=R2\n if R1<R2: (R1,R2) = (R2,R1);\n \n # CASE 1: circles do not intersect\n if R1+R2 <= d: return 0;\n \n # CASE 2: circle 2 completely in circle 1\n if d <= R1-R2: return np.pi*R2**2;\n \n # CASE 3: intersecting circles\n # [see e.g. http://mathworld.wolfram.com/Circle-CircleIntersection.html]\n r1 = (R1**2-R2**2+d**2)/2/d; \n r2 = (R2**2-R1**2+d**2)/2/d; # distance of origin from radical line\n \n C1 = R1**2 * np.arccos(r1/R1); # area of circle segments \n C2 = R2**2 * np.arccos(r2/R2); # defined by radical line\n \n T = d*np.sqrt(R1**2-r1**2); # area of the rhombus spanned by circle \n # origins and intersection points\n return C1+C2-T;","title":""},{"docid":"e64a1d89db2fc15ae03b37095d7133ee","score":"0.49084508","text":"def get_curve(start, end, peak, peak_max):\n points = np.array([(start, 0), (peak, peak_max), (end, 0)])\n # get x and y vectors\n x = points[:,0]\n y = points[:,1]\n\n # calculate polynomial\n z = np.polyfit(x, y, 2)\n f = np.poly1d(z)\n\n # calculate new x's and y's\n x_new = np.linspace(x[0], x[-1], 100)\n y_new = f(x_new)\n\n x_new = map(int, x_new)\n xy = []\n curr = None\n for x,y in zip(x_new, y_new):\n if x == curr:\n continue\n curr = x\n xy.append((x,y))\n x,y = zip(*xy)\n y = [abs(round(y_hat/peak_max,3)) for y_hat in y]\n return y","title":""},{"docid":"778ab236acfa7588c22d5e3acf81c4e3","score":"0.48993328","text":"def find_center(self, point1, point2):\r\n self. point1 = point1\r\n self. point2 = point2\r\n x1 = self.point1[0]\r\n x2 = self.point2[0]\r\n y1 = self.point1[1]\r\n y2 = self.point2[1]\r\n# (self.xc, self.yc) = (0, 0)\r\n self.xm = (x1+x2)/2\r\n self.ym = (y1+y2)/2\r\n \r\n secant = np.sqrt((x1-x2)**2 + (y1-y2)**2)\r\n A = (1/4)*(secant**2/(math.tan((point1[2]-point2[2])/2))**2)\r\n B = (A - self.R**2 - (self.xm**2+self.ym**2-x1**2-y1**2))/(2*y1 - 2*self.ym)\r\n k = (self.xm-x1)/(y1 - self.ym)\r\n criteria = (2*(k*B - k*y1 -x1))**2 - 4*(1+k**2)*((B-y1)**2 +x1**2-self.R**2)\r\n print(criteria)\r\n xc1 = (-2*(k*B-k*y1-x1) + np.sqrt(criteria))/(2*(1+k**2)) \r\n xc2 = (-2*(k*B-k*y1-x1) - np.sqrt(criteria))/(2*(1+k**2)) \r\n yc1 = k*xc1 + B\r\n yc2 = k*xc2 + B\r\n# self.point = point\r\n \r\n self.center1 = (xc1, yc1)\r\n self.center2 = (xc2, yc2)\r\n \r\n return (self.center1, self.center2)","title":""},{"docid":"e6b57bd2d15bcd3fb679797aa0bb4e87","score":"0.48988172","text":"def _intesectLines(pt1, pt2):\n (pt1, pt2), (pt3, pt4) = pt1, pt2\n denom = (pt1[0] - pt2[0]) * (pt3[1] - pt4[1]) - (pt1[1] - pt2[1]) * (pt3[0] - pt4[0])\n if _roundFloat(denom) == 0:\n return None\n x = (pt1[0] * pt2[1] - pt1[1] * pt2[0]) * (pt3[0] - pt4[0]) - (pt1[0] - pt2[0]) * (pt3[0] * pt4[1] - pt3[1] * pt4[0])\n x /= denom\n y = (pt1[0] * pt2[1] - pt1[1] * pt2[0]) * (pt3[1] - pt4[1]) - (pt1[1] - pt2[1]) * (pt3[0] * pt4[1] - pt3[1] * pt4[0])\n y /= denom\n return (x, y)","title":""},{"docid":"92975a9ccf2e4be63b02f1ab3ceb688c","score":"0.4889182","text":"def _ellipse(self,a2,b,r):\n return a2 * np.sqrt(1 - (r/b)**2)","title":""},{"docid":"0cfe92aa6244b616018dac0b97e8d24a","score":"0.4886405","text":"def get_r(Data, start=0):\n # pace IS A PROBLEM\n # construct the line\n Len = len(Data)\n a = (Data[0] - Data[-1]) / (1 - Len)\n b = Data[0] - a * start\n\n # calculate r2\n AveData = sum(Data[1:-1]) / Len - 2\n SStot = 0\n SSres = 0\n for i in range(1, Len - 1):\n SStot += abs(Data[i] - AveData)\n SSres += abs(Data[i] - (a * (start + i) + b))\n try:\n return - SSres / SStot\n except ZeroDivisionError:\n # this means that the dot are all (or nearly all because of rounding on the computer) on a horizontal straight line\n return 0 # the highest possible r.","title":""},{"docid":"4eee34b06cb4e1c2a2a390979b61d01a","score":"0.48860067","text":"def roundness2(self):\n\n if np.isnan(self.hx) or np.isnan(self.hy):\n return np.nan\n else:\n return 2.0 * (self.hx - self.hy) / (self.hx + self.hy)","title":""},{"docid":"76ede3ddbcc1598c7845364b5b6c322b","score":"0.4879624","text":"def PointBetweenPoints(pt1, pt2, t = .5):\n line = rg.Line(pt1, pt2)\n return line.PointAt(t)","title":""},{"docid":"2b794e4210faa6ef6d32f59adba573d6","score":"0.48759794","text":"def end_pose(start_pose, curvature, length):\r\n x, y, theta = start_pose\r\n if curvature == 0.0:\r\n # Linear movement.\r\n x += length * cos(theta)\r\n y += length * sin(theta)\r\n return (x, y, theta)\r\n else:\r\n # Curve segment of radius 1/curvature.\r\n tx = cos(theta)\r\n ty = sin(theta)\r\n radius = 1.0/curvature\r\n xc = x - radius * ty # Center of circle.\r\n yc = y + radius * tx\r\n angle = length / radius\r\n cosa = cos(angle)\r\n sina = sin(angle)\r\n nx = xc + radius * (cosa * ty + sina * tx)\r\n ny = yc + radius * (sina * ty - cosa * tx)\r\n ntheta = (theta + angle + pi) % (2*pi) - pi\r\n return (nx, ny, ntheta)","title":""},{"docid":"e7bdc5139df753d53f7a74aa8d03e219","score":"0.4874375","text":"def approximate(self, points, using=None, scheme=None, vb=True):\n # First, reset the interpolation scheme if one is passed\n # explicitly:\n if scheme is not None:\n self.scheme = scheme\n\n # Now make the interpolation, using the current scheme:\n self.interpolator = self.interpolate(using=using, vb=vb)\n if vb: print('interpolating between '+str(min(points))+' and '+str(max(points)))\n interpolated = self.interpolator(points)\n interpolated = qp.utils.normalize_gridded((points, interpolated), vb=False)\n # interpolated[interpolated<0.] = 0.\n\n return interpolated#(points, interpolated)","title":""},{"docid":"8ba729f4d7a40ca42e6114448566272c","score":"0.4874125","text":"def test_intersect_line_circle2(self):\n self.assertFalse(intersect_line_circle(0, 0, 10, -10, 10, 10, 11))","title":""},{"docid":"fd1f5e9cd067dce41939181bf47f3b0e","score":"0.48682445","text":"def bisect_rocs(rocpoints, predicate, start=0, end=None):\n if end is None:\n end = len(rocpoints)\n if end <= start:\n raise ValueError(\n 'Start (%d) must be less than end (%d).' % (start, end))\n new_index = (start + end) / 2\n if start == new_index:\n return end # We narrowed down the range completely\n else:\n if predicate(rocpoints[new_index]):\n # Look in lower half\n return bisect_rocs(rocpoints, predicate, start, new_index)\n else:\n # Look in upper half\n return bisect_rocs(rocpoints, predicate, new_index, end)","title":""},{"docid":"36e9e53bb8880aa266a58b582869a451","score":"0.48619306","text":"def test_point_in_circle2(self):\n pos = (5, -9)\n start = (0, 0)\n radius = 10\n self.assertFalse(point_in_circle(pos, start, radius))","title":""},{"docid":"19709577605e175bb9ea170076c5d567","score":"0.48590878","text":"def midpoint_2d(points):\n \n n = len(points)\n if n is 0:\n return None\n\n x_sum = 0\n y_sum = 0\n # center of set of finite points is average of those points\n for point in points:\n x_sum += point.x\n y_sum += point.y\n\n return (x_sum / n, y_sum / n)","title":""},{"docid":"72ad19cab377b0a6e5a019277d1579f8","score":"0.48504642","text":"def corners(self):\r\n\r\n return [(self.x_1, self.y_1), (self.x_1, self.y_2),\r\n (self.x_2, self.y_1), (self.x_2, self.y_2)]","title":""},{"docid":"b113c5c3ac7d2c60576fdb0ad191a7d1","score":"0.4845425","text":"def midpoint_to(self,obj):\n if isinstance(obj,Point):\n return obj.midpoint_to(obj.projected_on(self))\n elif isinstance(obj,Line):\n d = obj.r - self.r\n t1t2 = dot(self.t,obj.t)\n if abs(abs(t1t2)-1) < 1e-12: #parallel case \n d = orthogonalized_to(d,self.t)\n return Point(self.r + 0.5*d)\n else:\n t1d = dot(d,self.t)\n t2d = dot(d,obj.t)\n s = (t1t2*t2d - t1d)/(t1t2**2-1)\n u = (t1t2*t1d - t2d)/(t1t2**2-1)\n return Point(0.5*(obj.r + u*obj.t + self.r + s*self.t)) \n else:\n return obj.midpoint_to(self)","title":""},{"docid":"10e1ef73fb92d31ad2ae4306de42e096","score":"0.4842004","text":"def linear_interpolate(pt, corners, values):\n N = len(pt)\n\n if N==1:\n xd = (pt[0] - corners[0]) / (corners[1] - corners[0])\n return values[0]*(1 - xd) + values[1]*xd\n\n #interpolate out the first dimension\n new_values = np.empty(2**(N-1), dtype=np.float64)\n x_corners = np.array([corners[0,0], corners[0,1]])\n jump = 2**(N-1)\n for i in range(2**(N-1)):\n new_values[i] = linear_interpolate(np.array([pt[0]]), x_corners, \n np.array([values[i], values[i+jump]]))\n \n new_corners = np.empty((N-1, 2), dtype=np.float64)\n new_pt = np.empty(N-1, dtype=np.float64)\n for i in range(N-1):\n new_corners[i,0] = corners[i+1, 0]\n new_corners[i,1] = corners[i+1, 1]\n new_pt = pt[i+1]\n\n return linear_interpolate(new_pt, new_corners, new_values)","title":""}],"string":"[\n {\n \"docid\": \"71e372926e3cc59de3c212a9607ba596\",\n \"score\": \"0.6365892\",\n \"text\": \"def roundJoin(p1, p2, x, y, dist):\\n\\n (x0, y0) = p1.atend()\\n (x1, y1) = p2.atbegin()\\n\\n (dx0, dy0) = (pyx.unit.topt(x - x0), pyx.unit.topt(y - y0))\\n (dx1, dy1) = (pyx.unit.topt(x - x1), pyx.unit.topt(y - y1))\\n\\n alpha = math.atan2(dy0, dx0)\\n delta = math.atan2(dy1, dx1)\\n\\n angle = delta - alpha\\n\\n beta = (alpha + delta) * 0.5\\n\\n if (abs(angle) < math.pi / 2):\\n\\n p = []\\n\\n for stop in range(0, 4):\\n\\n beta = alpha + angle / 3.0 * stop\\n\\n (x1, y1) = (x - math.cos(beta) * abs(dist),\\n y - math.sin(beta) * abs(dist))\\n\\n p.append((x1, y1))\\n\\n A = Splines.naturalSpline(p)\\n\\n return pyx.path.curve(*A)\\n\\n else:\\n\\n p = []\\n\\n for stop in range(0, 4):\\n\\n beta = alpha + angle / 6.0 * stop\\n\\n (x1, y1) = (x - math.cos(beta) * abs(dist),\\n y - math.sin(beta) * abs(dist))\\n\\n p.append((x1, y1))\\n\\n A = Splines.naturalSpline(p)\\n\\n p = []\\n\\n for stop in range(3, 7):\\n\\n beta = alpha + angle / 6.0 * stop\\n\\n (x1, y1) = (x - math.cos(beta) * abs(dist),\\n y - math.sin(beta) * abs(dist))\\n\\n p.append((x1, y1))\\n\\n B = Splines.naturalSpline(p)\\n\\n c1 = pyx.path.curve(*A)\\n c2 = pyx.path.curve(*B)\\n\\n return c1 << c2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11bfb66bb55658a00f32adbbf3618155\",\n \"score\": \"0.6192633\",\n \"text\": \"def create_round_rectangle(self, x1, y1, x2, y2, radius=25, **kwargs):\\n points = [\\n x1 + radius,\\n y1,\\n x1 + radius,\\n y1,\\n x2 - radius,\\n y1,\\n x2 - radius,\\n y1,\\n x2,\\n y1,\\n x2,\\n y1 + radius,\\n x2,\\n y1 + radius,\\n x2,\\n y2 - radius,\\n x2,\\n y2 - radius,\\n x2,\\n y2,\\n x2 - radius,\\n y2,\\n x2 - radius,\\n y2,\\n x1 + radius,\\n y2,\\n x1 + radius,\\n y2,\\n x1,\\n y2,\\n x1,\\n y2 - radius,\\n x1,\\n y2 - radius,\\n x1,\\n y1 + radius,\\n x1,\\n y1 + radius,\\n x1,\\n y1,\\n ]\\n\\n kwargs[\\\"smooth\\\"] = True\\n return self._create(\\\"polygon\\\", points, kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16d1bc4813e2822eff52c1a3c6ab5c38\",\n \"score\": \"0.6036745\",\n \"text\": \"def _circle_nurbscurve(c1, n2):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"399096a92b583810797ece771742bdbe\",\n \"score\": \"0.60202044\",\n \"text\": \"def draw_curve(start_point, end_point, missing_ind=-1):\\r\\n result = \\\"X\\\"+str(start_point[0]) +\\\"Y\\\"+str(start_point[1])+\\\"D02*\\\\n\\\"\\r\\n result += \\\"D14*\\\\n\\\"\\r\\n result += \\\"D03*\\\\n\\\"\\r\\n result += \\\"D22*\\\\n\\\"\\r\\n start_cir_loc = (int((end_point[0] - start_point[0]) / 4 ), start_point[1] ) # initial line\\r\\n result += \\\"X\\\"+str(start_cir_loc[0]) +\\\"Y\\\"+str(start_cir_loc[1])+\\\"D01*\\\\n\\\"\\r\\n mid_point = (int((end_point[0] + start_point[0]) / 2 ), int((end_point[1] + start_point[1]) / 2 ) ) # mid_point\\r\\n dist_to_mid_point = (mid_point[0] - start_cir_loc[0], mid_point[1] - start_cir_loc[1])\\r\\n #rising circ\\r\\n num_cir_lines = 120\\r\\n for ind in range(num_cir_lines):\\r\\n x = int(start_cir_loc[0] + ind * dist_to_mid_point[0] / num_cir_lines)\\r\\n x_normalized_dist = (x - start_cir_loc[0]) / dist_to_mid_point[0]\\r\\n y = mid_point[1] - int(dist_to_mid_point[1]* (1 - (x_normalized_dist)**2 ) ** 0.5)\\r\\n if ind == missing_ind:\\r\\n x_btoken = int(start_cir_loc[0] + (ind-0.225) * dist_to_mid_point[0] / num_cir_lines)\\r\\n result += \\\"X\\\"+str(x_btoken) +\\\"Y\\\"+str(y)+\\\"D01*\\\\n\\\"\\r\\n else:\\r\\n result += \\\"X\\\"+str(x) +\\\"Y\\\"+str(y)+\\\"D01*\\\\n\\\"\\r\\n result += \\\"X\\\"+str(x) +\\\"Y\\\"+str(y)+\\\"D02*\\\\n\\\"\\r\\n result += \\\"X\\\"+str(mid_point[0]) +\\\"Y\\\"+str(mid_point[1])+\\\"D01*\\\\n\\\"#draw to mid point\\r\\n \\r\\n \\r\\n end_cir_loc = (int(3 * (end_point[0] - start_point[0]) / 4 ), end_point[1] )\\r\\n dist_to_end_cir = (end_cir_loc[0] - mid_point[0], end_cir_loc[1] - mid_point[1])\\r\\n for ind in range(num_cir_lines):\\r\\n x = int(mid_point[0] + ind * dist_to_end_cir[0] / num_cir_lines)\\r\\n x_normalized_dist = (x - mid_point[0]) / dist_to_end_cir[0]\\r\\n y = mid_point[1] + int(dist_to_end_cir[1]* (1 - (1 - x_normalized_dist)**2 ) ** 0.5)\\r\\n result += \\\"X\\\"+str(x) +\\\"Y\\\"+str(y)+\\\"D01*\\\\n\\\"\\r\\n result += \\\"X\\\"+str(x) +\\\"Y\\\"+str(y)+\\\"D02*\\\\n\\\"\\r\\n result += \\\"X\\\"+str(end_point[0]) +\\\"Y\\\"+str(end_point[1])+\\\"D01*\\\\n\\\"\\r\\n result += \\\"D14*\\\\n\\\"\\r\\n result += \\\"D03*\\\\n\\\"\\r\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e32ddb88c7f57ff0400644f8b3f4616\",\n \"score\": \"0.6000487\",\n \"text\": \"def midpoint(pt1, pt2):\\n return (0.5 * (pt1[0] + pt2[0]), 0.5 * (pt1[1] + pt2[1]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbde95649531856c1f44f4232369bbb0\",\n \"score\": \"0.5994136\",\n \"text\": \"def midpoint2(x1: float, y1: float, x2: float, y2: float) -> complex:\\n\\n x = (x1 + x2) / 2\\n y = (y1 + y2) / 2\\n\\n return complex(x, y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b02bfc629ba49a16f66b90cbbe95a288\",\n \"score\": \"0.5767674\",\n \"text\": \"def _geodesic_between_two_points(x1,y1,x2,y2):\\n pi=RR.pi()\\n #print \\\"z1=\\\",x1,y1\\n #print \\\"z2=\\\",x2,y2\\n if( abs(x1-x2)< 1E-10):\\n # The line segment [x=x1, y0<= y <= y1]\\n return line([[x1,y1],[x2,y2]]) #[0,0,x0,infinity]\\n c=RR(y1**2 -y2**2 +x1**2 -x2**2 )/RR(2 *(x1-x2))\\n r=RR(sqrt(y1**2 +(x1-c)**2 ))\\n r1=RR(y1/r); r2=RR(y2/r)\\n if(abs(r1-1 )< 1E-12 ):\\n r1=RR(1.0)\\n elif(abs(r2+1 )< 1E-12 ):\\n r2=-RR(1.0)\\n if(abs(r2-1 )< 1E-12 ):\\n r2=RR(1.0)\\n elif(abs(r2+1 )<1E-12 ):\\n r2=-RR(1.0)\\n if(x1>=c):\\n t1 = RR(arcsin(r1))\\n else:\\n t1 = RR(pi)-RR(arcsin(r1))\\n if(x2>=c):\\n t2 = RR(arcsin(r2))\\n else:\\n t2 = RR(pi)-arcsin(r2)\\n tmid = (t1+t2)*RR(0.5)\\n a0=min(t1,t2)\\n a1=max(t1,t2)\\n #print \\\"c,r=\\\",c,r\\n #print \\\"t1,t2=\\\",t1,t2\\n return _circ_arc(t1,t2,c,r)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"454df344681b6db68112b40bbf329a69\",\n \"score\": \"0.57438695\",\n \"text\": \"def getCorrection(start, end, pos):\\n (xs, ys) = start\\n (xe, ye) = end\\n (xp, yp) = pos\\n\\n # Discard edge cases with no sense\\n assert(xs != xe or ys != ye)\\n assert(xp != xe or yp != ye)\\n assert(xs != xp or ys != yp)\\n\\n # First get the line equation from start to end points.\\n # line equation follows the following pattern: y = m * x + b\\n m = 0.0\\n b = 0.0\\n if abs(xe - xs) > PRECISION:\\n m = (ye - ys) / (xe - xs)\\n b = ys - m * xs\\n else:\\n m = 1\\n b = - xs\\n\\n # Get the perpendicular line equation to the first line\\n mp = 0.0\\n bp = 0.0\\n if abs(xe - xs) < PRECISION:\\n bp = yp\\n elif abs(m) < PRECISION:\\n mp = 1\\n bp = - xp\\n else:\\n mp = - 1 / m\\n bp = yp - mp * xp\\n\\n # Get the point at the intersection of the two lines\\n xi = 0.0\\n yi = 0.0\\n if abs(xe - xs) < PRECISION:\\n xi = b\\n yi = bp\\n elif abs(m) < PRECISION:\\n xi = bp\\n yi = b\\n else:\\n xi = - (bp - b) / (mp - m)\\n yi = m * xi + b\\n\\n # Get the distance between the tree points\\n dist_pi = math.sqrt((xp - xi) * (xp - xi) + (yp - yi) * (yp - yi))\\n dist_pe = math.sqrt((xp - xe) * (xp - xe) + (yp - ye) * (yp - ye))\\n dist_sp = math.sqrt((xs - xp) * (xs - xp) + (ys - yp) * (ys - yp))\\n\\n # Get the offset angles alpha and beta\\n alpha = math.asin(dist_pi / dist_pe)\\n beta = math.asin(dist_pi / dist_sp)\\n\\n return - (alpha + beta)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"689afdbfd65017c9f3ecce09ffb99e5d\",\n \"score\": \"0.5736394\",\n \"text\": \"def rad_of_curvature(left_line, right_line):\\n\\n ploty = left_line.ally\\n leftx, rightx = left_line.allx, right_line.allx\\n\\n leftx = leftx[::-1]\\n rightx = rightx[::-1]\\n\\n width_lanes = abs(right_line.startx - left_line.startx)\\n ym_per_pix = 30 / 720\\n xm_per_pix = 3.7*(720/1280) / width_lanes\\n\\n y_eval = np.max(ploty)\\n\\n left_fit_cr = np.polyfit(ploty * ym_per_pix, leftx * xm_per_pix, 2)\\n right_fit_cr = np.polyfit(ploty * ym_per_pix, rightx * xm_per_pix, 2)\\n\\n left_curverad = ((1 + (2 * left_fit_cr[0] * y_eval * ym_per_pix + left_fit_cr[1]) ** 2) ** 1.5) / np.absolute(\\n 2 * left_fit_cr[0])\\n right_curverad = ((1 + (2 * right_fit_cr[0] * y_eval * ym_per_pix + right_fit_cr[1]) ** 2) ** 1.5) / np.absolute(\\n 2 * right_fit_cr[0])\\n\\n left_line.radius_of_curvature = left_curverad\\n right_line.radius_of_curvature = right_curverad\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d08d746bc0ba36a946ca9a6592c9c15a\",\n \"score\": \"0.5683035\",\n \"text\": \"def midpoint(ptA, ptB): \\n \\n return ((ptA[0] + ptB[0]) * 0.5, (ptA[1] + ptB[1]) * 0.5)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b7b0117a622b5f9b92a2307cba805ff\",\n \"score\": \"0.5608825\",\n \"text\": \"def corners(self, s, k_min, proximity, length):\\n return define_corners(self.mid, s, k_min, proximity, length)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73efb589155e1628f055b07d3803183d\",\n \"score\": \"0.56075686\",\n \"text\": \"def _point_nurbscurve(p1, n2):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87986489350c29f930184369b9d8620a\",\n \"score\": \"0.5597784\",\n \"text\": \"def positionAlongCurve():\\n\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"199f1d837a39a79fb7d4be31accd1cff\",\n \"score\": \"0.55913544\",\n \"text\": \"def midpoint(p1, p2):\\n return np.array([int((p1.x + p2.x) / 2), int((p1.y + p2.y) / 2)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68386dcd88582b043fff9383c0272af0\",\n \"score\": \"0.5589129\",\n \"text\": \"def midpoint(self, p1, p2):\\n \\n return ((p1[0] + p2[0]) * 0.5, (p1[1] + p2[1]) * 0.5)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"325b5134792b92be45a0e63c58d66ee7\",\n \"score\": \"0.55685306\",\n \"text\": \"def midpoint1(x1: float, y1: float) -> complex:\\n\\n x = x1 / 2\\n y = y1 / 2\\n\\n return complex(x, y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac8c02724121b56f720719a8fdf5e664\",\n \"score\": \"0.55669737\",\n \"text\": \"def _point_circle(p1, c2):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa425f3d03a5f0037bbd68835787f606\",\n \"score\": \"0.55107385\",\n \"text\": \"def createRational(self, controlPoints, degree, knots, weights, isPeriodic):\\n return NurbsCurve2D()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16f1ba5146c22ec972daddf380579743\",\n \"score\": \"0.55019015\",\n \"text\": \"def _circle_circle(c1, c2):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd5f2fd735b89d05deff24f3f84597e2\",\n \"score\": \"0.54760617\",\n \"text\": \"def find_points(self):\\n\\n radius = self.radius\\n thickness = self.thickness\\n bottom_outer_x, bottom_outer_y = self.outer_start_point\\n top_outer_y = bottom_outer_y + (4 * radius)\\n top_outer_x = bottom_outer_x\\n inner_r = radius - thickness\\n (bottom_outer_x, bottom_outer_y, thickness, radius, top_outer_x, top_outer_y, inner_r,) = (\\n float(bottom_outer_x),\\n float(bottom_outer_y),\\n float(thickness),\\n float(radius),\\n float(top_outer_x),\\n float(top_outer_y),\\n float(inner_r),\\n )\\n\\n point_1 = (bottom_outer_x, bottom_outer_y, \\\"circle\\\")\\n point_3 = (point_1[0] + radius, point_1[1] + radius, \\\"straight\\\")\\n point_4 = (point_3[0], point_3[1] + radius * 2, \\\"circle\\\")\\n point_6 = (top_outer_x, top_outer_y, \\\"straight\\\")\\n point_7 = (point_6[0], point_6[1] - thickness, \\\"circle\\\")\\n point_9 = (point_4[0] - thickness, point_4[1], \\\"straight\\\")\\n point_10 = (point_3[0] - thickness, point_3[1], \\\"circle\\\")\\n point_12 = (point_1[0], point_1[1] + thickness, \\\"straight\\\")\\n point_2 = (\\n (point_1[0]) + (radius * math.cos((3 * math.pi) / 8)),\\n (point_1[1] + radius) - (radius * math.sin((3 * math.pi) / 8)),\\n \\\"circle\\\",\\n )\\n point_5 = (\\n (point_6[0] + (radius * math.cos((2 * math.pi) / 8))),\\n (point_6[1] - radius) + (radius * math.sin((2 * math.pi) / 8)),\\n \\\"circle\\\",\\n )\\n point_8 = (\\n (point_7[0] + (inner_r * math.cos((2 * math.pi) / 8))),\\n (point_7[1] - inner_r) + (inner_r * math.sin((2 * math.pi) / 8)),\\n \\\"circle\\\",\\n )\\n point_11 = (\\n (point_12[0]) + (inner_r * math.cos((3 * math.pi) / 8)),\\n (point_12[1] + inner_r) - (inner_r * math.sin((3 * math.pi) / 8)),\\n \\\"circle\\\",\\n )\\n\\n self.points = [\\n point_1,\\n point_2,\\n point_3,\\n point_4,\\n point_5,\\n point_6,\\n point_7,\\n point_8,\\n point_9,\\n point_10,\\n point_11,\\n point_12,\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"731d5c320ac4e9c4b4d9c99104f3d5a0\",\n \"score\": \"0.5465036\",\n \"text\": \"def midpoint(a, b):\\n return (\\n (a[0]+b[0])/2,\\n (a[1]+b[1])/2,\\n (a[2]+b[2])/2 )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"698e358c81e75c8964414baf45e7e2ee\",\n \"score\": \"0.54537404\",\n \"text\": \"def _line_circle(l1, c2):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbbe1476fde579696dbc99fa0f972f81\",\n \"score\": \"0.53872186\",\n \"text\": \"def update_end_points(self):\\n \\\"\\\"\\\" Must do in pixel units because the scale doesn't allow line length to be converted, just points\\\"\\\"\\\"\\n\\n midx, midy = self.convert_arr_to_pixels(self.mid)\\n #theta = m.atan(self.slope) ## in radians\\n theta = m.radians(self.degrees) ## in radians\\n\\n ## Do trig\\n opp = m.sin(theta) * self.hypoteneuse\\n adj = m.cos(theta) * self.hypoteneuse\\n\\n ## Get start/end by applying deltas to mid coord\\n start_x = int(midx - adj)\\n start_y = int(midy + opp)\\n end_x = int(midx + adj)\\n end_y = int(midy - opp) # account for pygame negative stupidity\\n\\n self.start = self.convert_pixel_to_arr( (start_x, start_y) )\\n self.end = self.convert_pixel_to_arr( (end_x, end_y) )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5665891832b108af48c9de36eff0662a\",\n \"score\": \"0.53804934\",\n \"text\": \"def _line_nurbscurve(l1, n2):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebc7061641b942c5b9cfaac9e405bddd\",\n \"score\": \"0.53786194\",\n \"text\": \"def circle_segment(\\n radius: float,\\n start: np.ndarray,\\n stop: np.ndarray,\\n interval: float = 0.1,\\n minimum_times: int = 10,\\n times: Optional[int] = None,\\n) -> np.ndarray:\\n interval = np.arctan(float(interval) / radius)\\n start, stop = np.radians(start), np.radians(stop)\\n\\n if times is None:\\n times = max(int((stop - start) / interval), minimum_times)\\n ramp = cached_linspace(start, stop, times)\\n return radius * np.c_[np.cos(ramp), np.sin(ramp)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2022a798fe32d2c71f7b6ec46a861587\",\n \"score\": \"0.5376266\",\n \"text\": \"def midpoint(x1, y1, x2, y2):\\n\\n bracket1 = (x1 + x2)/2\\n bracket2 = (y1 + y2)/2\\n\\n return \\\"x: %s, y: %s.\\\" % (bracket1, bracket2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa537ecf7922ccd5f06aef0575098964\",\n \"score\": \"0.53521\",\n \"text\": \"def get_line_1(start, end):\\n # Setup initial conditions\\n x1, y1 = start\\n x2, y2 = end\\n dx = x2 - x1\\n dy = y2 - y1\\n \\n # Determine how steep the line is\\n is_steep = abs(dy) > abs(dx)\\n \\n # Rotate line\\n if is_steep:\\n x1, y1 = y1, x1\\n x2, y2 = y2, x2\\n \\n # Swap start and end points if necessary and store swap state\\n swapped = False\\n if x1 > x2:\\n x1, x2 = x2, x1\\n y1, y2 = y2, y1\\n swapped = True\\n \\n # Recalculate differentials\\n dx = x2 - x1\\n dy = y2 - y1\\n \\n # Calculate error\\n error = int(dx / 2.0)\\n ystep = 1 if y1 < y2 else -1\\n \\n # Iterate over bounding box generating points between start and end\\n y = y1\\n points = []\\n for x in range(x1, x2 + 1):\\n coord = (y, x) if is_steep else (x, y)\\n points.append(coord)\\n error -= abs(dy)\\n if error < 0:\\n y += ystep\\n error += dx\\n \\n # Reverse the list if the coordinates were swapped\\n if swapped:\\n points.reverse()\\n return points\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef7ba15884a622a0ce969e3075a20fa4\",\n \"score\": \"0.5349511\",\n \"text\": \"def __ComputeCurved(vpercent, w, vec, via, pts, segs):\\n\\n radius = via[1]/2.0\\n\\n #Compute the bezier middle points\\n req_angle = asin(vpercent/100.0);\\n oppside = tan(req_angle)*(radius-(w/sin(req_angle)))\\n length = sqrt(radius*radius + oppside*oppside)\\n d = req_angle - acos(radius/length)\\n vecBC = [vec[0]*cos(d)+vec[1]*sin(d) , -vec[0]*sin(d)+vec[1]*cos(d)]\\n pointBC = via[0] + wxPoint(int(vecBC[0] * length), int(vecBC[1] * length))\\n d = -d\\n vecAE = [vec[0]*cos(d)+vec[1]*sin(d) , -vec[0]*sin(d)+vec[1]*cos(d)]\\n pointAE = via[0] + wxPoint(int(vecAE[0] * length), int(vecAE[1] * length))\\n\\n curve1 = __Bezier(pts[1], pointBC, pts[2], n=segs)\\n curve2 = __Bezier(pts[4], pointAE, pts[0], n=segs)\\n\\n return curve1 + [pts[3]] + curve2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1439b05af4a2d60c3324425d38789920\",\n \"score\": \"0.5339895\",\n \"text\": \"def getMidpoint(p1, p2):\\r\\n return (p1.x + p2.x) / 2, (p1.y + p2.y) / 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33ce0a13f0286a5bc14bf0b13501e05f\",\n \"score\": \"0.5336291\",\n \"text\": \"def concentric_xys(self, radius, start):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5680e8674264dd194df9a1475a17833\",\n \"score\": \"0.53350806\",\n \"text\": \"def test_convert_circle_to_spline(self):\\n\\n new_points = paramak.utils.convert_circle_to_spline(\\n p_0=(200., 0.),\\n p_1=(250., 50.),\\n p_2=(200., 100.),\\n tolerance=0.2\\n )\\n\\n # these points can change from 200. to values like 200.00000000000009\\n assert pytest.approx(new_points[0][0], abs=0.0000000000001) == 200\\n assert pytest.approx(new_points[0][1], abs=0.0000000000001) == 0\\n assert pytest.approx(new_points[-1][0], abs=0.0000000000001) == 200\\n assert pytest.approx(new_points[-1][1], abs=0.0000000000001) == 100\\n\\n new_points_more_details = paramak.utils.convert_circle_to_spline(\\n p_0=(200, 0),\\n p_1=(250, 50),\\n p_2=(200, 100),\\n tolerance=0.1\\n )\\n\\n assert len(new_points_more_details) > len(new_points)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8edfb6edffa5684cd753573588c83b5a\",\n \"score\": \"0.53187805\",\n \"text\": \"def round_corner(self, radius):\\n corner = Image.new(\\\"L\\\", (radius, radius), 0)\\n draw = ImageDraw.Draw(corner)\\n draw.pieslice((0, 0, radius * 2, radius * 2), 180, 270, fill=255)\\n return corner\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3f40961cbf6248f1bab34a92b669973\",\n \"score\": \"0.52999246\",\n \"text\": \"def solve_curvature(p1, p2, n1, n2, fac, fallback):\\r\\n from mathutils.geometry import (intersect_line_line,\\r\\n )\\r\\n\\r\\n p1_a = p1 + n1\\r\\n p2_a = p2 - n2\\r\\n\\r\\n isect = intersect_line_line(p1,\\r\\n p1_a,\\r\\n p2,\\r\\n p2_a,\\r\\n )\\r\\n\\r\\n if isect:\\r\\n corner = isect[0].lerp(isect[1], 0.5)\\r\\n else:\\r\\n corner = None\\r\\n\\r\\n if corner:\\r\\n p1_first_order = p1.lerp(corner, fac)\\r\\n p2_first_order = corner.lerp(p2, fac)\\r\\n co = p1_first_order.lerp(p2_first_order, fac)\\r\\n\\r\\n return co\\r\\n else:\\r\\n # cant interpolate. just return interpolated value\\r\\n return fallback.copy() # p1.lerp(p2, fac)\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"707f882ffd2946f04fab3b764106ef64\",\n \"score\": \"0.52709275\",\n \"text\": \"def find_line_start_end(m, b, w, h):\\n intersect = []\\n # print(\\\"m =\\\", m, \\\"b =\\\", b)\\n if np.isnan(m) or np.isnan(b):\\n return None\\n\\n if b >= 0 and b <= h:\\n intersect.append((0, b))\\n if (-b / m) >= 0 and (-b / m) <= w:\\n if (-b / m, 0) not in intersect:\\n intersect.append((-b / m, 0))\\n if (m * w + b) >= 0 and (m * w + b) <= h:\\n if (w, m * w + b) not in intersect:\\n intersect.append((w, m * w + b))\\n if (h - b) / m >= 0 and (h - b) / m <= w:\\n if ((h - b) / m, h) not in intersect:\\n intersect.append(((h - b) / m, h))\\n if len(intersect) is not 2:\\n raise RuntimeError(\\n \\\"Unknown line end points calculation error. Intersect = \\\" +\\n str(intersect) + \\\", m = \\\" + str(m) + \\\", b = \\\" + str(b))\\n return [(int(round(i[0])), int(round(i[1]))) for i in intersect]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfc62035eb548cb637ef2e0338fa14e4\",\n \"score\": \"0.5266367\",\n \"text\": \"def interpolate_x(p1, p2, y):\\n (x1, y1) = p1\\n (x2, y2) = p2\\n # In case result is undefined, midpoint is as good as any value.\\n if y1 == y2:\\n return (x1 + x2) / 2.\\n x = x1 + (x2 - x1) * (y - y1) / float(y2 - y1)\\n # print \\\"interpolate_x [%g,%g,%g][%g,%g,%g]\\\" % (x1,x,x2,y1,y,y2)\\n return x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07082ba430d5c92fd708cabf1172d166\",\n \"score\": \"0.5264112\",\n \"text\": \"def extract(self, startParam, endParam):\\n return NurbsCurve2D()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc8b8a352981c39c337c60cb0de9df1a\",\n \"score\": \"0.5251435\",\n \"text\": \"def round_corners(self, radius, N):\\n\\n dpoly = super().round_corners(radius, radius, N)\\n self.assign(dpoly)\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f6eda4e1925810288c4b06b91f2682f\",\n \"score\": \"0.5242262\",\n \"text\": \"def round_rect(\\n self, x: int, y: int, width: int, height: int, radius: int, color: int\\n ) -> None:\\n self._gfx_mode()\\n self._curve_helper(x + radius, y + radius, radius, radius, 1, color, False)\\n self._curve_helper(\\n x + width - radius - 1, y + radius, radius, radius, 2, color, False\\n )\\n self._curve_helper(\\n x + radius, y + height - radius, radius, radius, 0, color, False\\n )\\n self._curve_helper(\\n x + width - radius - 1, y + height - radius, radius, radius, 3, color, False\\n )\\n self.hline(x + radius, y, width - (radius * 2) - 1, color)\\n self.hline(x + radius, y + height, width - (radius * 2) - 1, color)\\n self.vline(x, y + radius, height - (radius * 2), color)\\n self.vline(x + width - 1, y + radius, height - (radius * 2), color)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"559998ef561a0e76e729e17e8ab78fe8\",\n \"score\": \"0.52398854\",\n \"text\": \"def _nurbscurve_nurbscurve(n1, n2):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dac3aa474734f3356c82cc009eb06ca7\",\n \"score\": \"0.5228479\",\n \"text\": \"def find_points(self):\\n\\n # / p4\\n # / /¦\\n # / / ¦\\n # / / ¦\\n # / / ¦\\n # p1/ ¦\\n # ¦ ¦\\n # x ¦ ¦\\n # ¦ ¦\\n # p2\\\\ ¦\\n # \\\\ \\\\ ¦\\n # \\\\ \\\\ ¦\\n # \\\\ \\\\ ¦\\n # \\\\ p3\\n\\n if self.radius_type == \\\"corner\\\":\\n distance_to_inner_corner = self.inner_radius\\n distance_to_rear_corner = self.outer_radius\\n # this section calculates a new distance to the corners now that we\\n # know the user provided the distance to the straight\\n if self.radius_type == \\\"straight\\\":\\n angle = 360 / (self.number_of_coils * 2)\\n distance_to_inner_corner = self.inner_radius / math.cos(math.radians(angle))\\n distance_to_rear_corner = self.outer_radius / math.cos(math.radians(angle))\\n\\n if self.gap_size * self.number_of_coils > 2 * math.pi * distance_to_inner_corner:\\n msg = (\\n \\\"Gap_size is too large. The gap_size * number of coils must \\\"\\n \\\"be less than the circumference of the circle made by \\\"\\n \\\"the inner_radius\\\"\\n )\\n raise ValueError(msg)\\n\\n if distance_to_inner_corner != 0.0:\\n theta_inner = ((2 * math.pi * distance_to_inner_corner) - (self.gap_size * self.number_of_coils)) / (\\n distance_to_inner_corner * self.number_of_coils\\n )\\n omega_inner = math.asin(self.gap_size / (2 * distance_to_inner_corner))\\n\\n # inner points\\n point_1 = (\\n (distance_to_inner_corner * math.cos(-omega_inner)),\\n (-distance_to_inner_corner * math.sin(-omega_inner)),\\n )\\n point_2 = (\\n (\\n distance_to_inner_corner * math.cos(theta_inner) * math.cos(-omega_inner)\\n + distance_to_inner_corner * math.sin(theta_inner) * math.sin(-omega_inner)\\n ),\\n (\\n -distance_to_inner_corner * math.cos(theta_inner) * math.sin(-omega_inner)\\n + distance_to_inner_corner * math.sin(theta_inner) * math.cos(-omega_inner)\\n ),\\n )\\n points = [(point_1[0], point_1[1]), (point_2[0], point_2[1])]\\n\\n else:\\n\\n points = [(0, 0)]\\n\\n # print(point_1)\\n # print(point_2)\\n\\n theta_outer = ((2 * math.pi * distance_to_rear_corner) - (self.gap_size * self.number_of_coils)) / (\\n distance_to_rear_corner * self.number_of_coils\\n )\\n omega_outer = math.asin(self.gap_size / (2 * distance_to_rear_corner))\\n\\n # outer points\\n point_4 = (\\n (distance_to_rear_corner * math.cos(-omega_outer)),\\n (-distance_to_rear_corner * math.sin(-omega_outer)),\\n )\\n point_6 = (\\n (\\n distance_to_rear_corner * math.cos(theta_outer) * math.cos(-omega_outer)\\n + distance_to_rear_corner * math.sin(theta_outer) * math.sin(-omega_outer)\\n ),\\n (\\n -distance_to_rear_corner * math.cos(theta_outer) * math.sin(-omega_outer)\\n + distance_to_rear_corner * math.sin(theta_outer) * math.cos(-omega_outer)\\n ),\\n )\\n points.append((point_6[0], point_6[1]))\\n points.append((point_4[0], point_4[1]))\\n\\n self.points = points\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1f5de42017ddfc9418a4519b1e4280a\",\n \"score\": \"0.5222381\",\n \"text\": \"def closest_point(p1, p2, s):\\n \\n #if the line is a single point, the closest point is the only point\\n if p1==p2:\\n return (0,p1)\\n \\n seg_vector = vector_diff(p2,p1)\\n seg_mag = mag(seg_vector)\\n #print( \\\"seg_vector, length\\\", seg_vector, seg_mag )\\n seg_unit = vector_div( seg_vector, seg_mag )\\n stop_vector = vector_diff(s,p1)\\n #print( \\\"stop_vector\\\", stop_vector )\\n \\n #scalar projection of A onto B = (A dot B)/|B| = A dot unit(B)\\n sp = dot_product( stop_vector, seg_unit )\\n \\n #print( \\\"scalar projection\\\", sp )\\n \\n if sp < 0:\\n #closest point is startpoint\\n #print( \\\"startpoint\\\" )\\n return (0, p1)\\n elif sp > seg_mag:\\n #closest point is endpoint\\n #print( \\\"endpoint\\\" )\\n return (1, p2)\\n else:\\n #closest point is midspan\\n #print( \\\"midpoint\\\" )\\n return (sp/seg_mag, vector_sum(p1,vector_mult( seg_unit, sp )))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e974735b7206c7393dfaa1bf8906fc18\",\n \"score\": \"0.5222275\",\n \"text\": \"def createByThreePoints(self, startPoint, point, endPoint):\\n return Arc2D()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"565ab3ac57b73688b6f47209b32598f7\",\n \"score\": \"0.52110666\",\n \"text\": \"def curve():\\n\\n x1 = np.linspace(-2, .99, 100)\\n x2 = np.linspace(1.01,6, 100)\\n y1 = 1.0/(x1-1)\\n y2 = 1.0/(x2-1)\\n\\n plt.plot(x1,y1, \\\"m--\\\", x2,y2, \\\"m--\\\", linewidth = 5)\\n plt.ylim([-6,6])\\n #plt.plot(x2,y2)\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1240c234fe3e278b0e2a59a930558cd6\",\n \"score\": \"0.5203733\",\n \"text\": \"def get_line(start, end):\\n # Setup initial conditions\\n x1, y1 = start\\n x2, y2 = end\\n dx = x2 - x1\\n dy = y2 - y1\\n\\n # Determine how steep the line is\\n is_steep = abs(dy) > abs(dx)\\n\\n # Rotate line\\n if is_steep:\\n x1, y1 = y1, x1\\n x2, y2 = y2, x2\\n\\n # Swap start and end points if necessary and store swap state\\n swapped = False\\n if x1 > x2:\\n x1, x2 = x2, x1\\n y1, y2 = y2, y1\\n swapped = True\\n\\n # Recalculate differentials\\n dx = x2 - x1\\n dy = y2 - y1\\n\\n # Calculate error\\n error = int(dx / 2.0)\\n ystep = 1 if y1 < y2 else -1\\n\\n # Iterate over bounding box generating points between start and end\\n y = y1\\n points = []\\n for x in range(x1, x2 + 1):\\n coord = (y, x) if is_steep else (x, y)\\n points.append(coord)\\n error -= abs(dy)\\n if error < 0:\\n y += ystep\\n error += dx\\n\\n # Reverse the list if the coordinates were swapped\\n if swapped:\\n points.reverse()\\n return points\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1240c234fe3e278b0e2a59a930558cd6\",\n \"score\": \"0.5203733\",\n \"text\": \"def get_line(start, end):\\n # Setup initial conditions\\n x1, y1 = start\\n x2, y2 = end\\n dx = x2 - x1\\n dy = y2 - y1\\n\\n # Determine how steep the line is\\n is_steep = abs(dy) > abs(dx)\\n\\n # Rotate line\\n if is_steep:\\n x1, y1 = y1, x1\\n x2, y2 = y2, x2\\n\\n # Swap start and end points if necessary and store swap state\\n swapped = False\\n if x1 > x2:\\n x1, x2 = x2, x1\\n y1, y2 = y2, y1\\n swapped = True\\n\\n # Recalculate differentials\\n dx = x2 - x1\\n dy = y2 - y1\\n\\n # Calculate error\\n error = int(dx / 2.0)\\n ystep = 1 if y1 < y2 else -1\\n\\n # Iterate over bounding box generating points between start and end\\n y = y1\\n points = []\\n for x in range(x1, x2 + 1):\\n coord = (y, x) if is_steep else (x, y)\\n points.append(coord)\\n error -= abs(dy)\\n if error < 0:\\n y += ystep\\n error += dx\\n\\n # Reverse the list if the coordinates were swapped\\n if swapped:\\n points.reverse()\\n return points\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1240c234fe3e278b0e2a59a930558cd6\",\n \"score\": \"0.5203733\",\n \"text\": \"def get_line(start, end):\\n # Setup initial conditions\\n x1, y1 = start\\n x2, y2 = end\\n dx = x2 - x1\\n dy = y2 - y1\\n\\n # Determine how steep the line is\\n is_steep = abs(dy) > abs(dx)\\n\\n # Rotate line\\n if is_steep:\\n x1, y1 = y1, x1\\n x2, y2 = y2, x2\\n\\n # Swap start and end points if necessary and store swap state\\n swapped = False\\n if x1 > x2:\\n x1, x2 = x2, x1\\n y1, y2 = y2, y1\\n swapped = True\\n\\n # Recalculate differentials\\n dx = x2 - x1\\n dy = y2 - y1\\n\\n # Calculate error\\n error = int(dx / 2.0)\\n ystep = 1 if y1 < y2 else -1\\n\\n # Iterate over bounding box generating points between start and end\\n y = y1\\n points = []\\n for x in range(x1, x2 + 1):\\n coord = (y, x) if is_steep else (x, y)\\n points.append(coord)\\n error -= abs(dy)\\n if error < 0:\\n y += ystep\\n error += dx\\n\\n # Reverse the list if the coordinates were swapped\\n if swapped:\\n points.reverse()\\n return points\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fa28213e1fa5da162094c9e21598b4d\",\n \"score\": \"0.52012336\",\n \"text\": \"def generate_quad_corners(indices, x, y):\\n (i, j, k, l) = indices\\n\\n def gpi(index_1, index_2):\\n return generate_point_indices(index_1, index_2, len(x))\\n\\n xis = np.empty(4)\\n yis = np.empty(4)\\n xis.fill(np.nan)\\n yis.fill(np.nan)\\n\\n if j <= i or k <= j or l <= k:\\n pass\\n else:\\n (xis[0], yis[0]) = line_intersection(x[gpi(i, j)],\\n y[gpi(i, j)])\\n (xis[1], yis[1]) = line_intersection(x[gpi(j, k)],\\n y[gpi(j, k)])\\n (xis[2], yis[2]) = line_intersection(x[gpi(k, l)],\\n y[gpi(k, l)])\\n (xis[3], yis[3]) = line_intersection(x[gpi(l, i)],\\n y[gpi(l, i)])\\n\\n return (xis, yis)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49e8510c501cd5ff0cd40f442c306c6b\",\n \"score\": \"0.5193536\",\n \"text\": \"def midpoint(self,other):\\n return self.interpolant(other,0.5).toPoint()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49e8510c501cd5ff0cd40f442c306c6b\",\n \"score\": \"0.5193536\",\n \"text\": \"def midpoint(self,other):\\n return self.interpolant(other,0.5).toPoint()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3202b4aa5b4f8de759bca79ad134b91\",\n \"score\": \"0.51823646\",\n \"text\": \"def midpoint(self):\\n x = (self.a.x + self.b.x) / 2.0\\n y = (self.a.y + self.b.y) / 2.0\\n\\n return Point(x, y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d406af560861792730353d91f8e66443\",\n \"score\": \"0.5177173\",\n \"text\": \"def interpolate(pointImpl: 'PointImpl', pointImpl2: 'PointImpl', double: float) -> cern.accsoft.commons.value.Point:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d595ca085407f5800e0ac1a52bbe978\",\n \"score\": \"0.51695335\",\n \"text\": \"def arc(x, y, radius, start_angle, stop_angle, fill_color=\\\"\\\", stroke_color=\\\"\\\", stroke_width=-1):\\n raise NotImplementedError(\\\"arc() not implemented\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fa2f5dcf162cca0a32e0b0399d23c4a\",\n \"score\": \"0.5166751\",\n \"text\": \"def preparePixmap(self):\\n start = self.startPixmap()\\n end = self.endPixmap()\\n painter = QPainter(self.outPixmap())\\n painter.drawPixmap(0, 0, start)\\n size = start.size().expandedTo(end.size())\\n width = size.width()\\n height = size.height()\\n radius = int((width**2 + height**2) ** 0.5) / 2\\n start_rect = QRect(width / 2, height / 2, 0, 0)\\n end_rect = QRect(width / 2, height / 2, radius, radius)\\n return start_rect, end_rect\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4bfc1331af755077475b49d7955ffed\",\n \"score\": \"0.5150121\",\n \"text\": \"def circleCurve(h, k, t, r, v):\\n x = h + r * cos(v * t)\\n y = k + r * sin(v * t)\\n return x, y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08eba72e9f7b8a0ca1d557116dce5915\",\n \"score\": \"0.5129027\",\n \"text\": \"def bresenham_circle(center, curve):\\n radius = pts.distance_between_points(center, curve)\\n\\n f = 1 - radius\\n ddf_x = 1\\n ddf_y = -2 * radius\\n\\n x = 0\\n y = radius\\n\\n\\n points = list()\\n\\n points.append(pts.Point(center.x, round(center.y + radius)))\\n points.append(pts.Point(center.x, round(center.y - radius)))\\n points.append(pts.Point(round(center.x + radius), center.y))\\n points.append(pts.Point(round(center.x + radius), center.y))\\n\\n while x < y:\\n if f >= 0: \\n y -= 1\\n ddf_y += 2\\n f += ddf_y\\n x += 1\\n ddf_x += 2\\n f += ddf_x \\n points.append(pts.Point(round(center.x + x), round(center.y + y)))\\n points.append(pts.Point(round(center.x - x), round(center.y + y)))\\n points.append(pts.Point(round(center.x + x), round(center.y - y)))\\n points.append(pts.Point(round(center.x - x), round(center.y - y)))\\n points.append(pts.Point(round(center.x + y), round(center.y + x)))\\n points.append(pts.Point(round(center.x - y), round(center.y + x)))\\n points.append(pts.Point(round(center.x + y), round(center.y - x)))\\n points.append(pts.Point(round(center.x - y), round(center.y - x)))\\n\\n\\n return points\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39f67966efb822389c74f1600a60a4da\",\n \"score\": \"0.5126685\",\n \"text\": \"def createNonRational(self, controlPoints, degree, knots, isPeriodic):\\n return NurbsCurve2D()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06280bd0339e64197adc4a3a445da6f5\",\n \"score\": \"0.5114326\",\n \"text\": \"def get_curve_intersection(x1, y1, x2, y2, log=False):\\n if log:\\n x1 = np.log10(x1)\\n y1 = np.log10(y1)\\n x2 = np.log10(x2)\\n y2 = np.log10(y2)\\n\\n itp = interp1d(x1, y1, kind='linear', bounds_error=False)\\n x_root = []\\n y_root = []\\n for idx in range(len(y2)-1):\\n x_l, x_r = x2[[idx, idx+1]]\\n y2_l, y2_r = y2[[idx, idx+1]]\\n a = (y2_r - y2_l)/(x_r-x_l)\\n b = y2_l - a*x_l\\n err = lambda x: itp(x) - (a*x+b)\\n if err(x_l)*err(x_r)<0: # there is an intersection\\n x_sol = brentq(err, x_l, x_r)\\n y_sol = a*x_sol+b\\n x_root.append(x_sol)\\n y_root.append(y_sol)\\n else:\\n continue\\n x_root = np.array(x_root)\\n y_root = np.array(y_root)\\n if log:\\n x_root = np.power(10, x_root)\\n y_root = np.power(10, y_root)\\n return x_root, y_root\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c279dc64cc327ad9341a1576678fc10\",\n \"score\": \"0.50966436\",\n \"text\": \"def get_line_equation(start_point: np.ndarray,\\n end_point: np.ndarray) -> typing.Tuple[float, float, float]:\\n [x1, y1] = start_point\\n [x2, y2] = end_point\\n\\n x = x2 - x1\\n y = y2 - y1\\n\\n if x == 0 and y == 0:\\n raise ArithmeticError\\n if x == 0:\\n a = 1\\n b = 0\\n elif y == 0:\\n a = 0\\n b = 1\\n else:\\n b = 1\\n a = -b * y / x\\n\\n c = -a * x1 - b * y1\\n return a, b, c\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb6cb74ac1f80e3e22b32d9e28d11be4\",\n \"score\": \"0.50926507\",\n \"text\": \"def get_intersection_pts(p1, r1, p2, r2):\\n dist = np.linalg.norm(p1 - p2)\\n if dist >= (r1 + r2):\\n # circles are too far apart\\n return [], CircleIntersectionType.TOO_FAR\\n elif dist <= abs(r1 - r2):\\n if r1 < r2:\\n return [], CircleIntersectionType.CIRCLE1_IN_CIRCLE2\\n else:\\n return [], CircleIntersectionType.CIRCLE2_IN_CIRCLE1\\n else:\\n # two intersection pts\\n r1_sq = r1**2\\n r2_sq = r2**2\\n d_sq = dist**2\\n pt_base = 0.5 * (p1 + p2) + \\\\\\n 0.5 * ((r1_sq - r2_sq) / d_sq) * (p2 - p1)\\n \\n delta_y = p2[1] - p1[1]\\n delta_x = p1[0] - p2[0]\\n plus_minus = 0.5 * np.sqrt((2 * (r1_sq + r2_sq) / d_sq) - ((r1_sq - r2_sq)/d_sq)**2 - 1) * np.array([delta_y, delta_x])\\n intersect_pts = [pt_base+plus_minus, pt_base-plus_minus]\\n\\n\\n d1 = (r2_sq - r1_sq - d_sq) / (2 * dist)\\n if d1 > 0:\\n return intersect_pts, CircleIntersectionType.RADIAL_AXIS_NONCENTERED_SMALL_CIRCLE1\\n d2 = (r1_sq - r2_sq - d_sq) / (2 * dist)\\n if d2 > 0:\\n return intersect_pts, CircleIntersectionType.RADIAL_AXIS_NONCENTERED_SMALL_CIRCLE2\\n return intersect_pts, CircleIntersectionType.RADIAL_AXIS_CENTERED\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2097afecdbc6f5ae740a84ee23b759e5\",\n \"score\": \"0.5071573\",\n \"text\": \"def curvature(left_fit, right_fit):\\n ploty = np.linspace(0, 719, num=720) # to cover same y-range as image\\n\\n # For each y position generate random x position within +/-50 pix\\n # of the line base position in each case (x=200 for left, and x=900 for right)\\n leftx = np.array([200 + (y ** 2) * left_fit[0] + left_fit[1]\\n for y in ploty])\\n rightx = np.array([900 + (y ** 2) * right_fit[0] + right_fit[1]\\n for y in ploty])\\n\\n leftx = leftx[::-1] # Reverse to match top-to-bottom in y\\n rightx = rightx[::-1] # Reverse to match top-to-bottom in y\\n\\n y_eval = np.max(ploty)\\n\\n # Define conversions in x and y from pixels space to meters\\n ym_per_pix = 30 / 720 # meters per pixel in y dimension\\n xm_per_pix = 3.7 / 700 # meters per pixel in x dimension\\n\\n # Fit new polynomials to x,y in world space\\n left_fit_cr = np.polyfit(ploty * ym_per_pix, leftx * xm_per_pix, 2)\\n right_fit_cr = np.polyfit(ploty * ym_per_pix, rightx * xm_per_pix, 2)\\n # Calculate the new radii of curvature\\n left_curverad = ((1 + (2 * left_fit_cr[0] * y_eval * ym_per_pix + left_fit_cr[1]) ** 2) ** 1.5) / np.absolute(\\n 2 * left_fit_cr[0])\\n right_curverad = ((1 + (2 * right_fit_cr[0] * y_eval * ym_per_pix + right_fit_cr[1]) ** 2) ** 1.5) / np.absolute(\\n 2 * right_fit_cr[0])\\n\\n return np.average([left_curverad, right_curverad])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7db2dc99fdff2f02542de6f2f23a81c7\",\n \"score\": \"0.50624436\",\n \"text\": \"def make_circle(radius, an_points, rn_points, n_borders):\\n if an_points < 1:\\n Logger.serror('You must give a \\\"an_points\\\" of at least 1.', stackoffset=3)\\n if rn_points < 1:\\n Logger.serror('You must give a \\\"rn_points\\\" of at least 1.', stackoffset=3)\\n if isinstance(an_points, float):\\n Logger.slog('You passed in a float \\\"an_points\\\" parameter: the value will be '\\n 'truncated to an integer.', level='warning', stackoffset=3)\\n if isinstance(rn_points, float):\\n Logger.slog('You passed in a float \\\"rn_points\\\" parameter: the value will be '\\n 'truncated to an integer.', level='warning', stackoffset=3)\\n\\n # create nodes\\n radiuses = np.linspace(0, radius, rn_points+1)\\n lx, ly = [0.], [0.]\\n m = np.linspace(0,2*np.pi,an_points,endpoint=False)\\n for r in radiuses[1:]:\\n lx.extend(list(r*np.cos(m)))\\n ly.extend(list(r*np.sin(m)))\\n x, y = np.array(lx), np.array(ly)\\n z = np.zeros((len(x), 1))\\n nodes = np.column_stack((x,y,z))\\n\\n # create connectivity\\n elts = []\\n # .. create triangles (surface)\\n tri = mtri.Triangulation(nodes[:,0], nodes[:,1])\\n for elt in tri.triangles: elts.append([2,10,elt[0],elt[1],elt[2]])\\n # .. create edges (border)\\n if n_borders == 0: pass\\n elif n_borders == 1:\\n for n in range(len(m)-1):\\n elts.append([1,1,len(x)-len(m)+n,len(x)-len(m)+n+1,-1])\\n elts.append([1,1,len(x)-1,len(x)-len(m),-1])\\n else:\\n Logger.slog('A \\\"circle\\\" primitive can only be created with 0 or 1 '\\n 'distinct borders.\\\\nInvalid value: {} (the domain will '\\n 'be created with 1 border).'.format(n_borders),\\n level='warning')\\n for n in range(len(m)-1):\\n elts.append([1,1,len(x)-len(m)+n,len(x)-len(m)+n+1,-1])\\n elts.append([1,1,len(x)-1,len(x)-len(m),-1])\\n elements = np.array(elts)\\n \\n return nodes, elements\",\n \"title\": \"\"\n },\n {\n \"docid\": \"899821480d6d2ef30481dfe58860d716\",\n \"score\": \"0.50422966\",\n \"text\": \"def circle_from_two(points, p, q):\\r\\n circ = diameter(p, q)\\r\\n left = None\\r\\n right = None\\r\\n px, py = p\\r\\n qx, qy = q\\r\\n\\r\\n # For each point not in the two-point circle, form a circumcircle and\\r\\n # classify it on left or right side.\\r\\n for r in points:\\r\\n if in_circle(r, circ):\\r\\n continue\\r\\n\\r\\n cross = cross_prod(px, py, qx, qy, r[0], r[1])\\r\\n c = circumcircle(p, q, r)\\r\\n if c is None:\\r\\n continue\\r\\n elif cross > 0.0 and (left is None or cross_prod(px, py, qx, qy, c[0], c[1]) > cross_prod(px, py, qx, qy, left[0], left[1])):\\r\\n left = c\\r\\n elif cross < 0.0 and (right is None or cross_prod(px, py, qx, qy, c[0], c[1]) < cross_prod(px, py, qx, qy, right[0], right[1])):\\r\\n right = c\\r\\n\\r\\n # Select which circle to return.\\r\\n if left is None and right is None:\\r\\n return circ\\r\\n elif left is None:\\r\\n return right\\r\\n elif right is None:\\r\\n return left\\r\\n else:\\r\\n return left if (left[2] <= right[2]) else right\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ad83ccecb2632a5d2c5375f8c58453a\",\n \"score\": \"0.50414443\",\n \"text\": \"def midpoint(self, p):\\n return Point([simplify((a + b)*S.Half) for a, b in zip(self.args, p.args)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddaa8fd7d68095f5a446b0c071da1103\",\n \"score\": \"0.502761\",\n \"text\": \"def __ComputePoints(track, via, hpercent, vpercent, segs):\\n start = track.GetStart()\\n end = track.GetEnd()\\n\\n if (segs>2) and (vpercent>70.0):\\n #If curved via are selected, max angle is 45 degres --> 70%\\n vpercent = 70.0\\n\\n # ensure that start is at the via/pad end\\n d = end - via[0]\\n if sqrt(d.x * d.x + d.y * d.y) < via[1]:\\n start, end = end, start\\n\\n # get normalized track vector\\n # it will be used a base vector pointing in the track direction\\n pt = end - start\\n norm = sqrt(pt.x * pt.x + pt.y * pt.y)\\n vec = [t / norm for t in pt]\\n\\n # find point on the track, sharp end of the teardrop\\n w = track.GetWidth()/2\\n radius = via[1]/2\\n n = radius*(1+hpercent/100.0)\\n dist = sqrt(n*n + w*w)\\n d = atan2(w, n)\\n vecB = [vec[0]*cos(d)+vec[1]*sin(d) , -vec[0]*sin(d)+vec[1]*cos(d)]\\n pointB = start + wxPoint(int(vecB[0] * dist), int(vecB[1] * dist))\\n vecA = [vec[0]*cos(-d)+vec[1]*sin(-d) , -vec[0]*sin(-d)+vec[1]*cos(-d)]\\n pointA = start + wxPoint(int(vecA[0] * dist), int(vecA[1] * dist))\\n\\n # via side points\\n radius = via[1] / 2\\n d = asin(vpercent/100.0);\\n vecC = [vec[0]*cos(d)+vec[1]*sin(d) , -vec[0]*sin(d)+vec[1]*cos(d)]\\n d = asin(-vpercent/100.0);\\n vecE = [vec[0]*cos(d)+vec[1]*sin(d) , -vec[0]*sin(d)+vec[1]*cos(d)]\\n pointC = via[0] + wxPoint(int(vecC[0] * radius), int(vecC[1] * radius))\\n pointE = via[0] + wxPoint(int(vecE[0] * radius), int(vecE[1] * radius))\\n\\n # Introduce a last point in order to cover the via centre.\\n # If not, the zone won't be filled\\n vecD = [-vec[0], -vec[1]]\\n radius = (via[1]/2)*0.5 #50% of via radius is enough to include\\n pointD = via[0] + wxPoint(int(vecD[0] * radius), int(vecD[1] * radius))\\n\\n pts = [pointA, pointB, pointC, pointD, pointE]\\n if segs > 2:\\n pts = __ComputeCurved(vpercent, w, vec, via, pts, segs)\\n\\n return pts\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6e6ea7668c9557bb9e80def97f65140\",\n \"score\": \"0.5022576\",\n \"text\": \"def curve_intersection_points(self, i1, i2):\\n # Check for splits of this curve along axis i1\\n ci_points = np.full((2,3), self.c[self.i0])\\n cnt = 0\\n # If the curve just touches a bound along i1, add a single point...\\n if np.isclose(self.c[i1] + self.r, self.domain.L[i1]): # ...on the upper bound...\\n ci_points[cnt,i1] = self.domain.L[i1]\\n ci_points[cnt,i2] = self.c[i2]\\n cnt += 1\\n elif np.isclose(self.c[i1] - self.r, 0.): # ...or on the lower bound.\\n ci_points[cnt,i1] = 0.\\n ci_points[cnt,i2] = self.c[i2]\\n cnt += 1\\n # Otherwise, if the sphere is split along i1 the curve may cross the bounds.\\n elif self.sphere.split_axis[i1]:\\n # Add two points at upper bound along i1...\\n if self.c[i1] + self.r > self.domain.L[i1]:\\n ci_points[cnt:cnt+2,i1] = self.domain.L[i1]\\n # ...or add two points at lower bound along i1...\\n elif self.c[i1] - self.r < 0.:\\n ci_points[cnt:cnt+2,i1] = 0.\\n # ...or add no points at bounds along i1.\\n else:\\n return ci_points[:cnt]\\n di1 = ci_points[cnt,i1] - self.c[i1]\\n di2 = np.sqrt(self.r**2 - di1**2)\\n ci_points[cnt,i2] = self.c[i2] + di2\\n ci_points[cnt+1,i2] = self.c[i2] - di2\\n cnt += 2\\n return ci_points[:cnt] - self.c\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efa1e7eb944845077d42084af5df59ef\",\n \"score\": \"0.50172305\",\n \"text\": \"def boundDestination(pStart, pEnd):\\n (xStart, yStart, zStart) = pStart\\n zPenStart = zStart - PEN_DIST\\n \\n (xEnd, yEnd, zEnd) = pEnd\\n zPenEnd = zEnd - PEN_DIST\\n \\n (xOut, yOut, zOut) = (0, 0, 0)\\n \\n if penInCylinder(pStart):\\n if penInCylinder(pEnd):\\n (xOut, yOut, zOut) = pEnd\\n else: \\n # Project xy onto outer circle:\\n (xOut, yOut, zOut) = boundByCircle(pEnd)\\n # Deal with z issues:\\n if zEnd > Z_MAX: # Deal with z\\n zOut = Z_MAX\\n if not inRectangle(pStart):\\n if zPenEnd < Z_CLAMP:\\n zOut = Z_CLAMP + PEN_DIST\\n else: # if pStart is in rectangle\\n if zPenEnd < Z_CLAMP:\\n if inRectangle(pEnd):\\n if zPenEnd < Z_PAPER:\\n zOut = Z_PAPER + PEN_DIST\\n else: # if pEnd is not in rectangle\\n zOut = Z_CLAMP + PEN_DIST\\n elif penInPrism(pStart):\\n if penInPrism(pEnd):\\n (xOut, yOut, zOut) = pEnd\\n else:\\n # Project xy onto rectangle:\\n (xOut, yOut, zOut) = boundByRectangle(pEnd)\\n # Deal with z issues:\\n if zPenEnd < Z_PAPER:\\n zOut = Z_PAPER + PEN_DIST\\n if zEnd > Z_MAX:\\n zOut = Z_MAX\\n else:\\n # This should never be entered, because pStart should always be valid. If\\n # it isn't, send the point to home.\\n (xOut, yOut, zOut) = HOME\\n \\n return (xOut, yOut, zOut)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90570ec9f1798a02adce5c215f6db31d\",\n \"score\": \"0.5015211\",\n \"text\": \"def _generate_ring(radius: int):\\n ring = set()\\n ring.add((radius, 0))\\n if radius - 1 > 0:\\n for x in range(1, radius):\\n min_value = 1.0\\n for y in range(1, radius + 1):\\n value = abs(((x**2 + y**2) / radius**2) - 1)\\n if min_value > value:\\n min_value = value\\n min_x, min_y = x, y\\n ring.add((min_x, min_y))\\n\\n temp_ring = ring.copy()\\n for x, y in temp_ring:\\n ring.add((y, x))\\n\\n temp_ring = ring.copy()\\n for x, y in temp_ring:\\n ring.add((-x, y))\\n\\n temp_ring = ring.copy()\\n for x, y in temp_ring:\\n ring.add((x, -y))\\n\\n temp_ring = ring.copy()\\n for x, y in temp_ring:\\n ring.add((-x, -y))\\n\\n return ring\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04c23ad002952322f506445f090905f6\",\n \"score\": \"0.5014824\",\n \"text\": \"def _trim_path_ends_to_layer(vm, px, py, pz, pi=None):\\n # get points for portion of segment that crosses layer\\n if pi is None:\\n pi = assign_points_to_layers(vm, px, py, pz)\\n\\n # trim start of line\\n if pi[0] != pi[1]:\\n iref = pi[1] - 1\\n pt0 = [px[0], py[0], pz[0]]\\n pt1 = [px[1], py[1], pz[1]]\\n isect = find_line_intersection(vm, iref, pt0, pt1) \\n\\n assert isect is not None, 'Problem finding intersection with'\\\\\\n ' iref = {:} between {:} and {:}.'.format(iref, pt0, pt1)\\n\\n px[0], py[0], pz[0] = isect\\n\\n # trim end of line\\n if pi[-2] != pi[-1]:\\n iref = pi[-1] - 1\\n pt0 = [px[-2], py[-2], pz[-2]]\\n pt1 = [px[-1], py[-1], pz[-1]]\\n\\n isect = find_line_intersection(vm, iref, pt0, pt1) \\n\\n assert isect is not None, 'Problem finding intersection with'\\\\\\n ' iref = {:} between {:} and {:}.'.format(iref, pt0, pt1)\\n\\n px[-1], py[-1], pz[-1] = isect\\n\\n return px, py, pz\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d3c8ab36725a7647e3df319555e52ee\",\n \"score\": \"0.49978265\",\n \"text\": \"def create_RLine(RRmin,Rmin,xD):\\n global xR, yR\\n R = RRmin * Rmin\\n xR = np.linspace(0,1,num=1000)\\n yR = ((R/(R+1))*xR) + ((1/(R+1))*xD)\\n findintersect(yq, yR, xR)\\n xR = np.array([xint, xD])\\n yR = ((R/(R+1))*xR) + ((1/(R+1))*xD)\\n return yR, xR\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7498e496d1c298564d48cadd698586ba\",\n \"score\": \"0.49734038\",\n \"text\": \"def snap(self):\\n\\n x_values = [self.vertices[0].x, self.vertices[1].x, self.vertices[2].x, self.vertices[3].x]\\n y_values = [self.vertices[0].y, self.vertices[1].y, self.vertices[2].y, self.vertices[3].y]\\n\\n\\n for i in range(4):\\n if x_values[i] == 0 or isinstance(x_values[i], int):\\n pass\\n elif x_values[i] - math.floor(x_values[i]) < 0.5:\\n x_values[i] = math.floor(x_values[i])\\n else:\\n x_values[i] = math.ceil(x_values[i])\\n\\n for i in range(4):\\n if y_values[i] == 0 or isinstance(y_values[i], int):\\n pass\\n elif y_values[i] - math.floor(y_values[i]) < 0.5:\\n y_values[i] = math.floor(y_values[i])\\n else:\\n y_values[i] = math.ceil(y_values[i])\\n\\n if x_values[0] == x_values[1] == x_values[2] == x_values[3] and \\\\\\n y_values[0] == y_values[1] == y_values[2] == y_values[3]:\\n return self\\n\\n # for i in range(4):\\n # print(x_values[i], y_values[i])\\n\\n return Quadrilateral(x_values[0], y_values[0],\\n x_values[1], y_values[1],\\n x_values[2], y_values[2],\\n x_values[3], y_values[3]) # TODO\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a65f0cf79997f633cc7b1bf9c5938f5\",\n \"score\": \"0.49693984\",\n \"text\": \"def GetBendLineValues2(self, Up=defaultNamedNotOptArg, Angle=defaultNamedNotOptArg, Radius=defaultNamedNotOptArg, Points=defaultNamedNotOptArg):\\n\\t\\treturn self._ApplyTypes_(115, 1, (11, 0), ((16395, 3), (16389, 3), (16389, 3), (16396, 3)), u'GetBendLineValues2', None,Up\\n\\t\\t\\t, Angle, Radius, Points)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa6ba1c1a7b5507703ee8abf22f39c29\",\n \"score\": \"0.49614617\",\n \"text\": \"def test_two_segments_join_on_boundary(self):\\n # clang-format off\\n # 3========2========5\\n # | | |\\n # | 0====1=============2\\n # | | |\\n # 0--------1--------4\\n # clang-format on\\n\\n points = [(0, 0, 0), (1, 0, 0), (1, 1, 0), (0, 1, 0), (2, 0, 0), (2, 1, 0)]\\n cells = [UGrid.cell_type_enum.QUAD, 4, 0, 1, 2, 3, UGrid.cell_type_enum.QUAD, 4, 1, 4, 5, 2]\\n ugrid = UGrid(points, cells)\\n extractor = UGrid2dPolylineDataExtractor(ugrid, 'points')\\n\\n point_scalars = [0, 2, 3, 1, 4, 5]\\n extractor.set_grid_scalars(point_scalars, [], 'points')\\n\\n polyline = [(0.5, 0.5, 0.0), (1.0, 0.5, 0.0), (2.5, 0.5, 0.0)]\\n extractor.set_polyline(polyline)\\n extracted_locations = extractor.extract_locations\\n extracted_data = extractor.extract_data()\\n\\n expected_data = [1.5, 2.5, 3.5, 4.5, float('nan')]\\n np.testing.assert_array_equal(expected_data, extracted_data)\\n expected_locations = [(0.5, 0.5, 0.0), (1.0, 0.5, 0.0), (1.5, 0.5, 0.0),\\n (2.0, 0.5, 0.0), (2.5, 0.5, 0.0)]\\n np.testing.assert_array_equal(expected_locations, extracted_locations)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfe1e20e8415052a22afa553b3feed7a\",\n \"score\": \"0.49564895\",\n \"text\": \"def quadratic_bezier_curve(p0,p1,p2,n=10) :\\n curve_pts = []\\n for t in numpy.arange(0,1+1./n,1./n) :\\n curve_pts.append(quadratic_bezier_point(p0,p1,p2,t))\\n return curve_pts\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9731039ecc73ee00207b907c06fe41a7\",\n \"score\": \"0.49543917\",\n \"text\": \"def fill_round_rect(\\n self, x: int, y: int, width: int, height: int, radius: int, color: int\\n ) -> None:\\n self._gfx_mode()\\n self._curve_helper(x + radius, y + radius, radius, radius, 1, color, True)\\n self._curve_helper(\\n x + width - radius - 1, y + radius, radius, radius, 2, color, True\\n )\\n self._curve_helper(\\n x + radius, y + height - radius, radius, radius, 0, color, True\\n )\\n self._curve_helper(\\n x + width - radius - 1, y + height - radius, radius, radius, 3, color, True\\n )\\n self._rect_helper(\\n x + radius, y, x + width - radius - 1, y + height - 1, color, True\\n )\\n self._rect_helper(\\n x, y + radius, x + width - 1, y + height - radius - 1, color, True\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4ef37f4be6791b12df37b164fb4b90a\",\n \"score\": \"0.49534315\",\n \"text\": \"def _circular_radii(self):\\n shift = self.radius_step / 2\\n min_radius = self.min_radius - shift\\n max_radius = self.max_radius + shift\\n nsteps = int(math.floor((max_radius - min_radius)\\n / self.radius_step))\\n max_radius = min_radius + (nsteps * self.radius_step)\\n return np.linspace(min_radius, max_radius, nsteps + 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0dc0ea9e247be4cdac98570e634236a3\",\n \"score\": \"0.4951872\",\n \"text\": \"def get_subrectangle(x1,y1,x2,y2):\\r\\n diff_x = x2 - x1\\r\\n diff_y = y2 - y1\\r\\n x1_a = x1 + diff_x/3\\r\\n x2_a = x1 + 2*diff_x/3\\r\\n y1_a = y1 + diff_y/3\\r\\n y2_a = y1 + 2*diff_y/3\\r\\n \\r\\n return x1_a,y1_a,x2_a,y2_a\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd56c63aaf48b4575293d5ace0c8a8b5\",\n \"score\": \"0.4944744\",\n \"text\": \"def _fast_fit_circle(x,y,use_median=False) :\\n # init\\n nn=len(x)\\n i1=np.arange(nn)\\n ### i2=(i1+1)%nn\\n i2=(i1+nn//2-1)%nn\\n\\n # midpoints\\n mx=((x[i1]+x[i2])/2.)\\n my=((y[i1]+y[i2])/2.)\\n nx=(y[i2]-y[i1])\\n ny=-(x[i2]-x[i1])\\n\\n # solve for intersection of perpendicular bisectors\\n # with s1,s2 are affine parameters of 2 adjacent perpendicular bisectors\\n # 2 equations:\\n # mx1 + nx1*s1 = mx2 + nx2*s2\\n # my1 + ny1*s1 = my2 + ny2*s2\\n num = (ny[i2]*mx[i2]-nx[i2]*my[i2]-ny[i2]*mx[i1]+nx[i2]*my[i1])\\n denom = (ny[i2]*nx[i1]-nx[i2]*ny[i1])\\n ok = (denom!=0)\\n s1 = np.zeros(nn)\\n s1[ok] = num[ok]/denom[ok]\\n\\n # coordinates of intersections are estimates of center of circle\\n xc=mx[i1]+nx[i1]*s1[i1]\\n yc=my[i1]+ny[i1]*s1[i1]\\n\\n # first estimate of center is mean of all intersections\\n if use_median :\\n xc=np.median(xc)\\n yc=np.median(yc)\\n r=np.median(np.hypot(x-xc, y-yc))\\n else :\\n xc=np.mean(xc)\\n yc=np.mean(yc)\\n r=np.mean(np.hypot(x-xc, y-yc))\\n\\n return xc,yc,r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59df700aa7779ebf780d35869f92aa1e\",\n \"score\": \"0.49429443\",\n \"text\": \"def line_between_points(self, x1, y1, x2, y2, n_values=100):\\n x1_, y1_ = self.image_coords_as_array_coords(x1, y1)\\n x2_, y2_ = self.image_coords_as_array_coords(x2, y2)\\n n_values = ifloor(math.sqrt((x2_-x1_)**2 + (y2_-y1_)**2))\\n delta_x = (x2_ - x1_) / (n_values - 1)\\n delta_y = (y2_ - y1_) / (n_values - 1)\\n vals = []\\n img_coords = []\\n d = self._raw.linearintdata\\n # TODO remarkably, this is reasonably fast in Python, but it would\\n # probably be more at home in scitbx.math\\n for n in range(n_values):\\n x = x1_ + (n * delta_x)\\n y = y1_ + (n * delta_y)\\n xd, yd = self.array_coords_as_detector_coords(x, y)\\n img_coords.append((xd,yd))\\n x_1 = ifloor(x)\\n x_2 = iceil(x)\\n y_1 = ifloor(y)\\n y_2 = iceil(y)\\n v11 = d[(x_1, y_1)]\\n v12 = d[(x_1, y_2)]\\n v21 = d[(x_2, y_1)]\\n v22 = d[(x_2, y_2)]\\n if (x_2 == x_1):\\n if (y_2 == y_1):\\n vxy = v11\\n else :\\n vxy = ((v12 * (y - y_1)) + (v11 * (y_2 - y))) / (y_2 - y_1)\\n elif (y_2 == y_1):\\n vxy = ((v21 * (x - x_1)) + (v11 * (x_2 - x))) / (x_2 - x_1)\\n else :\\n dxdy = (y_2 - y_1) * (x_2 - x_1)\\n vxy = ((v11 / dxdy) * (x_2 - x) * (y_2 - y)) + \\\\\\n ((v21 / dxdy) * (x - x_1) * (y_2 - y)) + \\\\\\n ((v12 / dxdy) * (x_2 - x) * (y - y_1)) + \\\\\\n ((v22 / dxdy) * (x - x_1) * (y - y_1))\\n vals.append(vxy)\\n lattice_length = None\\n if (len(vals) > 5):\\n # first find peaks in the profile\\n peaks = []\\n avg = sum(vals) / len(vals)\\n filtered_vals = []\\n for x in vals :\\n if (x <= avg*3):\\n filtered_vals.append(x)\\n background = sum(filtered_vals) / len(filtered_vals)\\n i = 2\\n while (i < len(vals) - 2):\\n x = vals[i]\\n if (x <= background):\\n pass\\n elif ((x > vals[i-1]) and (x > vals[i-2]) and\\n (x > vals[i+1]) and (x > vals[i+2])):\\n peaks.append(i)\\n i += 1\\n if (len(peaks) > 0):\\n # calculate the average lattice length\\n center_x, center_y = self.get_beam_center_mm()\\n distances = []\\n i = 1\\n while (i < len(peaks)):\\n x1,y1 = img_coords[peaks[i-1]]\\n x2,y2 = img_coords[peaks[i]]\\n rs_distance = rstbx.utils.reciprocal_space_distance(x1, y1, x2, y2,\\n wavelength=self.get_wavelength(),\\n center_x=center_x,\\n center_y=center_y,\\n distance=self.get_detector_distance(),\\n detector_two_theta=self.get_detector_2theta(),\\n distance_is_corrected=True)\\n assert (rs_distance > 0)\\n distances.append(1 / rs_distance)\\n i += 1\\n lattice_length = sum(distances) / len(distances)\\n distance = self.distance_between_points(x1, y1, x2, y2)\\n return line_profile(vals, distance, lattice_length)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eea6d8fd47305207da2df37e671a3a3b\",\n \"score\": \"0.49427333\",\n \"text\": \"def curvature(self):\\n return 2.0 / (self.left.radius() + self.right.radius())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65cebad806a124548ebb3351039aac06\",\n \"score\": \"0.49414554\",\n \"text\": \"def __add__(self, other):\\n\\n # X9.62 B.3:\\n\\n if not isinstance(other, Point):\\n return NotImplemented\\n if other == INFINITY:\\n return self\\n if self == INFINITY:\\n return other\\n assert self.__curve == other.__curve\\n if self.__x == other.__x:\\n if (self.__y + other.__y) % self.__curve.p() == 0:\\n return INFINITY\\n else:\\n return self.double()\\n\\n p = self.__curve.p()\\n\\n l = ((other.__y - self.__y) * \\\\\\n numbertheory.inverse_mod(other.__x - self.__x, p)) % p\\n\\n x3 = (l * l - self.__x - other.__x) % p\\n y3 = (l * (self.__x - x3) - self.__y) % p\\n\\n return Point(self.__curve, x3, y3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5108a828f93ce340172759443fd52792\",\n \"score\": \"0.49352363\",\n \"text\": \"def quadratic_bezier_point(p0,p1,p2,t) :\\n return (1-t)**2*p0 + 2*(1-t)*t*p1 + t**2*p2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62727ab26e250091d1d51520e7499374\",\n \"score\": \"0.4930479\",\n \"text\": \"def _bezier_math(t: float, p1: Tuple[float, float], p2: Tuple[float, float]) -> Tuple[float, float]:\\n\\tx0, y0 = 0, 0\\n\\tx1, y1 = p1\\n\\tx2, y2 = p2\\n\\tx3, y3 = 1, 1\\n\\tx = (1 - t) ** 3 * x0 + 3 * (1 - t) ** 2 * t * x1 + 3 * (1 - t) * t ** 2 * x2 + t ** 3 * x3\\n\\ty = (1 - t) ** 3 * y0 + 3 * (1 - t) ** 2 * t * y1 + 3 * (1 - t) * t ** 2 * y2 + t ** 3 * y3\\n\\treturn x, y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"325bcbfcf6527df6e16d03eb78adcea6\",\n \"score\": \"0.49298626\",\n \"text\": \"def __newRoundCorners( self ):\\n\\n for l in self.newLines:\\n l.roundLowerRight = False\\n l.roundUpperRight = False\\n\\n lines = self.newLines\\n for i in range( len(lines)-1 ):\\n if lines[i+1].ragWidth < lines[i].ragWidth:\\n lines[i].roundLowerRight = True\\n\\n for i in range( len(lines)-1 ):\\n if lines[i].ragWidth < lines[i+1].ragWidth:\\n lines[i+1].roundUpperRight = True\\n\\n lines[-1].roundLowerRight = True\\n self.newLines = lines\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b6681c19f0ea78102a0939e1fed13c8\",\n \"score\": \"0.4919724\",\n \"text\": \"def __intersecting_circles(self,R1,R2,d):\\n # ensure standard order R1>=R2\\n if R1<R2: (R1,R2) = (R2,R1);\\n \\n # CASE 1: circles do not intersect\\n if R1+R2 <= d: return 0;\\n \\n # CASE 2: circle 2 completely in circle 1\\n if d <= R1-R2: return np.pi*R2**2;\\n \\n # CASE 3: intersecting circles\\n # [see e.g. http://mathworld.wolfram.com/Circle-CircleIntersection.html]\\n r1 = (R1**2-R2**2+d**2)/2/d; \\n r2 = (R2**2-R1**2+d**2)/2/d; # distance of origin from radical line\\n \\n C1 = R1**2 * np.arccos(r1/R1); # area of circle segments \\n C2 = R2**2 * np.arccos(r2/R2); # defined by radical line\\n \\n T = d*np.sqrt(R1**2-r1**2); # area of the rhombus spanned by circle \\n # origins and intersection points\\n return C1+C2-T;\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e64a1d89db2fc15ae03b37095d7133ee\",\n \"score\": \"0.49084508\",\n \"text\": \"def get_curve(start, end, peak, peak_max):\\n points = np.array([(start, 0), (peak, peak_max), (end, 0)])\\n # get x and y vectors\\n x = points[:,0]\\n y = points[:,1]\\n\\n # calculate polynomial\\n z = np.polyfit(x, y, 2)\\n f = np.poly1d(z)\\n\\n # calculate new x's and y's\\n x_new = np.linspace(x[0], x[-1], 100)\\n y_new = f(x_new)\\n\\n x_new = map(int, x_new)\\n xy = []\\n curr = None\\n for x,y in zip(x_new, y_new):\\n if x == curr:\\n continue\\n curr = x\\n xy.append((x,y))\\n x,y = zip(*xy)\\n y = [abs(round(y_hat/peak_max,3)) for y_hat in y]\\n return y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"778ab236acfa7588c22d5e3acf81c4e3\",\n \"score\": \"0.48993328\",\n \"text\": \"def find_center(self, point1, point2):\\r\\n self. point1 = point1\\r\\n self. point2 = point2\\r\\n x1 = self.point1[0]\\r\\n x2 = self.point2[0]\\r\\n y1 = self.point1[1]\\r\\n y2 = self.point2[1]\\r\\n# (self.xc, self.yc) = (0, 0)\\r\\n self.xm = (x1+x2)/2\\r\\n self.ym = (y1+y2)/2\\r\\n \\r\\n secant = np.sqrt((x1-x2)**2 + (y1-y2)**2)\\r\\n A = (1/4)*(secant**2/(math.tan((point1[2]-point2[2])/2))**2)\\r\\n B = (A - self.R**2 - (self.xm**2+self.ym**2-x1**2-y1**2))/(2*y1 - 2*self.ym)\\r\\n k = (self.xm-x1)/(y1 - self.ym)\\r\\n criteria = (2*(k*B - k*y1 -x1))**2 - 4*(1+k**2)*((B-y1)**2 +x1**2-self.R**2)\\r\\n print(criteria)\\r\\n xc1 = (-2*(k*B-k*y1-x1) + np.sqrt(criteria))/(2*(1+k**2)) \\r\\n xc2 = (-2*(k*B-k*y1-x1) - np.sqrt(criteria))/(2*(1+k**2)) \\r\\n yc1 = k*xc1 + B\\r\\n yc2 = k*xc2 + B\\r\\n# self.point = point\\r\\n \\r\\n self.center1 = (xc1, yc1)\\r\\n self.center2 = (xc2, yc2)\\r\\n \\r\\n return (self.center1, self.center2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6b57bd2d15bcd3fb679797aa0bb4e87\",\n \"score\": \"0.48988172\",\n \"text\": \"def _intesectLines(pt1, pt2):\\n (pt1, pt2), (pt3, pt4) = pt1, pt2\\n denom = (pt1[0] - pt2[0]) * (pt3[1] - pt4[1]) - (pt1[1] - pt2[1]) * (pt3[0] - pt4[0])\\n if _roundFloat(denom) == 0:\\n return None\\n x = (pt1[0] * pt2[1] - pt1[1] * pt2[0]) * (pt3[0] - pt4[0]) - (pt1[0] - pt2[0]) * (pt3[0] * pt4[1] - pt3[1] * pt4[0])\\n x /= denom\\n y = (pt1[0] * pt2[1] - pt1[1] * pt2[0]) * (pt3[1] - pt4[1]) - (pt1[1] - pt2[1]) * (pt3[0] * pt4[1] - pt3[1] * pt4[0])\\n y /= denom\\n return (x, y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92975a9ccf2e4be63b02f1ab3ceb688c\",\n \"score\": \"0.4889182\",\n \"text\": \"def _ellipse(self,a2,b,r):\\n return a2 * np.sqrt(1 - (r/b)**2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0cfe92aa6244b616018dac0b97e8d24a\",\n \"score\": \"0.4886405\",\n \"text\": \"def get_r(Data, start=0):\\n # pace IS A PROBLEM\\n # construct the line\\n Len = len(Data)\\n a = (Data[0] - Data[-1]) / (1 - Len)\\n b = Data[0] - a * start\\n\\n # calculate r2\\n AveData = sum(Data[1:-1]) / Len - 2\\n SStot = 0\\n SSres = 0\\n for i in range(1, Len - 1):\\n SStot += abs(Data[i] - AveData)\\n SSres += abs(Data[i] - (a * (start + i) + b))\\n try:\\n return - SSres / SStot\\n except ZeroDivisionError:\\n # this means that the dot are all (or nearly all because of rounding on the computer) on a horizontal straight line\\n return 0 # the highest possible r.\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4eee34b06cb4e1c2a2a390979b61d01a\",\n \"score\": \"0.48860067\",\n \"text\": \"def roundness2(self):\\n\\n if np.isnan(self.hx) or np.isnan(self.hy):\\n return np.nan\\n else:\\n return 2.0 * (self.hx - self.hy) / (self.hx + self.hy)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76ede3ddbcc1598c7845364b5b6c322b\",\n \"score\": \"0.4879624\",\n \"text\": \"def PointBetweenPoints(pt1, pt2, t = .5):\\n line = rg.Line(pt1, pt2)\\n return line.PointAt(t)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b794e4210faa6ef6d32f59adba573d6\",\n \"score\": \"0.48759794\",\n \"text\": \"def end_pose(start_pose, curvature, length):\\r\\n x, y, theta = start_pose\\r\\n if curvature == 0.0:\\r\\n # Linear movement.\\r\\n x += length * cos(theta)\\r\\n y += length * sin(theta)\\r\\n return (x, y, theta)\\r\\n else:\\r\\n # Curve segment of radius 1/curvature.\\r\\n tx = cos(theta)\\r\\n ty = sin(theta)\\r\\n radius = 1.0/curvature\\r\\n xc = x - radius * ty # Center of circle.\\r\\n yc = y + radius * tx\\r\\n angle = length / radius\\r\\n cosa = cos(angle)\\r\\n sina = sin(angle)\\r\\n nx = xc + radius * (cosa * ty + sina * tx)\\r\\n ny = yc + radius * (sina * ty - cosa * tx)\\r\\n ntheta = (theta + angle + pi) % (2*pi) - pi\\r\\n return (nx, ny, ntheta)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7bdc5139df753d53f7a74aa8d03e219\",\n \"score\": \"0.4874375\",\n \"text\": \"def approximate(self, points, using=None, scheme=None, vb=True):\\n # First, reset the interpolation scheme if one is passed\\n # explicitly:\\n if scheme is not None:\\n self.scheme = scheme\\n\\n # Now make the interpolation, using the current scheme:\\n self.interpolator = self.interpolate(using=using, vb=vb)\\n if vb: print('interpolating between '+str(min(points))+' and '+str(max(points)))\\n interpolated = self.interpolator(points)\\n interpolated = qp.utils.normalize_gridded((points, interpolated), vb=False)\\n # interpolated[interpolated<0.] = 0.\\n\\n return interpolated#(points, interpolated)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ba729f4d7a40ca42e6114448566272c\",\n \"score\": \"0.4874125\",\n \"text\": \"def test_intersect_line_circle2(self):\\n self.assertFalse(intersect_line_circle(0, 0, 10, -10, 10, 10, 11))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd1f5e9cd067dce41939181bf47f3b0e\",\n \"score\": \"0.48682445\",\n \"text\": \"def bisect_rocs(rocpoints, predicate, start=0, end=None):\\n if end is None:\\n end = len(rocpoints)\\n if end <= start:\\n raise ValueError(\\n 'Start (%d) must be less than end (%d).' % (start, end))\\n new_index = (start + end) / 2\\n if start == new_index:\\n return end # We narrowed down the range completely\\n else:\\n if predicate(rocpoints[new_index]):\\n # Look in lower half\\n return bisect_rocs(rocpoints, predicate, start, new_index)\\n else:\\n # Look in upper half\\n return bisect_rocs(rocpoints, predicate, new_index, end)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36e9e53bb8880aa266a58b582869a451\",\n \"score\": \"0.48619306\",\n \"text\": \"def test_point_in_circle2(self):\\n pos = (5, -9)\\n start = (0, 0)\\n radius = 10\\n self.assertFalse(point_in_circle(pos, start, radius))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19709577605e175bb9ea170076c5d567\",\n \"score\": \"0.48590878\",\n \"text\": \"def midpoint_2d(points):\\n \\n n = len(points)\\n if n is 0:\\n return None\\n\\n x_sum = 0\\n y_sum = 0\\n # center of set of finite points is average of those points\\n for point in points:\\n x_sum += point.x\\n y_sum += point.y\\n\\n return (x_sum / n, y_sum / n)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72ad19cab377b0a6e5a019277d1579f8\",\n \"score\": \"0.48504642\",\n \"text\": \"def corners(self):\\r\\n\\r\\n return [(self.x_1, self.y_1), (self.x_1, self.y_2),\\r\\n (self.x_2, self.y_1), (self.x_2, self.y_2)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b113c5c3ac7d2c60576fdb0ad191a7d1\",\n \"score\": \"0.4845425\",\n \"text\": \"def midpoint_to(self,obj):\\n if isinstance(obj,Point):\\n return obj.midpoint_to(obj.projected_on(self))\\n elif isinstance(obj,Line):\\n d = obj.r - self.r\\n t1t2 = dot(self.t,obj.t)\\n if abs(abs(t1t2)-1) < 1e-12: #parallel case \\n d = orthogonalized_to(d,self.t)\\n return Point(self.r + 0.5*d)\\n else:\\n t1d = dot(d,self.t)\\n t2d = dot(d,obj.t)\\n s = (t1t2*t2d - t1d)/(t1t2**2-1)\\n u = (t1t2*t1d - t2d)/(t1t2**2-1)\\n return Point(0.5*(obj.r + u*obj.t + self.r + s*self.t)) \\n else:\\n return obj.midpoint_to(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10e1ef73fb92d31ad2ae4306de42e096\",\n \"score\": \"0.4842004\",\n \"text\": \"def linear_interpolate(pt, corners, values):\\n N = len(pt)\\n\\n if N==1:\\n xd = (pt[0] - corners[0]) / (corners[1] - corners[0])\\n return values[0]*(1 - xd) + values[1]*xd\\n\\n #interpolate out the first dimension\\n new_values = np.empty(2**(N-1), dtype=np.float64)\\n x_corners = np.array([corners[0,0], corners[0,1]])\\n jump = 2**(N-1)\\n for i in range(2**(N-1)):\\n new_values[i] = linear_interpolate(np.array([pt[0]]), x_corners, \\n np.array([values[i], values[i+jump]]))\\n \\n new_corners = np.empty((N-1, 2), dtype=np.float64)\\n new_pt = np.empty(N-1, dtype=np.float64)\\n for i in range(N-1):\\n new_corners[i,0] = corners[i+1, 0]\\n new_corners[i,1] = corners[i+1, 1]\\n new_pt = pt[i+1]\\n\\n return linear_interpolate(new_pt, new_corners, new_values)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":874,"cells":{"query_id":{"kind":"string","value":"ecb0ee01b6ccb11e75677aa7d9ee8dfb"},"query":{"kind":"string","value":"Plot the strain data in the frequency domain"},"positive_passages":{"kind":"list like","value":[{"docid":"d9bd7a000ba278376e63a5fe428de478","score":"0.6397094","text":"def _frequency_domain_plot(self, *args, **kwargs):\n from pesummary.gw.plots.detchar import frequency_domain_strain_data\n\n return frequency_domain_strain_data(*args, **kwargs)[self.IFO]","title":""}],"string":"[\n {\n \"docid\": \"d9bd7a000ba278376e63a5fe428de478\",\n \"score\": \"0.6397094\",\n \"text\": \"def _frequency_domain_plot(self, *args, **kwargs):\\n from pesummary.gw.plots.detchar import frequency_domain_strain_data\\n\\n return frequency_domain_strain_data(*args, **kwargs)[self.IFO]\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"9a1712a9bdcd3cfbf3521c02ffd920db","score":"0.6559505","text":"def plot(self):\n x_range = np.arange(0, 1.0, 0.01)\n y_vals = [self.get_freq(x) for x in x_range]\n plt.plot(x_range, y_vals)\n plt.show()","title":""},{"docid":"32df4677233cf5ac08efc6bfb875f532","score":"0.6464078","text":"def plot_strain_rate(self, strain):\r\n self.acq_rate\r\n pass","title":""},{"docid":"10d0240a7b2c318a70684ee5aea6f0a1","score":"0.64597017","text":"def plotWaveandFrequencyRange(self):\n\n\n times = -np.arange(WWProperties.signalSamples) * WWProperties.signalTimestep + WWProperties.signalStart\n signal = self.waveMaker.makeWave(WWProperties.m1, WWProperties.m2, WWProperties.phic, WWProperties.tc, times) \n frequency = self.waveMaker.omega / (2 * np.pi)\n\n plt.figure(1)\n plt.subplot(211)\n plt.title(\"Inspiral for (10 M${}_\\odot$, 1.4M${}_\\odot$) non-spining system (WW fig 8)\")\n plt.plot(times, frequency)\n plt.ylabel('Orbital Frequency')\n\n plt.subplot(212)\n plt.plot(times, signal)\n plt.xlabel('Time (s)')\n plt.ylabel('$R h_+$')\n\n plt.savefig(self.figFolderName + \"/\" + Params + \" \" + GandC0 + \" units signal signalStart %f signalSamples %d signalTimestep %f (WW fig 8).pdf\" % (WWProperties.signalStart, WWProperties.signalSamples, WWProperties.signalTimestep), bbox_inches='tight')\n plt.cla()\n\n print (\"frequency hight\", frequency[0], \"frequency low\", frequency[-1], \"\\n\")","title":""},{"docid":"7cd38308831333d2194321b987d5e54a","score":"0.64455694","text":"def plotSpectrum(y,Fs):\n \n n = len(Data) # length of the signal\n k = arange(n)\n T = n/Fs\n frq = k/T # two sides frequency range\n frq = frq[range(n/2)] # one side frequency range\n\n Y = fft(y)/n # fft computing and normalization\n Y = Y[range(n/2)]\n \n plot(frq,abs(Y),'r') \n xlabel('Freq (Hz)')\n ylabel('|Y(freq)|')","title":""},{"docid":"1d146498a3afe5dbeb0b5200a35b7813","score":"0.6393334","text":"def plot(self, *args, **kw):\n return self.frequency.plot(*args, **kw)","title":""},{"docid":"43dc6d572557709820fdf37ce6d96e46","score":"0.6373058","text":"def plot(self):\n fig, (ax0,ax1) = plt.subplots(2,figsize=(14,14))\n ax0.plot(SineSignal.t,self.injected_signal)\n ax1.plot(self.inj_freq[:SineSignal.n_t//2],self.freq_prob)\n ax0.set(title='injected signal',ylabel='signal value',xlabel='signal time chunk')\n ax1.set(title='discrete FFT',ylabel='freq strength in signal',xlabel='frequency')\n plt.savefig('example_signal.pdf',bbox_inches='tight')\n plt.clf()","title":""},{"docid":"19aaefa1345a282c92b5900b512f451e","score":"0.6189974","text":"def plotSpectrum(y,Fs):\n n = len(y) # length of the signal\n k = arange(n)\n T = n/Fs\n frq = k/T # two sides frequency range\n frq = frq[range(n/2)] # one side frequency range\n \n Y = fft(y)/n # fft computing and normalization\n Y = Y[range(n/2)]\n plot(frq,abs(Y),'r') # plotting the spectrum\n xlabel('Freq (Hz)')\n ylabel('|IR_Spectrum(freq)|')\n rospy.loginfo('[sharp_listener] DONE PLOTING!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!')\n show()","title":""},{"docid":"01cc1e93736eff282d7d80f527ae3d74","score":"0.61449003","text":"def plot_wavelength(spectrum, wavelength):\n #print(len(spectrum[0]))\n #print(len(wavelength[0]))\n for index, element in enumerate(spectrum):\n plt.plot(wavelength[index], element)\n plt.xlabel(\"Angstroms\")\n plt.ylabel(\"Count\")\n plt.title(\"No Interpolation\")\n plt.show()","title":""},{"docid":"be1260d4ed484e7a2db67cbcc8b6dcf5","score":"0.60464585","text":"def plotSpectrum(self, ax, signal, color):\n n = len(signal) # length of the signal\n k = arange(n)\n T = n/self.sample_rate\n frq = k/T # two sides frequency range\n frq = frq[range(int(n/2))] # one side frequency range\n\n Y = fft(signal)/n # fft computing and normalization\n Y = Y[range(int(n/2))]\n\n ax.plot(frq, abs(Y), color) # plotting the spectrum\n xlabel('Freq (Hz)')\n ylabel('Gain')","title":""},{"docid":"1e21d7a7639204bf306e85af3f9fef3e","score":"0.60293746","text":"def plot_sfrd_data(ax):\n\n ObsSFRdensity = np.array([\n [0, 0.0158489, 0, 0, 0.0251189, 0.01000000],\n [0.150000, 0.0173780, 0, 0.300000, 0.0181970, 0.0165959],\n [0.0425000, 0.0239883, 0.0425000, 0.0425000, 0.0269153, 0.0213796],\n [0.200000, 0.0295121, 0.100000, 0.300000, 0.0323594, 0.0269154],\n [0.350000, 0.0147911, 0.200000, 0.500000, 0.0173780, 0.0125893],\n [0.625000, 0.0275423, 0.500000, 0.750000, 0.0331131, 0.0229087],\n [0.825000, 0.0549541, 0.750000, 1.00000, 0.0776247, 0.0389045],\n [0.625000, 0.0794328, 0.500000, 0.750000, 0.0954993, 0.0660693],\n [0.700000, 0.0323594, 0.575000, 0.825000, 0.0371535, 0.0281838],\n [1.25000, 0.0467735, 1.50000, 1.00000, 0.0660693, 0.0331131],\n [0.750000, 0.0549541, 0.500000, 1.00000, 0.0389045, 0.0776247],\n [1.25000, 0.0741310, 1.00000, 1.50000, 0.0524807, 0.104713],\n [1.75000, 0.0562341, 1.50000, 2.00000, 0.0398107, 0.0794328],\n [2.75000, 0.0794328, 2.00000, 3.50000, 0.0562341, 0.112202],\n [4.00000, 0.0309030, 3.50000, 4.50000, 0.0489779, 0.0194984],\n [0.250000, 0.0398107, 0.00000, 0.500000, 0.0239883, 0.0812831],\n [0.750000, 0.0446684, 0.500000, 1.00000, 0.0323594, 0.0776247],\n [1.25000, 0.0630957, 1.00000, 1.50000, 0.0478630, 0.109648],\n [1.75000, 0.0645654, 1.50000, 2.00000, 0.0489779, 0.112202],\n [2.50000, 0.0831764, 2.00000, 3.00000, 0.0512861, 0.158489],\n [3.50000, 0.0776247, 3.00000, 4.00000, 0.0416869, 0.169824],\n [4.50000, 0.0977237, 4.00000, 5.00000, 0.0416869, 0.269153],\n [5.50000, 0.0426580, 5.00000, 6.00000, 0.0177828, 0.165959],\n [3.00000, 0.120226, 2.00000, 4.00000, 0.173780, 0.0831764],\n [3.04000, 0.128825, 2.69000, 3.39000, 0.151356, 0.109648],\n [4.13000, 0.114815, 3.78000, 4.48000, 0.144544, 0.0912011],\n [0.350000, 0.0346737, 0.200000, 0.500000, 0.0537032, 0.0165959],\n [0.750000, 0.0512861, 0.500000, 1.00000, 0.0575440, 0.0436516],\n [1.50000, 0.0691831, 1.00000, 2.00000, 0.0758578, 0.0630957],\n [2.50000, 0.147911, 2.00000, 3.00000, 0.169824, 0.128825],\n [3.50000, 0.0645654, 3.00000, 4.00000, 0.0776247, 0.0512861],\n ], dtype=np.float32)\n\n ObsRedshift = ObsSFRdensity[:, 0]\n xErrLo = ObsSFRdensity[:, 0]-ObsSFRdensity[:, 2]\n xErrHi = ObsSFRdensity[:, 3]-ObsSFRdensity[:, 0]\n\n ObsSFR = np.log10(ObsSFRdensity[:, 1])\n yErrLo = np.log10(ObsSFRdensity[:, 1])-np.log10(ObsSFRdensity[:, 4])\n yErrHi = np.log10(ObsSFRdensity[:, 5])-np.log10(ObsSFRdensity[:, 1])\n\n ax.errorbar(\n ObsRedshift,\n ObsSFR,\n yerr=[yErrLo, yErrHi],\n xerr=[xErrLo, xErrHi],\n color='g',\n lw=1.0,\n alpha=0.3,\n marker='o',\n ls='none',\n label='Observations'\n )\n\n return ax","title":""},{"docid":"4eddb8873c3833a0aab5857e3cccbae8","score":"0.6019912","text":"def plot_ssd(self, condition='all', counts=False, bw=.01, xlims=None):\n cidx=self._get_cidx(condition)\n a=stats.itemfreq(self.SSD[(cidx) & np.isfinite(self.SSD)])#.astype(np.int)\n if counts==False:\n a[:,1]/=np.sum(a[:,1])\n pl.bar(a[:,0]-bw/2.0, a[:,1], width=bw)\n pl.xlabel('SSD')\n pl.ylabel('freq')\n pl.title('data=%s, condition=%s'%(self.name, 'all' if condition=='all' else \":\".join(self.design.condidx(condition))))\n if xlims!=None:\n pl.xlim(*xlims)\n if not counts:\n pl.ylim(0,1) # probs ","title":""},{"docid":"764745d5cc8079a761da3f6bce628b39","score":"0.60031587","text":"def plot(self):\n timebin = np.linspace(0, self.total_duration, self.specx.shape[0])\n freq_vector = [self.fc - (self.fs/2), (self.fs/2) + self.fc]\n \n plt.figure(figsize=(12,8)) \n plt.imshow(self.specx[::self.jump], extent=freq_vector + self.rightlim, origin='lower', aspect='auto') \n plt.plot(self.track_center[0], self.time_bins[0], color = 'k')\n plt.plot(self.raw_center[:, 0], timebin, color = 'white', marker='.', alpha=0.5)\n plt.xlabel('Frequency (Hz) \\n'+'\\nFile:'+ self.filename + '\\nRuntime:' + str(self.t_fs))\n plt.ylabel('Time (s)')\n plt.title('Waterfall')\n plt.xlim([self.f_chan[0] - self.BW[0]/2, self.f_chan[0] + self.BW[0]/2])\n plt.colorbar()\n plt.savefig('waterfal_plot.png', dpi=400, transparent=False)\n plt.show()","title":""},{"docid":"b38d8bba19a1986cf110f47f667f66fb","score":"0.59973717","text":"def plot_tsys(self, S):\n plot_scale = 0.\n nscale = 0\n for ipix in range(S.npix):\n indx_fin = np.where(np.isfinite(S.roach[ipix].tsys_spectrum))\n indx_inf = np.where(np.isinf(S.roach[ipix].tsys_spectrum))\n indx_nan = np.where(np.isnan(S.roach[ipix].tsys_spectrum))\n print(ipix, 'fin------------', indx_fin[0])\n print(ipix, 'inf------------', indx_inf[0])\n print(ipix, 'nan------------', indx_nan[0])\n l_fin = len(indx_fin[0])\n if l_fin > 0 and S.roach[ipix].tsys > 0 and \\\n S.roach[ipix].tsys < 500:\n plot_scale = plot_scale + S.roach[ipix].tsys\n nscale = nscale + 1\n if nscale > 0:\n plot_scale = plot_scale / nscale\n plot_order = [1,5,9,13,2,6,10,14,3,7,11,15,4,8,12,16];\n for ipix in range(S.npix):\n pixel_id = S.roach_pixel_ids[ipix]\n ax = pl.subplot(4, 4, plot_order[pixel_id])\n ax.tick_params(axis='both', which='major', labelsize=6)\n ax.tick_params(axis='both', which='minor', labelsize=6)\n indx_fin = np.where(np.isfinite(S.roach[ipix].tsys_spectrum))\n l_fin = len(indx_fin[0])\n if l_fin > 0:\n pl.plot(S.roach[ipix].tsys_spectrum)\n pl.text(S.nchan / 2, 10, '%d %6.0fK'%(pixel_id, \n S.roach[ipix].tsys), \n horizontalalignment='center')\n if plot_scale != 0:\n pl.axis([0, S.nchan, 0, plot_scale * 1.5])\n else:\n pl.text(0.1, 0.5, '%d NaN'%(pixel_id))\n pl.suptitle('TSys: ObsNum %d\\n%s %s GHz'%(S.obsnum, S.receiver, \n S.line_rest_frequency))","title":""},{"docid":"6861c4bf1e79fc5f1cb4ab9f65f58ddc","score":"0.5983932","text":"def plot(self, noised=False):\n\n if noised and self.ndata is None:\n raise ValueError(\n 'Can not display noised data when snr has been set to None.')\n\n # Show data\n hs_tmp = hs.signals.Signal2D(self.data if not noised else self.ndata)\n hs_tmp.axes_manager = self.hsdata.axes_manager\n hs_tmp.metadata = self.hsdata.metadata\n hs_tmp.plot()","title":""},{"docid":"3acd03f47ed6284b9846c2ff82dca9ac","score":"0.5945903","text":"def plotFourier(self, widgetName, data, pen):\n try:\n N = len(data)\n # Normalization\n yplot = 2* np.abs(data[: N//2]) / N\n widgetName.plotItem.plot(self.signalFourier['dataFrequencies'][: N//2], yplot, pen= pen)\n except:\n pass","title":""},{"docid":"91a1bfa74dc58b5ae7b3fe03e08df65e","score":"0.5928069","text":"def plot_data(self\n ) -> None:\n self.simulate_data()\n\n plt.plot(self.t, self.spleen_signal, color='red')\n plt.plot(self.t, self.liver_signal, color='blue')\n plt.plot(self.spleen_sampling_times,\n self.spleen_data, marker=\"x\", color='red', linewidth=0)\n plt.plot(self.liver_sampling_times, self.liver_data,\n marker=\"x\", color='blue', linewidth=0)\n plt.show()","title":""},{"docid":"020261c8d490d9b2375b232dd988f26f","score":"0.59164333","text":"def draw_latent_frequency(encoder=None):\n \n color_id = {1: 'red', 2: 'green', 3:'blue', 4: 'orange', 5: 'purple'}\n\n if encoder is None:\n encoder = load_model('encoder.h5')\n linear_1, amp_1 = sin_signal_freq()\n \n sin_latents_1 = encoder.predict(linear_1) \n plt.cla()\n plt.clf()\n clr = [color_id[i] for i in amp_1]\n plt.scatter(sin_latents_1[:, 0], sin_latents_1[:, 1], s=3.5, color=clr)\n #plt.savefig('frequency_with_phase_latents.png', dpi=200)\n plt.show()","title":""},{"docid":"b62a559972d796680c393dc4d7c9736e","score":"0.58984387","text":"def plot(self):\n from matplotlib import pyplot\n import numpy as np\n\n x = unit.Quantity(np.arange(0, np.pi, 0.1), unit=unit.radians)\n pyplot.plot(x,\n self.k * (1 + np.cos(self.periodicity * x - self.phase)),\n 'r--')\n\n pyplot.show()","title":""},{"docid":"b21b21cfb8950b17961e02645fbed45a","score":"0.58799034","text":"def plot_rx_f(data_z):\n freq, z_fft = fft_rx(data_z)\n plt.plot(freq, z_fft)\n plt.show()","title":""},{"docid":"3c0726ec78152ae796254111f38ed0a3","score":"0.58260477","text":"def simpleFreqPlot(x, y, plot=plt, xscale=False, yscale=False):\n plot.scatter(x, y)\n\n if xscale:\n plt.xscale('log') \n if yscale:\n plt.yscale('log')","title":""},{"docid":"60fa5d6ed28306c4d2c22893009d51b4","score":"0.5802264","text":"def draw(self):\n\t\tts = pd.Series(self.R[2:], index=xrange(2,self.N+2))\n\t\tts.plot()\n\t\tplt.show()","title":""},{"docid":"72f4c34f5e39fcdc2c4c161c2570dc46","score":"0.5779021","text":"def plot_as1D(self, noised=False):\n\n if noised and self.ndata is None:\n raise ValueError(\n 'Can not display noised data when snr has been set to None.')\n\n if noised:\n hs_data = hs.signals.Signal1D(self.ndata)\n else:\n hs_data = hs.signals.Signal1D(self.data)\n\n hs_data.axes_manager = self.hsdata.axes_manager\n hs_data.metadata = self.hsdata.metadata\n\n hs_data.plot()\n hs_data._plot.navigator_plot.ax.images[-1].set_cmap(\"viridis\")","title":""},{"docid":"e3899bf53a12913b0f5c1d2d78c61dce","score":"0.57586116","text":"def plotFrequency(b,a=1):\n w,h = signal.freqz(b,a)\n h_dB = abs(h);#20 * np.log(abs(h))/np.log(10)\n subplot(211)\n plot(w/max(w),h_dB)\n #plt.ylim(-150, 5)\n ylabel('Magnitude (db)')\n xlabel(r'Normalized Frequency (x$\\pi$rad/sample)')\n title(r'Frequency response')\n subplot(212)\n h_Phase = np.unwrap(np.arctan2(np.imag(h),np.real(h)))\n plot(w/max(w),h_Phase)\n ylabel('Phase (radians)')\n xlabel(r'Normalized Frequency (x$\\pi$rad/sample)')\n title(r'Phase response')\n plt.subplots_adjust(hspace=0.5)\n return w;","title":""},{"docid":"b3508db5d40f46209add10843063a723","score":"0.57523036","text":"def stability_plotter(df, waves):\n stability = []\n for header in df:\n array = df[header].to_numpy()\n\n std_dev = np.std(array)\n average = np.average(array)\n stability.append(std_dev/average) \n \n # nice plot for stability, how it changes per wavelength\n plt.plot(waves, stability) \n plt.title(\"Stability per wavelength\")\n plt.xlabel(\"Wavelength (nm)\")\n plt.ylabel(\"Stability\")\n plt.savefig(\"stability.png\")\n plt.show()\n plt.close() \n \n return stability","title":""},{"docid":"e81ae6b25f4095438c8c6ae76050a372","score":"0.5750488","text":"def plot_seabird(netcdffile):\n\n f = netcdf.netcdf_file(netcdffile, 'r')\n\n depth = f.variables['DEPTH'].data\n salinity = f.variables['PSAL'].data\n temp = f.variables['TEMP'].data\n\n f.close()\n\n fig = plt.figure()\n ax1 = fig.add_subplot(111)\n ln1 = ax1.plot(salinity, depth, 'r.', label='Salinity')\n ax1.invert_yaxis()\n\n ax2 = ax1.twiny()\n ln2 = ax2.plot(temp, depth, 'g.', label='Temperature')\n\n ax1.grid(True)\n ax1.set_ylabel('Depth (m)')\n ax1.set_xlabel('Salinity (PSU)')\n ax2.set_xlabel('Temperature ($^\\circ$C)')\n\n lns = ln1+ln2\n labs = [l.get_label() for l in lns]\n ax2.legend(lns, labs, loc=0)\n\n return fig","title":""},{"docid":"d7bac4221056958e0be52693fb1669b1","score":"0.5747702","text":"def plot_temporal_distribution(self, fname=None):\n fname = self._fname(fname)\n \n figname = 'temps1.Simulation.plot_temporal_distribution.' + fname.replace(\" \", \"_\")\n fig, axs = plt.subplots(2, 1, num=figname, clear=True, figsize=[6.4, 7.19])\n axs[0].set_title(f'{fname.capitalize()} filter\\nTemporal distribution of S1 candidates')\n \n ax = axs[0]\n ax.set_xlabel('Time relative to true S1 location [ns]')\n ax.set_ylabel('Inverse of neighbor temporal gap [ns$^{-1}$]')\n \n for k in ['all', 'dcr']:\n time = self.times[fname][k]\n time = np.sort(time)\n ddecdf = 1 / np.diff(time)\n x = time - self.s1loc\n y = np.concatenate([ddecdf, ddecdf[-1:]])\n ax.plot(x, y, drawstyle='steps-post', **self.plotkw[k])\n \n ax.axvspan(-self.deadradius, self.deadradius, color='#eee', zorder=-9, label='$\\\\pm$ dead radius')\n\n ax.legend(loc='upper right')\n ax.set_xlim(1.5 * self.deadradius * np.array([-1, 1]))\n ax.set_yscale('log')\n ax.minorticks_on()\n ax.grid(True, which='major', linestyle='--')\n ax.grid(True, which='minor', linestyle=':')\n \n \n ax = axs[1]\n ax.set_xlabel('Time relative to true S1 location [ns]')\n ax.set_ylabel('Histogram bin density [ns$^{-1}$]')\n \n times1 = self.hits1.reshape(-1) - self.s1loc \n time = self.times[fname]['all'] - self.s1loc\n signal = self.signal[fname]['all']\n time_match = time[signal]\n sigma = qsigma.qsigma(time_match)\n \n # t = np.linspace(..., ..., 1000)\n # ax.plot(t, pS1.p_S1_gauss(t, self.VL, self.tauV, self.tauL, self.tres), label='S1 pdf')\n histkw = dict(bins='auto', density=True, histtype='step', zorder=10)\n ax.hist(times1, label=f'S1 photons ({len(times1)})', linestyle=':', **histkw)\n ax.hist(time_match, label=f'matching candidates ($\\\\sigma_q$={sigma:.3g} ns)', **histkw)\n \n ax.axvspan(0, self.deadradius, color='#eee', zorder=-9, label=f'dead radius ({self.deadradius} ns)')\n\n textbox.textbox(ax, self.infotext(), loc='upper left', zorder=11)\n \n ax.legend(loc='upper right', fontsize='small')\n ax.set_yscale('log')\n linthreshx = 100 #10 ** np.ceil(np.log10(15 * sigma))\n ax.set_xscale('symlog', linthreshx=linthreshx)\n ax.minorticks_on()\n ax.xaxis.set_minor_locator(symloglocator.MinorSymLogLocator(linthreshx))\n ax.grid(True, which='major', linestyle='--')\n ax.grid(True, which='minor', linestyle=':')\n\n fig.tight_layout()\n \n return fig","title":""},{"docid":"42942931756a0fae0105213e21666ea5","score":"0.5739101","text":"def plot(self):\n from matplotlib import pyplot\n import numpy as np\n\n x = unit.Quantity(np.arange(0, np.pi, 0.1), unit=unit.radians)\n pyplot.plot(x, self.constant * (1 + self.prefactor\n * np.cos(self.periodicity * x\n + self.phase)),\n 'r--')\n\n pyplot.show()","title":""},{"docid":"97916c7a9496d26b389c541e02f8a532","score":"0.573355","text":"def do_stability_plotting() -> None:\n # data = gather_stability_data('500runs_lowerL')\n data = pickle_load('500runs_lowerL')\n plt.plot_stability(data)","title":""},{"docid":"4f1f1b272d5c909d338e7f4486fb3d7d","score":"0.5661861","text":"def plot(y, si):\n x = si.timer.series.magnitude\n plot_power(x, y, si)\n plot_reactivity(x, si)\n plot_power_w_reactivity(x=x, y=y, si=si)\n plot_zetas(x, y, si)\n if si.ne._ndg > 0:\n plot_omegas(x, y, si)\n plot_temps_together(x, y, si)\n plot_temps_separately(x, y, si)","title":""},{"docid":"f8cb646c3958e42160b2bfa23202d86f","score":"0.56223905","text":"def plot_data(self, cutoff=200):\n\n # If data not loaded, call load_data function\n if len(self.data) == 0:\n self.load_data()\n\n ix1 = index_of(self.energy, - min(self.energy))\n ix2 = index_of(self.energy, cutoff)\n yHeight = max(self.nor_counts[ix2:])\n\n x = self.energy[:ix1]\n y = self.counts[:ix1]\n model = GaussianModel()\n pars = model.guess(y, x=x)\n result = model.fit(y, pars, x=x)\n fwhm = result.params['fwhm'].value\n\n # Plot\n plt.figure(figsize=(10, 3))\n gs = gridspec.GridSpec(1, 3)\n ax0 = plt.subplot(gs[0, 0])\n ax0.plot(x, y, 'bo', markersize=4)\n ax0.plot(x, result.best_fit, 'r-')\n ax0.set_xlabel('wavenumber (cm-1)', fontsize=8)\n ax0.set_ylabel('counts', color='k', fontsize=8)\n plt.title('FWHM ='+\"{0:.0f}\".format(fwhm), fontsize=10)\n plt.xlim(-200, 200)\n ax0.locator_params(axis='x', nbins=5)\n ax0.locator_params(axis='y', nbins=8)\n\n ax1 = plt.subplot(gs[0, 1:3])\n ax1.plot(self.energy, self.nor_counts, 'k-')\n ax1.set_xlabel('wavenumber (cm-1)', fontsize=8)\n # Make the y-axis label and tick labels match the line color.\n for tl in ax1.get_yticklabels():\n tl.set_color('k')\n\n ax2 = ax1.twinx()\n ax2.plot(self.energy, self.nor_counts, 'r-')\n ax2.set_ylabel('counts', color='r', fontsize=8)\n for tl in ax2.get_yticklabels():\n tl.set_color('r')\n\n plt.ylim(0, yHeight)\n plt.xlim(min(self.energy), max(self.energy))\n plt.title(self.filename, fontsize=10)\n ax2.locator_params(axis='x', nbins=15)\n ax2.locator_params(axis='y', nbins=8)\n plt.show()","title":""},{"docid":"7184530a51eb0ca2b3baf9fd9e7ecb36","score":"0.56194925","text":"def plot(t, y, oversampling=5, figsize=(12, 20)):\n freqs, mag, phase, Pn, proba = extract(t, y, hifac=1, oversampling=oversampling)\n fig, ax = plt.subplots(6, 1, figsize=figsize)\n ax[0].scatter(t, y, color='black')\n ax[0].set_title('Flux')\n ax[0].set_xlabel('time [$mjd$]')\n \n ax[1].plot(freqs, mag, color='black')\n ax[1].set_title('Spectrum magnitude')\n ax[1].set_xlabel('Freq [$mjd^{-1}$]')\n \n ax[2].plot(freqs, phase, color='black')\n ax[2].set_title('Spectrum phase')\n ax[2].set_xlabel('Freq [$mjd^{-1}$]')\n \n ax[3].plot(1/freqs, Pn, color='black')\n ax[3].set_title('LombScargle periodogram')\n ax[3].set_xlabel('Period [$mjd$]')\n \n ax[4].plot(1/freqs, proba, color='black')\n ax[4].set_title('Periodogram true alarm probability')\n ax[4].set_xlabel('Period [$mjd$]')\n # Estimated phase\n h = 1/freqs[np.argmax(Pn)]\n phase_hat = (t/h)%1\n ax[5].scatter(phase_hat, y, color='black')\n ax[5].set_title('Phase from estimated period, where h=%.4f'%h)\n fig.tight_layout()\n return freqs, mag, phase, Pn, proba","title":""},{"docid":"68ce11b2bc075e32138591177c931ee5","score":"0.5619293","text":"def plot(self, iwidth=None):\n if iwidth is None:\n snr = self.snrs.max(axis=1)\n else:\n snr = self.snrs[:, iwidth]\n\n plt.plot(self.periods, snr, marker='o', markersize=2, alpha=0.5)\n plt.xlim(self.periods.min(), self.periods.max())\n plt.xlabel('Trial Period (s)', fontsize=16)\n plt.ylabel('S/N', fontsize=16)\n\n if iwidth is None:\n plt.title('Best S/N at any trial width', fontsize=18)\n else:\n width_bins = self.widths[iwidth]\n plt.title('S/N at trial width = %d' % width_bins, fontsize=18)\n\n plt.xticks(fontsize=14)\n plt.yticks(fontsize=14)\n plt.grid(linestyle=':')\n plt.tight_layout()","title":""},{"docid":"e6c72ac3c0c6b364b049b8287a00d8ca","score":"0.5612675","text":"def plot_freq_tau(self):\n plt.plot(self.frequency, self.tau)\n plt.xlabel(\"Frequency (MHz)\")\n plt.ylabel(r\"$\\tau (\\mu s)$\")\n plt.show()","title":""},{"docid":"ecbe146e7ef623fe833d3baac843b889","score":"0.5577341","text":"def plot_as2D(self, noised=False):\n\n if noised and self.ndata is None:\n raise ValueError(\n 'Can not display noised data when snr has been set to None.')\n\n if noised:\n hs_data = hs.signals.Signal2D(\n np.moveaxis(self.ndata, [0, 1, 2], [1, 2, 0]))\n else:\n hs_data = hs.signals.Signal2D(\n np.moveaxis(self.data, [0, 1, 2], [1, 2, 0]))\n\n hs_data.axes_manager = self.hsdata.axes_manager\n hs_data.metadata = self.hsdata.metadata\n\n hs_data.plot(cmap='viridis')","title":""},{"docid":"2a46e6e350f17c7266ca0fb74d298350","score":"0.55732363","text":"def plot_signals(self\n ) -> None:\n self.calculate_signals()\n\n plt.plot(self.t, self.spleen_signal, color='red')\n plt.plot(self.t, self.liver_signal, color='blue')\n plt.show()","title":""},{"docid":"e6b0d0ac14c98bef106090cdeba380f9","score":"0.5559552","text":"def plot_ant_gain(self):\n import pylab\n pylab.plot(self.config['freqs']/1e6,self.config['antenna_bandpass'])\n pylab.title('Antenna gain %s'%self.config['antenna_bandpass_calfile'])\n pylab.xlabel('Frequency (MHz)')\n pylab.ylabel('Relative response (dBi)')","title":""},{"docid":"e7744e0d9ac9a0ab4dc9b64addef70b9","score":"0.55551803","text":"def plot_converted(spectrum):\n for index, element in enumerate(spectrum):\n plt.plot(element[0][0], element[0][1])\n plt.xlabel(\"Angstroms\")\n plt.ylabel(\"Count\")\n plt.title(\"Interpolated\")\n plt.show()","title":""},{"docid":"5feb152157af5a94623b346dcc2104bb","score":"0.5549776","text":"def plot_waterfall(d, freqs, lsts, t_unit='hr', f_unit='MHz',\n raster=True):\n\n plt.imshow(d, aspect='auto', interpolation='none',\n rasterized=raster,\n extent=(freqs[0], freqs[-1], lsts[0], lsts[-1]))\n plt.xlabel(\"Frequency [%s]\" % f_unit)\n plt.ylabel(\"Time [%s]\" % t_unit)","title":""},{"docid":"4633c1fe42fab5ba5f5baaa1f8632282","score":"0.55475754","text":"def plot_spe_freq(res, points=None, record=-1, time=None, fig_name=''):\n # If time is given searched for record\n if time is not None:\n record = res.get_closest_record(time)\n time = res.times[record]\n else:\n time = res.times[record]\n\n if points is None:\n points = res.get_list_spectrum_points()\n\n fig, axe = plt.subplots()\n\n for point in points:\n # Getting list of frequencies and spectrum value\n freq, spectrum = res.get_spectrum(point, record)\n # Plotting it\n plot1d(axe, freq, spectrum, plot_label='point {:06d}'.format(point),\n x_label='Frequencies', y_label='Spectrum')\n\n axe.legend()\n\n axe.set_title(\"At time {}\".format(time))\n\n if fig_name != '':\n print(\" \"*8+\"~> Plotting {}\".format(fig_name))\n fig.savefig(fig_name)\n else:\n plt.show()\n\n plt.close(fig)","title":""},{"docid":"9d2121dbb71c965bf21c71ee0f085cd5","score":"0.5524251","text":"def plot(self):\r\n test_domain = np.linspace(0, 1, 100, dtype = intprim.constants.DTYPE)\r\n test_range = self.get_basis_functions(test_domain)\r\n\r\n fig = plt.figure()\r\n\r\n for basis_func in test_range:\r\n plt.plot(test_domain, basis_func)\r\n\r\n fig.suptitle('Basis Functions')\r\n\r\n plt.show(block = False)","title":""},{"docid":"321bcdea5c2ecb4b5d058d69d46fb14b","score":"0.55236715","text":"def plot_sxx(self, bloc, chan, num_samples=None):\n\n pol0, pol1 = self.get_data(bloc, chan, num_samples)\n sxx0, sxx1 = np.fft.fftshift(np.abs(np.fft.fft(pol0)) ** 2), np.fft.fftshift(np.abs(np.fft.fft(pol1)) ** 2)\n plt_title = \"SPD of bloc: {}, channel {}\".format(str(bloc), str(chan))\n lfreq, ufreq = self._calc_chan_freq_range(chan) # get lower bound and upper bound frequency of the channel\n freq_axis = np.linspace(lfreq, ufreq, len(sxx0))\n self._plot(sxx0, sxx1, plt_title, x=freq_axis)\n return sxx0, sxx1","title":""},{"docid":"2877ac82537c408c7259bbc2c2e06982","score":"0.5521294","text":"def generateSinewave(nrCycles, nrdataPointsPerCycles = 2048):\n cycles = np.linspace(0,nrCycles * 2 * np.pi, nrdataPointsPerCycles)\n cyclesArray = np.array(np.sin(cycles))\n #plt.plot(cyclesArray)\n #plt.xlabel('angle [rad]')\n #plt.ylabel('sin (x)')\n #plt.axis('tight')\n #plt.show()\n \n wave = int16scale(cyclesArray)\n plt.plot(wave)\n plt.xlabel('angle [rad]')\n plt.ylabel('sin (x)')\n plt.axis('tight')\n plt.show()","title":""},{"docid":"d8a7cb7b48c07db5456c3a268448f9ba","score":"0.55171424","text":"def display_stft(X,\n fs,\n len_sig,\n low_freq=0,\n high_freq=3000,\n min_db=-10,\n max_db=0,\n normalize=True):\n # normalize : largest coefficient magnitude is unity\n X_temp = X.copy()\n if normalize:\n X_temp /= np.amax(abs(X_temp))\n\n # compute frequencies array\n Freqs = np.array([low_freq, high_freq])\n Fd = (Freqs * X_temp.shape[0] / fs).astype(int)\n\n # compute values matrix\n Z = X_temp[Fd[1]:Fd[0]:-1, :]\n Z = np.clip(np.log(np.abs(Z) + 1e-50), min_db, max_db)\n Z = 255 * (Z - min_db) / (max_db - min_db)\n\n # compute duration\n time = float(len_sig) / float(fs)\n\n # plotting\n plt.imshow(Z,\n extent=[0, time, low_freq / 1000, high_freq / 1000],\n aspect=\"auto\")\n plt.ylabel('Frequency (Khz)')\n plt.xlabel('Time (sec)')\n plt.show()","title":""},{"docid":"1e633f063f5d07dbb8d1057a1cb1afda","score":"0.55149215","text":"def plot_restfreq_spectrum(SSC, band):\n\n def get_spectrum(SSC, band):\n spectrum = spectra[str(SSC['no'])][band]\n frequency = spectrum['frequency'].to(u.MHz)\n intensity = spectrum['spectrum'].to(u.K)\n\n # shift spectrum to rest frequency\n velshift = SSC['velshift']\n frequency = [(-vsys-velshift).to(u.GHz, equivalencies=u.doppler_optical(f)).value for f in frequency]*u.GHz\n\n # remove NaNs\n frequency, intensity = crossmatch(frequency.to(u.GHz).value, intensity.to(u.K).value)\n return frequency, intensity\n\n def set_up_figure(SSC, specie, band):\n fig,ax = plt.subplots(nrows=1, ncols=1, squeeze=True, sharex='col', sharey='row', figsize=(10,8))\n ax.text(0.05, 0.9, 'SSC '+str(SSC['no'])+': '+band, color='k', transform=ax.transAxes, ha='left', va='top', weight='bold', fontsize=16, bbox=props)\n return fig,ax\n\n def plot_spectrum(ax, frequency, spectrum):\n ax.plot(frequency, spectrum, ls='-', color='k', zorder=3)\n ax.fill_between(frequency, spectrum, [0. for f in frequency], color='grey', alpha=0.5, zorder=2)\n\n def get_detected_lines(band=None):\n # get detected species\n all_species = []\n for SSC in SSCs:\n for specie in detected_species[str(SSC['no'])]:\n if not specie in all_species:\n all_species.append(specie)\n # get all lines of the detected species\n all_lines = []\n for specie in all_species:\n slines = [l for l in lines if l['XCLASS']==specie]\n for sl in slines:\n all_lines.append(sl)\n # keep only lines of given band\n if not band==None:\n bandlines = []\n for line in all_lines:\n if band=='LSB':\n if line['restfreq']<350*u.GHz:\n bandlines.append(line)\n elif band=='USB':\n if line['restfreq']>350*u.GHz:\n bandlines.append(line)\n return sorted(bandlines, key=lambda k: k['restfreq'])\n else:\n return sorted(all_lines, key=lambda k: k['restfreq'])\n\n def label_lines(ax, spectrum, band):\n detected_lines = get_detected_lines(band=band)\n for idx,line in enumerate(detected_lines):\n restfreq = line['restfreq'].to(u.GHz).value\n if (restfreq>frequency[0] and restfreq<frequency[-1]):\n if band=='LSB':\n xlim = [342.4, 346.2]\n elif band=='USB':\n xlim = [354.3, 358.1]\n xloc = xlim[0] +((idx+0.5)/len(detected_lines))*(xlim[1]-xlim[0])\n ax.axvline(x=restfreq, ymin=0, ymax=1, color='dimgrey', ls='--', lw=0.5, zorder=1)\n ax.plot([restfreq,xloc], [1.05*np.nanmax(spectrum), 1.05*1.05*np.nanmax(spectrum)], color='dimgrey', ls='--', lw=0.5, zorder=1, clip_on=False)\n ax.text(xloc, 1.06*1.05*np.nanmax(spectrum), line_tex(line), color='dimgrey', fontsize=10, rotation=90, ha='center', va='bottom')\n\n\n def velocity_indicator(ax, frequency, spectrum):\n x = np.nanmedian(frequency)\n y = 0.95*np.nanmax(spectrum)\n ax.plot([x, x+0.05836398],[y,y], c='k', ls='-', lw='3', zorder=11)\n ax.text(x, y, r'- $\\sim 50$\\,km\\,s$^{-1}$', color='k', ha='left', va='center', weight='bold', bbox=props, zorder=10)\n\n def format_figure(ax, frequency, spectrum):\n ax.set_xlim([frequency[0], frequency[-1]])\n ax.set_ylim(-0.05*np.nanmax(spectrum), 1.05*np.nanmax(spectrum))\n ax.xaxis.set_major_locator(MultipleLocator(0.5))\n ax.xaxis.set_minor_locator(MultipleLocator(0.1))\n ax.yaxis.set_major_locator(MultipleLocator(10))\n ax.yaxis.set_minor_locator(MultipleLocator(2))\n ax.tick_params(axis='both', which='major', labelsize=12)\n ax.set_axisbelow(True)\n ax.grid(ls=':', c='grey')\n ax.set_xlabel(r'$\\nu_\\mathrm{rest}$ [GHz]', fontsize=12)\n ax.set_ylabel(r'T$_\\mathrm{b}$ [K]', fontsize=12)\n fig.set_tight_layout(True)\n\n def save_figure(fig, specie, band):\n savepath = escape_fname(os.path.join(plotdir, '03.XCLASS_fit', 'restfreq_spectra', str(SSC['no'])+'.'+band+'.restfreq.pdf'))\n os.system('mkdir -p '+os.path.dirname(savepath))\n fig.savefig(savepath, dpi=300, bbox_inches='tight')\n\n\n frequency, spectrum = get_spectrum(SSC, band)\n fig,ax = set_up_figure(SSC, specie, band)\n plot_spectrum(ax, frequency, spectrum)\n label_lines(ax, spectrum, band)\n velocity_indicator(ax, frequency, spectrum)\n format_figure(ax, frequency, spectrum)\n save_figure(fig, specie, band)","title":""},{"docid":"5ed08779bec6b098fe75436b5dcbf85f","score":"0.54813236","text":"def control_sinwave(num,filename=\"control_sinwave.txt\",display=False):\n filename = datadir + filename\n N = num\n min_period = 2\n max_period = 20\n n_changepoints = N/200\n rng = np.random.RandomState(42)\n\n changepoints = np.insert(np.sort(rng.randint(0,N,n_changepoints)),[0,n_changepoints],[0,N])\n const_intervals = zip(changepoints,np.roll(changepoints,-1))[:-1]\n frequency_control = np.zeros((N,1))\n for (t0,t1) in const_intervals:\n frequency_control[t0:t1] = rng.rand()\n periods = frequency_control * (max_period - min_period) + max_period\n frequency_output = np.zeros((N,1))\n z = 0\n for i in range(N):\n z = z + 2 * np.pi / periods[i]\n frequency_output[i] = (np.sin(z) + 1)/2\n input,output = map(lambda x:x[0],frequency_control),map(lambda x:x[0],frequency_output)\n pd.DataFrame(zip(input,output),columns=['input','output']).to_csv(filename,index=False)\n if display:\n display_range = 1000\n plt.figure(figsize =(12,1.5))\n plt.plot(range(display_range),input[:display_range])\n plt.plot(range(display_range),output[:display_range]) # output[:display_range]ではうまくいかない。なぜ？\n plt.show()","title":""},{"docid":"e1cda1f5c61d48cc3a8e5f6e447b11e7","score":"0.54711914","text":"def draw_figure(self, data):\n count_data = data[:, 1:len(self.get_channels())+1]\n time_data = data[:, 0]\n\n # Scale count values using SI prefix\n prefix = ['', 'k', 'M', 'G']\n prefix_index = 0\n while np.max(count_data) > 1000:\n count_data = count_data / 1000\n prefix_index = prefix_index + 1\n counts_prefix = prefix[prefix_index]\n\n # Use qudi style\n plt.style.use(self._save_logic.mpl_qd_style)\n\n # Create figure\n fig, ax = plt.subplots()\n ax.plot(time_data, count_data, linestyle=':', linewidth=0.5)\n ax.set_xlabel('Time (s)')\n ax.set_ylabel('Fluorescence (' + counts_prefix + 'c/s)')\n return fig","title":""},{"docid":"3fe8f2e42a540b5737fd6f01c8b9f78b","score":"0.5427506","text":"def plot_series(rr, hist=True):\r\n x1, x2 = cl.beats_to_lists(rr)\r\n return [go.Scatter(x=x1, y=x2)]","title":""},{"docid":"8dc8698e2675f02af917d8ee0af41af3","score":"0.5418549","text":"def SignalSeries():\r\n pass","title":""},{"docid":"a4240a5aadf84ac63cc47713c81c8741","score":"0.5413463","text":"def plot(self):\n pass","title":""},{"docid":"a4240a5aadf84ac63cc47713c81c8741","score":"0.5413463","text":"def plot(self):\n pass","title":""},{"docid":"a4240a5aadf84ac63cc47713c81c8741","score":"0.5413463","text":"def plot(self):\n pass","title":""},{"docid":"a4240a5aadf84ac63cc47713c81c8741","score":"0.5413463","text":"def plot(self):\n pass","title":""},{"docid":"325c82d73d4b3299aa9d72d0e620f80a","score":"0.5408115","text":"def plot_sine_wave(start_x, stop_x, amplitude):\n x_array = np.linspace(start_x, stop_x, 1000)\n print(x_array)\n y_array = amplitude * np.sin(x_array)\n plt.plot(x_array, y_array,\":\",color=\"red\")","title":""},{"docid":"e06bc26f07f27d7a19d7e80c20b15559","score":"0.54054606","text":"def graph(data, title=\"Graph\"):\n plt.figure(figsize=(7, 6))\n plt.title(title)\n plt.plot(data.keys(), data.values())\n plt.ylabel(\"Sound Level\")\n plt.xlabel(\"Time (s)\")\n plt.show()","title":""},{"docid":"76f87307f248dc083c3f817bc812cdf9","score":"0.54031557","text":"def plot_fits(direction_rates,fit_curve,title):","title":""},{"docid":"7bd4e06e92975f2fccc3c57ae66f54fc","score":"0.5398139","text":"def plot_trust_func(x):\r\n plt.plot(x, trust_value, 'ro')\r\n plt.xlabel('Variance value')\r\n plt.ylabel('Trust value')\r\n plt.title('Trust function (%s elements)' % (trust_value.shape[0]))\r\n plt.show()","title":""},{"docid":"20f6f18a7cd137d69fec0682a6e9826c","score":"0.5390575","text":"def plot_spectra(inst):\n for i in range(3):\n for j in range(4):\n if (i < 2 or j < 2):\n index = i*4 + j\n else:\n break\n lmda, flux = inst.data[index]\n plt.subplot(4, 1, j+1)\n plt.title('Day {}'.format(inst.days[index]))\n plt.plot(lmda, flux)\n plt.xlabel('Wavelength, [nm]')\n plt.ylabel('Relative flux')\n plt.suptitle('Flux relative to the continuum flux around spectral line')\n plt.show()","title":""},{"docid":"b436ce02d66b805de84dce1cea9dac0d","score":"0.5388317","text":"def fourierGraph(ivalueData=None, N=10):\n valueData = ivalueData\n #### Create a plot ####\n origPlot = figure(title=\"Fourier curve fitting\", plot_width=400, plot_height=200, responsive=True)\n origPlot.toolbar.logo = None\n origPlot.xaxis.minor_tick_line_color = None # turn off x-axis minor ticks\n origPlot.yaxis.minor_tick_line_color = None # turn off x-axis minor ticks\n\n # call the fourierTrigSeries() to generate lambda func series and the list of tuples trigTerms (coefs, trigFuncs)\n series, trigTerms = ta.fourierTrigSeries(valueData, n=N)\n # Original Line\n xline = [i for i in range(len(valueData))]\n origPlot.line(xline, valueData, legend=\"Original data\", color='green', line_width=3, line_dash='dashed')\n # Fourier Line\n xs = np.arange(0, len(xline), .3)\n origPlot.line(xs, series(xs), legend=\"Fourier Trig Series n={}\".format(N), line_width=3)\n # Legend color setup\n origPlot.legend.background_fill_color = \"LightGray\"\n\n #### Amplitude plot ####\n coef, trigFuncs = zip(*trigTerms)\n trigFuncsString = [str(trigfunc) for trigfunc in trigFuncs[2:]]\n ampPlot = figure(title=\"Frequency Amp\", plot_width=400, plot_height=200,x_range=trigFuncsString,responsive=True)\n ampPlot.toolbar.logo = None\n ampPlot.yaxis.minor_tick_line_color = None\n ampPlot.circle(trigFuncsString, coef[2:], line_width=3, size=6)\n ampPlot.xaxis.major_label_orientation = math.pi/4\n # Create Stem graph\n x0 = [i + 1 for i in range(len(coef)-2)]\n x1 = coef[2:]\n ampPlot.ray(x=0, y=0, angle=0, length=(N*2 + 2), line_width=3, line_color=\"purple\", line_alpha=0.5)\n ampPlot.segment(x0=x0, y0=0, x1=x0, y1=coef[2:], line_width=3)\n\n #### Frequency plot ####\n freqPlot = figure(title=\"Frequency decomposition\", plot_width=400, plot_height=400, responsive=True)\n freqPlot.toolbar.logo = None\n freqPlot.xaxis.minor_tick_line_color = None\n freqPlot.yaxis.minor_tick_line_color = None\n\n\n x = sp.Symbol('x')\n numLines = len(trigFuncs)\n color_pallete = Spectral11*numLines\n\n for i in range(numLines):\n # ignore the first two terms as they are not trig functions but numbers\n if i < 2:\n continue\n f = sp.lambdify(x, trigFuncs[i], modules=['numpy'])\n # plot the functions\n freqPlot.line(xs,coef[i]*f(xs), color=color_pallete[i])\n\n #### Convolve begin #####\n trendPlot = figure(title=\"Trend Plot\",plot_width=400, plot_height=200,responsive=True)\n\n trendPlot.line(xs, series(xs), line_width=3)\n\n origScript, origDiv = components(origPlot,INLINE)\n ampScript, ampDiv = components(ampPlot, INLINE)\n freqScript, freqDiv = components(freqPlot, INLINE)\n trendScript, trendDiv = components(trendPlot, INLINE)\n return (origScript, origDiv,ampScript, ampDiv,freqScript, freqDiv, trendScript, trendDiv)","title":""},{"docid":"f7460b939220ce4fbf6728465186e014","score":"0.53771937","text":"def plot(self):\n plt.figure(figsize=(14,7))\n plt.plot(SignalGrid.bin_time,\n SignalGrid.meander(SignalGrid.bin_time)+\n SignalGrid.initial_frequency,'.',label='meandering frequency') \n plt.plot(SignalGrid.bin_time,self.post_freq,'r.',label='highest strength frequency')\n plt.legend()\n plt.title('highest strength frequency and actual meandering frequency of signal')\n plt.ylabel('signal frequency')\n plt.xlabel('bin time')\n plt.savefig('meandering_frequency.pdf',bbox_inches='tight')\n plt.clf()\n\n plt.figure(figsize=(7,14))\n plt.imshow(self.ngrid, cmap='viridis')\n plt.gca().xaxis.tick_top()\n plt.gca().xaxis.set_label_position('top')\n cbar = plt.colorbar() \n cbar.set_label('frequency probability distribution')\n plt.title('grid of signals in frequency domain as frequency changes')\n plt.ylabel('signal frequency bins')\n plt.xlabel('long time bins')\n plt.savefig('signal_grid_raw.pdf',bbox_inches='tight')\n plt.clf()\n\n plt.figure(figsize=(7,14))\n plt.imshow(self.lngrid, cmap='viridis')\n plt.gca().xaxis.tick_top()\n plt.gca().xaxis.set_label_position('top') \n cbar = plt.colorbar() \n cbar.set_label('log(frequency) probability distribution')\n plt.title('grid of signals in log(frequency) domain as frequency changes')\n plt.ylabel('signal frequency bins')\n plt.xlabel('long time bins')\n plt.savefig('lnwandering.pdf',bbox_inches='tight')\n plt.clf()\n\n plt.figure(figsize=(7,14))\n plt.imshow(self.path_grid, cmap='viridis')\n plt.gca().xaxis.tick_top()\n plt.gca().xaxis.set_label_position('top') \n plt.title('viterbi path through signal frequency grid')\n plt.ylabel('signal frequency bins')\n plt.xlabel('long time bins')\n plt.savefig('viterbi_path.pdf',bbox_inches='tight')\n plt.clf()","title":""},{"docid":"6de07e4f38a23a0e036128fc9a64c3b9","score":"0.5373745","text":"def initializeSpectrumGraph(self):\r\n\r\n # X axis definition\r\n self.f = self.RATE * np.arange(self.N/2) / self.N\r\n\r\n # Initializing plot\r\n self.fig, axis = plt.subplots(1, 1)\r\n self.linef, = axis.plot(self.f, np.zeros(len(self.f)))\r\n\r\n # Setting axis labels\r\n axis.set_xlabel(\"Freq [Hz]\")\r\n axis.set_ylabel(\"X(f)\")\r\n\r\n # Setting axis limits\r\n axis.set_xlim(20, self.RATE / 2) # Nyquist\r\n axis.set_ylim(0, 200)\r\n plt.show(block = False)","title":""},{"docid":"3a597102eac7b1851de3951c85c28f66","score":"0.53708905","text":"def plot_v_frequency(self, y: NumberLike, *args, **kwargs):\n\n try:\n if len(npy.shape(y)) > 2:\n # perhaps the dimensions are empty, try to squeeze it down\n y = y.squeeze()\n if len(npy.shape(y)) > 2:\n # the dimensions are full, so lets loop and plot each\n for m in range(npy.shape(y)[1]):\n for n in range(npy.shape(y)[2]):\n self.plot(y[:, m, n], *args, **kwargs)\n return\n if len(y) == len(self):\n pass\n else:\n\n raise IndexError(['thing to plot doesn\\'t have same'\n ' number of points as f'])\n except(TypeError):\n y = y * npy.ones(len(self))\n\n # plt.plot(self.f_scaled, y, *args, **kwargs)\n plt.plot(self.f, y, *args, **kwargs)\n ax = plt.gca()\n scale_frequency_ticks(ax, self.unit)\n plt.autoscale(axis='x', tight=True)\n self.labelXAxis()","title":""},{"docid":"071ccad6af0331bddf513c0c2c2dd045","score":"0.5365488","text":"def plot_filter_response(\n frequency,\n sample_rate,\n filter_type,\n filter_order=2,\n show_grid=True,\n fig_size=(10, 5),\n):\n\n b, a = sensormotion.signal.build_filter(\n frequency, sample_rate, filter_type, filter_order\n )\n\n # Plot the frequency response\n w, h = freqz(b, a, worN=8000)\n f, axarr = plt.subplots(figsize=fig_size)\n axarr.plot(0.5 * sample_rate * w / np.pi, np.abs(h), \"b\")\n axarr.set_xlim(0, 0.5 * sample_rate)\n axarr.set_xlabel(\"Frequency (Hz)\")\n axarr.grid(show_grid)\n\n # Add lines and markers at the cutoff frequency/frequencies\n if filter_type == \"bandpass\":\n for i in range(len(frequency)):\n axarr.axvline(frequency[i], color=\"k\")\n axarr.plot(frequency[i], 0.5 * np.sqrt(2), \"ko\")\n else:\n axarr.axvline(frequency, color=\"k\")\n axarr.plot(frequency, 0.5 * np.sqrt(2), \"ko\")\n\n plt.suptitle(\"Filter Frequency Response\", size=16)\n plt.show()","title":""},{"docid":"17b033440e6d4eb79b494049bf1885e0","score":"0.5365277","text":"def plot_data(self):\n pass","title":""},{"docid":"82e83e8f050f1745066a1238f3180589","score":"0.5361473","text":"def FW_trend_plot(quant, empty=False): # Fig. 9c\n assert quant in ['FW', 'SALT']\n if quant=='FW': q, Q, Q1 = 0, 'F', 'W'\n elif quant=='SALT': q, Q, Q1 = 1, 'S', 'S'\n lat_bounds = ['90N', '60N', '45N', '10N', '10S', '34S']\n\n f = plt.figure(figsize=(6.4,2.5))\n hap = dict(x=0, dy=0, width=.04, length_includes_head=True, head_width=.06, head_length=.02, ec=None, lw=0, clip_on=False) # horizontal arrow properties\n vap = dict(y=0, dx=0, width=.06, length_includes_head=True, head_width=.08, head_length=.03, ec=None, lw=0, clip_on=False) # vertical arrow properties\n\n for i, (latS, latN) in enumerate(lat_bands):\n #region: define axes\n if i==0: # Atlantic 34S-60N\n ax = f.add_axes([13.5/16,.2,1/8,.55])\n ax.xaxis.set_ticks([])\n ax.yaxis.set_ticks([])\n\n axb = f.add_axes([13.5/16,-.05,1/8 ,.5 ])\n axt = f.add_axes([13.5/16,.5 ,1/8 ,.5 ])\n axl = f.add_axes([12 /16,.2 ,3/16,.55])\n axr = f.add_axes([14 /16,.2 ,3/16,.55])\n vert_lim = [(-.3,.3),(-8e7,8e7)][q]\n hor_lim = [(-.2,.2),(-3e8,3e8)][q]\n draw_scales(f=f,left=False,vlim=vert_lim[1],hlim=hor_lim[1])\n draw_labels(f,'Sv/century')\n\n else:\n ax = f.add_axes([(2*i+1)/16-1.5/16,.2,1/8,.55])\n ax.xaxis.set_ticks([])\n ax.yaxis.set_ticks([])\n\n axb = f.add_axes([(2*i+1)/16-1.5/16,-.05,1/8 ,.5 ])\n axt = f.add_axes([(2*i+1)/16-1.5/16,.5 ,1/8 ,.5 ])\n axl = f.add_axes([(2*i+1)/16-3/16 ,.2 ,3/16,.55])\n axr = f.add_axes([(2*i+1)/16-1/16 ,.2 ,3/16,.55])\n\n vert_lim = [(-.25,.25),(-2e7,2e7)][q]\n hor_lim = [(-.2,.2),(-.7e8,.7e8)][q]\n if i==1:\n draw_scales(f=f,left=True,vlim=vert_lim[1],hlim=hor_lim[1])\n for ax in [axl, axr]:\n ax.set_xlim(hor_lim)\n ax.set_ylim((-.35,1.3))\n\n for ax in [axb, axt, axl, axr]:\n # ax.patch.set_alpha(0)\n ax.axis('off')\n ax.xaxis.set_ticks([])\n\n for ax in [axt, axb]:\n ax.set_ylim(vert_lim)\n ax.set_xlim((-.25,1.45))\n\n #endregion\n #region: plotting\n for s, sim in enumerate(['HIGH', 'LOW']):\n if empty: continue\n nlat_ = nlat_at(sim, latS)\n run, rcp = ['ctrl','lpd'][s], ['rcp', 'lr1'][s]\n\n #region: d/dt [axb]\n if quant=='SALT': fac = 1\n elif quant=='FW': fac = -1e-6/S0\n ddtS_ctl, ddtS_rcp = get_ddt_SALT(sim=sim, latS=latS, latN=latN)\n label = [r'$\\Delta \\bar{W}$', r'$\\Delta \\bar{S}$'][q]\n axt.arrow(x=.8+s/10, dy=ddtS_rcp*fac, **vap, color=plt.cm.tab20(18+s))\n #endregion\n\n #region: meridional flux (convergences) means/trends, incl. BS/Med [axl, axr, axb]\n inflow, inflow_trend = get_BS_Med(sim)\n if latS==60: # BS in Arctic\n fluxes, conv = get_fluxes(sim=sim, quant=quant, lat=latS, latS=None, latN=None)\n # conv['ov'], conv['az'], conv['ed'] = 0, 0, 0\n # conv['to'] = fluxes['to'] + inflow[f'{Q}_BS'].values/[1e6,1][q] # [m^3/s] -> [Sv]\n conv['tov'], conv['taz'], conv['ted'], conv['tto'] = 0, 0, 0, fluxes['tto']\n \n axr.set_xlim(hor_lim)\n axr.arrow(y=.425+s/10, dx=-inflow_trend[f'{Q}_BS'].values/[1e6,1][q], color=plt.cm.tab20(16+s), **hap)\n\n else:\n fluxes, conv = get_fluxes(sim=sim, quant=quant, lat=latS, latS=latS, latN=latN)\n if latS<35 and latN>35: # Med\n axb.arrow(x=1.05+s/10, dy=-inflow_trend[f'{Q}_Med'].values/[1e6,1][q], color=plt.cm.tab20(16+s), **vap)\n\n axl.arrow(y=.55+s/10, dx=fluxes['tov'], color=plt.cm.tab20(0+s), **hap)\n axl.arrow(y=.3 +s/10, dx=fluxes['taz'], color=plt.cm.tab20(2+s), **hap)\n axl.arrow(y=.05+s/10, dx=fluxes['ted'], color=plt.cm.tab20(4+s), **hap)\n axl.arrow(y=.8 +s/10, dx=fluxes['tto'], color=plt.cm.tab20(6+s), **hap)\n \n if i==0: # draw 60 North values in Atlantic box\n fluxes_, conv_ = get_fluxes(sim=sim, quant=quant, lat=latN, latS=latS, latN=latN)\n axr.arrow(y=.55+s/10, dx=fluxes_['tov'], color=plt.cm.tab20(0+s), **hap)\n axr.arrow(y=.3 +s/10, dx=fluxes_['taz'], color=plt.cm.tab20(2+s), **hap)\n axr.arrow(y=.05+s/10, dx=fluxes_['ted'], color=plt.cm.tab20(4+s), **hap)\n axr.arrow(y=.8 +s/10, dx=fluxes_['tto'], color=plt.cm.tab20(6+s), **hap)\n \n axb.arrow(x=.3 +s/10, dy=conv['tov'], color=plt.cm.tab20(0+s), **vap)\n axb.arrow(x=.55+s/10, dy=conv['taz'], color=plt.cm.tab20(2+s), **vap)\n axb.arrow(x=.8 +s/10, dy=conv['ted'], color=plt.cm.tab20(4+s), **vap)\n axb.arrow(x=.05+s/10, dy=conv['tto'], color=plt.cm.tab20(6+s), **vap)\n #endregion\n\n #region: surface fluxes [axt]\n sfwf_mean, sfwf_trend = get_SFWF(sim=sim, quant=quant, latS=latS, latN=latN)\n axt.arrow(x=.05+s/10, dy=-sfwf_trend['SFWF'], color=plt.cm.tab20(8+s) , **vap)\n axt.arrow(x=.3 +s/10, dy=-sfwf_trend['R'] , color=plt.cm.tab20b(14+s), **vap)\n axt.arrow(x=.55+s/10, dy=-sfwf_trend['PE'] , color=plt.cm.tab20(12+s) , **vap)\n #endregion\n\n #endregion\n\n #region: legend, numbering, scales\n f.text(.01,.93,'(c)')\n #endregion\n if q==0 and empty==False:\n plt.savefig(f'{path_results}/FW-paper/Fig9c.eps')\n return","title":""},{"docid":"2dd552eb62f0a7b99152133339e48310","score":"0.53593385","text":"def plotTauNoise():\n fig = plt.figure(0,figsize=(5, 3.6))\n plt.rc('text', usetex=True)\n font = {'family' : 'sans-serif',\n 'serif' : 'Helvetica',\n 'weight' : 'normal',\n 'size' : 14 }\n plt.rc('lines', lw=2)\n plt.rc('font', **font)\n ax = fig.add_subplot(111)\n fig.subplots_adjust(left=0.15, right =0.9,\\\n bottom=0.15, top =0.9, wspace=0.25)\n\n # tau0 = (L/np.pi)**2/3\n T = np.linspace(1, 150, 100)\n ax.plot(T, tauTheory(1, T, 100), 'b-')\n ax.plot(T, tauTheory(0.1, T, 100), 'r-')\n # ax.plot(T, tauTheory(0.1, T, 100), '-')\n # ax.plot(T, tauTheory(0.01, T, 100), '-')\n\n # load the simulation data and plot as dots\n tauData = np.loadtxt('tauNoise_N100_F1.dat')\n ax.plot(tauData[:,0], tauData[:,1], 'bo')\n tauData = np.loadtxt('tauNoise_N100_F0.1.dat')\n ax.plot(tauData[:,0], tauData[:,1], 'ro')\n\n\n # set format of the figure\n ax.set_yscale('log')\n # ax.set_xlabel(r'$k_B T/\\Delta E$')\n # ax.set_ylabel(r'$\\tau_{\\parallel}/\\tau_{Rouse}$')\n ax.set_xlabel(r'$T$')\n ax.set_ylabel(r'$\\tau_{\\parallel}$')\n ax.legend([r'$F=1$', r'$F=0.1$'], loc='upper right', fontsize=15)\n ax.set_xlim([0,150])\n # ax.set_ylim([0,1.2])\n plt.show()","title":""},{"docid":"e139afc352661e5e4581608f7060213c","score":"0.53593016","text":"def plot(self, width=5.51, height=4, n_xticks=6, fname=None):\n plt.figure(dpi=300, figsize=(width, height))\n alpha = min(1, max(0.01, 50/self.n_simulations_))\n for i in range(self.n_simulations_):\n obs = self.data_[i]\n last_state = obs.iloc[-1]\n last_state.t = self.t_max_\n obs = obs.append([last_state], ignore_index=True)\n plt.plot(obs[[\"t\"]], obs[[\"s\"]], c=\"C0\", alpha=alpha) # S\n plt.plot(obs[[\"t\"]], obs[[\"i\"]], c=\"C1\", alpha=alpha) # I\n plt.plot(obs[[\"t\"]], obs[[\"r\"]], c=\"C2\", alpha=alpha) # R\n title = \"Stochastic SIR model \"\n if self.n_simulations_ > 1:\n title += \"(\" + str(self.n_simulations_) + \" samples) \"\n title += \"with \" \\\n \"$\\\\beta=\" + str(self.beta) + \"$ and \" \\\n \"$\\\\gamma=\" + str(self.gamma) + \"$\"\n plt.title(title)\n plt.xlim([0, self.t_max_])\n plt.ylim([0, self.n])\n plt.xticks(np.linspace(0, self.t_max_, n_xticks))\n plt.xlabel(\"$t$\")\n plt.ylabel(\"$S_t,\\ I_t,\\ R_t$\")\n plt.grid()\n legend = plt.legend([\"$S_t$\", \"$I_t$\", \"$R_t$\"])\n for l in legend.get_lines():\n l.set_alpha(1)\n if fname is not None:\n plt.savefig(fname + \".pdf\")","title":""},{"docid":"44f7cba35773a96c2187a59423d24515","score":"0.53471714","text":"def plot_sed(w_obs, f_obs, ferr_obs, # observed fluxes\n w_mod, f_mod, # flux measured for best fitting model\n wave_conv_fil, flux_conv_fil, # filter transmission curves\n labs, sedfilename='best_fit_sed.pdf'): # filter and plotfile name\n fig1, ax1 = plt.subplots(figsize=(6, 5))\n\n for f in range(len(w_mod)):\n ax1.scatter(w_mod[f], f_mod[f], alpha=0.7)\n ax1.plot(wave_conv_fil[f], flux_conv_fil[f], alpha=0.7, label=labs[f])\n\n ax1.errorbar(w_obs, f_obs, yerr=ferr_obs,\n ls='None', color='k', label='observations', marker='+')\n\n ax1.set_yscale('log')\n ax1.set_xlabel(r'Wavelength [$\\AA$]')\n ax1.set_ylabel(r'Flux [erg/cm$^2$/s/$\\AA$]')\n ax1.legend(loc='lower right')\n\n fig1.savefig(sedfilename, bbox_inches='tight')\n plt.show()","title":""},{"docid":"86950e208e87ef1d9695c1274e2bb17a","score":"0.5346543","text":"def water_freq(df):\n \n assert isinstance(df, xr.Dataset),\"Input has to be a xarray.Dataset.\"\n \n try:\n df.water\n except Exception:\n print(\"'water' band cannot be found. Please use pred_index() to acquire the required band.\")\n try:\n df.time\n except Exception:\n print(\"'time' cannot be found. Please check the time dimension of the dataset.\")\n try:\n df.longitude\n df.latitude\n except Exception:\n print(\"'longitude' or/and 'latitude' cannot be found. Please check the dimension of the dataset.\")\n \n frequency = df.water.sum(dim='time',skipna=True)/len(df.time)\n show(hv.render(frequency.hvplot.image(x=\"longitude\",y=\"latitude\",aspect=1,cmap='bmy_r')))","title":""},{"docid":"9ca67b04880c97c72c135c6551227754","score":"0.53425175","text":"def Task67():\r\n print(\"Lab 4 Tasks 6 and 7:\") \r\n\r\n\r\n # Low-pass filter design parameters\r\n Ntaps=16\r\n Fbands=[0, 0.15, 0.2, 0.25, 0.3, 0.5]\r\n Abands=[0, 1, 0]\r\n Weights= [100, 1, 100]\r\n #\r\n BPtaps = signal.remez(Ntaps, Fbands, Abands, Weights)\r\n #\r\n fig = plt.figure()\r\n plt.plot(BPtaps)\r\n plt.xlabel('Sample')\r\n plt.ylabel('Value')\r\n plt.title('Impulse Response of the FIR Filter')\r\n # Frequency response\r\n fig, pltArr = plt.subplots(2, sharex=True) \r\n fig.suptitle(\"Frequency Response\")\r\n [freq, response] = signal.freqz(BPtaps, [1], worN=2000)\r\n pltArr[0].plot(freq, np.log10(np.abs(response)))\r\n pltArr[0].set_title(\"Magnitude of Frequency Reponse\")\r\n angles = np.unwrap(np.angle(response))\r\n pltArr[1].plot(freq, angles)\r\n pltArr[1].set_title(\"Angle of Frequency Reponse\")\r\n # plt.xlabel('Normalized Frequency (pi is Nyquist Frequency)')\r\n # plt.ylabel(\"Magnitude of Frequency Response\")\r\n # plt.title(\"Magnitude of Frequency Response\")\r\n\r\n\r\n # Minimum phase version\r\n rt = np.roots(BPtaps)\r\n [b, r] = signal.deconvolve(BPtaps, [1,-rt[1]])\r\n MinPhTaps = signal.convolve(b,[1,-1/rt[1].conjugate()])\r\n\r\n #impulse response\r\n fig = plt.figure()\r\n plt.plot(MinPhTaps)\r\n plt.xlabel('Sample')\r\n plt.ylabel('Value')\r\n plt.title('Impulse Response of the Minimum Phase Filter')\r\n # Frequency response\r\n fig, pltArr = plt.subplots(2, sharex=True) \r\n fig.suptitle(\"Frequency Response\")\r\n [freq, response] = signal.freqz(MinPhTaps, [1], worN=2000)\r\n pltArr[0].plot(freq, np.log10(np.abs(response)))\r\n pltArr[0].set_title(\"Magnitude of Frequency Reponse\")\r\n angles = np.unwrap(np.angle(response))\r\n pltArr[1].plot(freq, angles)\r\n pltArr[1].set_title(\"Angle of Frequency Reponse\")\r\n # plt.xlabel('Normalized Frequency (pi is Nyquist Frequency)')\r\n # plt.ylabel(\"Magnitude of Frequency Response\")\r\n # plt.title(\"Magnitude of Frequency Response\")\r\n #\r\n plt.show()","title":""},{"docid":"1cbb3d7d23c1d95b8bf18627104c12b5","score":"0.53419256","text":"def wave_plot():\n PLOT_RANGE = [1, 2, 3, 4, 5] # Choose n's to plot corresponding psi_n's\n\n N = 100 # Number of points for discretization\n NUM_EIGVALS = max(PLOT_RANGE) # Number of eigenvalues to get\n\n pb = ParticleBox(N, NUM_EIGVALS) \n\n apsi = np.empty((NUM_EIGVALS, pb.N), dtype=float) \n for n in range(1, NUM_EIGVALS+1):\n apsi[n-1] = np.sqrt(2) * np.sin(n*np.pi*pb.x) * np.sign(pb.psi[1, n-1])\n \n plt.figure()\n plt.xlabel(\"x'\")\n plt.ylabel(r\"$\\psi_n(x)$\")\n for n in PLOT_RANGE:\n i = n - 1\n color = cmap(float(i)/NUM_EIGVALS)\n plt.plot(pb.x, pb.psi[:,i], marker=\".\", c=color)\n plt.plot(pb.x, apsi[i], label=r\"$E_%s$\" % str(n), c=color)\n plt.legend(title=r\"$E_n$\")\n plt.savefig(\"wavefuncs.pdf\")","title":""},{"docid":"b940c2c8fd8774760229735f7b3cf092","score":"0.5341725","text":"def plot_data(phi, data, mytitle, fnm):\n x = np.linspace(-4,10,100) # x values for plotting\n sample_sizes = [100, 1000, 10000]\n for ss in sample_sizes:\n sample = data[0:ss]\n p = pw.density_function(phi, sample)\n # Evaluate the density funtion for values of x,\n # using the zero-one window function.\n print(\"Plotting density for a sample size of\", ss)\n y = np.array([p(xi) for xi in x])\n plt.plot(x,y, label=str(ss))\n plt.legend(fancybox=True, title=\"Sample Size\", shadow=True)\n plt.title(mytitle, fontsize=18)\n plt.xlabel(\"x\", fontsize=16)\n plt.ylabel(\"p(x)\", fontsize=16)\n plt.show()","title":""},{"docid":"7eb54f008c02fdb89bb8d693e1cbef14","score":"0.53329366","text":"def plot(self, writeFiles = False, spec = 'sourceSpec'):\n# \t\tioff()\n# \t\tdata = readOObin(file)\n fig = figure(17, **ssxdef.specsize)\n fig.clear()\n a = axes()\n a.plot(self.wavelengths, self.data[spec], lw='.5')\n a.text(0.93, 0.92, self.shotname, horizontalalignment='right',\n verticalalignment='top', transform = a.transAxes)\n ylabel(self.ylabel)\n xlabel(self.xlabel)\n xlim(200, 1100)\n if writeFiles:\n ssxutil\n fName = ssxutil.ssxPath(self.shotname+'-oo.pdf', 'output',\n self.runYear + '/' + self.runDate + '/' + self.shotname, mkdir\n = True)\n fig.savefig(fName)\n else:\n fig.show()\n # ion()","title":""},{"docid":"eda9a426bc6353fe2a4d7471d12df01a","score":"0.5332477","text":"def exercise11():\n x = numpy.arange(11, step=0.01)\n sinx = numpy.sin(x)\n\n plt.figure()\n plt.plot(sinx)\n plt.xlabel('x value')\n plt.ylabel('sin(x)')\n plt.title('Sine Function for x from 0.0 to 10.0')\n plt.savefig('sin_plot.pdf', format='pdf')","title":""},{"docid":"232630898f055a6a0325cf97e338b403","score":"0.53291404","text":"def plot_sine_evaluation(real_samples, fake_samples, idx, identifier):\n ### frequency\n seq_length = len(real_samples[0]) # assumes samples are all the same length\n frate = seq_length\n freqs_hz = np.fft.rfftfreq(seq_length)*frate # this is for labelling the plot\n # TODO, just taking axis 0 for now...\n w_real = np.mean(np.abs(np.fft.rfft(real_samples[:, :, 0])), axis=0)\n w_fake = np.mean(np.abs(np.fft.rfft(fake_samples[:, :, 0])), axis=0)\n ### amplitude\n A_real = np.max(np.abs(real_samples[:, :, 0]), axis=1)\n A_fake = np.max(np.abs(fake_samples[:, :, 0]), axis=1)\n ### now plot\n nrow = 2\n ncol = 2\n fig, axarr = plt.subplots(nrow, ncol, sharex='col', figsize=(6, 6))\n # freq\n axarr[0, 0].vlines(freqs_hz, ymin=np.minimum(np.zeros_like(w_real), w_real), ymax=np.maximum(np.zeros_like(w_real), w_real), color='#30ba50')\n axarr[0, 0].set_title(\"frequency\", fontsize=16)\n axarr[0, 0].set_ylabel(\"real\", fontsize=16)\n axarr[1, 0].vlines(freqs_hz, ymin=np.minimum(np.zeros_like(w_fake), w_fake), ymax=np.maximum(np.zeros_like(w_fake), w_fake), color='#ba4730')\n axarr[1, 0].set_ylabel(\"generated\", fontsize=16)\n # amplitude\n axarr[0, 1].hist(A_real, normed=True, color='#30ba50', bins=30)\n axarr[0, 1].set_title(\"amplitude\", fontsize=16)\n axarr[1, 1].hist(A_fake, normed=True, color='#ba4730', bins=30)\n\n fig.savefig('./experiments/plots/' + identifier + '_eval' + str(idx).zfill(4) +'.png')\n plt.clf()\n plt.close()\n return True","title":""},{"docid":"ef580d4a3e7b53d3dde91cfce6f95fcf","score":"0.5324583","text":"def PlotData( self ):\n plt.title( 'q = ' + str( self.col ) )\n plt.plot( self.data[ : , 0 ] , self.data[ : , self.col ] , label = 'orig' )\n func = savgol_filter( self.data[ : , self.col ] , 51 , 3 )\n plt.plot( self.data[ : , 0 ] , func , label = 'smooth' )\n for i in range( self.Fits.shape[1] ):\n plt.plot( self.data[ : , 0 ] , self.Fits[ : , i ] , label = self.labels[ i ] )\n\n\n plt.scatter( self.FitParams[ self.indx , 0 ] , np.max( self.Fits ) )\n\n \n plt.legend()\n plt.show()","title":""},{"docid":"46990c8e8cc2aa32f9e48b07db569a20","score":"0.53186226","text":"def _time_domain_plot(self, *args, **kwargs):\n from pesummary.gw.plots.detchar import time_domain_strain_data\n\n return time_domain_strain_data(*args, **kwargs)[self.IFO]","title":""},{"docid":"2c0064397489775f8d93e5fe269e3106","score":"0.5317835","text":"def plot_n(self, **kwargs):\n if self.verbose > 1:\n print(\"RefractiveIndex.plot_n()\") \n\n self.plot_spectrum(y_unit = \"Refractive index n\", **kwargs)","title":""},{"docid":"6748e27ec5d8284573ee71832baaad52","score":"0.5315133","text":"def plot_spectrum(self):\n assert self.traced\n self.log.debug(npArrayInfo(self.twl*1e6,\"Wavlength\"))\n self.log.debug(npArrayInfo(self.tfl,\"Flux\"))\n plt.clf()\n plt.semilogy(self.twl*1e6,self.tfl,\"b.\")\n xmin,xmax,ymin,ymax = plt.axis()\n if ymin < 1e-4:\n ymin = 1e-4\n plt.axis((xmin,xmax,ymin,ymax))\n plt.title(\"Generated, Fluxed Spectra (%d)\" % self.num)\n plt.xlabel(\"Wavelength ($\\mu m$)\")\n plt.ylabel(\"Flux (Electrons)\")\n plt.savefig(\"%(Partials)s/Instrument-%(num)04d-Flux%(ext)s\" % dict(num=self.num, ext=self.config[\"Plots\"][\"format\"],**self.config[\"Dirs\"]))\n plt.clf()","title":""},{"docid":"5ba3e04bd866b846af1d337af2f9e534","score":"0.53136957","text":"def wavelength_plot(waves_list, waves,number_of_detectors):\n \n for k in range(0, number_of_detectors):\n newlist = []\n for i in range(k, len(waves_list), 8):\n newlist.append(waves_list[i])\n \n print(newlist) \n print(len(newlist))\n plt.title(\"Efficiency vs wavelength\") \n plt.plot(waves, newlist,label = \"Detector \"+str(k+1)) \n plt.legend()\n \n plt.show()\n plt.savefig(\"wavelength_eff.png\")\n plt.close()","title":""},{"docid":"8f68020d8832f3c46b31b1d1ea865aca","score":"0.5297882","text":"def plot_phonon_dispersion_bands(self):\n import matplotlib.pyplot as plt\n\n def replace_list(text_string):\n substitutions = {'GAMMA': u'$\\Gamma$',\n }\n\n for item in substitutions.items():\n text_string = text_string.replace(item[0], item[1])\n return text_string\n\n force_constants = self.get_force_constants()\n bands_and_labels = self.get_path_using_seek_path()\n\n _bands = obtain_phonon_dispersion_bands(self._structure,\n bands_and_labels['ranges'],\n force_constants,\n self._supercell_matrix,\n primitive_matrix=self._primitive_matrix,\n band_resolution=30)\n\n for i, freq in enumerate(_bands[1]):\n plt.plot(_bands[1][i], _bands[2][i], color='r')\n\n # plt.axes().get_xaxis().set_visible(False)\n plt.axes().get_xaxis().set_ticks([])\n\n plt.ylabel('Frequency [THz]')\n plt.xlabel('Wave vector')\n plt.xlim([0, _bands[1][-1][-1]])\n plt.axhline(y=0, color='k', ls='dashed')\n plt.suptitle('Phonon dispersion')\n\n if 'labels' in bands_and_labels:\n plt.rcParams.update({'mathtext.default': 'regular'})\n\n labels = bands_and_labels['labels']\n\n labels_e = []\n x_labels = []\n for i, freq in enumerate(_bands[1]):\n if labels[i][0] == labels[i - 1][1]:\n labels_e.append(replace_list(labels[i][0]))\n else:\n labels_e.append(\n replace_list(labels[i - 1][1]) + '/' + replace_list(labels[i][0]))\n x_labels.append(_bands[1][i][0])\n x_labels.append(_bands[1][-1][-1])\n labels_e.append(replace_list(labels[-1][1]))\n labels_e[0] = replace_list(labels[0][0])\n\n plt.xticks(x_labels, labels_e, rotation='horizontal')\n\n plt.show()","title":""},{"docid":"54b9b221d5f3188b39b57ce49859e4de","score":"0.5297838","text":"def BeamElementShearDiagram(f, L):\n \n x = [0, L]\n z = [f[0,0] , -f[2,0]]\n plt.plot(x,z)\n plt.ylabel('Shear Force (kN)')\n plt.show()","title":""},{"docid":"24bd2370716487e55b3f891cb0493efa","score":"0.5296585","text":"def plot_data(self, npz_filename):\r\n\t\t# #taken directly from ibmq quantum experience online\r\n\t\t# fig = plt.figure(figsize=(16,10))\r\n\t\t# # counts = {'0000': 60, '0001': 64.5, '0010': 61.5, '0100': 63.5, '0101': 62.5, '0110': 62.5, '0111': 59, '1000': 66, '1001': 57, '1010': 62.5, '1011': 68, '1100': 55, '1110': 62.5, \"1111\":70}\r\n\t\t# # frequencies = [60,64.5,61.5,63.5,62.5,62.5,59,66,57,62.5,68,55,62.5,70]\r\n\t\t# answers = [key for key in counts.keys()]\r\n\t\t# answers.sort()\r\n\t\t# plt.bar(range(len(counts)), frequencies, align='center', tick_label=answers)\r\n\t\t# plt.title('Results for n = 4 (grovers algorithm)', fontsize=25)\r\n\t\t# plt.xlabel('Measurement',fontsize=20)\r\n\t\t# plt.ylabel('Frequency of occurence',fontsize=20)\r\n\t\t# plt.xticks(fontsize=15)\r\n\t\t# plt.yticks(fontsize=15)\r\n\t\t\r\n\t\t\r\n\t\t# fig.savefig('figures/g_counts_histogram_4.png', bbox_inches='tight')\r\n\t\t\r\n\t\t# data = np.load(npz_filename)\r\n\r\n\t\t# run_time_arr = data['run_time_arr']\r\n\t\t# n_list = data['n_list']\r\n\t\t# n_list = [1,2,3,4]\r\n\r\n\t\t# avg_time = np.sum(run_time_arr,1)/np.shape(run_time_arr)[1]\r\n\t\t# avg_time = [10.8,11.5,14.5,31.4]\r\n\r\n\t\t# plt.rcParams[\"font.family\"] = \"serif\"\r\n\t\t# fig = plt.figure(figsize=(16,10))\r\n\r\n\t\t# z = np.polyfit(n_list, avg_time, 10)\r\n\t\t# p = np.poly1d(z)\r\n\r\n\t\t# plt.plot(np.linspace(n_list[0], n_list[-1] + 0.1, 100), p(np.linspace(n_list[0], n_list[-1] + 0.1, 100)), ls = '-', color = 'r')\r\n\t\t# plt.plot(n_list, avg_time, ls = '', markersize = 15, marker = '.',label = 'M')\r\n\t\t# plt.title('Execution time scaling for Grovers algorithm', fontsize=25)\r\n\t\t# plt.xlabel('n (bit string length)',fontsize=20)\r\n\t\t# plt.ylabel('Average time of execution (s)',fontsize=20)\r\n\t\t# plt.xticks(fontsize=15)\r\n\t\t# plt.yticks(fontsize=15)\r\n\r\n\t\t# fig.savefig('figures/g_run_time.png', bbox_inches='tight')\r\n\r\n\t\t# num_times = data['num_times']\r\n\t\t# variance_arr = data['variance_arr']\r\n\t\t# n = data['n']\r\n\r\n\t\t# plt.rcParams[\"font.family\"] = \"serif\"\r\n\t\t# fig = plt.figure(figsize=(16,10))\r\n\r\n\t\t# assemble_time_arr = data['assemble_time_arr']\r\n\t\t# n_list = data['n_list']\r\n\t\t# # n_list = [1,2,3,4]\r\n\r\n\t\t# avg_time = np.sum(assemble_time_arr,1)/np.shape(assemble_time_arr)[1]\r\n\t\t# # avg_time = [4.2,4.5,7.2,14.9] #taken directly from imbq experience\r\n\r\n\t\t# plt.rcParams[\"font.family\"] = \"serif\"\r\n\t\t# fig = plt.figure(figsize=(16,10))\r\n\r\n\t\t# z = np.polyfit(n_list, avg_time, 10)\r\n\t\t# p = np.poly1d(z)\r\n\r\n\t\t# plt.plot(np.linspace(n_list[0], n_list[-1] + 0.1, 100), p(np.linspace(n_list[0], n_list[-1] + 0.1, 100)), ls = '-', color = 'r')\r\n\t\t# plt.plot(n_list, avg_time, ls = '', markersize = 15, marker = '.',label = 'M')\r\n\t\t# plt.title('Compilation time scaling for Grovers algorithm', fontsize=25)\r\n\t\t# plt.xlabel('n (bit string length)',fontsize=20)\r\n\t\t# plt.ylabel('Average time of execution (s)',fontsize=20)\r\n\t\t# plt.xticks(fontsize=15)\r\n\t\t# plt.yticks(fontsize=15)\r\n\r\n\t\t# fig.savefig('figures/g_assemble_time.png', bbox_inches='tight')\r\n\r\n\t\t# correct_arr = data['correct_arr']\r\n\t\t# n_list = data['n_list']\r\n\t\t# # n_list = [1,2,3,4]\r\n\r\n\t\t# avg_correct = np.sum(correct_arr,1)/np.shape(correct_arr)[1]\r\n\t\t# # avg_correct = [55,96,20,1.5]\r\n\r\n\t\t# plt.rcParams[\"font.family\"] = \"serif\"\r\n\t\t# fig = plt.figure(figsize=(16,10))\r\n\r\n\t\t# z = np.polyfit(n_list, avg_correct, 10)\r\n\t\t# p = np.poly1d(z)\r\n\r\n\t\t# plt.plot(np.linspace(n_list[0], n_list[-1] + 0.1, 100), p(np.linspace(n_list[0], n_list[-1] + 0.1, 100)), ls = '-', color = 'r')\r\n\t\t# plt.plot(n_list, avg_correct, ls = '', markersize = 15, marker = '.',label = 'M')\r\n\t\t# plt.title('Percentage correct for grovers algorithm', fontsize=25)\r\n\t\t# plt.xlabel('n (bit string length)',fontsize=20)\r\n\t\t# plt.ylabel('Percentage correct',fontsize=20)\r\n\t\t# plt.xticks(fontsize=15)\r\n\t\t# plt.yticks(fontsize=15)\r\n\r\n\t\t# fig.savefig('figures/g_correct.png', bbox_inches='tight')\r\n\r\n\t\t\r\n\t\t# num_times = data['num_times']\r\n\t\t# variance_arr = data['variance_arr']\r\n\t\t# n = data['n']\r\n\r\n\t\t# plt.rcParams[\"font.family\"] = \"serif\"\r\n\t\t# fig = plt.figure(figsize=(16,10))\r\n\r\n\t\t\r\n\r\n\t\t# plt.hist(variance_arr)\r\n\t\t# plt.title('Dependence of execution time on $U_f$ (grovers algorithm)', fontsize=25)\r\n\t\t# plt.xlabel('Execution time (s)',fontsize=20)\r\n\t\t# plt.ylabel('Frequency of occurence',fontsize=20)\r\n\t\t# plt.xticks(fontsize=15)\r\n\t\t# plt.yticks(fontsize=15)\r\n\r\n\r\n\t\t# fig.savefig('figures/g_hist.png', bbox_inches='tight')\r\n\t\t\r\n\t\t\r\n\t\t#Obtain transpiled depths of circuits\r\n\t\t# list_of_depths = [2,5,309,2984]\r\n\t\t# n_list = [1,2,3,4]\r\n\t\t# plt.rcParams[\"font.family\"] = \"serif\"\r\n\t\t# fig = plt.figure(figsize=(16,10))\r\n\r\n\t\t# z = np.polyfit(n_list, list_of_depths, 10)\r\n\t\t# p = np.poly1d(z)\r\n\r\n\t\t# plt.plot(np.linspace(n_list[0], n_list[-1] + 0.1, 100), p(np.linspace(n_list[0], n_list[-1] + 0.1, 100)), ls = '-', color = 'r')\r\n\t\t# plt.plot(n_list, list_of_depths, ls = '', markersize = 15, marker = '.',label = 'M')\r\n\t\t# plt.title('depth of circuit for grovers algorithm', fontsize=25)\r\n\t\t# plt.xlabel('n (bit string length)',fontsize=20)\r\n\t\t# plt.ylabel('Depth',fontsize=20)\r\n\t\t# plt.xticks(fontsize=15)\r\n\t\t# plt.yticks(fontsize=15)\r\n\r\n\t\t# fig.savefig('figures/g_depth.png', bbox_inches='tight')\r","title":""},{"docid":"73911a61b4c40c2cc94b768075118074","score":"0.5291009","text":"def plotting():\n pass","title":""},{"docid":"cfde243e9b097e1d2580c2fd8b464e9c","score":"0.5280765","text":"def plot_ongoing_FFT_distribution(ax, fontproperties,\r\n stim_types = ['extended_stim', 'flash'],\r\n windows = [9000.0, 2000.0, 1000.0], \r\n gaps = [0.0, 200.0, 200.0],\r\n padding = 0.0,\r\n freq_range_for_FFT = (1.0, 5.0),\r\n replace_spikes = True, remove_sine_waves = True,\r\n get_new_FFT_results_all_cells = False,\r\n get_new_processed_traces = False,\r\n master_folder_path = 'E:\\\\correlated_variability'):\r\n\r\n fonts = fontproperties\r\n size = fonts.get_size()\r\n mpl.rcParams['mathtext.default'] = 'regular'\r\n \r\n FFT_dict_all_cells = get_FFT(stim_types, windows, gaps, padding,\r\n freq_range_for_FFT, replace_spikes, remove_sine_waves,\r\n get_new_FFT_results_all_cells, get_new_processed_traces,\r\n master_folder_path)\r\n \r\n FFTs_all_cells = []\r\n for cell in FFT_dict_all_cells:\r\n FFTs_all_cells.append(numpy.mean(FFT_dict_all_cells[cell]['ongoing']))\r\n\r\n ### plot the histogram ###\r\n\r\n hist1 = ax.hist(FFTs_all_cells, bins = 15, color = 'k', alpha = 0.4)\r\n\r\n ### format the histogram ### \r\n ax.spines['top'].set_visible(False)\r\n ax.spines['right'].set_visible(False)\r\n ax.spines['left'].set_visible(False)\r\n ax.yaxis.set_visible(False)\r\n ax.get_xaxis().tick_bottom()\r\n\r\n ax.tick_params(axis = 'x', length = 3.0, width = 1.0, direction = 'outward')\r\n loc = plticker.MultipleLocator(base = 200.0)\r\n ax.xaxis.set_major_locator(loc)\r\n x_labels = ax.get_xticks()\r\n ax.set_xticklabels(x_labels, fontsize = size)\r\n ax.set_xlabel(r'$\\langle FFT_\\delta\\rangle$ (mV)', fontsize = size) \r\n\r\n ### report number of cells and preparations ###\r\n experiments = []\r\n for cell_name in FFT_dict_all_cells:\r\n expt_date = cell_name.split('_')[0]\r\n if expt_date not in experiments:\r\n experiments.append(expt_date)\r\n print ' %s cells;'%str(len(FFT_dict_all_cells.keys())), \\\r\n '%s turtles'%str(len(experiments))","title":""},{"docid":"d146a9eff50a412c9d6eb3995bade567","score":"0.5272843","text":"def plot_rx_t(data_z):\n time = np.arange(len(data_z)) * (1/SAMPLE_RATE)\n plt.plot(time, data_z.real)\n plt.show()","title":""},{"docid":"1aeb1a8facff6190d400b6f7bf508226","score":"0.52714753","text":"def plot_encoded(t, u, s, fig_title='', file_name=''):\n\n dt = t[1]-t[0]\n cs = np.cumsum(s)\n if cs[-1] >= max(t)-min(t):\n raise ValueError('some spike times occur outside of signal''s support')\n\n p.clf()\n p.gcf().canvas.set_window_title(fig_title)\n p.axes([0.125, 0.3, 0.775, 0.6])\n p.vlines(cs+min(t), np.zeros(len(cs)), u[np.asarray(cs/dt, int)], 'b')\n p.hlines(0, 0, max(t), 'r')\n p.plot(t, u, hold=True)\n p.xlabel('t (s)')\n p.ylabel('u(t)')\n p.title(fig_title)\n p.gca().set_xlim(min(t), max(t))\n a = p.axes([0.125, 0.1, 0.775, 0.1])\n p.plot(cs+min(t), np.zeros(len(s)), 'ro')\n a.set_yticklabels([])\n p.xlabel('%d spikes' % len(s))\n p.gca().set_xlim(min(t), max(t))\n p.draw_if_interactive()\n if file_name:\n p.savefig(file_name)","title":""},{"docid":"a29923cc7dc87151a0c34aa43610a6d8","score":"0.52597255","text":"def f_sensitivity_per_band():\n\n fig = plt.figure()\n\n j_minimum = minimum.where((minimum.N_j > 50) & (minimum.Stetson < 0.5))\n h_minimum = minimum.where((minimum.N_h > 80) & (minimum.Stetson < 0.5))\n k_minimum = minimum.where((minimum.N_k > 80) & (minimum.Stetson < 0.5))\n\n s1 = plt.subplot(3,1,1)\n s2 = plt.subplot(3,1,2, sharex=s1)\n s3 = plt.subplot(3,1,3, sharex=s1)\n \n s1.plot(j_minimum.j_meanr, j_minimum.j_rmsr, 'b,')\n s2.plot(h_minimum.h_meanr, h_minimum.h_rmsr, 'g,')\n s3.plot(k_minimum.k_meanr, k_minimum.k_rmsr, 'r,')\n\n plt.xlim(10.5, 17.5)\n\n s3.set_xlabel(\"Magnitude\")\n s3.set_ylabel(\"Observed rms\")\n for s in [s1,s2,s3]:\n s.set_ylim(0,0.1)\n s.set_yticks([0, 0.05, 0.1])\n\n s1.text(0.2, 0.6, \"$J$\", fontsize=24, color='b', transform=s1.transAxes)\n s2.text(0.2, 0.6, \"$H$\", fontsize=24, color='g', transform=s2.transAxes)\n s3.text(0.2, 0.6, \"$K$\", fontsize=24, color='r', transform=s3.transAxes) \n\n plt.show()\n\n return fig","title":""},{"docid":"deec8fb4da515f311aa5f56e0bf7d914","score":"0.52595687","text":"def show_friction_line(self, sns_context=\"talk\"):\r\n self.mean_friction_frame()\r\n\r\n p_dat = self.mean_fric_frame[self.mean_fric_frame['direction'] == 0]\r\n n_dat = self.mean_fric_frame[self.mean_fric_frame['direction'] == 1]\r\n\r\n xbins = self.mean_fric_frame['load_index'].max() + 1\r\n\r\n sns.set_context(sns_context)\r\n\r\n f, (ax1, ax2) = plt.subplots(nrows=1, ncols=2)\r\n\r\n sns.regplot(x='N', y='F', data=p_dat, x_bins=xbins, ax=ax1)\r\n sns.regplot(x='N', y='F', data=n_dat, x_bins=xbins, ax=ax2)\r\n\r\n ax1.set_title(\"(+)\")\r\n ax2.set_title(\"(-)\")\r\n\r\n plt.tight_layout()\r\n\r\n plt.show()","title":""},{"docid":"7c4470402c93c029d205eb287970fec8","score":"0.52591443","text":"def space_plot(data, ax=None, **kwargs):\n x, y = space_from_dict(data)\n x_sm, y_sm = smooth(x, y, 200)\n ax.plot(x_sm, y_sm, **kwargs)\n ax.set(ylabel=f\"{settings.CLI} ($mM$)\", xlabel='Distance from soma ($\\mu m$)')","title":""},{"docid":"e04c6d60e6a67240c77f6180fc064a24","score":"0.5258989","text":"def scatter_plot(self):\r\n assert self.channel_count == 2\r\n plt.figure('Stochastic Signals Scatter Plot')\r\n plt.scatter(self.channel[0], self.channel[1])\r\n plt.show()","title":""},{"docid":"78cc970aa7034e2e743e039f26e6a096","score":"0.5256539","text":"def plot_fourier(u, fs, fmin=0.0, fmax=None, style='line'):\n\n if fmin < 0.0 or fmin >= fs/2:\n raise ValueError('invalid minimum frequency')\n\n if fmax is None:\n fmax = fs/2\n if fmax <= fmin or fmax > fs/2:\n raise ValueError('invalid maximum frequency')\n\n n = len(u)/2\n uf = fft(u)[0:n]\n f = (fs/2.0)*np.arange(0, n)/n\n\n a = int(2.0*n*fmin/fs)\n b = int(2.0*n*fmax/fs)\n\n p.clf()\n p.subplot(211)\n if style == 'stem':\n p.stem(f[a:b], np.real(uf)[a:b])\n p.ylabel('real')\n elif style == 'semilogy':\n p.semilogy(f[a:b], np.abs(np.real(uf)[a:b]))\n p.ylabel('|real|')\n else:\n p.plot(f[a:b], np.real(uf)[a:b])\n p.ylabel('real')\n p.xlim((f[a], f[b-1]))\n p.subplot(212)\n if style == 'stem':\n p.stem(f[a:b], np.imag(uf)[a:b])\n p.ylabel('imag')\n elif style == 'semilogy':\n p.semilogy(f[a:b], np.abs(np.imag(uf)[a:b]))\n p.ylabel('|imag|')\n else:\n p.plot(f[a:b], np.imag(uf)[a:b])\n p.ylabel('imag')\n p.xlim((f[a], f[b-1]))\n p.xlabel('f (Hz)')","title":""},{"docid":"0fbfb6678402efcb2ddb69854ae2cb56","score":"0.52535695","text":"def DFT(signal):\n ts = np.linspace(0, DURATION, (500*(DURATION))+1)\n plt.plot(ts, signal)\n freqs = np.linspace(0, 4*np.pi, 1000)\n dft = []\n for i in range(len(freqs)):\n dft.append(Fourier_Coeff(signal, freqs[i]))\n print(min(dft))\n print(freqs[dft.index(min(dft))])\n plt.figure()\n\n plt.plot(freqs, dft)\n plt.show()","title":""},{"docid":"6c85d6b274551db91812abf48d590831","score":"0.5251782","text":"def plot_sot_sig(filename, idxs):\n filename = \"/tmp/dg_\" + filename + \".dat\"\n data, name = read_tracer_file(filename)\n plot_select_traj(data, idxs, name)\n return","title":""},{"docid":"c208be70c9bc718b05411194d1943c0c","score":"0.524833","text":"def plot_n_sst_timeseries(satellites): \n ocean_area = 361900000.0\n labels = ['ATSR1','ATSR2','AATSR','NOAA07','NOAA09','NOAA11','NOAA12','NOAA14','NOAA15','NOAA16','NOAA17','NOAA18','NOAA19','METOPA']\n satellites = ['ATSR1','ATSR2','AATSR','AVHRR07_G','AVHRR09_G','AVHRR11_G','AVHRR12_G','AVHRR14_G','AVHRR15_G','AVHRR16_G','AVHRR17_G','AVHRR18_G','AVHRR19_G','AVHRRMTA_G']\n\n fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True)\n\n lab = []\n ncolors = len(satellites)\n ax1.set_prop_cycle('color',[plt.cm.gnuplot2(j) for j in np.linspace(0, 1, ncolors)])\n for i in range(0,len(satellites)):\n filename = satellites[i] + '_summary.nc'\n ds = xarray.open_dataset(filename)\n dates = ds['time']\n idx = np.argsort(dates, axis=0) \n t = np.array(dates)[idx]\n days = (t[-1] - t[0]).astype('timedelta64[D]') / np.timedelta64(1, 'D')\n years = days/365.0\n times_duplicates = pd.Series(t)\n times = times_duplicates.drop_duplicates()\n Q4_duplicates = pd.Series(ds['n_sst_q4'].values[idx], index=t)\n Q5_duplicates = pd.Series(ds['n_sst_q5'].values[idx], index=t)\n n_sst_q4 = 365.0 * Q4_duplicates.groupby(Q4_duplicates.index).sum() / ocean_area \n n_sst_q5 = 365.0 * Q5_duplicates.groupby(Q5_duplicates.index).sum() / ocean_area \n df = DataFrame({'Q4' : n_sst_q4, 'Q5' : n_sst_q5}) \n df['Sum'] = df['Q4'] + df['Q5']\n# df['Sum'] = df['Q4'].fillna(0) + df['Q5'].fillna(0)\n# df['Sum_mean'] = df['Sum'].resample(\"1d\").sum().fillna(0).rolling(window=31, min_periods=1).median()\n# df['Sum_mean'].plot(ax=ax1)\n\n lab.append(labels[i])\n ax1.plot(times, df['Sum'], '.', markersize=0.2)\n ax1.set_ylim([0,18])\n print(labels[i] + \",\" + str(df['Sum'].mean()) + \",\" + str(df['Sum'].shape[0]))\n\n plt.tick_params(labelsize=12)\n title_str = 'QL=4 & 5'\n ax1.set_title(title_str, fontsize=10)\n\n lab = []\n ncolors = len(satellites)\n ax2.set_prop_cycle('color',[plt.cm.gnuplot2(j) for j in np.linspace(0, 1, ncolors)])\n for i in range(0,len(satellites)):\n filename = satellites[i] + '_summary.nc'\n ds = xarray.open_dataset(filename)\n dates = ds['time']\n idx = np.argsort(dates, axis=0) \n t = np.array(dates)[idx]\n days = (t[-1] - t[0]).astype('timedelta64[D]') / np.timedelta64(1, 'D')\n years = days/365.0\n times_duplicates = pd.Series(t)\n times = times_duplicates.drop_duplicates()\n Q3_duplicates = pd.Series(ds['n_sst_q3'].values[idx], index=t)\n n_sst_q3 = 365.0 * Q3_duplicates.groupby(Q3_duplicates.index).sum() / ocean_area\n df = DataFrame({'Q3' : n_sst_q3})\n# df['Q3_mean'] = df['Q3'].resample(\"1d\").sum().rolling(window=31, min_periods=1).median()\n# df['Q3_mean'].plot(ax=ax2)\n\n lab.append(labels[i])\n ax2.plot(times, df['Q3'], '.', markersize=0.2)\n ax2.set_ylim([0,18])\n print(labels[i] + \",\" + str(df['Q3'].mean()) + \",\" + str(df['Q3'].shape[0]))\n\n plt.tick_params(labelsize=12)\n title_str = 'QL=3'\n ax2.set_title(title_str, fontsize=10)\n\n fig.legend(lab, fontsize=8, loc=7, markerscale=20, scatterpoints=5)\n fig.subplots_adjust(right=0.8) \n fig.text(0.01, 0.5, 'Observation density / $\\mathrm{km^{-2} \\ yr^{-1}}$', va='center', rotation='vertical')\n plt.savefig('n_sst_timeseries.pdf')\n# plt.savefig('n_sst_timeseries.png', dpi=600)\n# plt.savefig('n_sst_timeseries.eps', format='eps', rasterized=True, dpi=1200)\n plt.close('all')","title":""},{"docid":"9875abe0b1b5428c0aead1ff3f2ec260","score":"0.5241629","text":"def plot_freqs(files=FILES):\n def calc_freqs(f):\n t = tokenize(f)\n dist = Counter(t)\n return sorted(dist.values(), reverse=True)\n freqs = [calc_freqs(f) for f in files]\n\n def plot_one(f, n):\n corpora = len(files)\n width = 2 # linlin and loglog\n plt.subplot(corpora, width, n*2+1)\n plt.plot(f)\n plt.subplot(corpora, width, n*2+2)\n plt.loglog(f)\n\n for i, val in enumerate(freqs):\n plot_one(val, i)\n plt.show()","title":""},{"docid":"7493718cfec2906c4ff155a96006fce1","score":"0.52378213","text":"def plot_spectrum(gpi):\n # read ts, fill gaps\n ts = prepare_ts(gpi, start_date='2007-01-01', end_date='2011-12-31')\n \n # detrend\n ts_detrend = ts - ts.mean()\n \n # construct sample frequencies\n n = len(ts_detrend) # length of the signal\n \n # define cycles per unit: days = 1, week=7, month=31, years = 365.25\n days = 365.25\n time_step = 1.0 / days\n frq = np.fft.fftfreq(n, d = time_step)\n frq = frq[range(n/2)] # one side frequency range\n \n # fft computing and normalization\n TS = np.fft.fft(ts_detrend)/n \n TS = TS[range(n/2)]\n \n # plot \n f, ax = plt.subplots(3,figsize=(10,10))\n \n f.suptitle(\"Single Sided Amplitude Spectrum of ASCAT-Ts at Gpi: %d\" % gpi,\n fontsize=16)\n ax[0].plot(ts)\n ax[0].set_xlabel('Time')\n ax[0].set_ylabel('SM [%]')\n ax[0].set_yticks(np.arange(0,120,20))\n \n ax[1].plot(frq,abs(TS),'r') # plotting the spectrum\n ax[1].set_xlabel('Frequency (cycles per %d days)' % days)\n ax[1].set_ylabel('|TS(freq)|')\n ax[1].set_xlim(0,182.5)\n ax[1].set_ylim(0,6)\n ax[1].set_xticks(np.arange(0,200,25))\n ax[1].set_yticks(np.arange(0,8,1))\n \n ax[2].plot(frq,abs(TS),'r') # plotting the spectrum\n ax[2].set_xlabel('Frequency (cycles per %d days)' % days)\n ax[2].set_ylabel('|TS(freq)|')\n ax[2].set_xlim(0,13)\n ax[2].set_xticks(range(13))\n ax[2].set_yticks(np.arange(0,13,2))\n \n f.tight_layout()\n f.subplots_adjust(top=0.92)\n plt.show()","title":""},{"docid":"aa49ffe2b571bf04f4d138eb3f1bb17a","score":"0.5232876","text":"def plot_ts(data, ax=None, fig=None, fig_kwargs=None):\n # Grab station information to use in plot title\n station_id = data['Station'][0]\n station_dict = _get_station_dict()\n station_name = station_dict[station_id]\n\n # Create figure and/or axis if no existing figure/axis objects are passed\n fig, ax = _fig_setup(ax, fig, fig_kwargs)\n \n # Plot on axis\n ax.plot(data.index, data['Discharge'])\n ax.set_ylim([0, 1.2*data['Discharge'].max()])\n ax.set_xlabel('Date')\n ax.set_ylabel('Discharge (cfs)')\n ax.set_title(station_name)\n return fig, ax","title":""},{"docid":"17ada2679035692327d9814373245722","score":"0.5231238","text":"def plot_default(self):\n plt.figure(figsize=(12,8)) \n plt.imshow(self.specx[::2], extent=self.leftlim + self.rightlim, origin='lower', aspect='auto') \n plt.xlabel('Frequency Bins \\n'+'\\nFile:'+ self.filename + '\\nRuntime:' + str(self.t_fs))\n plt.ylabel('Time (s)')\n plt.title('Waterfall')\n plt.colorbar()\n plt.savefig('waterfal_just_plot.png', dpi=400, transparent=False)\n plt.show()","title":""},{"docid":"d9d8f3869d4c8a0dba30e5d30597d93b","score":"0.52307063","text":"def PlotPowerSpectrum(g, x0, N, args=()):\n sig = IterateList(g, x0, N+1000, args)[1000:]\n sig_freq = sp.fftpack.fftfreq(sig.size, d=1)\n sig_fft = sp.fftpack.fft(sig)\n pidxs = sp.where(sig_freq > 0)\n freqs, power = sig_freq[pidxs], sp.abs(sig_fft)[pidxs]\n pylab.plot(freqs, power)\n pylab.xlabel('Frequency [Hz]')\n pylab.ylabel('Power')\n pylab.show()\n print(\"Peak Frequency:\")\n print(freqs[power.argmax()])","title":""},{"docid":"b3c8ed7def21d86af46ce4b0aa1689ae","score":"0.52234256","text":"def plot_classification_frequency(classification_data):\n classification_frequency = scipy.stats.itemfreq(classification_data)\n\n plt.figure(1)\n\n for row_n, row in enumerate(classification_frequency):\n if row[0] == 'B':\n label = 'Benign'\n color = 'b'\n elif row[0] == 'M':\n label = 'Malignant'\n color = 'r'\n else:\n raise Exception(\"Unkown classification:\", row[0])\n frequency = int(row[1])\n plt.bar(left=row_n, height=frequency, color=color, label=label)\n\n plt.gca().axes.xaxis.set_ticklabels([])\n plt.legend()\n plt.xlabel(\"Diagnosis\")\n plt.ylabel(\"Frequency\")\n plt.title(\"Distribution of Classifications\")\n print(\n \"In order to have our classifier be adept at spotting all classes,\\n\"\n \"we must ensure our data has a reasonably equal distribution.\\n\"\n )\n plt.show()","title":""}],"string":"[\n {\n \"docid\": \"9a1712a9bdcd3cfbf3521c02ffd920db\",\n \"score\": \"0.6559505\",\n \"text\": \"def plot(self):\\n x_range = np.arange(0, 1.0, 0.01)\\n y_vals = [self.get_freq(x) for x in x_range]\\n plt.plot(x_range, y_vals)\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32df4677233cf5ac08efc6bfb875f532\",\n \"score\": \"0.6464078\",\n \"text\": \"def plot_strain_rate(self, strain):\\r\\n self.acq_rate\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10d0240a7b2c318a70684ee5aea6f0a1\",\n \"score\": \"0.64597017\",\n \"text\": \"def plotWaveandFrequencyRange(self):\\n\\n\\n times = -np.arange(WWProperties.signalSamples) * WWProperties.signalTimestep + WWProperties.signalStart\\n signal = self.waveMaker.makeWave(WWProperties.m1, WWProperties.m2, WWProperties.phic, WWProperties.tc, times) \\n frequency = self.waveMaker.omega / (2 * np.pi)\\n\\n plt.figure(1)\\n plt.subplot(211)\\n plt.title(\\\"Inspiral for (10 M${}_\\\\odot$, 1.4M${}_\\\\odot$) non-spining system (WW fig 8)\\\")\\n plt.plot(times, frequency)\\n plt.ylabel('Orbital Frequency')\\n\\n plt.subplot(212)\\n plt.plot(times, signal)\\n plt.xlabel('Time (s)')\\n plt.ylabel('$R h_+$')\\n\\n plt.savefig(self.figFolderName + \\\"/\\\" + Params + \\\" \\\" + GandC0 + \\\" units signal signalStart %f signalSamples %d signalTimestep %f (WW fig 8).pdf\\\" % (WWProperties.signalStart, WWProperties.signalSamples, WWProperties.signalTimestep), bbox_inches='tight')\\n plt.cla()\\n\\n print (\\\"frequency hight\\\", frequency[0], \\\"frequency low\\\", frequency[-1], \\\"\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cd38308831333d2194321b987d5e54a\",\n \"score\": \"0.64455694\",\n \"text\": \"def plotSpectrum(y,Fs):\\n \\n n = len(Data) # length of the signal\\n k = arange(n)\\n T = n/Fs\\n frq = k/T # two sides frequency range\\n frq = frq[range(n/2)] # one side frequency range\\n\\n Y = fft(y)/n # fft computing and normalization\\n Y = Y[range(n/2)]\\n \\n plot(frq,abs(Y),'r') \\n xlabel('Freq (Hz)')\\n ylabel('|Y(freq)|')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d146498a3afe5dbeb0b5200a35b7813\",\n \"score\": \"0.6393334\",\n \"text\": \"def plot(self, *args, **kw):\\n return self.frequency.plot(*args, **kw)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43dc6d572557709820fdf37ce6d96e46\",\n \"score\": \"0.6373058\",\n \"text\": \"def plot(self):\\n fig, (ax0,ax1) = plt.subplots(2,figsize=(14,14))\\n ax0.plot(SineSignal.t,self.injected_signal)\\n ax1.plot(self.inj_freq[:SineSignal.n_t//2],self.freq_prob)\\n ax0.set(title='injected signal',ylabel='signal value',xlabel='signal time chunk')\\n ax1.set(title='discrete FFT',ylabel='freq strength in signal',xlabel='frequency')\\n plt.savefig('example_signal.pdf',bbox_inches='tight')\\n plt.clf()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19aaefa1345a282c92b5900b512f451e\",\n \"score\": \"0.6189974\",\n \"text\": \"def plotSpectrum(y,Fs):\\n n = len(y) # length of the signal\\n k = arange(n)\\n T = n/Fs\\n frq = k/T # two sides frequency range\\n frq = frq[range(n/2)] # one side frequency range\\n \\n Y = fft(y)/n # fft computing and normalization\\n Y = Y[range(n/2)]\\n plot(frq,abs(Y),'r') # plotting the spectrum\\n xlabel('Freq (Hz)')\\n ylabel('|IR_Spectrum(freq)|')\\n rospy.loginfo('[sharp_listener] DONE PLOTING!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!')\\n show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01cc1e93736eff282d7d80f527ae3d74\",\n \"score\": \"0.61449003\",\n \"text\": \"def plot_wavelength(spectrum, wavelength):\\n #print(len(spectrum[0]))\\n #print(len(wavelength[0]))\\n for index, element in enumerate(spectrum):\\n plt.plot(wavelength[index], element)\\n plt.xlabel(\\\"Angstroms\\\")\\n plt.ylabel(\\\"Count\\\")\\n plt.title(\\\"No Interpolation\\\")\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be1260d4ed484e7a2db67cbcc8b6dcf5\",\n \"score\": \"0.60464585\",\n \"text\": \"def plotSpectrum(self, ax, signal, color):\\n n = len(signal) # length of the signal\\n k = arange(n)\\n T = n/self.sample_rate\\n frq = k/T # two sides frequency range\\n frq = frq[range(int(n/2))] # one side frequency range\\n\\n Y = fft(signal)/n # fft computing and normalization\\n Y = Y[range(int(n/2))]\\n\\n ax.plot(frq, abs(Y), color) # plotting the spectrum\\n xlabel('Freq (Hz)')\\n ylabel('Gain')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e21d7a7639204bf306e85af3f9fef3e\",\n \"score\": \"0.60293746\",\n \"text\": \"def plot_sfrd_data(ax):\\n\\n ObsSFRdensity = np.array([\\n [0, 0.0158489, 0, 0, 0.0251189, 0.01000000],\\n [0.150000, 0.0173780, 0, 0.300000, 0.0181970, 0.0165959],\\n [0.0425000, 0.0239883, 0.0425000, 0.0425000, 0.0269153, 0.0213796],\\n [0.200000, 0.0295121, 0.100000, 0.300000, 0.0323594, 0.0269154],\\n [0.350000, 0.0147911, 0.200000, 0.500000, 0.0173780, 0.0125893],\\n [0.625000, 0.0275423, 0.500000, 0.750000, 0.0331131, 0.0229087],\\n [0.825000, 0.0549541, 0.750000, 1.00000, 0.0776247, 0.0389045],\\n [0.625000, 0.0794328, 0.500000, 0.750000, 0.0954993, 0.0660693],\\n [0.700000, 0.0323594, 0.575000, 0.825000, 0.0371535, 0.0281838],\\n [1.25000, 0.0467735, 1.50000, 1.00000, 0.0660693, 0.0331131],\\n [0.750000, 0.0549541, 0.500000, 1.00000, 0.0389045, 0.0776247],\\n [1.25000, 0.0741310, 1.00000, 1.50000, 0.0524807, 0.104713],\\n [1.75000, 0.0562341, 1.50000, 2.00000, 0.0398107, 0.0794328],\\n [2.75000, 0.0794328, 2.00000, 3.50000, 0.0562341, 0.112202],\\n [4.00000, 0.0309030, 3.50000, 4.50000, 0.0489779, 0.0194984],\\n [0.250000, 0.0398107, 0.00000, 0.500000, 0.0239883, 0.0812831],\\n [0.750000, 0.0446684, 0.500000, 1.00000, 0.0323594, 0.0776247],\\n [1.25000, 0.0630957, 1.00000, 1.50000, 0.0478630, 0.109648],\\n [1.75000, 0.0645654, 1.50000, 2.00000, 0.0489779, 0.112202],\\n [2.50000, 0.0831764, 2.00000, 3.00000, 0.0512861, 0.158489],\\n [3.50000, 0.0776247, 3.00000, 4.00000, 0.0416869, 0.169824],\\n [4.50000, 0.0977237, 4.00000, 5.00000, 0.0416869, 0.269153],\\n [5.50000, 0.0426580, 5.00000, 6.00000, 0.0177828, 0.165959],\\n [3.00000, 0.120226, 2.00000, 4.00000, 0.173780, 0.0831764],\\n [3.04000, 0.128825, 2.69000, 3.39000, 0.151356, 0.109648],\\n [4.13000, 0.114815, 3.78000, 4.48000, 0.144544, 0.0912011],\\n [0.350000, 0.0346737, 0.200000, 0.500000, 0.0537032, 0.0165959],\\n [0.750000, 0.0512861, 0.500000, 1.00000, 0.0575440, 0.0436516],\\n [1.50000, 0.0691831, 1.00000, 2.00000, 0.0758578, 0.0630957],\\n [2.50000, 0.147911, 2.00000, 3.00000, 0.169824, 0.128825],\\n [3.50000, 0.0645654, 3.00000, 4.00000, 0.0776247, 0.0512861],\\n ], dtype=np.float32)\\n\\n ObsRedshift = ObsSFRdensity[:, 0]\\n xErrLo = ObsSFRdensity[:, 0]-ObsSFRdensity[:, 2]\\n xErrHi = ObsSFRdensity[:, 3]-ObsSFRdensity[:, 0]\\n\\n ObsSFR = np.log10(ObsSFRdensity[:, 1])\\n yErrLo = np.log10(ObsSFRdensity[:, 1])-np.log10(ObsSFRdensity[:, 4])\\n yErrHi = np.log10(ObsSFRdensity[:, 5])-np.log10(ObsSFRdensity[:, 1])\\n\\n ax.errorbar(\\n ObsRedshift,\\n ObsSFR,\\n yerr=[yErrLo, yErrHi],\\n xerr=[xErrLo, xErrHi],\\n color='g',\\n lw=1.0,\\n alpha=0.3,\\n marker='o',\\n ls='none',\\n label='Observations'\\n )\\n\\n return ax\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4eddb8873c3833a0aab5857e3cccbae8\",\n \"score\": \"0.6019912\",\n \"text\": \"def plot_ssd(self, condition='all', counts=False, bw=.01, xlims=None):\\n cidx=self._get_cidx(condition)\\n a=stats.itemfreq(self.SSD[(cidx) & np.isfinite(self.SSD)])#.astype(np.int)\\n if counts==False:\\n a[:,1]/=np.sum(a[:,1])\\n pl.bar(a[:,0]-bw/2.0, a[:,1], width=bw)\\n pl.xlabel('SSD')\\n pl.ylabel('freq')\\n pl.title('data=%s, condition=%s'%(self.name, 'all' if condition=='all' else \\\":\\\".join(self.design.condidx(condition))))\\n if xlims!=None:\\n pl.xlim(*xlims)\\n if not counts:\\n pl.ylim(0,1) # probs \",\n \"title\": \"\"\n },\n {\n \"docid\": \"764745d5cc8079a761da3f6bce628b39\",\n \"score\": \"0.60031587\",\n \"text\": \"def plot(self):\\n timebin = np.linspace(0, self.total_duration, self.specx.shape[0])\\n freq_vector = [self.fc - (self.fs/2), (self.fs/2) + self.fc]\\n \\n plt.figure(figsize=(12,8)) \\n plt.imshow(self.specx[::self.jump], extent=freq_vector + self.rightlim, origin='lower', aspect='auto') \\n plt.plot(self.track_center[0], self.time_bins[0], color = 'k')\\n plt.plot(self.raw_center[:, 0], timebin, color = 'white', marker='.', alpha=0.5)\\n plt.xlabel('Frequency (Hz) \\\\n'+'\\\\nFile:'+ self.filename + '\\\\nRuntime:' + str(self.t_fs))\\n plt.ylabel('Time (s)')\\n plt.title('Waterfall')\\n plt.xlim([self.f_chan[0] - self.BW[0]/2, self.f_chan[0] + self.BW[0]/2])\\n plt.colorbar()\\n plt.savefig('waterfal_plot.png', dpi=400, transparent=False)\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b38d8bba19a1986cf110f47f667f66fb\",\n \"score\": \"0.59973717\",\n \"text\": \"def plot_tsys(self, S):\\n plot_scale = 0.\\n nscale = 0\\n for ipix in range(S.npix):\\n indx_fin = np.where(np.isfinite(S.roach[ipix].tsys_spectrum))\\n indx_inf = np.where(np.isinf(S.roach[ipix].tsys_spectrum))\\n indx_nan = np.where(np.isnan(S.roach[ipix].tsys_spectrum))\\n print(ipix, 'fin------------', indx_fin[0])\\n print(ipix, 'inf------------', indx_inf[0])\\n print(ipix, 'nan------------', indx_nan[0])\\n l_fin = len(indx_fin[0])\\n if l_fin > 0 and S.roach[ipix].tsys > 0 and \\\\\\n S.roach[ipix].tsys < 500:\\n plot_scale = plot_scale + S.roach[ipix].tsys\\n nscale = nscale + 1\\n if nscale > 0:\\n plot_scale = plot_scale / nscale\\n plot_order = [1,5,9,13,2,6,10,14,3,7,11,15,4,8,12,16];\\n for ipix in range(S.npix):\\n pixel_id = S.roach_pixel_ids[ipix]\\n ax = pl.subplot(4, 4, plot_order[pixel_id])\\n ax.tick_params(axis='both', which='major', labelsize=6)\\n ax.tick_params(axis='both', which='minor', labelsize=6)\\n indx_fin = np.where(np.isfinite(S.roach[ipix].tsys_spectrum))\\n l_fin = len(indx_fin[0])\\n if l_fin > 0:\\n pl.plot(S.roach[ipix].tsys_spectrum)\\n pl.text(S.nchan / 2, 10, '%d %6.0fK'%(pixel_id, \\n S.roach[ipix].tsys), \\n horizontalalignment='center')\\n if plot_scale != 0:\\n pl.axis([0, S.nchan, 0, plot_scale * 1.5])\\n else:\\n pl.text(0.1, 0.5, '%d NaN'%(pixel_id))\\n pl.suptitle('TSys: ObsNum %d\\\\n%s %s GHz'%(S.obsnum, S.receiver, \\n S.line_rest_frequency))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6861c4bf1e79fc5f1cb4ab9f65f58ddc\",\n \"score\": \"0.5983932\",\n \"text\": \"def plot(self, noised=False):\\n\\n if noised and self.ndata is None:\\n raise ValueError(\\n 'Can not display noised data when snr has been set to None.')\\n\\n # Show data\\n hs_tmp = hs.signals.Signal2D(self.data if not noised else self.ndata)\\n hs_tmp.axes_manager = self.hsdata.axes_manager\\n hs_tmp.metadata = self.hsdata.metadata\\n hs_tmp.plot()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3acd03f47ed6284b9846c2ff82dca9ac\",\n \"score\": \"0.5945903\",\n \"text\": \"def plotFourier(self, widgetName, data, pen):\\n try:\\n N = len(data)\\n # Normalization\\n yplot = 2* np.abs(data[: N//2]) / N\\n widgetName.plotItem.plot(self.signalFourier['dataFrequencies'][: N//2], yplot, pen= pen)\\n except:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91a1bfa74dc58b5ae7b3fe03e08df65e\",\n \"score\": \"0.5928069\",\n \"text\": \"def plot_data(self\\n ) -> None:\\n self.simulate_data()\\n\\n plt.plot(self.t, self.spleen_signal, color='red')\\n plt.plot(self.t, self.liver_signal, color='blue')\\n plt.plot(self.spleen_sampling_times,\\n self.spleen_data, marker=\\\"x\\\", color='red', linewidth=0)\\n plt.plot(self.liver_sampling_times, self.liver_data,\\n marker=\\\"x\\\", color='blue', linewidth=0)\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"020261c8d490d9b2375b232dd988f26f\",\n \"score\": \"0.59164333\",\n \"text\": \"def draw_latent_frequency(encoder=None):\\n \\n color_id = {1: 'red', 2: 'green', 3:'blue', 4: 'orange', 5: 'purple'}\\n\\n if encoder is None:\\n encoder = load_model('encoder.h5')\\n linear_1, amp_1 = sin_signal_freq()\\n \\n sin_latents_1 = encoder.predict(linear_1) \\n plt.cla()\\n plt.clf()\\n clr = [color_id[i] for i in amp_1]\\n plt.scatter(sin_latents_1[:, 0], sin_latents_1[:, 1], s=3.5, color=clr)\\n #plt.savefig('frequency_with_phase_latents.png', dpi=200)\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b62a559972d796680c393dc4d7c9736e\",\n \"score\": \"0.58984387\",\n \"text\": \"def plot(self):\\n from matplotlib import pyplot\\n import numpy as np\\n\\n x = unit.Quantity(np.arange(0, np.pi, 0.1), unit=unit.radians)\\n pyplot.plot(x,\\n self.k * (1 + np.cos(self.periodicity * x - self.phase)),\\n 'r--')\\n\\n pyplot.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b21b21cfb8950b17961e02645fbed45a\",\n \"score\": \"0.58799034\",\n \"text\": \"def plot_rx_f(data_z):\\n freq, z_fft = fft_rx(data_z)\\n plt.plot(freq, z_fft)\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c0726ec78152ae796254111f38ed0a3\",\n \"score\": \"0.58260477\",\n \"text\": \"def simpleFreqPlot(x, y, plot=plt, xscale=False, yscale=False):\\n plot.scatter(x, y)\\n\\n if xscale:\\n plt.xscale('log') \\n if yscale:\\n plt.yscale('log')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60fa5d6ed28306c4d2c22893009d51b4\",\n \"score\": \"0.5802264\",\n \"text\": \"def draw(self):\\n\\t\\tts = pd.Series(self.R[2:], index=xrange(2,self.N+2))\\n\\t\\tts.plot()\\n\\t\\tplt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72f4c34f5e39fcdc2c4c161c2570dc46\",\n \"score\": \"0.5779021\",\n \"text\": \"def plot_as1D(self, noised=False):\\n\\n if noised and self.ndata is None:\\n raise ValueError(\\n 'Can not display noised data when snr has been set to None.')\\n\\n if noised:\\n hs_data = hs.signals.Signal1D(self.ndata)\\n else:\\n hs_data = hs.signals.Signal1D(self.data)\\n\\n hs_data.axes_manager = self.hsdata.axes_manager\\n hs_data.metadata = self.hsdata.metadata\\n\\n hs_data.plot()\\n hs_data._plot.navigator_plot.ax.images[-1].set_cmap(\\\"viridis\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3899bf53a12913b0f5c1d2d78c61dce\",\n \"score\": \"0.57586116\",\n \"text\": \"def plotFrequency(b,a=1):\\n w,h = signal.freqz(b,a)\\n h_dB = abs(h);#20 * np.log(abs(h))/np.log(10)\\n subplot(211)\\n plot(w/max(w),h_dB)\\n #plt.ylim(-150, 5)\\n ylabel('Magnitude (db)')\\n xlabel(r'Normalized Frequency (x$\\\\pi$rad/sample)')\\n title(r'Frequency response')\\n subplot(212)\\n h_Phase = np.unwrap(np.arctan2(np.imag(h),np.real(h)))\\n plot(w/max(w),h_Phase)\\n ylabel('Phase (radians)')\\n xlabel(r'Normalized Frequency (x$\\\\pi$rad/sample)')\\n title(r'Phase response')\\n plt.subplots_adjust(hspace=0.5)\\n return w;\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3508db5d40f46209add10843063a723\",\n \"score\": \"0.57523036\",\n \"text\": \"def stability_plotter(df, waves):\\n stability = []\\n for header in df:\\n array = df[header].to_numpy()\\n\\n std_dev = np.std(array)\\n average = np.average(array)\\n stability.append(std_dev/average) \\n \\n # nice plot for stability, how it changes per wavelength\\n plt.plot(waves, stability) \\n plt.title(\\\"Stability per wavelength\\\")\\n plt.xlabel(\\\"Wavelength (nm)\\\")\\n plt.ylabel(\\\"Stability\\\")\\n plt.savefig(\\\"stability.png\\\")\\n plt.show()\\n plt.close() \\n \\n return stability\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e81ae6b25f4095438c8c6ae76050a372\",\n \"score\": \"0.5750488\",\n \"text\": \"def plot_seabird(netcdffile):\\n\\n f = netcdf.netcdf_file(netcdffile, 'r')\\n\\n depth = f.variables['DEPTH'].data\\n salinity = f.variables['PSAL'].data\\n temp = f.variables['TEMP'].data\\n\\n f.close()\\n\\n fig = plt.figure()\\n ax1 = fig.add_subplot(111)\\n ln1 = ax1.plot(salinity, depth, 'r.', label='Salinity')\\n ax1.invert_yaxis()\\n\\n ax2 = ax1.twiny()\\n ln2 = ax2.plot(temp, depth, 'g.', label='Temperature')\\n\\n ax1.grid(True)\\n ax1.set_ylabel('Depth (m)')\\n ax1.set_xlabel('Salinity (PSU)')\\n ax2.set_xlabel('Temperature ($^\\\\circ$C)')\\n\\n lns = ln1+ln2\\n labs = [l.get_label() for l in lns]\\n ax2.legend(lns, labs, loc=0)\\n\\n return fig\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7bac4221056958e0be52693fb1669b1\",\n \"score\": \"0.5747702\",\n \"text\": \"def plot_temporal_distribution(self, fname=None):\\n fname = self._fname(fname)\\n \\n figname = 'temps1.Simulation.plot_temporal_distribution.' + fname.replace(\\\" \\\", \\\"_\\\")\\n fig, axs = plt.subplots(2, 1, num=figname, clear=True, figsize=[6.4, 7.19])\\n axs[0].set_title(f'{fname.capitalize()} filter\\\\nTemporal distribution of S1 candidates')\\n \\n ax = axs[0]\\n ax.set_xlabel('Time relative to true S1 location [ns]')\\n ax.set_ylabel('Inverse of neighbor temporal gap [ns$^{-1}$]')\\n \\n for k in ['all', 'dcr']:\\n time = self.times[fname][k]\\n time = np.sort(time)\\n ddecdf = 1 / np.diff(time)\\n x = time - self.s1loc\\n y = np.concatenate([ddecdf, ddecdf[-1:]])\\n ax.plot(x, y, drawstyle='steps-post', **self.plotkw[k])\\n \\n ax.axvspan(-self.deadradius, self.deadradius, color='#eee', zorder=-9, label='$\\\\\\\\pm$ dead radius')\\n\\n ax.legend(loc='upper right')\\n ax.set_xlim(1.5 * self.deadradius * np.array([-1, 1]))\\n ax.set_yscale('log')\\n ax.minorticks_on()\\n ax.grid(True, which='major', linestyle='--')\\n ax.grid(True, which='minor', linestyle=':')\\n \\n \\n ax = axs[1]\\n ax.set_xlabel('Time relative to true S1 location [ns]')\\n ax.set_ylabel('Histogram bin density [ns$^{-1}$]')\\n \\n times1 = self.hits1.reshape(-1) - self.s1loc \\n time = self.times[fname]['all'] - self.s1loc\\n signal = self.signal[fname]['all']\\n time_match = time[signal]\\n sigma = qsigma.qsigma(time_match)\\n \\n # t = np.linspace(..., ..., 1000)\\n # ax.plot(t, pS1.p_S1_gauss(t, self.VL, self.tauV, self.tauL, self.tres), label='S1 pdf')\\n histkw = dict(bins='auto', density=True, histtype='step', zorder=10)\\n ax.hist(times1, label=f'S1 photons ({len(times1)})', linestyle=':', **histkw)\\n ax.hist(time_match, label=f'matching candidates ($\\\\\\\\sigma_q$={sigma:.3g} ns)', **histkw)\\n \\n ax.axvspan(0, self.deadradius, color='#eee', zorder=-9, label=f'dead radius ({self.deadradius} ns)')\\n\\n textbox.textbox(ax, self.infotext(), loc='upper left', zorder=11)\\n \\n ax.legend(loc='upper right', fontsize='small')\\n ax.set_yscale('log')\\n linthreshx = 100 #10 ** np.ceil(np.log10(15 * sigma))\\n ax.set_xscale('symlog', linthreshx=linthreshx)\\n ax.minorticks_on()\\n ax.xaxis.set_minor_locator(symloglocator.MinorSymLogLocator(linthreshx))\\n ax.grid(True, which='major', linestyle='--')\\n ax.grid(True, which='minor', linestyle=':')\\n\\n fig.tight_layout()\\n \\n return fig\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42942931756a0fae0105213e21666ea5\",\n \"score\": \"0.5739101\",\n \"text\": \"def plot(self):\\n from matplotlib import pyplot\\n import numpy as np\\n\\n x = unit.Quantity(np.arange(0, np.pi, 0.1), unit=unit.radians)\\n pyplot.plot(x, self.constant * (1 + self.prefactor\\n * np.cos(self.periodicity * x\\n + self.phase)),\\n 'r--')\\n\\n pyplot.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97916c7a9496d26b389c541e02f8a532\",\n \"score\": \"0.573355\",\n \"text\": \"def do_stability_plotting() -> None:\\n # data = gather_stability_data('500runs_lowerL')\\n data = pickle_load('500runs_lowerL')\\n plt.plot_stability(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f1f1b272d5c909d338e7f4486fb3d7d\",\n \"score\": \"0.5661861\",\n \"text\": \"def plot(y, si):\\n x = si.timer.series.magnitude\\n plot_power(x, y, si)\\n plot_reactivity(x, si)\\n plot_power_w_reactivity(x=x, y=y, si=si)\\n plot_zetas(x, y, si)\\n if si.ne._ndg > 0:\\n plot_omegas(x, y, si)\\n plot_temps_together(x, y, si)\\n plot_temps_separately(x, y, si)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8cb646c3958e42160b2bfa23202d86f\",\n \"score\": \"0.56223905\",\n \"text\": \"def plot_data(self, cutoff=200):\\n\\n # If data not loaded, call load_data function\\n if len(self.data) == 0:\\n self.load_data()\\n\\n ix1 = index_of(self.energy, - min(self.energy))\\n ix2 = index_of(self.energy, cutoff)\\n yHeight = max(self.nor_counts[ix2:])\\n\\n x = self.energy[:ix1]\\n y = self.counts[:ix1]\\n model = GaussianModel()\\n pars = model.guess(y, x=x)\\n result = model.fit(y, pars, x=x)\\n fwhm = result.params['fwhm'].value\\n\\n # Plot\\n plt.figure(figsize=(10, 3))\\n gs = gridspec.GridSpec(1, 3)\\n ax0 = plt.subplot(gs[0, 0])\\n ax0.plot(x, y, 'bo', markersize=4)\\n ax0.plot(x, result.best_fit, 'r-')\\n ax0.set_xlabel('wavenumber (cm-1)', fontsize=8)\\n ax0.set_ylabel('counts', color='k', fontsize=8)\\n plt.title('FWHM ='+\\\"{0:.0f}\\\".format(fwhm), fontsize=10)\\n plt.xlim(-200, 200)\\n ax0.locator_params(axis='x', nbins=5)\\n ax0.locator_params(axis='y', nbins=8)\\n\\n ax1 = plt.subplot(gs[0, 1:3])\\n ax1.plot(self.energy, self.nor_counts, 'k-')\\n ax1.set_xlabel('wavenumber (cm-1)', fontsize=8)\\n # Make the y-axis label and tick labels match the line color.\\n for tl in ax1.get_yticklabels():\\n tl.set_color('k')\\n\\n ax2 = ax1.twinx()\\n ax2.plot(self.energy, self.nor_counts, 'r-')\\n ax2.set_ylabel('counts', color='r', fontsize=8)\\n for tl in ax2.get_yticklabels():\\n tl.set_color('r')\\n\\n plt.ylim(0, yHeight)\\n plt.xlim(min(self.energy), max(self.energy))\\n plt.title(self.filename, fontsize=10)\\n ax2.locator_params(axis='x', nbins=15)\\n ax2.locator_params(axis='y', nbins=8)\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7184530a51eb0ca2b3baf9fd9e7ecb36\",\n \"score\": \"0.56194925\",\n \"text\": \"def plot(t, y, oversampling=5, figsize=(12, 20)):\\n freqs, mag, phase, Pn, proba = extract(t, y, hifac=1, oversampling=oversampling)\\n fig, ax = plt.subplots(6, 1, figsize=figsize)\\n ax[0].scatter(t, y, color='black')\\n ax[0].set_title('Flux')\\n ax[0].set_xlabel('time [$mjd$]')\\n \\n ax[1].plot(freqs, mag, color='black')\\n ax[1].set_title('Spectrum magnitude')\\n ax[1].set_xlabel('Freq [$mjd^{-1}$]')\\n \\n ax[2].plot(freqs, phase, color='black')\\n ax[2].set_title('Spectrum phase')\\n ax[2].set_xlabel('Freq [$mjd^{-1}$]')\\n \\n ax[3].plot(1/freqs, Pn, color='black')\\n ax[3].set_title('LombScargle periodogram')\\n ax[3].set_xlabel('Period [$mjd$]')\\n \\n ax[4].plot(1/freqs, proba, color='black')\\n ax[4].set_title('Periodogram true alarm probability')\\n ax[4].set_xlabel('Period [$mjd$]')\\n # Estimated phase\\n h = 1/freqs[np.argmax(Pn)]\\n phase_hat = (t/h)%1\\n ax[5].scatter(phase_hat, y, color='black')\\n ax[5].set_title('Phase from estimated period, where h=%.4f'%h)\\n fig.tight_layout()\\n return freqs, mag, phase, Pn, proba\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68ce11b2bc075e32138591177c931ee5\",\n \"score\": \"0.5619293\",\n \"text\": \"def plot(self, iwidth=None):\\n if iwidth is None:\\n snr = self.snrs.max(axis=1)\\n else:\\n snr = self.snrs[:, iwidth]\\n\\n plt.plot(self.periods, snr, marker='o', markersize=2, alpha=0.5)\\n plt.xlim(self.periods.min(), self.periods.max())\\n plt.xlabel('Trial Period (s)', fontsize=16)\\n plt.ylabel('S/N', fontsize=16)\\n\\n if iwidth is None:\\n plt.title('Best S/N at any trial width', fontsize=18)\\n else:\\n width_bins = self.widths[iwidth]\\n plt.title('S/N at trial width = %d' % width_bins, fontsize=18)\\n\\n plt.xticks(fontsize=14)\\n plt.yticks(fontsize=14)\\n plt.grid(linestyle=':')\\n plt.tight_layout()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6c72ac3c0c6b364b049b8287a00d8ca\",\n \"score\": \"0.5612675\",\n \"text\": \"def plot_freq_tau(self):\\n plt.plot(self.frequency, self.tau)\\n plt.xlabel(\\\"Frequency (MHz)\\\")\\n plt.ylabel(r\\\"$\\\\tau (\\\\mu s)$\\\")\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecbe146e7ef623fe833d3baac843b889\",\n \"score\": \"0.5577341\",\n \"text\": \"def plot_as2D(self, noised=False):\\n\\n if noised and self.ndata is None:\\n raise ValueError(\\n 'Can not display noised data when snr has been set to None.')\\n\\n if noised:\\n hs_data = hs.signals.Signal2D(\\n np.moveaxis(self.ndata, [0, 1, 2], [1, 2, 0]))\\n else:\\n hs_data = hs.signals.Signal2D(\\n np.moveaxis(self.data, [0, 1, 2], [1, 2, 0]))\\n\\n hs_data.axes_manager = self.hsdata.axes_manager\\n hs_data.metadata = self.hsdata.metadata\\n\\n hs_data.plot(cmap='viridis')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a46e6e350f17c7266ca0fb74d298350\",\n \"score\": \"0.55732363\",\n \"text\": \"def plot_signals(self\\n ) -> None:\\n self.calculate_signals()\\n\\n plt.plot(self.t, self.spleen_signal, color='red')\\n plt.plot(self.t, self.liver_signal, color='blue')\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6b0d0ac14c98bef106090cdeba380f9\",\n \"score\": \"0.5559552\",\n \"text\": \"def plot_ant_gain(self):\\n import pylab\\n pylab.plot(self.config['freqs']/1e6,self.config['antenna_bandpass'])\\n pylab.title('Antenna gain %s'%self.config['antenna_bandpass_calfile'])\\n pylab.xlabel('Frequency (MHz)')\\n pylab.ylabel('Relative response (dBi)')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7744e0d9ac9a0ab4dc9b64addef70b9\",\n \"score\": \"0.55551803\",\n \"text\": \"def plot_converted(spectrum):\\n for index, element in enumerate(spectrum):\\n plt.plot(element[0][0], element[0][1])\\n plt.xlabel(\\\"Angstroms\\\")\\n plt.ylabel(\\\"Count\\\")\\n plt.title(\\\"Interpolated\\\")\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5feb152157af5a94623b346dcc2104bb\",\n \"score\": \"0.5549776\",\n \"text\": \"def plot_waterfall(d, freqs, lsts, t_unit='hr', f_unit='MHz',\\n raster=True):\\n\\n plt.imshow(d, aspect='auto', interpolation='none',\\n rasterized=raster,\\n extent=(freqs[0], freqs[-1], lsts[0], lsts[-1]))\\n plt.xlabel(\\\"Frequency [%s]\\\" % f_unit)\\n plt.ylabel(\\\"Time [%s]\\\" % t_unit)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4633c1fe42fab5ba5f5baaa1f8632282\",\n \"score\": \"0.55475754\",\n \"text\": \"def plot_spe_freq(res, points=None, record=-1, time=None, fig_name=''):\\n # If time is given searched for record\\n if time is not None:\\n record = res.get_closest_record(time)\\n time = res.times[record]\\n else:\\n time = res.times[record]\\n\\n if points is None:\\n points = res.get_list_spectrum_points()\\n\\n fig, axe = plt.subplots()\\n\\n for point in points:\\n # Getting list of frequencies and spectrum value\\n freq, spectrum = res.get_spectrum(point, record)\\n # Plotting it\\n plot1d(axe, freq, spectrum, plot_label='point {:06d}'.format(point),\\n x_label='Frequencies', y_label='Spectrum')\\n\\n axe.legend()\\n\\n axe.set_title(\\\"At time {}\\\".format(time))\\n\\n if fig_name != '':\\n print(\\\" \\\"*8+\\\"~> Plotting {}\\\".format(fig_name))\\n fig.savefig(fig_name)\\n else:\\n plt.show()\\n\\n plt.close(fig)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d2121dbb71c965bf21c71ee0f085cd5\",\n \"score\": \"0.5524251\",\n \"text\": \"def plot(self):\\r\\n test_domain = np.linspace(0, 1, 100, dtype = intprim.constants.DTYPE)\\r\\n test_range = self.get_basis_functions(test_domain)\\r\\n\\r\\n fig = plt.figure()\\r\\n\\r\\n for basis_func in test_range:\\r\\n plt.plot(test_domain, basis_func)\\r\\n\\r\\n fig.suptitle('Basis Functions')\\r\\n\\r\\n plt.show(block = False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"321bcdea5c2ecb4b5d058d69d46fb14b\",\n \"score\": \"0.55236715\",\n \"text\": \"def plot_sxx(self, bloc, chan, num_samples=None):\\n\\n pol0, pol1 = self.get_data(bloc, chan, num_samples)\\n sxx0, sxx1 = np.fft.fftshift(np.abs(np.fft.fft(pol0)) ** 2), np.fft.fftshift(np.abs(np.fft.fft(pol1)) ** 2)\\n plt_title = \\\"SPD of bloc: {}, channel {}\\\".format(str(bloc), str(chan))\\n lfreq, ufreq = self._calc_chan_freq_range(chan) # get lower bound and upper bound frequency of the channel\\n freq_axis = np.linspace(lfreq, ufreq, len(sxx0))\\n self._plot(sxx0, sxx1, plt_title, x=freq_axis)\\n return sxx0, sxx1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2877ac82537c408c7259bbc2c2e06982\",\n \"score\": \"0.5521294\",\n \"text\": \"def generateSinewave(nrCycles, nrdataPointsPerCycles = 2048):\\n cycles = np.linspace(0,nrCycles * 2 * np.pi, nrdataPointsPerCycles)\\n cyclesArray = np.array(np.sin(cycles))\\n #plt.plot(cyclesArray)\\n #plt.xlabel('angle [rad]')\\n #plt.ylabel('sin (x)')\\n #plt.axis('tight')\\n #plt.show()\\n \\n wave = int16scale(cyclesArray)\\n plt.plot(wave)\\n plt.xlabel('angle [rad]')\\n plt.ylabel('sin (x)')\\n plt.axis('tight')\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8a7cb7b48c07db5456c3a268448f9ba\",\n \"score\": \"0.55171424\",\n \"text\": \"def display_stft(X,\\n fs,\\n len_sig,\\n low_freq=0,\\n high_freq=3000,\\n min_db=-10,\\n max_db=0,\\n normalize=True):\\n # normalize : largest coefficient magnitude is unity\\n X_temp = X.copy()\\n if normalize:\\n X_temp /= np.amax(abs(X_temp))\\n\\n # compute frequencies array\\n Freqs = np.array([low_freq, high_freq])\\n Fd = (Freqs * X_temp.shape[0] / fs).astype(int)\\n\\n # compute values matrix\\n Z = X_temp[Fd[1]:Fd[0]:-1, :]\\n Z = np.clip(np.log(np.abs(Z) + 1e-50), min_db, max_db)\\n Z = 255 * (Z - min_db) / (max_db - min_db)\\n\\n # compute duration\\n time = float(len_sig) / float(fs)\\n\\n # plotting\\n plt.imshow(Z,\\n extent=[0, time, low_freq / 1000, high_freq / 1000],\\n aspect=\\\"auto\\\")\\n plt.ylabel('Frequency (Khz)')\\n plt.xlabel('Time (sec)')\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e633f063f5d07dbb8d1057a1cb1afda\",\n \"score\": \"0.55149215\",\n \"text\": \"def plot_restfreq_spectrum(SSC, band):\\n\\n def get_spectrum(SSC, band):\\n spectrum = spectra[str(SSC['no'])][band]\\n frequency = spectrum['frequency'].to(u.MHz)\\n intensity = spectrum['spectrum'].to(u.K)\\n\\n # shift spectrum to rest frequency\\n velshift = SSC['velshift']\\n frequency = [(-vsys-velshift).to(u.GHz, equivalencies=u.doppler_optical(f)).value for f in frequency]*u.GHz\\n\\n # remove NaNs\\n frequency, intensity = crossmatch(frequency.to(u.GHz).value, intensity.to(u.K).value)\\n return frequency, intensity\\n\\n def set_up_figure(SSC, specie, band):\\n fig,ax = plt.subplots(nrows=1, ncols=1, squeeze=True, sharex='col', sharey='row', figsize=(10,8))\\n ax.text(0.05, 0.9, 'SSC '+str(SSC['no'])+': '+band, color='k', transform=ax.transAxes, ha='left', va='top', weight='bold', fontsize=16, bbox=props)\\n return fig,ax\\n\\n def plot_spectrum(ax, frequency, spectrum):\\n ax.plot(frequency, spectrum, ls='-', color='k', zorder=3)\\n ax.fill_between(frequency, spectrum, [0. for f in frequency], color='grey', alpha=0.5, zorder=2)\\n\\n def get_detected_lines(band=None):\\n # get detected species\\n all_species = []\\n for SSC in SSCs:\\n for specie in detected_species[str(SSC['no'])]:\\n if not specie in all_species:\\n all_species.append(specie)\\n # get all lines of the detected species\\n all_lines = []\\n for specie in all_species:\\n slines = [l for l in lines if l['XCLASS']==specie]\\n for sl in slines:\\n all_lines.append(sl)\\n # keep only lines of given band\\n if not band==None:\\n bandlines = []\\n for line in all_lines:\\n if band=='LSB':\\n if line['restfreq']<350*u.GHz:\\n bandlines.append(line)\\n elif band=='USB':\\n if line['restfreq']>350*u.GHz:\\n bandlines.append(line)\\n return sorted(bandlines, key=lambda k: k['restfreq'])\\n else:\\n return sorted(all_lines, key=lambda k: k['restfreq'])\\n\\n def label_lines(ax, spectrum, band):\\n detected_lines = get_detected_lines(band=band)\\n for idx,line in enumerate(detected_lines):\\n restfreq = line['restfreq'].to(u.GHz).value\\n if (restfreq>frequency[0] and restfreq<frequency[-1]):\\n if band=='LSB':\\n xlim = [342.4, 346.2]\\n elif band=='USB':\\n xlim = [354.3, 358.1]\\n xloc = xlim[0] +((idx+0.5)/len(detected_lines))*(xlim[1]-xlim[0])\\n ax.axvline(x=restfreq, ymin=0, ymax=1, color='dimgrey', ls='--', lw=0.5, zorder=1)\\n ax.plot([restfreq,xloc], [1.05*np.nanmax(spectrum), 1.05*1.05*np.nanmax(spectrum)], color='dimgrey', ls='--', lw=0.5, zorder=1, clip_on=False)\\n ax.text(xloc, 1.06*1.05*np.nanmax(spectrum), line_tex(line), color='dimgrey', fontsize=10, rotation=90, ha='center', va='bottom')\\n\\n\\n def velocity_indicator(ax, frequency, spectrum):\\n x = np.nanmedian(frequency)\\n y = 0.95*np.nanmax(spectrum)\\n ax.plot([x, x+0.05836398],[y,y], c='k', ls='-', lw='3', zorder=11)\\n ax.text(x, y, r'- $\\\\sim 50$\\\\,km\\\\,s$^{-1}$', color='k', ha='left', va='center', weight='bold', bbox=props, zorder=10)\\n\\n def format_figure(ax, frequency, spectrum):\\n ax.set_xlim([frequency[0], frequency[-1]])\\n ax.set_ylim(-0.05*np.nanmax(spectrum), 1.05*np.nanmax(spectrum))\\n ax.xaxis.set_major_locator(MultipleLocator(0.5))\\n ax.xaxis.set_minor_locator(MultipleLocator(0.1))\\n ax.yaxis.set_major_locator(MultipleLocator(10))\\n ax.yaxis.set_minor_locator(MultipleLocator(2))\\n ax.tick_params(axis='both', which='major', labelsize=12)\\n ax.set_axisbelow(True)\\n ax.grid(ls=':', c='grey')\\n ax.set_xlabel(r'$\\\\nu_\\\\mathrm{rest}$ [GHz]', fontsize=12)\\n ax.set_ylabel(r'T$_\\\\mathrm{b}$ [K]', fontsize=12)\\n fig.set_tight_layout(True)\\n\\n def save_figure(fig, specie, band):\\n savepath = escape_fname(os.path.join(plotdir, '03.XCLASS_fit', 'restfreq_spectra', str(SSC['no'])+'.'+band+'.restfreq.pdf'))\\n os.system('mkdir -p '+os.path.dirname(savepath))\\n fig.savefig(savepath, dpi=300, bbox_inches='tight')\\n\\n\\n frequency, spectrum = get_spectrum(SSC, band)\\n fig,ax = set_up_figure(SSC, specie, band)\\n plot_spectrum(ax, frequency, spectrum)\\n label_lines(ax, spectrum, band)\\n velocity_indicator(ax, frequency, spectrum)\\n format_figure(ax, frequency, spectrum)\\n save_figure(fig, specie, band)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ed08779bec6b098fe75436b5dcbf85f\",\n \"score\": \"0.54813236\",\n \"text\": \"def control_sinwave(num,filename=\\\"control_sinwave.txt\\\",display=False):\\n filename = datadir + filename\\n N = num\\n min_period = 2\\n max_period = 20\\n n_changepoints = N/200\\n rng = np.random.RandomState(42)\\n\\n changepoints = np.insert(np.sort(rng.randint(0,N,n_changepoints)),[0,n_changepoints],[0,N])\\n const_intervals = zip(changepoints,np.roll(changepoints,-1))[:-1]\\n frequency_control = np.zeros((N,1))\\n for (t0,t1) in const_intervals:\\n frequency_control[t0:t1] = rng.rand()\\n periods = frequency_control * (max_period - min_period) + max_period\\n frequency_output = np.zeros((N,1))\\n z = 0\\n for i in range(N):\\n z = z + 2 * np.pi / periods[i]\\n frequency_output[i] = (np.sin(z) + 1)/2\\n input,output = map(lambda x:x[0],frequency_control),map(lambda x:x[0],frequency_output)\\n pd.DataFrame(zip(input,output),columns=['input','output']).to_csv(filename,index=False)\\n if display:\\n display_range = 1000\\n plt.figure(figsize =(12,1.5))\\n plt.plot(range(display_range),input[:display_range])\\n plt.plot(range(display_range),output[:display_range]) # output[:display_range]ではうまくいかない。なぜ？\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1cda1f5c61d48cc3a8e5f6e447b11e7\",\n \"score\": \"0.54711914\",\n \"text\": \"def draw_figure(self, data):\\n count_data = data[:, 1:len(self.get_channels())+1]\\n time_data = data[:, 0]\\n\\n # Scale count values using SI prefix\\n prefix = ['', 'k', 'M', 'G']\\n prefix_index = 0\\n while np.max(count_data) > 1000:\\n count_data = count_data / 1000\\n prefix_index = prefix_index + 1\\n counts_prefix = prefix[prefix_index]\\n\\n # Use qudi style\\n plt.style.use(self._save_logic.mpl_qd_style)\\n\\n # Create figure\\n fig, ax = plt.subplots()\\n ax.plot(time_data, count_data, linestyle=':', linewidth=0.5)\\n ax.set_xlabel('Time (s)')\\n ax.set_ylabel('Fluorescence (' + counts_prefix + 'c/s)')\\n return fig\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fe8f2e42a540b5737fd6f01c8b9f78b\",\n \"score\": \"0.5427506\",\n \"text\": \"def plot_series(rr, hist=True):\\r\\n x1, x2 = cl.beats_to_lists(rr)\\r\\n return [go.Scatter(x=x1, y=x2)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8dc8698e2675f02af917d8ee0af41af3\",\n \"score\": \"0.5418549\",\n \"text\": \"def SignalSeries():\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4240a5aadf84ac63cc47713c81c8741\",\n \"score\": \"0.5413463\",\n \"text\": \"def plot(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4240a5aadf84ac63cc47713c81c8741\",\n \"score\": \"0.5413463\",\n \"text\": \"def plot(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4240a5aadf84ac63cc47713c81c8741\",\n \"score\": \"0.5413463\",\n \"text\": \"def plot(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4240a5aadf84ac63cc47713c81c8741\",\n \"score\": \"0.5413463\",\n \"text\": \"def plot(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"325c82d73d4b3299aa9d72d0e620f80a\",\n \"score\": \"0.5408115\",\n \"text\": \"def plot_sine_wave(start_x, stop_x, amplitude):\\n x_array = np.linspace(start_x, stop_x, 1000)\\n print(x_array)\\n y_array = amplitude * np.sin(x_array)\\n plt.plot(x_array, y_array,\\\":\\\",color=\\\"red\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e06bc26f07f27d7a19d7e80c20b15559\",\n \"score\": \"0.54054606\",\n \"text\": \"def graph(data, title=\\\"Graph\\\"):\\n plt.figure(figsize=(7, 6))\\n plt.title(title)\\n plt.plot(data.keys(), data.values())\\n plt.ylabel(\\\"Sound Level\\\")\\n plt.xlabel(\\\"Time (s)\\\")\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76f87307f248dc083c3f817bc812cdf9\",\n \"score\": \"0.54031557\",\n \"text\": \"def plot_fits(direction_rates,fit_curve,title):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7bd4e06e92975f2fccc3c57ae66f54fc\",\n \"score\": \"0.5398139\",\n \"text\": \"def plot_trust_func(x):\\r\\n plt.plot(x, trust_value, 'ro')\\r\\n plt.xlabel('Variance value')\\r\\n plt.ylabel('Trust value')\\r\\n plt.title('Trust function (%s elements)' % (trust_value.shape[0]))\\r\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20f6f18a7cd137d69fec0682a6e9826c\",\n \"score\": \"0.5390575\",\n \"text\": \"def plot_spectra(inst):\\n for i in range(3):\\n for j in range(4):\\n if (i < 2 or j < 2):\\n index = i*4 + j\\n else:\\n break\\n lmda, flux = inst.data[index]\\n plt.subplot(4, 1, j+1)\\n plt.title('Day {}'.format(inst.days[index]))\\n plt.plot(lmda, flux)\\n plt.xlabel('Wavelength, [nm]')\\n plt.ylabel('Relative flux')\\n plt.suptitle('Flux relative to the continuum flux around spectral line')\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b436ce02d66b805de84dce1cea9dac0d\",\n \"score\": \"0.5388317\",\n \"text\": \"def fourierGraph(ivalueData=None, N=10):\\n valueData = ivalueData\\n #### Create a plot ####\\n origPlot = figure(title=\\\"Fourier curve fitting\\\", plot_width=400, plot_height=200, responsive=True)\\n origPlot.toolbar.logo = None\\n origPlot.xaxis.minor_tick_line_color = None # turn off x-axis minor ticks\\n origPlot.yaxis.minor_tick_line_color = None # turn off x-axis minor ticks\\n\\n # call the fourierTrigSeries() to generate lambda func series and the list of tuples trigTerms (coefs, trigFuncs)\\n series, trigTerms = ta.fourierTrigSeries(valueData, n=N)\\n # Original Line\\n xline = [i for i in range(len(valueData))]\\n origPlot.line(xline, valueData, legend=\\\"Original data\\\", color='green', line_width=3, line_dash='dashed')\\n # Fourier Line\\n xs = np.arange(0, len(xline), .3)\\n origPlot.line(xs, series(xs), legend=\\\"Fourier Trig Series n={}\\\".format(N), line_width=3)\\n # Legend color setup\\n origPlot.legend.background_fill_color = \\\"LightGray\\\"\\n\\n #### Amplitude plot ####\\n coef, trigFuncs = zip(*trigTerms)\\n trigFuncsString = [str(trigfunc) for trigfunc in trigFuncs[2:]]\\n ampPlot = figure(title=\\\"Frequency Amp\\\", plot_width=400, plot_height=200,x_range=trigFuncsString,responsive=True)\\n ampPlot.toolbar.logo = None\\n ampPlot.yaxis.minor_tick_line_color = None\\n ampPlot.circle(trigFuncsString, coef[2:], line_width=3, size=6)\\n ampPlot.xaxis.major_label_orientation = math.pi/4\\n # Create Stem graph\\n x0 = [i + 1 for i in range(len(coef)-2)]\\n x1 = coef[2:]\\n ampPlot.ray(x=0, y=0, angle=0, length=(N*2 + 2), line_width=3, line_color=\\\"purple\\\", line_alpha=0.5)\\n ampPlot.segment(x0=x0, y0=0, x1=x0, y1=coef[2:], line_width=3)\\n\\n #### Frequency plot ####\\n freqPlot = figure(title=\\\"Frequency decomposition\\\", plot_width=400, plot_height=400, responsive=True)\\n freqPlot.toolbar.logo = None\\n freqPlot.xaxis.minor_tick_line_color = None\\n freqPlot.yaxis.minor_tick_line_color = None\\n\\n\\n x = sp.Symbol('x')\\n numLines = len(trigFuncs)\\n color_pallete = Spectral11*numLines\\n\\n for i in range(numLines):\\n # ignore the first two terms as they are not trig functions but numbers\\n if i < 2:\\n continue\\n f = sp.lambdify(x, trigFuncs[i], modules=['numpy'])\\n # plot the functions\\n freqPlot.line(xs,coef[i]*f(xs), color=color_pallete[i])\\n\\n #### Convolve begin #####\\n trendPlot = figure(title=\\\"Trend Plot\\\",plot_width=400, plot_height=200,responsive=True)\\n\\n trendPlot.line(xs, series(xs), line_width=3)\\n\\n origScript, origDiv = components(origPlot,INLINE)\\n ampScript, ampDiv = components(ampPlot, INLINE)\\n freqScript, freqDiv = components(freqPlot, INLINE)\\n trendScript, trendDiv = components(trendPlot, INLINE)\\n return (origScript, origDiv,ampScript, ampDiv,freqScript, freqDiv, trendScript, trendDiv)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7460b939220ce4fbf6728465186e014\",\n \"score\": \"0.53771937\",\n \"text\": \"def plot(self):\\n plt.figure(figsize=(14,7))\\n plt.plot(SignalGrid.bin_time,\\n SignalGrid.meander(SignalGrid.bin_time)+\\n SignalGrid.initial_frequency,'.',label='meandering frequency') \\n plt.plot(SignalGrid.bin_time,self.post_freq,'r.',label='highest strength frequency')\\n plt.legend()\\n plt.title('highest strength frequency and actual meandering frequency of signal')\\n plt.ylabel('signal frequency')\\n plt.xlabel('bin time')\\n plt.savefig('meandering_frequency.pdf',bbox_inches='tight')\\n plt.clf()\\n\\n plt.figure(figsize=(7,14))\\n plt.imshow(self.ngrid, cmap='viridis')\\n plt.gca().xaxis.tick_top()\\n plt.gca().xaxis.set_label_position('top')\\n cbar = plt.colorbar() \\n cbar.set_label('frequency probability distribution')\\n plt.title('grid of signals in frequency domain as frequency changes')\\n plt.ylabel('signal frequency bins')\\n plt.xlabel('long time bins')\\n plt.savefig('signal_grid_raw.pdf',bbox_inches='tight')\\n plt.clf()\\n\\n plt.figure(figsize=(7,14))\\n plt.imshow(self.lngrid, cmap='viridis')\\n plt.gca().xaxis.tick_top()\\n plt.gca().xaxis.set_label_position('top') \\n cbar = plt.colorbar() \\n cbar.set_label('log(frequency) probability distribution')\\n plt.title('grid of signals in log(frequency) domain as frequency changes')\\n plt.ylabel('signal frequency bins')\\n plt.xlabel('long time bins')\\n plt.savefig('lnwandering.pdf',bbox_inches='tight')\\n plt.clf()\\n\\n plt.figure(figsize=(7,14))\\n plt.imshow(self.path_grid, cmap='viridis')\\n plt.gca().xaxis.tick_top()\\n plt.gca().xaxis.set_label_position('top') \\n plt.title('viterbi path through signal frequency grid')\\n plt.ylabel('signal frequency bins')\\n plt.xlabel('long time bins')\\n plt.savefig('viterbi_path.pdf',bbox_inches='tight')\\n plt.clf()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6de07e4f38a23a0e036128fc9a64c3b9\",\n \"score\": \"0.5373745\",\n \"text\": \"def initializeSpectrumGraph(self):\\r\\n\\r\\n # X axis definition\\r\\n self.f = self.RATE * np.arange(self.N/2) / self.N\\r\\n\\r\\n # Initializing plot\\r\\n self.fig, axis = plt.subplots(1, 1)\\r\\n self.linef, = axis.plot(self.f, np.zeros(len(self.f)))\\r\\n\\r\\n # Setting axis labels\\r\\n axis.set_xlabel(\\\"Freq [Hz]\\\")\\r\\n axis.set_ylabel(\\\"X(f)\\\")\\r\\n\\r\\n # Setting axis limits\\r\\n axis.set_xlim(20, self.RATE / 2) # Nyquist\\r\\n axis.set_ylim(0, 200)\\r\\n plt.show(block = False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a597102eac7b1851de3951c85c28f66\",\n \"score\": \"0.53708905\",\n \"text\": \"def plot_v_frequency(self, y: NumberLike, *args, **kwargs):\\n\\n try:\\n if len(npy.shape(y)) > 2:\\n # perhaps the dimensions are empty, try to squeeze it down\\n y = y.squeeze()\\n if len(npy.shape(y)) > 2:\\n # the dimensions are full, so lets loop and plot each\\n for m in range(npy.shape(y)[1]):\\n for n in range(npy.shape(y)[2]):\\n self.plot(y[:, m, n], *args, **kwargs)\\n return\\n if len(y) == len(self):\\n pass\\n else:\\n\\n raise IndexError(['thing to plot doesn\\\\'t have same'\\n ' number of points as f'])\\n except(TypeError):\\n y = y * npy.ones(len(self))\\n\\n # plt.plot(self.f_scaled, y, *args, **kwargs)\\n plt.plot(self.f, y, *args, **kwargs)\\n ax = plt.gca()\\n scale_frequency_ticks(ax, self.unit)\\n plt.autoscale(axis='x', tight=True)\\n self.labelXAxis()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"071ccad6af0331bddf513c0c2c2dd045\",\n \"score\": \"0.5365488\",\n \"text\": \"def plot_filter_response(\\n frequency,\\n sample_rate,\\n filter_type,\\n filter_order=2,\\n show_grid=True,\\n fig_size=(10, 5),\\n):\\n\\n b, a = sensormotion.signal.build_filter(\\n frequency, sample_rate, filter_type, filter_order\\n )\\n\\n # Plot the frequency response\\n w, h = freqz(b, a, worN=8000)\\n f, axarr = plt.subplots(figsize=fig_size)\\n axarr.plot(0.5 * sample_rate * w / np.pi, np.abs(h), \\\"b\\\")\\n axarr.set_xlim(0, 0.5 * sample_rate)\\n axarr.set_xlabel(\\\"Frequency (Hz)\\\")\\n axarr.grid(show_grid)\\n\\n # Add lines and markers at the cutoff frequency/frequencies\\n if filter_type == \\\"bandpass\\\":\\n for i in range(len(frequency)):\\n axarr.axvline(frequency[i], color=\\\"k\\\")\\n axarr.plot(frequency[i], 0.5 * np.sqrt(2), \\\"ko\\\")\\n else:\\n axarr.axvline(frequency, color=\\\"k\\\")\\n axarr.plot(frequency, 0.5 * np.sqrt(2), \\\"ko\\\")\\n\\n plt.suptitle(\\\"Filter Frequency Response\\\", size=16)\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17b033440e6d4eb79b494049bf1885e0\",\n \"score\": \"0.5365277\",\n \"text\": \"def plot_data(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82e83e8f050f1745066a1238f3180589\",\n \"score\": \"0.5361473\",\n \"text\": \"def FW_trend_plot(quant, empty=False): # Fig. 9c\\n assert quant in ['FW', 'SALT']\\n if quant=='FW': q, Q, Q1 = 0, 'F', 'W'\\n elif quant=='SALT': q, Q, Q1 = 1, 'S', 'S'\\n lat_bounds = ['90N', '60N', '45N', '10N', '10S', '34S']\\n\\n f = plt.figure(figsize=(6.4,2.5))\\n hap = dict(x=0, dy=0, width=.04, length_includes_head=True, head_width=.06, head_length=.02, ec=None, lw=0, clip_on=False) # horizontal arrow properties\\n vap = dict(y=0, dx=0, width=.06, length_includes_head=True, head_width=.08, head_length=.03, ec=None, lw=0, clip_on=False) # vertical arrow properties\\n\\n for i, (latS, latN) in enumerate(lat_bands):\\n #region: define axes\\n if i==0: # Atlantic 34S-60N\\n ax = f.add_axes([13.5/16,.2,1/8,.55])\\n ax.xaxis.set_ticks([])\\n ax.yaxis.set_ticks([])\\n\\n axb = f.add_axes([13.5/16,-.05,1/8 ,.5 ])\\n axt = f.add_axes([13.5/16,.5 ,1/8 ,.5 ])\\n axl = f.add_axes([12 /16,.2 ,3/16,.55])\\n axr = f.add_axes([14 /16,.2 ,3/16,.55])\\n vert_lim = [(-.3,.3),(-8e7,8e7)][q]\\n hor_lim = [(-.2,.2),(-3e8,3e8)][q]\\n draw_scales(f=f,left=False,vlim=vert_lim[1],hlim=hor_lim[1])\\n draw_labels(f,'Sv/century')\\n\\n else:\\n ax = f.add_axes([(2*i+1)/16-1.5/16,.2,1/8,.55])\\n ax.xaxis.set_ticks([])\\n ax.yaxis.set_ticks([])\\n\\n axb = f.add_axes([(2*i+1)/16-1.5/16,-.05,1/8 ,.5 ])\\n axt = f.add_axes([(2*i+1)/16-1.5/16,.5 ,1/8 ,.5 ])\\n axl = f.add_axes([(2*i+1)/16-3/16 ,.2 ,3/16,.55])\\n axr = f.add_axes([(2*i+1)/16-1/16 ,.2 ,3/16,.55])\\n\\n vert_lim = [(-.25,.25),(-2e7,2e7)][q]\\n hor_lim = [(-.2,.2),(-.7e8,.7e8)][q]\\n if i==1:\\n draw_scales(f=f,left=True,vlim=vert_lim[1],hlim=hor_lim[1])\\n for ax in [axl, axr]:\\n ax.set_xlim(hor_lim)\\n ax.set_ylim((-.35,1.3))\\n\\n for ax in [axb, axt, axl, axr]:\\n # ax.patch.set_alpha(0)\\n ax.axis('off')\\n ax.xaxis.set_ticks([])\\n\\n for ax in [axt, axb]:\\n ax.set_ylim(vert_lim)\\n ax.set_xlim((-.25,1.45))\\n\\n #endregion\\n #region: plotting\\n for s, sim in enumerate(['HIGH', 'LOW']):\\n if empty: continue\\n nlat_ = nlat_at(sim, latS)\\n run, rcp = ['ctrl','lpd'][s], ['rcp', 'lr1'][s]\\n\\n #region: d/dt [axb]\\n if quant=='SALT': fac = 1\\n elif quant=='FW': fac = -1e-6/S0\\n ddtS_ctl, ddtS_rcp = get_ddt_SALT(sim=sim, latS=latS, latN=latN)\\n label = [r'$\\\\Delta \\\\bar{W}$', r'$\\\\Delta \\\\bar{S}$'][q]\\n axt.arrow(x=.8+s/10, dy=ddtS_rcp*fac, **vap, color=plt.cm.tab20(18+s))\\n #endregion\\n\\n #region: meridional flux (convergences) means/trends, incl. BS/Med [axl, axr, axb]\\n inflow, inflow_trend = get_BS_Med(sim)\\n if latS==60: # BS in Arctic\\n fluxes, conv = get_fluxes(sim=sim, quant=quant, lat=latS, latS=None, latN=None)\\n # conv['ov'], conv['az'], conv['ed'] = 0, 0, 0\\n # conv['to'] = fluxes['to'] + inflow[f'{Q}_BS'].values/[1e6,1][q] # [m^3/s] -> [Sv]\\n conv['tov'], conv['taz'], conv['ted'], conv['tto'] = 0, 0, 0, fluxes['tto']\\n \\n axr.set_xlim(hor_lim)\\n axr.arrow(y=.425+s/10, dx=-inflow_trend[f'{Q}_BS'].values/[1e6,1][q], color=plt.cm.tab20(16+s), **hap)\\n\\n else:\\n fluxes, conv = get_fluxes(sim=sim, quant=quant, lat=latS, latS=latS, latN=latN)\\n if latS<35 and latN>35: # Med\\n axb.arrow(x=1.05+s/10, dy=-inflow_trend[f'{Q}_Med'].values/[1e6,1][q], color=plt.cm.tab20(16+s), **vap)\\n\\n axl.arrow(y=.55+s/10, dx=fluxes['tov'], color=plt.cm.tab20(0+s), **hap)\\n axl.arrow(y=.3 +s/10, dx=fluxes['taz'], color=plt.cm.tab20(2+s), **hap)\\n axl.arrow(y=.05+s/10, dx=fluxes['ted'], color=plt.cm.tab20(4+s), **hap)\\n axl.arrow(y=.8 +s/10, dx=fluxes['tto'], color=plt.cm.tab20(6+s), **hap)\\n \\n if i==0: # draw 60 North values in Atlantic box\\n fluxes_, conv_ = get_fluxes(sim=sim, quant=quant, lat=latN, latS=latS, latN=latN)\\n axr.arrow(y=.55+s/10, dx=fluxes_['tov'], color=plt.cm.tab20(0+s), **hap)\\n axr.arrow(y=.3 +s/10, dx=fluxes_['taz'], color=plt.cm.tab20(2+s), **hap)\\n axr.arrow(y=.05+s/10, dx=fluxes_['ted'], color=plt.cm.tab20(4+s), **hap)\\n axr.arrow(y=.8 +s/10, dx=fluxes_['tto'], color=plt.cm.tab20(6+s), **hap)\\n \\n axb.arrow(x=.3 +s/10, dy=conv['tov'], color=plt.cm.tab20(0+s), **vap)\\n axb.arrow(x=.55+s/10, dy=conv['taz'], color=plt.cm.tab20(2+s), **vap)\\n axb.arrow(x=.8 +s/10, dy=conv['ted'], color=plt.cm.tab20(4+s), **vap)\\n axb.arrow(x=.05+s/10, dy=conv['tto'], color=plt.cm.tab20(6+s), **vap)\\n #endregion\\n\\n #region: surface fluxes [axt]\\n sfwf_mean, sfwf_trend = get_SFWF(sim=sim, quant=quant, latS=latS, latN=latN)\\n axt.arrow(x=.05+s/10, dy=-sfwf_trend['SFWF'], color=plt.cm.tab20(8+s) , **vap)\\n axt.arrow(x=.3 +s/10, dy=-sfwf_trend['R'] , color=plt.cm.tab20b(14+s), **vap)\\n axt.arrow(x=.55+s/10, dy=-sfwf_trend['PE'] , color=plt.cm.tab20(12+s) , **vap)\\n #endregion\\n\\n #endregion\\n\\n #region: legend, numbering, scales\\n f.text(.01,.93,'(c)')\\n #endregion\\n if q==0 and empty==False:\\n plt.savefig(f'{path_results}/FW-paper/Fig9c.eps')\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2dd552eb62f0a7b99152133339e48310\",\n \"score\": \"0.53593385\",\n \"text\": \"def plotTauNoise():\\n fig = plt.figure(0,figsize=(5, 3.6))\\n plt.rc('text', usetex=True)\\n font = {'family' : 'sans-serif',\\n 'serif' : 'Helvetica',\\n 'weight' : 'normal',\\n 'size' : 14 }\\n plt.rc('lines', lw=2)\\n plt.rc('font', **font)\\n ax = fig.add_subplot(111)\\n fig.subplots_adjust(left=0.15, right =0.9,\\\\\\n bottom=0.15, top =0.9, wspace=0.25)\\n\\n # tau0 = (L/np.pi)**2/3\\n T = np.linspace(1, 150, 100)\\n ax.plot(T, tauTheory(1, T, 100), 'b-')\\n ax.plot(T, tauTheory(0.1, T, 100), 'r-')\\n # ax.plot(T, tauTheory(0.1, T, 100), '-')\\n # ax.plot(T, tauTheory(0.01, T, 100), '-')\\n\\n # load the simulation data and plot as dots\\n tauData = np.loadtxt('tauNoise_N100_F1.dat')\\n ax.plot(tauData[:,0], tauData[:,1], 'bo')\\n tauData = np.loadtxt('tauNoise_N100_F0.1.dat')\\n ax.plot(tauData[:,0], tauData[:,1], 'ro')\\n\\n\\n # set format of the figure\\n ax.set_yscale('log')\\n # ax.set_xlabel(r'$k_B T/\\\\Delta E$')\\n # ax.set_ylabel(r'$\\\\tau_{\\\\parallel}/\\\\tau_{Rouse}$')\\n ax.set_xlabel(r'$T$')\\n ax.set_ylabel(r'$\\\\tau_{\\\\parallel}$')\\n ax.legend([r'$F=1$', r'$F=0.1$'], loc='upper right', fontsize=15)\\n ax.set_xlim([0,150])\\n # ax.set_ylim([0,1.2])\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e139afc352661e5e4581608f7060213c\",\n \"score\": \"0.53593016\",\n \"text\": \"def plot(self, width=5.51, height=4, n_xticks=6, fname=None):\\n plt.figure(dpi=300, figsize=(width, height))\\n alpha = min(1, max(0.01, 50/self.n_simulations_))\\n for i in range(self.n_simulations_):\\n obs = self.data_[i]\\n last_state = obs.iloc[-1]\\n last_state.t = self.t_max_\\n obs = obs.append([last_state], ignore_index=True)\\n plt.plot(obs[[\\\"t\\\"]], obs[[\\\"s\\\"]], c=\\\"C0\\\", alpha=alpha) # S\\n plt.plot(obs[[\\\"t\\\"]], obs[[\\\"i\\\"]], c=\\\"C1\\\", alpha=alpha) # I\\n plt.plot(obs[[\\\"t\\\"]], obs[[\\\"r\\\"]], c=\\\"C2\\\", alpha=alpha) # R\\n title = \\\"Stochastic SIR model \\\"\\n if self.n_simulations_ > 1:\\n title += \\\"(\\\" + str(self.n_simulations_) + \\\" samples) \\\"\\n title += \\\"with \\\" \\\\\\n \\\"$\\\\\\\\beta=\\\" + str(self.beta) + \\\"$ and \\\" \\\\\\n \\\"$\\\\\\\\gamma=\\\" + str(self.gamma) + \\\"$\\\"\\n plt.title(title)\\n plt.xlim([0, self.t_max_])\\n plt.ylim([0, self.n])\\n plt.xticks(np.linspace(0, self.t_max_, n_xticks))\\n plt.xlabel(\\\"$t$\\\")\\n plt.ylabel(\\\"$S_t,\\\\ I_t,\\\\ R_t$\\\")\\n plt.grid()\\n legend = plt.legend([\\\"$S_t$\\\", \\\"$I_t$\\\", \\\"$R_t$\\\"])\\n for l in legend.get_lines():\\n l.set_alpha(1)\\n if fname is not None:\\n plt.savefig(fname + \\\".pdf\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44f7cba35773a96c2187a59423d24515\",\n \"score\": \"0.53471714\",\n \"text\": \"def plot_sed(w_obs, f_obs, ferr_obs, # observed fluxes\\n w_mod, f_mod, # flux measured for best fitting model\\n wave_conv_fil, flux_conv_fil, # filter transmission curves\\n labs, sedfilename='best_fit_sed.pdf'): # filter and plotfile name\\n fig1, ax1 = plt.subplots(figsize=(6, 5))\\n\\n for f in range(len(w_mod)):\\n ax1.scatter(w_mod[f], f_mod[f], alpha=0.7)\\n ax1.plot(wave_conv_fil[f], flux_conv_fil[f], alpha=0.7, label=labs[f])\\n\\n ax1.errorbar(w_obs, f_obs, yerr=ferr_obs,\\n ls='None', color='k', label='observations', marker='+')\\n\\n ax1.set_yscale('log')\\n ax1.set_xlabel(r'Wavelength [$\\\\AA$]')\\n ax1.set_ylabel(r'Flux [erg/cm$^2$/s/$\\\\AA$]')\\n ax1.legend(loc='lower right')\\n\\n fig1.savefig(sedfilename, bbox_inches='tight')\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86950e208e87ef1d9695c1274e2bb17a\",\n \"score\": \"0.5346543\",\n \"text\": \"def water_freq(df):\\n \\n assert isinstance(df, xr.Dataset),\\\"Input has to be a xarray.Dataset.\\\"\\n \\n try:\\n df.water\\n except Exception:\\n print(\\\"'water' band cannot be found. Please use pred_index() to acquire the required band.\\\")\\n try:\\n df.time\\n except Exception:\\n print(\\\"'time' cannot be found. Please check the time dimension of the dataset.\\\")\\n try:\\n df.longitude\\n df.latitude\\n except Exception:\\n print(\\\"'longitude' or/and 'latitude' cannot be found. Please check the dimension of the dataset.\\\")\\n \\n frequency = df.water.sum(dim='time',skipna=True)/len(df.time)\\n show(hv.render(frequency.hvplot.image(x=\\\"longitude\\\",y=\\\"latitude\\\",aspect=1,cmap='bmy_r')))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ca67b04880c97c72c135c6551227754\",\n \"score\": \"0.53425175\",\n \"text\": \"def Task67():\\r\\n print(\\\"Lab 4 Tasks 6 and 7:\\\") \\r\\n\\r\\n\\r\\n # Low-pass filter design parameters\\r\\n Ntaps=16\\r\\n Fbands=[0, 0.15, 0.2, 0.25, 0.3, 0.5]\\r\\n Abands=[0, 1, 0]\\r\\n Weights= [100, 1, 100]\\r\\n #\\r\\n BPtaps = signal.remez(Ntaps, Fbands, Abands, Weights)\\r\\n #\\r\\n fig = plt.figure()\\r\\n plt.plot(BPtaps)\\r\\n plt.xlabel('Sample')\\r\\n plt.ylabel('Value')\\r\\n plt.title('Impulse Response of the FIR Filter')\\r\\n # Frequency response\\r\\n fig, pltArr = plt.subplots(2, sharex=True) \\r\\n fig.suptitle(\\\"Frequency Response\\\")\\r\\n [freq, response] = signal.freqz(BPtaps, [1], worN=2000)\\r\\n pltArr[0].plot(freq, np.log10(np.abs(response)))\\r\\n pltArr[0].set_title(\\\"Magnitude of Frequency Reponse\\\")\\r\\n angles = np.unwrap(np.angle(response))\\r\\n pltArr[1].plot(freq, angles)\\r\\n pltArr[1].set_title(\\\"Angle of Frequency Reponse\\\")\\r\\n # plt.xlabel('Normalized Frequency (pi is Nyquist Frequency)')\\r\\n # plt.ylabel(\\\"Magnitude of Frequency Response\\\")\\r\\n # plt.title(\\\"Magnitude of Frequency Response\\\")\\r\\n\\r\\n\\r\\n # Minimum phase version\\r\\n rt = np.roots(BPtaps)\\r\\n [b, r] = signal.deconvolve(BPtaps, [1,-rt[1]])\\r\\n MinPhTaps = signal.convolve(b,[1,-1/rt[1].conjugate()])\\r\\n\\r\\n #impulse response\\r\\n fig = plt.figure()\\r\\n plt.plot(MinPhTaps)\\r\\n plt.xlabel('Sample')\\r\\n plt.ylabel('Value')\\r\\n plt.title('Impulse Response of the Minimum Phase Filter')\\r\\n # Frequency response\\r\\n fig, pltArr = plt.subplots(2, sharex=True) \\r\\n fig.suptitle(\\\"Frequency Response\\\")\\r\\n [freq, response] = signal.freqz(MinPhTaps, [1], worN=2000)\\r\\n pltArr[0].plot(freq, np.log10(np.abs(response)))\\r\\n pltArr[0].set_title(\\\"Magnitude of Frequency Reponse\\\")\\r\\n angles = np.unwrap(np.angle(response))\\r\\n pltArr[1].plot(freq, angles)\\r\\n pltArr[1].set_title(\\\"Angle of Frequency Reponse\\\")\\r\\n # plt.xlabel('Normalized Frequency (pi is Nyquist Frequency)')\\r\\n # plt.ylabel(\\\"Magnitude of Frequency Response\\\")\\r\\n # plt.title(\\\"Magnitude of Frequency Response\\\")\\r\\n #\\r\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1cbb3d7d23c1d95b8bf18627104c12b5\",\n \"score\": \"0.53419256\",\n \"text\": \"def wave_plot():\\n PLOT_RANGE = [1, 2, 3, 4, 5] # Choose n's to plot corresponding psi_n's\\n\\n N = 100 # Number of points for discretization\\n NUM_EIGVALS = max(PLOT_RANGE) # Number of eigenvalues to get\\n\\n pb = ParticleBox(N, NUM_EIGVALS) \\n\\n apsi = np.empty((NUM_EIGVALS, pb.N), dtype=float) \\n for n in range(1, NUM_EIGVALS+1):\\n apsi[n-1] = np.sqrt(2) * np.sin(n*np.pi*pb.x) * np.sign(pb.psi[1, n-1])\\n \\n plt.figure()\\n plt.xlabel(\\\"x'\\\")\\n plt.ylabel(r\\\"$\\\\psi_n(x)$\\\")\\n for n in PLOT_RANGE:\\n i = n - 1\\n color = cmap(float(i)/NUM_EIGVALS)\\n plt.plot(pb.x, pb.psi[:,i], marker=\\\".\\\", c=color)\\n plt.plot(pb.x, apsi[i], label=r\\\"$E_%s$\\\" % str(n), c=color)\\n plt.legend(title=r\\\"$E_n$\\\")\\n plt.savefig(\\\"wavefuncs.pdf\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b940c2c8fd8774760229735f7b3cf092\",\n \"score\": \"0.5341725\",\n \"text\": \"def plot_data(phi, data, mytitle, fnm):\\n x = np.linspace(-4,10,100) # x values for plotting\\n sample_sizes = [100, 1000, 10000]\\n for ss in sample_sizes:\\n sample = data[0:ss]\\n p = pw.density_function(phi, sample)\\n # Evaluate the density funtion for values of x,\\n # using the zero-one window function.\\n print(\\\"Plotting density for a sample size of\\\", ss)\\n y = np.array([p(xi) for xi in x])\\n plt.plot(x,y, label=str(ss))\\n plt.legend(fancybox=True, title=\\\"Sample Size\\\", shadow=True)\\n plt.title(mytitle, fontsize=18)\\n plt.xlabel(\\\"x\\\", fontsize=16)\\n plt.ylabel(\\\"p(x)\\\", fontsize=16)\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7eb54f008c02fdb89bb8d693e1cbef14\",\n \"score\": \"0.53329366\",\n \"text\": \"def plot(self, writeFiles = False, spec = 'sourceSpec'):\\n# \\t\\tioff()\\n# \\t\\tdata = readOObin(file)\\n fig = figure(17, **ssxdef.specsize)\\n fig.clear()\\n a = axes()\\n a.plot(self.wavelengths, self.data[spec], lw='.5')\\n a.text(0.93, 0.92, self.shotname, horizontalalignment='right',\\n verticalalignment='top', transform = a.transAxes)\\n ylabel(self.ylabel)\\n xlabel(self.xlabel)\\n xlim(200, 1100)\\n if writeFiles:\\n ssxutil\\n fName = ssxutil.ssxPath(self.shotname+'-oo.pdf', 'output',\\n self.runYear + '/' + self.runDate + '/' + self.shotname, mkdir\\n = True)\\n fig.savefig(fName)\\n else:\\n fig.show()\\n # ion()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eda9a426bc6353fe2a4d7471d12df01a\",\n \"score\": \"0.5332477\",\n \"text\": \"def exercise11():\\n x = numpy.arange(11, step=0.01)\\n sinx = numpy.sin(x)\\n\\n plt.figure()\\n plt.plot(sinx)\\n plt.xlabel('x value')\\n plt.ylabel('sin(x)')\\n plt.title('Sine Function for x from 0.0 to 10.0')\\n plt.savefig('sin_plot.pdf', format='pdf')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"232630898f055a6a0325cf97e338b403\",\n \"score\": \"0.53291404\",\n \"text\": \"def plot_sine_evaluation(real_samples, fake_samples, idx, identifier):\\n ### frequency\\n seq_length = len(real_samples[0]) # assumes samples are all the same length\\n frate = seq_length\\n freqs_hz = np.fft.rfftfreq(seq_length)*frate # this is for labelling the plot\\n # TODO, just taking axis 0 for now...\\n w_real = np.mean(np.abs(np.fft.rfft(real_samples[:, :, 0])), axis=0)\\n w_fake = np.mean(np.abs(np.fft.rfft(fake_samples[:, :, 0])), axis=0)\\n ### amplitude\\n A_real = np.max(np.abs(real_samples[:, :, 0]), axis=1)\\n A_fake = np.max(np.abs(fake_samples[:, :, 0]), axis=1)\\n ### now plot\\n nrow = 2\\n ncol = 2\\n fig, axarr = plt.subplots(nrow, ncol, sharex='col', figsize=(6, 6))\\n # freq\\n axarr[0, 0].vlines(freqs_hz, ymin=np.minimum(np.zeros_like(w_real), w_real), ymax=np.maximum(np.zeros_like(w_real), w_real), color='#30ba50')\\n axarr[0, 0].set_title(\\\"frequency\\\", fontsize=16)\\n axarr[0, 0].set_ylabel(\\\"real\\\", fontsize=16)\\n axarr[1, 0].vlines(freqs_hz, ymin=np.minimum(np.zeros_like(w_fake), w_fake), ymax=np.maximum(np.zeros_like(w_fake), w_fake), color='#ba4730')\\n axarr[1, 0].set_ylabel(\\\"generated\\\", fontsize=16)\\n # amplitude\\n axarr[0, 1].hist(A_real, normed=True, color='#30ba50', bins=30)\\n axarr[0, 1].set_title(\\\"amplitude\\\", fontsize=16)\\n axarr[1, 1].hist(A_fake, normed=True, color='#ba4730', bins=30)\\n\\n fig.savefig('./experiments/plots/' + identifier + '_eval' + str(idx).zfill(4) +'.png')\\n plt.clf()\\n plt.close()\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef580d4a3e7b53d3dde91cfce6f95fcf\",\n \"score\": \"0.5324583\",\n \"text\": \"def PlotData( self ):\\n plt.title( 'q = ' + str( self.col ) )\\n plt.plot( self.data[ : , 0 ] , self.data[ : , self.col ] , label = 'orig' )\\n func = savgol_filter( self.data[ : , self.col ] , 51 , 3 )\\n plt.plot( self.data[ : , 0 ] , func , label = 'smooth' )\\n for i in range( self.Fits.shape[1] ):\\n plt.plot( self.data[ : , 0 ] , self.Fits[ : , i ] , label = self.labels[ i ] )\\n\\n\\n plt.scatter( self.FitParams[ self.indx , 0 ] , np.max( self.Fits ) )\\n\\n \\n plt.legend()\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46990c8e8cc2aa32f9e48b07db569a20\",\n \"score\": \"0.53186226\",\n \"text\": \"def _time_domain_plot(self, *args, **kwargs):\\n from pesummary.gw.plots.detchar import time_domain_strain_data\\n\\n return time_domain_strain_data(*args, **kwargs)[self.IFO]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c0064397489775f8d93e5fe269e3106\",\n \"score\": \"0.5317835\",\n \"text\": \"def plot_n(self, **kwargs):\\n if self.verbose > 1:\\n print(\\\"RefractiveIndex.plot_n()\\\") \\n\\n self.plot_spectrum(y_unit = \\\"Refractive index n\\\", **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6748e27ec5d8284573ee71832baaad52\",\n \"score\": \"0.5315133\",\n \"text\": \"def plot_spectrum(self):\\n assert self.traced\\n self.log.debug(npArrayInfo(self.twl*1e6,\\\"Wavlength\\\"))\\n self.log.debug(npArrayInfo(self.tfl,\\\"Flux\\\"))\\n plt.clf()\\n plt.semilogy(self.twl*1e6,self.tfl,\\\"b.\\\")\\n xmin,xmax,ymin,ymax = plt.axis()\\n if ymin < 1e-4:\\n ymin = 1e-4\\n plt.axis((xmin,xmax,ymin,ymax))\\n plt.title(\\\"Generated, Fluxed Spectra (%d)\\\" % self.num)\\n plt.xlabel(\\\"Wavelength ($\\\\mu m$)\\\")\\n plt.ylabel(\\\"Flux (Electrons)\\\")\\n plt.savefig(\\\"%(Partials)s/Instrument-%(num)04d-Flux%(ext)s\\\" % dict(num=self.num, ext=self.config[\\\"Plots\\\"][\\\"format\\\"],**self.config[\\\"Dirs\\\"]))\\n plt.clf()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ba3e04bd866b846af1d337af2f9e534\",\n \"score\": \"0.53136957\",\n \"text\": \"def wavelength_plot(waves_list, waves,number_of_detectors):\\n \\n for k in range(0, number_of_detectors):\\n newlist = []\\n for i in range(k, len(waves_list), 8):\\n newlist.append(waves_list[i])\\n \\n print(newlist) \\n print(len(newlist))\\n plt.title(\\\"Efficiency vs wavelength\\\") \\n plt.plot(waves, newlist,label = \\\"Detector \\\"+str(k+1)) \\n plt.legend()\\n \\n plt.show()\\n plt.savefig(\\\"wavelength_eff.png\\\")\\n plt.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f68020d8832f3c46b31b1d1ea865aca\",\n \"score\": \"0.5297882\",\n \"text\": \"def plot_phonon_dispersion_bands(self):\\n import matplotlib.pyplot as plt\\n\\n def replace_list(text_string):\\n substitutions = {'GAMMA': u'$\\\\Gamma$',\\n }\\n\\n for item in substitutions.items():\\n text_string = text_string.replace(item[0], item[1])\\n return text_string\\n\\n force_constants = self.get_force_constants()\\n bands_and_labels = self.get_path_using_seek_path()\\n\\n _bands = obtain_phonon_dispersion_bands(self._structure,\\n bands_and_labels['ranges'],\\n force_constants,\\n self._supercell_matrix,\\n primitive_matrix=self._primitive_matrix,\\n band_resolution=30)\\n\\n for i, freq in enumerate(_bands[1]):\\n plt.plot(_bands[1][i], _bands[2][i], color='r')\\n\\n # plt.axes().get_xaxis().set_visible(False)\\n plt.axes().get_xaxis().set_ticks([])\\n\\n plt.ylabel('Frequency [THz]')\\n plt.xlabel('Wave vector')\\n plt.xlim([0, _bands[1][-1][-1]])\\n plt.axhline(y=0, color='k', ls='dashed')\\n plt.suptitle('Phonon dispersion')\\n\\n if 'labels' in bands_and_labels:\\n plt.rcParams.update({'mathtext.default': 'regular'})\\n\\n labels = bands_and_labels['labels']\\n\\n labels_e = []\\n x_labels = []\\n for i, freq in enumerate(_bands[1]):\\n if labels[i][0] == labels[i - 1][1]:\\n labels_e.append(replace_list(labels[i][0]))\\n else:\\n labels_e.append(\\n replace_list(labels[i - 1][1]) + '/' + replace_list(labels[i][0]))\\n x_labels.append(_bands[1][i][0])\\n x_labels.append(_bands[1][-1][-1])\\n labels_e.append(replace_list(labels[-1][1]))\\n labels_e[0] = replace_list(labels[0][0])\\n\\n plt.xticks(x_labels, labels_e, rotation='horizontal')\\n\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54b9b221d5f3188b39b57ce49859e4de\",\n \"score\": \"0.5297838\",\n \"text\": \"def BeamElementShearDiagram(f, L):\\n \\n x = [0, L]\\n z = [f[0,0] , -f[2,0]]\\n plt.plot(x,z)\\n plt.ylabel('Shear Force (kN)')\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24bd2370716487e55b3f891cb0493efa\",\n \"score\": \"0.5296585\",\n \"text\": \"def plot_data(self, npz_filename):\\r\\n\\t\\t# #taken directly from ibmq quantum experience online\\r\\n\\t\\t# fig = plt.figure(figsize=(16,10))\\r\\n\\t\\t# # counts = {'0000': 60, '0001': 64.5, '0010': 61.5, '0100': 63.5, '0101': 62.5, '0110': 62.5, '0111': 59, '1000': 66, '1001': 57, '1010': 62.5, '1011': 68, '1100': 55, '1110': 62.5, \\\"1111\\\":70}\\r\\n\\t\\t# # frequencies = [60,64.5,61.5,63.5,62.5,62.5,59,66,57,62.5,68,55,62.5,70]\\r\\n\\t\\t# answers = [key for key in counts.keys()]\\r\\n\\t\\t# answers.sort()\\r\\n\\t\\t# plt.bar(range(len(counts)), frequencies, align='center', tick_label=answers)\\r\\n\\t\\t# plt.title('Results for n = 4 (grovers algorithm)', fontsize=25)\\r\\n\\t\\t# plt.xlabel('Measurement',fontsize=20)\\r\\n\\t\\t# plt.ylabel('Frequency of occurence',fontsize=20)\\r\\n\\t\\t# plt.xticks(fontsize=15)\\r\\n\\t\\t# plt.yticks(fontsize=15)\\r\\n\\t\\t\\r\\n\\t\\t\\r\\n\\t\\t# fig.savefig('figures/g_counts_histogram_4.png', bbox_inches='tight')\\r\\n\\t\\t\\r\\n\\t\\t# data = np.load(npz_filename)\\r\\n\\r\\n\\t\\t# run_time_arr = data['run_time_arr']\\r\\n\\t\\t# n_list = data['n_list']\\r\\n\\t\\t# n_list = [1,2,3,4]\\r\\n\\r\\n\\t\\t# avg_time = np.sum(run_time_arr,1)/np.shape(run_time_arr)[1]\\r\\n\\t\\t# avg_time = [10.8,11.5,14.5,31.4]\\r\\n\\r\\n\\t\\t# plt.rcParams[\\\"font.family\\\"] = \\\"serif\\\"\\r\\n\\t\\t# fig = plt.figure(figsize=(16,10))\\r\\n\\r\\n\\t\\t# z = np.polyfit(n_list, avg_time, 10)\\r\\n\\t\\t# p = np.poly1d(z)\\r\\n\\r\\n\\t\\t# plt.plot(np.linspace(n_list[0], n_list[-1] + 0.1, 100), p(np.linspace(n_list[0], n_list[-1] + 0.1, 100)), ls = '-', color = 'r')\\r\\n\\t\\t# plt.plot(n_list, avg_time, ls = '', markersize = 15, marker = '.',label = 'M')\\r\\n\\t\\t# plt.title('Execution time scaling for Grovers algorithm', fontsize=25)\\r\\n\\t\\t# plt.xlabel('n (bit string length)',fontsize=20)\\r\\n\\t\\t# plt.ylabel('Average time of execution (s)',fontsize=20)\\r\\n\\t\\t# plt.xticks(fontsize=15)\\r\\n\\t\\t# plt.yticks(fontsize=15)\\r\\n\\r\\n\\t\\t# fig.savefig('figures/g_run_time.png', bbox_inches='tight')\\r\\n\\r\\n\\t\\t# num_times = data['num_times']\\r\\n\\t\\t# variance_arr = data['variance_arr']\\r\\n\\t\\t# n = data['n']\\r\\n\\r\\n\\t\\t# plt.rcParams[\\\"font.family\\\"] = \\\"serif\\\"\\r\\n\\t\\t# fig = plt.figure(figsize=(16,10))\\r\\n\\r\\n\\t\\t# assemble_time_arr = data['assemble_time_arr']\\r\\n\\t\\t# n_list = data['n_list']\\r\\n\\t\\t# # n_list = [1,2,3,4]\\r\\n\\r\\n\\t\\t# avg_time = np.sum(assemble_time_arr,1)/np.shape(assemble_time_arr)[1]\\r\\n\\t\\t# # avg_time = [4.2,4.5,7.2,14.9] #taken directly from imbq experience\\r\\n\\r\\n\\t\\t# plt.rcParams[\\\"font.family\\\"] = \\\"serif\\\"\\r\\n\\t\\t# fig = plt.figure(figsize=(16,10))\\r\\n\\r\\n\\t\\t# z = np.polyfit(n_list, avg_time, 10)\\r\\n\\t\\t# p = np.poly1d(z)\\r\\n\\r\\n\\t\\t# plt.plot(np.linspace(n_list[0], n_list[-1] + 0.1, 100), p(np.linspace(n_list[0], n_list[-1] + 0.1, 100)), ls = '-', color = 'r')\\r\\n\\t\\t# plt.plot(n_list, avg_time, ls = '', markersize = 15, marker = '.',label = 'M')\\r\\n\\t\\t# plt.title('Compilation time scaling for Grovers algorithm', fontsize=25)\\r\\n\\t\\t# plt.xlabel('n (bit string length)',fontsize=20)\\r\\n\\t\\t# plt.ylabel('Average time of execution (s)',fontsize=20)\\r\\n\\t\\t# plt.xticks(fontsize=15)\\r\\n\\t\\t# plt.yticks(fontsize=15)\\r\\n\\r\\n\\t\\t# fig.savefig('figures/g_assemble_time.png', bbox_inches='tight')\\r\\n\\r\\n\\t\\t# correct_arr = data['correct_arr']\\r\\n\\t\\t# n_list = data['n_list']\\r\\n\\t\\t# # n_list = [1,2,3,4]\\r\\n\\r\\n\\t\\t# avg_correct = np.sum(correct_arr,1)/np.shape(correct_arr)[1]\\r\\n\\t\\t# # avg_correct = [55,96,20,1.5]\\r\\n\\r\\n\\t\\t# plt.rcParams[\\\"font.family\\\"] = \\\"serif\\\"\\r\\n\\t\\t# fig = plt.figure(figsize=(16,10))\\r\\n\\r\\n\\t\\t# z = np.polyfit(n_list, avg_correct, 10)\\r\\n\\t\\t# p = np.poly1d(z)\\r\\n\\r\\n\\t\\t# plt.plot(np.linspace(n_list[0], n_list[-1] + 0.1, 100), p(np.linspace(n_list[0], n_list[-1] + 0.1, 100)), ls = '-', color = 'r')\\r\\n\\t\\t# plt.plot(n_list, avg_correct, ls = '', markersize = 15, marker = '.',label = 'M')\\r\\n\\t\\t# plt.title('Percentage correct for grovers algorithm', fontsize=25)\\r\\n\\t\\t# plt.xlabel('n (bit string length)',fontsize=20)\\r\\n\\t\\t# plt.ylabel('Percentage correct',fontsize=20)\\r\\n\\t\\t# plt.xticks(fontsize=15)\\r\\n\\t\\t# plt.yticks(fontsize=15)\\r\\n\\r\\n\\t\\t# fig.savefig('figures/g_correct.png', bbox_inches='tight')\\r\\n\\r\\n\\t\\t\\r\\n\\t\\t# num_times = data['num_times']\\r\\n\\t\\t# variance_arr = data['variance_arr']\\r\\n\\t\\t# n = data['n']\\r\\n\\r\\n\\t\\t# plt.rcParams[\\\"font.family\\\"] = \\\"serif\\\"\\r\\n\\t\\t# fig = plt.figure(figsize=(16,10))\\r\\n\\r\\n\\t\\t\\r\\n\\r\\n\\t\\t# plt.hist(variance_arr)\\r\\n\\t\\t# plt.title('Dependence of execution time on $U_f$ (grovers algorithm)', fontsize=25)\\r\\n\\t\\t# plt.xlabel('Execution time (s)',fontsize=20)\\r\\n\\t\\t# plt.ylabel('Frequency of occurence',fontsize=20)\\r\\n\\t\\t# plt.xticks(fontsize=15)\\r\\n\\t\\t# plt.yticks(fontsize=15)\\r\\n\\r\\n\\r\\n\\t\\t# fig.savefig('figures/g_hist.png', bbox_inches='tight')\\r\\n\\t\\t\\r\\n\\t\\t\\r\\n\\t\\t#Obtain transpiled depths of circuits\\r\\n\\t\\t# list_of_depths = [2,5,309,2984]\\r\\n\\t\\t# n_list = [1,2,3,4]\\r\\n\\t\\t# plt.rcParams[\\\"font.family\\\"] = \\\"serif\\\"\\r\\n\\t\\t# fig = plt.figure(figsize=(16,10))\\r\\n\\r\\n\\t\\t# z = np.polyfit(n_list, list_of_depths, 10)\\r\\n\\t\\t# p = np.poly1d(z)\\r\\n\\r\\n\\t\\t# plt.plot(np.linspace(n_list[0], n_list[-1] + 0.1, 100), p(np.linspace(n_list[0], n_list[-1] + 0.1, 100)), ls = '-', color = 'r')\\r\\n\\t\\t# plt.plot(n_list, list_of_depths, ls = '', markersize = 15, marker = '.',label = 'M')\\r\\n\\t\\t# plt.title('depth of circuit for grovers algorithm', fontsize=25)\\r\\n\\t\\t# plt.xlabel('n (bit string length)',fontsize=20)\\r\\n\\t\\t# plt.ylabel('Depth',fontsize=20)\\r\\n\\t\\t# plt.xticks(fontsize=15)\\r\\n\\t\\t# plt.yticks(fontsize=15)\\r\\n\\r\\n\\t\\t# fig.savefig('figures/g_depth.png', bbox_inches='tight')\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73911a61b4c40c2cc94b768075118074\",\n \"score\": \"0.5291009\",\n \"text\": \"def plotting():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cfde243e9b097e1d2580c2fd8b464e9c\",\n \"score\": \"0.5280765\",\n \"text\": \"def plot_ongoing_FFT_distribution(ax, fontproperties,\\r\\n stim_types = ['extended_stim', 'flash'],\\r\\n windows = [9000.0, 2000.0, 1000.0], \\r\\n gaps = [0.0, 200.0, 200.0],\\r\\n padding = 0.0,\\r\\n freq_range_for_FFT = (1.0, 5.0),\\r\\n replace_spikes = True, remove_sine_waves = True,\\r\\n get_new_FFT_results_all_cells = False,\\r\\n get_new_processed_traces = False,\\r\\n master_folder_path = 'E:\\\\\\\\correlated_variability'):\\r\\n\\r\\n fonts = fontproperties\\r\\n size = fonts.get_size()\\r\\n mpl.rcParams['mathtext.default'] = 'regular'\\r\\n \\r\\n FFT_dict_all_cells = get_FFT(stim_types, windows, gaps, padding,\\r\\n freq_range_for_FFT, replace_spikes, remove_sine_waves,\\r\\n get_new_FFT_results_all_cells, get_new_processed_traces,\\r\\n master_folder_path)\\r\\n \\r\\n FFTs_all_cells = []\\r\\n for cell in FFT_dict_all_cells:\\r\\n FFTs_all_cells.append(numpy.mean(FFT_dict_all_cells[cell]['ongoing']))\\r\\n\\r\\n ### plot the histogram ###\\r\\n\\r\\n hist1 = ax.hist(FFTs_all_cells, bins = 15, color = 'k', alpha = 0.4)\\r\\n\\r\\n ### format the histogram ### \\r\\n ax.spines['top'].set_visible(False)\\r\\n ax.spines['right'].set_visible(False)\\r\\n ax.spines['left'].set_visible(False)\\r\\n ax.yaxis.set_visible(False)\\r\\n ax.get_xaxis().tick_bottom()\\r\\n\\r\\n ax.tick_params(axis = 'x', length = 3.0, width = 1.0, direction = 'outward')\\r\\n loc = plticker.MultipleLocator(base = 200.0)\\r\\n ax.xaxis.set_major_locator(loc)\\r\\n x_labels = ax.get_xticks()\\r\\n ax.set_xticklabels(x_labels, fontsize = size)\\r\\n ax.set_xlabel(r'$\\\\langle FFT_\\\\delta\\\\rangle$ (mV)', fontsize = size) \\r\\n\\r\\n ### report number of cells and preparations ###\\r\\n experiments = []\\r\\n for cell_name in FFT_dict_all_cells:\\r\\n expt_date = cell_name.split('_')[0]\\r\\n if expt_date not in experiments:\\r\\n experiments.append(expt_date)\\r\\n print ' %s cells;'%str(len(FFT_dict_all_cells.keys())), \\\\\\r\\n '%s turtles'%str(len(experiments))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d146a9eff50a412c9d6eb3995bade567\",\n \"score\": \"0.5272843\",\n \"text\": \"def plot_rx_t(data_z):\\n time = np.arange(len(data_z)) * (1/SAMPLE_RATE)\\n plt.plot(time, data_z.real)\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1aeb1a8facff6190d400b6f7bf508226\",\n \"score\": \"0.52714753\",\n \"text\": \"def plot_encoded(t, u, s, fig_title='', file_name=''):\\n\\n dt = t[1]-t[0]\\n cs = np.cumsum(s)\\n if cs[-1] >= max(t)-min(t):\\n raise ValueError('some spike times occur outside of signal''s support')\\n\\n p.clf()\\n p.gcf().canvas.set_window_title(fig_title)\\n p.axes([0.125, 0.3, 0.775, 0.6])\\n p.vlines(cs+min(t), np.zeros(len(cs)), u[np.asarray(cs/dt, int)], 'b')\\n p.hlines(0, 0, max(t), 'r')\\n p.plot(t, u, hold=True)\\n p.xlabel('t (s)')\\n p.ylabel('u(t)')\\n p.title(fig_title)\\n p.gca().set_xlim(min(t), max(t))\\n a = p.axes([0.125, 0.1, 0.775, 0.1])\\n p.plot(cs+min(t), np.zeros(len(s)), 'ro')\\n a.set_yticklabels([])\\n p.xlabel('%d spikes' % len(s))\\n p.gca().set_xlim(min(t), max(t))\\n p.draw_if_interactive()\\n if file_name:\\n p.savefig(file_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a29923cc7dc87151a0c34aa43610a6d8\",\n \"score\": \"0.52597255\",\n \"text\": \"def f_sensitivity_per_band():\\n\\n fig = plt.figure()\\n\\n j_minimum = minimum.where((minimum.N_j > 50) & (minimum.Stetson < 0.5))\\n h_minimum = minimum.where((minimum.N_h > 80) & (minimum.Stetson < 0.5))\\n k_minimum = minimum.where((minimum.N_k > 80) & (minimum.Stetson < 0.5))\\n\\n s1 = plt.subplot(3,1,1)\\n s2 = plt.subplot(3,1,2, sharex=s1)\\n s3 = plt.subplot(3,1,3, sharex=s1)\\n \\n s1.plot(j_minimum.j_meanr, j_minimum.j_rmsr, 'b,')\\n s2.plot(h_minimum.h_meanr, h_minimum.h_rmsr, 'g,')\\n s3.plot(k_minimum.k_meanr, k_minimum.k_rmsr, 'r,')\\n\\n plt.xlim(10.5, 17.5)\\n\\n s3.set_xlabel(\\\"Magnitude\\\")\\n s3.set_ylabel(\\\"Observed rms\\\")\\n for s in [s1,s2,s3]:\\n s.set_ylim(0,0.1)\\n s.set_yticks([0, 0.05, 0.1])\\n\\n s1.text(0.2, 0.6, \\\"$J$\\\", fontsize=24, color='b', transform=s1.transAxes)\\n s2.text(0.2, 0.6, \\\"$H$\\\", fontsize=24, color='g', transform=s2.transAxes)\\n s3.text(0.2, 0.6, \\\"$K$\\\", fontsize=24, color='r', transform=s3.transAxes) \\n\\n plt.show()\\n\\n return fig\",\n \"title\": \"\"\n },\n {\n \"docid\": \"deec8fb4da515f311aa5f56e0bf7d914\",\n \"score\": \"0.52595687\",\n \"text\": \"def show_friction_line(self, sns_context=\\\"talk\\\"):\\r\\n self.mean_friction_frame()\\r\\n\\r\\n p_dat = self.mean_fric_frame[self.mean_fric_frame['direction'] == 0]\\r\\n n_dat = self.mean_fric_frame[self.mean_fric_frame['direction'] == 1]\\r\\n\\r\\n xbins = self.mean_fric_frame['load_index'].max() + 1\\r\\n\\r\\n sns.set_context(sns_context)\\r\\n\\r\\n f, (ax1, ax2) = plt.subplots(nrows=1, ncols=2)\\r\\n\\r\\n sns.regplot(x='N', y='F', data=p_dat, x_bins=xbins, ax=ax1)\\r\\n sns.regplot(x='N', y='F', data=n_dat, x_bins=xbins, ax=ax2)\\r\\n\\r\\n ax1.set_title(\\\"(+)\\\")\\r\\n ax2.set_title(\\\"(-)\\\")\\r\\n\\r\\n plt.tight_layout()\\r\\n\\r\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c4470402c93c029d205eb287970fec8\",\n \"score\": \"0.52591443\",\n \"text\": \"def space_plot(data, ax=None, **kwargs):\\n x, y = space_from_dict(data)\\n x_sm, y_sm = smooth(x, y, 200)\\n ax.plot(x_sm, y_sm, **kwargs)\\n ax.set(ylabel=f\\\"{settings.CLI} ($mM$)\\\", xlabel='Distance from soma ($\\\\mu m$)')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e04c6d60e6a67240c77f6180fc064a24\",\n \"score\": \"0.5258989\",\n \"text\": \"def scatter_plot(self):\\r\\n assert self.channel_count == 2\\r\\n plt.figure('Stochastic Signals Scatter Plot')\\r\\n plt.scatter(self.channel[0], self.channel[1])\\r\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78cc970aa7034e2e743e039f26e6a096\",\n \"score\": \"0.5256539\",\n \"text\": \"def plot_fourier(u, fs, fmin=0.0, fmax=None, style='line'):\\n\\n if fmin < 0.0 or fmin >= fs/2:\\n raise ValueError('invalid minimum frequency')\\n\\n if fmax is None:\\n fmax = fs/2\\n if fmax <= fmin or fmax > fs/2:\\n raise ValueError('invalid maximum frequency')\\n\\n n = len(u)/2\\n uf = fft(u)[0:n]\\n f = (fs/2.0)*np.arange(0, n)/n\\n\\n a = int(2.0*n*fmin/fs)\\n b = int(2.0*n*fmax/fs)\\n\\n p.clf()\\n p.subplot(211)\\n if style == 'stem':\\n p.stem(f[a:b], np.real(uf)[a:b])\\n p.ylabel('real')\\n elif style == 'semilogy':\\n p.semilogy(f[a:b], np.abs(np.real(uf)[a:b]))\\n p.ylabel('|real|')\\n else:\\n p.plot(f[a:b], np.real(uf)[a:b])\\n p.ylabel('real')\\n p.xlim((f[a], f[b-1]))\\n p.subplot(212)\\n if style == 'stem':\\n p.stem(f[a:b], np.imag(uf)[a:b])\\n p.ylabel('imag')\\n elif style == 'semilogy':\\n p.semilogy(f[a:b], np.abs(np.imag(uf)[a:b]))\\n p.ylabel('|imag|')\\n else:\\n p.plot(f[a:b], np.imag(uf)[a:b])\\n p.ylabel('imag')\\n p.xlim((f[a], f[b-1]))\\n p.xlabel('f (Hz)')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fbfb6678402efcb2ddb69854ae2cb56\",\n \"score\": \"0.52535695\",\n \"text\": \"def DFT(signal):\\n ts = np.linspace(0, DURATION, (500*(DURATION))+1)\\n plt.plot(ts, signal)\\n freqs = np.linspace(0, 4*np.pi, 1000)\\n dft = []\\n for i in range(len(freqs)):\\n dft.append(Fourier_Coeff(signal, freqs[i]))\\n print(min(dft))\\n print(freqs[dft.index(min(dft))])\\n plt.figure()\\n\\n plt.plot(freqs, dft)\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c85d6b274551db91812abf48d590831\",\n \"score\": \"0.5251782\",\n \"text\": \"def plot_sot_sig(filename, idxs):\\n filename = \\\"/tmp/dg_\\\" + filename + \\\".dat\\\"\\n data, name = read_tracer_file(filename)\\n plot_select_traj(data, idxs, name)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c208be70c9bc718b05411194d1943c0c\",\n \"score\": \"0.524833\",\n \"text\": \"def plot_n_sst_timeseries(satellites): \\n ocean_area = 361900000.0\\n labels = ['ATSR1','ATSR2','AATSR','NOAA07','NOAA09','NOAA11','NOAA12','NOAA14','NOAA15','NOAA16','NOAA17','NOAA18','NOAA19','METOPA']\\n satellites = ['ATSR1','ATSR2','AATSR','AVHRR07_G','AVHRR09_G','AVHRR11_G','AVHRR12_G','AVHRR14_G','AVHRR15_G','AVHRR16_G','AVHRR17_G','AVHRR18_G','AVHRR19_G','AVHRRMTA_G']\\n\\n fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True)\\n\\n lab = []\\n ncolors = len(satellites)\\n ax1.set_prop_cycle('color',[plt.cm.gnuplot2(j) for j in np.linspace(0, 1, ncolors)])\\n for i in range(0,len(satellites)):\\n filename = satellites[i] + '_summary.nc'\\n ds = xarray.open_dataset(filename)\\n dates = ds['time']\\n idx = np.argsort(dates, axis=0) \\n t = np.array(dates)[idx]\\n days = (t[-1] - t[0]).astype('timedelta64[D]') / np.timedelta64(1, 'D')\\n years = days/365.0\\n times_duplicates = pd.Series(t)\\n times = times_duplicates.drop_duplicates()\\n Q4_duplicates = pd.Series(ds['n_sst_q4'].values[idx], index=t)\\n Q5_duplicates = pd.Series(ds['n_sst_q5'].values[idx], index=t)\\n n_sst_q4 = 365.0 * Q4_duplicates.groupby(Q4_duplicates.index).sum() / ocean_area \\n n_sst_q5 = 365.0 * Q5_duplicates.groupby(Q5_duplicates.index).sum() / ocean_area \\n df = DataFrame({'Q4' : n_sst_q4, 'Q5' : n_sst_q5}) \\n df['Sum'] = df['Q4'] + df['Q5']\\n# df['Sum'] = df['Q4'].fillna(0) + df['Q5'].fillna(0)\\n# df['Sum_mean'] = df['Sum'].resample(\\\"1d\\\").sum().fillna(0).rolling(window=31, min_periods=1).median()\\n# df['Sum_mean'].plot(ax=ax1)\\n\\n lab.append(labels[i])\\n ax1.plot(times, df['Sum'], '.', markersize=0.2)\\n ax1.set_ylim([0,18])\\n print(labels[i] + \\\",\\\" + str(df['Sum'].mean()) + \\\",\\\" + str(df['Sum'].shape[0]))\\n\\n plt.tick_params(labelsize=12)\\n title_str = 'QL=4 & 5'\\n ax1.set_title(title_str, fontsize=10)\\n\\n lab = []\\n ncolors = len(satellites)\\n ax2.set_prop_cycle('color',[plt.cm.gnuplot2(j) for j in np.linspace(0, 1, ncolors)])\\n for i in range(0,len(satellites)):\\n filename = satellites[i] + '_summary.nc'\\n ds = xarray.open_dataset(filename)\\n dates = ds['time']\\n idx = np.argsort(dates, axis=0) \\n t = np.array(dates)[idx]\\n days = (t[-1] - t[0]).astype('timedelta64[D]') / np.timedelta64(1, 'D')\\n years = days/365.0\\n times_duplicates = pd.Series(t)\\n times = times_duplicates.drop_duplicates()\\n Q3_duplicates = pd.Series(ds['n_sst_q3'].values[idx], index=t)\\n n_sst_q3 = 365.0 * Q3_duplicates.groupby(Q3_duplicates.index).sum() / ocean_area\\n df = DataFrame({'Q3' : n_sst_q3})\\n# df['Q3_mean'] = df['Q3'].resample(\\\"1d\\\").sum().rolling(window=31, min_periods=1).median()\\n# df['Q3_mean'].plot(ax=ax2)\\n\\n lab.append(labels[i])\\n ax2.plot(times, df['Q3'], '.', markersize=0.2)\\n ax2.set_ylim([0,18])\\n print(labels[i] + \\\",\\\" + str(df['Q3'].mean()) + \\\",\\\" + str(df['Q3'].shape[0]))\\n\\n plt.tick_params(labelsize=12)\\n title_str = 'QL=3'\\n ax2.set_title(title_str, fontsize=10)\\n\\n fig.legend(lab, fontsize=8, loc=7, markerscale=20, scatterpoints=5)\\n fig.subplots_adjust(right=0.8) \\n fig.text(0.01, 0.5, 'Observation density / $\\\\mathrm{km^{-2} \\\\ yr^{-1}}$', va='center', rotation='vertical')\\n plt.savefig('n_sst_timeseries.pdf')\\n# plt.savefig('n_sst_timeseries.png', dpi=600)\\n# plt.savefig('n_sst_timeseries.eps', format='eps', rasterized=True, dpi=1200)\\n plt.close('all')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9875abe0b1b5428c0aead1ff3f2ec260\",\n \"score\": \"0.5241629\",\n \"text\": \"def plot_freqs(files=FILES):\\n def calc_freqs(f):\\n t = tokenize(f)\\n dist = Counter(t)\\n return sorted(dist.values(), reverse=True)\\n freqs = [calc_freqs(f) for f in files]\\n\\n def plot_one(f, n):\\n corpora = len(files)\\n width = 2 # linlin and loglog\\n plt.subplot(corpora, width, n*2+1)\\n plt.plot(f)\\n plt.subplot(corpora, width, n*2+2)\\n plt.loglog(f)\\n\\n for i, val in enumerate(freqs):\\n plot_one(val, i)\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7493718cfec2906c4ff155a96006fce1\",\n \"score\": \"0.52378213\",\n \"text\": \"def plot_spectrum(gpi):\\n # read ts, fill gaps\\n ts = prepare_ts(gpi, start_date='2007-01-01', end_date='2011-12-31')\\n \\n # detrend\\n ts_detrend = ts - ts.mean()\\n \\n # construct sample frequencies\\n n = len(ts_detrend) # length of the signal\\n \\n # define cycles per unit: days = 1, week=7, month=31, years = 365.25\\n days = 365.25\\n time_step = 1.0 / days\\n frq = np.fft.fftfreq(n, d = time_step)\\n frq = frq[range(n/2)] # one side frequency range\\n \\n # fft computing and normalization\\n TS = np.fft.fft(ts_detrend)/n \\n TS = TS[range(n/2)]\\n \\n # plot \\n f, ax = plt.subplots(3,figsize=(10,10))\\n \\n f.suptitle(\\\"Single Sided Amplitude Spectrum of ASCAT-Ts at Gpi: %d\\\" % gpi,\\n fontsize=16)\\n ax[0].plot(ts)\\n ax[0].set_xlabel('Time')\\n ax[0].set_ylabel('SM [%]')\\n ax[0].set_yticks(np.arange(0,120,20))\\n \\n ax[1].plot(frq,abs(TS),'r') # plotting the spectrum\\n ax[1].set_xlabel('Frequency (cycles per %d days)' % days)\\n ax[1].set_ylabel('|TS(freq)|')\\n ax[1].set_xlim(0,182.5)\\n ax[1].set_ylim(0,6)\\n ax[1].set_xticks(np.arange(0,200,25))\\n ax[1].set_yticks(np.arange(0,8,1))\\n \\n ax[2].plot(frq,abs(TS),'r') # plotting the spectrum\\n ax[2].set_xlabel('Frequency (cycles per %d days)' % days)\\n ax[2].set_ylabel('|TS(freq)|')\\n ax[2].set_xlim(0,13)\\n ax[2].set_xticks(range(13))\\n ax[2].set_yticks(np.arange(0,13,2))\\n \\n f.tight_layout()\\n f.subplots_adjust(top=0.92)\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa49ffe2b571bf04f4d138eb3f1bb17a\",\n \"score\": \"0.5232876\",\n \"text\": \"def plot_ts(data, ax=None, fig=None, fig_kwargs=None):\\n # Grab station information to use in plot title\\n station_id = data['Station'][0]\\n station_dict = _get_station_dict()\\n station_name = station_dict[station_id]\\n\\n # Create figure and/or axis if no existing figure/axis objects are passed\\n fig, ax = _fig_setup(ax, fig, fig_kwargs)\\n \\n # Plot on axis\\n ax.plot(data.index, data['Discharge'])\\n ax.set_ylim([0, 1.2*data['Discharge'].max()])\\n ax.set_xlabel('Date')\\n ax.set_ylabel('Discharge (cfs)')\\n ax.set_title(station_name)\\n return fig, ax\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17ada2679035692327d9814373245722\",\n \"score\": \"0.5231238\",\n \"text\": \"def plot_default(self):\\n plt.figure(figsize=(12,8)) \\n plt.imshow(self.specx[::2], extent=self.leftlim + self.rightlim, origin='lower', aspect='auto') \\n plt.xlabel('Frequency Bins \\\\n'+'\\\\nFile:'+ self.filename + '\\\\nRuntime:' + str(self.t_fs))\\n plt.ylabel('Time (s)')\\n plt.title('Waterfall')\\n plt.colorbar()\\n plt.savefig('waterfal_just_plot.png', dpi=400, transparent=False)\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9d8f3869d4c8a0dba30e5d30597d93b\",\n \"score\": \"0.52307063\",\n \"text\": \"def PlotPowerSpectrum(g, x0, N, args=()):\\n sig = IterateList(g, x0, N+1000, args)[1000:]\\n sig_freq = sp.fftpack.fftfreq(sig.size, d=1)\\n sig_fft = sp.fftpack.fft(sig)\\n pidxs = sp.where(sig_freq > 0)\\n freqs, power = sig_freq[pidxs], sp.abs(sig_fft)[pidxs]\\n pylab.plot(freqs, power)\\n pylab.xlabel('Frequency [Hz]')\\n pylab.ylabel('Power')\\n pylab.show()\\n print(\\\"Peak Frequency:\\\")\\n print(freqs[power.argmax()])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3c8ed7def21d86af46ce4b0aa1689ae\",\n \"score\": \"0.52234256\",\n \"text\": \"def plot_classification_frequency(classification_data):\\n classification_frequency = scipy.stats.itemfreq(classification_data)\\n\\n plt.figure(1)\\n\\n for row_n, row in enumerate(classification_frequency):\\n if row[0] == 'B':\\n label = 'Benign'\\n color = 'b'\\n elif row[0] == 'M':\\n label = 'Malignant'\\n color = 'r'\\n else:\\n raise Exception(\\\"Unkown classification:\\\", row[0])\\n frequency = int(row[1])\\n plt.bar(left=row_n, height=frequency, color=color, label=label)\\n\\n plt.gca().axes.xaxis.set_ticklabels([])\\n plt.legend()\\n plt.xlabel(\\\"Diagnosis\\\")\\n plt.ylabel(\\\"Frequency\\\")\\n plt.title(\\\"Distribution of Classifications\\\")\\n print(\\n \\\"In order to have our classifier be adept at spotting all classes,\\\\n\\\"\\n \\\"we must ensure our data has a reasonably equal distribution.\\\\n\\\"\\n )\\n plt.show()\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":875,"cells":{"query_id":{"kind":"string","value":"09c4e58955a8b4e19f869fca7078f2d8"},"query":{"kind":"string","value":"subscribe the web client to this socket"},"positive_passages":{"kind":"list like","value":[{"docid":"8806147f6d946aa383d7fb9f5fb8964f","score":"0.6003841","text":"def sub(self, uri, prefix=''):\n\n ident = self.ident(uri, prefix)\n idents = self.recievers.iterkeys()\n self.log('subscribe:', 'requested', ident)\n\n # we don't need to actually create more filtered sockets if we\n # already have a general one on the books.\n # the javascript end will handle choosing the most specific\n # subscriber callback for any message we send over the wire\n if ident in self.recievers or self.any_has_prefix(ident, idents):\n # already subscribed\n self.write_message({'success': 'already subscribed'})\n self.log('subscribe:', 'already has', ident)\n return\n\n # create a new reciever and a callback for it\n self.log('subscribe:', 'subscribing to prefix', repr(prefix), 'at uri', uri)\n rcvr = AsyncReciever(sub(uri), prefix)\n self.recievers[ident] = rcvr\n rcvr.on_recieve(self.write_squidwork)\n\n self.write_message({'success': 'subscribed {}'.format(ident)})","title":""}],"string":"[\n {\n \"docid\": \"8806147f6d946aa383d7fb9f5fb8964f\",\n \"score\": \"0.6003841\",\n \"text\": \"def sub(self, uri, prefix=''):\\n\\n ident = self.ident(uri, prefix)\\n idents = self.recievers.iterkeys()\\n self.log('subscribe:', 'requested', ident)\\n\\n # we don't need to actually create more filtered sockets if we\\n # already have a general one on the books.\\n # the javascript end will handle choosing the most specific\\n # subscriber callback for any message we send over the wire\\n if ident in self.recievers or self.any_has_prefix(ident, idents):\\n # already subscribed\\n self.write_message({'success': 'already subscribed'})\\n self.log('subscribe:', 'already has', ident)\\n return\\n\\n # create a new reciever and a callback for it\\n self.log('subscribe:', 'subscribing to prefix', repr(prefix), 'at uri', uri)\\n rcvr = AsyncReciever(sub(uri), prefix)\\n self.recievers[ident] = rcvr\\n rcvr.on_recieve(self.write_squidwork)\\n\\n self.write_message({'success': 'subscribed {}'.format(ident)})\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"109c83d9994e8d99aa6478bff90b40e6","score":"0.70212555","text":"def subscribe(self):\n try:\n self.client.subscribe()\n except Exception as e:\n logger.error(\"Unknown error: {}\".format(e))\n raise Exception","title":""},{"docid":"30af9950d2e712e3525c37be2730b9d6","score":"0.67332","text":"def on_connect(client, userdata, flags, rc):\n print(\"Connected with result code %s, subscribing to topic: %s\" % (str(rc), userdata[\"topic\"]))\n client.subscribe(userdata[\"topic\"])","title":""},{"docid":"1cb1f02a5e9dd4e21452f5f7b5134ec8","score":"0.6669246","text":"def on_connect(client, userdata, flags, rc):\r\n print(\"Connected with result code \"+str(rc))\r\n \r\n # TOPICS I SUBCRIBED TO\r\n \r\n client.subscribe(\"Francis/cpupct\") # (MY PUBLISHER)\r\n\r\n \r\n client.subscribe(\"Damare/cpupct\") # (CLASSMATE'S PUBLISHERS)\r\n client.subscribe(\"Miller/cpupct\")\r\n client.subscribe(\"Markham/cpupct\")\r\n client.subscribe(\"Bingham/cpupct\")","title":""},{"docid":"62a6bc5e285e4fcdc979ea01b8fd1651","score":"0.6598101","text":"async def __init_connection__(self): \n\t\tself.session = aiohttp.ClientSession()\n\t\tself.connection = self.session.ws_connect(\n\t\t\t\turl=self.WSS_FEED, heartbeat=self.WSS_TIMEOUT)\n\n\t\tself.websocket = await self.connection.__aenter__()\n\t\tawait self.websocket.send_json(self._subscribe)","title":""},{"docid":"a6b0dc8b8fa7163b13277619e79b2edc","score":"0.6485489","text":"def on_run(self, _):\n logging.info('Websocket connected.')\n self.get_and_subscribe_devices()\n self.get_events()","title":""},{"docid":"f6b5f89d88dbfe68a0490a5ab158841c","score":"0.64582074","text":"def on_connect(\n self,\n client: mqtt.Client,\n userdata: Any,\n rc: int,\n properties: mqtt.Properties = None,\n ) -> None:\n # subscribe to all topics\n client.subscribe(\"#\")","title":""},{"docid":"3ea6571cbf873c7a6a8e829256bb62a7","score":"0.64488345","text":"def subscribe_server(self):\n\t\tsubscribe_data = json_subscribe(MATRICULES, self.__client.port, self.__name)\n\t\tresponse = json_decode(self.__client.send_to_server(subscribe_data))\n\n\t\ttry:\n\t\t\tif response[\"response\"].upper() == \"OK\":\n\t\t\t\tlogging.info(\"Registered to the server\")\n\t\t\telse:\n\t\t\t\tlogging.critical(f\"Error while subscribing : {response['error']}\")\n\t\texcept Exception as e:\n\t\t\tlogging.critical(f\"Error on subscribing to the server : {e}\")\n\t\t\texit()","title":""},{"docid":"1773ee9752c0b4475e44f7118f584b18","score":"0.63788486","text":"def on_connect(client, userdata, flags, rc):\n print('Connected with result code ' + str(rc))\n client.subscribe(MQTT_TOPIC)","title":""},{"docid":"1773ee9752c0b4475e44f7118f584b18","score":"0.63788486","text":"def on_connect(client, userdata, flags, rc):\n print('Connected with result code ' + str(rc))\n client.subscribe(MQTT_TOPIC)","title":""},{"docid":"de83f252f24f417d7cd393a046a1cfef","score":"0.632665","text":"def on_connect(client, userdata, rc):\n print('Connected with result code ' + str(rc))\n # Subscribing in on_connect() means that if we lose the connection and\n # reconnect then subscriptions will be renewed.\n client.subscribe(MQTT_TOPIC, 1)","title":""},{"docid":"34ecf349b262dc88037422352aa62c1d","score":"0.63254875","text":"def on_connect(client, userdata, flags, rc):\n\tprint('Connected with result code ' + str(rc))\n\tclient.subscribe(MQTT_TOPIC)","title":""},{"docid":"208a7ef91d240bd0867a02a71e4a044c","score":"0.6249386","text":"def on_connect(self, client, userdata, flags, rc):\n\n if rc == 0:\n self.connected = True\n logger.info(self.parse_return_code(0))\n self.client.subscribe(self.appconfig.topic)\n else:\n logger.error(f\"{self.parse_return_code(rc)}\")\n self.connected = False","title":""},{"docid":"96ca8fd2138803d9ab75dc324644bc12","score":"0.62461495","text":"def on_subscribe(self):\n\n def subscribe_handler(handler: Callable):\n log_info.info(\"on_subscribe handler accepted\")\n self.client.on_subscribe = handler\n return handler\n\n return subscribe_handler","title":""},{"docid":"9ca443108e3022d9d58f794f0ced2b31","score":"0.6235847","text":"def subscribe(self, callback):\n # Avoid duplicate subscription\n if self._subscribe_id:\n return\n\n self._subscribe_id = 'subscribe:%s:%d' % (\n self.name, self.rosbridge.id_counter)\n\n self.rosbridge.on(self.name, callback)\n self.rosbridge.send_on_ready(Message({\n 'op': 'subscribe',\n 'id': self._subscribe_id,\n 'type': self.message_type,\n 'topic': self.name,\n 'compression': self.compression,\n 'throttle_rate': self.throttle_rate,\n 'queue_length': self.queue_length\n }))","title":""},{"docid":"30e9c2e4ccabe60518676a63aff16820","score":"0.6223954","text":"def subscribe(self, publisher):\n for line in publisher:\n self.data_socket.connect(\"tcp://\"+str(line[0])+\":\"+str(line[1]))\n self.subscribed_ports.append(line[1])\n self.data_socket.setsockopt(zmq.SUBSCRIBE, '')","title":""},{"docid":"2dee9ed9400f9c6041395dd1afb0bbef","score":"0.6219262","text":"async def hass_subscribe_events(websocket):\n # Subscribe to events.\n LOGGER.info(\"Subscribing to events...\")\n await websocket.send(json.dumps(dict(\n id=1,\n type=\"subscribe_events\",\n event_type=\"state_changed\"\n )))\n response = await websocket.recv()\n LOGGER.info(f\"Subscription result: {response}\")","title":""},{"docid":"4cb4db2cd2f949cb3525fe17bfeb4b15","score":"0.61780894","text":"async def listen(self, websocket, path):\n\n # send newly connected client initial timestamp\n await self.notify(websocket, 0)\n\n try:\n # incoming message event\n async for message in websocket:\n\n # decode incoming message into an associative array\n data = json.loads(message)\n\n # notify client with event for message with count \"c\"\n await self.notify(websocket, data[\"c\"])\n except asyncio.IncompleteReadError as e:\n pass","title":""},{"docid":"3a9deba0e0758b1168148eea8318f7e6","score":"0.61297876","text":"def subscribe(self, topic, url):\n # note: do NOT encode the URL, this is not decoded on the erlang side!\n # (only strings are allowed anyway)\n # url = self._conn.encode_value(url)\n result = self._conn.call('subscribe', [topic, url])\n self._conn.process_result_subscribe(result)","title":""},{"docid":"428dc3f6c80fa87868e02c64a0482b24","score":"0.6125694","text":"def subscribe(listener):\n _thread_local.communication_manager.subscribe(listener) # type: ignore[union-attr]","title":""},{"docid":"74056d62b32d840d3379628d4cb168a8","score":"0.6108331","text":"def subscribe(self):\n self.bus.subscribe('start', self.start)\n self.bus.subscribe('stop', self.stop)\n if self.subscribe_event is not None:\n self.bus.subscribe(self.subscribe_event, self.receive_input)","title":""},{"docid":"69c034eb6af951481f78d8a153ac4de5","score":"0.6104722","text":"def subscribe(self, destination, extra_headers=None):\n raise NotImplementedError(\"%s client does not implement SUBSCRIBE\" % (self.__class__,))","title":""},{"docid":"69c034eb6af951481f78d8a153ac4de5","score":"0.6104722","text":"def subscribe(self, destination, extra_headers=None):\n raise NotImplementedError(\"%s client does not implement SUBSCRIBE\" % (self.__class__,))","title":""},{"docid":"f2283ff5fa92e53216cc4f40a9a26c92","score":"0.60880274","text":"def subscribe(self, subscribe):\n\n self._subscribe = subscribe","title":""},{"docid":"a165097abb33ee878cb29395443ba874","score":"0.6080283","text":"def run(self):\n self.socket = self.context.socket(zmq.SUB)\n self.socket.setsockopt(zmq.SUBSCRIBE,'')\n self.socket.setsockopt(zmq.IDENTITY, self.session.session)\n self.socket.connect('tcp://%s:%i' % self.address)\n self.iostate = POLLIN|POLLERR\n self.ioloop.add_handler(self.socket, self._handle_events, \n self.iostate)\n self.ioloop.start()","title":""},{"docid":"044e96b0f6504480e85d0002d636d8c7","score":"0.6080151","text":"def send_subscription(self):\n\n print \"[i] Sending a Subscribe request\"\n\n # Generate the payload\n payload = SUBSCRIBE % ('http://' + self.WAN_IP, self.TOPIC)\n\n # Send the request\n self.send_request(payload)","title":""},{"docid":"a1fbcbacf2bad343fc3e64f67308aa3d","score":"0.60752404","text":"def on_connect(client, userdata, flags, return_code):\n\n print('Connected with result code {}'.format(return_code))\n\n client.subscribe(TTN_SUBSCRIPTIONS)","title":""},{"docid":"8730922ab3833b142c6b52138e508107","score":"0.6073351","text":"def process_connection():\n logging.debug(\"Processing new connection\")\n mqttc.subscribe(MQTT_TOPIC, 2)","title":""},{"docid":"a09b9f4cfe40ed455a6d9791186b5b54","score":"0.60667396","text":"def on_subscribe(client, userdata, mid, granted_qos):\n logging.debug(\"Subscribe with mid \" + str(mid) + \" received.\")","title":""},{"docid":"604fe2fcf17c64f470c26bca917eb12a","score":"0.6056498","text":"def listen(self):\n\n print('listening to kafka')\n\n self.source.subscribe(Emitter(self.ws))","title":""},{"docid":"bfcf7ea3905dd36a1e61e6d079cd9c5c","score":"0.6054609","text":"async def accept(self, subprotocol=None):\n await super().send({\"type\": \"websocket.accept\", \"subprotocol\": subprotocol})","title":""},{"docid":"c03ce8df3a0931cba47a033064b70a1b","score":"0.60300183","text":"def on_connect(self, client, userdata, flags, rc):\n if id(client) == id(self.roomba):\n self.debug(\"Connected to roomba mqtt broker with result code \"+str(rc))\n client.subscribe(\"#\")\n\n elif id(client) == id(self.upstream):\n self.debug(\"Connected to upstream mqtt broker with result code \"+str(rc))\n client.subscribe(self.settings[\"upstream\"][\"subscribe\"])","title":""},{"docid":"e7798393e531003822fc25fea1801866","score":"0.6025621","text":"def subscribe(self, publisher):\n for line in publisher:\n self.create_info_log_entry(\"LogWriter subscribes to: \"+str(line[0])+\":\"+str(line[1]))\n self.socket.connect(\"tcp://\"+str(line[0])+\":\"+str(line[1]))\n self.subscribed_ports.append(line[1])\n self.socket.setsockopt(zmq.SUBSCRIBE, '') # remove the subscription filter","title":""},{"docid":"5fd8ca6d8f7652e667e4ae3c65125329","score":"0.60156804","text":"def on_mqtt_connect(self, client: Client, userdata, flags, rc):\n logging.info(f\"Connected with result code {rc}\")\n # Subscribing in on_connect() means that if we lose the connection and\n # reconnect then subscriptions will be renewed.\n for t in self.args.topic:\n logging.info(f\"Subscribe to {t}\")\n self.mqtt_client.subscribe(t)","title":""},{"docid":"647db7120a3b62c29e476dd5ecff92ea","score":"0.59803313","text":"def on_connect(client, userdata, flags, rc):\n logger.info(f\"Connected with result code {rc}.\")\n # Subscribing in on_connect() means that if we lose the connection and reconnect then subscriptions will be renewed.\n client.subscribe(\"owntracks/+/+\")","title":""},{"docid":"b6404ce39582b128ff7db687c3ecbdbb","score":"0.5975095","text":"def on_client_connect(self, client):\n self.clients.append(client)\n log.msg('Client connected')","title":""},{"docid":"81d4d7cf7a27d74bb259be07b0ce69ba","score":"0.5970741","text":"def on_subscribe(self, client, userdata, mid, granted_qos):\n\n self.subscribed = True","title":""},{"docid":"e8eca22d65772bb0fae58f229e5d2851","score":"0.5969631","text":"def subscribe(self, message):\n try:\n channel_id = self.channel_id(message)\n self.server.connections.add_peer(channel_id, self, self.client_address)\n self.is_subscribed = True\n logger.debug('New peer {}:{} connected to channel: {}'.format(\n self.client_address[0], self.client_address[1], channel_id\n ))\n except Exception as e:\n logger.error('Failed to subscribe new peer {}:{} - {}'.format(\n self.client_address[0], self.client_address[1], str(e)\n ))","title":""},{"docid":"363a8fa65e57fe4a084c237ce48483df","score":"0.5913931","text":"def _on_connect(client, userdata, flags, rc):\n logging.info(f'Connected to broker, result code {str(rc)}')\n\n for topic in userdata.keys():\n client.subscribe(topic)\n logging.info(f'Subscribing to topic: {topic}')","title":""},{"docid":"4d804f333d2e59ba836f2b80a93cc046","score":"0.59032863","text":"def do_subscribe(self, arg: str) -> None:\n args = [arg]\n libwampli.ready_uri(args)\n\n task = Task(\"subscribe\", args)\n self._send_queue.put_nowait(task)","title":""},{"docid":"321d23e95b7b77305725017b1ad0cc16","score":"0.590251","text":"def connect():\n print('connection established')\n # ClientApplication.sio.emit(event='on_client_message', \n # data=data_pkg)","title":""},{"docid":"b77237ee54ab3ba67bd5e2ee6e4d29ca","score":"0.5901918","text":"def subscriber(self):\n pass","title":""},{"docid":"6053ec4a1abeb4f6420ef05c189e8e19","score":"0.5892358","text":"def _connect(self) -> None:\n super()._connect()\n self.pubsub = self.client.pubsub()","title":""},{"docid":"d81b36044ba981b02c1e9be0bc69af91","score":"0.5859421","text":"def on_connect(self, client, userdata, flags, rc):\n # srw2ho 20.04.2023: Threadnames umbiegen for debugging rausnehmen -> kann auch in Thread-Handler for MQTT Request zum Teil erledigt werden\n # threading.current_thread().name = (\n # \"client.py\" + \"_on_connect_\" + str(threading.get_ident())\n # )\n\n # print(\"MQTT-Connected with result code: \" + str_)\n\n # resubscribe to all previously subscribed topics\n # for topic, _ in self._subscriptions.items():\n # self._client.subscribe(topic)\n\n # qos default = 0\n try:\n qos = 0\n topictupels = [(key, qos)\n for key, value in self._subscriptions.items()]\n if len(topictupels) > 0:\n self._client.subscribe(topictupels)\n\n self._isconnected = True\n\n str_ = self.getConnectMessageByReturnValue(rc)\n logger.info(f\"MQTT-Connected with result code: {str_}\")\n\n if self._connectHandler != None:\n self._connectHandler(client, userdata, flags, rc)\n\n except Exception as e:\n logger.error(f\"MQTT-on_connect error result code: {e}\")","title":""},{"docid":"e7f4167d3aea9b93e4071415aa1d5a11","score":"0.58471465","text":"def on_connect(connected_client, _, __, rc) -> None:\n logger.info(\"Connected client with result code \" + str(rc))\n # Subscribe in on_connect callback to automatically re-subscribe if the connection was lost\n # Subscribe to all arduino hexnumber actions\n # '+' means single level wildcard. '#' means multi level wildcard.\n # See http://www.hivemq.com/blog/mqtt-essentials-part-5-mqtt-topics-best-practices\n logger.debug(\"Subscribing to \" + str(constants.iRulezTopic) + \"/\" + constants.virtual_IO_board_name + \"/\" +\n constants.actionTopic)\n connected_client.subscribe(constants.iRulezTopic + \"/\" + constants.virtual_IO_board_name + \"/\" +\n constants.actionTopic)","title":""},{"docid":"ffc22ad28f7193b853d28b4ad6d12799","score":"0.58453065","text":"def run(self):\n rospy.loginfo(\"Bridge accepted client: %s\", self.client_addr)\n\n try:\n while True:\n val, sig = self.dec.decode()\n if not sig:\n continue\n rosname = id2msg(sig.name)\n if val is not None: # Not as stupid as it looks.\n if rosname == 'subscribe':\n self._handle_subscribe(val)\n elif rosname == 'publish':\n self._handle_publish(val)\n elif sig.name in sample_in_hooks: # TODO: Check reg.?\n for conv in sample_in_hooks[sig.name]:\n conv.put_sample(sig.name, val)\n elif rosname in conf.IMPORTS: # TODO: out.\n self._handle_topic(val, sig)\n else:\n self._handle_service(val, sig)\n else:\n rospy.loginfo(\"Client registered: '%s'\", sig.name)\n except EOFError:\n # Clean up socket and subscriptions on EOF.\n self.client_socket.close()\n for topic,sub in self.subs.iteritems():\n sub.unregister()\n # TODO: Clean conv.\n for pt, conv in self.subscribed_conversions.iteritems():\n conv.unregister_sample_subscriber(pt, self)\n except socket.error as e:\n rospy.logerr(\"Socket error %d: %s\", e.errno, e.strerror)\n\n rospy.loginfo(\"Client disconnected\")","title":""},{"docid":"933058659501a1cf766ddaa6ddecf13b","score":"0.5836819","text":"def subscribe(self, feed):\n self.client.subscribe(feed[0], self.youtube_cat_id)","title":""},{"docid":"a4d555a9be9e7853198ab6ea23cfd4b2","score":"0.58281356","text":"def subscribe(self, board, channel):\n pass","title":""},{"docid":"231613a6a5cca367ea23dd0a7069a7be","score":"0.5827039","text":"def subscribe_rpc(self):\n self.subscribe(self._topic_rpc, qos=1)","title":""},{"docid":"49af65cd12db79cc94a800a9e6491cc9","score":"0.58243436","text":"def subscribe(self):\n return Subscribe(self)","title":""},{"docid":"cdb38fb58095d83ff05e91a14acdffdb","score":"0.58231837","text":"def on_open(self, info):\n\n LOGGER.info('[ChatWebsocketHandler] Websocket connecition opened: %s ' % self)\n\n # Initialize new pika rabbitmq client object for this websocket.\n self.rabbit_client = RabbitMqClient()\n # Assign websocket object to a Pika client object attribute.\n websocketParticipants.add(self)\n self.rabbit_client.websocket = self\n # connect to rabbitmq\n self.rabbit_client.start()","title":""},{"docid":"9414d002188eeda2419ac3d6b7666c50","score":"0.5794444","text":"def subscribe(self, observer: Callable):\n self.subscriber = observer","title":""},{"docid":"06318552a49798a9cecbbe695db29006","score":"0.578933","text":"def ReceiveSocketMessage(self):\n while(True):\n conn,addr = self.socketObj.accept()\n self.clients[addr] = c","title":""},{"docid":"ca92506f33ff1c0bf39fc75b3545b935","score":"0.57771236","text":"def subscribe(self):\n return self.reddit_session._subscribe(self.name)","title":""},{"docid":"eb1a9bbe722d57f7d2e2ec2c64b59cd8","score":"0.57768047","text":"async def ws_rpc_subscribe(self, payload):\n await self.channels.register(\n payload[\"channel\"], payload.get(\"event\"), self.channel_callback\n )\n return dict(subscribed=self.channels.get_subscribed(self.channel_callback))","title":""},{"docid":"a0979b72a46cf7220e9c3febd6f3e1de","score":"0.57747185","text":"def _on_subscribe_mqtt(\n self, client: MQTT, user_data: str, topic: int, qos: int\n ) -> None:\n if self.on_subscribe is not None:\n self.on_subscribe(self, user_data, topic, qos)","title":""},{"docid":"29af8ef24fd7803d78143a7057d45605","score":"0.5749867","text":"def subscribe(self, type='any'):\n return self.send_command(\n Command(\n 'clientnotifyregister',\n schandlerid=0,\n event=type\n )\n )","title":""},{"docid":"707137a02b24c0d11e8299d4c4c5cd20","score":"0.57385665","text":"def handshake(self):\n message = self.rp.raw.decode('utf-8', errors='strict').strip()\n upgrade = re.search('\\nupgrade[\\s]*:[\\s]*websocket', message.lower())\n\n if not upgrade:\n self.keep_alive = False\n self.handle_post()\n return\n\n key = re.search('\\nsec-websocket-key[\\s]*:[\\s]*(.*)\\r\\n', message, re.I)\n if key:\n key = key.group(1)\n else:\n logger.warning(\"Client tried to connect but was missing a key\")\n self.keep_alive = False\n return\n\n response = Adapter.handshake_response(key)\n self.handshake_done = self.request.send(response.encode('utf-8'))\n self.valid_client = True\n\n # Add new client\n self.subscribe(message)","title":""},{"docid":"f55b02acd05f5348c3aaa612d2a59217","score":"0.5734122","text":"def on_subscribe(mqtt_client, userdata, mid, granted_qos):\n logging.debug(\"Begin on_subscribe\")\n\n #TODO Add implementation specific logic here \n\n\n logging.debug(\"End on_subscribe\")","title":""},{"docid":"0b6c715a70da27099c956a35b02c4911","score":"0.57303196","text":"def on_connect():\n configuration = ioant.get_configuration()\n # There is now a connection\n topic = ioant.get_topic_structure()\n topic['top'] = \"live\"\n topic['global'] = configuration['ioant']['mqtt']['global']\n topic['local'] = configuration['ioant']['mqtt']['local']\n topic['client_id'] = configuration['ioant']['mqtt']['client_id']\n ioant.subscribe(topic)","title":""},{"docid":"923552b333b506bf79c04f8fd0c089f9","score":"0.5729532","text":"def handleSubscribe( self, inEvent ):\n sd = inEvent.getPayload()\n# sd = parseSubscribeData( inEvent.getPayload() )\n\n _log.debug( 'handleSubscribe %s' % ( sd ) )\n\n \"\"\"\n When subscriptions are received they could be wild carded by providing blank eventType or eventSource\n so we need to check for these\n \"\"\"\n if sd and (sd[0] > 0):\n et = sd[1]\n es = sd[2]\n if (et == \"http://id.webbrick.co.uk/events/config/get\") or (et == \"\") or (et == None):\n self.doPublish(es)","title":""},{"docid":"481d4fea3e5d9c492189a4a6171d715d","score":"0.572783","text":"async def run_client():\n async with websockets.connect(WEBSOCKET_SERVER) as websocket:\n await register_host_id(websocket)\n await response_consumer(websocket)","title":""},{"docid":"eb5d2686edf72f76037f042bd326f31f","score":"0.5723234","text":"def subscribe(self, callback_on_message, from_hub=None):\n self.callback = callback_on_message\n self.exchange_name = utils.make_exchange_name(self._namespace, self.EXCHANGE_TYPE, extra=from_hub)\n connection = self._connect()\n return connection.ioloop","title":""},{"docid":"2a56f0eb39aa0a0cd37f9fe9bb589748","score":"0.57232267","text":"def recieve_user_connection(self): # 20\n \n # 這是ptt的sample code，但要處理2個clients好像要別的方法，待修改\n with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as l_s:\n l_s.bind((self.host, self.port))\n l_s.listen()\n c_s, addr = l_s.accept()\n with c_s:\n print(\"Connected\", addr)\n while True:\n data = c_s.recv(1024)\n if not data:\n break\n c_s.sendall(data)\n # 待修改結束\n\n pass","title":""},{"docid":"9f5beb188c4b26919cf9b43206793c2a","score":"0.5701495","text":"def subscribeReceived(self, presence):\n log.debug(\"Auto-subscribing to %s\", presence.sender.userhost())\n self.subscribed(presence.sender)\n self.subscribe(presence.sender.userhostJID())","title":""},{"docid":"3b7f804cf63187e53da43b04928e5ddd","score":"0.5682295","text":"def on_subscribe(client, user_data, mid, granted_qos):\n print(\"Subscribed: \" + str(mid) + \" \" + str(granted_qos))","title":""},{"docid":"474b1f27522d4eb3d65ee983c742a460","score":"0.5674448","text":"def on_message(self, message):\n\n LOGGER.info('[ChatWebsocketHandler] message received on Websocket: %s ' % self)\n\n res = tornado.escape.json_decode(message)\n routing_key = res['routing_key']\n msg = res['msg']\n stage = msg['stage']\n\n if stage == 'start':\n LOGGER.info('[ChatWebsocketHandler] Message Stage : START')\n\n name = msg['name']\n # assign name to rabbit client\n self.rabbit_client._person = name\n # assign clientid to rabbit client\n self.rabbit_client._clientid = self.genid()\n # add no. of current participants/websocket connections\n self.rabbit_client._participants = len(websocketParticipants)\n msg['participants'] = len(websocketParticipants)\n\n msg['clientid'] = self.rabbit_client._clientid\n\n LOGGER.info('[ChatWebsocketHandler] Publishing the received message to RabbitMQ')\n\n self.rabbit_client.publish(msg, routing_key)","title":""},{"docid":"208be905cad6b3f83a5669c1f26b50f2","score":"0.56721705","text":"def websocket_handler(request):\n ws = web.WebSocketResponse()\n yield from ws.prepare(request)\n\n token = TOKEN(request)\n identity = yield from register_ws(ws, token)\n\n print('cookies', request.cookies)\n\n while True:\n try:\n msg = yield from ws.receive()\n\n if msg.tp == aiohttp.MsgType.text:\n topics = json.loads(msg.data)['topics']\n subscribe_to_topics(identity, ws, topics)\n\n elif msg.tp == aiohttp.MsgType.close:\n print('websocket connection closed')\n unregister_ws(identity, ws)\n break\n elif msg.tp == aiohttp.MsgType.error:\n print('ws connection closed with exception %s',\n aio.ws.exception())\n unregister_ws(identity, ws)\n break\n\n except RuntimeError:\n # clients.remove(ws)\n print('client disconnected')\n break\n\n return ws","title":""},{"docid":"124fbdb429162a7ccf624ba8f315bd6a","score":"0.5670587","text":"async def connect(self):\n self.websocket = await self.ws_connect(self.URL)","title":""},{"docid":"c0af300979cba8e47820d9e57d6744ef","score":"0.5667329","text":"def connectionMade(self):\n self.session = {}\n self.session[\"host\"] = self.transport.getHost().host\n self.buffer = \"\"\n Broadcast.get().addClient(self)","title":""},{"docid":"cec9fd0388a9b83a14c51f38982b31fe","score":"0.5665399","text":"def open(self):\n self.id = self.get_argument(\"Id\")\n self.stream.set_nodelay(True)\n clients[self.id] = {\"id\": self.id, \"object\": self}\n print(\"WebSocket opened\")","title":""},{"docid":"cec9fd0388a9b83a14c51f38982b31fe","score":"0.5665399","text":"def open(self):\n self.id = self.get_argument(\"Id\")\n self.stream.set_nodelay(True)\n clients[self.id] = {\"id\": self.id, \"object\": self}\n print(\"WebSocket opened\")","title":""},{"docid":"b199a24f89224737ce6c24154afe160f","score":"0.566445","text":"def run(self):\n # important to start the websocket server in run as opposed to init,\n # otherwise we'll start it in the main thread and block other requests\n # from coming in\n self._server = WebsocketServer(self.websocket_port, host='0.0.0.0')\n self._server.set_fn_new_client(self.new_client)\n self._server.run_forever()","title":""},{"docid":"c8ff23322dfb8d704b0ec629ab47362a","score":"0.56538796","text":"def subscribe(topic, callback):\n _global_message_broker.subscribe(topic, callback)","title":""},{"docid":"673e148136fccfcf0487abc64442d7c3","score":"0.5653784","text":"def run(self):\n data = self.s.recv(1024)\n headers = self.parse_headers(data)\n\n if not 'Sec-WebSocket-Key' in headers:\n raise ValueError('Missing header: Sec-WebSocket-Key')\n\n accept = base64.b64encode(hashlib.sha1(headers['Sec-WebSocket-Key'] + config.guid).digest())\n\n handshake = ('HTTP/1.1 101 Web Socket Protocol Handshake\\r\\n'\n 'Upgrade: WebSocket\\r\\n'\n 'Connection: Upgrade\\r\\n'\n 'WebSocket-Origin: http://%s\\r\\n'\n 'WebSocket-Location: ws://%s:%s/\\r\\n'\n 'WebSocket-Protocol: sample\\r\\n'\n 'Sec-WebSocket-Accept: %s\\r\\n\\r\\n'\n % (config.http_host, config.socket_host, config.socket_port, accept)\n )\n self.s.send(handshake.encode())\n\n while True:\n data = self.s.recv(1024)\n\n if not data: continue\n\n print('Data from', self.addr, ':', data)\n self.onreceive(data)\n\n self.close()","title":""},{"docid":"e0800383a5ffb0de48f24c277e3e36c1","score":"0.56505376","text":"def subscribe(job_id):\n\tdef gen():\n\t\tq = Queue()\n\t\tSUBSCRIPTIONS.append(q)\n\t\ttry:\n\t\t\twhile True:\n\t\t\t\tresult = q.get()\n\t\t\t\tev = ServerSentEvent(str(result))\n\t\t\t\tyield ev.encode()\n\t\texcept GeneratorExit:\n\t\t\tSUBSCRIPTIONS.remove(q)\n\t\n\treturn Response(gen(), mimetype='text/event-stream')","title":""},{"docid":"8cb984db70bfce5674829ca97345f922","score":"0.5648998","text":"def _listen_clients(self) -> None:\n\n # TODO: If receive a message call self.set(data.key, data.value, data.expiration_time, False, client_address)\n pass","title":""},{"docid":"8076531e916709fde6f744f89767196d","score":"0.56457824","text":"def _make_socket(self):\n import dallinger.db\n from dallinger.experiment_server.sockets import chat_backend\n\n self.redis = dallinger.db.redis_conn\n chat_backend.subscribe(self, 'griduniverse')\n\n self.publish({\n 'type': 'connect',\n 'player_id': self.participant_id\n })","title":""},{"docid":"cc3406805529266c606f02fad0f6d1b9","score":"0.5639633","text":"def onConnect(self, client):\n pass","title":""},{"docid":"3b72bf8d5365aead55c4004de4e1e8be","score":"0.56317765","text":"def on_subscribe(client, userdata, mid, granted_qos, properties=None):\n self.onsub_lock.acquire()\n if self.debug_flg:\n print(f\"subscribe accepted with QOS: {granted_qos} with mid: {mid}\")\n self.onsub_lock.release()","title":""},{"docid":"a1f651e6f780ec893cbe7bab0c3770b6","score":"0.56171685","text":"async def register(websocket,path):\n ws_clients.add(websocket)","title":""},{"docid":"ae1368c7698822c6b24ddcbe6ff12129","score":"0.5611535","text":"def _connect_to_chat(self):\n if self.chat_details is None:\n raise NotAuthenticatedError(\"You must first log in to Mixer!\")\n\n self.websocket = Socket(self.chat_details[\"endpoints\"])\n self.websocket.on(\"opened\", self._send_auth_packet)\n self.websocket.on(\"message\", lambda msg: self.emit(\"message\", msg))","title":""},{"docid":"b686b490ca5468aa198061b57bf5dd2f","score":"0.55902344","text":"def connect_to_websocket(self):\n # conn = yield websocket_connect(\"wss://ws-feed-public.sandbox.gdax.com\")\n conn = yield websocket_connect(\"wss://ws-feed.gdax.com\")\n req = {\n \"type\": \"subscribe\",\n \"product_ids\": [\n \"BTC-USD\"\n ],\n \"channels\": [\n \"level2\"\n ]\n }\n conn.write_message(json.dumps(req))\n while True:\n msg = yield conn.read_message()\n if msg:\n response = json.loads(msg)\n # print(response['type'])\n if response['type'] == 'l2update':\n # # Perform update in database\n # and emit update to client\n self.perform_update(response)\n\n elif response['type'] == 'snapshot':\n # Store snapshot in database\n for bid_data in response['bids']:\n # Intialize a new row of data\n item = self.add_new_gdax_item(\"bid\", bid_data[0], bid_data[1])\n self.session.add(item)\n\n for ask_data in response['asks']:\n item = self.add_new_gdax_item(\"ask\", ask_data[0], ask_data[1])\n self.session.add(item)\n\n self.session.commit()\n print(\"GDAX Snapshot Received\")\n # print(response)\n self.snapshot_received = True\n elif response['type'] == 'error':\n print(response)\n else:\n break","title":""},{"docid":"d850528e0d772d5adc3eca6c2ddf37da","score":"0.557667","text":"def handle(self):\n data, sock = self.request\n print ('HOST: UDPEchoClientHandler: Rx: \\n%s\\n' % data)\n sock.sendto(data, self.client_address)","title":""},{"docid":"a1130a4aed0cad229b79e289337b3857","score":"0.55707645","text":"def subscribe(self, topic, func=None, fargs=None):\r\n\r\n if (self.__mqtt is None) or (not self.__connected):\r\n raise MQTTClientError('connect to the server before calling subscribe()')\r\n\r\n if self.__listening:\r\n LOG.warning('already listened. Do nothing.')\r\n return\r\n\r\n self.__func = func\r\n self.__args = fargs if fargs else ()\r\n self.__mqtt.on_message = self.__on_message\r\n self.__sub_topic = self.__topic.topic(self.__uid, topic)\r\n self.__subscribe(self.__sub_topic)\r\n self.__listening = True","title":""},{"docid":"6543728a404ad89ec24620c8b88a342e","score":"0.55667305","text":"async def server_on_message(self, websocket, path):\n # Register websocket\n self.connected_clients.add(websocket)\n try:\n await self.on_connect(websocket)\n\n while True:\n try:\n data = await websocket.recv()\n message = json.loads(data)\n\n await self.handle_message_exchange(message, websocket)\n except Exception as e:\n logger.error(e)\n if websocket in self.connected_clients:\n self.connected_clients.remove(websocket)\n if websocket in self.raft_nodes:\n self.raft_nodes.remove(websocket)\n if websocket in self.control_connection:\n self.control_connection.remove(websocket)\n if websocket in self.node_connections:\n self.node_connections.remove(websocket)\n break\n finally:\n if websocket in self.connected_clients:\n self.connected_clients.remove(websocket)","title":""},{"docid":"8146967e1a7c78cf3a1c87216e797ab7","score":"0.5559368","text":"async def subscribe(self) -> None:\n try:\n self.ws = await websockets.connect(self.ws_endpoint)\n\n if self.request is not None:\n LOGGER.info('Requesting Book: {}'.format(self.request))\n await self.ws.send(self.request)\n LOGGER.info('BOOK %s: %s subscription request sent.' %\n (self.exchange.upper(), self.sym))\n\n if self.trades_request is not None:\n LOGGER.info('Requesting Trades: {}'.format(self.trades_request))\n await self.ws.send(self.trades_request)\n LOGGER.info('TRADES %s: %s subscription request sent.' %\n (self.exchange.upper(), self.sym))\n\n self.last_subscribe_time = dt.now(tz=TIMEZONE)\n\n # Add incoming messages to a queue, which is consumed and processed\n # in the run() method.\n while True:\n self.queue.put(json.loads(await self.ws.recv()))\n\n except websockets.ConnectionClosed as exception:\n LOGGER.warn('%s: subscription exception %s' % (self.exchange, exception))\n self.retry_counter += 1\n elapsed = (dt.now(tz=TIMEZONE) - self.last_subscribe_time).seconds\n\n if elapsed < 10:\n sleep_time = max(10 - elapsed, 1)\n time.sleep(sleep_time)\n LOGGER.info('%s - %s is sleeping %i seconds...' %\n (self.exchange, self.sym, sleep_time))\n\n if self.retry_counter < self.max_retries:\n LOGGER.info('%s: Retrying to connect... attempted #%i' %\n (self.exchange, self.retry_counter))\n await self.subscribe() # recursion\n else:\n LOGGER.warn('%s: %s Ran out of reconnection attempts. '\n 'Have already tried %i times.' %\n (self.exchange, self.sym, self.retry_counter))","title":""},{"docid":"ffeb58fce1952b02d2c48183b669e68c","score":"0.55575657","text":"def on_connect(self):\n print(\"You're connected to the streaming server.\")","title":""},{"docid":"41f00dbd1874994712749593498a41aa","score":"0.55537254","text":"def subscribeTo(self, aTopic, aFunctionBindTo):\n if(self.mAlreadySubscribed == False):\n self.mFunction = aFunctionBindTo\n self.mTopic = aTopic\n \n tMESSAGE = \"subscribe, \" + aTopic\n tMessageInBytes = bytes(tMESSAGE, 'UTF-8')\n self.mSock.sendto(tMessageInBytes, (self.mSubscriberServiceIPV4, self.mSubscriberServicePort))\n self.mAlreadySubscribed = True\n self.mSock.close()\n self.mListeningSocket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n self.mListeningSocket.bind(('', self.mListeningPort))\n #wont keep the thread up if main thread die\n self.mThread.daemon = True\n try:\n self.mThread.start()\n except: #catch all errors\n pass\n return True\n else:\n return False","title":""},{"docid":"7f9cbcf36f5f663c8c7b2e9349708f55","score":"0.55497867","text":"def subscribe(self, topic, callback):\n if topic not in self.callbacks:\n self.callbacks[topic] = [callback]\n else:\n if callback not in self.callbacks[topic]:\n self.callbacks[topic].append(callback)\n\n def callback_handler(client, userdata, message):\n # log = f\"{message.payload.decode()} {message.topic}\"\n if message.topic in self.callbacks:\n for callbk in self.callbacks[message.topic]:\n callbk(message.payload)\n\n self.connection.on_message = callback_handler\n return self.connection.subscribe(topic)","title":""},{"docid":"15cbb8a1f7bfcaeb39ff36c1ab694b7d","score":"0.5531924","text":"async def _start(self) -> None:\n await self.send({\"type\": \"websocket.connect\"})\n await self.receive()","title":""},{"docid":"b770eb5036f3c849bc84cdd54f3aad5c","score":"0.55316156","text":"def subscribe(self, device: str, control: str):\n topic = generate_topic(device, control)\n\n result_code: int\n message_id: int | None\n result_code, message_id = self.__client.subscribe(topic)\n self.__raise_error_if_any(result_code)","title":""},{"docid":"3101dc1fc0283932611f191e2582c8c7","score":"0.55312043","text":"def on_startup():\n mqtt.subscribe('client/speech/speak')","title":""},{"docid":"c282998f8bc443b180a92881e5a01d3c","score":"0.55235696","text":"def on_connect(client, userdata, flags, rc):\n main_logger.info(\"attempting on_connect\")\n if rc==0:\n mqtt_client.connected = True\n for topic in MQTT_SUB_TOPIC:\n client.subscribe(topic)\n main_logger.info(\"Subscribed to: {0}\".format(topic))\n main_logger.info(\"Successful Connection: {0}\".format(str(rc)))\n else:\n mqtt_client.failed_connection = True # If rc != 0 then failed to connect. Set flag to stop mqtt loop\n main_logger.info(\"Unsuccessful Connection - Code {0}\".format(str(rc)))","title":""},{"docid":"f56e3251fc326b3c247372eff566ceca","score":"0.5515813","text":"def listen(self, name, handler):\n self.client_endpoint.connect(...)\n self.handler = handler","title":""},{"docid":"2f11716bf717a126caea8b86537d9376","score":"0.5501132","text":"def subscribe(self, subscriber):\n self._subscribers[subscriber.uid] = subscriber","title":""},{"docid":"db596cfaddc21f03e25725b68cbf68cc","score":"0.54992354","text":"def listen(self):\n logging.info(\n \"ThreadedServer.listen(): listening for new client connection...\")\n self.sock.listen(5)\n\n while True:\n client, address = self.sock.accept()\n self.client = client\n new_player = Player()\n # client.settimeout(10)\n\n # if connected, make threads\n if self.verify_client_conn(client):\n logging.info(\n \"================================================================\\nThreadedServer.listen(): Connected to: {}:{}\\n==========================================================================\\n\".format(address[0], str(address[1])))\n t1 = threading.Thread(target=self.speak_to_client, name=\"send_\" + self.thread_name, args=(\n client, address, new_player))\n t2 = threading.Thread(target=self.listen_to_client, name=\"recv_\" + self.thread_name, args=(\n client, address, new_player))\n\n t1.start()\n t2.start()\n\n logging.info(\n f\"ThreadedServer.listen() Show active threads \\n{threading.enumerate()}\\n\")","title":""},{"docid":"4e473263cf14a22328bb34c3e230a0b1","score":"0.54958236","text":"def listening(self):\r\n while self.is_connected:\r\n msg_receiv = self.client_socket.recv(1024)\r\n if len(msg_receiv) > 0:\r\n msg_receiv = pickle.loads(msg_receiv)\r\n print \"j'ai bien recu \"+msg_receiv","title":""},{"docid":"69e6a05ad8e8814ca1b44ef4b808941b","score":"0.54850984","text":"def websocket(self):\n return self.websocket_client.wss","title":""},{"docid":"109c4648ec05697a54b2e612a53cdb50","score":"0.5478585","text":"def subscribe(self):\r\n for channel in self.bus.listeners:\r\n # Subscribe self.start, self.exit, etc. if present.\r\n method = getattr(self, channel, None)\r\n if method is not None:\r\n self.bus.subscribe(channel, method)","title":""},{"docid":"2ce1b57fc85b1882151f4f140c324245","score":"0.54769194","text":"async def _subscribe_channels(self, ws: WSAssistant):\n try:\n payload = {\n \"id\": str(get_tracking_nonce()),\n \"type\": \"subscribe\",\n \"topic\": f\"/market/candles:{self._ex_trading_pair}_{CONSTANTS.INTERVALS[self.interval]}\",\n \"privateChannel\": False,\n \"response\": False,\n }\n subscribe_candles_request: WSJSONRequest = WSJSONRequest(payload=payload)\n\n await ws.send(subscribe_candles_request)\n self.logger().info(\"Subscribed to public klines...\")\n except asyncio.CancelledError:\n raise\n except Exception:\n self.logger().error(\n \"Unexpected error occurred subscribing to public klines...\",\n exc_info=True\n )\n raise","title":""}],"string":"[\n {\n \"docid\": \"109c83d9994e8d99aa6478bff90b40e6\",\n \"score\": \"0.70212555\",\n \"text\": \"def subscribe(self):\\n try:\\n self.client.subscribe()\\n except Exception as e:\\n logger.error(\\\"Unknown error: {}\\\".format(e))\\n raise Exception\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30af9950d2e712e3525c37be2730b9d6\",\n \"score\": \"0.67332\",\n \"text\": \"def on_connect(client, userdata, flags, rc):\\n print(\\\"Connected with result code %s, subscribing to topic: %s\\\" % (str(rc), userdata[\\\"topic\\\"]))\\n client.subscribe(userdata[\\\"topic\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1cb1f02a5e9dd4e21452f5f7b5134ec8\",\n \"score\": \"0.6669246\",\n \"text\": \"def on_connect(client, userdata, flags, rc):\\r\\n print(\\\"Connected with result code \\\"+str(rc))\\r\\n \\r\\n # TOPICS I SUBCRIBED TO\\r\\n \\r\\n client.subscribe(\\\"Francis/cpupct\\\") # (MY PUBLISHER)\\r\\n\\r\\n \\r\\n client.subscribe(\\\"Damare/cpupct\\\") # (CLASSMATE'S PUBLISHERS)\\r\\n client.subscribe(\\\"Miller/cpupct\\\")\\r\\n client.subscribe(\\\"Markham/cpupct\\\")\\r\\n client.subscribe(\\\"Bingham/cpupct\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62a6bc5e285e4fcdc979ea01b8fd1651\",\n \"score\": \"0.6598101\",\n \"text\": \"async def __init_connection__(self): \\n\\t\\tself.session = aiohttp.ClientSession()\\n\\t\\tself.connection = self.session.ws_connect(\\n\\t\\t\\t\\turl=self.WSS_FEED, heartbeat=self.WSS_TIMEOUT)\\n\\n\\t\\tself.websocket = await self.connection.__aenter__()\\n\\t\\tawait self.websocket.send_json(self._subscribe)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6b0dc8b8fa7163b13277619e79b2edc\",\n \"score\": \"0.6485489\",\n \"text\": \"def on_run(self, _):\\n logging.info('Websocket connected.')\\n self.get_and_subscribe_devices()\\n self.get_events()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6b5f89d88dbfe68a0490a5ab158841c\",\n \"score\": \"0.64582074\",\n \"text\": \"def on_connect(\\n self,\\n client: mqtt.Client,\\n userdata: Any,\\n rc: int,\\n properties: mqtt.Properties = None,\\n ) -> None:\\n # subscribe to all topics\\n client.subscribe(\\\"#\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ea6571cbf873c7a6a8e829256bb62a7\",\n \"score\": \"0.64488345\",\n \"text\": \"def subscribe_server(self):\\n\\t\\tsubscribe_data = json_subscribe(MATRICULES, self.__client.port, self.__name)\\n\\t\\tresponse = json_decode(self.__client.send_to_server(subscribe_data))\\n\\n\\t\\ttry:\\n\\t\\t\\tif response[\\\"response\\\"].upper() == \\\"OK\\\":\\n\\t\\t\\t\\tlogging.info(\\\"Registered to the server\\\")\\n\\t\\t\\telse:\\n\\t\\t\\t\\tlogging.critical(f\\\"Error while subscribing : {response['error']}\\\")\\n\\t\\texcept Exception as e:\\n\\t\\t\\tlogging.critical(f\\\"Error on subscribing to the server : {e}\\\")\\n\\t\\t\\texit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1773ee9752c0b4475e44f7118f584b18\",\n \"score\": \"0.63788486\",\n \"text\": \"def on_connect(client, userdata, flags, rc):\\n print('Connected with result code ' + str(rc))\\n client.subscribe(MQTT_TOPIC)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1773ee9752c0b4475e44f7118f584b18\",\n \"score\": \"0.63788486\",\n \"text\": \"def on_connect(client, userdata, flags, rc):\\n print('Connected with result code ' + str(rc))\\n client.subscribe(MQTT_TOPIC)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de83f252f24f417d7cd393a046a1cfef\",\n \"score\": \"0.632665\",\n \"text\": \"def on_connect(client, userdata, rc):\\n print('Connected with result code ' + str(rc))\\n # Subscribing in on_connect() means that if we lose the connection and\\n # reconnect then subscriptions will be renewed.\\n client.subscribe(MQTT_TOPIC, 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34ecf349b262dc88037422352aa62c1d\",\n \"score\": \"0.63254875\",\n \"text\": \"def on_connect(client, userdata, flags, rc):\\n\\tprint('Connected with result code ' + str(rc))\\n\\tclient.subscribe(MQTT_TOPIC)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"208a7ef91d240bd0867a02a71e4a044c\",\n \"score\": \"0.6249386\",\n \"text\": \"def on_connect(self, client, userdata, flags, rc):\\n\\n if rc == 0:\\n self.connected = True\\n logger.info(self.parse_return_code(0))\\n self.client.subscribe(self.appconfig.topic)\\n else:\\n logger.error(f\\\"{self.parse_return_code(rc)}\\\")\\n self.connected = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96ca8fd2138803d9ab75dc324644bc12\",\n \"score\": \"0.62461495\",\n \"text\": \"def on_subscribe(self):\\n\\n def subscribe_handler(handler: Callable):\\n log_info.info(\\\"on_subscribe handler accepted\\\")\\n self.client.on_subscribe = handler\\n return handler\\n\\n return subscribe_handler\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ca443108e3022d9d58f794f0ced2b31\",\n \"score\": \"0.6235847\",\n \"text\": \"def subscribe(self, callback):\\n # Avoid duplicate subscription\\n if self._subscribe_id:\\n return\\n\\n self._subscribe_id = 'subscribe:%s:%d' % (\\n self.name, self.rosbridge.id_counter)\\n\\n self.rosbridge.on(self.name, callback)\\n self.rosbridge.send_on_ready(Message({\\n 'op': 'subscribe',\\n 'id': self._subscribe_id,\\n 'type': self.message_type,\\n 'topic': self.name,\\n 'compression': self.compression,\\n 'throttle_rate': self.throttle_rate,\\n 'queue_length': self.queue_length\\n }))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30e9c2e4ccabe60518676a63aff16820\",\n \"score\": \"0.6223954\",\n \"text\": \"def subscribe(self, publisher):\\n for line in publisher:\\n self.data_socket.connect(\\\"tcp://\\\"+str(line[0])+\\\":\\\"+str(line[1]))\\n self.subscribed_ports.append(line[1])\\n self.data_socket.setsockopt(zmq.SUBSCRIBE, '')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2dee9ed9400f9c6041395dd1afb0bbef\",\n \"score\": \"0.6219262\",\n \"text\": \"async def hass_subscribe_events(websocket):\\n # Subscribe to events.\\n LOGGER.info(\\\"Subscribing to events...\\\")\\n await websocket.send(json.dumps(dict(\\n id=1,\\n type=\\\"subscribe_events\\\",\\n event_type=\\\"state_changed\\\"\\n )))\\n response = await websocket.recv()\\n LOGGER.info(f\\\"Subscription result: {response}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4cb4db2cd2f949cb3525fe17bfeb4b15\",\n \"score\": \"0.61780894\",\n \"text\": \"async def listen(self, websocket, path):\\n\\n # send newly connected client initial timestamp\\n await self.notify(websocket, 0)\\n\\n try:\\n # incoming message event\\n async for message in websocket:\\n\\n # decode incoming message into an associative array\\n data = json.loads(message)\\n\\n # notify client with event for message with count \\\"c\\\"\\n await self.notify(websocket, data[\\\"c\\\"])\\n except asyncio.IncompleteReadError as e:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a9deba0e0758b1168148eea8318f7e6\",\n \"score\": \"0.61297876\",\n \"text\": \"def subscribe(self, topic, url):\\n # note: do NOT encode the URL, this is not decoded on the erlang side!\\n # (only strings are allowed anyway)\\n # url = self._conn.encode_value(url)\\n result = self._conn.call('subscribe', [topic, url])\\n self._conn.process_result_subscribe(result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"428dc3f6c80fa87868e02c64a0482b24\",\n \"score\": \"0.6125694\",\n \"text\": \"def subscribe(listener):\\n _thread_local.communication_manager.subscribe(listener) # type: ignore[union-attr]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74056d62b32d840d3379628d4cb168a8\",\n \"score\": \"0.6108331\",\n \"text\": \"def subscribe(self):\\n self.bus.subscribe('start', self.start)\\n self.bus.subscribe('stop', self.stop)\\n if self.subscribe_event is not None:\\n self.bus.subscribe(self.subscribe_event, self.receive_input)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69c034eb6af951481f78d8a153ac4de5\",\n \"score\": \"0.6104722\",\n \"text\": \"def subscribe(self, destination, extra_headers=None):\\n raise NotImplementedError(\\\"%s client does not implement SUBSCRIBE\\\" % (self.__class__,))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69c034eb6af951481f78d8a153ac4de5\",\n \"score\": \"0.6104722\",\n \"text\": \"def subscribe(self, destination, extra_headers=None):\\n raise NotImplementedError(\\\"%s client does not implement SUBSCRIBE\\\" % (self.__class__,))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2283ff5fa92e53216cc4f40a9a26c92\",\n \"score\": \"0.60880274\",\n \"text\": \"def subscribe(self, subscribe):\\n\\n self._subscribe = subscribe\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a165097abb33ee878cb29395443ba874\",\n \"score\": \"0.6080283\",\n \"text\": \"def run(self):\\n self.socket = self.context.socket(zmq.SUB)\\n self.socket.setsockopt(zmq.SUBSCRIBE,'')\\n self.socket.setsockopt(zmq.IDENTITY, self.session.session)\\n self.socket.connect('tcp://%s:%i' % self.address)\\n self.iostate = POLLIN|POLLERR\\n self.ioloop.add_handler(self.socket, self._handle_events, \\n self.iostate)\\n self.ioloop.start()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"044e96b0f6504480e85d0002d636d8c7\",\n \"score\": \"0.6080151\",\n \"text\": \"def send_subscription(self):\\n\\n print \\\"[i] Sending a Subscribe request\\\"\\n\\n # Generate the payload\\n payload = SUBSCRIBE % ('http://' + self.WAN_IP, self.TOPIC)\\n\\n # Send the request\\n self.send_request(payload)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1fbcbacf2bad343fc3e64f67308aa3d\",\n \"score\": \"0.60752404\",\n \"text\": \"def on_connect(client, userdata, flags, return_code):\\n\\n print('Connected with result code {}'.format(return_code))\\n\\n client.subscribe(TTN_SUBSCRIPTIONS)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8730922ab3833b142c6b52138e508107\",\n \"score\": \"0.6073351\",\n \"text\": \"def process_connection():\\n logging.debug(\\\"Processing new connection\\\")\\n mqttc.subscribe(MQTT_TOPIC, 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a09b9f4cfe40ed455a6d9791186b5b54\",\n \"score\": \"0.60667396\",\n \"text\": \"def on_subscribe(client, userdata, mid, granted_qos):\\n logging.debug(\\\"Subscribe with mid \\\" + str(mid) + \\\" received.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"604fe2fcf17c64f470c26bca917eb12a\",\n \"score\": \"0.6056498\",\n \"text\": \"def listen(self):\\n\\n print('listening to kafka')\\n\\n self.source.subscribe(Emitter(self.ws))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfcf7ea3905dd36a1e61e6d079cd9c5c\",\n \"score\": \"0.6054609\",\n \"text\": \"async def accept(self, subprotocol=None):\\n await super().send({\\\"type\\\": \\\"websocket.accept\\\", \\\"subprotocol\\\": subprotocol})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c03ce8df3a0931cba47a033064b70a1b\",\n \"score\": \"0.60300183\",\n \"text\": \"def on_connect(self, client, userdata, flags, rc):\\n if id(client) == id(self.roomba):\\n self.debug(\\\"Connected to roomba mqtt broker with result code \\\"+str(rc))\\n client.subscribe(\\\"#\\\")\\n\\n elif id(client) == id(self.upstream):\\n self.debug(\\\"Connected to upstream mqtt broker with result code \\\"+str(rc))\\n client.subscribe(self.settings[\\\"upstream\\\"][\\\"subscribe\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7798393e531003822fc25fea1801866\",\n \"score\": \"0.6025621\",\n \"text\": \"def subscribe(self, publisher):\\n for line in publisher:\\n self.create_info_log_entry(\\\"LogWriter subscribes to: \\\"+str(line[0])+\\\":\\\"+str(line[1]))\\n self.socket.connect(\\\"tcp://\\\"+str(line[0])+\\\":\\\"+str(line[1]))\\n self.subscribed_ports.append(line[1])\\n self.socket.setsockopt(zmq.SUBSCRIBE, '') # remove the subscription filter\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5fd8ca6d8f7652e667e4ae3c65125329\",\n \"score\": \"0.60156804\",\n \"text\": \"def on_mqtt_connect(self, client: Client, userdata, flags, rc):\\n logging.info(f\\\"Connected with result code {rc}\\\")\\n # Subscribing in on_connect() means that if we lose the connection and\\n # reconnect then subscriptions will be renewed.\\n for t in self.args.topic:\\n logging.info(f\\\"Subscribe to {t}\\\")\\n self.mqtt_client.subscribe(t)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"647db7120a3b62c29e476dd5ecff92ea\",\n \"score\": \"0.59803313\",\n \"text\": \"def on_connect(client, userdata, flags, rc):\\n logger.info(f\\\"Connected with result code {rc}.\\\")\\n # Subscribing in on_connect() means that if we lose the connection and reconnect then subscriptions will be renewed.\\n client.subscribe(\\\"owntracks/+/+\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6404ce39582b128ff7db687c3ecbdbb\",\n \"score\": \"0.5975095\",\n \"text\": \"def on_client_connect(self, client):\\n self.clients.append(client)\\n log.msg('Client connected')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81d4d7cf7a27d74bb259be07b0ce69ba\",\n \"score\": \"0.5970741\",\n \"text\": \"def on_subscribe(self, client, userdata, mid, granted_qos):\\n\\n self.subscribed = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8eca22d65772bb0fae58f229e5d2851\",\n \"score\": \"0.5969631\",\n \"text\": \"def subscribe(self, message):\\n try:\\n channel_id = self.channel_id(message)\\n self.server.connections.add_peer(channel_id, self, self.client_address)\\n self.is_subscribed = True\\n logger.debug('New peer {}:{} connected to channel: {}'.format(\\n self.client_address[0], self.client_address[1], channel_id\\n ))\\n except Exception as e:\\n logger.error('Failed to subscribe new peer {}:{} - {}'.format(\\n self.client_address[0], self.client_address[1], str(e)\\n ))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"363a8fa65e57fe4a084c237ce48483df\",\n \"score\": \"0.5913931\",\n \"text\": \"def _on_connect(client, userdata, flags, rc):\\n logging.info(f'Connected to broker, result code {str(rc)}')\\n\\n for topic in userdata.keys():\\n client.subscribe(topic)\\n logging.info(f'Subscribing to topic: {topic}')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d804f333d2e59ba836f2b80a93cc046\",\n \"score\": \"0.59032863\",\n \"text\": \"def do_subscribe(self, arg: str) -> None:\\n args = [arg]\\n libwampli.ready_uri(args)\\n\\n task = Task(\\\"subscribe\\\", args)\\n self._send_queue.put_nowait(task)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"321d23e95b7b77305725017b1ad0cc16\",\n \"score\": \"0.590251\",\n \"text\": \"def connect():\\n print('connection established')\\n # ClientApplication.sio.emit(event='on_client_message', \\n # data=data_pkg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b77237ee54ab3ba67bd5e2ee6e4d29ca\",\n \"score\": \"0.5901918\",\n \"text\": \"def subscriber(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6053ec4a1abeb4f6420ef05c189e8e19\",\n \"score\": \"0.5892358\",\n \"text\": \"def _connect(self) -> None:\\n super()._connect()\\n self.pubsub = self.client.pubsub()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d81b36044ba981b02c1e9be0bc69af91\",\n \"score\": \"0.5859421\",\n \"text\": \"def on_connect(self, client, userdata, flags, rc):\\n # srw2ho 20.04.2023: Threadnames umbiegen for debugging rausnehmen -> kann auch in Thread-Handler for MQTT Request zum Teil erledigt werden\\n # threading.current_thread().name = (\\n # \\\"client.py\\\" + \\\"_on_connect_\\\" + str(threading.get_ident())\\n # )\\n\\n # print(\\\"MQTT-Connected with result code: \\\" + str_)\\n\\n # resubscribe to all previously subscribed topics\\n # for topic, _ in self._subscriptions.items():\\n # self._client.subscribe(topic)\\n\\n # qos default = 0\\n try:\\n qos = 0\\n topictupels = [(key, qos)\\n for key, value in self._subscriptions.items()]\\n if len(topictupels) > 0:\\n self._client.subscribe(topictupels)\\n\\n self._isconnected = True\\n\\n str_ = self.getConnectMessageByReturnValue(rc)\\n logger.info(f\\\"MQTT-Connected with result code: {str_}\\\")\\n\\n if self._connectHandler != None:\\n self._connectHandler(client, userdata, flags, rc)\\n\\n except Exception as e:\\n logger.error(f\\\"MQTT-on_connect error result code: {e}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7f4167d3aea9b93e4071415aa1d5a11\",\n \"score\": \"0.58471465\",\n \"text\": \"def on_connect(connected_client, _, __, rc) -> None:\\n logger.info(\\\"Connected client with result code \\\" + str(rc))\\n # Subscribe in on_connect callback to automatically re-subscribe if the connection was lost\\n # Subscribe to all arduino hexnumber actions\\n # '+' means single level wildcard. '#' means multi level wildcard.\\n # See http://www.hivemq.com/blog/mqtt-essentials-part-5-mqtt-topics-best-practices\\n logger.debug(\\\"Subscribing to \\\" + str(constants.iRulezTopic) + \\\"/\\\" + constants.virtual_IO_board_name + \\\"/\\\" +\\n constants.actionTopic)\\n connected_client.subscribe(constants.iRulezTopic + \\\"/\\\" + constants.virtual_IO_board_name + \\\"/\\\" +\\n constants.actionTopic)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffc22ad28f7193b853d28b4ad6d12799\",\n \"score\": \"0.58453065\",\n \"text\": \"def run(self):\\n rospy.loginfo(\\\"Bridge accepted client: %s\\\", self.client_addr)\\n\\n try:\\n while True:\\n val, sig = self.dec.decode()\\n if not sig:\\n continue\\n rosname = id2msg(sig.name)\\n if val is not None: # Not as stupid as it looks.\\n if rosname == 'subscribe':\\n self._handle_subscribe(val)\\n elif rosname == 'publish':\\n self._handle_publish(val)\\n elif sig.name in sample_in_hooks: # TODO: Check reg.?\\n for conv in sample_in_hooks[sig.name]:\\n conv.put_sample(sig.name, val)\\n elif rosname in conf.IMPORTS: # TODO: out.\\n self._handle_topic(val, sig)\\n else:\\n self._handle_service(val, sig)\\n else:\\n rospy.loginfo(\\\"Client registered: '%s'\\\", sig.name)\\n except EOFError:\\n # Clean up socket and subscriptions on EOF.\\n self.client_socket.close()\\n for topic,sub in self.subs.iteritems():\\n sub.unregister()\\n # TODO: Clean conv.\\n for pt, conv in self.subscribed_conversions.iteritems():\\n conv.unregister_sample_subscriber(pt, self)\\n except socket.error as e:\\n rospy.logerr(\\\"Socket error %d: %s\\\", e.errno, e.strerror)\\n\\n rospy.loginfo(\\\"Client disconnected\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"933058659501a1cf766ddaa6ddecf13b\",\n \"score\": \"0.5836819\",\n \"text\": \"def subscribe(self, feed):\\n self.client.subscribe(feed[0], self.youtube_cat_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4d555a9be9e7853198ab6ea23cfd4b2\",\n \"score\": \"0.58281356\",\n \"text\": \"def subscribe(self, board, channel):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"231613a6a5cca367ea23dd0a7069a7be\",\n \"score\": \"0.5827039\",\n \"text\": \"def subscribe_rpc(self):\\n self.subscribe(self._topic_rpc, qos=1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49af65cd12db79cc94a800a9e6491cc9\",\n \"score\": \"0.58243436\",\n \"text\": \"def subscribe(self):\\n return Subscribe(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cdb38fb58095d83ff05e91a14acdffdb\",\n \"score\": \"0.58231837\",\n \"text\": \"def on_open(self, info):\\n\\n LOGGER.info('[ChatWebsocketHandler] Websocket connecition opened: %s ' % self)\\n\\n # Initialize new pika rabbitmq client object for this websocket.\\n self.rabbit_client = RabbitMqClient()\\n # Assign websocket object to a Pika client object attribute.\\n websocketParticipants.add(self)\\n self.rabbit_client.websocket = self\\n # connect to rabbitmq\\n self.rabbit_client.start()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9414d002188eeda2419ac3d6b7666c50\",\n \"score\": \"0.5794444\",\n \"text\": \"def subscribe(self, observer: Callable):\\n self.subscriber = observer\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06318552a49798a9cecbbe695db29006\",\n \"score\": \"0.578933\",\n \"text\": \"def ReceiveSocketMessage(self):\\n while(True):\\n conn,addr = self.socketObj.accept()\\n self.clients[addr] = c\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca92506f33ff1c0bf39fc75b3545b935\",\n \"score\": \"0.57771236\",\n \"text\": \"def subscribe(self):\\n return self.reddit_session._subscribe(self.name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb1a9bbe722d57f7d2e2ec2c64b59cd8\",\n \"score\": \"0.57768047\",\n \"text\": \"async def ws_rpc_subscribe(self, payload):\\n await self.channels.register(\\n payload[\\\"channel\\\"], payload.get(\\\"event\\\"), self.channel_callback\\n )\\n return dict(subscribed=self.channels.get_subscribed(self.channel_callback))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0979b72a46cf7220e9c3febd6f3e1de\",\n \"score\": \"0.57747185\",\n \"text\": \"def _on_subscribe_mqtt(\\n self, client: MQTT, user_data: str, topic: int, qos: int\\n ) -> None:\\n if self.on_subscribe is not None:\\n self.on_subscribe(self, user_data, topic, qos)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29af8ef24fd7803d78143a7057d45605\",\n \"score\": \"0.5749867\",\n \"text\": \"def subscribe(self, type='any'):\\n return self.send_command(\\n Command(\\n 'clientnotifyregister',\\n schandlerid=0,\\n event=type\\n )\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"707137a02b24c0d11e8299d4c4c5cd20\",\n \"score\": \"0.57385665\",\n \"text\": \"def handshake(self):\\n message = self.rp.raw.decode('utf-8', errors='strict').strip()\\n upgrade = re.search('\\\\nupgrade[\\\\s]*:[\\\\s]*websocket', message.lower())\\n\\n if not upgrade:\\n self.keep_alive = False\\n self.handle_post()\\n return\\n\\n key = re.search('\\\\nsec-websocket-key[\\\\s]*:[\\\\s]*(.*)\\\\r\\\\n', message, re.I)\\n if key:\\n key = key.group(1)\\n else:\\n logger.warning(\\\"Client tried to connect but was missing a key\\\")\\n self.keep_alive = False\\n return\\n\\n response = Adapter.handshake_response(key)\\n self.handshake_done = self.request.send(response.encode('utf-8'))\\n self.valid_client = True\\n\\n # Add new client\\n self.subscribe(message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f55b02acd05f5348c3aaa612d2a59217\",\n \"score\": \"0.5734122\",\n \"text\": \"def on_subscribe(mqtt_client, userdata, mid, granted_qos):\\n logging.debug(\\\"Begin on_subscribe\\\")\\n\\n #TODO Add implementation specific logic here \\n\\n\\n logging.debug(\\\"End on_subscribe\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b6c715a70da27099c956a35b02c4911\",\n \"score\": \"0.57303196\",\n \"text\": \"def on_connect():\\n configuration = ioant.get_configuration()\\n # There is now a connection\\n topic = ioant.get_topic_structure()\\n topic['top'] = \\\"live\\\"\\n topic['global'] = configuration['ioant']['mqtt']['global']\\n topic['local'] = configuration['ioant']['mqtt']['local']\\n topic['client_id'] = configuration['ioant']['mqtt']['client_id']\\n ioant.subscribe(topic)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"923552b333b506bf79c04f8fd0c089f9\",\n \"score\": \"0.5729532\",\n \"text\": \"def handleSubscribe( self, inEvent ):\\n sd = inEvent.getPayload()\\n# sd = parseSubscribeData( inEvent.getPayload() )\\n\\n _log.debug( 'handleSubscribe %s' % ( sd ) )\\n\\n \\\"\\\"\\\"\\n When subscriptions are received they could be wild carded by providing blank eventType or eventSource\\n so we need to check for these\\n \\\"\\\"\\\"\\n if sd and (sd[0] > 0):\\n et = sd[1]\\n es = sd[2]\\n if (et == \\\"http://id.webbrick.co.uk/events/config/get\\\") or (et == \\\"\\\") or (et == None):\\n self.doPublish(es)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"481d4fea3e5d9c492189a4a6171d715d\",\n \"score\": \"0.572783\",\n \"text\": \"async def run_client():\\n async with websockets.connect(WEBSOCKET_SERVER) as websocket:\\n await register_host_id(websocket)\\n await response_consumer(websocket)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb5d2686edf72f76037f042bd326f31f\",\n \"score\": \"0.5723234\",\n \"text\": \"def subscribe(self, callback_on_message, from_hub=None):\\n self.callback = callback_on_message\\n self.exchange_name = utils.make_exchange_name(self._namespace, self.EXCHANGE_TYPE, extra=from_hub)\\n connection = self._connect()\\n return connection.ioloop\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a56f0eb39aa0a0cd37f9fe9bb589748\",\n \"score\": \"0.57232267\",\n \"text\": \"def recieve_user_connection(self): # 20\\n \\n # 這是ptt的sample code，但要處理2個clients好像要別的方法，待修改\\n with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as l_s:\\n l_s.bind((self.host, self.port))\\n l_s.listen()\\n c_s, addr = l_s.accept()\\n with c_s:\\n print(\\\"Connected\\\", addr)\\n while True:\\n data = c_s.recv(1024)\\n if not data:\\n break\\n c_s.sendall(data)\\n # 待修改結束\\n\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f5beb188c4b26919cf9b43206793c2a\",\n \"score\": \"0.5701495\",\n \"text\": \"def subscribeReceived(self, presence):\\n log.debug(\\\"Auto-subscribing to %s\\\", presence.sender.userhost())\\n self.subscribed(presence.sender)\\n self.subscribe(presence.sender.userhostJID())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b7f804cf63187e53da43b04928e5ddd\",\n \"score\": \"0.5682295\",\n \"text\": \"def on_subscribe(client, user_data, mid, granted_qos):\\n print(\\\"Subscribed: \\\" + str(mid) + \\\" \\\" + str(granted_qos))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"474b1f27522d4eb3d65ee983c742a460\",\n \"score\": \"0.5674448\",\n \"text\": \"def on_message(self, message):\\n\\n LOGGER.info('[ChatWebsocketHandler] message received on Websocket: %s ' % self)\\n\\n res = tornado.escape.json_decode(message)\\n routing_key = res['routing_key']\\n msg = res['msg']\\n stage = msg['stage']\\n\\n if stage == 'start':\\n LOGGER.info('[ChatWebsocketHandler] Message Stage : START')\\n\\n name = msg['name']\\n # assign name to rabbit client\\n self.rabbit_client._person = name\\n # assign clientid to rabbit client\\n self.rabbit_client._clientid = self.genid()\\n # add no. of current participants/websocket connections\\n self.rabbit_client._participants = len(websocketParticipants)\\n msg['participants'] = len(websocketParticipants)\\n\\n msg['clientid'] = self.rabbit_client._clientid\\n\\n LOGGER.info('[ChatWebsocketHandler] Publishing the received message to RabbitMQ')\\n\\n self.rabbit_client.publish(msg, routing_key)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"208be905cad6b3f83a5669c1f26b50f2\",\n \"score\": \"0.56721705\",\n \"text\": \"def websocket_handler(request):\\n ws = web.WebSocketResponse()\\n yield from ws.prepare(request)\\n\\n token = TOKEN(request)\\n identity = yield from register_ws(ws, token)\\n\\n print('cookies', request.cookies)\\n\\n while True:\\n try:\\n msg = yield from ws.receive()\\n\\n if msg.tp == aiohttp.MsgType.text:\\n topics = json.loads(msg.data)['topics']\\n subscribe_to_topics(identity, ws, topics)\\n\\n elif msg.tp == aiohttp.MsgType.close:\\n print('websocket connection closed')\\n unregister_ws(identity, ws)\\n break\\n elif msg.tp == aiohttp.MsgType.error:\\n print('ws connection closed with exception %s',\\n aio.ws.exception())\\n unregister_ws(identity, ws)\\n break\\n\\n except RuntimeError:\\n # clients.remove(ws)\\n print('client disconnected')\\n break\\n\\n return ws\",\n \"title\": \"\"\n },\n {\n \"docid\": \"124fbdb429162a7ccf624ba8f315bd6a\",\n \"score\": \"0.5670587\",\n \"text\": \"async def connect(self):\\n self.websocket = await self.ws_connect(self.URL)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0af300979cba8e47820d9e57d6744ef\",\n \"score\": \"0.5667329\",\n \"text\": \"def connectionMade(self):\\n self.session = {}\\n self.session[\\\"host\\\"] = self.transport.getHost().host\\n self.buffer = \\\"\\\"\\n Broadcast.get().addClient(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cec9fd0388a9b83a14c51f38982b31fe\",\n \"score\": \"0.5665399\",\n \"text\": \"def open(self):\\n self.id = self.get_argument(\\\"Id\\\")\\n self.stream.set_nodelay(True)\\n clients[self.id] = {\\\"id\\\": self.id, \\\"object\\\": self}\\n print(\\\"WebSocket opened\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cec9fd0388a9b83a14c51f38982b31fe\",\n \"score\": \"0.5665399\",\n \"text\": \"def open(self):\\n self.id = self.get_argument(\\\"Id\\\")\\n self.stream.set_nodelay(True)\\n clients[self.id] = {\\\"id\\\": self.id, \\\"object\\\": self}\\n print(\\\"WebSocket opened\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b199a24f89224737ce6c24154afe160f\",\n \"score\": \"0.566445\",\n \"text\": \"def run(self):\\n # important to start the websocket server in run as opposed to init,\\n # otherwise we'll start it in the main thread and block other requests\\n # from coming in\\n self._server = WebsocketServer(self.websocket_port, host='0.0.0.0')\\n self._server.set_fn_new_client(self.new_client)\\n self._server.run_forever()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8ff23322dfb8d704b0ec629ab47362a\",\n \"score\": \"0.56538796\",\n \"text\": \"def subscribe(topic, callback):\\n _global_message_broker.subscribe(topic, callback)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"673e148136fccfcf0487abc64442d7c3\",\n \"score\": \"0.5653784\",\n \"text\": \"def run(self):\\n data = self.s.recv(1024)\\n headers = self.parse_headers(data)\\n\\n if not 'Sec-WebSocket-Key' in headers:\\n raise ValueError('Missing header: Sec-WebSocket-Key')\\n\\n accept = base64.b64encode(hashlib.sha1(headers['Sec-WebSocket-Key'] + config.guid).digest())\\n\\n handshake = ('HTTP/1.1 101 Web Socket Protocol Handshake\\\\r\\\\n'\\n 'Upgrade: WebSocket\\\\r\\\\n'\\n 'Connection: Upgrade\\\\r\\\\n'\\n 'WebSocket-Origin: http://%s\\\\r\\\\n'\\n 'WebSocket-Location: ws://%s:%s/\\\\r\\\\n'\\n 'WebSocket-Protocol: sample\\\\r\\\\n'\\n 'Sec-WebSocket-Accept: %s\\\\r\\\\n\\\\r\\\\n'\\n % (config.http_host, config.socket_host, config.socket_port, accept)\\n )\\n self.s.send(handshake.encode())\\n\\n while True:\\n data = self.s.recv(1024)\\n\\n if not data: continue\\n\\n print('Data from', self.addr, ':', data)\\n self.onreceive(data)\\n\\n self.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0800383a5ffb0de48f24c277e3e36c1\",\n \"score\": \"0.56505376\",\n \"text\": \"def subscribe(job_id):\\n\\tdef gen():\\n\\t\\tq = Queue()\\n\\t\\tSUBSCRIPTIONS.append(q)\\n\\t\\ttry:\\n\\t\\t\\twhile True:\\n\\t\\t\\t\\tresult = q.get()\\n\\t\\t\\t\\tev = ServerSentEvent(str(result))\\n\\t\\t\\t\\tyield ev.encode()\\n\\t\\texcept GeneratorExit:\\n\\t\\t\\tSUBSCRIPTIONS.remove(q)\\n\\t\\n\\treturn Response(gen(), mimetype='text/event-stream')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cb984db70bfce5674829ca97345f922\",\n \"score\": \"0.5648998\",\n \"text\": \"def _listen_clients(self) -> None:\\n\\n # TODO: If receive a message call self.set(data.key, data.value, data.expiration_time, False, client_address)\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8076531e916709fde6f744f89767196d\",\n \"score\": \"0.56457824\",\n \"text\": \"def _make_socket(self):\\n import dallinger.db\\n from dallinger.experiment_server.sockets import chat_backend\\n\\n self.redis = dallinger.db.redis_conn\\n chat_backend.subscribe(self, 'griduniverse')\\n\\n self.publish({\\n 'type': 'connect',\\n 'player_id': self.participant_id\\n })\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc3406805529266c606f02fad0f6d1b9\",\n \"score\": \"0.5639633\",\n \"text\": \"def onConnect(self, client):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b72bf8d5365aead55c4004de4e1e8be\",\n \"score\": \"0.56317765\",\n \"text\": \"def on_subscribe(client, userdata, mid, granted_qos, properties=None):\\n self.onsub_lock.acquire()\\n if self.debug_flg:\\n print(f\\\"subscribe accepted with QOS: {granted_qos} with mid: {mid}\\\")\\n self.onsub_lock.release()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1f651e6f780ec893cbe7bab0c3770b6\",\n \"score\": \"0.56171685\",\n \"text\": \"async def register(websocket,path):\\n ws_clients.add(websocket)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae1368c7698822c6b24ddcbe6ff12129\",\n \"score\": \"0.5611535\",\n \"text\": \"def _connect_to_chat(self):\\n if self.chat_details is None:\\n raise NotAuthenticatedError(\\\"You must first log in to Mixer!\\\")\\n\\n self.websocket = Socket(self.chat_details[\\\"endpoints\\\"])\\n self.websocket.on(\\\"opened\\\", self._send_auth_packet)\\n self.websocket.on(\\\"message\\\", lambda msg: self.emit(\\\"message\\\", msg))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b686b490ca5468aa198061b57bf5dd2f\",\n \"score\": \"0.55902344\",\n \"text\": \"def connect_to_websocket(self):\\n # conn = yield websocket_connect(\\\"wss://ws-feed-public.sandbox.gdax.com\\\")\\n conn = yield websocket_connect(\\\"wss://ws-feed.gdax.com\\\")\\n req = {\\n \\\"type\\\": \\\"subscribe\\\",\\n \\\"product_ids\\\": [\\n \\\"BTC-USD\\\"\\n ],\\n \\\"channels\\\": [\\n \\\"level2\\\"\\n ]\\n }\\n conn.write_message(json.dumps(req))\\n while True:\\n msg = yield conn.read_message()\\n if msg:\\n response = json.loads(msg)\\n # print(response['type'])\\n if response['type'] == 'l2update':\\n # # Perform update in database\\n # and emit update to client\\n self.perform_update(response)\\n\\n elif response['type'] == 'snapshot':\\n # Store snapshot in database\\n for bid_data in response['bids']:\\n # Intialize a new row of data\\n item = self.add_new_gdax_item(\\\"bid\\\", bid_data[0], bid_data[1])\\n self.session.add(item)\\n\\n for ask_data in response['asks']:\\n item = self.add_new_gdax_item(\\\"ask\\\", ask_data[0], ask_data[1])\\n self.session.add(item)\\n\\n self.session.commit()\\n print(\\\"GDAX Snapshot Received\\\")\\n # print(response)\\n self.snapshot_received = True\\n elif response['type'] == 'error':\\n print(response)\\n else:\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d850528e0d772d5adc3eca6c2ddf37da\",\n \"score\": \"0.557667\",\n \"text\": \"def handle(self):\\n data, sock = self.request\\n print ('HOST: UDPEchoClientHandler: Rx: \\\\n%s\\\\n' % data)\\n sock.sendto(data, self.client_address)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1130a4aed0cad229b79e289337b3857\",\n \"score\": \"0.55707645\",\n \"text\": \"def subscribe(self, topic, func=None, fargs=None):\\r\\n\\r\\n if (self.__mqtt is None) or (not self.__connected):\\r\\n raise MQTTClientError('connect to the server before calling subscribe()')\\r\\n\\r\\n if self.__listening:\\r\\n LOG.warning('already listened. Do nothing.')\\r\\n return\\r\\n\\r\\n self.__func = func\\r\\n self.__args = fargs if fargs else ()\\r\\n self.__mqtt.on_message = self.__on_message\\r\\n self.__sub_topic = self.__topic.topic(self.__uid, topic)\\r\\n self.__subscribe(self.__sub_topic)\\r\\n self.__listening = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6543728a404ad89ec24620c8b88a342e\",\n \"score\": \"0.55667305\",\n \"text\": \"async def server_on_message(self, websocket, path):\\n # Register websocket\\n self.connected_clients.add(websocket)\\n try:\\n await self.on_connect(websocket)\\n\\n while True:\\n try:\\n data = await websocket.recv()\\n message = json.loads(data)\\n\\n await self.handle_message_exchange(message, websocket)\\n except Exception as e:\\n logger.error(e)\\n if websocket in self.connected_clients:\\n self.connected_clients.remove(websocket)\\n if websocket in self.raft_nodes:\\n self.raft_nodes.remove(websocket)\\n if websocket in self.control_connection:\\n self.control_connection.remove(websocket)\\n if websocket in self.node_connections:\\n self.node_connections.remove(websocket)\\n break\\n finally:\\n if websocket in self.connected_clients:\\n self.connected_clients.remove(websocket)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8146967e1a7c78cf3a1c87216e797ab7\",\n \"score\": \"0.5559368\",\n \"text\": \"async def subscribe(self) -> None:\\n try:\\n self.ws = await websockets.connect(self.ws_endpoint)\\n\\n if self.request is not None:\\n LOGGER.info('Requesting Book: {}'.format(self.request))\\n await self.ws.send(self.request)\\n LOGGER.info('BOOK %s: %s subscription request sent.' %\\n (self.exchange.upper(), self.sym))\\n\\n if self.trades_request is not None:\\n LOGGER.info('Requesting Trades: {}'.format(self.trades_request))\\n await self.ws.send(self.trades_request)\\n LOGGER.info('TRADES %s: %s subscription request sent.' %\\n (self.exchange.upper(), self.sym))\\n\\n self.last_subscribe_time = dt.now(tz=TIMEZONE)\\n\\n # Add incoming messages to a queue, which is consumed and processed\\n # in the run() method.\\n while True:\\n self.queue.put(json.loads(await self.ws.recv()))\\n\\n except websockets.ConnectionClosed as exception:\\n LOGGER.warn('%s: subscription exception %s' % (self.exchange, exception))\\n self.retry_counter += 1\\n elapsed = (dt.now(tz=TIMEZONE) - self.last_subscribe_time).seconds\\n\\n if elapsed < 10:\\n sleep_time = max(10 - elapsed, 1)\\n time.sleep(sleep_time)\\n LOGGER.info('%s - %s is sleeping %i seconds...' %\\n (self.exchange, self.sym, sleep_time))\\n\\n if self.retry_counter < self.max_retries:\\n LOGGER.info('%s: Retrying to connect... attempted #%i' %\\n (self.exchange, self.retry_counter))\\n await self.subscribe() # recursion\\n else:\\n LOGGER.warn('%s: %s Ran out of reconnection attempts. '\\n 'Have already tried %i times.' %\\n (self.exchange, self.sym, self.retry_counter))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffeb58fce1952b02d2c48183b669e68c\",\n \"score\": \"0.55575657\",\n \"text\": \"def on_connect(self):\\n print(\\\"You're connected to the streaming server.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41f00dbd1874994712749593498a41aa\",\n \"score\": \"0.55537254\",\n \"text\": \"def subscribeTo(self, aTopic, aFunctionBindTo):\\n if(self.mAlreadySubscribed == False):\\n self.mFunction = aFunctionBindTo\\n self.mTopic = aTopic\\n \\n tMESSAGE = \\\"subscribe, \\\" + aTopic\\n tMessageInBytes = bytes(tMESSAGE, 'UTF-8')\\n self.mSock.sendto(tMessageInBytes, (self.mSubscriberServiceIPV4, self.mSubscriberServicePort))\\n self.mAlreadySubscribed = True\\n self.mSock.close()\\n self.mListeningSocket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\\n self.mListeningSocket.bind(('', self.mListeningPort))\\n #wont keep the thread up if main thread die\\n self.mThread.daemon = True\\n try:\\n self.mThread.start()\\n except: #catch all errors\\n pass\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f9cbcf36f5f663c8c7b2e9349708f55\",\n \"score\": \"0.55497867\",\n \"text\": \"def subscribe(self, topic, callback):\\n if topic not in self.callbacks:\\n self.callbacks[topic] = [callback]\\n else:\\n if callback not in self.callbacks[topic]:\\n self.callbacks[topic].append(callback)\\n\\n def callback_handler(client, userdata, message):\\n # log = f\\\"{message.payload.decode()} {message.topic}\\\"\\n if message.topic in self.callbacks:\\n for callbk in self.callbacks[message.topic]:\\n callbk(message.payload)\\n\\n self.connection.on_message = callback_handler\\n return self.connection.subscribe(topic)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15cbb8a1f7bfcaeb39ff36c1ab694b7d\",\n \"score\": \"0.5531924\",\n \"text\": \"async def _start(self) -> None:\\n await self.send({\\\"type\\\": \\\"websocket.connect\\\"})\\n await self.receive()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b770eb5036f3c849bc84cdd54f3aad5c\",\n \"score\": \"0.55316156\",\n \"text\": \"def subscribe(self, device: str, control: str):\\n topic = generate_topic(device, control)\\n\\n result_code: int\\n message_id: int | None\\n result_code, message_id = self.__client.subscribe(topic)\\n self.__raise_error_if_any(result_code)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3101dc1fc0283932611f191e2582c8c7\",\n \"score\": \"0.55312043\",\n \"text\": \"def on_startup():\\n mqtt.subscribe('client/speech/speak')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c282998f8bc443b180a92881e5a01d3c\",\n \"score\": \"0.55235696\",\n \"text\": \"def on_connect(client, userdata, flags, rc):\\n main_logger.info(\\\"attempting on_connect\\\")\\n if rc==0:\\n mqtt_client.connected = True\\n for topic in MQTT_SUB_TOPIC:\\n client.subscribe(topic)\\n main_logger.info(\\\"Subscribed to: {0}\\\".format(topic))\\n main_logger.info(\\\"Successful Connection: {0}\\\".format(str(rc)))\\n else:\\n mqtt_client.failed_connection = True # If rc != 0 then failed to connect. Set flag to stop mqtt loop\\n main_logger.info(\\\"Unsuccessful Connection - Code {0}\\\".format(str(rc)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f56e3251fc326b3c247372eff566ceca\",\n \"score\": \"0.5515813\",\n \"text\": \"def listen(self, name, handler):\\n self.client_endpoint.connect(...)\\n self.handler = handler\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f11716bf717a126caea8b86537d9376\",\n \"score\": \"0.5501132\",\n \"text\": \"def subscribe(self, subscriber):\\n self._subscribers[subscriber.uid] = subscriber\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db596cfaddc21f03e25725b68cbf68cc\",\n \"score\": \"0.54992354\",\n \"text\": \"def listen(self):\\n logging.info(\\n \\\"ThreadedServer.listen(): listening for new client connection...\\\")\\n self.sock.listen(5)\\n\\n while True:\\n client, address = self.sock.accept()\\n self.client = client\\n new_player = Player()\\n # client.settimeout(10)\\n\\n # if connected, make threads\\n if self.verify_client_conn(client):\\n logging.info(\\n \\\"================================================================\\\\nThreadedServer.listen(): Connected to: {}:{}\\\\n==========================================================================\\\\n\\\".format(address[0], str(address[1])))\\n t1 = threading.Thread(target=self.speak_to_client, name=\\\"send_\\\" + self.thread_name, args=(\\n client, address, new_player))\\n t2 = threading.Thread(target=self.listen_to_client, name=\\\"recv_\\\" + self.thread_name, args=(\\n client, address, new_player))\\n\\n t1.start()\\n t2.start()\\n\\n logging.info(\\n f\\\"ThreadedServer.listen() Show active threads \\\\n{threading.enumerate()}\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e473263cf14a22328bb34c3e230a0b1\",\n \"score\": \"0.54958236\",\n \"text\": \"def listening(self):\\r\\n while self.is_connected:\\r\\n msg_receiv = self.client_socket.recv(1024)\\r\\n if len(msg_receiv) > 0:\\r\\n msg_receiv = pickle.loads(msg_receiv)\\r\\n print \\\"j'ai bien recu \\\"+msg_receiv\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69e6a05ad8e8814ca1b44ef4b808941b\",\n \"score\": \"0.54850984\",\n \"text\": \"def websocket(self):\\n return self.websocket_client.wss\",\n \"title\": \"\"\n },\n {\n \"docid\": \"109c4648ec05697a54b2e612a53cdb50\",\n \"score\": \"0.5478585\",\n \"text\": \"def subscribe(self):\\r\\n for channel in self.bus.listeners:\\r\\n # Subscribe self.start, self.exit, etc. if present.\\r\\n method = getattr(self, channel, None)\\r\\n if method is not None:\\r\\n self.bus.subscribe(channel, method)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ce1b57fc85b1882151f4f140c324245\",\n \"score\": \"0.54769194\",\n \"text\": \"async def _subscribe_channels(self, ws: WSAssistant):\\n try:\\n payload = {\\n \\\"id\\\": str(get_tracking_nonce()),\\n \\\"type\\\": \\\"subscribe\\\",\\n \\\"topic\\\": f\\\"/market/candles:{self._ex_trading_pair}_{CONSTANTS.INTERVALS[self.interval]}\\\",\\n \\\"privateChannel\\\": False,\\n \\\"response\\\": False,\\n }\\n subscribe_candles_request: WSJSONRequest = WSJSONRequest(payload=payload)\\n\\n await ws.send(subscribe_candles_request)\\n self.logger().info(\\\"Subscribed to public klines...\\\")\\n except asyncio.CancelledError:\\n raise\\n except Exception:\\n self.logger().error(\\n \\\"Unexpected error occurred subscribing to public klines...\\\",\\n exc_info=True\\n )\\n raise\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":876,"cells":{"query_id":{"kind":"string","value":"9afc8cba396106e0c0e2d2cf5abd0df5"},"query":{"kind":"string","value":"Search a word's phonetic in 'WordReference'."},"positive_passages":{"kind":"list like","value":[{"docid":"a39f189fce2e8d571f78c75ac7966b03","score":"0.66305864","text":"def getPhonetic(word, language, variant=\"UK\") :\n\n\tlangFrom = \"\"\n\tlangTo = \"\"\n\n\tif language == \"English\":\n\t\tlangFrom = \"en\"\n\t\tlangTo = \"fr\"\n\n\ttry:\n\t\tpage = requests.get(f\"https://www.wordreference.com/{langFrom}{langTo}/{word}\")\n\texcept:\n\t\treturn NetworkError\n\n\tsoup = BeautifulSoup(page.content, 'html.parser')\n\tpronuncation_widget = soup.find(\"div\", {\"id\":\"pronunciation_widget\"})\n\n\tif pronuncation_widget != None:\n\t\tpronuncation = pronuncation_widget.text\n\telse :\n\t\treturn WordNotFoundError # Le mot cherché n'est pas disponible sur WordReference\n\n\ttry:\n\t\tif variant == \"US\" and \"US\" in pronuncation.split('/')[2]:\n\t\t\treturn pronuncation.split('/')[3]\n\t\telse :\n\t\t\treturn pronuncation.split('/')[1]\n\texcept:\n\t\treturn WordNotFoundError","title":""}],"string":"[\n {\n \"docid\": \"a39f189fce2e8d571f78c75ac7966b03\",\n \"score\": \"0.66305864\",\n \"text\": \"def getPhonetic(word, language, variant=\\\"UK\\\") :\\n\\n\\tlangFrom = \\\"\\\"\\n\\tlangTo = \\\"\\\"\\n\\n\\tif language == \\\"English\\\":\\n\\t\\tlangFrom = \\\"en\\\"\\n\\t\\tlangTo = \\\"fr\\\"\\n\\n\\ttry:\\n\\t\\tpage = requests.get(f\\\"https://www.wordreference.com/{langFrom}{langTo}/{word}\\\")\\n\\texcept:\\n\\t\\treturn NetworkError\\n\\n\\tsoup = BeautifulSoup(page.content, 'html.parser')\\n\\tpronuncation_widget = soup.find(\\\"div\\\", {\\\"id\\\":\\\"pronunciation_widget\\\"})\\n\\n\\tif pronuncation_widget != None:\\n\\t\\tpronuncation = pronuncation_widget.text\\n\\telse :\\n\\t\\treturn WordNotFoundError # Le mot cherché n'est pas disponible sur WordReference\\n\\n\\ttry:\\n\\t\\tif variant == \\\"US\\\" and \\\"US\\\" in pronuncation.split('/')[2]:\\n\\t\\t\\treturn pronuncation.split('/')[3]\\n\\t\\telse :\\n\\t\\t\\treturn pronuncation.split('/')[1]\\n\\texcept:\\n\\t\\treturn WordNotFoundError\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"f621e037c12702cc9d483850528970ea","score":"0.6215948","text":"def check_word_as_pounct(self,word):\t\t\n\t\tdetailed_result=[]\n\t\t# ToDo : fix it to isdigit, by moatz saad\n\t\tif word.isnumeric():\n\t\t\tdetailed_result.append(wordCase.wordCase({\n\t\t\t'word':word,\n\t\t\t'affix': ('', '', '', ''),\t\t\t\n\t\t\t'stem':'',\n\t\t\t'original':word,\n\t\t\t'vocalized':word,\n\t\t\t'tags':self.get_number_tags(word),\n\t\t\t'type':'NUMBER',\n\t\t\t'freq':0,\n\t\t\t'syntax':'',\t\t\t\n\t\t\t}));\t\n\t\tif word in stem_pounct_const.POUNCTUATION:\n\t\t\tdetailed_result.append(wordCase.wordCase({\n\t\t\t'word':word,\n\t\t\t'affix': ('', '', '', ''),\n\t\t\t'stem':'',\n\t\t\t'original':word,\n\t\t\t'vocalized':word,\n\t\t\t'tags':stem_pounct_const.POUNCTUATION[word]['tags'],\n\t\t\t'type':'POUNCT',\n\t\t\t'freq':0,\n\t\t\t'syntax':'',\t\t\t\n\t\t\t}));\n\n\t\treturn detailed_result;","title":""},{"docid":"f15c2c8e548933d5db43f035e0e157ee","score":"0.59108704","text":"def _searchWord(self, forward):\r\n document = core.workspace().currentDocument()\r\n\r\n cursor = document.qutepart.textCursor()\r\n if not cursor.hasSelection():\r\n cursor.select(cursor.WordUnderCursor)\r\n word = cursor.selectedText()\r\n wordStartAbsPos = cursor.anchor()\r\n wordEndAbsPos = cursor.position()\r\n\r\n if not word:\r\n return\r\n\r\n regExp = re.compile('\\\\b%s\\\\b' % re.escape(word))\r\n text = document.qutepart.text\r\n\r\n # avoid matching word under cursor\r\n if forward:\r\n startPoint = wordEndAbsPos\r\n else:\r\n startPoint = wordStartAbsPos\r\n\r\n self._updateFoundItemsHighlighting(regExp)\r\n\r\n match, matches = self._searchInText(regExp, document.qutepart.text, startPoint, forward)\r\n if match is not None:\r\n document.qutepart.absSelectedPosition = (match.start(), match.start() + len(match.group(0)))\r\n core.mainWindow().statusBar().showMessage('Match %d of %d' % \\\r\n (matches.index(match) + 1, len(matches)), 3000)\r\n else:\r\n core.workspace().currentDocument().qutepart.resetSelection()","title":""},{"docid":"119af19f789667feed1933f8c0940bbb","score":"0.5865671","text":"def lookup(self, word: str) -> Word:\n pass","title":""},{"docid":"49892e79152973faf6541ccd239a512f","score":"0.57349604","text":"def similar_words(word):\n\tpass","title":""},{"docid":"52a1aa05273ce8f7537946394e8f926e","score":"0.5687349","text":"def isMatchWord(self): #$NON-NLS-1$\r","title":""},{"docid":"ef1eee697a52f3b9d1a9cc95a527b609","score":"0.5653841","text":"def findText(self, wordList, forward=True):\n fullList = globalref.docRef.root.descendantList(True)\n currentPos = fullList.index(self.currentItem)\n fullList = fullList[currentPos+1:] + fullList[:currentPos]\n if not forward:\n fullList.reverse()\n for item in fullList:\n if item.matchWords(wordList):\n self.changeSearchOpen([item])\n return item\n return None","title":""},{"docid":"41302f76f4b995dbdfaf54a9acf1da4f","score":"0.56114215","text":"def search(self, word):\n p = self.helper(word)\n if p and p.isWord:\n return True\n return False","title":""},{"docid":"e706dab44c63ad4b71eb78a872fd5b52","score":"0.55804014","text":"def pronounSearch(filter):\n pronouns = fileToStringList(\"pronoun dictionary\")\n filteredText = []\n for line in poemList:\n line = line.split(\" \")\n words = \"\"\n for word in line:\n words += \" \"\n if word in pronouns:\n words += word.upper()\n else:\n words += word \n filteredText += [words]\n printPoem(filteredText)","title":""},{"docid":"634b4ba065aa9dc816a8038e5a6b7202","score":"0.5579486","text":"def search(self, word: str) -> bool:\n tree = self.look_up\n for w in word:\n if w not in tree:\n return False\n tree = tree[w]\n if '#' in tree:\n return True\n return False","title":""},{"docid":"d090b4b2009235bf089acd6ef97e9b48","score":"0.554672","text":"def setMatchWord(self, b): #$NON-NLS-1$\r","title":""},{"docid":"cc8a36b1931c5d7138df46c5692e3cc7","score":"0.55318964","text":"def search(self, word):\n node = self.trie\n for c in word:\n if c in node:\n node = node[c]\n else:\n return False\n return node.get('term') is not None","title":""},{"docid":"3be2be54c47feae253a6511406d75f0f","score":"0.55304444","text":"def search(self, word: str) -> bool:\n # 1. Initialize: cur = root\n # 2. for each char c in target string S:\n # 3. if cur does not have a child c:\n # 4. search fails\n # 5. cur = cur.children[c]\n # 6. search successes\n \n cur = self.root\n for c in word:\n if c not in cur.child:\n return False\n cur = cur.child[c]\n return cur.word","title":""},{"docid":"556576af3c8d07ab77912cf1477e6d69","score":"0.5497367","text":"def search(self, word):\n return word in self.s","title":""},{"docid":"bb0d92329c4e3314d64003defe172e5b","score":"0.5487036","text":"def search(self, word: str) -> bool:\r\n word_tuple = self.make_tuple(word)\r\n\r\n for w in self.words:\r\n diff_from = w - word_tuple\r\n diff_to = word_tuple - w\r\n if len(diff_from) == 1 and len(diff_to) == 1:\r\n return True\r\n\r\n return False","title":""},{"docid":"c2a3a9d7142fdc191a6f4135418508b5","score":"0.5475612","text":"def test_find_word():\n question = ['salut', 'grandpy', 'comment', 'vas', 'tu',\n 'peux', 'tu', 'me', 'dire', 'où', 'se', 'trouve',\n 'l', 'arc', 'de', 'triomphe']\n req = pars.find_word(question)\n result = \"où trouve arc triomphe \"\n assert str(req) == str(result)","title":""},{"docid":"2a6472a8616a373a711dfd938804744b","score":"0.547298","text":"def search(self, word: str) -> bool:\n node = self.links\n for c in word:\n if c not in node:\n return False\n node = node[c]\n # If at end after for loop, this is a word\n if '#' in node:\n return True\n # Else we are still in the middle of a word even though the letters matched up so far\n return False","title":""},{"docid":"58d0e8a7e52a09d7a00f86268fb2167d","score":"0.5467885","text":"def search(self, word):\n return self.find(self.root, word)","title":""},{"docid":"4eb54815db618751275ce31f4d211450","score":"0.5466823","text":"def search(self, word: str) -> bool:\n tree = self.lookup\n for a in word:\n if a not in tree:\n return False\n tree = tree[a]\n if \"#\" in tree:\n return True\n return False","title":""},{"docid":"06dff3a05a2a8aecf6193aab308874e2","score":"0.54580563","text":"def find_similar_words(self) -> None:\n for word, token in zip(self.words, self.tokens):\n if word not in self.synonyms:\n self.synonyms[word] = self._synonyms_generator.neighbors(word, token.tag_, self.perturb_opts['top_n'])","title":""},{"docid":"573197d52075cdf856037db98d220891","score":"0.5451955","text":"def search(self, word):\n if self.root == None:\n return False\n else:\n return self.root.getWord(word, isRoot=True)","title":""},{"docid":"a2dc36522701e660c82beb68b0a3f85e","score":"0.54393554","text":"def search(self, word: str) -> bool:\n t = self.trie\n for i in word:\n if i not in t:\n return False\n t = t[i]\n if '#' not in t:\n return False\n return True","title":""},{"docid":"e700d57593b0321889c80e89dcc3dde1","score":"0.5432258","text":"def search(self, word):\n return self.match(word, 0, self.root)","title":""},{"docid":"11f5bd470f95c5fb80259dd7afc11c8c","score":"0.54315823","text":"def search(self, word):\n n = len(word)\n w = word + '#'\n #if g(self.td, w, 0, n+1):\n # return False\n for i in xrange(n):\n loc = ord(word[i]) - ord('a') + 1\n w = word[:i] + '.' + word[i+1:] + '#'\n z = [0]\n if f(self.td, w, 0, n+1, z, loc):\n return True\n return False","title":""},{"docid":"674b018ce35745405a7173701c65d216","score":"0.54207706","text":"def search(self, word):\n cur = self.rootNode\n for w in word:\n if cur.children[ord(w) - ord('a')] is None:\n return False\n else:\n cur = cur.children[ord(w)-ord('a')]\n \n return cur.isWord","title":""},{"docid":"d62e27d835bbc446f0a221b8dd8bf299","score":"0.5379058","text":"def search(self, word, text):\n return bool(re.search(r'\\b{}\\b'.format(re.escape(word)), text))","title":""},{"docid":"77f817b649730a6d1e4ab4b685a3e9b6","score":"0.53673387","text":"def look_up_pronunciation(word: str, PronouncingTable) -> List[str]:\n for i in range(len(PronouncingTable[0])):\n if word == PronouncingTable[0][i]:\n return PronouncingTable[1][i]\n return []","title":""},{"docid":"9c3edf8cd651c8c2c542c95162c46c5e","score":"0.53462887","text":"def search(self, word):\n\n if len(word) not in self.dic: return False\n\n for candidate in self.dic[len(word)]:\n diff = 0\n\n for a, b in zip(candidate, word):\n if a != b: diff += 1\n\n if diff == 1: return True\n\n return False","title":""},{"docid":"dcd0b14bbc81546e11b7885e8e1aa545","score":"0.5334784","text":"def search(self, word):\n flag = False\n \n def recursive(word,trie):\n nonlocal flag\n \n if word == \"\" and \"#\" in trie:\n flag = True\n return \n ## 单词过长\n if word != \"\" and trie == {\"#\":\"#\"}:\n return\n\n \n if word[0] == \".\":\n for key in trie:\n recursive(word[1:],trie[key])\n \n elif word[0] in trie:\n recursive(word[1:],trie[word[0]])\n ## 不存在该前缀\n else:\n return\n\n recursive(word,self.trie)\n return flag","title":""},{"docid":"731e8b4e58170b5801a59201a75aae50","score":"0.5333343","text":"def search(self, word):\n return self.searchFrom(word, self.root)","title":""},{"docid":"d9a1b1c2f85f47dbec17764ee2d002db","score":"0.531149","text":"def search(self, word):\n for i in range(len(word)):\n for ch_idx in range(26):\n ch = chr(ord('a') + ch_idx)\n if ch != word[i]:\n replace_word = word[:i] + ch + word[i+1:]\n if replace_word in self.dictionary:\n return True\n return False","title":""},{"docid":"db484243b2bdbb1db1075f47ba4ea170","score":"0.53038156","text":"def search(self, word: 'str') -> 'bool':\n temp = self.root\n for ch in word:\n if ch not in temp.links:\n return False\n temp = temp.links[ch]\n return temp.isEnd","title":""},{"docid":"79a45cd0fca596fd841446ea08d4a3b4","score":"0.529919","text":"def search(self, word):\r\n current = self.root\r\n for w in word:\r\n current = current.children.get(w)#False\r\n if current == None:\r\n return False\r\n return current.isword# isword 将会在遍历完apple, 字母e之后，自动改成True,所以search('app') 返回\r","title":""},{"docid":"54c5d8a16717620fa7a995cd1f6c40ee","score":"0.5298913","text":"def __contains__(self, word):\n return word in self.word2id","title":""},{"docid":"286913f0ac3cef1aa8ee1c6fa17d430c","score":"0.52985764","text":"def word_search_and_compare(self):\r\n d = self.var.get()\r\n pattern = d\r\n return [w for w in word_list if re.match(pattern,w)]","title":""},{"docid":"cd78b2039c3cedf3c9e0a412f3df94a6","score":"0.52977455","text":"def findW(words, casesensitive=False, context=False, ordered=0, bk=None):\n t0 = tc()\n \n # unpack string for individual words\n word_array = ['']\n for element in words:\n # separate words by where spaces are\n if element == ' ':\n word_array.append('')\n else:\n word_array[-1] += element\n\n # change apostrophes?\n for i in range(len(word_array)):\n word = word_array[i]\n if \"'\" in word:\n word_split = word.split(\"'\")\n word = u\"\\u2019\".join(word_split)\n word_array[i] = word\n \n # analyze words if upper/lowercase does not matter\n if casesensitive == False:\n # redefine 'words'\n words = []\n Words = []\n for word_index in range(len(word_array)):\n UppercaseLetter = word_array[word_index][0].upper()\n LowercaseLetter = word_array[word_index][0].lower()\n RestOfWord = word_array[word_index][1:]\n # create uppercase word\n Word = UppercaseLetter + RestOfWord\n Words.append(Word)\n # create lowercase word\n word = LowercaseLetter + RestOfWord\n words.append(word)\n # print(words)\n # print(Words)\n else:\n words = word_array\n # print(words)\n \n\n verses_containing_words = 0\n verses_containing_extra_words = 0\n occurrences = 0\n \n # search entire Bible, or one book, or multiple books\n if bk is None:\n # If no book is specified, search entire Bible\n books_to_go_through = Book_names\n elif type(bk) == list:\n # If a list of books is specified, search those books\n books_to_go_through = bk\n for bkk in books_to_go_through:\n if bkk not in Book_names:\n print(bkk + ' not found in the books of the Bible.')\n return\n elif type(bk) == str:\n # If a single string is entered, check if it is a book, or the entire new/old testament\n books_to_go_through = [bk]\n if bk not in Book_names:\n if bk.upper() == 'OT':\n books_to_go_through = Book_names[:39]\n elif bk.upper() == 'NT':\n books_to_go_through = Book_names[39:]\n else:\n print(bk + ' not found in the books of the Bible.')\n return\n\n # go through books of the Bible\n for book_name in books_to_go_through:\n Book = Bible_searchable[book_name]\n \n # go through each chapter\n for chapter_index in range(len(Book)):\n chapter_number = chapter_index + 1\n Chapter = Book[chapter_index]\n \n # go through each verse\n for verse_name,EntireVerse in Chapter.items():\n\n # In case words should be matched in order, store a copy of the verse\n Verse = EntireVerse\n\n # check to see if each word is in the verse\n word_index = 0\n contains = True\n contains_added = True\n while contains and word_index < len(words):\n \n # If upper/lowercase is unimportant...\n if casesensitive == False:\n word = words[word_index]\n Word = Words[word_index]\n # Is word in verse? (also check non-original tongues)\n if (word not in Verse) and (Word not in Verse):\n contains = False\n elif ordered != 0:\n # If word is in verse, and order is important, let subsequent calls only search the rest of the verse\n split_index = min(try_index(Verse, [word, Word]))\n if (ordered == 2) and (word_index != 0) and (split_index != 0):\n contains = False\n Verse = Verse[split_index+1:]\n \n # If upper/lowercase is important...\n elif casesensitive == True:\n word = words[word_index]\n # Is word in verse? (also check non-original tongues)\n if word not in Verse:\n contains = False\n elif ordered != 0:\n # If word is in verse, and order is important, let subsequent calls only search the rest of the verse\n split_index = Verse.index(word)\n if (ordered == 2) and (word_index != 0) and (split_index != 0):\n contains = False\n Verse = Verse[split_index+1:]\n \n word_index += 1\n\n if contains == True:\n verses_containing_words += 1\n ActualVerse = Bible[book_name][chapter_index][verse_name]\n total_verse = verse_writeout(ActualVerse)\n print(book_name,chapter_number,':',verse_name)\n print(' ',total_verse)\n tf = tc()\n print(tf-t0) \n print('number of verses containing specified words: ',verses_containing_words)","title":""},{"docid":"e9e41d7e82f2c86ef3b9f6615e7e45c7","score":"0.52892405","text":"def __zh_word_tea_dictionary_search(self, chinese_word):\n general_dictionary = {}\n\n # Conduct a search.\n web_search = f\"http://babelcarp.org/babelcarp/babelcarp.cgi?phrase={chinese_word}&define=1\"\n eth_page = requests.get(web_search, headers=self.zw_useragent, timeout=15)\n try:\n tree = html.fromstring(eth_page.content) # now contains the whole HTML page\n word_content = tree.xpath('//fieldset[contains(@id,\"translation\")]//text()')\n except BaseException:\n return None\n\n # Get the headword of the entry.\n try:\n head_word = word_content[2].strip()\n except IndexError:\n return None\n\n if chinese_word not in head_word:\n # If the characters don't match: Exit. This includes null searches.\n return None\n else: # It exists.\n try:\n pinyin = re.search(r\"\\((.*?)\\)\", word_content[2]).group(1).lower()\n except AttributeError: # Never mind, it does not exist.\n return None\n\n meaning = word_content[3:]\n meaning = [item.strip() for item in meaning]\n\n # Format the entry to return\n formatted_line = f'\\n\\n**Tea Meanings**: \"{\" \".join(meaning)}.\"'\n formatted_line = formatted_line.replace(\" )\", \" \")\n formatted_line = formatted_line.replace(\" \", \" \")\n formatted_line += f\" ([Babelcarp]({web_search}))\" # Append source\n\n general_dictionary[\"meaning\"] = formatted_line\n general_dictionary[\"pinyin\"] = pinyin\n\n return general_dictionary","title":""},{"docid":"2f1fcb9dc073f9bd440e9f975ff4d84a","score":"0.52829","text":"def __zh_word_cccanto_search(self, cantonese_word: str) -> None | Dict[str, str]:\n general_dictionary = {}\n\n # We open the file.\n with open(FILE_ADDRESS_ZH_CCCANTO, encoding=\"utf-8\") as f:\n existing_data = f.read().split(\"\\n\")\n\n relevant_line = None\n\n # Look for the relevant word (should not take long.)\n for entry in existing_data:\n traditional_headword = entry.split(\" \", 1)[0]\n if cantonese_word == traditional_headword:\n relevant_line = entry\n break\n\n if relevant_line is not None:\n # Parse the entry (based on code from Marcanuy at https://github.com/marcanuy/cedict_utils, MIT license)\n hanzis = relevant_line.partition(\"[\")[0].split(\" \", 1)\n keywords = {\n \"meanings\": relevant_line.partition(\"/\")[2]\n .replace('\"', \"'\")\n .rstrip(\"/\")\n .strip()\n .split(\"/\"),\n \"traditional\": hanzis[0].strip(\" \"),\n \"simplified\": hanzis[1].strip(\" \"),\n # Take the content in between the two brackets\n \"pinyin\": relevant_line.partition(\"[\")[2].partition(\"]\")[0],\n \"jyutping\": relevant_line.partition(\"{\")[2].partition(\"}\")[0],\n \"raw_line\": relevant_line,\n }\n\n formatted_line = '\\n\\n**Cantonese Meanings**: \"{}.\"'.format(\n \"; \".join(keywords[\"meanings\"])\n )\n formatted_line += (\n f\" ([CC-Canto](https://cantonese.org/search.php?q={cantonese_word}))\"\n )\n for i in range(0, 9):\n keywords[\"jyutping\"] = keywords[\"jyutping\"].replace(\n str(i), f\"^{str(i)} \"\n ) # Adds syntax for tones\n keywords[\"jyutping\"] = (\n keywords[\"jyutping\"].replace(\" \", \" \").strip()\n ) # Replace double spaces\n\n general_dictionary[\"meaning\"] = formatted_line\n general_dictionary[\"pinyin\"] = keywords[\"pinyin\"]\n general_dictionary[\"jyutping\"] = keywords[\"jyutping\"]\n\n return general_dictionary","title":""},{"docid":"13a0ed020c7f75d885b6395a7a4eab35","score":"0.5277528","text":"def search(self, word):\n cur = self.root\n for c in word:\n if c in cur.d:\n cur = cur.d[c]\n else:\n return False\n return True if 0 in cur.d else False","title":""},{"docid":"49111f63cb2263f7c680c79069fbd7f9","score":"0.52603996","text":"def lookup_exact(self, normalized_spelling):\n if Word._get_cache().contains(normalized_spelling):\n return self.filter(words__normalized_spelling=normalized_spelling)\n else:\n return self.none()","title":""},{"docid":"803904b6ab3d9386c944a427b4068ff7","score":"0.5258035","text":"def search(self, word):\n current = self.root\n n = len(word)\n\n i = 0\n while i < n:\n node_found = False\n\n for trie_node in current.children:\n if trie_node.value == word[i]:\n current = trie_node\n i += 1\n node_found = True\n break\n\n if not node_found:\n return (False, False)\n\n # if end of word reached, then increment weight to indicate popularity/frequency of access\n if current.end_of_word:\n current.weight += 1\n\n return (True, True if current.end_of_word else False)","title":""},{"docid":"42edee9ef6aeba56c4525a2ed7d1dcee","score":"0.5256944","text":"def search(self, word):\n return self.searchre(word, self.root);","title":""},{"docid":"78d40b8ca14697e036b719ed5f03f2fa","score":"0.52565396","text":"def get_word (self, word):\n for w in self.words:\n if w.word == word:\n return w\n return None","title":""},{"docid":"2a145cf91aeed21da722c383ec995ae4","score":"0.52554923","text":"def check_word(self,word, guessedTag=\"\"):\t\n\t\tword=araby.stripTatweel(word);\n\t\tword_vocalised=word;\n\t\tword_nm=araby.stripTashkeel(word);\n\t\tresulted_text=u\"\";\n\t\tresulted_data=[];\n\t\t# if word is a pounctuation\n\t\tresulted_data+=self.check_word_as_pounct(word_nm);\n\t\t# Done: if the word is a stop word we have some problems,\n\t\t# the stop word can also be another normal word (verb or noun),\n\t\t# we must consider it in future works\n\t\t# if word is stopword allow stop words analysis\n\t\tresulted_data+=self.check_word_as_stopword(word_nm);\n\n\t\t#if word is verb\n\t\t# مشكلة بعض الكلمات المستبعدة تعتبر أفعلا أو اسماء\n\t\tif self.tagger.hasVerbTag(guessedTag) or self.tagger.isStopWordTag(guessedTag):\n\t\t\tresulted_data+=self.check_word_as_verb(word_nm);\n\t\t\t#print \"is verb\", rabti,len(resulted_data);\n\t\t#if word is noun\n\t\tif self.tagger.hasNounTag(guessedTag) or self.tagger.isStopWordTag(guessedTag):\t\t\t\n\t\t\tresulted_data+=self.check_word_as_noun(word_nm);\n\t\tif len(resulted_data)==0:\n\t\t\t#check the word as unkonwn\n\t\t\tresulted_data+=self.check_word_as_unknown(word_nm);\n\t\t\t#check if the word is nomralized and solution are equivalent\n\t\tresulted_data = self.check_normalized(word_vocalised, resulted_data)\n\t\t#check if the word is shadda like\n\t\tresulted_data = self.check_shadda(word_vocalised, resulted_data)\n\n\t\t#check if the word is vocalized like results\t\t\t\n\t\tif self.partial_vocalization_support:\n\t\t\tresulted_data=self.check_partial_vocalized(word_vocalised, resulted_data);\n\t\t# add word frequency information in tags\n\t\tresulted_data = self.addWordFrequency(resulted_data);\n\n\t\tif len(resulted_data)==0:\n\t\t\tresulted_data.append(wordCase.wordCase({\n\t\t\t'word':word, \n\t\t\t'affix': ('' , '', '', ''), \n\t\t\t'stem':'',\n\t\t\t'original':word,\n\t\t\t'vocalized':word,\n\t\t\t'tags':u'',\n\t\t\t'type':'unknown',\n\t\t\t'root':'',\n\t\t\t'template':'',\n\t\t\t'freq':self.wordfreq.getFreq(word, 'unknown'),\n\t\t\t'syntax':'',\n\t\t\t})\n\t\t\t);\n\t\treturn resulted_data;","title":""},{"docid":"2ad525da66c886ac0b4a95585aa3a0dd","score":"0.5247887","text":"def search(self, word):\n node = self.root\n for chars in word:\n node = node.data.get(chars)\n if not node:\n return False\n return node.is_word #","title":""},{"docid":"83d31c22d1e8665ce3abde1012f1138f","score":"0.52335036","text":"def find(self, word):\n if len(word) < 3:\n return \"at least 3 chars\"\n\n word = word.lower().strip()\n char_list = list(word)\n char_list.sort()\n sign = \"\".join(char_list)\n\n matched_words = self.trie.find(sign)\n self.trie.reset()\n if len(matched_words) == 0:\n return \"not find anagrams\"\n else:\n sign, score = self.high_score(matched_words)\n return dict.data[sign][0]","title":""},{"docid":"75bec44b7b4ae79e680262c4f945746f","score":"0.5222018","text":"def search_word(word):\r\n\r\n word = word.strip()\r\n if word.startswith(' '):\r\n return None\r\n\r\n for char in word:\r\n if char not in kanas and char not in letters and char not in splitch:\r\n break\r\n else:\r\n return None\r\n\r\n search_url = BASIC_URL + word\r\n try:\r\n content_str = requests.get(search_url).content.decode('utf-8')\r\n except requests.exceptions.ConnectionError:\r\n print('Error! Connection failed!\\nOn searching %s' % word)\r\n return None\r\n\r\n str_1_re = str_1_start + str_re_bracket + str_1_end\r\n re_1 = re.compile(str_1_re)\r\n result_1 = re_1.search(content_str)\r\n\r\n if result_1:\r\n return result_1.group(1)\r\n\r\n str_2_re = str_2_start + str_re_bracket + str_2_end\r\n re_2 = re.compile(str_2_re)\r\n result_2 = re_2.search(content_str)\r\n\r\n if result_2:\r\n return result_2.group(1)\r\n\r\n return None","title":""},{"docid":"95fbebcd5615fa5e42f692cecf9c6ccf","score":"0.5215799","text":"def check_all_dependents_of_word_in_ref_are_in_hyp(c, wordIndex):\n depIndex = wordIndex - 1\n# print('target =', wordIndex)\n # look for a dependent of word to its left in reference\n while (depIndex >=0) :\n# print('depIndex = ', depIndex)\n govRefIndex = int(c.getBuffer().getWord(depIndex).getFeat('GOVREF')) + depIndex\n# print(\"govRefIndex = \", govRefIndex)\n if govRefIndex == wordIndex : # dep is a dependent of word in ref \n #check that dep has the same governor in hyp \n govHypIndex = int(c.getBuffer().getWord(depIndex).getFeat('GOV')) + depIndex\n# print(depIndex, 'is dependent ')\n if govHypIndex != govRefIndex :\n# print('wrong gov (', govHypIndex, ')');\n return False\n depIndex -= 1\n\n sentenceChange = False\n depIndex = wordIndex + 1\n while depIndex < c.getBuffer().getLength() :\n# print('depIndex = ', depIndex)\n govRefIndex = int(c.getBuffer().getWord(depIndex).getFeat('GOVREF')) + depIndex\n# print(\"govRefIndex = \", govRefIndex)\n if(govRefIndex == wordIndex): # dep is a dependent of word in ref\n govHypIndex = int(c.getBuffer().getWord(depIndex).getFeat('GOV')) + depIndex\n# print(depIndex, 'is dependent ')\n if govHypIndex != govRefIndex :\n# print('wrong gov (', govHypIndex, ')');\n return False\n depIndex += 1\n\n return True","title":""},{"docid":"0f79bbbcaf448bb2753aa735ab0e90b7","score":"0.5213169","text":"def search(self, word):\n return self.search_word(self.root, word, 0, len(word))","title":""},{"docid":"089065648193f8050512ae756019ff02","score":"0.5206609","text":"def search(self, word):\n current = self.root\n for letter in word:\n current = current.children.get(letter)\n if current is None:\n return False\n return current.is_word","title":""},{"docid":"cc150e7652a471dad4efa9ae94880637","score":"0.5205561","text":"def paraphrase_sentence( sentence, model ):\n # 3 implementations:\n # if the first letter is equal to the first letter of w, move to the next one.\n # don't use the letter e at all\n # spelling corrections\n\n blob = textblob.TextBlob( sentence )\n print(\"The sentence's words are\")\n LoW = blob.words\n print(LoW)\n\n NewLoW = \"\"\n for w in LoW:\n \n if w not in model:\n NewLoW += w + \" \"\n else:\n \n w_alternatives = model.most_similar(positive=[w], topn=100)\n\n counter = 0\n\n for i in range(len(w_alternatives)):\n \n first_alternative, first_alternative_score = w_alternatives[i] # initial one!\n\n if (first_alternative[0] != w[0]):\n if 'e' not in first_alternative:\n break\n else:\n counter += 1\n \n if counter == len(w_alternatives):\n first_alternative, first_alternative_score = w_alternatives[0]\n else: \n NewLoW += first_alternative + \" \"\n \n # you should change this so that it returns a new string (a new sentence),\n # NOT just print a list of words (that's what's provided in this starter code)\n \n NewLoW = NewLoW[:-1]\n \n NewLoW = textblob.TextBlob(NewLoW)\n NewLoW = NewLoW.correct()\n NewLoW = NewLoW.words\n \n NewSentence = \"\"\n \n for x in NewLoW:\n NewSentence += x + \" \"\n \n NewSentence = NewSentence[:-1] \n \n return NewSentence","title":""},{"docid":"a9ed18f5bd662bfde151aaf8fe66b769","score":"0.51939875","text":"def fuzzy_lookup(tokens, rule_set):\n # we'll have to de-metaphone these to decide what part of\n # the phrase needs to be passed on...\n phonetic = metaphone(''.join(tokens))\n for start in range(len(phonetic)):\n table = rule_set\n for offset, char in enumerate(phonetic[start:]):\n if char in table:\n table = table[char]\n elif table is not rule_set:\n if PHRASE_MARKER in table:\n return table[PHRASE_MARKER], phonetic[start:]\n if WORD_MARKER in table:\n return table[WORD_MARKER], phonetic[start:]\n elif None in table:\n return table[None], phonetic[start : start + offset]\n return [], ''","title":""},{"docid":"dd82e57f0e8755748a931f546571fbeb","score":"0.51849705","text":"def search(self, answer, par_title):\n main_answer = self.ans.get_main_words(answer)\n #print '<br>main lemmata of answer: ',main_answer,'<br>' #for debugging\n\n main_par = self.ans.get_main_words(par_title)\n #print '<br>main lemmata of par_title: ',main_par,'<br><br>' #for debugging\n\n success = '1'\n\n for lemma in main_answer:\n if lemma not in main_par:\n success = '0'\n break\n return success","title":""},{"docid":"6b60a1acdfeb7595fa38611f7e45d8a9","score":"0.51840717","text":"def find_pronoun(sent):\n pronoun = None\n\n for word, part_of_speech in sent.pos_tags:\n # Disambiguate pronouns\n if part_of_speech == 'PRP' and word.lower() == 'you':\n pronoun = 'I'\n elif part_of_speech == 'PRP' and word == 'I':\n # If the user mentioned themselves, then they will definitely be the pronoun\n pronoun = 'You'\n return pronoun","title":""},{"docid":"518c9aa1c3370375c9ae9ef71f96d3d1","score":"0.5174752","text":"def search(self, word):\r\n return any(\r\n self.magicdict.get(c, 0) > 1\r\n or self.magicdict.get(c, 0) == 1 and word not in self.magicset\r\n for c in self._candidate(word))","title":""},{"docid":"ce7365e8581c90bfb23003d3a2ed6ead","score":"0.51745605","text":"def busqueda(self, xPers):","title":""},{"docid":"fd7ac766c0de25cc56089e8042174747","score":"0.5173696","text":"def searchTextString(source, topic, people):\r\n listWordsTopic = []\r\n if topic != -1:\r\n listWordsTopic = vocTopics[topic][language_in]\r\n for i in range(len(listWordsTopic)):\r\n listWordsTopic[i] = listWordsTopic[i].encode('utf-8')\r\n listWordsPeople = []\r\n if people != -1:\r\n listWordsPeople = vocPeople[people][language_in]\r\n for i in range(len(listWordsPeople)):\r\n listWordsPeople[i] = listWordsPeople[i].encode('utf-8')\r\n print(\"listWordsTopic\", listWordsTopic)\r\n print(\"listWordsPeople\", listWordsPeople)\r\n foundSource = None\r\n print(\"Searching....\")\r\n foundResults = {} #key, topic, people\r\n if (source == 'bible' or source == -1):\r\n foundSource = 'bible'\r\n for book in Bible:\r\n #print(\"book\", book)\r\n for bookNum in Bible[book]:\r\n #print(\"bookNum\", bookNum)\r\n for verseNum in Bible[book][bookNum]:\r\n #print(\"verseNum\", verseNum)\r\n #print(Bible[book][bookNum][verseNum])\r\n #print(Bible[book][bookNum][verseNum][language_out])\r\n if (language_out in Bible[book][bookNum][verseNum]): #check that language exists\r\n foundKey = BibleDataToID(book, bookNum, verseNum)\r\n wordInSentence(foundSource, listWordsTopic, 'topic', Bible[book][bookNum][verseNum][language_out], foundKey, foundResults)\r\n wordInSentence(foundSource, listWordsPeople, 'people', Bible[book][bookNum][verseNum][language_out], foundKey, foundResults)\r\n\r\n if (source == 'pray' or source == -1):\r\n foundSource = 'pray'\r\n for prayer in prayers:\r\n for part in prayers[prayer]['parts']:\r\n #print(prayers[prayer]['parts'][part])\r\n if (language_out in prayers[prayer]['parts'][part]): #check that language exists\r\n foundKey = prayer\r\n wordInSentence(foundSource, listWordsTopic, 'topic', prayers[prayer]['parts'][part][language_out], foundKey, foundResults)\r\n wordInSentence(foundSource, listWordsPeople, 'people', prayers[prayer]['parts'][part][language_out], foundKey, foundResults)\r\n\r\n\r\n if (source == 'pope' or source == -1): #exactly the same as prayers\r\n foundSource = 'pope'\r\n for popeword in pope:\r\n for part in pope[popeword]['parts']:\r\n if (language_out in pope[popeword]['parts'][part]): #check that language exists\r\n foundKey = popeword\r\n wordInSentence(foundSource, listWordsTopic, 'topic', pope[popeword]['parts'][part][language_out], foundKey, foundResults)\r\n wordInSentence(foundSource, listWordsPeople, 'people', pope[popeword]['parts'][part][language_out], foundKey, foundResults)\r\n\r\n\r\n if (source == 'quotes' or source == -1): #exactly the same as prayers\r\n foundSource = 'quotes'\r\n for quote in quotes:\r\n for part in quotes[quote]['parts']:\r\n if (language_out in quotes[quote]['parts'][part]): #check that language exists\r\n foundKey = quote\r\n wordInSentence(foundSource, listWordsTopic, 'topic', quotes[quote]['parts'][part][language_out], foundKey, foundResults)\r\n wordInSentence(foundSource, listWordsPeople, 'people', quotes[quote]['parts'][part][language_out], foundKey, foundResults)\r\n\r\n #print(\"foundResults before saints:\", foundResults)\r\n if (people != -1):\r\n if people[0].isdigit(): #if it's a date, the query is a saint\r\n foundSource = 'saints'\r\n foundKey = people[0:len(people)-1] + 's'\r\n if foundKey not in foundResults:\r\n foundResults[foundKey] = {}\r\n foundResults[foundKey]['source'] = foundSource\r\n foundResults[foundKey]['topic'] = 0\r\n foundResults[foundKey]['people'] = 1\r\n\r\n\r\n #print(\"foundResults:\", foundResults)\r\n #filter case of AND (as so far it collected OR)\r\n if (topic != -1 and people != -1):\r\n for result in list(foundResults): #converted to a list to force a copy of the keys to avoid runtime error\r\n if foundResults[result]['topic']==0 or foundResults[result]['people'] ==0:\r\n del foundResults[result]\r\n #print(\"deleted\", result)\r\n\r\n #print(\"entriesSkip\", entriesSkip)\r\n for result in list(foundResults):\r\n #print([-1, foundResults[result]['source'], topic, people, result])\r\n if ([-1, foundResults[result]['source'], topic, people, result] in entriesSkip):\r\n del foundResults[result]\r\n #print(\"skipping\", result)\r\n\r\n print(\"filtered foundResults:\", foundResults)\r\n\r\n \"\"\"\r\n foundResults contains for each key:\r\n the source where it has been found, and the flags topic and people, which are 0/1\r\n the variable foundSource will be overwritten and is not usable\r\n topic and people are the variables containing the original query parameters\r\n \"\"\"\r\n \r\n if len(foundResults) > 0: \r\n chosenResult = random.choice(list(foundResults)) #chosenResult is a key of dictionary\r\n if ([-1, source, topic, people, chosenResult, '0'] not in entries):\r\n print(\"adding to the entries\")\r\n entries.append([-1, source, topic, people, chosenResult, '0'])\r\n print(\"chosenResult:\",chosenResult)\r\n #print(\"entries\", entries)\r\n\r\n return [foundResults[chosenResult]['source'], chosenResult]\r\n else:\r\n return [None, None]","title":""},{"docid":"fce05c06e3b70acbfd536b5a5375ee95","score":"0.51694447","text":"def search(self, word: str) -> bool:\n trie = self.lookup\n\n def _helper(word, trie):\n if '#' in trie:\n return True\n\n if word[0]=='.':\n for k in trie.keys():\n if _helper(word[1:], trie[k]):\n return True\n return False\n elif word[0] in trie:\n return _helper(word[1:], trie[word[0]])\n else:\n return False\n \n return _helper(word, trie)","title":""},{"docid":"34b671a639e7f2f78a29b0e96a68b0ca","score":"0.51692384","text":"def search(self, sentence):\n docs = self.solr.query(nltk.word_tokenize(sentence))\n for doc in docs:\n print \" \".join(doc[\"tokens\"])","title":""},{"docid":"73f60bf9569920fa87bdb9f44707ccf4","score":"0.5167708","text":"def __contains__(self, word: str) -> bool:\n word = word.lower()\n for w in self.words:\n if word == w:\n return True\n return False","title":""},{"docid":"f8e1ce0bfed13370e770dedcdaafb566","score":"0.51632833","text":"def search(term):\n try:\n for ds in thesaurus.keys():\n if term in thesaurus[ds].speicher:\n #-->Ueberprufen ob das Term in self.speicher\n print thesaurus[ds]\n else:\n return False\n return True\n except:\n print \"Exception:\\nsearch() funktion.\"","title":""},{"docid":"2e8d81790ed622e692adc7fbc12c6243","score":"0.5159494","text":"def search(self, word):\n curr = self.root\n\n for char in word:\n if char in curr.children:\n curr = curr.children[char]\n else:\n return False\n\n return curr.word","title":""},{"docid":"0eddfb92df58fbf9cd8f12378037ebba","score":"0.51592755","text":"def phonetics(self):\n status = False\n for phile in self.files.all():\n for tag in phile.tags.all():\n if tag.name == 'Phonetics':\n status = phile.phile.name\n break\n return status","title":""},{"docid":"a9abed17f7360fba0ae9fdb6a7d3160b","score":"0.51582825","text":"def find(self, word):\n letters = list(word)\n node = self._root\n \n for letter in letters:\n if letter not in node._children:\n return False\n node = node._children[letter]\n if node.isWord:\n if node._value:\n return node._value\n else:\n return True\n else:\n return False","title":""},{"docid":"62d3d7fcae0609fe97563d2dd63891f3","score":"0.51580274","text":"def search(self, word):\n i = 0\n it = self.root\n while i < len(word):\n if len(it.children) > 0:\n k = bisect.bisect_left(it.children, TreeNode(word[i]))\n if k < len(it.children) and it.children[k].val == word[i]:\n it = it.children[k]\n i += 1\n else:\n break\n else:\n break\n if i != len(word) or not it.is_word:\n return False\n else:\n return True","title":""},{"docid":"e3cec28d40485563f092fa41b4c7ca69","score":"0.5153317","text":"def search(self, word):\n current = self.root\n for i in word:\n if current.dict.get(i,None)==None:\n return False\n current = current.dict[i]\n\n if current.isEOW==True:\n return True\n else:\n return False","title":""},{"docid":"7e50da15c7e6884f2df967d049fb96df","score":"0.51523215","text":"def search(self, word: str) -> bool:\n current = self.root\n for i in word:\n if i in current.node.keys():\n current = current.node[i]\n else:\n return False\n return current.isword","title":""},{"docid":"b30b7b67c28f90d3021da2d79927ad56","score":"0.5147014","text":"def easy_search_terms(self, sentence, esource, ths, rules, off_list=None):\n\t\treduced_term_list = []\n\t\tsentence_terms = []\n\t\tsentence_terms_text = []\n\t\tentity_tokens = []\n\n\n\t\t#Create a gazette with words that start with any word of the sentence\n\t\t#Why was this after next section?\n\t\tfor token in sentence.tokens:\n\t\t\tptoken = str(token.text.encode(\"utf-8\")).lower()\n\t\t\tif ptoken in self.term_dic:\n\t\t\t\tfor term in self.term_dic[ptoken]:\n\t\t\t\t\t#print term\n\t\t\t\t\treduced_term_list.append(term)\n\n\t\tif off_list != None:\n\t\t\tif \"twice_validated\" in rules:\n\t\t\t\tfor term in off_list + reduced_term_list:\n\t\t\t\t\ttsplit = term.split(\" \")\n\t\t\t\t\tif len(tsplit) == 2:\n\t\t\t\t\t\tt = tsplit[1] + \" in the \" + tsplit[0]\n\t\t\t\t\t\tt2 = tsplit[1] + \" of the \" + tsplit[0]\n\t\t\t\t\t\t#print sentence.sid, t\n\t\t\t\t\t\tif t in sentence.text.lower() or t2 in sentence.text.lower():\n\t\t\t\t\t\t\t#print t\n\t\t\t\t\t\t\treduced_term_list.append(tsplit[1] + \" in the \" + tsplit[0])\n\t\t\t\t\t\t\treduced_term_list.append(tsplit[1] + \" of the \" + tsplit[0])\n\t\t\t\t\tif \"type\" in term:\n\t\t\t\t\t\ti = term.find(\"type\")\n\t\t\t\t\t\tx = term[:i].strip()\n\t\t\t\t\t\ty = term[i:].strip()\n\t\t\t\t\t\treduced_term_list.append(x + \" \" + y)\n\t\t\t\t\t\treduced_term_list.append(x + \" \" + y.replace(\"type\", \"\"))\n\t\t\t\t\t\treduced_term_list.append(y + \" \" + x)\n\t\t\t\t\t\treduced_term_list.append(y + \" \" + x.replace(\"type\", \"\"))\n\n\n\n\t\t#Iterate gazette and check if the sentence has any exact match\n\t\tfor term in reduced_term_list:\n\t\t\tterm_tokens = nltk.word_tokenize(term.lower())\n\t\t\ttoken_flag = False\n\t\t\tfor token in sentence.tokens:\n\t\t\t\tif term_tokens[0].lower() == token.text.lower():\n\t\t\t\t\ttoken_flag = True\n\t\t\t\t\tstart_index = sentence.tokens.index(token)\n\t\t\tif token_flag:\t\t\t\t\t\n\t\t\t\tend_index = start_index + len(term_tokens)\n\t\t\t\tentity_tokens = []\n\t\t\t\tfor token in sentence.tokens[start_index:end_index]:\n\t\t\t\t\tentity_tokens.append(token.text)\n\t\t\t\t#Check if gazette term is the same as sentence tokens for that term.\t\t\t\t\t\t\t\t\n\t\t\t\tif term_tokens == [x.lower() for x in entity_tokens]:\n\t\t\t\t\tstart = sentence.tokens[start_index:end_index][0].dstart\n\t\t\t\t\tend = sentence.tokens[start_index:end_index][-1].dend\n\t\t\t\t\tterm = str(sentence.text[sentence.tokens[start_index:end_index][0].start:sentence.tokens[start_index:end_index][-1].end])\t\t\t\t\t\n\t\t\t\t\tsentence_terms.append((start, end, term))\n\t\t\t\t\tsentence_terms_text.append(term)\n\t\t\t\t\t#print term\n\t\t\t\t\t\n\n\t\t### RULES FOR VALIDATION (maybe should be in functions)\n\t\t#Undidented next line. See if it changes reslts.\n\t\tfor token in sentence.tokens: \t# sentence and adds the combination and next word\n\t\t\tif \"posgowords\" in rules: #Tries to find a combination of go_word and pos_go_word in\n\t\t\t\tpos_flag = False\t\t\t# if next word is not a noun, looks for next word.\n\t\t\t\ttok = str(token.text.encode(\"utf-8\").strip().lower())\n\t\t\t\tif tok in go_words:\n\t\t\t\t\ttlist = []\n\t\t\t\t\tfor word in pos_go_words:\n\t\t\t\t\t\tterm = tok + \" \" + word\n\t\t\t\t\t\tif term in str(sentence.text.encode(\"utf-8\")):\n\t\t\t\t\t\t\ttlist.append(term)\n\t\t\t\t\tif len(tlist) > 0:\n\t\t\t\t\t\tterm = max(tlist, key=len)\n\t\t\t\t\t\tl = len(term.split(\" \"))\n\t\t\t\t\t\tindex_start = sentence.tokens.index(token)\n\t\t\t\t\t\tindex_end = index_start + l + 1 #+1 for next word\n\t\t\t\t\t\tterm = sentence.text[sentence.tokens[index_start:index_end][0].start:sentence.tokens[index_start:index_end][-1].end]\n\t\t\t\t\t\tif sentence.tokens[index_end-1].pos != \"NN\" and sentence.tokens[index_end-1].pos != \"NNS\": #\n\t\t\t\t\t\t\tindex_end += 1\n\t\t\t\t\t\t\tterm = sentence.text[sentence.tokens[index_start:index_end][0].start:sentence.tokens[index_start:index_end][-1].end]\n\t\t\t\t\t\tif index_end < len(sentence.tokens):\n\t\t\t\t\t\t\tif sentence.tokens[index_end].pos == \"NN\" or sentence.tokens[index_end].pos == \"NNS\":\n\t\t\t\t\t\t\t\tindex_end += 1\n\t\t\t\t\t\t\t\tterm = sentence.text[sentence.tokens[index_start:index_end][0].start:sentence.tokens[index_start:index_end][-1].end]\n\t\t\t\t\t\tsentence_terms.append((sentence.tokens[index_start:index_end][0].dstart, sentence.tokens[index_start:index_end][-1].dend, str(term.strip())))\n\t\t\t\t\n\t\t\tif \"gowords\" in rules:\n\t\t\t\tgo_flag = False\n\t\t\t\tif str(token.text.encode(\"utf-8\")).strip().lower() in go_words:\n\t\t\t\t\tindex = sentence.tokens.index(token)\n\t\t\t\t\tfor word in go_words:\n\t\t\t\t\t\tterm = str(sentence.tokens[index-1].text) + \" \" + word\n\t\t\t\t\t\tif term in reduced_term_list:\n\t\t\t\t\t\t\t#print term, \"---\", token.text, \"---\", sentence.text\n\t\t\t\t\t\t\tgo_flag = True\n\t\t\t\tif go_flag and index-1 > 0 and index+1 < len(sentence.tokens):\n\t\t\t\t\tprint \"********\"\n\t\t\t\t\tstart = sentence.tokens[index-1:index+1][0].dstart\n\t\t\t\t\tend = sentence.tokens[index-1:index+1][-1].dend\n\t\t\t\t\tterm = sentence.text[sentence.tokens[index-1:index+1][0].start:sentence.tokens[index-1:index+1][-1].end]\n\t\t\t\t\tsentence_terms.append((start, end, str(term).strip()))\n\n\n\t\tif \"longterms\" in rules: #Add terms that are longer than the ones that exist.\n\t\t\tsentence_entities = [] \n\t\t\tfor s in sentence.entities.elist:\n\t\t\t\tif s.startswith(esource):\n\t\t\t\t\tsentence_entities = [str(x.text.encode(\"utf-8\")) for x in sentence.entities.elist[s]]\n\n\t\t\tfor term in [x[2] for x in sentence_terms] + sentence_entities:\n\t\t\t\tterm_tokens = nltk.word_tokenize(term.strip().lower())\n\t\t\t\tfor token in sentence.tokens:\n\t\t\t\t\tif term_tokens[0].lower() == token.text.lower():\n\t\t\t\t\t\tstart_index = sentence.tokens.index(token)\n\t\t\t\t\t\tend_index = start_index + len(term_tokens)\n\t\t\t\t\t\tif term_tokens == [str(x.text) for x in sentence.tokens[start_index:end_index]]:\n\t\t\t\t\t\t\t#Look for bigger term to the left\n\t\t\t\t\t\t\tif start_index > 0:\n\t\t\t\t\t\t\t\tif sentence.tokens[start_index-1].text == \"and\" and sentence.tokens[end_index-1].text in go_words:\n\t\t\t\t\t\t\t\t\ti = 2\n\t\t\t\t\t\t\t\t\twhile \",\" in sentence.tokens[start_index-i:start_index+1-i][0].text:\n\t\t\t\t\t\t\t\t\t\tterm = \" \".join([x.text for x in sentence.tokens[start_index-1-i:end_index]]).replace(\" ,\", \",\")\n\t\t\t\t\t\t\t\t\t\tstart = sentence.tokens[start_index-1-i:end_index][0].dstart\n\t\t\t\t\t\t\t\t\t\tend = sentence.tokens[start_index-1-i:end_index][-1].dend\n\t\t\t\t\t\t\t\t\t\tsentence_terms.append((start, end, str(term.strip())))\n\t\t\t\t\t\t\t\t\t\ti += 2\n\n\t\t\t\t\t\t\t#look for bigger term to the right (bigger than 2)\n\t\t\t\t\t\t\tif end_index < len(sentence.tokens):\n\t\t\t\t\t\t\t\tif sentence.tokens[end_index].text == \",\" and sentence.tokens[start_index].text in go_words:\n\t\t\t\t\t\t\t\t\ti = 2\n\t\t\t\t\t\t\t\t\twhile \",\" in sentence.tokens[end_index+i:end_index+1+i][0].text:\n\t\t\t\t\t\t\t\t\t\tterm = \" \".join([x.text for x in sentence.tokens[start_index:end_index+1+i]]).replace(\" ,\", \",\")\n\t\t\t\t\t\t\t\t\t\tstart = sentence.tokens[start_index:end_index+1+i][0].dstart\n\t\t\t\t\t\t\t\t\t\tend = sentence.tokens[start_index:end_index+1+i][-1].dend\n\t\t\t\t\t\t\t\t\t\tif sentence.tokens[start_index:end_index+1+i][-1].text == \",\":\n\t\t\t\t\t\t\t\t\t\t\tend -=1\n\t\t\t\t\t\t\t\t\t\t\tterm = term[:-1]\n\t\t\t\t\t\t\t\t\t\tsentence_terms.append((start, end, str(term.strip())))\n\t\t\t\t\t\t\t\t\t\ti += 2\n\t\t\t\t\t\t\t\t\tterm = \" \".join([x.text for x in sentence.tokens[start_index:end_index+1+i]]).replace(\" ,\", \",\")\n\t\t\t\t\t\t\t\t\tstart = sentence.tokens[start_index:end_index+1+i][0].dstart\n\t\t\t\t\t\t\t\t\tend = sentence.tokens[start_index:end_index+1+i][-1].dend\n\t\t\t\t\t\t\t\t\tif sentence.tokens[start_index:end_index+1+i][-1].text == \",\":\n\t\t\t\t\t\t\t\t\t\tend -=1\n\t\t\t\t\t\t\t\t\t\tterm = term[:-1]\n\t\t\t\t\t\t\t\t\tsentence_terms.append((start, end, str(term.strip())))\n\t\t\t\t\t\t\t#Check if its a double term ex: \"x and y anomalies\"\n\t\t\t\t\t\t\tif start_index > 0:\n\t\t\t\t\t\t\t\tif sentence.tokens[start_index-1].text == \"and\":\n\t\t\t\t\t\t\t\t\tterm_flag = False\n\t\t\t\t\t\t\t\t\ttok1 = sentence.tokens[start_index-2:start_index-1][0].text\n\t\t\t\t\t\t\t\t\ttok2 = sentence.tokens[end_index-1].text\n\t\t\t\t\t\t\t\t\tif str(tok2) in go_words:\n\t\t\t\t\t\t\t\t\t\tfor word in go_words:\n\t\t\t\t\t\t\t\t\t\t\tjoin_tok = str(tok1) + \" \" + word\n\t\t\t\t\t\t\t\t\t\t\tif join_tok in reduced_term_list:\n\t\t\t\t\t\t\t\t\t\t\t\tterm_flag = True\n\t\t\t\t\t\t\t\t\tif term_flag:\n\t\t\t\t\t\t\t\t\t\tstart = sentence.tokens[start_index-2:end_index][0].dstart\n\t\t\t\t\t\t\t\t\t\tend = sentence.tokens[start_index-2:end_index][-1].dend\n\t\t\t\t\t\t\t\t\t\tterm = sentence.text[sentence.tokens[start_index-2:end_index][0].start:sentence.tokens[start_index-2:end_index][-1].end]\n\t\t\t\t\t\t\t\t\t\tsentence_terms.append((start, end, str(term).strip()))\n\n\t\tif \"small_ent\" in rules: #Remove smaller entities\n\t\t\tsmaller_entities = set([])\n\t\t\tfor a in sentence_terms:\n\t\t\t\tfor b in sentence_terms_text + entity_tokens:\n\t\t\t\t\tif a[2].lower() in b and a[2].lower() != b:\n\t\t\t\t\t\t#logging.info(\"{}: small entity: {} / big entity: {}\".format(sentence.sid, a, b))\n\t\t\t\t\t\tsmaller_entities.add(a)\n\t\t\tfor x in smaller_entities:\n\t\t\t\tsentence_terms.remove(x)\n\t\t\t\t#logging.info(\"{}, removed smaller entity: {}\".format(sentence.sid, x))\n\n\t\tfor term in reduced_term_list: #Check if sentence is composed by a single entity\n\t\t\t#print term, str(sentence.text)\n\t\t\tif term.lower() == str(sentence.text).lower():\n\t\t\t\t#print term\n\t\t\t\tsentence_terms = []\n\t\t\t\tstart = 0\n\t\t\t\tend = len(term)\n\t\t\t\tterm = str(sentence.text)\t\t\t\t\t\n\t\t\t\tsentence_terms.append((start, end, term))\n\n\t\tsentence_terms = list(set(sentence_terms))\n\t\t#if len(sentence_terms) > 0:\n\t\t#\tprint sentence.sid, sentence_terms\n\t\treturn sentence_terms","title":""},{"docid":"603d36f4ea8dd5954778da1408869859","score":"0.5146544","text":"def search(self, word: str) -> bool:\n point = self.root\n for ch in word:\n if point.childers[ch] is None:\n return False\n point = point.childers[ch]\n return point.is_end","title":""},{"docid":"21b814248255a3308b1030b3e913265c","score":"0.5143131","text":"def search_word(self, word):\n current = self.root\n for letter in word:\n current = current.children.get(letter)\n if not current:\n return False\n return current.is_word","title":""},{"docid":"5129a7afd385ec5a88186b9bc4b1788b","score":"0.5137912","text":"def search(self, word):\n def _dfs(trie, idx):\n \n if idx == len(word) and trie.get(\"final\") and trie[\"final\"] == word:\n return True\n char = word[idx]\n if char != '.' and char in trie:\n return _dfs(trie[char], idx + 1)\n else:\n valid = False\n for char in trie.keys():\n valid = valid or _dfs(trie[char], idx + 1)\n if valid:\n return True\n return False\n \n return _dfs(self._trie, 0)","title":""},{"docid":"d4e18b7b98b229ae8b26a167c9a88a51","score":"0.51302934","text":"def match_nouns(self, nword):\n\n def filter_is_true(nid, test):\n \"\"\"Run a test on a particular noun, passed by ID.\"\"\"\n try:\n tmethod = self.filters[test.lstrip('!')]\n except KeyError:\n return True\n \n return tmethod(nid) ^ (test[0] == '!') # negate if test starts with !\n \n def get_matching_nouns(iword):\n return (self.get_nouns_by_name(self.sub_input_words(nword)) if nword[0] == '%'\n else filter(bool, (self.nouns.get(nid) for nid in nword.split(','))))\n \n if ':' in nword:\n # filter matching nouns using tests\n nword, tests = nword.split(':', 1)\n return set(noun for noun in get_matching_nouns(nword)\n if all(filter_is_true(noun.id, test) for test in tests.split(':')))\n else:\n return get_matching_nouns(nword)","title":""},{"docid":"2474539b8ee1273f7aeece86997bb162","score":"0.512346","text":"def zh_word(self, word: str) -> str:\n\n alternate_meanings = []\n alternate_pinyin = ()\n alternate_jyutping = None\n\n eth_page = requests.get(\n \"https://www.mdbg.net/chinese/dictionary?page=worddict&wdrst=0&wdqb=c:\"\n + word,\n timeout=15,\n headers=self.zw_useragent,\n )\n tree = html.fromstring(eth_page.content) # now contains the whole HTML page\n word_exists = str(\n tree.xpath('//p[contains(@class,\"nonprintable\")]/strong/text()')\n )\n cmn_pronunciation = tree.xpath('//div[contains(@class,\"pinyin\")]/a/span/text()')\n # We only want the pronunciations to be as long as the input\n cmn_pronunciation = cmn_pronunciation[0 : len(word)]\n # We don't need a dividing character per pinyin standards\n cmn_pronunciation = \"\".join(cmn_pronunciation)\n\n # Check to not return anything if the entry is invalid\n if \"No results found\" in word_exists:\n # First we try to check our specialty dictionaries. Buddhist dictionary first. Then the tea dictionary.\n search_results_buddhist = self.__zh_word_buddhist_dictionary_search(\n tradify(word)\n )\n search_results_tea = self.__zh_word_tea_dictionary_search(simplify(word))\n search_results_cccanto = self.__zh_word_cccanto_search(tradify(word))\n\n # If both have nothing, we kick it down to the character search.\n if (\n search_results_buddhist is None\n and search_results_tea is None\n and search_results_cccanto is None\n ):\n logger.info(\n \"ZH-Word: No results found. Getting individual characters instead.\"\n )\n # This will split the word into character chunks.\n if len(word) < 2:\n to_post = self.zh_character(word)\n else: # The word is longer than one character.\n to_post = \"\"\n search_characters = list(word)\n for character in search_characters:\n to_post += \"\\n\\n\" + self.zh_character(character)\n return to_post\n\n # Otherwise, let's try to format the data nicely.\n if search_results_buddhist is not None:\n alternate_meanings.append(search_results_buddhist[\"meaning\"])\n alternate_pinyin = search_results_buddhist[\"pinyin\"]\n if search_results_tea is not None:\n alternate_meanings.append(search_results_tea[\"meaning\"])\n alternate_pinyin = search_results_tea[\"pinyin\"]\n if search_results_cccanto is not None:\n alternate_meanings.append(search_results_cccanto[\"meaning\"])\n alternate_pinyin = search_results_cccanto[\"pinyin\"]\n alternate_jyutping = search_results_cccanto[\"jyutping\"]\n logger.info(\n f\"ZH-Word: No results for word {word}, but results are in specialty dictionaries.\"\n )\n\n if len(alternate_meanings) == 0:\n # The standard search function for regular words.\n # Get alternate pinyin from a separate function. We get Wade Giles and Yale. Like 'Guan1 yin1 Pu2 sa4'\n try:\n py_split_pronunciation = tree.xpath(\n '//div[contains(@class,\"pinyin\")]/a/@onclick'\n )\n py_split_pronunciation = re.search(\n r\"\\|(...+)\\'\\)\", py_split_pronunciation[0]\n ).group(0)\n # Format it nicely.\n py_split_pronunciation = py_split_pronunciation.split(\"'\", 1)[0][\n 1:\n ].strip()\n alt_romanize = self.__zh_word_alt_romanization(py_split_pronunciation)\n except IndexError:\n # This likely means that the page does not contain that information.\n alt_romanize = (\"---\", \"---\")\n\n meaning = [\n div.text_content()\n for div in tree.xpath('//div[contains(@class,\"defs\")]')\n ]\n # This removes any empty spaces or commas that are in the list.\n meaning = [x for x in meaning if x not in [\" \", \", \"]]\n meaning = \"/ \".join(meaning)\n meaning = meaning.strip()\n\n # Obtain the Cantonese information.\n yue_page = requests.get(\n \"https://cantonese.org/search.php?q=\" + word,\n timeout=15,\n headers=self.zw_useragent,\n )\n yue_tree = html.fromstring(\n yue_page.content\n ) # now contains the whole HTML page\n yue_pronunciation = yue_tree.xpath(\n '//h3[contains(@class,\"resulthead\")]/small/strong//text()'\n )\n # This Len needs to be double because of the numbers\n yue_pronunciation = yue_pronunciation[0 : (len(word) * 2)]\n yue_pronunciation = iter(yue_pronunciation)\n yue_pronunciation = [\n c + next(yue_pronunciation, \"\") for c in yue_pronunciation\n ]\n\n # Combines the tones and the syllables together\n yue_pronunciation = \" \".join(yue_pronunciation)\n for i in range(0, 9):\n # Adds Markdown syntax\n yue_pronunciation = yue_pronunciation.replace(str(i), f\"^({str(i)}) \")\n yue_pronunciation = yue_pronunciation.strip()\n\n else: # This is for the alternate search with the specialty dictionaries.\n cmn_pronunciation = self.__zh_word_decode_pinyin(alternate_pinyin)\n alt_romanize = self.__zh_word_alt_romanization(alternate_pinyin)\n if alternate_jyutping is not None:\n yue_pronunciation = alternate_jyutping\n else:\n yue_pronunciation = \"---\" # The non-Canto specialty dictionaries do not include Jyutping pronunciation.\n meaning = \"\\n\".join(alternate_meanings)\n\n # Format the header appropriately.\n if tradify(word) == simplify(word):\n lookup_line_1 = str(\n \"# [{0}](https://en.wiktionary.org/wiki/{0}#Chinese)\".format(word)\n )\n else:\n lookup_line_1 = (\n \"# [{0} ({1})](https://en.wiktionary.org/wiki/{0}#Chinese)\".format(\n tradify(word), simplify(word)\n )\n )\n\n # Format the rest.\n lookup_line_1 += \"\\n\\nLanguage | Pronunciation\\n---------|--------------\"\n lookup_line_1 += (\n f\"\\n**Mandarin** (Pinyin) | *{cmn_pronunciation}*\\n**Mandarin** (Wade-Giles) | *{alt_romanize[1]}*\"\n f\"\\n**Mandarin** (Yale) | *{alt_romanize[0]}*\\n**Cantonese** | *{yue_pronunciation}*\"\n )\n\n # Add Hokkien and Hakka data.\n lookup_line_1 += self.__zh_character_min_hak(tradify(word))\n\n # Format the meaning line.\n if len(alternate_meanings) == 0:\n # Format the regular results we have.\n lookup_line_2 = f'\\n\\n**Meanings**: \"{meaning}.\"'\n\n # Append chengyu data if the string is four characters.\n if len(word) == 4:\n chengyu_data = self.__zh_word_chengyu(word)\n if chengyu_data is not None:\n logger.info(\"ZH-Word: >> Added additional chengyu data.\")\n lookup_line_2 += chengyu_data\n\n # We append Buddhist results if we have them.\n mainline_search_results_buddhist = (\n self.__zh_word_buddhist_dictionary_search(tradify(word))\n )\n if mainline_search_results_buddhist is not None:\n lookup_line_2 += mainline_search_results_buddhist[\"meaning\"]\n\n else: # This is for the alternate dictionaries only.\n lookup_line_2 = \"\\n\" + meaning\n\n # Format the footer with the dictionary links.\n lookup_line_3 = (\n \"\\n\\n\\n^Information ^from \"\n \"[^CantoDict](https://www.cantonese.sheik.co.uk/dictionary/search/?searchtype=1&text={0}) ^| \"\n \"[^MDBG](https://www.mdbg.net/chinese/dictionary?page=worddict&wdrst=0&wdqb=c:{0}) ^| \"\n \"[^Yellowbridge](https://yellowbridge.com/chinese/dictionary.php?word={0}) ^| \"\n \"[^Youdao](https://dict.youdao.com/w/eng/{0}/#keyfrom=dict2.index)\"\n )\n lookup_line_3 = lookup_line_3.format(word)\n\n # Combine everything together.\n to_post = lookup_line_1 + lookup_line_2 + \"\\n\\n\" + lookup_line_3\n logger.info(\n f\"ZH-Word: Received a lookup command for {word} in Chinese. Returned search results.\"\n )\n return to_post","title":""},{"docid":"f11e2aedbf9cc55ef6eaa3a0d9f87405","score":"0.5122512","text":"def search(self, word: str) -> bool:\n stack = [(self.trie, 0)] \n while len(stack):\n ptr, i = stack.pop()\n \n if i == len(word):\n if ptr.is_word:\n return True\n elif word[i] == '.':\n for child in ptr.children:\n if child:\n stack.append((child, i+1))\n else:\n c = ord(word[i]) - ord('a')\n if ptr.children[c]:\n stack.append((ptr.children[c], i+1))\n \n return False","title":""},{"docid":"47c9e87dd0adb9194f38e3ce3743f15f","score":"0.51146907","text":"def test_recognize(self):\n expected = set([\"banana\"])\n assert self.recognizer.recognize(\"ana\") == expected # \"ana\" should match to \"banana\" within a levenshtein threshold of 3","title":""},{"docid":"b42b6b8b2de85ce68c9c2d7a629ebc82","score":"0.5110162","text":"def filter_pos(word, obj, linkage):\n # Filtering trigrams\n if obj.third_word:\n if word == obj.first_word:\n if not filter_check_conj(obj.second_word, obj.first_word,\n obj.first_word_pos, obj.third_word_pos, obj, linkage):\n if not filter_check_conj(obj.third_word, obj.first_word,\n obj.first_word_pos, obj.second_word_pos, obj, linkage):\n if not filter_check_trigrams(word, obj.first_word_pos, obj.second_word_pos,\n obj.third_word_pos, obj, linkage):\n filter_check_trigrams(word, obj.first_word_pos, obj.third_word_pos,\n obj.second_word_pos, obj, linkage)\n elif word == obj.second_word:\n if not filter_check_conj(obj.first_word, obj.second_word,\n obj.second_word_pos, obj.third_word_pos, obj, linkage):\n if not filter_check_conj(obj.third_word, obj.second_word,\n obj.first_word_pos, obj.second_word_pos, obj, linkage):\n if not filter_check_trigrams(word, obj.second_word_pos, obj.first_word_pos,\n obj.third_word_pos, obj, linkage):\n filter_check_trigrams(word, obj.second_word_pos, obj.third_word_pos,\n obj.first_word_pos, obj, linkage)\n else:\n if not filter_check_conj(obj.second_word, obj.third_word,\n obj.first_word_pos, obj.third_word_pos, obj, linkage):\n if not filter_check_conj(obj.first_word, obj.third_word,\n obj.third_word_pos, obj.second_word_pos, obj, linkage):\n if not filter_check_trigrams(word, obj.third_word_pos, obj.second_word_pos,\n obj.first_word_pos, obj, linkage):\n filter_check_trigrams(word, obj.third_word_pos, obj.first_word_pos,\n obj.second_word_pos, obj, linkage)\n # Filtering bigrams\n else:\n if word == obj.first_word:\n if obj.first_word_pos in self.possible_bigrams \\\n and obj.second_word_pos in self.possible_bigrams[obj.first_word_pos]:\n create_candidates_dict(self.filtered_candidates, word, linkage, [obj])\n else:\n if obj.second_word_pos in self.possible_bigrams \\\n and obj.first_word_pos in self.possible_bigrams[obj.second_word_pos]:\n create_candidates_dict(self.filtered_candidates, word, linkage, [obj])","title":""},{"docid":"bc62ea8916b0b471b876b1cad20095d6","score":"0.51050824","text":"def __contains__(self, word: str) -> bool:\n return self.search(word) > 0","title":""},{"docid":"3a481df1012cf275a9b824619bc2e25f","score":"0.510192","text":"def search(self, word: str) -> bool:\n cur = self\n i = 0\n while i < len(word) and word[i] in cur.children:\n cur = cur.children[word[i]]\n i += 1\n \n return i == len(word) and cur.count >= 1","title":""},{"docid":"61a932810d8b77e3f4b7a92447938e25","score":"0.51014376","text":"def noun(self, w):\n if w in self.jap_noun_dict.keys():\n return self.jap_noun_dict[w]\n elif w in self.eng_noun_dict.keys():\n return self.eng_noun_dict[w]\n else:\n print('Noun', w, 'not found')\n assert False","title":""},{"docid":"3fa4a689f9afd87da8e99c9a1e6fdb5d","score":"0.50992703","text":"def search(self, word):\n return self.root.search(word)","title":""},{"docid":"2dd50c62527f5cd09869f2a88189be14","score":"0.5097264","text":"def solve_coref(self):\n nlp = LangModel.get_instance()\n text = self.to_txt()\n doc = nlp(text)\n tok_list = list(token.text_with_ws for token in doc)\n for cluster in doc._.coref_clusters:\n cluster_main_words = set(cluster.main.text.split(' '))\n for coref in cluster:\n if coref != cluster.main.text and bool(set(coref.text.split(' ')).intersection(cluster_main_words)) == False:\n tok_list[coref.start] = cluster.main.text + doc[coref.end - 1].whitespace_\n for i in range(coref.start + 1, coref.end):\n tok_list[i] = \"\"\n self.txt = \"\".join(tok_list)\n return self.txt","title":""},{"docid":"8f7f0d35fa0e1cd283b287dc0393311f","score":"0.50957876","text":"def search(self, word):\n return self._search(self.root, word, 0)","title":""},{"docid":"77b4946d7e621ac42c917b80ad3abc14","score":"0.5095266","text":"def lookup_word(self, target: str):\n return en_dict.__lookup(self.d, target, 0)","title":""},{"docid":"a47dc1577d1ec3408a4ab60270bf47c2","score":"0.50942844","text":"def get_tags(self, word: str) -> Optional[str]:\n for tag, words in self._pos2words.items():\n if word.lower() in words:\n return tag","title":""},{"docid":"f9899bdb2db89808e040b11e590047d7","score":"0.5094146","text":"def search(self, word):\n curr = self.root\n for s in word:\n if s in curr.sub_node:\n curr = curr.sub_node[s]\n else:\n return False\n return curr.flag","title":""},{"docid":"d4630da3df1393ca25258f5a269e837b","score":"0.50895876","text":"def search(self, word: str) -> bool:\n cur = self.root\n for c in word:\n if c not in cur:\n return False\n cur = cur[c]\n return '#' in cur","title":""},{"docid":"672a7ec3f9f53eb1e02fa0e6b1406a09","score":"0.5087387","text":"def check_word_as_noun(self,noun):\n\t\treturn self.nounstemmer.stemming_noun(noun);","title":""},{"docid":"23a7d8dc2948f841c179d0b27f1ef186","score":"0.50758886","text":"def test_word_bound_regex_complex_search(parser: ShoulderBirdParser) -> None:\n msg = \"appreciated\"\n matches = parser.get_matches(\"101\", \"Delta\", msg)\n assert len(matches) == 0\n msg = \"preoct\"\n matches = parser.get_matches(\"101\", \"Delta\", msg)\n assert len(matches) == 1","title":""},{"docid":"8f8e834a6398fb8144a96755bd62b200","score":"0.5075861","text":"def search_by_lemma(cli, args):\n wn = get_wn_profile(cli, args)\n get_synsets_by_term(wn, args.lemma, args.pos, compact=not args.detail, lang=args.lang)\n pass","title":""},{"docid":"1e8e81a2e4d87b29ad435db03f4e5a7e","score":"0.5073183","text":"def analyze(self, word):\n res = self._get_constituents(word)\n res[\"related_words\"] = []\n if res[\"roots\"]:\n for root in res[\"roots\"]:\n res[\"related_words\"] += self._get_related_words(root)\n res[\"related_words\"] = list(set(res[\"related_words\"]))\n try:\n res[\"related_words\"].remove(res[\"original_word\"])\n except ValueError:\n pass\n return res","title":""},{"docid":"34cb44f25d6b51e685777d047ca3a563","score":"0.50690717","text":"def isin_cmu(word):\n if type(word) == list or len(word.split(\" \")) > 1:\n if type(word)==str:\n word = word.split(\" \")\n for w in word:\n if w.lower() not in word_dict:\n return False\n return True\n return word.lower() in word_dict","title":""},{"docid":"d4db5327b507b0ff07771e70cd404096","score":"0.50672233","text":"def search(self, word):\n\n def find(word, trie):\n\n if not word:\n return True\n\n first, rest = word[0], word[1:]\n\n if first == '.':\n\n\n for letter, child in trie.children.items():\n\n if find(rest, child):\n return True\n\n elif first not in trie.children:\n return False\n else:\n\n return find(rest, trie.children[first])\n\n return find(word, self.root) or False","title":""},{"docid":"0255114554a107656ecf5d808312eb1a","score":"0.5061024","text":"def search_dict(meaning_dict, word):\n found = False\n filtered_dict_list = []\n if word.casefold() in meaning_dict:\n found = True\n filtered_dict_list = meaning_dict[word.casefold()]\n return filtered_dict_list, found","title":""},{"docid":"c37748d7662274affe9d82e971554e25","score":"0.5060229","text":"def encontrando_letra(letra,word):\n a,b='áéíóú','aeiou'\n sintilde=str.maketrans(a,b)\n word=word.translate(sintilde)\n if letra in word:\n search= word.index(letra)\n posiciones=[i for i in range(0,len(word)) if letra==word[i]]\n else:\n posiciones=[]\n return posiciones","title":""},{"docid":"07cebe19add845cac509a6314e71125c","score":"0.50533223","text":"def backward_nounsearch(nounphraselist,nounhead):\n\t#print \"Backward-Nounsearch\"\n\t#print nounphraselist, nounhead\n\tnounphraselist.reverse()\n\tfor np in nounphraselist:\n\t#\tprint np[0].reveal()\n\t\tgetsense=find_in_Extension(nounhead,np[0].extension)\n\t\tif getsense>-1:\t\t\t\t\t#remove unccessary senses\n\t\t\tnp[0].extension=[np[0].extension[getsense]]\n\t\t\treturn np\n\treturn 0","title":""},{"docid":"0a0ad41eea397e29b7c3210eca591455","score":"0.50527984","text":"def test_common_words(self): \n filename = \"gutenberg.txt\"\n commonwords = CommonWords(filename)\n word = \"the \"\n count = commonwords.word_count(word)\n print(\"\\n\")\n print(f\"{word} exists {count} times\")","title":""},{"docid":"e0a4cdc7de5e9013914fe8a4932f5629","score":"0.50512457","text":"def _lookup_words(\n self, words: List[str], word_dict: Dict[str, List[str]], n: int = 5\n ) -> Dict[str, Dict[str, Any]]:\n\n pronunciations: Dict[str, Dict[str, Any]] = {}\n\n # Dictionary uses upper-case letters\n dictionary_upper = self.profile.get(\"speech_to_text.dictionary_upper\", False)\n\n # Check words against dictionary\n unknown_words = set()\n for word in words:\n if dictionary_upper:\n lookup_word = word.upper()\n else:\n lookup_word = word.lower()\n\n pronounces = list(word_dict.get(lookup_word, []))\n in_dictionary = len(pronounces) > 0\n\n pronunciations[word] = {\n \"in_dictionary\": in_dictionary,\n \"pronunciations\": pronounces,\n }\n if not in_dictionary:\n unknown_words.add(word)\n\n # Guess pronunciations for unknown word\n if unknown_words:\n # Path to phonetisaurus FST\n g2p_path = self.profile.read_path(\n self.profile.get(\n f\"speech_to_text.{self.speech_system}.g2p_model\", \"g2p.fst\"\n )\n )\n\n g2p_casing = self.profile.get(\"speech_to_text.g2p_casing\", \"\").lower()\n\n # Case-sensitive mapping from upper/lower cased word back to original casing\n word_map: Dict[str, str] = {}\n\n with tempfile.NamedTemporaryFile(mode=\"w+\", suffix=\".txt\") as wordlist_file:\n # Write words to a temporary word list file\n for word in unknown_words:\n original_word = word\n if g2p_casing == \"upper\":\n # FST was trained with upper-case letters\n word = word.upper()\n elif g2p_casing == \"lower\":\n # FST was trained with loser-case letters\n word = word.lower()\n\n print(word, file=wordlist_file)\n word_map[word] = original_word\n\n wordlist_file.seek(0)\n\n # Output phonetisaurus results to temporary file\n with tempfile.NamedTemporaryFile(\n mode=\"w+\", suffix=\".txt\"\n ) as pronounce_file:\n # Use phonetisaurus to guess pronunciations\n g2p_command = [\n \"phonetisaurus-apply\",\n \"--model\",\n g2p_path,\n \"--word_list\",\n wordlist_file.name,\n \"--nbest\",\n str(n),\n ]\n\n self._logger.debug(repr(g2p_command))\n subprocess.check_call(g2p_command, stdout=pronounce_file)\n\n pronounce_file.seek(0)\n\n # Read results\n ws_pattern = re.compile(r\"\\s+\")\n\n for line in pronounce_file:\n parts = ws_pattern.split(line)\n word = word_map[parts[0].strip()]\n phonemes = \" \".join(parts[1:]).strip()\n pronunciations[word][\"pronunciations\"].append(phonemes)\n\n return pronunciations","title":""},{"docid":"3680d23254344da739deaad8b23a4c0d","score":"0.50512004","text":"def lookup_query(query):\n if (dictionary_word.get(query, False)):\n return ('The reasult of search: ' + json.dumps(dictionary_word[query], indent=4))\n\n return ('Sorry, there is no such word :(')","title":""},{"docid":"48bfbd167cb6b821ef0d717900781793","score":"0.5049092","text":"def search(self, word):\n return self.searchnode(self,word)","title":""},{"docid":"c4899904cea8ebb8da156245d69d8793","score":"0.50404733","text":"def spellCheck(self, sentence: Sentence) -> Sentence:\n previousRoot = None\n result = Sentence()\n for i in range(sentence.wordCount()):\n word = sentence.getWord(i)\n fsmParses = self.fsm.morphologicalAnalysis(word.getName())\n if fsmParses.size() == 0:\n candidates = self.candidateList(word)\n bestCandidate = word.getName()\n bestRoot = word\n bestProbability = 0.0\n for candidate in candidates:\n fsmParses = self.fsm.morphologicalAnalysis(candidate)\n root = fsmParses.getParseWithLongestRootWord().getWord()\n if previousRoot is not None:\n probability = self.__nGram.getProbability(previousRoot.getName(), root.getName())\n else:\n probability = self.__nGram.getProbability(root.getName())\n if probability > bestProbability:\n bestCandidate = candidate\n bestRoot = root\n bestProbability = probability\n previousRoot = bestRoot\n result.addWord(Word(bestCandidate))\n else:\n result.addWord(word)\n previousRoot = fsmParses.getParseWithLongestRootWord().getWord()\n return result","title":""},{"docid":"4ba20aa1eebd8e49b5094c92452c15a5","score":"0.5039387","text":"def search(self, word):\n node = self.root\n for item in word:\n if item in node.childrenValue:\n node = node.children[node.childrenValue[item]]\n else:\n return False\n if \"\" in node.childrenValue:\n return True\n else:\n return False","title":""}],"string":"[\n {\n \"docid\": \"f621e037c12702cc9d483850528970ea\",\n \"score\": \"0.6215948\",\n \"text\": \"def check_word_as_pounct(self,word):\\t\\t\\n\\t\\tdetailed_result=[]\\n\\t\\t# ToDo : fix it to isdigit, by moatz saad\\n\\t\\tif word.isnumeric():\\n\\t\\t\\tdetailed_result.append(wordCase.wordCase({\\n\\t\\t\\t'word':word,\\n\\t\\t\\t'affix': ('', '', '', ''),\\t\\t\\t\\n\\t\\t\\t'stem':'',\\n\\t\\t\\t'original':word,\\n\\t\\t\\t'vocalized':word,\\n\\t\\t\\t'tags':self.get_number_tags(word),\\n\\t\\t\\t'type':'NUMBER',\\n\\t\\t\\t'freq':0,\\n\\t\\t\\t'syntax':'',\\t\\t\\t\\n\\t\\t\\t}));\\t\\n\\t\\tif word in stem_pounct_const.POUNCTUATION:\\n\\t\\t\\tdetailed_result.append(wordCase.wordCase({\\n\\t\\t\\t'word':word,\\n\\t\\t\\t'affix': ('', '', '', ''),\\n\\t\\t\\t'stem':'',\\n\\t\\t\\t'original':word,\\n\\t\\t\\t'vocalized':word,\\n\\t\\t\\t'tags':stem_pounct_const.POUNCTUATION[word]['tags'],\\n\\t\\t\\t'type':'POUNCT',\\n\\t\\t\\t'freq':0,\\n\\t\\t\\t'syntax':'',\\t\\t\\t\\n\\t\\t\\t}));\\n\\n\\t\\treturn detailed_result;\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f15c2c8e548933d5db43f035e0e157ee\",\n \"score\": \"0.59108704\",\n \"text\": \"def _searchWord(self, forward):\\r\\n document = core.workspace().currentDocument()\\r\\n\\r\\n cursor = document.qutepart.textCursor()\\r\\n if not cursor.hasSelection():\\r\\n cursor.select(cursor.WordUnderCursor)\\r\\n word = cursor.selectedText()\\r\\n wordStartAbsPos = cursor.anchor()\\r\\n wordEndAbsPos = cursor.position()\\r\\n\\r\\n if not word:\\r\\n return\\r\\n\\r\\n regExp = re.compile('\\\\\\\\b%s\\\\\\\\b' % re.escape(word))\\r\\n text = document.qutepart.text\\r\\n\\r\\n # avoid matching word under cursor\\r\\n if forward:\\r\\n startPoint = wordEndAbsPos\\r\\n else:\\r\\n startPoint = wordStartAbsPos\\r\\n\\r\\n self._updateFoundItemsHighlighting(regExp)\\r\\n\\r\\n match, matches = self._searchInText(regExp, document.qutepart.text, startPoint, forward)\\r\\n if match is not None:\\r\\n document.qutepart.absSelectedPosition = (match.start(), match.start() + len(match.group(0)))\\r\\n core.mainWindow().statusBar().showMessage('Match %d of %d' % \\\\\\r\\n (matches.index(match) + 1, len(matches)), 3000)\\r\\n else:\\r\\n core.workspace().currentDocument().qutepart.resetSelection()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"119af19f789667feed1933f8c0940bbb\",\n \"score\": \"0.5865671\",\n \"text\": \"def lookup(self, word: str) -> Word:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49892e79152973faf6541ccd239a512f\",\n \"score\": \"0.57349604\",\n \"text\": \"def similar_words(word):\\n\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52a1aa05273ce8f7537946394e8f926e\",\n \"score\": \"0.5687349\",\n \"text\": \"def isMatchWord(self): #$NON-NLS-1$\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef1eee697a52f3b9d1a9cc95a527b609\",\n \"score\": \"0.5653841\",\n \"text\": \"def findText(self, wordList, forward=True):\\n fullList = globalref.docRef.root.descendantList(True)\\n currentPos = fullList.index(self.currentItem)\\n fullList = fullList[currentPos+1:] + fullList[:currentPos]\\n if not forward:\\n fullList.reverse()\\n for item in fullList:\\n if item.matchWords(wordList):\\n self.changeSearchOpen([item])\\n return item\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41302f76f4b995dbdfaf54a9acf1da4f\",\n \"score\": \"0.56114215\",\n \"text\": \"def search(self, word):\\n p = self.helper(word)\\n if p and p.isWord:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e706dab44c63ad4b71eb78a872fd5b52\",\n \"score\": \"0.55804014\",\n \"text\": \"def pronounSearch(filter):\\n pronouns = fileToStringList(\\\"pronoun dictionary\\\")\\n filteredText = []\\n for line in poemList:\\n line = line.split(\\\" \\\")\\n words = \\\"\\\"\\n for word in line:\\n words += \\\" \\\"\\n if word in pronouns:\\n words += word.upper()\\n else:\\n words += word \\n filteredText += [words]\\n printPoem(filteredText)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"634b4ba065aa9dc816a8038e5a6b7202\",\n \"score\": \"0.5579486\",\n \"text\": \"def search(self, word: str) -> bool:\\n tree = self.look_up\\n for w in word:\\n if w not in tree:\\n return False\\n tree = tree[w]\\n if '#' in tree:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d090b4b2009235bf089acd6ef97e9b48\",\n \"score\": \"0.554672\",\n \"text\": \"def setMatchWord(self, b): #$NON-NLS-1$\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc8a36b1931c5d7138df46c5692e3cc7\",\n \"score\": \"0.55318964\",\n \"text\": \"def search(self, word):\\n node = self.trie\\n for c in word:\\n if c in node:\\n node = node[c]\\n else:\\n return False\\n return node.get('term') is not None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3be2be54c47feae253a6511406d75f0f\",\n \"score\": \"0.55304444\",\n \"text\": \"def search(self, word: str) -> bool:\\n # 1. Initialize: cur = root\\n # 2. for each char c in target string S:\\n # 3. if cur does not have a child c:\\n # 4. search fails\\n # 5. cur = cur.children[c]\\n # 6. search successes\\n \\n cur = self.root\\n for c in word:\\n if c not in cur.child:\\n return False\\n cur = cur.child[c]\\n return cur.word\",\n \"title\": \"\"\n },\n {\n \"docid\": \"556576af3c8d07ab77912cf1477e6d69\",\n \"score\": \"0.5497367\",\n \"text\": \"def search(self, word):\\n return word in self.s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb0d92329c4e3314d64003defe172e5b\",\n \"score\": \"0.5487036\",\n \"text\": \"def search(self, word: str) -> bool:\\r\\n word_tuple = self.make_tuple(word)\\r\\n\\r\\n for w in self.words:\\r\\n diff_from = w - word_tuple\\r\\n diff_to = word_tuple - w\\r\\n if len(diff_from) == 1 and len(diff_to) == 1:\\r\\n return True\\r\\n\\r\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2a3a9d7142fdc191a6f4135418508b5\",\n \"score\": \"0.5475612\",\n \"text\": \"def test_find_word():\\n question = ['salut', 'grandpy', 'comment', 'vas', 'tu',\\n 'peux', 'tu', 'me', 'dire', 'où', 'se', 'trouve',\\n 'l', 'arc', 'de', 'triomphe']\\n req = pars.find_word(question)\\n result = \\\"où trouve arc triomphe \\\"\\n assert str(req) == str(result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a6472a8616a373a711dfd938804744b\",\n \"score\": \"0.547298\",\n \"text\": \"def search(self, word: str) -> bool:\\n node = self.links\\n for c in word:\\n if c not in node:\\n return False\\n node = node[c]\\n # If at end after for loop, this is a word\\n if '#' in node:\\n return True\\n # Else we are still in the middle of a word even though the letters matched up so far\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58d0e8a7e52a09d7a00f86268fb2167d\",\n \"score\": \"0.5467885\",\n \"text\": \"def search(self, word):\\n return self.find(self.root, word)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4eb54815db618751275ce31f4d211450\",\n \"score\": \"0.5466823\",\n \"text\": \"def search(self, word: str) -> bool:\\n tree = self.lookup\\n for a in word:\\n if a not in tree:\\n return False\\n tree = tree[a]\\n if \\\"#\\\" in tree:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06dff3a05a2a8aecf6193aab308874e2\",\n \"score\": \"0.54580563\",\n \"text\": \"def find_similar_words(self) -> None:\\n for word, token in zip(self.words, self.tokens):\\n if word not in self.synonyms:\\n self.synonyms[word] = self._synonyms_generator.neighbors(word, token.tag_, self.perturb_opts['top_n'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"573197d52075cdf856037db98d220891\",\n \"score\": \"0.5451955\",\n \"text\": \"def search(self, word):\\n if self.root == None:\\n return False\\n else:\\n return self.root.getWord(word, isRoot=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2dc36522701e660c82beb68b0a3f85e\",\n \"score\": \"0.54393554\",\n \"text\": \"def search(self, word: str) -> bool:\\n t = self.trie\\n for i in word:\\n if i not in t:\\n return False\\n t = t[i]\\n if '#' not in t:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e700d57593b0321889c80e89dcc3dde1\",\n \"score\": \"0.5432258\",\n \"text\": \"def search(self, word):\\n return self.match(word, 0, self.root)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11f5bd470f95c5fb80259dd7afc11c8c\",\n \"score\": \"0.54315823\",\n \"text\": \"def search(self, word):\\n n = len(word)\\n w = word + '#'\\n #if g(self.td, w, 0, n+1):\\n # return False\\n for i in xrange(n):\\n loc = ord(word[i]) - ord('a') + 1\\n w = word[:i] + '.' + word[i+1:] + '#'\\n z = [0]\\n if f(self.td, w, 0, n+1, z, loc):\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"674b018ce35745405a7173701c65d216\",\n \"score\": \"0.54207706\",\n \"text\": \"def search(self, word):\\n cur = self.rootNode\\n for w in word:\\n if cur.children[ord(w) - ord('a')] is None:\\n return False\\n else:\\n cur = cur.children[ord(w)-ord('a')]\\n \\n return cur.isWord\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d62e27d835bbc446f0a221b8dd8bf299\",\n \"score\": \"0.5379058\",\n \"text\": \"def search(self, word, text):\\n return bool(re.search(r'\\\\b{}\\\\b'.format(re.escape(word)), text))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77f817b649730a6d1e4ab4b685a3e9b6\",\n \"score\": \"0.53673387\",\n \"text\": \"def look_up_pronunciation(word: str, PronouncingTable) -> List[str]:\\n for i in range(len(PronouncingTable[0])):\\n if word == PronouncingTable[0][i]:\\n return PronouncingTable[1][i]\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c3edf8cd651c8c2c542c95162c46c5e\",\n \"score\": \"0.53462887\",\n \"text\": \"def search(self, word):\\n\\n if len(word) not in self.dic: return False\\n\\n for candidate in self.dic[len(word)]:\\n diff = 0\\n\\n for a, b in zip(candidate, word):\\n if a != b: diff += 1\\n\\n if diff == 1: return True\\n\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dcd0b14bbc81546e11b7885e8e1aa545\",\n \"score\": \"0.5334784\",\n \"text\": \"def search(self, word):\\n flag = False\\n \\n def recursive(word,trie):\\n nonlocal flag\\n \\n if word == \\\"\\\" and \\\"#\\\" in trie:\\n flag = True\\n return \\n ## 单词过长\\n if word != \\\"\\\" and trie == {\\\"#\\\":\\\"#\\\"}:\\n return\\n\\n \\n if word[0] == \\\".\\\":\\n for key in trie:\\n recursive(word[1:],trie[key])\\n \\n elif word[0] in trie:\\n recursive(word[1:],trie[word[0]])\\n ## 不存在该前缀\\n else:\\n return\\n\\n recursive(word,self.trie)\\n return flag\",\n \"title\": \"\"\n },\n {\n \"docid\": \"731e8b4e58170b5801a59201a75aae50\",\n \"score\": \"0.5333343\",\n \"text\": \"def search(self, word):\\n return self.searchFrom(word, self.root)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9a1b1c2f85f47dbec17764ee2d002db\",\n \"score\": \"0.531149\",\n \"text\": \"def search(self, word):\\n for i in range(len(word)):\\n for ch_idx in range(26):\\n ch = chr(ord('a') + ch_idx)\\n if ch != word[i]:\\n replace_word = word[:i] + ch + word[i+1:]\\n if replace_word in self.dictionary:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db484243b2bdbb1db1075f47ba4ea170\",\n \"score\": \"0.53038156\",\n \"text\": \"def search(self, word: 'str') -> 'bool':\\n temp = self.root\\n for ch in word:\\n if ch not in temp.links:\\n return False\\n temp = temp.links[ch]\\n return temp.isEnd\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79a45cd0fca596fd841446ea08d4a3b4\",\n \"score\": \"0.529919\",\n \"text\": \"def search(self, word):\\r\\n current = self.root\\r\\n for w in word:\\r\\n current = current.children.get(w)#False\\r\\n if current == None:\\r\\n return False\\r\\n return current.isword# isword 将会在遍历完apple, 字母e之后，自动改成True,所以search('app') 返回\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54c5d8a16717620fa7a995cd1f6c40ee\",\n \"score\": \"0.5298913\",\n \"text\": \"def __contains__(self, word):\\n return word in self.word2id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"286913f0ac3cef1aa8ee1c6fa17d430c\",\n \"score\": \"0.52985764\",\n \"text\": \"def word_search_and_compare(self):\\r\\n d = self.var.get()\\r\\n pattern = d\\r\\n return [w for w in word_list if re.match(pattern,w)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd78b2039c3cedf3c9e0a412f3df94a6\",\n \"score\": \"0.52977455\",\n \"text\": \"def findW(words, casesensitive=False, context=False, ordered=0, bk=None):\\n t0 = tc()\\n \\n # unpack string for individual words\\n word_array = ['']\\n for element in words:\\n # separate words by where spaces are\\n if element == ' ':\\n word_array.append('')\\n else:\\n word_array[-1] += element\\n\\n # change apostrophes?\\n for i in range(len(word_array)):\\n word = word_array[i]\\n if \\\"'\\\" in word:\\n word_split = word.split(\\\"'\\\")\\n word = u\\\"\\\\u2019\\\".join(word_split)\\n word_array[i] = word\\n \\n # analyze words if upper/lowercase does not matter\\n if casesensitive == False:\\n # redefine 'words'\\n words = []\\n Words = []\\n for word_index in range(len(word_array)):\\n UppercaseLetter = word_array[word_index][0].upper()\\n LowercaseLetter = word_array[word_index][0].lower()\\n RestOfWord = word_array[word_index][1:]\\n # create uppercase word\\n Word = UppercaseLetter + RestOfWord\\n Words.append(Word)\\n # create lowercase word\\n word = LowercaseLetter + RestOfWord\\n words.append(word)\\n # print(words)\\n # print(Words)\\n else:\\n words = word_array\\n # print(words)\\n \\n\\n verses_containing_words = 0\\n verses_containing_extra_words = 0\\n occurrences = 0\\n \\n # search entire Bible, or one book, or multiple books\\n if bk is None:\\n # If no book is specified, search entire Bible\\n books_to_go_through = Book_names\\n elif type(bk) == list:\\n # If a list of books is specified, search those books\\n books_to_go_through = bk\\n for bkk in books_to_go_through:\\n if bkk not in Book_names:\\n print(bkk + ' not found in the books of the Bible.')\\n return\\n elif type(bk) == str:\\n # If a single string is entered, check if it is a book, or the entire new/old testament\\n books_to_go_through = [bk]\\n if bk not in Book_names:\\n if bk.upper() == 'OT':\\n books_to_go_through = Book_names[:39]\\n elif bk.upper() == 'NT':\\n books_to_go_through = Book_names[39:]\\n else:\\n print(bk + ' not found in the books of the Bible.')\\n return\\n\\n # go through books of the Bible\\n for book_name in books_to_go_through:\\n Book = Bible_searchable[book_name]\\n \\n # go through each chapter\\n for chapter_index in range(len(Book)):\\n chapter_number = chapter_index + 1\\n Chapter = Book[chapter_index]\\n \\n # go through each verse\\n for verse_name,EntireVerse in Chapter.items():\\n\\n # In case words should be matched in order, store a copy of the verse\\n Verse = EntireVerse\\n\\n # check to see if each word is in the verse\\n word_index = 0\\n contains = True\\n contains_added = True\\n while contains and word_index < len(words):\\n \\n # If upper/lowercase is unimportant...\\n if casesensitive == False:\\n word = words[word_index]\\n Word = Words[word_index]\\n # Is word in verse? (also check non-original tongues)\\n if (word not in Verse) and (Word not in Verse):\\n contains = False\\n elif ordered != 0:\\n # If word is in verse, and order is important, let subsequent calls only search the rest of the verse\\n split_index = min(try_index(Verse, [word, Word]))\\n if (ordered == 2) and (word_index != 0) and (split_index != 0):\\n contains = False\\n Verse = Verse[split_index+1:]\\n \\n # If upper/lowercase is important...\\n elif casesensitive == True:\\n word = words[word_index]\\n # Is word in verse? (also check non-original tongues)\\n if word not in Verse:\\n contains = False\\n elif ordered != 0:\\n # If word is in verse, and order is important, let subsequent calls only search the rest of the verse\\n split_index = Verse.index(word)\\n if (ordered == 2) and (word_index != 0) and (split_index != 0):\\n contains = False\\n Verse = Verse[split_index+1:]\\n \\n word_index += 1\\n\\n if contains == True:\\n verses_containing_words += 1\\n ActualVerse = Bible[book_name][chapter_index][verse_name]\\n total_verse = verse_writeout(ActualVerse)\\n print(book_name,chapter_number,':',verse_name)\\n print(' ',total_verse)\\n tf = tc()\\n print(tf-t0) \\n print('number of verses containing specified words: ',verses_containing_words)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9e41d7e82f2c86ef3b9f6615e7e45c7\",\n \"score\": \"0.52892405\",\n \"text\": \"def __zh_word_tea_dictionary_search(self, chinese_word):\\n general_dictionary = {}\\n\\n # Conduct a search.\\n web_search = f\\\"http://babelcarp.org/babelcarp/babelcarp.cgi?phrase={chinese_word}&define=1\\\"\\n eth_page = requests.get(web_search, headers=self.zw_useragent, timeout=15)\\n try:\\n tree = html.fromstring(eth_page.content) # now contains the whole HTML page\\n word_content = tree.xpath('//fieldset[contains(@id,\\\"translation\\\")]//text()')\\n except BaseException:\\n return None\\n\\n # Get the headword of the entry.\\n try:\\n head_word = word_content[2].strip()\\n except IndexError:\\n return None\\n\\n if chinese_word not in head_word:\\n # If the characters don't match: Exit. This includes null searches.\\n return None\\n else: # It exists.\\n try:\\n pinyin = re.search(r\\\"\\\\((.*?)\\\\)\\\", word_content[2]).group(1).lower()\\n except AttributeError: # Never mind, it does not exist.\\n return None\\n\\n meaning = word_content[3:]\\n meaning = [item.strip() for item in meaning]\\n\\n # Format the entry to return\\n formatted_line = f'\\\\n\\\\n**Tea Meanings**: \\\"{\\\" \\\".join(meaning)}.\\\"'\\n formatted_line = formatted_line.replace(\\\" )\\\", \\\" \\\")\\n formatted_line = formatted_line.replace(\\\" \\\", \\\" \\\")\\n formatted_line += f\\\" ([Babelcarp]({web_search}))\\\" # Append source\\n\\n general_dictionary[\\\"meaning\\\"] = formatted_line\\n general_dictionary[\\\"pinyin\\\"] = pinyin\\n\\n return general_dictionary\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f1fcb9dc073f9bd440e9f975ff4d84a\",\n \"score\": \"0.52829\",\n \"text\": \"def __zh_word_cccanto_search(self, cantonese_word: str) -> None | Dict[str, str]:\\n general_dictionary = {}\\n\\n # We open the file.\\n with open(FILE_ADDRESS_ZH_CCCANTO, encoding=\\\"utf-8\\\") as f:\\n existing_data = f.read().split(\\\"\\\\n\\\")\\n\\n relevant_line = None\\n\\n # Look for the relevant word (should not take long.)\\n for entry in existing_data:\\n traditional_headword = entry.split(\\\" \\\", 1)[0]\\n if cantonese_word == traditional_headword:\\n relevant_line = entry\\n break\\n\\n if relevant_line is not None:\\n # Parse the entry (based on code from Marcanuy at https://github.com/marcanuy/cedict_utils, MIT license)\\n hanzis = relevant_line.partition(\\\"[\\\")[0].split(\\\" \\\", 1)\\n keywords = {\\n \\\"meanings\\\": relevant_line.partition(\\\"/\\\")[2]\\n .replace('\\\"', \\\"'\\\")\\n .rstrip(\\\"/\\\")\\n .strip()\\n .split(\\\"/\\\"),\\n \\\"traditional\\\": hanzis[0].strip(\\\" \\\"),\\n \\\"simplified\\\": hanzis[1].strip(\\\" \\\"),\\n # Take the content in between the two brackets\\n \\\"pinyin\\\": relevant_line.partition(\\\"[\\\")[2].partition(\\\"]\\\")[0],\\n \\\"jyutping\\\": relevant_line.partition(\\\"{\\\")[2].partition(\\\"}\\\")[0],\\n \\\"raw_line\\\": relevant_line,\\n }\\n\\n formatted_line = '\\\\n\\\\n**Cantonese Meanings**: \\\"{}.\\\"'.format(\\n \\\"; \\\".join(keywords[\\\"meanings\\\"])\\n )\\n formatted_line += (\\n f\\\" ([CC-Canto](https://cantonese.org/search.php?q={cantonese_word}))\\\"\\n )\\n for i in range(0, 9):\\n keywords[\\\"jyutping\\\"] = keywords[\\\"jyutping\\\"].replace(\\n str(i), f\\\"^{str(i)} \\\"\\n ) # Adds syntax for tones\\n keywords[\\\"jyutping\\\"] = (\\n keywords[\\\"jyutping\\\"].replace(\\\" \\\", \\\" \\\").strip()\\n ) # Replace double spaces\\n\\n general_dictionary[\\\"meaning\\\"] = formatted_line\\n general_dictionary[\\\"pinyin\\\"] = keywords[\\\"pinyin\\\"]\\n general_dictionary[\\\"jyutping\\\"] = keywords[\\\"jyutping\\\"]\\n\\n return general_dictionary\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13a0ed020c7f75d885b6395a7a4eab35\",\n \"score\": \"0.5277528\",\n \"text\": \"def search(self, word):\\n cur = self.root\\n for c in word:\\n if c in cur.d:\\n cur = cur.d[c]\\n else:\\n return False\\n return True if 0 in cur.d else False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49111f63cb2263f7c680c79069fbd7f9\",\n \"score\": \"0.52603996\",\n \"text\": \"def lookup_exact(self, normalized_spelling):\\n if Word._get_cache().contains(normalized_spelling):\\n return self.filter(words__normalized_spelling=normalized_spelling)\\n else:\\n return self.none()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"803904b6ab3d9386c944a427b4068ff7\",\n \"score\": \"0.5258035\",\n \"text\": \"def search(self, word):\\n current = self.root\\n n = len(word)\\n\\n i = 0\\n while i < n:\\n node_found = False\\n\\n for trie_node in current.children:\\n if trie_node.value == word[i]:\\n current = trie_node\\n i += 1\\n node_found = True\\n break\\n\\n if not node_found:\\n return (False, False)\\n\\n # if end of word reached, then increment weight to indicate popularity/frequency of access\\n if current.end_of_word:\\n current.weight += 1\\n\\n return (True, True if current.end_of_word else False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42edee9ef6aeba56c4525a2ed7d1dcee\",\n \"score\": \"0.5256944\",\n \"text\": \"def search(self, word):\\n return self.searchre(word, self.root);\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78d40b8ca14697e036b719ed5f03f2fa\",\n \"score\": \"0.52565396\",\n \"text\": \"def get_word (self, word):\\n for w in self.words:\\n if w.word == word:\\n return w\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a145cf91aeed21da722c383ec995ae4\",\n \"score\": \"0.52554923\",\n \"text\": \"def check_word(self,word, guessedTag=\\\"\\\"):\\t\\n\\t\\tword=araby.stripTatweel(word);\\n\\t\\tword_vocalised=word;\\n\\t\\tword_nm=araby.stripTashkeel(word);\\n\\t\\tresulted_text=u\\\"\\\";\\n\\t\\tresulted_data=[];\\n\\t\\t# if word is a pounctuation\\n\\t\\tresulted_data+=self.check_word_as_pounct(word_nm);\\n\\t\\t# Done: if the word is a stop word we have some problems,\\n\\t\\t# the stop word can also be another normal word (verb or noun),\\n\\t\\t# we must consider it in future works\\n\\t\\t# if word is stopword allow stop words analysis\\n\\t\\tresulted_data+=self.check_word_as_stopword(word_nm);\\n\\n\\t\\t#if word is verb\\n\\t\\t# مشكلة بعض الكلمات المستبعدة تعتبر أفعلا أو اسماء\\n\\t\\tif self.tagger.hasVerbTag(guessedTag) or self.tagger.isStopWordTag(guessedTag):\\n\\t\\t\\tresulted_data+=self.check_word_as_verb(word_nm);\\n\\t\\t\\t#print \\\"is verb\\\", rabti,len(resulted_data);\\n\\t\\t#if word is noun\\n\\t\\tif self.tagger.hasNounTag(guessedTag) or self.tagger.isStopWordTag(guessedTag):\\t\\t\\t\\n\\t\\t\\tresulted_data+=self.check_word_as_noun(word_nm);\\n\\t\\tif len(resulted_data)==0:\\n\\t\\t\\t#check the word as unkonwn\\n\\t\\t\\tresulted_data+=self.check_word_as_unknown(word_nm);\\n\\t\\t\\t#check if the word is nomralized and solution are equivalent\\n\\t\\tresulted_data = self.check_normalized(word_vocalised, resulted_data)\\n\\t\\t#check if the word is shadda like\\n\\t\\tresulted_data = self.check_shadda(word_vocalised, resulted_data)\\n\\n\\t\\t#check if the word is vocalized like results\\t\\t\\t\\n\\t\\tif self.partial_vocalization_support:\\n\\t\\t\\tresulted_data=self.check_partial_vocalized(word_vocalised, resulted_data);\\n\\t\\t# add word frequency information in tags\\n\\t\\tresulted_data = self.addWordFrequency(resulted_data);\\n\\n\\t\\tif len(resulted_data)==0:\\n\\t\\t\\tresulted_data.append(wordCase.wordCase({\\n\\t\\t\\t'word':word, \\n\\t\\t\\t'affix': ('' , '', '', ''), \\n\\t\\t\\t'stem':'',\\n\\t\\t\\t'original':word,\\n\\t\\t\\t'vocalized':word,\\n\\t\\t\\t'tags':u'',\\n\\t\\t\\t'type':'unknown',\\n\\t\\t\\t'root':'',\\n\\t\\t\\t'template':'',\\n\\t\\t\\t'freq':self.wordfreq.getFreq(word, 'unknown'),\\n\\t\\t\\t'syntax':'',\\n\\t\\t\\t})\\n\\t\\t\\t);\\n\\t\\treturn resulted_data;\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ad525da66c886ac0b4a95585aa3a0dd\",\n \"score\": \"0.5247887\",\n \"text\": \"def search(self, word):\\n node = self.root\\n for chars in word:\\n node = node.data.get(chars)\\n if not node:\\n return False\\n return node.is_word #\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83d31c22d1e8665ce3abde1012f1138f\",\n \"score\": \"0.52335036\",\n \"text\": \"def find(self, word):\\n if len(word) < 3:\\n return \\\"at least 3 chars\\\"\\n\\n word = word.lower().strip()\\n char_list = list(word)\\n char_list.sort()\\n sign = \\\"\\\".join(char_list)\\n\\n matched_words = self.trie.find(sign)\\n self.trie.reset()\\n if len(matched_words) == 0:\\n return \\\"not find anagrams\\\"\\n else:\\n sign, score = self.high_score(matched_words)\\n return dict.data[sign][0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75bec44b7b4ae79e680262c4f945746f\",\n \"score\": \"0.5222018\",\n \"text\": \"def search_word(word):\\r\\n\\r\\n word = word.strip()\\r\\n if word.startswith(' '):\\r\\n return None\\r\\n\\r\\n for char in word:\\r\\n if char not in kanas and char not in letters and char not in splitch:\\r\\n break\\r\\n else:\\r\\n return None\\r\\n\\r\\n search_url = BASIC_URL + word\\r\\n try:\\r\\n content_str = requests.get(search_url).content.decode('utf-8')\\r\\n except requests.exceptions.ConnectionError:\\r\\n print('Error! Connection failed!\\\\nOn searching %s' % word)\\r\\n return None\\r\\n\\r\\n str_1_re = str_1_start + str_re_bracket + str_1_end\\r\\n re_1 = re.compile(str_1_re)\\r\\n result_1 = re_1.search(content_str)\\r\\n\\r\\n if result_1:\\r\\n return result_1.group(1)\\r\\n\\r\\n str_2_re = str_2_start + str_re_bracket + str_2_end\\r\\n re_2 = re.compile(str_2_re)\\r\\n result_2 = re_2.search(content_str)\\r\\n\\r\\n if result_2:\\r\\n return result_2.group(1)\\r\\n\\r\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95fbebcd5615fa5e42f692cecf9c6ccf\",\n \"score\": \"0.5215799\",\n \"text\": \"def check_all_dependents_of_word_in_ref_are_in_hyp(c, wordIndex):\\n depIndex = wordIndex - 1\\n# print('target =', wordIndex)\\n # look for a dependent of word to its left in reference\\n while (depIndex >=0) :\\n# print('depIndex = ', depIndex)\\n govRefIndex = int(c.getBuffer().getWord(depIndex).getFeat('GOVREF')) + depIndex\\n# print(\\\"govRefIndex = \\\", govRefIndex)\\n if govRefIndex == wordIndex : # dep is a dependent of word in ref \\n #check that dep has the same governor in hyp \\n govHypIndex = int(c.getBuffer().getWord(depIndex).getFeat('GOV')) + depIndex\\n# print(depIndex, 'is dependent ')\\n if govHypIndex != govRefIndex :\\n# print('wrong gov (', govHypIndex, ')');\\n return False\\n depIndex -= 1\\n\\n sentenceChange = False\\n depIndex = wordIndex + 1\\n while depIndex < c.getBuffer().getLength() :\\n# print('depIndex = ', depIndex)\\n govRefIndex = int(c.getBuffer().getWord(depIndex).getFeat('GOVREF')) + depIndex\\n# print(\\\"govRefIndex = \\\", govRefIndex)\\n if(govRefIndex == wordIndex): # dep is a dependent of word in ref\\n govHypIndex = int(c.getBuffer().getWord(depIndex).getFeat('GOV')) + depIndex\\n# print(depIndex, 'is dependent ')\\n if govHypIndex != govRefIndex :\\n# print('wrong gov (', govHypIndex, ')');\\n return False\\n depIndex += 1\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f79bbbcaf448bb2753aa735ab0e90b7\",\n \"score\": \"0.5213169\",\n \"text\": \"def search(self, word):\\n return self.search_word(self.root, word, 0, len(word))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"089065648193f8050512ae756019ff02\",\n \"score\": \"0.5206609\",\n \"text\": \"def search(self, word):\\n current = self.root\\n for letter in word:\\n current = current.children.get(letter)\\n if current is None:\\n return False\\n return current.is_word\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc150e7652a471dad4efa9ae94880637\",\n \"score\": \"0.5205561\",\n \"text\": \"def paraphrase_sentence( sentence, model ):\\n # 3 implementations:\\n # if the first letter is equal to the first letter of w, move to the next one.\\n # don't use the letter e at all\\n # spelling corrections\\n\\n blob = textblob.TextBlob( sentence )\\n print(\\\"The sentence's words are\\\")\\n LoW = blob.words\\n print(LoW)\\n\\n NewLoW = \\\"\\\"\\n for w in LoW:\\n \\n if w not in model:\\n NewLoW += w + \\\" \\\"\\n else:\\n \\n w_alternatives = model.most_similar(positive=[w], topn=100)\\n\\n counter = 0\\n\\n for i in range(len(w_alternatives)):\\n \\n first_alternative, first_alternative_score = w_alternatives[i] # initial one!\\n\\n if (first_alternative[0] != w[0]):\\n if 'e' not in first_alternative:\\n break\\n else:\\n counter += 1\\n \\n if counter == len(w_alternatives):\\n first_alternative, first_alternative_score = w_alternatives[0]\\n else: \\n NewLoW += first_alternative + \\\" \\\"\\n \\n # you should change this so that it returns a new string (a new sentence),\\n # NOT just print a list of words (that's what's provided in this starter code)\\n \\n NewLoW = NewLoW[:-1]\\n \\n NewLoW = textblob.TextBlob(NewLoW)\\n NewLoW = NewLoW.correct()\\n NewLoW = NewLoW.words\\n \\n NewSentence = \\\"\\\"\\n \\n for x in NewLoW:\\n NewSentence += x + \\\" \\\"\\n \\n NewSentence = NewSentence[:-1] \\n \\n return NewSentence\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9ed18f5bd662bfde151aaf8fe66b769\",\n \"score\": \"0.51939875\",\n \"text\": \"def fuzzy_lookup(tokens, rule_set):\\n # we'll have to de-metaphone these to decide what part of\\n # the phrase needs to be passed on...\\n phonetic = metaphone(''.join(tokens))\\n for start in range(len(phonetic)):\\n table = rule_set\\n for offset, char in enumerate(phonetic[start:]):\\n if char in table:\\n table = table[char]\\n elif table is not rule_set:\\n if PHRASE_MARKER in table:\\n return table[PHRASE_MARKER], phonetic[start:]\\n if WORD_MARKER in table:\\n return table[WORD_MARKER], phonetic[start:]\\n elif None in table:\\n return table[None], phonetic[start : start + offset]\\n return [], ''\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd82e57f0e8755748a931f546571fbeb\",\n \"score\": \"0.51849705\",\n \"text\": \"def search(self, answer, par_title):\\n main_answer = self.ans.get_main_words(answer)\\n #print '<br>main lemmata of answer: ',main_answer,'<br>' #for debugging\\n\\n main_par = self.ans.get_main_words(par_title)\\n #print '<br>main lemmata of par_title: ',main_par,'<br><br>' #for debugging\\n\\n success = '1'\\n\\n for lemma in main_answer:\\n if lemma not in main_par:\\n success = '0'\\n break\\n return success\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b60a1acdfeb7595fa38611f7e45d8a9\",\n \"score\": \"0.51840717\",\n \"text\": \"def find_pronoun(sent):\\n pronoun = None\\n\\n for word, part_of_speech in sent.pos_tags:\\n # Disambiguate pronouns\\n if part_of_speech == 'PRP' and word.lower() == 'you':\\n pronoun = 'I'\\n elif part_of_speech == 'PRP' and word == 'I':\\n # If the user mentioned themselves, then they will definitely be the pronoun\\n pronoun = 'You'\\n return pronoun\",\n \"title\": \"\"\n },\n {\n \"docid\": \"518c9aa1c3370375c9ae9ef71f96d3d1\",\n \"score\": \"0.5174752\",\n \"text\": \"def search(self, word):\\r\\n return any(\\r\\n self.magicdict.get(c, 0) > 1\\r\\n or self.magicdict.get(c, 0) == 1 and word not in self.magicset\\r\\n for c in self._candidate(word))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce7365e8581c90bfb23003d3a2ed6ead\",\n \"score\": \"0.51745605\",\n \"text\": \"def busqueda(self, xPers):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd7ac766c0de25cc56089e8042174747\",\n \"score\": \"0.5173696\",\n \"text\": \"def searchTextString(source, topic, people):\\r\\n listWordsTopic = []\\r\\n if topic != -1:\\r\\n listWordsTopic = vocTopics[topic][language_in]\\r\\n for i in range(len(listWordsTopic)):\\r\\n listWordsTopic[i] = listWordsTopic[i].encode('utf-8')\\r\\n listWordsPeople = []\\r\\n if people != -1:\\r\\n listWordsPeople = vocPeople[people][language_in]\\r\\n for i in range(len(listWordsPeople)):\\r\\n listWordsPeople[i] = listWordsPeople[i].encode('utf-8')\\r\\n print(\\\"listWordsTopic\\\", listWordsTopic)\\r\\n print(\\\"listWordsPeople\\\", listWordsPeople)\\r\\n foundSource = None\\r\\n print(\\\"Searching....\\\")\\r\\n foundResults = {} #key, topic, people\\r\\n if (source == 'bible' or source == -1):\\r\\n foundSource = 'bible'\\r\\n for book in Bible:\\r\\n #print(\\\"book\\\", book)\\r\\n for bookNum in Bible[book]:\\r\\n #print(\\\"bookNum\\\", bookNum)\\r\\n for verseNum in Bible[book][bookNum]:\\r\\n #print(\\\"verseNum\\\", verseNum)\\r\\n #print(Bible[book][bookNum][verseNum])\\r\\n #print(Bible[book][bookNum][verseNum][language_out])\\r\\n if (language_out in Bible[book][bookNum][verseNum]): #check that language exists\\r\\n foundKey = BibleDataToID(book, bookNum, verseNum)\\r\\n wordInSentence(foundSource, listWordsTopic, 'topic', Bible[book][bookNum][verseNum][language_out], foundKey, foundResults)\\r\\n wordInSentence(foundSource, listWordsPeople, 'people', Bible[book][bookNum][verseNum][language_out], foundKey, foundResults)\\r\\n\\r\\n if (source == 'pray' or source == -1):\\r\\n foundSource = 'pray'\\r\\n for prayer in prayers:\\r\\n for part in prayers[prayer]['parts']:\\r\\n #print(prayers[prayer]['parts'][part])\\r\\n if (language_out in prayers[prayer]['parts'][part]): #check that language exists\\r\\n foundKey = prayer\\r\\n wordInSentence(foundSource, listWordsTopic, 'topic', prayers[prayer]['parts'][part][language_out], foundKey, foundResults)\\r\\n wordInSentence(foundSource, listWordsPeople, 'people', prayers[prayer]['parts'][part][language_out], foundKey, foundResults)\\r\\n\\r\\n\\r\\n if (source == 'pope' or source == -1): #exactly the same as prayers\\r\\n foundSource = 'pope'\\r\\n for popeword in pope:\\r\\n for part in pope[popeword]['parts']:\\r\\n if (language_out in pope[popeword]['parts'][part]): #check that language exists\\r\\n foundKey = popeword\\r\\n wordInSentence(foundSource, listWordsTopic, 'topic', pope[popeword]['parts'][part][language_out], foundKey, foundResults)\\r\\n wordInSentence(foundSource, listWordsPeople, 'people', pope[popeword]['parts'][part][language_out], foundKey, foundResults)\\r\\n\\r\\n\\r\\n if (source == 'quotes' or source == -1): #exactly the same as prayers\\r\\n foundSource = 'quotes'\\r\\n for quote in quotes:\\r\\n for part in quotes[quote]['parts']:\\r\\n if (language_out in quotes[quote]['parts'][part]): #check that language exists\\r\\n foundKey = quote\\r\\n wordInSentence(foundSource, listWordsTopic, 'topic', quotes[quote]['parts'][part][language_out], foundKey, foundResults)\\r\\n wordInSentence(foundSource, listWordsPeople, 'people', quotes[quote]['parts'][part][language_out], foundKey, foundResults)\\r\\n\\r\\n #print(\\\"foundResults before saints:\\\", foundResults)\\r\\n if (people != -1):\\r\\n if people[0].isdigit(): #if it's a date, the query is a saint\\r\\n foundSource = 'saints'\\r\\n foundKey = people[0:len(people)-1] + 's'\\r\\n if foundKey not in foundResults:\\r\\n foundResults[foundKey] = {}\\r\\n foundResults[foundKey]['source'] = foundSource\\r\\n foundResults[foundKey]['topic'] = 0\\r\\n foundResults[foundKey]['people'] = 1\\r\\n\\r\\n\\r\\n #print(\\\"foundResults:\\\", foundResults)\\r\\n #filter case of AND (as so far it collected OR)\\r\\n if (topic != -1 and people != -1):\\r\\n for result in list(foundResults): #converted to a list to force a copy of the keys to avoid runtime error\\r\\n if foundResults[result]['topic']==0 or foundResults[result]['people'] ==0:\\r\\n del foundResults[result]\\r\\n #print(\\\"deleted\\\", result)\\r\\n\\r\\n #print(\\\"entriesSkip\\\", entriesSkip)\\r\\n for result in list(foundResults):\\r\\n #print([-1, foundResults[result]['source'], topic, people, result])\\r\\n if ([-1, foundResults[result]['source'], topic, people, result] in entriesSkip):\\r\\n del foundResults[result]\\r\\n #print(\\\"skipping\\\", result)\\r\\n\\r\\n print(\\\"filtered foundResults:\\\", foundResults)\\r\\n\\r\\n \\\"\\\"\\\"\\r\\n foundResults contains for each key:\\r\\n the source where it has been found, and the flags topic and people, which are 0/1\\r\\n the variable foundSource will be overwritten and is not usable\\r\\n topic and people are the variables containing the original query parameters\\r\\n \\\"\\\"\\\"\\r\\n \\r\\n if len(foundResults) > 0: \\r\\n chosenResult = random.choice(list(foundResults)) #chosenResult is a key of dictionary\\r\\n if ([-1, source, topic, people, chosenResult, '0'] not in entries):\\r\\n print(\\\"adding to the entries\\\")\\r\\n entries.append([-1, source, topic, people, chosenResult, '0'])\\r\\n print(\\\"chosenResult:\\\",chosenResult)\\r\\n #print(\\\"entries\\\", entries)\\r\\n\\r\\n return [foundResults[chosenResult]['source'], chosenResult]\\r\\n else:\\r\\n return [None, None]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fce05c06e3b70acbfd536b5a5375ee95\",\n \"score\": \"0.51694447\",\n \"text\": \"def search(self, word: str) -> bool:\\n trie = self.lookup\\n\\n def _helper(word, trie):\\n if '#' in trie:\\n return True\\n\\n if word[0]=='.':\\n for k in trie.keys():\\n if _helper(word[1:], trie[k]):\\n return True\\n return False\\n elif word[0] in trie:\\n return _helper(word[1:], trie[word[0]])\\n else:\\n return False\\n \\n return _helper(word, trie)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34b671a639e7f2f78a29b0e96a68b0ca\",\n \"score\": \"0.51692384\",\n \"text\": \"def search(self, sentence):\\n docs = self.solr.query(nltk.word_tokenize(sentence))\\n for doc in docs:\\n print \\\" \\\".join(doc[\\\"tokens\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73f60bf9569920fa87bdb9f44707ccf4\",\n \"score\": \"0.5167708\",\n \"text\": \"def __contains__(self, word: str) -> bool:\\n word = word.lower()\\n for w in self.words:\\n if word == w:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8e1ce0bfed13370e770dedcdaafb566\",\n \"score\": \"0.51632833\",\n \"text\": \"def search(term):\\n try:\\n for ds in thesaurus.keys():\\n if term in thesaurus[ds].speicher:\\n #-->Ueberprufen ob das Term in self.speicher\\n print thesaurus[ds]\\n else:\\n return False\\n return True\\n except:\\n print \\\"Exception:\\\\nsearch() funktion.\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e8d81790ed622e692adc7fbc12c6243\",\n \"score\": \"0.5159494\",\n \"text\": \"def search(self, word):\\n curr = self.root\\n\\n for char in word:\\n if char in curr.children:\\n curr = curr.children[char]\\n else:\\n return False\\n\\n return curr.word\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0eddfb92df58fbf9cd8f12378037ebba\",\n \"score\": \"0.51592755\",\n \"text\": \"def phonetics(self):\\n status = False\\n for phile in self.files.all():\\n for tag in phile.tags.all():\\n if tag.name == 'Phonetics':\\n status = phile.phile.name\\n break\\n return status\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9abed17f7360fba0ae9fdb6a7d3160b\",\n \"score\": \"0.51582825\",\n \"text\": \"def find(self, word):\\n letters = list(word)\\n node = self._root\\n \\n for letter in letters:\\n if letter not in node._children:\\n return False\\n node = node._children[letter]\\n if node.isWord:\\n if node._value:\\n return node._value\\n else:\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62d3d7fcae0609fe97563d2dd63891f3\",\n \"score\": \"0.51580274\",\n \"text\": \"def search(self, word):\\n i = 0\\n it = self.root\\n while i < len(word):\\n if len(it.children) > 0:\\n k = bisect.bisect_left(it.children, TreeNode(word[i]))\\n if k < len(it.children) and it.children[k].val == word[i]:\\n it = it.children[k]\\n i += 1\\n else:\\n break\\n else:\\n break\\n if i != len(word) or not it.is_word:\\n return False\\n else:\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3cec28d40485563f092fa41b4c7ca69\",\n \"score\": \"0.5153317\",\n \"text\": \"def search(self, word):\\n current = self.root\\n for i in word:\\n if current.dict.get(i,None)==None:\\n return False\\n current = current.dict[i]\\n\\n if current.isEOW==True:\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e50da15c7e6884f2df967d049fb96df\",\n \"score\": \"0.51523215\",\n \"text\": \"def search(self, word: str) -> bool:\\n current = self.root\\n for i in word:\\n if i in current.node.keys():\\n current = current.node[i]\\n else:\\n return False\\n return current.isword\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b30b7b67c28f90d3021da2d79927ad56\",\n \"score\": \"0.5147014\",\n \"text\": \"def easy_search_terms(self, sentence, esource, ths, rules, off_list=None):\\n\\t\\treduced_term_list = []\\n\\t\\tsentence_terms = []\\n\\t\\tsentence_terms_text = []\\n\\t\\tentity_tokens = []\\n\\n\\n\\t\\t#Create a gazette with words that start with any word of the sentence\\n\\t\\t#Why was this after next section?\\n\\t\\tfor token in sentence.tokens:\\n\\t\\t\\tptoken = str(token.text.encode(\\\"utf-8\\\")).lower()\\n\\t\\t\\tif ptoken in self.term_dic:\\n\\t\\t\\t\\tfor term in self.term_dic[ptoken]:\\n\\t\\t\\t\\t\\t#print term\\n\\t\\t\\t\\t\\treduced_term_list.append(term)\\n\\n\\t\\tif off_list != None:\\n\\t\\t\\tif \\\"twice_validated\\\" in rules:\\n\\t\\t\\t\\tfor term in off_list + reduced_term_list:\\n\\t\\t\\t\\t\\ttsplit = term.split(\\\" \\\")\\n\\t\\t\\t\\t\\tif len(tsplit) == 2:\\n\\t\\t\\t\\t\\t\\tt = tsplit[1] + \\\" in the \\\" + tsplit[0]\\n\\t\\t\\t\\t\\t\\tt2 = tsplit[1] + \\\" of the \\\" + tsplit[0]\\n\\t\\t\\t\\t\\t\\t#print sentence.sid, t\\n\\t\\t\\t\\t\\t\\tif t in sentence.text.lower() or t2 in sentence.text.lower():\\n\\t\\t\\t\\t\\t\\t\\t#print t\\n\\t\\t\\t\\t\\t\\t\\treduced_term_list.append(tsplit[1] + \\\" in the \\\" + tsplit[0])\\n\\t\\t\\t\\t\\t\\t\\treduced_term_list.append(tsplit[1] + \\\" of the \\\" + tsplit[0])\\n\\t\\t\\t\\t\\tif \\\"type\\\" in term:\\n\\t\\t\\t\\t\\t\\ti = term.find(\\\"type\\\")\\n\\t\\t\\t\\t\\t\\tx = term[:i].strip()\\n\\t\\t\\t\\t\\t\\ty = term[i:].strip()\\n\\t\\t\\t\\t\\t\\treduced_term_list.append(x + \\\" \\\" + y)\\n\\t\\t\\t\\t\\t\\treduced_term_list.append(x + \\\" \\\" + y.replace(\\\"type\\\", \\\"\\\"))\\n\\t\\t\\t\\t\\t\\treduced_term_list.append(y + \\\" \\\" + x)\\n\\t\\t\\t\\t\\t\\treduced_term_list.append(y + \\\" \\\" + x.replace(\\\"type\\\", \\\"\\\"))\\n\\n\\n\\n\\t\\t#Iterate gazette and check if the sentence has any exact match\\n\\t\\tfor term in reduced_term_list:\\n\\t\\t\\tterm_tokens = nltk.word_tokenize(term.lower())\\n\\t\\t\\ttoken_flag = False\\n\\t\\t\\tfor token in sentence.tokens:\\n\\t\\t\\t\\tif term_tokens[0].lower() == token.text.lower():\\n\\t\\t\\t\\t\\ttoken_flag = True\\n\\t\\t\\t\\t\\tstart_index = sentence.tokens.index(token)\\n\\t\\t\\tif token_flag:\\t\\t\\t\\t\\t\\n\\t\\t\\t\\tend_index = start_index + len(term_tokens)\\n\\t\\t\\t\\tentity_tokens = []\\n\\t\\t\\t\\tfor token in sentence.tokens[start_index:end_index]:\\n\\t\\t\\t\\t\\tentity_tokens.append(token.text)\\n\\t\\t\\t\\t#Check if gazette term is the same as sentence tokens for that term.\\t\\t\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\tif term_tokens == [x.lower() for x in entity_tokens]:\\n\\t\\t\\t\\t\\tstart = sentence.tokens[start_index:end_index][0].dstart\\n\\t\\t\\t\\t\\tend = sentence.tokens[start_index:end_index][-1].dend\\n\\t\\t\\t\\t\\tterm = str(sentence.text[sentence.tokens[start_index:end_index][0].start:sentence.tokens[start_index:end_index][-1].end])\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\tsentence_terms.append((start, end, term))\\n\\t\\t\\t\\t\\tsentence_terms_text.append(term)\\n\\t\\t\\t\\t\\t#print term\\n\\t\\t\\t\\t\\t\\n\\n\\t\\t### RULES FOR VALIDATION (maybe should be in functions)\\n\\t\\t#Undidented next line. See if it changes reslts.\\n\\t\\tfor token in sentence.tokens: \\t# sentence and adds the combination and next word\\n\\t\\t\\tif \\\"posgowords\\\" in rules: #Tries to find a combination of go_word and pos_go_word in\\n\\t\\t\\t\\tpos_flag = False\\t\\t\\t# if next word is not a noun, looks for next word.\\n\\t\\t\\t\\ttok = str(token.text.encode(\\\"utf-8\\\").strip().lower())\\n\\t\\t\\t\\tif tok in go_words:\\n\\t\\t\\t\\t\\ttlist = []\\n\\t\\t\\t\\t\\tfor word in pos_go_words:\\n\\t\\t\\t\\t\\t\\tterm = tok + \\\" \\\" + word\\n\\t\\t\\t\\t\\t\\tif term in str(sentence.text.encode(\\\"utf-8\\\")):\\n\\t\\t\\t\\t\\t\\t\\ttlist.append(term)\\n\\t\\t\\t\\t\\tif len(tlist) > 0:\\n\\t\\t\\t\\t\\t\\tterm = max(tlist, key=len)\\n\\t\\t\\t\\t\\t\\tl = len(term.split(\\\" \\\"))\\n\\t\\t\\t\\t\\t\\tindex_start = sentence.tokens.index(token)\\n\\t\\t\\t\\t\\t\\tindex_end = index_start + l + 1 #+1 for next word\\n\\t\\t\\t\\t\\t\\tterm = sentence.text[sentence.tokens[index_start:index_end][0].start:sentence.tokens[index_start:index_end][-1].end]\\n\\t\\t\\t\\t\\t\\tif sentence.tokens[index_end-1].pos != \\\"NN\\\" and sentence.tokens[index_end-1].pos != \\\"NNS\\\": #\\n\\t\\t\\t\\t\\t\\t\\tindex_end += 1\\n\\t\\t\\t\\t\\t\\t\\tterm = sentence.text[sentence.tokens[index_start:index_end][0].start:sentence.tokens[index_start:index_end][-1].end]\\n\\t\\t\\t\\t\\t\\tif index_end < len(sentence.tokens):\\n\\t\\t\\t\\t\\t\\t\\tif sentence.tokens[index_end].pos == \\\"NN\\\" or sentence.tokens[index_end].pos == \\\"NNS\\\":\\n\\t\\t\\t\\t\\t\\t\\t\\tindex_end += 1\\n\\t\\t\\t\\t\\t\\t\\t\\tterm = sentence.text[sentence.tokens[index_start:index_end][0].start:sentence.tokens[index_start:index_end][-1].end]\\n\\t\\t\\t\\t\\t\\tsentence_terms.append((sentence.tokens[index_start:index_end][0].dstart, sentence.tokens[index_start:index_end][-1].dend, str(term.strip())))\\n\\t\\t\\t\\t\\n\\t\\t\\tif \\\"gowords\\\" in rules:\\n\\t\\t\\t\\tgo_flag = False\\n\\t\\t\\t\\tif str(token.text.encode(\\\"utf-8\\\")).strip().lower() in go_words:\\n\\t\\t\\t\\t\\tindex = sentence.tokens.index(token)\\n\\t\\t\\t\\t\\tfor word in go_words:\\n\\t\\t\\t\\t\\t\\tterm = str(sentence.tokens[index-1].text) + \\\" \\\" + word\\n\\t\\t\\t\\t\\t\\tif term in reduced_term_list:\\n\\t\\t\\t\\t\\t\\t\\t#print term, \\\"---\\\", token.text, \\\"---\\\", sentence.text\\n\\t\\t\\t\\t\\t\\t\\tgo_flag = True\\n\\t\\t\\t\\tif go_flag and index-1 > 0 and index+1 < len(sentence.tokens):\\n\\t\\t\\t\\t\\tprint \\\"********\\\"\\n\\t\\t\\t\\t\\tstart = sentence.tokens[index-1:index+1][0].dstart\\n\\t\\t\\t\\t\\tend = sentence.tokens[index-1:index+1][-1].dend\\n\\t\\t\\t\\t\\tterm = sentence.text[sentence.tokens[index-1:index+1][0].start:sentence.tokens[index-1:index+1][-1].end]\\n\\t\\t\\t\\t\\tsentence_terms.append((start, end, str(term).strip()))\\n\\n\\n\\t\\tif \\\"longterms\\\" in rules: #Add terms that are longer than the ones that exist.\\n\\t\\t\\tsentence_entities = [] \\n\\t\\t\\tfor s in sentence.entities.elist:\\n\\t\\t\\t\\tif s.startswith(esource):\\n\\t\\t\\t\\t\\tsentence_entities = [str(x.text.encode(\\\"utf-8\\\")) for x in sentence.entities.elist[s]]\\n\\n\\t\\t\\tfor term in [x[2] for x in sentence_terms] + sentence_entities:\\n\\t\\t\\t\\tterm_tokens = nltk.word_tokenize(term.strip().lower())\\n\\t\\t\\t\\tfor token in sentence.tokens:\\n\\t\\t\\t\\t\\tif term_tokens[0].lower() == token.text.lower():\\n\\t\\t\\t\\t\\t\\tstart_index = sentence.tokens.index(token)\\n\\t\\t\\t\\t\\t\\tend_index = start_index + len(term_tokens)\\n\\t\\t\\t\\t\\t\\tif term_tokens == [str(x.text) for x in sentence.tokens[start_index:end_index]]:\\n\\t\\t\\t\\t\\t\\t\\t#Look for bigger term to the left\\n\\t\\t\\t\\t\\t\\t\\tif start_index > 0:\\n\\t\\t\\t\\t\\t\\t\\t\\tif sentence.tokens[start_index-1].text == \\\"and\\\" and sentence.tokens[end_index-1].text in go_words:\\n\\t\\t\\t\\t\\t\\t\\t\\t\\ti = 2\\n\\t\\t\\t\\t\\t\\t\\t\\t\\twhile \\\",\\\" in sentence.tokens[start_index-i:start_index+1-i][0].text:\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tterm = \\\" \\\".join([x.text for x in sentence.tokens[start_index-1-i:end_index]]).replace(\\\" ,\\\", \\\",\\\")\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tstart = sentence.tokens[start_index-1-i:end_index][0].dstart\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tend = sentence.tokens[start_index-1-i:end_index][-1].dend\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tsentence_terms.append((start, end, str(term.strip())))\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\ti += 2\\n\\n\\t\\t\\t\\t\\t\\t\\t#look for bigger term to the right (bigger than 2)\\n\\t\\t\\t\\t\\t\\t\\tif end_index < len(sentence.tokens):\\n\\t\\t\\t\\t\\t\\t\\t\\tif sentence.tokens[end_index].text == \\\",\\\" and sentence.tokens[start_index].text in go_words:\\n\\t\\t\\t\\t\\t\\t\\t\\t\\ti = 2\\n\\t\\t\\t\\t\\t\\t\\t\\t\\twhile \\\",\\\" in sentence.tokens[end_index+i:end_index+1+i][0].text:\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tterm = \\\" \\\".join([x.text for x in sentence.tokens[start_index:end_index+1+i]]).replace(\\\" ,\\\", \\\",\\\")\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tstart = sentence.tokens[start_index:end_index+1+i][0].dstart\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tend = sentence.tokens[start_index:end_index+1+i][-1].dend\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tif sentence.tokens[start_index:end_index+1+i][-1].text == \\\",\\\":\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tend -=1\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tterm = term[:-1]\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tsentence_terms.append((start, end, str(term.strip())))\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\ti += 2\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tterm = \\\" \\\".join([x.text for x in sentence.tokens[start_index:end_index+1+i]]).replace(\\\" ,\\\", \\\",\\\")\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tstart = sentence.tokens[start_index:end_index+1+i][0].dstart\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tend = sentence.tokens[start_index:end_index+1+i][-1].dend\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tif sentence.tokens[start_index:end_index+1+i][-1].text == \\\",\\\":\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tend -=1\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tterm = term[:-1]\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tsentence_terms.append((start, end, str(term.strip())))\\n\\t\\t\\t\\t\\t\\t\\t#Check if its a double term ex: \\\"x and y anomalies\\\"\\n\\t\\t\\t\\t\\t\\t\\tif start_index > 0:\\n\\t\\t\\t\\t\\t\\t\\t\\tif sentence.tokens[start_index-1].text == \\\"and\\\":\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tterm_flag = False\\n\\t\\t\\t\\t\\t\\t\\t\\t\\ttok1 = sentence.tokens[start_index-2:start_index-1][0].text\\n\\t\\t\\t\\t\\t\\t\\t\\t\\ttok2 = sentence.tokens[end_index-1].text\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tif str(tok2) in go_words:\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tfor word in go_words:\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tjoin_tok = str(tok1) + \\\" \\\" + word\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tif join_tok in reduced_term_list:\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tterm_flag = True\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tif term_flag:\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tstart = sentence.tokens[start_index-2:end_index][0].dstart\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tend = sentence.tokens[start_index-2:end_index][-1].dend\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tterm = sentence.text[sentence.tokens[start_index-2:end_index][0].start:sentence.tokens[start_index-2:end_index][-1].end]\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tsentence_terms.append((start, end, str(term).strip()))\\n\\n\\t\\tif \\\"small_ent\\\" in rules: #Remove smaller entities\\n\\t\\t\\tsmaller_entities = set([])\\n\\t\\t\\tfor a in sentence_terms:\\n\\t\\t\\t\\tfor b in sentence_terms_text + entity_tokens:\\n\\t\\t\\t\\t\\tif a[2].lower() in b and a[2].lower() != b:\\n\\t\\t\\t\\t\\t\\t#logging.info(\\\"{}: small entity: {} / big entity: {}\\\".format(sentence.sid, a, b))\\n\\t\\t\\t\\t\\t\\tsmaller_entities.add(a)\\n\\t\\t\\tfor x in smaller_entities:\\n\\t\\t\\t\\tsentence_terms.remove(x)\\n\\t\\t\\t\\t#logging.info(\\\"{}, removed smaller entity: {}\\\".format(sentence.sid, x))\\n\\n\\t\\tfor term in reduced_term_list: #Check if sentence is composed by a single entity\\n\\t\\t\\t#print term, str(sentence.text)\\n\\t\\t\\tif term.lower() == str(sentence.text).lower():\\n\\t\\t\\t\\t#print term\\n\\t\\t\\t\\tsentence_terms = []\\n\\t\\t\\t\\tstart = 0\\n\\t\\t\\t\\tend = len(term)\\n\\t\\t\\t\\tterm = str(sentence.text)\\t\\t\\t\\t\\t\\n\\t\\t\\t\\tsentence_terms.append((start, end, term))\\n\\n\\t\\tsentence_terms = list(set(sentence_terms))\\n\\t\\t#if len(sentence_terms) > 0:\\n\\t\\t#\\tprint sentence.sid, sentence_terms\\n\\t\\treturn sentence_terms\",\n \"title\": \"\"\n },\n {\n \"docid\": \"603d36f4ea8dd5954778da1408869859\",\n \"score\": \"0.5146544\",\n \"text\": \"def search(self, word: str) -> bool:\\n point = self.root\\n for ch in word:\\n if point.childers[ch] is None:\\n return False\\n point = point.childers[ch]\\n return point.is_end\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21b814248255a3308b1030b3e913265c\",\n \"score\": \"0.5143131\",\n \"text\": \"def search_word(self, word):\\n current = self.root\\n for letter in word:\\n current = current.children.get(letter)\\n if not current:\\n return False\\n return current.is_word\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5129a7afd385ec5a88186b9bc4b1788b\",\n \"score\": \"0.5137912\",\n \"text\": \"def search(self, word):\\n def _dfs(trie, idx):\\n \\n if idx == len(word) and trie.get(\\\"final\\\") and trie[\\\"final\\\"] == word:\\n return True\\n char = word[idx]\\n if char != '.' and char in trie:\\n return _dfs(trie[char], idx + 1)\\n else:\\n valid = False\\n for char in trie.keys():\\n valid = valid or _dfs(trie[char], idx + 1)\\n if valid:\\n return True\\n return False\\n \\n return _dfs(self._trie, 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4e18b7b98b229ae8b26a167c9a88a51\",\n \"score\": \"0.51302934\",\n \"text\": \"def match_nouns(self, nword):\\n\\n def filter_is_true(nid, test):\\n \\\"\\\"\\\"Run a test on a particular noun, passed by ID.\\\"\\\"\\\"\\n try:\\n tmethod = self.filters[test.lstrip('!')]\\n except KeyError:\\n return True\\n \\n return tmethod(nid) ^ (test[0] == '!') # negate if test starts with !\\n \\n def get_matching_nouns(iword):\\n return (self.get_nouns_by_name(self.sub_input_words(nword)) if nword[0] == '%'\\n else filter(bool, (self.nouns.get(nid) for nid in nword.split(','))))\\n \\n if ':' in nword:\\n # filter matching nouns using tests\\n nword, tests = nword.split(':', 1)\\n return set(noun for noun in get_matching_nouns(nword)\\n if all(filter_is_true(noun.id, test) for test in tests.split(':')))\\n else:\\n return get_matching_nouns(nword)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2474539b8ee1273f7aeece86997bb162\",\n \"score\": \"0.512346\",\n \"text\": \"def zh_word(self, word: str) -> str:\\n\\n alternate_meanings = []\\n alternate_pinyin = ()\\n alternate_jyutping = None\\n\\n eth_page = requests.get(\\n \\\"https://www.mdbg.net/chinese/dictionary?page=worddict&wdrst=0&wdqb=c:\\\"\\n + word,\\n timeout=15,\\n headers=self.zw_useragent,\\n )\\n tree = html.fromstring(eth_page.content) # now contains the whole HTML page\\n word_exists = str(\\n tree.xpath('//p[contains(@class,\\\"nonprintable\\\")]/strong/text()')\\n )\\n cmn_pronunciation = tree.xpath('//div[contains(@class,\\\"pinyin\\\")]/a/span/text()')\\n # We only want the pronunciations to be as long as the input\\n cmn_pronunciation = cmn_pronunciation[0 : len(word)]\\n # We don't need a dividing character per pinyin standards\\n cmn_pronunciation = \\\"\\\".join(cmn_pronunciation)\\n\\n # Check to not return anything if the entry is invalid\\n if \\\"No results found\\\" in word_exists:\\n # First we try to check our specialty dictionaries. Buddhist dictionary first. Then the tea dictionary.\\n search_results_buddhist = self.__zh_word_buddhist_dictionary_search(\\n tradify(word)\\n )\\n search_results_tea = self.__zh_word_tea_dictionary_search(simplify(word))\\n search_results_cccanto = self.__zh_word_cccanto_search(tradify(word))\\n\\n # If both have nothing, we kick it down to the character search.\\n if (\\n search_results_buddhist is None\\n and search_results_tea is None\\n and search_results_cccanto is None\\n ):\\n logger.info(\\n \\\"ZH-Word: No results found. Getting individual characters instead.\\\"\\n )\\n # This will split the word into character chunks.\\n if len(word) < 2:\\n to_post = self.zh_character(word)\\n else: # The word is longer than one character.\\n to_post = \\\"\\\"\\n search_characters = list(word)\\n for character in search_characters:\\n to_post += \\\"\\\\n\\\\n\\\" + self.zh_character(character)\\n return to_post\\n\\n # Otherwise, let's try to format the data nicely.\\n if search_results_buddhist is not None:\\n alternate_meanings.append(search_results_buddhist[\\\"meaning\\\"])\\n alternate_pinyin = search_results_buddhist[\\\"pinyin\\\"]\\n if search_results_tea is not None:\\n alternate_meanings.append(search_results_tea[\\\"meaning\\\"])\\n alternate_pinyin = search_results_tea[\\\"pinyin\\\"]\\n if search_results_cccanto is not None:\\n alternate_meanings.append(search_results_cccanto[\\\"meaning\\\"])\\n alternate_pinyin = search_results_cccanto[\\\"pinyin\\\"]\\n alternate_jyutping = search_results_cccanto[\\\"jyutping\\\"]\\n logger.info(\\n f\\\"ZH-Word: No results for word {word}, but results are in specialty dictionaries.\\\"\\n )\\n\\n if len(alternate_meanings) == 0:\\n # The standard search function for regular words.\\n # Get alternate pinyin from a separate function. We get Wade Giles and Yale. Like 'Guan1 yin1 Pu2 sa4'\\n try:\\n py_split_pronunciation = tree.xpath(\\n '//div[contains(@class,\\\"pinyin\\\")]/a/@onclick'\\n )\\n py_split_pronunciation = re.search(\\n r\\\"\\\\|(...+)\\\\'\\\\)\\\", py_split_pronunciation[0]\\n ).group(0)\\n # Format it nicely.\\n py_split_pronunciation = py_split_pronunciation.split(\\\"'\\\", 1)[0][\\n 1:\\n ].strip()\\n alt_romanize = self.__zh_word_alt_romanization(py_split_pronunciation)\\n except IndexError:\\n # This likely means that the page does not contain that information.\\n alt_romanize = (\\\"---\\\", \\\"---\\\")\\n\\n meaning = [\\n div.text_content()\\n for div in tree.xpath('//div[contains(@class,\\\"defs\\\")]')\\n ]\\n # This removes any empty spaces or commas that are in the list.\\n meaning = [x for x in meaning if x not in [\\\" \\\", \\\", \\\"]]\\n meaning = \\\"/ \\\".join(meaning)\\n meaning = meaning.strip()\\n\\n # Obtain the Cantonese information.\\n yue_page = requests.get(\\n \\\"https://cantonese.org/search.php?q=\\\" + word,\\n timeout=15,\\n headers=self.zw_useragent,\\n )\\n yue_tree = html.fromstring(\\n yue_page.content\\n ) # now contains the whole HTML page\\n yue_pronunciation = yue_tree.xpath(\\n '//h3[contains(@class,\\\"resulthead\\\")]/small/strong//text()'\\n )\\n # This Len needs to be double because of the numbers\\n yue_pronunciation = yue_pronunciation[0 : (len(word) * 2)]\\n yue_pronunciation = iter(yue_pronunciation)\\n yue_pronunciation = [\\n c + next(yue_pronunciation, \\\"\\\") for c in yue_pronunciation\\n ]\\n\\n # Combines the tones and the syllables together\\n yue_pronunciation = \\\" \\\".join(yue_pronunciation)\\n for i in range(0, 9):\\n # Adds Markdown syntax\\n yue_pronunciation = yue_pronunciation.replace(str(i), f\\\"^({str(i)}) \\\")\\n yue_pronunciation = yue_pronunciation.strip()\\n\\n else: # This is for the alternate search with the specialty dictionaries.\\n cmn_pronunciation = self.__zh_word_decode_pinyin(alternate_pinyin)\\n alt_romanize = self.__zh_word_alt_romanization(alternate_pinyin)\\n if alternate_jyutping is not None:\\n yue_pronunciation = alternate_jyutping\\n else:\\n yue_pronunciation = \\\"---\\\" # The non-Canto specialty dictionaries do not include Jyutping pronunciation.\\n meaning = \\\"\\\\n\\\".join(alternate_meanings)\\n\\n # Format the header appropriately.\\n if tradify(word) == simplify(word):\\n lookup_line_1 = str(\\n \\\"# [{0}](https://en.wiktionary.org/wiki/{0}#Chinese)\\\".format(word)\\n )\\n else:\\n lookup_line_1 = (\\n \\\"# [{0} ({1})](https://en.wiktionary.org/wiki/{0}#Chinese)\\\".format(\\n tradify(word), simplify(word)\\n )\\n )\\n\\n # Format the rest.\\n lookup_line_1 += \\\"\\\\n\\\\nLanguage | Pronunciation\\\\n---------|--------------\\\"\\n lookup_line_1 += (\\n f\\\"\\\\n**Mandarin** (Pinyin) | *{cmn_pronunciation}*\\\\n**Mandarin** (Wade-Giles) | *{alt_romanize[1]}*\\\"\\n f\\\"\\\\n**Mandarin** (Yale) | *{alt_romanize[0]}*\\\\n**Cantonese** | *{yue_pronunciation}*\\\"\\n )\\n\\n # Add Hokkien and Hakka data.\\n lookup_line_1 += self.__zh_character_min_hak(tradify(word))\\n\\n # Format the meaning line.\\n if len(alternate_meanings) == 0:\\n # Format the regular results we have.\\n lookup_line_2 = f'\\\\n\\\\n**Meanings**: \\\"{meaning}.\\\"'\\n\\n # Append chengyu data if the string is four characters.\\n if len(word) == 4:\\n chengyu_data = self.__zh_word_chengyu(word)\\n if chengyu_data is not None:\\n logger.info(\\\"ZH-Word: >> Added additional chengyu data.\\\")\\n lookup_line_2 += chengyu_data\\n\\n # We append Buddhist results if we have them.\\n mainline_search_results_buddhist = (\\n self.__zh_word_buddhist_dictionary_search(tradify(word))\\n )\\n if mainline_search_results_buddhist is not None:\\n lookup_line_2 += mainline_search_results_buddhist[\\\"meaning\\\"]\\n\\n else: # This is for the alternate dictionaries only.\\n lookup_line_2 = \\\"\\\\n\\\" + meaning\\n\\n # Format the footer with the dictionary links.\\n lookup_line_3 = (\\n \\\"\\\\n\\\\n\\\\n^Information ^from \\\"\\n \\\"[^CantoDict](https://www.cantonese.sheik.co.uk/dictionary/search/?searchtype=1&text={0}) ^| \\\"\\n \\\"[^MDBG](https://www.mdbg.net/chinese/dictionary?page=worddict&wdrst=0&wdqb=c:{0}) ^| \\\"\\n \\\"[^Yellowbridge](https://yellowbridge.com/chinese/dictionary.php?word={0}) ^| \\\"\\n \\\"[^Youdao](https://dict.youdao.com/w/eng/{0}/#keyfrom=dict2.index)\\\"\\n )\\n lookup_line_3 = lookup_line_3.format(word)\\n\\n # Combine everything together.\\n to_post = lookup_line_1 + lookup_line_2 + \\\"\\\\n\\\\n\\\" + lookup_line_3\\n logger.info(\\n f\\\"ZH-Word: Received a lookup command for {word} in Chinese. Returned search results.\\\"\\n )\\n return to_post\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f11e2aedbf9cc55ef6eaa3a0d9f87405\",\n \"score\": \"0.5122512\",\n \"text\": \"def search(self, word: str) -> bool:\\n stack = [(self.trie, 0)] \\n while len(stack):\\n ptr, i = stack.pop()\\n \\n if i == len(word):\\n if ptr.is_word:\\n return True\\n elif word[i] == '.':\\n for child in ptr.children:\\n if child:\\n stack.append((child, i+1))\\n else:\\n c = ord(word[i]) - ord('a')\\n if ptr.children[c]:\\n stack.append((ptr.children[c], i+1))\\n \\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47c9e87dd0adb9194f38e3ce3743f15f\",\n \"score\": \"0.51146907\",\n \"text\": \"def test_recognize(self):\\n expected = set([\\\"banana\\\"])\\n assert self.recognizer.recognize(\\\"ana\\\") == expected # \\\"ana\\\" should match to \\\"banana\\\" within a levenshtein threshold of 3\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b42b6b8b2de85ce68c9c2d7a629ebc82\",\n \"score\": \"0.5110162\",\n \"text\": \"def filter_pos(word, obj, linkage):\\n # Filtering trigrams\\n if obj.third_word:\\n if word == obj.first_word:\\n if not filter_check_conj(obj.second_word, obj.first_word,\\n obj.first_word_pos, obj.third_word_pos, obj, linkage):\\n if not filter_check_conj(obj.third_word, obj.first_word,\\n obj.first_word_pos, obj.second_word_pos, obj, linkage):\\n if not filter_check_trigrams(word, obj.first_word_pos, obj.second_word_pos,\\n obj.third_word_pos, obj, linkage):\\n filter_check_trigrams(word, obj.first_word_pos, obj.third_word_pos,\\n obj.second_word_pos, obj, linkage)\\n elif word == obj.second_word:\\n if not filter_check_conj(obj.first_word, obj.second_word,\\n obj.second_word_pos, obj.third_word_pos, obj, linkage):\\n if not filter_check_conj(obj.third_word, obj.second_word,\\n obj.first_word_pos, obj.second_word_pos, obj, linkage):\\n if not filter_check_trigrams(word, obj.second_word_pos, obj.first_word_pos,\\n obj.third_word_pos, obj, linkage):\\n filter_check_trigrams(word, obj.second_word_pos, obj.third_word_pos,\\n obj.first_word_pos, obj, linkage)\\n else:\\n if not filter_check_conj(obj.second_word, obj.third_word,\\n obj.first_word_pos, obj.third_word_pos, obj, linkage):\\n if not filter_check_conj(obj.first_word, obj.third_word,\\n obj.third_word_pos, obj.second_word_pos, obj, linkage):\\n if not filter_check_trigrams(word, obj.third_word_pos, obj.second_word_pos,\\n obj.first_word_pos, obj, linkage):\\n filter_check_trigrams(word, obj.third_word_pos, obj.first_word_pos,\\n obj.second_word_pos, obj, linkage)\\n # Filtering bigrams\\n else:\\n if word == obj.first_word:\\n if obj.first_word_pos in self.possible_bigrams \\\\\\n and obj.second_word_pos in self.possible_bigrams[obj.first_word_pos]:\\n create_candidates_dict(self.filtered_candidates, word, linkage, [obj])\\n else:\\n if obj.second_word_pos in self.possible_bigrams \\\\\\n and obj.first_word_pos in self.possible_bigrams[obj.second_word_pos]:\\n create_candidates_dict(self.filtered_candidates, word, linkage, [obj])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc62ea8916b0b471b876b1cad20095d6\",\n \"score\": \"0.51050824\",\n \"text\": \"def __contains__(self, word: str) -> bool:\\n return self.search(word) > 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a481df1012cf275a9b824619bc2e25f\",\n \"score\": \"0.510192\",\n \"text\": \"def search(self, word: str) -> bool:\\n cur = self\\n i = 0\\n while i < len(word) and word[i] in cur.children:\\n cur = cur.children[word[i]]\\n i += 1\\n \\n return i == len(word) and cur.count >= 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61a932810d8b77e3f4b7a92447938e25\",\n \"score\": \"0.51014376\",\n \"text\": \"def noun(self, w):\\n if w in self.jap_noun_dict.keys():\\n return self.jap_noun_dict[w]\\n elif w in self.eng_noun_dict.keys():\\n return self.eng_noun_dict[w]\\n else:\\n print('Noun', w, 'not found')\\n assert False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fa4a689f9afd87da8e99c9a1e6fdb5d\",\n \"score\": \"0.50992703\",\n \"text\": \"def search(self, word):\\n return self.root.search(word)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2dd50c62527f5cd09869f2a88189be14\",\n \"score\": \"0.5097264\",\n \"text\": \"def solve_coref(self):\\n nlp = LangModel.get_instance()\\n text = self.to_txt()\\n doc = nlp(text)\\n tok_list = list(token.text_with_ws for token in doc)\\n for cluster in doc._.coref_clusters:\\n cluster_main_words = set(cluster.main.text.split(' '))\\n for coref in cluster:\\n if coref != cluster.main.text and bool(set(coref.text.split(' ')).intersection(cluster_main_words)) == False:\\n tok_list[coref.start] = cluster.main.text + doc[coref.end - 1].whitespace_\\n for i in range(coref.start + 1, coref.end):\\n tok_list[i] = \\\"\\\"\\n self.txt = \\\"\\\".join(tok_list)\\n return self.txt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f7f0d35fa0e1cd283b287dc0393311f\",\n \"score\": \"0.50957876\",\n \"text\": \"def search(self, word):\\n return self._search(self.root, word, 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77b4946d7e621ac42c917b80ad3abc14\",\n \"score\": \"0.5095266\",\n \"text\": \"def lookup_word(self, target: str):\\n return en_dict.__lookup(self.d, target, 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a47dc1577d1ec3408a4ab60270bf47c2\",\n \"score\": \"0.50942844\",\n \"text\": \"def get_tags(self, word: str) -> Optional[str]:\\n for tag, words in self._pos2words.items():\\n if word.lower() in words:\\n return tag\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9899bdb2db89808e040b11e590047d7\",\n \"score\": \"0.5094146\",\n \"text\": \"def search(self, word):\\n curr = self.root\\n for s in word:\\n if s in curr.sub_node:\\n curr = curr.sub_node[s]\\n else:\\n return False\\n return curr.flag\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4630da3df1393ca25258f5a269e837b\",\n \"score\": \"0.50895876\",\n \"text\": \"def search(self, word: str) -> bool:\\n cur = self.root\\n for c in word:\\n if c not in cur:\\n return False\\n cur = cur[c]\\n return '#' in cur\",\n \"title\": \"\"\n },\n {\n \"docid\": \"672a7ec3f9f53eb1e02fa0e6b1406a09\",\n \"score\": \"0.5087387\",\n \"text\": \"def check_word_as_noun(self,noun):\\n\\t\\treturn self.nounstemmer.stemming_noun(noun);\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23a7d8dc2948f841c179d0b27f1ef186\",\n \"score\": \"0.50758886\",\n \"text\": \"def test_word_bound_regex_complex_search(parser: ShoulderBirdParser) -> None:\\n msg = \\\"appreciated\\\"\\n matches = parser.get_matches(\\\"101\\\", \\\"Delta\\\", msg)\\n assert len(matches) == 0\\n msg = \\\"preoct\\\"\\n matches = parser.get_matches(\\\"101\\\", \\\"Delta\\\", msg)\\n assert len(matches) == 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f8e834a6398fb8144a96755bd62b200\",\n \"score\": \"0.5075861\",\n \"text\": \"def search_by_lemma(cli, args):\\n wn = get_wn_profile(cli, args)\\n get_synsets_by_term(wn, args.lemma, args.pos, compact=not args.detail, lang=args.lang)\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e8e81a2e4d87b29ad435db03f4e5a7e\",\n \"score\": \"0.5073183\",\n \"text\": \"def analyze(self, word):\\n res = self._get_constituents(word)\\n res[\\\"related_words\\\"] = []\\n if res[\\\"roots\\\"]:\\n for root in res[\\\"roots\\\"]:\\n res[\\\"related_words\\\"] += self._get_related_words(root)\\n res[\\\"related_words\\\"] = list(set(res[\\\"related_words\\\"]))\\n try:\\n res[\\\"related_words\\\"].remove(res[\\\"original_word\\\"])\\n except ValueError:\\n pass\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34cb44f25d6b51e685777d047ca3a563\",\n \"score\": \"0.50690717\",\n \"text\": \"def isin_cmu(word):\\n if type(word) == list or len(word.split(\\\" \\\")) > 1:\\n if type(word)==str:\\n word = word.split(\\\" \\\")\\n for w in word:\\n if w.lower() not in word_dict:\\n return False\\n return True\\n return word.lower() in word_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4db5327b507b0ff07771e70cd404096\",\n \"score\": \"0.50672233\",\n \"text\": \"def search(self, word):\\n\\n def find(word, trie):\\n\\n if not word:\\n return True\\n\\n first, rest = word[0], word[1:]\\n\\n if first == '.':\\n\\n\\n for letter, child in trie.children.items():\\n\\n if find(rest, child):\\n return True\\n\\n elif first not in trie.children:\\n return False\\n else:\\n\\n return find(rest, trie.children[first])\\n\\n return find(word, self.root) or False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0255114554a107656ecf5d808312eb1a\",\n \"score\": \"0.5061024\",\n \"text\": \"def search_dict(meaning_dict, word):\\n found = False\\n filtered_dict_list = []\\n if word.casefold() in meaning_dict:\\n found = True\\n filtered_dict_list = meaning_dict[word.casefold()]\\n return filtered_dict_list, found\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c37748d7662274affe9d82e971554e25\",\n \"score\": \"0.5060229\",\n \"text\": \"def encontrando_letra(letra,word):\\n a,b='áéíóú','aeiou'\\n sintilde=str.maketrans(a,b)\\n word=word.translate(sintilde)\\n if letra in word:\\n search= word.index(letra)\\n posiciones=[i for i in range(0,len(word)) if letra==word[i]]\\n else:\\n posiciones=[]\\n return posiciones\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07cebe19add845cac509a6314e71125c\",\n \"score\": \"0.50533223\",\n \"text\": \"def backward_nounsearch(nounphraselist,nounhead):\\n\\t#print \\\"Backward-Nounsearch\\\"\\n\\t#print nounphraselist, nounhead\\n\\tnounphraselist.reverse()\\n\\tfor np in nounphraselist:\\n\\t#\\tprint np[0].reveal()\\n\\t\\tgetsense=find_in_Extension(nounhead,np[0].extension)\\n\\t\\tif getsense>-1:\\t\\t\\t\\t\\t#remove unccessary senses\\n\\t\\t\\tnp[0].extension=[np[0].extension[getsense]]\\n\\t\\t\\treturn np\\n\\treturn 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a0ad41eea397e29b7c3210eca591455\",\n \"score\": \"0.50527984\",\n \"text\": \"def test_common_words(self): \\n filename = \\\"gutenberg.txt\\\"\\n commonwords = CommonWords(filename)\\n word = \\\"the \\\"\\n count = commonwords.word_count(word)\\n print(\\\"\\\\n\\\")\\n print(f\\\"{word} exists {count} times\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0a4cdc7de5e9013914fe8a4932f5629\",\n \"score\": \"0.50512457\",\n \"text\": \"def _lookup_words(\\n self, words: List[str], word_dict: Dict[str, List[str]], n: int = 5\\n ) -> Dict[str, Dict[str, Any]]:\\n\\n pronunciations: Dict[str, Dict[str, Any]] = {}\\n\\n # Dictionary uses upper-case letters\\n dictionary_upper = self.profile.get(\\\"speech_to_text.dictionary_upper\\\", False)\\n\\n # Check words against dictionary\\n unknown_words = set()\\n for word in words:\\n if dictionary_upper:\\n lookup_word = word.upper()\\n else:\\n lookup_word = word.lower()\\n\\n pronounces = list(word_dict.get(lookup_word, []))\\n in_dictionary = len(pronounces) > 0\\n\\n pronunciations[word] = {\\n \\\"in_dictionary\\\": in_dictionary,\\n \\\"pronunciations\\\": pronounces,\\n }\\n if not in_dictionary:\\n unknown_words.add(word)\\n\\n # Guess pronunciations for unknown word\\n if unknown_words:\\n # Path to phonetisaurus FST\\n g2p_path = self.profile.read_path(\\n self.profile.get(\\n f\\\"speech_to_text.{self.speech_system}.g2p_model\\\", \\\"g2p.fst\\\"\\n )\\n )\\n\\n g2p_casing = self.profile.get(\\\"speech_to_text.g2p_casing\\\", \\\"\\\").lower()\\n\\n # Case-sensitive mapping from upper/lower cased word back to original casing\\n word_map: Dict[str, str] = {}\\n\\n with tempfile.NamedTemporaryFile(mode=\\\"w+\\\", suffix=\\\".txt\\\") as wordlist_file:\\n # Write words to a temporary word list file\\n for word in unknown_words:\\n original_word = word\\n if g2p_casing == \\\"upper\\\":\\n # FST was trained with upper-case letters\\n word = word.upper()\\n elif g2p_casing == \\\"lower\\\":\\n # FST was trained with loser-case letters\\n word = word.lower()\\n\\n print(word, file=wordlist_file)\\n word_map[word] = original_word\\n\\n wordlist_file.seek(0)\\n\\n # Output phonetisaurus results to temporary file\\n with tempfile.NamedTemporaryFile(\\n mode=\\\"w+\\\", suffix=\\\".txt\\\"\\n ) as pronounce_file:\\n # Use phonetisaurus to guess pronunciations\\n g2p_command = [\\n \\\"phonetisaurus-apply\\\",\\n \\\"--model\\\",\\n g2p_path,\\n \\\"--word_list\\\",\\n wordlist_file.name,\\n \\\"--nbest\\\",\\n str(n),\\n ]\\n\\n self._logger.debug(repr(g2p_command))\\n subprocess.check_call(g2p_command, stdout=pronounce_file)\\n\\n pronounce_file.seek(0)\\n\\n # Read results\\n ws_pattern = re.compile(r\\\"\\\\s+\\\")\\n\\n for line in pronounce_file:\\n parts = ws_pattern.split(line)\\n word = word_map[parts[0].strip()]\\n phonemes = \\\" \\\".join(parts[1:]).strip()\\n pronunciations[word][\\\"pronunciations\\\"].append(phonemes)\\n\\n return pronunciations\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3680d23254344da739deaad8b23a4c0d\",\n \"score\": \"0.50512004\",\n \"text\": \"def lookup_query(query):\\n if (dictionary_word.get(query, False)):\\n return ('The reasult of search: ' + json.dumps(dictionary_word[query], indent=4))\\n\\n return ('Sorry, there is no such word :(')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48bfbd167cb6b821ef0d717900781793\",\n \"score\": \"0.5049092\",\n \"text\": \"def search(self, word):\\n return self.searchnode(self,word)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4899904cea8ebb8da156245d69d8793\",\n \"score\": \"0.50404733\",\n \"text\": \"def spellCheck(self, sentence: Sentence) -> Sentence:\\n previousRoot = None\\n result = Sentence()\\n for i in range(sentence.wordCount()):\\n word = sentence.getWord(i)\\n fsmParses = self.fsm.morphologicalAnalysis(word.getName())\\n if fsmParses.size() == 0:\\n candidates = self.candidateList(word)\\n bestCandidate = word.getName()\\n bestRoot = word\\n bestProbability = 0.0\\n for candidate in candidates:\\n fsmParses = self.fsm.morphologicalAnalysis(candidate)\\n root = fsmParses.getParseWithLongestRootWord().getWord()\\n if previousRoot is not None:\\n probability = self.__nGram.getProbability(previousRoot.getName(), root.getName())\\n else:\\n probability = self.__nGram.getProbability(root.getName())\\n if probability > bestProbability:\\n bestCandidate = candidate\\n bestRoot = root\\n bestProbability = probability\\n previousRoot = bestRoot\\n result.addWord(Word(bestCandidate))\\n else:\\n result.addWord(word)\\n previousRoot = fsmParses.getParseWithLongestRootWord().getWord()\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ba20aa1eebd8e49b5094c92452c15a5\",\n \"score\": \"0.5039387\",\n \"text\": \"def search(self, word):\\n node = self.root\\n for item in word:\\n if item in node.childrenValue:\\n node = node.children[node.childrenValue[item]]\\n else:\\n return False\\n if \\\"\\\" in node.childrenValue:\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":877,"cells":{"query_id":{"kind":"string","value":"a62790cae6789b1d12d43b936f35c3a6"},"query":{"kind":"string","value":"Decoder block for albedo"},"positive_passages":{"kind":"list like","value":[{"docid":"e2767a3a5b8c9cf3aa792d1df864d5aa","score":"0.5774206","text":"def _albedo_decoder(albedo_mlp_output, encoder_op_each_layer):\n\t\tfilters = [ 256, 256, 256, 128, 64]\n\t\tdeconvolved = albedo_mlp_output\n\n\t\tfor i in range(5):\n\t\t\tdeconvolved_input = tf.concat(\n\t\t\t\t[deconvolved, encoder_op_each_layer[5-(i+1)]],\n\t\t\t\t3)\n\t\t\tdeconvolved = tf.layers.conv2d_transpose(\n\t\t\t\tdeconvolved_input,\n\t\t\t\tfilters = filters[i],\n\t\t\t\tkernel_size = 4,\n\t\t\t\tstrides = 2,\n\t\t\t\tpadding = \"SAME\",\n\t\t\t\tname = \"albedo_decoder_deconv2d_%d\" % i)\n\t\t\tdeconvlved = tf.layers.batch_normalization(\n\t\t\t\tdeconvolved,\n\t\t\t\ttraining=(mode == tf.estimator.ModeKeys.TRAIN),\n\t\t\t\tname = \"albedo_decoder_batch_norm_%d\" % i)\n\t\t\tdeconvolved = tf.nn.relu(\n\t\t\t\tdeconvolved,\n\t\t\t\tname=None)\n\t\talbedo_decoder_output = tf.layers.conv2d(\n\t\t\t\tdeconvolved,\n\t\t\t\tfilters = 3,\n\t\t\t\tkernel_size = 1,\n\t\t\t\tname = \"albedo_decoder_conv2d\")\n\t\treturn albedo_decoder_output","title":""}],"string":"[\n {\n \"docid\": \"e2767a3a5b8c9cf3aa792d1df864d5aa\",\n \"score\": \"0.5774206\",\n \"text\": \"def _albedo_decoder(albedo_mlp_output, encoder_op_each_layer):\\n\\t\\tfilters = [ 256, 256, 256, 128, 64]\\n\\t\\tdeconvolved = albedo_mlp_output\\n\\n\\t\\tfor i in range(5):\\n\\t\\t\\tdeconvolved_input = tf.concat(\\n\\t\\t\\t\\t[deconvolved, encoder_op_each_layer[5-(i+1)]],\\n\\t\\t\\t\\t3)\\n\\t\\t\\tdeconvolved = tf.layers.conv2d_transpose(\\n\\t\\t\\t\\tdeconvolved_input,\\n\\t\\t\\t\\tfilters = filters[i],\\n\\t\\t\\t\\tkernel_size = 4,\\n\\t\\t\\t\\tstrides = 2,\\n\\t\\t\\t\\tpadding = \\\"SAME\\\",\\n\\t\\t\\t\\tname = \\\"albedo_decoder_deconv2d_%d\\\" % i)\\n\\t\\t\\tdeconvlved = tf.layers.batch_normalization(\\n\\t\\t\\t\\tdeconvolved,\\n\\t\\t\\t\\ttraining=(mode == tf.estimator.ModeKeys.TRAIN),\\n\\t\\t\\t\\tname = \\\"albedo_decoder_batch_norm_%d\\\" % i)\\n\\t\\t\\tdeconvolved = tf.nn.relu(\\n\\t\\t\\t\\tdeconvolved,\\n\\t\\t\\t\\tname=None)\\n\\t\\talbedo_decoder_output = tf.layers.conv2d(\\n\\t\\t\\t\\tdeconvolved,\\n\\t\\t\\t\\tfilters = 3,\\n\\t\\t\\t\\tkernel_size = 1,\\n\\t\\t\\t\\tname = \\\"albedo_decoder_conv2d\\\")\\n\\t\\treturn albedo_decoder_output\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"626baf0b964a809a74e5496021d993f6","score":"0.6972518","text":"def _decode(self):\n pass","title":""},{"docid":"38895bd4f86a4a78899953d8615e9326","score":"0.68361384","text":"def decoder(self, decoder: decoders.Decoder):\n pass","title":""},{"docid":"cfdd22f5baf132fe9bbd0a8d7d221931","score":"0.66675913","text":"def _decode(self, decode_input):\n pass","title":""},{"docid":"fc8a4d920cc0a168f628c6a30a917442","score":"0.6611095","text":"def doDecode(self, data):\r\n\t\traise NotImplementedError","title":""},{"docid":"8172a3f12a2a7c8cc9be82e0e436f1d5","score":"0.6397546","text":"def getincrementaldecoder(encoding):\n\tpass","title":""},{"docid":"05394b827274cdc9bd803e34820c19f2","score":"0.6352971","text":"def decode(self, data: bytes) -> Any:","title":""},{"docid":"d25bfeabe6d7478697db77be9f0efeae","score":"0.63112146","text":"def PyCodec_Decoder(space, encoding):\n raise NotImplementedError","title":""},{"docid":"656e43c228720bab15917f95c975a86a","score":"0.62642276","text":"def decode(self, object,final):\n\t\tpass","title":""},{"docid":"02c7229d4908dae552e9e362d525bf82","score":"0.6207092","text":"def create_decoder(self):\n raise NotImplementedError","title":""},{"docid":"6838eda268384ea3705705d0a9e2155d","score":"0.6159395","text":"def _decode(self, input):\n return input","title":""},{"docid":"3b5279604d06a6f5f3644877ca5660ca","score":"0.6148942","text":"def decompress_stream(self,key): \n self.key = key \n #print \"++++++++++++ decompressing stream +++++++++++++++++++++++++\" ,key\n try: \n data = self.objects[self.key]\n start = data.find(\"stream\")\n end = data.find(\"endstream\")\n self.buff = data[start+6:].strip()\n if len(self.buff) < 2:return\n \n self.methods = self.get_compMethod(key,data)\n self.data = self.buff.strip() \n for self.method in self.methods:\n #print self.method\n if 'fl' == self.method.lower():\n self.data = decompress(self.data)\n if 'ascii85decode' == self.method.lower():\n self.data = ascii85decode(self.data)\n if 'asciihexdecode' == self.method.lower(): \n self.data = asciihexdecode(self.data)\n if 'lzwdecode' == self.method.lower():\n self.data = lzwdecode(self.data) \n \n if len(self.methods) == 0: \n self.handle_evasion(key,data[:start]) \n \n except Exception,err:\n pass \n #print sys.stderr.write('ERROR: %s\\n' % str(err)),key,self.methods\n y = \"\"\n #print filter(lambda x: chr(ord(x)),self.data)\n return self.data","title":""},{"docid":"22961228a5d8e0767be431da4d6b4ff7","score":"0.614481","text":"def getdecoder(encoding):\n\tpass","title":""},{"docid":"3a80e52aec8dd02a28150454c6640933","score":"0.61191016","text":"def decode(cls, b):\n raise NotImplementedError","title":""},{"docid":"815597b99c08003f75af554e6c32b357","score":"0.6056201","text":"def decoder(self) -> Optional[Decoder]:\n pass","title":""},{"docid":"ec94cd0717d46587ae681b9923388c24","score":"0.6045181","text":"def decode(self, *args, **kwargs):\n raise NotImplementedError","title":""},{"docid":"c9d8a3a06e710b06131968de49690b3b","score":"0.599665","text":"def _decode(*args):\n\t\treturn args","title":""},{"docid":"c9d8a3a06e710b06131968de49690b3b","score":"0.599665","text":"def _decode(*args):\n\t\treturn args","title":""},{"docid":"ec151e7c78054d10ee1a53e21416e15c","score":"0.5995815","text":"def create_decoder(self):\n batch_size = tf.shape(self.encode)[0]\n l = tf.multiply(self.encode, self.act_embed, name='merge')\n l = FC(l, 1024, 'dec')\n l = FC(l, 64 * 10 * 10, 'ip4')\n l = ReLu(l, 'relu1')\n l = tf.reshape(l, [-1, 10, 10, 64], name='dec-reshape')\n l = Deconv2D(l, [6, 6], [batch_size, 20, 20, 64], 64, 2, 'SAME', 'deconv3')\n l = ReLu(l, 'relu2')\n l = Deconv2D(l, [6, 6], [batch_size, 40, 40, 64], 64, 2, 'SAME', 'deconv2')\n l = ReLu(l, 'relu3')\n l = Deconv2D(l, [6, 6], [batch_size, 84, 84, 3], 3, 2, 'VALID', 'x_hat-05')\n return l","title":""},{"docid":"004738b5189ab156c3b08484230a4e63","score":"0.58919895","text":"def _streamEMB():\r\n pass","title":""},{"docid":"6cd76706583c27d28efc67cf8db75c6d","score":"0.5870717","text":"def decode(self):\n self.decoded = True\n if self.dtype == 'BTH0':\n self.subpack = BTH0(self.datablock, self.subheader['Beams'])\n elif self.dtype == 'SNI0':\n self.subpack = SNI0(self.datablock)\n elif self.dtype == 'WCD0':\n self.subpack = WCD0(self.datablock)\n else:\n self.decoded = False\n print \"Data record \" + str(self.header[0]) + \" decoding is not yet supported.\"","title":""},{"docid":"80d31190a67ca9809d6e8d81c532c0ca","score":"0.5848532","text":"def __init__(self, src_embed, encoder, tgt_embed, decoder, enc2dec, generator):\n super(EncoderDecoder, self).__init__()\n self.src_embed = src_embed\n self.encoder = encoder\n self.enc2dec = enc2dec\n self.tgt_embed = tgt_embed\n self.decoder = decoder\n self.generator = generator","title":""},{"docid":"cf288f0eb8fa271862df0176fbf97a8a","score":"0.58331096","text":"def decode(self, z):\n return self.generator(z)","title":""},{"docid":"9abd21a9925f2a7133048d0c6bace4dd","score":"0.58246773","text":"def PyCodec_Decode(space, object, encoding, errors):\n raise NotImplementedError","title":""},{"docid":"f3af54a011a660b2cdea7bd33bf90890","score":"0.58233213","text":"def on_decoded(self, packet):\n self.emit('packet', packet)","title":""},{"docid":"f15ca233b40a14500cf04cb2ee430e23","score":"0.5805976","text":"def _decode1(self, body, data):\n if \" \" in body:\n evtype,body = body.split(\" \",1)\n else:\n evtype,body = body,\"\"\n evtype = evtype.upper()\n if evtype == \"CIRC\":\n m = re.match(r\"(\\d+)\\s+(\\S+)(\\s\\S+)?(\\s\\S+)?(\\s\\S+)?(\\s\\S+)?\", body)\n if not m:\n raise ProtocolError(\"CIRC event misformatted.\")\n ident,status,path,purpose,reason,remote = m.groups()\n ident = int(ident)\n if path:\n if \"PURPOSE=\" in path:\n remote = reason\n reason = purpose\n purpose=path\n path=[]\n elif \"REASON=\" in path:\n remote = reason\n reason = path\n purpose = \"\"\n path=[]\n else:\n path_verb = path.strip().split(\",\")\n path = []\n for p in path_verb:\n path.append(p.replace(\"~\", \"=\").split(\"=\")[0])\n else:\n path = []\n\n if purpose and \"REASON=\" in purpose:\n remote=reason\n reason=purpose\n purpose=\"\"\n\n if purpose: purpose = purpose[9:]\n if reason: reason = reason[8:]\n if remote: remote = remote[15:]\n event = CircuitEvent(evtype, ident, status, path, purpose, reason,\n remote, body)\n elif evtype == \"STREAM\":\n #plog(\"DEBUG\", \"STREAM: \"+body)\n m = re.match(r\"(\\S+)\\s+(\\S+)\\s+(\\S+)\\s+(\\S+)?:(\\d+)(\\sREASON=\\S+)?(\\sREMOTE_REASON=\\S+)?(\\sSOURCE=\\S+)?(\\sSOURCE_ADDR=\\S+)?(\\s+PURPOSE=\\S+)?\", body)\n if not m:\n raise ProtocolError(\"STREAM event misformatted.\")\n ident,status,circ,target_host,target_port,reason,remote,source,source_addr,purpose = m.groups()\n ident,circ = map(int, (ident,circ))\n if not target_host: # This can happen on SOCKS_PROTOCOL failures\n target_host = \"(none)\"\n if reason: reason = reason[8:]\n if remote: remote = remote[15:]\n if source: source = source[8:]\n if source_addr: source_addr = source_addr[13:]\n if purpose:\n purpose = purpose.lstrip()\n purpose = purpose[8:]\n event = StreamEvent(evtype, ident, status, circ, target_host,\n int(target_port), reason, remote, source, source_addr,\n purpose, body)\n elif evtype == \"ORCONN\":\n m = re.match(r\"(\\S+)\\s+(\\S+)(\\sAGE=\\S+)?(\\sREAD=\\S+)?(\\sWRITTEN=\\S+)?(\\sREASON=\\S+)?(\\sNCIRCS=\\S+)?\", body)\n if not m:\n raise ProtocolError(\"ORCONN event misformatted.\")\n target, status, age, read, wrote, reason, ncircs = m.groups()\n\n #plog(\"DEBUG\", \"ORCONN: \"+body)\n if ncircs: ncircs = int(ncircs[8:])\n else: ncircs = 0\n if reason: reason = reason[8:]\n if age: age = int(age[5:])\n else: age = 0\n if read: read = int(read[6:])\n else: read = 0\n if wrote: wrote = int(wrote[9:])\n else: wrote = 0\n event = ORConnEvent(evtype, status, target, age, read, wrote,\n reason, ncircs, body)\n elif evtype == \"STREAM_BW\":\n m = re.match(r\"(\\d+)\\s+(\\d+)\\s+(\\d+)\", body)\n if not m:\n raise ProtocolError(\"STREAM_BW event misformatted.\")\n event = StreamBwEvent(evtype, body, *m.groups())\n elif evtype == \"BW\":\n m = re.match(r\"(\\d+)\\s+(\\d+)\", body)\n if not m:\n raise ProtocolError(\"BANDWIDTH event misformatted.\")\n read, written = map(long, m.groups())\n event = BWEvent(evtype, read, written, body)\n elif evtype in (\"DEBUG\", \"INFO\", \"NOTICE\", \"WARN\", \"ERR\"):\n event = LogEvent(evtype, body)\n elif evtype == \"NEWDESC\":\n ids_verb = body.split(\" \")\n ids = []\n for i in ids_verb:\n ids.append(i.replace(\"~\", \"=\").split(\"=\")[0].replace(\"$\",\"\"))\n event = NewDescEvent(evtype, ids, body)\n elif evtype == \"ADDRMAP\":\n # TODO: Also parse errors and GMTExpiry\n m = re.match(r'(\\S+)\\s+(\\S+)\\s+(\\\"[^\"]+\\\"|\\w+)', body)\n if not m:\n raise ProtocolError(\"ADDRMAP event misformatted.\")\n fromaddr, toaddr, when = m.groups()\n if when.upper() == \"NEVER\": \n when = None\n else:\n when = time.strptime(when[1:-1], \"%Y-%m-%d %H:%M:%S\")\n event = AddrMapEvent(evtype, fromaddr, toaddr, when, body)\n elif evtype == \"NS\":\n event = NetworkStatusEvent(evtype, parse_ns_body(data), data)\n elif evtype == \"NEWCONSENSUS\":\n event = NewConsensusEvent(evtype, parse_ns_body(data), data)\n elif evtype == \"BUILDTIMEOUT_SET\":\n m = re.match(\n r\"(\\S+)\\sTOTAL_TIMES=(\\d+)\\sTIMEOUT_MS=(\\d+)\\sXM=(\\d+)\\sALPHA=(\\S+)\\sCUTOFF_QUANTILE=(\\S+)\",\n body)\n set_type, total_times, timeout_ms, xm, alpha, quantile = m.groups()\n event = BuildTimeoutSetEvent(evtype, set_type, int(total_times),\n int(timeout_ms), int(xm), float(alpha),\n float(quantile), body)\n elif evtype == \"GUARD\":\n m = re.match(r\"(\\S+)\\s(\\S+)\\s(\\S+)\", body)\n entry, guard, status = m.groups()\n event = GuardEvent(evtype, entry, guard, status, body)\n elif evtype == \"TORCTL_TIMER\":\n event = TimerEvent(evtype, data)\n else:\n event = UnknownEvent(evtype, body)\n\n return event","title":""},{"docid":"b67d36b773ac1c98416946d255a8afb0","score":"0.5796752","text":"def get_decoder(aes_adapter):\n def decode(cipthertext):\n return aes_adapter.decrypt(cipthertext)\n \n return decode","title":""},{"docid":"dbf29e9bb0c4dbdc3a711c24cc9ceadc","score":"0.57395804","text":"def h264_frame_cb(self, h264_frame):","title":""},{"docid":"cb3db0e41e5ebfd55a462b37b6761e09","score":"0.57241505","text":"def forward(self, decoder_inner_states):\n return None","title":""},{"docid":"4d22563669cf14411d5c12414526ae2d","score":"0.56893575","text":"def dataDecoder(data):\n\treturn json.loads(base64.b64decode(data))","title":""},{"docid":"f68c28b8971dac56bacd45365aa29806","score":"0.561687","text":"def decode(data: dict):\n try:\n if data[\"sourceEndpoint\"] == \"0x00\":\n # decode ZDO\n if \"zdo\" not in CLUSTERS:\n zdo = CLUSTERS[\"zdo\"] = ZDO(None)\n else:\n zdo = CLUSTERS[\"zdo\"]\n\n cluster_id = int(data[\"clusterId\"], 0)\n raw = bytes.fromhex(data[\"APSPlayload\"][2:])\n hdr, args = zdo.deserialize(cluster_id, raw)\n cmd = ZDOCmd(hdr.command_id).name\n if hdr.command_id == ZDOCmd.Active_EP_rsp:\n return {\"command\": cmd, \"status\": str(args[0]), \"endpoints\": args[2]}\n elif hdr.command_id == ZDOCmd.Simple_Desc_rsp:\n desc: SizePrefixedSimpleDescriptor = args[2]\n return {\n \"command\": cmd,\n \"status\": str(args[0]),\n \"device_type\": desc.device_type,\n \"device_version\": desc.device_version,\n \"endpoint\": desc.endpoint,\n \"input_clusters\": desc.input_clusters,\n \"output_clusters\": desc.output_clusters,\n \"profile\": desc.profile,\n }\n elif hdr.command_id == ZDOCmd.Node_Desc_rsp:\n desc: NodeDescriptor = args[2]\n return {\n \"command\": cmd,\n \"status\": str(args[0]),\n \"is_mains_powered\": desc.is_mains_powered,\n \"logical_type\": str(desc.logical_type),\n \"manufacturer_code\": desc.manufacturer_code,\n }\n elif hdr.command_id in (ZDOCmd.Bind_rsp, ZDOCmd.Mgmt_Leave_rsp):\n return {\n \"command\": cmd,\n \"status\": str(args[0]),\n }\n elif hdr.command_id in (ZDOCmd.Node_Desc_req, ZDOCmd.Active_EP_req):\n return {\"command\": cmd}\n elif hdr.command_id == ZDOCmd.Simple_Desc_req:\n return {\n \"command\": cmd,\n \"endpoint\": args[0],\n }\n elif hdr.command_id == ZDOCmd.Bind_req:\n return {\n \"command\": cmd,\n \"src_addr\": args[0],\n \"src_endpoint\": args[1],\n \"cluster\": args[2],\n \"dst_addr\": args[3],\n }\n elif hdr.command_id == ZDOCmd.IEEE_addr_rsp:\n return {\n \"command\": cmd,\n \"status\": args[0],\n \"ieee\": args[1],\n \"nwk\": args[2],\n }\n elif hdr.command_id == ZDOCmd.Mgmt_Leave_req:\n return {\n \"command\": cmd,\n \"ieee\": args[0],\n }\n elif hdr.command_id == ZDOCmd.Mgmt_NWK_Update_rsp:\n return {\n \"command\": cmd,\n \"status\": args[0],\n \"channels\": args[1],\n \"total\": args[2],\n \"failures\": args[3],\n \"energy\": args[4],\n }\n elif hdr.command_id == ZDOCmd.NWK_addr_rsp:\n return {\n \"command\": cmd,\n \"status\": args[0],\n \"ieee\": args[1],\n \"nwk\": args[2],\n }\n else:\n raise NotImplemented\n\n # decode ZCL\n cluster_id = int(data[\"clusterId\"], 0)\n if cluster_id not in CLUSTERS:\n cluster = CLUSTERS[cluster_id] = Cluster.from_id(None, cluster_id)\n cluster._log = lambda *_, **__: None\n else:\n cluster = CLUSTERS[cluster_id]\n\n raw = bytes.fromhex(data[\"APSPlayload\"][2:])\n try:\n hdr, args = cluster.deserialize(raw)\n hdr: ZCLHeader\n except ValueError as e:\n return {\"cluster_id\": cluster_id, \"error\": str(e)}\n except KeyError as e:\n return {\"cluster_id\": cluster_id, \"error\": f\"Key error: {e}\"}\n\n payload = {\n \"endpoint\": int(data[\"sourceEndpoint\"], 0),\n \"seq\": hdr.tsn,\n }\n\n if cluster.ep_attribute:\n payload[\"cluster\"] = cluster.ep_attribute\n else:\n payload[\"cluster_id\"] = cluster_id\n\n if hdr.frame_control.is_general:\n payload[\"command\"] = Command(hdr.command_id).name\n\n if (\n hdr.command_id == Command.Report_Attributes\n or hdr.command_id == Command.Write_Attributes\n ):\n (attrs,) = args\n for attr in attrs:\n assert isinstance(attr, Attribute)\n if attr.attrid in cluster.attributes:\n name = cluster.attributes[attr.attrid].name\n else:\n name = attr.attrid\n\n value = attr.value.value\n if isinstance(value, bytes) and value:\n payload[name] = \"0x\" + value.hex()\n elif isinstance(value, list) and not isinstance(value, EUI64):\n payload[name] = [v.value for v in value]\n elif isinstance(value, int):\n payload[name] = int(value)\n else:\n payload[name] = value\n\n elif hdr.command_id == Command.Read_Attributes_rsp:\n (attrs,) = args\n for attr in attrs:\n assert isinstance(attr, ReadAttributeRecord)\n if attr.attrid in cluster.attributes:\n name = cluster.attributes[attr.attrid].name\n else:\n name = attr.attrid\n\n if attr.value is not None:\n value = attr.value.value\n if isinstance(value, bytes) and value:\n payload[name] = \"0x\" + value.hex()\n elif isinstance(value, list):\n payload[name] = [v.value for v in value]\n elif isinstance(value, int):\n payload[name] = int(value)\n else:\n payload[name] = value\n else:\n payload[name] = str(attr.status)\n\n elif hdr.command_id == Command.Read_Attributes:\n (attrs,) = args\n payload[\"value\"] = attrs\n\n elif hdr.command_id == Command.Configure_Reporting:\n (attrs,) = args\n # fix __repr__ bug\n for attr in attrs:\n if not hasattr(attr, \"reportable_change\"):\n attr.reportable_change = None\n payload[\"value\"] = attrs\n\n elif (\n hdr.command_id == Command.Write_Attributes_rsp\n or hdr.command_id == Command.Configure_Reporting_rsp\n ):\n (resp,) = args\n payload[\"status\"] = [str(attr.status) for attr in resp]\n\n elif hdr.command_id == Command.Discover_Commands_Received_rsp:\n payload[\"status\"] = bool(args[0])\n payload[\"value\"] = args[1]\n\n elif hdr.command_id == Command.Default_Response:\n payload[\"value\"] = args[0]\n payload[\"status\"] = str(args[1])\n\n else:\n if isinstance(args, bytes) and args:\n args = \"0x\" + args.hex()\n payload[\"command_id\"] = int(hdr.command_id)\n payload[\"value\"] = args\n\n elif hdr.frame_control.is_cluster:\n # if isinstance(args, bytes) and args:\n # args = \"0x\" + args.hex()\n\n payload[\"command_id\"] = hdr.command_id\n if hdr.command_id < len(cluster.commands):\n payload[\"command\"] = cluster.commands[hdr.command_id]\n if args:\n payload[\"value\"] = args\n\n else:\n if isinstance(args, bytes) and args:\n args = \"0x\" + args.hex()\n\n payload.update({\"command_id\": hdr.command_id, \"value\": args})\n\n return payload\n\n except Exception as e:\n _LOGGER.debug(\"Error while parsing zigbee\", exc_info=e)\n return None","title":""},{"docid":"4efe99706bc450b6087bc45900faadf5","score":"0.5615661","text":"def decryptor(self):","title":""},{"docid":"ac5f7ffd6048c15111f770cbc783b3e4","score":"0.5596908","text":"def ice_postUnmarshal(self):\n pass # Currently unused","title":""},{"docid":"ac5f7ffd6048c15111f770cbc783b3e4","score":"0.5596908","text":"def ice_postUnmarshal(self):\n pass # Currently unused","title":""},{"docid":"8427df3257328528c26a61b4d7ad32c4","score":"0.5574899","text":"def pre_unpack(cls, packet: 'dict[str, Any]') -> 'None':","title":""},{"docid":"22982f63a768d2129c9b93dc2ab39052","score":"0.5568638","text":"def handlePacket(hdr, data):\n print(decoder.decode(data))","title":""},{"docid":"c846256b4df06d00b751d7b53bf36892","score":"0.55586743","text":"def __enter__(self) -> MP3Decoder:\n ...","title":""},{"docid":"58029eecd5aabab55fbede1e828a1f3a","score":"0.5558033","text":"def __init__(self, raw):\n self._hal = cetacean._parse_hal(raw)","title":""},{"docid":"43c13a1b1a08edd9fe819b08990185bb","score":"0.5526881","text":"def DecodeCardData(datalist):\r\n #if RunBBBHW():\r\n # decode the data stread read from the card reader\r\n pass","title":""},{"docid":"f5633445cd2196f481ae05ee201dba9b","score":"0.55074894","text":"def incremental_decode(decoder, *args, **kwargs):\n kwargs[\"return_tuple\"] = False\n return decode(*args, **kwargs)","title":""},{"docid":"5f35f6a760294a29a935abd567435cf6","score":"0.55020064","text":"def sample_decode(self, z, greedy=False):\n raise NotImplementedError()","title":""},{"docid":"01f3e6fca1b5ba32a14fc1e90d4b5c6c","score":"0.55010587","text":"def Decode(self, encoded_data):\n return b'', b''","title":""},{"docid":"8d0db0b47a416a38b6a2ebb772b77989","score":"0.5474095","text":"def _decode_text(self):\n \n print(f\"AtBash decode; received message is {self.message}\")\n return self._applyCipher()","title":""},{"docid":"d6eb14817741e876cbd69c2bcdb65af4","score":"0.5462845","text":"def decode_msg(self, message):\n\t\tpass","title":""},{"docid":"806ef27c4098f00577cdb7fe063f8974","score":"0.5461527","text":"def start_decode_by_msg(interface_obj,\n msg):\n msg = msg.split(\" \", 2)\n print(msg)\n status_code = msg[0]\n if status_code == \"*1\" and len(msg) > 2:\n address = msg[1].capitalize()\n value = msg[2]\n decodes = interface_obj.get_dict_decodes()\n if address in decodes:\n decodes[address](value)","title":""},{"docid":"147edb0ee1a2ac1af51651bfabda424b","score":"0.5460968","text":"def fix_stream():\n content_iter = res.iter_content(8192)\n data = next(content_iter)\n yield b'FLV' + data[3:]\n for chunk in content_iter:\n yield chunk","title":""},{"docid":"cd61f8673a70275330464ecd88305f50","score":"0.5457824","text":"def decode(self, decoder_input, step=0):\n\n cell_input = torch.cat((decoder_input, self.attention_context), -1)\n self.attention_hidden, self.attention_cell = self.attention_rnn(cell_input, (self.attention_hidden, self.attention_cell))\n\n\n self.attention_hidden = F.dropout(self.attention_hidden, self.p_attention_dropout, self.training)\n\n attention_weights_cat = torch.cat((self.attention_weights.unsqueeze(1),self.attention_weights_cum.unsqueeze(1)), dim=1)\n\n\n self.attention_context, self.attention_weights = self.attention_layer(self.attention_hidden, self.memory, self.processed_memory,attention_weights_cat, self.mask)\n\n self.attention_weights_cum += self.attention_weights\n\n decoder_input = torch.cat((self.attention_hidden, self.attention_context), -1)\n\n self.decoder_hidden, self.decoder_cell = self.decoder_rnn(decoder_input, (self.decoder_hidden, self.decoder_cell))\n self.decoder_hidden = F.dropout(self.decoder_hidden, self.p_decoder_dropout, self.training)\n\n decoder_hidden_attention_context = torch.cat((self.decoder_hidden, self.attention_context), dim=1)\n\n decoder_output = self.linear_projection(decoder_hidden_attention_context)\n\n gate_prediction = self.gate_layer(decoder_hidden_attention_context)\n return decoder_output, gate_prediction, self.attention_weights","title":""},{"docid":"8c93a45c991e36ea8f5341956eb71b4c","score":"0.5449894","text":"def __init__(self, resolver_context, path_spec):\n super(EncodedStream, self).__init__(resolver_context, path_spec)\n self._current_offset = 0\n self._decoded_data = b''\n self._decoded_data_offset = 0\n self._decoded_data_size = 0\n self._decoded_stream_size = None\n self._decoder = None\n self._encoded_data = b''\n self._encoding_method = None\n self._file_object = None\n self._realign_offset = True","title":""},{"docid":"805bfc1d0d84d611045e63a34befabe5","score":"0.54455596","text":"def DecoderBlock(d_model, d_ff, n_heads,\n dropout, mode, ff_activation):\n \n # Add list of two Residual blocks: the attention with normalization and dropout and feed-forward blocks\n return [\n tl.Residual(\n # Normalize layer input\n tl.LayerNorm(), \n # Add causal attention \n tl.CausalAttention(d_feature, n_heads=n_heads, dropout=dropout, mode=mode) \n ),\n tl.Residual(\n # Add feed-forward block\n # We don't need to normalize the layer inputs here. The feed-forward block takes care of that for us.\n FeedForward(d_model, d_ff, dropout, mode, ff_activation)\n ),\n ]","title":""},{"docid":"b00d79e19317529639a7bad50df3b3b6","score":"0.54431593","text":"def decode(self):\n super(Decoder, self).calculate_i_symbols()","title":""},{"docid":"74339f9854916975a3e85433c580a91a","score":"0.5421054","text":"def __init__(self, encoder):\n self.encoder = encoder","title":""},{"docid":"8aaf705c4b5b6a4fdf86e4a7f64bf3a9","score":"0.54164857","text":"def decode_character(self, pbs):\r\n raise NotImplementedError()","title":""},{"docid":"bad4a2184e3204c3f04f16c542c233d8","score":"0.54144484","text":"def decode(self, data: bytes) -> Any:\n return self.decoder.decode(data.decode(\"utf8\"))","title":""},{"docid":"db8378125524870f8c622862220a7662","score":"0.5387947","text":"def decode(self, z):\n z = self.fc3(z)\n z = self.decoder(z)\n return z","title":""},{"docid":"dfad4d9b0e6981d4c35c0b4d9f28dc39","score":"0.5386913","text":"def __init__(self, data):\n self.data = EncodingBytes(data)\n self.encoding = None","title":""},{"docid":"268445abb2b0eb9942a26009dc57c96d","score":"0.5371185","text":"def load_decoder(autoencoder):\n dim = len(autoencoder.get_config()['input_layers'])\n mag_phase_flag = False\n decoders = []\n if dim == 2: \n mag_phase_flag = True\n decoders.append(autoencoder.get_layer('mag_decoder'))\n decoders.append(autoencoder.get_layer('phase_decoder'))\n else:\n decoders.append(autoencoder.get_layer('decoder'))\n return decoders,mag_phase_flag","title":""},{"docid":"f4c126d16254ae48575123acbade7757","score":"0.5360108","text":"def decrypt(self, data):","title":""},{"docid":"e0ae1fc618748ee86c844dfd04c99d69","score":"0.53584963","text":"def decode(self, *args, **kwargs):\n return self.tokenizer.decode(*args, **kwargs)","title":""},{"docid":"95cb7be13ca1975eeced4ece06ee909d","score":"0.5352908","text":"def decoder(self):\n return{'dec_units':self.units, 'embedding_dim':self.embedding_dim,\n 'num_chars':self.num_chars, 'K':self.K}","title":""},{"docid":"2c2dc0d7babd52208dfff995f598f790","score":"0.5345057","text":"def decompress(bytestream: bytes, padding: int, tree: Node) -> str:\n # TODO: Implement this function\n ...","title":""},{"docid":"cdae5e70056f45b66cdaa165b78bc33f","score":"0.53391147","text":"def Decode(self, input_batch):\n return self._BeamSearchDecode(input_batch)","title":""},{"docid":"7706f52d1c0875bf6c8922ed0669d81f","score":"0.53332925","text":"def decode(bdata):\n\n return parse_blist(bdata)[0]","title":""},{"docid":"15a452c3d9b2dd806c2c6756f9a455f5","score":"0.5331376","text":"def decode(self, file, filename, streaming):\n raise NotImplementedError()","title":""},{"docid":"966e871e5aa7ea2e730454460aa0da25","score":"0.5329762","text":"def decode_57(p_pyhouse_obj, p_controller_obj):\n l_message = p_controller_obj._Message\n l_obj = utilDecode.get_obj_from_message(p_pyhouse_obj, l_message[4:7])\n l_link_obj = LinkData()\n l_link_obj.Flag = l_flags = l_message[2]\n l_link_obj.Group = l_group = l_message[3]\n l_link_obj.InsteonAddess = l_obj.InsteonAddress\n l_link_obj.Data = l_data = [l_message[7], l_message[8], l_message[9]]\n l_flag_control = l_flags & 0x40\n l_type = 'Responder'\n if l_flag_control != 0:\n l_type = 'Controller'\n LOG.info(\"All-Link response-57 - Group={:#02X}, Name={}, Flags={:#x}, Data={}, {}\".format(l_group, l_obj.Name, l_flags, l_data, l_type))\n l_ret = True\n return l_ret","title":""},{"docid":"2fb36d89daf5003a61d47b57052bcf10","score":"0.5325287","text":"def __init__(self, decoder_args):\n super(SimpleDFSDecoder, self).__init__(decoder_args)\n #self._min_length_ratio = 0.25 # TODO: Make configurable\n self._min_length_ratio = -0.1\n self._min_length = -100","title":""},{"docid":"521c7da474a2e658edf0f15e39fa23cb","score":"0.53212535","text":"def decode(self, decoder_input, time_step):\n t = time_step#0.5 - 0.5*np.cos(min(1, time_step / 500)*np.pi)\n cell_input = torch.cat((\n decoder_input, self.attention_context, self.get_latents(t)), -1)\n self.attention_hidden = self.attention_rnn(\n cell_input, self.attention_hidden)\n self.attention_hidden = F.dropout(\n self.attention_hidden, self.p_attention_dropout, self.training)\n\n attention_weights_cat = torch.cat(\n (self.attention_weights.unsqueeze(1),\n self.attention_weights_cum.unsqueeze(1)), dim=1)\n self.attention_context, self.attention_weights = self.attention_layer(\n self.attention_hidden, self.memory, self.processed_memory,\n attention_weights_cat, self.mask)\n\n self.attention_weights_cum += self.attention_weights\n decoder_input = torch.cat(\n (self.attention_hidden, self.attention_context), -1)\n self.decoder_hidden = self.decoder_rnn(\n decoder_input, self.decoder_hidden)\n self.decoder_hidden = F.dropout(\n self.decoder_hidden, self.p_decoder_dropout, self.training)\n\n decoder_hidden_attention_context = torch.cat(\n (self.decoder_hidden, self.attention_context), dim=1)\n decoder_output = self.linear_projection(\n decoder_hidden_attention_context)\n\n decoder_output = self.mel_params(decoder_output.squeeze(1))\n\n gate_prediction = self.gate_layer(decoder_hidden_attention_context).squeeze(1)\n return decoder_output, gate_prediction, self.attention_weights","title":""},{"docid":"53ea7a47eeb122d96e3e5bc68044bdd9","score":"0.53130627","text":"def decode(self, data):\n decoded_data = self._decoder(data)\n return decoded_data","title":""},{"docid":"c607b1dfe86d7cef0547e69e343b1978","score":"0.5308986","text":"def decode(self, data):\n if data[0] == '#':\n # list of names\n self.names = [self.sub_re.sub('_', name) for name in data[1:].split('!')]\n print \"JobySimDecoder got names:\", self.names\n return None\n\n vals = map(eval, data.split('!'))\n d = dict(zip(self.names, vals))\n #print d\n return d","title":""},{"docid":"2f756994e847b09b50842af820384a48","score":"0.53000313","text":"def get_encoder(self):","title":""},{"docid":"406e826cf4e87db8a9dd2d576e52d22c","score":"0.52965343","text":"def decode(self, z):\n h2 = self.de1(z)\n return self.decoder(h2)","title":""},{"docid":"2d4b6412fe217a1c185cc32f5760b363","score":"0.52954984","text":"def __init__(self) -> None:\n self.path = None\n self.replay = None\n self.bitstream = None\n self.netstream = None\n self.header_raw = None\n self.body_raw = None\n\n # Decoder Settings\n self.ignore_network = None\n self.verbose = None","title":""},{"docid":"9c9e17c253279a74f20744d5b78c7385","score":"0.52928096","text":"def decode(self, data_batch):\n warnings.warn('`decode` method not found, falling back to `forward`')\n return self.forward(data_batch)","title":""},{"docid":"91d7633f9a48468f46543b4aaabcfdb1","score":"0.5284167","text":"def batch_decode(self, sequences, **kwargs):\n return super().batch_decode(sequences, **kwargs)","title":""},{"docid":"2c61d8d687f6258ee1ff152daf2d01ab","score":"0.5271713","text":"def __build_decoder(self, z, y_dim):\n\n self.logger.debug(\"create decoder\")\n\n # define network\n net = slim.fully_connected(z, 50, activation_fn=tf.nn.softplus)\n net = slim.fully_connected(net, 50, activation_fn=tf.nn.softplus)\n\n # get moments\n y_hat = slim.fully_connected(net, y_dim, activation_fn=None)\n y_ls2 = slim.fully_connected(net, y_dim, activation_fn=None)\n\n return y_hat, y_ls2","title":""},{"docid":"579aec73593b9435d511da13b2ec45fc","score":"0.5259557","text":"def get_decoding(self):\n return self._dual.get_values('decoding')","title":""},{"docid":"b88ccb62c6cc5eca19fb0e7987dbffb5","score":"0.525838","text":"def __init__(self, data=None):\n self._converter = None\n if data:\n self.decode(data)","title":""},{"docid":"ff74bb77d713042f9dd9af85b3e65aad","score":"0.5253929","text":"def _build_decoder(self, arch, **kwargs):\n # usual non-bottleneck transformer decoder\n if (not arch) or (arch == \"seq2seq\"):\n decoder = self.decoder\n else:\n raise ValueError(f\"Unknown arch = {self.arch}, supported arch = {self.supported_arch}\")\n\n return decoder","title":""},{"docid":"ac943a10e87260c5a8487f6b1913a2b3","score":"0.52502054","text":"def build_decoder(opt, embeddings, pos = None):\n # 根据transformer论文，整个decoder层由dec_layers=6层堆叠而成，d_model = dec_rnn_size = 512,\n # multi-head attention中 h = heads = 8, feed-forward层大小为transformer_ff=2048 \n return TransformerDecoder(opt.dec_layers, opt.dec_rnn_size,\n opt.heads, opt.transformer_ff,\n opt.dropout, embeddings, pos)","title":""},{"docid":"308dc5ae38319d61e558b24711bbdc69","score":"0.52474207","text":"def _decode_nalu(self, nalu_bytes):\n if \"0x\" + nalu_bytes[0 : 4 * 8].hex == self.START_CODE_PREFIX:\n start_code = nalu_bytes.read(\"bytes:4\")\n else:\n start_code = nalu_bytes.read(\"bytes:3\")\n forbidden_zero_bit = nalu_bytes.read(1)\n nal_ref_idc = nalu_bytes.read(\"uint:2\")\n nal_unit_type = nalu_bytes.read(\"uint:5\")\n nal_unit_payload = nalu_bytes[nalu_bytes.pos :]\n\n rbsp_payload = BitStream()\n for i in range(int(len(nal_unit_payload) / 8)):\n if (\n len(nal_unit_payload) - nal_unit_payload.pos >= 24\n and nal_unit_payload.peek(\"bits:24\") == \"0x000003\"\n ):\n rbsp_payload.append(nal_unit_payload.read(\"bits:8\"))\n rbsp_payload.append(nal_unit_payload.read(\"bits:8\"))\n nal_unit_payload.read(\"bits:8\")\n else:\n if nal_unit_payload.pos == len(nal_unit_payload):\n continue\n rbsp_payload.append(nal_unit_payload.read(\"bits:8\"))\n\n return nal_unit_type, rbsp_payload","title":""},{"docid":"ad8b777e9d9c17b87a40241244905996","score":"0.5245915","text":"def handlePacket(self, data):\n r = self.mres()\n r.deserialize(data)\n self.response = r","title":""},{"docid":"e6370326a0e4aebb4a8c87ab5c66bdea","score":"0.5229287","text":"def deserialize(self, data):","title":""},{"docid":"0100d9ec3e1372f9c672f26173225f86","score":"0.5208996","text":"def feed(self, binary):\r\n\t\temit = False\r\n\t\tif self.decoder is None:\r\n\t\t\tself.decoder = codecs.getincrementaldecoder(self.encoding)()\r\n\t\tbuf = self.buf + self.decoder.decode(binary)\r\n\t\tif self.jsondec is None:\r\n\t\t\tself.jsondec = json.JSONDecoder()\r\n\t\twhile True:\r\n\t\t\ttry:\r\n\t\t\t\tobj,pos = self.jsondec.raw_decode(buf)\r\n\t\t\texcept ValueError,e:\r\n\t\t\t\tself.buf = buf\r\n\t\t\t\tbreak\r\n\t\t\t\r\n\t\t\temit = True\r\n\t\t\tself.callback(obj)\r\n\t\t\t\r\n\t\t\twhile pos<len(buf) and buf[pos] in \"\\r\\n\":\r\n\t\t\t\tpos += 1\r\n\t\t\r\n\t\t\tbuf = buf[pos:]\r\n\t\treturn emit","title":""},{"docid":"a43708002b86a7213edbba69a6dad5b1","score":"0.52077204","text":"def decoder(self, init_state):\n # pd.Print(init_state)\n # define counter variable in the decoding\n array_len = pd.fill_constant(shape=[1], dtype='int64', value=self.max_length)\n counter = pd.zeros(shape=[1], dtype='int64', force_cpu=True)\n static_count = pd.zeros(shape=[1], dtype='int64', force_cpu=True)\n\n # define tensor array to save content at each time step, and write initial id, score and state\n state_h_array = pd.create_array('float32')\n pd.array_write(self.h, array=state_h_array, i=counter)\n state_c_array = pd.create_array('float32')\n pd.array_write(self.c, array=state_c_array, i=counter)\n\n src_indexes = fluid.layers.data(\n name='source_index', shape=[1], dtype='int64', lod_level=1)\n src_index_array = pd.create_array('int64')\n pd.array_write(src_indexes, array=src_index_array, i=counter)\n\n ids_array = pd.create_array('int64')\n scores_array = pd.create_array('float32')\n\n init_ids = fluid.layers.data(\n name=\"init_ids\", shape=[1], dtype=\"int64\", lod_level=2)\n init_scores = fluid.layers.data(\n name=\"init_scores\", shape=[1], dtype=\"float32\", lod_level=2)\n\n pd.array_write(init_ids, array=ids_array, i=counter)\n pd.array_write(init_scores, array=scores_array, i=counter)\n\n encoder_vec_array = pd.create_array('float32')\n pd.array_write(self.encoder_vec, array=encoder_vec_array, i=static_count)\n encoder_vec_full_array = pd.create_array('float32')\n pd.array_write(self.encoder_vec_full, array=encoder_vec_full_array, i=static_count)\n encoder_proj_array = pd.create_array('float32')\n pd.array_write(self.encoder_proj, array=encoder_proj_array, i=static_count)\n\n event_embedding_array = pd.create_array('float32')\n pd.array_write(self.event_embedding, array=event_embedding_array, i=static_count)\n\n # define conditional variable to stop loop\n cond = pd.less_than(x=counter, y=array_len)\n # define while_op\n while_op = pd.While(cond=cond)\n with while_op.block(): # define the computing of each step\n # pd.Print(counter)\n\n # obtain input at present step of decoder, including id chosen at previous step, corresponding score and state at previous step.\n pre_ids = pd.array_read(array=ids_array, i=counter)\n pre_h_state = pd.array_read(array=state_h_array, i=counter)\n pre_c_state = pd.array_read(array=state_c_array, i=counter)\n\n # pre_score = pd.array_read(array=scores_array, i=counter)\n pre_score = pd.array_read(array=scores_array, i=static_count)\n\n _encoder_input_ids = pd.array_read(\n array=src_index_array, i=static_count)\n\n event_embedding = pd.array_read(\n array=event_embedding_array, i=static_count)\n\n # print(\"pre_h_state\", pre_h_state)\n encoder_vec = pd.array_read(\n array=encoder_vec_array, i=static_count)\n encoder_vec_full = pd.array_read(\n array=encoder_vec_full_array, i=static_count)\n encoder_proj = pd.array_read(\n array=encoder_proj_array, i=static_count)\n\n # # update input state as state correspondent with id chosen at previous step\n # pre_h_state_expanded = pd.sequence_expand(pre_h_state, pre_score)\n # pre_c_state_expanded = pd.sequence_expand(pre_c_state, pre_score)\n # computing logic of decoder under the same train mode, including input vector and computing unit of decoder\n # compute predicting probability of normalized word\n pre_ids_emb = pd.embedding(\n input=pre_ids,\n size=[self.target_dict_dim, self.embedding_dim],\n dtype='float32',\n param_attr=fluid.ParamAttr(name=\"trg_embedding\"))\n\n # pd.Print(pre_ids_emb)\n att_context = self.simple_attention(\n encoder_vec, encoder_proj, pre_h_state)\n # print(\"att_context\", att_context)\n # print(\"pre_ids_emb\", pre_ids_emb)\n # pd.Print(att_context)\n\n prob_c = fluid.layers.sequence_expand_as(pre_score,\n encoder_vec)\n # pd.Print(prob_c)\n\n current_score, current_h, current_c, this_prob_c = self.copy_decoder(\n pre_ids_emb, \n encoder_vec, encoder_vec_full, encoder_proj, \n _encoder_input_ids, pre_ids,\n prob_c, att_context,\n pre_h_state, pre_c_state,\n event_embedding)\n\n # decoder_inputs = fluid.layers.concat(\n # input=[att_context, pre_ids_emb], axis=1)\n # current_h, current_c = self.lstm_step(\n # decoder_inputs, pre_h_state, pre_c_state, self.decoder_size)\n # # compute predicting probability of nomarlized word\n # current_score = fluid.layers.fc(input=current_h,\n # size=self.target_dict_dim,\n # act='softmax',\n # param_attr=fluid.ParamAttr(name=\"out_softmax_w\"),\n # bias_attr=fluid.ParamAttr(name=\"out_softmax_b\"))\n\n # # current_state = pd.fc(input=[pre_state_expanded, pre_ids_emb],\n # # size=decoder_size,\n # # act='tanh')\n # current_state_with_lod = pd.lod_reset(x=current_h, y=pre_score)\n # current_score = pd.fc(input=current_state_with_lod,\n # size=self.target_dict_dim,\n # act='softmax',\n # param_attr=fluid.ParamAttr(name=\"out_softmax_w\"),\n # bias_attr=fluid.ParamAttr(name=\"out_softmax_b\"))\n # print(current_score)\n topk_scores, topk_indices = pd.topk(current_score, k=self.beam_size)\n # pd.Print(topk_indices)\n # pd.Print(topk_scores)\n selected_ids, selected_scores = topk_indices, topk_scores\n\n # # compute accumulated score and perform beam search\n # accu_scores = pd.elementwise_add(\n # x=pd.log(topk_scores), y=pd.reshape(pre_score, shape=[-1]), axis=0)\n # selected_ids, selected_scores = pd.beam_search(\n # pre_ids,\n # pre_score,\n # topk_indices,\n # accu_scores,\n # self.beam_size,\n # # end_id=self.end_id,\n # end_id=999999,\n # level=0)\n\n # pd.Print(selected_ids)\n # pd.Print(selected_scores)\n\n pd.increment(x=counter, value=1, in_place=True)\n # write search result and corresponding hidden layer into tensor array\n pd.array_write(current_h, array=state_h_array, i=counter)\n pd.array_write(current_c, array=state_c_array, i=counter)\n pd.array_write(selected_ids, array=ids_array, i=counter)\n pd.array_write(selected_scores, array=scores_array, i=counter)\n # pd.Print(selected_ids)\n # pd.Print(selected_scores)\n\n # update condition to stop loop\n length_cond = pd.less_than(x=counter, y=array_len)\n finish_cond = pd.logical_not(pd.is_empty(x=selected_ids))\n pd.logical_and(x=length_cond, y=finish_cond, out=cond)\n\n # pd.Print(array_len)\n # translation_ids, translation_scores = pd.beam_search_decode(\n # ids=ids_array, scores=scores_array, beam_size=self.beam_size, end_id=self.end_id)\n # pd.Print(translation_ids)\n translation_ids, translation_ids_index = pd.tensor_array_to_tensor(ids_array, axis=1)\n translation_scores, translation_scores_index = pd.tensor_array_to_tensor(scores_array, axis=1)\n\n return translation_ids, translation_scores","title":""},{"docid":"57eb5221e001f9fabf8c29663de5e28e","score":"0.51929384","text":"def _decoder(self, z):\n # Number of outputs for decoder\n num_outputs = self._ndims\n\n # Dense Layer\n dense = fully_connected(inputs=z, num_outputs=50,\n activation_fn=tf.nn.softplus)\n\n # Hidden Layer\n hidden = fully_connected(\n inputs=dense, num_outputs=100, activation_fn=tf.nn.softplus)\n\n # Output Layer\n output = fully_connected(\n inputs=hidden, num_outputs=num_outputs, activation_fn=tf.nn.sigmoid)\n return output","title":""},{"docid":"1ea99a0495a5692a97a2e97355893bac","score":"0.5192917","text":"def decoded(self):\n self.offset = self.offset * -1\n return self.cipher()","title":""},{"docid":"bc4ff94c92ae04bd0640007f450ae366","score":"0.51878834","text":"def decode_ufocapture(fname):\n raise NotImplementedError","title":""},{"docid":"bba883b82b163a975b522ef68405d732","score":"0.5181577","text":"def decoder(self, y_tilde):\n nb_examples = y_tilde.shape[0]\n hidden_decoder = tls.leaky_relu(numpy.dot(y_tilde,\n self.__parameters_eae['weights_decoder']['l1']) +\n numpy.tile(self.__parameters_eae['biases_decoder']['l1'],\n (nb_examples, 1)))\n reconstruction = numpy.dot(hidden_decoder,\n self.__parameters_eae['weights_decoder']['mean']) + \\\n numpy.tile(self.__parameters_eae['biases_decoder']['mean'],\n (nb_examples, 1))\n return (hidden_decoder, reconstruction)","title":""},{"docid":"581f22bae8b648b865a8a10e93ef2867","score":"0.5176327","text":"def _extract_frame(self, data):\n if (data == self.A0FLAG or data == self.C0FLAG) and (self.stepindex == 0):\n # print(\"Head: \", hex(data))\n self.stepindex += 1\n self._buffer.append(hex(data))\n elif self.stepindex < 4 and self.stepindex >= 1:\n self.stepindex += 1\n self._buffer.append(hex(data))\n elif self.stepindex == 4:\n self.stepindex += 1\n self._framelen = int(data)\n # print(\"length: \", self._framelen)\n self._buffer.append(hex(data))\n elif self.stepindex == 5:\n if self._framecurlen < self._framelen:\n self._buffer.append(hex(data))\n self._framecurlen += 1\n elif self._framecurlen == self._framelen:\n self._buffer.append(hex(data))\n resu_crc = checkcrc.xorcrc_str(self._buffer)\n if data == resu_crc:\n self.frame_received(self._buffer)\n self.reset()","title":""},{"docid":"d478ca8698ce386c64c931afc59305d9","score":"0.5167792","text":"def msg_decoder(msg):\n\n if msg.encoding == 'bgr8':\n image = np.frombuffer(msg.data, np.uint8).reshape((msg.height, msg.width, 3))[:, :, ::-1]\n elif msg.encoding == 'bgra8':\n image = np.frombuffer(msg.data, np.uint8).reshape((msg.height, msg.width, 4))[:, :, ::-1][:,:,1:]\n elif msg.encoding == 'rgb8':\n image = np.frombuffer(msg.data, np.uint8).reshape((msg.height, msg.width, 3))\n elif msg.encoding == 'mono8' or msg.encoding == '8UC1':\n image = np.frombuffer(msg.data, np.uint8).reshape((msg.height, msg.width))\n image = np.stack((image,) * 3, axis = -1) # greyscale to RGB\n elif msg.encoding == 'mono16' or msg.encoding == '16UC1':\n image = np.frombuffer(msg.data, np.uint16).reshape((msg.height, msg.width)).astype(np.float)\n image_max = np.percentile(image, 95)\n image_min = np.percentile(image, 5)\n image = 255*((image - image_min)/(image_max - image_min))\n image = np.clip(image, 0, 255)\n image = np.stack((image,) * 3, axis = -1) # greyscale to RGB\n else:\n print(\"Image encoding \" + msg.encoding + \" not supported yet.\")\n return None\n\n return image","title":""},{"docid":"b82ab7e92d92f37fb0b92377893805b6","score":"0.5167117","text":"def decompress(textrep):\n pass","title":""},{"docid":"e067f956f99d3c5a32b3c5a2e8a1be4d","score":"0.5162957","text":"def deserialize_bar_code_model(\n serialized_item_handle: HHandle\n) -> HHandle:\n with HalconOperator(1986) as proc:\n proc.set_input_tuple(0, serialized_item_handle)\n proc.init_oct(0)\n proc.execute()\n bar_code_handle = proc.get_output_tuple_s(0)\n return bar_code_handle # type: ignore","title":""},{"docid":"04d54ed3527234da59bfdb4a080b6d14","score":"0.51594746","text":"def _build_decoder(self, encoder_outputs, encoder_state, hps):\n\n tgt_sos_id = tf.cast(self.vocab_table.lookup(tf.constant(hps.sos)),\n tf.int32)\n tgt_eos_id = tf.cast(self.vocab_table.lookup(tf.constant(hps.eos)),\n tf.int32)\n\n iterator = self.iterator\n\n with tf.variable_scope('decoder') as decoder_scope:\n sample_id, final_context_state,coverage_loss= tf.no_op(), tf.no_op(),tf.no_op()\n\n dec_in_state = self._reduce_states(encoder_state[0], encoder_state[1])\n\n cell, decoder_initial_state = self._build_decoder_cell(hps, None, dec_in_state, None)\n # target_output = iterator.target_output\n # target_input = iterator.target_input\n source_sequence_length = iterator.source_sequence_length\n # target_sequence_length = iterator.target_sequence_length\n\n source = iterator.source\n\n\n if self.time_major:\n print(\"it's time major\")\n\n source = tf.transpose(source)\n source_sequence_length = tf.transpose(source_sequence_length)\n\n if self.mode != tf.contrib.learn.ModeKeys.INFER:\n target_input = iterator.target_input\n target_output = iterator.target_output\n target_sequence_length = iterator.target_sequence_length\n\n if self.time_major:\n utils.print_out(\"time major\")\n target_input = tf.transpose(target_input)\n target_output = tf.transpose(target_output)\n target_sequence_length = tf.transpose(target_sequence_length)\n\n target_max_time = self.get_max_time(target_output)\n dec_target_mask = tf.sequence_mask(target_sequence_length, target_max_time, dtype=tf.float32)\n self._target_batch = target_output\n\n\n emb_dec_inputs = [tf.nn.embedding_lookup(self.embedding_decoder, x) for x in tf.unstack(target_input,\n axis=1, num =150)] # list length max_dec_steps containing shape (batch_size, emb_size)\n\n\n\n source_max_time = self.get_max_time(source)\n\n en_src_mask = tf.sequence_mask(source_sequence_length, source_max_time, dtype=tf.float32)\n if self.time_major:\n en_src_mask = tf.transpose(en_src_mask)\n\n decoder_outputs, dec_out_state, attn_dists, p_gens, coverage = self.attention_decoder(hps, emb_dec_inputs, dec_in_state, encoder_outputs, en_src_mask, cell,\n initial_state_attention=False,\n copy_source=hps.copy_source,\n use_coverage=True,\n prev_coverage=None, scope =decoder_scope)\n\n\n logits = self.output_layer(tf.stack(decoder_outputs))\n\n coverage_loss = _coverage_loss(attn_dists, dec_target_mask)\n\n\n\n else:\n # maximum_iteration: The maximum decoding steps.\n maximum_iterations = self._get_infer_maximum_iterations(\n hps, source_sequence_length)\n beam_width = hps.beam_width\n length_penalty_weight = hps.length_penalty_weight\n start_tokens = tf.fill([self.batch_size], tgt_sos_id)\n end_token = tgt_eos_id\n\n if beam_width > 0:\n utils.print_out(\"beam search activated\")\n my_decoder = tf.contrib.seq2seq.BeamSearchDecoder(\n cell=cell,\n embedding=self.embedding_decoder,\n start_tokens=start_tokens,\n end_token=end_token,\n initial_state=decoder_initial_state,\n beam_width=beam_width,\n output_layer=self.output_layer,\n length_penalty_weight=length_penalty_weight)\n else:\n # Helper\n utils.print_out(\"greedy decoding activated\")\n\n helper = tf.contrib.seq2seq.GreedyEmbeddingHelper(\n self.embedding_decoder, start_tokens, end_token)\n\n # Decoder\n my_decoder = tf.contrib.seq2seq.BasicDecoder(\n cell,\n helper,\n dec_in_state,\n output_layer=self.output_layer # applied per timestep\n )\n\n # Dynamic decoding\n outputs, final_context_state, _ = tf.contrib.seq2seq.dynamic_decode(\n my_decoder,\n maximum_iterations=maximum_iterations,\n output_time_major=self.time_major,\n swap_memory=True,\n scope=decoder_scope)\n\n if beam_width > 0:\n logits = tf.no_op()\n sample_id = outputs.predicted_ids\n # if self.time_major:\n # sample_id = tf.transpose(sample_id, perm=[1, 2, 0])\n # utils.print_out(\"transpose activated for sampleid \")\n\n\n\n else:\n logits = outputs.rnn_output\n sample_id = outputs.sample_id\n\n return coverage_loss,logits, sample_id, final_context_state","title":""},{"docid":"50fb28d8eceeebd3b6d5fbe52fee43a2","score":"0.515891","text":"def frameReceived(opcode, data, fin):","title":""},{"docid":"c3a1d27deddb7c84802e783c0bb8789f","score":"0.5157705","text":"def parser_hook(self, raw: bytes) -> List[Dict]:\n raise NotImplementedError","title":""},{"docid":"17759e404ca12de5f656c9267afbe4b9","score":"0.51533115","text":"def _decoder(self, inputs, all_states):\n with tf.variable_scope(\"decoder\", reuse=tf.AUTO_REUSE):\n enc_pad_mask, enc_doc_mask, dec_in = inputs[: 3]\n\n # Concatenating the pad masks of all sentences.\n enc_pad_mask = tf.reshape(enc_pad_mask,\n [-1, FLAGS.max_enc_sent*FLAGS.max_enc_steps_per_sent]) # Shape: B x T_enc.\n\n # Converting ID's to word vectors.\n dec_in_vecs = tf.nn.embedding_lookup(params=self._word_embedding, ids=dec_in) # Shape: B x T_dec x D.\n dec_in_vecs = tf.cast(dec_in_vecs, dtype=tf.float32) # Shape: B x T_dec x D.\n\n # Memory masks.\n mem_masks = [enc_pad_mask, enc_doc_mask] # Shape: [B x T_enc, B x S_in].\n\n # NSE internal states.\n sent_mems, doc_mem = all_states[0] # Shape: B x T_enc x D, B x S_in x D.\n state = [[sent_mems, doc_mem]] + all_states[1:]\n\n writes = []\n p_attns = []\n p_gens = []\n for i in range(FLAGS.dec_steps):\n\n x_t = dec_in_vecs[:, i, :] # Shape: B x D.\n output, state = self._nse.step(\n x_t=x_t, mem_masks=mem_masks, prev_state=state, use_pgen=FLAGS.use_pgen\n )\n\n # Appending the outputs.\n writes.append(output[0])\n p_attns.append(output[1])\n\n if FLAGS.use_pgen:\n p_gens.append(output[2])\n\n p_vocabs = self._get_vocab_dist(writes) # Shape: T_dec * [B x vsize].\n\n p_cums = p_vocabs\n if FLAGS.use_pgen:\n enc_in_ext_vocab = inputs[3]\n p_cums = self._get_cumulative_dist(\n p_vocabs, p_gens, p_attns, enc_in_ext_vocab\n ) # Shape: T_dec * [B x ext_vsize].\n\n if FLAGS.mode.lower() == \"test\":\n p_final = p_cums[0] # Shape: Bm x V.\n\n topk_probs, topk_ids = tf.nn.top_k(p_final, k=2*FLAGS.beam_size, name=\"topk_preds\")\n topk_log_probs = tf.log(tf.clip_by_value(topk_probs, 1e-10, 1.0))\n\n outputs = [topk_ids, topk_log_probs, state, p_attns]\n if FLAGS.use_pgen:\n outputs.append(p_gens)\n\n return outputs\n else:\n return p_cums, p_attns","title":""},{"docid":"191eb1a3af3210354b12e9bae77ef99b","score":"0.51462364","text":"def __init__(self):\n self._last_decoding = None\n self._cs = None\n self._firstaddr = None\n self._arch = None\n self.valid_archs = None\n self.modes = None\n Backend.__init__(self)","title":""},{"docid":"7310867d5f707f28d8c7bd81dd1363e9","score":"0.51425415","text":"def decode(self, decoder_input, tgt_lang=None):\n\n cell_input = torch.cat((decoder_input, self.attention_context), -1)\n # B, prenet_dim + model_dim\n self.attention_hidden, self.attention_cell = self.attention_rnn(\n cell_input, (self.attention_hidden, self.attention_cell), tgt_lang)\n # B, model_dim\n self.attention_hidden = F.dropout(\n self.attention_hidden, self.p_attention_dropout, self.training)\n\n attention_weights_cat = torch.cat(\n (self.attention_weights.unsqueeze(1),\n self.attention_weights_cum.unsqueeze(1)), dim=1)\n # B, 2 , Max_time\n # attention_hidden should be query\n self.attention_context, self.attention_weights = self.attention_layer(\n self.attention_hidden, self.memory, self.processed_memory,\n attention_weights_cat, self.mask, tgt_lang)\n\n self.attention_weights_cum += self.attention_weights\n decoder_input = torch.cat(\n (self.attention_hidden, self.attention_context), -1)\n # B, 2* model_dim\n self.decoder_hidden, self.decoder_cell = self.decoder_rnn(\n decoder_input, (self.decoder_hidden, self.decoder_cell), tgt_lang)\n self.decoder_hidden = F.dropout(\n self.decoder_hidden, self.p_decoder_dropout, self.training)\n\n decoder_hidden_attention_context = torch.cat(\n (self.decoder_hidden, self.attention_context), dim=1)\n decoder_output = self.linear_projection(\n decoder_hidden_attention_context, tgt_lang)\n # B, nmel * nframeperstep\n gate_prediction = self.gate_layer(decoder_hidden_attention_context, tgt_lang)\n\n return decoder_output, gate_prediction, self.attention_weights, self.attention_context","title":""},{"docid":"3110cb915aad4720aa2bf939b094889f","score":"0.5138669","text":"def post_decrypt_data(self, data):\n# print(\"---RawRNCryptor---\")#debug\n# print(data) #debug\n# print(bord(data[-1]))#debug\n data = data[:-bord(data[-1])]\n \n return data","title":""},{"docid":"33d5885b022fcf07af88b51eb092b2d0","score":"0.5122174","text":"def __init__(self, fileblock):\n hdr_sz = Datagram.hdr_dtype.itemsize\n H0_sz = Datagram.H0_dtype.itemsize\n self.header = np.frombuffer(fileblock, dtype = Datagram.hdr_dtype, count = 1)[0]\n self.decoded = False\n self.dtype = self.header[0]\n if np.frombuffer(fileblock[hdr_sz:hdr_sz+2], dtype = '>S2') == 'H0':\n self.subheader = np.frombuffer(fileblock, dtype = Datagram.H0_dtype, offset = hdr_sz, count = 1)[0]\n self.datablock = fileblock[hdr_sz+H0_sz:]\n # the snippet and truepix datagrams have a larger header...\n if self.dtype == 'SNI0' or self.dtype == 'TPX0':\n self.datablock = self.datablock[6*4:]\n else:\n self.datablock = fileblock[hdr_sz:]","title":""},{"docid":"cad359e1ea1c317bc873c6e1fcd5d0f1","score":"0.51208204","text":"def get_decoder( model ):\n decod = [ l for l in model.layers if 'decoder' in l.name ]\n\n for dec in decod:\n if 'decoder' not in dec.name: \n print( \"Structure '{}' not valid as decoder architecture\".format( dec.name ) )\n sys.exit( 1 )\n\n if dec.layers[ 0 ].input_shape[ -1 ] != cnfg[ 'latent_size' ]: \n if 'latent_subsize' not in cnfg: \n print( \"Input size '{:d}' is not the expected '{}'\".format(\n dec.layers[ 0 ].input_shape[ -1 ], cnfg[ 'latent_size' ] ) )\n sys.exit( 1 )\n\n elif dec.layers[ 0 ].input_shape[ -1 ] != cnfg[ 'latent_subsize' ]: \n print( \"Input size '{:d}' is not the expected '{}'\".format(\n dec.layers[ 0 ].input_shape[ -1 ], cnfg[ 'latent_subsize' ] ) )\n sys.exit( 1 )\n\n # just to try a random latent space\n # ts.save_image( dec.predict( rnd.uniform(-3, 3) * np.random.rand(1,128) ), True, \"i.jpg\" )\n\n if len( decod ) == 1:\n return decod[ 0 ]\n\n return decod","title":""},{"docid":"8a9c0da991ec2080f575adf808bd88b3","score":"0.51069516","text":"def _decode(self, data):\n return base64.b64decode(data)","title":""}],"string":"[\n {\n \"docid\": \"626baf0b964a809a74e5496021d993f6\",\n \"score\": \"0.6972518\",\n \"text\": \"def _decode(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38895bd4f86a4a78899953d8615e9326\",\n \"score\": \"0.68361384\",\n \"text\": \"def decoder(self, decoder: decoders.Decoder):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cfdd22f5baf132fe9bbd0a8d7d221931\",\n \"score\": \"0.66675913\",\n \"text\": \"def _decode(self, decode_input):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc8a4d920cc0a168f628c6a30a917442\",\n \"score\": \"0.6611095\",\n \"text\": \"def doDecode(self, data):\\r\\n\\t\\traise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8172a3f12a2a7c8cc9be82e0e436f1d5\",\n \"score\": \"0.6397546\",\n \"text\": \"def getincrementaldecoder(encoding):\\n\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05394b827274cdc9bd803e34820c19f2\",\n \"score\": \"0.6352971\",\n \"text\": \"def decode(self, data: bytes) -> Any:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d25bfeabe6d7478697db77be9f0efeae\",\n \"score\": \"0.63112146\",\n \"text\": \"def PyCodec_Decoder(space, encoding):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"656e43c228720bab15917f95c975a86a\",\n \"score\": \"0.62642276\",\n \"text\": \"def decode(self, object,final):\\n\\t\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02c7229d4908dae552e9e362d525bf82\",\n \"score\": \"0.6207092\",\n \"text\": \"def create_decoder(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6838eda268384ea3705705d0a9e2155d\",\n \"score\": \"0.6159395\",\n \"text\": \"def _decode(self, input):\\n return input\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b5279604d06a6f5f3644877ca5660ca\",\n \"score\": \"0.6148942\",\n \"text\": \"def decompress_stream(self,key): \\n self.key = key \\n #print \\\"++++++++++++ decompressing stream +++++++++++++++++++++++++\\\" ,key\\n try: \\n data = self.objects[self.key]\\n start = data.find(\\\"stream\\\")\\n end = data.find(\\\"endstream\\\")\\n self.buff = data[start+6:].strip()\\n if len(self.buff) < 2:return\\n \\n self.methods = self.get_compMethod(key,data)\\n self.data = self.buff.strip() \\n for self.method in self.methods:\\n #print self.method\\n if 'fl' == self.method.lower():\\n self.data = decompress(self.data)\\n if 'ascii85decode' == self.method.lower():\\n self.data = ascii85decode(self.data)\\n if 'asciihexdecode' == self.method.lower(): \\n self.data = asciihexdecode(self.data)\\n if 'lzwdecode' == self.method.lower():\\n self.data = lzwdecode(self.data) \\n \\n if len(self.methods) == 0: \\n self.handle_evasion(key,data[:start]) \\n \\n except Exception,err:\\n pass \\n #print sys.stderr.write('ERROR: %s\\\\n' % str(err)),key,self.methods\\n y = \\\"\\\"\\n #print filter(lambda x: chr(ord(x)),self.data)\\n return self.data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22961228a5d8e0767be431da4d6b4ff7\",\n \"score\": \"0.614481\",\n \"text\": \"def getdecoder(encoding):\\n\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a80e52aec8dd02a28150454c6640933\",\n \"score\": \"0.61191016\",\n \"text\": \"def decode(cls, b):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"815597b99c08003f75af554e6c32b357\",\n \"score\": \"0.6056201\",\n \"text\": \"def decoder(self) -> Optional[Decoder]:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec94cd0717d46587ae681b9923388c24\",\n \"score\": \"0.6045181\",\n \"text\": \"def decode(self, *args, **kwargs):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9d8a3a06e710b06131968de49690b3b\",\n \"score\": \"0.599665\",\n \"text\": \"def _decode(*args):\\n\\t\\treturn args\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9d8a3a06e710b06131968de49690b3b\",\n \"score\": \"0.599665\",\n \"text\": \"def _decode(*args):\\n\\t\\treturn args\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec151e7c78054d10ee1a53e21416e15c\",\n \"score\": \"0.5995815\",\n \"text\": \"def create_decoder(self):\\n batch_size = tf.shape(self.encode)[0]\\n l = tf.multiply(self.encode, self.act_embed, name='merge')\\n l = FC(l, 1024, 'dec')\\n l = FC(l, 64 * 10 * 10, 'ip4')\\n l = ReLu(l, 'relu1')\\n l = tf.reshape(l, [-1, 10, 10, 64], name='dec-reshape')\\n l = Deconv2D(l, [6, 6], [batch_size, 20, 20, 64], 64, 2, 'SAME', 'deconv3')\\n l = ReLu(l, 'relu2')\\n l = Deconv2D(l, [6, 6], [batch_size, 40, 40, 64], 64, 2, 'SAME', 'deconv2')\\n l = ReLu(l, 'relu3')\\n l = Deconv2D(l, [6, 6], [batch_size, 84, 84, 3], 3, 2, 'VALID', 'x_hat-05')\\n return l\",\n \"title\": \"\"\n },\n {\n \"docid\": \"004738b5189ab156c3b08484230a4e63\",\n \"score\": \"0.58919895\",\n \"text\": \"def _streamEMB():\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6cd76706583c27d28efc67cf8db75c6d\",\n \"score\": \"0.5870717\",\n \"text\": \"def decode(self):\\n self.decoded = True\\n if self.dtype == 'BTH0':\\n self.subpack = BTH0(self.datablock, self.subheader['Beams'])\\n elif self.dtype == 'SNI0':\\n self.subpack = SNI0(self.datablock)\\n elif self.dtype == 'WCD0':\\n self.subpack = WCD0(self.datablock)\\n else:\\n self.decoded = False\\n print \\\"Data record \\\" + str(self.header[0]) + \\\" decoding is not yet supported.\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80d31190a67ca9809d6e8d81c532c0ca\",\n \"score\": \"0.5848532\",\n \"text\": \"def __init__(self, src_embed, encoder, tgt_embed, decoder, enc2dec, generator):\\n super(EncoderDecoder, self).__init__()\\n self.src_embed = src_embed\\n self.encoder = encoder\\n self.enc2dec = enc2dec\\n self.tgt_embed = tgt_embed\\n self.decoder = decoder\\n self.generator = generator\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf288f0eb8fa271862df0176fbf97a8a\",\n \"score\": \"0.58331096\",\n \"text\": \"def decode(self, z):\\n return self.generator(z)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9abd21a9925f2a7133048d0c6bace4dd\",\n \"score\": \"0.58246773\",\n \"text\": \"def PyCodec_Decode(space, object, encoding, errors):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3af54a011a660b2cdea7bd33bf90890\",\n \"score\": \"0.58233213\",\n \"text\": \"def on_decoded(self, packet):\\n self.emit('packet', packet)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f15ca233b40a14500cf04cb2ee430e23\",\n \"score\": \"0.5805976\",\n \"text\": \"def _decode1(self, body, data):\\n if \\\" \\\" in body:\\n evtype,body = body.split(\\\" \\\",1)\\n else:\\n evtype,body = body,\\\"\\\"\\n evtype = evtype.upper()\\n if evtype == \\\"CIRC\\\":\\n m = re.match(r\\\"(\\\\d+)\\\\s+(\\\\S+)(\\\\s\\\\S+)?(\\\\s\\\\S+)?(\\\\s\\\\S+)?(\\\\s\\\\S+)?\\\", body)\\n if not m:\\n raise ProtocolError(\\\"CIRC event misformatted.\\\")\\n ident,status,path,purpose,reason,remote = m.groups()\\n ident = int(ident)\\n if path:\\n if \\\"PURPOSE=\\\" in path:\\n remote = reason\\n reason = purpose\\n purpose=path\\n path=[]\\n elif \\\"REASON=\\\" in path:\\n remote = reason\\n reason = path\\n purpose = \\\"\\\"\\n path=[]\\n else:\\n path_verb = path.strip().split(\\\",\\\")\\n path = []\\n for p in path_verb:\\n path.append(p.replace(\\\"~\\\", \\\"=\\\").split(\\\"=\\\")[0])\\n else:\\n path = []\\n\\n if purpose and \\\"REASON=\\\" in purpose:\\n remote=reason\\n reason=purpose\\n purpose=\\\"\\\"\\n\\n if purpose: purpose = purpose[9:]\\n if reason: reason = reason[8:]\\n if remote: remote = remote[15:]\\n event = CircuitEvent(evtype, ident, status, path, purpose, reason,\\n remote, body)\\n elif evtype == \\\"STREAM\\\":\\n #plog(\\\"DEBUG\\\", \\\"STREAM: \\\"+body)\\n m = re.match(r\\\"(\\\\S+)\\\\s+(\\\\S+)\\\\s+(\\\\S+)\\\\s+(\\\\S+)?:(\\\\d+)(\\\\sREASON=\\\\S+)?(\\\\sREMOTE_REASON=\\\\S+)?(\\\\sSOURCE=\\\\S+)?(\\\\sSOURCE_ADDR=\\\\S+)?(\\\\s+PURPOSE=\\\\S+)?\\\", body)\\n if not m:\\n raise ProtocolError(\\\"STREAM event misformatted.\\\")\\n ident,status,circ,target_host,target_port,reason,remote,source,source_addr,purpose = m.groups()\\n ident,circ = map(int, (ident,circ))\\n if not target_host: # This can happen on SOCKS_PROTOCOL failures\\n target_host = \\\"(none)\\\"\\n if reason: reason = reason[8:]\\n if remote: remote = remote[15:]\\n if source: source = source[8:]\\n if source_addr: source_addr = source_addr[13:]\\n if purpose:\\n purpose = purpose.lstrip()\\n purpose = purpose[8:]\\n event = StreamEvent(evtype, ident, status, circ, target_host,\\n int(target_port), reason, remote, source, source_addr,\\n purpose, body)\\n elif evtype == \\\"ORCONN\\\":\\n m = re.match(r\\\"(\\\\S+)\\\\s+(\\\\S+)(\\\\sAGE=\\\\S+)?(\\\\sREAD=\\\\S+)?(\\\\sWRITTEN=\\\\S+)?(\\\\sREASON=\\\\S+)?(\\\\sNCIRCS=\\\\S+)?\\\", body)\\n if not m:\\n raise ProtocolError(\\\"ORCONN event misformatted.\\\")\\n target, status, age, read, wrote, reason, ncircs = m.groups()\\n\\n #plog(\\\"DEBUG\\\", \\\"ORCONN: \\\"+body)\\n if ncircs: ncircs = int(ncircs[8:])\\n else: ncircs = 0\\n if reason: reason = reason[8:]\\n if age: age = int(age[5:])\\n else: age = 0\\n if read: read = int(read[6:])\\n else: read = 0\\n if wrote: wrote = int(wrote[9:])\\n else: wrote = 0\\n event = ORConnEvent(evtype, status, target, age, read, wrote,\\n reason, ncircs, body)\\n elif evtype == \\\"STREAM_BW\\\":\\n m = re.match(r\\\"(\\\\d+)\\\\s+(\\\\d+)\\\\s+(\\\\d+)\\\", body)\\n if not m:\\n raise ProtocolError(\\\"STREAM_BW event misformatted.\\\")\\n event = StreamBwEvent(evtype, body, *m.groups())\\n elif evtype == \\\"BW\\\":\\n m = re.match(r\\\"(\\\\d+)\\\\s+(\\\\d+)\\\", body)\\n if not m:\\n raise ProtocolError(\\\"BANDWIDTH event misformatted.\\\")\\n read, written = map(long, m.groups())\\n event = BWEvent(evtype, read, written, body)\\n elif evtype in (\\\"DEBUG\\\", \\\"INFO\\\", \\\"NOTICE\\\", \\\"WARN\\\", \\\"ERR\\\"):\\n event = LogEvent(evtype, body)\\n elif evtype == \\\"NEWDESC\\\":\\n ids_verb = body.split(\\\" \\\")\\n ids = []\\n for i in ids_verb:\\n ids.append(i.replace(\\\"~\\\", \\\"=\\\").split(\\\"=\\\")[0].replace(\\\"$\\\",\\\"\\\"))\\n event = NewDescEvent(evtype, ids, body)\\n elif evtype == \\\"ADDRMAP\\\":\\n # TODO: Also parse errors and GMTExpiry\\n m = re.match(r'(\\\\S+)\\\\s+(\\\\S+)\\\\s+(\\\\\\\"[^\\\"]+\\\\\\\"|\\\\w+)', body)\\n if not m:\\n raise ProtocolError(\\\"ADDRMAP event misformatted.\\\")\\n fromaddr, toaddr, when = m.groups()\\n if when.upper() == \\\"NEVER\\\": \\n when = None\\n else:\\n when = time.strptime(when[1:-1], \\\"%Y-%m-%d %H:%M:%S\\\")\\n event = AddrMapEvent(evtype, fromaddr, toaddr, when, body)\\n elif evtype == \\\"NS\\\":\\n event = NetworkStatusEvent(evtype, parse_ns_body(data), data)\\n elif evtype == \\\"NEWCONSENSUS\\\":\\n event = NewConsensusEvent(evtype, parse_ns_body(data), data)\\n elif evtype == \\\"BUILDTIMEOUT_SET\\\":\\n m = re.match(\\n r\\\"(\\\\S+)\\\\sTOTAL_TIMES=(\\\\d+)\\\\sTIMEOUT_MS=(\\\\d+)\\\\sXM=(\\\\d+)\\\\sALPHA=(\\\\S+)\\\\sCUTOFF_QUANTILE=(\\\\S+)\\\",\\n body)\\n set_type, total_times, timeout_ms, xm, alpha, quantile = m.groups()\\n event = BuildTimeoutSetEvent(evtype, set_type, int(total_times),\\n int(timeout_ms), int(xm), float(alpha),\\n float(quantile), body)\\n elif evtype == \\\"GUARD\\\":\\n m = re.match(r\\\"(\\\\S+)\\\\s(\\\\S+)\\\\s(\\\\S+)\\\", body)\\n entry, guard, status = m.groups()\\n event = GuardEvent(evtype, entry, guard, status, body)\\n elif evtype == \\\"TORCTL_TIMER\\\":\\n event = TimerEvent(evtype, data)\\n else:\\n event = UnknownEvent(evtype, body)\\n\\n return event\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b67d36b773ac1c98416946d255a8afb0\",\n \"score\": \"0.5796752\",\n \"text\": \"def get_decoder(aes_adapter):\\n def decode(cipthertext):\\n return aes_adapter.decrypt(cipthertext)\\n \\n return decode\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbf29e9bb0c4dbdc3a711c24cc9ceadc\",\n \"score\": \"0.57395804\",\n \"text\": \"def h264_frame_cb(self, h264_frame):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb3db0e41e5ebfd55a462b37b6761e09\",\n \"score\": \"0.57241505\",\n \"text\": \"def forward(self, decoder_inner_states):\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d22563669cf14411d5c12414526ae2d\",\n \"score\": \"0.56893575\",\n \"text\": \"def dataDecoder(data):\\n\\treturn json.loads(base64.b64decode(data))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f68c28b8971dac56bacd45365aa29806\",\n \"score\": \"0.561687\",\n \"text\": \"def decode(data: dict):\\n try:\\n if data[\\\"sourceEndpoint\\\"] == \\\"0x00\\\":\\n # decode ZDO\\n if \\\"zdo\\\" not in CLUSTERS:\\n zdo = CLUSTERS[\\\"zdo\\\"] = ZDO(None)\\n else:\\n zdo = CLUSTERS[\\\"zdo\\\"]\\n\\n cluster_id = int(data[\\\"clusterId\\\"], 0)\\n raw = bytes.fromhex(data[\\\"APSPlayload\\\"][2:])\\n hdr, args = zdo.deserialize(cluster_id, raw)\\n cmd = ZDOCmd(hdr.command_id).name\\n if hdr.command_id == ZDOCmd.Active_EP_rsp:\\n return {\\\"command\\\": cmd, \\\"status\\\": str(args[0]), \\\"endpoints\\\": args[2]}\\n elif hdr.command_id == ZDOCmd.Simple_Desc_rsp:\\n desc: SizePrefixedSimpleDescriptor = args[2]\\n return {\\n \\\"command\\\": cmd,\\n \\\"status\\\": str(args[0]),\\n \\\"device_type\\\": desc.device_type,\\n \\\"device_version\\\": desc.device_version,\\n \\\"endpoint\\\": desc.endpoint,\\n \\\"input_clusters\\\": desc.input_clusters,\\n \\\"output_clusters\\\": desc.output_clusters,\\n \\\"profile\\\": desc.profile,\\n }\\n elif hdr.command_id == ZDOCmd.Node_Desc_rsp:\\n desc: NodeDescriptor = args[2]\\n return {\\n \\\"command\\\": cmd,\\n \\\"status\\\": str(args[0]),\\n \\\"is_mains_powered\\\": desc.is_mains_powered,\\n \\\"logical_type\\\": str(desc.logical_type),\\n \\\"manufacturer_code\\\": desc.manufacturer_code,\\n }\\n elif hdr.command_id in (ZDOCmd.Bind_rsp, ZDOCmd.Mgmt_Leave_rsp):\\n return {\\n \\\"command\\\": cmd,\\n \\\"status\\\": str(args[0]),\\n }\\n elif hdr.command_id in (ZDOCmd.Node_Desc_req, ZDOCmd.Active_EP_req):\\n return {\\\"command\\\": cmd}\\n elif hdr.command_id == ZDOCmd.Simple_Desc_req:\\n return {\\n \\\"command\\\": cmd,\\n \\\"endpoint\\\": args[0],\\n }\\n elif hdr.command_id == ZDOCmd.Bind_req:\\n return {\\n \\\"command\\\": cmd,\\n \\\"src_addr\\\": args[0],\\n \\\"src_endpoint\\\": args[1],\\n \\\"cluster\\\": args[2],\\n \\\"dst_addr\\\": args[3],\\n }\\n elif hdr.command_id == ZDOCmd.IEEE_addr_rsp:\\n return {\\n \\\"command\\\": cmd,\\n \\\"status\\\": args[0],\\n \\\"ieee\\\": args[1],\\n \\\"nwk\\\": args[2],\\n }\\n elif hdr.command_id == ZDOCmd.Mgmt_Leave_req:\\n return {\\n \\\"command\\\": cmd,\\n \\\"ieee\\\": args[0],\\n }\\n elif hdr.command_id == ZDOCmd.Mgmt_NWK_Update_rsp:\\n return {\\n \\\"command\\\": cmd,\\n \\\"status\\\": args[0],\\n \\\"channels\\\": args[1],\\n \\\"total\\\": args[2],\\n \\\"failures\\\": args[3],\\n \\\"energy\\\": args[4],\\n }\\n elif hdr.command_id == ZDOCmd.NWK_addr_rsp:\\n return {\\n \\\"command\\\": cmd,\\n \\\"status\\\": args[0],\\n \\\"ieee\\\": args[1],\\n \\\"nwk\\\": args[2],\\n }\\n else:\\n raise NotImplemented\\n\\n # decode ZCL\\n cluster_id = int(data[\\\"clusterId\\\"], 0)\\n if cluster_id not in CLUSTERS:\\n cluster = CLUSTERS[cluster_id] = Cluster.from_id(None, cluster_id)\\n cluster._log = lambda *_, **__: None\\n else:\\n cluster = CLUSTERS[cluster_id]\\n\\n raw = bytes.fromhex(data[\\\"APSPlayload\\\"][2:])\\n try:\\n hdr, args = cluster.deserialize(raw)\\n hdr: ZCLHeader\\n except ValueError as e:\\n return {\\\"cluster_id\\\": cluster_id, \\\"error\\\": str(e)}\\n except KeyError as e:\\n return {\\\"cluster_id\\\": cluster_id, \\\"error\\\": f\\\"Key error: {e}\\\"}\\n\\n payload = {\\n \\\"endpoint\\\": int(data[\\\"sourceEndpoint\\\"], 0),\\n \\\"seq\\\": hdr.tsn,\\n }\\n\\n if cluster.ep_attribute:\\n payload[\\\"cluster\\\"] = cluster.ep_attribute\\n else:\\n payload[\\\"cluster_id\\\"] = cluster_id\\n\\n if hdr.frame_control.is_general:\\n payload[\\\"command\\\"] = Command(hdr.command_id).name\\n\\n if (\\n hdr.command_id == Command.Report_Attributes\\n or hdr.command_id == Command.Write_Attributes\\n ):\\n (attrs,) = args\\n for attr in attrs:\\n assert isinstance(attr, Attribute)\\n if attr.attrid in cluster.attributes:\\n name = cluster.attributes[attr.attrid].name\\n else:\\n name = attr.attrid\\n\\n value = attr.value.value\\n if isinstance(value, bytes) and value:\\n payload[name] = \\\"0x\\\" + value.hex()\\n elif isinstance(value, list) and not isinstance(value, EUI64):\\n payload[name] = [v.value for v in value]\\n elif isinstance(value, int):\\n payload[name] = int(value)\\n else:\\n payload[name] = value\\n\\n elif hdr.command_id == Command.Read_Attributes_rsp:\\n (attrs,) = args\\n for attr in attrs:\\n assert isinstance(attr, ReadAttributeRecord)\\n if attr.attrid in cluster.attributes:\\n name = cluster.attributes[attr.attrid].name\\n else:\\n name = attr.attrid\\n\\n if attr.value is not None:\\n value = attr.value.value\\n if isinstance(value, bytes) and value:\\n payload[name] = \\\"0x\\\" + value.hex()\\n elif isinstance(value, list):\\n payload[name] = [v.value for v in value]\\n elif isinstance(value, int):\\n payload[name] = int(value)\\n else:\\n payload[name] = value\\n else:\\n payload[name] = str(attr.status)\\n\\n elif hdr.command_id == Command.Read_Attributes:\\n (attrs,) = args\\n payload[\\\"value\\\"] = attrs\\n\\n elif hdr.command_id == Command.Configure_Reporting:\\n (attrs,) = args\\n # fix __repr__ bug\\n for attr in attrs:\\n if not hasattr(attr, \\\"reportable_change\\\"):\\n attr.reportable_change = None\\n payload[\\\"value\\\"] = attrs\\n\\n elif (\\n hdr.command_id == Command.Write_Attributes_rsp\\n or hdr.command_id == Command.Configure_Reporting_rsp\\n ):\\n (resp,) = args\\n payload[\\\"status\\\"] = [str(attr.status) for attr in resp]\\n\\n elif hdr.command_id == Command.Discover_Commands_Received_rsp:\\n payload[\\\"status\\\"] = bool(args[0])\\n payload[\\\"value\\\"] = args[1]\\n\\n elif hdr.command_id == Command.Default_Response:\\n payload[\\\"value\\\"] = args[0]\\n payload[\\\"status\\\"] = str(args[1])\\n\\n else:\\n if isinstance(args, bytes) and args:\\n args = \\\"0x\\\" + args.hex()\\n payload[\\\"command_id\\\"] = int(hdr.command_id)\\n payload[\\\"value\\\"] = args\\n\\n elif hdr.frame_control.is_cluster:\\n # if isinstance(args, bytes) and args:\\n # args = \\\"0x\\\" + args.hex()\\n\\n payload[\\\"command_id\\\"] = hdr.command_id\\n if hdr.command_id < len(cluster.commands):\\n payload[\\\"command\\\"] = cluster.commands[hdr.command_id]\\n if args:\\n payload[\\\"value\\\"] = args\\n\\n else:\\n if isinstance(args, bytes) and args:\\n args = \\\"0x\\\" + args.hex()\\n\\n payload.update({\\\"command_id\\\": hdr.command_id, \\\"value\\\": args})\\n\\n return payload\\n\\n except Exception as e:\\n _LOGGER.debug(\\\"Error while parsing zigbee\\\", exc_info=e)\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4efe99706bc450b6087bc45900faadf5\",\n \"score\": \"0.5615661\",\n \"text\": \"def decryptor(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac5f7ffd6048c15111f770cbc783b3e4\",\n \"score\": \"0.5596908\",\n \"text\": \"def ice_postUnmarshal(self):\\n pass # Currently unused\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac5f7ffd6048c15111f770cbc783b3e4\",\n \"score\": \"0.5596908\",\n \"text\": \"def ice_postUnmarshal(self):\\n pass # Currently unused\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8427df3257328528c26a61b4d7ad32c4\",\n \"score\": \"0.5574899\",\n \"text\": \"def pre_unpack(cls, packet: 'dict[str, Any]') -> 'None':\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22982f63a768d2129c9b93dc2ab39052\",\n \"score\": \"0.5568638\",\n \"text\": \"def handlePacket(hdr, data):\\n print(decoder.decode(data))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c846256b4df06d00b751d7b53bf36892\",\n \"score\": \"0.55586743\",\n \"text\": \"def __enter__(self) -> MP3Decoder:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58029eecd5aabab55fbede1e828a1f3a\",\n \"score\": \"0.5558033\",\n \"text\": \"def __init__(self, raw):\\n self._hal = cetacean._parse_hal(raw)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43c13a1b1a08edd9fe819b08990185bb\",\n \"score\": \"0.5526881\",\n \"text\": \"def DecodeCardData(datalist):\\r\\n #if RunBBBHW():\\r\\n # decode the data stread read from the card reader\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5633445cd2196f481ae05ee201dba9b\",\n \"score\": \"0.55074894\",\n \"text\": \"def incremental_decode(decoder, *args, **kwargs):\\n kwargs[\\\"return_tuple\\\"] = False\\n return decode(*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f35f6a760294a29a935abd567435cf6\",\n \"score\": \"0.55020064\",\n \"text\": \"def sample_decode(self, z, greedy=False):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01f3e6fca1b5ba32a14fc1e90d4b5c6c\",\n \"score\": \"0.55010587\",\n \"text\": \"def Decode(self, encoded_data):\\n return b'', b''\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d0db0b47a416a38b6a2ebb772b77989\",\n \"score\": \"0.5474095\",\n \"text\": \"def _decode_text(self):\\n \\n print(f\\\"AtBash decode; received message is {self.message}\\\")\\n return self._applyCipher()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6eb14817741e876cbd69c2bcdb65af4\",\n \"score\": \"0.5462845\",\n \"text\": \"def decode_msg(self, message):\\n\\t\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"806ef27c4098f00577cdb7fe063f8974\",\n \"score\": \"0.5461527\",\n \"text\": \"def start_decode_by_msg(interface_obj,\\n msg):\\n msg = msg.split(\\\" \\\", 2)\\n print(msg)\\n status_code = msg[0]\\n if status_code == \\\"*1\\\" and len(msg) > 2:\\n address = msg[1].capitalize()\\n value = msg[2]\\n decodes = interface_obj.get_dict_decodes()\\n if address in decodes:\\n decodes[address](value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"147edb0ee1a2ac1af51651bfabda424b\",\n \"score\": \"0.5460968\",\n \"text\": \"def fix_stream():\\n content_iter = res.iter_content(8192)\\n data = next(content_iter)\\n yield b'FLV' + data[3:]\\n for chunk in content_iter:\\n yield chunk\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd61f8673a70275330464ecd88305f50\",\n \"score\": \"0.5457824\",\n \"text\": \"def decode(self, decoder_input, step=0):\\n\\n cell_input = torch.cat((decoder_input, self.attention_context), -1)\\n self.attention_hidden, self.attention_cell = self.attention_rnn(cell_input, (self.attention_hidden, self.attention_cell))\\n\\n\\n self.attention_hidden = F.dropout(self.attention_hidden, self.p_attention_dropout, self.training)\\n\\n attention_weights_cat = torch.cat((self.attention_weights.unsqueeze(1),self.attention_weights_cum.unsqueeze(1)), dim=1)\\n\\n\\n self.attention_context, self.attention_weights = self.attention_layer(self.attention_hidden, self.memory, self.processed_memory,attention_weights_cat, self.mask)\\n\\n self.attention_weights_cum += self.attention_weights\\n\\n decoder_input = torch.cat((self.attention_hidden, self.attention_context), -1)\\n\\n self.decoder_hidden, self.decoder_cell = self.decoder_rnn(decoder_input, (self.decoder_hidden, self.decoder_cell))\\n self.decoder_hidden = F.dropout(self.decoder_hidden, self.p_decoder_dropout, self.training)\\n\\n decoder_hidden_attention_context = torch.cat((self.decoder_hidden, self.attention_context), dim=1)\\n\\n decoder_output = self.linear_projection(decoder_hidden_attention_context)\\n\\n gate_prediction = self.gate_layer(decoder_hidden_attention_context)\\n return decoder_output, gate_prediction, self.attention_weights\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c93a45c991e36ea8f5341956eb71b4c\",\n \"score\": \"0.5449894\",\n \"text\": \"def __init__(self, resolver_context, path_spec):\\n super(EncodedStream, self).__init__(resolver_context, path_spec)\\n self._current_offset = 0\\n self._decoded_data = b''\\n self._decoded_data_offset = 0\\n self._decoded_data_size = 0\\n self._decoded_stream_size = None\\n self._decoder = None\\n self._encoded_data = b''\\n self._encoding_method = None\\n self._file_object = None\\n self._realign_offset = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"805bfc1d0d84d611045e63a34befabe5\",\n \"score\": \"0.54455596\",\n \"text\": \"def DecoderBlock(d_model, d_ff, n_heads,\\n dropout, mode, ff_activation):\\n \\n # Add list of two Residual blocks: the attention with normalization and dropout and feed-forward blocks\\n return [\\n tl.Residual(\\n # Normalize layer input\\n tl.LayerNorm(), \\n # Add causal attention \\n tl.CausalAttention(d_feature, n_heads=n_heads, dropout=dropout, mode=mode) \\n ),\\n tl.Residual(\\n # Add feed-forward block\\n # We don't need to normalize the layer inputs here. The feed-forward block takes care of that for us.\\n FeedForward(d_model, d_ff, dropout, mode, ff_activation)\\n ),\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b00d79e19317529639a7bad50df3b3b6\",\n \"score\": \"0.54431593\",\n \"text\": \"def decode(self):\\n super(Decoder, self).calculate_i_symbols()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74339f9854916975a3e85433c580a91a\",\n \"score\": \"0.5421054\",\n \"text\": \"def __init__(self, encoder):\\n self.encoder = encoder\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8aaf705c4b5b6a4fdf86e4a7f64bf3a9\",\n \"score\": \"0.54164857\",\n \"text\": \"def decode_character(self, pbs):\\r\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bad4a2184e3204c3f04f16c542c233d8\",\n \"score\": \"0.54144484\",\n \"text\": \"def decode(self, data: bytes) -> Any:\\n return self.decoder.decode(data.decode(\\\"utf8\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db8378125524870f8c622862220a7662\",\n \"score\": \"0.5387947\",\n \"text\": \"def decode(self, z):\\n z = self.fc3(z)\\n z = self.decoder(z)\\n return z\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfad4d9b0e6981d4c35c0b4d9f28dc39\",\n \"score\": \"0.5386913\",\n \"text\": \"def __init__(self, data):\\n self.data = EncodingBytes(data)\\n self.encoding = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"268445abb2b0eb9942a26009dc57c96d\",\n \"score\": \"0.5371185\",\n \"text\": \"def load_decoder(autoencoder):\\n dim = len(autoencoder.get_config()['input_layers'])\\n mag_phase_flag = False\\n decoders = []\\n if dim == 2: \\n mag_phase_flag = True\\n decoders.append(autoencoder.get_layer('mag_decoder'))\\n decoders.append(autoencoder.get_layer('phase_decoder'))\\n else:\\n decoders.append(autoencoder.get_layer('decoder'))\\n return decoders,mag_phase_flag\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4c126d16254ae48575123acbade7757\",\n \"score\": \"0.5360108\",\n \"text\": \"def decrypt(self, data):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0ae1fc618748ee86c844dfd04c99d69\",\n \"score\": \"0.53584963\",\n \"text\": \"def decode(self, *args, **kwargs):\\n return self.tokenizer.decode(*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95cb7be13ca1975eeced4ece06ee909d\",\n \"score\": \"0.5352908\",\n \"text\": \"def decoder(self):\\n return{'dec_units':self.units, 'embedding_dim':self.embedding_dim,\\n 'num_chars':self.num_chars, 'K':self.K}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c2dc0d7babd52208dfff995f598f790\",\n \"score\": \"0.5345057\",\n \"text\": \"def decompress(bytestream: bytes, padding: int, tree: Node) -> str:\\n # TODO: Implement this function\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cdae5e70056f45b66cdaa165b78bc33f\",\n \"score\": \"0.53391147\",\n \"text\": \"def Decode(self, input_batch):\\n return self._BeamSearchDecode(input_batch)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7706f52d1c0875bf6c8922ed0669d81f\",\n \"score\": \"0.53332925\",\n \"text\": \"def decode(bdata):\\n\\n return parse_blist(bdata)[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15a452c3d9b2dd806c2c6756f9a455f5\",\n \"score\": \"0.5331376\",\n \"text\": \"def decode(self, file, filename, streaming):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"966e871e5aa7ea2e730454460aa0da25\",\n \"score\": \"0.5329762\",\n \"text\": \"def decode_57(p_pyhouse_obj, p_controller_obj):\\n l_message = p_controller_obj._Message\\n l_obj = utilDecode.get_obj_from_message(p_pyhouse_obj, l_message[4:7])\\n l_link_obj = LinkData()\\n l_link_obj.Flag = l_flags = l_message[2]\\n l_link_obj.Group = l_group = l_message[3]\\n l_link_obj.InsteonAddess = l_obj.InsteonAddress\\n l_link_obj.Data = l_data = [l_message[7], l_message[8], l_message[9]]\\n l_flag_control = l_flags & 0x40\\n l_type = 'Responder'\\n if l_flag_control != 0:\\n l_type = 'Controller'\\n LOG.info(\\\"All-Link response-57 - Group={:#02X}, Name={}, Flags={:#x}, Data={}, {}\\\".format(l_group, l_obj.Name, l_flags, l_data, l_type))\\n l_ret = True\\n return l_ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fb36d89daf5003a61d47b57052bcf10\",\n \"score\": \"0.5325287\",\n \"text\": \"def __init__(self, decoder_args):\\n super(SimpleDFSDecoder, self).__init__(decoder_args)\\n #self._min_length_ratio = 0.25 # TODO: Make configurable\\n self._min_length_ratio = -0.1\\n self._min_length = -100\",\n \"title\": \"\"\n },\n {\n \"docid\": \"521c7da474a2e658edf0f15e39fa23cb\",\n \"score\": \"0.53212535\",\n \"text\": \"def decode(self, decoder_input, time_step):\\n t = time_step#0.5 - 0.5*np.cos(min(1, time_step / 500)*np.pi)\\n cell_input = torch.cat((\\n decoder_input, self.attention_context, self.get_latents(t)), -1)\\n self.attention_hidden = self.attention_rnn(\\n cell_input, self.attention_hidden)\\n self.attention_hidden = F.dropout(\\n self.attention_hidden, self.p_attention_dropout, self.training)\\n\\n attention_weights_cat = torch.cat(\\n (self.attention_weights.unsqueeze(1),\\n self.attention_weights_cum.unsqueeze(1)), dim=1)\\n self.attention_context, self.attention_weights = self.attention_layer(\\n self.attention_hidden, self.memory, self.processed_memory,\\n attention_weights_cat, self.mask)\\n\\n self.attention_weights_cum += self.attention_weights\\n decoder_input = torch.cat(\\n (self.attention_hidden, self.attention_context), -1)\\n self.decoder_hidden = self.decoder_rnn(\\n decoder_input, self.decoder_hidden)\\n self.decoder_hidden = F.dropout(\\n self.decoder_hidden, self.p_decoder_dropout, self.training)\\n\\n decoder_hidden_attention_context = torch.cat(\\n (self.decoder_hidden, self.attention_context), dim=1)\\n decoder_output = self.linear_projection(\\n decoder_hidden_attention_context)\\n\\n decoder_output = self.mel_params(decoder_output.squeeze(1))\\n\\n gate_prediction = self.gate_layer(decoder_hidden_attention_context).squeeze(1)\\n return decoder_output, gate_prediction, self.attention_weights\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53ea7a47eeb122d96e3e5bc68044bdd9\",\n \"score\": \"0.53130627\",\n \"text\": \"def decode(self, data):\\n decoded_data = self._decoder(data)\\n return decoded_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c607b1dfe86d7cef0547e69e343b1978\",\n \"score\": \"0.5308986\",\n \"text\": \"def decode(self, data):\\n if data[0] == '#':\\n # list of names\\n self.names = [self.sub_re.sub('_', name) for name in data[1:].split('!')]\\n print \\\"JobySimDecoder got names:\\\", self.names\\n return None\\n\\n vals = map(eval, data.split('!'))\\n d = dict(zip(self.names, vals))\\n #print d\\n return d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f756994e847b09b50842af820384a48\",\n \"score\": \"0.53000313\",\n \"text\": \"def get_encoder(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"406e826cf4e87db8a9dd2d576e52d22c\",\n \"score\": \"0.52965343\",\n \"text\": \"def decode(self, z):\\n h2 = self.de1(z)\\n return self.decoder(h2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d4b6412fe217a1c185cc32f5760b363\",\n \"score\": \"0.52954984\",\n \"text\": \"def __init__(self) -> None:\\n self.path = None\\n self.replay = None\\n self.bitstream = None\\n self.netstream = None\\n self.header_raw = None\\n self.body_raw = None\\n\\n # Decoder Settings\\n self.ignore_network = None\\n self.verbose = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c9e17c253279a74f20744d5b78c7385\",\n \"score\": \"0.52928096\",\n \"text\": \"def decode(self, data_batch):\\n warnings.warn('`decode` method not found, falling back to `forward`')\\n return self.forward(data_batch)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91d7633f9a48468f46543b4aaabcfdb1\",\n \"score\": \"0.5284167\",\n \"text\": \"def batch_decode(self, sequences, **kwargs):\\n return super().batch_decode(sequences, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c61d8d687f6258ee1ff152daf2d01ab\",\n \"score\": \"0.5271713\",\n \"text\": \"def __build_decoder(self, z, y_dim):\\n\\n self.logger.debug(\\\"create decoder\\\")\\n\\n # define network\\n net = slim.fully_connected(z, 50, activation_fn=tf.nn.softplus)\\n net = slim.fully_connected(net, 50, activation_fn=tf.nn.softplus)\\n\\n # get moments\\n y_hat = slim.fully_connected(net, y_dim, activation_fn=None)\\n y_ls2 = slim.fully_connected(net, y_dim, activation_fn=None)\\n\\n return y_hat, y_ls2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"579aec73593b9435d511da13b2ec45fc\",\n \"score\": \"0.5259557\",\n \"text\": \"def get_decoding(self):\\n return self._dual.get_values('decoding')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b88ccb62c6cc5eca19fb0e7987dbffb5\",\n \"score\": \"0.525838\",\n \"text\": \"def __init__(self, data=None):\\n self._converter = None\\n if data:\\n self.decode(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff74bb77d713042f9dd9af85b3e65aad\",\n \"score\": \"0.5253929\",\n \"text\": \"def _build_decoder(self, arch, **kwargs):\\n # usual non-bottleneck transformer decoder\\n if (not arch) or (arch == \\\"seq2seq\\\"):\\n decoder = self.decoder\\n else:\\n raise ValueError(f\\\"Unknown arch = {self.arch}, supported arch = {self.supported_arch}\\\")\\n\\n return decoder\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac943a10e87260c5a8487f6b1913a2b3\",\n \"score\": \"0.52502054\",\n \"text\": \"def build_decoder(opt, embeddings, pos = None):\\n # 根据transformer论文，整个decoder层由dec_layers=6层堆叠而成，d_model = dec_rnn_size = 512,\\n # multi-head attention中 h = heads = 8, feed-forward层大小为transformer_ff=2048 \\n return TransformerDecoder(opt.dec_layers, opt.dec_rnn_size,\\n opt.heads, opt.transformer_ff,\\n opt.dropout, embeddings, pos)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"308dc5ae38319d61e558b24711bbdc69\",\n \"score\": \"0.52474207\",\n \"text\": \"def _decode_nalu(self, nalu_bytes):\\n if \\\"0x\\\" + nalu_bytes[0 : 4 * 8].hex == self.START_CODE_PREFIX:\\n start_code = nalu_bytes.read(\\\"bytes:4\\\")\\n else:\\n start_code = nalu_bytes.read(\\\"bytes:3\\\")\\n forbidden_zero_bit = nalu_bytes.read(1)\\n nal_ref_idc = nalu_bytes.read(\\\"uint:2\\\")\\n nal_unit_type = nalu_bytes.read(\\\"uint:5\\\")\\n nal_unit_payload = nalu_bytes[nalu_bytes.pos :]\\n\\n rbsp_payload = BitStream()\\n for i in range(int(len(nal_unit_payload) / 8)):\\n if (\\n len(nal_unit_payload) - nal_unit_payload.pos >= 24\\n and nal_unit_payload.peek(\\\"bits:24\\\") == \\\"0x000003\\\"\\n ):\\n rbsp_payload.append(nal_unit_payload.read(\\\"bits:8\\\"))\\n rbsp_payload.append(nal_unit_payload.read(\\\"bits:8\\\"))\\n nal_unit_payload.read(\\\"bits:8\\\")\\n else:\\n if nal_unit_payload.pos == len(nal_unit_payload):\\n continue\\n rbsp_payload.append(nal_unit_payload.read(\\\"bits:8\\\"))\\n\\n return nal_unit_type, rbsp_payload\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad8b777e9d9c17b87a40241244905996\",\n \"score\": \"0.5245915\",\n \"text\": \"def handlePacket(self, data):\\n r = self.mres()\\n r.deserialize(data)\\n self.response = r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6370326a0e4aebb4a8c87ab5c66bdea\",\n \"score\": \"0.5229287\",\n \"text\": \"def deserialize(self, data):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0100d9ec3e1372f9c672f26173225f86\",\n \"score\": \"0.5208996\",\n \"text\": \"def feed(self, binary):\\r\\n\\t\\temit = False\\r\\n\\t\\tif self.decoder is None:\\r\\n\\t\\t\\tself.decoder = codecs.getincrementaldecoder(self.encoding)()\\r\\n\\t\\tbuf = self.buf + self.decoder.decode(binary)\\r\\n\\t\\tif self.jsondec is None:\\r\\n\\t\\t\\tself.jsondec = json.JSONDecoder()\\r\\n\\t\\twhile True:\\r\\n\\t\\t\\ttry:\\r\\n\\t\\t\\t\\tobj,pos = self.jsondec.raw_decode(buf)\\r\\n\\t\\t\\texcept ValueError,e:\\r\\n\\t\\t\\t\\tself.buf = buf\\r\\n\\t\\t\\t\\tbreak\\r\\n\\t\\t\\t\\r\\n\\t\\t\\temit = True\\r\\n\\t\\t\\tself.callback(obj)\\r\\n\\t\\t\\t\\r\\n\\t\\t\\twhile pos<len(buf) and buf[pos] in \\\"\\\\r\\\\n\\\":\\r\\n\\t\\t\\t\\tpos += 1\\r\\n\\t\\t\\r\\n\\t\\t\\tbuf = buf[pos:]\\r\\n\\t\\treturn emit\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a43708002b86a7213edbba69a6dad5b1\",\n \"score\": \"0.52077204\",\n \"text\": \"def decoder(self, init_state):\\n # pd.Print(init_state)\\n # define counter variable in the decoding\\n array_len = pd.fill_constant(shape=[1], dtype='int64', value=self.max_length)\\n counter = pd.zeros(shape=[1], dtype='int64', force_cpu=True)\\n static_count = pd.zeros(shape=[1], dtype='int64', force_cpu=True)\\n\\n # define tensor array to save content at each time step, and write initial id, score and state\\n state_h_array = pd.create_array('float32')\\n pd.array_write(self.h, array=state_h_array, i=counter)\\n state_c_array = pd.create_array('float32')\\n pd.array_write(self.c, array=state_c_array, i=counter)\\n\\n src_indexes = fluid.layers.data(\\n name='source_index', shape=[1], dtype='int64', lod_level=1)\\n src_index_array = pd.create_array('int64')\\n pd.array_write(src_indexes, array=src_index_array, i=counter)\\n\\n ids_array = pd.create_array('int64')\\n scores_array = pd.create_array('float32')\\n\\n init_ids = fluid.layers.data(\\n name=\\\"init_ids\\\", shape=[1], dtype=\\\"int64\\\", lod_level=2)\\n init_scores = fluid.layers.data(\\n name=\\\"init_scores\\\", shape=[1], dtype=\\\"float32\\\", lod_level=2)\\n\\n pd.array_write(init_ids, array=ids_array, i=counter)\\n pd.array_write(init_scores, array=scores_array, i=counter)\\n\\n encoder_vec_array = pd.create_array('float32')\\n pd.array_write(self.encoder_vec, array=encoder_vec_array, i=static_count)\\n encoder_vec_full_array = pd.create_array('float32')\\n pd.array_write(self.encoder_vec_full, array=encoder_vec_full_array, i=static_count)\\n encoder_proj_array = pd.create_array('float32')\\n pd.array_write(self.encoder_proj, array=encoder_proj_array, i=static_count)\\n\\n event_embedding_array = pd.create_array('float32')\\n pd.array_write(self.event_embedding, array=event_embedding_array, i=static_count)\\n\\n # define conditional variable to stop loop\\n cond = pd.less_than(x=counter, y=array_len)\\n # define while_op\\n while_op = pd.While(cond=cond)\\n with while_op.block(): # define the computing of each step\\n # pd.Print(counter)\\n\\n # obtain input at present step of decoder, including id chosen at previous step, corresponding score and state at previous step.\\n pre_ids = pd.array_read(array=ids_array, i=counter)\\n pre_h_state = pd.array_read(array=state_h_array, i=counter)\\n pre_c_state = pd.array_read(array=state_c_array, i=counter)\\n\\n # pre_score = pd.array_read(array=scores_array, i=counter)\\n pre_score = pd.array_read(array=scores_array, i=static_count)\\n\\n _encoder_input_ids = pd.array_read(\\n array=src_index_array, i=static_count)\\n\\n event_embedding = pd.array_read(\\n array=event_embedding_array, i=static_count)\\n\\n # print(\\\"pre_h_state\\\", pre_h_state)\\n encoder_vec = pd.array_read(\\n array=encoder_vec_array, i=static_count)\\n encoder_vec_full = pd.array_read(\\n array=encoder_vec_full_array, i=static_count)\\n encoder_proj = pd.array_read(\\n array=encoder_proj_array, i=static_count)\\n\\n # # update input state as state correspondent with id chosen at previous step\\n # pre_h_state_expanded = pd.sequence_expand(pre_h_state, pre_score)\\n # pre_c_state_expanded = pd.sequence_expand(pre_c_state, pre_score)\\n # computing logic of decoder under the same train mode, including input vector and computing unit of decoder\\n # compute predicting probability of normalized word\\n pre_ids_emb = pd.embedding(\\n input=pre_ids,\\n size=[self.target_dict_dim, self.embedding_dim],\\n dtype='float32',\\n param_attr=fluid.ParamAttr(name=\\\"trg_embedding\\\"))\\n\\n # pd.Print(pre_ids_emb)\\n att_context = self.simple_attention(\\n encoder_vec, encoder_proj, pre_h_state)\\n # print(\\\"att_context\\\", att_context)\\n # print(\\\"pre_ids_emb\\\", pre_ids_emb)\\n # pd.Print(att_context)\\n\\n prob_c = fluid.layers.sequence_expand_as(pre_score,\\n encoder_vec)\\n # pd.Print(prob_c)\\n\\n current_score, current_h, current_c, this_prob_c = self.copy_decoder(\\n pre_ids_emb, \\n encoder_vec, encoder_vec_full, encoder_proj, \\n _encoder_input_ids, pre_ids,\\n prob_c, att_context,\\n pre_h_state, pre_c_state,\\n event_embedding)\\n\\n # decoder_inputs = fluid.layers.concat(\\n # input=[att_context, pre_ids_emb], axis=1)\\n # current_h, current_c = self.lstm_step(\\n # decoder_inputs, pre_h_state, pre_c_state, self.decoder_size)\\n # # compute predicting probability of nomarlized word\\n # current_score = fluid.layers.fc(input=current_h,\\n # size=self.target_dict_dim,\\n # act='softmax',\\n # param_attr=fluid.ParamAttr(name=\\\"out_softmax_w\\\"),\\n # bias_attr=fluid.ParamAttr(name=\\\"out_softmax_b\\\"))\\n\\n # # current_state = pd.fc(input=[pre_state_expanded, pre_ids_emb],\\n # # size=decoder_size,\\n # # act='tanh')\\n # current_state_with_lod = pd.lod_reset(x=current_h, y=pre_score)\\n # current_score = pd.fc(input=current_state_with_lod,\\n # size=self.target_dict_dim,\\n # act='softmax',\\n # param_attr=fluid.ParamAttr(name=\\\"out_softmax_w\\\"),\\n # bias_attr=fluid.ParamAttr(name=\\\"out_softmax_b\\\"))\\n # print(current_score)\\n topk_scores, topk_indices = pd.topk(current_score, k=self.beam_size)\\n # pd.Print(topk_indices)\\n # pd.Print(topk_scores)\\n selected_ids, selected_scores = topk_indices, topk_scores\\n\\n # # compute accumulated score and perform beam search\\n # accu_scores = pd.elementwise_add(\\n # x=pd.log(topk_scores), y=pd.reshape(pre_score, shape=[-1]), axis=0)\\n # selected_ids, selected_scores = pd.beam_search(\\n # pre_ids,\\n # pre_score,\\n # topk_indices,\\n # accu_scores,\\n # self.beam_size,\\n # # end_id=self.end_id,\\n # end_id=999999,\\n # level=0)\\n\\n # pd.Print(selected_ids)\\n # pd.Print(selected_scores)\\n\\n pd.increment(x=counter, value=1, in_place=True)\\n # write search result and corresponding hidden layer into tensor array\\n pd.array_write(current_h, array=state_h_array, i=counter)\\n pd.array_write(current_c, array=state_c_array, i=counter)\\n pd.array_write(selected_ids, array=ids_array, i=counter)\\n pd.array_write(selected_scores, array=scores_array, i=counter)\\n # pd.Print(selected_ids)\\n # pd.Print(selected_scores)\\n\\n # update condition to stop loop\\n length_cond = pd.less_than(x=counter, y=array_len)\\n finish_cond = pd.logical_not(pd.is_empty(x=selected_ids))\\n pd.logical_and(x=length_cond, y=finish_cond, out=cond)\\n\\n # pd.Print(array_len)\\n # translation_ids, translation_scores = pd.beam_search_decode(\\n # ids=ids_array, scores=scores_array, beam_size=self.beam_size, end_id=self.end_id)\\n # pd.Print(translation_ids)\\n translation_ids, translation_ids_index = pd.tensor_array_to_tensor(ids_array, axis=1)\\n translation_scores, translation_scores_index = pd.tensor_array_to_tensor(scores_array, axis=1)\\n\\n return translation_ids, translation_scores\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57eb5221e001f9fabf8c29663de5e28e\",\n \"score\": \"0.51929384\",\n \"text\": \"def _decoder(self, z):\\n # Number of outputs for decoder\\n num_outputs = self._ndims\\n\\n # Dense Layer\\n dense = fully_connected(inputs=z, num_outputs=50,\\n activation_fn=tf.nn.softplus)\\n\\n # Hidden Layer\\n hidden = fully_connected(\\n inputs=dense, num_outputs=100, activation_fn=tf.nn.softplus)\\n\\n # Output Layer\\n output = fully_connected(\\n inputs=hidden, num_outputs=num_outputs, activation_fn=tf.nn.sigmoid)\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ea99a0495a5692a97a2e97355893bac\",\n \"score\": \"0.5192917\",\n \"text\": \"def decoded(self):\\n self.offset = self.offset * -1\\n return self.cipher()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc4ff94c92ae04bd0640007f450ae366\",\n \"score\": \"0.51878834\",\n \"text\": \"def decode_ufocapture(fname):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bba883b82b163a975b522ef68405d732\",\n \"score\": \"0.5181577\",\n \"text\": \"def decoder(self, y_tilde):\\n nb_examples = y_tilde.shape[0]\\n hidden_decoder = tls.leaky_relu(numpy.dot(y_tilde,\\n self.__parameters_eae['weights_decoder']['l1']) +\\n numpy.tile(self.__parameters_eae['biases_decoder']['l1'],\\n (nb_examples, 1)))\\n reconstruction = numpy.dot(hidden_decoder,\\n self.__parameters_eae['weights_decoder']['mean']) + \\\\\\n numpy.tile(self.__parameters_eae['biases_decoder']['mean'],\\n (nb_examples, 1))\\n return (hidden_decoder, reconstruction)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"581f22bae8b648b865a8a10e93ef2867\",\n \"score\": \"0.5176327\",\n \"text\": \"def _extract_frame(self, data):\\n if (data == self.A0FLAG or data == self.C0FLAG) and (self.stepindex == 0):\\n # print(\\\"Head: \\\", hex(data))\\n self.stepindex += 1\\n self._buffer.append(hex(data))\\n elif self.stepindex < 4 and self.stepindex >= 1:\\n self.stepindex += 1\\n self._buffer.append(hex(data))\\n elif self.stepindex == 4:\\n self.stepindex += 1\\n self._framelen = int(data)\\n # print(\\\"length: \\\", self._framelen)\\n self._buffer.append(hex(data))\\n elif self.stepindex == 5:\\n if self._framecurlen < self._framelen:\\n self._buffer.append(hex(data))\\n self._framecurlen += 1\\n elif self._framecurlen == self._framelen:\\n self._buffer.append(hex(data))\\n resu_crc = checkcrc.xorcrc_str(self._buffer)\\n if data == resu_crc:\\n self.frame_received(self._buffer)\\n self.reset()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d478ca8698ce386c64c931afc59305d9\",\n \"score\": \"0.5167792\",\n \"text\": \"def msg_decoder(msg):\\n\\n if msg.encoding == 'bgr8':\\n image = np.frombuffer(msg.data, np.uint8).reshape((msg.height, msg.width, 3))[:, :, ::-1]\\n elif msg.encoding == 'bgra8':\\n image = np.frombuffer(msg.data, np.uint8).reshape((msg.height, msg.width, 4))[:, :, ::-1][:,:,1:]\\n elif msg.encoding == 'rgb8':\\n image = np.frombuffer(msg.data, np.uint8).reshape((msg.height, msg.width, 3))\\n elif msg.encoding == 'mono8' or msg.encoding == '8UC1':\\n image = np.frombuffer(msg.data, np.uint8).reshape((msg.height, msg.width))\\n image = np.stack((image,) * 3, axis = -1) # greyscale to RGB\\n elif msg.encoding == 'mono16' or msg.encoding == '16UC1':\\n image = np.frombuffer(msg.data, np.uint16).reshape((msg.height, msg.width)).astype(np.float)\\n image_max = np.percentile(image, 95)\\n image_min = np.percentile(image, 5)\\n image = 255*((image - image_min)/(image_max - image_min))\\n image = np.clip(image, 0, 255)\\n image = np.stack((image,) * 3, axis = -1) # greyscale to RGB\\n else:\\n print(\\\"Image encoding \\\" + msg.encoding + \\\" not supported yet.\\\")\\n return None\\n\\n return image\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b82ab7e92d92f37fb0b92377893805b6\",\n \"score\": \"0.5167117\",\n \"text\": \"def decompress(textrep):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e067f956f99d3c5a32b3c5a2e8a1be4d\",\n \"score\": \"0.5162957\",\n \"text\": \"def deserialize_bar_code_model(\\n serialized_item_handle: HHandle\\n) -> HHandle:\\n with HalconOperator(1986) as proc:\\n proc.set_input_tuple(0, serialized_item_handle)\\n proc.init_oct(0)\\n proc.execute()\\n bar_code_handle = proc.get_output_tuple_s(0)\\n return bar_code_handle # type: ignore\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04d54ed3527234da59bfdb4a080b6d14\",\n \"score\": \"0.51594746\",\n \"text\": \"def _build_decoder(self, encoder_outputs, encoder_state, hps):\\n\\n tgt_sos_id = tf.cast(self.vocab_table.lookup(tf.constant(hps.sos)),\\n tf.int32)\\n tgt_eos_id = tf.cast(self.vocab_table.lookup(tf.constant(hps.eos)),\\n tf.int32)\\n\\n iterator = self.iterator\\n\\n with tf.variable_scope('decoder') as decoder_scope:\\n sample_id, final_context_state,coverage_loss= tf.no_op(), tf.no_op(),tf.no_op()\\n\\n dec_in_state = self._reduce_states(encoder_state[0], encoder_state[1])\\n\\n cell, decoder_initial_state = self._build_decoder_cell(hps, None, dec_in_state, None)\\n # target_output = iterator.target_output\\n # target_input = iterator.target_input\\n source_sequence_length = iterator.source_sequence_length\\n # target_sequence_length = iterator.target_sequence_length\\n\\n source = iterator.source\\n\\n\\n if self.time_major:\\n print(\\\"it's time major\\\")\\n\\n source = tf.transpose(source)\\n source_sequence_length = tf.transpose(source_sequence_length)\\n\\n if self.mode != tf.contrib.learn.ModeKeys.INFER:\\n target_input = iterator.target_input\\n target_output = iterator.target_output\\n target_sequence_length = iterator.target_sequence_length\\n\\n if self.time_major:\\n utils.print_out(\\\"time major\\\")\\n target_input = tf.transpose(target_input)\\n target_output = tf.transpose(target_output)\\n target_sequence_length = tf.transpose(target_sequence_length)\\n\\n target_max_time = self.get_max_time(target_output)\\n dec_target_mask = tf.sequence_mask(target_sequence_length, target_max_time, dtype=tf.float32)\\n self._target_batch = target_output\\n\\n\\n emb_dec_inputs = [tf.nn.embedding_lookup(self.embedding_decoder, x) for x in tf.unstack(target_input,\\n axis=1, num =150)] # list length max_dec_steps containing shape (batch_size, emb_size)\\n\\n\\n\\n source_max_time = self.get_max_time(source)\\n\\n en_src_mask = tf.sequence_mask(source_sequence_length, source_max_time, dtype=tf.float32)\\n if self.time_major:\\n en_src_mask = tf.transpose(en_src_mask)\\n\\n decoder_outputs, dec_out_state, attn_dists, p_gens, coverage = self.attention_decoder(hps, emb_dec_inputs, dec_in_state, encoder_outputs, en_src_mask, cell,\\n initial_state_attention=False,\\n copy_source=hps.copy_source,\\n use_coverage=True,\\n prev_coverage=None, scope =decoder_scope)\\n\\n\\n logits = self.output_layer(tf.stack(decoder_outputs))\\n\\n coverage_loss = _coverage_loss(attn_dists, dec_target_mask)\\n\\n\\n\\n else:\\n # maximum_iteration: The maximum decoding steps.\\n maximum_iterations = self._get_infer_maximum_iterations(\\n hps, source_sequence_length)\\n beam_width = hps.beam_width\\n length_penalty_weight = hps.length_penalty_weight\\n start_tokens = tf.fill([self.batch_size], tgt_sos_id)\\n end_token = tgt_eos_id\\n\\n if beam_width > 0:\\n utils.print_out(\\\"beam search activated\\\")\\n my_decoder = tf.contrib.seq2seq.BeamSearchDecoder(\\n cell=cell,\\n embedding=self.embedding_decoder,\\n start_tokens=start_tokens,\\n end_token=end_token,\\n initial_state=decoder_initial_state,\\n beam_width=beam_width,\\n output_layer=self.output_layer,\\n length_penalty_weight=length_penalty_weight)\\n else:\\n # Helper\\n utils.print_out(\\\"greedy decoding activated\\\")\\n\\n helper = tf.contrib.seq2seq.GreedyEmbeddingHelper(\\n self.embedding_decoder, start_tokens, end_token)\\n\\n # Decoder\\n my_decoder = tf.contrib.seq2seq.BasicDecoder(\\n cell,\\n helper,\\n dec_in_state,\\n output_layer=self.output_layer # applied per timestep\\n )\\n\\n # Dynamic decoding\\n outputs, final_context_state, _ = tf.contrib.seq2seq.dynamic_decode(\\n my_decoder,\\n maximum_iterations=maximum_iterations,\\n output_time_major=self.time_major,\\n swap_memory=True,\\n scope=decoder_scope)\\n\\n if beam_width > 0:\\n logits = tf.no_op()\\n sample_id = outputs.predicted_ids\\n # if self.time_major:\\n # sample_id = tf.transpose(sample_id, perm=[1, 2, 0])\\n # utils.print_out(\\\"transpose activated for sampleid \\\")\\n\\n\\n\\n else:\\n logits = outputs.rnn_output\\n sample_id = outputs.sample_id\\n\\n return coverage_loss,logits, sample_id, final_context_state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50fb28d8eceeebd3b6d5fbe52fee43a2\",\n \"score\": \"0.515891\",\n \"text\": \"def frameReceived(opcode, data, fin):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3a1d27deddb7c84802e783c0bb8789f\",\n \"score\": \"0.5157705\",\n \"text\": \"def parser_hook(self, raw: bytes) -> List[Dict]:\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17759e404ca12de5f656c9267afbe4b9\",\n \"score\": \"0.51533115\",\n \"text\": \"def _decoder(self, inputs, all_states):\\n with tf.variable_scope(\\\"decoder\\\", reuse=tf.AUTO_REUSE):\\n enc_pad_mask, enc_doc_mask, dec_in = inputs[: 3]\\n\\n # Concatenating the pad masks of all sentences.\\n enc_pad_mask = tf.reshape(enc_pad_mask,\\n [-1, FLAGS.max_enc_sent*FLAGS.max_enc_steps_per_sent]) # Shape: B x T_enc.\\n\\n # Converting ID's to word vectors.\\n dec_in_vecs = tf.nn.embedding_lookup(params=self._word_embedding, ids=dec_in) # Shape: B x T_dec x D.\\n dec_in_vecs = tf.cast(dec_in_vecs, dtype=tf.float32) # Shape: B x T_dec x D.\\n\\n # Memory masks.\\n mem_masks = [enc_pad_mask, enc_doc_mask] # Shape: [B x T_enc, B x S_in].\\n\\n # NSE internal states.\\n sent_mems, doc_mem = all_states[0] # Shape: B x T_enc x D, B x S_in x D.\\n state = [[sent_mems, doc_mem]] + all_states[1:]\\n\\n writes = []\\n p_attns = []\\n p_gens = []\\n for i in range(FLAGS.dec_steps):\\n\\n x_t = dec_in_vecs[:, i, :] # Shape: B x D.\\n output, state = self._nse.step(\\n x_t=x_t, mem_masks=mem_masks, prev_state=state, use_pgen=FLAGS.use_pgen\\n )\\n\\n # Appending the outputs.\\n writes.append(output[0])\\n p_attns.append(output[1])\\n\\n if FLAGS.use_pgen:\\n p_gens.append(output[2])\\n\\n p_vocabs = self._get_vocab_dist(writes) # Shape: T_dec * [B x vsize].\\n\\n p_cums = p_vocabs\\n if FLAGS.use_pgen:\\n enc_in_ext_vocab = inputs[3]\\n p_cums = self._get_cumulative_dist(\\n p_vocabs, p_gens, p_attns, enc_in_ext_vocab\\n ) # Shape: T_dec * [B x ext_vsize].\\n\\n if FLAGS.mode.lower() == \\\"test\\\":\\n p_final = p_cums[0] # Shape: Bm x V.\\n\\n topk_probs, topk_ids = tf.nn.top_k(p_final, k=2*FLAGS.beam_size, name=\\\"topk_preds\\\")\\n topk_log_probs = tf.log(tf.clip_by_value(topk_probs, 1e-10, 1.0))\\n\\n outputs = [topk_ids, topk_log_probs, state, p_attns]\\n if FLAGS.use_pgen:\\n outputs.append(p_gens)\\n\\n return outputs\\n else:\\n return p_cums, p_attns\",\n \"title\": \"\"\n },\n {\n \"docid\": \"191eb1a3af3210354b12e9bae77ef99b\",\n \"score\": \"0.51462364\",\n \"text\": \"def __init__(self):\\n self._last_decoding = None\\n self._cs = None\\n self._firstaddr = None\\n self._arch = None\\n self.valid_archs = None\\n self.modes = None\\n Backend.__init__(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7310867d5f707f28d8c7bd81dd1363e9\",\n \"score\": \"0.51425415\",\n \"text\": \"def decode(self, decoder_input, tgt_lang=None):\\n\\n cell_input = torch.cat((decoder_input, self.attention_context), -1)\\n # B, prenet_dim + model_dim\\n self.attention_hidden, self.attention_cell = self.attention_rnn(\\n cell_input, (self.attention_hidden, self.attention_cell), tgt_lang)\\n # B, model_dim\\n self.attention_hidden = F.dropout(\\n self.attention_hidden, self.p_attention_dropout, self.training)\\n\\n attention_weights_cat = torch.cat(\\n (self.attention_weights.unsqueeze(1),\\n self.attention_weights_cum.unsqueeze(1)), dim=1)\\n # B, 2 , Max_time\\n # attention_hidden should be query\\n self.attention_context, self.attention_weights = self.attention_layer(\\n self.attention_hidden, self.memory, self.processed_memory,\\n attention_weights_cat, self.mask, tgt_lang)\\n\\n self.attention_weights_cum += self.attention_weights\\n decoder_input = torch.cat(\\n (self.attention_hidden, self.attention_context), -1)\\n # B, 2* model_dim\\n self.decoder_hidden, self.decoder_cell = self.decoder_rnn(\\n decoder_input, (self.decoder_hidden, self.decoder_cell), tgt_lang)\\n self.decoder_hidden = F.dropout(\\n self.decoder_hidden, self.p_decoder_dropout, self.training)\\n\\n decoder_hidden_attention_context = torch.cat(\\n (self.decoder_hidden, self.attention_context), dim=1)\\n decoder_output = self.linear_projection(\\n decoder_hidden_attention_context, tgt_lang)\\n # B, nmel * nframeperstep\\n gate_prediction = self.gate_layer(decoder_hidden_attention_context, tgt_lang)\\n\\n return decoder_output, gate_prediction, self.attention_weights, self.attention_context\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3110cb915aad4720aa2bf939b094889f\",\n \"score\": \"0.5138669\",\n \"text\": \"def post_decrypt_data(self, data):\\n# print(\\\"---RawRNCryptor---\\\")#debug\\n# print(data) #debug\\n# print(bord(data[-1]))#debug\\n data = data[:-bord(data[-1])]\\n \\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33d5885b022fcf07af88b51eb092b2d0\",\n \"score\": \"0.5122174\",\n \"text\": \"def __init__(self, fileblock):\\n hdr_sz = Datagram.hdr_dtype.itemsize\\n H0_sz = Datagram.H0_dtype.itemsize\\n self.header = np.frombuffer(fileblock, dtype = Datagram.hdr_dtype, count = 1)[0]\\n self.decoded = False\\n self.dtype = self.header[0]\\n if np.frombuffer(fileblock[hdr_sz:hdr_sz+2], dtype = '>S2') == 'H0':\\n self.subheader = np.frombuffer(fileblock, dtype = Datagram.H0_dtype, offset = hdr_sz, count = 1)[0]\\n self.datablock = fileblock[hdr_sz+H0_sz:]\\n # the snippet and truepix datagrams have a larger header...\\n if self.dtype == 'SNI0' or self.dtype == 'TPX0':\\n self.datablock = self.datablock[6*4:]\\n else:\\n self.datablock = fileblock[hdr_sz:]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cad359e1ea1c317bc873c6e1fcd5d0f1\",\n \"score\": \"0.51208204\",\n \"text\": \"def get_decoder( model ):\\n decod = [ l for l in model.layers if 'decoder' in l.name ]\\n\\n for dec in decod:\\n if 'decoder' not in dec.name: \\n print( \\\"Structure '{}' not valid as decoder architecture\\\".format( dec.name ) )\\n sys.exit( 1 )\\n\\n if dec.layers[ 0 ].input_shape[ -1 ] != cnfg[ 'latent_size' ]: \\n if 'latent_subsize' not in cnfg: \\n print( \\\"Input size '{:d}' is not the expected '{}'\\\".format(\\n dec.layers[ 0 ].input_shape[ -1 ], cnfg[ 'latent_size' ] ) )\\n sys.exit( 1 )\\n\\n elif dec.layers[ 0 ].input_shape[ -1 ] != cnfg[ 'latent_subsize' ]: \\n print( \\\"Input size '{:d}' is not the expected '{}'\\\".format(\\n dec.layers[ 0 ].input_shape[ -1 ], cnfg[ 'latent_subsize' ] ) )\\n sys.exit( 1 )\\n\\n # just to try a random latent space\\n # ts.save_image( dec.predict( rnd.uniform(-3, 3) * np.random.rand(1,128) ), True, \\\"i.jpg\\\" )\\n\\n if len( decod ) == 1:\\n return decod[ 0 ]\\n\\n return decod\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a9c0da991ec2080f575adf808bd88b3\",\n \"score\": \"0.51069516\",\n \"text\": \"def _decode(self, data):\\n return base64.b64decode(data)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":878,"cells":{"query_id":{"kind":"string","value":"814a86ccb163c5aa3b30a811286a1675"},"query":{"kind":"string","value":"Get the currently selected job"},"positive_passages":{"kind":"list like","value":[{"docid":"9ccb19c631ea05b86427f4c455904752","score":"0.83990645","text":"def get_job(self) -> Job:\n return self.jobs_list[self.sel_idx]","title":""}],"string":"[\n {\n \"docid\": \"9ccb19c631ea05b86427f4c455904752\",\n \"score\": \"0.83990645\",\n \"text\": \"def get_job(self) -> Job:\\n return self.jobs_list[self.sel_idx]\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"af8b12dff6f2530fbbc6ac201ccc6a00","score":"0.76543224","text":"def job(self):\n return self.batch[self.job_id]","title":""},{"docid":"c77c5c99ee3ce12ea54d8fa4e31cad56","score":"0.74811244","text":"def current_job(self):\n assert(ExecutorThread.executor_object is not None)\n return self.__job","title":""},{"docid":"9ecc71e9f9dcc8380aa3973f37a4d3c5","score":"0.74108416","text":"def getJob(self, name=None):\n if name == None: \n name = self.jobstable.get_selectedRecordNames()[0]\n if name == None:\n return None, name\n jobid = self.DB.meta.peatsa_jobs[name]\n try:\n job = PEATSA.WebApp.Data.Job(jobid, self.connection)\n except:\n #print 'job not in database'\n return None,name\n return job, name","title":""},{"docid":"0f4be7825549e1aa7c028eaea001baaf","score":"0.73360646","text":"def job(self):\n\n if self.current_bead is None:\n return None\n\n if self.jobs is None:\n RosProxy().notify(\"Can not find jobs.\", STATE.ERROR)\n return None\n\n _job = None\n for job in self.jobs.configurations:\n if job.job_number == self.current_bead.wps_job_number:\n return job\n\n return None","title":""},{"docid":"c7f730bd9a2326fbb1000e8495107d10","score":"0.7218985","text":"def get_job(self) -> Dict[Text, Text]:\n request = self._client.projects().jobs().get(name=self._job_name)\n return request.execute()","title":""},{"docid":"3d208b9f6ce656558644faa6d6530e96","score":"0.70712274","text":"def latest_job(self):\n return self.jobmanagers[self.current_network].latest_job","title":""},{"docid":"50814c6ef52744dc8990d89a072cf79a","score":"0.6990164","text":"def get_job(self, identifier: str):\n self._log_operation('Getting job {i}'.format(i=identifier))\n return self._job_queue.get_job_details(identifier)","title":""},{"docid":"aad51da0917c85589f9f232b8c594f8c","score":"0.69777465","text":"def job(self) -> str:\n return self._job","title":""},{"docid":"aad51da0917c85589f9f232b8c594f8c","score":"0.69777465","text":"def job(self) -> str:\n return self._job","title":""},{"docid":"5f1bdfb45c40c9b081591080fa4afdd6","score":"0.693246","text":"def get_job(self) -> CustomJob:\n return self._client.get_custom_job(name=self._job_name)","title":""},{"docid":"5fabe3444209b9b8c69e97f9238714d6","score":"0.6889623","text":"def get_job(jid=None):\n if not jid:\n raise CommandExecutionError(\"ID option must not be none.\")\n\n query = {\"type\": \"op\", \"cmd\": \"<show><jobs><id>{}</id></jobs></show>\".format(jid)}\n\n return __proxy__[\"panos.call\"](query)","title":""},{"docid":"d0f6443556c64dd8a6a0c80da380d552","score":"0.6888683","text":"def get_object(self) -> Job:\n project = ProjectPermissionsMixin.get_object(self)\n return project.jobs.get(id=self.kwargs[\"job\"])","title":""},{"docid":"5067befa595f885794f913ec2cb322b5","score":"0.6761618","text":"def name(self):\n return self._job","title":""},{"docid":"f5c480b954de55cb76b39c7da4a4aec9","score":"0.67527044","text":"def get_current_job(connection: Optional['Redis'] = None, job_class: Optional['Job'] = None) -> Optional['Job']:\n if connection:\n warnings.warn(\"connection argument for get_current_job is deprecated.\", DeprecationWarning)\n if job_class:\n warnings.warn(\"job_class argument for get_current_job is deprecated.\", DeprecationWarning)\n return _job_stack.top","title":""},{"docid":"4b7cd238aad2a9761a46438d11c2daa1","score":"0.66537917","text":"def jobid(self):\n return self.get_db('jobid')","title":""},{"docid":"78e3f2e10d4cf60bc8ea6f8525ff88ea","score":"0.664405","text":"def get_job(self, job_name):\n try:\n return self.json_dict['job definitions'][job_name]\n except KeyError:\n print('No job \"%s\" in %s' % (job_name, self.filepath))\n return None","title":""},{"docid":"b6933f5b50956edf9b0c0726a8fcca91","score":"0.66311663","text":"def get_job(self, job_reference):\n url = 'jobs/{0}'.format(job_reference)\n result = self.get(url)\n return result.get('job', result)","title":""},{"docid":"2d38772ab8b7b8ad1802c89a71c7a45f","score":"0.6616254","text":"def _get_job(self, uid):\n try:\n return self._jobs[uid]\n except KeyError:\n raise JobNotFoundError('job \\'%s\\' is not found' % (uid,))","title":""},{"docid":"93b6ad4303cba70a1a992da909f5ba2c","score":"0.65424514","text":"def getNode(self):\r\n try:\r\n output,error = Popen(\"qstat | grep \"+self.jobId, shell=True, stdout=PIPE, stderr=PIPE).communicate()\r\n if self.jobId in output:\r\n return output.split(\"\\t\")[7]\r\n if len(error) > 0:\r\n logging.error(error)\r\n except ValueError:\r\n logging.info(\"Error: waiting for not submitted job...\")","title":""},{"docid":"0b5833b47c71c6580a7c4caffdaae616","score":"0.6541083","text":"def job_id(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"job_id\")","title":""},{"docid":"8a5c43603b02703bf2d1d585b8a374cd","score":"0.64970195","text":"def current_task(self):\n try:\n return self.active_item(remove=False)\n except queue.Empty:\n return None","title":""},{"docid":"7547812ad097f1785dbbad4e18e7eba3","score":"0.6488299","text":"def fetchJob(self):\n \n mpDlg = MultipleValDialog(title='Get Job',\n initialvalues=('','my job1'),\n labels=('ID','Your label',),\n types=('string','string'),\n parent=self.mainwin)\n if mpDlg.result == True:\n jobid = mpDlg.results[0]\n name = mpDlg.results[1]\n else:\n return\n job = PEATSA.WebApp.Data.Job(jobid, self.connection) \n if job != None: \n print 'adding job id %s to list' %job.identification\n self.storeJob(name, job)\n self.updateJobs()\n return","title":""},{"docid":"3c3ef46cb2d48b5039f1f46284060519","score":"0.64742666","text":"def job_id(self):\n return self._job_id","title":""},{"docid":"18e6652ba9e4bcab35480341824c784c","score":"0.6451212","text":"def get_job(arn=None):\n pass","title":""},{"docid":"9029ee9214b222b84e27cdbce6fb4666","score":"0.64294446","text":"def get_current_url(self,\n job_name: str) -> str:\n\n self.cur.callproc('job_get_current_url_SP', (job_name, ))\n job_state = self.cur.fetchone()\n\n if job_state is None:\n raise ValueError('Job is unknown!')\n if job_state[0] is not None:\n # Field 0 contains the status: finished (not None) or not (None)\n raise RuntimeError(f\"Job {job_name} already finished.\")\n # Field 1 contains either the current or the start URL\n return job_state[1]","title":""},{"docid":"06483d882088e84f35e61319a12d4a6e","score":"0.6415561","text":"def get_job(job_name: str):\n\n job_details = redis_controller.get_job_details(job_name=job_name)\n return job_details","title":""},{"docid":"84b55ffbeedd0ff6fba729ba8cffee39","score":"0.64076096","text":"def get_job_shared(self, job, context=None):\n return self._client.call_method(\n 'UserAndJobState.get_job_shared',\n [job], self._service_ver, context)","title":""},{"docid":"d8b638f76c9ceacb45d3117d7aca9c33","score":"0.64054716","text":"def getJobID(self):\n return self.__nupicJobID","title":""},{"docid":"b44597406b159ab9d484fab9d4d69923","score":"0.63929665","text":"def get_current_task(self):\n return self.get_current_step().get_last_task()","title":""},{"docid":"e7f504259ec7155499cb42dc084a3206","score":"0.6363812","text":"def get_job_name(self) -> Text:\n return self._job_name","title":""},{"docid":"c5f784aecdda3e0e6f0e0006c09128a5","score":"0.6363635","text":"def GetSelection(self):\r\n\r\n return self._current","title":""},{"docid":"3c336258e50fc9c183446912b0b6c3b6","score":"0.63534665","text":"def getSelected(self):\n selected = self.defaultChoice\n if self.tableSelected is not None:\n selected = self.tableSelected.getString(self.defaultChoice)\n return self.map.get(selected)","title":""},{"docid":"6990697f8730aa95d7b6826bddfe5f8f","score":"0.63476473","text":"def getSelectedItem(self):\n return self.selected","title":""},{"docid":"6b61c917a7fc487b76212e3453451189","score":"0.6322638","text":"def get_job_for_worker(self, worker_name):\n job = self._in_queue.pop(0)\n self._workers[worker_name].active_jobs.append(job)\n return job.arguments","title":""},{"docid":"dda09c5deca9e8e16820e57870902346","score":"0.629666","text":"def get_job(self, job_id):\n\n try:\n exposure = Job.objects.filter(id=job_id)\n except:\n exposure = None\n\n return exposure","title":""},{"docid":"48ea24f4da972ded10e31515a51f3a0d","score":"0.62954223","text":"def getSelectedItem(self):\n currentIndex = self.table.selectionModel().currentIndex()\n return self.model.itemFromIndex(currentIndex)","title":""},{"docid":"7aa6d233a7345f50ef72c40ab0641c37","score":"0.6293708","text":"def get_selected(self):\n return self.selected","title":""},{"docid":"c2a5b93c970dbaf6db845cd3b0618f2a","score":"0.62937015","text":"def selected(self):\n return self._choices[self._selected][0]","title":""},{"docid":"e87e801ef46c550a8a8a41324d368793","score":"0.62896395","text":"def getJob(uniq):\n return Job(Cuebot.getStub('job').GetJob(\n job_pb2.JobGetJobRequest(id=uniq), timeout=Cuebot.Timeout).job)","title":""},{"docid":"8799724c5d97b8bb12c3e70cc82f850b","score":"0.62876314","text":"def current_choice(self):\n\t\treturn self.choice_data_list[self.select_index]","title":""},{"docid":"00aa089818698a12166c474195780082","score":"0.628369","text":"def _get_current_task():\r\n return current_task","title":""},{"docid":"00aa089818698a12166c474195780082","score":"0.628369","text":"def _get_current_task():\r\n return current_task","title":""},{"docid":"61950c11040d5cb19c6d357215e525e6","score":"0.62808716","text":"def job_name(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"job_name\")","title":""},{"docid":"d057c3b5b1e5bd91b71dcb5fc03b12f5","score":"0.6272014","text":"def current_val(self):\n try:\n return self.listbox.get(self.listbox.curselection()[0])\n except IndexError:\n raise KeyError(\"Nothing selected\")","title":""},{"docid":"34c3dadd642a1b239b1a06a27ac28726","score":"0.62645125","text":"def current_job(user):\n logs = user.log_set.filter(finish__isnull=True)[:1]\n if logs:\n log = logs[0]\n result = LabelResponse(log.job.name,\n log.get_duration_display())\n else:\n log = user.log_set.latest()\n result = LabelResponse('Not Working',\n str(timezone.localtime(log.start).date()))\n\n return result","title":""},{"docid":"a304b5fe9b384b5ce8d6417267e66456","score":"0.6261799","text":"def current_state(self) -> Optional[pulumi.Input['JobCurrentState']]:\n return pulumi.get(self, \"current_state\")","title":""},{"docid":"a696973960bc6764197621cc4a83ad67","score":"0.6252568","text":"def get_job(self) -> Union[Dict[Text, Text], CustomJob]:\n pass","title":""},{"docid":"b93d4e1971fc66390e4efc9f8c09438f","score":"0.6224905","text":"def retrieve_job(self, job_id):\n job = {}\n with self._lock:\n if job_id not in self._jobs:\n return None\n job = self._jobs[job_id]\n return job","title":""},{"docid":"ba73ba8de2269a2cad9d8eb0750e9399","score":"0.6220498","text":"def last_job(self): # TOFIX model the job and return an object instead of dictionary\n return self._data.get('summary_fields', {}).get('last_job')","title":""},{"docid":"94a3a533b396c02b1190034b7e21e977","score":"0.6218048","text":"def retrieve_job(self, job_id) -> AzureQuantumJob:\n return self._provider.get_job(job_id)","title":""},{"docid":"9b2d9b92e8816572122138224818709d","score":"0.61945635","text":"def get_job_by_id(self, job_id):\n return self.get_resource(category=SYSTEM, resource_level=JOB,\n resource_level_id=job_id)","title":""},{"docid":"8b46be80f8dd216a7bee66c7f522528e","score":"0.61901975","text":"def __jobSelectedHandle(self, job):\n if job:\n self.__jobSelectedLineEdit.setText(job.name())\n else:\n self.__jobSelectedLineEdit.setText(\"\")","title":""},{"docid":"0c4b8260e2688fee5cba617ca1b02eb7","score":"0.61812234","text":"def get_selected(self):\n\n # if there are items in the list\n if self.currentIndex() != -1:\n modelitem = self._dictmodel.itemFromIndex(\n self._modelindexes[ self.currentIndex() ]\n )\n return modelitem","title":""},{"docid":"e04b55768b1e2385bb292f248653450c","score":"0.61712205","text":"def job_name(self) -> str:\n return pulumi.get(self, \"job_name\")","title":""},{"docid":"c6184e26543769efb4fc3d0c88ae5ab8","score":"0.6162487","text":"def get_currently_selected_configuration(self):\n return self.get_container().currently_selected_configuration","title":""},{"docid":"d0a9d2cdb4d593d07b230b49e7c7c5ac","score":"0.6156431","text":"def job_id(self):\n return self._properties.get(\"jobReference\", {}).get(\"jobId\")","title":""},{"docid":"573b6befb6f32323630c4b1e95ebc7a9","score":"0.6136343","text":"def get_jobs_id(self, ti) -> None:\n return self.get_hook().get_jobs_id(ti)","title":""},{"docid":"17fe9d59c386516b87879a2e119685b2","score":"0.6074745","text":"def GetSelection(self):\n return self.__selected_item","title":""},{"docid":"10d2cae850564ddc6c57257a4b75b7d3","score":"0.60608876","text":"def GetSelection(self):\n \n return self.selected","title":""},{"docid":"e27824548380df250ad465f6b8d9594a","score":"0.60586756","text":"async def get_jobs(jobId: int) -> str: \n return mngr.getJob(str(jobId))","title":""},{"docid":"3f0e2fde101431fcae6ef47742544cba","score":"0.6044516","text":"def job_id(self) -> JobId:\r\n return self._job_id","title":""},{"docid":"36ae7856d81d2a3194dac50821ca271f","score":"0.6035905","text":"def get_current(self):\n return self.current","title":""},{"docid":"520d86fc8aae26bbe4bb10391ad5b948","score":"0.60348165","text":"def get_label(self):\n return self.job[self.label_key]","title":""},{"docid":"cb3828c0f8848e71ae6696f2990e718b","score":"0.6017808","text":"def jobId(self):\n returnself._ShREEKConfig.jobId()","title":""},{"docid":"5235decd2a9a2db28bec2e88630097db","score":"0.6017586","text":"def current_workflow():\n try:\n return current_worker_pool.workflow\n except AttributeError:\n return None","title":""},{"docid":"28c59d575fa715d62c15403eccd5b89e","score":"0.60086626","text":"def job_name(self):\n return self._stub.List(self._message).job_name","title":""},{"docid":"9f73e5bdf81008391aafc240cec8a3c2","score":"0.6006928","text":"def get_state(self):\n\t\treturn Job(SDK.PrlVm_GetState(self.handle)[0])","title":""},{"docid":"6e3374bbc7061f1cb82a9df8d0622399","score":"0.6006506","text":"def retrieve(received_job_id: str) -> Union[Job, None]:\n # todo: add error handling\n found_job = db.Jobs().get_by_id(received_job_id)\n if not found_job:\n return\n return found_job","title":""},{"docid":"7c67b16d573ad63bd0b8d0a768ef6227","score":"0.6005077","text":"def get_job(self, _id):\n data = {\n 'class': 'Job',\n 'id': _id,\n 'attrs': {},\n }\n job = self.db_client.send_request('list', json.dumps(data))\n\n return Job(\n _id=job['id'],\n _type=job['type'],\n task=job['task'],\n command=job['command'],\n input_parameters=job['inputParameters'],\n status=job['status'],\n runner_id=job['runner'],\n )","title":""},{"docid":"38c3db93e876eb6ee1d8016539e54fd4","score":"0.59741336","text":"def getCurrentRow(self):\n item = self.getSelectedItem()\n if item:\n return item.row()\n return None","title":""},{"docid":"9b9933fb10f52a3bd51ab80868a1aa3d","score":"0.5969369","text":"def get_jobs_connection(self):\n return self.m_connection.jobs","title":""},{"docid":"bbaebc5bd6fe50426934c552c1f7ffc2","score":"0.5965098","text":"def current_worker():\n try:\n return worker_thread_data.worker\n except AttributeError:\n return None","title":""},{"docid":"878489e7c2a1603e25b729f1341fc9a7","score":"0.5961013","text":"def get_current_option(self) -> str:\n return self.options[self.current_option_index]","title":""},{"docid":"1c9c4404705a410a6ea6a61cbc01a613","score":"0.59596807","text":"def getJobName():\n return os.environ['LCATR_JOB']","title":""},{"docid":"045565465f0aa8d4f7385cec41a11693","score":"0.59542495","text":"def job_type(self):\n return self._job_type","title":""},{"docid":"cfcfa494646e3e8a703b0af529d15b25","score":"0.5949728","text":"def GetCurrentItem(self):\r\n\r\n return self._current","title":""},{"docid":"973ee774435ab56cd5ab0a1d36d9926b","score":"0.5926058","text":"def _get_job_id(self) -> str:\n return self.split_name[2][3:]","title":""},{"docid":"512270a56166f65baecbffc96e2d95eb","score":"0.5916962","text":"async def get_job_state(jobId: int) -> State: \n return mngr.getStateJob(str(jobId))","title":""},{"docid":"eff3e39af6070d08e905aa20fa9ce090","score":"0.59167755","text":"def find(self, job):\n for i, candidate_job in pendulate(self.jobs):\n if candidate_job == job:\n return i\n raise ValueError(\"Job not found in batch\")","title":""},{"docid":"9403f019c08356c1fc189609fd60bb60","score":"0.5916563","text":"def job_name(self) -> str:\n return self._step_execution_context.job_name","title":""},{"docid":"8cc211e741a2fe21697242139e70911d","score":"0.59097177","text":"def getSelection(self):\n return self.selection","title":""},{"docid":"e783a91342650ad40ee9a456bf210691","score":"0.59074605","text":"def get_job_id(self):\n return {'job_id': self._job_id}","title":""},{"docid":"3bd56a3cbdea81175a199406a62cc71a","score":"0.59040135","text":"def get_task(self, profile):\n task = None\n if self._value.has_option(profile, 'task'):\n task = self._value.get(profile, 'task')\n\n self.logger.info(\"%s is selected as task\" % task)\n return task","title":""},{"docid":"e83f2595d6df75f56c2be24e96bfcaea","score":"0.58960843","text":"def getCurrent(self):\n return self.__current","title":""},{"docid":"3ccc3b35a0173536aed4513549531203","score":"0.5889516","text":"def get_current(self, event=None):\n childes = self.nb.winfo_children() # return the list objects of child widgets of notebook[tab widget]\n return childes[self.nb.index('current')].winfo_children()[0]","title":""},{"docid":"84cc4e6ed7bced348b0b1c7b01ed0d52","score":"0.5887606","text":"def get_job(self, id, jobstore=None):\n\n return self._scheduler.get_job(id, jobstore)","title":""},{"docid":"e27689e9b4349ce17f0bd0022e7bda56","score":"0.5877996","text":"def get_task(self):\n return self.queue.get()","title":""},{"docid":"8525cc2fb6d0683d168b8e34cfa10804","score":"0.58687586","text":"def job_type(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"job_type\")","title":""},{"docid":"4a277c3be41e9a6591244a8554a5611b","score":"0.5860497","text":"def GetSelection(self):\r\n\r\n return self.selection","title":""},{"docid":"8957f0306906c63c116652f12e99f457","score":"0.58585596","text":"def get_thread(self):\n return self.threads[self.thread_id]","title":""},{"docid":"19d37f5ca57dcec4c1760f90d8a64851","score":"0.58583784","text":"def latest_job():\n try:\n return models.SyncJob.objects.latest('start')\n except models.SyncJob.DoesNotExist:\n return False","title":""},{"docid":"46f4e1f0b8c9d2bf7c3b3107a67e09fe","score":"0.58579504","text":"def current_item(self) -> MenuItem | None:\n if not self.all_items:\n return None\n else:\n return self.all_items[self.current_option]","title":""},{"docid":"900ccee2e5ea45bd7bffdc7dda61f13d","score":"0.584532","text":"def reference(self) -> pulumi.Output['outputs.JobReference']:\n return pulumi.get(self, \"reference\")","title":""},{"docid":"f6a5bcf3e0c88bf1affe903e77cc038e","score":"0.5831991","text":"def _retrieve_job_id(job_name, res_id):\n active_jobs = celery_inspector.active()\n job_id = _retrieve_task_id(job_name, res_id, active_jobs)\n if not job_id:\n reserved_jobs = celery_inspector.reserved()\n job_id = _retrieve_task_id(job_name, res_id, reserved_jobs)\n if not job_id:\n scheduled_jobs = celery_inspector.scheduled()\n job_id = _retrieve_task_id(job_name, res_id, scheduled_jobs)\n return job_id","title":""},{"docid":"3f8b9eba1601c0ad75cf1873a5f36f52","score":"0.5828973","text":"def job(job_name):\n ClientID = Job.get_client_id(job_name)\n return tasks_for_client_job(ClientID, job_name)","title":""},{"docid":"088cec76e613d82c4abf916190234e8b","score":"0.58223146","text":"def get_object(self, queryset=None):\n # 404 if job doesn't exist\n try:\n job = Job.objects.select_related().get(pk=self.kwargs['pk'])\n except Job.DoesNotExist:\n raise Http404(\"No Job with PK#{} found.\".format(self.kwargs['pk']))\n\n # Staff can see all jobs\n if self.request.user.is_staff:\n return job\n\n # Creator can see their own jobs no matter the status\n if job.creator == self.request.user:\n return job\n\n # For everyone else the job needs to be visible\n if job.visible:\n return job\n\n # Return None to signal 401 unauthorized\n return None","title":""},{"docid":"99be3a98c227ea75ed580c60a5620fa8","score":"0.5821088","text":"def getJob(appName, jobId):\n jobs = db.getJobs(jobId=jobId)\n job = None if len(jobs) == 0 else jobs[0]\n k3job = dispatcher.getJob(int(jobId))\n\n if job == None:\n return returnError(\"Job ID, %s, does not exist\" % jobId, 404)\n\n thisjob = dict(job, url=dispatcher.getSandboxURL(jobId))\n if k3job != None:\n thisjob['master'] = k3job.master\n local = os.path.join(webapp.config['UPLOADED_JOBS_DEST'], appName, str(jobId)).encode(encoding='utf8', errors='ignore')\n path = os.path.join(webapp.config['UPLOADED_JOBS_DEST'], appName, str(jobId),'role.yaml').encode(encoding='utf8', errors='ignore')\n if os.path.exists(local) and os.path.exists(path):\n with open(path, 'r') as role:\n thisjob['roles'] = role.read()\n else:\n return returnError(\"Job Data no longer exists\", 400)\n\n thisjob['sandbox'] = sorted (os.listdir(local))\n\n if 'application/json' in request.headers['Accept']:\n return jsonify(thisjob)\n else:\n return render_template(\"last.html\", appName=appName, lastjob=thisjob)","title":""},{"docid":"fd85fbcf857c13ba9729a8ea751e478e","score":"0.5818568","text":"def getSelected(self):\n\n return self._streams[self._selectedStream]","title":""},{"docid":"c0db0d692b30ab8db20386f7587f5100","score":"0.581538","text":"def next_and_lock(self):\n\n if self._cur_job:\n log.error(\"There is already an active job! Please call done() or failed() on the job first.\")\n return None\n\n # Loop until we found a job\n while True:\n\n jobs = self._ls_waiting_jobs()\n\n # No more jobs\n if not jobs:\n return None\n\n if self._rnd_job:\n job = random.choice(jobs)\n else:\n job = sorted(jobs)[0]\n\n log.debug(\"Selected job: %s\" % job)\n\n wjob = \"%s.%s\" % (self._wid, job)\n\n src = pjoin(self._jobsdir, \"00_waiting\", job)\n dst = pjoin(self._jobsdir, \"01_running\", wjob)\n\n try:\n self._move(src, dst)\n except FileNotFoundError:\n log.warning(\"Job %s does not exist anymore. Moving on\" % src)\n continue\n\n if not self._worker_sync:\n return Job(self, \"01_running\", job)\n else:\n\n job = self._job_worker_selection(job)\n\n if job is not None:\n return job","title":""},{"docid":"4f9fd1262ddea417bb84a554bbce314d","score":"0.58028716","text":"def selected_item(self) -> MenuItem | None:\n if self.selected_option == -1:\n return None\n else:\n return self.all_items[self.selected_option]","title":""}],"string":"[\n {\n \"docid\": \"af8b12dff6f2530fbbc6ac201ccc6a00\",\n \"score\": \"0.76543224\",\n \"text\": \"def job(self):\\n return self.batch[self.job_id]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c77c5c99ee3ce12ea54d8fa4e31cad56\",\n \"score\": \"0.74811244\",\n \"text\": \"def current_job(self):\\n assert(ExecutorThread.executor_object is not None)\\n return self.__job\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ecc71e9f9dcc8380aa3973f37a4d3c5\",\n \"score\": \"0.74108416\",\n \"text\": \"def getJob(self, name=None):\\n if name == None: \\n name = self.jobstable.get_selectedRecordNames()[0]\\n if name == None:\\n return None, name\\n jobid = self.DB.meta.peatsa_jobs[name]\\n try:\\n job = PEATSA.WebApp.Data.Job(jobid, self.connection)\\n except:\\n #print 'job not in database'\\n return None,name\\n return job, name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f4be7825549e1aa7c028eaea001baaf\",\n \"score\": \"0.73360646\",\n \"text\": \"def job(self):\\n\\n if self.current_bead is None:\\n return None\\n\\n if self.jobs is None:\\n RosProxy().notify(\\\"Can not find jobs.\\\", STATE.ERROR)\\n return None\\n\\n _job = None\\n for job in self.jobs.configurations:\\n if job.job_number == self.current_bead.wps_job_number:\\n return job\\n\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7f730bd9a2326fbb1000e8495107d10\",\n \"score\": \"0.7218985\",\n \"text\": \"def get_job(self) -> Dict[Text, Text]:\\n request = self._client.projects().jobs().get(name=self._job_name)\\n return request.execute()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d208b9f6ce656558644faa6d6530e96\",\n \"score\": \"0.70712274\",\n \"text\": \"def latest_job(self):\\n return self.jobmanagers[self.current_network].latest_job\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50814c6ef52744dc8990d89a072cf79a\",\n \"score\": \"0.6990164\",\n \"text\": \"def get_job(self, identifier: str):\\n self._log_operation('Getting job {i}'.format(i=identifier))\\n return self._job_queue.get_job_details(identifier)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aad51da0917c85589f9f232b8c594f8c\",\n \"score\": \"0.69777465\",\n \"text\": \"def job(self) -> str:\\n return self._job\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aad51da0917c85589f9f232b8c594f8c\",\n \"score\": \"0.69777465\",\n \"text\": \"def job(self) -> str:\\n return self._job\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f1bdfb45c40c9b081591080fa4afdd6\",\n \"score\": \"0.693246\",\n \"text\": \"def get_job(self) -> CustomJob:\\n return self._client.get_custom_job(name=self._job_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5fabe3444209b9b8c69e97f9238714d6\",\n \"score\": \"0.6889623\",\n \"text\": \"def get_job(jid=None):\\n if not jid:\\n raise CommandExecutionError(\\\"ID option must not be none.\\\")\\n\\n query = {\\\"type\\\": \\\"op\\\", \\\"cmd\\\": \\\"<show><jobs><id>{}</id></jobs></show>\\\".format(jid)}\\n\\n return __proxy__[\\\"panos.call\\\"](query)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0f6443556c64dd8a6a0c80da380d552\",\n \"score\": \"0.6888683\",\n \"text\": \"def get_object(self) -> Job:\\n project = ProjectPermissionsMixin.get_object(self)\\n return project.jobs.get(id=self.kwargs[\\\"job\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5067befa595f885794f913ec2cb322b5\",\n \"score\": \"0.6761618\",\n \"text\": \"def name(self):\\n return self._job\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5c480b954de55cb76b39c7da4a4aec9\",\n \"score\": \"0.67527044\",\n \"text\": \"def get_current_job(connection: Optional['Redis'] = None, job_class: Optional['Job'] = None) -> Optional['Job']:\\n if connection:\\n warnings.warn(\\\"connection argument for get_current_job is deprecated.\\\", DeprecationWarning)\\n if job_class:\\n warnings.warn(\\\"job_class argument for get_current_job is deprecated.\\\", DeprecationWarning)\\n return _job_stack.top\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b7cd238aad2a9761a46438d11c2daa1\",\n \"score\": \"0.66537917\",\n \"text\": \"def jobid(self):\\n return self.get_db('jobid')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78e3f2e10d4cf60bc8ea6f8525ff88ea\",\n \"score\": \"0.664405\",\n \"text\": \"def get_job(self, job_name):\\n try:\\n return self.json_dict['job definitions'][job_name]\\n except KeyError:\\n print('No job \\\"%s\\\" in %s' % (job_name, self.filepath))\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6933f5b50956edf9b0c0726a8fcca91\",\n \"score\": \"0.66311663\",\n \"text\": \"def get_job(self, job_reference):\\n url = 'jobs/{0}'.format(job_reference)\\n result = self.get(url)\\n return result.get('job', result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d38772ab8b7b8ad1802c89a71c7a45f\",\n \"score\": \"0.6616254\",\n \"text\": \"def _get_job(self, uid):\\n try:\\n return self._jobs[uid]\\n except KeyError:\\n raise JobNotFoundError('job \\\\'%s\\\\' is not found' % (uid,))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93b6ad4303cba70a1a992da909f5ba2c\",\n \"score\": \"0.65424514\",\n \"text\": \"def getNode(self):\\r\\n try:\\r\\n output,error = Popen(\\\"qstat | grep \\\"+self.jobId, shell=True, stdout=PIPE, stderr=PIPE).communicate()\\r\\n if self.jobId in output:\\r\\n return output.split(\\\"\\\\t\\\")[7]\\r\\n if len(error) > 0:\\r\\n logging.error(error)\\r\\n except ValueError:\\r\\n logging.info(\\\"Error: waiting for not submitted job...\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b5833b47c71c6580a7c4caffdaae616\",\n \"score\": \"0.6541083\",\n \"text\": \"def job_id(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"job_id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a5c43603b02703bf2d1d585b8a374cd\",\n \"score\": \"0.64970195\",\n \"text\": \"def current_task(self):\\n try:\\n return self.active_item(remove=False)\\n except queue.Empty:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7547812ad097f1785dbbad4e18e7eba3\",\n \"score\": \"0.6488299\",\n \"text\": \"def fetchJob(self):\\n \\n mpDlg = MultipleValDialog(title='Get Job',\\n initialvalues=('','my job1'),\\n labels=('ID','Your label',),\\n types=('string','string'),\\n parent=self.mainwin)\\n if mpDlg.result == True:\\n jobid = mpDlg.results[0]\\n name = mpDlg.results[1]\\n else:\\n return\\n job = PEATSA.WebApp.Data.Job(jobid, self.connection) \\n if job != None: \\n print 'adding job id %s to list' %job.identification\\n self.storeJob(name, job)\\n self.updateJobs()\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c3ef46cb2d48b5039f1f46284060519\",\n \"score\": \"0.64742666\",\n \"text\": \"def job_id(self):\\n return self._job_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18e6652ba9e4bcab35480341824c784c\",\n \"score\": \"0.6451212\",\n \"text\": \"def get_job(arn=None):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9029ee9214b222b84e27cdbce6fb4666\",\n \"score\": \"0.64294446\",\n \"text\": \"def get_current_url(self,\\n job_name: str) -> str:\\n\\n self.cur.callproc('job_get_current_url_SP', (job_name, ))\\n job_state = self.cur.fetchone()\\n\\n if job_state is None:\\n raise ValueError('Job is unknown!')\\n if job_state[0] is not None:\\n # Field 0 contains the status: finished (not None) or not (None)\\n raise RuntimeError(f\\\"Job {job_name} already finished.\\\")\\n # Field 1 contains either the current or the start URL\\n return job_state[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06483d882088e84f35e61319a12d4a6e\",\n \"score\": \"0.6415561\",\n \"text\": \"def get_job(job_name: str):\\n\\n job_details = redis_controller.get_job_details(job_name=job_name)\\n return job_details\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84b55ffbeedd0ff6fba729ba8cffee39\",\n \"score\": \"0.64076096\",\n \"text\": \"def get_job_shared(self, job, context=None):\\n return self._client.call_method(\\n 'UserAndJobState.get_job_shared',\\n [job], self._service_ver, context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8b638f76c9ceacb45d3117d7aca9c33\",\n \"score\": \"0.64054716\",\n \"text\": \"def getJobID(self):\\n return self.__nupicJobID\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b44597406b159ab9d484fab9d4d69923\",\n \"score\": \"0.63929665\",\n \"text\": \"def get_current_task(self):\\n return self.get_current_step().get_last_task()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7f504259ec7155499cb42dc084a3206\",\n \"score\": \"0.6363812\",\n \"text\": \"def get_job_name(self) -> Text:\\n return self._job_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5f784aecdda3e0e6f0e0006c09128a5\",\n \"score\": \"0.6363635\",\n \"text\": \"def GetSelection(self):\\r\\n\\r\\n return self._current\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c336258e50fc9c183446912b0b6c3b6\",\n \"score\": \"0.63534665\",\n \"text\": \"def getSelected(self):\\n selected = self.defaultChoice\\n if self.tableSelected is not None:\\n selected = self.tableSelected.getString(self.defaultChoice)\\n return self.map.get(selected)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6990697f8730aa95d7b6826bddfe5f8f\",\n \"score\": \"0.63476473\",\n \"text\": \"def getSelectedItem(self):\\n return self.selected\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b61c917a7fc487b76212e3453451189\",\n \"score\": \"0.6322638\",\n \"text\": \"def get_job_for_worker(self, worker_name):\\n job = self._in_queue.pop(0)\\n self._workers[worker_name].active_jobs.append(job)\\n return job.arguments\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dda09c5deca9e8e16820e57870902346\",\n \"score\": \"0.629666\",\n \"text\": \"def get_job(self, job_id):\\n\\n try:\\n exposure = Job.objects.filter(id=job_id)\\n except:\\n exposure = None\\n\\n return exposure\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48ea24f4da972ded10e31515a51f3a0d\",\n \"score\": \"0.62954223\",\n \"text\": \"def getSelectedItem(self):\\n currentIndex = self.table.selectionModel().currentIndex()\\n return self.model.itemFromIndex(currentIndex)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7aa6d233a7345f50ef72c40ab0641c37\",\n \"score\": \"0.6293708\",\n \"text\": \"def get_selected(self):\\n return self.selected\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2a5b93c970dbaf6db845cd3b0618f2a\",\n \"score\": \"0.62937015\",\n \"text\": \"def selected(self):\\n return self._choices[self._selected][0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e87e801ef46c550a8a8a41324d368793\",\n \"score\": \"0.62896395\",\n \"text\": \"def getJob(uniq):\\n return Job(Cuebot.getStub('job').GetJob(\\n job_pb2.JobGetJobRequest(id=uniq), timeout=Cuebot.Timeout).job)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8799724c5d97b8bb12c3e70cc82f850b\",\n \"score\": \"0.62876314\",\n \"text\": \"def current_choice(self):\\n\\t\\treturn self.choice_data_list[self.select_index]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00aa089818698a12166c474195780082\",\n \"score\": \"0.628369\",\n \"text\": \"def _get_current_task():\\r\\n return current_task\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00aa089818698a12166c474195780082\",\n \"score\": \"0.628369\",\n \"text\": \"def _get_current_task():\\r\\n return current_task\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61950c11040d5cb19c6d357215e525e6\",\n \"score\": \"0.62808716\",\n \"text\": \"def job_name(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"job_name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d057c3b5b1e5bd91b71dcb5fc03b12f5\",\n \"score\": \"0.6272014\",\n \"text\": \"def current_val(self):\\n try:\\n return self.listbox.get(self.listbox.curselection()[0])\\n except IndexError:\\n raise KeyError(\\\"Nothing selected\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34c3dadd642a1b239b1a06a27ac28726\",\n \"score\": \"0.62645125\",\n \"text\": \"def current_job(user):\\n logs = user.log_set.filter(finish__isnull=True)[:1]\\n if logs:\\n log = logs[0]\\n result = LabelResponse(log.job.name,\\n log.get_duration_display())\\n else:\\n log = user.log_set.latest()\\n result = LabelResponse('Not Working',\\n str(timezone.localtime(log.start).date()))\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a304b5fe9b384b5ce8d6417267e66456\",\n \"score\": \"0.6261799\",\n \"text\": \"def current_state(self) -> Optional[pulumi.Input['JobCurrentState']]:\\n return pulumi.get(self, \\\"current_state\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a696973960bc6764197621cc4a83ad67\",\n \"score\": \"0.6252568\",\n \"text\": \"def get_job(self) -> Union[Dict[Text, Text], CustomJob]:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b93d4e1971fc66390e4efc9f8c09438f\",\n \"score\": \"0.6224905\",\n \"text\": \"def retrieve_job(self, job_id):\\n job = {}\\n with self._lock:\\n if job_id not in self._jobs:\\n return None\\n job = self._jobs[job_id]\\n return job\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba73ba8de2269a2cad9d8eb0750e9399\",\n \"score\": \"0.6220498\",\n \"text\": \"def last_job(self): # TOFIX model the job and return an object instead of dictionary\\n return self._data.get('summary_fields', {}).get('last_job')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94a3a533b396c02b1190034b7e21e977\",\n \"score\": \"0.6218048\",\n \"text\": \"def retrieve_job(self, job_id) -> AzureQuantumJob:\\n return self._provider.get_job(job_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b2d9b92e8816572122138224818709d\",\n \"score\": \"0.61945635\",\n \"text\": \"def get_job_by_id(self, job_id):\\n return self.get_resource(category=SYSTEM, resource_level=JOB,\\n resource_level_id=job_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b46be80f8dd216a7bee66c7f522528e\",\n \"score\": \"0.61901975\",\n \"text\": \"def __jobSelectedHandle(self, job):\\n if job:\\n self.__jobSelectedLineEdit.setText(job.name())\\n else:\\n self.__jobSelectedLineEdit.setText(\\\"\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c4b8260e2688fee5cba617ca1b02eb7\",\n \"score\": \"0.61812234\",\n \"text\": \"def get_selected(self):\\n\\n # if there are items in the list\\n if self.currentIndex() != -1:\\n modelitem = self._dictmodel.itemFromIndex(\\n self._modelindexes[ self.currentIndex() ]\\n )\\n return modelitem\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e04b55768b1e2385bb292f248653450c\",\n \"score\": \"0.61712205\",\n \"text\": \"def job_name(self) -> str:\\n return pulumi.get(self, \\\"job_name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6184e26543769efb4fc3d0c88ae5ab8\",\n \"score\": \"0.6162487\",\n \"text\": \"def get_currently_selected_configuration(self):\\n return self.get_container().currently_selected_configuration\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0a9d2cdb4d593d07b230b49e7c7c5ac\",\n \"score\": \"0.6156431\",\n \"text\": \"def job_id(self):\\n return self._properties.get(\\\"jobReference\\\", {}).get(\\\"jobId\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"573b6befb6f32323630c4b1e95ebc7a9\",\n \"score\": \"0.6136343\",\n \"text\": \"def get_jobs_id(self, ti) -> None:\\n return self.get_hook().get_jobs_id(ti)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17fe9d59c386516b87879a2e119685b2\",\n \"score\": \"0.6074745\",\n \"text\": \"def GetSelection(self):\\n return self.__selected_item\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10d2cae850564ddc6c57257a4b75b7d3\",\n \"score\": \"0.60608876\",\n \"text\": \"def GetSelection(self):\\n \\n return self.selected\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e27824548380df250ad465f6b8d9594a\",\n \"score\": \"0.60586756\",\n \"text\": \"async def get_jobs(jobId: int) -> str: \\n return mngr.getJob(str(jobId))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f0e2fde101431fcae6ef47742544cba\",\n \"score\": \"0.6044516\",\n \"text\": \"def job_id(self) -> JobId:\\r\\n return self._job_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36ae7856d81d2a3194dac50821ca271f\",\n \"score\": \"0.6035905\",\n \"text\": \"def get_current(self):\\n return self.current\",\n \"title\": \"\"\n },\n {\n \"docid\": \"520d86fc8aae26bbe4bb10391ad5b948\",\n \"score\": \"0.60348165\",\n \"text\": \"def get_label(self):\\n return self.job[self.label_key]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb3828c0f8848e71ae6696f2990e718b\",\n \"score\": \"0.6017808\",\n \"text\": \"def jobId(self):\\n returnself._ShREEKConfig.jobId()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5235decd2a9a2db28bec2e88630097db\",\n \"score\": \"0.6017586\",\n \"text\": \"def current_workflow():\\n try:\\n return current_worker_pool.workflow\\n except AttributeError:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28c59d575fa715d62c15403eccd5b89e\",\n \"score\": \"0.60086626\",\n \"text\": \"def job_name(self):\\n return self._stub.List(self._message).job_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f73e5bdf81008391aafc240cec8a3c2\",\n \"score\": \"0.6006928\",\n \"text\": \"def get_state(self):\\n\\t\\treturn Job(SDK.PrlVm_GetState(self.handle)[0])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e3374bbc7061f1cb82a9df8d0622399\",\n \"score\": \"0.6006506\",\n \"text\": \"def retrieve(received_job_id: str) -> Union[Job, None]:\\n # todo: add error handling\\n found_job = db.Jobs().get_by_id(received_job_id)\\n if not found_job:\\n return\\n return found_job\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c67b16d573ad63bd0b8d0a768ef6227\",\n \"score\": \"0.6005077\",\n \"text\": \"def get_job(self, _id):\\n data = {\\n 'class': 'Job',\\n 'id': _id,\\n 'attrs': {},\\n }\\n job = self.db_client.send_request('list', json.dumps(data))\\n\\n return Job(\\n _id=job['id'],\\n _type=job['type'],\\n task=job['task'],\\n command=job['command'],\\n input_parameters=job['inputParameters'],\\n status=job['status'],\\n runner_id=job['runner'],\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38c3db93e876eb6ee1d8016539e54fd4\",\n \"score\": \"0.59741336\",\n \"text\": \"def getCurrentRow(self):\\n item = self.getSelectedItem()\\n if item:\\n return item.row()\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b9933fb10f52a3bd51ab80868a1aa3d\",\n \"score\": \"0.5969369\",\n \"text\": \"def get_jobs_connection(self):\\n return self.m_connection.jobs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbaebc5bd6fe50426934c552c1f7ffc2\",\n \"score\": \"0.5965098\",\n \"text\": \"def current_worker():\\n try:\\n return worker_thread_data.worker\\n except AttributeError:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"878489e7c2a1603e25b729f1341fc9a7\",\n \"score\": \"0.5961013\",\n \"text\": \"def get_current_option(self) -> str:\\n return self.options[self.current_option_index]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c9c4404705a410a6ea6a61cbc01a613\",\n \"score\": \"0.59596807\",\n \"text\": \"def getJobName():\\n return os.environ['LCATR_JOB']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"045565465f0aa8d4f7385cec41a11693\",\n \"score\": \"0.59542495\",\n \"text\": \"def job_type(self):\\n return self._job_type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cfcfa494646e3e8a703b0af529d15b25\",\n \"score\": \"0.5949728\",\n \"text\": \"def GetCurrentItem(self):\\r\\n\\r\\n return self._current\",\n \"title\": \"\"\n },\n {\n \"docid\": \"973ee774435ab56cd5ab0a1d36d9926b\",\n \"score\": \"0.5926058\",\n \"text\": \"def _get_job_id(self) -> str:\\n return self.split_name[2][3:]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"512270a56166f65baecbffc96e2d95eb\",\n \"score\": \"0.5916962\",\n \"text\": \"async def get_job_state(jobId: int) -> State: \\n return mngr.getStateJob(str(jobId))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eff3e39af6070d08e905aa20fa9ce090\",\n \"score\": \"0.59167755\",\n \"text\": \"def find(self, job):\\n for i, candidate_job in pendulate(self.jobs):\\n if candidate_job == job:\\n return i\\n raise ValueError(\\\"Job not found in batch\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9403f019c08356c1fc189609fd60bb60\",\n \"score\": \"0.5916563\",\n \"text\": \"def job_name(self) -> str:\\n return self._step_execution_context.job_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cc211e741a2fe21697242139e70911d\",\n \"score\": \"0.59097177\",\n \"text\": \"def getSelection(self):\\n return self.selection\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e783a91342650ad40ee9a456bf210691\",\n \"score\": \"0.59074605\",\n \"text\": \"def get_job_id(self):\\n return {'job_id': self._job_id}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bd56a3cbdea81175a199406a62cc71a\",\n \"score\": \"0.59040135\",\n \"text\": \"def get_task(self, profile):\\n task = None\\n if self._value.has_option(profile, 'task'):\\n task = self._value.get(profile, 'task')\\n\\n self.logger.info(\\\"%s is selected as task\\\" % task)\\n return task\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e83f2595d6df75f56c2be24e96bfcaea\",\n \"score\": \"0.58960843\",\n \"text\": \"def getCurrent(self):\\n return self.__current\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ccc3b35a0173536aed4513549531203\",\n \"score\": \"0.5889516\",\n \"text\": \"def get_current(self, event=None):\\n childes = self.nb.winfo_children() # return the list objects of child widgets of notebook[tab widget]\\n return childes[self.nb.index('current')].winfo_children()[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84cc4e6ed7bced348b0b1c7b01ed0d52\",\n \"score\": \"0.5887606\",\n \"text\": \"def get_job(self, id, jobstore=None):\\n\\n return self._scheduler.get_job(id, jobstore)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e27689e9b4349ce17f0bd0022e7bda56\",\n \"score\": \"0.5877996\",\n \"text\": \"def get_task(self):\\n return self.queue.get()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8525cc2fb6d0683d168b8e34cfa10804\",\n \"score\": \"0.58687586\",\n \"text\": \"def job_type(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"job_type\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a277c3be41e9a6591244a8554a5611b\",\n \"score\": \"0.5860497\",\n \"text\": \"def GetSelection(self):\\r\\n\\r\\n return self.selection\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8957f0306906c63c116652f12e99f457\",\n \"score\": \"0.58585596\",\n \"text\": \"def get_thread(self):\\n return self.threads[self.thread_id]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19d37f5ca57dcec4c1760f90d8a64851\",\n \"score\": \"0.58583784\",\n \"text\": \"def latest_job():\\n try:\\n return models.SyncJob.objects.latest('start')\\n except models.SyncJob.DoesNotExist:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46f4e1f0b8c9d2bf7c3b3107a67e09fe\",\n \"score\": \"0.58579504\",\n \"text\": \"def current_item(self) -> MenuItem | None:\\n if not self.all_items:\\n return None\\n else:\\n return self.all_items[self.current_option]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"900ccee2e5ea45bd7bffdc7dda61f13d\",\n \"score\": \"0.584532\",\n \"text\": \"def reference(self) -> pulumi.Output['outputs.JobReference']:\\n return pulumi.get(self, \\\"reference\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6a5bcf3e0c88bf1affe903e77cc038e\",\n \"score\": \"0.5831991\",\n \"text\": \"def _retrieve_job_id(job_name, res_id):\\n active_jobs = celery_inspector.active()\\n job_id = _retrieve_task_id(job_name, res_id, active_jobs)\\n if not job_id:\\n reserved_jobs = celery_inspector.reserved()\\n job_id = _retrieve_task_id(job_name, res_id, reserved_jobs)\\n if not job_id:\\n scheduled_jobs = celery_inspector.scheduled()\\n job_id = _retrieve_task_id(job_name, res_id, scheduled_jobs)\\n return job_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f8b9eba1601c0ad75cf1873a5f36f52\",\n \"score\": \"0.5828973\",\n \"text\": \"def job(job_name):\\n ClientID = Job.get_client_id(job_name)\\n return tasks_for_client_job(ClientID, job_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"088cec76e613d82c4abf916190234e8b\",\n \"score\": \"0.58223146\",\n \"text\": \"def get_object(self, queryset=None):\\n # 404 if job doesn't exist\\n try:\\n job = Job.objects.select_related().get(pk=self.kwargs['pk'])\\n except Job.DoesNotExist:\\n raise Http404(\\\"No Job with PK#{} found.\\\".format(self.kwargs['pk']))\\n\\n # Staff can see all jobs\\n if self.request.user.is_staff:\\n return job\\n\\n # Creator can see their own jobs no matter the status\\n if job.creator == self.request.user:\\n return job\\n\\n # For everyone else the job needs to be visible\\n if job.visible:\\n return job\\n\\n # Return None to signal 401 unauthorized\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99be3a98c227ea75ed580c60a5620fa8\",\n \"score\": \"0.5821088\",\n \"text\": \"def getJob(appName, jobId):\\n jobs = db.getJobs(jobId=jobId)\\n job = None if len(jobs) == 0 else jobs[0]\\n k3job = dispatcher.getJob(int(jobId))\\n\\n if job == None:\\n return returnError(\\\"Job ID, %s, does not exist\\\" % jobId, 404)\\n\\n thisjob = dict(job, url=dispatcher.getSandboxURL(jobId))\\n if k3job != None:\\n thisjob['master'] = k3job.master\\n local = os.path.join(webapp.config['UPLOADED_JOBS_DEST'], appName, str(jobId)).encode(encoding='utf8', errors='ignore')\\n path = os.path.join(webapp.config['UPLOADED_JOBS_DEST'], appName, str(jobId),'role.yaml').encode(encoding='utf8', errors='ignore')\\n if os.path.exists(local) and os.path.exists(path):\\n with open(path, 'r') as role:\\n thisjob['roles'] = role.read()\\n else:\\n return returnError(\\\"Job Data no longer exists\\\", 400)\\n\\n thisjob['sandbox'] = sorted (os.listdir(local))\\n\\n if 'application/json' in request.headers['Accept']:\\n return jsonify(thisjob)\\n else:\\n return render_template(\\\"last.html\\\", appName=appName, lastjob=thisjob)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd85fbcf857c13ba9729a8ea751e478e\",\n \"score\": \"0.5818568\",\n \"text\": \"def getSelected(self):\\n\\n return self._streams[self._selectedStream]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0db0d692b30ab8db20386f7587f5100\",\n \"score\": \"0.581538\",\n \"text\": \"def next_and_lock(self):\\n\\n if self._cur_job:\\n log.error(\\\"There is already an active job! Please call done() or failed() on the job first.\\\")\\n return None\\n\\n # Loop until we found a job\\n while True:\\n\\n jobs = self._ls_waiting_jobs()\\n\\n # No more jobs\\n if not jobs:\\n return None\\n\\n if self._rnd_job:\\n job = random.choice(jobs)\\n else:\\n job = sorted(jobs)[0]\\n\\n log.debug(\\\"Selected job: %s\\\" % job)\\n\\n wjob = \\\"%s.%s\\\" % (self._wid, job)\\n\\n src = pjoin(self._jobsdir, \\\"00_waiting\\\", job)\\n dst = pjoin(self._jobsdir, \\\"01_running\\\", wjob)\\n\\n try:\\n self._move(src, dst)\\n except FileNotFoundError:\\n log.warning(\\\"Job %s does not exist anymore. Moving on\\\" % src)\\n continue\\n\\n if not self._worker_sync:\\n return Job(self, \\\"01_running\\\", job)\\n else:\\n\\n job = self._job_worker_selection(job)\\n\\n if job is not None:\\n return job\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f9fd1262ddea417bb84a554bbce314d\",\n \"score\": \"0.58028716\",\n \"text\": \"def selected_item(self) -> MenuItem | None:\\n if self.selected_option == -1:\\n return None\\n else:\\n return self.all_items[self.selected_option]\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":879,"cells":{"query_id":{"kind":"string","value":"02855788f1ab0f7e4223d629dd005483"},"query":{"kind":"string","value":"Recursive function that checks for nonindexable columns."},"positive_passages":{"kind":"list like","value":[{"docid":"8a7947e76ac9931b926d1f3a3c9fbd55","score":"0.5383323","text":"def check_indexable(token_list):\n for tok in token_list.tokens:\n if tok.ttype == tokens.Name:\n col = self.schema.get_column(self.table.value, tok.value)\n if col and not col.indexable:\n raise NotIndexableError()\n elif isinstance(tok, (sql.Comparison, sql.Identifier)):\n check_indexable(tok)","title":""}],"string":"[\n {\n \"docid\": \"8a7947e76ac9931b926d1f3a3c9fbd55\",\n \"score\": \"0.5383323\",\n \"text\": \"def check_indexable(token_list):\\n for tok in token_list.tokens:\\n if tok.ttype == tokens.Name:\\n col = self.schema.get_column(self.table.value, tok.value)\\n if col and not col.indexable:\\n raise NotIndexableError()\\n elif isinstance(tok, (sql.Comparison, sql.Identifier)):\\n check_indexable(tok)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"e3fa63a2b75dd4515481dc104bb244c8","score":"0.6160125","text":"def _check(self, df):\n cols_to_check = []\n try:\n cols_to_check.append(getattr(self, 'field'))\n except AttributeError:\n pass\n try:\n cols_to_check += getattr(self, 'fields')\n except AttributeError:\n pass\n _check_columns(df, cols_to_check)","title":""},{"docid":"41f4981bab0519aa652e7f6bc27d2192","score":"0.61310595","text":"def check_cols_not_present(df, disallowed_cols):\n\n if len(disallowed_cols & set(df.columns)) > 0:\n raise ValueError(\n \"Input metadata file already includes at least one of the \"\n \"following columns: {}\".format(disallowed_cols)\n )","title":""},{"docid":"9c00f30bb00881dda27be3615d504edd","score":"0.60815316","text":"def _check_columns(self, df: pd.DataFrame, col_list: list, additional_str=\"\"):\n\n df_cols = df.columns\n for col in col_list:\n if col not in df_cols:\n print(\"Available colunms:\", df_cols)\n raise KeyError(f\"Column '{col}' doesn't exist in data. {additional_str}\")","title":""},{"docid":"18ea98cbf60a3533d605aecf61976dfb","score":"0.60659856","text":"def _test_no_missing_columns(self, model):\n obj = model()\n for field in obj._meta.fields:\n if field.name == 'id':\n continue\n if _has_default(field):\n continue\n if field.name not in self and field.name not in self.index.names:\n raise PandasLovesPoniesException('missing column: %s' % field.name)\n return True","title":""},{"docid":"bbd5c403b3e3800609f7151c43170526","score":"0.5997924","text":"def find_multi_val_cols(df, ignore_index_col=True, exception_cols=[]):\r\n multi_val_cols = []\r\n first_col = ignore_index_col\r\n for col in df:\r\n if first_col:\r\n first_col = False\r\n continue\r\n else:\r\n if col in exception_cols:\r\n continue\r\n else:\r\n if df[col].nunique() > 1:\r\n multi_val_cols.append(col)\r\n else:\r\n pass\r\n return multi_val_cols","title":""},{"docid":"524ab3b6258fe3dcaa5510f45933c5ab","score":"0.58972174","text":"def _emptyColumn(self, index):\n list = [x[index] for x in self._aliens]\n count = 0\n for spot in list:\n if spot == None:\n count -= 1\n else:\n count += 1\n if count >= 0:\n return False\n else:\n return True","title":""},{"docid":"69eb5f1ab0f68ed85866ced30689a978","score":"0.5878816","text":"def test_is_indexable(self):\n # verify -----------------------\n try:\n self.columns[0]\n except TypeError:\n msg = \"'_ColumnCollection' object does not support indexing\"\n self.fail(msg)\n except IndexError:\n pass","title":""},{"docid":"69eb5f1ab0f68ed85866ced30689a978","score":"0.5878816","text":"def test_is_indexable(self):\n # verify -----------------------\n try:\n self.columns[0]\n except TypeError:\n msg = \"'_ColumnCollection' object does not support indexing\"\n self.fail(msg)\n except IndexError:\n pass","title":""},{"docid":"bcc3efaa1ed1a08a54850274da46f2eb","score":"0.5871422","text":"def _all_nat_check(df):\n return np.all(pd.isnull(df))","title":""},{"docid":"8eed673dbfb0c0b54fba0094851b0601","score":"0.5846668","text":"def test_col_idx_bad(self, api_objects, data):\n with pytest.raises(pytan3.api_objects.exceptions.ModuleError):\n data.columns[\"Nope\"]","title":""},{"docid":"8eed673dbfb0c0b54fba0094851b0601","score":"0.5846668","text":"def test_col_idx_bad(self, api_objects, data):\n with pytest.raises(pytan3.api_objects.exceptions.ModuleError):\n data.columns[\"Nope\"]","title":""},{"docid":"2485e4da00783debc26c3c850372f1d8","score":"0.5808011","text":"def __nonzero__(self):\n return bool(self._columns)","title":""},{"docid":"a2c577628194b9f3df5994966c09e99f","score":"0.5775897","text":"def validate(self):\n first = self.statement.tokens[0].value\n if first != 'SELECT':\n raise UnexpectedError(first)\n elif self.statement.token_next(1).match(tokens.Wildcard, '*'):\n raise WildcardError()\n elif not self.where:\n raise UnexpectedEndError()\n elif not self.table:\n raise UnexpectedError('WHERE')\n\n def check_indexable(token_list):\n \"\"\"Recursive function that checks for non-indexable columns.\"\"\"\n for tok in token_list.tokens:\n if tok.ttype == tokens.Name:\n col = self.schema.get_column(self.table.value, tok.value)\n if col and not col.indexable:\n raise NotIndexableError()\n elif isinstance(tok, (sql.Comparison, sql.Identifier)):\n check_indexable(tok)\n\n check_indexable(self.where)","title":""},{"docid":"1c9b91431a2b18c1e99fa980220819a2","score":"0.5774767","text":"def check_for_missing_columns(df, col_names):\n missing_columns = list(OrderedSet(col_names) - OrderedSet(df.schema.names))\n\n if len(missing_columns) > 0:\n RaiseIt.value_error(missing_columns, df.columns)\n\n return False","title":""},{"docid":"1accd1105a1deb7bbd500dfdc83f7ea0","score":"0.57646555","text":"def missing_key_columns(args, unreconciled):\n error = False\n for column in [args.group_by, args.key_column]:\n if column not in unreconciled.columns:\n error = True\n print('ERROR: \"{}\" is not a column header'.format(column))\n return error","title":""},{"docid":"806c0a2d3888cbd6b2fc360a48b12649","score":"0.57543796","text":"def validate_columns(self, df):\n expected_df_columns = pd.DataFrame(columns=self.expected_columns)\n\n columns_too_many = df.columns.difference(expected_df_columns.columns)\n if not len(columns_too_many) == 0:\n print('The provided dataframe has too many columns:', *columns_too_many, sep='\\n')\n\n columns_too_few = expected_df_columns.columns.difference(df.columns)\n if not len(columns_too_few) == 0:\n print('The provided dataframe is missing the following columns:', *columns_too_few, sep='\\n')\n\n return len(columns_too_many) + len(columns_too_few) == 0","title":""},{"docid":"f31b795808c49920c0eb8bf8b06c5f01","score":"0.5747699","text":"def missing_columns(self):\n return self._row.isnull()","title":""},{"docid":"5df080767c2941dda03c486ba9b47ce4","score":"0.5742666","text":"def verification(df: pd.DataFrame, column_name: str) -> None:\r\n types = []\r\n for index in df.index:\r\n if type(df.loc[index, column_name]) not in types:\r\n types.append(type(df.loc[index, column_name]))\r\n print(column_name, ':\\t', types)","title":""},{"docid":"d5ffe361bcea013654e11771c715bdaa","score":"0.5742278","text":"def check_columns(self, df: pd.DataFrame) -> None:\n\n try:\n df = df.loc[:, [\"group\", self.normalizer, self.target]]\n except KeyError:\n LOGGER.info(\n f\" KeyError. Columns: group, target, an/or normalizer not in table \"\n f\"columns:{df.columns}\"\n )\n # import pdb; pdb.set_trace()\n raise","title":""},{"docid":"4d4cd993dae4ce2d0ace105e836ae8e8","score":"0.57144254","text":"def _is_unreferenced_column_node(parent, node):\n return parent.label() == 'COLUMN_REFERENCE' \\\n and len(parent) > 1 \\\n and parent[1].label() == 'COLUMN_NAMES' \\\n and node.label() == 'COLUMN_NAMES'","title":""},{"docid":"27d1a5422342d4eb826dc324df8c9be4","score":"0.57092535","text":"def _check_nan(df, *columns):\n\n for col in columns:\n if col not in df.columns:\n raise ValueError(\"Column not found: {}\".format(col))\n\n if df[col].isnull().any():\n raise ValueError(\"Column must not contain NaN values: {}\".format(col))","title":""},{"docid":"bb5a9af27fef7ede67606a6e79e876eb","score":"0.5707324","text":"def get_notnull_columns(df: pd.DataFrame, candidates: Iterable):\n return [col for col in candidates\n if col in df and not np.all(df[col].isnull())]","title":""},{"docid":"f893ded9eaaff04d2800bcee88bee7d8","score":"0.57054573","text":"def ignored_column_indicator(self):\n return self.match_results == -2","title":""},{"docid":"8ff13a201a4797a8ec4fbbda969e2b70","score":"0.5684672","text":"def colinspection(df):\n columns = df.columns.tolist()\n for col in columns:\n print(df[col].head())\n# print(5*'//')\n# print('Percentage of Missing data:', df[col].isnull().mean())\n print(10*'----')","title":""},{"docid":"c8b9bb021e9788f04969b6aa07f20c60","score":"0.56765926","text":"def get_non_numerical_cols(df):\n\tnon_numerical_cols = []\n\n\tfor col_name in df.columns:\n\t\tif df[col_name].dtypes not in ['uint8', 'uint32', 'uint64', 'int8', 'int32', 'int64', 'float16', 'float32',\n\t\t\t\t\t\t\t\t\t 'float64']:\n\t\t\tnon_numerical_cols.append(col_name)\n\n\treturn non_numerical_cols","title":""},{"docid":"051b6cbc64f588d9fc3691ea4b71dea0","score":"0.56352067","text":"def handle_missing_threshold(df, prop_required_column = .3, prop_required_row = .9):\n threshold = int(round(prop_required_column*len(df.index),0))\n df.dropna(axis=1, thresh=threshold, inplace=True)\n threshold = int(round(prop_required_row*len(df.columns),0))\n df.dropna(axis=0, thresh=threshold, inplace=True)\n return df","title":""},{"docid":"a51a136d3bfcbe78acd877e1f9e78906","score":"0.5632437","text":"def validate_columns(args, column_types, unreconciled):\n plugins = util.get_plugins('column_types')\n plugin_types = list(plugins.keys())\n\n error = missing_headers(unreconciled, column_types, plugin_types)\n error |= missing_key_columns(args, unreconciled)\n\n if error:\n error_exit(unreconciled, plugin_types)","title":""},{"docid":"4c5440de8118a8e6781f3be6051164c2","score":"0.5624304","text":"def check_missing(df, col): \n return df[pd.isnull(df[col])]","title":""},{"docid":"f95c9658c42145b083486bbb4c5d482c","score":"0.5571803","text":"def _test_invalid_nulls(self, model):\n from django.db.models import fields\n obj = model()\n nonnull_fields = [x for x in obj._meta.fields\n if not x.null and x.name != 'id']\n for field in nonnull_fields:\n if _has_default(field):\n continue\n nonnull_cols_series = _column_getter(self, field.name)\n if nonnull_cols_series.isnull().any():\n error_msg = '%s column contains nulls' % field.name\n raise PandasLovesPoniesException(error_msg)\n return True","title":""},{"docid":"3fc3b4b9e82dc3b85244e676231b9d00","score":"0.55682826","text":"def _any_nat_check(df):\n return np.any(pd.isnull(df))","title":""},{"docid":"842aaf30eeae134892185c43443c5b63","score":"0.5563729","text":"def check_data(self, df, *args):\n column_headers = df.columns\n print(\"Checking input data, all data headers: {}\".format(column_headers))\n for column in args:\n if column not in column_headers:\n print(\"Missing or mis-configured column: {}\".format(column))\n return False\n return True","title":""},{"docid":"674d3c3b8445b44d99fbe88e3b23f7d8","score":"0.5540896","text":"def _continuous_col_finder(df, drop_na = False):\n df = df.copy()\n bin_cols = _binary_col_finder(df, drop_na = drop_na)\n cont_cols = [col for col in df.columns.tolist() if col not in bin_cols]\n \n return cont_cols","title":""},{"docid":"42b52d755ff577ab939d7a4641fe72b5","score":"0.5515423","text":"def validate_data_columns(self, data_columns, min_itemsize, non_index_axes):\n if not len(non_index_axes):\n return []\n\n axis, axis_labels = non_index_axes[0]\n info = self.info.get(axis, {})\n if info.get(\"type\") == \"MultiIndex\" and data_columns:\n raise ValueError(\n f\"cannot use a multi-index on axis [{axis}] with \"\n f\"data_columns {data_columns}\"\n )\n\n # evaluate the passed data_columns, True == use all columns\n # take only valid axis labels\n if data_columns is True:\n data_columns = list(axis_labels)\n elif data_columns is None:\n data_columns = []\n\n # if min_itemsize is a dict, add the keys (exclude 'values')\n if isinstance(min_itemsize, dict):\n existing_data_columns = set(data_columns)\n data_columns = list(data_columns) # ensure we do not modify\n data_columns.extend(\n [\n k\n for k in min_itemsize.keys()\n if k != \"values\" and k not in existing_data_columns\n ]\n )\n\n # return valid columns in the order of our axis\n return [c for c in data_columns if c in axis_labels]","title":""},{"docid":"7ad738750770e34e125efad157a9890b","score":"0.54954255","text":"def search_missing_values(self):\n log.info(\"Searching for missing values\")\n columns_with_missing = self.df.columns[self.df.isnull().any()].tolist()\n if columns_with_missing:\n for col in columns_with_missing:\n self.column_meta[col][\"missing\"] = True\n log.warning(f\"Missing values found in column: {col}\")\n else:\n log.info(\"No missing values found\")","title":""},{"docid":"4e3c30cde06bfc2ab05acae79daba783","score":"0.54807353","text":"def row_has_all_fields(self, row):\n for f in self.mapping:\n if f[0] != '_' and f[1] and not row[f[0]]:\n return False\n return True","title":""},{"docid":"7351ce515279d4551abb01fabab6f203","score":"0.54802614","text":"def test_column_exclude_rules(self):\n col_rules = deepcopy(self.basespecs['iris']['column_rules'])\n col_rules['Species']['exclude'] = ['virginica', 'versicolor']\n dframe_val = DataFrameValidator(data=self.iris_dframe.copy(),\n column_rules=col_rules,\n rules={'drop_duplicates': False})\n cleaned_species = dframe_val.clean()['Species']\n self.assertItemsEqual(cleaned_species.unique().tolist(), ['setosa'])\n self.assertEqual(cleaned_species.shape[0], 50)","title":""},{"docid":"a9226f0091b25bccd15e094eefa8b3de","score":"0.5437251","text":"def check_and_drop_duplicate_columns(dataframe: pd.DataFrame):\n extra_columns = dataframe.columns[dataframe.columns.duplicated()]\n if len(extra_columns) == 0:\n return dataframe\n for column in extra_columns:\n test_equality = dataframe.loc[:, column] == dataframe.loc[:, column]\n if not test_equality.all(axis=None):\n raise ValueError\n return dataframe.loc[:, ~dataframe.columns.duplicated()]","title":""},{"docid":"e29d468a67a26db8825dfc00511fe131","score":"0.5436097","text":"def collect_invalid_members_of(subset_df, missing=subs_missing_sibs):\n subset_df.apply(lambda sub: get_invalid_subset_member(sub, missing),\n axis=\"columns\")","title":""},{"docid":"3edc66b5898cd55b7a8fb4535d9d87de","score":"0.54328555","text":"def validate_columns_names(df, col_names, index=0):\n\n columns = val_to_list(col_names)\n\n if is_list_of_tuples(columns):\n columns = [c[index] for c in columns]\n\n # Remove duplicates in the list\n if is_list_of_strings(columns):\n columns = OrderedSet(columns)\n\n check_for_missing_columns(df, columns)\n\n return True","title":""},{"docid":"64de9c8f0a0a2b1a306871348930e610","score":"0.543095","text":"def test_is_indexable(self):\n # verify -----------------------\n try:\n self.rows[0]\n except TypeError:\n msg = \"'_RowCollection' object does not support indexing\"\n self.fail(msg)\n except IndexError:\n pass","title":""},{"docid":"64de9c8f0a0a2b1a306871348930e610","score":"0.543095","text":"def test_is_indexable(self):\n # verify -----------------------\n try:\n self.rows[0]\n except TypeError:\n msg = \"'_RowCollection' object does not support indexing\"\n self.fail(msg)\n except IndexError:\n pass","title":""},{"docid":"eb07a2d0f1822e472f0d0a7c058e9323","score":"0.54210275","text":"def _check_colname(*columns):\n\n for col in columns:\n if str(col).endswith(\"_\"):\n raise ValueError(\n \"Dict keys are not allowed to end with '_': {}\".format(col)\n )\n\n if \"__\" in str(col):\n raise ValueError(\n \"Dict keys are not allowed to contain '__': {}\".format(col)\n )","title":""},{"docid":"c604dc2a2158947d13ff6de8c64907d6","score":"0.5411608","text":"def check_column_names(columns, *args):\n for arg in args:\n if isinstance(arg, (tuple, list)):\n missing = set(arg) - set(columns)\n if missing:\n raise ValueError(\"Following columns were requested but are \"\n \"not available: %s.\\n\"\n \"All requested columns: %s\\n\"\n \"Available columns: %s\"\n \"\" % (missing, arg, columns))","title":""},{"docid":"e49c6ea7f798d0074137b8114b8fdb89","score":"0.5401765","text":"def test_filter_multiple_invalid_columns(self):\n self.dicom.filter_by_keywords({ \"invalid\" : \"bla\", \"another_invalid_col\" : \"bla\" })\n self.assertEqual(0, self.dicom.metadata.count())","title":""},{"docid":"75924c096bfde057ca6e8a6acb26337c","score":"0.5392577","text":"def _validate_data(df: pd.DataFrame, schema: input_schema.Schema):\n\n for key, value in schema.input_schema_map.items():\n _ = value # TODO(mikebernico) Implement type checking.\n if key not in df.columns:\n schema_keys = list(schema.input_schema_map.keys())\n raise AttributeError(\n f'DataFrame does not contain expected column: {key}. '\n f'Ensure header matches schema keys: {schema_keys}.')","title":""},{"docid":"ff9e9ae9dcd4defcb83d47f9c52d7e05","score":"0.53670734","text":"def fail_struct_and_ancestors(struct, disparate_column, fails=0):\n\n disparate_column[struct.idx] = fails\n if struct.level > 0:\n fail_struct_and_ancestors(struct.parent, disparate_column, fails=fails)","title":""},{"docid":"b382c1957cabdff476ec67b392ae97ae","score":"0.53512156","text":"def check_data_frame(df: pd.DataFrame) -> None:\n\n required_cols = {\n \"Time\",\n \"Exchange\",\n \"Asset Class\",\n \"Product Name\",\n \"Product Code\",\n \"Start Period\",\n \"End Period\",\n \"Maintenance\",\n \"Currency\",\n \"Maint. Vol. Scan\",\n }\n assert required_cols.issubset(df.columns)","title":""},{"docid":"81ff25db8120d5cc58f473ee78e3a6a9","score":"0.5350528","text":"def valid_type_in_col(arch):\r\n for attrib in arch.xpath('//*/@col'):\r\n try:\r\n int(attrib)\r\n except:\r\n return False\r\n return True","title":""},{"docid":"8342f02d0f9f18a9899440e19fe69542","score":"0.53426254","text":"def index_like(index):\n return not (isinstance(index, pd.RangeIndex) and\n index._start == 0 and\n index._stop == len(index) and\n index._step == 1 and index.name is None)","title":""},{"docid":"8342f02d0f9f18a9899440e19fe69542","score":"0.53426254","text":"def index_like(index):\n return not (isinstance(index, pd.RangeIndex) and\n index._start == 0 and\n index._stop == len(index) and\n index._step == 1 and index.name is None)","title":""},{"docid":"93b7986ef6dd3b112286c38fc7d292a4","score":"0.531596","text":"def test_is_indexable(self):\n # verify -----------------------\n try:\n self.cells[0]\n except TypeError:\n msg = \"'_CellCollection' object does not support indexing\"\n self.fail(msg)\n except IndexError:\n pass","title":""},{"docid":"93b7986ef6dd3b112286c38fc7d292a4","score":"0.531596","text":"def test_is_indexable(self):\n # verify -----------------------\n try:\n self.cells[0]\n except TypeError:\n msg = \"'_CellCollection' object does not support indexing\"\n self.fail(msg)\n except IndexError:\n pass","title":""},{"docid":"1f699f42b9373549e2e92c9efd322583","score":"0.5309775","text":"def findSingleValueColumns(originalData):\n uselessColumns = [];\n\n for i in range(0, len(originalData[0])):\n isUsefulColumn = False;\n firstEntry = originalData[0][i];\n for j in range(1, len(originalData)):\n if (firstEntry != originalData[j][i]):\n isUsefulColumn = True;\n break;\n\n if (not isUsefulColumn):\n uselessColumns.append(i);\n \n return uselessColumns;","title":""},{"docid":"3989fdac9dd1eadd0ef26c8e169c58ef","score":"0.52963823","text":"def check_for_empty_index(comparable: Compare):\n index_column = comparable.data_frame[idx.get_index_name(comparable)]\n comparable.empty_index = [index for index, value in enumerate(index_column)\n if value == Field.empty_string.value\n or value != value or value is None]","title":""},{"docid":"601ceb455c2239064745f4e8c861c1d9","score":"0.5295186","text":"def check_valid_column(observation):\n\n valid_columns = set(columns)\n keys = set(observation.keys())\n\n if len(valid_columns - keys) > 0:\n missing = valid_columns - keys\n error = \"Missing columns: {}\".format(missing)\n return False, error\n\n if len(keys - valid_columns) > 0:\n extra = keys - valid_columns\n error = \"Unrecognized columns provided: {}\".format(extra)\n return False, error\n\n if len(keys - valid_columns) == 0:\n return True, \"\"","title":""},{"docid":"40a868f60e69c3431bd9b2d3969fc8d9","score":"0.5292685","text":"def _index_check(self):\r\n for temp in self.data_dict.values():\r\n if (self.data_dict['open'].index.difference(temp.index).shape[0]\r\n != 0) or (self.data_dict['open'].shape != temp.shape):\r\n \r\n raise ValueError('Dataframe indices are not aligned')","title":""},{"docid":"220b6ae2239ae2b79c1866ae8cd2e57a","score":"0.528538","text":"def test_df_iterate_over_columns2(self):\n from sdc.hiframes.api import get_nan_mask\n\n @self.jit\n def jitted_func():\n cols = ('A', 'B', 'C', 'D')\n df = pd.DataFrame({\n 'A': ['a', 'b', None, 'a', '', None, 'b'],\n 'B': ['a', 'b', 'd', 'a', '', 'c', 'b'],\n 'C': [np.nan, 1, 2, 1, np.nan, 2, 1],\n 'D': [1, 2, 9, 5, 2, 1, 0]\n })\n res_nan_mask = np.zeros(len(df), dtype=np.bool_)\n for col in literal_unroll(cols):\n res_nan_mask += get_nan_mask(df[col].values)\n return res_nan_mask\n\n # expected is a boolean mask of df rows that have None values\n expected = np.asarray([True, False, True, False, True, True, False])\n result = jitted_func()\n np.testing.assert_array_equal(result, expected)","title":""},{"docid":"b0e148ecaf122b56e0f98404fd395b26","score":"0.5284561","text":"def is_valid(self,row):\n # Do we know how to deal with all the columns?\n coldiff = set(row.columns) - set(self.COLUMN_NAMES)\n if coldiff:\n raise Exception(\"Row contains unknown columns: \" + str(coldiff) )","title":""},{"docid":"ff3debd100e78e5ef90274f68d69a6b1","score":"0.5276152","text":"def ColFilter(col):\n return not hiddencols or col not in hiddencols","title":""},{"docid":"94c5574835260a85edd84885fef2c151","score":"0.527597","text":"def __validIndex__(self, i):\n try:\n int(i) # Not great here as it would pass floats and prolly others\n except ValueError:\n return False\n return True","title":""},{"docid":"c11d1af3340601b79646c0035db15ca1","score":"0.5256806","text":"def validate_data_columns(self, data_columns, min_itemsize):\r\n\r\n if not len(self.non_index_axes):\r\n return []\r\n\r\n axis, axis_labels = self.non_index_axes[0]\r\n info = self.info.get(axis, dict())\r\n if info.get('type') == 'MultiIndex' and data_columns:\r\n raise ValueError(\"cannot use a multi-index on axis [{0}] with \"\r\n \"data_columns {1}\".format(axis, data_columns))\r\n\r\n # evaluate the passed data_columns, True == use all columns\r\n # take only valide axis labels\r\n if data_columns is True:\r\n data_columns = axis_labels\r\n elif data_columns is None:\r\n data_columns = []\r\n\r\n # if min_itemsize is a dict, add the keys (exclude 'values')\r\n if isinstance(min_itemsize, dict):\r\n\r\n existing_data_columns = set(data_columns)\r\n data_columns.extend([\r\n k for k in min_itemsize.keys()\r\n if k != 'values' and k not in existing_data_columns\r\n ])\r\n\r\n # return valid columns in the order of our axis\r\n return [c for c in data_columns if c in axis_labels]","title":""},{"docid":"751177ea15f5dca1040a232b69cc189f","score":"0.52445453","text":"def check_valid_column(observation):\n \n valid_columns = {\n \"SubjectRaceCode\",\n \"SubjectSexCode\",\n \"SubjectEthnicityCode\",\n \"StatuteReason\", \n \"InterventionReasonCode\", \n \"ResidentIndicator\", \n \"SearchAuthorizationCode\",\n \"SubjectAge\",\n \"hour\",\n \"day_of_week\",\n }\n \n keys = set(observation.keys())\n \n if len(valid_columns - keys) > 0: \n missing = valid_columns - keys\n error = \"Missing columns: {}\".format(missing)\n return False, error\n \n if len(keys - valid_columns) > 0: \n extra = keys - valid_columns\n error = \"Unrecognized columns provided: {}\".format(extra)\n return False, error \n\n return True, \"\"","title":""},{"docid":"1dcc38d104d90f34739b5e7bfef05e15","score":"0.52435005","text":"def fn_check_missing_data(df):\r\n return df.isnull().sum().sort_values(ascending=False)","title":""},{"docid":"f133da545a2c8f390d9d2ed08bdec144","score":"0.5239565","text":"def boolean_col_expressions(self):\r\n return skip_if([\r\n no_support('firebird', 'not supported by database'),\r\n no_support('oracle', 'not supported by database'),\r\n no_support('mssql', 'not supported by database'),\r\n no_support('sybase', 'not supported by database'),\r\n no_support('maxdb', 'FIXME: verify not supported by database'),\r\n no_support('informix', 'not supported by database'),\r\n ])","title":""},{"docid":"73629530698a773e87a8d298f85c31a4","score":"0.5235513","text":"def format_disjunctive_column(comparable_a: Compare, comparable_b: Compare):\n for index, header in enumerate(comparable_a.header):\n\n is_not_index = not header_validator.is_index(comparable_a, header)\n is_not_in_other = not header_validator.is_column(comparable_b, header)\n is_empty_type = header[Field.column_type.value] == Field.empty_string.value\n is_not_mapped = not header_validator.is_mapped(comparable_a, header)\n\n is_disjunctive = all([is_not_index, is_not_in_other, is_empty_type, is_not_mapped])\n\n if is_disjunctive:\n comparable_a.header[index][Field.column_type.value] = Field.disjunctive.value\n comparable_a.header[index][Field.column_name.value] = change_value(comparable_a, index)","title":""},{"docid":"8d61c2dd845c44d1c6299ca9024c1227","score":"0.5213469","text":"def test_multiple_cols_incl_no_op():\n assert json_to_table(data, [\"name\", \"adict_col\"]).equals(\n pd.DataFrame(\n [\n {\n \"name\": \"blah\",\n \"adict_col.a\": 1,\n \"adict_col.b.c\": 1,\n \"list_col\": [{\"a\": 1}, {\"a\": 2}],\n \"adict_col\": {\"a\": 1, \"b\": {\"c\": 1}},\n \"bdict_list_col\": {\"a\": [{\"b\": 1}, {\"b\": 2}]},\n }\n ]\n )\n )","title":""},{"docid":"57d2fd28e6347de38594e9da2c99c28f","score":"0.52077776","text":"def test_filter_invalid_column(self):\n self.dicom.filter_by_keywords({ \"invalid keyword\" : \"value\" })\n self.assertEqual(0, self.dicom.metadata.count())","title":""},{"docid":"59830e994f7cf540c4eee242768e673f","score":"0.520426","text":"def notnull(self) -> DataFrame:\n return ~self.isna()","title":""},{"docid":"3583f74ae4057d4ba2f5d84abbc105ed","score":"0.52028775","text":"def check_column(sudoku, n, pos,inicial):\n columna = sudoku[:,pos[1]]\n contador = 0\n for i in columna:\n if i == n:\n contador += 1\n \n if inicial:\n if contador == 1:\n return True\n else:\n return False \n else:\n if contador >= 1:\n return False\n else:\n return True","title":""},{"docid":"0bcaa9767ea24b71f9589d3f5e4619b3","score":"0.518393","text":"def check_finite(dataframe, file, errorfile, exclude_cols=[]):\n nans = False\n df = dataframe.drop(list(set(exclude_cols).intersection(set(dataframe.columns))), axis=1)\n column_sums = np.sum(~np.isfinite(df))\n if column_sums.sum() > 0:\n nans = True\n errorfile.write('{}:\\n'.format(file))\n errorfile.write('Number of non-finite by column:\\n')\n errorfile.write(column_sums.to_string())\n errorfile.write('\\nIndices of non-finite values:\\n')\n isnans = np.isnan(df)\n inds = dict([(c, df.ix[isnans[c].values, c].index.tolist()) for c in df.columns])\n for k, v in inds.iteritems():\n if len(v) > 0:\n errorfile.write('{}:'.format(k))\n for i in v:\n errorfile.write(' {}'.format(i))\n errorfile.write('\\n')\n errorfile.write('\\n')\n return nans","title":""},{"docid":"3fc0391e82aea940f6eebf46b41f2097","score":"0.5182957","text":"def test_null_rows(fast_out, df_name, thresh):\n # These are columns that only exist in earlier years\n pv.no_null_rows(\n df=fast_out.__getattribute__(df_name)(),\n df_name=df_name,\n thresh=thresh,\n )","title":""},{"docid":"55b74f2ff1dbe073bdeeed39b8f41be5","score":"0.51813966","text":"def get_cols_with_negative_values(df: DataFrame) -> list:\n\tnumCols = get_numerical_cols(df)\n\treturn df[numCols].columns[(df[numCols] < 0).any()].tolist()","title":""},{"docid":"6554b9d79c4848925ffc41d91c58da5c","score":"0.518076","text":"def test_raises_on_idx_out_of_range(self):\n with self.assertRaises(IndexError):\n self.columns[9]","title":""},{"docid":"6554b9d79c4848925ffc41d91c58da5c","score":"0.518076","text":"def test_raises_on_idx_out_of_range(self):\n with self.assertRaises(IndexError):\n self.columns[9]","title":""},{"docid":"ffaf8bc12e32eebc46c123561a14eaf6","score":"0.51789826","text":"def print_invalid_rows(gdf):\n for i, row in gdf.iterrows():\n if not row['geometry'].is_valid:\n print('not valid index', i, '\\n', row)","title":""},{"docid":"462234af756e770b2cbb7443dcb3a5fa","score":"0.51781344","text":"def test_nan(self):\n for i in list(DATA.columns):\n self.assertTrue(not pd.isnull(DATA[i]).any())","title":""},{"docid":"d41bacbdc0607a7ff1b5383e8cc8467d","score":"0.51697177","text":"def test_cols_negative(self):\n self.assertRaises(BaseException, basic.alphanum, -1)","title":""},{"docid":"62542c96fc8123f5522923129c598331","score":"0.51590306","text":"def sanity_checker(self, verbose = True):\n for col in self.bcols:\n if col not in self.cat.colnames:\n print(f'Warning: {col} is missing in catalogue Table')\n raise Exception('Missing Column')\n if verbose:\n print('Checking catalogue columns (ra, dec, parallax, pmra, phot_g_mean_mag, etc) - OK')\n self.add_extra_cols(verbose = verbose)","title":""},{"docid":"47e9a1c660ddf4b7604819f46667f362","score":"0.5158693","text":"def missing_headers(unreconciled, column_types, plugin_types):\n error = False\n for column, column_type in column_types.items():\n if column not in unreconciled.columns:\n error = True\n print('ERROR: \"{}\" is not a column header'.format(column))\n if column_type['type'] not in plugin_types:\n error = True\n print('ERROR: \"{}\" is not a column type'.format(\n column_type['type']))\n return error","title":""},{"docid":"8d1f84015c7f809bd23875eacb2cf7a4","score":"0.51507735","text":"def check_cols_present(df, required_cols):\n\n if len(required_cols & set(df.columns)) < len(required_cols):\n raise ValueError(\n \"Input metadata file must include the following \"\n \"columns: {}\".format(required_cols)\n )","title":""},{"docid":"930d36fd1945365e7a127181adb85b38","score":"0.5149544","text":"def check_column(\n df: pd.DataFrame, old_column_names: List, present: bool = True\n):\n for column_name in old_column_names:\n if present:\n if column_name not in df.columns:\n raise ValueError(\n f\"{column_name} not present in dataframe columns!\"\n )\n else: # Tests for exclusion\n if column_name in df.columns:\n raise ValueError(\n f\"{column_name} already present in dataframe columns!\"\n )","title":""},{"docid":"7d6695bbf5767a28dbc46539990c2cd6","score":"0.5132296","text":"def check_valid_column(observation):\n \n valid_columns = {\n \"observation_id\",\n \"Type\",\n \"Date\",\n \"Part of a policing operation\",\n \"Latitude\",\n \"Longitude\",\n \"Gender\",\n \"Age range\",\n \"Officer-defined ethnicity\",\n \"Legislation\",\n \"Object of search\",\n \"station\"\n}\n\n keys = set(observation.keys())\n \n if len(valid_columns - keys) > 0: \n missing = valid_columns - keys\n error = \"Missing columns: {}\".format(missing)\n return False, error\n \n if len(keys - valid_columns) > 0: \n extra = keys - valid_columns\n error = \"Unrecognized columns provided: {}\".format(extra)\n return False, error \n\n return True, \"\"","title":""},{"docid":"2e7497fdf4666e0af31ba55039d1a7e0","score":"0.5127166","text":"def _check_data_length(\n self,\n columns: Sequence[Hashable],\n data: Sequence[ArrayLike],\n ) -> None:\n if not self.index_col and len(columns) != len(data) and columns:\n empty_str = is_object_dtype(data[-1]) and data[-1] == \"\"\n # error: No overload variant of \"__ror__\" of \"ndarray\" matches\n # argument type \"ExtensionArray\"\n empty_str_or_na = empty_str | isna(data[-1]) # type: ignore[operator]\n if len(columns) == len(data) - 1 and np.all(empty_str_or_na):\n return\n warnings.warn(\n \"Length of header or names does not match length of data. This leads \"\n \"to a loss of data with index_col=False.\",\n ParserWarning,\n stacklevel=find_stack_level(),\n )","title":""},{"docid":"c824c10d4001f501f292582b4e7896dd","score":"0.5123544","text":"def test_column_values(self):\n for column in self.table.columns:\n self.assertEqual(len(column.values), 0)","title":""},{"docid":"a16d7cdee4929b20a0e5e4c9ef3b5cad","score":"0.5120725","text":"def unmatched_column_indicator(self):\n return self.match_results == -1","title":""},{"docid":"e94a86eba0a9c73c2665950a7bbf9049","score":"0.51171285","text":"def cell_is_not_empty(row, column_id):\n for cell in row.cells:\n if cell.column_id == column_id:\n if cell.value:\n return True\n else:\n return False","title":""},{"docid":"30e08e12955e3bdb38f55464b8919957","score":"0.51115584","text":"def is_admissible(index, index_set):\n back_neighbors = backward_neighbors(index)\n for ind_b in back_neighbors:\n if ind_b not in index_set:\n return False\n return True","title":""},{"docid":"be1fcb412111a2ae440729ca099cbe20","score":"0.5111317","text":"def is_indexed(self):\r\n try:\r\n return getattr(self.table.cols, self.cname).is_indexed\r\n except:\r\n False","title":""},{"docid":"eaf4c0d9fdc1bfb2837a3d3f4df8881f","score":"0.51064456","text":"def count_missing(df, is_print=True, return_df=False):\n \n nans_l = {}\n fir_valid = dict().fromkeys(df.columns)\n last_valid = dict().fromkeys(df.columns)\n \n for col in df.columns:\n ts = df[[col]].copy()\n fir_ix = ts.first_valid_index()\n if fir_ix is not None:\n fir_ix = ts.index.get_loc(fir_ix)\n last_ix = ts.index.get_loc(ts.last_valid_index())\n ts = ts.iloc[fir_ix:last_ix+1, :]\n #tuple for cnt nan and no-nan\n nans_l[col] = (ts.isna().sum().values[0], ts.notnull().sum().values[0])\n #to print first and last valid indexes\n fir_valid[col] = ts.first_valid_index()\n last_valid[col] = ts.last_valid_index()\n if is_print: \n columns_names = 'Element | Count NaN | Count no-NaN | First valid date| Last valid date'\n print(' '*(len(columns_names)//3) + 'Some information about data')\n print('-'*len(columns_names))\n print(columns_names)\n print('-'*len(columns_names))\n for col, val in nans_l.items():\n fir_val_ix = str(fir_valid[col]).split(' ')[0]\n last_val_ix = str(last_valid[col]).split(' ')[0]\n cnt_nan = val[0]\n cnt_nonan = val[1]\n print(f'{col:<7}{cnt_nan:>8}{cnt_nonan:>15}{fir_val_ix:>18}{last_val_ix:>18}')\n\n print(\"\\nP.S. valid date means that before or after this date there're only missing values.\\n\\\nOf course nonvalid dates are omitted\")\n if return_df:\n i = 0\n df = pd.DataFrame(columns=['Element', 'Count NaN', 'Count no-NaN', 'First valid date', 'Last valid date'])\n for col, val in nans_l.items():\n fir_val_ix = str(fir_valid[col]).split(' ')[0]\n last_val_ix = str(last_valid[col]).split(' ')[0]\n cnt_nan = val[0]\n cnt_nonan = val[1]\n \n df.loc[i] = [col, cnt_nan, cnt_nonan, fir_val_ix, last_val_ix]\n i += 1\n \n return df","title":""},{"docid":"8bad98b811b467f84c095f3f084ee903","score":"0.5100679","text":"def check_input_df(input_df, expected_cols):\n\n if all(col in input_df.columns for col in expected_cols):\n pass\n else:\n raise ValueError(\n \"Input must be of type DataFrame and contain the following columns: {}\".format(\n \", \".join(expected_cols)\n )\n )","title":""},{"docid":"6f4cf733fb3c1935f8166f9f8d58507e","score":"0.50980073","text":"def sanitize_df(df: pd.DataFrame) -> pd.DataFrame:\n # Delete empty rows\n for i in df.index.tolist()[-1::-1]:\n if df.iloc[i].isna().all():\n df.drop(i, inplace=True)\n else:\n break\n # Delete empty columns\n cols = []\n for column in df:\n if not df[column].isnull().all():\n cols.append(column)\n df = df[cols]\n return df","title":""},{"docid":"e6ee1a04f6a6e56a8301fa2d7140cdfb","score":"0.5087057","text":"def _dict_validation(self, data: DataFrame, verbose: bool = False) -> DataFrame:\n\n dict_column_valid_values = self._read_dict_column_valid_values()\n\n if not dict_column_valid_values:\n if verbose:\n print(f'\\t| No columns to clean.')\n\n print(f'\\t| Row count: {data.count()} before cleaning')\n for column, values in dict_column_valid_values.items():\n if column in data.columns:\n data = data.filter(data[column].isin(values))\n if verbose:\n print(f'\\t| Row count: {data.count()}, after cleaning column: {column}')\n\n return data","title":""},{"docid":"369605ce9128255a6fc840b2252e3193","score":"0.50847715","text":"def test_column_presence(profile: pd.DataFrame):\n KMCPProfile.validate(profile)","title":""},{"docid":"3e682d51600e6609a33d18d8d2bbe52b","score":"0.50722545","text":"def apply(self, data_source):\r\n cols_with_nan = []\r\n for column in self.columns:\r\n if data_source[column].isnull().values.any():\r\n cols_with_nan.append(column)\r\n if cols_with_nan:\r\n raise ValidDataException(\r\n \"Columns containing NaN values\", cols_with_nan)\r\n\r\n # ============== Possible additional dataset checks =====================\r","title":""},{"docid":"c72b1077f6c223737f1279516169c86e","score":"0.50645167","text":"def drop_mostly_missing_cols(hpdf):\n copy = hpdf.data.copy()\n # drop columns with more than 20% values missing\n notna_col_mask = ~ (copy.isna().sum()/len(copy) > 0.20)\n notna_col_mask.loc['SalePrice'] = True\n copy = copy.loc[:, notna_col_mask]\n # drop columns associated with those\n copy.drop(columns=['MiscVal'])\n return copy","title":""},{"docid":"236290a51636775aa37a8e2a005e3d55","score":"0.50597477","text":"def test_invalid_column_as_index_raises_keyerror(self):\n self.assertRaises(KeyError, rad.preprocess, self.frame, [\"invalid\"])","title":""},{"docid":"6912649baf46bbbe7ccc5166b1f4e012","score":"0.50557643","text":"def test_df_iterate_over_columns1(self):\n from sdc.hiframes.pd_dataframe_ext import get_dataframe_data\n from sdc.hiframes.api import get_nan_mask\n\n @self.jit\n def jitted_func():\n df = pd.DataFrame({\n 'A': ['a', 'b', None, 'a', '', None, 'b'],\n 'B': ['a', 'b', 'd', 'a', '', 'c', 'b'],\n 'C': [np.nan, 1, 2, 1, np.nan, 2, 1],\n 'D': [1, 2, 9, 5, 2, 1, 0]\n })\n\n # tuple of literals has to be created in a jitted function, otherwise\n # col_id won't be literal and unboxing in get_dataframe_data won't compile\n column_ids = (0, 1, 2, 3)\n res_nan_mask = np.zeros(len(df), dtype=np.bool_)\n for col_id in literal_unroll(column_ids):\n res_nan_mask += get_nan_mask(get_dataframe_data(df, col_id))\n return res_nan_mask\n\n # expected is a boolean mask of df rows that have None values\n expected = np.asarray([True, False, True, False, True, True, False])\n result = jitted_func()\n np.testing.assert_array_equal(result, expected)","title":""},{"docid":"0b9aa472e431a4ec17013f374b59b55e","score":"0.5053048","text":"def check_index(self, index):\n\n if index.bottom != 1:\n pass\n #Only access arrays with whole indices!\n elif index.top >= len(self):\n pass\n #Array out of bounds error!\n elif index.sign == -1:\n pass\n #Indexes can't be negative!","title":""},{"docid":"1af4f5770eb242513967f65b95444e88","score":"0.50512403","text":"def _is_valid_erp(d , / ):\r\n if not hasattr(d, \"columns\"): \r\n raise TypeError (\r\n \"ERP 'resistivity' and station measurement data expect\"\r\n f\" to be arranged in a dataframe. Got {type (d).__name__!r}\"\r\n )\r\n return not len(d) ==0 and ('resistivity' and 'station') in d.columns","title":""},{"docid":"6766aa344d9214a4f55119bbdcaecec1","score":"0.5050591","text":"def all_better(self):\n df = self.compare_with_soa()\n idxs = [all(df[col] > 0) for col in df.columns]\n\n return df.iloc[:, idxs]","title":""}],"string":"[\n {\n \"docid\": \"e3fa63a2b75dd4515481dc104bb244c8\",\n \"score\": \"0.6160125\",\n \"text\": \"def _check(self, df):\\n cols_to_check = []\\n try:\\n cols_to_check.append(getattr(self, 'field'))\\n except AttributeError:\\n pass\\n try:\\n cols_to_check += getattr(self, 'fields')\\n except AttributeError:\\n pass\\n _check_columns(df, cols_to_check)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41f4981bab0519aa652e7f6bc27d2192\",\n \"score\": \"0.61310595\",\n \"text\": \"def check_cols_not_present(df, disallowed_cols):\\n\\n if len(disallowed_cols & set(df.columns)) > 0:\\n raise ValueError(\\n \\\"Input metadata file already includes at least one of the \\\"\\n \\\"following columns: {}\\\".format(disallowed_cols)\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c00f30bb00881dda27be3615d504edd\",\n \"score\": \"0.60815316\",\n \"text\": \"def _check_columns(self, df: pd.DataFrame, col_list: list, additional_str=\\\"\\\"):\\n\\n df_cols = df.columns\\n for col in col_list:\\n if col not in df_cols:\\n print(\\\"Available colunms:\\\", df_cols)\\n raise KeyError(f\\\"Column '{col}' doesn't exist in data. {additional_str}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18ea98cbf60a3533d605aecf61976dfb\",\n \"score\": \"0.60659856\",\n \"text\": \"def _test_no_missing_columns(self, model):\\n obj = model()\\n for field in obj._meta.fields:\\n if field.name == 'id':\\n continue\\n if _has_default(field):\\n continue\\n if field.name not in self and field.name not in self.index.names:\\n raise PandasLovesPoniesException('missing column: %s' % field.name)\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbd5c403b3e3800609f7151c43170526\",\n \"score\": \"0.5997924\",\n \"text\": \"def find_multi_val_cols(df, ignore_index_col=True, exception_cols=[]):\\r\\n multi_val_cols = []\\r\\n first_col = ignore_index_col\\r\\n for col in df:\\r\\n if first_col:\\r\\n first_col = False\\r\\n continue\\r\\n else:\\r\\n if col in exception_cols:\\r\\n continue\\r\\n else:\\r\\n if df[col].nunique() > 1:\\r\\n multi_val_cols.append(col)\\r\\n else:\\r\\n pass\\r\\n return multi_val_cols\",\n \"title\": \"\"\n },\n {\n \"docid\": \"524ab3b6258fe3dcaa5510f45933c5ab\",\n \"score\": \"0.58972174\",\n \"text\": \"def _emptyColumn(self, index):\\n list = [x[index] for x in self._aliens]\\n count = 0\\n for spot in list:\\n if spot == None:\\n count -= 1\\n else:\\n count += 1\\n if count >= 0:\\n return False\\n else:\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69eb5f1ab0f68ed85866ced30689a978\",\n \"score\": \"0.5878816\",\n \"text\": \"def test_is_indexable(self):\\n # verify -----------------------\\n try:\\n self.columns[0]\\n except TypeError:\\n msg = \\\"'_ColumnCollection' object does not support indexing\\\"\\n self.fail(msg)\\n except IndexError:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69eb5f1ab0f68ed85866ced30689a978\",\n \"score\": \"0.5878816\",\n \"text\": \"def test_is_indexable(self):\\n # verify -----------------------\\n try:\\n self.columns[0]\\n except TypeError:\\n msg = \\\"'_ColumnCollection' object does not support indexing\\\"\\n self.fail(msg)\\n except IndexError:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bcc3efaa1ed1a08a54850274da46f2eb\",\n \"score\": \"0.5871422\",\n \"text\": \"def _all_nat_check(df):\\n return np.all(pd.isnull(df))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8eed673dbfb0c0b54fba0094851b0601\",\n \"score\": \"0.5846668\",\n \"text\": \"def test_col_idx_bad(self, api_objects, data):\\n with pytest.raises(pytan3.api_objects.exceptions.ModuleError):\\n data.columns[\\\"Nope\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8eed673dbfb0c0b54fba0094851b0601\",\n \"score\": \"0.5846668\",\n \"text\": \"def test_col_idx_bad(self, api_objects, data):\\n with pytest.raises(pytan3.api_objects.exceptions.ModuleError):\\n data.columns[\\\"Nope\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2485e4da00783debc26c3c850372f1d8\",\n \"score\": \"0.5808011\",\n \"text\": \"def __nonzero__(self):\\n return bool(self._columns)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2c577628194b9f3df5994966c09e99f\",\n \"score\": \"0.5775897\",\n \"text\": \"def validate(self):\\n first = self.statement.tokens[0].value\\n if first != 'SELECT':\\n raise UnexpectedError(first)\\n elif self.statement.token_next(1).match(tokens.Wildcard, '*'):\\n raise WildcardError()\\n elif not self.where:\\n raise UnexpectedEndError()\\n elif not self.table:\\n raise UnexpectedError('WHERE')\\n\\n def check_indexable(token_list):\\n \\\"\\\"\\\"Recursive function that checks for non-indexable columns.\\\"\\\"\\\"\\n for tok in token_list.tokens:\\n if tok.ttype == tokens.Name:\\n col = self.schema.get_column(self.table.value, tok.value)\\n if col and not col.indexable:\\n raise NotIndexableError()\\n elif isinstance(tok, (sql.Comparison, sql.Identifier)):\\n check_indexable(tok)\\n\\n check_indexable(self.where)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c9b91431a2b18c1e99fa980220819a2\",\n \"score\": \"0.5774767\",\n \"text\": \"def check_for_missing_columns(df, col_names):\\n missing_columns = list(OrderedSet(col_names) - OrderedSet(df.schema.names))\\n\\n if len(missing_columns) > 0:\\n RaiseIt.value_error(missing_columns, df.columns)\\n\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1accd1105a1deb7bbd500dfdc83f7ea0\",\n \"score\": \"0.57646555\",\n \"text\": \"def missing_key_columns(args, unreconciled):\\n error = False\\n for column in [args.group_by, args.key_column]:\\n if column not in unreconciled.columns:\\n error = True\\n print('ERROR: \\\"{}\\\" is not a column header'.format(column))\\n return error\",\n \"title\": \"\"\n },\n {\n \"docid\": \"806c0a2d3888cbd6b2fc360a48b12649\",\n \"score\": \"0.57543796\",\n \"text\": \"def validate_columns(self, df):\\n expected_df_columns = pd.DataFrame(columns=self.expected_columns)\\n\\n columns_too_many = df.columns.difference(expected_df_columns.columns)\\n if not len(columns_too_many) == 0:\\n print('The provided dataframe has too many columns:', *columns_too_many, sep='\\\\n')\\n\\n columns_too_few = expected_df_columns.columns.difference(df.columns)\\n if not len(columns_too_few) == 0:\\n print('The provided dataframe is missing the following columns:', *columns_too_few, sep='\\\\n')\\n\\n return len(columns_too_many) + len(columns_too_few) == 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f31b795808c49920c0eb8bf8b06c5f01\",\n \"score\": \"0.5747699\",\n \"text\": \"def missing_columns(self):\\n return self._row.isnull()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5df080767c2941dda03c486ba9b47ce4\",\n \"score\": \"0.5742666\",\n \"text\": \"def verification(df: pd.DataFrame, column_name: str) -> None:\\r\\n types = []\\r\\n for index in df.index:\\r\\n if type(df.loc[index, column_name]) not in types:\\r\\n types.append(type(df.loc[index, column_name]))\\r\\n print(column_name, ':\\\\t', types)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5ffe361bcea013654e11771c715bdaa\",\n \"score\": \"0.5742278\",\n \"text\": \"def check_columns(self, df: pd.DataFrame) -> None:\\n\\n try:\\n df = df.loc[:, [\\\"group\\\", self.normalizer, self.target]]\\n except KeyError:\\n LOGGER.info(\\n f\\\" KeyError. Columns: group, target, an/or normalizer not in table \\\"\\n f\\\"columns:{df.columns}\\\"\\n )\\n # import pdb; pdb.set_trace()\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d4cd993dae4ce2d0ace105e836ae8e8\",\n \"score\": \"0.57144254\",\n \"text\": \"def _is_unreferenced_column_node(parent, node):\\n return parent.label() == 'COLUMN_REFERENCE' \\\\\\n and len(parent) > 1 \\\\\\n and parent[1].label() == 'COLUMN_NAMES' \\\\\\n and node.label() == 'COLUMN_NAMES'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27d1a5422342d4eb826dc324df8c9be4\",\n \"score\": \"0.57092535\",\n \"text\": \"def _check_nan(df, *columns):\\n\\n for col in columns:\\n if col not in df.columns:\\n raise ValueError(\\\"Column not found: {}\\\".format(col))\\n\\n if df[col].isnull().any():\\n raise ValueError(\\\"Column must not contain NaN values: {}\\\".format(col))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb5a9af27fef7ede67606a6e79e876eb\",\n \"score\": \"0.5707324\",\n \"text\": \"def get_notnull_columns(df: pd.DataFrame, candidates: Iterable):\\n return [col for col in candidates\\n if col in df and not np.all(df[col].isnull())]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f893ded9eaaff04d2800bcee88bee7d8\",\n \"score\": \"0.57054573\",\n \"text\": \"def ignored_column_indicator(self):\\n return self.match_results == -2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ff13a201a4797a8ec4fbbda969e2b70\",\n \"score\": \"0.5684672\",\n \"text\": \"def colinspection(df):\\n columns = df.columns.tolist()\\n for col in columns:\\n print(df[col].head())\\n# print(5*'//')\\n# print('Percentage of Missing data:', df[col].isnull().mean())\\n print(10*'----')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8b9bb021e9788f04969b6aa07f20c60\",\n \"score\": \"0.56765926\",\n \"text\": \"def get_non_numerical_cols(df):\\n\\tnon_numerical_cols = []\\n\\n\\tfor col_name in df.columns:\\n\\t\\tif df[col_name].dtypes not in ['uint8', 'uint32', 'uint64', 'int8', 'int32', 'int64', 'float16', 'float32',\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t 'float64']:\\n\\t\\t\\tnon_numerical_cols.append(col_name)\\n\\n\\treturn non_numerical_cols\",\n \"title\": \"\"\n },\n {\n \"docid\": \"051b6cbc64f588d9fc3691ea4b71dea0\",\n \"score\": \"0.56352067\",\n \"text\": \"def handle_missing_threshold(df, prop_required_column = .3, prop_required_row = .9):\\n threshold = int(round(prop_required_column*len(df.index),0))\\n df.dropna(axis=1, thresh=threshold, inplace=True)\\n threshold = int(round(prop_required_row*len(df.columns),0))\\n df.dropna(axis=0, thresh=threshold, inplace=True)\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a51a136d3bfcbe78acd877e1f9e78906\",\n \"score\": \"0.5632437\",\n \"text\": \"def validate_columns(args, column_types, unreconciled):\\n plugins = util.get_plugins('column_types')\\n plugin_types = list(plugins.keys())\\n\\n error = missing_headers(unreconciled, column_types, plugin_types)\\n error |= missing_key_columns(args, unreconciled)\\n\\n if error:\\n error_exit(unreconciled, plugin_types)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c5440de8118a8e6781f3be6051164c2\",\n \"score\": \"0.5624304\",\n \"text\": \"def check_missing(df, col): \\n return df[pd.isnull(df[col])]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f95c9658c42145b083486bbb4c5d482c\",\n \"score\": \"0.5571803\",\n \"text\": \"def _test_invalid_nulls(self, model):\\n from django.db.models import fields\\n obj = model()\\n nonnull_fields = [x for x in obj._meta.fields\\n if not x.null and x.name != 'id']\\n for field in nonnull_fields:\\n if _has_default(field):\\n continue\\n nonnull_cols_series = _column_getter(self, field.name)\\n if nonnull_cols_series.isnull().any():\\n error_msg = '%s column contains nulls' % field.name\\n raise PandasLovesPoniesException(error_msg)\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fc3b4b9e82dc3b85244e676231b9d00\",\n \"score\": \"0.55682826\",\n \"text\": \"def _any_nat_check(df):\\n return np.any(pd.isnull(df))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"842aaf30eeae134892185c43443c5b63\",\n \"score\": \"0.5563729\",\n \"text\": \"def check_data(self, df, *args):\\n column_headers = df.columns\\n print(\\\"Checking input data, all data headers: {}\\\".format(column_headers))\\n for column in args:\\n if column not in column_headers:\\n print(\\\"Missing or mis-configured column: {}\\\".format(column))\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"674d3c3b8445b44d99fbe88e3b23f7d8\",\n \"score\": \"0.5540896\",\n \"text\": \"def _continuous_col_finder(df, drop_na = False):\\n df = df.copy()\\n bin_cols = _binary_col_finder(df, drop_na = drop_na)\\n cont_cols = [col for col in df.columns.tolist() if col not in bin_cols]\\n \\n return cont_cols\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42b52d755ff577ab939d7a4641fe72b5\",\n \"score\": \"0.5515423\",\n \"text\": \"def validate_data_columns(self, data_columns, min_itemsize, non_index_axes):\\n if not len(non_index_axes):\\n return []\\n\\n axis, axis_labels = non_index_axes[0]\\n info = self.info.get(axis, {})\\n if info.get(\\\"type\\\") == \\\"MultiIndex\\\" and data_columns:\\n raise ValueError(\\n f\\\"cannot use a multi-index on axis [{axis}] with \\\"\\n f\\\"data_columns {data_columns}\\\"\\n )\\n\\n # evaluate the passed data_columns, True == use all columns\\n # take only valid axis labels\\n if data_columns is True:\\n data_columns = list(axis_labels)\\n elif data_columns is None:\\n data_columns = []\\n\\n # if min_itemsize is a dict, add the keys (exclude 'values')\\n if isinstance(min_itemsize, dict):\\n existing_data_columns = set(data_columns)\\n data_columns = list(data_columns) # ensure we do not modify\\n data_columns.extend(\\n [\\n k\\n for k in min_itemsize.keys()\\n if k != \\\"values\\\" and k not in existing_data_columns\\n ]\\n )\\n\\n # return valid columns in the order of our axis\\n return [c for c in data_columns if c in axis_labels]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ad738750770e34e125efad157a9890b\",\n \"score\": \"0.54954255\",\n \"text\": \"def search_missing_values(self):\\n log.info(\\\"Searching for missing values\\\")\\n columns_with_missing = self.df.columns[self.df.isnull().any()].tolist()\\n if columns_with_missing:\\n for col in columns_with_missing:\\n self.column_meta[col][\\\"missing\\\"] = True\\n log.warning(f\\\"Missing values found in column: {col}\\\")\\n else:\\n log.info(\\\"No missing values found\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e3c30cde06bfc2ab05acae79daba783\",\n \"score\": \"0.54807353\",\n \"text\": \"def row_has_all_fields(self, row):\\n for f in self.mapping:\\n if f[0] != '_' and f[1] and not row[f[0]]:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7351ce515279d4551abb01fabab6f203\",\n \"score\": \"0.54802614\",\n \"text\": \"def test_column_exclude_rules(self):\\n col_rules = deepcopy(self.basespecs['iris']['column_rules'])\\n col_rules['Species']['exclude'] = ['virginica', 'versicolor']\\n dframe_val = DataFrameValidator(data=self.iris_dframe.copy(),\\n column_rules=col_rules,\\n rules={'drop_duplicates': False})\\n cleaned_species = dframe_val.clean()['Species']\\n self.assertItemsEqual(cleaned_species.unique().tolist(), ['setosa'])\\n self.assertEqual(cleaned_species.shape[0], 50)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9226f0091b25bccd15e094eefa8b3de\",\n \"score\": \"0.5437251\",\n \"text\": \"def check_and_drop_duplicate_columns(dataframe: pd.DataFrame):\\n extra_columns = dataframe.columns[dataframe.columns.duplicated()]\\n if len(extra_columns) == 0:\\n return dataframe\\n for column in extra_columns:\\n test_equality = dataframe.loc[:, column] == dataframe.loc[:, column]\\n if not test_equality.all(axis=None):\\n raise ValueError\\n return dataframe.loc[:, ~dataframe.columns.duplicated()]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e29d468a67a26db8825dfc00511fe131\",\n \"score\": \"0.5436097\",\n \"text\": \"def collect_invalid_members_of(subset_df, missing=subs_missing_sibs):\\n subset_df.apply(lambda sub: get_invalid_subset_member(sub, missing),\\n axis=\\\"columns\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3edc66b5898cd55b7a8fb4535d9d87de\",\n \"score\": \"0.54328555\",\n \"text\": \"def validate_columns_names(df, col_names, index=0):\\n\\n columns = val_to_list(col_names)\\n\\n if is_list_of_tuples(columns):\\n columns = [c[index] for c in columns]\\n\\n # Remove duplicates in the list\\n if is_list_of_strings(columns):\\n columns = OrderedSet(columns)\\n\\n check_for_missing_columns(df, columns)\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64de9c8f0a0a2b1a306871348930e610\",\n \"score\": \"0.543095\",\n \"text\": \"def test_is_indexable(self):\\n # verify -----------------------\\n try:\\n self.rows[0]\\n except TypeError:\\n msg = \\\"'_RowCollection' object does not support indexing\\\"\\n self.fail(msg)\\n except IndexError:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64de9c8f0a0a2b1a306871348930e610\",\n \"score\": \"0.543095\",\n \"text\": \"def test_is_indexable(self):\\n # verify -----------------------\\n try:\\n self.rows[0]\\n except TypeError:\\n msg = \\\"'_RowCollection' object does not support indexing\\\"\\n self.fail(msg)\\n except IndexError:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb07a2d0f1822e472f0d0a7c058e9323\",\n \"score\": \"0.54210275\",\n \"text\": \"def _check_colname(*columns):\\n\\n for col in columns:\\n if str(col).endswith(\\\"_\\\"):\\n raise ValueError(\\n \\\"Dict keys are not allowed to end with '_': {}\\\".format(col)\\n )\\n\\n if \\\"__\\\" in str(col):\\n raise ValueError(\\n \\\"Dict keys are not allowed to contain '__': {}\\\".format(col)\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c604dc2a2158947d13ff6de8c64907d6\",\n \"score\": \"0.5411608\",\n \"text\": \"def check_column_names(columns, *args):\\n for arg in args:\\n if isinstance(arg, (tuple, list)):\\n missing = set(arg) - set(columns)\\n if missing:\\n raise ValueError(\\\"Following columns were requested but are \\\"\\n \\\"not available: %s.\\\\n\\\"\\n \\\"All requested columns: %s\\\\n\\\"\\n \\\"Available columns: %s\\\"\\n \\\"\\\" % (missing, arg, columns))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e49c6ea7f798d0074137b8114b8fdb89\",\n \"score\": \"0.5401765\",\n \"text\": \"def test_filter_multiple_invalid_columns(self):\\n self.dicom.filter_by_keywords({ \\\"invalid\\\" : \\\"bla\\\", \\\"another_invalid_col\\\" : \\\"bla\\\" })\\n self.assertEqual(0, self.dicom.metadata.count())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75924c096bfde057ca6e8a6acb26337c\",\n \"score\": \"0.5392577\",\n \"text\": \"def _validate_data(df: pd.DataFrame, schema: input_schema.Schema):\\n\\n for key, value in schema.input_schema_map.items():\\n _ = value # TODO(mikebernico) Implement type checking.\\n if key not in df.columns:\\n schema_keys = list(schema.input_schema_map.keys())\\n raise AttributeError(\\n f'DataFrame does not contain expected column: {key}. '\\n f'Ensure header matches schema keys: {schema_keys}.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff9e9ae9dcd4defcb83d47f9c52d7e05\",\n \"score\": \"0.53670734\",\n \"text\": \"def fail_struct_and_ancestors(struct, disparate_column, fails=0):\\n\\n disparate_column[struct.idx] = fails\\n if struct.level > 0:\\n fail_struct_and_ancestors(struct.parent, disparate_column, fails=fails)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b382c1957cabdff476ec67b392ae97ae\",\n \"score\": \"0.53512156\",\n \"text\": \"def check_data_frame(df: pd.DataFrame) -> None:\\n\\n required_cols = {\\n \\\"Time\\\",\\n \\\"Exchange\\\",\\n \\\"Asset Class\\\",\\n \\\"Product Name\\\",\\n \\\"Product Code\\\",\\n \\\"Start Period\\\",\\n \\\"End Period\\\",\\n \\\"Maintenance\\\",\\n \\\"Currency\\\",\\n \\\"Maint. Vol. Scan\\\",\\n }\\n assert required_cols.issubset(df.columns)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81ff25db8120d5cc58f473ee78e3a6a9\",\n \"score\": \"0.5350528\",\n \"text\": \"def valid_type_in_col(arch):\\r\\n for attrib in arch.xpath('//*/@col'):\\r\\n try:\\r\\n int(attrib)\\r\\n except:\\r\\n return False\\r\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8342f02d0f9f18a9899440e19fe69542\",\n \"score\": \"0.53426254\",\n \"text\": \"def index_like(index):\\n return not (isinstance(index, pd.RangeIndex) and\\n index._start == 0 and\\n index._stop == len(index) and\\n index._step == 1 and index.name is None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8342f02d0f9f18a9899440e19fe69542\",\n \"score\": \"0.53426254\",\n \"text\": \"def index_like(index):\\n return not (isinstance(index, pd.RangeIndex) and\\n index._start == 0 and\\n index._stop == len(index) and\\n index._step == 1 and index.name is None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93b7986ef6dd3b112286c38fc7d292a4\",\n \"score\": \"0.531596\",\n \"text\": \"def test_is_indexable(self):\\n # verify -----------------------\\n try:\\n self.cells[0]\\n except TypeError:\\n msg = \\\"'_CellCollection' object does not support indexing\\\"\\n self.fail(msg)\\n except IndexError:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93b7986ef6dd3b112286c38fc7d292a4\",\n \"score\": \"0.531596\",\n \"text\": \"def test_is_indexable(self):\\n # verify -----------------------\\n try:\\n self.cells[0]\\n except TypeError:\\n msg = \\\"'_CellCollection' object does not support indexing\\\"\\n self.fail(msg)\\n except IndexError:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f699f42b9373549e2e92c9efd322583\",\n \"score\": \"0.5309775\",\n \"text\": \"def findSingleValueColumns(originalData):\\n uselessColumns = [];\\n\\n for i in range(0, len(originalData[0])):\\n isUsefulColumn = False;\\n firstEntry = originalData[0][i];\\n for j in range(1, len(originalData)):\\n if (firstEntry != originalData[j][i]):\\n isUsefulColumn = True;\\n break;\\n\\n if (not isUsefulColumn):\\n uselessColumns.append(i);\\n \\n return uselessColumns;\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3989fdac9dd1eadd0ef26c8e169c58ef\",\n \"score\": \"0.52963823\",\n \"text\": \"def check_for_empty_index(comparable: Compare):\\n index_column = comparable.data_frame[idx.get_index_name(comparable)]\\n comparable.empty_index = [index for index, value in enumerate(index_column)\\n if value == Field.empty_string.value\\n or value != value or value is None]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"601ceb455c2239064745f4e8c861c1d9\",\n \"score\": \"0.5295186\",\n \"text\": \"def check_valid_column(observation):\\n\\n valid_columns = set(columns)\\n keys = set(observation.keys())\\n\\n if len(valid_columns - keys) > 0:\\n missing = valid_columns - keys\\n error = \\\"Missing columns: {}\\\".format(missing)\\n return False, error\\n\\n if len(keys - valid_columns) > 0:\\n extra = keys - valid_columns\\n error = \\\"Unrecognized columns provided: {}\\\".format(extra)\\n return False, error\\n\\n if len(keys - valid_columns) == 0:\\n return True, \\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40a868f60e69c3431bd9b2d3969fc8d9\",\n \"score\": \"0.5292685\",\n \"text\": \"def _index_check(self):\\r\\n for temp in self.data_dict.values():\\r\\n if (self.data_dict['open'].index.difference(temp.index).shape[0]\\r\\n != 0) or (self.data_dict['open'].shape != temp.shape):\\r\\n \\r\\n raise ValueError('Dataframe indices are not aligned')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"220b6ae2239ae2b79c1866ae8cd2e57a\",\n \"score\": \"0.528538\",\n \"text\": \"def test_df_iterate_over_columns2(self):\\n from sdc.hiframes.api import get_nan_mask\\n\\n @self.jit\\n def jitted_func():\\n cols = ('A', 'B', 'C', 'D')\\n df = pd.DataFrame({\\n 'A': ['a', 'b', None, 'a', '', None, 'b'],\\n 'B': ['a', 'b', 'd', 'a', '', 'c', 'b'],\\n 'C': [np.nan, 1, 2, 1, np.nan, 2, 1],\\n 'D': [1, 2, 9, 5, 2, 1, 0]\\n })\\n res_nan_mask = np.zeros(len(df), dtype=np.bool_)\\n for col in literal_unroll(cols):\\n res_nan_mask += get_nan_mask(df[col].values)\\n return res_nan_mask\\n\\n # expected is a boolean mask of df rows that have None values\\n expected = np.asarray([True, False, True, False, True, True, False])\\n result = jitted_func()\\n np.testing.assert_array_equal(result, expected)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0e148ecaf122b56e0f98404fd395b26\",\n \"score\": \"0.5284561\",\n \"text\": \"def is_valid(self,row):\\n # Do we know how to deal with all the columns?\\n coldiff = set(row.columns) - set(self.COLUMN_NAMES)\\n if coldiff:\\n raise Exception(\\\"Row contains unknown columns: \\\" + str(coldiff) )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff3debd100e78e5ef90274f68d69a6b1\",\n \"score\": \"0.5276152\",\n \"text\": \"def ColFilter(col):\\n return not hiddencols or col not in hiddencols\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94c5574835260a85edd84885fef2c151\",\n \"score\": \"0.527597\",\n \"text\": \"def __validIndex__(self, i):\\n try:\\n int(i) # Not great here as it would pass floats and prolly others\\n except ValueError:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c11d1af3340601b79646c0035db15ca1\",\n \"score\": \"0.5256806\",\n \"text\": \"def validate_data_columns(self, data_columns, min_itemsize):\\r\\n\\r\\n if not len(self.non_index_axes):\\r\\n return []\\r\\n\\r\\n axis, axis_labels = self.non_index_axes[0]\\r\\n info = self.info.get(axis, dict())\\r\\n if info.get('type') == 'MultiIndex' and data_columns:\\r\\n raise ValueError(\\\"cannot use a multi-index on axis [{0}] with \\\"\\r\\n \\\"data_columns {1}\\\".format(axis, data_columns))\\r\\n\\r\\n # evaluate the passed data_columns, True == use all columns\\r\\n # take only valide axis labels\\r\\n if data_columns is True:\\r\\n data_columns = axis_labels\\r\\n elif data_columns is None:\\r\\n data_columns = []\\r\\n\\r\\n # if min_itemsize is a dict, add the keys (exclude 'values')\\r\\n if isinstance(min_itemsize, dict):\\r\\n\\r\\n existing_data_columns = set(data_columns)\\r\\n data_columns.extend([\\r\\n k for k in min_itemsize.keys()\\r\\n if k != 'values' and k not in existing_data_columns\\r\\n ])\\r\\n\\r\\n # return valid columns in the order of our axis\\r\\n return [c for c in data_columns if c in axis_labels]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"751177ea15f5dca1040a232b69cc189f\",\n \"score\": \"0.52445453\",\n \"text\": \"def check_valid_column(observation):\\n \\n valid_columns = {\\n \\\"SubjectRaceCode\\\",\\n \\\"SubjectSexCode\\\",\\n \\\"SubjectEthnicityCode\\\",\\n \\\"StatuteReason\\\", \\n \\\"InterventionReasonCode\\\", \\n \\\"ResidentIndicator\\\", \\n \\\"SearchAuthorizationCode\\\",\\n \\\"SubjectAge\\\",\\n \\\"hour\\\",\\n \\\"day_of_week\\\",\\n }\\n \\n keys = set(observation.keys())\\n \\n if len(valid_columns - keys) > 0: \\n missing = valid_columns - keys\\n error = \\\"Missing columns: {}\\\".format(missing)\\n return False, error\\n \\n if len(keys - valid_columns) > 0: \\n extra = keys - valid_columns\\n error = \\\"Unrecognized columns provided: {}\\\".format(extra)\\n return False, error \\n\\n return True, \\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1dcc38d104d90f34739b5e7bfef05e15\",\n \"score\": \"0.52435005\",\n \"text\": \"def fn_check_missing_data(df):\\r\\n return df.isnull().sum().sort_values(ascending=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f133da545a2c8f390d9d2ed08bdec144\",\n \"score\": \"0.5239565\",\n \"text\": \"def boolean_col_expressions(self):\\r\\n return skip_if([\\r\\n no_support('firebird', 'not supported by database'),\\r\\n no_support('oracle', 'not supported by database'),\\r\\n no_support('mssql', 'not supported by database'),\\r\\n no_support('sybase', 'not supported by database'),\\r\\n no_support('maxdb', 'FIXME: verify not supported by database'),\\r\\n no_support('informix', 'not supported by database'),\\r\\n ])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73629530698a773e87a8d298f85c31a4\",\n \"score\": \"0.5235513\",\n \"text\": \"def format_disjunctive_column(comparable_a: Compare, comparable_b: Compare):\\n for index, header in enumerate(comparable_a.header):\\n\\n is_not_index = not header_validator.is_index(comparable_a, header)\\n is_not_in_other = not header_validator.is_column(comparable_b, header)\\n is_empty_type = header[Field.column_type.value] == Field.empty_string.value\\n is_not_mapped = not header_validator.is_mapped(comparable_a, header)\\n\\n is_disjunctive = all([is_not_index, is_not_in_other, is_empty_type, is_not_mapped])\\n\\n if is_disjunctive:\\n comparable_a.header[index][Field.column_type.value] = Field.disjunctive.value\\n comparable_a.header[index][Field.column_name.value] = change_value(comparable_a, index)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d61c2dd845c44d1c6299ca9024c1227\",\n \"score\": \"0.5213469\",\n \"text\": \"def test_multiple_cols_incl_no_op():\\n assert json_to_table(data, [\\\"name\\\", \\\"adict_col\\\"]).equals(\\n pd.DataFrame(\\n [\\n {\\n \\\"name\\\": \\\"blah\\\",\\n \\\"adict_col.a\\\": 1,\\n \\\"adict_col.b.c\\\": 1,\\n \\\"list_col\\\": [{\\\"a\\\": 1}, {\\\"a\\\": 2}],\\n \\\"adict_col\\\": {\\\"a\\\": 1, \\\"b\\\": {\\\"c\\\": 1}},\\n \\\"bdict_list_col\\\": {\\\"a\\\": [{\\\"b\\\": 1}, {\\\"b\\\": 2}]},\\n }\\n ]\\n )\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57d2fd28e6347de38594e9da2c99c28f\",\n \"score\": \"0.52077776\",\n \"text\": \"def test_filter_invalid_column(self):\\n self.dicom.filter_by_keywords({ \\\"invalid keyword\\\" : \\\"value\\\" })\\n self.assertEqual(0, self.dicom.metadata.count())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59830e994f7cf540c4eee242768e673f\",\n \"score\": \"0.520426\",\n \"text\": \"def notnull(self) -> DataFrame:\\n return ~self.isna()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3583f74ae4057d4ba2f5d84abbc105ed\",\n \"score\": \"0.52028775\",\n \"text\": \"def check_column(sudoku, n, pos,inicial):\\n columna = sudoku[:,pos[1]]\\n contador = 0\\n for i in columna:\\n if i == n:\\n contador += 1\\n \\n if inicial:\\n if contador == 1:\\n return True\\n else:\\n return False \\n else:\\n if contador >= 1:\\n return False\\n else:\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0bcaa9767ea24b71f9589d3f5e4619b3\",\n \"score\": \"0.518393\",\n \"text\": \"def check_finite(dataframe, file, errorfile, exclude_cols=[]):\\n nans = False\\n df = dataframe.drop(list(set(exclude_cols).intersection(set(dataframe.columns))), axis=1)\\n column_sums = np.sum(~np.isfinite(df))\\n if column_sums.sum() > 0:\\n nans = True\\n errorfile.write('{}:\\\\n'.format(file))\\n errorfile.write('Number of non-finite by column:\\\\n')\\n errorfile.write(column_sums.to_string())\\n errorfile.write('\\\\nIndices of non-finite values:\\\\n')\\n isnans = np.isnan(df)\\n inds = dict([(c, df.ix[isnans[c].values, c].index.tolist()) for c in df.columns])\\n for k, v in inds.iteritems():\\n if len(v) > 0:\\n errorfile.write('{}:'.format(k))\\n for i in v:\\n errorfile.write(' {}'.format(i))\\n errorfile.write('\\\\n')\\n errorfile.write('\\\\n')\\n return nans\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fc0391e82aea940f6eebf46b41f2097\",\n \"score\": \"0.5182957\",\n \"text\": \"def test_null_rows(fast_out, df_name, thresh):\\n # These are columns that only exist in earlier years\\n pv.no_null_rows(\\n df=fast_out.__getattribute__(df_name)(),\\n df_name=df_name,\\n thresh=thresh,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55b74f2ff1dbe073bdeeed39b8f41be5\",\n \"score\": \"0.51813966\",\n \"text\": \"def get_cols_with_negative_values(df: DataFrame) -> list:\\n\\tnumCols = get_numerical_cols(df)\\n\\treturn df[numCols].columns[(df[numCols] < 0).any()].tolist()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6554b9d79c4848925ffc41d91c58da5c\",\n \"score\": \"0.518076\",\n \"text\": \"def test_raises_on_idx_out_of_range(self):\\n with self.assertRaises(IndexError):\\n self.columns[9]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6554b9d79c4848925ffc41d91c58da5c\",\n \"score\": \"0.518076\",\n \"text\": \"def test_raises_on_idx_out_of_range(self):\\n with self.assertRaises(IndexError):\\n self.columns[9]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffaf8bc12e32eebc46c123561a14eaf6\",\n \"score\": \"0.51789826\",\n \"text\": \"def print_invalid_rows(gdf):\\n for i, row in gdf.iterrows():\\n if not row['geometry'].is_valid:\\n print('not valid index', i, '\\\\n', row)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"462234af756e770b2cbb7443dcb3a5fa\",\n \"score\": \"0.51781344\",\n \"text\": \"def test_nan(self):\\n for i in list(DATA.columns):\\n self.assertTrue(not pd.isnull(DATA[i]).any())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d41bacbdc0607a7ff1b5383e8cc8467d\",\n \"score\": \"0.51697177\",\n \"text\": \"def test_cols_negative(self):\\n self.assertRaises(BaseException, basic.alphanum, -1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62542c96fc8123f5522923129c598331\",\n \"score\": \"0.51590306\",\n \"text\": \"def sanity_checker(self, verbose = True):\\n for col in self.bcols:\\n if col not in self.cat.colnames:\\n print(f'Warning: {col} is missing in catalogue Table')\\n raise Exception('Missing Column')\\n if verbose:\\n print('Checking catalogue columns (ra, dec, parallax, pmra, phot_g_mean_mag, etc) - OK')\\n self.add_extra_cols(verbose = verbose)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47e9a1c660ddf4b7604819f46667f362\",\n \"score\": \"0.5158693\",\n \"text\": \"def missing_headers(unreconciled, column_types, plugin_types):\\n error = False\\n for column, column_type in column_types.items():\\n if column not in unreconciled.columns:\\n error = True\\n print('ERROR: \\\"{}\\\" is not a column header'.format(column))\\n if column_type['type'] not in plugin_types:\\n error = True\\n print('ERROR: \\\"{}\\\" is not a column type'.format(\\n column_type['type']))\\n return error\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d1f84015c7f809bd23875eacb2cf7a4\",\n \"score\": \"0.51507735\",\n \"text\": \"def check_cols_present(df, required_cols):\\n\\n if len(required_cols & set(df.columns)) < len(required_cols):\\n raise ValueError(\\n \\\"Input metadata file must include the following \\\"\\n \\\"columns: {}\\\".format(required_cols)\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"930d36fd1945365e7a127181adb85b38\",\n \"score\": \"0.5149544\",\n \"text\": \"def check_column(\\n df: pd.DataFrame, old_column_names: List, present: bool = True\\n):\\n for column_name in old_column_names:\\n if present:\\n if column_name not in df.columns:\\n raise ValueError(\\n f\\\"{column_name} not present in dataframe columns!\\\"\\n )\\n else: # Tests for exclusion\\n if column_name in df.columns:\\n raise ValueError(\\n f\\\"{column_name} already present in dataframe columns!\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d6695bbf5767a28dbc46539990c2cd6\",\n \"score\": \"0.5132296\",\n \"text\": \"def check_valid_column(observation):\\n \\n valid_columns = {\\n \\\"observation_id\\\",\\n \\\"Type\\\",\\n \\\"Date\\\",\\n \\\"Part of a policing operation\\\",\\n \\\"Latitude\\\",\\n \\\"Longitude\\\",\\n \\\"Gender\\\",\\n \\\"Age range\\\",\\n \\\"Officer-defined ethnicity\\\",\\n \\\"Legislation\\\",\\n \\\"Object of search\\\",\\n \\\"station\\\"\\n}\\n\\n keys = set(observation.keys())\\n \\n if len(valid_columns - keys) > 0: \\n missing = valid_columns - keys\\n error = \\\"Missing columns: {}\\\".format(missing)\\n return False, error\\n \\n if len(keys - valid_columns) > 0: \\n extra = keys - valid_columns\\n error = \\\"Unrecognized columns provided: {}\\\".format(extra)\\n return False, error \\n\\n return True, \\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e7497fdf4666e0af31ba55039d1a7e0\",\n \"score\": \"0.5127166\",\n \"text\": \"def _check_data_length(\\n self,\\n columns: Sequence[Hashable],\\n data: Sequence[ArrayLike],\\n ) -> None:\\n if not self.index_col and len(columns) != len(data) and columns:\\n empty_str = is_object_dtype(data[-1]) and data[-1] == \\\"\\\"\\n # error: No overload variant of \\\"__ror__\\\" of \\\"ndarray\\\" matches\\n # argument type \\\"ExtensionArray\\\"\\n empty_str_or_na = empty_str | isna(data[-1]) # type: ignore[operator]\\n if len(columns) == len(data) - 1 and np.all(empty_str_or_na):\\n return\\n warnings.warn(\\n \\\"Length of header or names does not match length of data. This leads \\\"\\n \\\"to a loss of data with index_col=False.\\\",\\n ParserWarning,\\n stacklevel=find_stack_level(),\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c824c10d4001f501f292582b4e7896dd\",\n \"score\": \"0.5123544\",\n \"text\": \"def test_column_values(self):\\n for column in self.table.columns:\\n self.assertEqual(len(column.values), 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a16d7cdee4929b20a0e5e4c9ef3b5cad\",\n \"score\": \"0.5120725\",\n \"text\": \"def unmatched_column_indicator(self):\\n return self.match_results == -1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e94a86eba0a9c73c2665950a7bbf9049\",\n \"score\": \"0.51171285\",\n \"text\": \"def cell_is_not_empty(row, column_id):\\n for cell in row.cells:\\n if cell.column_id == column_id:\\n if cell.value:\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30e08e12955e3bdb38f55464b8919957\",\n \"score\": \"0.51115584\",\n \"text\": \"def is_admissible(index, index_set):\\n back_neighbors = backward_neighbors(index)\\n for ind_b in back_neighbors:\\n if ind_b not in index_set:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be1fcb412111a2ae440729ca099cbe20\",\n \"score\": \"0.5111317\",\n \"text\": \"def is_indexed(self):\\r\\n try:\\r\\n return getattr(self.table.cols, self.cname).is_indexed\\r\\n except:\\r\\n False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eaf4c0d9fdc1bfb2837a3d3f4df8881f\",\n \"score\": \"0.51064456\",\n \"text\": \"def count_missing(df, is_print=True, return_df=False):\\n \\n nans_l = {}\\n fir_valid = dict().fromkeys(df.columns)\\n last_valid = dict().fromkeys(df.columns)\\n \\n for col in df.columns:\\n ts = df[[col]].copy()\\n fir_ix = ts.first_valid_index()\\n if fir_ix is not None:\\n fir_ix = ts.index.get_loc(fir_ix)\\n last_ix = ts.index.get_loc(ts.last_valid_index())\\n ts = ts.iloc[fir_ix:last_ix+1, :]\\n #tuple for cnt nan and no-nan\\n nans_l[col] = (ts.isna().sum().values[0], ts.notnull().sum().values[0])\\n #to print first and last valid indexes\\n fir_valid[col] = ts.first_valid_index()\\n last_valid[col] = ts.last_valid_index()\\n if is_print: \\n columns_names = 'Element | Count NaN | Count no-NaN | First valid date| Last valid date'\\n print(' '*(len(columns_names)//3) + 'Some information about data')\\n print('-'*len(columns_names))\\n print(columns_names)\\n print('-'*len(columns_names))\\n for col, val in nans_l.items():\\n fir_val_ix = str(fir_valid[col]).split(' ')[0]\\n last_val_ix = str(last_valid[col]).split(' ')[0]\\n cnt_nan = val[0]\\n cnt_nonan = val[1]\\n print(f'{col:<7}{cnt_nan:>8}{cnt_nonan:>15}{fir_val_ix:>18}{last_val_ix:>18}')\\n\\n print(\\\"\\\\nP.S. valid date means that before or after this date there're only missing values.\\\\n\\\\\\nOf course nonvalid dates are omitted\\\")\\n if return_df:\\n i = 0\\n df = pd.DataFrame(columns=['Element', 'Count NaN', 'Count no-NaN', 'First valid date', 'Last valid date'])\\n for col, val in nans_l.items():\\n fir_val_ix = str(fir_valid[col]).split(' ')[0]\\n last_val_ix = str(last_valid[col]).split(' ')[0]\\n cnt_nan = val[0]\\n cnt_nonan = val[1]\\n \\n df.loc[i] = [col, cnt_nan, cnt_nonan, fir_val_ix, last_val_ix]\\n i += 1\\n \\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8bad98b811b467f84c095f3f084ee903\",\n \"score\": \"0.5100679\",\n \"text\": \"def check_input_df(input_df, expected_cols):\\n\\n if all(col in input_df.columns for col in expected_cols):\\n pass\\n else:\\n raise ValueError(\\n \\\"Input must be of type DataFrame and contain the following columns: {}\\\".format(\\n \\\", \\\".join(expected_cols)\\n )\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f4cf733fb3c1935f8166f9f8d58507e\",\n \"score\": \"0.50980073\",\n \"text\": \"def sanitize_df(df: pd.DataFrame) -> pd.DataFrame:\\n # Delete empty rows\\n for i in df.index.tolist()[-1::-1]:\\n if df.iloc[i].isna().all():\\n df.drop(i, inplace=True)\\n else:\\n break\\n # Delete empty columns\\n cols = []\\n for column in df:\\n if not df[column].isnull().all():\\n cols.append(column)\\n df = df[cols]\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6ee1a04f6a6e56a8301fa2d7140cdfb\",\n \"score\": \"0.5087057\",\n \"text\": \"def _dict_validation(self, data: DataFrame, verbose: bool = False) -> DataFrame:\\n\\n dict_column_valid_values = self._read_dict_column_valid_values()\\n\\n if not dict_column_valid_values:\\n if verbose:\\n print(f'\\\\t| No columns to clean.')\\n\\n print(f'\\\\t| Row count: {data.count()} before cleaning')\\n for column, values in dict_column_valid_values.items():\\n if column in data.columns:\\n data = data.filter(data[column].isin(values))\\n if verbose:\\n print(f'\\\\t| Row count: {data.count()}, after cleaning column: {column}')\\n\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"369605ce9128255a6fc840b2252e3193\",\n \"score\": \"0.50847715\",\n \"text\": \"def test_column_presence(profile: pd.DataFrame):\\n KMCPProfile.validate(profile)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e682d51600e6609a33d18d8d2bbe52b\",\n \"score\": \"0.50722545\",\n \"text\": \"def apply(self, data_source):\\r\\n cols_with_nan = []\\r\\n for column in self.columns:\\r\\n if data_source[column].isnull().values.any():\\r\\n cols_with_nan.append(column)\\r\\n if cols_with_nan:\\r\\n raise ValidDataException(\\r\\n \\\"Columns containing NaN values\\\", cols_with_nan)\\r\\n\\r\\n # ============== Possible additional dataset checks =====================\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c72b1077f6c223737f1279516169c86e\",\n \"score\": \"0.50645167\",\n \"text\": \"def drop_mostly_missing_cols(hpdf):\\n copy = hpdf.data.copy()\\n # drop columns with more than 20% values missing\\n notna_col_mask = ~ (copy.isna().sum()/len(copy) > 0.20)\\n notna_col_mask.loc['SalePrice'] = True\\n copy = copy.loc[:, notna_col_mask]\\n # drop columns associated with those\\n copy.drop(columns=['MiscVal'])\\n return copy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"236290a51636775aa37a8e2a005e3d55\",\n \"score\": \"0.50597477\",\n \"text\": \"def test_invalid_column_as_index_raises_keyerror(self):\\n self.assertRaises(KeyError, rad.preprocess, self.frame, [\\\"invalid\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6912649baf46bbbe7ccc5166b1f4e012\",\n \"score\": \"0.50557643\",\n \"text\": \"def test_df_iterate_over_columns1(self):\\n from sdc.hiframes.pd_dataframe_ext import get_dataframe_data\\n from sdc.hiframes.api import get_nan_mask\\n\\n @self.jit\\n def jitted_func():\\n df = pd.DataFrame({\\n 'A': ['a', 'b', None, 'a', '', None, 'b'],\\n 'B': ['a', 'b', 'd', 'a', '', 'c', 'b'],\\n 'C': [np.nan, 1, 2, 1, np.nan, 2, 1],\\n 'D': [1, 2, 9, 5, 2, 1, 0]\\n })\\n\\n # tuple of literals has to be created in a jitted function, otherwise\\n # col_id won't be literal and unboxing in get_dataframe_data won't compile\\n column_ids = (0, 1, 2, 3)\\n res_nan_mask = np.zeros(len(df), dtype=np.bool_)\\n for col_id in literal_unroll(column_ids):\\n res_nan_mask += get_nan_mask(get_dataframe_data(df, col_id))\\n return res_nan_mask\\n\\n # expected is a boolean mask of df rows that have None values\\n expected = np.asarray([True, False, True, False, True, True, False])\\n result = jitted_func()\\n np.testing.assert_array_equal(result, expected)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b9aa472e431a4ec17013f374b59b55e\",\n \"score\": \"0.5053048\",\n \"text\": \"def check_index(self, index):\\n\\n if index.bottom != 1:\\n pass\\n #Only access arrays with whole indices!\\n elif index.top >= len(self):\\n pass\\n #Array out of bounds error!\\n elif index.sign == -1:\\n pass\\n #Indexes can't be negative!\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1af4f5770eb242513967f65b95444e88\",\n \"score\": \"0.50512403\",\n \"text\": \"def _is_valid_erp(d , / ):\\r\\n if not hasattr(d, \\\"columns\\\"): \\r\\n raise TypeError (\\r\\n \\\"ERP 'resistivity' and station measurement data expect\\\"\\r\\n f\\\" to be arranged in a dataframe. Got {type (d).__name__!r}\\\"\\r\\n )\\r\\n return not len(d) ==0 and ('resistivity' and 'station') in d.columns\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6766aa344d9214a4f55119bbdcaecec1\",\n \"score\": \"0.5050591\",\n \"text\": \"def all_better(self):\\n df = self.compare_with_soa()\\n idxs = [all(df[col] > 0) for col in df.columns]\\n\\n return df.iloc[:, idxs]\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":880,"cells":{"query_id":{"kind":"string","value":"7d4440ed8fc28e2206639809e8615ddc"},"query":{"kind":"string","value":"Tests if this user can change the hierarchy. A return of true does not guarantee successful authorization. A return of false indicates that it is known performing any update will result in a ``PermissionDenied``. This is intended as a hint to an application that may opt not to offer these operations to an unauthorized user."},"positive_passages":{"kind":"list like","value":[{"docid":"96a1e125c7c2c93baf4e3b63c5b3e402","score":"0.66537386","text":"def can_modify_family_hierarchy(self):\n return # boolean","title":""}],"string":"[\n {\n \"docid\": \"96a1e125c7c2c93baf4e3b63c5b3e402\",\n \"score\": \"0.66537386\",\n \"text\": \"def can_modify_family_hierarchy(self):\\n return # boolean\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"f086a73bf3494ee3a3cea9a84eaad19a","score":"0.7123733","text":"def can_modify_objective_hierarchy(self):\n return # boolean","title":""},{"docid":"4ae42bba99802e77eb731d9315fbcaf2","score":"0.71223724","text":"def change(self, user, energy, *args):\n if user.is_anonymous or user.is_client:\n return False\n\n if user.is_administrator:\n return True\n\n if user.is_manager:\n return False\n\n if user.is_advisor and user.group == energy.group:\n return True\n\n return self.admin_permission(user, energy, *args)","title":""},{"docid":"326dab2fcfc11c6a3385a81bc894a803","score":"0.70367","text":"def can_modify_log_hierarchy(self):\n # Implemented from template for\n # osid.resource.BinHierarchyDesignSession.can_modify_bin_hierarchy_template\n # NOTE: It is expected that real authentication hints will be\n # handled in a service adapter above the pay grade of this impl.\n if self._catalog_session is not None:\n return self._catalog_session.can_modify_catalog_hierarchy()\n return True","title":""},{"docid":"c63b93a3cf33cf0081f182be0ad3e7e3","score":"0.70354694","text":"def can_modify_location_hierarchy(self):\n return # boolean","title":""},{"docid":"eba8c36ed3d0fc460ce735581e8b7fca","score":"0.6934173","text":"def can_modify_objective_bank_hierarchy(self):\n return # boolean","title":""},{"docid":"eba8c36ed3d0fc460ce735581e8b7fca","score":"0.6934173","text":"def can_modify_objective_bank_hierarchy(self):\n return # boolean","title":""},{"docid":"eba8c36ed3d0fc460ce735581e8b7fca","score":"0.6934173","text":"def can_modify_objective_bank_hierarchy(self):\n return # boolean","title":""},{"docid":"e3e191f8d0d0b022f488249f14cb5ad4","score":"0.66498375","text":"def has_change_permission(self, request, obj=None):\n return False","title":""},{"docid":"e3e191f8d0d0b022f488249f14cb5ad4","score":"0.66498375","text":"def has_change_permission(self, request, obj=None):\n return False","title":""},{"docid":"e3e191f8d0d0b022f488249f14cb5ad4","score":"0.66498375","text":"def has_change_permission(self, request, obj=None):\n return False","title":""},{"docid":"16ab91f4e764f1c93cc98de15a17b6da","score":"0.65783924","text":"def check_change_permission(self, user, perm, obj):\n return None","title":""},{"docid":"54e0f8ee1ff50cf83eb0d9d9792eeb84","score":"0.6500337","text":"def can_modify_map_hierarchy(self):\n return # boolean","title":""},{"docid":"1ec207bfdcc96bf32208253eaddb4661","score":"0.64400214","text":"def check_change_object_permission(self, user, perm, obj):\n return user.has_perm(self.get_permission_name('change'))","title":""},{"docid":"dc0e9522125ac95a83e422aaafda8609","score":"0.64384574","text":"def has_change_permission(self, request, obj=None):\n perm = self.object_change_permission\n if settings.FEINCMS_TREE_EDITOR_OBJECT_PERMISSIONS:\n r = request.user.has_perm(perm, obj)\n else:\n r = request.user.has_perm(perm)\n\n return r and super(TreeEditor, self).has_change_permission(\n request, obj)","title":""},{"docid":"9101230496d06bdc9f8d50b9a5c3e01d","score":"0.6308406","text":"def change(self, user, yearlyenergyprice, *args):\n if user.is_anonymous or user.is_client:\n return False\n\n if user.is_administrator:\n return True\n\n if user.is_manager:\n return False\n\n if user.is_advisor and user.group == yearlyenergyprice.group:\n return True\n\n return self.admin_permission(user, yearlyenergyprice, *args)","title":""},{"docid":"3ee419014ef54c5d0221eb7251c19df0","score":"0.63054144","text":"def change(self, user, professional, *args):\n if user.is_anonymous or user.is_client:\n return False\n\n if user.is_professional:\n return True\n\n if user.is_administrator:\n return True\n\n if user.is_advisor:\n return True\n\n return self.admin_permission(user, professional, *args)","title":""},{"docid":"21116e49d78228aca38949b5a8fd33bb","score":"0.62654173","text":"def check_permission(self, context, instance):\n permission = '{0}.change_{1}'.format(instance._meta.app_label,\n instance._meta.module_name)\n return context['user'].has_perm(permission)","title":""},{"docid":"9c24be45e6f53c353c5fac1fe1057a16","score":"0.6231308","text":"def can_access_objective_hierarchy(self):\n return # boolean","title":""},{"docid":"6f01ca44f3d7b1375ec2255247908e53","score":"0.6134754","text":"def check_edit_permission(self):\n if not any('change' in item for item in self.get_permissions()):\n raise PermissionDenied()","title":""},{"docid":"a52df81f115fb14cef89e5c89377dccb","score":"0.6119095","text":"def check_read_permission(self, user, perm, obj):\n return user.has_perm(self.get_permission_name('change'))","title":""},{"docid":"695d6dbe0711f095b7443ecf67bcc3e3","score":"0.6055337","text":"def can_access_location_hierarchy(self):\n return # boolean","title":""},{"docid":"e173a769d9ffd166694679d08f8e276c","score":"0.60433006","text":"def has_change_permission(self, request, obj=None):\r\n return request.user.is_staff and (obj is not None) and (obj.id % 2 == 0)","title":""},{"docid":"1afbb28dfdd377291f475208186804b5","score":"0.6035344","text":"def has_change_permission(self, request, obj=None, strict=False):\n change_perm = super().has_change_permission(request, obj=obj)\n approve_perm = self.has_signoff_approve_permission(request, obj=obj)\n either_perm = change_perm or (approve_perm and not strict)\n return either_perm and (not obj or self.is_pending_signoff(obj))","title":""},{"docid":"57cde41a3ad6fe26e840c64d84b425aa","score":"0.60105664","text":"def can_access_objective_bank_hierarchy(self):\n return # boolean","title":""},{"docid":"57cde41a3ad6fe26e840c64d84b425aa","score":"0.60105664","text":"def can_access_objective_bank_hierarchy(self):\n return # boolean","title":""},{"docid":"57cde41a3ad6fe26e840c64d84b425aa","score":"0.60105664","text":"def can_access_objective_bank_hierarchy(self):\n return # boolean","title":""},{"docid":"30ff08b8b26ea6ad926c40328b89cb1f","score":"0.59874535","text":"def has_change_permission(self, request, obj=None):\n info = self.parent_model._meta.app_label, self.parent_model._meta.model_name\n url_name = u'{}_{}_change'.format(*info)\n return url_name == request.resolver_match.url_name","title":""},{"docid":"7b480cdd29b962b00eac1ef13451b738","score":"0.5984149","text":"def perform_update(self, serializer):\n\n user = self.request.user\n obj = self.get_object()\n data = serializer.validated_data\n\n is_active_change = ('is_active' in data and\n obj.is_active != data['is_active'])\n is_staff_change = ('is_staff' in data and\n obj.is_staff != data['is_staff'])\n\n if ((is_staff_change or is_active_change) and not user.is_staff):\n raise PermissionDenied(\"Non-staff user cannot change \"\n \"'is_staff' or 'is_active'\")\n serializer.save()","title":""},{"docid":"8c71c665b6da6712db8637bb041f9011","score":"0.59692115","text":"def check_permissions(cls, info: ResolverInfo) -> bool:\n user = info.context.user\n\n if not cls._meta.public and not check_authenticated(user):\n return False\n\n if not cls._meta.permissions:\n return True\n\n return check_perms(user, cls._meta.permissions, any_perm=cls._meta.permissions_any)","title":""},{"docid":"becf3ca384cc189cf53ace9dcc61a7a8","score":"0.5965556","text":"def has_change_permission(self, request, obj=None):\r\n opts = self.opts\r\n codename = get_permission_codename('change', opts)\r\n return request.user.has_perm(\"%s.%s\" % (opts.app_label, codename))","title":""},{"docid":"119a7787b5edf9a3886512232aa46c73","score":"0.5964482","text":"def can(self):\n identity = get_identity(self.user)\n return (\n is_owner(self.user, self.record) or\n (\n Permission(menrva_edit_published_record).allows(identity) and\n has_published(self.record)\n ) or\n Permission(menrva_edit).allows(identity)\n # NOTE: by default any Permission has a super-user Need\n )","title":""},{"docid":"5cfe3a8beb83ef352de7dbb63bb04837","score":"0.5953403","text":"def can_write(self, user):\n return user.is_superuser or self.is_owner(user)","title":""},{"docid":"9ecdc7c251ba998ee68c2a48b26dcf0a","score":"0.5952299","text":"def has_edit_permission(self, user: settings.AUTH_USER_MODEL) -> bool:\n return user.is_superuser or user == self.owner","title":""},{"docid":"97782ee43b7acc12486517a1201fdd7f","score":"0.5916611","text":"def check_list_permission(self, user, perm, obj):\n return user.has_perm(self.get_permission_name('change'))","title":""},{"docid":"f44abee2275fb7376af120e654474ede","score":"0.5910422","text":"def is_admin(self):\n return self._is_allowed(self.ADMIN_PERMISSION)","title":""},{"docid":"a23e70d7ae4615c4fe6bb24422abd6fa","score":"0.59097165","text":"def has_change_permission(self, request, obj=None):\n opts = self.opts\n codename = get_permission_codename(\"change\", opts)\n return request.user.has_perm(\"%s.%s\" % (opts.app_label, codename))","title":""},{"docid":"dc7baf806a8252b35fe7b3cb854516c5","score":"0.5898846","text":"def can_write(self, user):\n return user.is_superuser or self.is_in_users(user) or self.is_owner(user)","title":""},{"docid":"29869365906d3f277f35fb029cbd5490","score":"0.58698875","text":"def canEdit(self):\n return self.getDetails().getPermissions().canEdit()","title":""},{"docid":"ba4461ef7f171df7fff18e9c1f424cb7","score":"0.58698493","text":"def check_permissions(self, cleaned_data):\n user = self.request.user\n if not co.CheckPermissions(user, self.instance, co.CAN_EDIT):\n raise ValidationError('Operation can not be performed.')","title":""},{"docid":"82f7d30c4f6577e2348331a6448dbcf7","score":"0.5846985","text":"def can_access_family_hierarchy(self):\n return # boolean","title":""},{"docid":"82f7d30c4f6577e2348331a6448dbcf7","score":"0.5846985","text":"def can_access_family_hierarchy(self):\n return # boolean","title":""},{"docid":"82f7d30c4f6577e2348331a6448dbcf7","score":"0.5846985","text":"def can_access_family_hierarchy(self):\n return # boolean","title":""},{"docid":"d471640b2e580f0a4c409696fb8558bf","score":"0.58413994","text":"def has_permission(self):\n return True","title":""},{"docid":"9908e48ad7c9445b462ec6e8d90106b0","score":"0.58315104","text":"def is_accessible(self):\n return admin_permission.can()","title":""},{"docid":"9908e48ad7c9445b462ec6e8d90106b0","score":"0.58315104","text":"def is_accessible(self):\n return admin_permission.can()","title":""},{"docid":"9908e48ad7c9445b462ec6e8d90106b0","score":"0.58315104","text":"def is_accessible(self):\n return admin_permission.can()","title":""},{"docid":"9908e48ad7c9445b462ec6e8d90106b0","score":"0.58315104","text":"def is_accessible(self):\n return admin_permission.can()","title":""},{"docid":"9908e48ad7c9445b462ec6e8d90106b0","score":"0.58315104","text":"def is_accessible(self):\n return admin_permission.can()","title":""},{"docid":"9908e48ad7c9445b462ec6e8d90106b0","score":"0.58315104","text":"def is_accessible(self):\n return admin_permission.can()","title":""},{"docid":"32e5a7a6fc1bda99c95f5a2d6af78e6e","score":"0.5829296","text":"def change_allow_level(self):\n return self._change_allow_level","title":""},{"docid":"13db659673f91961e8200c5d30fa74f5","score":"0.58086085","text":"def can_edit(user, simulation):\n if simulation.user == user or user.is_superuser:\n return True\n else:\n return False","title":""},{"docid":"0696d73ff32c02341df9f1e42a3a4a04","score":"0.5786341","text":"def has_permission(self, user):\n if user.is_anonymous:\n return False\n if user.groups.filter(\n pk=get_admin_group().pk).count() == 0:\n # We give full permissions to anyone in the admin group\n\n if \"func\" in self.kwargs:\n if not self.kwargs[\"func\"](user):\n return False\n\n for p in self.permissions:\n if \".\" not in p:\n # Probably on the happeningsite object\n p = \"happening.%s\" % p\n if not user.has_perm(p):\n return False\n return True","title":""},{"docid":"4c81b54ae600ff37a5b1b11bf575bd14","score":"0.5747423","text":"def is_superuser(self):\r\n return self.access_level >= self.ACCESS_ROOT","title":""},{"docid":"4f3c1c38ef1f8d323ea254f07624d585","score":"0.5737781","text":"def can_access_map_hierarchy(self):\n return # boolean","title":""},{"docid":"9f8c9494aebab33a70e14563d399de35","score":"0.5727718","text":"def has_permission(self, request, view):\r\n return request.method in permissions.SAFE_METHODS or request.user.is_superuser","title":""},{"docid":"5fcb9eab7c2d34499bdbb166481582d9","score":"0.5710465","text":"def test_authorized_change_self_true_to_false(self):\n target_user_id = AUTHORIZED_UPDATER_ID\n self.assertTrue(app.find_authorization_in_db(\n target_user_id, app.app.config['COLLECTION']))\n self.client.post('/v1/authorization/update', data={\n 'target_user_id': target_user_id,\n 'is_organizer': False,\n **DUMMY_GAUTH_REQUEST_DATA})\n self.assertFalse(app.find_authorization_in_db(\n target_user_id, app.app.config['COLLECTION']))","title":""},{"docid":"0728738f67e3a352d19495f003f26205","score":"0.5674512","text":"async def hierarchy(self, ctx):\n server = ctx.message.server\n toggled = self.settings[server.id].get(\"respect_hierarchy\",\n default_settings[\"respect_hierarchy\"])\n if not toggled:\n self.settings[server.id][\"respect_hierarchy\"] = True\n await self.bot.say(\"Role hierarchy will be checked when \"\n \"moderation commands are issued.\")\n else:\n self.settings[server.id][\"respect_hierarchy\"] = False\n await self.bot.say(\"Role hierarchy will be ignored when \"\n \"moderation commands are issued.\")\n dataIO.save_json(\"data/mod/settings.json\", self.settings)","title":""},{"docid":"b41b08a0fc69b6a4ecacdfb453bd9d63","score":"0.56687194","text":"def canWrite (self, obj):\n \n return self.isAdmin() or (self.getUserId() == obj.getDetails().getOwner().getId() and\n obj.getDetails().getPermissions().isUserWrite())","title":""},{"docid":"5c599fd13ba67711e23071a898251fbc","score":"0.5655981","text":"async def is_admin(self):\n for user in self._users:\n if user[\"userName\"] == self._username:\n if user[\"level\"] == \"admin\":\n _LOGGER.debug(\n \"User %s has authorisation level %s\",\n self._username,\n user[\"level\"],\n )\n else:\n _LOGGER.warning(\n \"\"\"User %s has authorisation level %s. Only admin users can change\n camera settings! Switches will not work.\"\"\",\n self._username,\n user[\"level\"],\n )","title":""},{"docid":"d4d8be9fd1811be004b57f3487466c23","score":"0.5655306","text":"def can(self):\n return self.identity.can(self.permission)","title":""},{"docid":"f323651b4f73f44c03dc32facf452a8f","score":"0.56519675","text":"def is_accessible(self):\n return auth.check()","title":""},{"docid":"53f0f6b387f2c47df178560c8312752c","score":"0.5645688","text":"def has_permission(self, request, view):\r\n return request.user.is_superuser","title":""},{"docid":"4f15d14f085c1e67fb047f0ed158f8f9","score":"0.56189257","text":"def checkAttributePermission( self, attr, perm ):\n #It should work as follows (numbers denotes the order of roles search):\n #\n #for each attribute in each state may be set:\n # list of roles who can view attr and\n # list of roles who can change attr in current state\n #1. If roles are set (changed) for attribute in the state:\n # all users having one of listed roles can access to the attribute value.\n #\n #2. If roles are not yet set (changed) for attribute, try to acquire roles from state:\n # for viewing - permission 'View',\n # for editing - permission 'Modify portal content'\n #\n #3. If there is category inheritance and in the parent category there is\n # same state and same attribute, try to do as 1,2 but in parent category\n #\n #4. And so forth.\n\n category = self.getCategory()\n\n if not category:\n return None\n\n if type(attr) is StringType:\n attr = category.getAttributeDefinition(attr)\n\n if attr is None:\n return None\n\n attribute_id = attr.getId()\n\n membership = getToolByName(self, 'portal_membership', None)\n workflow = getToolByName( self, 'portal_workflow', None )\n if None in ( membership, workflow ):\n return 0\n\n user = membership.getAuthenticatedMember()\n user_roles = user.getRolesInContext(self)\n\n state = workflow.getStateFor( self )\n if state is None:\n #Most likely - no default states in category settings.\n return 0\n\n bases = [ category ]\n bases.extend( category.listBases(expand=1) )\n\n for category in bases:\n wf = category.__of__(self).getWorkflow()\n result = wf.getAttributePermissionInfo( state, attribute_id, perm )\n\n if result['acquired']: # and not result['roles']:\n #use parent's props\n sd = getattr( wf.states, state, None )\n if sd is None:\n continue\n\n if wf.states.isPrivateItem( sd ):\n #simply check permission\n #...hm, is it correct?\n return _checkPermission( perm, self )\n else:\n for u_role in user_roles:\n if u_role in result['roles']:\n return 1\n return 0\n return 0","title":""},{"docid":"febd8422a0020ba8a554a22d85030cff","score":"0.56091917","text":"def HasPermission(data):\n if not Parent.HasPermission(data.User, MySet.Permission, MySet.PermissionInfo):\n message = MySet.PermissionResponse.replace(\"$username\", data.UserName).replace(\"$permission\", MySet.Permission).replace(\"$permissioninfo\", MySet.PermissionInfo)\n SendResp(data, message)\n return False\n return True","title":""},{"docid":"abf85c8dde4dda067f9d92609ed45dc8","score":"0.56049603","text":"def can_update_relationships(self):\n return # boolean","title":""},{"docid":"e016799eb4a56aafd8410b154c0d5958","score":"0.5603641","text":"def can_modify(self, student):\n return student.user.has_perm('change_course', self) \\\n and student.user.has_perm('delete_course', self)","title":""},{"docid":"71f4e104b38d12d853279fb5c8b15d4d","score":"0.5594958","text":"def is_accessible(self):\n\n return current_user.is_authenticated and admin_permission.can()","title":""},{"docid":"71f4e104b38d12d853279fb5c8b15d4d","score":"0.5594958","text":"def is_accessible(self):\n\n return current_user.is_authenticated and admin_permission.can()","title":""},{"docid":"71f4e104b38d12d853279fb5c8b15d4d","score":"0.5594958","text":"def is_accessible(self):\n\n return current_user.is_authenticated and admin_permission.can()","title":""},{"docid":"31b84d1dbc59acaf2ef717189e1c1bca","score":"0.5586617","text":"def can_access_log_hierarchy(self):\n # Implemented from template for\n # osid.resource.BinHierarchySession.can_access_bin_hierarchy\n # NOTE: It is expected that real authentication hints will be\n # handled in a service adapter above the pay grade of this impl.\n if self._catalog_session is not None:\n return self._catalog_session.can_access_catalog_hierarchy()\n return True","title":""},{"docid":"230bcf0cf55a11ca067b326c83435d32","score":"0.5585937","text":"def canBeAdmin (self):\n return 0 in self.getEventContext().memberOfGroups","title":""},{"docid":"10c7d77fbc3469d08c1d8c1a79602dd9","score":"0.5579623","text":"def is_accessible(self):\n return False","title":""},{"docid":"f9a0336b54ed6bb28176b9b6912d6af4","score":"0.55739427","text":"def is_admin(self, bot, update):\n user = self.get_user_from_update(update)\n if not user.is_admin:\n bot.send_message(chat_id=update.effective_message.chat_id, text=Msg.missing_permission)\n return False\n return True","title":""},{"docid":"1f4cb7b41ebf2888d2bdd69e9817a040","score":"0.55646855","text":"def check_viewlog_permission(self, user, perm, obj):\n return user.has_perm(self.get_permission_name('change'))","title":""},{"docid":"4de747e5b8a114ad5aa2079fe4fa8241","score":"0.5556951","text":"def is_accessible(self):\n return True","title":""},{"docid":"bb0e8c1c76dd213c7946adb1a2c17c75","score":"0.5556138","text":"def _set_permissions (self):\n \n if self.data_object and self.parameter:\n for checker in self.checkers:\n checker.Enable ( )\n if self.check_method (self.data_object, self.parameter, checker.element):\n checker.SetValue (True)\n else:\n checker.SetValue (False)\n else:\n for checker in self.checkers:\n checker.SetValue (False)\n checker.Disable ( )","title":""},{"docid":"125855a6042fbc63a72040024cba67ec","score":"0.55527806","text":"def test_check_can_edit_super(self):\n self.assertTrue(check_can_edit(self.dat, self.user4))","title":""},{"docid":"5ff23ca27bfff044acc742f33b54cc4c","score":"0.5550484","text":"def canOwnerWrite (self, obj):\n return obj.getDetails().getPermissions().isUserWrite()","title":""},{"docid":"a0adba3dc7d59887e3c43fc8bd90550a","score":"0.55479497","text":"def __set_permissions(self):\n if not self.data_object:\n self.workplaces.Disable ( )\n for role in self.roles:\n role.SetValue (False)\n role.Disable ( )\n else:\n try:\n self.workplaces.Enable ( )\n workplace = self.workplaces.get_value ( )\n roles = self.data_object.roles[workplace]\n for role_checker in self.roles:\n if role_checker.element in workplace.roles:\n role_checker.Enable ( )\n role_checker.SetValue (role_checker.element in roles)\n else:\n role_checker.SetValue (False)\n role_checker.Disable ( )\n except:\n self.workplaces.Disable ( )\n for role in self.roles:\n role.SetValue (False)\n role.Disable ( )","title":""},{"docid":"ab34ad9a4e185416ad931497b8f4cc6c","score":"0.5531983","text":"def _set_permissions (self):\n \n if self.data_object:\n for checker in self.checkers:\n checker.Enable ( )\n if self.check_method (self.data_object, checker.element):\n checker.SetValue (True)\n else:\n checker.SetValue (False)\n else:\n for checker in self.checkers:\n checker.SetValue (False)\n checker.Disable ( )","title":""},{"docid":"2e033c8136ffe413b36875c508129aae","score":"0.5530145","text":"def writable(self) -> bool:\n\n user = self._target.session.current_user()\n group = self._target.session.find_group(gid=user.gid)\n mode = self.stat().st_mode\n uid = self.stat().st_uid\n gid = self.stat().st_gid\n\n if uid == user.id and (mode & stat.S_IWUSR):\n return True\n elif group.id == gid and (mode & stat.S_IWGRP):\n return True\n else:\n if group.id == gid and (mode & stat.S_IWGRP):\n return True\n else:\n if mode & stat.S_IWOTH:\n return True\n\n return False","title":""},{"docid":"92b7c76ccf80c13776a9610cb27d50d8","score":"0.55254436","text":"def has_access(self, group):\n is_in_group = self.__usermanager.user_is_in_group(group,\n user_id=self.__id)\n is_admin = self.__usermanager.user_is_in_group(\"admin\",\n user_id=self.__id)\n\n if is_in_group or is_admin:\n self.save()\n return True\n\n is_admin = self.__usermanager.verify_user(self.__id, self.__name,\n \"admin\")\n is_in_group = self.__usermanager.verify_user(self.__id, self.__name,\n group)\n\n return is_admin or is_in_group","title":""},{"docid":"0b700a16adfce4b2df9ba8fad1f3010d","score":"0.5516191","text":"def user_has_perm(self, user):\n if user.is_superuser:\n return True\n permission = self.permission.format(self.model.__name__.lower())\n permission = \"{}.{}\".format(self.model._meta.app_label, permission)\n return user.has_perm(permission)","title":""},{"docid":"3ae456d9023f1cf5225c8035230de53a","score":"0.55139494","text":"def check_view_permissions(self):\n if self.action in ('list', 'retrieve', 'create'):\n return self.get_access_permissions().check_permissions(self.request.user)\n return False","title":""},{"docid":"c5e26acd78e706dba47aa2f321e48f2d","score":"0.550224","text":"def can_change_userlevel(request_user, profile_user):\n request_user = validate_user(request_user)\n # admin can change user to all levels\n if request_user.has_admin_auth():\n return True\n # judge can change user to sub-judge, user\n user_level = profile_user.user_level\n if request_user.has_judge_auth() and \\\n (user_level == User.SUB_JUDGE or user_level == User.USER):\n return True\n\n return False","title":""},{"docid":"0e5564ca7a946f17d9aceda27ae008c3","score":"0.5476059","text":"def is_cannot_set_permissions(self):\n return self._tag == 'cannot_set_permissions'","title":""},{"docid":"0e5564ca7a946f17d9aceda27ae008c3","score":"0.5476059","text":"def is_cannot_set_permissions(self):\n return self._tag == 'cannot_set_permissions'","title":""},{"docid":"a43cf0ab3f263dd02883b69414cd7d3f","score":"0.5454196","text":"def has_perm(self, perm, obj=None):\n return self.is_admin","title":""},{"docid":"1a90e3743ed04c4d4cebe4d0517859a3","score":"0.54512495","text":"def has_access(self):\n return True","title":""},{"docid":"c14f5d8958324c40796a407cf3fb78e6","score":"0.5440727","text":"def is_owner_or_raise(self):\n\t\t# Enforce access rights\n\t\tif not self.has_full_access:\n\t\t\traise self._denier\n\t\treturn True","title":""},{"docid":"8f784457e99e3eb20000d30116ede014","score":"0.5440518","text":"def _check_perms(self):\n if self.skip_perms_check:\n return\n required_perm = COMMAND_TO_PERM[self.command]\n # first, check foruser if set. This will leave self.repo_perm set\n # to RepoPerm for foruser, so any error can be reported\n if self.foruser:\n LOG.debug(\"_check_perms for {}\".format(self.foruser))\n if not self.check_permissions(required_perm, user=self.foruser):\n self._raise_perms_error()\n # if that worked, check for authenticated user, resetting self.repo_perm\n if not self.check_permissions(required_perm):\n self._raise_perms_error()","title":""},{"docid":"2764c32e6b9a4d4e8566d1b5b821e1dc","score":"0.5431048","text":"def can_set(self) -> Result[bool]:\n\n return self._execute_command('canSet')","title":""},{"docid":"261d9f1f56fae736ab6f716ed7b4a51f","score":"0.54293495","text":"def can(self, user):\n if self.can_user(user):\n return True\n elif self.can_group(user):\n return True\n elif self.can_percentage(user):\n return True\n else:\n return False","title":""},{"docid":"fd5a6d3bd8cba3ca094405dfb4412d11","score":"0.5425347","text":"def check_object_permissions(self, request, obj):\n return True","title":""},{"docid":"831098857948384024310ee6e3b198f2","score":"0.54191947","text":"def check_view_permissions(self):\n if self.action in (\n \"create\",\n \"partial_update\",\n \"update\",\n \"destroy\",\n \"sort_related_users\",\n ):\n result = has_perm(self.request.user, \"assignments.can_see\") and has_perm(\n self.request.user, \"assignments.can_manage\"\n )\n elif self.action == \"candidature_self\":\n result = has_perm(self.request.user, \"assignments.can_see\") and has_perm(\n self.request.user, \"assignments.can_nominate_self\"\n )\n elif self.action == \"candidature_other\":\n result = has_perm(self.request.user, \"assignments.can_see\") and has_perm(\n self.request.user, \"assignments.can_nominate_other\"\n )\n else:\n result = False\n return result","title":""},{"docid":"4e2bdc5487d373a569f75734632877eb","score":"0.5412812","text":"def can_change(self, request):\n return True","title":""},{"docid":"7f954cbb8d304b31499651a4966b3779","score":"0.54066485","text":"def is_editable_by_current_user(self):\n\n return api.user.has_permission(\n 'Modify portal content', obj=self.context)","title":""},{"docid":"2c8c30f2efe0be511ef894ab98777ef9","score":"0.5394684","text":"def can_user_set(self, user, channel):\n perms = channel.permissions_for(user)\n return user.manage_channels or user.administrator","title":""}],"string":"[\n {\n \"docid\": \"f086a73bf3494ee3a3cea9a84eaad19a\",\n \"score\": \"0.7123733\",\n \"text\": \"def can_modify_objective_hierarchy(self):\\n return # boolean\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ae42bba99802e77eb731d9315fbcaf2\",\n \"score\": \"0.71223724\",\n \"text\": \"def change(self, user, energy, *args):\\n if user.is_anonymous or user.is_client:\\n return False\\n\\n if user.is_administrator:\\n return True\\n\\n if user.is_manager:\\n return False\\n\\n if user.is_advisor and user.group == energy.group:\\n return True\\n\\n return self.admin_permission(user, energy, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"326dab2fcfc11c6a3385a81bc894a803\",\n \"score\": \"0.70367\",\n \"text\": \"def can_modify_log_hierarchy(self):\\n # Implemented from template for\\n # osid.resource.BinHierarchyDesignSession.can_modify_bin_hierarchy_template\\n # NOTE: It is expected that real authentication hints will be\\n # handled in a service adapter above the pay grade of this impl.\\n if self._catalog_session is not None:\\n return self._catalog_session.can_modify_catalog_hierarchy()\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c63b93a3cf33cf0081f182be0ad3e7e3\",\n \"score\": \"0.70354694\",\n \"text\": \"def can_modify_location_hierarchy(self):\\n return # boolean\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eba8c36ed3d0fc460ce735581e8b7fca\",\n \"score\": \"0.6934173\",\n \"text\": \"def can_modify_objective_bank_hierarchy(self):\\n return # boolean\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eba8c36ed3d0fc460ce735581e8b7fca\",\n \"score\": \"0.6934173\",\n \"text\": \"def can_modify_objective_bank_hierarchy(self):\\n return # boolean\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eba8c36ed3d0fc460ce735581e8b7fca\",\n \"score\": \"0.6934173\",\n \"text\": \"def can_modify_objective_bank_hierarchy(self):\\n return # boolean\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3e191f8d0d0b022f488249f14cb5ad4\",\n \"score\": \"0.66498375\",\n \"text\": \"def has_change_permission(self, request, obj=None):\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3e191f8d0d0b022f488249f14cb5ad4\",\n \"score\": \"0.66498375\",\n \"text\": \"def has_change_permission(self, request, obj=None):\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3e191f8d0d0b022f488249f14cb5ad4\",\n \"score\": \"0.66498375\",\n \"text\": \"def has_change_permission(self, request, obj=None):\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16ab91f4e764f1c93cc98de15a17b6da\",\n \"score\": \"0.65783924\",\n \"text\": \"def check_change_permission(self, user, perm, obj):\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54e0f8ee1ff50cf83eb0d9d9792eeb84\",\n \"score\": \"0.6500337\",\n \"text\": \"def can_modify_map_hierarchy(self):\\n return # boolean\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ec207bfdcc96bf32208253eaddb4661\",\n \"score\": \"0.64400214\",\n \"text\": \"def check_change_object_permission(self, user, perm, obj):\\n return user.has_perm(self.get_permission_name('change'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc0e9522125ac95a83e422aaafda8609\",\n \"score\": \"0.64384574\",\n \"text\": \"def has_change_permission(self, request, obj=None):\\n perm = self.object_change_permission\\n if settings.FEINCMS_TREE_EDITOR_OBJECT_PERMISSIONS:\\n r = request.user.has_perm(perm, obj)\\n else:\\n r = request.user.has_perm(perm)\\n\\n return r and super(TreeEditor, self).has_change_permission(\\n request, obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9101230496d06bdc9f8d50b9a5c3e01d\",\n \"score\": \"0.6308406\",\n \"text\": \"def change(self, user, yearlyenergyprice, *args):\\n if user.is_anonymous or user.is_client:\\n return False\\n\\n if user.is_administrator:\\n return True\\n\\n if user.is_manager:\\n return False\\n\\n if user.is_advisor and user.group == yearlyenergyprice.group:\\n return True\\n\\n return self.admin_permission(user, yearlyenergyprice, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ee419014ef54c5d0221eb7251c19df0\",\n \"score\": \"0.63054144\",\n \"text\": \"def change(self, user, professional, *args):\\n if user.is_anonymous or user.is_client:\\n return False\\n\\n if user.is_professional:\\n return True\\n\\n if user.is_administrator:\\n return True\\n\\n if user.is_advisor:\\n return True\\n\\n return self.admin_permission(user, professional, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21116e49d78228aca38949b5a8fd33bb\",\n \"score\": \"0.62654173\",\n \"text\": \"def check_permission(self, context, instance):\\n permission = '{0}.change_{1}'.format(instance._meta.app_label,\\n instance._meta.module_name)\\n return context['user'].has_perm(permission)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c24be45e6f53c353c5fac1fe1057a16\",\n \"score\": \"0.6231308\",\n \"text\": \"def can_access_objective_hierarchy(self):\\n return # boolean\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f01ca44f3d7b1375ec2255247908e53\",\n \"score\": \"0.6134754\",\n \"text\": \"def check_edit_permission(self):\\n if not any('change' in item for item in self.get_permissions()):\\n raise PermissionDenied()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a52df81f115fb14cef89e5c89377dccb\",\n \"score\": \"0.6119095\",\n \"text\": \"def check_read_permission(self, user, perm, obj):\\n return user.has_perm(self.get_permission_name('change'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"695d6dbe0711f095b7443ecf67bcc3e3\",\n \"score\": \"0.6055337\",\n \"text\": \"def can_access_location_hierarchy(self):\\n return # boolean\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e173a769d9ffd166694679d08f8e276c\",\n \"score\": \"0.60433006\",\n \"text\": \"def has_change_permission(self, request, obj=None):\\r\\n return request.user.is_staff and (obj is not None) and (obj.id % 2 == 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1afbb28dfdd377291f475208186804b5\",\n \"score\": \"0.6035344\",\n \"text\": \"def has_change_permission(self, request, obj=None, strict=False):\\n change_perm = super().has_change_permission(request, obj=obj)\\n approve_perm = self.has_signoff_approve_permission(request, obj=obj)\\n either_perm = change_perm or (approve_perm and not strict)\\n return either_perm and (not obj or self.is_pending_signoff(obj))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57cde41a3ad6fe26e840c64d84b425aa\",\n \"score\": \"0.60105664\",\n \"text\": \"def can_access_objective_bank_hierarchy(self):\\n return # boolean\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57cde41a3ad6fe26e840c64d84b425aa\",\n \"score\": \"0.60105664\",\n \"text\": \"def can_access_objective_bank_hierarchy(self):\\n return # boolean\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57cde41a3ad6fe26e840c64d84b425aa\",\n \"score\": \"0.60105664\",\n \"text\": \"def can_access_objective_bank_hierarchy(self):\\n return # boolean\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30ff08b8b26ea6ad926c40328b89cb1f\",\n \"score\": \"0.59874535\",\n \"text\": \"def has_change_permission(self, request, obj=None):\\n info = self.parent_model._meta.app_label, self.parent_model._meta.model_name\\n url_name = u'{}_{}_change'.format(*info)\\n return url_name == request.resolver_match.url_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b480cdd29b962b00eac1ef13451b738\",\n \"score\": \"0.5984149\",\n \"text\": \"def perform_update(self, serializer):\\n\\n user = self.request.user\\n obj = self.get_object()\\n data = serializer.validated_data\\n\\n is_active_change = ('is_active' in data and\\n obj.is_active != data['is_active'])\\n is_staff_change = ('is_staff' in data and\\n obj.is_staff != data['is_staff'])\\n\\n if ((is_staff_change or is_active_change) and not user.is_staff):\\n raise PermissionDenied(\\\"Non-staff user cannot change \\\"\\n \\\"'is_staff' or 'is_active'\\\")\\n serializer.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c71c665b6da6712db8637bb041f9011\",\n \"score\": \"0.59692115\",\n \"text\": \"def check_permissions(cls, info: ResolverInfo) -> bool:\\n user = info.context.user\\n\\n if not cls._meta.public and not check_authenticated(user):\\n return False\\n\\n if not cls._meta.permissions:\\n return True\\n\\n return check_perms(user, cls._meta.permissions, any_perm=cls._meta.permissions_any)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"becf3ca384cc189cf53ace9dcc61a7a8\",\n \"score\": \"0.5965556\",\n \"text\": \"def has_change_permission(self, request, obj=None):\\r\\n opts = self.opts\\r\\n codename = get_permission_codename('change', opts)\\r\\n return request.user.has_perm(\\\"%s.%s\\\" % (opts.app_label, codename))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"119a7787b5edf9a3886512232aa46c73\",\n \"score\": \"0.5964482\",\n \"text\": \"def can(self):\\n identity = get_identity(self.user)\\n return (\\n is_owner(self.user, self.record) or\\n (\\n Permission(menrva_edit_published_record).allows(identity) and\\n has_published(self.record)\\n ) or\\n Permission(menrva_edit).allows(identity)\\n # NOTE: by default any Permission has a super-user Need\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5cfe3a8beb83ef352de7dbb63bb04837\",\n \"score\": \"0.5953403\",\n \"text\": \"def can_write(self, user):\\n return user.is_superuser or self.is_owner(user)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ecdc7c251ba998ee68c2a48b26dcf0a\",\n \"score\": \"0.5952299\",\n \"text\": \"def has_edit_permission(self, user: settings.AUTH_USER_MODEL) -> bool:\\n return user.is_superuser or user == self.owner\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97782ee43b7acc12486517a1201fdd7f\",\n \"score\": \"0.5916611\",\n \"text\": \"def check_list_permission(self, user, perm, obj):\\n return user.has_perm(self.get_permission_name('change'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f44abee2275fb7376af120e654474ede\",\n \"score\": \"0.5910422\",\n \"text\": \"def is_admin(self):\\n return self._is_allowed(self.ADMIN_PERMISSION)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a23e70d7ae4615c4fe6bb24422abd6fa\",\n \"score\": \"0.59097165\",\n \"text\": \"def has_change_permission(self, request, obj=None):\\n opts = self.opts\\n codename = get_permission_codename(\\\"change\\\", opts)\\n return request.user.has_perm(\\\"%s.%s\\\" % (opts.app_label, codename))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc7baf806a8252b35fe7b3cb854516c5\",\n \"score\": \"0.5898846\",\n \"text\": \"def can_write(self, user):\\n return user.is_superuser or self.is_in_users(user) or self.is_owner(user)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29869365906d3f277f35fb029cbd5490\",\n \"score\": \"0.58698875\",\n \"text\": \"def canEdit(self):\\n return self.getDetails().getPermissions().canEdit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba4461ef7f171df7fff18e9c1f424cb7\",\n \"score\": \"0.58698493\",\n \"text\": \"def check_permissions(self, cleaned_data):\\n user = self.request.user\\n if not co.CheckPermissions(user, self.instance, co.CAN_EDIT):\\n raise ValidationError('Operation can not be performed.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82f7d30c4f6577e2348331a6448dbcf7\",\n \"score\": \"0.5846985\",\n \"text\": \"def can_access_family_hierarchy(self):\\n return # boolean\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82f7d30c4f6577e2348331a6448dbcf7\",\n \"score\": \"0.5846985\",\n \"text\": \"def can_access_family_hierarchy(self):\\n return # boolean\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82f7d30c4f6577e2348331a6448dbcf7\",\n \"score\": \"0.5846985\",\n \"text\": \"def can_access_family_hierarchy(self):\\n return # boolean\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d471640b2e580f0a4c409696fb8558bf\",\n \"score\": \"0.58413994\",\n \"text\": \"def has_permission(self):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9908e48ad7c9445b462ec6e8d90106b0\",\n \"score\": \"0.58315104\",\n \"text\": \"def is_accessible(self):\\n return admin_permission.can()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9908e48ad7c9445b462ec6e8d90106b0\",\n \"score\": \"0.58315104\",\n \"text\": \"def is_accessible(self):\\n return admin_permission.can()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9908e48ad7c9445b462ec6e8d90106b0\",\n \"score\": \"0.58315104\",\n \"text\": \"def is_accessible(self):\\n return admin_permission.can()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9908e48ad7c9445b462ec6e8d90106b0\",\n \"score\": \"0.58315104\",\n \"text\": \"def is_accessible(self):\\n return admin_permission.can()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9908e48ad7c9445b462ec6e8d90106b0\",\n \"score\": \"0.58315104\",\n \"text\": \"def is_accessible(self):\\n return admin_permission.can()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9908e48ad7c9445b462ec6e8d90106b0\",\n \"score\": \"0.58315104\",\n \"text\": \"def is_accessible(self):\\n return admin_permission.can()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32e5a7a6fc1bda99c95f5a2d6af78e6e\",\n \"score\": \"0.5829296\",\n \"text\": \"def change_allow_level(self):\\n return self._change_allow_level\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13db659673f91961e8200c5d30fa74f5\",\n \"score\": \"0.58086085\",\n \"text\": \"def can_edit(user, simulation):\\n if simulation.user == user or user.is_superuser:\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0696d73ff32c02341df9f1e42a3a4a04\",\n \"score\": \"0.5786341\",\n \"text\": \"def has_permission(self, user):\\n if user.is_anonymous:\\n return False\\n if user.groups.filter(\\n pk=get_admin_group().pk).count() == 0:\\n # We give full permissions to anyone in the admin group\\n\\n if \\\"func\\\" in self.kwargs:\\n if not self.kwargs[\\\"func\\\"](user):\\n return False\\n\\n for p in self.permissions:\\n if \\\".\\\" not in p:\\n # Probably on the happeningsite object\\n p = \\\"happening.%s\\\" % p\\n if not user.has_perm(p):\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c81b54ae600ff37a5b1b11bf575bd14\",\n \"score\": \"0.5747423\",\n \"text\": \"def is_superuser(self):\\r\\n return self.access_level >= self.ACCESS_ROOT\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f3c1c38ef1f8d323ea254f07624d585\",\n \"score\": \"0.5737781\",\n \"text\": \"def can_access_map_hierarchy(self):\\n return # boolean\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f8c9494aebab33a70e14563d399de35\",\n \"score\": \"0.5727718\",\n \"text\": \"def has_permission(self, request, view):\\r\\n return request.method in permissions.SAFE_METHODS or request.user.is_superuser\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5fcb9eab7c2d34499bdbb166481582d9\",\n \"score\": \"0.5710465\",\n \"text\": \"def test_authorized_change_self_true_to_false(self):\\n target_user_id = AUTHORIZED_UPDATER_ID\\n self.assertTrue(app.find_authorization_in_db(\\n target_user_id, app.app.config['COLLECTION']))\\n self.client.post('/v1/authorization/update', data={\\n 'target_user_id': target_user_id,\\n 'is_organizer': False,\\n **DUMMY_GAUTH_REQUEST_DATA})\\n self.assertFalse(app.find_authorization_in_db(\\n target_user_id, app.app.config['COLLECTION']))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0728738f67e3a352d19495f003f26205\",\n \"score\": \"0.5674512\",\n \"text\": \"async def hierarchy(self, ctx):\\n server = ctx.message.server\\n toggled = self.settings[server.id].get(\\\"respect_hierarchy\\\",\\n default_settings[\\\"respect_hierarchy\\\"])\\n if not toggled:\\n self.settings[server.id][\\\"respect_hierarchy\\\"] = True\\n await self.bot.say(\\\"Role hierarchy will be checked when \\\"\\n \\\"moderation commands are issued.\\\")\\n else:\\n self.settings[server.id][\\\"respect_hierarchy\\\"] = False\\n await self.bot.say(\\\"Role hierarchy will be ignored when \\\"\\n \\\"moderation commands are issued.\\\")\\n dataIO.save_json(\\\"data/mod/settings.json\\\", self.settings)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b41b08a0fc69b6a4ecacdfb453bd9d63\",\n \"score\": \"0.56687194\",\n \"text\": \"def canWrite (self, obj):\\n \\n return self.isAdmin() or (self.getUserId() == obj.getDetails().getOwner().getId() and\\n obj.getDetails().getPermissions().isUserWrite())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c599fd13ba67711e23071a898251fbc\",\n \"score\": \"0.5655981\",\n \"text\": \"async def is_admin(self):\\n for user in self._users:\\n if user[\\\"userName\\\"] == self._username:\\n if user[\\\"level\\\"] == \\\"admin\\\":\\n _LOGGER.debug(\\n \\\"User %s has authorisation level %s\\\",\\n self._username,\\n user[\\\"level\\\"],\\n )\\n else:\\n _LOGGER.warning(\\n \\\"\\\"\\\"User %s has authorisation level %s. Only admin users can change\\n camera settings! Switches will not work.\\\"\\\"\\\",\\n self._username,\\n user[\\\"level\\\"],\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4d8be9fd1811be004b57f3487466c23\",\n \"score\": \"0.5655306\",\n \"text\": \"def can(self):\\n return self.identity.can(self.permission)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f323651b4f73f44c03dc32facf452a8f\",\n \"score\": \"0.56519675\",\n \"text\": \"def is_accessible(self):\\n return auth.check()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53f0f6b387f2c47df178560c8312752c\",\n \"score\": \"0.5645688\",\n \"text\": \"def has_permission(self, request, view):\\r\\n return request.user.is_superuser\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f15d14f085c1e67fb047f0ed158f8f9\",\n \"score\": \"0.56189257\",\n \"text\": \"def checkAttributePermission( self, attr, perm ):\\n #It should work as follows (numbers denotes the order of roles search):\\n #\\n #for each attribute in each state may be set:\\n # list of roles who can view attr and\\n # list of roles who can change attr in current state\\n #1. If roles are set (changed) for attribute in the state:\\n # all users having one of listed roles can access to the attribute value.\\n #\\n #2. If roles are not yet set (changed) for attribute, try to acquire roles from state:\\n # for viewing - permission 'View',\\n # for editing - permission 'Modify portal content'\\n #\\n #3. If there is category inheritance and in the parent category there is\\n # same state and same attribute, try to do as 1,2 but in parent category\\n #\\n #4. And so forth.\\n\\n category = self.getCategory()\\n\\n if not category:\\n return None\\n\\n if type(attr) is StringType:\\n attr = category.getAttributeDefinition(attr)\\n\\n if attr is None:\\n return None\\n\\n attribute_id = attr.getId()\\n\\n membership = getToolByName(self, 'portal_membership', None)\\n workflow = getToolByName( self, 'portal_workflow', None )\\n if None in ( membership, workflow ):\\n return 0\\n\\n user = membership.getAuthenticatedMember()\\n user_roles = user.getRolesInContext(self)\\n\\n state = workflow.getStateFor( self )\\n if state is None:\\n #Most likely - no default states in category settings.\\n return 0\\n\\n bases = [ category ]\\n bases.extend( category.listBases(expand=1) )\\n\\n for category in bases:\\n wf = category.__of__(self).getWorkflow()\\n result = wf.getAttributePermissionInfo( state, attribute_id, perm )\\n\\n if result['acquired']: # and not result['roles']:\\n #use parent's props\\n sd = getattr( wf.states, state, None )\\n if sd is None:\\n continue\\n\\n if wf.states.isPrivateItem( sd ):\\n #simply check permission\\n #...hm, is it correct?\\n return _checkPermission( perm, self )\\n else:\\n for u_role in user_roles:\\n if u_role in result['roles']:\\n return 1\\n return 0\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"febd8422a0020ba8a554a22d85030cff\",\n \"score\": \"0.56091917\",\n \"text\": \"def HasPermission(data):\\n if not Parent.HasPermission(data.User, MySet.Permission, MySet.PermissionInfo):\\n message = MySet.PermissionResponse.replace(\\\"$username\\\", data.UserName).replace(\\\"$permission\\\", MySet.Permission).replace(\\\"$permissioninfo\\\", MySet.PermissionInfo)\\n SendResp(data, message)\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abf85c8dde4dda067f9d92609ed45dc8\",\n \"score\": \"0.56049603\",\n \"text\": \"def can_update_relationships(self):\\n return # boolean\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e016799eb4a56aafd8410b154c0d5958\",\n \"score\": \"0.5603641\",\n \"text\": \"def can_modify(self, student):\\n return student.user.has_perm('change_course', self) \\\\\\n and student.user.has_perm('delete_course', self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71f4e104b38d12d853279fb5c8b15d4d\",\n \"score\": \"0.5594958\",\n \"text\": \"def is_accessible(self):\\n\\n return current_user.is_authenticated and admin_permission.can()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71f4e104b38d12d853279fb5c8b15d4d\",\n \"score\": \"0.5594958\",\n \"text\": \"def is_accessible(self):\\n\\n return current_user.is_authenticated and admin_permission.can()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71f4e104b38d12d853279fb5c8b15d4d\",\n \"score\": \"0.5594958\",\n \"text\": \"def is_accessible(self):\\n\\n return current_user.is_authenticated and admin_permission.can()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31b84d1dbc59acaf2ef717189e1c1bca\",\n \"score\": \"0.5586617\",\n \"text\": \"def can_access_log_hierarchy(self):\\n # Implemented from template for\\n # osid.resource.BinHierarchySession.can_access_bin_hierarchy\\n # NOTE: It is expected that real authentication hints will be\\n # handled in a service adapter above the pay grade of this impl.\\n if self._catalog_session is not None:\\n return self._catalog_session.can_access_catalog_hierarchy()\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"230bcf0cf55a11ca067b326c83435d32\",\n \"score\": \"0.5585937\",\n \"text\": \"def canBeAdmin (self):\\n return 0 in self.getEventContext().memberOfGroups\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10c7d77fbc3469d08c1d8c1a79602dd9\",\n \"score\": \"0.5579623\",\n \"text\": \"def is_accessible(self):\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9a0336b54ed6bb28176b9b6912d6af4\",\n \"score\": \"0.55739427\",\n \"text\": \"def is_admin(self, bot, update):\\n user = self.get_user_from_update(update)\\n if not user.is_admin:\\n bot.send_message(chat_id=update.effective_message.chat_id, text=Msg.missing_permission)\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f4cb7b41ebf2888d2bdd69e9817a040\",\n \"score\": \"0.55646855\",\n \"text\": \"def check_viewlog_permission(self, user, perm, obj):\\n return user.has_perm(self.get_permission_name('change'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4de747e5b8a114ad5aa2079fe4fa8241\",\n \"score\": \"0.5556951\",\n \"text\": \"def is_accessible(self):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb0e8c1c76dd213c7946adb1a2c17c75\",\n \"score\": \"0.5556138\",\n \"text\": \"def _set_permissions (self):\\n \\n if self.data_object and self.parameter:\\n for checker in self.checkers:\\n checker.Enable ( )\\n if self.check_method (self.data_object, self.parameter, checker.element):\\n checker.SetValue (True)\\n else:\\n checker.SetValue (False)\\n else:\\n for checker in self.checkers:\\n checker.SetValue (False)\\n checker.Disable ( )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"125855a6042fbc63a72040024cba67ec\",\n \"score\": \"0.55527806\",\n \"text\": \"def test_check_can_edit_super(self):\\n self.assertTrue(check_can_edit(self.dat, self.user4))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ff23ca27bfff044acc742f33b54cc4c\",\n \"score\": \"0.5550484\",\n \"text\": \"def canOwnerWrite (self, obj):\\n return obj.getDetails().getPermissions().isUserWrite()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0adba3dc7d59887e3c43fc8bd90550a\",\n \"score\": \"0.55479497\",\n \"text\": \"def __set_permissions(self):\\n if not self.data_object:\\n self.workplaces.Disable ( )\\n for role in self.roles:\\n role.SetValue (False)\\n role.Disable ( )\\n else:\\n try:\\n self.workplaces.Enable ( )\\n workplace = self.workplaces.get_value ( )\\n roles = self.data_object.roles[workplace]\\n for role_checker in self.roles:\\n if role_checker.element in workplace.roles:\\n role_checker.Enable ( )\\n role_checker.SetValue (role_checker.element in roles)\\n else:\\n role_checker.SetValue (False)\\n role_checker.Disable ( )\\n except:\\n self.workplaces.Disable ( )\\n for role in self.roles:\\n role.SetValue (False)\\n role.Disable ( )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab34ad9a4e185416ad931497b8f4cc6c\",\n \"score\": \"0.5531983\",\n \"text\": \"def _set_permissions (self):\\n \\n if self.data_object:\\n for checker in self.checkers:\\n checker.Enable ( )\\n if self.check_method (self.data_object, checker.element):\\n checker.SetValue (True)\\n else:\\n checker.SetValue (False)\\n else:\\n for checker in self.checkers:\\n checker.SetValue (False)\\n checker.Disable ( )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e033c8136ffe413b36875c508129aae\",\n \"score\": \"0.5530145\",\n \"text\": \"def writable(self) -> bool:\\n\\n user = self._target.session.current_user()\\n group = self._target.session.find_group(gid=user.gid)\\n mode = self.stat().st_mode\\n uid = self.stat().st_uid\\n gid = self.stat().st_gid\\n\\n if uid == user.id and (mode & stat.S_IWUSR):\\n return True\\n elif group.id == gid and (mode & stat.S_IWGRP):\\n return True\\n else:\\n if group.id == gid and (mode & stat.S_IWGRP):\\n return True\\n else:\\n if mode & stat.S_IWOTH:\\n return True\\n\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92b7c76ccf80c13776a9610cb27d50d8\",\n \"score\": \"0.55254436\",\n \"text\": \"def has_access(self, group):\\n is_in_group = self.__usermanager.user_is_in_group(group,\\n user_id=self.__id)\\n is_admin = self.__usermanager.user_is_in_group(\\\"admin\\\",\\n user_id=self.__id)\\n\\n if is_in_group or is_admin:\\n self.save()\\n return True\\n\\n is_admin = self.__usermanager.verify_user(self.__id, self.__name,\\n \\\"admin\\\")\\n is_in_group = self.__usermanager.verify_user(self.__id, self.__name,\\n group)\\n\\n return is_admin or is_in_group\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b700a16adfce4b2df9ba8fad1f3010d\",\n \"score\": \"0.5516191\",\n \"text\": \"def user_has_perm(self, user):\\n if user.is_superuser:\\n return True\\n permission = self.permission.format(self.model.__name__.lower())\\n permission = \\\"{}.{}\\\".format(self.model._meta.app_label, permission)\\n return user.has_perm(permission)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ae456d9023f1cf5225c8035230de53a\",\n \"score\": \"0.55139494\",\n \"text\": \"def check_view_permissions(self):\\n if self.action in ('list', 'retrieve', 'create'):\\n return self.get_access_permissions().check_permissions(self.request.user)\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5e26acd78e706dba47aa2f321e48f2d\",\n \"score\": \"0.550224\",\n \"text\": \"def can_change_userlevel(request_user, profile_user):\\n request_user = validate_user(request_user)\\n # admin can change user to all levels\\n if request_user.has_admin_auth():\\n return True\\n # judge can change user to sub-judge, user\\n user_level = profile_user.user_level\\n if request_user.has_judge_auth() and \\\\\\n (user_level == User.SUB_JUDGE or user_level == User.USER):\\n return True\\n\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e5564ca7a946f17d9aceda27ae008c3\",\n \"score\": \"0.5476059\",\n \"text\": \"def is_cannot_set_permissions(self):\\n return self._tag == 'cannot_set_permissions'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e5564ca7a946f17d9aceda27ae008c3\",\n \"score\": \"0.5476059\",\n \"text\": \"def is_cannot_set_permissions(self):\\n return self._tag == 'cannot_set_permissions'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a43cf0ab3f263dd02883b69414cd7d3f\",\n \"score\": \"0.5454196\",\n \"text\": \"def has_perm(self, perm, obj=None):\\n return self.is_admin\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a90e3743ed04c4d4cebe4d0517859a3\",\n \"score\": \"0.54512495\",\n \"text\": \"def has_access(self):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c14f5d8958324c40796a407cf3fb78e6\",\n \"score\": \"0.5440727\",\n \"text\": \"def is_owner_or_raise(self):\\n\\t\\t# Enforce access rights\\n\\t\\tif not self.has_full_access:\\n\\t\\t\\traise self._denier\\n\\t\\treturn True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f784457e99e3eb20000d30116ede014\",\n \"score\": \"0.5440518\",\n \"text\": \"def _check_perms(self):\\n if self.skip_perms_check:\\n return\\n required_perm = COMMAND_TO_PERM[self.command]\\n # first, check foruser if set. This will leave self.repo_perm set\\n # to RepoPerm for foruser, so any error can be reported\\n if self.foruser:\\n LOG.debug(\\\"_check_perms for {}\\\".format(self.foruser))\\n if not self.check_permissions(required_perm, user=self.foruser):\\n self._raise_perms_error()\\n # if that worked, check for authenticated user, resetting self.repo_perm\\n if not self.check_permissions(required_perm):\\n self._raise_perms_error()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2764c32e6b9a4d4e8566d1b5b821e1dc\",\n \"score\": \"0.5431048\",\n \"text\": \"def can_set(self) -> Result[bool]:\\n\\n return self._execute_command('canSet')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"261d9f1f56fae736ab6f716ed7b4a51f\",\n \"score\": \"0.54293495\",\n \"text\": \"def can(self, user):\\n if self.can_user(user):\\n return True\\n elif self.can_group(user):\\n return True\\n elif self.can_percentage(user):\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd5a6d3bd8cba3ca094405dfb4412d11\",\n \"score\": \"0.5425347\",\n \"text\": \"def check_object_permissions(self, request, obj):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"831098857948384024310ee6e3b198f2\",\n \"score\": \"0.54191947\",\n \"text\": \"def check_view_permissions(self):\\n if self.action in (\\n \\\"create\\\",\\n \\\"partial_update\\\",\\n \\\"update\\\",\\n \\\"destroy\\\",\\n \\\"sort_related_users\\\",\\n ):\\n result = has_perm(self.request.user, \\\"assignments.can_see\\\") and has_perm(\\n self.request.user, \\\"assignments.can_manage\\\"\\n )\\n elif self.action == \\\"candidature_self\\\":\\n result = has_perm(self.request.user, \\\"assignments.can_see\\\") and has_perm(\\n self.request.user, \\\"assignments.can_nominate_self\\\"\\n )\\n elif self.action == \\\"candidature_other\\\":\\n result = has_perm(self.request.user, \\\"assignments.can_see\\\") and has_perm(\\n self.request.user, \\\"assignments.can_nominate_other\\\"\\n )\\n else:\\n result = False\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e2bdc5487d373a569f75734632877eb\",\n \"score\": \"0.5412812\",\n \"text\": \"def can_change(self, request):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f954cbb8d304b31499651a4966b3779\",\n \"score\": \"0.54066485\",\n \"text\": \"def is_editable_by_current_user(self):\\n\\n return api.user.has_permission(\\n 'Modify portal content', obj=self.context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c8c30f2efe0be511ef894ab98777ef9\",\n \"score\": \"0.5394684\",\n \"text\": \"def can_user_set(self, user, channel):\\n perms = channel.permissions_for(user)\\n return user.manage_channels or user.administrator\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":881,"cells":{"query_id":{"kind":"string","value":"1659d94502ed19e7b08785e4a0c75390"},"query":{"kind":"string","value":"Add a service variable of type ``ty`` to this model"},"positive_passages":{"kind":"list like","value":[{"docid":"88bde6386277d2c2911bb53b42cd1230","score":"0.6668062","text":"def service_define(self, service, ty):\n\n if service in self._data:\n raise NameError('Service variable <{}> already defined in _data'.format(service))\n if service in self._algebs:\n raise NameError('Service variable <{}> already defined in _algebs'.format(service))\n if service in self._states:\n raise NameError('Service variable <{}> already defined in _states'.format(service))\n\n self._service.append(service)\n self._service_ty.append(ty)","title":""}],"string":"[\n {\n \"docid\": \"88bde6386277d2c2911bb53b42cd1230\",\n \"score\": \"0.6668062\",\n \"text\": \"def service_define(self, service, ty):\\n\\n if service in self._data:\\n raise NameError('Service variable <{}> already defined in _data'.format(service))\\n if service in self._algebs:\\n raise NameError('Service variable <{}> already defined in _algebs'.format(service))\\n if service in self._states:\\n raise NameError('Service variable <{}> already defined in _states'.format(service))\\n\\n self._service.append(service)\\n self._service_ty.append(ty)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"9defd39fc9f8510cf3e2c3a479654431","score":"0.6076422","text":"def add(stype: ServiceType, service, name: str = None, *args, **kwargs):\n name = service.__name__ if name is None else name\n dynamic_provider = stype.value(service, *args, **kwargs)\n containers.DynamicContainer.__setattr__(\n Services.dynamic, name, dynamic_provider)","title":""},{"docid":"3592212c5dee85235a01bbbcff19fa73","score":"0.5992147","text":"def add_variable(\n self,\n name: str,\n value: typing.Any,\n type_: str = None,\n value_info: typing.Mapping = None\n ):\n self.variables[name] = {'value': value, 'type': type_, 'valueInfo': value_info}\n\n return self","title":""},{"docid":"1164a49bd4570e37925ffefd43c67229","score":"0.5949808","text":"def add_variable(\n self,\n name: str,\n value: typing.Any,\n type_: str = None,\n value_info: typing.Mapping = None\n ) -> None:\n self.variables[name] = {'value': value, 'type': type_, 'valueInfo': value_info}","title":""},{"docid":"d3fa2a549b3fbcb831f3296c938707b2","score":"0.5577186","text":"def add_additional_variables(self, var_name, var_type):\n self.additional_variables.append(Variable(var_name, '{}, intent(in):: {}'.format(var_type, var_name)))","title":""},{"docid":"5ef8f1293cb0d78bed7dd9506d03fa4f","score":"0.5563456","text":"def add_variable(self, name, attributes):\n self.variables[name] = self.VarStub(attributes)","title":""},{"docid":"e9645ccb11b2eae161856c0bb1f830a5","score":"0.55307704","text":"def add_service_def_to_schema(self, service_type):\n self.schema_dict_tmpl[SERVICE_KEY][service_type] = \\\n copy.deepcopy(SERVICE_DICT_TMPL)","title":""},{"docid":"d91270f4f3e5aadde2f796bfc6cbe8ee","score":"0.55147415","text":"def ServiceAdd(self, name, type, addr, required, optional):\n\t\tprint \"ServiceAdd\", self, name, type, addr, required, optional","title":""},{"docid":"2f3d8348ebc7bc29cf3c5c9e357c746a","score":"0.5456986","text":"def var_define(self, variable, ty, fname, descr='', uname=''):\n if ty not in ('x', 'y'):\n raise ValueError('Type <{}> of the variable <{}> is invalid'.format(ty, variable))\n\n if not uname:\n uname = variable\n\n if ty == 'x':\n self._states.append(variable)\n self._fnamex.append(fname)\n self._unamex.append(uname)\n if descr:\n self._states_descr.update({variable: descr})\n elif ty == 'y':\n self._algebs.append(variable)\n self._fnamey.append(fname)\n self._unamey.append(uname)\n if descr:\n self._algebs_descr.update({variable: descr})","title":""},{"docid":"69e6d6c7f0416249e3d1b62b72b62b78","score":"0.53908265","text":"async def async_set_variable_service_handler(service: ServiceCall) -> None:\n address = service.data.get(CONF_ADDRESS)\n vtype = service.data.get(CONF_TYPE)\n name = service.data.get(CONF_NAME)\n value = service.data.get(CONF_VALUE)\n init = service.data.get(CONF_INIT, False)\n isy_name = service.data.get(CONF_ISY)\n\n for config_entry_id in hass.data[DOMAIN]:\n isy_data = hass.data[DOMAIN][config_entry_id]\n isy = isy_data.root\n if isy_name and isy_name != isy.conf[\"name\"]:\n continue\n variable = None\n if name:\n variable = isy.variables.get_by_name(name)\n if address and vtype:\n variable = isy.variables.vobjs[vtype].get(address)\n if variable is not None:\n await variable.set_value(value, init)\n entity_registry = er.async_get(hass)\n async_log_deprecated_service_call(\n hass,\n call=service,\n alternate_service=\"number.set_value\",\n alternate_target=entity_registry.async_get_entity_id(\n Platform.NUMBER,\n DOMAIN,\n f\"{isy.uuid}_{address}{'_init' if init else ''}\",\n ),\n breaks_in_ha_version=\"2023.5.0\",\n )\n return\n _LOGGER.error(\"Could not set variable value; not found or enabled on the ISY\")","title":""},{"docid":"0408b07492ffe40ff87b32d5a79318f5","score":"0.5369281","text":"def add_variable(self, name, value):\n self[name] = value","title":""},{"docid":"b0541000e7bb74fb26ade412e014fb97","score":"0.5238458","text":"def add_variable(self, var: 'Constant'):\n self.__variables[var.name] = var","title":""},{"docid":"962a46b03c91a0e89c14d384e7f631a0","score":"0.5218415","text":"def register_service(self, service):\n self.services.append(service)","title":""},{"docid":"298f859d4d66ab8dc5ba7c58739bd3a9","score":"0.5217007","text":"def add(self, var, expression):\n expression = Polynomial.cast(expression)\n var.type = VariableType.TYPE # make var a type variable\n self._equations[var] = expression\n\n # register variables in the system.\n self._register_variable(var)\n self._register_expressions(expression)","title":""},{"docid":"6ced6762373522c7466b293e13335e11","score":"0.517509","text":"def register_variable(variable, is_independent=False, is_equation=False):\n PuddleRepository.variables.add(variable)\n if is_independent:\n PuddleRepository.independent_variables.add(variable)\n if is_equation:\n PuddleRepository.equations.add(variable)","title":""},{"docid":"72d2314cb8d744553cc0aa449ef03596","score":"0.5170673","text":"def add_var(self, var_name, required, dtype=None, dim=None, units=None):\n self.variables[var_name] = Variable(dtype, dim, units)\n if required:\n self.required_vars.append(var_name)\n else:\n self.optional_vars.append(var_name)","title":""},{"docid":"778596abb7f52bbd18b1fc1f3d2ca999","score":"0.5154649","text":"def Potential(name, var, model=None):\r\n\r\n var.name = name\r\n modelcontext(model).potentials.append(var)\r\n return var","title":""},{"docid":"9197efc30dfe97dcddc7a0e45e6e2f33","score":"0.51336783","text":"def add(self, var, value, where='dataset', reload=True):\n self._run(['--add', var, value], where=where, reload=reload, log_stderr=True)","title":""},{"docid":"84559dc0b26564911b7b60b9d01aeb1f","score":"0.51019806","text":"def add_variable(self, var, domain):\n if var in self.variables:\n raise Exception(\"Variable name already exists: %s\" % str(var))\n\n self.numVars += 1\n self.variables.append(var)\n self.values[var] = domain\n self.unaryFactors[var] = None\n self.binaryFactors[var] = dict()","title":""},{"docid":"4fc8b3c9b4bdb49630113516ed29ff6b","score":"0.5091044","text":"def add_variable(self,name, opt_value=0):\n\t\tself.variables[name] = opt_value","title":""},{"docid":"73a95e41a740930f2898481d8b6f9784","score":"0.5074586","text":"def var(self, name, type=\"float32\", shape=None):\n if isinstance(name, Variable): return name\n v = self.vars.get(name, None)\n if v == None:\n v = Variable(name)\n self.vars[name] = v\n v.type = type\n if shape is not None: v.shape = shape\n return v","title":""},{"docid":"be9535e3cf2217e4ec40a85589ca8352","score":"0.50581855","text":"def Variable(initial_value, name, trainable=None):\n raise NotImplementedError","title":""},{"docid":"845a7fb0185db44efeeefd6c4d0ce4fb","score":"0.5056541","text":"def addService(self, id_service, desc_service, host, port):\n # TODO\n self.services[id_service] = [desc_service, host, port]","title":""},{"docid":"1784a3c81f67489f3af2f67762e854ec","score":"0.5042215","text":"def var():\n\n return variable.Variable(\"generic_var\",template_units.kg_s,\"A generic var\")","title":""},{"docid":"b28ccf3f3e5504994d442fd086f76baa","score":"0.5020835","text":"def __setattr__(self, key, var):\n if not self._after_init:\n object.__setattr__(self, key, var)\n else:\n if type(var) is VariableInfo or var is None:\n object.__setattr__(self, key, var)\n # add it to the list of variables\n self._variables.append(key)\n else:\n raise TypeError(\"Can only set attributes of type VariableInfo.\")","title":""},{"docid":"b18b911166b4e57113a96dad0363061b","score":"0.5010512","text":"def add_workflowservice(wes_id):\n set_yaml(section='workflowservices', var2add=wes_id)","title":""},{"docid":"dbcbd25faabfc615d9d61ae83d298409","score":"0.49794123","text":"def add_service_ref_to_solution(self, service_type):\n self.schema_dict_tmpl[SOLUTION_KEY][self.app_name][SERVICE_KEY]\\\n .append({\"$ref\": topic_ref_transform(\"#/services/{0}\", service_type)})","title":""},{"docid":"024678819d4cc34a71ae2e45740064fc","score":"0.49627677","text":"def add_variable(self, variable):\n if not isinstance(variable, Variable):\n raise TypeError(f\"Expected {Variable}; got {type(variable)}.\")\n self.variables[variable.name] = variable","title":""},{"docid":"024678819d4cc34a71ae2e45740064fc","score":"0.49627677","text":"def add_variable(self, variable):\n if not isinstance(variable, Variable):\n raise TypeError(f\"Expected {Variable}; got {type(variable)}.\")\n self.variables[variable.name] = variable","title":""},{"docid":"70b9fb29bb32820944e7ba15bb2ca145","score":"0.4937653","text":"def step_impl(context, name, type_env):\n type_value = eval(type_env)\n context.data['type_env'][name] = type_value","title":""},{"docid":"5684265b28e6fec35c1f0f0f8f53a1b8","score":"0.49179962","text":"def register_service(self, service):\n self._services.append(service)","title":""},{"docid":"054391b1e377bed457c433e23cd3aed3","score":"0.49073106","text":"def add_var(self, name: str, size: int) -> None:\n self.named_variables.append(name)\n self.var_offset[name] = self.var_total_size\n self.var_size[name] = size\n self.var_total_size += size","title":""},{"docid":"2fb56f52cd21385c775b4130a8a4f625","score":"0.49039307","text":"def __inject(model, model_context):\n __kwargs[variable_injector.VARIABLE_FILE_APPEND_ARG] = variable_injector.VARIABLE_FILE_UPDATE\n inserted, variable_model, variable_file_name = VariableInjector(_program_name, model, model_context,\n WebLogicHelper(\n __logger).get_actual_weblogic_version()). \\\n inject_variables_keyword_file(**__kwargs)\n if inserted:\n model = Model(variable_model)\n return inserted, model","title":""},{"docid":"a8539f3cad57b511a87fe52c09debd7f","score":"0.4900647","text":"def add_symbol(self, key, ty, val): # pylint: disable=invalid-name\n if key in self.symbols.keys():\n old = str(self.symbols[key])\n new = str((ty, val))\n _internal_assert(False, f\"Name conflict in symbol table! [{key}] {old} -> {new}\")\n\n self.symbols[key] = ty, val\n\n if ty == Symbol.ThreadBind:\n if val.var.name not in self.binds.keys():\n self.binds[val.var.name] = val\n return\n val_ = self.binds[val.var.name]\n _internal_assert(\n tvm.tir.analysis.expr_deep_equal(val_.dom.extent, val.dom.extent),\n \"Thread extents should be uniform!\",\n )\n self.symbols[key] = ty, val_","title":""},{"docid":"ecfff0f9c2e52140e4db0843aacbf130","score":"0.4894691","text":"def addVarReg():\n\n cl.cycleOverList('addVarReg')","title":""},{"docid":"e3b72b81307247c0f690038494214360","score":"0.48906937","text":"def add_variable(self, name, label, style='.-', color=None, use_std=True):\n\n plot = {\n 'name': name,\n 'label': label,\n 'style': style,\n 'use_std': use_std,\n 'color': color,\n 'data': [],\n 'data_inf': [],\n 'data_sup': [],\n\n 'line': None,\n 'line_std': None\n }\n\n self._variables.append(plot)","title":""},{"docid":"f9afe34c14c0a26fdf21f2109b334199","score":"0.48824722","text":"def add_service(\n self,\n service_type: str,\n handler: ScanHandler,\n device_info_extractor: DevInfoExtractor,\n ) -> None:\n self._services[service_type] = (handler, device_info_extractor)","title":""},{"docid":"78d4a1aaad48ce397bf59ebe8bfa94f5","score":"0.4875754","text":"def define(self, name, var_type, kind):\n # creates new var with the next index\n new_var = Variable(name, var_type, kind, self.__var_segment_count[kind])\n # adds the var to the correct dictionary\n if kind in CLASS_VAR_DEC_KEYWORDS:\n self.__class_variables[name] = new_var\n else:\n self.__subroutine_variables[name] = new_var\n\n self.__var_segment_count[kind] += 1 # increment the next index of that kind","title":""},{"docid":"ea6c241bbd5399df19bbb65a56f574d9","score":"0.48703286","text":"def set_service(self, name, obj):\r\n self.__service_map[name] = obj","title":""},{"docid":"5a48106cf07f26247012102f727f824a","score":"0.48689204","text":"def set_variable(self, id: str, value: Any) -> None:\n self.__variables[id] = value","title":""},{"docid":"cb13ea8e2033127b154cd4d49a762287","score":"0.4868534","text":"def add_service(session):\n service = SpeechLocalization(session)\n id = session.registerService(SpeechLocalization.service_name, service)\n return [service, id]","title":""},{"docid":"4c0beb438d0c5b3661bf7a00284d14c1","score":"0.48595047","text":"def create_variable(self): #, name, is_input_feed=False, is_python=False, local=False, **kwargs):\n raise NotImplementedError","title":""},{"docid":"2eda9ea2d8b4802327cc277d959b7527","score":"0.48450002","text":"def add(self, tier: str):\n self.tiers.append(tier)","title":""},{"docid":"254a2541b304e2672a5c235e4e0f585d","score":"0.48305318","text":"def variable(type_name, name):\n var_type = ee_types.nameToClass(type_name) or computedobject.ComputedObject\n result = var_type.__new__(var_type)\n result.func = None\n result.args = None\n result.varName = name\n return result","title":""},{"docid":"1d94069190d94f57b6b28eca7ab223f9","score":"0.48005414","text":"def _register_variable(self, v):\n\n if v.idx is not None:\n return # nothing to do.\n\n v.idx = self._next_idx()\n self._all_variables[v.idx] = v\n\n if v.tuning_param is not None:\n self._tuned_variables.add(v)\n\n # case over the variable's type\n if isinstance(v, Seq):\n self._seq_variables.add(v)\n self._register_expressions(v.inner_expressions)\n\n elif isinstance(v, MSet):\n self._mset_variables.add(v)\n self._register_expressions(v.inner_expressions)\n\n elif isinstance(v, UCyc):\n self._ucyc_variables.add(v)\n self._register_expressions(v.inner_expressions)\n\n elif isinstance(v, Set):\n self._set_variables.add(v)\n self._register_expressions(v.inner_expressions)\n\n elif isinstance(v, Cyc):\n self._cyc_variables.add(v)\n self._register_expressions(v.inner_expressions)","title":""},{"docid":"4f27d76eed78930dd0a6d872d994d0c8","score":"0.47718805","text":"def add_input_variable(self, name, vartype, **kw):\n input_var = Variable(name, precision = vartype, **kw)\n self.arg_list.append(input_var)\n # WARNING: self.function_type.arg_list_precision is not updated\n return input_var","title":""},{"docid":"06e929d45e3aae61aacaa7abf3bf071d","score":"0.47694758","text":"def add_variable(self, vartype, var):\n var.body = self.id\n\n super(Body, self).add_variable(vartype, var)","title":""},{"docid":"9cf5cb78b6c06dcdcac406a34cb9724a","score":"0.47619912","text":"def add_var_hook(self, name, cb = None):\n\n if type(name).__name__ == 'dict':\n self._hooks.update(name)\n \n elif type(name).__name__ == 'str' and callable(cb):\n self._hooks[name] = cb","title":""},{"docid":"8c762d1905afc842ad483a8e1474ec48","score":"0.47408482","text":"def __setattr__(self, name, value):\n if name == 'recipe':\n # set the prodtype list\n try:\n self.prodtypes = [self.prodtype_map[step] for step in value]\n except AttributeError:\n self.prodtypes = list()\n super().__setattr__(name, value)","title":""},{"docid":"25aa544b97659805f627a20f7bdd61cc","score":"0.4736419","text":"def add_stkvar(*args):\r\n return _idaapi.add_stkvar(*args)","title":""},{"docid":"42f196f2e87744d06f70d24d61967f5b","score":"0.4714275","text":"def opt_var(self, var_name, dtype=None, dim=None, units=None):\n self.variables[var_name] = Variable(dtype, dim, units)\n self.optional_vars.append(var_name)","title":""},{"docid":"e80022b5ed6edbf664ca32526d5c11be","score":"0.46884003","text":"def _type_variable(self):\n v = Variable()\n v.type = VariableType.TYPE\n self._register_variable(v)\n return v","title":""},{"docid":"83c93cd48db82f68b8389bc667e637f1","score":"0.4688399","text":"def add_regvar(*args):\r\n return _idaapi.add_regvar(*args)","title":""},{"docid":"09de7da085bd94d00d2db85aab74c540","score":"0.46875712","text":"def add_var(self, name, definition):\n if name in self.variables:\n raise UpdateMakefilesException('variable already exists: %s' % name)\n self.variables[name] = Makefile.Variable(name, definition)","title":""},{"docid":"e3ca0e04715da7487d455188deb04a64","score":"0.46831843","text":"def _set_tvar(self, key, val):\n keyt, edit = translate(str(key), str(val))\n # if keyt == \"SCHOST\" and val == \"ccb\": raise DefError\n if self.load is None:\n self.load = ecflow.Variable(keyt, edit)\n else:\n next = self.next\n self.next = Edit(keyt, edit, next)","title":""},{"docid":"c4b68749a428281109e1143307749eb8","score":"0.46808255","text":"def register_parameter(self, variable, as_weight=False, as_bias=False, name=None, **collection_key_value_pairs):\n # Get variable name from variable if name is not given\n name = name if name is not None else variable.name\n # Attach collection key-value pairs\n utils.add_to_antipasti_collection(variable, antipasti_name=name, **collection_key_value_pairs)\n # Write to dict\n self._parameters['[LayerID:{}][{}]'.format(self.name, name)] = variable\n # Add to GraphKeys.TRAINABLE_VARIABLES if trainable\n if utils.is_antipasti_trainable(variable):\n A.add_to_collection(A.Collections.TRAINABLE_VARIABLES, variable)\n # Add to Collections.REGULARIZABLE_VARIABLES if regularizable\n if utils.is_antipasti_regularizable(variable):\n A.add_to_collection(A.Collections.REGULARIZABLE_VARIABLES, variable)\n # Add to GraphKeys.WEIGHTS if parameter is being registered as weight\n if as_weight:\n A.add_to_collection(A.Collections.WEIGHTS, variable)\n # Add to GraphKeys.BIASES if parameter is being registered as a bias\n if as_bias:\n A.add_to_collection(A.Collections.BIASES, variable)\n # Done.\n return variable","title":""},{"docid":"a48f9c6b5dbc432a584cc5096d9a1c44","score":"0.4678225","text":"def addVariable(\n self, component, group, parm, value=None, lower=None, upper=None, scale=1.0, scaledStep=True, dh=1e-6\n ):\n\n container_id = self.vspModel.FindContainer(component, 0)\n if container_id == \"\":\n raise Error(\"Bad component for DV: %s\" % component)\n\n parm_id = self.vspModel.FindParm(container_id, parm, group)\n if parm_id == \"\":\n raise Error(f\"Bad group or parm: {component} {group} {parm}\")\n\n # Now we know the parmID is ok. So we just get the value\n val = self.vspModel.GetParmVal(parm_id)\n\n dvName = f\"{component}:{group}:{parm}\"\n\n if value is None:\n value = val\n\n if scaledStep:\n dh = dh * value\n\n if value == 0:\n raise Error(\n \"Initial value is exactly 0. scaledStep option cannot be used\"\n \"Specify an explicit dh with scaledStep=False\"\n )\n\n self.DVs[dvName] = vspDV(parm_id, dvName, component, group, parm, value, lower, upper, scale, dh)","title":""},{"docid":"0429bf31ef91086fdf672452fa5981d1","score":"0.4675014","text":"def add_service(self, svc_id: str, service: ServiceBase):\n self._services[svc_id] = service","title":""},{"docid":"b5d4224122a95fee641111e54b867ad2","score":"0.46585572","text":"def add_component_to_skymodel(sm: SkyModel, comp: SkyComponent):\n sm.components.append(comp)\n return sm","title":""},{"docid":"28149eb10f1ec09b7c4f0d0206de5074","score":"0.46506104","text":"def provide(self, typ: Type[T], inst: T):\n self._instances[typ] = inst","title":""},{"docid":"5d17fde5c15b1eb590666033e4bce0dd","score":"0.46450153","text":"def Variable(self, variables, workSpace):","title":""},{"docid":"2b884a4f3754b090de619b0001e86300","score":"0.4641824","text":"def add_random_variable(self, var):\r\n self.named_vars[var.name] = var\r\n if not hasattr(self, var.name):\r\n setattr(self, var.name, var)","title":""},{"docid":"6d888912c210c69e4fc5f7f10eed23ca","score":"0.46408108","text":"def make_var(step, var_type, options):\n switch = {\"linear\": make_linear_var, \"exponential\": make_exponential_var}\n return switch[var_type](step=step, **options)","title":""},{"docid":"3783e07dfbaf9f1ee738e37d858513ad","score":"0.46239263","text":"def req_var(self, var_name, dtype=None, dim=None, units=None):\n self.variables[var_name] = Variable(dtype, dim, units)\n self.required_vars.append(var_name)","title":""},{"docid":"1cc61cc199f2c634257e661fb5ed7322","score":"0.46196917","text":"def addFeatureSpec(self, name, spec):\n spec = dict(spec)\n typeName = spec.pop('type', None)\n featureCls = self.getFeatureClass(typeName)\n self._features[name] = featureCls(**spec)","title":""},{"docid":"f384b2f19e43a291fab1ecdc762c8035","score":"0.4615893","text":"def Deterministic(name, var, model=None):\r\n var.name = name\r\n modelcontext(model).deterministics.append(var)\r\n modelcontext(model).add_random_variable(var)\r\n return var","title":""},{"docid":"b31a73c73065b6d3b29698a4d041edb1","score":"0.4614608","text":"def get_trained_variables(self, Session,atom_types=[]):","title":""},{"docid":"4a02774a85dda2b741bbe669873dae17","score":"0.46116033","text":"def service_add(self, **args) -> \"Service\":\n proto = self.bundle().service_prototype(**args)\n with allure_step(f\"Add service {proto.name} to cluster {self.name}\"):\n data = self._subcall(\"service\", \"create\", prototype_id=proto.id, cluster_id=self.id)\n return Service(self._api, id=data['id'])","title":""},{"docid":"6dcb00737cc618fffe02427e40f5ce16","score":"0.4608607","text":"def add_feat( self, feat ) :\n self.features.add( feat.name, feat.value )","title":""},{"docid":"9f685857a5e4295baacff6a0b90be24d","score":"0.4605806","text":"def add_servicepoint(self, identifier, callback):\n self._services[identifier] = callback","title":""},{"docid":"51a939c890994fe01e003ece3f5e371b","score":"0.46022916","text":"def add_triple(domain_name,objectproprety,range_name,path):\n onto = get_ontology(path).load()\n with onto:\n exec('onto.{0}.{1} = [onto.{2}]'.format(domain_name,objectproprety,range_name))\n # onto.test1.has_for_ingredient = [onto.acetaminophen]\n onto.save(path)","title":""},{"docid":"050ab371dc3ef3d31b4a907fadbd7500","score":"0.45820224","text":"def addSpeciesType(self, *args):\n return _libsbml.Model_addSpeciesType(self, *args)","title":""},{"docid":"2423f46247ca24f3df7dd52ca5107061","score":"0.45807213","text":"def add_service(self,service):\n if service in self.services:\n pass #service already exists\n else:\n self.services.append(service)","title":""},{"docid":"b5b31a2384dfa09acd7ef2d25ee1101c","score":"0.45603275","text":"def __init__(\n self_,\n name: str,\n type: SyntheticsConfigVariableType,\n example: Union[str, UnsetType] = unset,\n id: Union[str, UnsetType] = unset,\n pattern: Union[str, UnsetType] = unset,\n secure: Union[bool, UnsetType] = unset,\n **kwargs,\n ):\n if example is not unset:\n kwargs[\"example\"] = example\n if id is not unset:\n kwargs[\"id\"] = id\n if pattern is not unset:\n kwargs[\"pattern\"] = pattern\n if secure is not unset:\n kwargs[\"secure\"] = secure\n super().__init__(kwargs)\n\n self_.name = name\n self_.type = type","title":""},{"docid":"26d7714c1c17b8c30d6beaf0b459e65e","score":"0.45563242","text":"def _set_service_config_var(service, var_name, value):\n run(\n \"echo '%s: %s' | sudo tee -a /var/opt/magma/configs/%s.yml\" % (\n var_name, str(value), service,\n ),\n )","title":""},{"docid":"51bc5594f03c033631733987a8b94c67","score":"0.4554668","text":"def type(self, type):\n\n self.container['type'] = type","title":""},{"docid":"3d5183973eee60784baa08a7d0ea35e1","score":"0.45537454","text":"def _hook_variable(self):\n # Overload 'special' methods here\n self._hook___new__(torch.autograd.variable.Variable)\n self._hook_var_contents()\n self._hook_var_owners()\n\n for attr in dir(torch.autograd.variable.Variable):\n\n # Conditions for inclusion/exclusion\n if attr in self.exclude + self.var_exclude:\n continue\n lit = getattr(torch.autograd.variable.Variable, attr)\n is_base = attr in dir(object)\n is_desc = inspect.ismethoddescriptor(lit)\n # is_func = isinstance(type(lit), types.FunctionType)\n is_func = isinstance(lit, types.FunctionType)\n try:\n is_service_func = 'HookService' in lit.__qualname__\n except:\n is_service_func = False\n is_old = re.match('old*', attr) is not None\n\n # Where the overloading happens\n if ((is_desc or (is_func and not is_service_func)) and not is_base and not is_old):\n passer = utils.pass_method_args(lit)\n new_attr = self._get_overload_method_in_tensor_or_var(passer)\n setattr(torch.autograd.variable.Variable,\n 'old_{}'.format(attr), lit)\n setattr(torch.autograd.variable.Variable, attr, new_attr)\n\n self._hook_send_(torch.autograd.variable.Variable)\n self._hook_get_(torch.autograd.variable.Variable)\n self._hook_var_serde()","title":""},{"docid":"4e929d8b87b5e081f80a4ee8f822f056","score":"0.45482057","text":"def model(self, name, model):\n\n self.models[name] = model","title":""},{"docid":"954250acfbdac177818e95e9c1eda8ed","score":"0.45444962","text":"def add_modelservice_to_registry(self, modelservice_name, ModelServiceClassDef):\n self.modelservice_registry[modelservice_name] = ModelServiceClassDef","title":""},{"docid":"1b13ea8eba5ea76709b973cd73e7f7e4","score":"0.45395696","text":"def add_variable(nc, var):\n if nc is None:\n raise AttributeError(\"No file was specified\")\n if isinstance(nc, netCDF4._netCDF4.Dataset):\n pass\n else:\n nc = netCDF4.Dataset(nc, \"a\")\n\n # Handle the dimensions by enforcing a tuple list rather\n # than a list of strings, then add whatever we have\n try:\n dims = var['dims'].replace(\" \", \"\").split(',')\n except:\n dims = var['dims']\n try:\n nvar = nc.createVariable(var[\"name\"], var[\"type\"], dims)\n except:\n nvar = nc.createVariable(var[\"name\"], var[\"type\"])\n\n try:\n for key in var[\"attr\"]:\n # Check if it is a number and convert\n astr = __number_or_string(var[\"attr\"][key])\n setattr(nvar, key, astr)\n except KeyError:\n pass\n\n return nc","title":""},{"docid":"e2007eed04f9d79d37cb2d12f40eaa1c","score":"0.45244172","text":"def addVariable(self, name : str, value) -> LookUpTable:\n # Check if variable exists \n result = self.modVariable(name, value) \n if result is None:\n newItem = LookUpItem(name, value)\n return LookUpTable(self.items + [newItem])\n else:\n return result","title":""},{"docid":"6fe30ca8aac6e53b9cfcfa2c78760ec5","score":"0.45215234","text":"def seed_type(self, name, typ):\n self.typevars[name].lock(typ)","title":""},{"docid":"1a554f5e1ad78308080dd2e8dcf41c03","score":"0.4517167","text":"def AddSymbol(self,name,stype,body=None):\n if self.IsSymbol(name):\n raise Exception('Program Bug -- name \"%s\" already exists is symbols' % name);\n self.symbols['list'].append(name);\n self.symbols['type'].append(stype);\n self.symbols['body'].append(body);","title":""},{"docid":"afd0af436c849e3a11d8e676caf08cf9","score":"0.45027423","text":"def _add_weight(self, name, initial_value, dtype=None):\n variable = variable_v1.VariableV1(\n initial_value=initial_value,\n name=name,\n dtype=dtype,\n trainable=False,\n use_resource=True,\n synchronization=variables.VariableSynchronization.AUTO,\n # Set aggregation to NONE, as loss scaling variables should never be\n # aggregated.\n aggregation=variables.VariableAggregation.NONE)\n if context.executing_eagerly():\n graph_key = None\n else:\n graph = ops.get_default_graph()\n graph_key = graph._graph_key # pylint: disable=protected-access\n\n key = (name, graph_key)\n if self._weights.get(key, None) is not None:\n raise RuntimeError('Duplicate variables detected. {}'.format(key))\n self._weights[key] = variable\n self._handle_deferred_dependencies(name=name, trackable=variable)\n return variable","title":""},{"docid":"19c9f61ffb201c3cd36364280d3f3321","score":"0.44942945","text":"def _add_to_dict(self, name, attr):\n if name not in self._dict:\n logger.debug(\"Added variable '%s' to collection\", name)\n self._dict[name] = attr","title":""},{"docid":"1d64d7b1e9d7dcd83285585c625cbce8","score":"0.44941947","text":"def AddVariable(self, variable):\n callResult = self._Call(\"AddVariable\", variable)","title":""},{"docid":"4387e584cf37f38ca7b3b40f91b7d189","score":"0.4493591","text":"def _add_ttl_ns(self, line):\n lg = logging.getLogger(\"%s.%s\" % (self.ln, inspect.stack()[0][3]))\n lg.setLevel(self.log_level)\n\n lg.debug(\"line:\\n%s\", line)\n line = str(line).strip()\n # if the line is not a prefix line exit\n if line is None or line == 'none' or line == '' \\\n or not line.lower().startswith('@prefix'):\n return\n # parse the turtle line\n line = line.replace(\"@prefix\",\"\",1).strip()\n if line.endswith(\".\"):\n line = line[:-1]\n prefix = line[:line.find(\":\")].strip()\n uri = self.clean_iri(line[line.find(\":\")+1:].strip())\n # add the namespace to the class\n lg.debug(\"\\nprefix: %s uri: %s\", prefix, uri)\n self.bind(prefix, uri, override=False, calc=False)","title":""},{"docid":"e51362f31c5b35dd0258c125ae5c7aa3","score":"0.44895974","text":"def service(self, service):\n\n self._service = service","title":""},{"docid":"7c3493e6d4ddd2c8d1d1c25202443291","score":"0.44788188","text":"def __setitem__(self, name: str, value):\n attribute = self.model[name]\n if not isinstance(value, attribute.type):\n value = attribute.type(value)\n super().__setitem__(name, value)","title":""},{"docid":"4e05e4f277b531c1119e4daec0dc6084","score":"0.44782597","text":"def add_vars(self, **names):\n for (name, attr) in names.items():\n if isinstance(attr, Variable):\n attr_var = attr\n else:\n attr_var = Variable(\n name,\n attr.get(n.STANDARD_NAME, DEFAULT_INFO),\n attr.get(n.LONG_NAME, DEFAULT_INFO),\n attr.get(n.UNITS, DEFAULT_INFO))\n self._add_to_dict(name, attr_var)","title":""},{"docid":"75ae13c69cf34e6bee58d23bb49ea474","score":"0.44745833","text":"def add(self, val: object) -> None:\n self._ctx[type(val)] = val","title":""},{"docid":"65ef7ee407a390011f398cc52e7e352c","score":"0.4469828","text":"def put_variable(self, col: str, name: str, value: Any):\n if self.scope is None:\n raise ValueError(\"Can't access variables on unbound modules\")\n self.scope.put_variable(col, name, value)","title":""},{"docid":"f15198a08547e33a19f010e2898717e3","score":"0.445989","text":"def add_variable(self, name, description, default=None, min=None, max=None, edit_method=\"\"):\n if type(default) == int:\n if edit_method == \"\":\n self.add(name, \"int\", 0, description, default, min, max)\n else: # enum\n self.add(name, \"int\", 0, description, default, min, max, edit_method)\n elif type(default) == float:\n self.add(name, \"double\", 0, description, default, min, max)\n elif type(default) == str:\n self.add(name, \"str\", 0, description, default)\n elif type(default) == bool:\n self.add(name, \"bool\", 0, description, default)\n\n return default","title":""},{"docid":"07789a59bae2a38738250a04f45259e9","score":"0.44525698","text":"def add_auto_stkpnt(*args):\r\n return _idaapi.add_auto_stkpnt(*args)","title":""},{"docid":"0156454b525cdc3e2964299e2ec54f9b","score":"0.44474766","text":"def substitutetype(ty):\n\n ty = ty.replace(\"std::\", \"\")\n ty = re.sub(r\"(.*)<(.*)>\",\"\\\\1< \\\\2 >\",ty)\n ty = re.sub(r\"glm::tvec(.*)< T, P >\", \"glm::vec\\\\1\", ty)\n ty = re.sub(r\"tvec(.*)< T, P >\", \"glm::vec\\\\1\", ty)\n ty = re.sub(r\"glm::tmat(.*)< T, P >\", \"glm::mat\\\\1\", ty)\n ty = re.sub(r\"tmat(.*)< T, P >\", \"glm::mat\\\\1\", ty)\n ty = ty.replace(\"glm::mat2x2\", \"glm::mat2\")\n ty = ty.replace(\"glm::mat3x3\", \"glm::mat3\")\n ty = ty.replace(\"glm::mat4x4\", \"glm::mat4\")\n ty = re.sub(r\"glm::tquat(.*)< T, P >\", \"glm::quat\\\\1\", ty)\n ty = re.sub(r\"tquat(.*)< T, P >\", \"glm::quat\\\\1\", ty)\n return ty","title":""},{"docid":"614db44ba135598f6aa013d6d3e24fbf","score":"0.44427925","text":"def add_feature(self, word, feature_name, is_test):\n self.features[word][feature_name] = 1\n if not is_test:\n self.feat_freq[feature_name] += 1","title":""},{"docid":"3cc64afd02b922e3516aaa2d0f8e9d57","score":"0.44409594","text":"def define(self, key, value):\n self.bindings[key] = value","title":""},{"docid":"a29d39a72159d29329b007249c0ff8a1","score":"0.44374675","text":"def add_type(type, ext, strict=True):\n if _db is None:\n init()\n return _db.add_type(type, ext, strict)","title":""},{"docid":"507292c0496fcaa2f2d177e85e5dcf6a","score":"0.443726","text":"def add_variable(self):\n\n self._max_var_id += 1\n\n return CNFExprVar(self._max_var_id)","title":""},{"docid":"193fce564413aab280711e344891165c","score":"0.44345352","text":"def __setattr__(self, name, value):\n self.scope[name] = value","title":""},{"docid":"520d0e4dc24e362c3395271899874cc0","score":"0.44314107","text":"def add(self, name, value):\n self.local_dictionary[name] = value","title":""}],"string":"[\n {\n \"docid\": \"9defd39fc9f8510cf3e2c3a479654431\",\n \"score\": \"0.6076422\",\n \"text\": \"def add(stype: ServiceType, service, name: str = None, *args, **kwargs):\\n name = service.__name__ if name is None else name\\n dynamic_provider = stype.value(service, *args, **kwargs)\\n containers.DynamicContainer.__setattr__(\\n Services.dynamic, name, dynamic_provider)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3592212c5dee85235a01bbbcff19fa73\",\n \"score\": \"0.5992147\",\n \"text\": \"def add_variable(\\n self,\\n name: str,\\n value: typing.Any,\\n type_: str = None,\\n value_info: typing.Mapping = None\\n ):\\n self.variables[name] = {'value': value, 'type': type_, 'valueInfo': value_info}\\n\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1164a49bd4570e37925ffefd43c67229\",\n \"score\": \"0.5949808\",\n \"text\": \"def add_variable(\\n self,\\n name: str,\\n value: typing.Any,\\n type_: str = None,\\n value_info: typing.Mapping = None\\n ) -> None:\\n self.variables[name] = {'value': value, 'type': type_, 'valueInfo': value_info}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3fa2a549b3fbcb831f3296c938707b2\",\n \"score\": \"0.5577186\",\n \"text\": \"def add_additional_variables(self, var_name, var_type):\\n self.additional_variables.append(Variable(var_name, '{}, intent(in):: {}'.format(var_type, var_name)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ef8f1293cb0d78bed7dd9506d03fa4f\",\n \"score\": \"0.5563456\",\n \"text\": \"def add_variable(self, name, attributes):\\n self.variables[name] = self.VarStub(attributes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9645ccb11b2eae161856c0bb1f830a5\",\n \"score\": \"0.55307704\",\n \"text\": \"def add_service_def_to_schema(self, service_type):\\n self.schema_dict_tmpl[SERVICE_KEY][service_type] = \\\\\\n copy.deepcopy(SERVICE_DICT_TMPL)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d91270f4f3e5aadde2f796bfc6cbe8ee\",\n \"score\": \"0.55147415\",\n \"text\": \"def ServiceAdd(self, name, type, addr, required, optional):\\n\\t\\tprint \\\"ServiceAdd\\\", self, name, type, addr, required, optional\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f3d8348ebc7bc29cf3c5c9e357c746a\",\n \"score\": \"0.5456986\",\n \"text\": \"def var_define(self, variable, ty, fname, descr='', uname=''):\\n if ty not in ('x', 'y'):\\n raise ValueError('Type <{}> of the variable <{}> is invalid'.format(ty, variable))\\n\\n if not uname:\\n uname = variable\\n\\n if ty == 'x':\\n self._states.append(variable)\\n self._fnamex.append(fname)\\n self._unamex.append(uname)\\n if descr:\\n self._states_descr.update({variable: descr})\\n elif ty == 'y':\\n self._algebs.append(variable)\\n self._fnamey.append(fname)\\n self._unamey.append(uname)\\n if descr:\\n self._algebs_descr.update({variable: descr})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69e6d6c7f0416249e3d1b62b72b62b78\",\n \"score\": \"0.53908265\",\n \"text\": \"async def async_set_variable_service_handler(service: ServiceCall) -> None:\\n address = service.data.get(CONF_ADDRESS)\\n vtype = service.data.get(CONF_TYPE)\\n name = service.data.get(CONF_NAME)\\n value = service.data.get(CONF_VALUE)\\n init = service.data.get(CONF_INIT, False)\\n isy_name = service.data.get(CONF_ISY)\\n\\n for config_entry_id in hass.data[DOMAIN]:\\n isy_data = hass.data[DOMAIN][config_entry_id]\\n isy = isy_data.root\\n if isy_name and isy_name != isy.conf[\\\"name\\\"]:\\n continue\\n variable = None\\n if name:\\n variable = isy.variables.get_by_name(name)\\n if address and vtype:\\n variable = isy.variables.vobjs[vtype].get(address)\\n if variable is not None:\\n await variable.set_value(value, init)\\n entity_registry = er.async_get(hass)\\n async_log_deprecated_service_call(\\n hass,\\n call=service,\\n alternate_service=\\\"number.set_value\\\",\\n alternate_target=entity_registry.async_get_entity_id(\\n Platform.NUMBER,\\n DOMAIN,\\n f\\\"{isy.uuid}_{address}{'_init' if init else ''}\\\",\\n ),\\n breaks_in_ha_version=\\\"2023.5.0\\\",\\n )\\n return\\n _LOGGER.error(\\\"Could not set variable value; not found or enabled on the ISY\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0408b07492ffe40ff87b32d5a79318f5\",\n \"score\": \"0.5369281\",\n \"text\": \"def add_variable(self, name, value):\\n self[name] = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0541000e7bb74fb26ade412e014fb97\",\n \"score\": \"0.5238458\",\n \"text\": \"def add_variable(self, var: 'Constant'):\\n self.__variables[var.name] = var\",\n \"title\": \"\"\n },\n {\n \"docid\": \"962a46b03c91a0e89c14d384e7f631a0\",\n \"score\": \"0.5218415\",\n \"text\": \"def register_service(self, service):\\n self.services.append(service)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"298f859d4d66ab8dc5ba7c58739bd3a9\",\n \"score\": \"0.5217007\",\n \"text\": \"def add(self, var, expression):\\n expression = Polynomial.cast(expression)\\n var.type = VariableType.TYPE # make var a type variable\\n self._equations[var] = expression\\n\\n # register variables in the system.\\n self._register_variable(var)\\n self._register_expressions(expression)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ced6762373522c7466b293e13335e11\",\n \"score\": \"0.517509\",\n \"text\": \"def register_variable(variable, is_independent=False, is_equation=False):\\n PuddleRepository.variables.add(variable)\\n if is_independent:\\n PuddleRepository.independent_variables.add(variable)\\n if is_equation:\\n PuddleRepository.equations.add(variable)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72d2314cb8d744553cc0aa449ef03596\",\n \"score\": \"0.5170673\",\n \"text\": \"def add_var(self, var_name, required, dtype=None, dim=None, units=None):\\n self.variables[var_name] = Variable(dtype, dim, units)\\n if required:\\n self.required_vars.append(var_name)\\n else:\\n self.optional_vars.append(var_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"778596abb7f52bbd18b1fc1f3d2ca999\",\n \"score\": \"0.5154649\",\n \"text\": \"def Potential(name, var, model=None):\\r\\n\\r\\n var.name = name\\r\\n modelcontext(model).potentials.append(var)\\r\\n return var\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9197efc30dfe97dcddc7a0e45e6e2f33\",\n \"score\": \"0.51336783\",\n \"text\": \"def add(self, var, value, where='dataset', reload=True):\\n self._run(['--add', var, value], where=where, reload=reload, log_stderr=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84559dc0b26564911b7b60b9d01aeb1f\",\n \"score\": \"0.51019806\",\n \"text\": \"def add_variable(self, var, domain):\\n if var in self.variables:\\n raise Exception(\\\"Variable name already exists: %s\\\" % str(var))\\n\\n self.numVars += 1\\n self.variables.append(var)\\n self.values[var] = domain\\n self.unaryFactors[var] = None\\n self.binaryFactors[var] = dict()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fc8b3c9b4bdb49630113516ed29ff6b\",\n \"score\": \"0.5091044\",\n \"text\": \"def add_variable(self,name, opt_value=0):\\n\\t\\tself.variables[name] = opt_value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73a95e41a740930f2898481d8b6f9784\",\n \"score\": \"0.5074586\",\n \"text\": \"def var(self, name, type=\\\"float32\\\", shape=None):\\n if isinstance(name, Variable): return name\\n v = self.vars.get(name, None)\\n if v == None:\\n v = Variable(name)\\n self.vars[name] = v\\n v.type = type\\n if shape is not None: v.shape = shape\\n return v\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be9535e3cf2217e4ec40a85589ca8352\",\n \"score\": \"0.50581855\",\n \"text\": \"def Variable(initial_value, name, trainable=None):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"845a7fb0185db44efeeefd6c4d0ce4fb\",\n \"score\": \"0.5056541\",\n \"text\": \"def addService(self, id_service, desc_service, host, port):\\n # TODO\\n self.services[id_service] = [desc_service, host, port]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1784a3c81f67489f3af2f67762e854ec\",\n \"score\": \"0.5042215\",\n \"text\": \"def var():\\n\\n return variable.Variable(\\\"generic_var\\\",template_units.kg_s,\\\"A generic var\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b28ccf3f3e5504994d442fd086f76baa\",\n \"score\": \"0.5020835\",\n \"text\": \"def __setattr__(self, key, var):\\n if not self._after_init:\\n object.__setattr__(self, key, var)\\n else:\\n if type(var) is VariableInfo or var is None:\\n object.__setattr__(self, key, var)\\n # add it to the list of variables\\n self._variables.append(key)\\n else:\\n raise TypeError(\\\"Can only set attributes of type VariableInfo.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b18b911166b4e57113a96dad0363061b\",\n \"score\": \"0.5010512\",\n \"text\": \"def add_workflowservice(wes_id):\\n set_yaml(section='workflowservices', var2add=wes_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbcbd25faabfc615d9d61ae83d298409\",\n \"score\": \"0.49794123\",\n \"text\": \"def add_service_ref_to_solution(self, service_type):\\n self.schema_dict_tmpl[SOLUTION_KEY][self.app_name][SERVICE_KEY]\\\\\\n .append({\\\"$ref\\\": topic_ref_transform(\\\"#/services/{0}\\\", service_type)})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"024678819d4cc34a71ae2e45740064fc\",\n \"score\": \"0.49627677\",\n \"text\": \"def add_variable(self, variable):\\n if not isinstance(variable, Variable):\\n raise TypeError(f\\\"Expected {Variable}; got {type(variable)}.\\\")\\n self.variables[variable.name] = variable\",\n \"title\": \"\"\n },\n {\n \"docid\": \"024678819d4cc34a71ae2e45740064fc\",\n \"score\": \"0.49627677\",\n \"text\": \"def add_variable(self, variable):\\n if not isinstance(variable, Variable):\\n raise TypeError(f\\\"Expected {Variable}; got {type(variable)}.\\\")\\n self.variables[variable.name] = variable\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70b9fb29bb32820944e7ba15bb2ca145\",\n \"score\": \"0.4937653\",\n \"text\": \"def step_impl(context, name, type_env):\\n type_value = eval(type_env)\\n context.data['type_env'][name] = type_value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5684265b28e6fec35c1f0f0f8f53a1b8\",\n \"score\": \"0.49179962\",\n \"text\": \"def register_service(self, service):\\n self._services.append(service)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"054391b1e377bed457c433e23cd3aed3\",\n \"score\": \"0.49073106\",\n \"text\": \"def add_var(self, name: str, size: int) -> None:\\n self.named_variables.append(name)\\n self.var_offset[name] = self.var_total_size\\n self.var_size[name] = size\\n self.var_total_size += size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fb56f52cd21385c775b4130a8a4f625\",\n \"score\": \"0.49039307\",\n \"text\": \"def __inject(model, model_context):\\n __kwargs[variable_injector.VARIABLE_FILE_APPEND_ARG] = variable_injector.VARIABLE_FILE_UPDATE\\n inserted, variable_model, variable_file_name = VariableInjector(_program_name, model, model_context,\\n WebLogicHelper(\\n __logger).get_actual_weblogic_version()). \\\\\\n inject_variables_keyword_file(**__kwargs)\\n if inserted:\\n model = Model(variable_model)\\n return inserted, model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8539f3cad57b511a87fe52c09debd7f\",\n \"score\": \"0.4900647\",\n \"text\": \"def add_symbol(self, key, ty, val): # pylint: disable=invalid-name\\n if key in self.symbols.keys():\\n old = str(self.symbols[key])\\n new = str((ty, val))\\n _internal_assert(False, f\\\"Name conflict in symbol table! [{key}] {old} -> {new}\\\")\\n\\n self.symbols[key] = ty, val\\n\\n if ty == Symbol.ThreadBind:\\n if val.var.name not in self.binds.keys():\\n self.binds[val.var.name] = val\\n return\\n val_ = self.binds[val.var.name]\\n _internal_assert(\\n tvm.tir.analysis.expr_deep_equal(val_.dom.extent, val.dom.extent),\\n \\\"Thread extents should be uniform!\\\",\\n )\\n self.symbols[key] = ty, val_\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecfff0f9c2e52140e4db0843aacbf130\",\n \"score\": \"0.4894691\",\n \"text\": \"def addVarReg():\\n\\n cl.cycleOverList('addVarReg')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3b72b81307247c0f690038494214360\",\n \"score\": \"0.48906937\",\n \"text\": \"def add_variable(self, name, label, style='.-', color=None, use_std=True):\\n\\n plot = {\\n 'name': name,\\n 'label': label,\\n 'style': style,\\n 'use_std': use_std,\\n 'color': color,\\n 'data': [],\\n 'data_inf': [],\\n 'data_sup': [],\\n\\n 'line': None,\\n 'line_std': None\\n }\\n\\n self._variables.append(plot)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9afe34c14c0a26fdf21f2109b334199\",\n \"score\": \"0.48824722\",\n \"text\": \"def add_service(\\n self,\\n service_type: str,\\n handler: ScanHandler,\\n device_info_extractor: DevInfoExtractor,\\n ) -> None:\\n self._services[service_type] = (handler, device_info_extractor)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78d4a1aaad48ce397bf59ebe8bfa94f5\",\n \"score\": \"0.4875754\",\n \"text\": \"def define(self, name, var_type, kind):\\n # creates new var with the next index\\n new_var = Variable(name, var_type, kind, self.__var_segment_count[kind])\\n # adds the var to the correct dictionary\\n if kind in CLASS_VAR_DEC_KEYWORDS:\\n self.__class_variables[name] = new_var\\n else:\\n self.__subroutine_variables[name] = new_var\\n\\n self.__var_segment_count[kind] += 1 # increment the next index of that kind\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea6c241bbd5399df19bbb65a56f574d9\",\n \"score\": \"0.48703286\",\n \"text\": \"def set_service(self, name, obj):\\r\\n self.__service_map[name] = obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a48106cf07f26247012102f727f824a\",\n \"score\": \"0.48689204\",\n \"text\": \"def set_variable(self, id: str, value: Any) -> None:\\n self.__variables[id] = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb13ea8e2033127b154cd4d49a762287\",\n \"score\": \"0.4868534\",\n \"text\": \"def add_service(session):\\n service = SpeechLocalization(session)\\n id = session.registerService(SpeechLocalization.service_name, service)\\n return [service, id]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c0beb438d0c5b3661bf7a00284d14c1\",\n \"score\": \"0.48595047\",\n \"text\": \"def create_variable(self): #, name, is_input_feed=False, is_python=False, local=False, **kwargs):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2eda9ea2d8b4802327cc277d959b7527\",\n \"score\": \"0.48450002\",\n \"text\": \"def add(self, tier: str):\\n self.tiers.append(tier)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"254a2541b304e2672a5c235e4e0f585d\",\n \"score\": \"0.48305318\",\n \"text\": \"def variable(type_name, name):\\n var_type = ee_types.nameToClass(type_name) or computedobject.ComputedObject\\n result = var_type.__new__(var_type)\\n result.func = None\\n result.args = None\\n result.varName = name\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d94069190d94f57b6b28eca7ab223f9\",\n \"score\": \"0.48005414\",\n \"text\": \"def _register_variable(self, v):\\n\\n if v.idx is not None:\\n return # nothing to do.\\n\\n v.idx = self._next_idx()\\n self._all_variables[v.idx] = v\\n\\n if v.tuning_param is not None:\\n self._tuned_variables.add(v)\\n\\n # case over the variable's type\\n if isinstance(v, Seq):\\n self._seq_variables.add(v)\\n self._register_expressions(v.inner_expressions)\\n\\n elif isinstance(v, MSet):\\n self._mset_variables.add(v)\\n self._register_expressions(v.inner_expressions)\\n\\n elif isinstance(v, UCyc):\\n self._ucyc_variables.add(v)\\n self._register_expressions(v.inner_expressions)\\n\\n elif isinstance(v, Set):\\n self._set_variables.add(v)\\n self._register_expressions(v.inner_expressions)\\n\\n elif isinstance(v, Cyc):\\n self._cyc_variables.add(v)\\n self._register_expressions(v.inner_expressions)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f27d76eed78930dd0a6d872d994d0c8\",\n \"score\": \"0.47718805\",\n \"text\": \"def add_input_variable(self, name, vartype, **kw):\\n input_var = Variable(name, precision = vartype, **kw)\\n self.arg_list.append(input_var)\\n # WARNING: self.function_type.arg_list_precision is not updated\\n return input_var\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06e929d45e3aae61aacaa7abf3bf071d\",\n \"score\": \"0.47694758\",\n \"text\": \"def add_variable(self, vartype, var):\\n var.body = self.id\\n\\n super(Body, self).add_variable(vartype, var)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9cf5cb78b6c06dcdcac406a34cb9724a\",\n \"score\": \"0.47619912\",\n \"text\": \"def add_var_hook(self, name, cb = None):\\n\\n if type(name).__name__ == 'dict':\\n self._hooks.update(name)\\n \\n elif type(name).__name__ == 'str' and callable(cb):\\n self._hooks[name] = cb\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c762d1905afc842ad483a8e1474ec48\",\n \"score\": \"0.47408482\",\n \"text\": \"def __setattr__(self, name, value):\\n if name == 'recipe':\\n # set the prodtype list\\n try:\\n self.prodtypes = [self.prodtype_map[step] for step in value]\\n except AttributeError:\\n self.prodtypes = list()\\n super().__setattr__(name, value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25aa544b97659805f627a20f7bdd61cc\",\n \"score\": \"0.4736419\",\n \"text\": \"def add_stkvar(*args):\\r\\n return _idaapi.add_stkvar(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42f196f2e87744d06f70d24d61967f5b\",\n \"score\": \"0.4714275\",\n \"text\": \"def opt_var(self, var_name, dtype=None, dim=None, units=None):\\n self.variables[var_name] = Variable(dtype, dim, units)\\n self.optional_vars.append(var_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e80022b5ed6edbf664ca32526d5c11be\",\n \"score\": \"0.46884003\",\n \"text\": \"def _type_variable(self):\\n v = Variable()\\n v.type = VariableType.TYPE\\n self._register_variable(v)\\n return v\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83c93cd48db82f68b8389bc667e637f1\",\n \"score\": \"0.4688399\",\n \"text\": \"def add_regvar(*args):\\r\\n return _idaapi.add_regvar(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09de7da085bd94d00d2db85aab74c540\",\n \"score\": \"0.46875712\",\n \"text\": \"def add_var(self, name, definition):\\n if name in self.variables:\\n raise UpdateMakefilesException('variable already exists: %s' % name)\\n self.variables[name] = Makefile.Variable(name, definition)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3ca0e04715da7487d455188deb04a64\",\n \"score\": \"0.46831843\",\n \"text\": \"def _set_tvar(self, key, val):\\n keyt, edit = translate(str(key), str(val))\\n # if keyt == \\\"SCHOST\\\" and val == \\\"ccb\\\": raise DefError\\n if self.load is None:\\n self.load = ecflow.Variable(keyt, edit)\\n else:\\n next = self.next\\n self.next = Edit(keyt, edit, next)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4b68749a428281109e1143307749eb8\",\n \"score\": \"0.46808255\",\n \"text\": \"def register_parameter(self, variable, as_weight=False, as_bias=False, name=None, **collection_key_value_pairs):\\n # Get variable name from variable if name is not given\\n name = name if name is not None else variable.name\\n # Attach collection key-value pairs\\n utils.add_to_antipasti_collection(variable, antipasti_name=name, **collection_key_value_pairs)\\n # Write to dict\\n self._parameters['[LayerID:{}][{}]'.format(self.name, name)] = variable\\n # Add to GraphKeys.TRAINABLE_VARIABLES if trainable\\n if utils.is_antipasti_trainable(variable):\\n A.add_to_collection(A.Collections.TRAINABLE_VARIABLES, variable)\\n # Add to Collections.REGULARIZABLE_VARIABLES if regularizable\\n if utils.is_antipasti_regularizable(variable):\\n A.add_to_collection(A.Collections.REGULARIZABLE_VARIABLES, variable)\\n # Add to GraphKeys.WEIGHTS if parameter is being registered as weight\\n if as_weight:\\n A.add_to_collection(A.Collections.WEIGHTS, variable)\\n # Add to GraphKeys.BIASES if parameter is being registered as a bias\\n if as_bias:\\n A.add_to_collection(A.Collections.BIASES, variable)\\n # Done.\\n return variable\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a48f9c6b5dbc432a584cc5096d9a1c44\",\n \"score\": \"0.4678225\",\n \"text\": \"def addVariable(\\n self, component, group, parm, value=None, lower=None, upper=None, scale=1.0, scaledStep=True, dh=1e-6\\n ):\\n\\n container_id = self.vspModel.FindContainer(component, 0)\\n if container_id == \\\"\\\":\\n raise Error(\\\"Bad component for DV: %s\\\" % component)\\n\\n parm_id = self.vspModel.FindParm(container_id, parm, group)\\n if parm_id == \\\"\\\":\\n raise Error(f\\\"Bad group or parm: {component} {group} {parm}\\\")\\n\\n # Now we know the parmID is ok. So we just get the value\\n val = self.vspModel.GetParmVal(parm_id)\\n\\n dvName = f\\\"{component}:{group}:{parm}\\\"\\n\\n if value is None:\\n value = val\\n\\n if scaledStep:\\n dh = dh * value\\n\\n if value == 0:\\n raise Error(\\n \\\"Initial value is exactly 0. scaledStep option cannot be used\\\"\\n \\\"Specify an explicit dh with scaledStep=False\\\"\\n )\\n\\n self.DVs[dvName] = vspDV(parm_id, dvName, component, group, parm, value, lower, upper, scale, dh)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0429bf31ef91086fdf672452fa5981d1\",\n \"score\": \"0.4675014\",\n \"text\": \"def add_service(self, svc_id: str, service: ServiceBase):\\n self._services[svc_id] = service\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5d4224122a95fee641111e54b867ad2\",\n \"score\": \"0.46585572\",\n \"text\": \"def add_component_to_skymodel(sm: SkyModel, comp: SkyComponent):\\n sm.components.append(comp)\\n return sm\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28149eb10f1ec09b7c4f0d0206de5074\",\n \"score\": \"0.46506104\",\n \"text\": \"def provide(self, typ: Type[T], inst: T):\\n self._instances[typ] = inst\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d17fde5c15b1eb590666033e4bce0dd\",\n \"score\": \"0.46450153\",\n \"text\": \"def Variable(self, variables, workSpace):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b884a4f3754b090de619b0001e86300\",\n \"score\": \"0.4641824\",\n \"text\": \"def add_random_variable(self, var):\\r\\n self.named_vars[var.name] = var\\r\\n if not hasattr(self, var.name):\\r\\n setattr(self, var.name, var)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d888912c210c69e4fc5f7f10eed23ca\",\n \"score\": \"0.46408108\",\n \"text\": \"def make_var(step, var_type, options):\\n switch = {\\\"linear\\\": make_linear_var, \\\"exponential\\\": make_exponential_var}\\n return switch[var_type](step=step, **options)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3783e07dfbaf9f1ee738e37d858513ad\",\n \"score\": \"0.46239263\",\n \"text\": \"def req_var(self, var_name, dtype=None, dim=None, units=None):\\n self.variables[var_name] = Variable(dtype, dim, units)\\n self.required_vars.append(var_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1cc61cc199f2c634257e661fb5ed7322\",\n \"score\": \"0.46196917\",\n \"text\": \"def addFeatureSpec(self, name, spec):\\n spec = dict(spec)\\n typeName = spec.pop('type', None)\\n featureCls = self.getFeatureClass(typeName)\\n self._features[name] = featureCls(**spec)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f384b2f19e43a291fab1ecdc762c8035\",\n \"score\": \"0.4615893\",\n \"text\": \"def Deterministic(name, var, model=None):\\r\\n var.name = name\\r\\n modelcontext(model).deterministics.append(var)\\r\\n modelcontext(model).add_random_variable(var)\\r\\n return var\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b31a73c73065b6d3b29698a4d041edb1\",\n \"score\": \"0.4614608\",\n \"text\": \"def get_trained_variables(self, Session,atom_types=[]):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a02774a85dda2b741bbe669873dae17\",\n \"score\": \"0.46116033\",\n \"text\": \"def service_add(self, **args) -> \\\"Service\\\":\\n proto = self.bundle().service_prototype(**args)\\n with allure_step(f\\\"Add service {proto.name} to cluster {self.name}\\\"):\\n data = self._subcall(\\\"service\\\", \\\"create\\\", prototype_id=proto.id, cluster_id=self.id)\\n return Service(self._api, id=data['id'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6dcb00737cc618fffe02427e40f5ce16\",\n \"score\": \"0.4608607\",\n \"text\": \"def add_feat( self, feat ) :\\n self.features.add( feat.name, feat.value )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f685857a5e4295baacff6a0b90be24d\",\n \"score\": \"0.4605806\",\n \"text\": \"def add_servicepoint(self, identifier, callback):\\n self._services[identifier] = callback\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51a939c890994fe01e003ece3f5e371b\",\n \"score\": \"0.46022916\",\n \"text\": \"def add_triple(domain_name,objectproprety,range_name,path):\\n onto = get_ontology(path).load()\\n with onto:\\n exec('onto.{0}.{1} = [onto.{2}]'.format(domain_name,objectproprety,range_name))\\n # onto.test1.has_for_ingredient = [onto.acetaminophen]\\n onto.save(path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"050ab371dc3ef3d31b4a907fadbd7500\",\n \"score\": \"0.45820224\",\n \"text\": \"def addSpeciesType(self, *args):\\n return _libsbml.Model_addSpeciesType(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2423f46247ca24f3df7dd52ca5107061\",\n \"score\": \"0.45807213\",\n \"text\": \"def add_service(self,service):\\n if service in self.services:\\n pass #service already exists\\n else:\\n self.services.append(service)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5b31a2384dfa09acd7ef2d25ee1101c\",\n \"score\": \"0.45603275\",\n \"text\": \"def __init__(\\n self_,\\n name: str,\\n type: SyntheticsConfigVariableType,\\n example: Union[str, UnsetType] = unset,\\n id: Union[str, UnsetType] = unset,\\n pattern: Union[str, UnsetType] = unset,\\n secure: Union[bool, UnsetType] = unset,\\n **kwargs,\\n ):\\n if example is not unset:\\n kwargs[\\\"example\\\"] = example\\n if id is not unset:\\n kwargs[\\\"id\\\"] = id\\n if pattern is not unset:\\n kwargs[\\\"pattern\\\"] = pattern\\n if secure is not unset:\\n kwargs[\\\"secure\\\"] = secure\\n super().__init__(kwargs)\\n\\n self_.name = name\\n self_.type = type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26d7714c1c17b8c30d6beaf0b459e65e\",\n \"score\": \"0.45563242\",\n \"text\": \"def _set_service_config_var(service, var_name, value):\\n run(\\n \\\"echo '%s: %s' | sudo tee -a /var/opt/magma/configs/%s.yml\\\" % (\\n var_name, str(value), service,\\n ),\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51bc5594f03c033631733987a8b94c67\",\n \"score\": \"0.4554668\",\n \"text\": \"def type(self, type):\\n\\n self.container['type'] = type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d5183973eee60784baa08a7d0ea35e1\",\n \"score\": \"0.45537454\",\n \"text\": \"def _hook_variable(self):\\n # Overload 'special' methods here\\n self._hook___new__(torch.autograd.variable.Variable)\\n self._hook_var_contents()\\n self._hook_var_owners()\\n\\n for attr in dir(torch.autograd.variable.Variable):\\n\\n # Conditions for inclusion/exclusion\\n if attr in self.exclude + self.var_exclude:\\n continue\\n lit = getattr(torch.autograd.variable.Variable, attr)\\n is_base = attr in dir(object)\\n is_desc = inspect.ismethoddescriptor(lit)\\n # is_func = isinstance(type(lit), types.FunctionType)\\n is_func = isinstance(lit, types.FunctionType)\\n try:\\n is_service_func = 'HookService' in lit.__qualname__\\n except:\\n is_service_func = False\\n is_old = re.match('old*', attr) is not None\\n\\n # Where the overloading happens\\n if ((is_desc or (is_func and not is_service_func)) and not is_base and not is_old):\\n passer = utils.pass_method_args(lit)\\n new_attr = self._get_overload_method_in_tensor_or_var(passer)\\n setattr(torch.autograd.variable.Variable,\\n 'old_{}'.format(attr), lit)\\n setattr(torch.autograd.variable.Variable, attr, new_attr)\\n\\n self._hook_send_(torch.autograd.variable.Variable)\\n self._hook_get_(torch.autograd.variable.Variable)\\n self._hook_var_serde()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e929d8b87b5e081f80a4ee8f822f056\",\n \"score\": \"0.45482057\",\n \"text\": \"def model(self, name, model):\\n\\n self.models[name] = model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"954250acfbdac177818e95e9c1eda8ed\",\n \"score\": \"0.45444962\",\n \"text\": \"def add_modelservice_to_registry(self, modelservice_name, ModelServiceClassDef):\\n self.modelservice_registry[modelservice_name] = ModelServiceClassDef\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b13ea8eba5ea76709b973cd73e7f7e4\",\n \"score\": \"0.45395696\",\n \"text\": \"def add_variable(nc, var):\\n if nc is None:\\n raise AttributeError(\\\"No file was specified\\\")\\n if isinstance(nc, netCDF4._netCDF4.Dataset):\\n pass\\n else:\\n nc = netCDF4.Dataset(nc, \\\"a\\\")\\n\\n # Handle the dimensions by enforcing a tuple list rather\\n # than a list of strings, then add whatever we have\\n try:\\n dims = var['dims'].replace(\\\" \\\", \\\"\\\").split(',')\\n except:\\n dims = var['dims']\\n try:\\n nvar = nc.createVariable(var[\\\"name\\\"], var[\\\"type\\\"], dims)\\n except:\\n nvar = nc.createVariable(var[\\\"name\\\"], var[\\\"type\\\"])\\n\\n try:\\n for key in var[\\\"attr\\\"]:\\n # Check if it is a number and convert\\n astr = __number_or_string(var[\\\"attr\\\"][key])\\n setattr(nvar, key, astr)\\n except KeyError:\\n pass\\n\\n return nc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2007eed04f9d79d37cb2d12f40eaa1c\",\n \"score\": \"0.45244172\",\n \"text\": \"def addVariable(self, name : str, value) -> LookUpTable:\\n # Check if variable exists \\n result = self.modVariable(name, value) \\n if result is None:\\n newItem = LookUpItem(name, value)\\n return LookUpTable(self.items + [newItem])\\n else:\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fe30ca8aac6e53b9cfcfa2c78760ec5\",\n \"score\": \"0.45215234\",\n \"text\": \"def seed_type(self, name, typ):\\n self.typevars[name].lock(typ)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a554f5e1ad78308080dd2e8dcf41c03\",\n \"score\": \"0.4517167\",\n \"text\": \"def AddSymbol(self,name,stype,body=None):\\n if self.IsSymbol(name):\\n raise Exception('Program Bug -- name \\\"%s\\\" already exists is symbols' % name);\\n self.symbols['list'].append(name);\\n self.symbols['type'].append(stype);\\n self.symbols['body'].append(body);\",\n \"title\": \"\"\n },\n {\n \"docid\": \"afd0af436c849e3a11d8e676caf08cf9\",\n \"score\": \"0.45027423\",\n \"text\": \"def _add_weight(self, name, initial_value, dtype=None):\\n variable = variable_v1.VariableV1(\\n initial_value=initial_value,\\n name=name,\\n dtype=dtype,\\n trainable=False,\\n use_resource=True,\\n synchronization=variables.VariableSynchronization.AUTO,\\n # Set aggregation to NONE, as loss scaling variables should never be\\n # aggregated.\\n aggregation=variables.VariableAggregation.NONE)\\n if context.executing_eagerly():\\n graph_key = None\\n else:\\n graph = ops.get_default_graph()\\n graph_key = graph._graph_key # pylint: disable=protected-access\\n\\n key = (name, graph_key)\\n if self._weights.get(key, None) is not None:\\n raise RuntimeError('Duplicate variables detected. {}'.format(key))\\n self._weights[key] = variable\\n self._handle_deferred_dependencies(name=name, trackable=variable)\\n return variable\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19c9f61ffb201c3cd36364280d3f3321\",\n \"score\": \"0.44942945\",\n \"text\": \"def _add_to_dict(self, name, attr):\\n if name not in self._dict:\\n logger.debug(\\\"Added variable '%s' to collection\\\", name)\\n self._dict[name] = attr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d64d7b1e9d7dcd83285585c625cbce8\",\n \"score\": \"0.44941947\",\n \"text\": \"def AddVariable(self, variable):\\n callResult = self._Call(\\\"AddVariable\\\", variable)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4387e584cf37f38ca7b3b40f91b7d189\",\n \"score\": \"0.4493591\",\n \"text\": \"def _add_ttl_ns(self, line):\\n lg = logging.getLogger(\\\"%s.%s\\\" % (self.ln, inspect.stack()[0][3]))\\n lg.setLevel(self.log_level)\\n\\n lg.debug(\\\"line:\\\\n%s\\\", line)\\n line = str(line).strip()\\n # if the line is not a prefix line exit\\n if line is None or line == 'none' or line == '' \\\\\\n or not line.lower().startswith('@prefix'):\\n return\\n # parse the turtle line\\n line = line.replace(\\\"@prefix\\\",\\\"\\\",1).strip()\\n if line.endswith(\\\".\\\"):\\n line = line[:-1]\\n prefix = line[:line.find(\\\":\\\")].strip()\\n uri = self.clean_iri(line[line.find(\\\":\\\")+1:].strip())\\n # add the namespace to the class\\n lg.debug(\\\"\\\\nprefix: %s uri: %s\\\", prefix, uri)\\n self.bind(prefix, uri, override=False, calc=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e51362f31c5b35dd0258c125ae5c7aa3\",\n \"score\": \"0.44895974\",\n \"text\": \"def service(self, service):\\n\\n self._service = service\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c3493e6d4ddd2c8d1d1c25202443291\",\n \"score\": \"0.44788188\",\n \"text\": \"def __setitem__(self, name: str, value):\\n attribute = self.model[name]\\n if not isinstance(value, attribute.type):\\n value = attribute.type(value)\\n super().__setitem__(name, value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e05e4f277b531c1119e4daec0dc6084\",\n \"score\": \"0.44782597\",\n \"text\": \"def add_vars(self, **names):\\n for (name, attr) in names.items():\\n if isinstance(attr, Variable):\\n attr_var = attr\\n else:\\n attr_var = Variable(\\n name,\\n attr.get(n.STANDARD_NAME, DEFAULT_INFO),\\n attr.get(n.LONG_NAME, DEFAULT_INFO),\\n attr.get(n.UNITS, DEFAULT_INFO))\\n self._add_to_dict(name, attr_var)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75ae13c69cf34e6bee58d23bb49ea474\",\n \"score\": \"0.44745833\",\n \"text\": \"def add(self, val: object) -> None:\\n self._ctx[type(val)] = val\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65ef7ee407a390011f398cc52e7e352c\",\n \"score\": \"0.4469828\",\n \"text\": \"def put_variable(self, col: str, name: str, value: Any):\\n if self.scope is None:\\n raise ValueError(\\\"Can't access variables on unbound modules\\\")\\n self.scope.put_variable(col, name, value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f15198a08547e33a19f010e2898717e3\",\n \"score\": \"0.445989\",\n \"text\": \"def add_variable(self, name, description, default=None, min=None, max=None, edit_method=\\\"\\\"):\\n if type(default) == int:\\n if edit_method == \\\"\\\":\\n self.add(name, \\\"int\\\", 0, description, default, min, max)\\n else: # enum\\n self.add(name, \\\"int\\\", 0, description, default, min, max, edit_method)\\n elif type(default) == float:\\n self.add(name, \\\"double\\\", 0, description, default, min, max)\\n elif type(default) == str:\\n self.add(name, \\\"str\\\", 0, description, default)\\n elif type(default) == bool:\\n self.add(name, \\\"bool\\\", 0, description, default)\\n\\n return default\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07789a59bae2a38738250a04f45259e9\",\n \"score\": \"0.44525698\",\n \"text\": \"def add_auto_stkpnt(*args):\\r\\n return _idaapi.add_auto_stkpnt(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0156454b525cdc3e2964299e2ec54f9b\",\n \"score\": \"0.44474766\",\n \"text\": \"def substitutetype(ty):\\n\\n ty = ty.replace(\\\"std::\\\", \\\"\\\")\\n ty = re.sub(r\\\"(.*)<(.*)>\\\",\\\"\\\\\\\\1< \\\\\\\\2 >\\\",ty)\\n ty = re.sub(r\\\"glm::tvec(.*)< T, P >\\\", \\\"glm::vec\\\\\\\\1\\\", ty)\\n ty = re.sub(r\\\"tvec(.*)< T, P >\\\", \\\"glm::vec\\\\\\\\1\\\", ty)\\n ty = re.sub(r\\\"glm::tmat(.*)< T, P >\\\", \\\"glm::mat\\\\\\\\1\\\", ty)\\n ty = re.sub(r\\\"tmat(.*)< T, P >\\\", \\\"glm::mat\\\\\\\\1\\\", ty)\\n ty = ty.replace(\\\"glm::mat2x2\\\", \\\"glm::mat2\\\")\\n ty = ty.replace(\\\"glm::mat3x3\\\", \\\"glm::mat3\\\")\\n ty = ty.replace(\\\"glm::mat4x4\\\", \\\"glm::mat4\\\")\\n ty = re.sub(r\\\"glm::tquat(.*)< T, P >\\\", \\\"glm::quat\\\\\\\\1\\\", ty)\\n ty = re.sub(r\\\"tquat(.*)< T, P >\\\", \\\"glm::quat\\\\\\\\1\\\", ty)\\n return ty\",\n \"title\": \"\"\n },\n {\n \"docid\": \"614db44ba135598f6aa013d6d3e24fbf\",\n \"score\": \"0.44427925\",\n \"text\": \"def add_feature(self, word, feature_name, is_test):\\n self.features[word][feature_name] = 1\\n if not is_test:\\n self.feat_freq[feature_name] += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cc64afd02b922e3516aaa2d0f8e9d57\",\n \"score\": \"0.44409594\",\n \"text\": \"def define(self, key, value):\\n self.bindings[key] = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a29d39a72159d29329b007249c0ff8a1\",\n \"score\": \"0.44374675\",\n \"text\": \"def add_type(type, ext, strict=True):\\n if _db is None:\\n init()\\n return _db.add_type(type, ext, strict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"507292c0496fcaa2f2d177e85e5dcf6a\",\n \"score\": \"0.443726\",\n \"text\": \"def add_variable(self):\\n\\n self._max_var_id += 1\\n\\n return CNFExprVar(self._max_var_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"193fce564413aab280711e344891165c\",\n \"score\": \"0.44345352\",\n \"text\": \"def __setattr__(self, name, value):\\n self.scope[name] = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"520d0e4dc24e362c3395271899874cc0\",\n \"score\": \"0.44314107\",\n \"text\": \"def add(self, name, value):\\n self.local_dictionary[name] = value\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":882,"cells":{"query_id":{"kind":"string","value":"16d45c9acf46f40f244ccc434e3d5dfb"},"query":{"kind":"string","value":"Adds liquid fuel or atomic fuel tanks to design"},"positive_passages":{"kind":"list like","value":[{"docid":"6723dea64db5b09e54a14db3774b557c","score":"0.69049513","text":"def add_conventional_tanks(self, lf):\n if self.mainengine.name == \"LFB Twin-Boar\":\n lf = max(lf, 36000)\n self.notes.append(\"6400 units of liquid fuel are already included in the engine\")\n if self.fueltype is parts.FuelTypes.LiquidFuel:\n smalltankcount = ceil(lf / parts.RocketFuelTanks[parts.SmallestTank[self.size]].m_full)\n else:\n # atomic fuel\n # Adomic Fuel is liquid fuel without oxidizer.\n smalltankcount = ceil(lf / (parts.RocketFuelTanks[parts.SmallestTank[self.size]].m_full *\n parts.AtomicTankFactor))\n self.notes.append(\"Atomic fuel is regular liquid fuel w/out oxidizer (remove oxidizer in VAB!)\")\n # Fuel tank calculation:\n # We use that\n # - Tank size is 2^n times the size of smallest tank with that radius\n # - It is cheapest to use the biggest tank possible\n for i in range(parts.SmallestTank[self.size], parts.BiggestTank[self.size]+1):\n if i != parts.BiggestTank[self.size]:\n if smalltankcount % 2 != 0:\n self.fueltanks.append((1, parts.RocketFuelTanks[i]))\n smalltankcount = smalltankcount // 2\n else:\n if self.mainengine.name == \"LFB Twin-Boar\":\n smalltankcount -= 1\n if smalltankcount > 0:\n self.fueltanks.append((smalltankcount, parts.RocketFuelTanks[i]))\n self.fueltanks.append((1, parts.TwinBoarPseudoTank))\n else:\n if smalltankcount > 0:\n self.fueltanks.append((smalltankcount, parts.RocketFuelTanks[i]))","title":""}],"string":"[\n {\n \"docid\": \"6723dea64db5b09e54a14db3774b557c\",\n \"score\": \"0.69049513\",\n \"text\": \"def add_conventional_tanks(self, lf):\\n if self.mainengine.name == \\\"LFB Twin-Boar\\\":\\n lf = max(lf, 36000)\\n self.notes.append(\\\"6400 units of liquid fuel are already included in the engine\\\")\\n if self.fueltype is parts.FuelTypes.LiquidFuel:\\n smalltankcount = ceil(lf / parts.RocketFuelTanks[parts.SmallestTank[self.size]].m_full)\\n else:\\n # atomic fuel\\n # Adomic Fuel is liquid fuel without oxidizer.\\n smalltankcount = ceil(lf / (parts.RocketFuelTanks[parts.SmallestTank[self.size]].m_full *\\n parts.AtomicTankFactor))\\n self.notes.append(\\\"Atomic fuel is regular liquid fuel w/out oxidizer (remove oxidizer in VAB!)\\\")\\n # Fuel tank calculation:\\n # We use that\\n # - Tank size is 2^n times the size of smallest tank with that radius\\n # - It is cheapest to use the biggest tank possible\\n for i in range(parts.SmallestTank[self.size], parts.BiggestTank[self.size]+1):\\n if i != parts.BiggestTank[self.size]:\\n if smalltankcount % 2 != 0:\\n self.fueltanks.append((1, parts.RocketFuelTanks[i]))\\n smalltankcount = smalltankcount // 2\\n else:\\n if self.mainengine.name == \\\"LFB Twin-Boar\\\":\\n smalltankcount -= 1\\n if smalltankcount > 0:\\n self.fueltanks.append((smalltankcount, parts.RocketFuelTanks[i]))\\n self.fueltanks.append((1, parts.TwinBoarPseudoTank))\\n else:\\n if smalltankcount > 0:\\n self.fueltanks.append((smalltankcount, parts.RocketFuelTanks[i]))\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"c4376dbfe4ecc36c450fad9f25cf7233","score":"0.63634956","text":"def add_special_tanks(self, xf, tank):\n tankcount = ceil(xf / tank.m_full)\n if tank.size == parts.RadialSize.RadiallyMounted:\n tankcount = max(tankcount, 2)\n self.requiredscience.add(tank.level)\n self.fueltanks.append((tankcount, tank))","title":""},{"docid":"082cfc2b4b7fa373b344bef0cb13906c","score":"0.5959416","text":"def build(self):\n\n # Create a custom grid, fe_set \n # why create an fe_set instead of using Transformation \n # factory?\n nfe = 6\n fe_a = 1/4.0\n fe_b = 0.2\n fe_set = [0, 0.004]\n for i in range(1,nfe+1):\n if i < nfe*fe_a:\n fe_set.append(i*fe_b/(nfe*fe_a))\n elif i == nfe: \n fe_set.append(1)\n else:\n fe_set.append(fe_b + (i-nfe*fe_a)*(1-fe_b)/(nfe*(1-fe_a)))\n\n \"\"\"\n Args: (to MB_CLC_fuel object, as defined in model file)\n dae_method = method to use for calcuating derivatives (default = OCLR)\n - BFD1 - 1st order backwards finite difference\n - OCLR - Orthogonal collocation, Lagrange-Radau\n - OCLL - Orthogonal collocation, Lagrange-Legendre\n press_drop = Pressure drop correlation for superficial velocity calc.\n - SimplifiedP - simplified pressure correlations \n - Ergun - Ergun equation\n fe_set = set of normalised finite element locations\n nfe = number of finite elements for bed discretization (default = 15)\n (not used if fe_set specified)\n ncp = number of collocation points (OCLR or OCLL only, default = 3)\n\n fe_set_t\n nfe_t \n ^ adding time set as a model-level continuous set...\n will change when moving to new framework\n \"\"\" \n # Create unit model for fuel reactor\n # unit model - an attribute of the flowsheet model\n # \"a block within a block\"\n #self.MB_fuel = MB_CLC_fuel.MB(\n # parent=self,\n # dae_method = 'OCLR',\n # press_drop = 'Ergun',\n # fe_set = fe_set,\n # ncp = 3)\n\n # need to add time set to the above\n # open question still:\n # how long of a horizon should I simulate?\n #\n # why is nfe in z-dimension not an input here?\n self.MB_fuel = MB_CLC_fuel.MB(\n parent=self,\n dae_method = 'OCLR',\n press_drop = 'Ergun',\n fe_set = fe_set,\n ncp = 3,\n horizon = 1, # was 10\n nfe_t = 1, # \" \"\n ncp_t = 3)","title":""},{"docid":"fe2ee9896aa4f33c95c28e736bdbce24","score":"0.57530856","text":"def create_lf_design(payload, pressure, dv, acc, eng,\n size=None, count=1, fueltype=parts.FuelTypes.LiquidFuel, tank=None):\n if size is None:\n size = eng.size\n design = Design(payload, eng, count, size, fueltype)\n if fueltype is parts.FuelTypes.LiquidFuel:\n f_e = 1 / 8\n elif fueltype is parts.FuelTypes.AtomicFuel:\n f_e = parts.AtomicTank_f_e\n else:\n f_e = tank.f_e\n m_p = payload + count*eng.m\n lf = physics.lf_needed_fuel(dv, physics.engine_isp(eng, pressure), m_p, f_e)\n if lf is None:\n return None\n if fueltype is parts.FuelTypes.LiquidFuel or fueltype is parts.FuelTypes.AtomicFuel:\n design.add_conventional_tanks((1 + f_e) * lf)\n else:\n design.add_special_tanks((1 + f_e) * lf, tank)\n design.calculate_performance(dv, pressure)\n if not design.has_enough_acceleration(acc):\n return None\n return design","title":""},{"docid":"3426d73dc82411122530ba3c274ebc77","score":"0.5537668","text":"def add_credit_calculation_fuel_types_0111(apps, schema_editor):\n db_alias = schema_editor.connection.alias\n\n approved_fuel = apps.get_model('api', 'ApprovedFuel')\n\n approved_fuel.objects.using(db_alias).filter(\n credit_calculation_only=True\n ).delete()\n\n approved_fuel.objects.using(db_alias).bulk_create([\n approved_fuel(\n name=\"Petroleum-based diesel\",\n effective_date=\"2017-01-01\",\n credit_calculation_only=True\n ),\n approved_fuel(\n name=\"Petroleum-based gasoline\",\n effective_date=\"2017-01-01\",\n credit_calculation_only=True\n )\n ])","title":""},{"docid":"4fa7dc54cbd0731fe1fd8be6249b9b1e","score":"0.55242157","text":"def add_design(design, eosscontext: EOSSContext, active: bool):\n\n if active:\n Design.objects.create(activecontext=eosscontext.activecontext,\n id=design['id'],\n inputs=json.dumps(design['inputs']),\n outputs=json.dumps(design['outputs']))\n else:\n Design.objects.create(eosscontext=eosscontext,\n id=design['id'],\n inputs=json.dumps(design['inputs']),\n outputs=json.dumps(design['outputs']))\n eosscontext.added_archs_count += 1\n\n eosscontext.last_arch_id += 1\n\n if eosscontext.added_archs_count >= 5:\n eosscontext.added_archs_count = 0\n activate_diversifier(eosscontext)\n\n eosscontext.save()","title":""},{"docid":"3ff7db4149d11baca3408731d4d1b4d1","score":"0.54325074","text":"def fuel(self, fuel):\n\n self._fuel = fuel","title":""},{"docid":"521030d08546c9b055941c622782262f","score":"0.5401777","text":"def fuel(self, f: float):\n if isinstance(f, float) is False:\n raise Warning(\"[WARNING]Fuel is not Float type.\")\n if f < 0: # <= ?\n raise Warning(\"[WARNING]Fuel have to Positive.\")\n self.__gas_remained += f\n if self.__gas_remained > self.__GAS_CAPACITY:\n # 최대 용량까지는 채우고 넘친건 안채우고\n self.__gas_remained = self.__GAS_CAPACITY\n raise Warning(\"[WARNING]Fuel is overfilled.\")","title":""},{"docid":"466f2aecdee189b7b185bc345ef5fbbf","score":"0.5339595","text":"def create_model(\n time_set=None,\n time_units=pyo.units.s,\n nfe=5,\n tee=False,\n calc_integ=True,\n):\n fs_cfg = {\"dynamic\": True, \"time_set\": time_set, \"time_units\": time_units}\n model_name = \"Steam Tank, Dynamic\"\n\n if time_set is None:\n time_set = [0, 3]\n\n m = pyo.ConcreteModel(name=model_name)\n m.fs = FlowsheetBlock(**fs_cfg)\n # Create a property parameter block\n m.fs.prop_water = iapws95.Iapws95ParameterBlock(\n phase_presentation=iapws95.PhaseType.LG\n )\n # Create the valve and tank models\n m.fs.valve_1 = Valve(\n dynamic=False,\n has_holdup=False,\n pressure_flow_callback=_valve_pressure_flow_cb,\n material_balance_type=MaterialBalanceType.componentTotal,\n property_package=m.fs.prop_water,\n )\n m.fs.tank = Heater(\n has_holdup=True,\n material_balance_type=MaterialBalanceType.componentTotal,\n property_package=m.fs.prop_water,\n )\n m.fs.valve_2 = Valve(\n dynamic=False,\n has_holdup=False,\n pressure_flow_callback=_valve_pressure_flow_cb,\n material_balance_type=MaterialBalanceType.componentTotal,\n property_package=m.fs.prop_water,\n )\n # Add a controller\n m.fs.ctrl = PIDController(\n process_var=m.fs.tank.control_volume.properties_out[:].pressure,\n manipulated_var=m.fs.valve_1.valve_opening,\n calculate_initial_integral=calc_integ,\n mv_bound_type=ControllerMVBoundType.SMOOTH_BOUND,\n controller_type=ControllerType.PI,\n )\n\n # The control volume block doesn't assume the two phases are in equilibrium\n # by default, so I'll make that assumption here, I don't actually expect\n # liquid to form but who knows. The phase_fraction in the control volume is\n # volumetric phase fraction hence the densities.\n @m.fs.tank.Constraint(m.fs.time)\n def vol_frac_vap(b, t):\n return (\n b.control_volume.properties_out[t].phase_frac[\"Vap\"]\n * b.control_volume.properties_out[t].dens_mol\n / b.control_volume.properties_out[t].dens_mol_phase[\"Vap\"]\n ) == (b.control_volume.phase_fraction[t, \"Vap\"])\n\n # Connect the models\n m.fs.v1_to_tank = Arc(source=m.fs.valve_1.outlet, destination=m.fs.tank.inlet)\n m.fs.tank_to_v2 = Arc(source=m.fs.tank.outlet, destination=m.fs.valve_2.inlet)\n\n # Add the stream constraints and do the DAE transformation\n pyo.TransformationFactory(\"network.expand_arcs\").apply_to(m.fs)\n pyo.TransformationFactory(\"dae.finite_difference\").apply_to(\n m.fs, nfe=nfe, wrt=m.fs.time, scheme=\"BACKWARD\"\n )\n\n # Fix the derivative variables to zero at time 0 (steady state assumption)\n m.fs.fix_initial_conditions()\n\n # Fix the input variables\n m.fs.valve_1.inlet.enth_mol.fix(50000)\n m.fs.valve_1.inlet.pressure.fix(5e5)\n m.fs.valve_2.outlet.pressure.fix(101325)\n m.fs.valve_1.Cv.fix(0.001)\n m.fs.valve_2.Cv.fix(0.001)\n m.fs.valve_1.valve_opening.fix(1)\n m.fs.valve_2.valve_opening.fix(1)\n m.fs.tank.heat_duty.fix(0)\n m.fs.tank.control_volume.volume.fix(2.0)\n\n # Fix controller settings\n m.fs.ctrl.gain_p.fix(1e-6)\n m.fs.ctrl.gain_i.fix(1e-5)\n # m.fs.ctrl.gain_d.fix(1e-6)\n # m.fs.ctrl.derivative_of_error[m.fs.time.first()].fix(0)\n m.fs.ctrl.setpoint.fix(3e5)\n m.fs.ctrl.mv_ref.fix(0)\n m.fs.ctrl.mv_lb = 0.0\n m.fs.ctrl.mv_ub = 1.0\n\n for t in m.fs.time:\n m.fs.valve_1.inlet.flow_mol[t] = 100 # initial guess on flow\n # simple initialize\n m.fs.valve_1.initialize()\n propagate_state(m.fs.v1_to_tank)\n m.fs.tank.initialize()\n propagate_state(m.fs.tank_to_v2)\n # Can't specify both flow and outlet pressure so free the outlet pressure\n # for initialization and refix it after. Inlet flow gets fixed in init, but\n # is unfixed for the final problem\n m.fs.valve_2.outlet.pressure.unfix()\n m.fs.valve_2.initialize()\n m.fs.valve_2.outlet.pressure.fix(101325)\n m.fs.valve_1.valve_opening.unfix()\n m.fs.valve_1.valve_opening[m.fs.time.first()].fix()\n # Return the model and solver\n return m","title":""},{"docid":"90134edba142744be072caf15ee8cd90","score":"0.53340214","text":"def create_sfb_design(payload, pressure, dv, acc, sfb_allowed, eng, eng_F_percentage, size, count, sfb, sfbcount):\n design = Design(payload, eng, count, size, parts.FuelTypes.LiquidFuel)\n design.add_sfb(sfb, sfbcount)\n # lpsr = Fl * I_sps / Fs / I_spl\n lpsr = count * eng.F_vac * sfb.isp_vac / sfbcount / sfb.F_vac / eng.isp_vac\n design.eng_F_percentage = eng_F_percentage\n m_p = payload + count*eng.m\n lf = physics.sflf_concurrent_needed_fuel(dv, physics.engine_isp(eng, pressure),\n physics.engine_isp(sfb, pressure),\n m_p,\n design.get_sfbmountmass(), sfbcount * sfb.m_full, sfbcount * sfb.m_empty,\n lpsr * eng_F_percentage)\n if lf is None:\n return None\n design.add_conventional_tanks(9 / 8 * lf)\n design.calculate_performance(dv, pressure)\n if not design.has_enough_acceleration(acc):\n return None\n if not design.sfb_burning_when_allowed(sfb_allowed):\n return None\n if sfbcount != 1:\n design.notes.append(\"Set liquid fuel engine thrust to {:.0%} while SFB are burning\".format(eng_F_percentage))\n return design","title":""},{"docid":"e1d92090255f7c6be24bcf6ab4eef980","score":"0.5296159","text":"def buildFuelModels(allowDynamicModels=True,allowNonBurningModels=False):\n fuelModels = dict()\n \n # Code FMx: Original 13 Fuel Models\n fuelModels[\"FM1\"] = FuelModel(\n 1, \"FM1\", \"Short grass [1]\",\n 1.0, 0.12, 8000, 8000,\n 0.034, 0, 0, 0, 0,\n 3500, 1500, 1500,\n False, True)\n fuelModels[\"FM2\"] = FuelModel(\n 2, \"FM2\", \"Timber grass and understory [2]\",\n 1.0, 0.15, 8000, 8000,\n 0.092, 0.046, 0.023, 0.023,\n 0,3000, 1500, 1500,\n False, True)\n fuelModels[\"FM3\"] = FuelModel(\n 3, \"FM3\", \"Tall grass [3]\",\n 2.5, 0.25, 8000, 8000,\n 0.138, 0, 0, 0, 0,\n 1500, 1500, 1500,\n False, True)\n fuelModels[\"FM4\"] = FuelModel(\n 4, \"FM4\", \"Chaparral [4]\",\n 6.0, 0.2, 8000, 8000,\n 0.230, 0.184, 0.092, 0, 0.230,\n 2000, 1500, 1500,\n False, True)\n fuelModels[\"FM5\"] = FuelModel(\n 5, \"FM5\", \"Brush [5]\",\n 2.0, 0.20, 8000, 8000,\n 0.046, 0.023, 0, 0, 0.092,\n 2000, 1500, 1500,\n False, True)\n fuelModels[\"FM6\"] = FuelModel(\n 6, \"FM6\", \"Dormant brush, hardwood slash [6]\",\n 2.5, 0.25, 8000, 8000,\n 0.069, 0.115, 0.092, 0, 0,\n 1750, 1500, 1500,\n False, True)\n fuelModels[\"FM7\"] = FuelModel(\n 7, \"FM7\", \"Southern rough [7]\",\n 2.5, 0.40, 8000, 8000,\n 0.052, 0.086, 0.069, 0, 0.017,\n 1750, 1500, 1500,\n False, True)\n fuelModels[\"FM8\"] = FuelModel(\n 8, \"FM8\", \"Short needle litter [8]\",\n 0.2, 0.3, 8000, 8000,\n 0.069, 0.046, 0.115, 0, 0,\n 2000, 1500, 1500,\n False, True)\n fuelModels[\"FM9\"] = FuelModel(\n 9, \"FM9\", \"Long needle or hardwood litter [9]\",\n 0.2, 0.25, 8000, 8000,\n 0.134, 0.019, 0.007, 0, 0,\n 2500, 1500, 1500,\n False, True)\n fuelModels[\"FM10\"] = FuelModel(\n 10, \"FM10\", \"Timber litter & understory [10]\",\n 1.0, 0.25, 8000, 8000,\n 0.138, 0.092, 0.230, 0, 0.092,\n 2000, 1500, 1500,\n False, True)\n fuelModels[\"FM11\"] = FuelModel(\n 11, \"FM11\", \"Light logging slash [11]\",\n 1.0, 0.15, 8000, 8000,\n 0.069, 0.207, 0.253, 0, 0,\n 1500, 1500, 1500,\n False, True)\n fuelModels[\"FM12\"] = FuelModel(\n 12, \"FM12\", \"Medium logging slash [12]\",\n 2.3, 0.20, 8000, 8000,\n 0.184, 0.644, 0.759, 0, 0,\n 1500, 1500, 1500,\n False, True)\n fuelModels[\"FM13\"] = FuelModel(\n 13, \"FM13\", \"Heavy logging slash [13]\",\n 3.0, 0.25, 8000, 8000,\n 0.322, 1.058, 1.288, 0, 0,\n 1500, 1500, 1500,\n False, True)\n \n if not allowDynamicModels:\n return fuelModels\n else:\n pass\n # 14-89 Available for custom models\n\n if allowNonBurningModels:\n # Code NBx: Non-burnable\n # 90 Available for custom NB model \n fuelModels[\"NB1\"] = FuelModel(\n 91, \"NB1\", \"Urban, developed [91]\",\n 1.0, 0.10, 8000, 8000,\n 0, 0, 0, 0, 0,\n 1500, 1500, 1500,\n False, True)\n fuelModels[\"NB2\"] = FuelModel(\n 92, \"NB2\", \"Snow, ice [92]\",\n 1.0, 0.10, 8000, 8000,\n 0, 0, 0, 0, 0,\n 1500, 1500, 1500,\n False, True)\n fuelModels[\"NB3\"] = FuelModel(\n 93, \"NB3\", \"Agricultural [93]\",\n 1.0, 0.10, 8000, 8000,\n 0, 0, 0, 0, 0,\n 1500, 1500, 1500,\n False, True)\n \n # Indices 94-95 Reserved for future standard non-burnable models\n \n fuelModels[\"NB4\"] = FuelModel(\n 94, \"NB4\", \"Future standard non-burnable [94]\",\n 1.0, 0.10, 8000, 8000,\n 0, 0, 0, 0, 0,\n 1500, 1500, 1500,\n False, True)\n fuelModels[\"NB5\"] = FuelModel(\n 95, \"NB5\", \"Future standard non-burnable [95]\",\n 1.0, 0.10, 8000, 8000,\n 0, 0, 0, 0, 0,\n 1500, 1500, 1500,\n False, True)\n \n # Indices 96-97 Available for custom NB model\n \n fuelModels[\"NB8\"] = FuelModel(\n 98, \"NB8\", \"Open water [98]\",\n 1.0, 0.10, 8000, 8000,\n 0, 0, 0, 0, 0,\n 1500, 1500, 1500,\n False, True)\n fuelModels[\"NB9\"] = FuelModel(\n 99, \"NB9\", \"Bare ground [99]\",\n 1.0, 0.10, 8000, 8000,\n 0, 0, 0, 0, 0,\n 1500, 1500, 1500,\n False, True)\n\n # Code GRx: Grass\n # Index 100 Available for custom GR model\n f = 2000.0 / 43560.0\n fuelModels[\"GR1\"] = FuelModel(\n 101, \"GR1\", \"Short, sparse, dry climate grass (D)\",\n 0.4, 0.15, 8000, 8000,\n 0.10*f, 0, 0, 0.30*f, 0,\n 2200, 2000, 1500,\n True, True)\n fuelModels[\"GR2\"] = FuelModel(\n 102, \"GR2\", \"Low load, dry climate grass (D)\",\n 1.0, 0.15, 8000, 8000,\n 0.10*f, 0, 0, 1.0*f, 0,\n 2000, 1800, 1500,\n True, True)\n fuelModels[\"GR3\"] = FuelModel(\n 103, \"GR3\", \"Low load, very coarse, humid climate grass (D)\",\n 2.0, 0.30, 8000, 8000,\n 0.10*f, 0.40*f, 0, 1.50*f, 0,\n 1500, 1300, 1500,\n True, True)\n fuelModels[\"GR4\"] = FuelModel(\n 104, \"GR4\", \"Moderate load, dry climate grass (D)\",\n 2.0, 0.15, 8000, 8000,\n 0.25*f, 0, 0, 1.9*f, 0,\n 2000, 1800, 1500,\n True, True)\n fuelModels[\"GR5\"] = FuelModel(\n 105, \"GR5\", \"Low load, humid climate grass (D)\",\n 1.5, 0.40, 8000, 8000,\n 0.40*f, 0.0, 0.0, 2.50*f, 0.0,\n 1800, 1600, 1500,\n True, True)\n fuelModels[\"GR6\"] = FuelModel(\n 106, \"GR6\", \"Moderate load, humid climate grass (D)\",\n 1.5, 0.40, 9000, 9000,\n 0.10*f, 0, 0, 3.4*f, 0,\n 2200, 2000, 1500,\n True, True)\n fuelModels[\"GR7\"] = FuelModel(\n 107, \"GR7\", \"High load, dry climate grass (D)\",\n 3.0, 0.15, 8000, 8000,\n 1.0*f, 0, 0, 5.4*f, 0,\n 2000, 1800, 1500,\n True, True)\n fuelModels[\"GR8\"] = FuelModel(\n 108, \"GR8\", \"High load, very coarse, humid climate grass (D)\",\n 4.0, 0.30, 8000, 8000,\n 0.5*f, 1.0*f, 0, 7.3*f, 0,\n 1500, 1300, 1500,\n True, True)\n fuelModels[\"GR9\"] = FuelModel(\n 109, \"GR9\", \"Very high load, humid climate grass (D)\",\n 5.0, 0.40, 8000, 8000,\n 1.0*f, 1.0*f, 0, 9.0*f, 0,\n 1800, 1600, 1500,\n True, True)\n # 110-112 are reserved for future standard grass models\n # 113-119 are available for custom grass models\n\n # Code GSx: Grass and shrub\n # 120 available for custom grass and shrub model\n fuelModels[\"GS1\"] = FuelModel(\n 121, \"GS1\", \"Low load, dry climate grass-shrub (D)\",\n 0.9, 0.15, 8000, 8000,\n 0.2*f, 0, 0, 0.5*f, 0.65*f,\n 2000, 1800, 1800,\n True, True)\n fuelModels[\"GS2\"] = FuelModel(\n 122, \"GS2\", \"Moderate load, dry climate grass-shrub (D)\",\n 1.5, 0.15, 8000, 8000,\n 0.5*f, 0.5*f, 0, 0.6*f, 1.0*f,\n 2000, 1800, 1800,\n True, True)\n fuelModels[\"GS3\"] = FuelModel(\n 123, \"GS3\", \"Moderate load, humid climate grass-shrub (D)\",\n 1.8, 0.40, 8000, 8000,\n 0.3*f, 0.25*f, 0, 1.45*f, 1.25*f,\n 1800, 1600, 1600,\n True, True)\n fuelModels[\"GS4\"] = FuelModel(\n 124, \"GS4\", \"High load, humid climate grass-shrub (D)\",\n 2.1, 0.40, 8000, 8000,\n 1.9*f, 0.3*f, 0.1*f, 3.4*f, 7.1*f,\n 1800, 1600, 1600,\n True, True)\n # 125-130 reserved for future standard grass and shrub models\n # 131-139 available for custom grass and shrub models\n\n # Shrub\n # 140 available for custom shrub model\n fuelModels[\"SH1\"] = FuelModel(\n 141, \"SH1\", \"Low load, dry climate shrub (D)\",\n 1.0, 0.15, 8000, 8000,\n 0.25*f, 0.25*f, 0, 0.15*f, 1.3*f,\n 2000, 1800, 1600,\n True, True)\n fuelModels[\"SH2\"] = FuelModel(\n 142, \"SH2\", \"Moderate load, dry climate shrub (S)\",\n 1.0, 0.15, 8000, 8000,\n 1.35*f, 2.4*f, 0.75*f, 0, 3.85*f,\n 2000, 1800, 1600,\n True, True)\n fuelModels[\"SH3\"] = FuelModel(\n 143, \"SH3\", \"Moderate load, humid climate shrub (S)\",\n 2.4, 0.40, 8000., 8000.,\n 0.45*f, 3.0*f, 0, 0, 6.2*f,\n 1600, 1800, 1400,\n True, True)\n fuelModels[\"SH4\"] = FuelModel(\n 144, \"SH4\", \"Low load, humid climate timber-shrub (S)\",\n 3.0, 0.30, 8000, 8000,\n 0.85*f, 1.15*f, 0.2*f, 0, 2.55*f,\n 2000, 1800, 1600,\n True, True)\n fuelModels[\"SH5\"] = FuelModel(\n 145, \"SH5\", \"High load, dry climate shrub (S)\",\n 6.0, 0.15, 8000, 8000,\n 3.6*f, 2.1*f, 0, 0, 2.9*f,\n 750, 1800, 1600,\n True, True)\n fuelModels[\"SH6\"] = FuelModel(\n 146, \"SH6\", \"Low load, humid climate shrub (S)\",\n 2.0, 0.30, 8000, 8000,\n 2.9*f, 1.45*f, 0, 0, 1.4*f,\n 750, 1800, 1600,\n True, True)\n fuelModels[\"SH7\"] = FuelModel(\n 147, \"SH7\", \"Very high load, dry climate shrub (S)\",\n 6.0, 0.15, 8000, 8000,\n 3.5*f, 5.3*f, 2.2*f, 0, 3.4*f,\n 750, 1800, 1600,\n True, True)\n fuelModels[\"SH8\"] = FuelModel(\n 148, \"SH8\", \"High load, humid climate shrub (S)\",\n 3.0, 0.40, 8000, 8000,\n 2.05*f, 3.4*f, 0.85*f, 0, 4.35*f,\n 750, 1800, 1600,\n True, True)\n fuelModels[\"SH9\"] = FuelModel(\n 149, \"SH9\", \"Very high load, humid climate shrub (D)\",\n 4.4, 0.40, 8000, 8000,\n 4.5*f, 2.45*f, 0, 1.55*f, 7.0*f,\n 750, 1800, 1500,\n True, True)\n # 150-152 reserved for future standard shrub models\n # 153-159 available for custom shrub models\n\n # Timber and understory\n # 160 available for custom timber and understory model\n fuelModels[\"TU1\"] = FuelModel(\n 161, \"TU1\", \"Light load, dry climate timber-grass-shrub (D)\",\n 0.6, 0.20, 8000, 8000,\n 0.2*f, 0.9*f, 1.5*f, 0.2*f, 0.9*f,\n 2000, 1800, 1600,\n True, True)\n fuelModels[\"TU2\"] = FuelModel(\n 162, \"TU2\", \"Moderate load, humid climate timber-shrub (S)\",\n 1.0, 0.30, 8000, 8000,\n 0.95*f, 1.8*f, 1.25*f, 0, 0.2*f,\n 2000, 1800, 1600,\n True, True)\n fuelModels[\"TU3\"] = FuelModel(\n 163, \"TU3\", \"Moderate load, humid climate timber-grass-shrub (D)\",\n 1.3, 0.30, 8000, 8000,\n 1.1*f, 0.15*f, 0.25*f, 0.65*f, 1.1*f,\n 1800, 1600, 1400,\n True, True)\n fuelModels[\"TU4\"] = FuelModel(\n 164, \"TU4\", \"Dwarf conifer understory (S)\",\n 0.5, 0.12, 8000, 8000,\n 4.5*f, 0, 0, 0, 2.0*f,\n 2300, 1800, 2000,\n True, True)\n fuelModels[\"TU5\"] = FuelModel(\n 165, \"TU5\", \"Very high load, dry climate timber-shrub (S)\",\n 1.0, 0.25, 8000, 8000,\n 4.0*f, 4.0*f, 3.0*f, 0, 3.0*f,\n 1500, 1800, 750,\n True, True)\n # 166-170 reserved for future standard timber and understory models\n # 171-179 available for custom timber and understory models\n # Timber and litter\n # 180 available for custom timber and litter models\n fuelModels[\"TL1\"] = FuelModel(\n 181, \"TL1\", \"Low load, compact conifer litter (S)\",\n 0.2, 0.30, 8000, 8000,\n 1.0*f, 2.2*f, 3.6*f, 0, 0,\n 2000, 1800, 1600,\n True, True)\n fuelModels[\"TL2\"] = FuelModel(\n 182, \"TL2\", \"Low load broadleaf litter (S)\",\n 0.2, 0.25, 8000, 8000,\n 1.4*f, 2.3*f, 2.2*f, 0, 0,\n 2000, 1800, 1600,\n True, True)\n fuelModels[\"TL3\"] = FuelModel(\n 183, \"TL3\", \"Moderate load conifer litter (S)\",\n 0.3, 0.20, 8000, 8000,\n 0.5*f, 2.2*f, 2.8*f, 0, 0,\n 2000, 1800, 1600,\n True, True)\n fuelModels[\"TL4\"] = FuelModel(\n 184, \"TL4\", \"Small downed logs (S)\",\n 0.4, 0.25, 8000, 8000,\n 0.5*f, 1.5*f, 4.2*f, 0, 0,\n 2000, 1800, 1600,\n True, True)\n fuelModels[\"TL5\"] = FuelModel(\n 185, \"TL5\", \"High load conifer litter (S)\",\n 0.6, 0.25, 8000, 8000,\n 1.15*f, 2.5*f, 4.4*f, 0, 0,\n 2000, 1800, 160,\n True, True)\n fuelModels[\"TL6\"] = FuelModel(\n 186, \"TL6\", \"High load broadleaf litter (S)\",\n 0.3, 0.25, 8000, 8000,\n 2.4*f, 1.2*f, 1.2*f, 0, 0,\n 2000, 1800, 1600,\n True, True)\n fuelModels[\"TL7\"] = FuelModel(\n 187, \"TL7\", \"Large downed logs (S)\",\n 0.4, 0.25, 8000, 8000,\n 0.3*f, 1.4*f, 8.1*f, 0, 0,\n 2000, 1800, 1600,\n True, True)\n fuelModels[\"TL8\"] = FuelModel(\n 188, \"TL8\", \"Long-needle litter (S)\",\n 0.3, 0.35, 8000, 8000,\n 5.8*f, 1.4*f, 1.1*f, 0, 0,\n 1800, 1800, 1600,\n True, True)\n fuelModels[\"TL9\"] = FuelModel(\n 189, \"TL9\", \"Very high load broadleaf litter (S)\",\n 0.6, 0.35, 8000, 8000,\n 6.65*f, 3.30*f, 4.15*f, 0, 0,\n 1800, 1800, 1600,\n True, True)\n # 190-192 reserved for future standard timber and litter models\n # 193-199 available for custom timber and litter models\n # Slash and blowdown\n # 200 available for custom slash and blowdown model\n fuelModels[\"SB1\"] = FuelModel(\n 201, \"SB1\", \"Low load activity fuel (S)\",\n 1.0, 0.25, 8000, 8000,\n 1.5*f, 3.0*f, 11.0*f, 0, 0,\n 2000, 1800, 1600,\n True, True)\n fuelModels[\"SB2\"] = FuelModel(\n 202, \"SB2\", \"Moderate load activity or low load blowdown (S)\",\n 1.0, 0.25, 8000, 8000,\n 4.5*f, 4.25*f, 4.0*f, 0, 0,\n 2000, 1800, 1600,\n True, True)\n fuelModels[\"SB3\"] = FuelModel(\n 203, \"SB3\", \"High load activity fuel or moderate load blowdown (S)\",\n 1.2, 0.25, 8000, 8000,\n 5.5*f, 2.75*f, 3.0*f, 0, 0,\n 2000, 1800, 1600,\n True, True)\n fuelModels[\"SB4\"] = FuelModel(\n 204, \"SB4\", \"High load blowdown (S)\",\n 2.7, 0.25, 8000, 8000,\n 5.25*f, 3.5*f, 5.25*f, 0, 0,\n 2000, 1800, 1600,\n True, True)\n \n return fuelModels","title":""},{"docid":"0ba14d04319d1cc764dd5c167c56f100","score":"0.5286508","text":"def addFacility():\n\tname = getInput(lambda x: x != 'Admin' and x not in facilityList.keys(), \"Enter name of new facility: \")\n\tcapacity = getInput(isPositive, \"Enter capacity: \")\n\t\n\tMedicalFacility(name, None, int(capacity))","title":""},{"docid":"0ff787ddc530d02f3867535c70b2e31b","score":"0.525206","text":"def parse_bouquet_design(self, bouquet_design):\n\n match = re.match(self.bouquet_design_pattern, bouquet_design)\n\n if not match:\n print(f'{bouquet_design} is not a valid design')\n\n bouquet_name, size, flowers_str, total = match.groups()\n\n flowers = re.findall(self.flowers_pattern, flowers_str)\n flowers_dict = {}\n\n certain_amount = 0\n for amount, name in flowers:\n amount = int(amount)\n flowers_dict[name] = amount\n certain_amount += amount\n\n total = int(total)\n any_amount = total - certain_amount\n\n self.bouquet_designs[size].append({\n 'flowers': Counter(flowers_dict),\n 'total': total,\n 'any': any_amount,\n 'name': bouquet_name,\n })","title":""},{"docid":"a56b3b65feed66b8d5147fda5ec1b31e","score":"0.5206796","text":"def design(self, lch, w_dict, th_dict, seg_dict, stack_dict, dum_info):\n # design dummies\n w_tail = w_dict['tail']\n w_in = w_dict['in']\n w_load = w_dict['load']\n th_tail = th_dict['tail']\n th_in = th_dict['in']\n th_load = th_dict['load']\n\n # design main transistors\n seg_tail1 = seg_dict['tail1']\n seg_tail2 = seg_dict['tail2']\n seg_tailcm = seg_dict['tailcm']\n seg_in = seg_dict['in']\n seg_ref = seg_dict['ref']\n seg_diode1 = seg_dict['diode1']\n seg_ngm1 = seg_dict['ngm1']\n seg_diode2 = seg_dict['diode2']\n seg_ngm2 = seg_dict['ngm2']\n\n stack_tail = stack_dict['tail']\n stack_in = stack_dict['in']\n stack_diode = stack_dict['diode']\n stack_ngm = stack_dict['ngm']\n\n self.instances['XTAIL'].design(w=w_tail, l=lch, seg=seg_tail1 * 2, intent=th_tail,\n stack=stack_tail)\n self.instances['XTAIL2L'].design(w=w_tail, l=lch, seg=seg_tail2, intent=th_tail,\n stack=stack_tail)\n self.instances['XTAIL2R'].design(w=w_tail, l=lch, seg=seg_tail2, intent=th_tail,\n stack=stack_tail)\n self.instances['XCML'].design(w=w_tail, l=lch, seg=seg_tailcm, intent=th_tail,\n stack=stack_tail)\n self.instances['XCMR'].design(w=w_tail, l=lch, seg=seg_tailcm, intent=th_tail,\n stack=stack_tail)\n self.instances['XREF'].design(w=w_tail, l=lch, seg=seg_ref, intent=th_tail,\n stack=stack_tail)\n self.instances['XINL'].design(w=w_in, l=lch, seg=seg_in, intent=th_in, stack=stack_in)\n self.instances['XINR'].design(w=w_in, l=lch, seg=seg_in, intent=th_in, stack=stack_in)\n self.instances['XRES'].design(w=w_in, l=lch, seg=seg_ref, intent=th_in, stack=stack_in)\n self.instances['XDIOL'].design(w=w_load, l=lch, seg=seg_diode1, intent=th_load,\n stack=stack_diode)\n self.instances['XDIOR'].design(w=w_load, l=lch, seg=seg_diode1, intent=th_load,\n stack=stack_diode)\n self.instances['XNGML'].design(w=w_load, l=lch, seg=seg_ngm1, intent=th_load,\n stack=stack_ngm)\n self.instances['XNGMR'].design(w=w_load, l=lch, seg=seg_ngm1, intent=th_load,\n stack=stack_ngm)\n self.instances['XDIO2L'].design(w=w_load, l=lch, seg=seg_diode2, intent=th_load,\n stack=stack_diode)\n self.instances['XDIO2R'].design(w=w_load, l=lch, seg=seg_diode2, intent=th_load,\n stack=stack_diode)\n self.instances['XNGM2L'].design(w=w_load, l=lch, seg=seg_ngm2, intent=th_load,\n stack=stack_ngm)\n self.instances['XNGM2R'].design(w=w_load, l=lch, seg=seg_ngm2, intent=th_load,\n stack=stack_ngm)\n\n # design dummies\n self.design_dummy_transistors(dum_info, 'XDUM', 'VDD', 'VSS')","title":""},{"docid":"3c39be295a4a95228c591f714729ad29","score":"0.520205","text":"def stoich_flue_gas(self, nw):\n lamb = 1\n n_fuel = 1\n m_fuel = 1 / molar_mass_flow(self.fuel.val) * n_fuel\n m_fuel_fg = m_fuel\n m_co2 = 0\n m_h2o = 0\n molar_masses[self.h2o] = CP.PropsSI('M', self.h2o)\n molar_masses[self.co2] = CP.PropsSI('M', self.co2)\n molar_masses[self.o2] = CP.PropsSI('M', self.o2)\n\n self.fg = {}\n self.fg[self.co2] = 0\n self.fg[self.h2o] = 0\n\n for f, x in self.fuel.val.items():\n fl = set(list(self.fuels())).intersection(\n set([a.replace(' ', '') for a in CP.get_aliases(f)]))\n\n if len(fl) == 0:\n if f in self.fg.keys():\n self.fg[f] += x * m_fuel\n else:\n self.fg[f] = x * m_fuel\n else:\n n_fluid = x * m_fuel / molar_masses[f]\n m_fuel_fg -= n_fluid * molar_masses[f]\n structure = fluid_structure(f)\n n = {}\n for el in ['C', 'H', 'O']:\n if el in structure.keys():\n n[el] = structure[el]\n else:\n n[el] = 0\n\n m_co2 += n_fluid * n['C'] * molar_masses[self.co2]\n m_h2o += n_fluid * n['H'] / 2 * molar_masses[self.h2o]\n\n self.fg[self.co2] += m_co2\n self.fg[self.h2o] += m_h2o\n\n n_o2 = (m_co2 / molar_masses[self.co2] +\n 0.5 * m_h2o / molar_masses[self.h2o]) * lamb\n m_air = n_o2 * molar_masses[self.o2] / self.air.val[self.o2]\n\n self.air_min = m_air / m_fuel\n\n for f, x in self.air.val.items():\n if f != self.o2:\n if f in self.fg.keys():\n self.fg[f] += m_air * x\n else:\n self.fg[f] = m_air * x\n\n m_fg = m_fuel + m_air\n\n for f in self.fg.keys():\n self.fg[f] /= m_fg\n\n if not self.path.is_set:\n self.path.val = None\n tespy_fluid(self.fuel_alias.val, self.fuel.val,\n [1000, nw.p_range_SI[1]], nw.T_range_SI,\n path=self.path.val)\n tespy_fluid(self.fuel_alias.val + '_fg', self.fg,\n [1000, nw.p_range_SI[1]], nw.T_range_SI,\n path=self.path.val)\n msg = ('Generated lookup table for ' + self.fuel_alias.val +\n ' and for stoichiometric flue gas at stoichiometric '\n 'combustion chamber ' + self.label + '.')\n logging.debug(msg)\n\n if self.air_alias.val not in ['Air', 'air']:\n tespy_fluid(self.air_alias.val, self.air.val,\n [1000, nw.p_range_SI[1]], nw.T_range_SI,\n path=self.path.val)\n msg = ('Generated lookup table for ' + self.air_alias.val +\n ' at stoichiometric combustion chamber ' + self.label + '.')\n else:\n msg = ('Using CoolProp air at stoichiometric combustion chamber ' +\n self.label + '.')\n logging.debug(msg)","title":""},{"docid":"318dd7356e6b6227392f6d735ba6f03d","score":"0.51707864","text":"def fuelProduction(self):\r\n try:\r\n for bid in self.bids:\r\n if bid.plant.source == \"Gas\":\r\n self.gas_fuel += bid.sold_amount * self.hours_for_bidround / bid.plant.efficiency\r\n self.total_gas_production += bid.sold_amount * self.hours_for_bidround\r\n elif bid.plant.source == \"Coal\":\r\n self.coal_fuel += bid.sold_amount * self.hours_for_bidround / bid.plant.efficiency\r\n self.total_coal_production += bid.sold_amount*self.hours_for_bidround\r\n elif bid.plant.source == \"PV\":\r\n self.total_pv_production += bid.sold_amount * self.hours_for_bidround\r\n except Exception as e:\r\n print(\"Exception in fuelProduction()\")\r\n print(e)","title":""},{"docid":"ff358730aa52bfa6675dfc42dc466f56","score":"0.516541","text":"def fill_gas_tank(self):\n\t\tprint(\"This car does not need a gas tank!\")","title":""},{"docid":"20c7d8c865f35c319dc225d3491f80ec","score":"0.5164768","text":"def create_bouquet(self, design, size):\n \n bouquet = Counter(design['flowers'])\n flowers = self.flowers[size]\n flowers -= bouquet\n\n left = design['any']\n if left:\n for flower_name, amount in flowers.items():\n if amount >= left:\n bouquet[flower_name] += left\n flowers[flower_name] = amount - left\n break\n else:\n bouquet[flower_name] += amount\n flowers[flower_name] = 0\n left -= amount\n\n self.total_count[size] -= design['total']\n\n bouquet_flowers = ''.join(\n [f'{name}{amount}' for name, amount in bouquet.items()],\n )\n bouquet_str = design['name'] + size + bouquet_flowers\n\n return bouquet_str","title":""},{"docid":"a6cb6a7348f3f06072c4d3add05a91d6","score":"0.5162991","text":"def add_technology(self, name):\n if name in self.technologies:\n logging.debug('Warning: Technology {} was added twice to supply node {}'.format(name, self.name))\n return\n self.technologies[name] = SupplyTechnology(name, self.cost_of_capital, self.scenario)\n self.tech_names.append(name)","title":""},{"docid":"4affdcd64d064bb2bdd05616ff703ec1","score":"0.51550865","text":"def add_technology(self, name):\n if name in self.technologies:\n logging.debug(\"Warning, storage tech {} was added twice to node {}\".format(name, self.name))\n return\n self.technologies[name] = StorageTechnology(name, self.cost_of_capital, self.scenario)\n self.tech_names.append(name)","title":""},{"docid":"545bac376e6696710cb0c912734d1716","score":"0.5147059","text":"def fill_gas_tank(self):\r\n print(\"This car doesn't need a gas tank!\")","title":""},{"docid":"3d247b02eb9d3ca679fadf6738181db5","score":"0.5142591","text":"def __init__(self, name, fuel, fanciness):\n super().__init__(name, fuel)\n self.price_per_km *= fanciness","title":""},{"docid":"e03ff0af14f3cd9139524ca593c9de9d","score":"0.51414496","text":"def add_stock(self):\n self.stock = SupplyStock(supply_node=self.name, scenario=self.scenario)\n self.stock.unit = cfg.calculation_energy_unit + \"/\" + cfg.getParam('time_step')","title":""},{"docid":"1e641585a8d590730cd937fe17c420e1","score":"0.51261896","text":"def initialise_fluids(self, nw):\n if self.air_alias.val in ['air', 'Air']:\n air = self.air_alias.val\n else:\n air = 'TESPy::' + self.air_alias.val\n flue_gas = 'TESPy::' + self.fuel_alias.val + \"_fg\"\n\n for c in nw.comps.loc[self].o:\n if not c.fluid.val_set[air]:\n c.fluid.val[air] = 0.8\n if not c.fluid.val_set[flue_gas]:\n c.fluid.val[flue_gas] = 0.2","title":""},{"docid":"7d00d428a9f0692471e23a9d5ad41c68","score":"0.5125038","text":"def addObjects(self):\n\n self.normal = self.guide.blades[\"blade\"].z * -1\n self.binormal = self.guide.blades[\"blade\"].x\n\n self.WIP = self.options[\"mode\"]\n\n if self.negate and self.settings[\"overrideNegate\"]:\n self.negate = False\n self.n_factor = 1\n\n # FK controllers ------------------------------------\n self.fk_npo = []\n self.fk_ctl = []\n self.tweak_npo = []\n self.tweak_ctl = []\n self.curv_pos = []\n self.upv_curv_pos = []\n self.upv_curv_lvl = []\n self.tangentsCtl = []\n t = self.guide.tra[\"root\"]\n\n parent = self.root\n tOld = False\n fk_ctl = None\n self.previusTag = self.parentCtlTag\n for i, t in enumerate(transform.getChainTransform(self.guide.apos,\n self.normal,\n self.negate)):\n self.dist = vector.getDistance(self.guide.apos[i],\n self.guide.apos[i + 1])\n if self.settings[\"neutralpose\"] or not tOld:\n tnpo = t\n else:\n tnpo = transform.setMatrixPosition(\n tOld,\n transform.getPositionFromMatrix(t))\n\n fk_npo = primitive.addTransform(\n parent, self.getName(\"fk%s_npo\" % i), tnpo)\n\n fk_ctl = self.addCtl(\n fk_npo,\n \"fk%s_ctl\" % i,\n t,\n self.color_fk,\n \"cube\",\n w=self.dist,\n h=self.size * .1,\n d=self.size * .1,\n po=datatypes.Vector(self.dist * .5 * self.n_factor, 0, 0),\n tp=self.previusTag)\n\n tweak_npo = primitive.addTransform(\n parent, self.getName(\"tweak%s_npo\" % i), tnpo)\n\n self.tweak_npo.append(tweak_npo)\n\n tweak_ctl = self.addCtl(\n tweak_npo,\n \"tweak%s_ctl\" % i,\n t,\n self.color_ik,\n \"cube\",\n w=self.size * .15,\n h=self.size * .05,\n d=self.size * .15,\n ro=datatypes.Vector([0, 0, 1.5708]),\n tp=self.previusTag)\n\n upv_curv_lvl = primitive.addTransform(\n tweak_ctl, self.getName(\"upv%s_lvl\" % i), t)\n upv_curv_lvl.attr(\"tz\").set(.01)\n\n self.fk_npo.append(fk_npo)\n self.fk_ctl.append(fk_ctl)\n self.tweak_ctl.append(tweak_ctl)\n self.upv_curv_lvl.append(upv_curv_lvl)\n tOld = t\n self.previusTag = fk_ctl\n parent = fk_ctl\n\n # TANGENTS\n tangents = []\n tangents_npo = []\n tangents_upv = []\n\n if not i:\n letters = \"A\"\n else:\n letters = \"AB\"\n\n for tang in letters:\n tang_npo = primitive.addTransform(\n tweak_ctl,\n self.getName(\"tng{}{}_npo\".format(tang, str(i))),\n t)\n\n tangents_npo.append(tang_npo)\n\n tang_ctl = self.addCtl(\n tang_npo,\n \"tng{}{}_ctl\".format(tang, str(i)),\n t,\n self.color_ik,\n \"square\",\n w=self.size * .07,\n h=self.size * .07,\n d=self.size * .07,\n ro=datatypes.Vector([0, 0, 1.5708]),\n tp=self.previusTag)\n\n upv_tang_curv_lvl = primitive.addTransform(\n tang_ctl,\n self.getName(\"tngUpv{}{}_lvl\".format(tang, str(i))),\n t)\n upv_tang_curv_lvl.attr(\"tz\").set(.01)\n tangents_upv.append(upv_tang_curv_lvl)\n\n tangents.append(tang_ctl)\n\n tangents_npo[0].attr(\"tx\").set(self.dist * .3333)\n\n # delete the first B tangent\n if not i:\n self.curv_pos.append(tweak_ctl)\n self.curv_pos.append(tangents[0])\n self.upv_curv_pos.append(upv_curv_lvl)\n self.upv_curv_pos.append(tangents_upv[0])\n else:\n self.curv_pos.append(tangents[1])\n self.curv_pos.append(tweak_ctl)\n self.curv_pos.append(tangents[0])\n self.upv_curv_pos.append(tangents_upv[1])\n self.upv_curv_pos.append(upv_curv_lvl)\n self.upv_curv_pos.append(tangents_upv[0])\n tangents_npo[1].attr(\"tx\").set(self.dist * -.3333)\n\n self.tangentsCtl.extend(tangents)\n\n # ==========\n\n # self.jnt_pos.append([fk_ctl, i, None, False])\n\n # add end control\n tweak_npo = primitive.addTransform(\n fk_ctl, self.getName(\"tweakEnd_npo\"), t)\n tweak_ctl = self.addCtl(\n tweak_npo,\n \"tweakEnd_ctl\",\n t,\n self.color_ik,\n \"cube\",\n w=self.size * .15,\n h=self.size * .05,\n d=self.size * .15,\n ro=datatypes.Vector([0, 0, 1.5708]),\n tp=self.previusTag)\n\n upv_curv_lvl = primitive.addTransform(\n tweak_ctl, self.getName(\"upvEnd_lvl\"), t)\n upv_curv_lvl.attr(\"tz\").set(.01)\n\n if self.negate:\n self.off_dist = self.dist * -1\n else:\n self.off_dist = self.dist\n tweak_npo.attr(\"tx\").set(self.off_dist)\n\n self.tweak_ctl.append(tweak_ctl)\n self.upv_curv_lvl.append(upv_curv_lvl)\n\n # tangent END\n tang_npo = primitive.addTransform(\n tweak_ctl,\n self.getName(\"tngEnd{}_npo\".format(tang, str(i))),\n t)\n\n tang_ctl = self.addCtl(\n tang_npo,\n \"tngEnd{}_ctl\".format(tang, str(i)),\n t,\n self.color_ik,\n \"square\",\n w=self.size * .07,\n h=self.size * .07,\n d=self.size * .07,\n ro=datatypes.Vector([0, 0, 1.5708]),\n tp=self.previusTag)\n\n upv_tang_curv_lvl = primitive.addTransform(\n tang_ctl,\n self.getName(\"tngUpv{}{}_lvl\".format(tang, str(i))),\n t)\n upv_tang_curv_lvl.attr(\"tz\").set(.01)\n tangents_upv.append(upv_tang_curv_lvl)\n\n tang_npo.attr(\"tx\").set(self.dist * -.3333)\n\n self.curv_pos.append(tang_ctl)\n self.curv_pos.append(tweak_ctl)\n self.upv_curv_pos.append(tang_ctl)\n self.upv_curv_pos.append(upv_curv_lvl)\n\n self.tangentsCtl.append(tang_ctl)\n\n # add length offset control if keep length\n # This option will be added only if keep length is active\n if self.settings[\"keepLength\"]:\n self.tweakTip_npo = primitive.addTransform(\n tweak_ctl, self.getName(\"tweakTip_npo\"), t)\n tweak_ctl = self.addCtl(\n self.tweakTip_npo,\n \"tweakTip_ctl\",\n t,\n self.color_fk,\n \"square\",\n w=self.size * .1,\n h=self.size * .1,\n d=self.size * .1,\n ro=datatypes.Vector([0, 0, 1.5708]),\n tp=self.previusTag)\n\n upv_curv_lvl = primitive.addTransform(\n tweak_ctl, self.getName(\"upvTip_lvl\"), t)\n upv_curv_lvl.attr(\"tz\").set(.01)\n\n # move to align with the parent\n self.tweakTip_npo.attr(\"tx\").set(0)\n\n self.tweak_ctl.append(tweak_ctl)\n self.upv_curv_lvl.append(upv_curv_lvl)\n\n # add visual reference\n self.line_ref = icon.connection_display_curve(\n self.getName(\"visualRef\"),\n [self.tweakTip_npo.getParent(), tweak_ctl])\n\n # Curves -------------------------------------------\n self.mst_crv = curve.addCnsCurve(self.root,\n self.getName(\"mst_crv\"),\n self.curv_pos,\n 3)\n\n self.slv_crv = curve.addCurve(self.root, self.getName(\"slv_crv\"),\n [datatypes.Vector()] * 32,\n False,\n 3)\n\n self.upv_crv = curve.addCnsCurve(self.root,\n self.getName(\"upv_crv\"),\n self.upv_curv_pos,\n 3)\n\n self.slv_upv_crv = curve.addCurve(self.root,\n self.getName(\"slv_upv_crv\"),\n [datatypes.Vector()] * 32,\n False,\n 3)\n\n self.mst_crv.setAttr(\"template\", True)\n self.slv_crv.setAttr(\"visibility\", False)\n self.upv_crv.setAttr(\"visibility\", False)\n self.slv_upv_crv.setAttr(\"visibility\", False)\n\n # Divisions\n self.div_cns = []\n self.upv_cns = []\n\n tagP = self.parentCtlTag\n self.Extra_tweak_npo = []\n self.Extra_tweak_ctl = []\n\n if self.settings[\"overrideJntNb\"]:\n self.def_number = self.settings[\"jntNb\"]\n else:\n self.def_number = len(self.guide.apos)\n\n for i in range(self.def_number):\n # References\n div_cns = primitive.addTransform(self.root,\n self.getName(\"%s_cns\" % i))\n\n pm.setAttr(div_cns + \".inheritsTransform\", False)\n self.div_cns.append(div_cns)\n\n upv_cns = primitive.addTransform(self.root,\n self.getName(\"%s_upv\" % i))\n\n pm.setAttr(upv_cns + \".inheritsTransform\", False)\n self.upv_cns.append(upv_cns)\n\n t = transform.getTransform(div_cns)\n\n extraTweak_npo = primitive.addTransform(\n div_cns,\n self.getName(\"extraTweak{}_npo\".format(tang, str(i))),\n t)\n self.Extra_tweak_npo.append(extraTweak_npo)\n Extra_tweak_ctl = self.addCtl(extraTweak_npo,\n \"extraTweak%s_ctl\" % i,\n t,\n self.color_fk,\n \"circle\",\n w=self.size * .15,\n d=self.size * .15,\n ro=datatypes.Vector([0, 0, 1.5708]),\n tp=tagP)\n attribute.setKeyableAttributes(Extra_tweak_ctl)\n self.Extra_tweak_ctl.append(Extra_tweak_ctl)\n self.jnt_pos.append([Extra_tweak_ctl, i])","title":""},{"docid":"4122803c711ee914e60ec481e887655d","score":"0.5112155","text":"def add_unit_of_measures_0117(apps, schema_editor):\n db_alias = schema_editor.connection.alias\n\n approved_fuel = apps.get_model('api', 'ApprovedFuel')\n unit_of_measure = apps.get_model('api', 'UnitOfMeasure')\n\n uom_l = unit_of_measure.objects.using(db_alias).create(\n name=\"L\",\n effective_date=\"2017-01-01\"\n )\n uom_kg = unit_of_measure.objects.using(db_alias).create(\n name=\"kg\",\n effective_date=\"2017-01-01\"\n )\n uom_kwh = unit_of_measure.objects.using(db_alias).create(\n name=\"kWh\",\n effective_date=\"2017-01-01\"\n )\n uom_m3 = unit_of_measure.objects.using(db_alias).create(\n name=\"m³\",\n effective_date=\"2017-01-01\"\n )\n\n approved_fuel.objects.using(db_alias).filter(\n name__in=[\n \"Biodiesel\", \"Ethanol\", \"HDRD\", \"Natural gas-based gasoline\",\n \"Petroleum-based diesel\", \"Petroleum-based gasoline\",\n \"Propane\", \"Renewable diesel\", \"Renewable gasoline\"\n ]\n ).update(\n unit_of_measure=uom_l\n )\n approved_fuel.objects.using(db_alias).filter(\n name__in=[\n \"Hydrogen\", \"LNG\"\n ]\n ).update(\n unit_of_measure=uom_kg\n )\n approved_fuel.objects.using(db_alias).filter(\n name=\"Electricity\"\n ).update(\n unit_of_measure=uom_kwh\n )\n approved_fuel.objects.using(db_alias).filter(\n name=\"CNG\"\n ).update(\n unit_of_measure=uom_m3\n )","title":""},{"docid":"3314b2ee6dbd4e64347b914b83ad4c42","score":"0.50818443","text":"def determine_features(self, designs, preferredsize, bestgimbal, prefergenerators,\n prefershortengines, prefermonopropellant):\n lowest_mass = True\n lowest_cost = True\n lowest_requirements = True\n best_gimbal_range = True\n shortest_engine = True\n for e in designs:\n if not e.is_best:\n continue\n if lowest_mass and e.get_mass() < self.get_mass():\n lowest_mass = False\n if lowest_cost and e.get_cost() < self.get_cost():\n lowest_cost = False\n if lowest_requirements and e.requiredscience.is_easier_than(self.requiredscience):\n lowest_requirements = False\n if best_gimbal_range and e.mainengine.tvc > self.mainengine.tvc:\n best_gimbal_range = False\n if shortest_engine and e.mainengine.length < self.mainengine.length:\n shortest_engine = False\n if lowest_mass:\n self.features.add(Features.mass)\n if lowest_cost:\n self.features.add(Features.cost)\n if lowest_requirements:\n # this extra condition is false, but it looks strange if requiring 'only'\n # VeryHeavRocketry is presented as something good\n if (techtree.Node.VeryHeavyRocketry not in self.requiredscience.nodes and\n techtree.Node.HypersonicFlight not in self.requiredscience.nodes and\n techtree.Node.IonPropulsion not in self.requiredscience.nodes):\n self.features.add(Features.low_requirements)\n if prefershortengines and shortest_engine:\n self.features.add(Features.short_engine)\n if ((bestgimbal == 1 and self.mainengine.tvc > 0.0) or\n (bestgimbal == 2 and best_gimbal_range)):\n self.features.add(Features.gimbal)\n if prefermonopropellant and self.fueltype is parts.FuelTypes.Monopropellant:\n self.features.add(Features.monopropellant)\n if prefergenerators and self.mainengine.electricity:\n self.features.add(Features.generator)\n if preferredsize is not None and \\\n (self.size is preferredsize or self.size is parts.RadialSize.RadiallyMounted):\n self.features.add(Features.radial_size)","title":""},{"docid":"13f28693e051937cf3c4d5a99e2bf4fa","score":"0.50801796","text":"def unit_techs(name, build_time, metal, energy, zetium, volume_coefs,\n points_mult, max_planets_required,\n attack_mod, crit_abs_coef,\n is_space, unlocker, npc=False):\n build_time = mins2sec(build_time)\n\n specs = get_specs(name, is_space)\n\n ### Base technology\n\n num_of_base_levels = max_lvl_for_unit_tech(name, unlocker)\n\n start_mult = unit_tech_start_multiplier(npc)\n end_mult = start_mult if num_of_base_levels == 1 \\\n else unit_tech_end_multiplier(npc)\n\n list = tuple()\n list += unit_tech_base(\n name, build_time, start_mult, end_mult, num_of_base_levels,\n metal, energy, zetium, volume_coefs, points_mult, max_planets_required, npc\n )\n\n for spec_name, func in specs:\n # Pass attack_mod to function, because it expects it!\n max = func(attack_mod, crit_abs_coef) * base_proportion * 100\n coefs = lin_dep_raw(0, max, num_of_base_levels) if unlocker \\\n else lin_dep_raw_with_first_lvl(max, num_of_base_levels)\n\n list += (name, spec_name.lower() + \" mod\") + coefs + (0, 1),\n list += empty_row\n\n ### Specialization technologies\n\n for spec_name, func in specs:\n tech_name = name + \" \" + spec_name\n list += unit_tech_base(\n tech_name, build_time, start_mult, end_mult, 10,\n metal, energy, zetium, volume_coefs, points_mult, max_planets_required,\n npc\n )\n\n # Pass attack_mod to function, because it expects it!\n max_value = func(attack_mod, crit_abs_coef) * specialized_proportion * 100\n coefs = lin_dep_raw_with_first_lvl(max_value, 10)\n list += (tech_name, spec_name.lower() + \" mod\") + coefs + (0, 1),\n list += empty_row\n\n return list","title":""},{"docid":"b972d67f55845810dda3970c430b7271","score":"0.5071852","text":"def enrich_fuel(enrich, fuel):\n fuel_comp = {}\n # build fuel comp. starting with bounded compound\n for isotope in mats[fuel]['comp']:\n if isotope == 92000:\n # add enriched Uranium to fuel composition\n fuel_comp.update({92235 : mats[fuel]['comp'][92000]*enrich,\n 92238 : mats[fuel]['comp'][92000]*(1-enrich) })\n else:\n fuel_comp.update({isotope : mats[fuel]['comp'][isotope]})\n \n mmO = 32\n mmN = 14.0067\n mmU25 = 235.04\n mmU28 = 238.05\n\n if fuel == 'UN':\n mmfuel = mmN + 1 / ((enrich / mmU25) + ((1-enrich) / mmU28))\n if fuel == 'UO2':\n mmfuel = mmO + 1 / ((enrich / mmU25) + ((1-enrich) / mmU28))\n \n return fuel_comp, mmfuel","title":""},{"docid":"cb1924df1adaa539fe9f8312c33f6485","score":"0.5071585","text":"def fly(self):\n if self.fuel > 1:\n self.fuel = self.fuel - 1\n else:\n print('Not enough fuel!')","title":""},{"docid":"76a562111b5675a76dcf0d2b8b9f37cb","score":"0.5070485","text":"def fill_gas_tank(self):\n print(\"This car doesn't need a gas tank!\")","title":""},{"docid":"76a562111b5675a76dcf0d2b8b9f37cb","score":"0.5070485","text":"def fill_gas_tank(self):\n print(\"This car doesn't need a gas tank!\")","title":""},{"docid":"76a562111b5675a76dcf0d2b8b9f37cb","score":"0.5070485","text":"def fill_gas_tank(self):\n print(\"This car doesn't need a gas tank!\")","title":""},{"docid":"336ef0b924a50959bd07d3c4ad4c3ec0","score":"0.50611955","text":"def fill_gas_tank(self):\n print(\"No need for gas in an electric car!\")","title":""},{"docid":"3528219963d4da17b5b9d15fd41c6142","score":"0.5050947","text":"def _setup(self,\n loglevel=10,\n electronic_conductance='electronic_conductance',\n occupancy='occupancy',\n voltage='voltage',\n **params):\n self._fluid = params['active_fluid']\n try: self._fluid = self.find_object_by_name(self._fluid) \n except: pass #Accept object\n self._X_name = voltage\n self._boundary_conditions_setup()\n g = self._fluid.get_throat_data(prop=electronic_conductance)\n s = self._fluid.get_throat_data(prop=occupancy)\n self._conductance = g*s+g*(-s)/1e3","title":""},{"docid":"beb93b038c111e1bd728757ba264fc08","score":"0.5036853","text":"def reaction_balance(self, fluid):\n if isinstance(self, combustion_engine):\n inl = self.inl[2:]\n outl = self.outl[2:]\n else:\n inl = self.inl\n outl = self.outl\n\n ######################################################################\n # molar mass flow for fuel and oxygen\n n_fuel = {}\n n_oxy_stoich = {}\n n_h = 0\n n_c = 0\n for f in self.fuel_list:\n n_fuel[f] = 0\n for i in inl:\n n = i.m.val_SI * i.fluid.val[f] / molar_masses[f]\n n_fuel[f] += n\n n_h += n * self.fuels[f]['H']\n n_c += n * self.fuels[f]['C']\n\n # stoichiometric oxygen requirement for each fuel\n n_oxy_stoich[f] = n_fuel[f] * (self.fuels[f]['H'] / 4 +\n self.fuels[f]['C'])\n\n n_oxygen = 0\n for i in inl:\n n_oxygen += (i.m.val_SI * i.fluid.val[self.o2] /\n molar_masses[self.o2])\n\n ######################################################################\n # calculate stoichiometric oxygen\n n_oxygen_stoich = n_h / 4 + n_c\n\n ######################################################################\n # calculate lambda if not set\n if not self.lamb.is_set:\n self.lamb.val = n_oxygen / n_oxygen_stoich\n\n ######################################################################\n # calculate excess fuel if lambda is lower than 1\n if self.lamb.val < 1:\n n_h_exc = (n_oxygen_stoich - n_oxygen) * 4\n n_c_exc = (n_oxygen_stoich - n_oxygen)\n else:\n n_h_exc = 0\n n_c_exc = 0\n\n ######################################################################\n # equation for carbondioxide\n if fluid == self.co2:\n dm = (n_c - n_c_exc) * molar_masses[self.co2]\n\n ######################################################################\n # equation for water\n elif fluid == self.h2o:\n dm = (n_h - n_h_exc) / 2 * molar_masses[self.h2o]\n\n ######################################################################\n # equation for oxygen\n elif fluid == self.o2:\n if self.lamb.val < 1:\n dm = -n_oxygen * molar_masses[self.o2]\n else:\n dm = -n_oxygen / self.lamb.val * molar_masses[self.o2]\n\n ######################################################################\n # equation for fuel\n elif fluid in self.fuel_list:\n if self.lamb.val < 1:\n n_fuel_exc = (-(n_oxygen / n_oxygen_stoich - 1) *\n n_oxy_stoich[fluid] *\n (self.fuels[f]['H'] / 4 + self.fuels[f]['C']))\n else:\n n_fuel_exc = 0\n dm = -(n_fuel[fluid] - n_fuel_exc) * molar_masses[fluid]\n\n ######################################################################\n # equation for other fluids\n else:\n dm = 0\n\n res = dm\n for i in inl:\n res += i.fluid.val[fluid] * i.m.val_SI\n for o in outl:\n res -= o.fluid.val[fluid] * o.m.val_SI\n return res","title":""},{"docid":"8fae5051ef38731e65efa832af478585","score":"0.5029067","text":"def fill_gas_tank(self):\n print(\"This car doesn't have a gas tank!\")","title":""},{"docid":"2c97738b9001ff6d453fe3ce8a118cb5","score":"0.5022136","text":"def add_unit_models(m):\n fs = m.fs_main.fs_stc\n prop_water = m.fs_main.prop_water\n\n # Custom valve function for throttle valve\n def throttle_valve_function(blk):\n blk.Cv.fix(1)\n a = blk.vfa = pyo.Var(initialize=2.8904e-02, doc=\"Valve function parameter a\")\n b = blk.vfb = pyo.Var(initialize=3.3497e-02, doc=\"Valve function parameter b\")\n c = blk.vfc = pyo.Var(initialize=1.4514e-02, doc=\"Valve function parameter c\")\n d = blk.vfd = pyo.Var(initialize=1.4533e-03, doc=\"Valve function parameter d\")\n a.fix()\n b.fix()\n c.fix()\n d.fix()\n o = blk.valve_opening\n\n @blk.Expression(m.fs_main.time)\n def valve_function(bd, t):\n return a * o[t] ** 3 - b * o[t] ** 2 + c * o[t] - d\n\n # Unit model for multistage turbine including throttle valve\n fs.turb = TurbineMultistage(\n dynamic=False,\n property_package=prop_water,\n num_parallel_inlet_stages=1,\n throttle_valve_function=ValveFunctionType.custom,\n throttle_valve_function_callback=throttle_valve_function,\n num_hp=14,\n num_ip=9,\n num_lp=5,\n hp_split_locations=[14],\n ip_split_locations=[6, 9],\n lp_split_locations=[2, 4, 5],\n hp_disconnect=[14],\n hp_split_num_outlets={14: 2},\n ip_split_num_outlets={9: 3},\n )\n\n # Unit model for regulating valve of BFPT (boiler feed pump turbine)\n fs.bfp_turb_valve = Valve(dynamic=False, property_package=prop_water)\n\n # Unit model for main stage of BFPT\n fs.bfp_turb = TurbineStage(dynamic=False, property_package=prop_water)\n\n # Unit model for outlet stage of BFPT\n fs.bfp_turb_os = TurbineOutletStage(dynamic=False, property_package=prop_water)\n\n # Unit model for main condenser\n fs.condenser = Condenser(\n dynamic=False,\n shell={\"has_pressure_change\": False, \"property_package\": prop_water},\n tube={\"has_pressure_change\": False, \"property_package\": prop_water},\n )\n\n # Unit model for auxiliary condenser\n fs.aux_condenser = Condenser(\n dynamic=False,\n shell={\"has_pressure_change\": False, \"property_package\": prop_water},\n tube={\"has_pressure_change\": False, \"property_package\": prop_water},\n )\n\n # Unit model for condenser hotwell (hotwell tank modeled separately)\n # Modeled as a mixer of makeup, main, and auxiliary condenser water streams\n # Set momentum_mixing_type to none since aux_condenser outlet pressure\n # is usually not equal to the main condenser outlet pressure\n # We impose the constraints to let the mixed pressure equal to\n # the main condenser pressure and makeup water pressure\n fs.condenser_hotwell = Mixer(\n dynamic=False,\n momentum_mixing_type=MomentumMixingType.none,\n inlet_list=[\"main_condensate\", \"makeup\", \"aux_condensate\"],\n property_package=prop_water,\n )\n\n # Unit model for water control valve between makeup tank and hotwell\n fs.makeup_valve = Valve(\n dynamic=False, has_holdup=False, phase=\"Liq\", property_package=prop_water\n )\n\n # Unit model for hotwell tank with holdup for dynamic model\n # Modeled as a simple tank with constant cross section area and tank level\n fs.hotwell_tank = WaterTank(\n tank_type=\"simple_tank\", has_holdup=True, property_package=prop_water\n )\n\n # Unit model for condensate pump\n fs.cond_pump = WaterPump(dynamic=False, property_package=prop_water)\n\n # Unit model for water control valve after hotwell tank\n # Used to control deaerator level\n fs.cond_valve = Valve(\n dynamic=False, has_holdup=False, phase=\"Liq\", property_package=prop_water\n )\n\n # Unit model for feed water heater 1\n fs.fwh1 = FWH0D(\n has_desuperheat=False,\n has_drain_cooling=False,\n has_drain_mixer=True,\n condense={\n \"tube\": {\"has_pressure_change\": True},\n \"shell\": {\"has_pressure_change\": True},\n \"has_holdup\": True,\n },\n property_package=prop_water,\n )\n\n # Unit model for drain pump of FWH1\n fs.fwh1_drain_pump = WaterPump(dynamic=False, property_package=prop_water)\n\n # Unit model for mixer of FWH1 drain and condensate\n fs.fwh1_drain_return = Mixer(\n dynamic=False,\n inlet_list=[\"feedwater\", \"fwh1_drain\"],\n property_package=prop_water,\n momentum_mixing_type=MomentumMixingType.equality,\n )\n\n # Unit model for feed water heater 2\n fs.fwh2 = FWH0D(\n has_desuperheat=False,\n has_drain_cooling=True,\n has_drain_mixer=True,\n condense={\n \"tube\": {\"has_pressure_change\": True},\n \"shell\": {\"has_pressure_change\": True},\n \"has_holdup\": True,\n },\n cooling={\"dynamic\": False, \"has_holdup\": False},\n property_package=prop_water,\n )\n\n # Unit model for water control valve between drain of fwh2 and fwh1\n fs.fwh2_valve = Valve(\n dynamic=False, has_holdup=False, phase=\"Liq\", property_package=prop_water\n )\n\n # Unit model for feed water heater 3\n fs.fwh3 = FWH0D(\n has_desuperheat=False,\n has_drain_cooling=True,\n has_drain_mixer=False,\n condense={\n \"tube\": {\"has_pressure_change\": True},\n \"shell\": {\"has_pressure_change\": True},\n \"has_holdup\": True,\n },\n cooling={\"dynamic\": False, \"has_holdup\": False},\n property_package=prop_water,\n )\n\n # Unit model for control valve between drain of fwh3 and fwh2\n fs.fwh3_valve = Valve(\n dynamic=False, has_holdup=False, phase=\"Liq\", property_package=prop_water\n )\n\n # Unit model for deaerator also known as fwh4\n # Modeled as mixer to mix extracted steam with condensate and drain\n # from fwh5\n # Using MomentumMixingType.equality for momentum_mixing_type\n # deaerator tank modeled separately for holdup in dyyamic model\n fs.fwh4_deair = Mixer(\n dynamic=False,\n momentum_mixing_type=MomentumMixingType.equality,\n inlet_list=[\"steam\", \"drain\", \"feedwater\"],\n property_package=prop_water,\n )\n\n # Unit model for deaerator water tank\n # Modeled as a horizontal cylindrical tank\n fs.da_tank = WaterTank(\n tank_type=\"horizontal_cylindrical_tank\",\n has_holdup=True,\n property_package=prop_water,\n )\n\n # Unit model for electrical feedwater booster pump\n fs.booster = WaterPump(dynamic=False, property_package=prop_water)\n\n # Unit model for main boiler feed water pump driven by steam turbine\n fs.bfp = WaterPump(dynamic=False, property_package=prop_water)\n\n # Unit model for splitter for spray water stream for main attemperator\n fs.split_attemp = Separator(\n dynamic=False, property_package=prop_water, outlet_list=[\"FeedWater\", \"Spray\"]\n )\n\n # Unit model for attemperator spray control valve\n fs.spray_valve = Valve(\n dynamic=False, has_holdup=False, phase=\"Liq\", property_package=prop_water\n )\n\n # Unit model for feed water heater 5\n fs.fwh5 = FWH0D(\n has_desuperheat=True,\n has_drain_cooling=True,\n has_drain_mixer=True,\n condense={\n \"tube\": {\"has_pressure_change\": True},\n \"shell\": {\"has_pressure_change\": True},\n \"has_holdup\": True,\n },\n desuperheat={\"dynamic\": False},\n cooling={\"dynamic\": False, \"has_holdup\": False},\n property_package=prop_water,\n )\n\n # Unit model for water control valve drain of fwh5 and deaerator\n fs.fwh5_valve = Valve(\n dynamic=False, has_holdup=False, phase=\"Liq\", property_package=prop_water\n )\n\n # Unit model for feed water heater 6\n fs.fwh6 = FWH0D(\n has_desuperheat=True,\n has_drain_cooling=True,\n has_drain_mixer=False,\n condense={\n \"tube\": {\"has_pressure_change\": True},\n \"shell\": {\"has_pressure_change\": True},\n \"has_holdup\": True,\n },\n desuperheat={\"dynamic\": False},\n cooling={\"dynamic\": False, \"has_holdup\": False},\n property_package=prop_water,\n )\n\n # Unit model for water control valve between drain of fwh6 and fwh5\n fs.fwh6_valve = Valve(\n dynamic=False, has_holdup=False, phase=\"Liq\", property_package=prop_water\n )\n\n # Important process variables, declared and used in PID controllers\n # Variable for main steam temperature\n fs.temperature_main_steam = pyo.Var(fs.time, initialize=810)\n\n # Constraint to calculate main steam temperature\n @fs.Constraint(fs.time)\n def temperature_main_steam_eqn(b, t):\n return (\n b.temperature_main_steam[t]\n == b.turb.throttle_valve[1].control_volume.properties_in[t].temperature\n )\n\n # Variable for gross power output in MW\n fs.power_output = pyo.Var(fs.time, initialize=300, doc=\"gross power output in MW\")\n\n # Constraint to calculate gross power output\n @fs.Constraint(fs.time)\n def power_output_eqn(b, t):\n return b.power_output[t] == -b.turb.power[t] / 1e6\n\n if m.dynamic is True:\n # Add PID controllers if the flowsheet model is a dynamic model\n # PI controller to control level of fwh2\n fs.fwh2_ctrl = PIDController(\n process_var=fs.fwh2.condense.level,\n manipulated_var=fs.fwh2_valve.valve_opening,\n controller_type=ControllerType.PI,\n calculate_initial_integral=False,\n )\n\n # PI controller to control level of fwh3\n fs.fwh3_ctrl = PIDController(\n process_var=fs.fwh3.condense.level,\n manipulated_var=fs.fwh3_valve.valve_opening,\n controller_type=ControllerType.PI,\n calculate_initial_integral=False,\n )\n\n # PI controller to control level of fwh5\n fs.fwh5_ctrl = PIDController(\n process_var=fs.fwh5.condense.level,\n manipulated_var=fs.fwh5_valve.valve_opening,\n controller_type=ControllerType.PI,\n calculate_initial_integral=False,\n )\n\n # PI controller to control level of fwh6\n fs.fwh6_ctrl = PIDController(\n process_var=fs.fwh6.condense.level,\n manipulated_var=fs.fwh6_valve.valve_opening,\n controller_type=ControllerType.PI,\n calculate_initial_integral=False,\n )\n\n # PI controller to control level of deaerator tank\n fs.da_ctrl = PIDController(\n process_var=fs.da_tank.tank_level,\n manipulated_var=fs.cond_valve.valve_opening,\n controller_type=ControllerType.PI,\n calculate_initial_integral=False,\n )\n\n # PI controller to control level of hotwell tank\n fs.makeup_ctrl = PIDController(\n process_var=fs.hotwell_tank.tank_level,\n manipulated_var=fs.makeup_valve.valve_opening,\n controller_type=ControllerType.PI,\n mv_bound_type=ControllerMVBoundType.SMOOTH_BOUND,\n calculate_initial_integral=False,\n )\n\n # PID controller to control main steam temperature\n fs.spray_ctrl = PIDController(\n process_var=fs.temperature_main_steam,\n manipulated_var=fs.spray_valve.valve_opening,\n controller_type=ControllerType.PID,\n mv_bound_type=ControllerMVBoundType.SMOOTH_BOUND,\n calculate_initial_integral=False,\n )\n\n return m","title":""},{"docid":"ef423f73af216d7275e52a7b54e355d7","score":"0.5018522","text":"def update_technology_dispatch(self, year):\n self.stock.act_tech = self.stock.technology.loc[:,year].to_frame()\n self.stock.act_rem = (self.stock.remaining.loc[:,year].to_frame().groupby(level=util.ix_incl(self.stock.act_tech,self.stock.act_tech.index.names)).sum())\n self.stock.act_tech_or_rem = self.stock.act_tech.fillna(self.stock.act_rem)\n self.stock.act_tech_or_rem = DfOper.add([self.stock.act_tech_or_rem,util.remove_df_levels(self.stock.dispatch_cap.loc[:,year].to_frame(),'vintage')])","title":""},{"docid":"998cd84bba23c8645a73a206dd061b5b","score":"0.50183886","text":"def calculate_input_stock(self):\n levels = self.stock.rollover_group_levels + [self.years] + [self.tech_names]\n names = self.stock.rollover_group_names + ['year'] + ['supply_technology']\n index = pd.MultiIndex.from_product(levels,names=names)\n if self.stock._has_data is True and 'supply_technology' in self.stock.raw_values.index.names:\n #remap to technology stocks\n self.stock.years = self.years\n self.stock.remap(map_from='raw_values', map_to='technology', converted_geography=GeoMapper.supply_primary_geography, fill_timeseries=True, fill_value=np.nan)\n #TODO add to clean timeseries. Don't allow filling of timseries before raw values.\n self.stock.technology[self.stock.technology.index.get_level_values('year')<min(self.stock.raw_values.index.get_level_values('year'))] = np.nan\n self.convert_stock('stock', 'technology')\n self.stock.technology = self.stock.technology.reorder_levels(names)\n self.stock.technology = self.stock.technology.reindex(index)\n #if there's case_specific stock data, we must use that to replace reference technology stocks\n if hasattr(self.case_stock,'technology'):\n # if there are levels in the case specific stock that are not in the reference stock, we must remove that level from the case stock\n mismatched_levels = [x for x in self.case_stock.technology.index.names if x not in self.stock.technology.index.names]\n if len(mismatched_levels):\n self.case_stock.technology= util.remove_df_levels(self.case_stock.technology,mismatched_levels)\n #if there are still level mismatches, it means the reference stock has more levels, which returns an error\n if np.any(util.difference_in_df_names(self.case_stock.technology, self.stock.technology,return_bool=True)):\n raise ValueError(\"technology stock indices in node %s do not match input energy system stock data\" %self.name)\n else:\n #if the previous test is passed, we use the reference stock to fill in the Nans of the case stock\n self.case_stock.technology = self.case_stock.technology.reorder_levels(names)\n self.case_stock.technology = self.case_stock.technology.reindex(index)\n self.stock.technology = self.case_stock.technology.fillna(self.stock.technology)\n self.stock.technology = self.stock.technology.unstack('year')\n self.stock.technology.columns = self.stock.technology.columns.droplevel()\n self.stock.technology = util.reindex_df_level_with_new_elements(self.stock.technology,'supply_technology',self.tech_names)\n elif hasattr(self.case_stock,'technology'):\n # if there are levels in the case specific stock that are not in the rollover groups, we must remove that level from the case stock\n mismatched_levels = [x for x in self.case_stock.technology.index.names if x not in names]\n if len(mismatched_levels):\n self.case_stock.technology = util.remove_df_levels(self.case_stock.technology,mismatched_levels)\n #if there are still level mismatches, it means the rollover has more levels, which returns an error\n if len([x for x in self.stock.rollover_group_names if x not in self.case_stock.technology.index.names]) :\n raise ValueError(\"technology stock levels in node %s do not match other node input data\" %self.name)\n else:\n #if the previous test is passed we reindex the case stock for unspecified technologies\n self.case_stock.technology = self.case_stock.technology.reorder_levels(names)\n structure_df = pd.DataFrame(1,index=index,columns=['value'])\n self.case_stock.technology = self.case_stock.technology.reindex(index)\n self.stock.technology = self.case_stock.technology\n self.stock.technology = self.stock.technology.unstack('year')\n self.stock.technology.columns = self.stock.technology.columns.droplevel()\n self.stock.technology = util.reindex_df_level_with_new_elements(self.stock.technology,'supply_technology',self.tech_names)\n else:\n levels = self.stock.rollover_group_levels + [self.tech_names]\n names = self.stock.rollover_group_names + ['supply_technology']\n index = pd.MultiIndex.from_product(levels,names=names)\n self.stock.technology = util.empty_df(index=index,columns=self.years,fill_value=np.NaN)\n\n if self.stock._has_data is True and 'supply_technology' not in self.stock.raw_values.index.names:\n levels = self.stock.rollover_group_levels + [self.years]\n names = self.stock.rollover_group_names + ['year']\n index = pd.MultiIndex.from_product(levels,names=names)\n structure_df = pd.DataFrame(1,index=index,columns=['value'])\n self.stock.remap(map_from='raw_values', map_to='total', converted_geography=GeoMapper.supply_primary_geography, time_index = self.years,fill_timeseries=True, fill_value=np.nan)\n #TODO add to clean timeseries. Don't allow filling of timseries before raw values.\n self.stock.total[self.stock.total.index.get_level_values('year')<min(self.stock.raw_values.index.get_level_values('year'))] = np.nan\n self.stock.total = DfOper.mult([self.stock.total,structure_df],fill_value=np.nan)\n self.convert_stock('stock', 'total')\n if hasattr(self.case_stock,'total'):\n mismatched_levels = [x for x in self.case_stock.total.index.names if x not in names]\n if len(mismatched_levels):\n self.case_stock.total = util.remove_df_levels(self.case_stock.total,mismatched_levels)\n #if there are still level mismatches, it means the reference stock has more levels, which returns an error\n if np.any(util.difference_in_df_names(self.case_stock.total, self.stock.total,return_bool=True)):\n raise ValueError(\"total stock indices in node %s do not match input energy system stock data\" %self.name)\n else:\n #if the previous test is passed, we use the reference stock to fill in the Nans of the case stock\n self.case_stock.total= self.case_stock.total.reorder_levels(names)\n self.stock.total = self.stock.total.reorder_levels(names)\n structure_df = pd.DataFrame(1,index=index,columns=['value'])\n self.case_stock.total = DfOper.mult([self.case_stock.total,structure_df],fill_value=np.nan)\n self.stock.total = DfOper.mult([self.stock.total,structure_df],fill_value=np.nan)\n self.stock.total = self.case_stock.total.fillna(self.stock.total)\n\n self.stock.total = self.stock.total.unstack('year')\n self.stock.total.columns = self.stock.total.columns.droplevel()\n elif hasattr(self.case_stock,'total'):\n levels = self.stock.rollover_group_levels + [self.years]\n names = self.stock.rollover_group_names + ['year']\n index = pd.MultiIndex.from_product(levels,names=names)\n # if there are levels in the case specific stock that are not in the rollover groups, we must remove that level from the case stock\n mismatched_levels = [x for x in self.case_stock.total.index.names if x not in names]\n if len(mismatched_levels):\n self.case_stock.total = util.remove_df_levels(self.case_stock.total,mismatched_levels)\n #if there are still level mismatches, it means the rollover has more levels, which returns an error\n if len([x for x in names if x not in self.case_stock.total.index.names]) :\n raise ValueError(\"total stock levels in node %s do not match other node input data\" %self.name)\n else:\n self.case_stock.total= self.case_stock.total.reorder_levels(names)\n self.case_stock.total = self.case_stock.total.reindex(index)\n self.stock.total = self.case_stock.total\n self.stock.total = self.stock.total.unstack('year')\n self.stock.total.columns = self.stock.total.columns.droplevel()\n else:\n index = pd.MultiIndex.from_product(self.stock.rollover_group_levels,names=self.stock.rollover_group_names)\n self.stock.total = util.empty_df(index=index,columns=self.years,fill_value=np.NaN)\n if self.stock._has_data or hasattr(self.case_stock,'data') and self.case_stock._has_data == True:\n self.stock._has_data = True\n self.max_total()\n if cfg.rio_supply_run and self.name not in cfg.rio_excluded_nodes:\n self.stock.technology.loc[:, cfg.supply_years] = self.stock.technology.loc[:, cfg.supply_years].fillna(0)\n self.format_rollover_stocks()","title":""},{"docid":"d04f362ef14e86d3c526cf128b1c32d4","score":"0.5014126","text":"def __init__(self, name, fuel, fanciness):\n super().__init__(name, fuel)\n self.price_per_km *= fanciness\n self.current_fare_distance = 0","title":""},{"docid":"58f96e662628372d9a0614b07194af13","score":"0.5006129","text":"def add_categories_0116(apps, schema_editor):\n db_alias = schema_editor.connection.alias\n\n approved_fuel = apps.get_model('api', 'ApprovedFuel')\n default_carbon_intensity_category = apps.get_model(\n 'api', 'DefaultCarbonIntensityCategory')\n energy_density_category = apps.get_model(\n 'api', 'EnergyDensityCategory')\n energy_effectiveness_ratio_category = apps.get_model(\n 'api', 'EnergyEffectivenessRatioCategory')\n\n approved_fuel.objects.using(db_alias).filter(\n name=\"Biodiesel\"\n ).update(\n default_carbon_intensity_category=default_carbon_intensity_category\n .objects.using(db_alias).get(\n name=\"Renewable Fuel in relation to diesel class fuel\"\n ),\n energy_density_category=energy_density_category\n .objects.using(db_alias).get(\n name=\"Biodiesel\"\n ),\n energy_effectiveness_ratio_category=energy_effectiveness_ratio_category\n .objects.using(db_alias).get(\n name=\"Petroleum-based diesel fuel or renewable fuel in relation \"\n \"to diesel class fuel\"\n )\n )\n\n approved_fuel.objects.using(db_alias).filter(\n name=\"CNG\"\n ).update(\n default_carbon_intensity_category=default_carbon_intensity_category\n .objects.using(db_alias).get(\n name=\"CNG\"\n ),\n energy_density_category=energy_density_category\n .objects.using(db_alias).get(\n name=\"CNG\"\n ),\n energy_effectiveness_ratio_category=energy_effectiveness_ratio_category\n .objects.using(db_alias).get(\n name=\"CNG\"\n )\n )\n\n approved_fuel.objects.using(db_alias).filter(\n name=\"Electricity\"\n ).update(\n default_carbon_intensity_category=default_carbon_intensity_category\n .objects.using(db_alias).get(\n name=\"Electricity\"\n ),\n energy_density_category=energy_density_category\n .objects.using(db_alias).get(\n name=\"Electricity\"\n ),\n energy_effectiveness_ratio_category=energy_effectiveness_ratio_category\n .objects.using(db_alias).get(\n name=\"Electricity\"\n )\n )\n\n approved_fuel.objects.using(db_alias).filter(\n name=\"Ethanol\"\n ).update(\n default_carbon_intensity_category=default_carbon_intensity_category\n .objects.using(db_alias).get(\n name=\"Renewable Fuel in relation to gasoline class fuel\"\n ),\n energy_density_category=energy_density_category\n .objects.using(db_alias).get(\n name=\"Ethanol\"\n ),\n energy_effectiveness_ratio_category=energy_effectiveness_ratio_category\n .objects.using(db_alias).get(\n name=\"Petroleum-based gasoline, natural gas-based gasoline or \"\n \"renewable fuel in relation to gasoline class fuel\"\n )\n )\n\n approved_fuel.objects.using(db_alias).filter(\n name=\"HDRD\"\n ).update(\n default_carbon_intensity_category=default_carbon_intensity_category\n .objects.using(db_alias).get(\n name=\"Renewable Fuel in relation to diesel class fuel\"\n ),\n energy_density_category=energy_density_category\n .objects.using(db_alias).get(\n name=\"Hydrogenation-derived renewable diesel fuel\"\n ),\n energy_effectiveness_ratio_category=energy_effectiveness_ratio_category\n .objects.using(db_alias).get(\n name=\"Petroleum-based diesel fuel or renewable fuel in relation \"\n \"to diesel class fuel\"\n )\n )\n\n approved_fuel.objects.using(db_alias).filter(\n name=\"Hydrogen\"\n ).update(\n default_carbon_intensity_category=default_carbon_intensity_category\n .objects.using(db_alias).get(\n name=\"Hydrogen\"\n ),\n energy_density_category=energy_density_category\n .objects.using(db_alias).get(\n name=\"Hydrogen\"\n ),\n energy_effectiveness_ratio_category=energy_effectiveness_ratio_category\n .objects.using(db_alias).get(\n name=\"Hydrogen\"\n )\n )\n\n approved_fuel.objects.using(db_alias).filter(\n name=\"LNG\"\n ).update(\n default_carbon_intensity_category=default_carbon_intensity_category\n .objects.using(db_alias).get(\n name=\"LNG\"\n ),\n energy_density_category=energy_density_category\n .objects.using(db_alias).get(\n name=\"LNG\"\n ),\n energy_effectiveness_ratio_category=energy_effectiveness_ratio_category\n .objects.using(db_alias).get(\n name=\"LNG\"\n )\n )\n\n approved_fuel.objects.using(db_alias).filter(\n name=\"Natural gas-based gasoline\"\n ).update(\n default_carbon_intensity_category=default_carbon_intensity_category\n .objects.using(db_alias).get(\n name=\"Natural gas-based gasoline\"\n ),\n energy_density_category=energy_density_category\n .objects.using(db_alias).get(\n name=\"Petroleum-based gasoline, natural gas-based gasoline or \"\n \"gasoline produced from biomass\"\n ),\n energy_effectiveness_ratio_category=energy_effectiveness_ratio_category\n .objects.using(db_alias).get(\n name=\"Petroleum-based gasoline, natural gas-based gasoline or \"\n \"renewable fuel in relation to gasoline class fuel\"\n )\n )\n\n approved_fuel.objects.using(db_alias).filter(\n name=\"Petroleum-based diesel\"\n ).update(\n default_carbon_intensity_category=default_carbon_intensity_category\n .objects.using(db_alias).get(\n name=\"Petroleum-based diesel\"\n ),\n energy_density_category=energy_density_category\n .objects.using(db_alias).get(\n name=\"Petroleum-based diesel fuel or diesel fuel produced from \"\n \"biomass\"\n ),\n energy_effectiveness_ratio_category=energy_effectiveness_ratio_category\n .objects.using(db_alias).get(\n name=\"Petroleum-based diesel fuel or renewable fuel in relation \"\n \"to diesel class fuel\"\n )\n )\n\n approved_fuel.objects.using(db_alias).filter(\n name=\"Petroleum-based gasoline\"\n ).update(\n default_carbon_intensity_category=default_carbon_intensity_category\n .objects.using(db_alias).get(\n name=\"Petroleum-based gasoline\"\n ),\n energy_density_category=energy_density_category\n .objects.using(db_alias).get(\n name=\"Petroleum-based gasoline, natural gas-based gasoline or \"\n \"gasoline produced from biomass\"\n ),\n energy_effectiveness_ratio_category=energy_effectiveness_ratio_category\n .objects.using(db_alias).get(\n name=\"Petroleum-based gasoline, natural gas-based gasoline or \"\n \"renewable fuel in relation to gasoline class fuel\"\n )\n )\n\n approved_fuel.objects.using(db_alias).filter(\n name=\"Propane\"\n ).update(\n default_carbon_intensity_category=default_carbon_intensity_category\n .objects.using(db_alias).get(\n name=\"Propane\"\n ),\n energy_density_category=energy_density_category\n .objects.using(db_alias).get(\n name=\"Propane\"\n ),\n energy_effectiveness_ratio_category=energy_effectiveness_ratio_category\n .objects.using(db_alias).get(\n name=\"Propane\"\n )\n )\n\n approved_fuel.objects.using(db_alias).filter(\n name=\"Renewable diesel\"\n ).update(\n default_carbon_intensity_category=default_carbon_intensity_category\n .objects.using(db_alias).get(\n name=\"Petroleum-based diesel\"\n ),\n energy_density_category=energy_density_category\n .objects.using(db_alias).get(\n name=\"Petroleum-based diesel fuel or diesel fuel produced from \"\n \"biomass\"\n ),\n energy_effectiveness_ratio_category=energy_effectiveness_ratio_category\n .objects.using(db_alias).get(\n name=\"Petroleum-based diesel fuel or renewable fuel in relation \"\n \"to diesel class fuel\"\n )\n )\n\n approved_fuel.objects.using(db_alias).filter(\n name=\"Renewable gasoline\"\n ).update(\n default_carbon_intensity_category=default_carbon_intensity_category\n .objects.using(db_alias).get(\n name=\"Petroleum-based gasoline\"\n ),\n energy_density_category=energy_density_category\n .objects.using(db_alias).get(\n name=\"Petroleum-based gasoline, natural gas-based gasoline or \"\n \"gasoline produced from biomass\"\n ),\n energy_effectiveness_ratio_category=energy_effectiveness_ratio_category\n .objects.using(db_alias).get(\n name=\"Petroleum-based gasoline, natural gas-based gasoline or \"\n \"renewable fuel in relation to gasoline class fuel\"\n )\n )","title":""},{"docid":"85b57ef7b85f54516d9e56610bb0104f","score":"0.49957237","text":"def update_technology(self, year):\n self.stock.act_tech_energy = util.DfOper.mult([self.stock.technology.loc[:,year].to_frame(), self.stock.act_energy_capacity_ratio],fill_value=np.nan)\n self.stock.act_tech_or_rem_energy = self.stock.act_tech_energy.fillna(self.stock.act_rem_energy.groupby(level=self.stock.act_tech_energy.index.names).sum())\n self.stock.act_tech_or_rem = util.remove_df_levels(util.DfOper.divi([self.stock.act_tech_or_rem_energy, self.stock.act_energy_capacity_ratio]),'supply_technology')","title":""},{"docid":"5fd420f76875d9e99ab84a88224acfa5","score":"0.49942207","text":"def add_concrete_material(self, name, fc, fu, Ec, eu):\n if name in self.materials_dictionary:\n return\n else:\n self.materials_dictionary[name] = MF.Concrete_Material(name, fc, fu, Ec, eu)\n self.Lb.insert(tk.END, name)\n self.controller.update_material_dropdown()","title":""},{"docid":"4109f63c0f39b8f4c8c1dae29e129450","score":"0.49941784","text":"def main():\n ss_flowsheet = ss_sim.main()\n flowsheet = Flowsheet(name='MB_Model') \n\n # fill in values of IC parameters from steady state solve\n setICs(flowsheet,ss_flowsheet)\n \n # Fix variables\n setInputs(flowsheet) \n\n # Initialize at steady state\n initialize_ss(flowsheet,ss_flowsheet)\n\n mb = flowsheet.MB_fuel\n\n # Then perturb\n # ^ that function should go in this file, probably\n # input: dict mapping state names (strings) to new values\n\n # this seems like as much work as doing the perturbation...\n # maybe just make a self contained function\n #input_perturbation = { ('Solid_In_x',mb.t) : { ['Fe2O3'] : 0.25 },\n # ('Solid_In_x',mb.t) : { ['Al2O3'] : 0.75 } }\n\n perturbInputs(flowsheet)\n\n # perturb states\n\n # should put this in a dedicated ~intialize~ function\n # that also intelligently initializes the model after perturbation\n mb.eq_d4.deactivate()\n mb.eq_d5.deactivate()\n mb.eq_d8.deactivate()\n mb.eq_d9.deactivate()\n mb.eq_d10.deactivate()\n mb.eq_g7.deactivate()\n mb.eq_g8.deactivate()\n mb.eq_g10.deactivate()\n mb.eq_g11.deactivate()\n mb.eq_g12.deactivate()\n mb.eq_g13.deactivate()\n mb.eq_g14.deactivate()\n mb.eq_g4.deactivate()\n mb.eq_g5.deactivate()\n mb.eq_g2.deactivate()\n mb.Tg_GW.fix(0.0)\n mb.Tg_refractory.fix(0.0)\n mb.Tw_Wamb.fix()\n mb.Tw.fix()\n mb.Nuw.fix()\n mb.Nu_ext.fix()\n mb.hw.fix()\n mb.hext.fix()\n mb.hext2.fix()\n mb.U.fix()\n mb.Uw.fix()\n mb.Pr_ext.fix()\n mb.Ra.fix()\n mb.Re.fix()\n ###\n \n\n '''\n ts = time.time() \n # Initialize fuel reactor\n flowsheet.MB_fuel._initialize(outlvl=1,\n optarg={\"tol\" : 1e-8,\n \"max_cpu_time\" : 600,\n \"print_level\" : 5,\n \"halt_on_ampl_error\": 'yes'}) \n '''\n\n # Create a solver\n opt = SolverFactory('ipopt')\n opt.options = {'tol': 1e-8,\n 'linear_solver' : 'ma27',\n 'bound_push': 1e-8,\n 'max_cpu_time': 600,\n 'print_level': 5,\n 'halt_on_ampl_error': 'yes'}\n flowsheet.write('fs.nl')\n\n with open('dyn_fs.txt','w') as f:\n flowsheet.display(ostream=f)\n\n print('Constraints violated pre-solve:')\n for const in flowsheet.MB_fuel.component_objects(Constraint,active=True):\n if not isinstance(const,SimpleConstraint):\n for idx in const:\n if (value(const[idx].body) > value(const[idx].upper) + 1.0e-7) or \\\n (value(const[idx].body) < value(const[idx].lower) - 1.0e-7):\n print(const.name,idx)\n else:\n if (value(const.body) > value(const.upper) + 1.0e-7) or \\\n (value(const.body) < value(const.lower) - 1.0e-7):\n print(const.name)\n print('- - -\\n')\n\n print('Variable bounds violated pre-solve:')\n for var in flowsheet.MB_fuel.component_objects(Var,active=True):\n # don't use IndexedComponent here, variables are always indexed components \n # could also do this by iterating over component_objects(SimpleVar)...?\n if not isinstance(var,SimpleVar):\n for idx in var:\n if not (var[idx].lb is None):\n if (var[idx].value < var[idx].lb - 1.0e-7):\n pdb.set_trace()\n print(var.name,idx)\n if not (var[idx].ub is None):\n if (var[idx].value > var[idx].ub + 1.0e-7):\n pdb.set_trace()\n print(var.name,idx)\n else:\n if (var.value > var.ub + 1.0e-7) or \\\n (var.value < var.lb - 1.0e-7):\n print(var.name)\n print('- - -\\n')\n\n # initialized at steady state, works regardless:\n flowsheet.strip_bounds()\n\n for z in mb.z:\n for t in mb.t:\n mb.Cg[z,'CH4',t].setlb(1e-6)\n\n\n # want a function to integrate the model one step from a specified time point\n # will call for all t\n integrate(flowsheet,0)\n \n #results = opt.solve(flowsheet,tee=True,symbolic_solver_labels=False,\n # keepfiles=False)\n\n #with open('dyn_fs_sol.txt','w') as f:\n # flowsheet.display(ostream=f)\n\n\n \n '''\n \n print(\"\\n\")\n print(\"----------------------------------------------------------\")\n print('Total simulation time: ', value(time.time() - ts), \" s\")\n print(\"----------------------------------------------------------\")\n\n \n # Print some variables \n print_summary_fuel_reactor(flowsheet) \n\n # Plot some variables \n #results_plot_fuel_reactor(flowsheet) \n\n #with open('m_fs.txt','w') as f:\n # flowsheet.display(ostream=f)\n\n # Store the flowsheet \n '''\n return flowsheet","title":""},{"docid":"03b4e4ce6255aeb472f7ce4bfbf83a15","score":"0.4981729","text":"def fuel_for_fuel(fuel: int) -> int:\n fuel_needed = 0\n additional_fuel = fuel\n\n while True:\n additional_fuel = launch_fuel_required(additional_fuel)\n if additional_fuel <= 0:\n break\n \n fuel_needed += additional_fuel\n \n return fuel_needed","title":""},{"docid":"54aa4b9cdbe040d77b4b7f7e7035ba0e","score":"0.49801105","text":"def testAddTax(self):\n\tdates = pd.date_range(dt.datetime(2015,01,01), dt.datetime(2025,01,01), freq = 'D')\n\ta = range(0,len(dates))\n\ta1 = np.array([100*1.0001**i for i in a])\n\tb = df.DataFrame({'income':a1}, index = dates)\n\ta2 = a1*.05\n\ta3 = a1*.03\n\td = df.DataFrame({'depreciation':a2,'interest':a3}, index = dates)\n\tr = 0.35\n\tkw2 = {'basis':b}\n\tcr = pf.TaxCredit(name = 'ITC', refundable = False, kind = 'Fractional', rate = 0.15, **kw2)\n\tc = [cr]\n\n\tkwargs = {}\n\tkwargs['name'] = 'fed1'\n\tkwargs['rate'] = r\n\tt = pf.FractionalTax(**kwargs)\n\tmanager = pf.TaxManager()\n\tmanager.create_tax(kind = 'Fractional', **kwargs)\n\t\n\tself.assertTrue(t.rate == manager.taxes['fed1'].rate)\n\tself.assertTrue(t.name == manager.taxes['fed1'].name)\n\n\n\tmanager= pf.TaxManager()\n\tmanager.create_tax(kind= 'Fractional', **kwargs)\n\tmanager.add_revenue(b, name = 'US')\n\tmanager.add_deductions(d, name = 'US')\t\n\n\tmanager.associate_revenue('fed1', ['US_income'])\n\tmanager.associate_deductions('fed1', ['US_depreciation','US_interest'])\n\tkwargs2 = {}\n\tkwargs2['name'] = 'fed1'\n\tkwargs2['rate'] = r\n\tkwargs2['basis'] = b\n\tkwargs2['deductions'] = d\n\t\n\tt = pf.FractionalTax(**kwargs2)\n\tt.basis.rename(columns = lambda x: \"US_%s\" % x, inplace = True)\n\tt.deductions.rename(columns = lambda x: \"US_%s\" % x, inplace = True)\n\tmanager.build_tax_schedule()\n\tself.assertTrue((t.basis == manager.taxes['fed1'].basis).all().all())\n\tself.assertTrue((t.deductions == manager.taxes['fed1'].deductions).all().all())\n\tself.assertTrue(t.rate == manager.taxes['fed1'].rate)\n\tself.assertTrue(t.name == manager.taxes['fed1'].name)\n\n\t#Test that fractional tax is created correctly\n\t\n\n\t#Test that tax is created correctly with associated income, depreciation columns","title":""},{"docid":"b175bbeb09505ebd751e60f0ceb93f8a","score":"0.49752685","text":"def plant(self, *legumes: veg.Vegetable):\n legumes = [self.__get_vegetable(dict) for dict in legumes ]\n\n for leg in legumes:\n\n reste = self.mygarden.add(leg)\n print(f\"Gardener plants {leg.get_nb_graine()} {type(leg).__name__}.s\")\n\n if reste:\n legumes.insert(legumes.index(leg) + 1, reste)\n self.__next_garden()\n continue","title":""},{"docid":"1ea6775487bbea1d069597779ace8883","score":"0.49738714","text":"def add_transport_modes_and_approved_fuels_0093(apps, schema_editor):\n\n db_alias = schema_editor.connection.alias\n\n # By retrieving the models via apps.get_model, we get one appropriately\n # versioned for this migration (so this shouldn't ever need to be\n # maintained if fields change)\n transport_mode = apps.get_model('api', 'TransportMode')\n transport_mode.objects.using(db_alias).bulk_create([\n transport_mode(\n name='Truck',\n effective_date='2017-01-01'\n ),\n transport_mode(\n name='Rail',\n effective_date='2017-01-01'\n ),\n transport_mode(\n name='Marine',\n effective_date='2017-01-01'\n ),\n transport_mode(\n name='Adjacent',\n effective_date='2017-01-01'\n ),\n transport_mode(\n name='Pipeline',\n effective_date='2017-01-01'\n )\n ])\n\n approved_fuel = apps.get_model('api', 'ApprovedFuel')\n approved_fuel.objects.using(db_alias).bulk_create(\n map(lambda af: approved_fuel(\n name=af,\n effective_date='2017-01-01'\n ), approved_fuel_names_0093)\n )","title":""},{"docid":"8ef4ee47703a673fc606c21b38202c26","score":"0.49727106","text":"def Fuel_type_reefer_DEF(self):\n\n #Input constants\n\n generic_trans_amount = \"$5.00\" # any value that gets an approval from the host\n card_to_use_NGFC = 'NGFC' # using this card to get all commercial prompts\n \n amounts_to_dispense = {\n \"buffer_1\":{\n \"grade\": 3,\n \"value\": \"3\"\n },\n \"buffer_2\":{\n \"grade\": 1,\n \"value\": \"2\"\n }\n } \n\n prompts = {\n \"Additional Products Y/N?\": { \n \"buttons\": [\"Yes\"]\n },\n \"Do you want to print a receipt?\": {\n \"buttons\": [\"No\"]\n },\n \"Select fuel products to dispense\": {\n \"buttons\": [\"Reefer fuel\"]\n },\n \"DEF?\": {\n \"buttons\": [\"Yes\"]\n }\n }\n\n messages_to_verify = {\n 'preauth_request_verifications': {\n 'Fuel Purchase': '500', \n '001 - Wex OTR Flags': 'C - Commercial'\n },\n 'preauth_response_verifications': {\n 'Response Code': '2', \n 'Approved Amount': '000000500',\n '002 - Wex OTR Non-Fuel Product Data - Product code: CADV (Company funds cash advance)': 'Amount: 0.00',\n '002 - Wex OTR Non-Fuel Product Data - Product code: MERC (Default category for merchandise)': 'Amount: 30.00', \n f'003 - Wex OTR Fuel Product Limits - Product Code: {self.helpers.grade_1_Product }': 'Product limit: 50.00',\n f'003 - Wex OTR Fuel Product Limits - Product Code: {self.helpers.grade_2_Product }': 'Product limit: 50.00',\n f'003 - Wex OTR Fuel Product Limits - Product Code: {self.helpers.grade_3_Product }': 'Product limit: 50.00',\n '005 - Wex OTR Customer Information': 'ABC TRUCKING DENVER CO234W987'\n },\n 'capture_request_verifications': {\n 'Fuel Purchase': '500', \n '001 - Wex OTR Flags': 'C - Commercial'\n },\n 'capture_response_verifications': {\n 'Response Code': '0', \n 'Approved Amount': '000000500',\n '002 - Wex OTR Non-Fuel Product Data - Product code: CADV (Company funds cash advance)': 'Amount: 20.00',\n '002 - Wex OTR Non-Fuel Product Data - Product code: MERC (Default category for merchandise)': 'Amount: 30.00',\n f'003 - Wex OTR Fuel Product Limits - Product Code: {self.helpers.grade_1_Product }': 'Product limit: 50.00',\n f'003 - Wex OTR Fuel Product Limits - Product Code: {self.helpers.grade_2_Product }': 'Product limit: 50.00',\n f'003 - Wex OTR Fuel Product Limits - Product Code: {self.helpers.grade_3_Product }': 'Product limit: 50.00',\n '005 - Wex OTR Customer Information': 'ABC TRUCKING DENVER CO234W987'}\n }\n\n #Output verificacions\n\n receipt_data = [f\"{self.helpers.grade_1_Name_reefer} CA PUMP# 1\",\n \"2.000 GAL @ $1.000/GAL $2.00 99\",\n f\"{self.helpers.grade_3_Name} CA PUMP# 1\",\n \"3.000 GAL @ $1.000/GAL $3.00 99\",\n \"Subtotal = $5.00\",\n \"Tax = $0.00\",\n \"Total = $5.00\",\n \"Change Due = $0.00\",\n \"Credit $5.00\"]\n\n self.helpers.prepay_transaction(\n card=card_to_use_NGFC,\n prepay_amount=generic_trans_amount,\n prompts=prompts,\n amounts_to_dispense=amounts_to_dispense,\n messages_to_verify=messages_to_verify,\n receipt_data=receipt_data\n )","title":""},{"docid":"69ba4745de36cf5acc1b2bf59f3f30b1","score":"0.4964572","text":"def add_stock(self):\n self.stock = SupplyStock(supply_node=self.name, scenario=self.scenario)","title":""},{"docid":"0ce187e0acf4d0801e01fc0f66411853","score":"0.49607182","text":"def addFleet(self, fleet:Fleet):\n print (\"ADDING FLEET\")\n self.fleet = fleet","title":""},{"docid":"2cc26d2414953d16f8db2422d86f4f7e","score":"0.4944704","text":"def __init__(self, name, fuel, reliability):\n super().__init__(name, fuel)\n self.reliability = reliability","title":""},{"docid":"2cc26d2414953d16f8db2422d86f4f7e","score":"0.4944704","text":"def __init__(self, name, fuel, reliability):\n super().__init__(name, fuel)\n self.reliability = reliability","title":""},{"docid":"7d01c2e4ccd3531f3eb532b265db7a39","score":"0.49305838","text":"def _add_species_basket(self, weight, count):\n self._logger.debug('Add species basket. wt: {}, ct: {}'.format(weight, count))\n if self._current_species_comp_item is None:\n self._logger.error('Species ID / Current Species Comp ID is None')\n return\n\n try:\n new_basket = SpeciesCompositionBaskets.create(\n species_comp_item=self._current_species_comp_item,\n basket_weight_itq=weight,\n fish_number_itq=count,\n created_by=ObserverDBUtil.get_current_user_id(),\n # FIELD-2087: Using default dateformat for time display; reformat before sync later\n created_date=ObserverDBUtil.get_arrow_datestr(date_format=ObserverDBUtil.default_dateformat),\n is_fg_tally_local=1 if self._is_fixed_gear else None,\n is_subsample=None\n )\n self._baskets_model.add_basket(new_basket)\n self._logger.info(f'Added basket wt: {weight} ct: {count}')\n\n finally:\n self._calculate_totals()\n self.basketAdded.emit()\n # self.dataExistsChanged.emit()","title":""},{"docid":"36e7bc57113f2a17351d5bfe5b5d9052","score":"0.49286556","text":"def __init__(self, name, color, max_size=2, fuel=10):\n Backpack.__init__(self,name,color,max_size)\n self.fuel = fuel","title":""},{"docid":"fbcd41a83b91d34ba26cdcd9f66eae16","score":"0.49237648","text":"def __init__(self,name,color,max_size=2,fuel=10):\n Backpack.__init__(self,name,color,max_size)\n self.fuel=fuel","title":""},{"docid":"e8dacbd577ced23b408179ae46706c72","score":"0.49079818","text":"def fly(self,fuel):\n if self.fuel-fuel>=0:\n self.fuel-=fuel\n else:\n print(\"Not enough fuel!\")","title":""},{"docid":"bda09fcd2f27d0272d4d2a849788262c","score":"0.48983583","text":"def calc_proposed_HF_consumption (self):\n building_data = self.comp_specs['building inventory']\n percent_savings = self.comp_specs['cohort savings percent'] / 100.0\n #~ fuel_type = 'Fuel Oil'\n #~ try:\n fuel_types = ['Fuel Oil',\n 'Natural Gas',\n 'HW District',\n 'Propane',\n 'Biomass']\n for fuel_type in fuel_types:\n fuel_vals = building_data[fuel_type + ' Post']\n idx = fuel_vals.isnull()\n fuel_vals[idx] = building_data[fuel_type][idx] *\\\n (1 - percent_savings)\n\n #~ except TypeError:\n\n\n\n #~ self.comp_specs['building inventory'][\"Fuel Oil Post\"] = \\\n #~ self.comp_specs['building inventory'][\"Fuel Oil\"]*\\\n #~ self.comp_specs['cohort savings multiplier']\n\n\n\n\n # by fuel type\n self.proposed_fuel_Hoil_consumption = \\\n building_data['Fuel Oil Post'].sum()\n self.proposed_fuel_lng_consumption = \\\n building_data['Natural Gas Post'].fillna(0).sum()\n self.proposed_fuel_hr_consumption = \\\n building_data['HW District Post'].fillna(0).sum()\n self.proposed_fuel_propane_consumption = \\\n building_data['Propane Post'].fillna(0).sum()\n self.proposed_fuel_biomass_consumption = \\\n building_data['Biomass Post'].fillna(0).sum()\n\n # mmbtu\n self.proposed_HF_consumption = \\\n self.proposed_fuel_Hoil_consumption / constants.mmbtu_to_gal_HF +\\\n self.proposed_fuel_hr_consumption/constants.mmbtu_to_gal_HF+\\\n self.proposed_fuel_lng_consumption/constants.mmbtu_to_Mcf+\\\n self.proposed_fuel_propane_consumption/constants.mmbtu_to_gal_LP+\\\n self.proposed_fuel_biomass_consumption/constants.mmbtu_to_cords","title":""},{"docid":"0eba9dbb71df991f6d1d8c0e75cdfb7d","score":"0.48977476","text":"def vessel_bloodflow(bf_cr, bf_ms, bf_fat, bf_sk, bf_ava_hand, bf_ava_foot):\n xbf = bf_cr + bf_ms + bf_fat + bf_sk\n\n bf_art = np.zeros(17)\n bf_vein = np.zeros(17)\n\n #Head\n bf_art[0] = xbf[0]\n bf_vein[0] = xbf[0]\n\n #Neck (+Head)\n bf_art[1] = xbf[1] + xbf[0]\n bf_vein[1] = xbf[1] + xbf[0]\n\n #Chest\n bf_art[2] = xbf[2]\n bf_vein[2] = xbf[2]\n\n #Back\n bf_art[3] = xbf[3]\n bf_vein[3] = xbf[3]\n\n #Pelvis (+Thighs, Legs, Feet, AVA_Feet)\n bf_art[4] = xbf[4] + xbf[11:17].sum() + 2*bf_ava_foot\n bf_vein[4] = xbf[4] + xbf[11:17].sum() + 2*bf_ava_foot\n\n #L.Shoulder (+Arm, Hand, (arteryのみAVA_Hand))\n bf_art[5] = xbf[5:8].sum() + bf_ava_hand\n bf_vein[5] = xbf[5:8].sum()\n\n #L.Arm (+Hand)\n bf_art[6] = xbf[6:8].sum() + bf_ava_hand\n bf_vein[6] = xbf[6:8].sum()\n\n #L.Hand\n bf_art[7] = xbf[7] + bf_ava_hand\n bf_vein[7] = xbf[7]\n\n #R.Shoulder (+Arm, Hand, (arteryのみAVA_Hand))\n bf_art[8] = xbf[8:11].sum() + bf_ava_hand\n bf_vein[8] = xbf[8:11].sum()\n\n #R.Arm (+Hand)\n bf_art[9] = xbf[9:11].sum() + bf_ava_hand\n bf_vein[9] = xbf[9:11].sum()\n\n #R.Hand\n bf_art[10] = xbf[10] + bf_ava_hand\n bf_vein[10] = xbf[10]\n\n #L.Thigh (+Leg, Foot, (arteryのみAVA_Foot))\n bf_art[11] = xbf[11:14].sum() + bf_ava_foot\n bf_vein[11] = xbf[11:14].sum()\n\n #L.Leg (+Foot)\n bf_art[12] = xbf[12:14].sum() + bf_ava_foot\n bf_vein[12] = xbf[12:14].sum()\n\n #L.Foot\n bf_art[13] = xbf[13] + bf_ava_foot\n bf_vein[13] = xbf[13]\n\n #R.Thigh (+Leg, Foot, (arteryのみAVA_Foot))\n bf_art[14] = xbf[14:17].sum() + bf_ava_foot\n bf_vein[14] = xbf[14:17].sum()\n\n #R.Leg (+Foot)\n bf_art[15] = xbf[15:17].sum() + bf_ava_foot\n bf_vein[15] = xbf[15:17].sum()\n\n #R.Foot\n bf_art[16] = xbf[16] + bf_ava_foot\n bf_vein[16] = xbf[16]\n\n return bf_art, bf_vein","title":""},{"docid":"3e17c90bb505b040d69dd15f68570980","score":"0.4893315","text":"def fill_gas_tank():\n print(\"This car doesn't need a gas tank!\")","title":""},{"docid":"c892f382168fd2d1e7ff5ba96624b188","score":"0.48805892","text":"def calculate_fuel_costs(self):\r\n self.coal_price = self.coal_cost_fixed + self.coal_cost_variable*self.coal_fuel\r\n self.gas_price= self.gas_cost_fixed+self.gas_cost_variable*self.gas_fuel","title":""},{"docid":"81c9bebc65222ad959a0d1280b3e8ac9","score":"0.48712495","text":"def berth_invest(self, year, handysize, handymax, panamax):\n\n # report on the status of all berth elements\n self.report_element(Berth, year)\n self.report_element(Quay_wall, year)\n self.report_element(Cyclic_Unloader, year)\n self.report_element(Continuous_Unloader, year)\n self.report_element(Conveyor_Quay, year)\n self.report_element(Storage, year)\n self.report_element(Conveyor_Hinter, year)\n self.report_element(Unloading_station, year)\n if self.debug:\n print('')\n print(' Start analysis:')\n\n # calculate berth occupancy\n berth_occupancy_planned, berth_occupancy_online, crane_occupancy_planned, crane_occupancy_online = self.calculate_berth_occupancy(\n year, handysize, handymax, panamax)\n factor, waiting_time_occupancy = self.waiting_time(year)\n if self.debug:\n print(' Berth occupancy planned (@ start of year): {}'.format(berth_occupancy_planned))\n print(' Berth occupancy online (@ start of year): {}'.format(berth_occupancy_online))\n print(' Crane occupancy planned (@ start of year): {}'.format(crane_occupancy_planned))\n print(' Crane occupancy online (@ start of year): {}'.format(crane_occupancy_online))\n print(' waiting time factor (@ start of year): {}'.format(factor))\n print(' waiting time occupancy (@ start of year): {}'.format(waiting_time_occupancy))\n\n while berth_occupancy_planned > self.allowable_berth_occupancy:\n\n # add a berth when no crane slots are available\n if not (self.check_crane_slot_available()):\n if self.debug:\n print(' *** add Berth to elements')\n berth = Berth(**agribulk_defaults.berth_data)\n berth.year_online = year + berth.delivery_time\n self.elements.append(berth)\n\n berth_occupancy_planned, berth_occupancy_online, crane_occupancy_planned, crane_occupancy_online = self.calculate_berth_occupancy(\n year, handysize, handymax, panamax)\n if self.debug:\n print(' Berth occupancy planned (after adding berth): {}'.format(berth_occupancy_planned))\n print(' Berth occupancy online (after adding berth): {}'.format(berth_occupancy_online))\n\n # check if a quay is needed\n berths = len(self.find_elements(Berth))\n quay_walls = len(self.find_elements(Quay_wall))\n if berths > quay_walls:\n length_v = max(agribulk_defaults.handysize_data[\"LOA\"],agribulk_defaults.handymax_data[\"LOA\"],\n agribulk_defaults.panamax_data[\"LOA\"]) # average size\n draft = max(agribulk_defaults.handysize_data[\"draft\"],agribulk_defaults.handymax_data[\"draft\"],\n agribulk_defaults.panamax_data[\"draft\"])\n # apply PIANC 2014:\n # see Ijzermans, 2019 - infrastructure.py line 107 - 111\n if quay_walls == 0:\n # - length when next quay is n = 1\n length = length_v + 2 * 15 # ref: PIANC 2014\n elif quay_walls == 1:\n # - length when next quay is n > 1\n length = 1.1 * berths * (length_v + 15) - (length_v + 2 * 15) # ref: PIANC 2014\n else:\n length = 1.1 * berths * (length_v + 15) - 1.1 * (berths - 1) * (length_v + 15)\n\n # - depth\n quay_wall = Quay_wall(**agribulk_defaults.quay_wall_data)\n depth = np.sum([draft, quay_wall.max_sinkage, quay_wall.wave_motion, quay_wall.safety_margin])\n self.quay_invest(year, length, depth)\n\n berth_occupancy_planned, berth_occupancy_online, crane_occupancy_planned, crane_occupancy_online = self.calculate_berth_occupancy(\n year, handysize, handymax, panamax)\n if self.debug:\n print(' Berth occupancy planned (after adding quay): {}'.format(berth_occupancy_planned))\n print(' Berth occupancy online (after adding quay): {}'.format(berth_occupancy_online))\n\n # check if a crane is needed\n if self.check_crane_slot_available():\n self.crane_invest(year)\n\n berth_occupancy_planned, berth_occupancy_online, crane_occupancy_planned, crane_occupancy_online = self.calculate_berth_occupancy(\n year, handysize, handymax, panamax)\n if self.debug:\n print(' Berth occupancy planned (after adding crane): {}'.format(berth_occupancy_planned))\n print(' Berth occupancy online (after adding crane): {}'.format(berth_occupancy_online))","title":""},{"docid":"be0ed39569cb97ebe043e32bbf2808bc","score":"0.4857554","text":"def refill(self, fuel):\n if self.fuel_amount == self.fuel_capacity:\n raise ToMuchFuel('You already have full tank!')\n\n self._fuel_amount += fuel\n\n if self.fuel_amount > self.fuel_capacity:\n self._fuel_amount = self.fuel_capacity","title":""},{"docid":"7c310cb5f6a3542e5fec828b27279159","score":"0.4856016","text":"def remove_credit_calculation_fuel_types_0111(apps, schema_editor):\n db_alias = schema_editor.connection.alias\n\n approved_fuel = apps.get_model('api', 'ApprovedFuel')\n\n approved_fuel.objects.using(db_alias).bulk_create([\n approved_fuel(\n name=\"Petroleum-based diesel fuel or renewable fuel in relation \"\n \"to diesel class fuel\",\n effective_date=\"2017-01-01\",\n credit_calculation_only=True\n ),\n approved_fuel(\n name=\"Petroleum-based gasoline, natural gas-based gasoline or \"\n \"renewable fuel in relation to gasoline class fuel\",\n effective_date=\"2017-01-01\",\n credit_calculation_only=True\n ),\n approved_fuel(\n name=\"Petroleum-based diesel fuel or diesel fuel produced from \"\n \"biomass\",\n effective_date=\"2017-01-01\",\n credit_calculation_only=True\n ),\n approved_fuel(\n name=\"Petroleum-based gasoline, natural gas-based gasoline or \"\n \"gasoline produced from biomass\",\n effective_date=\"2017-01-01\",\n credit_calculation_only=True\n ),\n approved_fuel(\n name=\"Hydrogenation-derived renewable diesel fuel\",\n effective_date=\"2017-01-01\",\n credit_calculation_only=True\n ),\n approved_fuel(\n name=\"Renewable fuel in relation to diesel class fuel\",\n effective_date=\"2017-01-01\",\n credit_calculation_only=True\n ),\n approved_fuel(\n name=\"Renewable fuel in relation to gasoline class fuel\",\n effective_date=\"2017-01-01\",\n credit_calculation_only=True\n )\n ])","title":""},{"docid":"bb1821a0dbd0c9c1f90f2f8d46a3bf54","score":"0.48557195","text":"def test_additional_fuel_requirements():\n assert list(day_1.additional_fuel_requirements(2)) == []\n assert list(day_1.additional_fuel_requirements(654)) == [216, 70, 21, 5]\n assert list(day_1.additional_fuel_requirements(33584)) == [\n 11192, 3728, 1240, 411, 135, 43, 12, 2\n ]","title":""},{"docid":"338e251f70d29585f913f8c0635001e6","score":"0.48507738","text":"def initialise_fluids(self, nw):\n N_2 = 0.7655\n O_2 = 0.2345\n\n n_fuel = 1\n lamb = 3\n\n fact_fuel = {}\n sum_fuel = 0\n for f in self.fuel_list:\n fact_fuel[f] = 0\n for i in self.inl:\n fact_fuel[f] += i.fluid.val[f] / 2\n sum_fuel += fact_fuel[f]\n\n for f in self.fuel_list:\n fact_fuel[f] /= sum_fuel\n\n m_co2 = 0\n m_h2o = 0\n m_fuel = 0\n for f in self.fuel_list:\n m_co2 += (n_fuel * self.fuels[f]['C'] * molar_masses[self.co2] *\n fact_fuel[f])\n m_h2o += (n_fuel * self.fuels[f]['H'] /\n 2 * molar_masses[self.h2o] * fact_fuel[f])\n m_fuel += n_fuel * molar_masses[f] * fact_fuel[f]\n\n n_o2 = (m_co2 / molar_masses[self.co2] +\n 0.5 * m_h2o / molar_masses[self.h2o]) * lamb\n\n m_air = n_o2 * molar_masses[self.o2] / O_2\n m_fg = m_air + m_fuel\n\n m_o2 = n_o2 * molar_masses[self.o2] * (1 - 1 / lamb)\n m_n2 = N_2 * m_air\n\n fg = {\n self.n2: m_n2 / m_fg,\n self.co2: m_co2 / m_fg,\n self.o2: m_o2 / m_fg,\n self.h2o: m_h2o / m_fg\n }\n\n o = self.outl[2]\n for fluid, x in o.fluid.val.items():\n if not o.fluid.val_set[fluid] and fluid in fg.keys():\n o.fluid.val[fluid] = fg[fluid]","title":""},{"docid":"85cbeb6d0afecead5d1523bd689337f2","score":"0.48417068","text":"def calculate_storage_tank_obj(self, tsm, hk_inp_temp, hk_inp_volfl_m3s, hk_out_temp, chp_inp_temp, chp_out_temp,\n chp_inp_volfl_m3s, gb_inp_temp, gp_out_temp, gb_inp_volfl_m3s, dhw_inp_temp, dhw_out_temp, dhw_inp_volfl_m3s, \n el_heat_status, current_time, t_ambient):\n # inp enters unit & leaves storage tank, out leaves unit & enters storage tank\n # hk - heating circuit, chp - chp unit, gb - gas boiler\n # temp - temperature; volfl - volume flow\n \n # heating circuit - output from the system - water taken out of the storage tank by the heating system: hk_inp_temp\n hk_rho = utils.rho_fluid_water(hk_inp_temp, self.p_atm, 1)\n hk_cp = utils.cp_fluid_water(hk_inp_temp, self.p_atm, 1)\n hk_mstr = hk_rho * hk_inp_volfl_m3s # in kg/s = kg/m3 * m3/s \n # combined power and heat unit - input to the storage tank: chp_out_temp\n chp_rho = utils.rho_fluid_water(chp_out_temp, self.p_atm, 1)\n chp_cp = utils.cp_fluid_water(chp_out_temp, self.p_atm, 1)\n chp_mstr = chp_rho * chp_inp_volfl_m3s # in kg/s = kg/m3 * m3/s \n # gas boiler unit - input to the storage tank\n gb_rho = utils.rho_fluid_water(gp_out_temp, self.p_atm, 1)\n gb_cp = utils.cp_fluid_water(gp_out_temp, self.p_atm, 1)\n gb_mstr = gb_rho * gb_inp_volfl_m3s # in kg/s = kg/m3 * m3/s \n\n # mixing of streams from chp and gas boiler units - input to the storage tank\n mix_up_mstr = chp_mstr + gb_mstr\n if(mix_up_mstr != 0.0):\n t_mix_up = (gb_mstr * gb_cp * gp_out_temp + chp_mstr * chp_cp * chp_out_temp) / (mix_up_mstr * hk_cp)\n # iterate out the temperature of the mixed flow of hot water\n mix_cp = utils.cp_fluid_water(t_mix_up, self.p_atm, 1)\n t_1 = (gb_mstr * gb_cp * gp_out_temp + chp_mstr * chp_cp * chp_out_temp) / (mix_up_mstr * mix_cp)\n else:\n t_mix_up = min(gp_out_temp, chp_out_temp)\n mix_cp = utils.cp_fluid_water(t_mix_up, self.p_atm, 1)\n t_1 = t_mix_up\n anz_iter = 0\n while ((abs(t_1 - t_mix_up) > 0.000001) and (anz_iter < 1000)):\n t_mix_up = t_1\n mix_cp = utils.cp_fluid_water(t_mix_up, self.p_atm, 1)\n t_1 = (gb_mstr * gb_cp * gp_out_temp + chp_mstr * chp_cp * chp_out_temp) / (mix_up_mstr * mix_cp)\n anz_iter = anz_iter + 1\n if (anz_iter == 1000):\n print('actual time = {}'.format(self.acttime))\n print('error = {}'.format(t_mix_up - t_1))\n print(' | t in C | mstr in kg/s ')\n print(' mixed | {} | {} '.format(t_mix_up,mix_up_mstr))\n print(' chp | {} | {} '.format(chp_out_temp,chp_mstr))\n print(' gas boi | {} | {} '.format(gp_out_temp,gb_mstr))\n print('---------+----------+-----------------')\n \n # balance of the hot water in the upper part of the storage tank - plus means input of hot water, minus means more hot water being taken out from the tank\n netto_hot_mstr = mix_up_mstr - hk_mstr # in kg/s = kg/m3 * m3/s \n netto_cold_mstr = hk_mstr - mix_up_mstr # in kg/s = kg/m3 * m3/s \n #print('mix_up_mstr = {}; hk_mstr = {}'.format(mix_up_mstr, hk_mstr))\n # netto gain of hot mass in the upper part of storage\n hot_gain = netto_hot_mstr * self.tsm.get_timestep() # in kg = kg/s * s\n hot_volume = hot_gain / utils.rho_fluid_water(t_mix_up, self.p_atm, 1) # in m3 = kg / kg/m3\n # misc number of slices that are fully filled hot water being pumped in m3\n #print('hot vol = {}; sliceVol = {}: mix_mstr = {}; hk_mstr = {};chp_mstr = {}; gb_mstr = {};hk_inp_volfl_m3s = {}, typ ={}'.format(hot_volume, self.slice_volume,mix_up_mstr , hk_mstr, chp_mstr, gb_mstr, hk_inp_volfl_m3s, type(hk_inp_volfl_m3s)))\n anz_slices = int(hot_volume / self.slice_volume)\n # height_slices --> self.effective_heigth\n # hot_volume --> self.effective_volume\n height_slices = hot_volume * self.effective_height / self.effective_volume\n #print('hot_gain = {}; hot_volume = {}; anz_slices = {}; height_slices = {}'.format(hot_gain,hot_volume,anz_slices,height_slices))\n # hk_inp_temp = self.t24\n cold_cp = utils.cp_fluid_water(hk_inp_temp, self.p_atm, 1)\n \n \n \n mix_down_mstr = hk_mstr + netto_hot_mstr # = mix_up_mstr = chp_mstr + gb_mstr\n t_st = self.get_output_temperature()\n cptst = utils.cp_fluid_water(t_st, self.p_atm, 1)\n hk_cp_out = utils.cp_fluid_water(hk_out_temp, self.p_atm, 1)\n if(mix_down_mstr != 0.0):\n t_mix_down = (t_st * cptst * self.mstr_hw + hk_out_temp * hk_cp_out * hk_mstr) / (mix_down_mstr * cptst)\n mix_cp = utils.cp_fluid_water(t_mix_down, self.p_atm, 1)\n t_2 = (t_st * cptst * self.mstr_hw + hk_out_temp * hk_cp_out * hk_mstr) / (mix_down_mstr * mix_cp)\n else:\n # whole flow comes from from heating system - chp and boiler are most likely off\n t_mix_down = hk_out_temp\n mix_cp = utils.cp_fluid_water(t_mix_down, self.p_atm, 1)\n t_2 = t_mix_down\n anz_iter = 0\n while((abs(t_2 - t_mix_down) > 0.000001) and (anz_iter < 1000)):\n t_mix_down = t_2\n mix_cp = utils.cp_fluid_water(t_mix_down, self.p_atm, 1)\n t_2 = (t_st * cptst * self.mstr_hw + hk_out_temp * hk_cp_out * hk_mstr) / (mix_down_mstr * mix_cp)\n anz_iter = anz_iter + 1\n if (anz_iter == 1000):\n print('actual time = {}'.format(self.acttime))\n print('error = {}'.format(t_mix_down - t_2))\n print(' | t in C | mstr in kg/s ')\n print(' mixed | {} | {} '.format(t_mix_down,mix_down_mstr))\n print(' st.tank | {} | {} '.format(t_st,mix_down_mstr))\n print(' heating | {} | {} '.format(hk_out_temp,hk_mstr))\n print('---------+----------+-----------------')\n \n \n\n #print('t_mix_up = {}; mstr = {}; wechsel = {}'.format(t_mix_up,netto_cold_mstr,self.slice_volume/(netto_cold_mstr*3.6),))\n\n # heat flow from heating appliances to the storage tank - convective influx with t_1, in W\n Q_c_hot_inp = netto_hot_mstr * mix_cp * (t_mix_up - self.temp[0])\n # heat flow from storage tank to the heating appliances to the heating appliances balanced with the input from heating system - convective influx\n Q_c_cold_inp = netto_hot_mstr * cold_cp * (hk_inp_temp - self.temp[self.nr_calc - 1])\n#\n# ================================================================\n # DOMESTIC HOT WATER - always from 0 to nr_calc-1 - only one flow direction possible\n #tinp_dhw = dhw_inp_temp\n #in_idx_dhw = 0\n #out_idx_dhw = self.nr_calc - 1\n dhw_mstr = dhw_inp_volfl_m3s * utils.rho_fluid_water(dhw_inp_temp, self.p_atm, 1)\n#\n# ----------------------------------------------------------------\n\n [tinp_hw, in_idx_hw, out_idx_hw] = self.calc_inputs_hw(netto_cold_mstr, hk_out_temp, t_mix_up)\n# ----------------------------------------------------------------\n self.calc_init_hydraulik(current_time, netto_cold_mstr, dhw_mstr, tinp_hw, dhw_inp_temp, in_idx_hw, out_idx_hw)\n# ----------------------------------------------------------------\n # domestic hot water\n t_new_dhw = []\n # heating water\n t_new_hw = []\n # \n [t_new_hw, t_new_dhw] = self.solve_hydraulic(t_new_dhw, t_new_hw, tinp_hw, dhw_inp_temp, in_idx_hw, 0, out_idx_hw, self.nr_calc - 1, el_heat_status, t_ambient) \n\n# ----------------------------------------------------------------\n # balances for all elements and heat exchange between them\n\n # \n#\n self.temp_dhw = t_new_dhw\n for elem in self.t_dhw:\n elem.set_average_temp(t_new_dhw[elem.get_id()])\n self.temp = t_new_hw\n\n for elem in self.t_hw:\n elem.set_average_temp(t_new_hw[elem.get_id()])\n #print(' {:8.3f} {} {}; hk_inp_volfl_m3s = {}; gb_inp_volfl_m3s = {}; chp_inp_volfl_m3s = {}; dhw_inp_volfl_m3s = {}'.format(self.acttime, t_new_hw[6], self.t_hw[6].get_mstr(), hk_inp_volfl_m3s, gb_inp_volfl_m3s, chp_inp_volfl_m3s, dhw_inp_volfl_m3s))\n #print(' {:8.3f} {} {} {} {} {}'.format(self.acttime, t_new_dhw[0], self.t_dhw[0].get_mstr(), self.t_dhw[0].get_mstr(), self.t_dhw[0].get_input_temp(), self.t_dhw[0].get_average_temp(), self.t_dhw[0].get_output_temp()))\n#\n# ----------------------------------------------------------------\n # OUTPUTS\n self.mstr_hw = netto_cold_mstr\n self.mstr_dhw = dhw_mstr\n self.out_idx_hw = out_idx_hw\n if(self.mstr_hw !=0.0):\n self.wechsel_zeit_in_h = self.slice_volume * utils.rho_fluid_water(tinp_hw, self.p_atm, 1) / (self.mstr_hw * 3600.0) # h = kg / kg/h = m3 * kg/m3 / (kg/s * 3600 s/h)\n else:\n self.wechsel_zeit_in_h = 0.0","title":""},{"docid":"68d31bf48746d89f05abf28f44abe8d8","score":"0.48378253","text":"def setUp(self,):\n\n\t\tself.make_constraint()\n\n\t\tself.set_types()\n\t\tself.feasible = 1 # 1 = infeasible, 0 = feasible - store the value here because we'll reference it layer in a closure\n\n\t\tavailable_species = {}\n\t\tfor huc in self.hucs: # prepopulate all the species so we can skip a condition later - don't use all species because it's possible that some won't be present. Only use the species in all the hucs\n\t\t\tfor species in huc.assemblage.all():\n\t\t\t\tavailable_species[species.common_name] = 1\n\n\t\tself.available_species = available_species.keys()\n\t\tlog.debug(\"Total Species in Area: {}\".format(len(available_species.keys())))\n\n\t\tself.eflows_nfe = 0","title":""},{"docid":"c5afd3a65bdacc695828d423a78707bf","score":"0.48360804","text":"def upadate_leverage(self):\n \n all_asset = [0 for _ in range(self.n)] \n all_liability = [0 for _ in range(self.n)] \n all_equity = [0 for _ in range(self.n)] \n for bank_index in range(self.n):\n all_asset[bank_index] += self.AssetMat.get_int_asset(bank_index)\n all_asset[bank_index] += self.ext_asset[bank_index]\n \n all_liability[bank_index] += self.AssetMat.get_int_liability(bank_index)\n all_liability[bank_index] += self.ext_asset[bank_index]\n \n equity = all_asset[bank_index] - all_liability[bank_index] \n\n if equity < 0 : \n self.default_set.add(bank_index)\n equity = 0\n # \n \n all_equity[bank_index] = equity\n # \n \n for bank_idx_i in range(self.n):\n for bank_index_j in range(self.n):\n val = min(self.AssetMat.get_mat()[bank_idx_i, bank_index_j] , 0) / equity[bank_index_j]\n self.LevMat.update_element(bank_idx_i, bank_index_j, val)\n #\n #\n pass","title":""},{"docid":"2860a835873ec7e19fad60f2f0cc5aa5","score":"0.48321462","text":"def Fuel_type_both_NotDEF(self):\n \n #Input constants\n\n generic_trans_amount = \"$5.00\" # any value that gets an approval from the host \n card_to_use_NGFC = 'NGFC' # using this card to get all commercial prompts\n \n amounts_to_dispense = {\n \"buffer_1\":{\n \"grade\": 1,\n \"value\": \"2\"\n },\n \"buffer_2\":{\n \"grade\": 1,\n \"value\": \"3\"\n }\n \n }\n prompts = {\n \"Additional Products Y/N?\": { \n \"buttons\": [\"Yes\"]\n },\n \"Do you want to print a receipt?\": {\n \"buttons\": [\"No\"]\n },\n \"Select fuel products to dispense\": {\n \"buttons\": [\"Both fuels\"]\n },\n \"DEF?\": {\n \"buttons\": [\"No\"]\n }\n }\n\n #Output verifications\n\n messages_to_verify = {\n 'preauth_request_verifications': {\n 'Fuel Purchase': '500', \n '001 - Wex OTR Flags': 'C - Commercial'\n },\n 'preauth_response_verifications': {\n 'Response Code': '2', \n 'Approved Amount': '000000500',\n '002 - Wex OTR Non-Fuel Product Data - Product code: CADV (Company funds cash advance)': 'Amount: 0.00',\n '002 - Wex OTR Non-Fuel Product Data - Product code: MERC (Default category for merchandise)': 'Amount: 30.00',\n f'003 - Wex OTR Fuel Product Limits - Product Code: {self.helpers.grade_1_Product }': 'Product limit: 50.00',\n f'003 - Wex OTR Fuel Product Limits - Product Code: {self.helpers.grade_2_Product }': 'Product limit: 50.00',\n f'003 - Wex OTR Fuel Product Limits - Product Code: {self.helpers.grade_3_Product }': 'Product limit: 50.00',\n '005 - Wex OTR Customer Information': 'ABC TRUCKING DENVER CO234W987'\n },\n 'capture_request_verifications': {\n 'Fuel Purchase': '500', \n '001 - Wex OTR Flags': 'C - Commercial'\n },\n 'capture_response_verifications': {\n 'Response Code': '0', \n 'Approved Amount': '000000500',\n '002 - Wex OTR Non-Fuel Product Data - Product code: CADV (Company funds cash advance)': 'Amount: 20.00',\n '002 - Wex OTR Non-Fuel Product Data - Product code: MERC (Default category for merchandise)': 'Amount: 30.00',\n f'003 - Wex OTR Fuel Product Limits - Product Code: {self.helpers.grade_1_Product }': 'Product limit: 50.00',\n f'003 - Wex OTR Fuel Product Limits - Product Code: {self.helpers.grade_2_Product }': 'Product limit: 50.00',\n f'003 - Wex OTR Fuel Product Limits - Product Code: {self.helpers.grade_3_Product }': 'Product limit: 50.00',\n '005 - Wex OTR Customer Information': 'ABC TRUCKING DENVER CO234W987'\n }\n }\n \n receipt_data = [f\"{self.helpers.grade_1_Name} CA PUMP# 1\",\n \"2.000 GAL @ $1.000/GAL $2.00 99\",\n f\"{self.helpers.grade_1_Name_reefer} CA PUMP# 1\",\n \"3.000 GAL @ $1.000/GAL $3.00 99\", \n \"Subtotal = $5.00\",\n \"Tax = $0.00\",\n \"Total = $5.00\",\n \"Change Due = $0.00\",\n \"Credit $5.00\"]\n \n self.helpers.prepay_transaction(\n card=card_to_use_NGFC,\n prepay_amount=generic_trans_amount,\n prompts=prompts,\n amounts_to_dispense=amounts_to_dispense,\n messages_to_verify=messages_to_verify,\n receipt_data=receipt_data\n )","title":""},{"docid":"479e595ee4bd9ccd2266755690e50ae8","score":"0.48295414","text":"def calc_baseline_HF_consumption (self):\n HDD_ests = self.comp_specs[\"consumption estimates\"][\"HDD\"]\n gal_sf_ests = self.comp_specs[\"consumption estimates\"][\"Gal/sf\"]\n\n measure = \"Fuel Oil\"\n data = self.comp_specs['building inventory']\n keys = data.T.keys()\n keys = set(keys)\n data[\"HDD ESTS\"] = 0\n data[\"GAL/SF\"] = 0\n for key in keys:\n #~ print key\n try:\n data[\"HDD ESTS\"].ix[key] = \\\n self.cd[\"heating degree days\"]/HDD_ests[key]\n data[\"GAL/SF\"].ix[key] = gal_sf_ests.ix[key]\n except KeyError:\n data[\"HDD ESTS\"].ix[key] = \\\n self.cd[\"heating degree days\"]/HDD_ests.ix['Other']#unitless\n data[\"GAL/SF\"].ix[key] = gal_sf_ests.ix['Other'] # (gal)/sqft\n\n\n idx = data[['Fuel Oil', \"Natural Gas\",\n 'HW District','Propane',\"Biomass\"]].isnull().all(1)\n if not self.cd['natural gas used']:\n data['Fuel Oil'].ix[idx] = data[idx]['Square Feet'] * \\\n data[idx]['HDD ESTS'] * \\\n data[idx]['GAL/SF']\n else:\n data['Natural Gas'].ix[idx] = data[idx]['Square Feet'] * \\\n data[idx]['HDD ESTS'] * \\\n data[idx]['GAL/SF'] / \\\n constants.mmbtu_to_gal_HF *\\\n constants.mmbtu_to_Mcf\n del data[\"GAL/SF\"]\n del data[\"HDD ESTS\"]\n\n\n\n self.baseline_fuel_Hoil_consumption = data['Fuel Oil'].fillna(0).sum()\n self.baseline_fuel_lng_consumption = data['Natural Gas'].fillna(0).sum()\n self.baseline_fuel_hr_consumption = data['HW District'].fillna(0).sum()\n self.baseline_fuel_propane_consumption = data['Propane'].fillna(0).sum()\n self.baseline_fuel_biomass_consumption = data['Biomass'].fillna(0).sum()\n\n\n self.baseline_HF_consumption = \\\n self.baseline_fuel_Hoil_consumption / constants.mmbtu_to_gal_HF +\\\n self.baseline_fuel_hr_consumption/constants.mmbtu_to_gal_HF+\\\n self.baseline_fuel_lng_consumption/constants.mmbtu_to_Mcf+\\\n self.baseline_fuel_propane_consumption/constants.mmbtu_to_gal_LP+\\\n self.baseline_fuel_biomass_consumption/constants.mmbtu_to_cords","title":""},{"docid":"b4429dc783690bfcec9cc7f15d16b710","score":"0.4825849","text":"def calculate_storage_tank(self, Timestep, hk_inp_temp, hk_inp_volfl_m3s, hk_out_temp, chp_inp_temp, chp_out_temp,\n chp_inp_volfl_m3s, gb_inp_temp, gp_out_temp, gb_inp_volfl_m3s, dhw_inp_temp, dhw_out_temp, dhw_inp_volfl_m3s):\n # inp enters unit & leaves storage tank, out leaves unit & enters storage tank\n # hk - heating circuit, chp - chp unit, gb - gas boiler\n # temp - temperature; volfl - volume flow\n \n # heating circuit - output from the system - water taken out of the storage tank by the heating system\n hk_rho = utils.rho_fluid_water(hk_inp_temp, self.p_atm, 1)\n hk_cp = utils.cp_fluid_water(hk_inp_temp, self.p_atm, 1)\n hk_mstr = hk_rho * hk_inp_volfl_m3s # in kg/s = kg/m3 * m3/s \n # combined power and heat unit - input to the storage tank\n chp_rho = utils.rho_fluid_water(chp_out_temp, self.p_atm, 1)\n chp_cp = utils.cp_fluid_water(chp_out_temp, self.p_atm, 1)\n chp_mstr = chp_rho * chp_inp_volfl_m3s # in kg/s = kg/m3 * m3/s \n # gas boiler unit - input to the storage tank\n gb_rho = utils.rho_fluid_water(gb_inp_temp, self.p_atm, 1)\n gb_cp = utils.cp_fluid_water(gb_inp_temp, self.p_atm, 1)\n gb_mstr = gb_rho * gb_inp_volfl_m3s # in kg/s = kg/m3 * m3/s \n # mixing of streams from chp and gas boiler units - input to the storage tank\n mix_up_mstr = chp_mstr + gb_mstr\n if(mix_up_mstr != 0.0):\n t_mix_up = (gb_mstr * gb_cp * gb_inp_temp + chp_mstr * chp_cp * chp_out_temp) / (mix_up_mstr * hk_cp)\n # iterate out the temperature of the mixed flow of hot water\n mix_cp = utils.cp_fluid_water(t_mix_up, self.p_atm, 1)\n t_1 = (gb_mstr * gb_cp * gb_inp_temp + chp_mstr * chp_cp * chp_out_temp) / (mix_up_mstr * mix_cp)\n else:\n t_mix_up = 0.5 * (gb_inp_temp + chp_out_temp)\n anz_iter = 0\n while ((abs(t_1 - t_mix_up) > 0.000001) and (anz_iter < 1000)):\n t_mix_up = t_1\n mix_cp = utils.cp_fluid_water(t_mix_up, self.p_atm, 1)\n t_1 = (gb_mstr * gb_cp * gb_inp_temp + chp_mstr * chp_cp * chp_out_temp) / (mix_up_mstr * mix_cp)\n anz_iter = anz_iter + 1\n if (anz_iter == 1000):\n print('actual time = {}'.format(self.acttime))\n print('error = {}'.format(t_mix_up - t_1))\n print(' | t in C | mstr in kg/s ')\n print(' mixed | {} | {} '.format(t_mix_up,mix_up_mstr))\n print(' chp | {} | {} '.format(chp_out_temp,chp_mstr))\n print(' gas boi | {} | {} '.format(gb_out_temp,gb_mstr))\n print('---------+----------+-----------------')\n \n # balance of the hot water in the upper part of the storage tank - plus means input of hot water, minus means more hot water being taken out from the tank\n netto_hot_mstr = mix_up_mstr - hk_mstr # in kg/s = kg/m3 * m3/s \n # netto gain of hot mass in the upper part of storage\n hot_gain = netto_hot_mstr * Timestep # in kg = kg/s * s\n hot_volume = hot_gain / utils.rho_fluid_water(t_mix_up, self.p_atm, 1) # in m3 = kg / kg/m3\n # misc number of slices that are fully filled hot water being pumped in m3\n anz_slices = int(hot_volume / self.slice_volume)\n # height_slices --> self.effective_heigth\n # hot_volume --> self.effective_volume\n height_slices = hot_volume * self.effective_height / self.effective_volume\n #print('hot_gain = {}; hot_volume = {}; anz_slices = {}; height_slices = {}'.format(hot_gain,hot_volume,anz_slices,height_slices))\n # hk_out_temp = self.t24\n cold_cp = utils.cp_fluid_water(hk_out_temp, self.p_atm, 1)\n \n\n # heat flow from heating appliances to the storage tank - convective influx with t_1, in W\n Q_c_hot_inp = netto_hot_mstr * mix_cp * (t_mix_up - self.temp[0])\n # heat flow from storage tank to the heating appliances to the heating appliances balanced with the input from heating system - convective influx\n Q_c_cold_inp = netto_hot_mstr * cold_cp * (hk_out_temp - self.temp[self.nr_calc - 1])\n \n if (netto_hot_mstr >= 0.0): # flow direction from above down\n tinp = t_mix_up\n downwards = True\n else: # flow direction from below up\n tinp = hk_out_temp\n downwards = False\n\n tinp_dhw = dhw_inp_temp\n\n t_new_dhw = []\n t_new = []\n # \n # calculate output temperatures\n for ii in range(self.nr_calc - 2):\n self.tout_dhw[ii] = 0.5 * (self.temp_dhw[ii] + self.temp_dhw[ii + 1])\n self.tout[ii] = 0.5 * (self.temp[ii] + self.temp[ii + 1])\n self.tout_dhw[self.nr_calc - 1] = self.temp_dhw[self.nr_calc - 1]\n self.tout[self.nr_calc - 1] = self.temp[self.nr_calc - 1]\n# for ii in range(self.nr_calc):\n# if (ii < (self.nr_calc - 1)):\n# self.tout_dhw[ii] = 0.5 * (self.temp_dhw[ii] + self.temp_dhw[ii + 1])\n# else:\n# self.tout_dhw[ii] = self.temp_dhw[ii]\n#\n# if downwards: # flow direction from above down\n# if (ii == 0): # outflow temp - first element is the lowerst one\n# self.tout[ii] = self.temp[ii]\n# else:\n# self.tout[ii] = 0.5 * (self.temp[ii] + self.temp[ii - 1])\n# \n# else: # flow direction from below up\n# if (ii < (self.nr_calc - 1)):\n# self.tout[ii] = 0.5 * (self.temp[ii] + self.temp[ii + 1])\n# else:\n# self.tout[ii] = self.temp[ii]\n\n # dhw calculation\n for ii in range(self.nr_calc):\n # domestic hot water calculation\n if(ii == 0):\n Qdhw_from_below = utils.cp_fluid_water(tinp_dhw, self.p_atm, 1) * netto_hot_mstr * tinp_dhw # kW = kJ/kg/K * kg/s * K\n else:\n Qdhw_from_below = utils.cp_fluid_water(self.tout_dhw[ii - 1], self.p_atm, 1) * netto_hot_mstr * self.tout_dhw[ii - 1] # kW = kJ/kg/K * kg/s * K\n Qdhw_from_above = utils.cp_fluid_water(self.tout_dhw[ii], self.p_atm, 1) * netto_hot_mstr * self.tout_dhw[ii] # kW = kJ/kg/K * kg/s * K\n # interface between domestic hot water and heating water \n Rwall = (1.0 / self.alpha_dhw + 1.0 / self.k_coef + 1.0 / self.alpha_tank) / self.pipe_surf_slice # K/W = m2*K/W / m2\n Q_wall = (self.temp[ii] - self.temp_dhw[ii]) / Rwall # in W = K * W/K\n Q_netto_dhw = Q_wall - Qdhw_from_below + Qdhw_from_above\n \n # heating water calculation\n Q_loss = self.alpha_loss * self.red_out_surf_slice * (self.t_outside - self.temp[ii])\n if(downwards): # flow direction from above down\n if(ii == self.nr_calc):\n Qhk_from_above = utils.cp_fluid_water(tinp, self.p_atm, 1) * netto_hot_mstr * tinp # kW = kJ/kg/K * kg/s * K\n else:\n Qhk_from_above = utils.cp_fluid_water(self.tout[ii], self.p_atm, 1) * netto_hot_mstr * self.tout[ii] # kW = kJ/kg/K * kg/s * K\n Qhk_from_below = utils.cp_fluid_water(self.tout[ii - 1], self.p_atm, 1) * netto_hot_mstr * self.tout[ii - 1] # kW = kJ/kg/K * kg/s * K\n # balance for domestic hot water\n \n Q_netto_heatWater = Q_loss + Qhk_from_above - Qhk_from_below - Q_wall\n twyn = self.temp[0]\n \n else: # flow direction from below up\n if(ii == 0):\n Qhk_from_below = utils.cp_fluid_water(tinp, self.p_atm, 1) * netto_hot_mstr * tinp # kW = kJ/kg/K * kg/s * K\n else:\n Qhk_from_below = utils.cp_fluid_water(self.tout[ii - 1], self.p_atm, 1) * netto_hot_mstr * self.tout[ii - 1] # kW = kJ/kg/K * kg/s * K\n Qhk_from_above = utils.cp_fluid_water(self.tout[ii - 1], self.p_atm, 1) * netto_hot_mstr * self.tout[ii] # kW = kJ/kg/K * kg/s * K\n # balance for domestic hot water\n Q_netto_heatWater = Q_loss + Qhk_from_above - Qhk_from_below - Q_wall\n twyn = self.temp[self.nr_calc - 1]\n \n \n \n # balance for storage tank water\n Q_netto_heatWater = Q_loss + Qhk_from_above - Qhk_from_below - Q_wall\n \n\n t_new_dhw.append(self.temp_dhw[ii] + Timestep * Q_netto_dhw / (\n self.pipe_volume_slice \n * utils.rho_fluid_water(self.temp_dhw[ii], self.p_atm, 1) \n * utils.cp_fluid_water(self.temp_dhw[ii], self.p_atm, 1)))\n t_new.append(self.temp[ii] - Timestep * Q_netto_heatWater / (\n (self.slice_volume - self.pipe_volume_slice) \n * utils.rho_fluid_water(self.temp[ii], self.p_atm, 1)\n * utils.cp_fluid_water(self.temp[ii],self.p_atm, 1)))\n \n self.temp = t_new\n self.temp_dhw = t_new_dhw\n \n \n #print('time = {}; ii = {}; Q_netto_dhw ={}; Q_netto_heatWater = {}'.format(self.acttime, ii, Q_netto_dhw, Q_netto_heatWater))\n #print('time = {}; ii = {}; Q_wall ={}; self.temp[99] = {}; self.temp_dhw[99] = {}'.format(self.acttime, ii, Q_wall, self.temp[99], self.temp_dhw[99]))\n #print('time = {}; ii = {}; Q_wall ={}; Q_loss = {};'.format(self.acttime, ii, Q_wall, Q_loss))\n #print('time = {}; ii = {}; Q_c_hot_inp ={}; Q_c_cold_inp = {}; netto_hot_mstr = {}'.format(self.acttime, ii, Q_c_hot_inp, Q_c_cold_inp, netto_hot_mstr))\n #print('time = {}; ii = {}; t_mix_up ={}; hk_out_temp = {}; netto_hot_mstr = {}; self.temp[self.nr_calc - 1] = {}'.format(self.acttime, ii, t_mix_up, hk_out_temp, netto_hot_mstr,self.temp[self.nr_calc - 1]))\n #print('time = {}; ii = {}; self.temp[0] ={}; self.temp[1] = {}; netto_hot_mstr = {}; self.temp_dhw[0] = {}'.format(self.acttime, ii, self.temp[0], self.temp[1], netto_hot_mstr,self.temp_dhw[0]))\n #print('time = {}; ii = {}; n[0] ={}; self.temp[0] = {}; netto_hot_mstr = {}; self.temp_dhw[0] = {}'.format(self.acttime, ii, hot_volume/self.pipe_volume_slice, self.temp[0], netto_hot_mstr,self.temp_dhw[0]))\n #sleep(1)","title":""},{"docid":"379f15768dc6fec75c4f842c47d730de","score":"0.4823592","text":"def setUp(self):\n self.system1 = seapy.system.System()\n self.system1.frequency.center = [500.0, 1000.0, 2000.0, 4000.0, 8000.0]\n self.steel = self.system1.addMaterial(\n \"steel\",\n \"MaterialSolid\",\n loss_factor=np.ones(len(self.system1.frequency.center)) * 0.2,\n )","title":""},{"docid":"5b9c030135c2b1eea66402d0b11de258","score":"0.48214135","text":"def tank_with_field(tank: Tank.Tank) -> None:\n arr_x, arr_y = Rectangle.points_rectangle(tank.body.rectangle)\n tank_max_x = FIELD_X + FIELD_WIDTH - BORDER_WIDTH - max(arr_x)\n tank_max_y = FIELD_Y + FIELD_HEIGHT - BORDER_WIDTH - max(arr_y)\n tank_min_x = FIELD_X + BORDER_WIDTH + max(arr_x)\n tank_min_y = FIELD_Y + BORDER_WIDTH + max(arr_y)\n touch = contact_with_field_boundaries(tank.body)\n if touch == \"x\":\n new_x = -tank.body.rectangle.centre.x + max(tank_min_x, min(tank.body.rectangle.centre.x, tank_max_x))\n Tank.move_tank(tank, Rectangle.create_point(new_x, 0))\n tank.speed.x = 0\n elif touch == \"y\":\n new_y = -tank.body.rectangle.centre.y + max(tank_min_y, min(tank.body.rectangle.centre.y, tank_max_y))\n Tank.move_tank(tank, Rectangle.create_point(0, new_y))\n tank.speed.y = 0","title":""},{"docid":"79f89dbcc7db479d9243123baf9b8b27","score":"0.48129183","text":"def initialise_fluids(self, nw):\n N_2 = 0.7655\n O_2 = 0.2345\n\n n_fuel = 1\n lamb = 3\n\n fact_fuel = {}\n sum_fuel = 0\n for f in self.fuel_list:\n fact_fuel[f] = 0\n for i in self.inl:\n fact_fuel[f] += i.fluid.val[f] / 2\n sum_fuel += fact_fuel[f]\n\n for f in self.fuel_list:\n fact_fuel[f] /= sum_fuel\n\n m_co2 = 0\n m_h2o = 0\n m_fuel = 0\n for f in self.fuel_list:\n m_co2 += (n_fuel * self.fuels[f]['C'] * molar_masses[self.co2] *\n fact_fuel[f])\n m_h2o += (n_fuel * self.fuels[f]['H'] / 2 *\n molar_masses[self.h2o] * fact_fuel[f])\n m_fuel += n_fuel * molar_masses[f] * fact_fuel[f]\n\n n_o2 = (m_co2 / molar_masses[self.co2] +\n 0.5 * m_h2o / molar_masses[self.h2o]) * lamb\n\n m_air = n_o2 * molar_masses[self.o2] / O_2\n m_fg = m_air + m_fuel\n\n m_o2 = n_o2 * molar_masses[self.o2] * (1 - 1 / lamb)\n m_n2 = N_2 * m_air\n\n fg = {\n self.n2: m_n2 / m_fg,\n self.co2: m_co2 / m_fg,\n self.o2: m_o2 / m_fg,\n self.h2o: m_h2o / m_fg\n }\n\n for o in self.outl:\n for fluid, x in o.fluid.val.items():\n if not o.fluid.val_set[fluid] and fluid in fg.keys():\n o.fluid.val[fluid] = fg[fluid]","title":""},{"docid":"8dd910b1051fa60aed1b8b0b58895061","score":"0.47961774","text":"def prepare_electricity_storage_nodes(self,year,loop):\n self.storage_efficiency_dict = util.recursivedict()\n self.storage_capacity_dict = util.recursivedict()\n for node in [x for x in self.nodes.values() if x.supply_type == 'Storage']:\n for zone in self.dispatch_zones:\n node.calculate_dispatch_coefficients(year, loop)\n if hasattr(node,'active_dispatch_coefficients'):\n storage_node_location = list(set(node.active_dispatch_coefficients.index.get_level_values('supply_node')))\n if len(storage_node_location)>1:\n raise ValueError('StorageNode %s has technologies with two different supply node locations' %node.name)\n if storage_node_location[0] in self.electricity_nodes[zone]+[zone]:\n capacity = node.stock.values.loc[:,year].to_frame().groupby(level=[GeoMapper.supply_primary_geography,'supply_technology']).sum()\n efficiency = copy.deepcopy(node.active_dispatch_coefficients)\n if 'demand_sector' not in capacity.index.names and zone == self.distribution_node_name:\n capacity = pd.concat([capacity]*len(self.demand_sectors), keys=self.demand_sectors, names=['demand_sector']) / len(self.demand_sectors)\n efficiency = pd.concat([efficiency]*len(self.demand_sectors), keys=self.demand_sectors, names=['demand_sector'])\n if GeoMapper.dispatch_geography != GeoMapper.supply_primary_geography:\n geography_map_key = node.geography_map_key if hasattr(node, 'geography_map_key') and node.geography_map_key is not None else GeoMapper.default_geography_map_key\n map_df = GeoMapper.get_instance().map_df(GeoMapper.supply_primary_geography,GeoMapper.dispatch_geography, normalize_as='total', map_key=geography_map_key, eliminate_zeros=False)\n capacity = DfOper.mult([capacity, map_df],fill_value=0.0)\n efficiency = DfOper.divi([util.remove_df_levels(DfOper.mult([efficiency, capacity]), GeoMapper.supply_primary_geography),util.remove_df_levels(capacity,GeoMapper.supply_primary_geography)]).fillna(0)\n capacity = util.remove_df_levels(capacity, GeoMapper.supply_primary_geography)\n # creates an empty database to fill with duration values, which are a technology parameter\n duration = copy.deepcopy(capacity) * 0\n duration = duration.sort_index()\n for tech in node.technologies.values():\n for geography in GeoMapper.supply_geographies:\n tech_indexer = util.level_specific_indexer(duration,['supply_technology',GeoMapper.supply_primary_geography], [tech.name,geography])\n year_indexer = util.level_specific_indexer(tech.duration.values,['year',GeoMapper.supply_primary_geography],[year,geography])\n duration.loc[tech_indexer,:] = tech.duration.values.loc[year_indexer,:].values[0]\n efficiency = util.remove_df_levels(efficiency,'supply_node') \n if zone == self.distribution_node_name:\n indexer = util.level_specific_indexer(self.dispatch_feeder_allocation, 'year', year)\n capacity = util.DfOper.mult([capacity, self.dispatch_feeder_allocation.loc[indexer, ]])\n duration = DfOper.divi([util.remove_df_levels(DfOper.mult([duration, capacity]),'demand_sector'),util.remove_df_levels(capacity,'demand_sector')]).fillna(0)\n efficiency = DfOper.divi([util.remove_df_levels(DfOper.mult([efficiency, capacity]),'demand_sector'),util.remove_df_levels(capacity,'demand_sector')]).fillna(1)\n capacity = util.remove_df_levels(capacity,'demand_sector')\n for geography in GeoMapper.dispatch_geographies:\n for dispatch_feeder in self.dispatch_feeders:\n for technology in node.technologies.keys():\n indexer = util.level_specific_indexer(efficiency, [GeoMapper.dispatch_geography, 'dispatch_feeder','supply_technology'],[geography,dispatch_feeder,technology])\n self.storage_efficiency_dict[geography][zone][dispatch_feeder][technology] = efficiency.loc[indexer,:].values[0][0]\n indexer = util.level_specific_indexer(capacity, [GeoMapper.dispatch_geography, 'dispatch_feeder','supply_technology'],[geography,dispatch_feeder,technology])\n self.storage_capacity_dict['power'][geography][zone][dispatch_feeder][technology] = capacity.loc[indexer,:].values[0][0]\n indexer = util.level_specific_indexer(duration, [GeoMapper.dispatch_geography, 'dispatch_feeder','supply_technology'],[geography,dispatch_feeder,technology])\n self.storage_capacity_dict['duration'][geography][zone][dispatch_feeder][technology] = duration.loc[indexer,:].values[0][0]\n else:\n for geography in GeoMapper.dispatch_geographies:\n for technology in node.technologies.keys():\n indexer = util.level_specific_indexer(capacity, [GeoMapper.dispatch_geography, 'supply_technology'],[geography,technology])\n tech_capacity = self.ensure_frame(util.remove_df_levels(capacity.loc[indexer,:], 'demand_sector'))\n indexer = util.level_specific_indexer(duration, [GeoMapper.dispatch_geography,'supply_technology'],[geography,technology])\n tech_duration = self.ensure_frame(util.remove_df_levels(duration.loc[indexer,:], 'demand_sector'))\n indexer = util.level_specific_indexer(efficiency, [GeoMapper.dispatch_geography, 'supply_technology'],[geography,technology])\n tech_efficiency = self.ensure_frame(util.remove_df_levels(efficiency.loc[indexer,:], 'demand_sector'))\n if tech_capacity.values[0][0] == 0:\n continue\n else:\n self.storage_capacity_dict['power'][geography][zone]['bulk'][technology] = tech_capacity.values[0][0]\n self.storage_capacity_dict['duration'][geography][zone]['bulk'][technology] = tech_duration.values[0][0]\n self.storage_efficiency_dict[geography][zone]['bulk'][technology] = tech_efficiency.values[0][0]","title":""},{"docid":"e9ff8083d62b246131cc5e5dee8c4849","score":"0.4795844","text":"def reaction_balance(self, fluid):\n if self.air_alias.val in ['air', 'Air']:\n air = self.air_alias.val\n else:\n air = 'TESPy::' + self.air_alias.val\n fuel = 'TESPy::' + self.fuel_alias.val\n flue_gas = 'TESPy::' + self.fuel_alias.val + '_fg'\n\n ######################################################################\n # calculate fuel and air mass flow\n m_fuel = 0\n for i in self.inl:\n m_fuel += i.m.val_SI * i.fluid.val[fuel]\n\n m_air = 0\n for i in self.inl:\n m_air += i.m.val_SI * i.fluid.val[air]\n\n m_air_min = self.air_min * m_fuel\n\n ######################################################################\n # calculate lambda if not specified\n if not self.lamb.is_set:\n self.lamb.val = m_air / (self.air_min * m_fuel)\n\n ######################################################################\n # calculate excess fuel if lambda is smaller than 1\n m_fuel_exc = 0\n if self.lamb.val < 1:\n m_fuel_exc = m_fuel - m_air / (self.lamb.val * self.air_min)\n\n ######################################################################\n # equation for air\n if fluid == air:\n if self.lamb.val >= 1:\n dm = -m_air_min\n else:\n dm = -m_air\n\n ######################################################################\n # equation for fuel\n elif fluid == fuel:\n dm = -(m_fuel - m_fuel_exc)\n\n ######################################################################\n # equation for flue gas\n elif fluid == flue_gas:\n dm = m_air_min + m_fuel\n\n ######################################################################\n # equation for other components\n else:\n dm = 0\n\n res = dm\n for i in self.inl:\n res += i.fluid.val[fluid] * i.m.val_SI\n for o in self.outl:\n res -= o.fluid.val[fluid] * o.m.val_SI\n return res","title":""},{"docid":"7f67f994547c8bc87f79d72fd2f745ef","score":"0.47861287","text":"def module_fuel_required(mass: int) -> int:\n module_fuel = launch_fuel_required(mass)\n fuel_fuel = fuel_for_fuel(module_fuel)\n\n return module_fuel + fuel_fuel","title":""},{"docid":"80ffa279879c68d92ea3b4f58763a5de","score":"0.4780072","text":"def add_steel_material(self, name, fy, fu, Es, ey, eu):\n if name in self.materials_dictionary:\n return\n else:\n self.materials_dictionary[name] = MF.Steel_Material(name, fy, fu, Es, ey, eu)\n self.Lb.insert(tk.END, name)\n self.controller.update_material_dropdown()","title":""},{"docid":"a031256b7bf2f6dda4f6263a6f46c089","score":"0.47720638","text":"def Fuel_type_both_DEF(self):\n \n #Input constants\n\n generic_trans_amount = \"$5.00\" # any value that gets an approval from the host\n card_to_use_NGFC = 'NGFC' # using this card to get all commercial prompts\n \n amounts_to_dispense = {\n \"buffer_1\":{\n \"grade\": 1,\n \"value\": \"2\"\n },\n \"buffer_2\":{\n \"grade\": 2,\n \"value\": \"1\"\n },\n \"buffer_3\":{\n \"grade\": 1,\n \"value\": \"2\"\n }\n }\n \n prompts = {\n \"Additional Products Y/N?\": { \n \"buttons\": [\"Yes\"]\n },\n \"Do you want to print a receipt?\": {\n \"buttons\": [\"No\"]\n },\n \"Select fuel products to dispense\": {\n \"buttons\": [\"Both fuels\"]\n },\n \"DEF?\": {\n \"buttons\": [\"Yes\"]\n }\n }\n #Output verifications\n\n messages_to_verify = {\n 'preauth_request_verifications': {\n 'Fuel Purchase': '500', \n '001 - Wex OTR Flags': 'C - Commercial'\n },\n 'preauth_response_verifications': {\n 'Response Code': '2', \n 'Approved Amount': '000000500',\n '002 - Wex OTR Non-Fuel Product Data - Product code: CADV (Company funds cash advance)': 'Amount: 0.00',\n '002 - Wex OTR Non-Fuel Product Data - Product code: MERC (Default category for merchandise)': 'Amount: 30.00',\n f'003 - Wex OTR Fuel Product Limits - Product Code: {self.helpers.grade_1_Product }': 'Product limit: 50.00',\n f'003 - Wex OTR Fuel Product Limits - Product Code: {self.helpers.grade_2_Product }': 'Product limit: 50.00',\n f'003 - Wex OTR Fuel Product Limits - Product Code: {self.helpers.grade_3_Product }': 'Product limit: 50.00',\n '005 - Wex OTR Customer Information': 'ABC TRUCKING DENVER CO234W987'\n },\n 'capture_request_verifications': {\n 'Fuel Purchase': '500', \n '001 - Wex OTR Flags': 'C - Commercial'\n },\n 'capture_response_verifications': {\n 'Response Code': '0', \n 'Approved Amount': '000000500',\n '002 - Wex OTR Non-Fuel Product Data - Product code: CADV (Company funds cash advance)': 'Amount: 20.00',\n '002 - Wex OTR Non-Fuel Product Data - Product code: MERC (Default category for merchandise)': 'Amount: 30.00',\n f'003 - Wex OTR Fuel Product Limits - Product Code: {self.helpers.grade_1_Product }': 'Product limit: 50.00',\n f'003 - Wex OTR Fuel Product Limits - Product Code: {self.helpers.grade_2_Product }': 'Product limit: 50.00',\n f'003 - Wex OTR Fuel Product Limits - Product Code: {self.helpers.grade_3_Product }': 'Product limit: 50.00',\n '005 - Wex OTR Customer Information': 'ABC TRUCKING DENVER CO234W987'\n }\n }\n\n receipt_data = [f\"{self.helpers.grade_1_Name} CA PUMP# 1\",\n \"2.000 GAL @ $1.000/GAL $2.00 99\",\n f\"{self.helpers.grade_2_Name} CA PUMP# 1\",\n \"1.000 GAL @ $1.000/GAL $1.00 99\",\n f\"{self.helpers.grade_1_Name_reefer} CA PUMP# 1\",\n \"2.000 GAL @ $1.000/GAL $2.00 99\",\n \"Subtotal = $5.00\",\n \"Tax = $0.00\",\n \"Total = $5.00\",\n \"Change Due = $0.00\",\n \"Credit $5.00\"]\n \n self.helpers.prepay_transaction(\n card=card_to_use_NGFC,\n prepay_amount=generic_trans_amount,\n prompts=prompts,\n amounts_to_dispense=amounts_to_dispense,\n messages_to_verify=messages_to_verify,\n receipt_data=receipt_data\n )","title":""},{"docid":"134cb08469e6040ec67750aa4bb34996","score":"0.4766695","text":"def tank_up(self, amount):\r\n if amount <= 0:\r\n raise FuelError(amount)\r\n if self.fuel + amount > self.tank_volume:\r\n raise TankUpError(self.tank_volume, self.fuel, amount)\r\n self.fuel += amount\r\n print(\"The {} has been fueled up.\".format(self.__class__.__name__.lower()))","title":""},{"docid":"c9b168dcd959437cb4231bbcaf25cc62","score":"0.47617945","text":"def design(self, clk_inv_params, drv_tinv_params, ff_inv_params, fb_tinv_params,\n dum_info, debug, **kwargs):\n self.instances['INV0'].design(**ff_inv_params)\n self.instances['INV1'].design(**ff_inv_params)\n self.instances['INV2'].design(**clk_inv_params)\n self.instances['INV3'].design(**clk_inv_params)\n\n self.instances['TINV0'].design(**drv_tinv_params)\n self.instances['TINV1'].design(**drv_tinv_params)\n self.instances['TINV2'].design(**fb_tinv_params)\n self.instances['TINV3'].design(**fb_tinv_params)\n\n self.design_dummy_transistors(dum_info, 'XDUM', 'VDD', 'VSS')\n\n if debug is False:\n self.remove_pin('mem1')\n self.remove_pin('mem2')\n self.remove_pin('latch')\n self.remove_pin('iclk')\n self.remove_pin('iclkb')","title":""},{"docid":"af6bc6c18a7c8095162973f603f44fac","score":"0.4755927","text":"def __init__(self, species, qty):\n\n # user super method to use superclass init on species and qty\n super(DomesticMelonOrder, self).__init__(species, qty, 'domestic', 0.08, None)","title":""},{"docid":"21cc92a9c234e33cec66c176148f1934","score":"0.47447366","text":"def setup_gratingcoupler_3d_base_project(self, fdtd): \n \n ## CLEAR SESSION\n #fdtd.clear()\n fdtd.newproject()\n \n ## Start adding base components\n fdtd.redrawoff()\n\n ## Set FDTD properties\n props = OrderedDict([\n (\"dimension\", \"3D\"),\n (\"x min\", self.x_min),\n (\"x max\", self.x_max), \n (\"y min\", self.y_min), \n (\"y max\", self.y_max), \n (\"z min\", self.z_min), \n (\"z max\", self.z_max), \n (\"background material\", self.mat_bg),\n (\"y min bc\", \"anti-symmetric\"),\n (\"simulation time\", 5000e-15), \n (\"auto shutoff min\", 1e-6),\n (\"mesh refinement\", \"conformal variant 0\"),\n (\"meshing tolerance\", 1.2e-15), \n (\"use legacy conformal interface detection\", False)\n ])\n\n if self.mat_bg == \"<Object defined dielectric>\":\n props[\"index\"] = self.n_bg\n\n if self.optim:\n props[\"mesh refinement\"] = \"precise volume average\"\n props[\"meshing refinement\"] = 11\n\n if self.pol_angle == 0:\n props[\"y min bc\"] = \"symmetric\"\n\n fdtd.addfdtd(properties=props)\n \n fdtd.addgaussian(name=\"source\", injection_axis=\"z-axis\", direction=\"backward\", polarization_angle=self.pol_angle, x=self.x_fib, x_span=self.x_fib_span, y_min=self.y_min, y_max=self.y_max, z=self.z_fib,\n beam_parameters=\"Waist size and position\", waist_radius_w0=5.2e-6, distance_from_waist=0.0, angle_theta=self.theta_fib_mat,\n center_wavelength=self.lambda0, wavelength_span=0.1e-6, optimize_for_short_pulse=False)\n \n fdtd.setglobalsource(\"center wavelength\",self.lambda0)\n fdtd.setglobalsource(\"wavelength span\",0.1e-6)\n fdtd.setglobalsource(\"optimize for short pulse\",False)\n fdtd.setglobalmonitor(\"frequency points\",11)\n fdtd.setglobalmonitor(\"use wavelength spacing\",True)\n \n fdtd.addmesh(name=\"source_mesh\",x=self.x_fib, x_span=24e-6, y_min=self.y_min, y_max=self.y_max, z=self.z_fib, z_span=2*self.dz, override_x_mesh=False, override_y_mesh=False, override_z_mesh=True, dz=self.dz)\n fdtd.setnamed(\"source_mesh\",\"enabled\",False) #< Disable by default but need to check the effect\n \n if self.material_name == \"<Object defined dielectric>\":\n fdtd.addrect(name=\"substrate\", x_min=(self.x_min-2e-6), x_max=(self.x_max+2e-6), y_min=(self.y_min-2e-6), y_max=(self.y_max+2e-6), z_min=-4e-6, z_max=-2e-6, material=self.material_name, index=self.index_wg, alpha=0.1)\n else:\n fdtd.addrect(name=\"substrate\", x_min=(self.x_min-2e-6), x_max=(self.x_max+2e-6), y_min=(self.y_min-2e-6), y_max=(self.y_max+2e-6), z_min=-4e-6, z_max=-2e-6, material=self.material_name, alpha=0.1)\n\n \n fdtd.addpower(name=\"fom\", monitor_type=\"2D X-normal\", x=self.mode_pos_x, y=0, y_span=self.mode_span_y, z=0, z_span=self.mode_span_z)\n fdtd.addmesh(name=\"fom_mesh\", x=self.mode_pos_x, x_span=2*self.dx, y=0, y_span=self.mode_span_y, z=0, z_span=self.mode_span_z, override_x_mesh=True, dx=self.dx, override_y_mesh=False, override_z_mesh=False )\n \n fdtd.addpower(name=\"opt_fields\",monitor_type=\"3D\", x_min=self.x_min_opt_region, x_max=self.x_max, y_min=self.y_min, y_max=self.y_max, z_min=self.wg_height-self.etch_depth, z_max=self.wg_height,\n output_Hx=False, output_Hy=False, output_Hz=False, output_power=False)\n fdtd.addmesh(name=\"opt_fields_mesh\", x_min=self.x_min_opt_region, x_max=self.x_max, y_min=self.y_min, y_max=self.y_max, z_min=self.wg_height-self.etch_depth, z_max=self.wg_height, dx=self.dx, dy=self.dy, dz=self.dz)\n \n fdtd.addindex(name=\"index_xy\", monitor_type=\"2D Z-normal\", x_min=self.x_min, x_max=self.x_max, y_min=self.y_min, y_max=self.y_max, z=self.wg_height-(self.etch_depth/2.), spatial_interpolation='none', enabled=False)\n fdtd.addindex(name=\"index_xz\", monitor_type=\"2D Y-normal\", x_min=self.x_min, x_max=self.x_max, y=0, z_min=self.z_min, z_max=self.z_max, spatial_interpolation='none', enabled=False)\n \n \n if self.material_name == \"<Object defined dielectric>\":\n fdtd.addrect(name='wg', x_min=(self.x_min-2e-6), x_max=2e-6, y=0, y_span=self.wg_width, z_min=0, z_max=self.wg_height, material=self.material_name, index=self.index_wg)\n else:\n fdtd.addrect(name='wg', x_min=(self.x_min-2e-6), x_max=2e-6, y=0, y_span=self.wg_width, z_min=0, z_max=self.wg_height, material=self.material_name)\n\n\n theta_start = self.initial_theta_taper\n theta_stop = 360.0 - theta_start\n\n if self.material_name == \"<Object defined dielectric>\":\n fdtd.addring(name='silicon', x=0, y=0, z_min=0, z_max=self.wg_height, inner_radius=0, outer_radius=60e-6, theta_start=theta_stop, theta_stop=theta_start, material=self.material_name, index=self.index_wg)\n else:\n fdtd.addring(name='silicon', x=0, y=0, z_min=0, z_max=self.wg_height, inner_radius=0, outer_radius=60e-6, theta_start=theta_stop, theta_stop=theta_start, material=self.material_name)\n\n \n fdtd.redrawon()","title":""},{"docid":"97df60dae26b41b9302a3ee0fc182462","score":"0.47366068","text":"def prepare_simulation(self, components):\n # Check level of destination storage component: if it is below specified threshold,\n # implement low artificial costs (to encourage system to fill it)\n # Check level of all non-central storage component and use the one with the\n # highest amount of h2:\n # if it is below specified threshold, the trailer cannot take any hydrogen from it\n\n # In the model definition, the foreign states must be defined in the following order:\n # 1) all origin storage levels [kg]\n # 2) all origin minimum storage levels [kg]\n # 3) all origin capacities [kg]\n # 4) destination storage level [kg]\n # 5) destination scapacity [kg]\n # The order for each of these in terms of production sites must also be the same e.g.\n # the first entry relates to the first site, the second entry relates\n # to the second site etc.\n if self.fs_component_name is not None:\n\n # n is the number of production sites that the trailer is connected to\n n = int((len(self.fs_component_name) - 2) / 3)\n # Creates an index list for the number of foreign states considered\n index_list = list(range(0, len(self.fs_component_name)))\n # List containing the origin storage levels [kg]\n fs_origin_storage_levels = []\n # List containing the origin minimum storage levels [kg]\n fs_origin_min_storage_levels = []\n # List containing the origin capacities [kg]\n fs_origin_capacities = []\n # List containing the origin available masses [kg]\n fs_origin_available_masses = []\n\n # Obtains a list of the origin storage levels for n sites\n for i in index_list[0:n]:\n this_origin_storage_level = self.get_foreign_state_value(components, index=i)\n fs_origin_storage_levels.append(this_origin_storage_level)\n\n # Obtains a list of the origin minimum storage levels for n sites\n for i in index_list[n:2 * n]:\n this_min_storage_level = self.get_foreign_state_value(components, index=i)\n fs_origin_min_storage_levels.append(this_min_storage_level)\n\n # Obtains a list of the origin capacity levels for n sites\n for i in index_list[2 * n:3 * n]:\n this_capacity = self.get_foreign_state_value(components, index=i)\n fs_origin_capacities.append(this_capacity)\n\n # Obtains a list for the available masses that can be taken from the\n # origin storage [kg].\n # It cannot take more than half of the capacity into account\n for i in range(int(n)):\n this_available_kg = min((fs_origin_storage_levels[i]\n - fs_origin_min_storage_levels[i]),\n fs_origin_capacities[i] / 2)\n fs_origin_available_masses.append(this_available_kg)\n\n # Get the availability mass of hydrogen of the fullest origin storage\n self.fs_origin_available_kg = max(fs_origin_available_masses)\n # Obtains the destination storage level [kg]\n fs_destination_storage_level = \\\n self.get_foreign_state_value(components, index=index_list[-2])\n # Obtains the destination storage capacity [kg]\n fs_destination_capacity = \\\n self.get_foreign_state_value(components, index=index_list[-1])\n # Obtains the available mass that can be delivered to the destination storage [kg]\n fs_destination_available_storage = \\\n fs_destination_capacity - fs_destination_storage_level\n\n # Checks if the destination storage level is below the threshold:\n # if yes, delivery possible\n\n # todo: implement multiple storage delivery in one time step from different wind\n # parks - low priority\n\n if fs_destination_storage_level \\\n < self.fs_destination_storage_threshold * fs_destination_capacity:\n\n # If the available mass [kg] in the destination storage and the amount of\n # available hydrogen [kg] in the origin storage exceed the trailer capacity,\n # the trailer should be completely filled\n if fs_destination_available_storage >= self.trailer_capacity \\\n and self.fs_origin_available_kg >= self.trailer_capacity:\n self.hydrogen_needed = self.trailer_capacity\n # If the available mass [kg] in the destination storage exceeds the trailer\n # capacity but the amount of available hydrogen in the origin storage is less\n # than the trailer capacity, the trailer should be filled with the maximum amount\n # of available hydrogen from the origin storage\n elif fs_destination_available_storage > \\\n self.trailer_capacity > self.fs_origin_available_kg:\n self.hydrogen_needed = self.fs_origin_available_kg\n # Else, the trailer should deliver the maximum amount of hydrogen that can fit\n # into the destination storage\n else:\n self.hydrogen_needed = fs_destination_available_storage\n # If the destination storage level is not below the threshold no delivery possible\n else:\n self.hydrogen_needed = 0\n\n self.current_ac = self.get_costs_and_art_costs()","title":""},{"docid":"2e8ce950ec3f60a3c592b78c62649117","score":"0.47321063","text":"def createGases(self):\n #Building objects for fuel and air\n self.fuel = ct.Solution(self.mechs[0])\n self.atmosphere = ct.Solution(self.mechs[1])\n self.exhausts = [ct.Solution(mech) for mech in self.mechs[2:]]","title":""},{"docid":"bfcb772888cc842bd209911b20bcad52","score":"0.47316712","text":"def add_liquidity(self, add_liquidity):\n\n self._add_liquidity = add_liquidity","title":""},{"docid":"66bbc098093daf9dc728ffaeb907d685","score":"0.47211936","text":"def define_components(mod):\n\n # TODO: maybe rename fuel_cost_per_timepoint component and/or .csv file to fuel_cost\n # (but that could cause confusion in the documentation?)\n mod.ZONE_FUEL_TIMEPOINTS = Set(\n dimen=3,\n validate=lambda m, z, f, p: (\n z in m.LOAD_ZONES and f in m.FUELS and p in m.TIMEPOINTS\n ),\n )\n mod.fuel_cost_per_timepoint = Param(\n mod.ZONE_FUEL_TIMEPOINTS, within=NonNegativeReals\n )\n mod.min_data_check(\"ZONE_FUEL_TIMEPOINTS\", \"fuel_cost_per_timepoint\")\n\n # don't allow use of a fuel when no cost has been specified\n mod.GEN_TP_FUELS_UNAVAILABLE = Set(\n dimen=3,\n initialize=mod.GEN_TP_FUELS,\n filter=lambda m, g, t, f: (m.gen_load_zone[g], f, t)\n not in m.ZONE_FUEL_TIMEPOINTS,\n )\n mod.Enforce_Fuel_Unavailability = Constraint(\n mod.GEN_TP_FUELS_UNAVAILABLE,\n rule=lambda m, g, t, f: m.GenFuelUseRate[g, t, f] == 0,\n )\n\n # Summarize total fuel costs in each timepoint for the objective function\n def FuelCostsPerTP_rule(m, t):\n if not hasattr(m, \"FuelCostsPerTP_dict\"):\n # cache all Fuel_Cost_TP values in a dictionary (created in one pass)\n m.FuelCostsPerTP_dict = {t2: 0.0 for t2 in m.TIMEPOINTS}\n for (g, t2, f) in m.GEN_TP_FUELS:\n if (g, t2, f) not in m.GEN_TP_FUELS_UNAVAILABLE:\n m.FuelCostsPerTP_dict[t2] += (\n m.GenFuelUseRate[g, t2, f]\n * m.fuel_cost_per_timepoint[m.gen_load_zone[g], f, t2]\n )\n # return a result from the dictionary and pop the element each time\n # to release memory\n return m.FuelCostsPerTP_dict.pop(t)\n\n mod.FuelCostsPerTP = Expression(mod.TIMEPOINTS, rule=FuelCostsPerTP_rule)\n mod.Cost_Components_Per_TP.append(\"FuelCostsPerTP\")","title":""},{"docid":"0ca4d83062a265d26174cd8194f4be6d","score":"0.47184825","text":"def start_engine(self):\r\n if self.fuel == 0:\r\n raise FuelError(self.fuel)\r\n print(\"Starting the engine...\")","title":""},{"docid":"d1ca68fdc819c414de5c5a717d62bb91","score":"0.47082067","text":"def accept(self, button=None):\n self.getParallelRebarsData()\n self.getCrossRebarsData()\n self.getColumnsData()\n self.getTiesData()\n self.getMainRebarsData()\n self.getSecondaryRebarsData()\n if not self.FootingReinforcementGroup:\n FootingReinforcementGroup = makeFootingReinforcement(\n parallel_rebar_type=self.parallel_rebars_type,\n parallel_front_cover=self.parallel_front_cover,\n parallel_rear_cover=self.parallel_rear_cover,\n parallel_left_cover=self.parallel_left_cover,\n parallel_right_cover=self.parallel_right_cover,\n parallel_top_cover=self.parallel_top_cover,\n parallel_bottom_cover=self.parallel_bottom_cover,\n parallel_diameter=self.parallel_diameter,\n parallel_amount_spacing_check=self.parallel_amount_spacing_check,\n parallel_amount_spacing_value=self.parallel_amount_spacing_value,\n cross_rebar_type=self.cross_rebars_type,\n cross_front_cover=self.cross_front_cover,\n cross_rear_cover=self.cross_rear_cover,\n cross_left_cover=self.cross_left_cover,\n cross_right_cover=self.cross_right_cover,\n cross_top_cover=self.cross_top_cover,\n cross_bottom_cover=self.cross_bottom_cover,\n cross_diameter=self.cross_diameter,\n cross_amount_spacing_check=self.cross_amount_spacing_check,\n column_front_spacing=self.column_front_spacing,\n column_left_spacing=self.column_left_spacing,\n column_right_spacing=self.column_right_spacing,\n column_rear_spacing=self.column_rear_spacing,\n tie_top_cover=self.tie_top_cover,\n tie_bottom_cover=self.tie_bottom_cover,\n tie_bent_angle=self.tie_bent_angle,\n tie_extension_factor=self.tie_extension_factor,\n tie_diameter=self.tie_diameter,\n tie_number_spacing_check=self.tie_number_spacing_check,\n tie_number_spacing_value=self.tie_number_spacing_value,\n column_main_rebar_diameter=self.column_main_rebar_diameter,\n column_main_rebars_t_offset=self.column_main_rebars_t_offset,\n cross_amount_spacing_value=self.cross_amount_spacing_value,\n column_width=self.column_width,\n column_length=self.column_length,\n xdir_column_amount_spacing_check=self.xdir_column_amount_spacing_check,\n xdir_column_amount_spacing_value=self.xdir_column_amount_spacing_value,\n ydir_column_amount_spacing_check=self.ydir_column_amount_spacing_check,\n ydir_column_amount_spacing_value=self.ydir_column_amount_spacing_value,\n parallel_rounding=self.parallel_rounding,\n parallel_l_shape_hook_orintation=self.parallel_l_shape_hook_orintation,\n cross_rounding=self.cross_rounding,\n cross_l_shape_hook_orintation=self.cross_l_shape_hook_orintation,\n column_main_rebars_type=self.column_main_rebars_type,\n column_main_hook_orientation=self.column_main_hook_orientation,\n column_main_hook_extend_along=self.column_main_hook_extend_along,\n column_l_main_rebar_rounding=self.column_l_main_rebar_rounding,\n column_main_hook_extension=self.column_main_hook_extension,\n column_sec_rebar_check=self.column_sec_rebar_check,\n column_sec_rebars_t_offset=self.column_sec_rebars_t_offset,\n column_sec_rebars_number_diameter=self.column_sec_rebars_number_diameter,\n column_sec_rebars_type=self.column_sec_rebars_type,\n column_sec_hook_orientation=self.column_sec_hook_orientation,\n column_l_sec_rebar_rounding=self.column_l_sec_rebar_rounding,\n column_sec_hook_extension=self.column_sec_hook_extension,\n mesh_cover_along=self.mesh_cover_along,\n structure=self.SelectedObj,\n facename=self.FaceName,\n )\n else:\n FootingReinforcementGroup = editFootingReinforcement(\n self.FootingReinforcementGroup,\n parallel_rebar_type=self.parallel_rebars_type,\n parallel_front_cover=self.parallel_front_cover,\n parallel_rear_cover=self.parallel_rear_cover,\n parallel_left_cover=self.parallel_left_cover,\n parallel_right_cover=self.parallel_right_cover,\n parallel_top_cover=self.parallel_top_cover,\n parallel_bottom_cover=self.parallel_bottom_cover,\n parallel_diameter=self.parallel_diameter,\n parallel_amount_spacing_check=self.parallel_amount_spacing_check,\n parallel_amount_spacing_value=self.parallel_amount_spacing_value,\n cross_rebar_type=self.cross_rebars_type,\n cross_front_cover=self.cross_front_cover,\n cross_rear_cover=self.cross_rear_cover,\n cross_left_cover=self.cross_left_cover,\n cross_right_cover=self.cross_right_cover,\n cross_top_cover=self.cross_top_cover,\n cross_bottom_cover=self.cross_bottom_cover,\n cross_diameter=self.cross_diameter,\n cross_amount_spacing_check=self.cross_amount_spacing_check,\n column_front_spacing=self.column_front_spacing,\n column_left_spacing=self.column_left_spacing,\n column_right_spacing=self.column_right_spacing,\n column_rear_spacing=self.column_rear_spacing,\n tie_top_cover=self.tie_top_cover,\n tie_bottom_cover=self.tie_bottom_cover,\n tie_bent_angle=self.tie_bent_angle,\n tie_extension_factor=self.tie_extension_factor,\n tie_diameter=self.tie_diameter,\n tie_number_spacing_check=self.tie_number_spacing_check,\n tie_number_spacing_value=self.tie_number_spacing_value,\n column_main_rebar_diameter=self.column_main_rebar_diameter,\n column_main_rebars_t_offset=self.column_main_rebars_t_offset,\n cross_amount_spacing_value=self.cross_amount_spacing_value,\n column_width=self.column_width,\n column_length=self.column_length,\n xdir_column_amount_spacing_check=self.xdir_column_amount_spacing_check,\n xdir_column_amount_spacing_value=self.xdir_column_amount_spacing_value,\n ydir_column_amount_spacing_check=self.ydir_column_amount_spacing_check,\n ydir_column_amount_spacing_value=self.ydir_column_amount_spacing_value,\n parallel_rounding=self.parallel_rounding,\n parallel_l_shape_hook_orintation=self.parallel_l_shape_hook_orintation,\n cross_rounding=self.cross_rounding,\n cross_l_shape_hook_orintation=self.cross_l_shape_hook_orintation,\n column_main_rebars_type=self.column_main_rebars_type,\n column_main_hook_orientation=self.column_main_hook_orientation,\n column_main_hook_extend_along=self.column_main_hook_extend_along,\n column_l_main_rebar_rounding=self.column_l_main_rebar_rounding,\n column_main_hook_extension=self.column_main_hook_extension,\n column_sec_rebar_check=self.column_sec_rebar_check,\n column_sec_rebars_t_offset=self.column_sec_rebars_t_offset,\n column_sec_rebars_number_diameter=self.column_sec_rebars_number_diameter,\n column_sec_rebars_type=self.column_sec_rebars_type,\n column_sec_hook_orientation=self.column_sec_hook_orientation,\n column_l_sec_rebar_rounding=self.column_l_sec_rebar_rounding,\n column_sec_hook_extension=self.column_sec_hook_extension,\n mesh_cover_along=self.mesh_cover_along,\n structure=self.SelectedObj,\n facename=self.FaceName,\n )\n\n self.FootingReinforcementGroup = FootingReinforcementGroup\n if (\n self.form.standardButtonBox.buttonRole(button)\n != QtWidgets.QDialogButtonBox.ApplyRole\n ):\n self.form.close()","title":""},{"docid":"c79e3f5389f572ebcc61dcdb92976a8e","score":"0.47073346","text":"def crane_invest(self, year):\n if self.debug:\n print(' *** add Harbour crane to elements')\n # add unloader object\n if (self.crane_type_defaults[\"crane_type\"] == 'Gantry crane' or\n self.crane_type_defaults[\"crane_type\"] == 'Harbour crane' or\n self.crane_type_defaults[\"crane_type\"] == 'Mobile crane'):\n crane = Cyclic_Unloader(**self.crane_type_defaults)\n elif self.crane_type_defaults[\"crane_type\"] == 'Screw unloader':\n crane = Continuous_Unloader(**self.crane_type_defaults)\n\n # - capex\n unit_rate = crane.unit_rate\n mobilisation = unit_rate * crane.mobilisation_perc\n crane.capex = int(unit_rate + mobilisation)\n\n # - opex\n crane.insurance = unit_rate * crane.insurance_perc\n crane.maintenance = unit_rate * crane.maintenance_perc\n\n # labour\n labour = Labour(**agribulk_defaults.labour_data)\n '''old formula --> crane.labour = crane.crew * self.operational_hours / labour.shift_length '''\n crane.shift = ((crane.crew * self.operational_hours) / (\n labour.shift_length * labour.annual_shifts))\n crane.labour = crane.shift * labour.operational_salary\n\n # apply proper timing for the crane to come online (in the same year as the latest Quay_wall)\n years_online = []\n for element in self.find_elements(Quay_wall):\n years_online.append(element.year_online)\n crane.year_online = max([year + crane.delivery_time, max(years_online)])\n\n # add cash flow information to quay_wall object in a dataframe\n crane = self.add_cashflow_data_to_element(crane)\n\n # add object to elements\n self.elements.append(crane)","title":""},{"docid":"4e1f22653df3a6afced7978588cef094","score":"0.46998996","text":"def simulate_scheil_solidification(dbf, comps, phases, composition,\n start_temperature, step_temperature=1.0,\n liquid_phase_name='LIQUID', eq_kwargs=None,\n stop=0.0001, verbose=False, adaptive=True):\n eq_kwargs = eq_kwargs or dict()\n STEP_SCALE_FACTOR = 1.2 # How much to try to adapt the temperature step by\n MAXIMUM_STEP_SIZE_REDUCTION = 5.0\n T_STEP_ORIG = step_temperature\n phases = filter_phases(dbf, unpack_components(dbf, comps), phases)\n models = instantiate_models(dbf, comps, phases)\n if verbose:\n print('building callables... ', end='')\n cbs = build_callables(dbf, comps, phases, models, additional_statevars={v.P, v.T, v.N}, build_gradients=True, build_hessians=True)\n if verbose:\n print('done')\n solid_phases = sorted(set(phases) - {liquid_phase_name})\n temp = start_temperature\n independent_comps = sorted([str(comp)[2:] for comp in composition.keys()])\n x_liquid = {comp: [composition[v.X(comp)]] for comp in independent_comps}\n fraction_solid = [0.0]\n temperatures = [temp]\n phase_amounts = {ph: [0.0] for ph in solid_phases}\n ord_disord_dict = order_disorder_dict(dbf, comps, phases)\n\n if adaptive and ('points' in eq_kwargs.get('calc_opts', {})):\n # Dynamically add points as the simulation runs\n species = unpack_components(dbf, comps)\n dof_dict = {ph: generate_dof(dbf.phases[ph], species)[1] for ph in phases}\n else:\n adaptive = False\n\n converged = False\n phases_seen = {liquid_phase_name, ''}\n liquid_comp = composition\n while fraction_solid[-1] < 1:\n conds = {v.T: temp, v.P: 101325.0, v.N: 1.0}\n comp_conds = liquid_comp\n fmt_comp_conds = ', '.join([f'{c}={val:0.2f}' for c, val in comp_conds.items()])\n conds.update(comp_conds)\n eq = equilibrium(dbf, comps, phases, conds, callables=cbs, model=models, **eq_kwargs)\n if adaptive:\n # Update the points dictionary with local samples around the equilibrium site fractions\n points_dict = eq_kwargs['calc_opts']['points']\n for vtx in range(eq.vertex.size):\n masked = eq.isel(vertex=vtx)\n ph = str(masked.Phase.values.squeeze())\n pts = points_dict.get(ph)\n if pts is not None:\n if verbose:\n print(f'Adding points to {ph}. ', end='')\n dof = dof_dict[ph]\n points_dict[ph] = np.concatenate([pts, local_sample(masked.Y.values.squeeze()[:sum(dof)].reshape(1, -1), dof, pdens=20)], axis=0)\n\n eq_phases = order_disorder_eq_phases(eq, ord_disord_dict)\n num_eq_phases = np.nansum(np.array([str(ph) for ph in eq_phases]) != '')\n new_phases_seen = set(eq_phases).difference(phases_seen)\n if len(new_phases_seen) > 0:\n if verbose:\n print(f'New phases seen: {new_phases_seen}. ', end='')\n phases_seen |= new_phases_seen\n if liquid_phase_name not in eq[\"Phase\"].values.squeeze():\n found_ph = set(eq_phases) - {''}\n if verbose:\n print(f'No liquid phase found at T={temp:0.3f}, {fmt_comp_conds}. (Found {found_ph}) ', end='')\n if len(found_ph) == 0:\n # No phases found in equilibrium. Just continue on lowering the temperature without changing anything\n if verbose:\n print(f'(Convergence failure) ', end='')\n if T_STEP_ORIG / step_temperature > MAXIMUM_STEP_SIZE_REDUCTION:\n # Only found solid phases and the step size has already been reduced. Stop running without converging.\n if verbose:\n print('Maximum step size reduction exceeded. Stopping.')\n converged = False\n break\n else:\n # Only found solid phases. Try reducing the step size to zero-in on the correct phases\n if verbose:\n print(f'Stepping back and reducing step size.')\n temp += step_temperature\n step_temperature /= STEP_SCALE_FACTOR\n continue\n # TODO: Will break if there is a liquid miscibility gap\n liquid_vertex = sorted(np.nonzero(eq[\"Phase\"].values.squeeze().flat == liquid_phase_name))[0]\n liquid_comp = {}\n for comp in independent_comps:\n x = float(eq[\"X\"].isel(vertex=liquid_vertex).squeeze().sel(component=comp).values)\n x_liquid[comp].append(x)\n liquid_comp[v.X(comp)] = x\n np_liq = np.nansum(eq.where(eq[\"Phase\"] == liquid_phase_name).NP.values)\n current_fraction_solid = float(fraction_solid[-1])\n found_phase_amounts = [(liquid_phase_name, np_liq)] # tuples of phase name, amount\n for solid_phase in solid_phases:\n if solid_phase not in eq_phases:\n phase_amounts[solid_phase].append(0.0)\n continue\n np_tieline = np.nansum(eq.isel(vertex=eq_phases.index(solid_phase))[\"NP\"].values.squeeze())\n found_phase_amounts.append((solid_phase, np_tieline))\n delta_fraction_solid = (1 - current_fraction_solid) * np_tieline\n current_fraction_solid += delta_fraction_solid\n phase_amounts[solid_phase].append(delta_fraction_solid)\n fraction_solid.append(current_fraction_solid)\n temperatures.append(temp)\n NL = 1 - fraction_solid[-1]\n if verbose:\n phase_amnts = ' '.join([f'NP({ph})={amnt:0.3f}' for ph, amnt in found_phase_amounts])\n if NL < 1.0e-3:\n print(f'T={temp:0.3f}, {fmt_comp_conds}, ΔT={step_temperature:0.3f}, NL: {NL:.2E}, {phase_amnts} ', end='')\n else:\n print(f'T={temp:0.3f}, {fmt_comp_conds}, ΔT={step_temperature:0.3f}, NL: {NL:0.3f}, {phase_amnts} ', end='')\n if NL < stop:\n if verbose:\n print(f'Liquid fraction below criterion {stop} . Stopping at {fmt_comp_conds}')\n converged = True\n break\n if verbose:\n print() # add line break\n temp -= step_temperature\n\n if fraction_solid[-1] < 1:\n for comp in independent_comps:\n x_liquid[comp].append(np.nan)\n fraction_solid.append(1.0)\n temperatures.append(temp)\n # set the final phase amount to the phase fractions in the eutectic\n # this method gives the sum total phase amounts of 1.0 by construction\n for solid_phase in solid_phases:\n if solid_phase in eq_phases:\n amount = np.nansum(eq.isel(vertex=eq_phases.index(solid_phase))[\"NP\"].values.squeeze())\n phase_amounts[solid_phase].append(float(amount) * (1 - current_fraction_solid))\n else:\n phase_amounts[solid_phase].append(0.0)\n\n return SolidificationResult(x_liquid, fraction_solid, temperatures, phase_amounts, converged, \"scheil\")","title":""},{"docid":"a4f42a97b845fb2ecc0c9406e3dbab64","score":"0.4697101","text":"def placeBets(self):\n raise NotImplementedError","title":""},{"docid":"bd91de486ae217716303491368e6e8ca","score":"0.46919805","text":"def fuel_required_for_part1(self) -> int:\n return self.mass//3 - 2","title":""},{"docid":"47824ae1f4bd2b5705be6f1c75a20c83","score":"0.46808332","text":"def fill_gas_tank(self):\n print(f\"This {self.make.title()} now has a full tank of gas!\")","title":""},{"docid":"17f94e9d43a9c0e15882d8974ca9afc4","score":"0.46776524","text":"def Calculate_Defect_Carrier_Concentration(self):\r\n\t\t\r\n\t\t# Initialize\r\n\t\tintrinsic_defect_carrier_concentration_temperature = {}\r\n\t\textrinsic_defect_carrier_concentration_temperature = {}\r\n\t\tfor temperature in self.temperature_array:\r\n\t\t\tintrinsic_defect_carrier_concentration_temperature[temperature] = np.zeros(len(self.fermi_energy_array))\r\n\t\t\textrinsic_defect_carrier_concentration_temperature[temperature] = np.zeros(len(self.fermi_energy_array))\r\n\t\t\r\n\t\t# Obtain intrinsic defect carrier concentration\r\n\t\tfor defect in self.ternary_defects_data.keys():\r\n\t\t\t\r\n\t\t\t# Check that item is truly a defect\r\n\t\t\tif \"_\" not in defect:\r\n\t\t\t\tcontinue\r\n\t\t\t\r\n\t\t\tif self.ternary_defects_data[defect][\"Extrinsic\"] == \"No\":\r\n\t\t\t\t\r\n\t\t\t\tfor charge in self.ternary_defects_data[defect][\"charge\"].keys():\r\n\t\t\t\t\t\r\n\t\t\t\t\t# Formation energy\r\n\t\t\t\t\t\"\"\"\r\n\t\t\t\t\tdefect_formation_enthalpy = self.ternary_defects_data[defect][\"charge\"][charge][\"Energy\"] \\\r\n\t\t\t\t\t\t\t\t\t\t\t\t- self.ternary_defects_data[\"supercellsize\"] * self.main_compound_total_energy \\\r\n\t\t\t\t\t\t\t\t\t\t\t\t- self.ternary_defects_data[defect][\"n_\"+self.species_a] * ( self.first_element_mu0 + float(dmu_a) ) \\\r\n\t\t\t\t\t\t\t\t\t\t\t\t- self.ternary_defects_data[defect][\"n_\"+self.species_b] * ( self.second_element_mu0 + float(dmu_b) ) \\\r\n\t\t\t\t\t\t\t\t\t\t\t\t- self.ternary_defects_data[defect][\"n_\"+self.species_c] * ( self.third_element_mu0 + float(dmu_c) ) \\\r\n\t\t\t\t\t\t\t\t\t\t\t\t+ float(charge) * self.fermi_energy_array \\\r\n\t\t\t\t\t\t\t\t\t\t\t\t+ self.ternary_defects_data[defect][\"charge\"][charge][\"ECorr\"]\r\n\t\t\t\t\t\"\"\"\r\n\t\t\t\t\tdefect_formation_enthalpy = self.ternary_defects_data[defect][\"charge\"][charge][\"Energy\"] \\\r\n\t\t\t\t\t\t\t\t\t\t\t\t- self.ternary_defects_data[\"supercellsize\"] * self.main_compound_total_energy \\\r\n\t\t\t\t\t\t\t\t\t\t\t\t+ float(charge) * self.fermi_energy_array \\\r\n\t\t\t\t\t\t\t\t\t\t\t\t+ self.ternary_defects_data[defect][\"charge\"][charge][\"ECorr\"]\r\n\t\t\t\t\tfor element in self.elements_list:\r\n\t\t\t\t\t\tdefect_formation_enthalpy -= self.ternary_defects_data[defect][\"n_\"+element] * ( self.mu_elements[element][\"mu0\"] + self.mu_elements[element][\"deltamu\"] )\r\n\t\t\t\t\t\r\n\t\t\t\t\t# Defect concentration\r\n\t\t\t\t\tif self.first_element == defect.split(\"_\")[-1]:\r\n\t\t\t\t\t\tN = self.main_compound_number_first_specie/self.vol\r\n\t\t\t\t\telif self.second_element == defect.split(\"_\")[-1]:\r\n\t\t\t\t\t\tN = self.main_compound_number_second_specie/self.vol\r\n\t\t\t\t\telif self.third_element == defect.split(\"_\")[-1]:\r\n\t\t\t\t\t\tN = self.main_compound_number_third_specie/self.vol\r\n\t\t\t\t\t\r\n\t\t\t\t\tfor temperature in self.temperature_array:\r\n\t\t\t\t\t\tdefect_carrier_concentration = float(charge) * N * np.exp(-defect_formation_enthalpy / (self.k * temperature) )\r\n\t\t\t\t\t\tintrinsic_defect_carrier_concentration_temperature[temperature] += defect_carrier_concentration\r\n\t\t\r\n\t\t# Check that user-selected extrinsic defect is not \"None\"\r\n\t\tif self.extrinsic_defect == \"None\":\r\n\t\t\treturn intrinsic_defect_carrier_concentration_temperature, extrinsic_defect_carrier_concentration_temperature\r\n\t\t\r\n\t\t# Obtain extrinsic defect carrier concentration\r\n\t\tfor charge in self.ternary_defects_data[self.extrinsic_defect][\"charge\"].keys():\r\n\t\t\t\r\n\t\t\t# Formation energy\r\n\t\t\t\"\"\"\r\n\t\t\textrinsic_defect_formation_enthalpy = self.ternary_defects_data[self.extrinsic_defect][\"charge\"][charge][\"Energy\"] \\\r\n\t\t\t\t\t\t\t\t\t\t\t\t- self.ternary_defects_data[\"supercellsize\"] * self.main_compound_total_energy \\\r\n\t\t\t\t\t\t\t\t\t\t\t\t- self.ternary_defects_data[self.extrinsic_defect][\"n_\"+self.species_a] * ( self.first_element_mu0 + float(dmu_a) ) \\\r\n\t\t\t\t\t\t\t\t\t\t\t\t- self.ternary_defects_data[self.extrinsic_defect][\"n_\"+self.species_b] * ( self.second_element_mu0 + float(dmu_b) ) \\\r\n\t\t\t\t\t\t\t\t\t\t\t\t- self.ternary_defects_data[self.extrinsic_defect][\"n_\"+self.species_c] * ( self.third_element_mu0 + float(dmu_c) ) \\\r\n\t\t\t\t\t\t\t\t\t\t\t\t- (self.extrinsic_defect_mu0 + self.extrinsic_defect_deltamu) \\\r\n\t\t\t\t\t\t\t\t\t\t\t\t+ float(charge) * self.fermi_energy_array \\\r\n\t\t\t\t\t\t\t\t\t\t\t\t+ self.ternary_defects_data[self.extrinsic_defect][\"charge\"][charge][\"ECorr\"]\r\n\t\t\t\"\"\"\r\n\t\t\textrinsic_defect_formation_enthalpy = self.ternary_defects_data[self.extrinsic_defect][\"charge\"][charge][\"Energy\"] \\\r\n\t\t\t\t\t\t\t\t\t\t\t\t- self.ternary_defects_data[\"supercellsize\"] * self.main_compound_total_energy \\\r\n\t\t\t\t\t\t\t\t\t\t\t\t- (self.extrinsic_defect_mu0 + self.extrinsic_defect_deltamu) \\\r\n\t\t\t\t\t\t\t\t\t\t\t\t+ float(charge) * self.fermi_energy_array \\\r\n\t\t\t\t\t\t\t\t\t\t\t\t+ self.ternary_defects_data[self.extrinsic_defect][\"charge\"][charge][\"ECorr\"]\r\n\t\t\tfor element in self.elements_list:\r\n\t\t\t\textrinsic_defect_formation_enthalpy -= self.ternary_defects_data[self.extrinsic_defect][\"n_\"+element] * ( self.mu_elements[element][\"mu0\"] + self.mu_elements[element][\"deltamu\"] )\r\n\t\t\t\r\n\t\t\t# Extrinsic defect concentration\r\n\t\t\tif self.first_element == defect.split(\"_\")[-1]:\r\n\t\t\t\tN = self.main_compound_number_first_specie/self.vol\r\n\t\t\telif self.second_element == defect.split(\"_\")[-1]:\r\n\t\t\t\tN = self.main_compound_number_second_specie/self.vol\r\n\t\t\telif self.third_element == defect.split(\"_\")[-1]:\r\n\t\t\t\tN = self.main_compound_number_third_specie/self.vol\r\n\t\t\t\r\n\t\t\tfor temperature in self.temperature_array:\r\n\t\t\t\textrinsic_defect_carrier_concentration = float(charge) * N * np.exp(-extrinsic_defect_formation_enthalpy / (self.k * temperature) )\r\n\t\t\t\textrinsic_defect_carrier_concentration_temperature[temperature] += extrinsic_defect_carrier_concentration\r\n\t\t\r\n\t\treturn intrinsic_defect_carrier_concentration_temperature, extrinsic_defect_carrier_concentration_temperature\t\t# Returns a dictionary with temperature as keys and array of defect-induced carrier concentrations (not defect concentrations) as values\r","title":""},{"docid":"1f3df972e7d3930717bf34d2d564ee3c","score":"0.46776056","text":"def __init__(self, name, fuel, price_per_km):\n super().__init__(name, fuel)\n self.price_per_km = price_per_km\n self.current_fare_distance = 0","title":""},{"docid":"8a4d2db6eeeaa9890edc5bf449d25db9","score":"0.4672864","text":"def fuel_required_for_part2(self) -> int:\n total_fuel_required = added_fuel_required = self.mass//3 - 2\n while (added_fuel_required := added_fuel_required//3 - 2) > 0:\n total_fuel_required += added_fuel_required\n return total_fuel_required","title":""},{"docid":"847ffdc16f66838897c4995bddee8f9c","score":"0.46675283","text":"def Fuel_type_reefer_NotDEF(self):\n \n #Input constants\n\n generic_trans_amount = \"$5.00\" # any value that gets an approval from the host\n card_to_use_NGFC = 'NGFC' # using this card to get all commercial prompts\n \n prompts = {\n \"Additional Products Y/N?\": { \n \"buttons\": [\"Yes\"]\n },\n \"Do you want to print a receipt?\": {\n \"buttons\": [\"No\"]\n },\n \"Select fuel products to dispense\": {\n \"buttons\": [\"Reefer fuel\"]\n },\n \"DEF?\": {\n \"buttons\": [\"No\"]\n }\n }\n\n messages_to_verify = {\n 'preauth_request_verifications': {\n 'Fuel Purchase': '500', \n '001 - Wex OTR Flags': 'C - Commercial'\n },\n 'preauth_response_verifications': {\n 'Response Code': '2', \n 'Approved Amount': '000000500',\n '002 - Wex OTR Non-Fuel Product Data - Product code: CADV (Company funds cash advance)': 'Amount: 0.00',\n '002 - Wex OTR Non-Fuel Product Data - Product code: MERC (Default category for merchandise)': 'Amount: 30.00',\n f'003 - Wex OTR Fuel Product Limits - Product Code: {self.helpers.grade_1_Product }': 'Product limit: 50.00',\n f'003 - Wex OTR Fuel Product Limits - Product Code: {self.helpers.grade_2_Product }': 'Product limit: 50.00',\n f'003 - Wex OTR Fuel Product Limits - Product Code: {self.helpers.grade_3_Product }': 'Product limit: 50.00',\n '005 - Wex OTR Customer Information': 'ABC TRUCKING DENVER CO234W987'\n },\n 'capture_request_verifications': {\n 'Fuel Purchase': '500', \n '001 - Wex OTR Flags': 'C - Commercial'\n },\n 'capture_response_verifications': {\n 'Response Code': '0', \n 'Approved Amount': '000000500',\n '002 - Wex OTR Non-Fuel Product Data - Product code: CADV (Company funds cash advance)': 'Amount: 20.00',\n '002 - Wex OTR Non-Fuel Product Data - Product code: MERC (Default category for merchandise)': 'Amount: 30.00',\n f'003 - Wex OTR Fuel Product Limits - Product Code: {self.helpers.grade_1_Product }': 'Product limit: 50.00',\n f'003 - Wex OTR Fuel Product Limits - Product Code: {self.helpers.grade_2_Product }': 'Product limit: 50.00',\n f'003 - Wex OTR Fuel Product Limits - Product Code: {self.helpers.grade_3_Product }': 'Product limit: 50.00',\n '005 - Wex OTR Customer Information': 'ABC TRUCKING DENVER CO234W987'\n }\n }\n\n #Output verifications\n\n receipt_data = [f\"{self.helpers.grade_1_Name_reefer} CA PUMP# 1\",\n \"5.000 GAL @ $1.000/GAL $5.00 99\",\n \"Subtotal = $5.00\",\n \"Tax = $0.00\",\n \"Total = $5.00\",\n \"Change Due = $0.00\",\n \"Credit $5.00\"]\n\n self.helpers.prepay_transaction(\n card=card_to_use_NGFC,\n prepay_amount=generic_trans_amount,\n prompts=prompts,\n messages_to_verify=messages_to_verify,\n receipt_data=receipt_data\n )","title":""},{"docid":"9163deaae2f5ae34d9616a47b87654a0","score":"0.46662685","text":"def fk_system(self):\n\n self.fk_controls = []\n \n self.fk_chain = joints_utils.related_clean_joint_chain(self.main_chain, self.side, \"fk\", True)\n self.fk_system_objs.append(self.fk_chain[0])\n\n # cmds.error(\"number of joint: {}\".format(len(self.fk_chain)))\n\n for i, jnt in enumerate(self.fk_chain):\n \n if i == (len(self.fk_chain) - 1):\n break\n ctrl = None\n if i != 0:\n ctrl = controller.Control(\"{}\".format(jnt[:len(jnt)-4]), 5.0, 'circle', jnt, jnt, self.fk_controls[i-1].get_control(), ['v'], '', True, True, False)\n elif i == 0:\n ctrl = controller.Control(\"{}\".format(jnt[:len(jnt)-4]), 5.0, 'circle', jnt, jnt, '', ['v'], '', True, True, False)\n else:\n ctrl = controller.Control(\"{}\".format(jnt[:len(jnt)-4]), 5.0, 'circle', jnt, jnt, '', ['v'], '', True, True, False)\n self.fk_controls.append(ctrl)\n \n cmds.parentConstraint(ctrl.get_control(), jnt, maintainOffset=True)\n cmds.scaleConstraint(ctrl.get_control(), jnt, maintainOffset=True)\n\n self.fk_system_grp = cmds.group(empty=True, name=\"{}_{}_fkSystem_GRP\".format(self.side, self.name))\n cmds.parent(self.fk_system_objs, self.fk_system_grp)\n cmds.group(empty=True, name=self.fk_ctrls_main_grp)\n transforms_utils.align_objs(self.clavicle_jnt, self.fk_ctrls_main_grp)\n cmds.parentConstraint(self.clavicle_jnt, self.fk_ctrls_main_grp, maintainOffset=True)\n # scale fix\n for axis in [\"X\", \"Y\", \"Z\"]:\n cmds.connectAttr(\"{}.scale{}\".format(self.clavicle_jnt, axis), \"{}.scale{}\".format(self.fk_ctrls_main_grp, axis), force=True)\n cmds.parent(self.fk_controls[0].get_offset_grp(), self.fk_ctrls_main_grp)\n self.module_main_grp(self.fk_system_grp)\n\n return True","title":""}],"string":"[\n {\n \"docid\": \"c4376dbfe4ecc36c450fad9f25cf7233\",\n \"score\": \"0.63634956\",\n \"text\": \"def add_special_tanks(self, xf, tank):\\n tankcount = ceil(xf / tank.m_full)\\n if tank.size == parts.RadialSize.RadiallyMounted:\\n tankcount = max(tankcount, 2)\\n self.requiredscience.add(tank.level)\\n self.fueltanks.append((tankcount, tank))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"082cfc2b4b7fa373b344bef0cb13906c\",\n \"score\": \"0.5959416\",\n \"text\": \"def build(self):\\n\\n # Create a custom grid, fe_set \\n # why create an fe_set instead of using Transformation \\n # factory?\\n nfe = 6\\n fe_a = 1/4.0\\n fe_b = 0.2\\n fe_set = [0, 0.004]\\n for i in range(1,nfe+1):\\n if i < nfe*fe_a:\\n fe_set.append(i*fe_b/(nfe*fe_a))\\n elif i == nfe: \\n fe_set.append(1)\\n else:\\n fe_set.append(fe_b + (i-nfe*fe_a)*(1-fe_b)/(nfe*(1-fe_a)))\\n\\n \\\"\\\"\\\"\\n Args: (to MB_CLC_fuel object, as defined in model file)\\n dae_method = method to use for calcuating derivatives (default = OCLR)\\n - BFD1 - 1st order backwards finite difference\\n - OCLR - Orthogonal collocation, Lagrange-Radau\\n - OCLL - Orthogonal collocation, Lagrange-Legendre\\n press_drop = Pressure drop correlation for superficial velocity calc.\\n - SimplifiedP - simplified pressure correlations \\n - Ergun - Ergun equation\\n fe_set = set of normalised finite element locations\\n nfe = number of finite elements for bed discretization (default = 15)\\n (not used if fe_set specified)\\n ncp = number of collocation points (OCLR or OCLL only, default = 3)\\n\\n fe_set_t\\n nfe_t \\n ^ adding time set as a model-level continuous set...\\n will change when moving to new framework\\n \\\"\\\"\\\" \\n # Create unit model for fuel reactor\\n # unit model - an attribute of the flowsheet model\\n # \\\"a block within a block\\\"\\n #self.MB_fuel = MB_CLC_fuel.MB(\\n # parent=self,\\n # dae_method = 'OCLR',\\n # press_drop = 'Ergun',\\n # fe_set = fe_set,\\n # ncp = 3)\\n\\n # need to add time set to the above\\n # open question still:\\n # how long of a horizon should I simulate?\\n #\\n # why is nfe in z-dimension not an input here?\\n self.MB_fuel = MB_CLC_fuel.MB(\\n parent=self,\\n dae_method = 'OCLR',\\n press_drop = 'Ergun',\\n fe_set = fe_set,\\n ncp = 3,\\n horizon = 1, # was 10\\n nfe_t = 1, # \\\" \\\"\\n ncp_t = 3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe2ee9896aa4f33c95c28e736bdbce24\",\n \"score\": \"0.57530856\",\n \"text\": \"def create_lf_design(payload, pressure, dv, acc, eng,\\n size=None, count=1, fueltype=parts.FuelTypes.LiquidFuel, tank=None):\\n if size is None:\\n size = eng.size\\n design = Design(payload, eng, count, size, fueltype)\\n if fueltype is parts.FuelTypes.LiquidFuel:\\n f_e = 1 / 8\\n elif fueltype is parts.FuelTypes.AtomicFuel:\\n f_e = parts.AtomicTank_f_e\\n else:\\n f_e = tank.f_e\\n m_p = payload + count*eng.m\\n lf = physics.lf_needed_fuel(dv, physics.engine_isp(eng, pressure), m_p, f_e)\\n if lf is None:\\n return None\\n if fueltype is parts.FuelTypes.LiquidFuel or fueltype is parts.FuelTypes.AtomicFuel:\\n design.add_conventional_tanks((1 + f_e) * lf)\\n else:\\n design.add_special_tanks((1 + f_e) * lf, tank)\\n design.calculate_performance(dv, pressure)\\n if not design.has_enough_acceleration(acc):\\n return None\\n return design\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3426d73dc82411122530ba3c274ebc77\",\n \"score\": \"0.5537668\",\n \"text\": \"def add_credit_calculation_fuel_types_0111(apps, schema_editor):\\n db_alias = schema_editor.connection.alias\\n\\n approved_fuel = apps.get_model('api', 'ApprovedFuel')\\n\\n approved_fuel.objects.using(db_alias).filter(\\n credit_calculation_only=True\\n ).delete()\\n\\n approved_fuel.objects.using(db_alias).bulk_create([\\n approved_fuel(\\n name=\\\"Petroleum-based diesel\\\",\\n effective_date=\\\"2017-01-01\\\",\\n credit_calculation_only=True\\n ),\\n approved_fuel(\\n name=\\\"Petroleum-based gasoline\\\",\\n effective_date=\\\"2017-01-01\\\",\\n credit_calculation_only=True\\n )\\n ])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fa7dc54cbd0731fe1fd8be6249b9b1e\",\n \"score\": \"0.55242157\",\n \"text\": \"def add_design(design, eosscontext: EOSSContext, active: bool):\\n\\n if active:\\n Design.objects.create(activecontext=eosscontext.activecontext,\\n id=design['id'],\\n inputs=json.dumps(design['inputs']),\\n outputs=json.dumps(design['outputs']))\\n else:\\n Design.objects.create(eosscontext=eosscontext,\\n id=design['id'],\\n inputs=json.dumps(design['inputs']),\\n outputs=json.dumps(design['outputs']))\\n eosscontext.added_archs_count += 1\\n\\n eosscontext.last_arch_id += 1\\n\\n if eosscontext.added_archs_count >= 5:\\n eosscontext.added_archs_count = 0\\n activate_diversifier(eosscontext)\\n\\n eosscontext.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ff7db4149d11baca3408731d4d1b4d1\",\n \"score\": \"0.54325074\",\n \"text\": \"def fuel(self, fuel):\\n\\n self._fuel = fuel\",\n \"title\": \"\"\n },\n {\n \"docid\": \"521030d08546c9b055941c622782262f\",\n \"score\": \"0.5401777\",\n \"text\": \"def fuel(self, f: float):\\n if isinstance(f, float) is False:\\n raise Warning(\\\"[WARNING]Fuel is not Float type.\\\")\\n if f < 0: # <= ?\\n raise Warning(\\\"[WARNING]Fuel have to Positive.\\\")\\n self.__gas_remained += f\\n if self.__gas_remained > self.__GAS_CAPACITY:\\n # 최대 용량까지는 채우고 넘친건 안채우고\\n self.__gas_remained = self.__GAS_CAPACITY\\n raise Warning(\\\"[WARNING]Fuel is overfilled.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"466f2aecdee189b7b185bc345ef5fbbf\",\n \"score\": \"0.5339595\",\n \"text\": \"def create_model(\\n time_set=None,\\n time_units=pyo.units.s,\\n nfe=5,\\n tee=False,\\n calc_integ=True,\\n):\\n fs_cfg = {\\\"dynamic\\\": True, \\\"time_set\\\": time_set, \\\"time_units\\\": time_units}\\n model_name = \\\"Steam Tank, Dynamic\\\"\\n\\n if time_set is None:\\n time_set = [0, 3]\\n\\n m = pyo.ConcreteModel(name=model_name)\\n m.fs = FlowsheetBlock(**fs_cfg)\\n # Create a property parameter block\\n m.fs.prop_water = iapws95.Iapws95ParameterBlock(\\n phase_presentation=iapws95.PhaseType.LG\\n )\\n # Create the valve and tank models\\n m.fs.valve_1 = Valve(\\n dynamic=False,\\n has_holdup=False,\\n pressure_flow_callback=_valve_pressure_flow_cb,\\n material_balance_type=MaterialBalanceType.componentTotal,\\n property_package=m.fs.prop_water,\\n )\\n m.fs.tank = Heater(\\n has_holdup=True,\\n material_balance_type=MaterialBalanceType.componentTotal,\\n property_package=m.fs.prop_water,\\n )\\n m.fs.valve_2 = Valve(\\n dynamic=False,\\n has_holdup=False,\\n pressure_flow_callback=_valve_pressure_flow_cb,\\n material_balance_type=MaterialBalanceType.componentTotal,\\n property_package=m.fs.prop_water,\\n )\\n # Add a controller\\n m.fs.ctrl = PIDController(\\n process_var=m.fs.tank.control_volume.properties_out[:].pressure,\\n manipulated_var=m.fs.valve_1.valve_opening,\\n calculate_initial_integral=calc_integ,\\n mv_bound_type=ControllerMVBoundType.SMOOTH_BOUND,\\n controller_type=ControllerType.PI,\\n )\\n\\n # The control volume block doesn't assume the two phases are in equilibrium\\n # by default, so I'll make that assumption here, I don't actually expect\\n # liquid to form but who knows. The phase_fraction in the control volume is\\n # volumetric phase fraction hence the densities.\\n @m.fs.tank.Constraint(m.fs.time)\\n def vol_frac_vap(b, t):\\n return (\\n b.control_volume.properties_out[t].phase_frac[\\\"Vap\\\"]\\n * b.control_volume.properties_out[t].dens_mol\\n / b.control_volume.properties_out[t].dens_mol_phase[\\\"Vap\\\"]\\n ) == (b.control_volume.phase_fraction[t, \\\"Vap\\\"])\\n\\n # Connect the models\\n m.fs.v1_to_tank = Arc(source=m.fs.valve_1.outlet, destination=m.fs.tank.inlet)\\n m.fs.tank_to_v2 = Arc(source=m.fs.tank.outlet, destination=m.fs.valve_2.inlet)\\n\\n # Add the stream constraints and do the DAE transformation\\n pyo.TransformationFactory(\\\"network.expand_arcs\\\").apply_to(m.fs)\\n pyo.TransformationFactory(\\\"dae.finite_difference\\\").apply_to(\\n m.fs, nfe=nfe, wrt=m.fs.time, scheme=\\\"BACKWARD\\\"\\n )\\n\\n # Fix the derivative variables to zero at time 0 (steady state assumption)\\n m.fs.fix_initial_conditions()\\n\\n # Fix the input variables\\n m.fs.valve_1.inlet.enth_mol.fix(50000)\\n m.fs.valve_1.inlet.pressure.fix(5e5)\\n m.fs.valve_2.outlet.pressure.fix(101325)\\n m.fs.valve_1.Cv.fix(0.001)\\n m.fs.valve_2.Cv.fix(0.001)\\n m.fs.valve_1.valve_opening.fix(1)\\n m.fs.valve_2.valve_opening.fix(1)\\n m.fs.tank.heat_duty.fix(0)\\n m.fs.tank.control_volume.volume.fix(2.0)\\n\\n # Fix controller settings\\n m.fs.ctrl.gain_p.fix(1e-6)\\n m.fs.ctrl.gain_i.fix(1e-5)\\n # m.fs.ctrl.gain_d.fix(1e-6)\\n # m.fs.ctrl.derivative_of_error[m.fs.time.first()].fix(0)\\n m.fs.ctrl.setpoint.fix(3e5)\\n m.fs.ctrl.mv_ref.fix(0)\\n m.fs.ctrl.mv_lb = 0.0\\n m.fs.ctrl.mv_ub = 1.0\\n\\n for t in m.fs.time:\\n m.fs.valve_1.inlet.flow_mol[t] = 100 # initial guess on flow\\n # simple initialize\\n m.fs.valve_1.initialize()\\n propagate_state(m.fs.v1_to_tank)\\n m.fs.tank.initialize()\\n propagate_state(m.fs.tank_to_v2)\\n # Can't specify both flow and outlet pressure so free the outlet pressure\\n # for initialization and refix it after. Inlet flow gets fixed in init, but\\n # is unfixed for the final problem\\n m.fs.valve_2.outlet.pressure.unfix()\\n m.fs.valve_2.initialize()\\n m.fs.valve_2.outlet.pressure.fix(101325)\\n m.fs.valve_1.valve_opening.unfix()\\n m.fs.valve_1.valve_opening[m.fs.time.first()].fix()\\n # Return the model and solver\\n return m\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90134edba142744be072caf15ee8cd90\",\n \"score\": \"0.53340214\",\n \"text\": \"def create_sfb_design(payload, pressure, dv, acc, sfb_allowed, eng, eng_F_percentage, size, count, sfb, sfbcount):\\n design = Design(payload, eng, count, size, parts.FuelTypes.LiquidFuel)\\n design.add_sfb(sfb, sfbcount)\\n # lpsr = Fl * I_sps / Fs / I_spl\\n lpsr = count * eng.F_vac * sfb.isp_vac / sfbcount / sfb.F_vac / eng.isp_vac\\n design.eng_F_percentage = eng_F_percentage\\n m_p = payload + count*eng.m\\n lf = physics.sflf_concurrent_needed_fuel(dv, physics.engine_isp(eng, pressure),\\n physics.engine_isp(sfb, pressure),\\n m_p,\\n design.get_sfbmountmass(), sfbcount * sfb.m_full, sfbcount * sfb.m_empty,\\n lpsr * eng_F_percentage)\\n if lf is None:\\n return None\\n design.add_conventional_tanks(9 / 8 * lf)\\n design.calculate_performance(dv, pressure)\\n if not design.has_enough_acceleration(acc):\\n return None\\n if not design.sfb_burning_when_allowed(sfb_allowed):\\n return None\\n if sfbcount != 1:\\n design.notes.append(\\\"Set liquid fuel engine thrust to {:.0%} while SFB are burning\\\".format(eng_F_percentage))\\n return design\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1d92090255f7c6be24bcf6ab4eef980\",\n \"score\": \"0.5296159\",\n \"text\": \"def buildFuelModels(allowDynamicModels=True,allowNonBurningModels=False):\\n fuelModels = dict()\\n \\n # Code FMx: Original 13 Fuel Models\\n fuelModels[\\\"FM1\\\"] = FuelModel(\\n 1, \\\"FM1\\\", \\\"Short grass [1]\\\",\\n 1.0, 0.12, 8000, 8000,\\n 0.034, 0, 0, 0, 0,\\n 3500, 1500, 1500,\\n False, True)\\n fuelModels[\\\"FM2\\\"] = FuelModel(\\n 2, \\\"FM2\\\", \\\"Timber grass and understory [2]\\\",\\n 1.0, 0.15, 8000, 8000,\\n 0.092, 0.046, 0.023, 0.023,\\n 0,3000, 1500, 1500,\\n False, True)\\n fuelModels[\\\"FM3\\\"] = FuelModel(\\n 3, \\\"FM3\\\", \\\"Tall grass [3]\\\",\\n 2.5, 0.25, 8000, 8000,\\n 0.138, 0, 0, 0, 0,\\n 1500, 1500, 1500,\\n False, True)\\n fuelModels[\\\"FM4\\\"] = FuelModel(\\n 4, \\\"FM4\\\", \\\"Chaparral [4]\\\",\\n 6.0, 0.2, 8000, 8000,\\n 0.230, 0.184, 0.092, 0, 0.230,\\n 2000, 1500, 1500,\\n False, True)\\n fuelModels[\\\"FM5\\\"] = FuelModel(\\n 5, \\\"FM5\\\", \\\"Brush [5]\\\",\\n 2.0, 0.20, 8000, 8000,\\n 0.046, 0.023, 0, 0, 0.092,\\n 2000, 1500, 1500,\\n False, True)\\n fuelModels[\\\"FM6\\\"] = FuelModel(\\n 6, \\\"FM6\\\", \\\"Dormant brush, hardwood slash [6]\\\",\\n 2.5, 0.25, 8000, 8000,\\n 0.069, 0.115, 0.092, 0, 0,\\n 1750, 1500, 1500,\\n False, True)\\n fuelModels[\\\"FM7\\\"] = FuelModel(\\n 7, \\\"FM7\\\", \\\"Southern rough [7]\\\",\\n 2.5, 0.40, 8000, 8000,\\n 0.052, 0.086, 0.069, 0, 0.017,\\n 1750, 1500, 1500,\\n False, True)\\n fuelModels[\\\"FM8\\\"] = FuelModel(\\n 8, \\\"FM8\\\", \\\"Short needle litter [8]\\\",\\n 0.2, 0.3, 8000, 8000,\\n 0.069, 0.046, 0.115, 0, 0,\\n 2000, 1500, 1500,\\n False, True)\\n fuelModels[\\\"FM9\\\"] = FuelModel(\\n 9, \\\"FM9\\\", \\\"Long needle or hardwood litter [9]\\\",\\n 0.2, 0.25, 8000, 8000,\\n 0.134, 0.019, 0.007, 0, 0,\\n 2500, 1500, 1500,\\n False, True)\\n fuelModels[\\\"FM10\\\"] = FuelModel(\\n 10, \\\"FM10\\\", \\\"Timber litter & understory [10]\\\",\\n 1.0, 0.25, 8000, 8000,\\n 0.138, 0.092, 0.230, 0, 0.092,\\n 2000, 1500, 1500,\\n False, True)\\n fuelModels[\\\"FM11\\\"] = FuelModel(\\n 11, \\\"FM11\\\", \\\"Light logging slash [11]\\\",\\n 1.0, 0.15, 8000, 8000,\\n 0.069, 0.207, 0.253, 0, 0,\\n 1500, 1500, 1500,\\n False, True)\\n fuelModels[\\\"FM12\\\"] = FuelModel(\\n 12, \\\"FM12\\\", \\\"Medium logging slash [12]\\\",\\n 2.3, 0.20, 8000, 8000,\\n 0.184, 0.644, 0.759, 0, 0,\\n 1500, 1500, 1500,\\n False, True)\\n fuelModels[\\\"FM13\\\"] = FuelModel(\\n 13, \\\"FM13\\\", \\\"Heavy logging slash [13]\\\",\\n 3.0, 0.25, 8000, 8000,\\n 0.322, 1.058, 1.288, 0, 0,\\n 1500, 1500, 1500,\\n False, True)\\n \\n if not allowDynamicModels:\\n return fuelModels\\n else:\\n pass\\n # 14-89 Available for custom models\\n\\n if allowNonBurningModels:\\n # Code NBx: Non-burnable\\n # 90 Available for custom NB model \\n fuelModels[\\\"NB1\\\"] = FuelModel(\\n 91, \\\"NB1\\\", \\\"Urban, developed [91]\\\",\\n 1.0, 0.10, 8000, 8000,\\n 0, 0, 0, 0, 0,\\n 1500, 1500, 1500,\\n False, True)\\n fuelModels[\\\"NB2\\\"] = FuelModel(\\n 92, \\\"NB2\\\", \\\"Snow, ice [92]\\\",\\n 1.0, 0.10, 8000, 8000,\\n 0, 0, 0, 0, 0,\\n 1500, 1500, 1500,\\n False, True)\\n fuelModels[\\\"NB3\\\"] = FuelModel(\\n 93, \\\"NB3\\\", \\\"Agricultural [93]\\\",\\n 1.0, 0.10, 8000, 8000,\\n 0, 0, 0, 0, 0,\\n 1500, 1500, 1500,\\n False, True)\\n \\n # Indices 94-95 Reserved for future standard non-burnable models\\n \\n fuelModels[\\\"NB4\\\"] = FuelModel(\\n 94, \\\"NB4\\\", \\\"Future standard non-burnable [94]\\\",\\n 1.0, 0.10, 8000, 8000,\\n 0, 0, 0, 0, 0,\\n 1500, 1500, 1500,\\n False, True)\\n fuelModels[\\\"NB5\\\"] = FuelModel(\\n 95, \\\"NB5\\\", \\\"Future standard non-burnable [95]\\\",\\n 1.0, 0.10, 8000, 8000,\\n 0, 0, 0, 0, 0,\\n 1500, 1500, 1500,\\n False, True)\\n \\n # Indices 96-97 Available for custom NB model\\n \\n fuelModels[\\\"NB8\\\"] = FuelModel(\\n 98, \\\"NB8\\\", \\\"Open water [98]\\\",\\n 1.0, 0.10, 8000, 8000,\\n 0, 0, 0, 0, 0,\\n 1500, 1500, 1500,\\n False, True)\\n fuelModels[\\\"NB9\\\"] = FuelModel(\\n 99, \\\"NB9\\\", \\\"Bare ground [99]\\\",\\n 1.0, 0.10, 8000, 8000,\\n 0, 0, 0, 0, 0,\\n 1500, 1500, 1500,\\n False, True)\\n\\n # Code GRx: Grass\\n # Index 100 Available for custom GR model\\n f = 2000.0 / 43560.0\\n fuelModels[\\\"GR1\\\"] = FuelModel(\\n 101, \\\"GR1\\\", \\\"Short, sparse, dry climate grass (D)\\\",\\n 0.4, 0.15, 8000, 8000,\\n 0.10*f, 0, 0, 0.30*f, 0,\\n 2200, 2000, 1500,\\n True, True)\\n fuelModels[\\\"GR2\\\"] = FuelModel(\\n 102, \\\"GR2\\\", \\\"Low load, dry climate grass (D)\\\",\\n 1.0, 0.15, 8000, 8000,\\n 0.10*f, 0, 0, 1.0*f, 0,\\n 2000, 1800, 1500,\\n True, True)\\n fuelModels[\\\"GR3\\\"] = FuelModel(\\n 103, \\\"GR3\\\", \\\"Low load, very coarse, humid climate grass (D)\\\",\\n 2.0, 0.30, 8000, 8000,\\n 0.10*f, 0.40*f, 0, 1.50*f, 0,\\n 1500, 1300, 1500,\\n True, True)\\n fuelModels[\\\"GR4\\\"] = FuelModel(\\n 104, \\\"GR4\\\", \\\"Moderate load, dry climate grass (D)\\\",\\n 2.0, 0.15, 8000, 8000,\\n 0.25*f, 0, 0, 1.9*f, 0,\\n 2000, 1800, 1500,\\n True, True)\\n fuelModels[\\\"GR5\\\"] = FuelModel(\\n 105, \\\"GR5\\\", \\\"Low load, humid climate grass (D)\\\",\\n 1.5, 0.40, 8000, 8000,\\n 0.40*f, 0.0, 0.0, 2.50*f, 0.0,\\n 1800, 1600, 1500,\\n True, True)\\n fuelModels[\\\"GR6\\\"] = FuelModel(\\n 106, \\\"GR6\\\", \\\"Moderate load, humid climate grass (D)\\\",\\n 1.5, 0.40, 9000, 9000,\\n 0.10*f, 0, 0, 3.4*f, 0,\\n 2200, 2000, 1500,\\n True, True)\\n fuelModels[\\\"GR7\\\"] = FuelModel(\\n 107, \\\"GR7\\\", \\\"High load, dry climate grass (D)\\\",\\n 3.0, 0.15, 8000, 8000,\\n 1.0*f, 0, 0, 5.4*f, 0,\\n 2000, 1800, 1500,\\n True, True)\\n fuelModels[\\\"GR8\\\"] = FuelModel(\\n 108, \\\"GR8\\\", \\\"High load, very coarse, humid climate grass (D)\\\",\\n 4.0, 0.30, 8000, 8000,\\n 0.5*f, 1.0*f, 0, 7.3*f, 0,\\n 1500, 1300, 1500,\\n True, True)\\n fuelModels[\\\"GR9\\\"] = FuelModel(\\n 109, \\\"GR9\\\", \\\"Very high load, humid climate grass (D)\\\",\\n 5.0, 0.40, 8000, 8000,\\n 1.0*f, 1.0*f, 0, 9.0*f, 0,\\n 1800, 1600, 1500,\\n True, True)\\n # 110-112 are reserved for future standard grass models\\n # 113-119 are available for custom grass models\\n\\n # Code GSx: Grass and shrub\\n # 120 available for custom grass and shrub model\\n fuelModels[\\\"GS1\\\"] = FuelModel(\\n 121, \\\"GS1\\\", \\\"Low load, dry climate grass-shrub (D)\\\",\\n 0.9, 0.15, 8000, 8000,\\n 0.2*f, 0, 0, 0.5*f, 0.65*f,\\n 2000, 1800, 1800,\\n True, True)\\n fuelModels[\\\"GS2\\\"] = FuelModel(\\n 122, \\\"GS2\\\", \\\"Moderate load, dry climate grass-shrub (D)\\\",\\n 1.5, 0.15, 8000, 8000,\\n 0.5*f, 0.5*f, 0, 0.6*f, 1.0*f,\\n 2000, 1800, 1800,\\n True, True)\\n fuelModels[\\\"GS3\\\"] = FuelModel(\\n 123, \\\"GS3\\\", \\\"Moderate load, humid climate grass-shrub (D)\\\",\\n 1.8, 0.40, 8000, 8000,\\n 0.3*f, 0.25*f, 0, 1.45*f, 1.25*f,\\n 1800, 1600, 1600,\\n True, True)\\n fuelModels[\\\"GS4\\\"] = FuelModel(\\n 124, \\\"GS4\\\", \\\"High load, humid climate grass-shrub (D)\\\",\\n 2.1, 0.40, 8000, 8000,\\n 1.9*f, 0.3*f, 0.1*f, 3.4*f, 7.1*f,\\n 1800, 1600, 1600,\\n True, True)\\n # 125-130 reserved for future standard grass and shrub models\\n # 131-139 available for custom grass and shrub models\\n\\n # Shrub\\n # 140 available for custom shrub model\\n fuelModels[\\\"SH1\\\"] = FuelModel(\\n 141, \\\"SH1\\\", \\\"Low load, dry climate shrub (D)\\\",\\n 1.0, 0.15, 8000, 8000,\\n 0.25*f, 0.25*f, 0, 0.15*f, 1.3*f,\\n 2000, 1800, 1600,\\n True, True)\\n fuelModels[\\\"SH2\\\"] = FuelModel(\\n 142, \\\"SH2\\\", \\\"Moderate load, dry climate shrub (S)\\\",\\n 1.0, 0.15, 8000, 8000,\\n 1.35*f, 2.4*f, 0.75*f, 0, 3.85*f,\\n 2000, 1800, 1600,\\n True, True)\\n fuelModels[\\\"SH3\\\"] = FuelModel(\\n 143, \\\"SH3\\\", \\\"Moderate load, humid climate shrub (S)\\\",\\n 2.4, 0.40, 8000., 8000.,\\n 0.45*f, 3.0*f, 0, 0, 6.2*f,\\n 1600, 1800, 1400,\\n True, True)\\n fuelModels[\\\"SH4\\\"] = FuelModel(\\n 144, \\\"SH4\\\", \\\"Low load, humid climate timber-shrub (S)\\\",\\n 3.0, 0.30, 8000, 8000,\\n 0.85*f, 1.15*f, 0.2*f, 0, 2.55*f,\\n 2000, 1800, 1600,\\n True, True)\\n fuelModels[\\\"SH5\\\"] = FuelModel(\\n 145, \\\"SH5\\\", \\\"High load, dry climate shrub (S)\\\",\\n 6.0, 0.15, 8000, 8000,\\n 3.6*f, 2.1*f, 0, 0, 2.9*f,\\n 750, 1800, 1600,\\n True, True)\\n fuelModels[\\\"SH6\\\"] = FuelModel(\\n 146, \\\"SH6\\\", \\\"Low load, humid climate shrub (S)\\\",\\n 2.0, 0.30, 8000, 8000,\\n 2.9*f, 1.45*f, 0, 0, 1.4*f,\\n 750, 1800, 1600,\\n True, True)\\n fuelModels[\\\"SH7\\\"] = FuelModel(\\n 147, \\\"SH7\\\", \\\"Very high load, dry climate shrub (S)\\\",\\n 6.0, 0.15, 8000, 8000,\\n 3.5*f, 5.3*f, 2.2*f, 0, 3.4*f,\\n 750, 1800, 1600,\\n True, True)\\n fuelModels[\\\"SH8\\\"] = FuelModel(\\n 148, \\\"SH8\\\", \\\"High load, humid climate shrub (S)\\\",\\n 3.0, 0.40, 8000, 8000,\\n 2.05*f, 3.4*f, 0.85*f, 0, 4.35*f,\\n 750, 1800, 1600,\\n True, True)\\n fuelModels[\\\"SH9\\\"] = FuelModel(\\n 149, \\\"SH9\\\", \\\"Very high load, humid climate shrub (D)\\\",\\n 4.4, 0.40, 8000, 8000,\\n 4.5*f, 2.45*f, 0, 1.55*f, 7.0*f,\\n 750, 1800, 1500,\\n True, True)\\n # 150-152 reserved for future standard shrub models\\n # 153-159 available for custom shrub models\\n\\n # Timber and understory\\n # 160 available for custom timber and understory model\\n fuelModels[\\\"TU1\\\"] = FuelModel(\\n 161, \\\"TU1\\\", \\\"Light load, dry climate timber-grass-shrub (D)\\\",\\n 0.6, 0.20, 8000, 8000,\\n 0.2*f, 0.9*f, 1.5*f, 0.2*f, 0.9*f,\\n 2000, 1800, 1600,\\n True, True)\\n fuelModels[\\\"TU2\\\"] = FuelModel(\\n 162, \\\"TU2\\\", \\\"Moderate load, humid climate timber-shrub (S)\\\",\\n 1.0, 0.30, 8000, 8000,\\n 0.95*f, 1.8*f, 1.25*f, 0, 0.2*f,\\n 2000, 1800, 1600,\\n True, True)\\n fuelModels[\\\"TU3\\\"] = FuelModel(\\n 163, \\\"TU3\\\", \\\"Moderate load, humid climate timber-grass-shrub (D)\\\",\\n 1.3, 0.30, 8000, 8000,\\n 1.1*f, 0.15*f, 0.25*f, 0.65*f, 1.1*f,\\n 1800, 1600, 1400,\\n True, True)\\n fuelModels[\\\"TU4\\\"] = FuelModel(\\n 164, \\\"TU4\\\", \\\"Dwarf conifer understory (S)\\\",\\n 0.5, 0.12, 8000, 8000,\\n 4.5*f, 0, 0, 0, 2.0*f,\\n 2300, 1800, 2000,\\n True, True)\\n fuelModels[\\\"TU5\\\"] = FuelModel(\\n 165, \\\"TU5\\\", \\\"Very high load, dry climate timber-shrub (S)\\\",\\n 1.0, 0.25, 8000, 8000,\\n 4.0*f, 4.0*f, 3.0*f, 0, 3.0*f,\\n 1500, 1800, 750,\\n True, True)\\n # 166-170 reserved for future standard timber and understory models\\n # 171-179 available for custom timber and understory models\\n # Timber and litter\\n # 180 available for custom timber and litter models\\n fuelModels[\\\"TL1\\\"] = FuelModel(\\n 181, \\\"TL1\\\", \\\"Low load, compact conifer litter (S)\\\",\\n 0.2, 0.30, 8000, 8000,\\n 1.0*f, 2.2*f, 3.6*f, 0, 0,\\n 2000, 1800, 1600,\\n True, True)\\n fuelModels[\\\"TL2\\\"] = FuelModel(\\n 182, \\\"TL2\\\", \\\"Low load broadleaf litter (S)\\\",\\n 0.2, 0.25, 8000, 8000,\\n 1.4*f, 2.3*f, 2.2*f, 0, 0,\\n 2000, 1800, 1600,\\n True, True)\\n fuelModels[\\\"TL3\\\"] = FuelModel(\\n 183, \\\"TL3\\\", \\\"Moderate load conifer litter (S)\\\",\\n 0.3, 0.20, 8000, 8000,\\n 0.5*f, 2.2*f, 2.8*f, 0, 0,\\n 2000, 1800, 1600,\\n True, True)\\n fuelModels[\\\"TL4\\\"] = FuelModel(\\n 184, \\\"TL4\\\", \\\"Small downed logs (S)\\\",\\n 0.4, 0.25, 8000, 8000,\\n 0.5*f, 1.5*f, 4.2*f, 0, 0,\\n 2000, 1800, 1600,\\n True, True)\\n fuelModels[\\\"TL5\\\"] = FuelModel(\\n 185, \\\"TL5\\\", \\\"High load conifer litter (S)\\\",\\n 0.6, 0.25, 8000, 8000,\\n 1.15*f, 2.5*f, 4.4*f, 0, 0,\\n 2000, 1800, 160,\\n True, True)\\n fuelModels[\\\"TL6\\\"] = FuelModel(\\n 186, \\\"TL6\\\", \\\"High load broadleaf litter (S)\\\",\\n 0.3, 0.25, 8000, 8000,\\n 2.4*f, 1.2*f, 1.2*f, 0, 0,\\n 2000, 1800, 1600,\\n True, True)\\n fuelModels[\\\"TL7\\\"] = FuelModel(\\n 187, \\\"TL7\\\", \\\"Large downed logs (S)\\\",\\n 0.4, 0.25, 8000, 8000,\\n 0.3*f, 1.4*f, 8.1*f, 0, 0,\\n 2000, 1800, 1600,\\n True, True)\\n fuelModels[\\\"TL8\\\"] = FuelModel(\\n 188, \\\"TL8\\\", \\\"Long-needle litter (S)\\\",\\n 0.3, 0.35, 8000, 8000,\\n 5.8*f, 1.4*f, 1.1*f, 0, 0,\\n 1800, 1800, 1600,\\n True, True)\\n fuelModels[\\\"TL9\\\"] = FuelModel(\\n 189, \\\"TL9\\\", \\\"Very high load broadleaf litter (S)\\\",\\n 0.6, 0.35, 8000, 8000,\\n 6.65*f, 3.30*f, 4.15*f, 0, 0,\\n 1800, 1800, 1600,\\n True, True)\\n # 190-192 reserved for future standard timber and litter models\\n # 193-199 available for custom timber and litter models\\n # Slash and blowdown\\n # 200 available for custom slash and blowdown model\\n fuelModels[\\\"SB1\\\"] = FuelModel(\\n 201, \\\"SB1\\\", \\\"Low load activity fuel (S)\\\",\\n 1.0, 0.25, 8000, 8000,\\n 1.5*f, 3.0*f, 11.0*f, 0, 0,\\n 2000, 1800, 1600,\\n True, True)\\n fuelModels[\\\"SB2\\\"] = FuelModel(\\n 202, \\\"SB2\\\", \\\"Moderate load activity or low load blowdown (S)\\\",\\n 1.0, 0.25, 8000, 8000,\\n 4.5*f, 4.25*f, 4.0*f, 0, 0,\\n 2000, 1800, 1600,\\n True, True)\\n fuelModels[\\\"SB3\\\"] = FuelModel(\\n 203, \\\"SB3\\\", \\\"High load activity fuel or moderate load blowdown (S)\\\",\\n 1.2, 0.25, 8000, 8000,\\n 5.5*f, 2.75*f, 3.0*f, 0, 0,\\n 2000, 1800, 1600,\\n True, True)\\n fuelModels[\\\"SB4\\\"] = FuelModel(\\n 204, \\\"SB4\\\", \\\"High load blowdown (S)\\\",\\n 2.7, 0.25, 8000, 8000,\\n 5.25*f, 3.5*f, 5.25*f, 0, 0,\\n 2000, 1800, 1600,\\n True, True)\\n \\n return fuelModels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ba14d04319d1cc764dd5c167c56f100\",\n \"score\": \"0.5286508\",\n \"text\": \"def addFacility():\\n\\tname = getInput(lambda x: x != 'Admin' and x not in facilityList.keys(), \\\"Enter name of new facility: \\\")\\n\\tcapacity = getInput(isPositive, \\\"Enter capacity: \\\")\\n\\t\\n\\tMedicalFacility(name, None, int(capacity))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ff787ddc530d02f3867535c70b2e31b\",\n \"score\": \"0.525206\",\n \"text\": \"def parse_bouquet_design(self, bouquet_design):\\n\\n match = re.match(self.bouquet_design_pattern, bouquet_design)\\n\\n if not match:\\n print(f'{bouquet_design} is not a valid design')\\n\\n bouquet_name, size, flowers_str, total = match.groups()\\n\\n flowers = re.findall(self.flowers_pattern, flowers_str)\\n flowers_dict = {}\\n\\n certain_amount = 0\\n for amount, name in flowers:\\n amount = int(amount)\\n flowers_dict[name] = amount\\n certain_amount += amount\\n\\n total = int(total)\\n any_amount = total - certain_amount\\n\\n self.bouquet_designs[size].append({\\n 'flowers': Counter(flowers_dict),\\n 'total': total,\\n 'any': any_amount,\\n 'name': bouquet_name,\\n })\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a56b3b65feed66b8d5147fda5ec1b31e\",\n \"score\": \"0.5206796\",\n \"text\": \"def design(self, lch, w_dict, th_dict, seg_dict, stack_dict, dum_info):\\n # design dummies\\n w_tail = w_dict['tail']\\n w_in = w_dict['in']\\n w_load = w_dict['load']\\n th_tail = th_dict['tail']\\n th_in = th_dict['in']\\n th_load = th_dict['load']\\n\\n # design main transistors\\n seg_tail1 = seg_dict['tail1']\\n seg_tail2 = seg_dict['tail2']\\n seg_tailcm = seg_dict['tailcm']\\n seg_in = seg_dict['in']\\n seg_ref = seg_dict['ref']\\n seg_diode1 = seg_dict['diode1']\\n seg_ngm1 = seg_dict['ngm1']\\n seg_diode2 = seg_dict['diode2']\\n seg_ngm2 = seg_dict['ngm2']\\n\\n stack_tail = stack_dict['tail']\\n stack_in = stack_dict['in']\\n stack_diode = stack_dict['diode']\\n stack_ngm = stack_dict['ngm']\\n\\n self.instances['XTAIL'].design(w=w_tail, l=lch, seg=seg_tail1 * 2, intent=th_tail,\\n stack=stack_tail)\\n self.instances['XTAIL2L'].design(w=w_tail, l=lch, seg=seg_tail2, intent=th_tail,\\n stack=stack_tail)\\n self.instances['XTAIL2R'].design(w=w_tail, l=lch, seg=seg_tail2, intent=th_tail,\\n stack=stack_tail)\\n self.instances['XCML'].design(w=w_tail, l=lch, seg=seg_tailcm, intent=th_tail,\\n stack=stack_tail)\\n self.instances['XCMR'].design(w=w_tail, l=lch, seg=seg_tailcm, intent=th_tail,\\n stack=stack_tail)\\n self.instances['XREF'].design(w=w_tail, l=lch, seg=seg_ref, intent=th_tail,\\n stack=stack_tail)\\n self.instances['XINL'].design(w=w_in, l=lch, seg=seg_in, intent=th_in, stack=stack_in)\\n self.instances['XINR'].design(w=w_in, l=lch, seg=seg_in, intent=th_in, stack=stack_in)\\n self.instances['XRES'].design(w=w_in, l=lch, seg=seg_ref, intent=th_in, stack=stack_in)\\n self.instances['XDIOL'].design(w=w_load, l=lch, seg=seg_diode1, intent=th_load,\\n stack=stack_diode)\\n self.instances['XDIOR'].design(w=w_load, l=lch, seg=seg_diode1, intent=th_load,\\n stack=stack_diode)\\n self.instances['XNGML'].design(w=w_load, l=lch, seg=seg_ngm1, intent=th_load,\\n stack=stack_ngm)\\n self.instances['XNGMR'].design(w=w_load, l=lch, seg=seg_ngm1, intent=th_load,\\n stack=stack_ngm)\\n self.instances['XDIO2L'].design(w=w_load, l=lch, seg=seg_diode2, intent=th_load,\\n stack=stack_diode)\\n self.instances['XDIO2R'].design(w=w_load, l=lch, seg=seg_diode2, intent=th_load,\\n stack=stack_diode)\\n self.instances['XNGM2L'].design(w=w_load, l=lch, seg=seg_ngm2, intent=th_load,\\n stack=stack_ngm)\\n self.instances['XNGM2R'].design(w=w_load, l=lch, seg=seg_ngm2, intent=th_load,\\n stack=stack_ngm)\\n\\n # design dummies\\n self.design_dummy_transistors(dum_info, 'XDUM', 'VDD', 'VSS')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c39be295a4a95228c591f714729ad29\",\n \"score\": \"0.520205\",\n \"text\": \"def stoich_flue_gas(self, nw):\\n lamb = 1\\n n_fuel = 1\\n m_fuel = 1 / molar_mass_flow(self.fuel.val) * n_fuel\\n m_fuel_fg = m_fuel\\n m_co2 = 0\\n m_h2o = 0\\n molar_masses[self.h2o] = CP.PropsSI('M', self.h2o)\\n molar_masses[self.co2] = CP.PropsSI('M', self.co2)\\n molar_masses[self.o2] = CP.PropsSI('M', self.o2)\\n\\n self.fg = {}\\n self.fg[self.co2] = 0\\n self.fg[self.h2o] = 0\\n\\n for f, x in self.fuel.val.items():\\n fl = set(list(self.fuels())).intersection(\\n set([a.replace(' ', '') for a in CP.get_aliases(f)]))\\n\\n if len(fl) == 0:\\n if f in self.fg.keys():\\n self.fg[f] += x * m_fuel\\n else:\\n self.fg[f] = x * m_fuel\\n else:\\n n_fluid = x * m_fuel / molar_masses[f]\\n m_fuel_fg -= n_fluid * molar_masses[f]\\n structure = fluid_structure(f)\\n n = {}\\n for el in ['C', 'H', 'O']:\\n if el in structure.keys():\\n n[el] = structure[el]\\n else:\\n n[el] = 0\\n\\n m_co2 += n_fluid * n['C'] * molar_masses[self.co2]\\n m_h2o += n_fluid * n['H'] / 2 * molar_masses[self.h2o]\\n\\n self.fg[self.co2] += m_co2\\n self.fg[self.h2o] += m_h2o\\n\\n n_o2 = (m_co2 / molar_masses[self.co2] +\\n 0.5 * m_h2o / molar_masses[self.h2o]) * lamb\\n m_air = n_o2 * molar_masses[self.o2] / self.air.val[self.o2]\\n\\n self.air_min = m_air / m_fuel\\n\\n for f, x in self.air.val.items():\\n if f != self.o2:\\n if f in self.fg.keys():\\n self.fg[f] += m_air * x\\n else:\\n self.fg[f] = m_air * x\\n\\n m_fg = m_fuel + m_air\\n\\n for f in self.fg.keys():\\n self.fg[f] /= m_fg\\n\\n if not self.path.is_set:\\n self.path.val = None\\n tespy_fluid(self.fuel_alias.val, self.fuel.val,\\n [1000, nw.p_range_SI[1]], nw.T_range_SI,\\n path=self.path.val)\\n tespy_fluid(self.fuel_alias.val + '_fg', self.fg,\\n [1000, nw.p_range_SI[1]], nw.T_range_SI,\\n path=self.path.val)\\n msg = ('Generated lookup table for ' + self.fuel_alias.val +\\n ' and for stoichiometric flue gas at stoichiometric '\\n 'combustion chamber ' + self.label + '.')\\n logging.debug(msg)\\n\\n if self.air_alias.val not in ['Air', 'air']:\\n tespy_fluid(self.air_alias.val, self.air.val,\\n [1000, nw.p_range_SI[1]], nw.T_range_SI,\\n path=self.path.val)\\n msg = ('Generated lookup table for ' + self.air_alias.val +\\n ' at stoichiometric combustion chamber ' + self.label + '.')\\n else:\\n msg = ('Using CoolProp air at stoichiometric combustion chamber ' +\\n self.label + '.')\\n logging.debug(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"318dd7356e6b6227392f6d735ba6f03d\",\n \"score\": \"0.51707864\",\n \"text\": \"def fuelProduction(self):\\r\\n try:\\r\\n for bid in self.bids:\\r\\n if bid.plant.source == \\\"Gas\\\":\\r\\n self.gas_fuel += bid.sold_amount * self.hours_for_bidround / bid.plant.efficiency\\r\\n self.total_gas_production += bid.sold_amount * self.hours_for_bidround\\r\\n elif bid.plant.source == \\\"Coal\\\":\\r\\n self.coal_fuel += bid.sold_amount * self.hours_for_bidround / bid.plant.efficiency\\r\\n self.total_coal_production += bid.sold_amount*self.hours_for_bidround\\r\\n elif bid.plant.source == \\\"PV\\\":\\r\\n self.total_pv_production += bid.sold_amount * self.hours_for_bidround\\r\\n except Exception as e:\\r\\n print(\\\"Exception in fuelProduction()\\\")\\r\\n print(e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff358730aa52bfa6675dfc42dc466f56\",\n \"score\": \"0.516541\",\n \"text\": \"def fill_gas_tank(self):\\n\\t\\tprint(\\\"This car does not need a gas tank!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20c7d8c865f35c319dc225d3491f80ec\",\n \"score\": \"0.5164768\",\n \"text\": \"def create_bouquet(self, design, size):\\n \\n bouquet = Counter(design['flowers'])\\n flowers = self.flowers[size]\\n flowers -= bouquet\\n\\n left = design['any']\\n if left:\\n for flower_name, amount in flowers.items():\\n if amount >= left:\\n bouquet[flower_name] += left\\n flowers[flower_name] = amount - left\\n break\\n else:\\n bouquet[flower_name] += amount\\n flowers[flower_name] = 0\\n left -= amount\\n\\n self.total_count[size] -= design['total']\\n\\n bouquet_flowers = ''.join(\\n [f'{name}{amount}' for name, amount in bouquet.items()],\\n )\\n bouquet_str = design['name'] + size + bouquet_flowers\\n\\n return bouquet_str\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6cb6a7348f3f06072c4d3add05a91d6\",\n \"score\": \"0.5162991\",\n \"text\": \"def add_technology(self, name):\\n if name in self.technologies:\\n logging.debug('Warning: Technology {} was added twice to supply node {}'.format(name, self.name))\\n return\\n self.technologies[name] = SupplyTechnology(name, self.cost_of_capital, self.scenario)\\n self.tech_names.append(name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4affdcd64d064bb2bdd05616ff703ec1\",\n \"score\": \"0.51550865\",\n \"text\": \"def add_technology(self, name):\\n if name in self.technologies:\\n logging.debug(\\\"Warning, storage tech {} was added twice to node {}\\\".format(name, self.name))\\n return\\n self.technologies[name] = StorageTechnology(name, self.cost_of_capital, self.scenario)\\n self.tech_names.append(name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"545bac376e6696710cb0c912734d1716\",\n \"score\": \"0.5147059\",\n \"text\": \"def fill_gas_tank(self):\\r\\n print(\\\"This car doesn't need a gas tank!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d247b02eb9d3ca679fadf6738181db5\",\n \"score\": \"0.5142591\",\n \"text\": \"def __init__(self, name, fuel, fanciness):\\n super().__init__(name, fuel)\\n self.price_per_km *= fanciness\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e03ff0af14f3cd9139524ca593c9de9d\",\n \"score\": \"0.51414496\",\n \"text\": \"def add_stock(self):\\n self.stock = SupplyStock(supply_node=self.name, scenario=self.scenario)\\n self.stock.unit = cfg.calculation_energy_unit + \\\"/\\\" + cfg.getParam('time_step')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e641585a8d590730cd937fe17c420e1\",\n \"score\": \"0.51261896\",\n \"text\": \"def initialise_fluids(self, nw):\\n if self.air_alias.val in ['air', 'Air']:\\n air = self.air_alias.val\\n else:\\n air = 'TESPy::' + self.air_alias.val\\n flue_gas = 'TESPy::' + self.fuel_alias.val + \\\"_fg\\\"\\n\\n for c in nw.comps.loc[self].o:\\n if not c.fluid.val_set[air]:\\n c.fluid.val[air] = 0.8\\n if not c.fluid.val_set[flue_gas]:\\n c.fluid.val[flue_gas] = 0.2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d00d428a9f0692471e23a9d5ad41c68\",\n \"score\": \"0.5125038\",\n \"text\": \"def addObjects(self):\\n\\n self.normal = self.guide.blades[\\\"blade\\\"].z * -1\\n self.binormal = self.guide.blades[\\\"blade\\\"].x\\n\\n self.WIP = self.options[\\\"mode\\\"]\\n\\n if self.negate and self.settings[\\\"overrideNegate\\\"]:\\n self.negate = False\\n self.n_factor = 1\\n\\n # FK controllers ------------------------------------\\n self.fk_npo = []\\n self.fk_ctl = []\\n self.tweak_npo = []\\n self.tweak_ctl = []\\n self.curv_pos = []\\n self.upv_curv_pos = []\\n self.upv_curv_lvl = []\\n self.tangentsCtl = []\\n t = self.guide.tra[\\\"root\\\"]\\n\\n parent = self.root\\n tOld = False\\n fk_ctl = None\\n self.previusTag = self.parentCtlTag\\n for i, t in enumerate(transform.getChainTransform(self.guide.apos,\\n self.normal,\\n self.negate)):\\n self.dist = vector.getDistance(self.guide.apos[i],\\n self.guide.apos[i + 1])\\n if self.settings[\\\"neutralpose\\\"] or not tOld:\\n tnpo = t\\n else:\\n tnpo = transform.setMatrixPosition(\\n tOld,\\n transform.getPositionFromMatrix(t))\\n\\n fk_npo = primitive.addTransform(\\n parent, self.getName(\\\"fk%s_npo\\\" % i), tnpo)\\n\\n fk_ctl = self.addCtl(\\n fk_npo,\\n \\\"fk%s_ctl\\\" % i,\\n t,\\n self.color_fk,\\n \\\"cube\\\",\\n w=self.dist,\\n h=self.size * .1,\\n d=self.size * .1,\\n po=datatypes.Vector(self.dist * .5 * self.n_factor, 0, 0),\\n tp=self.previusTag)\\n\\n tweak_npo = primitive.addTransform(\\n parent, self.getName(\\\"tweak%s_npo\\\" % i), tnpo)\\n\\n self.tweak_npo.append(tweak_npo)\\n\\n tweak_ctl = self.addCtl(\\n tweak_npo,\\n \\\"tweak%s_ctl\\\" % i,\\n t,\\n self.color_ik,\\n \\\"cube\\\",\\n w=self.size * .15,\\n h=self.size * .05,\\n d=self.size * .15,\\n ro=datatypes.Vector([0, 0, 1.5708]),\\n tp=self.previusTag)\\n\\n upv_curv_lvl = primitive.addTransform(\\n tweak_ctl, self.getName(\\\"upv%s_lvl\\\" % i), t)\\n upv_curv_lvl.attr(\\\"tz\\\").set(.01)\\n\\n self.fk_npo.append(fk_npo)\\n self.fk_ctl.append(fk_ctl)\\n self.tweak_ctl.append(tweak_ctl)\\n self.upv_curv_lvl.append(upv_curv_lvl)\\n tOld = t\\n self.previusTag = fk_ctl\\n parent = fk_ctl\\n\\n # TANGENTS\\n tangents = []\\n tangents_npo = []\\n tangents_upv = []\\n\\n if not i:\\n letters = \\\"A\\\"\\n else:\\n letters = \\\"AB\\\"\\n\\n for tang in letters:\\n tang_npo = primitive.addTransform(\\n tweak_ctl,\\n self.getName(\\\"tng{}{}_npo\\\".format(tang, str(i))),\\n t)\\n\\n tangents_npo.append(tang_npo)\\n\\n tang_ctl = self.addCtl(\\n tang_npo,\\n \\\"tng{}{}_ctl\\\".format(tang, str(i)),\\n t,\\n self.color_ik,\\n \\\"square\\\",\\n w=self.size * .07,\\n h=self.size * .07,\\n d=self.size * .07,\\n ro=datatypes.Vector([0, 0, 1.5708]),\\n tp=self.previusTag)\\n\\n upv_tang_curv_lvl = primitive.addTransform(\\n tang_ctl,\\n self.getName(\\\"tngUpv{}{}_lvl\\\".format(tang, str(i))),\\n t)\\n upv_tang_curv_lvl.attr(\\\"tz\\\").set(.01)\\n tangents_upv.append(upv_tang_curv_lvl)\\n\\n tangents.append(tang_ctl)\\n\\n tangents_npo[0].attr(\\\"tx\\\").set(self.dist * .3333)\\n\\n # delete the first B tangent\\n if not i:\\n self.curv_pos.append(tweak_ctl)\\n self.curv_pos.append(tangents[0])\\n self.upv_curv_pos.append(upv_curv_lvl)\\n self.upv_curv_pos.append(tangents_upv[0])\\n else:\\n self.curv_pos.append(tangents[1])\\n self.curv_pos.append(tweak_ctl)\\n self.curv_pos.append(tangents[0])\\n self.upv_curv_pos.append(tangents_upv[1])\\n self.upv_curv_pos.append(upv_curv_lvl)\\n self.upv_curv_pos.append(tangents_upv[0])\\n tangents_npo[1].attr(\\\"tx\\\").set(self.dist * -.3333)\\n\\n self.tangentsCtl.extend(tangents)\\n\\n # ==========\\n\\n # self.jnt_pos.append([fk_ctl, i, None, False])\\n\\n # add end control\\n tweak_npo = primitive.addTransform(\\n fk_ctl, self.getName(\\\"tweakEnd_npo\\\"), t)\\n tweak_ctl = self.addCtl(\\n tweak_npo,\\n \\\"tweakEnd_ctl\\\",\\n t,\\n self.color_ik,\\n \\\"cube\\\",\\n w=self.size * .15,\\n h=self.size * .05,\\n d=self.size * .15,\\n ro=datatypes.Vector([0, 0, 1.5708]),\\n tp=self.previusTag)\\n\\n upv_curv_lvl = primitive.addTransform(\\n tweak_ctl, self.getName(\\\"upvEnd_lvl\\\"), t)\\n upv_curv_lvl.attr(\\\"tz\\\").set(.01)\\n\\n if self.negate:\\n self.off_dist = self.dist * -1\\n else:\\n self.off_dist = self.dist\\n tweak_npo.attr(\\\"tx\\\").set(self.off_dist)\\n\\n self.tweak_ctl.append(tweak_ctl)\\n self.upv_curv_lvl.append(upv_curv_lvl)\\n\\n # tangent END\\n tang_npo = primitive.addTransform(\\n tweak_ctl,\\n self.getName(\\\"tngEnd{}_npo\\\".format(tang, str(i))),\\n t)\\n\\n tang_ctl = self.addCtl(\\n tang_npo,\\n \\\"tngEnd{}_ctl\\\".format(tang, str(i)),\\n t,\\n self.color_ik,\\n \\\"square\\\",\\n w=self.size * .07,\\n h=self.size * .07,\\n d=self.size * .07,\\n ro=datatypes.Vector([0, 0, 1.5708]),\\n tp=self.previusTag)\\n\\n upv_tang_curv_lvl = primitive.addTransform(\\n tang_ctl,\\n self.getName(\\\"tngUpv{}{}_lvl\\\".format(tang, str(i))),\\n t)\\n upv_tang_curv_lvl.attr(\\\"tz\\\").set(.01)\\n tangents_upv.append(upv_tang_curv_lvl)\\n\\n tang_npo.attr(\\\"tx\\\").set(self.dist * -.3333)\\n\\n self.curv_pos.append(tang_ctl)\\n self.curv_pos.append(tweak_ctl)\\n self.upv_curv_pos.append(tang_ctl)\\n self.upv_curv_pos.append(upv_curv_lvl)\\n\\n self.tangentsCtl.append(tang_ctl)\\n\\n # add length offset control if keep length\\n # This option will be added only if keep length is active\\n if self.settings[\\\"keepLength\\\"]:\\n self.tweakTip_npo = primitive.addTransform(\\n tweak_ctl, self.getName(\\\"tweakTip_npo\\\"), t)\\n tweak_ctl = self.addCtl(\\n self.tweakTip_npo,\\n \\\"tweakTip_ctl\\\",\\n t,\\n self.color_fk,\\n \\\"square\\\",\\n w=self.size * .1,\\n h=self.size * .1,\\n d=self.size * .1,\\n ro=datatypes.Vector([0, 0, 1.5708]),\\n tp=self.previusTag)\\n\\n upv_curv_lvl = primitive.addTransform(\\n tweak_ctl, self.getName(\\\"upvTip_lvl\\\"), t)\\n upv_curv_lvl.attr(\\\"tz\\\").set(.01)\\n\\n # move to align with the parent\\n self.tweakTip_npo.attr(\\\"tx\\\").set(0)\\n\\n self.tweak_ctl.append(tweak_ctl)\\n self.upv_curv_lvl.append(upv_curv_lvl)\\n\\n # add visual reference\\n self.line_ref = icon.connection_display_curve(\\n self.getName(\\\"visualRef\\\"),\\n [self.tweakTip_npo.getParent(), tweak_ctl])\\n\\n # Curves -------------------------------------------\\n self.mst_crv = curve.addCnsCurve(self.root,\\n self.getName(\\\"mst_crv\\\"),\\n self.curv_pos,\\n 3)\\n\\n self.slv_crv = curve.addCurve(self.root, self.getName(\\\"slv_crv\\\"),\\n [datatypes.Vector()] * 32,\\n False,\\n 3)\\n\\n self.upv_crv = curve.addCnsCurve(self.root,\\n self.getName(\\\"upv_crv\\\"),\\n self.upv_curv_pos,\\n 3)\\n\\n self.slv_upv_crv = curve.addCurve(self.root,\\n self.getName(\\\"slv_upv_crv\\\"),\\n [datatypes.Vector()] * 32,\\n False,\\n 3)\\n\\n self.mst_crv.setAttr(\\\"template\\\", True)\\n self.slv_crv.setAttr(\\\"visibility\\\", False)\\n self.upv_crv.setAttr(\\\"visibility\\\", False)\\n self.slv_upv_crv.setAttr(\\\"visibility\\\", False)\\n\\n # Divisions\\n self.div_cns = []\\n self.upv_cns = []\\n\\n tagP = self.parentCtlTag\\n self.Extra_tweak_npo = []\\n self.Extra_tweak_ctl = []\\n\\n if self.settings[\\\"overrideJntNb\\\"]:\\n self.def_number = self.settings[\\\"jntNb\\\"]\\n else:\\n self.def_number = len(self.guide.apos)\\n\\n for i in range(self.def_number):\\n # References\\n div_cns = primitive.addTransform(self.root,\\n self.getName(\\\"%s_cns\\\" % i))\\n\\n pm.setAttr(div_cns + \\\".inheritsTransform\\\", False)\\n self.div_cns.append(div_cns)\\n\\n upv_cns = primitive.addTransform(self.root,\\n self.getName(\\\"%s_upv\\\" % i))\\n\\n pm.setAttr(upv_cns + \\\".inheritsTransform\\\", False)\\n self.upv_cns.append(upv_cns)\\n\\n t = transform.getTransform(div_cns)\\n\\n extraTweak_npo = primitive.addTransform(\\n div_cns,\\n self.getName(\\\"extraTweak{}_npo\\\".format(tang, str(i))),\\n t)\\n self.Extra_tweak_npo.append(extraTweak_npo)\\n Extra_tweak_ctl = self.addCtl(extraTweak_npo,\\n \\\"extraTweak%s_ctl\\\" % i,\\n t,\\n self.color_fk,\\n \\\"circle\\\",\\n w=self.size * .15,\\n d=self.size * .15,\\n ro=datatypes.Vector([0, 0, 1.5708]),\\n tp=tagP)\\n attribute.setKeyableAttributes(Extra_tweak_ctl)\\n self.Extra_tweak_ctl.append(Extra_tweak_ctl)\\n self.jnt_pos.append([Extra_tweak_ctl, i])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4122803c711ee914e60ec481e887655d\",\n \"score\": \"0.5112155\",\n \"text\": \"def add_unit_of_measures_0117(apps, schema_editor):\\n db_alias = schema_editor.connection.alias\\n\\n approved_fuel = apps.get_model('api', 'ApprovedFuel')\\n unit_of_measure = apps.get_model('api', 'UnitOfMeasure')\\n\\n uom_l = unit_of_measure.objects.using(db_alias).create(\\n name=\\\"L\\\",\\n effective_date=\\\"2017-01-01\\\"\\n )\\n uom_kg = unit_of_measure.objects.using(db_alias).create(\\n name=\\\"kg\\\",\\n effective_date=\\\"2017-01-01\\\"\\n )\\n uom_kwh = unit_of_measure.objects.using(db_alias).create(\\n name=\\\"kWh\\\",\\n effective_date=\\\"2017-01-01\\\"\\n )\\n uom_m3 = unit_of_measure.objects.using(db_alias).create(\\n name=\\\"m³\\\",\\n effective_date=\\\"2017-01-01\\\"\\n )\\n\\n approved_fuel.objects.using(db_alias).filter(\\n name__in=[\\n \\\"Biodiesel\\\", \\\"Ethanol\\\", \\\"HDRD\\\", \\\"Natural gas-based gasoline\\\",\\n \\\"Petroleum-based diesel\\\", \\\"Petroleum-based gasoline\\\",\\n \\\"Propane\\\", \\\"Renewable diesel\\\", \\\"Renewable gasoline\\\"\\n ]\\n ).update(\\n unit_of_measure=uom_l\\n )\\n approved_fuel.objects.using(db_alias).filter(\\n name__in=[\\n \\\"Hydrogen\\\", \\\"LNG\\\"\\n ]\\n ).update(\\n unit_of_measure=uom_kg\\n )\\n approved_fuel.objects.using(db_alias).filter(\\n name=\\\"Electricity\\\"\\n ).update(\\n unit_of_measure=uom_kwh\\n )\\n approved_fuel.objects.using(db_alias).filter(\\n name=\\\"CNG\\\"\\n ).update(\\n unit_of_measure=uom_m3\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3314b2ee6dbd4e64347b914b83ad4c42\",\n \"score\": \"0.50818443\",\n \"text\": \"def determine_features(self, designs, preferredsize, bestgimbal, prefergenerators,\\n prefershortengines, prefermonopropellant):\\n lowest_mass = True\\n lowest_cost = True\\n lowest_requirements = True\\n best_gimbal_range = True\\n shortest_engine = True\\n for e in designs:\\n if not e.is_best:\\n continue\\n if lowest_mass and e.get_mass() < self.get_mass():\\n lowest_mass = False\\n if lowest_cost and e.get_cost() < self.get_cost():\\n lowest_cost = False\\n if lowest_requirements and e.requiredscience.is_easier_than(self.requiredscience):\\n lowest_requirements = False\\n if best_gimbal_range and e.mainengine.tvc > self.mainengine.tvc:\\n best_gimbal_range = False\\n if shortest_engine and e.mainengine.length < self.mainengine.length:\\n shortest_engine = False\\n if lowest_mass:\\n self.features.add(Features.mass)\\n if lowest_cost:\\n self.features.add(Features.cost)\\n if lowest_requirements:\\n # this extra condition is false, but it looks strange if requiring 'only'\\n # VeryHeavRocketry is presented as something good\\n if (techtree.Node.VeryHeavyRocketry not in self.requiredscience.nodes and\\n techtree.Node.HypersonicFlight not in self.requiredscience.nodes and\\n techtree.Node.IonPropulsion not in self.requiredscience.nodes):\\n self.features.add(Features.low_requirements)\\n if prefershortengines and shortest_engine:\\n self.features.add(Features.short_engine)\\n if ((bestgimbal == 1 and self.mainengine.tvc > 0.0) or\\n (bestgimbal == 2 and best_gimbal_range)):\\n self.features.add(Features.gimbal)\\n if prefermonopropellant and self.fueltype is parts.FuelTypes.Monopropellant:\\n self.features.add(Features.monopropellant)\\n if prefergenerators and self.mainengine.electricity:\\n self.features.add(Features.generator)\\n if preferredsize is not None and \\\\\\n (self.size is preferredsize or self.size is parts.RadialSize.RadiallyMounted):\\n self.features.add(Features.radial_size)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13f28693e051937cf3c4d5a99e2bf4fa\",\n \"score\": \"0.50801796\",\n \"text\": \"def unit_techs(name, build_time, metal, energy, zetium, volume_coefs,\\n points_mult, max_planets_required,\\n attack_mod, crit_abs_coef,\\n is_space, unlocker, npc=False):\\n build_time = mins2sec(build_time)\\n\\n specs = get_specs(name, is_space)\\n\\n ### Base technology\\n\\n num_of_base_levels = max_lvl_for_unit_tech(name, unlocker)\\n\\n start_mult = unit_tech_start_multiplier(npc)\\n end_mult = start_mult if num_of_base_levels == 1 \\\\\\n else unit_tech_end_multiplier(npc)\\n\\n list = tuple()\\n list += unit_tech_base(\\n name, build_time, start_mult, end_mult, num_of_base_levels,\\n metal, energy, zetium, volume_coefs, points_mult, max_planets_required, npc\\n )\\n\\n for spec_name, func in specs:\\n # Pass attack_mod to function, because it expects it!\\n max = func(attack_mod, crit_abs_coef) * base_proportion * 100\\n coefs = lin_dep_raw(0, max, num_of_base_levels) if unlocker \\\\\\n else lin_dep_raw_with_first_lvl(max, num_of_base_levels)\\n\\n list += (name, spec_name.lower() + \\\" mod\\\") + coefs + (0, 1),\\n list += empty_row\\n\\n ### Specialization technologies\\n\\n for spec_name, func in specs:\\n tech_name = name + \\\" \\\" + spec_name\\n list += unit_tech_base(\\n tech_name, build_time, start_mult, end_mult, 10,\\n metal, energy, zetium, volume_coefs, points_mult, max_planets_required,\\n npc\\n )\\n\\n # Pass attack_mod to function, because it expects it!\\n max_value = func(attack_mod, crit_abs_coef) * specialized_proportion * 100\\n coefs = lin_dep_raw_with_first_lvl(max_value, 10)\\n list += (tech_name, spec_name.lower() + \\\" mod\\\") + coefs + (0, 1),\\n list += empty_row\\n\\n return list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b972d67f55845810dda3970c430b7271\",\n \"score\": \"0.5071852\",\n \"text\": \"def enrich_fuel(enrich, fuel):\\n fuel_comp = {}\\n # build fuel comp. starting with bounded compound\\n for isotope in mats[fuel]['comp']:\\n if isotope == 92000:\\n # add enriched Uranium to fuel composition\\n fuel_comp.update({92235 : mats[fuel]['comp'][92000]*enrich,\\n 92238 : mats[fuel]['comp'][92000]*(1-enrich) })\\n else:\\n fuel_comp.update({isotope : mats[fuel]['comp'][isotope]})\\n \\n mmO = 32\\n mmN = 14.0067\\n mmU25 = 235.04\\n mmU28 = 238.05\\n\\n if fuel == 'UN':\\n mmfuel = mmN + 1 / ((enrich / mmU25) + ((1-enrich) / mmU28))\\n if fuel == 'UO2':\\n mmfuel = mmO + 1 / ((enrich / mmU25) + ((1-enrich) / mmU28))\\n \\n return fuel_comp, mmfuel\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb1924df1adaa539fe9f8312c33f6485\",\n \"score\": \"0.5071585\",\n \"text\": \"def fly(self):\\n if self.fuel > 1:\\n self.fuel = self.fuel - 1\\n else:\\n print('Not enough fuel!')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76a562111b5675a76dcf0d2b8b9f37cb\",\n \"score\": \"0.5070485\",\n \"text\": \"def fill_gas_tank(self):\\n print(\\\"This car doesn't need a gas tank!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76a562111b5675a76dcf0d2b8b9f37cb\",\n \"score\": \"0.5070485\",\n \"text\": \"def fill_gas_tank(self):\\n print(\\\"This car doesn't need a gas tank!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76a562111b5675a76dcf0d2b8b9f37cb\",\n \"score\": \"0.5070485\",\n \"text\": \"def fill_gas_tank(self):\\n print(\\\"This car doesn't need a gas tank!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"336ef0b924a50959bd07d3c4ad4c3ec0\",\n \"score\": \"0.50611955\",\n \"text\": \"def fill_gas_tank(self):\\n print(\\\"No need for gas in an electric car!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3528219963d4da17b5b9d15fd41c6142\",\n \"score\": \"0.5050947\",\n \"text\": \"def _setup(self,\\n loglevel=10,\\n electronic_conductance='electronic_conductance',\\n occupancy='occupancy',\\n voltage='voltage',\\n **params):\\n self._fluid = params['active_fluid']\\n try: self._fluid = self.find_object_by_name(self._fluid) \\n except: pass #Accept object\\n self._X_name = voltage\\n self._boundary_conditions_setup()\\n g = self._fluid.get_throat_data(prop=electronic_conductance)\\n s = self._fluid.get_throat_data(prop=occupancy)\\n self._conductance = g*s+g*(-s)/1e3\",\n \"title\": \"\"\n },\n {\n \"docid\": \"beb93b038c111e1bd728757ba264fc08\",\n \"score\": \"0.5036853\",\n \"text\": \"def reaction_balance(self, fluid):\\n if isinstance(self, combustion_engine):\\n inl = self.inl[2:]\\n outl = self.outl[2:]\\n else:\\n inl = self.inl\\n outl = self.outl\\n\\n ######################################################################\\n # molar mass flow for fuel and oxygen\\n n_fuel = {}\\n n_oxy_stoich = {}\\n n_h = 0\\n n_c = 0\\n for f in self.fuel_list:\\n n_fuel[f] = 0\\n for i in inl:\\n n = i.m.val_SI * i.fluid.val[f] / molar_masses[f]\\n n_fuel[f] += n\\n n_h += n * self.fuels[f]['H']\\n n_c += n * self.fuels[f]['C']\\n\\n # stoichiometric oxygen requirement for each fuel\\n n_oxy_stoich[f] = n_fuel[f] * (self.fuels[f]['H'] / 4 +\\n self.fuels[f]['C'])\\n\\n n_oxygen = 0\\n for i in inl:\\n n_oxygen += (i.m.val_SI * i.fluid.val[self.o2] /\\n molar_masses[self.o2])\\n\\n ######################################################################\\n # calculate stoichiometric oxygen\\n n_oxygen_stoich = n_h / 4 + n_c\\n\\n ######################################################################\\n # calculate lambda if not set\\n if not self.lamb.is_set:\\n self.lamb.val = n_oxygen / n_oxygen_stoich\\n\\n ######################################################################\\n # calculate excess fuel if lambda is lower than 1\\n if self.lamb.val < 1:\\n n_h_exc = (n_oxygen_stoich - n_oxygen) * 4\\n n_c_exc = (n_oxygen_stoich - n_oxygen)\\n else:\\n n_h_exc = 0\\n n_c_exc = 0\\n\\n ######################################################################\\n # equation for carbondioxide\\n if fluid == self.co2:\\n dm = (n_c - n_c_exc) * molar_masses[self.co2]\\n\\n ######################################################################\\n # equation for water\\n elif fluid == self.h2o:\\n dm = (n_h - n_h_exc) / 2 * molar_masses[self.h2o]\\n\\n ######################################################################\\n # equation for oxygen\\n elif fluid == self.o2:\\n if self.lamb.val < 1:\\n dm = -n_oxygen * molar_masses[self.o2]\\n else:\\n dm = -n_oxygen / self.lamb.val * molar_masses[self.o2]\\n\\n ######################################################################\\n # equation for fuel\\n elif fluid in self.fuel_list:\\n if self.lamb.val < 1:\\n n_fuel_exc = (-(n_oxygen / n_oxygen_stoich - 1) *\\n n_oxy_stoich[fluid] *\\n (self.fuels[f]['H'] / 4 + self.fuels[f]['C']))\\n else:\\n n_fuel_exc = 0\\n dm = -(n_fuel[fluid] - n_fuel_exc) * molar_masses[fluid]\\n\\n ######################################################################\\n # equation for other fluids\\n else:\\n dm = 0\\n\\n res = dm\\n for i in inl:\\n res += i.fluid.val[fluid] * i.m.val_SI\\n for o in outl:\\n res -= o.fluid.val[fluid] * o.m.val_SI\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fae5051ef38731e65efa832af478585\",\n \"score\": \"0.5029067\",\n \"text\": \"def fill_gas_tank(self):\\n print(\\\"This car doesn't have a gas tank!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c97738b9001ff6d453fe3ce8a118cb5\",\n \"score\": \"0.5022136\",\n \"text\": \"def add_unit_models(m):\\n fs = m.fs_main.fs_stc\\n prop_water = m.fs_main.prop_water\\n\\n # Custom valve function for throttle valve\\n def throttle_valve_function(blk):\\n blk.Cv.fix(1)\\n a = blk.vfa = pyo.Var(initialize=2.8904e-02, doc=\\\"Valve function parameter a\\\")\\n b = blk.vfb = pyo.Var(initialize=3.3497e-02, doc=\\\"Valve function parameter b\\\")\\n c = blk.vfc = pyo.Var(initialize=1.4514e-02, doc=\\\"Valve function parameter c\\\")\\n d = blk.vfd = pyo.Var(initialize=1.4533e-03, doc=\\\"Valve function parameter d\\\")\\n a.fix()\\n b.fix()\\n c.fix()\\n d.fix()\\n o = blk.valve_opening\\n\\n @blk.Expression(m.fs_main.time)\\n def valve_function(bd, t):\\n return a * o[t] ** 3 - b * o[t] ** 2 + c * o[t] - d\\n\\n # Unit model for multistage turbine including throttle valve\\n fs.turb = TurbineMultistage(\\n dynamic=False,\\n property_package=prop_water,\\n num_parallel_inlet_stages=1,\\n throttle_valve_function=ValveFunctionType.custom,\\n throttle_valve_function_callback=throttle_valve_function,\\n num_hp=14,\\n num_ip=9,\\n num_lp=5,\\n hp_split_locations=[14],\\n ip_split_locations=[6, 9],\\n lp_split_locations=[2, 4, 5],\\n hp_disconnect=[14],\\n hp_split_num_outlets={14: 2},\\n ip_split_num_outlets={9: 3},\\n )\\n\\n # Unit model for regulating valve of BFPT (boiler feed pump turbine)\\n fs.bfp_turb_valve = Valve(dynamic=False, property_package=prop_water)\\n\\n # Unit model for main stage of BFPT\\n fs.bfp_turb = TurbineStage(dynamic=False, property_package=prop_water)\\n\\n # Unit model for outlet stage of BFPT\\n fs.bfp_turb_os = TurbineOutletStage(dynamic=False, property_package=prop_water)\\n\\n # Unit model for main condenser\\n fs.condenser = Condenser(\\n dynamic=False,\\n shell={\\\"has_pressure_change\\\": False, \\\"property_package\\\": prop_water},\\n tube={\\\"has_pressure_change\\\": False, \\\"property_package\\\": prop_water},\\n )\\n\\n # Unit model for auxiliary condenser\\n fs.aux_condenser = Condenser(\\n dynamic=False,\\n shell={\\\"has_pressure_change\\\": False, \\\"property_package\\\": prop_water},\\n tube={\\\"has_pressure_change\\\": False, \\\"property_package\\\": prop_water},\\n )\\n\\n # Unit model for condenser hotwell (hotwell tank modeled separately)\\n # Modeled as a mixer of makeup, main, and auxiliary condenser water streams\\n # Set momentum_mixing_type to none since aux_condenser outlet pressure\\n # is usually not equal to the main condenser outlet pressure\\n # We impose the constraints to let the mixed pressure equal to\\n # the main condenser pressure and makeup water pressure\\n fs.condenser_hotwell = Mixer(\\n dynamic=False,\\n momentum_mixing_type=MomentumMixingType.none,\\n inlet_list=[\\\"main_condensate\\\", \\\"makeup\\\", \\\"aux_condensate\\\"],\\n property_package=prop_water,\\n )\\n\\n # Unit model for water control valve between makeup tank and hotwell\\n fs.makeup_valve = Valve(\\n dynamic=False, has_holdup=False, phase=\\\"Liq\\\", property_package=prop_water\\n )\\n\\n # Unit model for hotwell tank with holdup for dynamic model\\n # Modeled as a simple tank with constant cross section area and tank level\\n fs.hotwell_tank = WaterTank(\\n tank_type=\\\"simple_tank\\\", has_holdup=True, property_package=prop_water\\n )\\n\\n # Unit model for condensate pump\\n fs.cond_pump = WaterPump(dynamic=False, property_package=prop_water)\\n\\n # Unit model for water control valve after hotwell tank\\n # Used to control deaerator level\\n fs.cond_valve = Valve(\\n dynamic=False, has_holdup=False, phase=\\\"Liq\\\", property_package=prop_water\\n )\\n\\n # Unit model for feed water heater 1\\n fs.fwh1 = FWH0D(\\n has_desuperheat=False,\\n has_drain_cooling=False,\\n has_drain_mixer=True,\\n condense={\\n \\\"tube\\\": {\\\"has_pressure_change\\\": True},\\n \\\"shell\\\": {\\\"has_pressure_change\\\": True},\\n \\\"has_holdup\\\": True,\\n },\\n property_package=prop_water,\\n )\\n\\n # Unit model for drain pump of FWH1\\n fs.fwh1_drain_pump = WaterPump(dynamic=False, property_package=prop_water)\\n\\n # Unit model for mixer of FWH1 drain and condensate\\n fs.fwh1_drain_return = Mixer(\\n dynamic=False,\\n inlet_list=[\\\"feedwater\\\", \\\"fwh1_drain\\\"],\\n property_package=prop_water,\\n momentum_mixing_type=MomentumMixingType.equality,\\n )\\n\\n # Unit model for feed water heater 2\\n fs.fwh2 = FWH0D(\\n has_desuperheat=False,\\n has_drain_cooling=True,\\n has_drain_mixer=True,\\n condense={\\n \\\"tube\\\": {\\\"has_pressure_change\\\": True},\\n \\\"shell\\\": {\\\"has_pressure_change\\\": True},\\n \\\"has_holdup\\\": True,\\n },\\n cooling={\\\"dynamic\\\": False, \\\"has_holdup\\\": False},\\n property_package=prop_water,\\n )\\n\\n # Unit model for water control valve between drain of fwh2 and fwh1\\n fs.fwh2_valve = Valve(\\n dynamic=False, has_holdup=False, phase=\\\"Liq\\\", property_package=prop_water\\n )\\n\\n # Unit model for feed water heater 3\\n fs.fwh3 = FWH0D(\\n has_desuperheat=False,\\n has_drain_cooling=True,\\n has_drain_mixer=False,\\n condense={\\n \\\"tube\\\": {\\\"has_pressure_change\\\": True},\\n \\\"shell\\\": {\\\"has_pressure_change\\\": True},\\n \\\"has_holdup\\\": True,\\n },\\n cooling={\\\"dynamic\\\": False, \\\"has_holdup\\\": False},\\n property_package=prop_water,\\n )\\n\\n # Unit model for control valve between drain of fwh3 and fwh2\\n fs.fwh3_valve = Valve(\\n dynamic=False, has_holdup=False, phase=\\\"Liq\\\", property_package=prop_water\\n )\\n\\n # Unit model for deaerator also known as fwh4\\n # Modeled as mixer to mix extracted steam with condensate and drain\\n # from fwh5\\n # Using MomentumMixingType.equality for momentum_mixing_type\\n # deaerator tank modeled separately for holdup in dyyamic model\\n fs.fwh4_deair = Mixer(\\n dynamic=False,\\n momentum_mixing_type=MomentumMixingType.equality,\\n inlet_list=[\\\"steam\\\", \\\"drain\\\", \\\"feedwater\\\"],\\n property_package=prop_water,\\n )\\n\\n # Unit model for deaerator water tank\\n # Modeled as a horizontal cylindrical tank\\n fs.da_tank = WaterTank(\\n tank_type=\\\"horizontal_cylindrical_tank\\\",\\n has_holdup=True,\\n property_package=prop_water,\\n )\\n\\n # Unit model for electrical feedwater booster pump\\n fs.booster = WaterPump(dynamic=False, property_package=prop_water)\\n\\n # Unit model for main boiler feed water pump driven by steam turbine\\n fs.bfp = WaterPump(dynamic=False, property_package=prop_water)\\n\\n # Unit model for splitter for spray water stream for main attemperator\\n fs.split_attemp = Separator(\\n dynamic=False, property_package=prop_water, outlet_list=[\\\"FeedWater\\\", \\\"Spray\\\"]\\n )\\n\\n # Unit model for attemperator spray control valve\\n fs.spray_valve = Valve(\\n dynamic=False, has_holdup=False, phase=\\\"Liq\\\", property_package=prop_water\\n )\\n\\n # Unit model for feed water heater 5\\n fs.fwh5 = FWH0D(\\n has_desuperheat=True,\\n has_drain_cooling=True,\\n has_drain_mixer=True,\\n condense={\\n \\\"tube\\\": {\\\"has_pressure_change\\\": True},\\n \\\"shell\\\": {\\\"has_pressure_change\\\": True},\\n \\\"has_holdup\\\": True,\\n },\\n desuperheat={\\\"dynamic\\\": False},\\n cooling={\\\"dynamic\\\": False, \\\"has_holdup\\\": False},\\n property_package=prop_water,\\n )\\n\\n # Unit model for water control valve drain of fwh5 and deaerator\\n fs.fwh5_valve = Valve(\\n dynamic=False, has_holdup=False, phase=\\\"Liq\\\", property_package=prop_water\\n )\\n\\n # Unit model for feed water heater 6\\n fs.fwh6 = FWH0D(\\n has_desuperheat=True,\\n has_drain_cooling=True,\\n has_drain_mixer=False,\\n condense={\\n \\\"tube\\\": {\\\"has_pressure_change\\\": True},\\n \\\"shell\\\": {\\\"has_pressure_change\\\": True},\\n \\\"has_holdup\\\": True,\\n },\\n desuperheat={\\\"dynamic\\\": False},\\n cooling={\\\"dynamic\\\": False, \\\"has_holdup\\\": False},\\n property_package=prop_water,\\n )\\n\\n # Unit model for water control valve between drain of fwh6 and fwh5\\n fs.fwh6_valve = Valve(\\n dynamic=False, has_holdup=False, phase=\\\"Liq\\\", property_package=prop_water\\n )\\n\\n # Important process variables, declared and used in PID controllers\\n # Variable for main steam temperature\\n fs.temperature_main_steam = pyo.Var(fs.time, initialize=810)\\n\\n # Constraint to calculate main steam temperature\\n @fs.Constraint(fs.time)\\n def temperature_main_steam_eqn(b, t):\\n return (\\n b.temperature_main_steam[t]\\n == b.turb.throttle_valve[1].control_volume.properties_in[t].temperature\\n )\\n\\n # Variable for gross power output in MW\\n fs.power_output = pyo.Var(fs.time, initialize=300, doc=\\\"gross power output in MW\\\")\\n\\n # Constraint to calculate gross power output\\n @fs.Constraint(fs.time)\\n def power_output_eqn(b, t):\\n return b.power_output[t] == -b.turb.power[t] / 1e6\\n\\n if m.dynamic is True:\\n # Add PID controllers if the flowsheet model is a dynamic model\\n # PI controller to control level of fwh2\\n fs.fwh2_ctrl = PIDController(\\n process_var=fs.fwh2.condense.level,\\n manipulated_var=fs.fwh2_valve.valve_opening,\\n controller_type=ControllerType.PI,\\n calculate_initial_integral=False,\\n )\\n\\n # PI controller to control level of fwh3\\n fs.fwh3_ctrl = PIDController(\\n process_var=fs.fwh3.condense.level,\\n manipulated_var=fs.fwh3_valve.valve_opening,\\n controller_type=ControllerType.PI,\\n calculate_initial_integral=False,\\n )\\n\\n # PI controller to control level of fwh5\\n fs.fwh5_ctrl = PIDController(\\n process_var=fs.fwh5.condense.level,\\n manipulated_var=fs.fwh5_valve.valve_opening,\\n controller_type=ControllerType.PI,\\n calculate_initial_integral=False,\\n )\\n\\n # PI controller to control level of fwh6\\n fs.fwh6_ctrl = PIDController(\\n process_var=fs.fwh6.condense.level,\\n manipulated_var=fs.fwh6_valve.valve_opening,\\n controller_type=ControllerType.PI,\\n calculate_initial_integral=False,\\n )\\n\\n # PI controller to control level of deaerator tank\\n fs.da_ctrl = PIDController(\\n process_var=fs.da_tank.tank_level,\\n manipulated_var=fs.cond_valve.valve_opening,\\n controller_type=ControllerType.PI,\\n calculate_initial_integral=False,\\n )\\n\\n # PI controller to control level of hotwell tank\\n fs.makeup_ctrl = PIDController(\\n process_var=fs.hotwell_tank.tank_level,\\n manipulated_var=fs.makeup_valve.valve_opening,\\n controller_type=ControllerType.PI,\\n mv_bound_type=ControllerMVBoundType.SMOOTH_BOUND,\\n calculate_initial_integral=False,\\n )\\n\\n # PID controller to control main steam temperature\\n fs.spray_ctrl = PIDController(\\n process_var=fs.temperature_main_steam,\\n manipulated_var=fs.spray_valve.valve_opening,\\n controller_type=ControllerType.PID,\\n mv_bound_type=ControllerMVBoundType.SMOOTH_BOUND,\\n calculate_initial_integral=False,\\n )\\n\\n return m\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef423f73af216d7275e52a7b54e355d7\",\n \"score\": \"0.5018522\",\n \"text\": \"def update_technology_dispatch(self, year):\\n self.stock.act_tech = self.stock.technology.loc[:,year].to_frame()\\n self.stock.act_rem = (self.stock.remaining.loc[:,year].to_frame().groupby(level=util.ix_incl(self.stock.act_tech,self.stock.act_tech.index.names)).sum())\\n self.stock.act_tech_or_rem = self.stock.act_tech.fillna(self.stock.act_rem)\\n self.stock.act_tech_or_rem = DfOper.add([self.stock.act_tech_or_rem,util.remove_df_levels(self.stock.dispatch_cap.loc[:,year].to_frame(),'vintage')])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"998cd84bba23c8645a73a206dd061b5b\",\n \"score\": \"0.50183886\",\n \"text\": \"def calculate_input_stock(self):\\n levels = self.stock.rollover_group_levels + [self.years] + [self.tech_names]\\n names = self.stock.rollover_group_names + ['year'] + ['supply_technology']\\n index = pd.MultiIndex.from_product(levels,names=names)\\n if self.stock._has_data is True and 'supply_technology' in self.stock.raw_values.index.names:\\n #remap to technology stocks\\n self.stock.years = self.years\\n self.stock.remap(map_from='raw_values', map_to='technology', converted_geography=GeoMapper.supply_primary_geography, fill_timeseries=True, fill_value=np.nan)\\n #TODO add to clean timeseries. Don't allow filling of timseries before raw values.\\n self.stock.technology[self.stock.technology.index.get_level_values('year')<min(self.stock.raw_values.index.get_level_values('year'))] = np.nan\\n self.convert_stock('stock', 'technology')\\n self.stock.technology = self.stock.technology.reorder_levels(names)\\n self.stock.technology = self.stock.technology.reindex(index)\\n #if there's case_specific stock data, we must use that to replace reference technology stocks\\n if hasattr(self.case_stock,'technology'):\\n # if there are levels in the case specific stock that are not in the reference stock, we must remove that level from the case stock\\n mismatched_levels = [x for x in self.case_stock.technology.index.names if x not in self.stock.technology.index.names]\\n if len(mismatched_levels):\\n self.case_stock.technology= util.remove_df_levels(self.case_stock.technology,mismatched_levels)\\n #if there are still level mismatches, it means the reference stock has more levels, which returns an error\\n if np.any(util.difference_in_df_names(self.case_stock.technology, self.stock.technology,return_bool=True)):\\n raise ValueError(\\\"technology stock indices in node %s do not match input energy system stock data\\\" %self.name)\\n else:\\n #if the previous test is passed, we use the reference stock to fill in the Nans of the case stock\\n self.case_stock.technology = self.case_stock.technology.reorder_levels(names)\\n self.case_stock.technology = self.case_stock.technology.reindex(index)\\n self.stock.technology = self.case_stock.technology.fillna(self.stock.technology)\\n self.stock.technology = self.stock.technology.unstack('year')\\n self.stock.technology.columns = self.stock.technology.columns.droplevel()\\n self.stock.technology = util.reindex_df_level_with_new_elements(self.stock.technology,'supply_technology',self.tech_names)\\n elif hasattr(self.case_stock,'technology'):\\n # if there are levels in the case specific stock that are not in the rollover groups, we must remove that level from the case stock\\n mismatched_levels = [x for x in self.case_stock.technology.index.names if x not in names]\\n if len(mismatched_levels):\\n self.case_stock.technology = util.remove_df_levels(self.case_stock.technology,mismatched_levels)\\n #if there are still level mismatches, it means the rollover has more levels, which returns an error\\n if len([x for x in self.stock.rollover_group_names if x not in self.case_stock.technology.index.names]) :\\n raise ValueError(\\\"technology stock levels in node %s do not match other node input data\\\" %self.name)\\n else:\\n #if the previous test is passed we reindex the case stock for unspecified technologies\\n self.case_stock.technology = self.case_stock.technology.reorder_levels(names)\\n structure_df = pd.DataFrame(1,index=index,columns=['value'])\\n self.case_stock.technology = self.case_stock.technology.reindex(index)\\n self.stock.technology = self.case_stock.technology\\n self.stock.technology = self.stock.technology.unstack('year')\\n self.stock.technology.columns = self.stock.technology.columns.droplevel()\\n self.stock.technology = util.reindex_df_level_with_new_elements(self.stock.technology,'supply_technology',self.tech_names)\\n else:\\n levels = self.stock.rollover_group_levels + [self.tech_names]\\n names = self.stock.rollover_group_names + ['supply_technology']\\n index = pd.MultiIndex.from_product(levels,names=names)\\n self.stock.technology = util.empty_df(index=index,columns=self.years,fill_value=np.NaN)\\n\\n if self.stock._has_data is True and 'supply_technology' not in self.stock.raw_values.index.names:\\n levels = self.stock.rollover_group_levels + [self.years]\\n names = self.stock.rollover_group_names + ['year']\\n index = pd.MultiIndex.from_product(levels,names=names)\\n structure_df = pd.DataFrame(1,index=index,columns=['value'])\\n self.stock.remap(map_from='raw_values', map_to='total', converted_geography=GeoMapper.supply_primary_geography, time_index = self.years,fill_timeseries=True, fill_value=np.nan)\\n #TODO add to clean timeseries. Don't allow filling of timseries before raw values.\\n self.stock.total[self.stock.total.index.get_level_values('year')<min(self.stock.raw_values.index.get_level_values('year'))] = np.nan\\n self.stock.total = DfOper.mult([self.stock.total,structure_df],fill_value=np.nan)\\n self.convert_stock('stock', 'total')\\n if hasattr(self.case_stock,'total'):\\n mismatched_levels = [x for x in self.case_stock.total.index.names if x not in names]\\n if len(mismatched_levels):\\n self.case_stock.total = util.remove_df_levels(self.case_stock.total,mismatched_levels)\\n #if there are still level mismatches, it means the reference stock has more levels, which returns an error\\n if np.any(util.difference_in_df_names(self.case_stock.total, self.stock.total,return_bool=True)):\\n raise ValueError(\\\"total stock indices in node %s do not match input energy system stock data\\\" %self.name)\\n else:\\n #if the previous test is passed, we use the reference stock to fill in the Nans of the case stock\\n self.case_stock.total= self.case_stock.total.reorder_levels(names)\\n self.stock.total = self.stock.total.reorder_levels(names)\\n structure_df = pd.DataFrame(1,index=index,columns=['value'])\\n self.case_stock.total = DfOper.mult([self.case_stock.total,structure_df],fill_value=np.nan)\\n self.stock.total = DfOper.mult([self.stock.total,structure_df],fill_value=np.nan)\\n self.stock.total = self.case_stock.total.fillna(self.stock.total)\\n\\n self.stock.total = self.stock.total.unstack('year')\\n self.stock.total.columns = self.stock.total.columns.droplevel()\\n elif hasattr(self.case_stock,'total'):\\n levels = self.stock.rollover_group_levels + [self.years]\\n names = self.stock.rollover_group_names + ['year']\\n index = pd.MultiIndex.from_product(levels,names=names)\\n # if there are levels in the case specific stock that are not in the rollover groups, we must remove that level from the case stock\\n mismatched_levels = [x for x in self.case_stock.total.index.names if x not in names]\\n if len(mismatched_levels):\\n self.case_stock.total = util.remove_df_levels(self.case_stock.total,mismatched_levels)\\n #if there are still level mismatches, it means the rollover has more levels, which returns an error\\n if len([x for x in names if x not in self.case_stock.total.index.names]) :\\n raise ValueError(\\\"total stock levels in node %s do not match other node input data\\\" %self.name)\\n else:\\n self.case_stock.total= self.case_stock.total.reorder_levels(names)\\n self.case_stock.total = self.case_stock.total.reindex(index)\\n self.stock.total = self.case_stock.total\\n self.stock.total = self.stock.total.unstack('year')\\n self.stock.total.columns = self.stock.total.columns.droplevel()\\n else:\\n index = pd.MultiIndex.from_product(self.stock.rollover_group_levels,names=self.stock.rollover_group_names)\\n self.stock.total = util.empty_df(index=index,columns=self.years,fill_value=np.NaN)\\n if self.stock._has_data or hasattr(self.case_stock,'data') and self.case_stock._has_data == True:\\n self.stock._has_data = True\\n self.max_total()\\n if cfg.rio_supply_run and self.name not in cfg.rio_excluded_nodes:\\n self.stock.technology.loc[:, cfg.supply_years] = self.stock.technology.loc[:, cfg.supply_years].fillna(0)\\n self.format_rollover_stocks()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d04f362ef14e86d3c526cf128b1c32d4\",\n \"score\": \"0.5014126\",\n \"text\": \"def __init__(self, name, fuel, fanciness):\\n super().__init__(name, fuel)\\n self.price_per_km *= fanciness\\n self.current_fare_distance = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58f96e662628372d9a0614b07194af13\",\n \"score\": \"0.5006129\",\n \"text\": \"def add_categories_0116(apps, schema_editor):\\n db_alias = schema_editor.connection.alias\\n\\n approved_fuel = apps.get_model('api', 'ApprovedFuel')\\n default_carbon_intensity_category = apps.get_model(\\n 'api', 'DefaultCarbonIntensityCategory')\\n energy_density_category = apps.get_model(\\n 'api', 'EnergyDensityCategory')\\n energy_effectiveness_ratio_category = apps.get_model(\\n 'api', 'EnergyEffectivenessRatioCategory')\\n\\n approved_fuel.objects.using(db_alias).filter(\\n name=\\\"Biodiesel\\\"\\n ).update(\\n default_carbon_intensity_category=default_carbon_intensity_category\\n .objects.using(db_alias).get(\\n name=\\\"Renewable Fuel in relation to diesel class fuel\\\"\\n ),\\n energy_density_category=energy_density_category\\n .objects.using(db_alias).get(\\n name=\\\"Biodiesel\\\"\\n ),\\n energy_effectiveness_ratio_category=energy_effectiveness_ratio_category\\n .objects.using(db_alias).get(\\n name=\\\"Petroleum-based diesel fuel or renewable fuel in relation \\\"\\n \\\"to diesel class fuel\\\"\\n )\\n )\\n\\n approved_fuel.objects.using(db_alias).filter(\\n name=\\\"CNG\\\"\\n ).update(\\n default_carbon_intensity_category=default_carbon_intensity_category\\n .objects.using(db_alias).get(\\n name=\\\"CNG\\\"\\n ),\\n energy_density_category=energy_density_category\\n .objects.using(db_alias).get(\\n name=\\\"CNG\\\"\\n ),\\n energy_effectiveness_ratio_category=energy_effectiveness_ratio_category\\n .objects.using(db_alias).get(\\n name=\\\"CNG\\\"\\n )\\n )\\n\\n approved_fuel.objects.using(db_alias).filter(\\n name=\\\"Electricity\\\"\\n ).update(\\n default_carbon_intensity_category=default_carbon_intensity_category\\n .objects.using(db_alias).get(\\n name=\\\"Electricity\\\"\\n ),\\n energy_density_category=energy_density_category\\n .objects.using(db_alias).get(\\n name=\\\"Electricity\\\"\\n ),\\n energy_effectiveness_ratio_category=energy_effectiveness_ratio_category\\n .objects.using(db_alias).get(\\n name=\\\"Electricity\\\"\\n )\\n )\\n\\n approved_fuel.objects.using(db_alias).filter(\\n name=\\\"Ethanol\\\"\\n ).update(\\n default_carbon_intensity_category=default_carbon_intensity_category\\n .objects.using(db_alias).get(\\n name=\\\"Renewable Fuel in relation to gasoline class fuel\\\"\\n ),\\n energy_density_category=energy_density_category\\n .objects.using(db_alias).get(\\n name=\\\"Ethanol\\\"\\n ),\\n energy_effectiveness_ratio_category=energy_effectiveness_ratio_category\\n .objects.using(db_alias).get(\\n name=\\\"Petroleum-based gasoline, natural gas-based gasoline or \\\"\\n \\\"renewable fuel in relation to gasoline class fuel\\\"\\n )\\n )\\n\\n approved_fuel.objects.using(db_alias).filter(\\n name=\\\"HDRD\\\"\\n ).update(\\n default_carbon_intensity_category=default_carbon_intensity_category\\n .objects.using(db_alias).get(\\n name=\\\"Renewable Fuel in relation to diesel class fuel\\\"\\n ),\\n energy_density_category=energy_density_category\\n .objects.using(db_alias).get(\\n name=\\\"Hydrogenation-derived renewable diesel fuel\\\"\\n ),\\n energy_effectiveness_ratio_category=energy_effectiveness_ratio_category\\n .objects.using(db_alias).get(\\n name=\\\"Petroleum-based diesel fuel or renewable fuel in relation \\\"\\n \\\"to diesel class fuel\\\"\\n )\\n )\\n\\n approved_fuel.objects.using(db_alias).filter(\\n name=\\\"Hydrogen\\\"\\n ).update(\\n default_carbon_intensity_category=default_carbon_intensity_category\\n .objects.using(db_alias).get(\\n name=\\\"Hydrogen\\\"\\n ),\\n energy_density_category=energy_density_category\\n .objects.using(db_alias).get(\\n name=\\\"Hydrogen\\\"\\n ),\\n energy_effectiveness_ratio_category=energy_effectiveness_ratio_category\\n .objects.using(db_alias).get(\\n name=\\\"Hydrogen\\\"\\n )\\n )\\n\\n approved_fuel.objects.using(db_alias).filter(\\n name=\\\"LNG\\\"\\n ).update(\\n default_carbon_intensity_category=default_carbon_intensity_category\\n .objects.using(db_alias).get(\\n name=\\\"LNG\\\"\\n ),\\n energy_density_category=energy_density_category\\n .objects.using(db_alias).get(\\n name=\\\"LNG\\\"\\n ),\\n energy_effectiveness_ratio_category=energy_effectiveness_ratio_category\\n .objects.using(db_alias).get(\\n name=\\\"LNG\\\"\\n )\\n )\\n\\n approved_fuel.objects.using(db_alias).filter(\\n name=\\\"Natural gas-based gasoline\\\"\\n ).update(\\n default_carbon_intensity_category=default_carbon_intensity_category\\n .objects.using(db_alias).get(\\n name=\\\"Natural gas-based gasoline\\\"\\n ),\\n energy_density_category=energy_density_category\\n .objects.using(db_alias).get(\\n name=\\\"Petroleum-based gasoline, natural gas-based gasoline or \\\"\\n \\\"gasoline produced from biomass\\\"\\n ),\\n energy_effectiveness_ratio_category=energy_effectiveness_ratio_category\\n .objects.using(db_alias).get(\\n name=\\\"Petroleum-based gasoline, natural gas-based gasoline or \\\"\\n \\\"renewable fuel in relation to gasoline class fuel\\\"\\n )\\n )\\n\\n approved_fuel.objects.using(db_alias).filter(\\n name=\\\"Petroleum-based diesel\\\"\\n ).update(\\n default_carbon_intensity_category=default_carbon_intensity_category\\n .objects.using(db_alias).get(\\n name=\\\"Petroleum-based diesel\\\"\\n ),\\n energy_density_category=energy_density_category\\n .objects.using(db_alias).get(\\n name=\\\"Petroleum-based diesel fuel or diesel fuel produced from \\\"\\n \\\"biomass\\\"\\n ),\\n energy_effectiveness_ratio_category=energy_effectiveness_ratio_category\\n .objects.using(db_alias).get(\\n name=\\\"Petroleum-based diesel fuel or renewable fuel in relation \\\"\\n \\\"to diesel class fuel\\\"\\n )\\n )\\n\\n approved_fuel.objects.using(db_alias).filter(\\n name=\\\"Petroleum-based gasoline\\\"\\n ).update(\\n default_carbon_intensity_category=default_carbon_intensity_category\\n .objects.using(db_alias).get(\\n name=\\\"Petroleum-based gasoline\\\"\\n ),\\n energy_density_category=energy_density_category\\n .objects.using(db_alias).get(\\n name=\\\"Petroleum-based gasoline, natural gas-based gasoline or \\\"\\n \\\"gasoline produced from biomass\\\"\\n ),\\n energy_effectiveness_ratio_category=energy_effectiveness_ratio_category\\n .objects.using(db_alias).get(\\n name=\\\"Petroleum-based gasoline, natural gas-based gasoline or \\\"\\n \\\"renewable fuel in relation to gasoline class fuel\\\"\\n )\\n )\\n\\n approved_fuel.objects.using(db_alias).filter(\\n name=\\\"Propane\\\"\\n ).update(\\n default_carbon_intensity_category=default_carbon_intensity_category\\n .objects.using(db_alias).get(\\n name=\\\"Propane\\\"\\n ),\\n energy_density_category=energy_density_category\\n .objects.using(db_alias).get(\\n name=\\\"Propane\\\"\\n ),\\n energy_effectiveness_ratio_category=energy_effectiveness_ratio_category\\n .objects.using(db_alias).get(\\n name=\\\"Propane\\\"\\n )\\n )\\n\\n approved_fuel.objects.using(db_alias).filter(\\n name=\\\"Renewable diesel\\\"\\n ).update(\\n default_carbon_intensity_category=default_carbon_intensity_category\\n .objects.using(db_alias).get(\\n name=\\\"Petroleum-based diesel\\\"\\n ),\\n energy_density_category=energy_density_category\\n .objects.using(db_alias).get(\\n name=\\\"Petroleum-based diesel fuel or diesel fuel produced from \\\"\\n \\\"biomass\\\"\\n ),\\n energy_effectiveness_ratio_category=energy_effectiveness_ratio_category\\n .objects.using(db_alias).get(\\n name=\\\"Petroleum-based diesel fuel or renewable fuel in relation \\\"\\n \\\"to diesel class fuel\\\"\\n )\\n )\\n\\n approved_fuel.objects.using(db_alias).filter(\\n name=\\\"Renewable gasoline\\\"\\n ).update(\\n default_carbon_intensity_category=default_carbon_intensity_category\\n .objects.using(db_alias).get(\\n name=\\\"Petroleum-based gasoline\\\"\\n ),\\n energy_density_category=energy_density_category\\n .objects.using(db_alias).get(\\n name=\\\"Petroleum-based gasoline, natural gas-based gasoline or \\\"\\n \\\"gasoline produced from biomass\\\"\\n ),\\n energy_effectiveness_ratio_category=energy_effectiveness_ratio_category\\n .objects.using(db_alias).get(\\n name=\\\"Petroleum-based gasoline, natural gas-based gasoline or \\\"\\n \\\"renewable fuel in relation to gasoline class fuel\\\"\\n )\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85b57ef7b85f54516d9e56610bb0104f\",\n \"score\": \"0.49957237\",\n \"text\": \"def update_technology(self, year):\\n self.stock.act_tech_energy = util.DfOper.mult([self.stock.technology.loc[:,year].to_frame(), self.stock.act_energy_capacity_ratio],fill_value=np.nan)\\n self.stock.act_tech_or_rem_energy = self.stock.act_tech_energy.fillna(self.stock.act_rem_energy.groupby(level=self.stock.act_tech_energy.index.names).sum())\\n self.stock.act_tech_or_rem = util.remove_df_levels(util.DfOper.divi([self.stock.act_tech_or_rem_energy, self.stock.act_energy_capacity_ratio]),'supply_technology')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5fd420f76875d9e99ab84a88224acfa5\",\n \"score\": \"0.49942207\",\n \"text\": \"def add_concrete_material(self, name, fc, fu, Ec, eu):\\n if name in self.materials_dictionary:\\n return\\n else:\\n self.materials_dictionary[name] = MF.Concrete_Material(name, fc, fu, Ec, eu)\\n self.Lb.insert(tk.END, name)\\n self.controller.update_material_dropdown()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4109f63c0f39b8f4c8c1dae29e129450\",\n \"score\": \"0.49941784\",\n \"text\": \"def main():\\n ss_flowsheet = ss_sim.main()\\n flowsheet = Flowsheet(name='MB_Model') \\n\\n # fill in values of IC parameters from steady state solve\\n setICs(flowsheet,ss_flowsheet)\\n \\n # Fix variables\\n setInputs(flowsheet) \\n\\n # Initialize at steady state\\n initialize_ss(flowsheet,ss_flowsheet)\\n\\n mb = flowsheet.MB_fuel\\n\\n # Then perturb\\n # ^ that function should go in this file, probably\\n # input: dict mapping state names (strings) to new values\\n\\n # this seems like as much work as doing the perturbation...\\n # maybe just make a self contained function\\n #input_perturbation = { ('Solid_In_x',mb.t) : { ['Fe2O3'] : 0.25 },\\n # ('Solid_In_x',mb.t) : { ['Al2O3'] : 0.75 } }\\n\\n perturbInputs(flowsheet)\\n\\n # perturb states\\n\\n # should put this in a dedicated ~intialize~ function\\n # that also intelligently initializes the model after perturbation\\n mb.eq_d4.deactivate()\\n mb.eq_d5.deactivate()\\n mb.eq_d8.deactivate()\\n mb.eq_d9.deactivate()\\n mb.eq_d10.deactivate()\\n mb.eq_g7.deactivate()\\n mb.eq_g8.deactivate()\\n mb.eq_g10.deactivate()\\n mb.eq_g11.deactivate()\\n mb.eq_g12.deactivate()\\n mb.eq_g13.deactivate()\\n mb.eq_g14.deactivate()\\n mb.eq_g4.deactivate()\\n mb.eq_g5.deactivate()\\n mb.eq_g2.deactivate()\\n mb.Tg_GW.fix(0.0)\\n mb.Tg_refractory.fix(0.0)\\n mb.Tw_Wamb.fix()\\n mb.Tw.fix()\\n mb.Nuw.fix()\\n mb.Nu_ext.fix()\\n mb.hw.fix()\\n mb.hext.fix()\\n mb.hext2.fix()\\n mb.U.fix()\\n mb.Uw.fix()\\n mb.Pr_ext.fix()\\n mb.Ra.fix()\\n mb.Re.fix()\\n ###\\n \\n\\n '''\\n ts = time.time() \\n # Initialize fuel reactor\\n flowsheet.MB_fuel._initialize(outlvl=1,\\n optarg={\\\"tol\\\" : 1e-8,\\n \\\"max_cpu_time\\\" : 600,\\n \\\"print_level\\\" : 5,\\n \\\"halt_on_ampl_error\\\": 'yes'}) \\n '''\\n\\n # Create a solver\\n opt = SolverFactory('ipopt')\\n opt.options = {'tol': 1e-8,\\n 'linear_solver' : 'ma27',\\n 'bound_push': 1e-8,\\n 'max_cpu_time': 600,\\n 'print_level': 5,\\n 'halt_on_ampl_error': 'yes'}\\n flowsheet.write('fs.nl')\\n\\n with open('dyn_fs.txt','w') as f:\\n flowsheet.display(ostream=f)\\n\\n print('Constraints violated pre-solve:')\\n for const in flowsheet.MB_fuel.component_objects(Constraint,active=True):\\n if not isinstance(const,SimpleConstraint):\\n for idx in const:\\n if (value(const[idx].body) > value(const[idx].upper) + 1.0e-7) or \\\\\\n (value(const[idx].body) < value(const[idx].lower) - 1.0e-7):\\n print(const.name,idx)\\n else:\\n if (value(const.body) > value(const.upper) + 1.0e-7) or \\\\\\n (value(const.body) < value(const.lower) - 1.0e-7):\\n print(const.name)\\n print('- - -\\\\n')\\n\\n print('Variable bounds violated pre-solve:')\\n for var in flowsheet.MB_fuel.component_objects(Var,active=True):\\n # don't use IndexedComponent here, variables are always indexed components \\n # could also do this by iterating over component_objects(SimpleVar)...?\\n if not isinstance(var,SimpleVar):\\n for idx in var:\\n if not (var[idx].lb is None):\\n if (var[idx].value < var[idx].lb - 1.0e-7):\\n pdb.set_trace()\\n print(var.name,idx)\\n if not (var[idx].ub is None):\\n if (var[idx].value > var[idx].ub + 1.0e-7):\\n pdb.set_trace()\\n print(var.name,idx)\\n else:\\n if (var.value > var.ub + 1.0e-7) or \\\\\\n (var.value < var.lb - 1.0e-7):\\n print(var.name)\\n print('- - -\\\\n')\\n\\n # initialized at steady state, works regardless:\\n flowsheet.strip_bounds()\\n\\n for z in mb.z:\\n for t in mb.t:\\n mb.Cg[z,'CH4',t].setlb(1e-6)\\n\\n\\n # want a function to integrate the model one step from a specified time point\\n # will call for all t\\n integrate(flowsheet,0)\\n \\n #results = opt.solve(flowsheet,tee=True,symbolic_solver_labels=False,\\n # keepfiles=False)\\n\\n #with open('dyn_fs_sol.txt','w') as f:\\n # flowsheet.display(ostream=f)\\n\\n\\n \\n '''\\n \\n print(\\\"\\\\n\\\")\\n print(\\\"----------------------------------------------------------\\\")\\n print('Total simulation time: ', value(time.time() - ts), \\\" s\\\")\\n print(\\\"----------------------------------------------------------\\\")\\n\\n \\n # Print some variables \\n print_summary_fuel_reactor(flowsheet) \\n\\n # Plot some variables \\n #results_plot_fuel_reactor(flowsheet) \\n\\n #with open('m_fs.txt','w') as f:\\n # flowsheet.display(ostream=f)\\n\\n # Store the flowsheet \\n '''\\n return flowsheet\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03b4e4ce6255aeb472f7ce4bfbf83a15\",\n \"score\": \"0.4981729\",\n \"text\": \"def fuel_for_fuel(fuel: int) -> int:\\n fuel_needed = 0\\n additional_fuel = fuel\\n\\n while True:\\n additional_fuel = launch_fuel_required(additional_fuel)\\n if additional_fuel <= 0:\\n break\\n \\n fuel_needed += additional_fuel\\n \\n return fuel_needed\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54aa4b9cdbe040d77b4b7f7e7035ba0e\",\n \"score\": \"0.49801105\",\n \"text\": \"def testAddTax(self):\\n\\tdates = pd.date_range(dt.datetime(2015,01,01), dt.datetime(2025,01,01), freq = 'D')\\n\\ta = range(0,len(dates))\\n\\ta1 = np.array([100*1.0001**i for i in a])\\n\\tb = df.DataFrame({'income':a1}, index = dates)\\n\\ta2 = a1*.05\\n\\ta3 = a1*.03\\n\\td = df.DataFrame({'depreciation':a2,'interest':a3}, index = dates)\\n\\tr = 0.35\\n\\tkw2 = {'basis':b}\\n\\tcr = pf.TaxCredit(name = 'ITC', refundable = False, kind = 'Fractional', rate = 0.15, **kw2)\\n\\tc = [cr]\\n\\n\\tkwargs = {}\\n\\tkwargs['name'] = 'fed1'\\n\\tkwargs['rate'] = r\\n\\tt = pf.FractionalTax(**kwargs)\\n\\tmanager = pf.TaxManager()\\n\\tmanager.create_tax(kind = 'Fractional', **kwargs)\\n\\t\\n\\tself.assertTrue(t.rate == manager.taxes['fed1'].rate)\\n\\tself.assertTrue(t.name == manager.taxes['fed1'].name)\\n\\n\\n\\tmanager= pf.TaxManager()\\n\\tmanager.create_tax(kind= 'Fractional', **kwargs)\\n\\tmanager.add_revenue(b, name = 'US')\\n\\tmanager.add_deductions(d, name = 'US')\\t\\n\\n\\tmanager.associate_revenue('fed1', ['US_income'])\\n\\tmanager.associate_deductions('fed1', ['US_depreciation','US_interest'])\\n\\tkwargs2 = {}\\n\\tkwargs2['name'] = 'fed1'\\n\\tkwargs2['rate'] = r\\n\\tkwargs2['basis'] = b\\n\\tkwargs2['deductions'] = d\\n\\t\\n\\tt = pf.FractionalTax(**kwargs2)\\n\\tt.basis.rename(columns = lambda x: \\\"US_%s\\\" % x, inplace = True)\\n\\tt.deductions.rename(columns = lambda x: \\\"US_%s\\\" % x, inplace = True)\\n\\tmanager.build_tax_schedule()\\n\\tself.assertTrue((t.basis == manager.taxes['fed1'].basis).all().all())\\n\\tself.assertTrue((t.deductions == manager.taxes['fed1'].deductions).all().all())\\n\\tself.assertTrue(t.rate == manager.taxes['fed1'].rate)\\n\\tself.assertTrue(t.name == manager.taxes['fed1'].name)\\n\\n\\t#Test that fractional tax is created correctly\\n\\t\\n\\n\\t#Test that tax is created correctly with associated income, depreciation columns\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b175bbeb09505ebd751e60f0ceb93f8a\",\n \"score\": \"0.49752685\",\n \"text\": \"def plant(self, *legumes: veg.Vegetable):\\n legumes = [self.__get_vegetable(dict) for dict in legumes ]\\n\\n for leg in legumes:\\n\\n reste = self.mygarden.add(leg)\\n print(f\\\"Gardener plants {leg.get_nb_graine()} {type(leg).__name__}.s\\\")\\n\\n if reste:\\n legumes.insert(legumes.index(leg) + 1, reste)\\n self.__next_garden()\\n continue\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ea6775487bbea1d069597779ace8883\",\n \"score\": \"0.49738714\",\n \"text\": \"def add_transport_modes_and_approved_fuels_0093(apps, schema_editor):\\n\\n db_alias = schema_editor.connection.alias\\n\\n # By retrieving the models via apps.get_model, we get one appropriately\\n # versioned for this migration (so this shouldn't ever need to be\\n # maintained if fields change)\\n transport_mode = apps.get_model('api', 'TransportMode')\\n transport_mode.objects.using(db_alias).bulk_create([\\n transport_mode(\\n name='Truck',\\n effective_date='2017-01-01'\\n ),\\n transport_mode(\\n name='Rail',\\n effective_date='2017-01-01'\\n ),\\n transport_mode(\\n name='Marine',\\n effective_date='2017-01-01'\\n ),\\n transport_mode(\\n name='Adjacent',\\n effective_date='2017-01-01'\\n ),\\n transport_mode(\\n name='Pipeline',\\n effective_date='2017-01-01'\\n )\\n ])\\n\\n approved_fuel = apps.get_model('api', 'ApprovedFuel')\\n approved_fuel.objects.using(db_alias).bulk_create(\\n map(lambda af: approved_fuel(\\n name=af,\\n effective_date='2017-01-01'\\n ), approved_fuel_names_0093)\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ef4ee47703a673fc606c21b38202c26\",\n \"score\": \"0.49727106\",\n \"text\": \"def Fuel_type_reefer_DEF(self):\\n\\n #Input constants\\n\\n generic_trans_amount = \\\"$5.00\\\" # any value that gets an approval from the host\\n card_to_use_NGFC = 'NGFC' # using this card to get all commercial prompts\\n \\n amounts_to_dispense = {\\n \\\"buffer_1\\\":{\\n \\\"grade\\\": 3,\\n \\\"value\\\": \\\"3\\\"\\n },\\n \\\"buffer_2\\\":{\\n \\\"grade\\\": 1,\\n \\\"value\\\": \\\"2\\\"\\n }\\n } \\n\\n prompts = {\\n \\\"Additional Products Y/N?\\\": { \\n \\\"buttons\\\": [\\\"Yes\\\"]\\n },\\n \\\"Do you want to print a receipt?\\\": {\\n \\\"buttons\\\": [\\\"No\\\"]\\n },\\n \\\"Select fuel products to dispense\\\": {\\n \\\"buttons\\\": [\\\"Reefer fuel\\\"]\\n },\\n \\\"DEF?\\\": {\\n \\\"buttons\\\": [\\\"Yes\\\"]\\n }\\n }\\n\\n messages_to_verify = {\\n 'preauth_request_verifications': {\\n 'Fuel Purchase': '500', \\n '001 - Wex OTR Flags': 'C - Commercial'\\n },\\n 'preauth_response_verifications': {\\n 'Response Code': '2', \\n 'Approved Amount': '000000500',\\n '002 - Wex OTR Non-Fuel Product Data - Product code: CADV (Company funds cash advance)': 'Amount: 0.00',\\n '002 - Wex OTR Non-Fuel Product Data - Product code: MERC (Default category for merchandise)': 'Amount: 30.00', \\n f'003 - Wex OTR Fuel Product Limits - Product Code: {self.helpers.grade_1_Product }': 'Product limit: 50.00',\\n f'003 - Wex OTR Fuel Product Limits - Product Code: {self.helpers.grade_2_Product }': 'Product limit: 50.00',\\n f'003 - Wex OTR Fuel Product Limits - Product Code: {self.helpers.grade_3_Product }': 'Product limit: 50.00',\\n '005 - Wex OTR Customer Information': 'ABC TRUCKING DENVER CO234W987'\\n },\\n 'capture_request_verifications': {\\n 'Fuel Purchase': '500', \\n '001 - Wex OTR Flags': 'C - Commercial'\\n },\\n 'capture_response_verifications': {\\n 'Response Code': '0', \\n 'Approved Amount': '000000500',\\n '002 - Wex OTR Non-Fuel Product Data - Product code: CADV (Company funds cash advance)': 'Amount: 20.00',\\n '002 - Wex OTR Non-Fuel Product Data - Product code: MERC (Default category for merchandise)': 'Amount: 30.00',\\n f'003 - Wex OTR Fuel Product Limits - Product Code: {self.helpers.grade_1_Product }': 'Product limit: 50.00',\\n f'003 - Wex OTR Fuel Product Limits - Product Code: {self.helpers.grade_2_Product }': 'Product limit: 50.00',\\n f'003 - Wex OTR Fuel Product Limits - Product Code: {self.helpers.grade_3_Product }': 'Product limit: 50.00',\\n '005 - Wex OTR Customer Information': 'ABC TRUCKING DENVER CO234W987'}\\n }\\n\\n #Output verificacions\\n\\n receipt_data = [f\\\"{self.helpers.grade_1_Name_reefer} CA PUMP# 1\\\",\\n \\\"2.000 GAL @ $1.000/GAL $2.00 99\\\",\\n f\\\"{self.helpers.grade_3_Name} CA PUMP# 1\\\",\\n \\\"3.000 GAL @ $1.000/GAL $3.00 99\\\",\\n \\\"Subtotal = $5.00\\\",\\n \\\"Tax = $0.00\\\",\\n \\\"Total = $5.00\\\",\\n \\\"Change Due = $0.00\\\",\\n \\\"Credit $5.00\\\"]\\n\\n self.helpers.prepay_transaction(\\n card=card_to_use_NGFC,\\n prepay_amount=generic_trans_amount,\\n prompts=prompts,\\n amounts_to_dispense=amounts_to_dispense,\\n messages_to_verify=messages_to_verify,\\n receipt_data=receipt_data\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69ba4745de36cf5acc1b2bf59f3f30b1\",\n \"score\": \"0.4964572\",\n \"text\": \"def add_stock(self):\\n self.stock = SupplyStock(supply_node=self.name, scenario=self.scenario)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ce187e0acf4d0801e01fc0f66411853\",\n \"score\": \"0.49607182\",\n \"text\": \"def addFleet(self, fleet:Fleet):\\n print (\\\"ADDING FLEET\\\")\\n self.fleet = fleet\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2cc26d2414953d16f8db2422d86f4f7e\",\n \"score\": \"0.4944704\",\n \"text\": \"def __init__(self, name, fuel, reliability):\\n super().__init__(name, fuel)\\n self.reliability = reliability\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2cc26d2414953d16f8db2422d86f4f7e\",\n \"score\": \"0.4944704\",\n \"text\": \"def __init__(self, name, fuel, reliability):\\n super().__init__(name, fuel)\\n self.reliability = reliability\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d01c2e4ccd3531f3eb532b265db7a39\",\n \"score\": \"0.49305838\",\n \"text\": \"def _add_species_basket(self, weight, count):\\n self._logger.debug('Add species basket. wt: {}, ct: {}'.format(weight, count))\\n if self._current_species_comp_item is None:\\n self._logger.error('Species ID / Current Species Comp ID is None')\\n return\\n\\n try:\\n new_basket = SpeciesCompositionBaskets.create(\\n species_comp_item=self._current_species_comp_item,\\n basket_weight_itq=weight,\\n fish_number_itq=count,\\n created_by=ObserverDBUtil.get_current_user_id(),\\n # FIELD-2087: Using default dateformat for time display; reformat before sync later\\n created_date=ObserverDBUtil.get_arrow_datestr(date_format=ObserverDBUtil.default_dateformat),\\n is_fg_tally_local=1 if self._is_fixed_gear else None,\\n is_subsample=None\\n )\\n self._baskets_model.add_basket(new_basket)\\n self._logger.info(f'Added basket wt: {weight} ct: {count}')\\n\\n finally:\\n self._calculate_totals()\\n self.basketAdded.emit()\\n # self.dataExistsChanged.emit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36e7bc57113f2a17351d5bfe5b5d9052\",\n \"score\": \"0.49286556\",\n \"text\": \"def __init__(self, name, color, max_size=2, fuel=10):\\n Backpack.__init__(self,name,color,max_size)\\n self.fuel = fuel\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbcd41a83b91d34ba26cdcd9f66eae16\",\n \"score\": \"0.49237648\",\n \"text\": \"def __init__(self,name,color,max_size=2,fuel=10):\\n Backpack.__init__(self,name,color,max_size)\\n self.fuel=fuel\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8dacbd577ced23b408179ae46706c72\",\n \"score\": \"0.49079818\",\n \"text\": \"def fly(self,fuel):\\n if self.fuel-fuel>=0:\\n self.fuel-=fuel\\n else:\\n print(\\\"Not enough fuel!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bda09fcd2f27d0272d4d2a849788262c\",\n \"score\": \"0.48983583\",\n \"text\": \"def calc_proposed_HF_consumption (self):\\n building_data = self.comp_specs['building inventory']\\n percent_savings = self.comp_specs['cohort savings percent'] / 100.0\\n #~ fuel_type = 'Fuel Oil'\\n #~ try:\\n fuel_types = ['Fuel Oil',\\n 'Natural Gas',\\n 'HW District',\\n 'Propane',\\n 'Biomass']\\n for fuel_type in fuel_types:\\n fuel_vals = building_data[fuel_type + ' Post']\\n idx = fuel_vals.isnull()\\n fuel_vals[idx] = building_data[fuel_type][idx] *\\\\\\n (1 - percent_savings)\\n\\n #~ except TypeError:\\n\\n\\n\\n #~ self.comp_specs['building inventory'][\\\"Fuel Oil Post\\\"] = \\\\\\n #~ self.comp_specs['building inventory'][\\\"Fuel Oil\\\"]*\\\\\\n #~ self.comp_specs['cohort savings multiplier']\\n\\n\\n\\n\\n # by fuel type\\n self.proposed_fuel_Hoil_consumption = \\\\\\n building_data['Fuel Oil Post'].sum()\\n self.proposed_fuel_lng_consumption = \\\\\\n building_data['Natural Gas Post'].fillna(0).sum()\\n self.proposed_fuel_hr_consumption = \\\\\\n building_data['HW District Post'].fillna(0).sum()\\n self.proposed_fuel_propane_consumption = \\\\\\n building_data['Propane Post'].fillna(0).sum()\\n self.proposed_fuel_biomass_consumption = \\\\\\n building_data['Biomass Post'].fillna(0).sum()\\n\\n # mmbtu\\n self.proposed_HF_consumption = \\\\\\n self.proposed_fuel_Hoil_consumption / constants.mmbtu_to_gal_HF +\\\\\\n self.proposed_fuel_hr_consumption/constants.mmbtu_to_gal_HF+\\\\\\n self.proposed_fuel_lng_consumption/constants.mmbtu_to_Mcf+\\\\\\n self.proposed_fuel_propane_consumption/constants.mmbtu_to_gal_LP+\\\\\\n self.proposed_fuel_biomass_consumption/constants.mmbtu_to_cords\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0eba9dbb71df991f6d1d8c0e75cdfb7d\",\n \"score\": \"0.48977476\",\n \"text\": \"def vessel_bloodflow(bf_cr, bf_ms, bf_fat, bf_sk, bf_ava_hand, bf_ava_foot):\\n xbf = bf_cr + bf_ms + bf_fat + bf_sk\\n\\n bf_art = np.zeros(17)\\n bf_vein = np.zeros(17)\\n\\n #Head\\n bf_art[0] = xbf[0]\\n bf_vein[0] = xbf[0]\\n\\n #Neck (+Head)\\n bf_art[1] = xbf[1] + xbf[0]\\n bf_vein[1] = xbf[1] + xbf[0]\\n\\n #Chest\\n bf_art[2] = xbf[2]\\n bf_vein[2] = xbf[2]\\n\\n #Back\\n bf_art[3] = xbf[3]\\n bf_vein[3] = xbf[3]\\n\\n #Pelvis (+Thighs, Legs, Feet, AVA_Feet)\\n bf_art[4] = xbf[4] + xbf[11:17].sum() + 2*bf_ava_foot\\n bf_vein[4] = xbf[4] + xbf[11:17].sum() + 2*bf_ava_foot\\n\\n #L.Shoulder (+Arm, Hand, (arteryのみAVA_Hand))\\n bf_art[5] = xbf[5:8].sum() + bf_ava_hand\\n bf_vein[5] = xbf[5:8].sum()\\n\\n #L.Arm (+Hand)\\n bf_art[6] = xbf[6:8].sum() + bf_ava_hand\\n bf_vein[6] = xbf[6:8].sum()\\n\\n #L.Hand\\n bf_art[7] = xbf[7] + bf_ava_hand\\n bf_vein[7] = xbf[7]\\n\\n #R.Shoulder (+Arm, Hand, (arteryのみAVA_Hand))\\n bf_art[8] = xbf[8:11].sum() + bf_ava_hand\\n bf_vein[8] = xbf[8:11].sum()\\n\\n #R.Arm (+Hand)\\n bf_art[9] = xbf[9:11].sum() + bf_ava_hand\\n bf_vein[9] = xbf[9:11].sum()\\n\\n #R.Hand\\n bf_art[10] = xbf[10] + bf_ava_hand\\n bf_vein[10] = xbf[10]\\n\\n #L.Thigh (+Leg, Foot, (arteryのみAVA_Foot))\\n bf_art[11] = xbf[11:14].sum() + bf_ava_foot\\n bf_vein[11] = xbf[11:14].sum()\\n\\n #L.Leg (+Foot)\\n bf_art[12] = xbf[12:14].sum() + bf_ava_foot\\n bf_vein[12] = xbf[12:14].sum()\\n\\n #L.Foot\\n bf_art[13] = xbf[13] + bf_ava_foot\\n bf_vein[13] = xbf[13]\\n\\n #R.Thigh (+Leg, Foot, (arteryのみAVA_Foot))\\n bf_art[14] = xbf[14:17].sum() + bf_ava_foot\\n bf_vein[14] = xbf[14:17].sum()\\n\\n #R.Leg (+Foot)\\n bf_art[15] = xbf[15:17].sum() + bf_ava_foot\\n bf_vein[15] = xbf[15:17].sum()\\n\\n #R.Foot\\n bf_art[16] = xbf[16] + bf_ava_foot\\n bf_vein[16] = xbf[16]\\n\\n return bf_art, bf_vein\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e17c90bb505b040d69dd15f68570980\",\n \"score\": \"0.4893315\",\n \"text\": \"def fill_gas_tank():\\n print(\\\"This car doesn't need a gas tank!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c892f382168fd2d1e7ff5ba96624b188\",\n \"score\": \"0.48805892\",\n \"text\": \"def calculate_fuel_costs(self):\\r\\n self.coal_price = self.coal_cost_fixed + self.coal_cost_variable*self.coal_fuel\\r\\n self.gas_price= self.gas_cost_fixed+self.gas_cost_variable*self.gas_fuel\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81c9bebc65222ad959a0d1280b3e8ac9\",\n \"score\": \"0.48712495\",\n \"text\": \"def berth_invest(self, year, handysize, handymax, panamax):\\n\\n # report on the status of all berth elements\\n self.report_element(Berth, year)\\n self.report_element(Quay_wall, year)\\n self.report_element(Cyclic_Unloader, year)\\n self.report_element(Continuous_Unloader, year)\\n self.report_element(Conveyor_Quay, year)\\n self.report_element(Storage, year)\\n self.report_element(Conveyor_Hinter, year)\\n self.report_element(Unloading_station, year)\\n if self.debug:\\n print('')\\n print(' Start analysis:')\\n\\n # calculate berth occupancy\\n berth_occupancy_planned, berth_occupancy_online, crane_occupancy_planned, crane_occupancy_online = self.calculate_berth_occupancy(\\n year, handysize, handymax, panamax)\\n factor, waiting_time_occupancy = self.waiting_time(year)\\n if self.debug:\\n print(' Berth occupancy planned (@ start of year): {}'.format(berth_occupancy_planned))\\n print(' Berth occupancy online (@ start of year): {}'.format(berth_occupancy_online))\\n print(' Crane occupancy planned (@ start of year): {}'.format(crane_occupancy_planned))\\n print(' Crane occupancy online (@ start of year): {}'.format(crane_occupancy_online))\\n print(' waiting time factor (@ start of year): {}'.format(factor))\\n print(' waiting time occupancy (@ start of year): {}'.format(waiting_time_occupancy))\\n\\n while berth_occupancy_planned > self.allowable_berth_occupancy:\\n\\n # add a berth when no crane slots are available\\n if not (self.check_crane_slot_available()):\\n if self.debug:\\n print(' *** add Berth to elements')\\n berth = Berth(**agribulk_defaults.berth_data)\\n berth.year_online = year + berth.delivery_time\\n self.elements.append(berth)\\n\\n berth_occupancy_planned, berth_occupancy_online, crane_occupancy_planned, crane_occupancy_online = self.calculate_berth_occupancy(\\n year, handysize, handymax, panamax)\\n if self.debug:\\n print(' Berth occupancy planned (after adding berth): {}'.format(berth_occupancy_planned))\\n print(' Berth occupancy online (after adding berth): {}'.format(berth_occupancy_online))\\n\\n # check if a quay is needed\\n berths = len(self.find_elements(Berth))\\n quay_walls = len(self.find_elements(Quay_wall))\\n if berths > quay_walls:\\n length_v = max(agribulk_defaults.handysize_data[\\\"LOA\\\"],agribulk_defaults.handymax_data[\\\"LOA\\\"],\\n agribulk_defaults.panamax_data[\\\"LOA\\\"]) # average size\\n draft = max(agribulk_defaults.handysize_data[\\\"draft\\\"],agribulk_defaults.handymax_data[\\\"draft\\\"],\\n agribulk_defaults.panamax_data[\\\"draft\\\"])\\n # apply PIANC 2014:\\n # see Ijzermans, 2019 - infrastructure.py line 107 - 111\\n if quay_walls == 0:\\n # - length when next quay is n = 1\\n length = length_v + 2 * 15 # ref: PIANC 2014\\n elif quay_walls == 1:\\n # - length when next quay is n > 1\\n length = 1.1 * berths * (length_v + 15) - (length_v + 2 * 15) # ref: PIANC 2014\\n else:\\n length = 1.1 * berths * (length_v + 15) - 1.1 * (berths - 1) * (length_v + 15)\\n\\n # - depth\\n quay_wall = Quay_wall(**agribulk_defaults.quay_wall_data)\\n depth = np.sum([draft, quay_wall.max_sinkage, quay_wall.wave_motion, quay_wall.safety_margin])\\n self.quay_invest(year, length, depth)\\n\\n berth_occupancy_planned, berth_occupancy_online, crane_occupancy_planned, crane_occupancy_online = self.calculate_berth_occupancy(\\n year, handysize, handymax, panamax)\\n if self.debug:\\n print(' Berth occupancy planned (after adding quay): {}'.format(berth_occupancy_planned))\\n print(' Berth occupancy online (after adding quay): {}'.format(berth_occupancy_online))\\n\\n # check if a crane is needed\\n if self.check_crane_slot_available():\\n self.crane_invest(year)\\n\\n berth_occupancy_planned, berth_occupancy_online, crane_occupancy_planned, crane_occupancy_online = self.calculate_berth_occupancy(\\n year, handysize, handymax, panamax)\\n if self.debug:\\n print(' Berth occupancy planned (after adding crane): {}'.format(berth_occupancy_planned))\\n print(' Berth occupancy online (after adding crane): {}'.format(berth_occupancy_online))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be0ed39569cb97ebe043e32bbf2808bc\",\n \"score\": \"0.4857554\",\n \"text\": \"def refill(self, fuel):\\n if self.fuel_amount == self.fuel_capacity:\\n raise ToMuchFuel('You already have full tank!')\\n\\n self._fuel_amount += fuel\\n\\n if self.fuel_amount > self.fuel_capacity:\\n self._fuel_amount = self.fuel_capacity\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c310cb5f6a3542e5fec828b27279159\",\n \"score\": \"0.4856016\",\n \"text\": \"def remove_credit_calculation_fuel_types_0111(apps, schema_editor):\\n db_alias = schema_editor.connection.alias\\n\\n approved_fuel = apps.get_model('api', 'ApprovedFuel')\\n\\n approved_fuel.objects.using(db_alias).bulk_create([\\n approved_fuel(\\n name=\\\"Petroleum-based diesel fuel or renewable fuel in relation \\\"\\n \\\"to diesel class fuel\\\",\\n effective_date=\\\"2017-01-01\\\",\\n credit_calculation_only=True\\n ),\\n approved_fuel(\\n name=\\\"Petroleum-based gasoline, natural gas-based gasoline or \\\"\\n \\\"renewable fuel in relation to gasoline class fuel\\\",\\n effective_date=\\\"2017-01-01\\\",\\n credit_calculation_only=True\\n ),\\n approved_fuel(\\n name=\\\"Petroleum-based diesel fuel or diesel fuel produced from \\\"\\n \\\"biomass\\\",\\n effective_date=\\\"2017-01-01\\\",\\n credit_calculation_only=True\\n ),\\n approved_fuel(\\n name=\\\"Petroleum-based gasoline, natural gas-based gasoline or \\\"\\n \\\"gasoline produced from biomass\\\",\\n effective_date=\\\"2017-01-01\\\",\\n credit_calculation_only=True\\n ),\\n approved_fuel(\\n name=\\\"Hydrogenation-derived renewable diesel fuel\\\",\\n effective_date=\\\"2017-01-01\\\",\\n credit_calculation_only=True\\n ),\\n approved_fuel(\\n name=\\\"Renewable fuel in relation to diesel class fuel\\\",\\n effective_date=\\\"2017-01-01\\\",\\n credit_calculation_only=True\\n ),\\n approved_fuel(\\n name=\\\"Renewable fuel in relation to gasoline class fuel\\\",\\n effective_date=\\\"2017-01-01\\\",\\n credit_calculation_only=True\\n )\\n ])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb1821a0dbd0c9c1f90f2f8d46a3bf54\",\n \"score\": \"0.48557195\",\n \"text\": \"def test_additional_fuel_requirements():\\n assert list(day_1.additional_fuel_requirements(2)) == []\\n assert list(day_1.additional_fuel_requirements(654)) == [216, 70, 21, 5]\\n assert list(day_1.additional_fuel_requirements(33584)) == [\\n 11192, 3728, 1240, 411, 135, 43, 12, 2\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"338e251f70d29585f913f8c0635001e6\",\n \"score\": \"0.48507738\",\n \"text\": \"def initialise_fluids(self, nw):\\n N_2 = 0.7655\\n O_2 = 0.2345\\n\\n n_fuel = 1\\n lamb = 3\\n\\n fact_fuel = {}\\n sum_fuel = 0\\n for f in self.fuel_list:\\n fact_fuel[f] = 0\\n for i in self.inl:\\n fact_fuel[f] += i.fluid.val[f] / 2\\n sum_fuel += fact_fuel[f]\\n\\n for f in self.fuel_list:\\n fact_fuel[f] /= sum_fuel\\n\\n m_co2 = 0\\n m_h2o = 0\\n m_fuel = 0\\n for f in self.fuel_list:\\n m_co2 += (n_fuel * self.fuels[f]['C'] * molar_masses[self.co2] *\\n fact_fuel[f])\\n m_h2o += (n_fuel * self.fuels[f]['H'] /\\n 2 * molar_masses[self.h2o] * fact_fuel[f])\\n m_fuel += n_fuel * molar_masses[f] * fact_fuel[f]\\n\\n n_o2 = (m_co2 / molar_masses[self.co2] +\\n 0.5 * m_h2o / molar_masses[self.h2o]) * lamb\\n\\n m_air = n_o2 * molar_masses[self.o2] / O_2\\n m_fg = m_air + m_fuel\\n\\n m_o2 = n_o2 * molar_masses[self.o2] * (1 - 1 / lamb)\\n m_n2 = N_2 * m_air\\n\\n fg = {\\n self.n2: m_n2 / m_fg,\\n self.co2: m_co2 / m_fg,\\n self.o2: m_o2 / m_fg,\\n self.h2o: m_h2o / m_fg\\n }\\n\\n o = self.outl[2]\\n for fluid, x in o.fluid.val.items():\\n if not o.fluid.val_set[fluid] and fluid in fg.keys():\\n o.fluid.val[fluid] = fg[fluid]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85cbeb6d0afecead5d1523bd689337f2\",\n \"score\": \"0.48417068\",\n \"text\": \"def calculate_storage_tank_obj(self, tsm, hk_inp_temp, hk_inp_volfl_m3s, hk_out_temp, chp_inp_temp, chp_out_temp,\\n chp_inp_volfl_m3s, gb_inp_temp, gp_out_temp, gb_inp_volfl_m3s, dhw_inp_temp, dhw_out_temp, dhw_inp_volfl_m3s, \\n el_heat_status, current_time, t_ambient):\\n # inp enters unit & leaves storage tank, out leaves unit & enters storage tank\\n # hk - heating circuit, chp - chp unit, gb - gas boiler\\n # temp - temperature; volfl - volume flow\\n \\n # heating circuit - output from the system - water taken out of the storage tank by the heating system: hk_inp_temp\\n hk_rho = utils.rho_fluid_water(hk_inp_temp, self.p_atm, 1)\\n hk_cp = utils.cp_fluid_water(hk_inp_temp, self.p_atm, 1)\\n hk_mstr = hk_rho * hk_inp_volfl_m3s # in kg/s = kg/m3 * m3/s \\n # combined power and heat unit - input to the storage tank: chp_out_temp\\n chp_rho = utils.rho_fluid_water(chp_out_temp, self.p_atm, 1)\\n chp_cp = utils.cp_fluid_water(chp_out_temp, self.p_atm, 1)\\n chp_mstr = chp_rho * chp_inp_volfl_m3s # in kg/s = kg/m3 * m3/s \\n # gas boiler unit - input to the storage tank\\n gb_rho = utils.rho_fluid_water(gp_out_temp, self.p_atm, 1)\\n gb_cp = utils.cp_fluid_water(gp_out_temp, self.p_atm, 1)\\n gb_mstr = gb_rho * gb_inp_volfl_m3s # in kg/s = kg/m3 * m3/s \\n\\n # mixing of streams from chp and gas boiler units - input to the storage tank\\n mix_up_mstr = chp_mstr + gb_mstr\\n if(mix_up_mstr != 0.0):\\n t_mix_up = (gb_mstr * gb_cp * gp_out_temp + chp_mstr * chp_cp * chp_out_temp) / (mix_up_mstr * hk_cp)\\n # iterate out the temperature of the mixed flow of hot water\\n mix_cp = utils.cp_fluid_water(t_mix_up, self.p_atm, 1)\\n t_1 = (gb_mstr * gb_cp * gp_out_temp + chp_mstr * chp_cp * chp_out_temp) / (mix_up_mstr * mix_cp)\\n else:\\n t_mix_up = min(gp_out_temp, chp_out_temp)\\n mix_cp = utils.cp_fluid_water(t_mix_up, self.p_atm, 1)\\n t_1 = t_mix_up\\n anz_iter = 0\\n while ((abs(t_1 - t_mix_up) > 0.000001) and (anz_iter < 1000)):\\n t_mix_up = t_1\\n mix_cp = utils.cp_fluid_water(t_mix_up, self.p_atm, 1)\\n t_1 = (gb_mstr * gb_cp * gp_out_temp + chp_mstr * chp_cp * chp_out_temp) / (mix_up_mstr * mix_cp)\\n anz_iter = anz_iter + 1\\n if (anz_iter == 1000):\\n print('actual time = {}'.format(self.acttime))\\n print('error = {}'.format(t_mix_up - t_1))\\n print(' | t in C | mstr in kg/s ')\\n print(' mixed | {} | {} '.format(t_mix_up,mix_up_mstr))\\n print(' chp | {} | {} '.format(chp_out_temp,chp_mstr))\\n print(' gas boi | {} | {} '.format(gp_out_temp,gb_mstr))\\n print('---------+----------+-----------------')\\n \\n # balance of the hot water in the upper part of the storage tank - plus means input of hot water, minus means more hot water being taken out from the tank\\n netto_hot_mstr = mix_up_mstr - hk_mstr # in kg/s = kg/m3 * m3/s \\n netto_cold_mstr = hk_mstr - mix_up_mstr # in kg/s = kg/m3 * m3/s \\n #print('mix_up_mstr = {}; hk_mstr = {}'.format(mix_up_mstr, hk_mstr))\\n # netto gain of hot mass in the upper part of storage\\n hot_gain = netto_hot_mstr * self.tsm.get_timestep() # in kg = kg/s * s\\n hot_volume = hot_gain / utils.rho_fluid_water(t_mix_up, self.p_atm, 1) # in m3 = kg / kg/m3\\n # misc number of slices that are fully filled hot water being pumped in m3\\n #print('hot vol = {}; sliceVol = {}: mix_mstr = {}; hk_mstr = {};chp_mstr = {}; gb_mstr = {};hk_inp_volfl_m3s = {}, typ ={}'.format(hot_volume, self.slice_volume,mix_up_mstr , hk_mstr, chp_mstr, gb_mstr, hk_inp_volfl_m3s, type(hk_inp_volfl_m3s)))\\n anz_slices = int(hot_volume / self.slice_volume)\\n # height_slices --> self.effective_heigth\\n # hot_volume --> self.effective_volume\\n height_slices = hot_volume * self.effective_height / self.effective_volume\\n #print('hot_gain = {}; hot_volume = {}; anz_slices = {}; height_slices = {}'.format(hot_gain,hot_volume,anz_slices,height_slices))\\n # hk_inp_temp = self.t24\\n cold_cp = utils.cp_fluid_water(hk_inp_temp, self.p_atm, 1)\\n \\n \\n \\n mix_down_mstr = hk_mstr + netto_hot_mstr # = mix_up_mstr = chp_mstr + gb_mstr\\n t_st = self.get_output_temperature()\\n cptst = utils.cp_fluid_water(t_st, self.p_atm, 1)\\n hk_cp_out = utils.cp_fluid_water(hk_out_temp, self.p_atm, 1)\\n if(mix_down_mstr != 0.0):\\n t_mix_down = (t_st * cptst * self.mstr_hw + hk_out_temp * hk_cp_out * hk_mstr) / (mix_down_mstr * cptst)\\n mix_cp = utils.cp_fluid_water(t_mix_down, self.p_atm, 1)\\n t_2 = (t_st * cptst * self.mstr_hw + hk_out_temp * hk_cp_out * hk_mstr) / (mix_down_mstr * mix_cp)\\n else:\\n # whole flow comes from from heating system - chp and boiler are most likely off\\n t_mix_down = hk_out_temp\\n mix_cp = utils.cp_fluid_water(t_mix_down, self.p_atm, 1)\\n t_2 = t_mix_down\\n anz_iter = 0\\n while((abs(t_2 - t_mix_down) > 0.000001) and (anz_iter < 1000)):\\n t_mix_down = t_2\\n mix_cp = utils.cp_fluid_water(t_mix_down, self.p_atm, 1)\\n t_2 = (t_st * cptst * self.mstr_hw + hk_out_temp * hk_cp_out * hk_mstr) / (mix_down_mstr * mix_cp)\\n anz_iter = anz_iter + 1\\n if (anz_iter == 1000):\\n print('actual time = {}'.format(self.acttime))\\n print('error = {}'.format(t_mix_down - t_2))\\n print(' | t in C | mstr in kg/s ')\\n print(' mixed | {} | {} '.format(t_mix_down,mix_down_mstr))\\n print(' st.tank | {} | {} '.format(t_st,mix_down_mstr))\\n print(' heating | {} | {} '.format(hk_out_temp,hk_mstr))\\n print('---------+----------+-----------------')\\n \\n \\n\\n #print('t_mix_up = {}; mstr = {}; wechsel = {}'.format(t_mix_up,netto_cold_mstr,self.slice_volume/(netto_cold_mstr*3.6),))\\n\\n # heat flow from heating appliances to the storage tank - convective influx with t_1, in W\\n Q_c_hot_inp = netto_hot_mstr * mix_cp * (t_mix_up - self.temp[0])\\n # heat flow from storage tank to the heating appliances to the heating appliances balanced with the input from heating system - convective influx\\n Q_c_cold_inp = netto_hot_mstr * cold_cp * (hk_inp_temp - self.temp[self.nr_calc - 1])\\n#\\n# ================================================================\\n # DOMESTIC HOT WATER - always from 0 to nr_calc-1 - only one flow direction possible\\n #tinp_dhw = dhw_inp_temp\\n #in_idx_dhw = 0\\n #out_idx_dhw = self.nr_calc - 1\\n dhw_mstr = dhw_inp_volfl_m3s * utils.rho_fluid_water(dhw_inp_temp, self.p_atm, 1)\\n#\\n# ----------------------------------------------------------------\\n\\n [tinp_hw, in_idx_hw, out_idx_hw] = self.calc_inputs_hw(netto_cold_mstr, hk_out_temp, t_mix_up)\\n# ----------------------------------------------------------------\\n self.calc_init_hydraulik(current_time, netto_cold_mstr, dhw_mstr, tinp_hw, dhw_inp_temp, in_idx_hw, out_idx_hw)\\n# ----------------------------------------------------------------\\n # domestic hot water\\n t_new_dhw = []\\n # heating water\\n t_new_hw = []\\n # \\n [t_new_hw, t_new_dhw] = self.solve_hydraulic(t_new_dhw, t_new_hw, tinp_hw, dhw_inp_temp, in_idx_hw, 0, out_idx_hw, self.nr_calc - 1, el_heat_status, t_ambient) \\n\\n# ----------------------------------------------------------------\\n # balances for all elements and heat exchange between them\\n\\n # \\n#\\n self.temp_dhw = t_new_dhw\\n for elem in self.t_dhw:\\n elem.set_average_temp(t_new_dhw[elem.get_id()])\\n self.temp = t_new_hw\\n\\n for elem in self.t_hw:\\n elem.set_average_temp(t_new_hw[elem.get_id()])\\n #print(' {:8.3f} {} {}; hk_inp_volfl_m3s = {}; gb_inp_volfl_m3s = {}; chp_inp_volfl_m3s = {}; dhw_inp_volfl_m3s = {}'.format(self.acttime, t_new_hw[6], self.t_hw[6].get_mstr(), hk_inp_volfl_m3s, gb_inp_volfl_m3s, chp_inp_volfl_m3s, dhw_inp_volfl_m3s))\\n #print(' {:8.3f} {} {} {} {} {}'.format(self.acttime, t_new_dhw[0], self.t_dhw[0].get_mstr(), self.t_dhw[0].get_mstr(), self.t_dhw[0].get_input_temp(), self.t_dhw[0].get_average_temp(), self.t_dhw[0].get_output_temp()))\\n#\\n# ----------------------------------------------------------------\\n # OUTPUTS\\n self.mstr_hw = netto_cold_mstr\\n self.mstr_dhw = dhw_mstr\\n self.out_idx_hw = out_idx_hw\\n if(self.mstr_hw !=0.0):\\n self.wechsel_zeit_in_h = self.slice_volume * utils.rho_fluid_water(tinp_hw, self.p_atm, 1) / (self.mstr_hw * 3600.0) # h = kg / kg/h = m3 * kg/m3 / (kg/s * 3600 s/h)\\n else:\\n self.wechsel_zeit_in_h = 0.0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68d31bf48746d89f05abf28f44abe8d8\",\n \"score\": \"0.48378253\",\n \"text\": \"def setUp(self,):\\n\\n\\t\\tself.make_constraint()\\n\\n\\t\\tself.set_types()\\n\\t\\tself.feasible = 1 # 1 = infeasible, 0 = feasible - store the value here because we'll reference it layer in a closure\\n\\n\\t\\tavailable_species = {}\\n\\t\\tfor huc in self.hucs: # prepopulate all the species so we can skip a condition later - don't use all species because it's possible that some won't be present. Only use the species in all the hucs\\n\\t\\t\\tfor species in huc.assemblage.all():\\n\\t\\t\\t\\tavailable_species[species.common_name] = 1\\n\\n\\t\\tself.available_species = available_species.keys()\\n\\t\\tlog.debug(\\\"Total Species in Area: {}\\\".format(len(available_species.keys())))\\n\\n\\t\\tself.eflows_nfe = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5afd3a65bdacc695828d423a78707bf\",\n \"score\": \"0.48360804\",\n \"text\": \"def upadate_leverage(self):\\n \\n all_asset = [0 for _ in range(self.n)] \\n all_liability = [0 for _ in range(self.n)] \\n all_equity = [0 for _ in range(self.n)] \\n for bank_index in range(self.n):\\n all_asset[bank_index] += self.AssetMat.get_int_asset(bank_index)\\n all_asset[bank_index] += self.ext_asset[bank_index]\\n \\n all_liability[bank_index] += self.AssetMat.get_int_liability(bank_index)\\n all_liability[bank_index] += self.ext_asset[bank_index]\\n \\n equity = all_asset[bank_index] - all_liability[bank_index] \\n\\n if equity < 0 : \\n self.default_set.add(bank_index)\\n equity = 0\\n # \\n \\n all_equity[bank_index] = equity\\n # \\n \\n for bank_idx_i in range(self.n):\\n for bank_index_j in range(self.n):\\n val = min(self.AssetMat.get_mat()[bank_idx_i, bank_index_j] , 0) / equity[bank_index_j]\\n self.LevMat.update_element(bank_idx_i, bank_index_j, val)\\n #\\n #\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2860a835873ec7e19fad60f2f0cc5aa5\",\n \"score\": \"0.48321462\",\n \"text\": \"def Fuel_type_both_NotDEF(self):\\n \\n #Input constants\\n\\n generic_trans_amount = \\\"$5.00\\\" # any value that gets an approval from the host \\n card_to_use_NGFC = 'NGFC' # using this card to get all commercial prompts\\n \\n amounts_to_dispense = {\\n \\\"buffer_1\\\":{\\n \\\"grade\\\": 1,\\n \\\"value\\\": \\\"2\\\"\\n },\\n \\\"buffer_2\\\":{\\n \\\"grade\\\": 1,\\n \\\"value\\\": \\\"3\\\"\\n }\\n \\n }\\n prompts = {\\n \\\"Additional Products Y/N?\\\": { \\n \\\"buttons\\\": [\\\"Yes\\\"]\\n },\\n \\\"Do you want to print a receipt?\\\": {\\n \\\"buttons\\\": [\\\"No\\\"]\\n },\\n \\\"Select fuel products to dispense\\\": {\\n \\\"buttons\\\": [\\\"Both fuels\\\"]\\n },\\n \\\"DEF?\\\": {\\n \\\"buttons\\\": [\\\"No\\\"]\\n }\\n }\\n\\n #Output verifications\\n\\n messages_to_verify = {\\n 'preauth_request_verifications': {\\n 'Fuel Purchase': '500', \\n '001 - Wex OTR Flags': 'C - Commercial'\\n },\\n 'preauth_response_verifications': {\\n 'Response Code': '2', \\n 'Approved Amount': '000000500',\\n '002 - Wex OTR Non-Fuel Product Data - Product code: CADV (Company funds cash advance)': 'Amount: 0.00',\\n '002 - Wex OTR Non-Fuel Product Data - Product code: MERC (Default category for merchandise)': 'Amount: 30.00',\\n f'003 - Wex OTR Fuel Product Limits - Product Code: {self.helpers.grade_1_Product }': 'Product limit: 50.00',\\n f'003 - Wex OTR Fuel Product Limits - Product Code: {self.helpers.grade_2_Product }': 'Product limit: 50.00',\\n f'003 - Wex OTR Fuel Product Limits - Product Code: {self.helpers.grade_3_Product }': 'Product limit: 50.00',\\n '005 - Wex OTR Customer Information': 'ABC TRUCKING DENVER CO234W987'\\n },\\n 'capture_request_verifications': {\\n 'Fuel Purchase': '500', \\n '001 - Wex OTR Flags': 'C - Commercial'\\n },\\n 'capture_response_verifications': {\\n 'Response Code': '0', \\n 'Approved Amount': '000000500',\\n '002 - Wex OTR Non-Fuel Product Data - Product code: CADV (Company funds cash advance)': 'Amount: 20.00',\\n '002 - Wex OTR Non-Fuel Product Data - Product code: MERC (Default category for merchandise)': 'Amount: 30.00',\\n f'003 - Wex OTR Fuel Product Limits - Product Code: {self.helpers.grade_1_Product }': 'Product limit: 50.00',\\n f'003 - Wex OTR Fuel Product Limits - Product Code: {self.helpers.grade_2_Product }': 'Product limit: 50.00',\\n f'003 - Wex OTR Fuel Product Limits - Product Code: {self.helpers.grade_3_Product }': 'Product limit: 50.00',\\n '005 - Wex OTR Customer Information': 'ABC TRUCKING DENVER CO234W987'\\n }\\n }\\n \\n receipt_data = [f\\\"{self.helpers.grade_1_Name} CA PUMP# 1\\\",\\n \\\"2.000 GAL @ $1.000/GAL $2.00 99\\\",\\n f\\\"{self.helpers.grade_1_Name_reefer} CA PUMP# 1\\\",\\n \\\"3.000 GAL @ $1.000/GAL $3.00 99\\\", \\n \\\"Subtotal = $5.00\\\",\\n \\\"Tax = $0.00\\\",\\n \\\"Total = $5.00\\\",\\n \\\"Change Due = $0.00\\\",\\n \\\"Credit $5.00\\\"]\\n \\n self.helpers.prepay_transaction(\\n card=card_to_use_NGFC,\\n prepay_amount=generic_trans_amount,\\n prompts=prompts,\\n amounts_to_dispense=amounts_to_dispense,\\n messages_to_verify=messages_to_verify,\\n receipt_data=receipt_data\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"479e595ee4bd9ccd2266755690e50ae8\",\n \"score\": \"0.48295414\",\n \"text\": \"def calc_baseline_HF_consumption (self):\\n HDD_ests = self.comp_specs[\\\"consumption estimates\\\"][\\\"HDD\\\"]\\n gal_sf_ests = self.comp_specs[\\\"consumption estimates\\\"][\\\"Gal/sf\\\"]\\n\\n measure = \\\"Fuel Oil\\\"\\n data = self.comp_specs['building inventory']\\n keys = data.T.keys()\\n keys = set(keys)\\n data[\\\"HDD ESTS\\\"] = 0\\n data[\\\"GAL/SF\\\"] = 0\\n for key in keys:\\n #~ print key\\n try:\\n data[\\\"HDD ESTS\\\"].ix[key] = \\\\\\n self.cd[\\\"heating degree days\\\"]/HDD_ests[key]\\n data[\\\"GAL/SF\\\"].ix[key] = gal_sf_ests.ix[key]\\n except KeyError:\\n data[\\\"HDD ESTS\\\"].ix[key] = \\\\\\n self.cd[\\\"heating degree days\\\"]/HDD_ests.ix['Other']#unitless\\n data[\\\"GAL/SF\\\"].ix[key] = gal_sf_ests.ix['Other'] # (gal)/sqft\\n\\n\\n idx = data[['Fuel Oil', \\\"Natural Gas\\\",\\n 'HW District','Propane',\\\"Biomass\\\"]].isnull().all(1)\\n if not self.cd['natural gas used']:\\n data['Fuel Oil'].ix[idx] = data[idx]['Square Feet'] * \\\\\\n data[idx]['HDD ESTS'] * \\\\\\n data[idx]['GAL/SF']\\n else:\\n data['Natural Gas'].ix[idx] = data[idx]['Square Feet'] * \\\\\\n data[idx]['HDD ESTS'] * \\\\\\n data[idx]['GAL/SF'] / \\\\\\n constants.mmbtu_to_gal_HF *\\\\\\n constants.mmbtu_to_Mcf\\n del data[\\\"GAL/SF\\\"]\\n del data[\\\"HDD ESTS\\\"]\\n\\n\\n\\n self.baseline_fuel_Hoil_consumption = data['Fuel Oil'].fillna(0).sum()\\n self.baseline_fuel_lng_consumption = data['Natural Gas'].fillna(0).sum()\\n self.baseline_fuel_hr_consumption = data['HW District'].fillna(0).sum()\\n self.baseline_fuel_propane_consumption = data['Propane'].fillna(0).sum()\\n self.baseline_fuel_biomass_consumption = data['Biomass'].fillna(0).sum()\\n\\n\\n self.baseline_HF_consumption = \\\\\\n self.baseline_fuel_Hoil_consumption / constants.mmbtu_to_gal_HF +\\\\\\n self.baseline_fuel_hr_consumption/constants.mmbtu_to_gal_HF+\\\\\\n self.baseline_fuel_lng_consumption/constants.mmbtu_to_Mcf+\\\\\\n self.baseline_fuel_propane_consumption/constants.mmbtu_to_gal_LP+\\\\\\n self.baseline_fuel_biomass_consumption/constants.mmbtu_to_cords\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4429dc783690bfcec9cc7f15d16b710\",\n \"score\": \"0.4825849\",\n \"text\": \"def calculate_storage_tank(self, Timestep, hk_inp_temp, hk_inp_volfl_m3s, hk_out_temp, chp_inp_temp, chp_out_temp,\\n chp_inp_volfl_m3s, gb_inp_temp, gp_out_temp, gb_inp_volfl_m3s, dhw_inp_temp, dhw_out_temp, dhw_inp_volfl_m3s):\\n # inp enters unit & leaves storage tank, out leaves unit & enters storage tank\\n # hk - heating circuit, chp - chp unit, gb - gas boiler\\n # temp - temperature; volfl - volume flow\\n \\n # heating circuit - output from the system - water taken out of the storage tank by the heating system\\n hk_rho = utils.rho_fluid_water(hk_inp_temp, self.p_atm, 1)\\n hk_cp = utils.cp_fluid_water(hk_inp_temp, self.p_atm, 1)\\n hk_mstr = hk_rho * hk_inp_volfl_m3s # in kg/s = kg/m3 * m3/s \\n # combined power and heat unit - input to the storage tank\\n chp_rho = utils.rho_fluid_water(chp_out_temp, self.p_atm, 1)\\n chp_cp = utils.cp_fluid_water(chp_out_temp, self.p_atm, 1)\\n chp_mstr = chp_rho * chp_inp_volfl_m3s # in kg/s = kg/m3 * m3/s \\n # gas boiler unit - input to the storage tank\\n gb_rho = utils.rho_fluid_water(gb_inp_temp, self.p_atm, 1)\\n gb_cp = utils.cp_fluid_water(gb_inp_temp, self.p_atm, 1)\\n gb_mstr = gb_rho * gb_inp_volfl_m3s # in kg/s = kg/m3 * m3/s \\n # mixing of streams from chp and gas boiler units - input to the storage tank\\n mix_up_mstr = chp_mstr + gb_mstr\\n if(mix_up_mstr != 0.0):\\n t_mix_up = (gb_mstr * gb_cp * gb_inp_temp + chp_mstr * chp_cp * chp_out_temp) / (mix_up_mstr * hk_cp)\\n # iterate out the temperature of the mixed flow of hot water\\n mix_cp = utils.cp_fluid_water(t_mix_up, self.p_atm, 1)\\n t_1 = (gb_mstr * gb_cp * gb_inp_temp + chp_mstr * chp_cp * chp_out_temp) / (mix_up_mstr * mix_cp)\\n else:\\n t_mix_up = 0.5 * (gb_inp_temp + chp_out_temp)\\n anz_iter = 0\\n while ((abs(t_1 - t_mix_up) > 0.000001) and (anz_iter < 1000)):\\n t_mix_up = t_1\\n mix_cp = utils.cp_fluid_water(t_mix_up, self.p_atm, 1)\\n t_1 = (gb_mstr * gb_cp * gb_inp_temp + chp_mstr * chp_cp * chp_out_temp) / (mix_up_mstr * mix_cp)\\n anz_iter = anz_iter + 1\\n if (anz_iter == 1000):\\n print('actual time = {}'.format(self.acttime))\\n print('error = {}'.format(t_mix_up - t_1))\\n print(' | t in C | mstr in kg/s ')\\n print(' mixed | {} | {} '.format(t_mix_up,mix_up_mstr))\\n print(' chp | {} | {} '.format(chp_out_temp,chp_mstr))\\n print(' gas boi | {} | {} '.format(gb_out_temp,gb_mstr))\\n print('---------+----------+-----------------')\\n \\n # balance of the hot water in the upper part of the storage tank - plus means input of hot water, minus means more hot water being taken out from the tank\\n netto_hot_mstr = mix_up_mstr - hk_mstr # in kg/s = kg/m3 * m3/s \\n # netto gain of hot mass in the upper part of storage\\n hot_gain = netto_hot_mstr * Timestep # in kg = kg/s * s\\n hot_volume = hot_gain / utils.rho_fluid_water(t_mix_up, self.p_atm, 1) # in m3 = kg / kg/m3\\n # misc number of slices that are fully filled hot water being pumped in m3\\n anz_slices = int(hot_volume / self.slice_volume)\\n # height_slices --> self.effective_heigth\\n # hot_volume --> self.effective_volume\\n height_slices = hot_volume * self.effective_height / self.effective_volume\\n #print('hot_gain = {}; hot_volume = {}; anz_slices = {}; height_slices = {}'.format(hot_gain,hot_volume,anz_slices,height_slices))\\n # hk_out_temp = self.t24\\n cold_cp = utils.cp_fluid_water(hk_out_temp, self.p_atm, 1)\\n \\n\\n # heat flow from heating appliances to the storage tank - convective influx with t_1, in W\\n Q_c_hot_inp = netto_hot_mstr * mix_cp * (t_mix_up - self.temp[0])\\n # heat flow from storage tank to the heating appliances to the heating appliances balanced with the input from heating system - convective influx\\n Q_c_cold_inp = netto_hot_mstr * cold_cp * (hk_out_temp - self.temp[self.nr_calc - 1])\\n \\n if (netto_hot_mstr >= 0.0): # flow direction from above down\\n tinp = t_mix_up\\n downwards = True\\n else: # flow direction from below up\\n tinp = hk_out_temp\\n downwards = False\\n\\n tinp_dhw = dhw_inp_temp\\n\\n t_new_dhw = []\\n t_new = []\\n # \\n # calculate output temperatures\\n for ii in range(self.nr_calc - 2):\\n self.tout_dhw[ii] = 0.5 * (self.temp_dhw[ii] + self.temp_dhw[ii + 1])\\n self.tout[ii] = 0.5 * (self.temp[ii] + self.temp[ii + 1])\\n self.tout_dhw[self.nr_calc - 1] = self.temp_dhw[self.nr_calc - 1]\\n self.tout[self.nr_calc - 1] = self.temp[self.nr_calc - 1]\\n# for ii in range(self.nr_calc):\\n# if (ii < (self.nr_calc - 1)):\\n# self.tout_dhw[ii] = 0.5 * (self.temp_dhw[ii] + self.temp_dhw[ii + 1])\\n# else:\\n# self.tout_dhw[ii] = self.temp_dhw[ii]\\n#\\n# if downwards: # flow direction from above down\\n# if (ii == 0): # outflow temp - first element is the lowerst one\\n# self.tout[ii] = self.temp[ii]\\n# else:\\n# self.tout[ii] = 0.5 * (self.temp[ii] + self.temp[ii - 1])\\n# \\n# else: # flow direction from below up\\n# if (ii < (self.nr_calc - 1)):\\n# self.tout[ii] = 0.5 * (self.temp[ii] + self.temp[ii + 1])\\n# else:\\n# self.tout[ii] = self.temp[ii]\\n\\n # dhw calculation\\n for ii in range(self.nr_calc):\\n # domestic hot water calculation\\n if(ii == 0):\\n Qdhw_from_below = utils.cp_fluid_water(tinp_dhw, self.p_atm, 1) * netto_hot_mstr * tinp_dhw # kW = kJ/kg/K * kg/s * K\\n else:\\n Qdhw_from_below = utils.cp_fluid_water(self.tout_dhw[ii - 1], self.p_atm, 1) * netto_hot_mstr * self.tout_dhw[ii - 1] # kW = kJ/kg/K * kg/s * K\\n Qdhw_from_above = utils.cp_fluid_water(self.tout_dhw[ii], self.p_atm, 1) * netto_hot_mstr * self.tout_dhw[ii] # kW = kJ/kg/K * kg/s * K\\n # interface between domestic hot water and heating water \\n Rwall = (1.0 / self.alpha_dhw + 1.0 / self.k_coef + 1.0 / self.alpha_tank) / self.pipe_surf_slice # K/W = m2*K/W / m2\\n Q_wall = (self.temp[ii] - self.temp_dhw[ii]) / Rwall # in W = K * W/K\\n Q_netto_dhw = Q_wall - Qdhw_from_below + Qdhw_from_above\\n \\n # heating water calculation\\n Q_loss = self.alpha_loss * self.red_out_surf_slice * (self.t_outside - self.temp[ii])\\n if(downwards): # flow direction from above down\\n if(ii == self.nr_calc):\\n Qhk_from_above = utils.cp_fluid_water(tinp, self.p_atm, 1) * netto_hot_mstr * tinp # kW = kJ/kg/K * kg/s * K\\n else:\\n Qhk_from_above = utils.cp_fluid_water(self.tout[ii], self.p_atm, 1) * netto_hot_mstr * self.tout[ii] # kW = kJ/kg/K * kg/s * K\\n Qhk_from_below = utils.cp_fluid_water(self.tout[ii - 1], self.p_atm, 1) * netto_hot_mstr * self.tout[ii - 1] # kW = kJ/kg/K * kg/s * K\\n # balance for domestic hot water\\n \\n Q_netto_heatWater = Q_loss + Qhk_from_above - Qhk_from_below - Q_wall\\n twyn = self.temp[0]\\n \\n else: # flow direction from below up\\n if(ii == 0):\\n Qhk_from_below = utils.cp_fluid_water(tinp, self.p_atm, 1) * netto_hot_mstr * tinp # kW = kJ/kg/K * kg/s * K\\n else:\\n Qhk_from_below = utils.cp_fluid_water(self.tout[ii - 1], self.p_atm, 1) * netto_hot_mstr * self.tout[ii - 1] # kW = kJ/kg/K * kg/s * K\\n Qhk_from_above = utils.cp_fluid_water(self.tout[ii - 1], self.p_atm, 1) * netto_hot_mstr * self.tout[ii] # kW = kJ/kg/K * kg/s * K\\n # balance for domestic hot water\\n Q_netto_heatWater = Q_loss + Qhk_from_above - Qhk_from_below - Q_wall\\n twyn = self.temp[self.nr_calc - 1]\\n \\n \\n \\n # balance for storage tank water\\n Q_netto_heatWater = Q_loss + Qhk_from_above - Qhk_from_below - Q_wall\\n \\n\\n t_new_dhw.append(self.temp_dhw[ii] + Timestep * Q_netto_dhw / (\\n self.pipe_volume_slice \\n * utils.rho_fluid_water(self.temp_dhw[ii], self.p_atm, 1) \\n * utils.cp_fluid_water(self.temp_dhw[ii], self.p_atm, 1)))\\n t_new.append(self.temp[ii] - Timestep * Q_netto_heatWater / (\\n (self.slice_volume - self.pipe_volume_slice) \\n * utils.rho_fluid_water(self.temp[ii], self.p_atm, 1)\\n * utils.cp_fluid_water(self.temp[ii],self.p_atm, 1)))\\n \\n self.temp = t_new\\n self.temp_dhw = t_new_dhw\\n \\n \\n #print('time = {}; ii = {}; Q_netto_dhw ={}; Q_netto_heatWater = {}'.format(self.acttime, ii, Q_netto_dhw, Q_netto_heatWater))\\n #print('time = {}; ii = {}; Q_wall ={}; self.temp[99] = {}; self.temp_dhw[99] = {}'.format(self.acttime, ii, Q_wall, self.temp[99], self.temp_dhw[99]))\\n #print('time = {}; ii = {}; Q_wall ={}; Q_loss = {};'.format(self.acttime, ii, Q_wall, Q_loss))\\n #print('time = {}; ii = {}; Q_c_hot_inp ={}; Q_c_cold_inp = {}; netto_hot_mstr = {}'.format(self.acttime, ii, Q_c_hot_inp, Q_c_cold_inp, netto_hot_mstr))\\n #print('time = {}; ii = {}; t_mix_up ={}; hk_out_temp = {}; netto_hot_mstr = {}; self.temp[self.nr_calc - 1] = {}'.format(self.acttime, ii, t_mix_up, hk_out_temp, netto_hot_mstr,self.temp[self.nr_calc - 1]))\\n #print('time = {}; ii = {}; self.temp[0] ={}; self.temp[1] = {}; netto_hot_mstr = {}; self.temp_dhw[0] = {}'.format(self.acttime, ii, self.temp[0], self.temp[1], netto_hot_mstr,self.temp_dhw[0]))\\n #print('time = {}; ii = {}; n[0] ={}; self.temp[0] = {}; netto_hot_mstr = {}; self.temp_dhw[0] = {}'.format(self.acttime, ii, hot_volume/self.pipe_volume_slice, self.temp[0], netto_hot_mstr,self.temp_dhw[0]))\\n #sleep(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"379f15768dc6fec75c4f842c47d730de\",\n \"score\": \"0.4823592\",\n \"text\": \"def setUp(self):\\n self.system1 = seapy.system.System()\\n self.system1.frequency.center = [500.0, 1000.0, 2000.0, 4000.0, 8000.0]\\n self.steel = self.system1.addMaterial(\\n \\\"steel\\\",\\n \\\"MaterialSolid\\\",\\n loss_factor=np.ones(len(self.system1.frequency.center)) * 0.2,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b9c030135c2b1eea66402d0b11de258\",\n \"score\": \"0.48214135\",\n \"text\": \"def tank_with_field(tank: Tank.Tank) -> None:\\n arr_x, arr_y = Rectangle.points_rectangle(tank.body.rectangle)\\n tank_max_x = FIELD_X + FIELD_WIDTH - BORDER_WIDTH - max(arr_x)\\n tank_max_y = FIELD_Y + FIELD_HEIGHT - BORDER_WIDTH - max(arr_y)\\n tank_min_x = FIELD_X + BORDER_WIDTH + max(arr_x)\\n tank_min_y = FIELD_Y + BORDER_WIDTH + max(arr_y)\\n touch = contact_with_field_boundaries(tank.body)\\n if touch == \\\"x\\\":\\n new_x = -tank.body.rectangle.centre.x + max(tank_min_x, min(tank.body.rectangle.centre.x, tank_max_x))\\n Tank.move_tank(tank, Rectangle.create_point(new_x, 0))\\n tank.speed.x = 0\\n elif touch == \\\"y\\\":\\n new_y = -tank.body.rectangle.centre.y + max(tank_min_y, min(tank.body.rectangle.centre.y, tank_max_y))\\n Tank.move_tank(tank, Rectangle.create_point(0, new_y))\\n tank.speed.y = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79f89dbcc7db479d9243123baf9b8b27\",\n \"score\": \"0.48129183\",\n \"text\": \"def initialise_fluids(self, nw):\\n N_2 = 0.7655\\n O_2 = 0.2345\\n\\n n_fuel = 1\\n lamb = 3\\n\\n fact_fuel = {}\\n sum_fuel = 0\\n for f in self.fuel_list:\\n fact_fuel[f] = 0\\n for i in self.inl:\\n fact_fuel[f] += i.fluid.val[f] / 2\\n sum_fuel += fact_fuel[f]\\n\\n for f in self.fuel_list:\\n fact_fuel[f] /= sum_fuel\\n\\n m_co2 = 0\\n m_h2o = 0\\n m_fuel = 0\\n for f in self.fuel_list:\\n m_co2 += (n_fuel * self.fuels[f]['C'] * molar_masses[self.co2] *\\n fact_fuel[f])\\n m_h2o += (n_fuel * self.fuels[f]['H'] / 2 *\\n molar_masses[self.h2o] * fact_fuel[f])\\n m_fuel += n_fuel * molar_masses[f] * fact_fuel[f]\\n\\n n_o2 = (m_co2 / molar_masses[self.co2] +\\n 0.5 * m_h2o / molar_masses[self.h2o]) * lamb\\n\\n m_air = n_o2 * molar_masses[self.o2] / O_2\\n m_fg = m_air + m_fuel\\n\\n m_o2 = n_o2 * molar_masses[self.o2] * (1 - 1 / lamb)\\n m_n2 = N_2 * m_air\\n\\n fg = {\\n self.n2: m_n2 / m_fg,\\n self.co2: m_co2 / m_fg,\\n self.o2: m_o2 / m_fg,\\n self.h2o: m_h2o / m_fg\\n }\\n\\n for o in self.outl:\\n for fluid, x in o.fluid.val.items():\\n if not o.fluid.val_set[fluid] and fluid in fg.keys():\\n o.fluid.val[fluid] = fg[fluid]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8dd910b1051fa60aed1b8b0b58895061\",\n \"score\": \"0.47961774\",\n \"text\": \"def prepare_electricity_storage_nodes(self,year,loop):\\n self.storage_efficiency_dict = util.recursivedict()\\n self.storage_capacity_dict = util.recursivedict()\\n for node in [x for x in self.nodes.values() if x.supply_type == 'Storage']:\\n for zone in self.dispatch_zones:\\n node.calculate_dispatch_coefficients(year, loop)\\n if hasattr(node,'active_dispatch_coefficients'):\\n storage_node_location = list(set(node.active_dispatch_coefficients.index.get_level_values('supply_node')))\\n if len(storage_node_location)>1:\\n raise ValueError('StorageNode %s has technologies with two different supply node locations' %node.name)\\n if storage_node_location[0] in self.electricity_nodes[zone]+[zone]:\\n capacity = node.stock.values.loc[:,year].to_frame().groupby(level=[GeoMapper.supply_primary_geography,'supply_technology']).sum()\\n efficiency = copy.deepcopy(node.active_dispatch_coefficients)\\n if 'demand_sector' not in capacity.index.names and zone == self.distribution_node_name:\\n capacity = pd.concat([capacity]*len(self.demand_sectors), keys=self.demand_sectors, names=['demand_sector']) / len(self.demand_sectors)\\n efficiency = pd.concat([efficiency]*len(self.demand_sectors), keys=self.demand_sectors, names=['demand_sector'])\\n if GeoMapper.dispatch_geography != GeoMapper.supply_primary_geography:\\n geography_map_key = node.geography_map_key if hasattr(node, 'geography_map_key') and node.geography_map_key is not None else GeoMapper.default_geography_map_key\\n map_df = GeoMapper.get_instance().map_df(GeoMapper.supply_primary_geography,GeoMapper.dispatch_geography, normalize_as='total', map_key=geography_map_key, eliminate_zeros=False)\\n capacity = DfOper.mult([capacity, map_df],fill_value=0.0)\\n efficiency = DfOper.divi([util.remove_df_levels(DfOper.mult([efficiency, capacity]), GeoMapper.supply_primary_geography),util.remove_df_levels(capacity,GeoMapper.supply_primary_geography)]).fillna(0)\\n capacity = util.remove_df_levels(capacity, GeoMapper.supply_primary_geography)\\n # creates an empty database to fill with duration values, which are a technology parameter\\n duration = copy.deepcopy(capacity) * 0\\n duration = duration.sort_index()\\n for tech in node.technologies.values():\\n for geography in GeoMapper.supply_geographies:\\n tech_indexer = util.level_specific_indexer(duration,['supply_technology',GeoMapper.supply_primary_geography], [tech.name,geography])\\n year_indexer = util.level_specific_indexer(tech.duration.values,['year',GeoMapper.supply_primary_geography],[year,geography])\\n duration.loc[tech_indexer,:] = tech.duration.values.loc[year_indexer,:].values[0]\\n efficiency = util.remove_df_levels(efficiency,'supply_node') \\n if zone == self.distribution_node_name:\\n indexer = util.level_specific_indexer(self.dispatch_feeder_allocation, 'year', year)\\n capacity = util.DfOper.mult([capacity, self.dispatch_feeder_allocation.loc[indexer, ]])\\n duration = DfOper.divi([util.remove_df_levels(DfOper.mult([duration, capacity]),'demand_sector'),util.remove_df_levels(capacity,'demand_sector')]).fillna(0)\\n efficiency = DfOper.divi([util.remove_df_levels(DfOper.mult([efficiency, capacity]),'demand_sector'),util.remove_df_levels(capacity,'demand_sector')]).fillna(1)\\n capacity = util.remove_df_levels(capacity,'demand_sector')\\n for geography in GeoMapper.dispatch_geographies:\\n for dispatch_feeder in self.dispatch_feeders:\\n for technology in node.technologies.keys():\\n indexer = util.level_specific_indexer(efficiency, [GeoMapper.dispatch_geography, 'dispatch_feeder','supply_technology'],[geography,dispatch_feeder,technology])\\n self.storage_efficiency_dict[geography][zone][dispatch_feeder][technology] = efficiency.loc[indexer,:].values[0][0]\\n indexer = util.level_specific_indexer(capacity, [GeoMapper.dispatch_geography, 'dispatch_feeder','supply_technology'],[geography,dispatch_feeder,technology])\\n self.storage_capacity_dict['power'][geography][zone][dispatch_feeder][technology] = capacity.loc[indexer,:].values[0][0]\\n indexer = util.level_specific_indexer(duration, [GeoMapper.dispatch_geography, 'dispatch_feeder','supply_technology'],[geography,dispatch_feeder,technology])\\n self.storage_capacity_dict['duration'][geography][zone][dispatch_feeder][technology] = duration.loc[indexer,:].values[0][0]\\n else:\\n for geography in GeoMapper.dispatch_geographies:\\n for technology in node.technologies.keys():\\n indexer = util.level_specific_indexer(capacity, [GeoMapper.dispatch_geography, 'supply_technology'],[geography,technology])\\n tech_capacity = self.ensure_frame(util.remove_df_levels(capacity.loc[indexer,:], 'demand_sector'))\\n indexer = util.level_specific_indexer(duration, [GeoMapper.dispatch_geography,'supply_technology'],[geography,technology])\\n tech_duration = self.ensure_frame(util.remove_df_levels(duration.loc[indexer,:], 'demand_sector'))\\n indexer = util.level_specific_indexer(efficiency, [GeoMapper.dispatch_geography, 'supply_technology'],[geography,technology])\\n tech_efficiency = self.ensure_frame(util.remove_df_levels(efficiency.loc[indexer,:], 'demand_sector'))\\n if tech_capacity.values[0][0] == 0:\\n continue\\n else:\\n self.storage_capacity_dict['power'][geography][zone]['bulk'][technology] = tech_capacity.values[0][0]\\n self.storage_capacity_dict['duration'][geography][zone]['bulk'][technology] = tech_duration.values[0][0]\\n self.storage_efficiency_dict[geography][zone]['bulk'][technology] = tech_efficiency.values[0][0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9ff8083d62b246131cc5e5dee8c4849\",\n \"score\": \"0.4795844\",\n \"text\": \"def reaction_balance(self, fluid):\\n if self.air_alias.val in ['air', 'Air']:\\n air = self.air_alias.val\\n else:\\n air = 'TESPy::' + self.air_alias.val\\n fuel = 'TESPy::' + self.fuel_alias.val\\n flue_gas = 'TESPy::' + self.fuel_alias.val + '_fg'\\n\\n ######################################################################\\n # calculate fuel and air mass flow\\n m_fuel = 0\\n for i in self.inl:\\n m_fuel += i.m.val_SI * i.fluid.val[fuel]\\n\\n m_air = 0\\n for i in self.inl:\\n m_air += i.m.val_SI * i.fluid.val[air]\\n\\n m_air_min = self.air_min * m_fuel\\n\\n ######################################################################\\n # calculate lambda if not specified\\n if not self.lamb.is_set:\\n self.lamb.val = m_air / (self.air_min * m_fuel)\\n\\n ######################################################################\\n # calculate excess fuel if lambda is smaller than 1\\n m_fuel_exc = 0\\n if self.lamb.val < 1:\\n m_fuel_exc = m_fuel - m_air / (self.lamb.val * self.air_min)\\n\\n ######################################################################\\n # equation for air\\n if fluid == air:\\n if self.lamb.val >= 1:\\n dm = -m_air_min\\n else:\\n dm = -m_air\\n\\n ######################################################################\\n # equation for fuel\\n elif fluid == fuel:\\n dm = -(m_fuel - m_fuel_exc)\\n\\n ######################################################################\\n # equation for flue gas\\n elif fluid == flue_gas:\\n dm = m_air_min + m_fuel\\n\\n ######################################################################\\n # equation for other components\\n else:\\n dm = 0\\n\\n res = dm\\n for i in self.inl:\\n res += i.fluid.val[fluid] * i.m.val_SI\\n for o in self.outl:\\n res -= o.fluid.val[fluid] * o.m.val_SI\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f67f994547c8bc87f79d72fd2f745ef\",\n \"score\": \"0.47861287\",\n \"text\": \"def module_fuel_required(mass: int) -> int:\\n module_fuel = launch_fuel_required(mass)\\n fuel_fuel = fuel_for_fuel(module_fuel)\\n\\n return module_fuel + fuel_fuel\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80ffa279879c68d92ea3b4f58763a5de\",\n \"score\": \"0.4780072\",\n \"text\": \"def add_steel_material(self, name, fy, fu, Es, ey, eu):\\n if name in self.materials_dictionary:\\n return\\n else:\\n self.materials_dictionary[name] = MF.Steel_Material(name, fy, fu, Es, ey, eu)\\n self.Lb.insert(tk.END, name)\\n self.controller.update_material_dropdown()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a031256b7bf2f6dda4f6263a6f46c089\",\n \"score\": \"0.47720638\",\n \"text\": \"def Fuel_type_both_DEF(self):\\n \\n #Input constants\\n\\n generic_trans_amount = \\\"$5.00\\\" # any value that gets an approval from the host\\n card_to_use_NGFC = 'NGFC' # using this card to get all commercial prompts\\n \\n amounts_to_dispense = {\\n \\\"buffer_1\\\":{\\n \\\"grade\\\": 1,\\n \\\"value\\\": \\\"2\\\"\\n },\\n \\\"buffer_2\\\":{\\n \\\"grade\\\": 2,\\n \\\"value\\\": \\\"1\\\"\\n },\\n \\\"buffer_3\\\":{\\n \\\"grade\\\": 1,\\n \\\"value\\\": \\\"2\\\"\\n }\\n }\\n \\n prompts = {\\n \\\"Additional Products Y/N?\\\": { \\n \\\"buttons\\\": [\\\"Yes\\\"]\\n },\\n \\\"Do you want to print a receipt?\\\": {\\n \\\"buttons\\\": [\\\"No\\\"]\\n },\\n \\\"Select fuel products to dispense\\\": {\\n \\\"buttons\\\": [\\\"Both fuels\\\"]\\n },\\n \\\"DEF?\\\": {\\n \\\"buttons\\\": [\\\"Yes\\\"]\\n }\\n }\\n #Output verifications\\n\\n messages_to_verify = {\\n 'preauth_request_verifications': {\\n 'Fuel Purchase': '500', \\n '001 - Wex OTR Flags': 'C - Commercial'\\n },\\n 'preauth_response_verifications': {\\n 'Response Code': '2', \\n 'Approved Amount': '000000500',\\n '002 - Wex OTR Non-Fuel Product Data - Product code: CADV (Company funds cash advance)': 'Amount: 0.00',\\n '002 - Wex OTR Non-Fuel Product Data - Product code: MERC (Default category for merchandise)': 'Amount: 30.00',\\n f'003 - Wex OTR Fuel Product Limits - Product Code: {self.helpers.grade_1_Product }': 'Product limit: 50.00',\\n f'003 - Wex OTR Fuel Product Limits - Product Code: {self.helpers.grade_2_Product }': 'Product limit: 50.00',\\n f'003 - Wex OTR Fuel Product Limits - Product Code: {self.helpers.grade_3_Product }': 'Product limit: 50.00',\\n '005 - Wex OTR Customer Information': 'ABC TRUCKING DENVER CO234W987'\\n },\\n 'capture_request_verifications': {\\n 'Fuel Purchase': '500', \\n '001 - Wex OTR Flags': 'C - Commercial'\\n },\\n 'capture_response_verifications': {\\n 'Response Code': '0', \\n 'Approved Amount': '000000500',\\n '002 - Wex OTR Non-Fuel Product Data - Product code: CADV (Company funds cash advance)': 'Amount: 20.00',\\n '002 - Wex OTR Non-Fuel Product Data - Product code: MERC (Default category for merchandise)': 'Amount: 30.00',\\n f'003 - Wex OTR Fuel Product Limits - Product Code: {self.helpers.grade_1_Product }': 'Product limit: 50.00',\\n f'003 - Wex OTR Fuel Product Limits - Product Code: {self.helpers.grade_2_Product }': 'Product limit: 50.00',\\n f'003 - Wex OTR Fuel Product Limits - Product Code: {self.helpers.grade_3_Product }': 'Product limit: 50.00',\\n '005 - Wex OTR Customer Information': 'ABC TRUCKING DENVER CO234W987'\\n }\\n }\\n\\n receipt_data = [f\\\"{self.helpers.grade_1_Name} CA PUMP# 1\\\",\\n \\\"2.000 GAL @ $1.000/GAL $2.00 99\\\",\\n f\\\"{self.helpers.grade_2_Name} CA PUMP# 1\\\",\\n \\\"1.000 GAL @ $1.000/GAL $1.00 99\\\",\\n f\\\"{self.helpers.grade_1_Name_reefer} CA PUMP# 1\\\",\\n \\\"2.000 GAL @ $1.000/GAL $2.00 99\\\",\\n \\\"Subtotal = $5.00\\\",\\n \\\"Tax = $0.00\\\",\\n \\\"Total = $5.00\\\",\\n \\\"Change Due = $0.00\\\",\\n \\\"Credit $5.00\\\"]\\n \\n self.helpers.prepay_transaction(\\n card=card_to_use_NGFC,\\n prepay_amount=generic_trans_amount,\\n prompts=prompts,\\n amounts_to_dispense=amounts_to_dispense,\\n messages_to_verify=messages_to_verify,\\n receipt_data=receipt_data\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"134cb08469e6040ec67750aa4bb34996\",\n \"score\": \"0.4766695\",\n \"text\": \"def tank_up(self, amount):\\r\\n if amount <= 0:\\r\\n raise FuelError(amount)\\r\\n if self.fuel + amount > self.tank_volume:\\r\\n raise TankUpError(self.tank_volume, self.fuel, amount)\\r\\n self.fuel += amount\\r\\n print(\\\"The {} has been fueled up.\\\".format(self.__class__.__name__.lower()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9b168dcd959437cb4231bbcaf25cc62\",\n \"score\": \"0.47617945\",\n \"text\": \"def design(self, clk_inv_params, drv_tinv_params, ff_inv_params, fb_tinv_params,\\n dum_info, debug, **kwargs):\\n self.instances['INV0'].design(**ff_inv_params)\\n self.instances['INV1'].design(**ff_inv_params)\\n self.instances['INV2'].design(**clk_inv_params)\\n self.instances['INV3'].design(**clk_inv_params)\\n\\n self.instances['TINV0'].design(**drv_tinv_params)\\n self.instances['TINV1'].design(**drv_tinv_params)\\n self.instances['TINV2'].design(**fb_tinv_params)\\n self.instances['TINV3'].design(**fb_tinv_params)\\n\\n self.design_dummy_transistors(dum_info, 'XDUM', 'VDD', 'VSS')\\n\\n if debug is False:\\n self.remove_pin('mem1')\\n self.remove_pin('mem2')\\n self.remove_pin('latch')\\n self.remove_pin('iclk')\\n self.remove_pin('iclkb')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af6bc6c18a7c8095162973f603f44fac\",\n \"score\": \"0.4755927\",\n \"text\": \"def __init__(self, species, qty):\\n\\n # user super method to use superclass init on species and qty\\n super(DomesticMelonOrder, self).__init__(species, qty, 'domestic', 0.08, None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21cc92a9c234e33cec66c176148f1934\",\n \"score\": \"0.47447366\",\n \"text\": \"def setup_gratingcoupler_3d_base_project(self, fdtd): \\n \\n ## CLEAR SESSION\\n #fdtd.clear()\\n fdtd.newproject()\\n \\n ## Start adding base components\\n fdtd.redrawoff()\\n\\n ## Set FDTD properties\\n props = OrderedDict([\\n (\\\"dimension\\\", \\\"3D\\\"),\\n (\\\"x min\\\", self.x_min),\\n (\\\"x max\\\", self.x_max), \\n (\\\"y min\\\", self.y_min), \\n (\\\"y max\\\", self.y_max), \\n (\\\"z min\\\", self.z_min), \\n (\\\"z max\\\", self.z_max), \\n (\\\"background material\\\", self.mat_bg),\\n (\\\"y min bc\\\", \\\"anti-symmetric\\\"),\\n (\\\"simulation time\\\", 5000e-15), \\n (\\\"auto shutoff min\\\", 1e-6),\\n (\\\"mesh refinement\\\", \\\"conformal variant 0\\\"),\\n (\\\"meshing tolerance\\\", 1.2e-15), \\n (\\\"use legacy conformal interface detection\\\", False)\\n ])\\n\\n if self.mat_bg == \\\"<Object defined dielectric>\\\":\\n props[\\\"index\\\"] = self.n_bg\\n\\n if self.optim:\\n props[\\\"mesh refinement\\\"] = \\\"precise volume average\\\"\\n props[\\\"meshing refinement\\\"] = 11\\n\\n if self.pol_angle == 0:\\n props[\\\"y min bc\\\"] = \\\"symmetric\\\"\\n\\n fdtd.addfdtd(properties=props)\\n \\n fdtd.addgaussian(name=\\\"source\\\", injection_axis=\\\"z-axis\\\", direction=\\\"backward\\\", polarization_angle=self.pol_angle, x=self.x_fib, x_span=self.x_fib_span, y_min=self.y_min, y_max=self.y_max, z=self.z_fib,\\n beam_parameters=\\\"Waist size and position\\\", waist_radius_w0=5.2e-6, distance_from_waist=0.0, angle_theta=self.theta_fib_mat,\\n center_wavelength=self.lambda0, wavelength_span=0.1e-6, optimize_for_short_pulse=False)\\n \\n fdtd.setglobalsource(\\\"center wavelength\\\",self.lambda0)\\n fdtd.setglobalsource(\\\"wavelength span\\\",0.1e-6)\\n fdtd.setglobalsource(\\\"optimize for short pulse\\\",False)\\n fdtd.setglobalmonitor(\\\"frequency points\\\",11)\\n fdtd.setglobalmonitor(\\\"use wavelength spacing\\\",True)\\n \\n fdtd.addmesh(name=\\\"source_mesh\\\",x=self.x_fib, x_span=24e-6, y_min=self.y_min, y_max=self.y_max, z=self.z_fib, z_span=2*self.dz, override_x_mesh=False, override_y_mesh=False, override_z_mesh=True, dz=self.dz)\\n fdtd.setnamed(\\\"source_mesh\\\",\\\"enabled\\\",False) #< Disable by default but need to check the effect\\n \\n if self.material_name == \\\"<Object defined dielectric>\\\":\\n fdtd.addrect(name=\\\"substrate\\\", x_min=(self.x_min-2e-6), x_max=(self.x_max+2e-6), y_min=(self.y_min-2e-6), y_max=(self.y_max+2e-6), z_min=-4e-6, z_max=-2e-6, material=self.material_name, index=self.index_wg, alpha=0.1)\\n else:\\n fdtd.addrect(name=\\\"substrate\\\", x_min=(self.x_min-2e-6), x_max=(self.x_max+2e-6), y_min=(self.y_min-2e-6), y_max=(self.y_max+2e-6), z_min=-4e-6, z_max=-2e-6, material=self.material_name, alpha=0.1)\\n\\n \\n fdtd.addpower(name=\\\"fom\\\", monitor_type=\\\"2D X-normal\\\", x=self.mode_pos_x, y=0, y_span=self.mode_span_y, z=0, z_span=self.mode_span_z)\\n fdtd.addmesh(name=\\\"fom_mesh\\\", x=self.mode_pos_x, x_span=2*self.dx, y=0, y_span=self.mode_span_y, z=0, z_span=self.mode_span_z, override_x_mesh=True, dx=self.dx, override_y_mesh=False, override_z_mesh=False )\\n \\n fdtd.addpower(name=\\\"opt_fields\\\",monitor_type=\\\"3D\\\", x_min=self.x_min_opt_region, x_max=self.x_max, y_min=self.y_min, y_max=self.y_max, z_min=self.wg_height-self.etch_depth, z_max=self.wg_height,\\n output_Hx=False, output_Hy=False, output_Hz=False, output_power=False)\\n fdtd.addmesh(name=\\\"opt_fields_mesh\\\", x_min=self.x_min_opt_region, x_max=self.x_max, y_min=self.y_min, y_max=self.y_max, z_min=self.wg_height-self.etch_depth, z_max=self.wg_height, dx=self.dx, dy=self.dy, dz=self.dz)\\n \\n fdtd.addindex(name=\\\"index_xy\\\", monitor_type=\\\"2D Z-normal\\\", x_min=self.x_min, x_max=self.x_max, y_min=self.y_min, y_max=self.y_max, z=self.wg_height-(self.etch_depth/2.), spatial_interpolation='none', enabled=False)\\n fdtd.addindex(name=\\\"index_xz\\\", monitor_type=\\\"2D Y-normal\\\", x_min=self.x_min, x_max=self.x_max, y=0, z_min=self.z_min, z_max=self.z_max, spatial_interpolation='none', enabled=False)\\n \\n \\n if self.material_name == \\\"<Object defined dielectric>\\\":\\n fdtd.addrect(name='wg', x_min=(self.x_min-2e-6), x_max=2e-6, y=0, y_span=self.wg_width, z_min=0, z_max=self.wg_height, material=self.material_name, index=self.index_wg)\\n else:\\n fdtd.addrect(name='wg', x_min=(self.x_min-2e-6), x_max=2e-6, y=0, y_span=self.wg_width, z_min=0, z_max=self.wg_height, material=self.material_name)\\n\\n\\n theta_start = self.initial_theta_taper\\n theta_stop = 360.0 - theta_start\\n\\n if self.material_name == \\\"<Object defined dielectric>\\\":\\n fdtd.addring(name='silicon', x=0, y=0, z_min=0, z_max=self.wg_height, inner_radius=0, outer_radius=60e-6, theta_start=theta_stop, theta_stop=theta_start, material=self.material_name, index=self.index_wg)\\n else:\\n fdtd.addring(name='silicon', x=0, y=0, z_min=0, z_max=self.wg_height, inner_radius=0, outer_radius=60e-6, theta_start=theta_stop, theta_stop=theta_start, material=self.material_name)\\n\\n \\n fdtd.redrawon()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97df60dae26b41b9302a3ee0fc182462\",\n \"score\": \"0.47366068\",\n \"text\": \"def prepare_simulation(self, components):\\n # Check level of destination storage component: if it is below specified threshold,\\n # implement low artificial costs (to encourage system to fill it)\\n # Check level of all non-central storage component and use the one with the\\n # highest amount of h2:\\n # if it is below specified threshold, the trailer cannot take any hydrogen from it\\n\\n # In the model definition, the foreign states must be defined in the following order:\\n # 1) all origin storage levels [kg]\\n # 2) all origin minimum storage levels [kg]\\n # 3) all origin capacities [kg]\\n # 4) destination storage level [kg]\\n # 5) destination scapacity [kg]\\n # The order for each of these in terms of production sites must also be the same e.g.\\n # the first entry relates to the first site, the second entry relates\\n # to the second site etc.\\n if self.fs_component_name is not None:\\n\\n # n is the number of production sites that the trailer is connected to\\n n = int((len(self.fs_component_name) - 2) / 3)\\n # Creates an index list for the number of foreign states considered\\n index_list = list(range(0, len(self.fs_component_name)))\\n # List containing the origin storage levels [kg]\\n fs_origin_storage_levels = []\\n # List containing the origin minimum storage levels [kg]\\n fs_origin_min_storage_levels = []\\n # List containing the origin capacities [kg]\\n fs_origin_capacities = []\\n # List containing the origin available masses [kg]\\n fs_origin_available_masses = []\\n\\n # Obtains a list of the origin storage levels for n sites\\n for i in index_list[0:n]:\\n this_origin_storage_level = self.get_foreign_state_value(components, index=i)\\n fs_origin_storage_levels.append(this_origin_storage_level)\\n\\n # Obtains a list of the origin minimum storage levels for n sites\\n for i in index_list[n:2 * n]:\\n this_min_storage_level = self.get_foreign_state_value(components, index=i)\\n fs_origin_min_storage_levels.append(this_min_storage_level)\\n\\n # Obtains a list of the origin capacity levels for n sites\\n for i in index_list[2 * n:3 * n]:\\n this_capacity = self.get_foreign_state_value(components, index=i)\\n fs_origin_capacities.append(this_capacity)\\n\\n # Obtains a list for the available masses that can be taken from the\\n # origin storage [kg].\\n # It cannot take more than half of the capacity into account\\n for i in range(int(n)):\\n this_available_kg = min((fs_origin_storage_levels[i]\\n - fs_origin_min_storage_levels[i]),\\n fs_origin_capacities[i] / 2)\\n fs_origin_available_masses.append(this_available_kg)\\n\\n # Get the availability mass of hydrogen of the fullest origin storage\\n self.fs_origin_available_kg = max(fs_origin_available_masses)\\n # Obtains the destination storage level [kg]\\n fs_destination_storage_level = \\\\\\n self.get_foreign_state_value(components, index=index_list[-2])\\n # Obtains the destination storage capacity [kg]\\n fs_destination_capacity = \\\\\\n self.get_foreign_state_value(components, index=index_list[-1])\\n # Obtains the available mass that can be delivered to the destination storage [kg]\\n fs_destination_available_storage = \\\\\\n fs_destination_capacity - fs_destination_storage_level\\n\\n # Checks if the destination storage level is below the threshold:\\n # if yes, delivery possible\\n\\n # todo: implement multiple storage delivery in one time step from different wind\\n # parks - low priority\\n\\n if fs_destination_storage_level \\\\\\n < self.fs_destination_storage_threshold * fs_destination_capacity:\\n\\n # If the available mass [kg] in the destination storage and the amount of\\n # available hydrogen [kg] in the origin storage exceed the trailer capacity,\\n # the trailer should be completely filled\\n if fs_destination_available_storage >= self.trailer_capacity \\\\\\n and self.fs_origin_available_kg >= self.trailer_capacity:\\n self.hydrogen_needed = self.trailer_capacity\\n # If the available mass [kg] in the destination storage exceeds the trailer\\n # capacity but the amount of available hydrogen in the origin storage is less\\n # than the trailer capacity, the trailer should be filled with the maximum amount\\n # of available hydrogen from the origin storage\\n elif fs_destination_available_storage > \\\\\\n self.trailer_capacity > self.fs_origin_available_kg:\\n self.hydrogen_needed = self.fs_origin_available_kg\\n # Else, the trailer should deliver the maximum amount of hydrogen that can fit\\n # into the destination storage\\n else:\\n self.hydrogen_needed = fs_destination_available_storage\\n # If the destination storage level is not below the threshold no delivery possible\\n else:\\n self.hydrogen_needed = 0\\n\\n self.current_ac = self.get_costs_and_art_costs()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e8ce950ec3f60a3c592b78c62649117\",\n \"score\": \"0.47321063\",\n \"text\": \"def createGases(self):\\n #Building objects for fuel and air\\n self.fuel = ct.Solution(self.mechs[0])\\n self.atmosphere = ct.Solution(self.mechs[1])\\n self.exhausts = [ct.Solution(mech) for mech in self.mechs[2:]]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfcb772888cc842bd209911b20bcad52\",\n \"score\": \"0.47316712\",\n \"text\": \"def add_liquidity(self, add_liquidity):\\n\\n self._add_liquidity = add_liquidity\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66bbc098093daf9dc728ffaeb907d685\",\n \"score\": \"0.47211936\",\n \"text\": \"def define_components(mod):\\n\\n # TODO: maybe rename fuel_cost_per_timepoint component and/or .csv file to fuel_cost\\n # (but that could cause confusion in the documentation?)\\n mod.ZONE_FUEL_TIMEPOINTS = Set(\\n dimen=3,\\n validate=lambda m, z, f, p: (\\n z in m.LOAD_ZONES and f in m.FUELS and p in m.TIMEPOINTS\\n ),\\n )\\n mod.fuel_cost_per_timepoint = Param(\\n mod.ZONE_FUEL_TIMEPOINTS, within=NonNegativeReals\\n )\\n mod.min_data_check(\\\"ZONE_FUEL_TIMEPOINTS\\\", \\\"fuel_cost_per_timepoint\\\")\\n\\n # don't allow use of a fuel when no cost has been specified\\n mod.GEN_TP_FUELS_UNAVAILABLE = Set(\\n dimen=3,\\n initialize=mod.GEN_TP_FUELS,\\n filter=lambda m, g, t, f: (m.gen_load_zone[g], f, t)\\n not in m.ZONE_FUEL_TIMEPOINTS,\\n )\\n mod.Enforce_Fuel_Unavailability = Constraint(\\n mod.GEN_TP_FUELS_UNAVAILABLE,\\n rule=lambda m, g, t, f: m.GenFuelUseRate[g, t, f] == 0,\\n )\\n\\n # Summarize total fuel costs in each timepoint for the objective function\\n def FuelCostsPerTP_rule(m, t):\\n if not hasattr(m, \\\"FuelCostsPerTP_dict\\\"):\\n # cache all Fuel_Cost_TP values in a dictionary (created in one pass)\\n m.FuelCostsPerTP_dict = {t2: 0.0 for t2 in m.TIMEPOINTS}\\n for (g, t2, f) in m.GEN_TP_FUELS:\\n if (g, t2, f) not in m.GEN_TP_FUELS_UNAVAILABLE:\\n m.FuelCostsPerTP_dict[t2] += (\\n m.GenFuelUseRate[g, t2, f]\\n * m.fuel_cost_per_timepoint[m.gen_load_zone[g], f, t2]\\n )\\n # return a result from the dictionary and pop the element each time\\n # to release memory\\n return m.FuelCostsPerTP_dict.pop(t)\\n\\n mod.FuelCostsPerTP = Expression(mod.TIMEPOINTS, rule=FuelCostsPerTP_rule)\\n mod.Cost_Components_Per_TP.append(\\\"FuelCostsPerTP\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ca4d83062a265d26174cd8194f4be6d\",\n \"score\": \"0.47184825\",\n \"text\": \"def start_engine(self):\\r\\n if self.fuel == 0:\\r\\n raise FuelError(self.fuel)\\r\\n print(\\\"Starting the engine...\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1ca68fdc819c414de5c5a717d62bb91\",\n \"score\": \"0.47082067\",\n \"text\": \"def accept(self, button=None):\\n self.getParallelRebarsData()\\n self.getCrossRebarsData()\\n self.getColumnsData()\\n self.getTiesData()\\n self.getMainRebarsData()\\n self.getSecondaryRebarsData()\\n if not self.FootingReinforcementGroup:\\n FootingReinforcementGroup = makeFootingReinforcement(\\n parallel_rebar_type=self.parallel_rebars_type,\\n parallel_front_cover=self.parallel_front_cover,\\n parallel_rear_cover=self.parallel_rear_cover,\\n parallel_left_cover=self.parallel_left_cover,\\n parallel_right_cover=self.parallel_right_cover,\\n parallel_top_cover=self.parallel_top_cover,\\n parallel_bottom_cover=self.parallel_bottom_cover,\\n parallel_diameter=self.parallel_diameter,\\n parallel_amount_spacing_check=self.parallel_amount_spacing_check,\\n parallel_amount_spacing_value=self.parallel_amount_spacing_value,\\n cross_rebar_type=self.cross_rebars_type,\\n cross_front_cover=self.cross_front_cover,\\n cross_rear_cover=self.cross_rear_cover,\\n cross_left_cover=self.cross_left_cover,\\n cross_right_cover=self.cross_right_cover,\\n cross_top_cover=self.cross_top_cover,\\n cross_bottom_cover=self.cross_bottom_cover,\\n cross_diameter=self.cross_diameter,\\n cross_amount_spacing_check=self.cross_amount_spacing_check,\\n column_front_spacing=self.column_front_spacing,\\n column_left_spacing=self.column_left_spacing,\\n column_right_spacing=self.column_right_spacing,\\n column_rear_spacing=self.column_rear_spacing,\\n tie_top_cover=self.tie_top_cover,\\n tie_bottom_cover=self.tie_bottom_cover,\\n tie_bent_angle=self.tie_bent_angle,\\n tie_extension_factor=self.tie_extension_factor,\\n tie_diameter=self.tie_diameter,\\n tie_number_spacing_check=self.tie_number_spacing_check,\\n tie_number_spacing_value=self.tie_number_spacing_value,\\n column_main_rebar_diameter=self.column_main_rebar_diameter,\\n column_main_rebars_t_offset=self.column_main_rebars_t_offset,\\n cross_amount_spacing_value=self.cross_amount_spacing_value,\\n column_width=self.column_width,\\n column_length=self.column_length,\\n xdir_column_amount_spacing_check=self.xdir_column_amount_spacing_check,\\n xdir_column_amount_spacing_value=self.xdir_column_amount_spacing_value,\\n ydir_column_amount_spacing_check=self.ydir_column_amount_spacing_check,\\n ydir_column_amount_spacing_value=self.ydir_column_amount_spacing_value,\\n parallel_rounding=self.parallel_rounding,\\n parallel_l_shape_hook_orintation=self.parallel_l_shape_hook_orintation,\\n cross_rounding=self.cross_rounding,\\n cross_l_shape_hook_orintation=self.cross_l_shape_hook_orintation,\\n column_main_rebars_type=self.column_main_rebars_type,\\n column_main_hook_orientation=self.column_main_hook_orientation,\\n column_main_hook_extend_along=self.column_main_hook_extend_along,\\n column_l_main_rebar_rounding=self.column_l_main_rebar_rounding,\\n column_main_hook_extension=self.column_main_hook_extension,\\n column_sec_rebar_check=self.column_sec_rebar_check,\\n column_sec_rebars_t_offset=self.column_sec_rebars_t_offset,\\n column_sec_rebars_number_diameter=self.column_sec_rebars_number_diameter,\\n column_sec_rebars_type=self.column_sec_rebars_type,\\n column_sec_hook_orientation=self.column_sec_hook_orientation,\\n column_l_sec_rebar_rounding=self.column_l_sec_rebar_rounding,\\n column_sec_hook_extension=self.column_sec_hook_extension,\\n mesh_cover_along=self.mesh_cover_along,\\n structure=self.SelectedObj,\\n facename=self.FaceName,\\n )\\n else:\\n FootingReinforcementGroup = editFootingReinforcement(\\n self.FootingReinforcementGroup,\\n parallel_rebar_type=self.parallel_rebars_type,\\n parallel_front_cover=self.parallel_front_cover,\\n parallel_rear_cover=self.parallel_rear_cover,\\n parallel_left_cover=self.parallel_left_cover,\\n parallel_right_cover=self.parallel_right_cover,\\n parallel_top_cover=self.parallel_top_cover,\\n parallel_bottom_cover=self.parallel_bottom_cover,\\n parallel_diameter=self.parallel_diameter,\\n parallel_amount_spacing_check=self.parallel_amount_spacing_check,\\n parallel_amount_spacing_value=self.parallel_amount_spacing_value,\\n cross_rebar_type=self.cross_rebars_type,\\n cross_front_cover=self.cross_front_cover,\\n cross_rear_cover=self.cross_rear_cover,\\n cross_left_cover=self.cross_left_cover,\\n cross_right_cover=self.cross_right_cover,\\n cross_top_cover=self.cross_top_cover,\\n cross_bottom_cover=self.cross_bottom_cover,\\n cross_diameter=self.cross_diameter,\\n cross_amount_spacing_check=self.cross_amount_spacing_check,\\n column_front_spacing=self.column_front_spacing,\\n column_left_spacing=self.column_left_spacing,\\n column_right_spacing=self.column_right_spacing,\\n column_rear_spacing=self.column_rear_spacing,\\n tie_top_cover=self.tie_top_cover,\\n tie_bottom_cover=self.tie_bottom_cover,\\n tie_bent_angle=self.tie_bent_angle,\\n tie_extension_factor=self.tie_extension_factor,\\n tie_diameter=self.tie_diameter,\\n tie_number_spacing_check=self.tie_number_spacing_check,\\n tie_number_spacing_value=self.tie_number_spacing_value,\\n column_main_rebar_diameter=self.column_main_rebar_diameter,\\n column_main_rebars_t_offset=self.column_main_rebars_t_offset,\\n cross_amount_spacing_value=self.cross_amount_spacing_value,\\n column_width=self.column_width,\\n column_length=self.column_length,\\n xdir_column_amount_spacing_check=self.xdir_column_amount_spacing_check,\\n xdir_column_amount_spacing_value=self.xdir_column_amount_spacing_value,\\n ydir_column_amount_spacing_check=self.ydir_column_amount_spacing_check,\\n ydir_column_amount_spacing_value=self.ydir_column_amount_spacing_value,\\n parallel_rounding=self.parallel_rounding,\\n parallel_l_shape_hook_orintation=self.parallel_l_shape_hook_orintation,\\n cross_rounding=self.cross_rounding,\\n cross_l_shape_hook_orintation=self.cross_l_shape_hook_orintation,\\n column_main_rebars_type=self.column_main_rebars_type,\\n column_main_hook_orientation=self.column_main_hook_orientation,\\n column_main_hook_extend_along=self.column_main_hook_extend_along,\\n column_l_main_rebar_rounding=self.column_l_main_rebar_rounding,\\n column_main_hook_extension=self.column_main_hook_extension,\\n column_sec_rebar_check=self.column_sec_rebar_check,\\n column_sec_rebars_t_offset=self.column_sec_rebars_t_offset,\\n column_sec_rebars_number_diameter=self.column_sec_rebars_number_diameter,\\n column_sec_rebars_type=self.column_sec_rebars_type,\\n column_sec_hook_orientation=self.column_sec_hook_orientation,\\n column_l_sec_rebar_rounding=self.column_l_sec_rebar_rounding,\\n column_sec_hook_extension=self.column_sec_hook_extension,\\n mesh_cover_along=self.mesh_cover_along,\\n structure=self.SelectedObj,\\n facename=self.FaceName,\\n )\\n\\n self.FootingReinforcementGroup = FootingReinforcementGroup\\n if (\\n self.form.standardButtonBox.buttonRole(button)\\n != QtWidgets.QDialogButtonBox.ApplyRole\\n ):\\n self.form.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c79e3f5389f572ebcc61dcdb92976a8e\",\n \"score\": \"0.47073346\",\n \"text\": \"def crane_invest(self, year):\\n if self.debug:\\n print(' *** add Harbour crane to elements')\\n # add unloader object\\n if (self.crane_type_defaults[\\\"crane_type\\\"] == 'Gantry crane' or\\n self.crane_type_defaults[\\\"crane_type\\\"] == 'Harbour crane' or\\n self.crane_type_defaults[\\\"crane_type\\\"] == 'Mobile crane'):\\n crane = Cyclic_Unloader(**self.crane_type_defaults)\\n elif self.crane_type_defaults[\\\"crane_type\\\"] == 'Screw unloader':\\n crane = Continuous_Unloader(**self.crane_type_defaults)\\n\\n # - capex\\n unit_rate = crane.unit_rate\\n mobilisation = unit_rate * crane.mobilisation_perc\\n crane.capex = int(unit_rate + mobilisation)\\n\\n # - opex\\n crane.insurance = unit_rate * crane.insurance_perc\\n crane.maintenance = unit_rate * crane.maintenance_perc\\n\\n # labour\\n labour = Labour(**agribulk_defaults.labour_data)\\n '''old formula --> crane.labour = crane.crew * self.operational_hours / labour.shift_length '''\\n crane.shift = ((crane.crew * self.operational_hours) / (\\n labour.shift_length * labour.annual_shifts))\\n crane.labour = crane.shift * labour.operational_salary\\n\\n # apply proper timing for the crane to come online (in the same year as the latest Quay_wall)\\n years_online = []\\n for element in self.find_elements(Quay_wall):\\n years_online.append(element.year_online)\\n crane.year_online = max([year + crane.delivery_time, max(years_online)])\\n\\n # add cash flow information to quay_wall object in a dataframe\\n crane = self.add_cashflow_data_to_element(crane)\\n\\n # add object to elements\\n self.elements.append(crane)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e1f22653df3a6afced7978588cef094\",\n \"score\": \"0.46998996\",\n \"text\": \"def simulate_scheil_solidification(dbf, comps, phases, composition,\\n start_temperature, step_temperature=1.0,\\n liquid_phase_name='LIQUID', eq_kwargs=None,\\n stop=0.0001, verbose=False, adaptive=True):\\n eq_kwargs = eq_kwargs or dict()\\n STEP_SCALE_FACTOR = 1.2 # How much to try to adapt the temperature step by\\n MAXIMUM_STEP_SIZE_REDUCTION = 5.0\\n T_STEP_ORIG = step_temperature\\n phases = filter_phases(dbf, unpack_components(dbf, comps), phases)\\n models = instantiate_models(dbf, comps, phases)\\n if verbose:\\n print('building callables... ', end='')\\n cbs = build_callables(dbf, comps, phases, models, additional_statevars={v.P, v.T, v.N}, build_gradients=True, build_hessians=True)\\n if verbose:\\n print('done')\\n solid_phases = sorted(set(phases) - {liquid_phase_name})\\n temp = start_temperature\\n independent_comps = sorted([str(comp)[2:] for comp in composition.keys()])\\n x_liquid = {comp: [composition[v.X(comp)]] for comp in independent_comps}\\n fraction_solid = [0.0]\\n temperatures = [temp]\\n phase_amounts = {ph: [0.0] for ph in solid_phases}\\n ord_disord_dict = order_disorder_dict(dbf, comps, phases)\\n\\n if adaptive and ('points' in eq_kwargs.get('calc_opts', {})):\\n # Dynamically add points as the simulation runs\\n species = unpack_components(dbf, comps)\\n dof_dict = {ph: generate_dof(dbf.phases[ph], species)[1] for ph in phases}\\n else:\\n adaptive = False\\n\\n converged = False\\n phases_seen = {liquid_phase_name, ''}\\n liquid_comp = composition\\n while fraction_solid[-1] < 1:\\n conds = {v.T: temp, v.P: 101325.0, v.N: 1.0}\\n comp_conds = liquid_comp\\n fmt_comp_conds = ', '.join([f'{c}={val:0.2f}' for c, val in comp_conds.items()])\\n conds.update(comp_conds)\\n eq = equilibrium(dbf, comps, phases, conds, callables=cbs, model=models, **eq_kwargs)\\n if adaptive:\\n # Update the points dictionary with local samples around the equilibrium site fractions\\n points_dict = eq_kwargs['calc_opts']['points']\\n for vtx in range(eq.vertex.size):\\n masked = eq.isel(vertex=vtx)\\n ph = str(masked.Phase.values.squeeze())\\n pts = points_dict.get(ph)\\n if pts is not None:\\n if verbose:\\n print(f'Adding points to {ph}. ', end='')\\n dof = dof_dict[ph]\\n points_dict[ph] = np.concatenate([pts, local_sample(masked.Y.values.squeeze()[:sum(dof)].reshape(1, -1), dof, pdens=20)], axis=0)\\n\\n eq_phases = order_disorder_eq_phases(eq, ord_disord_dict)\\n num_eq_phases = np.nansum(np.array([str(ph) for ph in eq_phases]) != '')\\n new_phases_seen = set(eq_phases).difference(phases_seen)\\n if len(new_phases_seen) > 0:\\n if verbose:\\n print(f'New phases seen: {new_phases_seen}. ', end='')\\n phases_seen |= new_phases_seen\\n if liquid_phase_name not in eq[\\\"Phase\\\"].values.squeeze():\\n found_ph = set(eq_phases) - {''}\\n if verbose:\\n print(f'No liquid phase found at T={temp:0.3f}, {fmt_comp_conds}. (Found {found_ph}) ', end='')\\n if len(found_ph) == 0:\\n # No phases found in equilibrium. Just continue on lowering the temperature without changing anything\\n if verbose:\\n print(f'(Convergence failure) ', end='')\\n if T_STEP_ORIG / step_temperature > MAXIMUM_STEP_SIZE_REDUCTION:\\n # Only found solid phases and the step size has already been reduced. Stop running without converging.\\n if verbose:\\n print('Maximum step size reduction exceeded. Stopping.')\\n converged = False\\n break\\n else:\\n # Only found solid phases. Try reducing the step size to zero-in on the correct phases\\n if verbose:\\n print(f'Stepping back and reducing step size.')\\n temp += step_temperature\\n step_temperature /= STEP_SCALE_FACTOR\\n continue\\n # TODO: Will break if there is a liquid miscibility gap\\n liquid_vertex = sorted(np.nonzero(eq[\\\"Phase\\\"].values.squeeze().flat == liquid_phase_name))[0]\\n liquid_comp = {}\\n for comp in independent_comps:\\n x = float(eq[\\\"X\\\"].isel(vertex=liquid_vertex).squeeze().sel(component=comp).values)\\n x_liquid[comp].append(x)\\n liquid_comp[v.X(comp)] = x\\n np_liq = np.nansum(eq.where(eq[\\\"Phase\\\"] == liquid_phase_name).NP.values)\\n current_fraction_solid = float(fraction_solid[-1])\\n found_phase_amounts = [(liquid_phase_name, np_liq)] # tuples of phase name, amount\\n for solid_phase in solid_phases:\\n if solid_phase not in eq_phases:\\n phase_amounts[solid_phase].append(0.0)\\n continue\\n np_tieline = np.nansum(eq.isel(vertex=eq_phases.index(solid_phase))[\\\"NP\\\"].values.squeeze())\\n found_phase_amounts.append((solid_phase, np_tieline))\\n delta_fraction_solid = (1 - current_fraction_solid) * np_tieline\\n current_fraction_solid += delta_fraction_solid\\n phase_amounts[solid_phase].append(delta_fraction_solid)\\n fraction_solid.append(current_fraction_solid)\\n temperatures.append(temp)\\n NL = 1 - fraction_solid[-1]\\n if verbose:\\n phase_amnts = ' '.join([f'NP({ph})={amnt:0.3f}' for ph, amnt in found_phase_amounts])\\n if NL < 1.0e-3:\\n print(f'T={temp:0.3f}, {fmt_comp_conds}, ΔT={step_temperature:0.3f}, NL: {NL:.2E}, {phase_amnts} ', end='')\\n else:\\n print(f'T={temp:0.3f}, {fmt_comp_conds}, ΔT={step_temperature:0.3f}, NL: {NL:0.3f}, {phase_amnts} ', end='')\\n if NL < stop:\\n if verbose:\\n print(f'Liquid fraction below criterion {stop} . Stopping at {fmt_comp_conds}')\\n converged = True\\n break\\n if verbose:\\n print() # add line break\\n temp -= step_temperature\\n\\n if fraction_solid[-1] < 1:\\n for comp in independent_comps:\\n x_liquid[comp].append(np.nan)\\n fraction_solid.append(1.0)\\n temperatures.append(temp)\\n # set the final phase amount to the phase fractions in the eutectic\\n # this method gives the sum total phase amounts of 1.0 by construction\\n for solid_phase in solid_phases:\\n if solid_phase in eq_phases:\\n amount = np.nansum(eq.isel(vertex=eq_phases.index(solid_phase))[\\\"NP\\\"].values.squeeze())\\n phase_amounts[solid_phase].append(float(amount) * (1 - current_fraction_solid))\\n else:\\n phase_amounts[solid_phase].append(0.0)\\n\\n return SolidificationResult(x_liquid, fraction_solid, temperatures, phase_amounts, converged, \\\"scheil\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4f42a97b845fb2ecc0c9406e3dbab64\",\n \"score\": \"0.4697101\",\n \"text\": \"def placeBets(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd91de486ae217716303491368e6e8ca\",\n \"score\": \"0.46919805\",\n \"text\": \"def fuel_required_for_part1(self) -> int:\\n return self.mass//3 - 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47824ae1f4bd2b5705be6f1c75a20c83\",\n \"score\": \"0.46808332\",\n \"text\": \"def fill_gas_tank(self):\\n print(f\\\"This {self.make.title()} now has a full tank of gas!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17f94e9d43a9c0e15882d8974ca9afc4\",\n \"score\": \"0.46776524\",\n \"text\": \"def Calculate_Defect_Carrier_Concentration(self):\\r\\n\\t\\t\\r\\n\\t\\t# Initialize\\r\\n\\t\\tintrinsic_defect_carrier_concentration_temperature = {}\\r\\n\\t\\textrinsic_defect_carrier_concentration_temperature = {}\\r\\n\\t\\tfor temperature in self.temperature_array:\\r\\n\\t\\t\\tintrinsic_defect_carrier_concentration_temperature[temperature] = np.zeros(len(self.fermi_energy_array))\\r\\n\\t\\t\\textrinsic_defect_carrier_concentration_temperature[temperature] = np.zeros(len(self.fermi_energy_array))\\r\\n\\t\\t\\r\\n\\t\\t# Obtain intrinsic defect carrier concentration\\r\\n\\t\\tfor defect in self.ternary_defects_data.keys():\\r\\n\\t\\t\\t\\r\\n\\t\\t\\t# Check that item is truly a defect\\r\\n\\t\\t\\tif \\\"_\\\" not in defect:\\r\\n\\t\\t\\t\\tcontinue\\r\\n\\t\\t\\t\\r\\n\\t\\t\\tif self.ternary_defects_data[defect][\\\"Extrinsic\\\"] == \\\"No\\\":\\r\\n\\t\\t\\t\\t\\r\\n\\t\\t\\t\\tfor charge in self.ternary_defects_data[defect][\\\"charge\\\"].keys():\\r\\n\\t\\t\\t\\t\\t\\r\\n\\t\\t\\t\\t\\t# Formation energy\\r\\n\\t\\t\\t\\t\\t\\\"\\\"\\\"\\r\\n\\t\\t\\t\\t\\tdefect_formation_enthalpy = self.ternary_defects_data[defect][\\\"charge\\\"][charge][\\\"Energy\\\"] \\\\\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t- self.ternary_defects_data[\\\"supercellsize\\\"] * self.main_compound_total_energy \\\\\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t- self.ternary_defects_data[defect][\\\"n_\\\"+self.species_a] * ( self.first_element_mu0 + float(dmu_a) ) \\\\\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t- self.ternary_defects_data[defect][\\\"n_\\\"+self.species_b] * ( self.second_element_mu0 + float(dmu_b) ) \\\\\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t- self.ternary_defects_data[defect][\\\"n_\\\"+self.species_c] * ( self.third_element_mu0 + float(dmu_c) ) \\\\\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t+ float(charge) * self.fermi_energy_array \\\\\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t+ self.ternary_defects_data[defect][\\\"charge\\\"][charge][\\\"ECorr\\\"]\\r\\n\\t\\t\\t\\t\\t\\\"\\\"\\\"\\r\\n\\t\\t\\t\\t\\tdefect_formation_enthalpy = self.ternary_defects_data[defect][\\\"charge\\\"][charge][\\\"Energy\\\"] \\\\\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t- self.ternary_defects_data[\\\"supercellsize\\\"] * self.main_compound_total_energy \\\\\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t+ float(charge) * self.fermi_energy_array \\\\\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t+ self.ternary_defects_data[defect][\\\"charge\\\"][charge][\\\"ECorr\\\"]\\r\\n\\t\\t\\t\\t\\tfor element in self.elements_list:\\r\\n\\t\\t\\t\\t\\t\\tdefect_formation_enthalpy -= self.ternary_defects_data[defect][\\\"n_\\\"+element] * ( self.mu_elements[element][\\\"mu0\\\"] + self.mu_elements[element][\\\"deltamu\\\"] )\\r\\n\\t\\t\\t\\t\\t\\r\\n\\t\\t\\t\\t\\t# Defect concentration\\r\\n\\t\\t\\t\\t\\tif self.first_element == defect.split(\\\"_\\\")[-1]:\\r\\n\\t\\t\\t\\t\\t\\tN = self.main_compound_number_first_specie/self.vol\\r\\n\\t\\t\\t\\t\\telif self.second_element == defect.split(\\\"_\\\")[-1]:\\r\\n\\t\\t\\t\\t\\t\\tN = self.main_compound_number_second_specie/self.vol\\r\\n\\t\\t\\t\\t\\telif self.third_element == defect.split(\\\"_\\\")[-1]:\\r\\n\\t\\t\\t\\t\\t\\tN = self.main_compound_number_third_specie/self.vol\\r\\n\\t\\t\\t\\t\\t\\r\\n\\t\\t\\t\\t\\tfor temperature in self.temperature_array:\\r\\n\\t\\t\\t\\t\\t\\tdefect_carrier_concentration = float(charge) * N * np.exp(-defect_formation_enthalpy / (self.k * temperature) )\\r\\n\\t\\t\\t\\t\\t\\tintrinsic_defect_carrier_concentration_temperature[temperature] += defect_carrier_concentration\\r\\n\\t\\t\\r\\n\\t\\t# Check that user-selected extrinsic defect is not \\\"None\\\"\\r\\n\\t\\tif self.extrinsic_defect == \\\"None\\\":\\r\\n\\t\\t\\treturn intrinsic_defect_carrier_concentration_temperature, extrinsic_defect_carrier_concentration_temperature\\r\\n\\t\\t\\r\\n\\t\\t# Obtain extrinsic defect carrier concentration\\r\\n\\t\\tfor charge in self.ternary_defects_data[self.extrinsic_defect][\\\"charge\\\"].keys():\\r\\n\\t\\t\\t\\r\\n\\t\\t\\t# Formation energy\\r\\n\\t\\t\\t\\\"\\\"\\\"\\r\\n\\t\\t\\textrinsic_defect_formation_enthalpy = self.ternary_defects_data[self.extrinsic_defect][\\\"charge\\\"][charge][\\\"Energy\\\"] \\\\\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t- self.ternary_defects_data[\\\"supercellsize\\\"] * self.main_compound_total_energy \\\\\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t- self.ternary_defects_data[self.extrinsic_defect][\\\"n_\\\"+self.species_a] * ( self.first_element_mu0 + float(dmu_a) ) \\\\\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t- self.ternary_defects_data[self.extrinsic_defect][\\\"n_\\\"+self.species_b] * ( self.second_element_mu0 + float(dmu_b) ) \\\\\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t- self.ternary_defects_data[self.extrinsic_defect][\\\"n_\\\"+self.species_c] * ( self.third_element_mu0 + float(dmu_c) ) \\\\\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t- (self.extrinsic_defect_mu0 + self.extrinsic_defect_deltamu) \\\\\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t+ float(charge) * self.fermi_energy_array \\\\\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t+ self.ternary_defects_data[self.extrinsic_defect][\\\"charge\\\"][charge][\\\"ECorr\\\"]\\r\\n\\t\\t\\t\\\"\\\"\\\"\\r\\n\\t\\t\\textrinsic_defect_formation_enthalpy = self.ternary_defects_data[self.extrinsic_defect][\\\"charge\\\"][charge][\\\"Energy\\\"] \\\\\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t- self.ternary_defects_data[\\\"supercellsize\\\"] * self.main_compound_total_energy \\\\\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t- (self.extrinsic_defect_mu0 + self.extrinsic_defect_deltamu) \\\\\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t+ float(charge) * self.fermi_energy_array \\\\\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t+ self.ternary_defects_data[self.extrinsic_defect][\\\"charge\\\"][charge][\\\"ECorr\\\"]\\r\\n\\t\\t\\tfor element in self.elements_list:\\r\\n\\t\\t\\t\\textrinsic_defect_formation_enthalpy -= self.ternary_defects_data[self.extrinsic_defect][\\\"n_\\\"+element] * ( self.mu_elements[element][\\\"mu0\\\"] + self.mu_elements[element][\\\"deltamu\\\"] )\\r\\n\\t\\t\\t\\r\\n\\t\\t\\t# Extrinsic defect concentration\\r\\n\\t\\t\\tif self.first_element == defect.split(\\\"_\\\")[-1]:\\r\\n\\t\\t\\t\\tN = self.main_compound_number_first_specie/self.vol\\r\\n\\t\\t\\telif self.second_element == defect.split(\\\"_\\\")[-1]:\\r\\n\\t\\t\\t\\tN = self.main_compound_number_second_specie/self.vol\\r\\n\\t\\t\\telif self.third_element == defect.split(\\\"_\\\")[-1]:\\r\\n\\t\\t\\t\\tN = self.main_compound_number_third_specie/self.vol\\r\\n\\t\\t\\t\\r\\n\\t\\t\\tfor temperature in self.temperature_array:\\r\\n\\t\\t\\t\\textrinsic_defect_carrier_concentration = float(charge) * N * np.exp(-extrinsic_defect_formation_enthalpy / (self.k * temperature) )\\r\\n\\t\\t\\t\\textrinsic_defect_carrier_concentration_temperature[temperature] += extrinsic_defect_carrier_concentration\\r\\n\\t\\t\\r\\n\\t\\treturn intrinsic_defect_carrier_concentration_temperature, extrinsic_defect_carrier_concentration_temperature\\t\\t# Returns a dictionary with temperature as keys and array of defect-induced carrier concentrations (not defect concentrations) as values\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f3df972e7d3930717bf34d2d564ee3c\",\n \"score\": \"0.46776056\",\n \"text\": \"def __init__(self, name, fuel, price_per_km):\\n super().__init__(name, fuel)\\n self.price_per_km = price_per_km\\n self.current_fare_distance = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a4d2db6eeeaa9890edc5bf449d25db9\",\n \"score\": \"0.4672864\",\n \"text\": \"def fuel_required_for_part2(self) -> int:\\n total_fuel_required = added_fuel_required = self.mass//3 - 2\\n while (added_fuel_required := added_fuel_required//3 - 2) > 0:\\n total_fuel_required += added_fuel_required\\n return total_fuel_required\",\n \"title\": \"\"\n },\n {\n \"docid\": \"847ffdc16f66838897c4995bddee8f9c\",\n \"score\": \"0.46675283\",\n \"text\": \"def Fuel_type_reefer_NotDEF(self):\\n \\n #Input constants\\n\\n generic_trans_amount = \\\"$5.00\\\" # any value that gets an approval from the host\\n card_to_use_NGFC = 'NGFC' # using this card to get all commercial prompts\\n \\n prompts = {\\n \\\"Additional Products Y/N?\\\": { \\n \\\"buttons\\\": [\\\"Yes\\\"]\\n },\\n \\\"Do you want to print a receipt?\\\": {\\n \\\"buttons\\\": [\\\"No\\\"]\\n },\\n \\\"Select fuel products to dispense\\\": {\\n \\\"buttons\\\": [\\\"Reefer fuel\\\"]\\n },\\n \\\"DEF?\\\": {\\n \\\"buttons\\\": [\\\"No\\\"]\\n }\\n }\\n\\n messages_to_verify = {\\n 'preauth_request_verifications': {\\n 'Fuel Purchase': '500', \\n '001 - Wex OTR Flags': 'C - Commercial'\\n },\\n 'preauth_response_verifications': {\\n 'Response Code': '2', \\n 'Approved Amount': '000000500',\\n '002 - Wex OTR Non-Fuel Product Data - Product code: CADV (Company funds cash advance)': 'Amount: 0.00',\\n '002 - Wex OTR Non-Fuel Product Data - Product code: MERC (Default category for merchandise)': 'Amount: 30.00',\\n f'003 - Wex OTR Fuel Product Limits - Product Code: {self.helpers.grade_1_Product }': 'Product limit: 50.00',\\n f'003 - Wex OTR Fuel Product Limits - Product Code: {self.helpers.grade_2_Product }': 'Product limit: 50.00',\\n f'003 - Wex OTR Fuel Product Limits - Product Code: {self.helpers.grade_3_Product }': 'Product limit: 50.00',\\n '005 - Wex OTR Customer Information': 'ABC TRUCKING DENVER CO234W987'\\n },\\n 'capture_request_verifications': {\\n 'Fuel Purchase': '500', \\n '001 - Wex OTR Flags': 'C - Commercial'\\n },\\n 'capture_response_verifications': {\\n 'Response Code': '0', \\n 'Approved Amount': '000000500',\\n '002 - Wex OTR Non-Fuel Product Data - Product code: CADV (Company funds cash advance)': 'Amount: 20.00',\\n '002 - Wex OTR Non-Fuel Product Data - Product code: MERC (Default category for merchandise)': 'Amount: 30.00',\\n f'003 - Wex OTR Fuel Product Limits - Product Code: {self.helpers.grade_1_Product }': 'Product limit: 50.00',\\n f'003 - Wex OTR Fuel Product Limits - Product Code: {self.helpers.grade_2_Product }': 'Product limit: 50.00',\\n f'003 - Wex OTR Fuel Product Limits - Product Code: {self.helpers.grade_3_Product }': 'Product limit: 50.00',\\n '005 - Wex OTR Customer Information': 'ABC TRUCKING DENVER CO234W987'\\n }\\n }\\n\\n #Output verifications\\n\\n receipt_data = [f\\\"{self.helpers.grade_1_Name_reefer} CA PUMP# 1\\\",\\n \\\"5.000 GAL @ $1.000/GAL $5.00 99\\\",\\n \\\"Subtotal = $5.00\\\",\\n \\\"Tax = $0.00\\\",\\n \\\"Total = $5.00\\\",\\n \\\"Change Due = $0.00\\\",\\n \\\"Credit $5.00\\\"]\\n\\n self.helpers.prepay_transaction(\\n card=card_to_use_NGFC,\\n prepay_amount=generic_trans_amount,\\n prompts=prompts,\\n messages_to_verify=messages_to_verify,\\n receipt_data=receipt_data\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9163deaae2f5ae34d9616a47b87654a0\",\n \"score\": \"0.46662685\",\n \"text\": \"def fk_system(self):\\n\\n self.fk_controls = []\\n \\n self.fk_chain = joints_utils.related_clean_joint_chain(self.main_chain, self.side, \\\"fk\\\", True)\\n self.fk_system_objs.append(self.fk_chain[0])\\n\\n # cmds.error(\\\"number of joint: {}\\\".format(len(self.fk_chain)))\\n\\n for i, jnt in enumerate(self.fk_chain):\\n \\n if i == (len(self.fk_chain) - 1):\\n break\\n ctrl = None\\n if i != 0:\\n ctrl = controller.Control(\\\"{}\\\".format(jnt[:len(jnt)-4]), 5.0, 'circle', jnt, jnt, self.fk_controls[i-1].get_control(), ['v'], '', True, True, False)\\n elif i == 0:\\n ctrl = controller.Control(\\\"{}\\\".format(jnt[:len(jnt)-4]), 5.0, 'circle', jnt, jnt, '', ['v'], '', True, True, False)\\n else:\\n ctrl = controller.Control(\\\"{}\\\".format(jnt[:len(jnt)-4]), 5.0, 'circle', jnt, jnt, '', ['v'], '', True, True, False)\\n self.fk_controls.append(ctrl)\\n \\n cmds.parentConstraint(ctrl.get_control(), jnt, maintainOffset=True)\\n cmds.scaleConstraint(ctrl.get_control(), jnt, maintainOffset=True)\\n\\n self.fk_system_grp = cmds.group(empty=True, name=\\\"{}_{}_fkSystem_GRP\\\".format(self.side, self.name))\\n cmds.parent(self.fk_system_objs, self.fk_system_grp)\\n cmds.group(empty=True, name=self.fk_ctrls_main_grp)\\n transforms_utils.align_objs(self.clavicle_jnt, self.fk_ctrls_main_grp)\\n cmds.parentConstraint(self.clavicle_jnt, self.fk_ctrls_main_grp, maintainOffset=True)\\n # scale fix\\n for axis in [\\\"X\\\", \\\"Y\\\", \\\"Z\\\"]:\\n cmds.connectAttr(\\\"{}.scale{}\\\".format(self.clavicle_jnt, axis), \\\"{}.scale{}\\\".format(self.fk_ctrls_main_grp, axis), force=True)\\n cmds.parent(self.fk_controls[0].get_offset_grp(), self.fk_ctrls_main_grp)\\n self.module_main_grp(self.fk_system_grp)\\n\\n return True\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":883,"cells":{"query_id":{"kind":"string","value":"53115a545456a4f0c2aade0d3a8c94ed"},"query":{"kind":"string","value":"loads the specified image."},"positive_passages":{"kind":"list like","value":[{"docid":"49beec7092879a0b08ea6d7fed006276","score":"0.0","text":"def __load__(self, item):\n \n # the name for parent of parent directory where the image is located and the name of the image are same.\n # an example directory breakup is shown below -\n # - data-science-bowl-2018/\n # - stage1_train/\n # - abc\n # - image\n # - abc\n # - mask\n full_image_path = os.path.join(self.path, item, \"images\", item) + \".png\"\n mask_dir_path = os.path.join(self.path, item, \"masks/\")\n all_masks = os.listdir(mask_dir_path)\n \n # load the images\n image = cv2.imread(full_image_path, 1)\n image = cv2.resize(image, (self.image_size, self.image_size))\n \n masked_img = np.zeros((self.image_size, self.image_size, 1))\n \n # load and prepare the corresponding mask.\n for mask in all_masks:\n fullPath = mask_dir_path + mask\n _masked_img = cv2.imread(fullPath, -1)\n _masked_img = cv2.resize(_masked_img, (self.image_size, self.image_size))\n _masked_img = np.expand_dims(_masked_img, axis = -1)\n masked_img = np.maximum(masked_img, _masked_img)\n \n # mormalize the mask and the image. \n image = image/255.0\n masked_img = masked_img/255.0\n \n return image, masked_img","title":""}],"string":"[\n {\n \"docid\": \"49beec7092879a0b08ea6d7fed006276\",\n \"score\": \"0.0\",\n \"text\": \"def __load__(self, item):\\n \\n # the name for parent of parent directory where the image is located and the name of the image are same.\\n # an example directory breakup is shown below -\\n # - data-science-bowl-2018/\\n # - stage1_train/\\n # - abc\\n # - image\\n # - abc\\n # - mask\\n full_image_path = os.path.join(self.path, item, \\\"images\\\", item) + \\\".png\\\"\\n mask_dir_path = os.path.join(self.path, item, \\\"masks/\\\")\\n all_masks = os.listdir(mask_dir_path)\\n \\n # load the images\\n image = cv2.imread(full_image_path, 1)\\n image = cv2.resize(image, (self.image_size, self.image_size))\\n \\n masked_img = np.zeros((self.image_size, self.image_size, 1))\\n \\n # load and prepare the corresponding mask.\\n for mask in all_masks:\\n fullPath = mask_dir_path + mask\\n _masked_img = cv2.imread(fullPath, -1)\\n _masked_img = cv2.resize(_masked_img, (self.image_size, self.image_size))\\n _masked_img = np.expand_dims(_masked_img, axis = -1)\\n masked_img = np.maximum(masked_img, _masked_img)\\n \\n # mormalize the mask and the image. \\n image = image/255.0\\n masked_img = masked_img/255.0\\n \\n return image, masked_img\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"8738269cbd579759308a2a386d6ce210","score":"0.77812254","text":"def load_image(self, image_id):\n info = self.image_info[image_id]\n image = info['img']\n return image","title":""},{"docid":"a978904aa9088dba70ec448b95ab2849","score":"0.7713859","text":"def load_image(self):\n self.load('Load image',self.place_image)","title":""},{"docid":"56fd55d3e32390ff34cff6d91cf8d4ec","score":"0.7456635","text":"def load_image(self, image_id):\n return self.image_info[image_id][\"image\"]","title":""},{"docid":"459cbeebfc55aa7463f304ec9ba50945","score":"0.7322855","text":"def load_image(self, image: Union[widgets.Image, str, pathlib.Path]):\n image = load_img(image)\n self.current_image = image\n self._display_image()\n self.init_empty_data()","title":""},{"docid":"862d16b5b7c544e8c666c6a0b9e7c558","score":"0.729733","text":"def load_image(self, image_id):\n # Load image\n path = self.image_info[image_id]['path']\n url = self.image_info[image_id]['url']\n\n is_url = urllib.request.urlopen(url).getcode() == 200\n\n if is_url:\n image = skimage.io.imread(url)\n else:\n image = skimage.io.imread(path)\n # If grayscale. Convert to RGB for consistency.\n if image.ndim != 3:\n image = skimage.color.gray2rgb(image)\n # If has an alpha channel, remove it for consistency\n if image.shape[-1] == 4:\n image = image[..., :3]\n return image","title":""},{"docid":"f5a722d84b84784e9f67de7dff96dc8f","score":"0.72675246","text":"def load_image(namespace, filename):\n try:\n file_obj = HTTPLoader.get_fileobj(namespace, filename)\n img = Image.open(file_obj)\n\n except NotFoundError as e:\n raise NotFoundError(\"An error occurred: '%s'\" % str(e))\n\n return image","title":""},{"docid":"f2f7e9d9d4c51963558c77bfde2b267b","score":"0.72297585","text":"def load_image(filename):\n return load('image', filename)","title":""},{"docid":"506ff2394e31bfd629ebe307a65485cc","score":"0.71853673","text":"def load_image(image_path):\n with open(image_path, \"rb\") as image_file:\n return load_image_from_bytes(image_file.read())","title":""},{"docid":"1966443bd0c4e8281ddc0f0f2b8aa437","score":"0.71544623","text":"def load_image(self, image_id):\n # Load image\n image = cv2.imread(self.image_info[image_id]['path'])\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n # If grayscale. Convert to RGB for consistency.\n if image.ndim != 3:\n image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)\n # If has an alpha channel, remove it for consistency\n if image.shape[-1] == 4:\n image = image[..., :3]\n if self.use_rgbd:\n # TODO: think about using the loading of open3d\n depth = cv2.imread(self.image_info[image_id]['depth'], -1)\n image = np.concatenate((image, np.expand_dims(depth / 10000., 2)), axis=2)\n return image","title":""},{"docid":"73fdaf195e2454197c512d03295bc5c8","score":"0.7046602","text":"def loadImage(self, inDirectory, inFilename):\n success = self.load(inDirectory + inFilename)\n self.imageLoaded = success","title":""},{"docid":"e260af3ad6ad71732aa720463dabfd25","score":"0.6961212","text":"def loadImage(self, imagePath=None):\n if imagePath is None:\n imagePath = self.getModel().getImagePath()\n if not self.__imageName: \n self.__imageName = self.getModel().getSpriteName()\n imageName = os.path.join(imagePath, self.__imageName) \n self.imgPath = GG.genteguada.GenteGuada.getInstance().getDataPath(imageName) \n self.anchor = guiobjects.getOffset(self.imgPath)\n self.topAnchor = guiobjects.getTopOffset(self.anchor, imageName)\n pos = self.__position\n scrPos = GG.utils.p3dToP2d(pos, self.anchor)\n zOrder = (pow(pos[0], 2) + pow(pos[1], 2))*10\n self.__img = guiobjects.getSprite(imageName, scrPos, zOrder)","title":""},{"docid":"ca931d033556fdc01dccc714498da3b4","score":"0.6921727","text":"def load_image(self, image_id, cache=True):\n full_image = self.frames.load_image(image_id, cache=cache)\n return full_image","title":""},{"docid":"7c3f9bcf359379c5bae2ac6d1e46d14e","score":"0.6917364","text":"def load_image_async(self, image_url, *params):\n print \"reading image from url %s\" % image_url\n rb_utils.get_url_async(image_url, self.load_image_cb, *params)","title":""},{"docid":"8947aaec0c5427dbabbcf9789f089a19","score":"0.69082767","text":"def load_image(self, image_path=None):\n if image_path is None:\n # Prompt user to choose an image\n image_path, sel_filter = QtWidgets.QFileDialog.getOpenFileName(\n self,\n \"Load image\"\n )\n if not image_path:\n return\n\n self.image_plane.load_image(image_path)\n\n # Input color space or the current view could be changed by file and\n # viewing rules on image load. Update the GUI without triggering a\n # re-render.\n with self._ocio_signals_blocked():\n self.set_input_color_space(self.image_plane.input_colorspace())\n self.set_display_view(\n self.image_plane.display(),\n self.image_plane.view()\n )","title":""},{"docid":"6479bd2bb00ad482508c65c82360a6ac","score":"0.6821809","text":"def load_image(self):\r\n try:\r\n image_file, _ = QtWidgets.QFileDialog.getOpenFileName(parent=self, caption=\"Load image\")\r\n if image_file == '':\r\n return\r\n with open(image_file, \"rb\") as f:\r\n buffer = BytesIO(f.read())\r\n image = Image.open(buffer).convert(\"RGB\")\r\n imgarray = np.array(image)\r\n imgdata = (imgarray, buffer)\r\n self.process_new_image(imgdata)\r\n\r\n except Exception as e:\r\n self.statusbar.showMessage(\"[Error] {}: {}\".format(type(e).__name__, str(e)))\r\n return","title":""},{"docid":"c8604f57578844e7ff4a8b5eae93b9c8","score":"0.677367","text":"def load_image(self, image_id):\n # Load image\n image = skimage.io.imread(self.image_info[image_id]['path'])\n # If grayscale. Convert to RGB for consistency.\n if image.ndim != 3:\n image = skimage.color.gray2rgb(image)\n return image","title":""},{"docid":"6a4f97fd482f36dfd666400ba3f16ec9","score":"0.6760915","text":"def load_image(namespace, filename):\n # Path to the great grandparent directory of this file\n try:\n image_bytes = FileSystemLoader.get_fileobj(namespace, filename)\n image = Image.open(image_bytes)\n except FileNotFoundError as e:\n raise NotFoundError(\"File Not Found at %s\" % (Path(namespace + \"/\" + filename)))\n\n return image","title":""},{"docid":"32d280ecc7da2e00d1b6ecb065ca6633","score":"0.67478627","text":"def load_image(path):\n try:\n canonical_path = str(path).replace(\"/\", os.sep).replace(\"\\\\\", os.sep)\n if canonical_path in ImageManager._images_dict.keys():\n # load image from img_dict\n _image = ImageManager._images_dict[canonical_path]\n else:\n try:\n _image = pygame.image.load(canonical_path).convert_alpha()\n ImageManager._images_dict[canonical_path] = _image\n except pygame.error:\n raise FileExistsError(\"File '{0}' does not exist. Check your path to the image.\".format(path))\n return _image\n except FileExistsError:\n raise FileExistsError(\"File '{0}' does not exist. Check your path to the image.\".format(path))","title":""},{"docid":"f85c9d69dc48b6be9f99f1c67f269dee","score":"0.67160517","text":"def load_img(path, img_id):\n return imread(path + '/images/' + img_id + '.png')","title":""},{"docid":"db00023f1545e7799f11d43deda99482","score":"0.6700112","text":"def load_image(fname):\n\n if not os.path.isfile(fname):\n raise FileNotFoundError(fname)\n\n try:\n import freeimgldr\n return _load_image_frimgldr(fname)\n except ImportError:\n pass\n\n typ = _detect_format(fname)\n if typ is None:\n return None #unknown type of image file\n typ = typ.lower()\n if typ in _image_loaders:\n return _image_loaders[typ](fname)\n\n return None #we don't have loader for requested type of image file","title":""},{"docid":"7b2d58b40db3432159585d79e7c86c2e","score":"0.66812533","text":"def loadImage(self, imagePath: str):\n\n # Load image. Converts to QPixmap automatically.\n self.imageView.setImage(imagePath)\n\n # Check fit status\n self.fitImage()\n\n # This will run only when the first image is loaded.\n if self.currentImage is None:\n # Replace the intro text with the image.\n self.mainLayout.replaceWidget(self.introLabel, self.imageView)\n\n # Enable buttons. They will be hidden/shown later.\n self.buttonPrevious.setEnabled(True)\n self.buttonNext.setEnabled(True)\n\n # Enable action.\n self.fitToWindowAct.setEnabled(True)\n\n # Store the current image.\n self.currentImage = imagePath\n\n # Scan the current image's directory.\n self.scanDir(os.path.dirname(self.currentImage))\n\n # Update the index\n self.updateIndex()","title":""},{"docid":"32006ada3795acb0b04bf5112741be10","score":"0.66740733","text":"def load_sample_image(image_name):\n ...","title":""},{"docid":"bad76fdb0c358caf6e263a0f7c410253","score":"0.664944","text":"def load_image(path,image_name) :\n data_path = os.path.join(path)\n image = skimage.io.imread(os.path.join(data_path, image_name))\n \n return image","title":""},{"docid":"bfc64962a14b5a3d6d205a5e24a29647","score":"0.6573593","text":"def load(self, filepath):\n image = loader.load_data(filepath, logger=self.logger)\n self.set_image(image)","title":""},{"docid":"849bc063050a8d33fe77dc8c1e4bf05c","score":"0.6561718","text":"def load_img(path):\n try:\n return Image.open(path)\n except IOError:\n return None","title":""},{"docid":"225c5ca3e728716e2e420bcb9fff9b9e","score":"0.65608877","text":"def image_load(path):\n from PIL import Image\n image = Image.open(path)\n\n #\n # import cv2\n # image = cv2.imread(path)\n return image","title":""},{"docid":"6fca5189e24afcc1d2cb32dbfc40c178","score":"0.6538374","text":"def image_loader(image_name):\r\n image = Image.open(img_path)\r\n image = loader(image).float()\r\n image = Variable(image, requires_grad=True)\r\n image = image.unsqueeze(0) #this is for VGG, may not be needed for ResNet\r\n return image.cuda() #assumes that you're using GPU\r","title":""},{"docid":"7dc7ba0da1702b832c3f1a1f44470f2c","score":"0.6520938","text":"def load_image(img_path):\n image = image_to_tensor(Image.open(img_path).convert('RGB'))\n return image","title":""},{"docid":"abebecf4ac59e891945db31f87ea34f5","score":"0.6464524","text":"def load_image(image):\r\n if isinstance(image, pygame.Surface):\r\n return image\r\n\r\n try:\r\n surface = pygame.image.load(image)\r\n return surface\r\n except pygame.error as message:\r\n print(\"Cannot load : \", image)\r\n print(\"Reason : \", message)\r\n\r\n return pygame.Surface((0, 0))","title":""},{"docid":"361f465558c1a2c6ab789d9df56eb77e","score":"0.6419893","text":"def loadImage(imagePath):\n return cv2.imread(imagePath)","title":""},{"docid":"7507358536e4de90b135962e8c855294","score":"0.6398999","text":"def image_loader(self,image_path):\n image = Image.open(image_path).convert(\"RGB\")\n image = loader(image).float()\n image = Variable(image, requires_grad=True)\n image = image.unsqueeze(0) #this is for VGG, may not be needed for ResNet\n return image","title":""},{"docid":"c3c20ad3648e6c1e221c7ae7af7c3efe","score":"0.63935006","text":"def loadReferenceImage(self):\n refFileName = QtWidgets.QFileDialog.getOpenFileName(self, caption = \"Open reference image\", filter=\"Image Files (*.png *.jpg)\")[0]\n try:\n self.refImage.load(refFileName)\n self.redisplayAnsi()\n except:\n pass","title":""},{"docid":"43b46e64cafcbb49987fe2fc68b98c61","score":"0.63855404","text":"def image_loader(img):\n loader = transforms.Compose([transforms.Resize((256, 256), Image.BICUBIC), \n transforms.ToTensor(), \n transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n \n image = loader(img).float()\n image = image.unsqueeze(0) #this is for VGG, may not be needed for ResNet\n return image #assumes that you're using GPU","title":""},{"docid":"1691766d8b3532a2c5376a7ef9586927","score":"0.638254","text":"def load_image(directory, image_file):\n return mpimg.imread(os.path.join(directory, image_file.strip()))","title":""},{"docid":"1ca1049d6c735c6794b30e568b961b8e","score":"0.6379491","text":"def loadImage(cls, image_data_stream):\n raise NotImplementedError()","title":""},{"docid":"f5a0da8d07279b27fa99a1348f1a9e39","score":"0.636756","text":"def load_image(data_dir, image_file):\n return mpimg.imread(os.path.join(data_dir, image_file.strip()))","title":""},{"docid":"c386d8322d7bfe873d27645811ead625","score":"0.6356187","text":"def load_image(self,image_id):\r\n image = skimage.io.imread(self.image_info[image_id][\"path\"])\r\n if image.ndim != 3:\r\n image = skimage.color.gray2rgb(image)\r\n if image.shape[-1] == 4:\r\n image = image[:,:,:3]\r\n return image","title":""},{"docid":"aa873429e0be8dfd1be7b2276ca27963","score":"0.63477254","text":"def image_loader(image_name):\n image = Image.open(image_name).convert(\"RGB\")\n image = loader(image).float()\n image = Variable(image, requires_grad=True)\n image = image.unsqueeze(0) #this is for VGG, may not be needed for ResNet\n return image","title":""},{"docid":"ac242ced77ac1dd11ae83ce5bd43aa10","score":"0.63471216","text":"def load_image(self):\n img = wx.Image(self.current_image, wx.BITMAP_TYPE_ANY)\n\n # scale the image, preserving the aspect ratio\n W = img.GetWidth()\n H = img.GetHeight()\n if W > H:\n new_w = self.max_size\n new_h = int(self.max_size * H / W)\n else:\n new_h = self.max_size\n new_w = int(self.max_size * W / H)\n img = img.Scale(new_w, new_h)\n\n self.image_ctrl.SetBitmap(wx.Bitmap(img))\n self.Refresh()","title":""},{"docid":"f9aa5749aa3d8a1ff57470f174317f7e","score":"0.6313478","text":"def load_image(name):\r\n fullname = os.path.join('data/images', name)\r\n try:\r\n image = pygame.image.load(fullname).convert_alpha()\r\n except pygame.error, message:\r\n print 'Cannot load image:', name\r\n raise SystemExit, message\r\n return image","title":""},{"docid":"bfdee46e4189d22fabafa73808aa0ce4","score":"0.6292713","text":"def load_full_image(image_tag_string, image_type='oci', load_until_layer=0):\n if image_type == 'oci':\n image = OCIImage(image_tag_string.replace('docker-daemon:', ''))\n elif image_type == 'docker':\n image = DockerImage(image_tag_string.replace('docker-daemon:', ''))\n failure_origin = formats.image_load_failure.format(\n testimage=image.repotag)\n try:\n image.load_image(load_until_layer)\n except (NameError,\n subprocess.CalledProcessError,\n IOError,\n docker.errors.APIError,\n ValueError,\n EOFError) as error:\n logger.warning('Error in loading image: %s', str(error))\n image.origins.add_notice_to_origins(\n failure_origin, Notice(str(error), 'error'))\n return image","title":""},{"docid":"423dcb12a40c43fa77814d8512417c75","score":"0.62877816","text":"def load_img(img_path):\r\n assert os.path.isfile(img_path), f'img file not exist: {img_path}'\r\n img = Image.open(img_path)\r\n return img.convert('RGB')","title":""},{"docid":"7c5f5cf10a9bf4a4a1327709d38d05ac","score":"0.62840444","text":"def LoadImage(self, path):\n # load the image\n bmp = wx.Bitmap(path)\n self.SetImage(bmp)\n self.SetScale(self.GetScale())\n\n # hide the button\n if self.btn:\n self.btn.Hide()\n self.btn = None\n\n # set members\n self.path = path\n self.orig = bmp\n self.GetParent().SetFocus()","title":""},{"docid":"fb2883457f5b09adf74434051ef1a8c2","score":"0.62824696","text":"def checkLoadImage(self):\n try:\n self.imageHyper.loadImage(self.imageFile)\n except Warning:\n self.msgError = \"Please select a correct npy file that contains image data\"\n QMessageBox.warning(self, \"Wrong npy file\", self.msgError)\n return\n\n self.boolImageLoaded = True","title":""},{"docid":"90129f3ad2ad70f4281dc952ceedd376","score":"0.6280957","text":"def loadSourceImage( self ):\n imgPath = os.path.join(self.directory, self.imageID + \".tif\")\n img = ImagePlus(imgPath)\n if img is None:\n raise ValueError(\"Couldn't find the image\")\n self.sourceImage = img","title":""},{"docid":"72939799ab3431634d37996cfa58e1f7","score":"0.62765235","text":"def imgLoadPil(imgPath):\n # load\n from PIL import Image\n img = Image.open(imgPath)\n return img","title":""},{"docid":"c516df5c7c03ec3e02a0f134d350cbc1","score":"0.62756073","text":"def load_image(self, image_id):\n\n info = self.image_info[image_id]\n\n # If not a WAD dataset image, delegate to parent class\n if info[\"source\"] != 'WAD':\n return super(self.__class__, self).load_image(image_id)\n\n # Load image\n path = join(self.root_dir + '_color', info['path'])\n image = skimage.io.imread(path)\n\n # If has an alpha channel, remove it for consistency\n if image.shape[-1] == 4:\n image = image[..., :3]\n\n return image","title":""},{"docid":"e5dded7c9cfc976e96996cf99a1956bb","score":"0.6262443","text":"def load_image(filename, url=None):\n img = None\n\n if url:\n img = Image.open(StringIO(urllib2.urlopen(url).read()))\n\n if filename:\n img = Image.open(filename)\n\n return img","title":""},{"docid":"ccedc097e769459652207cba3203abe3","score":"0.62621444","text":"def load_image(self, image_id):\n info = self.image_info[image_id]\n image = np.load(info['path'])\n image = image[:, :, 0] # To make image 2D for padding\n image = np.concatenate([np.zeros((256, 42)), image], axis=1)\n if image.ndim != 3:\n image = skimage.color.gray2rgb(image)\n return image","title":""},{"docid":"c75f54bacbd282c97e68a98b544e9a42","score":"0.62394977","text":"def load_image(data_dir, image_path):\n image_path = os.path.join(data_dir, image_path)\n if not os.path.isfile(image_path):\n raise Exception('{} is not a file, check your data_dir argument'.format(data_dir))\n image = cv2.imread(image_path)\n return image","title":""},{"docid":"f4628ceddf5fad659e4c8e7eb34f3900","score":"0.62305474","text":"def load_image(path): \n return imread(path, mode=\"L\") / 255","title":""},{"docid":"3334fdb484fd86ef3d2d938328c0404b","score":"0.62272096","text":"def __read_image(self, filename):\n\n with urllib.request.urlopen(filename) as url:\n s = url.read()\n\n resp = urllib.request.urlopen(filename)\n img = np.asarray(bytearray(s), dtype=\"uint8\")\n\n self.image = cv2.imdecode(img, cv2.IMREAD_COLOR)\n\n self.image = cv2.cvtColor(self.image, cv2.COLOR_BGR2RGB)\n\n self.im_copy = self.image.copy()\n\n self.height, self.width = self.image.shape[:2]\n\n self.debug = 0","title":""},{"docid":"c57e8c9f1fab3229dc81799521b2a0a5","score":"0.6222295","text":"def image_loader(img):\r\n image = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\r\n image = Image.fromarray(img)\r\n image = loader(image).float()\r\n image = image.unsqueeze(0)\r\n return image","title":""},{"docid":"258f7ecab574749c7811a4ac7e33eb0f","score":"0.62199837","text":"def open(self, image_path=None):\n try:\n self.image = imread(image_path, mode='RGB')\n self.path = image_path\n except (FileNotFoundError, AttributeError):\n self.image = None\n print(f'image {image_path} not found !!!')","title":""},{"docid":"6d723b3976c3122da0df31e17d98203d","score":"0.6210197","text":"def load_image(type):\n st.write(f\"### Upload {type} Image\")\n f = st.file_uploader(f\"Upload {type} Image\")\n img = None\n path = \"\"\n file_name = \"\"\n if f is None:\n return \"\", \"\", \"\"\n if f is not None:\n file_name = f.name\n format_file = file_name.split(\".\")\n if len(format_file) == 1:\n return \"_\", \"_\", \"_\"\n elif format_file[1] not in [\"png\", \"jpg\", \"jpeg\"]:\n return \"_\", \"_\", \"_\"\n tfile = tempfile.NamedTemporaryFile(delete=False)\n tfile.write(f.read())\n path = tfile.name\n img = img_as_float(io.imread(tfile.name))\n st.write(f\"### {type} Image\")\n st.write(f\"Shape of {type} Image: \", img.shape)\n im = st.image(img)\n return img, path, file_name","title":""},{"docid":"39ce9e29bca85025edc3e3544b980a97","score":"0.62019885","text":"def load_img(self, images_input_path):\n self.img_name = self.queue.pop()\n img_path = os.path.join(images_input_path, self.img_name)\n\n self.img = cv2.imread(img_path)\n return self.img, self.img_name","title":""},{"docid":"ee68a31b0910c3bcd12d6282eba3b950","score":"0.61911076","text":"def load(self, *latgs):\n content = LoadDialog(load=self._load_helper, cancel=self._dismiss_popup)\n self._popup = Popup(title=\"Load image\", content=content, size_hint=(0.9, 0.9))\n self._popup.open()","title":""},{"docid":"9046d5a165360b30d2f0cc65de332101","score":"0.6184551","text":"def load_img(path):\n return cv2.imread(path)","title":""},{"docid":"1066fccb62f61f8f3304730788711b73","score":"0.6183681","text":"def load_image(image_path):\n image = Image.open(image_path)\n image = np.array(image)\n return image","title":""},{"docid":"bac3b6226b4b8733339848902e13bf27","score":"0.618037","text":"def image_loader(image):\n my_image = Image.open(os.path.realpath(image))\n return extract_features(my_image)","title":""},{"docid":"d5a626b875781e6b90d74227fc451091","score":"0.6177321","text":"def image_loader(image_name):\r\n image = Image.open(image_name).convert('RGB') #load an image\r\n image = transform(image).float() #apply transformation\r\n image = Variable(image, requires_grad=True) #Convert to tensor\r\n image = image.unsqueeze(0)\r\n return image","title":""},{"docid":"b61cee7f16723260c45b0470b2f7e5f9","score":"0.61764014","text":"def load_image(path):\n print(\"Loading an image from :\", path)\n\n #Scales, crops, and normalizes a PIL image for a PyTorch model, returns an Numpy array\n transformations = transforms.Compose([transforms.Resize(256),\n transforms.CenterCrop(224),\n transforms.ToTensor(),\n transforms.Normalize([0.485, 0.456, 0.406],\n [0.229, 0.224, 0.225])])\n\n img = Image.open(path)\n img_tensor = transformations(img)\n img_tensor.unsqueeze_(0)\n\n print(\"Image loaded...\")\n\n return im_tensor","title":""},{"docid":"7b4f2fba1c4ca4cf7147f32efa019f7d","score":"0.6133668","text":"def loadImage(self, imgNo, dataset='train'):\n if dataset == 'train':\n image = self.dh.train[imgNo]\n elif dataset == 'test':\n image = self.dh.test[imgNo]\n else:\n logger.error('[loadImage] Only \\'test\\' or \\'train\\' datasets can be used')\n raise ValueError\n\n if image.shape != self.v.shape:\n logger.error('[loadImage] Size of provided image does not match v layer size!')\n raise ValueError\n self.v = image\n self.imgInfo = (dataset, imgNo)","title":""},{"docid":"9285b6a3b10ece80782f6a23ee014afa","score":"0.61222655","text":"def load_image(self, image_path):\n config = ocio.GetCurrentConfig()\n\n # Get input color space (file rule)\n cs_name, rule_idx = config.getColorSpaceFromFilepath(image_path)\n if not cs_name:\n # Use previous or config default\n if self._ocio_input_cs:\n cs_name = self._ocio_input_cs\n else:\n cs_name = ocio.ROLE_DEFAULT\n self._ocio_input_cs = cs_name\n\n # Get default view for input color space (viewing rule)\n self._ocio_view = config.getDefaultView(\n self._ocio_display,\n self._ocio_input_cs\n )\n\n buf = oiio.ImageBuf(image_path)\n spec = buf.spec()\n\n # Convert to RGBA, filling missing color channels with 0.0, and a\n # missing alpha with 1.0.\n if spec.nchannels < 4:\n buf = oiio.ImageBufAlgo.channels(\n buf,\n tuple(\n list(range(spec.nchannels))\n + ([0.0] * (4 - spec.nchannels - 1))\n + [1.0]\n ),\n newchannelnames=(\"R\", \"G\", \"B\", \"A\")\n )\n elif spec.nchannels > 4:\n buf = oiio.ImageBufAlgo.channels(\n buf,\n (0, 1, 2, 3),\n newchannelnames=(\"R\", \"G\", \"B\", \"A\")\n )\n\n # Get pixels as 32-bit float NumPy array\n data = buf.get_pixels(oiio.FLOAT)\n\n # Stash image size for pan/zoom calculations\n self._image_pos.x = spec.x\n self._image_pos.y = spec.y\n self._image_size.x = spec.width\n self._image_size.y = spec.height\n\n # Load image data into texture\n self.makeCurrent()\n\n GL.glBindTexture(GL.GL_TEXTURE_2D, self._image_tex)\n GL.glTexImage2D(\n GL.GL_TEXTURE_2D,\n 0,\n GL.GL_RGBA32F,\n spec.width,\n spec.height,\n 0,\n GL.GL_RGBA,\n GL.GL_FLOAT,\n data.ravel()\n )\n\n self._update_model_view_mat(update=False)\n\n self.update_ocio_proc(\n input_cs=self._ocio_input_cs,\n view=self._ocio_view\n )","title":""},{"docid":"9da15bdccbce1cbd3aebfedbf5d35501","score":"0.6121519","text":"def load_person_image(self, person):\n media_list = person.get_media_list()\n if media_list:\n media_ref = media_list[0]\n object_handle = media_ref.get_reference_handle()\n obj = self.dbstate.db.get_object_from_handle(object_handle)\n full_path = media_path_full(self.dbstate.db, obj.get_path())\n mime_type = obj.get_mime_type()\n if mime_type and mime_type.startswith(\"image\"):\n self.photo.set_image(full_path, mime_type,\n media_ref.get_rectangle())\n self.photo.set_uistate(self.uistate, object_handle)\n else:\n self.photo.set_image(None)\n self.photo.set_uistate(None, None)\n else:\n self.photo.set_image(None)\n self.photo.set_uistate(None, None)","title":""},{"docid":"d8c5f35230b75651a2017c3199f90a98","score":"0.61072826","text":"def load_sample_images():\n ...","title":""},{"docid":"2505bd4fce44e630ef2b26e5b96e1460","score":"0.61036724","text":"def load(self):\n img = Image.open(self.path).convert(\"RGBA\")\n img_data = img.tobytes()\n width, height = img.size\n self.texture = gl.glGenTextures(1)\n gl.glBindTexture(gl.GL_TEXTURE_2D, self.texture)\n gl.glTexParameterf(\n gl.GL_TEXTURE_2D, gl.GL_TEXTURE_MAG_FILTER, gl.GL_LINEAR)\n gl.glTexParameterf(\n gl.GL_TEXTURE_2D, gl.GL_TEXTURE_MIN_FILTER, gl.GL_LINEAR)\n gl.glTexImage2D(gl.GL_TEXTURE_2D, 0, gl.GL_RGBA, width, height, 0,\n gl.GL_RGBA, gl.GL_UNSIGNED_BYTE, img_data)\n gl.glEnable(gl.GL_TEXTURE_2D)\n self.loaded = True","title":""},{"docid":"57009f4934b881dd9e9de8b1973dc6c9","score":"0.60727423","text":"def loadSourceImage(self,srcImg):\n self.srcImg = srcImg # File basename, without full path\n self.imgOrig = mpimg.imread(os.path.join(projRoot,srcImg))\n imgDims = np.shape(self.imgOrig)\n self.imgDimX = imgDims[1] # Yeah, this isn't IDL, deal with it\n self.imgDimY = imgDims[0] # Yeah, this still isn't IDL, deal with it\n self.numChan = imgDims[2]\n print(\"Loaded source image: %s\" % self.srcImg)","title":""},{"docid":"b9f94654a4a7fcfc2ab94967eb23edc6","score":"0.60721624","text":"def load_image(file_path):\n assert os.path.exists(file_path), 'File does NOT exist! (' + file_path + ')'\n return cv2.imread(file_path)","title":""},{"docid":"f36e7b21b2cd837a9d2848114f31188d","score":"0.60709524","text":"def reload_nonauto(self):\n self.get_img()\n self.load_img()","title":""},{"docid":"3da04338bd86a8924d290e315857eaef","score":"0.6063735","text":"def loadimage(self, uuid: str):\n image_path = \"{}/images/{}.jpg\".format(self.storage_path, uuid)\n return img_to_array(load_img(image_path), dtype=np.float32)","title":""},{"docid":"8221e538bf4aae2a7c002263c91f3903","score":"0.6063564","text":"def load_image(data_dir, image_file):\n path = os.path.join(data_dir,\n image_file.strip().split(\"\\\\\")[-3],\n image_file.strip().split(\"\\\\\")[-2],\n image_file.strip().split(\"\\\\\")[-1])\n # Transform to RGB, so that preprocessing is same for train and realtime (unity sends rgb image)\n return cv2.cvtColor(cv2.imread(path), cv2.COLOR_BGR2RGB)","title":""},{"docid":"2543c8ee46c097fe4aa1ab8fd2e6bba6","score":"0.60453176","text":"def read_image(image_dir):\n pass","title":""},{"docid":"e7e9db8d648300104114f46eda83b01a","score":"0.60251856","text":"def loadImage(self, imageFilePath, flag=cv2.IMREAD_UNCHANGED):\n\n self.original_img = cv2.imread(imageFilePath, flag)\n # check for an error opening the file: this openCV function returns none if the file did not load\n\n if self.original_img is None:\n raise IOError(\"OpenCV failed to load an image using this file path: \" + imageFilePath)","title":""},{"docid":"bbb92a2f69ea502a81ea08c4d05caa44","score":"0.6018281","text":"def load_image(path):\n max_dim=512 # To make a perfect view\n img = Image.open(path) # Opening image\n print(img.size)\n long = max(img.size) # Max Dimension of image\n scale = max_dim/long # scale is made so that image can be scaled down in both directions appropriately\n img = img.resize((round(img.size[0]*scale), round(img.size[1]*scale)), Image.ANTIALIAS)\n img = img_to_array(img) # For converting image to array\n # We need to broadcast the image array such that it has a batch dimension \n img = np.expand_dims(img, axis=0)\n return img","title":""},{"docid":"445270903caad4ed5f13dac6f42fec34","score":"0.6017126","text":"def load_image(filename=None):\n if filename is None:\n raise ValueError(\"filename is None\")\n return cv2.imread(filename, 0)","title":""},{"docid":"3401b5d772aaea62780693286b89f6fb","score":"0.60169935","text":"def genericLoader(self, fileName, flag=0):\n\n fif = self.genericFileDeducer(fileName)\n\n #If a type is found, return the loaded image\n if (fif != FIF_UNKNOWN) and self.FIFSupportsReading(fif):\n dib = self.Load(fif, fileName, flag);\n return dib\n #else return none\n return None","title":""},{"docid":"7297fe469a24f524e83284487460365b","score":"0.5994823","text":"def load_img(self,image_name):\n path_to_project = os.path.join(os.path.dirname(os.path.abspath(__file__)),'..')\n image = cv2.imread(os.path.join(path_to_project,\"assets\", \"towers\", image_name))\n # image [height, width]\n scale = image.shape[0]//self.height\n tower_img = pygame.image.load(os.path.join(path_to_project,\"assets\", \"towers\", image_name))\n # pygame image [width, height]\n tower_img = pygame.transform.scale(tower_img, (image.shape[1]//scale, image.shape[0]//scale))\n return tower_img","title":""},{"docid":"2d9fd8bd2a5507cb0d65c47080f53d43","score":"0.59943676","text":"def load_image(image_file: str):\n try:\n image = resize(pygame.image.load(image_file).convert())\n except pygame.error as message: # pylint: disable=no-member\n print('Image at', image_file, 'unreachable')\n raise SystemExit(message)\n\n return image","title":""},{"docid":"05d59aec3ff2502ae8ec3c645183b4b8","score":"0.5994102","text":"def loadImage(self):\n print(\"here\")\n self.filePath, self.format = QtWidgets.QFileDialog.getOpenFileName(\n None,\n \"Load Image\",\n \"\",\n \"Images (*.png *.xpm *.jpg);;\",\n options=QtWidgets.QFileDialog.DontUseNativeDialog,\n )\n self.inputCornersImage = cv2.imread(self.filePath)\n self.cornersInput.setImage(self.inputCornersImage.T)","title":""},{"docid":"e355caef48973c54a4ebdd04df500f7c","score":"0.59938085","text":"def load_single_image(imdb_title, attempt_download=False):\r\n\r\n import imageio\r\n import os.path\r\n\r\n image_path = 'data/images/{}.jpg'.format(imdb_title)\r\n\r\n if not os.path.isfile(image_path):\r\n if not attempt_download:\r\n return None\r\n\r\n status_code = download_image(imdb_title)\r\n if status_code != 0:\r\n return None\r\n\r\n img_array = imageio.imread(image_path)\r\n return img_array","title":""},{"docid":"c97609315a077fc81f10c50e580518a7","score":"0.5987319","text":"def load_image(cls, name):\r\n image = cls._images.get(name)\r\n if image is None:\r\n image = QImage(':dock_images/%s.png' % name)\r\n cls._images[name] = image\r\n return image","title":""},{"docid":"1cdb02277fbb22a38e19b0e25e07a351","score":"0.59783894","text":"def _load_raw_image(filename:str):\n\n # WARNING: THIS ONE IS BAD, something strange happens when we load binary images: its non-detemrinsitc, smthimes the loaded image is crap!!\n # img = scipy.misc.imread(filename) \n return skimage.io.imread(filename)","title":""},{"docid":"25c1ad4a00e6770d7f97f85618cb8216","score":"0.5976029","text":"def load_image(self, image_id):\n image = skimage.io.imread(self.image_info[image_id]['path'])\n y,x,i = image.shape\n c = random.randint(0,2)\n D = image[:,:,c].reshape((y,x,1))\n image = np.concatenate((image, D), axis=2)\n return image","title":""},{"docid":"5ade7c3c18c0e002d077f73ed62e2ecf","score":"0.59722924","text":"def load_gifti(img):\n\n try:\n img = nib.load(img)\n except (ImageFileError, TypeError) as err:\n # it's gzipped, so read the gzip and pipe it in\n if isinstance(err, ImageFileError) and str(err).endswith('.gii.gz\"'):\n with gzip.GzipFile(img) as gz:\n img = nib.GiftiImage.from_bytes(gz.read())\n # it's not a pre-loaded GiftiImage so error out\n elif (isinstance(err, TypeError)\n and not str(err) == 'stat: path should be string, bytes, os.'\n 'PathLike or integer, not GiftiImage'):\n raise err\n\n return img","title":""},{"docid":"96783b9d396e01f502db134c11fddeb6","score":"0.59709334","text":"def imread(url):\n return Image.open(url)","title":""},{"docid":"50f1e2c9a592e68f61a6b7f4a7d1561f","score":"0.5969777","text":"def load(url):\n response = requests.get(url)\n pil_image = Image.open(BytesIO(response.content)).convert(\"RGB\")\n # convert to BGR format\n image = np.array(pil_image)[:, :, [2, 1, 0]]\n return image","title":""},{"docid":"d0fbc5a17738217f4e6e3ff69714fd33","score":"0.59675467","text":"def image_load(target):\r\n image = cv2.imread(target, 0)\r\n image = image[75:135, :]\r\n image = cv2.resize(image, (64,64))\r\n return image","title":""},{"docid":"57e38cd63e3a7a53582ed3dce329457a","score":"0.59487796","text":"def load(self, begin=None, end=None):\n self.img_dict.clear()\n if begin == None:\n start = (\n self.list_idx\n if self.list_idx < self.maxLen\n else self.list_idx - self.maxLen\n )\n load_len = (\n self.maxLen + start\n if self.maxLen + start < self.vidLen\n else self.vidLen\n )\n elif begin != None:\n start = begin\n if self.list_idx >= self.vidLen:\n logging.info(\n \"Skip Load {0} {1} {2}\".format(self.list_idx, self.vidLen, start)\n )\n\n load_len = (\n start + self.maxLen + 1\n if start + self.maxLen + 1 < self.vidLen\n else self.vidLen\n )\n self.list_idx = start\n img = Image.open(self.img_list[start])\n img = self.prepareImg(img)\n self.current = img\n logging.info(\"Loading... {0} {1}\".format(start, load_len))\n for i in range(start, load_len):\n worker = ImageLoader.Worker(\n self.loadImg, self.img_list[self.list_idx], self.list_idx % self.maxLen\n ) # Any other args, kwargs are passed to the run function\n self.threadpool.start(worker)\n self.list_idx += 1","title":""},{"docid":"d8d66dd735fe1e4cd8a8365ba8855a24","score":"0.59438246","text":"def get_image(self, image_id):\n res = self.do_request(\"GET\", \"/images/%s\" % image_id)\n data = json.loads(res.read())['image']\n return data","title":""},{"docid":"b61d2a8f126e517af3ef81454bec1968","score":"0.59420204","text":"def load_image(file_path, verbose=0):\n check_path(file_path)\n im = Image.open(file_path).convert(mode=\"RGB\")\n if verbose:\n print('image {} loaded'.format(file_path))\n print(im.format, im.size, im.mode)\n return im","title":""},{"docid":"fe46384270ee092b38082d91f1071b0e","score":"0.59418887","text":"def load_image(img_path: str) -> Image:\n original_image = PILImage.open(img_path)\n\n exif_data = original_image.getexif()\n if exif_data is not None:\n parsed_data = {}\n for tag, value in exif_data.items():\n if tag in TAGS:\n parsed_data[TAGS.get(tag)] = value\n elif tag in GPSTAGS:\n parsed_data[GPSTAGS.get(tag)] = value\n else:\n parsed_data[tag] = value\n\n exif_data = parsed_data\n\n return Image(np.asarray(original_image), exif_data)","title":""},{"docid":"59cdc1647d34236867f876694b6fa68c","score":"0.59353197","text":"def load_image(image_url):\n try:\n image_get_response = urllib.request.urlopen(image_url)\n image_array = np.asarray(bytearray(image_get_response.read()), dtype=np.uint8)\n image = cv2.imdecode(image_array, cv2.IMREAD_COLOR)\n image = cv2.resize(image, (224, 224))\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n image = image / 255\n except HTTPError as e:\n log.error('Server couldn\\'t fulfill the request. For url:' + image_url)\n return []\n except URLError as e:\n log.error('We failed to reach the server. For url:' + image_url)\n return []\n else:\n return image","title":""},{"docid":"ffbc4c58bebe99b3ab1786e3fa926ee6","score":"0.5927383","text":"def load_image(image_path):\n image = None\n if image_path.endswith(\"jpg\") or image_path.endswith(\"jpeg\"):\n image = Image.open(image_path)\n pixels = list(image.getdata())\n width, height = image.size\n pixels = [pixels[i * width:(i + 1) * width] for i in range(height)]\n size = image.size\n image = np.asarray(pixels)\n image = image * (10**19)\n elif image_path.endswith(\"mat\"):\n image = loadmat(image_path, appendmat=False)['dxImage']['img'][0][0]\n image_rescaled = image\n return image_rescaled","title":""},{"docid":"fe66656262021980d0f5945bb73c200d","score":"0.59122163","text":"def load_image(self, image):\n\n # Make sure it's an RGB with correct width and height\n\t\timage = image.resize((self.width, self.height), Image.ANTIALIAS)\n\t\timage = image.convert(\"RGB\")\n\n # Extract the pixels\n\t\tpix = image.load()\n\n\t\t# Add each pixel to the buffer\n\t\ti = 0\n\t\tw, h = image.size\n\t\tfor col in xrange(0, w):\n\t\t\tfor row in xrange(0, h):\n\t\t\t\tr,g,b = pix[col, row]\n\t\t\t\tcolor = color565(r, g, b)\n\t\t\t\tself._buffer[i] = color\n\t\t\t\ti += 1","title":""},{"docid":"bb683be3ba4f28a13d7aa4b967975fc5","score":"0.5903647","text":"def load_image(name):\n try:\n image = pygame.image.load(name)\n except pygame.error:\n print('Cannot load image:', name)\n return None\n return image.convert(), image.get_rect()","title":""},{"docid":"9f3fe6eace255b17a4c86ccccaf3b079","score":"0.59035844","text":"def load(self):\n for image in self:\n image.load()","title":""},{"docid":"422c776dfc8676bd01d5baf5cdc60605","score":"0.58976287","text":"def imread(self):\n try:\n return imageio.imread(self.local)\n except OSError:\n print(\n \"You need to download this image first. \"\n \"Use this_image.download(local_directory).\"\n )\n raise","title":""},{"docid":"99eb6eb86a8d91863e8d23333e24dbaa","score":"0.5889638","text":"def load_image(name, colorkey=None):\r\n fullname = os.path.join('images', name)\r\n try:\r\n image = pygame.image.load(fullname)\r\n except pygame.error, message:\r\n print 'Cannot load image:', fullname\r\n raise SystemExit, message\r\n image = image.convert()\r\n if colorkey is not None:\r\n if colorkey is -1:\r\n colorkey = image.get_at((0,0))\r\n image.set_colorkey(colorkey)\r\n return image","title":""},{"docid":"7a31bfd5ed891cd6a33db9b638069f58","score":"0.58886814","text":"def loadImg(self, img_path, idx):\n img = Image.open(img_path)\n img = self.prepareImg(img)\n logging.info(\"Added: {0}\".format(idx))\n self.img_dict[idx] = (img, idx)","title":""},{"docid":"fbf51920fe46c82f72b6ecc1a400e8e5","score":"0.5888152","text":"def read_image(path):\n try:\n image = Image.open(path)\n return image\n except Exception as e:\n print(e)","title":""}],"string":"[\n {\n \"docid\": \"8738269cbd579759308a2a386d6ce210\",\n \"score\": \"0.77812254\",\n \"text\": \"def load_image(self, image_id):\\n info = self.image_info[image_id]\\n image = info['img']\\n return image\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a978904aa9088dba70ec448b95ab2849\",\n \"score\": \"0.7713859\",\n \"text\": \"def load_image(self):\\n self.load('Load image',self.place_image)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56fd55d3e32390ff34cff6d91cf8d4ec\",\n \"score\": \"0.7456635\",\n \"text\": \"def load_image(self, image_id):\\n return self.image_info[image_id][\\\"image\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"459cbeebfc55aa7463f304ec9ba50945\",\n \"score\": \"0.7322855\",\n \"text\": \"def load_image(self, image: Union[widgets.Image, str, pathlib.Path]):\\n image = load_img(image)\\n self.current_image = image\\n self._display_image()\\n self.init_empty_data()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"862d16b5b7c544e8c666c6a0b9e7c558\",\n \"score\": \"0.729733\",\n \"text\": \"def load_image(self, image_id):\\n # Load image\\n path = self.image_info[image_id]['path']\\n url = self.image_info[image_id]['url']\\n\\n is_url = urllib.request.urlopen(url).getcode() == 200\\n\\n if is_url:\\n image = skimage.io.imread(url)\\n else:\\n image = skimage.io.imread(path)\\n # If grayscale. Convert to RGB for consistency.\\n if image.ndim != 3:\\n image = skimage.color.gray2rgb(image)\\n # If has an alpha channel, remove it for consistency\\n if image.shape[-1] == 4:\\n image = image[..., :3]\\n return image\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5a722d84b84784e9f67de7dff96dc8f\",\n \"score\": \"0.72675246\",\n \"text\": \"def load_image(namespace, filename):\\n try:\\n file_obj = HTTPLoader.get_fileobj(namespace, filename)\\n img = Image.open(file_obj)\\n\\n except NotFoundError as e:\\n raise NotFoundError(\\\"An error occurred: '%s'\\\" % str(e))\\n\\n return image\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2f7e9d9d4c51963558c77bfde2b267b\",\n \"score\": \"0.72297585\",\n \"text\": \"def load_image(filename):\\n return load('image', filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"506ff2394e31bfd629ebe307a65485cc\",\n \"score\": \"0.71853673\",\n \"text\": \"def load_image(image_path):\\n with open(image_path, \\\"rb\\\") as image_file:\\n return load_image_from_bytes(image_file.read())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1966443bd0c4e8281ddc0f0f2b8aa437\",\n \"score\": \"0.71544623\",\n \"text\": \"def load_image(self, image_id):\\n # Load image\\n image = cv2.imread(self.image_info[image_id]['path'])\\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\\n # If grayscale. Convert to RGB for consistency.\\n if image.ndim != 3:\\n image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)\\n # If has an alpha channel, remove it for consistency\\n if image.shape[-1] == 4:\\n image = image[..., :3]\\n if self.use_rgbd:\\n # TODO: think about using the loading of open3d\\n depth = cv2.imread(self.image_info[image_id]['depth'], -1)\\n image = np.concatenate((image, np.expand_dims(depth / 10000., 2)), axis=2)\\n return image\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73fdaf195e2454197c512d03295bc5c8\",\n \"score\": \"0.7046602\",\n \"text\": \"def loadImage(self, inDirectory, inFilename):\\n success = self.load(inDirectory + inFilename)\\n self.imageLoaded = success\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e260af3ad6ad71732aa720463dabfd25\",\n \"score\": \"0.6961212\",\n \"text\": \"def loadImage(self, imagePath=None):\\n if imagePath is None:\\n imagePath = self.getModel().getImagePath()\\n if not self.__imageName: \\n self.__imageName = self.getModel().getSpriteName()\\n imageName = os.path.join(imagePath, self.__imageName) \\n self.imgPath = GG.genteguada.GenteGuada.getInstance().getDataPath(imageName) \\n self.anchor = guiobjects.getOffset(self.imgPath)\\n self.topAnchor = guiobjects.getTopOffset(self.anchor, imageName)\\n pos = self.__position\\n scrPos = GG.utils.p3dToP2d(pos, self.anchor)\\n zOrder = (pow(pos[0], 2) + pow(pos[1], 2))*10\\n self.__img = guiobjects.getSprite(imageName, scrPos, zOrder)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca931d033556fdc01dccc714498da3b4\",\n \"score\": \"0.6921727\",\n \"text\": \"def load_image(self, image_id, cache=True):\\n full_image = self.frames.load_image(image_id, cache=cache)\\n return full_image\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c3f9bcf359379c5bae2ac6d1e46d14e\",\n \"score\": \"0.6917364\",\n \"text\": \"def load_image_async(self, image_url, *params):\\n print \\\"reading image from url %s\\\" % image_url\\n rb_utils.get_url_async(image_url, self.load_image_cb, *params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8947aaec0c5427dbabbcf9789f089a19\",\n \"score\": \"0.69082767\",\n \"text\": \"def load_image(self, image_path=None):\\n if image_path is None:\\n # Prompt user to choose an image\\n image_path, sel_filter = QtWidgets.QFileDialog.getOpenFileName(\\n self,\\n \\\"Load image\\\"\\n )\\n if not image_path:\\n return\\n\\n self.image_plane.load_image(image_path)\\n\\n # Input color space or the current view could be changed by file and\\n # viewing rules on image load. Update the GUI without triggering a\\n # re-render.\\n with self._ocio_signals_blocked():\\n self.set_input_color_space(self.image_plane.input_colorspace())\\n self.set_display_view(\\n self.image_plane.display(),\\n self.image_plane.view()\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6479bd2bb00ad482508c65c82360a6ac\",\n \"score\": \"0.6821809\",\n \"text\": \"def load_image(self):\\r\\n try:\\r\\n image_file, _ = QtWidgets.QFileDialog.getOpenFileName(parent=self, caption=\\\"Load image\\\")\\r\\n if image_file == '':\\r\\n return\\r\\n with open(image_file, \\\"rb\\\") as f:\\r\\n buffer = BytesIO(f.read())\\r\\n image = Image.open(buffer).convert(\\\"RGB\\\")\\r\\n imgarray = np.array(image)\\r\\n imgdata = (imgarray, buffer)\\r\\n self.process_new_image(imgdata)\\r\\n\\r\\n except Exception as e:\\r\\n self.statusbar.showMessage(\\\"[Error] {}: {}\\\".format(type(e).__name__, str(e)))\\r\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8604f57578844e7ff4a8b5eae93b9c8\",\n \"score\": \"0.677367\",\n \"text\": \"def load_image(self, image_id):\\n # Load image\\n image = skimage.io.imread(self.image_info[image_id]['path'])\\n # If grayscale. Convert to RGB for consistency.\\n if image.ndim != 3:\\n image = skimage.color.gray2rgb(image)\\n return image\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a4f97fd482f36dfd666400ba3f16ec9\",\n \"score\": \"0.6760915\",\n \"text\": \"def load_image(namespace, filename):\\n # Path to the great grandparent directory of this file\\n try:\\n image_bytes = FileSystemLoader.get_fileobj(namespace, filename)\\n image = Image.open(image_bytes)\\n except FileNotFoundError as e:\\n raise NotFoundError(\\\"File Not Found at %s\\\" % (Path(namespace + \\\"/\\\" + filename)))\\n\\n return image\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32d280ecc7da2e00d1b6ecb065ca6633\",\n \"score\": \"0.67478627\",\n \"text\": \"def load_image(path):\\n try:\\n canonical_path = str(path).replace(\\\"/\\\", os.sep).replace(\\\"\\\\\\\\\\\", os.sep)\\n if canonical_path in ImageManager._images_dict.keys():\\n # load image from img_dict\\n _image = ImageManager._images_dict[canonical_path]\\n else:\\n try:\\n _image = pygame.image.load(canonical_path).convert_alpha()\\n ImageManager._images_dict[canonical_path] = _image\\n except pygame.error:\\n raise FileExistsError(\\\"File '{0}' does not exist. Check your path to the image.\\\".format(path))\\n return _image\\n except FileExistsError:\\n raise FileExistsError(\\\"File '{0}' does not exist. Check your path to the image.\\\".format(path))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f85c9d69dc48b6be9f99f1c67f269dee\",\n \"score\": \"0.67160517\",\n \"text\": \"def load_img(path, img_id):\\n return imread(path + '/images/' + img_id + '.png')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db00023f1545e7799f11d43deda99482\",\n \"score\": \"0.6700112\",\n \"text\": \"def load_image(fname):\\n\\n if not os.path.isfile(fname):\\n raise FileNotFoundError(fname)\\n\\n try:\\n import freeimgldr\\n return _load_image_frimgldr(fname)\\n except ImportError:\\n pass\\n\\n typ = _detect_format(fname)\\n if typ is None:\\n return None #unknown type of image file\\n typ = typ.lower()\\n if typ in _image_loaders:\\n return _image_loaders[typ](fname)\\n\\n return None #we don't have loader for requested type of image file\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b2d58b40db3432159585d79e7c86c2e\",\n \"score\": \"0.66812533\",\n \"text\": \"def loadImage(self, imagePath: str):\\n\\n # Load image. Converts to QPixmap automatically.\\n self.imageView.setImage(imagePath)\\n\\n # Check fit status\\n self.fitImage()\\n\\n # This will run only when the first image is loaded.\\n if self.currentImage is None:\\n # Replace the intro text with the image.\\n self.mainLayout.replaceWidget(self.introLabel, self.imageView)\\n\\n # Enable buttons. They will be hidden/shown later.\\n self.buttonPrevious.setEnabled(True)\\n self.buttonNext.setEnabled(True)\\n\\n # Enable action.\\n self.fitToWindowAct.setEnabled(True)\\n\\n # Store the current image.\\n self.currentImage = imagePath\\n\\n # Scan the current image's directory.\\n self.scanDir(os.path.dirname(self.currentImage))\\n\\n # Update the index\\n self.updateIndex()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32006ada3795acb0b04bf5112741be10\",\n \"score\": \"0.66740733\",\n \"text\": \"def load_sample_image(image_name):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bad76fdb0c358caf6e263a0f7c410253\",\n \"score\": \"0.664944\",\n \"text\": \"def load_image(path,image_name) :\\n data_path = os.path.join(path)\\n image = skimage.io.imread(os.path.join(data_path, image_name))\\n \\n return image\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfc64962a14b5a3d6d205a5e24a29647\",\n \"score\": \"0.6573593\",\n \"text\": \"def load(self, filepath):\\n image = loader.load_data(filepath, logger=self.logger)\\n self.set_image(image)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"849bc063050a8d33fe77dc8c1e4bf05c\",\n \"score\": \"0.6561718\",\n \"text\": \"def load_img(path):\\n try:\\n return Image.open(path)\\n except IOError:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"225c5ca3e728716e2e420bcb9fff9b9e\",\n \"score\": \"0.65608877\",\n \"text\": \"def image_load(path):\\n from PIL import Image\\n image = Image.open(path)\\n\\n #\\n # import cv2\\n # image = cv2.imread(path)\\n return image\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fca5189e24afcc1d2cb32dbfc40c178\",\n \"score\": \"0.6538374\",\n \"text\": \"def image_loader(image_name):\\r\\n image = Image.open(img_path)\\r\\n image = loader(image).float()\\r\\n image = Variable(image, requires_grad=True)\\r\\n image = image.unsqueeze(0) #this is for VGG, may not be needed for ResNet\\r\\n return image.cuda() #assumes that you're using GPU\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7dc7ba0da1702b832c3f1a1f44470f2c\",\n \"score\": \"0.6520938\",\n \"text\": \"def load_image(img_path):\\n image = image_to_tensor(Image.open(img_path).convert('RGB'))\\n return image\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abebecf4ac59e891945db31f87ea34f5\",\n \"score\": \"0.6464524\",\n \"text\": \"def load_image(image):\\r\\n if isinstance(image, pygame.Surface):\\r\\n return image\\r\\n\\r\\n try:\\r\\n surface = pygame.image.load(image)\\r\\n return surface\\r\\n except pygame.error as message:\\r\\n print(\\\"Cannot load : \\\", image)\\r\\n print(\\\"Reason : \\\", message)\\r\\n\\r\\n return pygame.Surface((0, 0))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"361f465558c1a2c6ab789d9df56eb77e\",\n \"score\": \"0.6419893\",\n \"text\": \"def loadImage(imagePath):\\n return cv2.imread(imagePath)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7507358536e4de90b135962e8c855294\",\n \"score\": \"0.6398999\",\n \"text\": \"def image_loader(self,image_path):\\n image = Image.open(image_path).convert(\\\"RGB\\\")\\n image = loader(image).float()\\n image = Variable(image, requires_grad=True)\\n image = image.unsqueeze(0) #this is for VGG, may not be needed for ResNet\\n return image\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3c20ad3648e6c1e221c7ae7af7c3efe\",\n \"score\": \"0.63935006\",\n \"text\": \"def loadReferenceImage(self):\\n refFileName = QtWidgets.QFileDialog.getOpenFileName(self, caption = \\\"Open reference image\\\", filter=\\\"Image Files (*.png *.jpg)\\\")[0]\\n try:\\n self.refImage.load(refFileName)\\n self.redisplayAnsi()\\n except:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43b46e64cafcbb49987fe2fc68b98c61\",\n \"score\": \"0.63855404\",\n \"text\": \"def image_loader(img):\\n loader = transforms.Compose([transforms.Resize((256, 256), Image.BICUBIC), \\n transforms.ToTensor(), \\n transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\\n \\n image = loader(img).float()\\n image = image.unsqueeze(0) #this is for VGG, may not be needed for ResNet\\n return image #assumes that you're using GPU\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1691766d8b3532a2c5376a7ef9586927\",\n \"score\": \"0.638254\",\n \"text\": \"def load_image(directory, image_file):\\n return mpimg.imread(os.path.join(directory, image_file.strip()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ca1049d6c735c6794b30e568b961b8e\",\n \"score\": \"0.6379491\",\n \"text\": \"def loadImage(cls, image_data_stream):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5a0da8d07279b27fa99a1348f1a9e39\",\n \"score\": \"0.636756\",\n \"text\": \"def load_image(data_dir, image_file):\\n return mpimg.imread(os.path.join(data_dir, image_file.strip()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c386d8322d7bfe873d27645811ead625\",\n \"score\": \"0.6356187\",\n \"text\": \"def load_image(self,image_id):\\r\\n image = skimage.io.imread(self.image_info[image_id][\\\"path\\\"])\\r\\n if image.ndim != 3:\\r\\n image = skimage.color.gray2rgb(image)\\r\\n if image.shape[-1] == 4:\\r\\n image = image[:,:,:3]\\r\\n return image\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa873429e0be8dfd1be7b2276ca27963\",\n \"score\": \"0.63477254\",\n \"text\": \"def image_loader(image_name):\\n image = Image.open(image_name).convert(\\\"RGB\\\")\\n image = loader(image).float()\\n image = Variable(image, requires_grad=True)\\n image = image.unsqueeze(0) #this is for VGG, may not be needed for ResNet\\n return image\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac242ced77ac1dd11ae83ce5bd43aa10\",\n \"score\": \"0.63471216\",\n \"text\": \"def load_image(self):\\n img = wx.Image(self.current_image, wx.BITMAP_TYPE_ANY)\\n\\n # scale the image, preserving the aspect ratio\\n W = img.GetWidth()\\n H = img.GetHeight()\\n if W > H:\\n new_w = self.max_size\\n new_h = int(self.max_size * H / W)\\n else:\\n new_h = self.max_size\\n new_w = int(self.max_size * W / H)\\n img = img.Scale(new_w, new_h)\\n\\n self.image_ctrl.SetBitmap(wx.Bitmap(img))\\n self.Refresh()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9aa5749aa3d8a1ff57470f174317f7e\",\n \"score\": \"0.6313478\",\n \"text\": \"def load_image(name):\\r\\n fullname = os.path.join('data/images', name)\\r\\n try:\\r\\n image = pygame.image.load(fullname).convert_alpha()\\r\\n except pygame.error, message:\\r\\n print 'Cannot load image:', name\\r\\n raise SystemExit, message\\r\\n return image\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfdee46e4189d22fabafa73808aa0ce4\",\n \"score\": \"0.6292713\",\n \"text\": \"def load_full_image(image_tag_string, image_type='oci', load_until_layer=0):\\n if image_type == 'oci':\\n image = OCIImage(image_tag_string.replace('docker-daemon:', ''))\\n elif image_type == 'docker':\\n image = DockerImage(image_tag_string.replace('docker-daemon:', ''))\\n failure_origin = formats.image_load_failure.format(\\n testimage=image.repotag)\\n try:\\n image.load_image(load_until_layer)\\n except (NameError,\\n subprocess.CalledProcessError,\\n IOError,\\n docker.errors.APIError,\\n ValueError,\\n EOFError) as error:\\n logger.warning('Error in loading image: %s', str(error))\\n image.origins.add_notice_to_origins(\\n failure_origin, Notice(str(error), 'error'))\\n return image\",\n \"title\": \"\"\n },\n {\n \"docid\": \"423dcb12a40c43fa77814d8512417c75\",\n \"score\": \"0.62877816\",\n \"text\": \"def load_img(img_path):\\r\\n assert os.path.isfile(img_path), f'img file not exist: {img_path}'\\r\\n img = Image.open(img_path)\\r\\n return img.convert('RGB')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c5f5cf10a9bf4a4a1327709d38d05ac\",\n \"score\": \"0.62840444\",\n \"text\": \"def LoadImage(self, path):\\n # load the image\\n bmp = wx.Bitmap(path)\\n self.SetImage(bmp)\\n self.SetScale(self.GetScale())\\n\\n # hide the button\\n if self.btn:\\n self.btn.Hide()\\n self.btn = None\\n\\n # set members\\n self.path = path\\n self.orig = bmp\\n self.GetParent().SetFocus()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb2883457f5b09adf74434051ef1a8c2\",\n \"score\": \"0.62824696\",\n \"text\": \"def checkLoadImage(self):\\n try:\\n self.imageHyper.loadImage(self.imageFile)\\n except Warning:\\n self.msgError = \\\"Please select a correct npy file that contains image data\\\"\\n QMessageBox.warning(self, \\\"Wrong npy file\\\", self.msgError)\\n return\\n\\n self.boolImageLoaded = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90129f3ad2ad70f4281dc952ceedd376\",\n \"score\": \"0.6280957\",\n \"text\": \"def loadSourceImage( self ):\\n imgPath = os.path.join(self.directory, self.imageID + \\\".tif\\\")\\n img = ImagePlus(imgPath)\\n if img is None:\\n raise ValueError(\\\"Couldn't find the image\\\")\\n self.sourceImage = img\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72939799ab3431634d37996cfa58e1f7\",\n \"score\": \"0.62765235\",\n \"text\": \"def imgLoadPil(imgPath):\\n # load\\n from PIL import Image\\n img = Image.open(imgPath)\\n return img\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c516df5c7c03ec3e02a0f134d350cbc1\",\n \"score\": \"0.62756073\",\n \"text\": \"def load_image(self, image_id):\\n\\n info = self.image_info[image_id]\\n\\n # If not a WAD dataset image, delegate to parent class\\n if info[\\\"source\\\"] != 'WAD':\\n return super(self.__class__, self).load_image(image_id)\\n\\n # Load image\\n path = join(self.root_dir + '_color', info['path'])\\n image = skimage.io.imread(path)\\n\\n # If has an alpha channel, remove it for consistency\\n if image.shape[-1] == 4:\\n image = image[..., :3]\\n\\n return image\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5dded7c9cfc976e96996cf99a1956bb\",\n \"score\": \"0.6262443\",\n \"text\": \"def load_image(filename, url=None):\\n img = None\\n\\n if url:\\n img = Image.open(StringIO(urllib2.urlopen(url).read()))\\n\\n if filename:\\n img = Image.open(filename)\\n\\n return img\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ccedc097e769459652207cba3203abe3\",\n \"score\": \"0.62621444\",\n \"text\": \"def load_image(self, image_id):\\n info = self.image_info[image_id]\\n image = np.load(info['path'])\\n image = image[:, :, 0] # To make image 2D for padding\\n image = np.concatenate([np.zeros((256, 42)), image], axis=1)\\n if image.ndim != 3:\\n image = skimage.color.gray2rgb(image)\\n return image\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c75f54bacbd282c97e68a98b544e9a42\",\n \"score\": \"0.62394977\",\n \"text\": \"def load_image(data_dir, image_path):\\n image_path = os.path.join(data_dir, image_path)\\n if not os.path.isfile(image_path):\\n raise Exception('{} is not a file, check your data_dir argument'.format(data_dir))\\n image = cv2.imread(image_path)\\n return image\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4628ceddf5fad659e4c8e7eb34f3900\",\n \"score\": \"0.62305474\",\n \"text\": \"def load_image(path): \\n return imread(path, mode=\\\"L\\\") / 255\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3334fdb484fd86ef3d2d938328c0404b\",\n \"score\": \"0.62272096\",\n \"text\": \"def __read_image(self, filename):\\n\\n with urllib.request.urlopen(filename) as url:\\n s = url.read()\\n\\n resp = urllib.request.urlopen(filename)\\n img = np.asarray(bytearray(s), dtype=\\\"uint8\\\")\\n\\n self.image = cv2.imdecode(img, cv2.IMREAD_COLOR)\\n\\n self.image = cv2.cvtColor(self.image, cv2.COLOR_BGR2RGB)\\n\\n self.im_copy = self.image.copy()\\n\\n self.height, self.width = self.image.shape[:2]\\n\\n self.debug = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c57e8c9f1fab3229dc81799521b2a0a5\",\n \"score\": \"0.6222295\",\n \"text\": \"def image_loader(img):\\r\\n image = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\\r\\n image = Image.fromarray(img)\\r\\n image = loader(image).float()\\r\\n image = image.unsqueeze(0)\\r\\n return image\",\n \"title\": \"\"\n },\n {\n \"docid\": \"258f7ecab574749c7811a4ac7e33eb0f\",\n \"score\": \"0.62199837\",\n \"text\": \"def open(self, image_path=None):\\n try:\\n self.image = imread(image_path, mode='RGB')\\n self.path = image_path\\n except (FileNotFoundError, AttributeError):\\n self.image = None\\n print(f'image {image_path} not found !!!')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d723b3976c3122da0df31e17d98203d\",\n \"score\": \"0.6210197\",\n \"text\": \"def load_image(type):\\n st.write(f\\\"### Upload {type} Image\\\")\\n f = st.file_uploader(f\\\"Upload {type} Image\\\")\\n img = None\\n path = \\\"\\\"\\n file_name = \\\"\\\"\\n if f is None:\\n return \\\"\\\", \\\"\\\", \\\"\\\"\\n if f is not None:\\n file_name = f.name\\n format_file = file_name.split(\\\".\\\")\\n if len(format_file) == 1:\\n return \\\"_\\\", \\\"_\\\", \\\"_\\\"\\n elif format_file[1] not in [\\\"png\\\", \\\"jpg\\\", \\\"jpeg\\\"]:\\n return \\\"_\\\", \\\"_\\\", \\\"_\\\"\\n tfile = tempfile.NamedTemporaryFile(delete=False)\\n tfile.write(f.read())\\n path = tfile.name\\n img = img_as_float(io.imread(tfile.name))\\n st.write(f\\\"### {type} Image\\\")\\n st.write(f\\\"Shape of {type} Image: \\\", img.shape)\\n im = st.image(img)\\n return img, path, file_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39ce9e29bca85025edc3e3544b980a97\",\n \"score\": \"0.62019885\",\n \"text\": \"def load_img(self, images_input_path):\\n self.img_name = self.queue.pop()\\n img_path = os.path.join(images_input_path, self.img_name)\\n\\n self.img = cv2.imread(img_path)\\n return self.img, self.img_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee68a31b0910c3bcd12d6282eba3b950\",\n \"score\": \"0.61911076\",\n \"text\": \"def load(self, *latgs):\\n content = LoadDialog(load=self._load_helper, cancel=self._dismiss_popup)\\n self._popup = Popup(title=\\\"Load image\\\", content=content, size_hint=(0.9, 0.9))\\n self._popup.open()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9046d5a165360b30d2f0cc65de332101\",\n \"score\": \"0.6184551\",\n \"text\": \"def load_img(path):\\n return cv2.imread(path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1066fccb62f61f8f3304730788711b73\",\n \"score\": \"0.6183681\",\n \"text\": \"def load_image(image_path):\\n image = Image.open(image_path)\\n image = np.array(image)\\n return image\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bac3b6226b4b8733339848902e13bf27\",\n \"score\": \"0.618037\",\n \"text\": \"def image_loader(image):\\n my_image = Image.open(os.path.realpath(image))\\n return extract_features(my_image)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5a626b875781e6b90d74227fc451091\",\n \"score\": \"0.6177321\",\n \"text\": \"def image_loader(image_name):\\r\\n image = Image.open(image_name).convert('RGB') #load an image\\r\\n image = transform(image).float() #apply transformation\\r\\n image = Variable(image, requires_grad=True) #Convert to tensor\\r\\n image = image.unsqueeze(0)\\r\\n return image\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b61cee7f16723260c45b0470b2f7e5f9\",\n \"score\": \"0.61764014\",\n \"text\": \"def load_image(path):\\n print(\\\"Loading an image from :\\\", path)\\n\\n #Scales, crops, and normalizes a PIL image for a PyTorch model, returns an Numpy array\\n transformations = transforms.Compose([transforms.Resize(256),\\n transforms.CenterCrop(224),\\n transforms.ToTensor(),\\n transforms.Normalize([0.485, 0.456, 0.406],\\n [0.229, 0.224, 0.225])])\\n\\n img = Image.open(path)\\n img_tensor = transformations(img)\\n img_tensor.unsqueeze_(0)\\n\\n print(\\\"Image loaded...\\\")\\n\\n return im_tensor\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b4f2fba1c4ca4cf7147f32efa019f7d\",\n \"score\": \"0.6133668\",\n \"text\": \"def loadImage(self, imgNo, dataset='train'):\\n if dataset == 'train':\\n image = self.dh.train[imgNo]\\n elif dataset == 'test':\\n image = self.dh.test[imgNo]\\n else:\\n logger.error('[loadImage] Only \\\\'test\\\\' or \\\\'train\\\\' datasets can be used')\\n raise ValueError\\n\\n if image.shape != self.v.shape:\\n logger.error('[loadImage] Size of provided image does not match v layer size!')\\n raise ValueError\\n self.v = image\\n self.imgInfo = (dataset, imgNo)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9285b6a3b10ece80782f6a23ee014afa\",\n \"score\": \"0.61222655\",\n \"text\": \"def load_image(self, image_path):\\n config = ocio.GetCurrentConfig()\\n\\n # Get input color space (file rule)\\n cs_name, rule_idx = config.getColorSpaceFromFilepath(image_path)\\n if not cs_name:\\n # Use previous or config default\\n if self._ocio_input_cs:\\n cs_name = self._ocio_input_cs\\n else:\\n cs_name = ocio.ROLE_DEFAULT\\n self._ocio_input_cs = cs_name\\n\\n # Get default view for input color space (viewing rule)\\n self._ocio_view = config.getDefaultView(\\n self._ocio_display,\\n self._ocio_input_cs\\n )\\n\\n buf = oiio.ImageBuf(image_path)\\n spec = buf.spec()\\n\\n # Convert to RGBA, filling missing color channels with 0.0, and a\\n # missing alpha with 1.0.\\n if spec.nchannels < 4:\\n buf = oiio.ImageBufAlgo.channels(\\n buf,\\n tuple(\\n list(range(spec.nchannels))\\n + ([0.0] * (4 - spec.nchannels - 1))\\n + [1.0]\\n ),\\n newchannelnames=(\\\"R\\\", \\\"G\\\", \\\"B\\\", \\\"A\\\")\\n )\\n elif spec.nchannels > 4:\\n buf = oiio.ImageBufAlgo.channels(\\n buf,\\n (0, 1, 2, 3),\\n newchannelnames=(\\\"R\\\", \\\"G\\\", \\\"B\\\", \\\"A\\\")\\n )\\n\\n # Get pixels as 32-bit float NumPy array\\n data = buf.get_pixels(oiio.FLOAT)\\n\\n # Stash image size for pan/zoom calculations\\n self._image_pos.x = spec.x\\n self._image_pos.y = spec.y\\n self._image_size.x = spec.width\\n self._image_size.y = spec.height\\n\\n # Load image data into texture\\n self.makeCurrent()\\n\\n GL.glBindTexture(GL.GL_TEXTURE_2D, self._image_tex)\\n GL.glTexImage2D(\\n GL.GL_TEXTURE_2D,\\n 0,\\n GL.GL_RGBA32F,\\n spec.width,\\n spec.height,\\n 0,\\n GL.GL_RGBA,\\n GL.GL_FLOAT,\\n data.ravel()\\n )\\n\\n self._update_model_view_mat(update=False)\\n\\n self.update_ocio_proc(\\n input_cs=self._ocio_input_cs,\\n view=self._ocio_view\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9da15bdccbce1cbd3aebfedbf5d35501\",\n \"score\": \"0.6121519\",\n \"text\": \"def load_person_image(self, person):\\n media_list = person.get_media_list()\\n if media_list:\\n media_ref = media_list[0]\\n object_handle = media_ref.get_reference_handle()\\n obj = self.dbstate.db.get_object_from_handle(object_handle)\\n full_path = media_path_full(self.dbstate.db, obj.get_path())\\n mime_type = obj.get_mime_type()\\n if mime_type and mime_type.startswith(\\\"image\\\"):\\n self.photo.set_image(full_path, mime_type,\\n media_ref.get_rectangle())\\n self.photo.set_uistate(self.uistate, object_handle)\\n else:\\n self.photo.set_image(None)\\n self.photo.set_uistate(None, None)\\n else:\\n self.photo.set_image(None)\\n self.photo.set_uistate(None, None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8c5f35230b75651a2017c3199f90a98\",\n \"score\": \"0.61072826\",\n \"text\": \"def load_sample_images():\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2505bd4fce44e630ef2b26e5b96e1460\",\n \"score\": \"0.61036724\",\n \"text\": \"def load(self):\\n img = Image.open(self.path).convert(\\\"RGBA\\\")\\n img_data = img.tobytes()\\n width, height = img.size\\n self.texture = gl.glGenTextures(1)\\n gl.glBindTexture(gl.GL_TEXTURE_2D, self.texture)\\n gl.glTexParameterf(\\n gl.GL_TEXTURE_2D, gl.GL_TEXTURE_MAG_FILTER, gl.GL_LINEAR)\\n gl.glTexParameterf(\\n gl.GL_TEXTURE_2D, gl.GL_TEXTURE_MIN_FILTER, gl.GL_LINEAR)\\n gl.glTexImage2D(gl.GL_TEXTURE_2D, 0, gl.GL_RGBA, width, height, 0,\\n gl.GL_RGBA, gl.GL_UNSIGNED_BYTE, img_data)\\n gl.glEnable(gl.GL_TEXTURE_2D)\\n self.loaded = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57009f4934b881dd9e9de8b1973dc6c9\",\n \"score\": \"0.60727423\",\n \"text\": \"def loadSourceImage(self,srcImg):\\n self.srcImg = srcImg # File basename, without full path\\n self.imgOrig = mpimg.imread(os.path.join(projRoot,srcImg))\\n imgDims = np.shape(self.imgOrig)\\n self.imgDimX = imgDims[1] # Yeah, this isn't IDL, deal with it\\n self.imgDimY = imgDims[0] # Yeah, this still isn't IDL, deal with it\\n self.numChan = imgDims[2]\\n print(\\\"Loaded source image: %s\\\" % self.srcImg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9f94654a4a7fcfc2ab94967eb23edc6\",\n \"score\": \"0.60721624\",\n \"text\": \"def load_image(file_path):\\n assert os.path.exists(file_path), 'File does NOT exist! (' + file_path + ')'\\n return cv2.imread(file_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f36e7b21b2cd837a9d2848114f31188d\",\n \"score\": \"0.60709524\",\n \"text\": \"def reload_nonauto(self):\\n self.get_img()\\n self.load_img()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3da04338bd86a8924d290e315857eaef\",\n \"score\": \"0.6063735\",\n \"text\": \"def loadimage(self, uuid: str):\\n image_path = \\\"{}/images/{}.jpg\\\".format(self.storage_path, uuid)\\n return img_to_array(load_img(image_path), dtype=np.float32)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8221e538bf4aae2a7c002263c91f3903\",\n \"score\": \"0.6063564\",\n \"text\": \"def load_image(data_dir, image_file):\\n path = os.path.join(data_dir,\\n image_file.strip().split(\\\"\\\\\\\\\\\")[-3],\\n image_file.strip().split(\\\"\\\\\\\\\\\")[-2],\\n image_file.strip().split(\\\"\\\\\\\\\\\")[-1])\\n # Transform to RGB, so that preprocessing is same for train and realtime (unity sends rgb image)\\n return cv2.cvtColor(cv2.imread(path), cv2.COLOR_BGR2RGB)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2543c8ee46c097fe4aa1ab8fd2e6bba6\",\n \"score\": \"0.60453176\",\n \"text\": \"def read_image(image_dir):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7e9db8d648300104114f46eda83b01a\",\n \"score\": \"0.60251856\",\n \"text\": \"def loadImage(self, imageFilePath, flag=cv2.IMREAD_UNCHANGED):\\n\\n self.original_img = cv2.imread(imageFilePath, flag)\\n # check for an error opening the file: this openCV function returns none if the file did not load\\n\\n if self.original_img is None:\\n raise IOError(\\\"OpenCV failed to load an image using this file path: \\\" + imageFilePath)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbb92a2f69ea502a81ea08c4d05caa44\",\n \"score\": \"0.6018281\",\n \"text\": \"def load_image(path):\\n max_dim=512 # To make a perfect view\\n img = Image.open(path) # Opening image\\n print(img.size)\\n long = max(img.size) # Max Dimension of image\\n scale = max_dim/long # scale is made so that image can be scaled down in both directions appropriately\\n img = img.resize((round(img.size[0]*scale), round(img.size[1]*scale)), Image.ANTIALIAS)\\n img = img_to_array(img) # For converting image to array\\n # We need to broadcast the image array such that it has a batch dimension \\n img = np.expand_dims(img, axis=0)\\n return img\",\n \"title\": \"\"\n },\n {\n \"docid\": \"445270903caad4ed5f13dac6f42fec34\",\n \"score\": \"0.6017126\",\n \"text\": \"def load_image(filename=None):\\n if filename is None:\\n raise ValueError(\\\"filename is None\\\")\\n return cv2.imread(filename, 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3401b5d772aaea62780693286b89f6fb\",\n \"score\": \"0.60169935\",\n \"text\": \"def genericLoader(self, fileName, flag=0):\\n\\n fif = self.genericFileDeducer(fileName)\\n\\n #If a type is found, return the loaded image\\n if (fif != FIF_UNKNOWN) and self.FIFSupportsReading(fif):\\n dib = self.Load(fif, fileName, flag);\\n return dib\\n #else return none\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7297fe469a24f524e83284487460365b\",\n \"score\": \"0.5994823\",\n \"text\": \"def load_img(self,image_name):\\n path_to_project = os.path.join(os.path.dirname(os.path.abspath(__file__)),'..')\\n image = cv2.imread(os.path.join(path_to_project,\\\"assets\\\", \\\"towers\\\", image_name))\\n # image [height, width]\\n scale = image.shape[0]//self.height\\n tower_img = pygame.image.load(os.path.join(path_to_project,\\\"assets\\\", \\\"towers\\\", image_name))\\n # pygame image [width, height]\\n tower_img = pygame.transform.scale(tower_img, (image.shape[1]//scale, image.shape[0]//scale))\\n return tower_img\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d9fd8bd2a5507cb0d65c47080f53d43\",\n \"score\": \"0.59943676\",\n \"text\": \"def load_image(image_file: str):\\n try:\\n image = resize(pygame.image.load(image_file).convert())\\n except pygame.error as message: # pylint: disable=no-member\\n print('Image at', image_file, 'unreachable')\\n raise SystemExit(message)\\n\\n return image\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05d59aec3ff2502ae8ec3c645183b4b8\",\n \"score\": \"0.5994102\",\n \"text\": \"def loadImage(self):\\n print(\\\"here\\\")\\n self.filePath, self.format = QtWidgets.QFileDialog.getOpenFileName(\\n None,\\n \\\"Load Image\\\",\\n \\\"\\\",\\n \\\"Images (*.png *.xpm *.jpg);;\\\",\\n options=QtWidgets.QFileDialog.DontUseNativeDialog,\\n )\\n self.inputCornersImage = cv2.imread(self.filePath)\\n self.cornersInput.setImage(self.inputCornersImage.T)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e355caef48973c54a4ebdd04df500f7c\",\n \"score\": \"0.59938085\",\n \"text\": \"def load_single_image(imdb_title, attempt_download=False):\\r\\n\\r\\n import imageio\\r\\n import os.path\\r\\n\\r\\n image_path = 'data/images/{}.jpg'.format(imdb_title)\\r\\n\\r\\n if not os.path.isfile(image_path):\\r\\n if not attempt_download:\\r\\n return None\\r\\n\\r\\n status_code = download_image(imdb_title)\\r\\n if status_code != 0:\\r\\n return None\\r\\n\\r\\n img_array = imageio.imread(image_path)\\r\\n return img_array\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c97609315a077fc81f10c50e580518a7\",\n \"score\": \"0.5987319\",\n \"text\": \"def load_image(cls, name):\\r\\n image = cls._images.get(name)\\r\\n if image is None:\\r\\n image = QImage(':dock_images/%s.png' % name)\\r\\n cls._images[name] = image\\r\\n return image\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1cdb02277fbb22a38e19b0e25e07a351\",\n \"score\": \"0.59783894\",\n \"text\": \"def _load_raw_image(filename:str):\\n\\n # WARNING: THIS ONE IS BAD, something strange happens when we load binary images: its non-detemrinsitc, smthimes the loaded image is crap!!\\n # img = scipy.misc.imread(filename) \\n return skimage.io.imread(filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25c1ad4a00e6770d7f97f85618cb8216\",\n \"score\": \"0.5976029\",\n \"text\": \"def load_image(self, image_id):\\n image = skimage.io.imread(self.image_info[image_id]['path'])\\n y,x,i = image.shape\\n c = random.randint(0,2)\\n D = image[:,:,c].reshape((y,x,1))\\n image = np.concatenate((image, D), axis=2)\\n return image\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ade7c3c18c0e002d077f73ed62e2ecf\",\n \"score\": \"0.59722924\",\n \"text\": \"def load_gifti(img):\\n\\n try:\\n img = nib.load(img)\\n except (ImageFileError, TypeError) as err:\\n # it's gzipped, so read the gzip and pipe it in\\n if isinstance(err, ImageFileError) and str(err).endswith('.gii.gz\\\"'):\\n with gzip.GzipFile(img) as gz:\\n img = nib.GiftiImage.from_bytes(gz.read())\\n # it's not a pre-loaded GiftiImage so error out\\n elif (isinstance(err, TypeError)\\n and not str(err) == 'stat: path should be string, bytes, os.'\\n 'PathLike or integer, not GiftiImage'):\\n raise err\\n\\n return img\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96783b9d396e01f502db134c11fddeb6\",\n \"score\": \"0.59709334\",\n \"text\": \"def imread(url):\\n return Image.open(url)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50f1e2c9a592e68f61a6b7f4a7d1561f\",\n \"score\": \"0.5969777\",\n \"text\": \"def load(url):\\n response = requests.get(url)\\n pil_image = Image.open(BytesIO(response.content)).convert(\\\"RGB\\\")\\n # convert to BGR format\\n image = np.array(pil_image)[:, :, [2, 1, 0]]\\n return image\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0fbc5a17738217f4e6e3ff69714fd33\",\n \"score\": \"0.59675467\",\n \"text\": \"def image_load(target):\\r\\n image = cv2.imread(target, 0)\\r\\n image = image[75:135, :]\\r\\n image = cv2.resize(image, (64,64))\\r\\n return image\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57e38cd63e3a7a53582ed3dce329457a\",\n \"score\": \"0.59487796\",\n \"text\": \"def load(self, begin=None, end=None):\\n self.img_dict.clear()\\n if begin == None:\\n start = (\\n self.list_idx\\n if self.list_idx < self.maxLen\\n else self.list_idx - self.maxLen\\n )\\n load_len = (\\n self.maxLen + start\\n if self.maxLen + start < self.vidLen\\n else self.vidLen\\n )\\n elif begin != None:\\n start = begin\\n if self.list_idx >= self.vidLen:\\n logging.info(\\n \\\"Skip Load {0} {1} {2}\\\".format(self.list_idx, self.vidLen, start)\\n )\\n\\n load_len = (\\n start + self.maxLen + 1\\n if start + self.maxLen + 1 < self.vidLen\\n else self.vidLen\\n )\\n self.list_idx = start\\n img = Image.open(self.img_list[start])\\n img = self.prepareImg(img)\\n self.current = img\\n logging.info(\\\"Loading... {0} {1}\\\".format(start, load_len))\\n for i in range(start, load_len):\\n worker = ImageLoader.Worker(\\n self.loadImg, self.img_list[self.list_idx], self.list_idx % self.maxLen\\n ) # Any other args, kwargs are passed to the run function\\n self.threadpool.start(worker)\\n self.list_idx += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8d66dd735fe1e4cd8a8365ba8855a24\",\n \"score\": \"0.59438246\",\n \"text\": \"def get_image(self, image_id):\\n res = self.do_request(\\\"GET\\\", \\\"/images/%s\\\" % image_id)\\n data = json.loads(res.read())['image']\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b61d2a8f126e517af3ef81454bec1968\",\n \"score\": \"0.59420204\",\n \"text\": \"def load_image(file_path, verbose=0):\\n check_path(file_path)\\n im = Image.open(file_path).convert(mode=\\\"RGB\\\")\\n if verbose:\\n print('image {} loaded'.format(file_path))\\n print(im.format, im.size, im.mode)\\n return im\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe46384270ee092b38082d91f1071b0e\",\n \"score\": \"0.59418887\",\n \"text\": \"def load_image(img_path: str) -> Image:\\n original_image = PILImage.open(img_path)\\n\\n exif_data = original_image.getexif()\\n if exif_data is not None:\\n parsed_data = {}\\n for tag, value in exif_data.items():\\n if tag in TAGS:\\n parsed_data[TAGS.get(tag)] = value\\n elif tag in GPSTAGS:\\n parsed_data[GPSTAGS.get(tag)] = value\\n else:\\n parsed_data[tag] = value\\n\\n exif_data = parsed_data\\n\\n return Image(np.asarray(original_image), exif_data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59cdc1647d34236867f876694b6fa68c\",\n \"score\": \"0.59353197\",\n \"text\": \"def load_image(image_url):\\n try:\\n image_get_response = urllib.request.urlopen(image_url)\\n image_array = np.asarray(bytearray(image_get_response.read()), dtype=np.uint8)\\n image = cv2.imdecode(image_array, cv2.IMREAD_COLOR)\\n image = cv2.resize(image, (224, 224))\\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\\n image = image / 255\\n except HTTPError as e:\\n log.error('Server couldn\\\\'t fulfill the request. For url:' + image_url)\\n return []\\n except URLError as e:\\n log.error('We failed to reach the server. For url:' + image_url)\\n return []\\n else:\\n return image\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffbc4c58bebe99b3ab1786e3fa926ee6\",\n \"score\": \"0.5927383\",\n \"text\": \"def load_image(image_path):\\n image = None\\n if image_path.endswith(\\\"jpg\\\") or image_path.endswith(\\\"jpeg\\\"):\\n image = Image.open(image_path)\\n pixels = list(image.getdata())\\n width, height = image.size\\n pixels = [pixels[i * width:(i + 1) * width] for i in range(height)]\\n size = image.size\\n image = np.asarray(pixels)\\n image = image * (10**19)\\n elif image_path.endswith(\\\"mat\\\"):\\n image = loadmat(image_path, appendmat=False)['dxImage']['img'][0][0]\\n image_rescaled = image\\n return image_rescaled\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe66656262021980d0f5945bb73c200d\",\n \"score\": \"0.59122163\",\n \"text\": \"def load_image(self, image):\\n\\n # Make sure it's an RGB with correct width and height\\n\\t\\timage = image.resize((self.width, self.height), Image.ANTIALIAS)\\n\\t\\timage = image.convert(\\\"RGB\\\")\\n\\n # Extract the pixels\\n\\t\\tpix = image.load()\\n\\n\\t\\t# Add each pixel to the buffer\\n\\t\\ti = 0\\n\\t\\tw, h = image.size\\n\\t\\tfor col in xrange(0, w):\\n\\t\\t\\tfor row in xrange(0, h):\\n\\t\\t\\t\\tr,g,b = pix[col, row]\\n\\t\\t\\t\\tcolor = color565(r, g, b)\\n\\t\\t\\t\\tself._buffer[i] = color\\n\\t\\t\\t\\ti += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb683be3ba4f28a13d7aa4b967975fc5\",\n \"score\": \"0.5903647\",\n \"text\": \"def load_image(name):\\n try:\\n image = pygame.image.load(name)\\n except pygame.error:\\n print('Cannot load image:', name)\\n return None\\n return image.convert(), image.get_rect()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f3fe6eace255b17a4c86ccccaf3b079\",\n \"score\": \"0.59035844\",\n \"text\": \"def load(self):\\n for image in self:\\n image.load()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"422c776dfc8676bd01d5baf5cdc60605\",\n \"score\": \"0.58976287\",\n \"text\": \"def imread(self):\\n try:\\n return imageio.imread(self.local)\\n except OSError:\\n print(\\n \\\"You need to download this image first. \\\"\\n \\\"Use this_image.download(local_directory).\\\"\\n )\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99eb6eb86a8d91863e8d23333e24dbaa\",\n \"score\": \"0.5889638\",\n \"text\": \"def load_image(name, colorkey=None):\\r\\n fullname = os.path.join('images', name)\\r\\n try:\\r\\n image = pygame.image.load(fullname)\\r\\n except pygame.error, message:\\r\\n print 'Cannot load image:', fullname\\r\\n raise SystemExit, message\\r\\n image = image.convert()\\r\\n if colorkey is not None:\\r\\n if colorkey is -1:\\r\\n colorkey = image.get_at((0,0))\\r\\n image.set_colorkey(colorkey)\\r\\n return image\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a31bfd5ed891cd6a33db9b638069f58\",\n \"score\": \"0.58886814\",\n \"text\": \"def loadImg(self, img_path, idx):\\n img = Image.open(img_path)\\n img = self.prepareImg(img)\\n logging.info(\\\"Added: {0}\\\".format(idx))\\n self.img_dict[idx] = (img, idx)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbf51920fe46c82f72b6ecc1a400e8e5\",\n \"score\": \"0.5888152\",\n \"text\": \"def read_image(path):\\n try:\\n image = Image.open(path)\\n return image\\n except Exception as e:\\n print(e)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":884,"cells":{"query_id":{"kind":"string","value":"06fa4b50e745e149999779d1fefdcc29"},"query":{"kind":"string","value":"Get this user's given name."},"positive_passages":{"kind":"list like","value":[{"docid":"a9bedb947418ec71ca1ce03e332b80e8","score":"0.7923014","text":"def get_given_name(self):\r\n return self.__given_name","title":""}],"string":"[\n {\n \"docid\": \"a9bedb947418ec71ca1ce03e332b80e8\",\n \"score\": \"0.7923014\",\n \"text\": \"def get_given_name(self):\\r\\n return self.__given_name\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"d3e35db1a2327055baa7cf8197681877","score":"0.85153365","text":"def get_name(self):\n return self._user.name","title":""},{"docid":"cd962d543d325b6cb86da8222ad995fe","score":"0.84088993","text":"def get_user_name(self):\n user = User.by_id(self.user_id)\n return user.name","title":""},{"docid":"cd962d543d325b6cb86da8222ad995fe","score":"0.84088993","text":"def get_user_name(self):\n user = User.by_id(self.user_id)\n return user.name","title":""},{"docid":"c38c7a91662edff55079ca9d841f1a65","score":"0.83776337","text":"def user_name(self) -> str:\n return pulumi.get(self, \"user_name\")","title":""},{"docid":"f7bab5c0a5b6f6e241debda84af97c71","score":"0.83414084","text":"def get_user_name(self):\n return self._user_name","title":""},{"docid":"fe817f7455c01924b62a4c06ff8cf370","score":"0.8308692","text":"def get_user_name(self):\n return self.user.get_full_name()","title":""},{"docid":"75ffdb0315385c149937b055f51ad796","score":"0.82164556","text":"def user_name(self) -> Optional[str]:\n return pulumi.get(self, \"user_name\")","title":""},{"docid":"cd78d8a314967636155e57271b078b67","score":"0.81838244","text":"def user_name(self) -> str:\n return self._user_name","title":""},{"docid":"d1a78600d0b4f612c20b3e5cbaed86e7","score":"0.8176067","text":"def name(self):\n if self.user is not None:\n return self.user.get_full_name()\n else:\n return self.user_name","title":""},{"docid":"487b7448bc34fa92e472a685658555ed","score":"0.81500876","text":"def username(self):\n name = self['user'].get('name', None)\n if name:\n return name\n else:\n return self['user'].get('username', None)","title":""},{"docid":"b7afbe8548d6c6124d2b87297dfa8928","score":"0.81100714","text":"def get_full_name(self):\n return self.username","title":""},{"docid":"b7afbe8548d6c6124d2b87297dfa8928","score":"0.81100714","text":"def get_full_name(self):\n return self.username","title":""},{"docid":"b7afbe8548d6c6124d2b87297dfa8928","score":"0.81100714","text":"def get_full_name(self):\n return self.username","title":""},{"docid":"e5120174d6c1af71c91b2dc402a16ab5","score":"0.8067261","text":"def user_name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"user_name\")","title":""},{"docid":"e5120174d6c1af71c91b2dc402a16ab5","score":"0.8067261","text":"def user_name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"user_name\")","title":""},{"docid":"e5120174d6c1af71c91b2dc402a16ab5","score":"0.8067261","text":"def user_name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"user_name\")","title":""},{"docid":"e5120174d6c1af71c91b2dc402a16ab5","score":"0.8067261","text":"def user_name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"user_name\")","title":""},{"docid":"e5120174d6c1af71c91b2dc402a16ab5","score":"0.8067261","text":"def user_name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"user_name\")","title":""},{"docid":"e5120174d6c1af71c91b2dc402a16ab5","score":"0.8067261","text":"def user_name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"user_name\")","title":""},{"docid":"e5120174d6c1af71c91b2dc402a16ab5","score":"0.8067261","text":"def user_name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"user_name\")","title":""},{"docid":"e5120174d6c1af71c91b2dc402a16ab5","score":"0.8067261","text":"def user_name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"user_name\")","title":""},{"docid":"e5120174d6c1af71c91b2dc402a16ab5","score":"0.8067261","text":"def user_name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"user_name\")","title":""},{"docid":"e5120174d6c1af71c91b2dc402a16ab5","score":"0.8067261","text":"def user_name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"user_name\")","title":""},{"docid":"e5120174d6c1af71c91b2dc402a16ab5","score":"0.8067261","text":"def user_name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"user_name\")","title":""},{"docid":"2eed35af81cd943dae63c5ad4b1b6f24","score":"0.80647093","text":"def user_name(self):\n return self._user_name","title":""},{"docid":"e195f343b3f09c9f7c3d90450c15f36c","score":"0.80296254","text":"def user_name(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"user_name\")","title":""},{"docid":"e195f343b3f09c9f7c3d90450c15f36c","score":"0.80296254","text":"def user_name(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"user_name\")","title":""},{"docid":"17c744ece37ee95e4966af26d33877e1","score":"0.8010694","text":"def name(self) -> Optional[str]:\n return self._user","title":""},{"docid":"11ef98b342879ff22bc36ee8764234c4","score":"0.8006749","text":"def user_name(self) -> pulumi.Output[Optional[str]]:\n return pulumi.get(self, \"user_name\")","title":""},{"docid":"11ef98b342879ff22bc36ee8764234c4","score":"0.8006749","text":"def user_name(self) -> pulumi.Output[Optional[str]]:\n return pulumi.get(self, \"user_name\")","title":""},{"docid":"2209fa258ff098919b22ccc5670c9c73","score":"0.7958158","text":"def get_full_name(self):\n return six.text_type(self.get_username())","title":""},{"docid":"8b7e2af122fcd9bdbd43f77500e4a5d2","score":"0.780032","text":"def get_username():\n return user","title":""},{"docid":"e3aafb6a92f942cc513ec0d3a01dc742","score":"0.777432","text":"def getUserName(self):\n return self.getId()","title":""},{"docid":"ee064f3d24b3ab0154b6c10107fa98b1","score":"0.7714885","text":"def get_username(self) -> str:\n try:\n user = self.user.username\n except ObjectDoesNotExist:\n user = \"\"\n return user","title":""},{"docid":"a0a5f5c8881b0276540c99ac64b6318b","score":"0.7699066","text":"def full_name(self):\n return self.user.get_full_name()","title":""},{"docid":"3caacaad65f672faf9d1a77a9d60675f","score":"0.76468724","text":"def _get_user_name(**kwargs):\n return kwargs['user'].user_name","title":""},{"docid":"c3fa275510b41c0afd0dca7c00638721","score":"0.7643679","text":"def getUserName():","title":""},{"docid":"bbf8426ada05e08426d5efbbaab67419","score":"0.76348865","text":"def given_name(self):\n return self.getattr('given_name')","title":""},{"docid":"425379f12160c4d8be11e9515768ffea","score":"0.7619373","text":"def username(self):\n return self.person.usernames[0] if self.person and len(self.person.usernames) > 0 else None","title":""},{"docid":"92e04997314ffdf726ce2ee97ac1f6a2","score":"0.7575072","text":"def getUserName(self):\n return self.context.getUserName()","title":""},{"docid":"9cc1066737f4579f46b8c123d7f734e5","score":"0.75701725","text":"def user_name(self):\r\n return self.update.effective_user.full_name","title":""},{"docid":"7bb843d9693ecc4234421ed1920cfe1d","score":"0.75541955","text":"def get_full_name(self):\n full_name = '%s %s' % (self.first_name, self.last_name)\n return full_name.strip() or self.username","title":""},{"docid":"7730103ff724d43f61b294833b2801ee","score":"0.75505465","text":"def user_profile_name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"user_profile_name\")","title":""},{"docid":"8e817303567f35fd453c9bf2b94dcecb","score":"0.7534759","text":"def full_name(self) -> str:\n if self.name or self.family_name:\n return f\"{self.name or ''} {self.family_name or ''}\".strip()\n return self.username","title":""},{"docid":"91e255011c16a0b6118a9ef0019dc136","score":"0.7512216","text":"def get_short_name(self):\n return self.username","title":""},{"docid":"91e255011c16a0b6118a9ef0019dc136","score":"0.7512216","text":"def get_short_name(self):\n return self.username","title":""},{"docid":"91e255011c16a0b6118a9ef0019dc136","score":"0.7512216","text":"def get_short_name(self):\n return self.username","title":""},{"docid":"91e255011c16a0b6118a9ef0019dc136","score":"0.7512216","text":"def get_short_name(self):\n return self.username","title":""},{"docid":"91e255011c16a0b6118a9ef0019dc136","score":"0.7512216","text":"def get_short_name(self):\n return self.username","title":""},{"docid":"91e255011c16a0b6118a9ef0019dc136","score":"0.7512216","text":"def get_short_name(self):\n return self.username","title":""},{"docid":"91e255011c16a0b6118a9ef0019dc136","score":"0.7512216","text":"def get_short_name(self):\n return self.username","title":""},{"docid":"0060f4227c0e0906f8da6878e69e5274","score":"0.7488508","text":"def get_user_name(self, user_id: str) -> str:\n return self.SqlData.get_user_name(user_id)","title":""},{"docid":"7516dd39e058547ce68b2a17044d0820","score":"0.7442402","text":"def user_profile_name(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"user_profile_name\")","title":""},{"docid":"b9f3d48e59dd013ff027376c19cf5463","score":"0.7441075","text":"def username(self) -> str:\n return pulumi.get(self, \"username\")","title":""},{"docid":"b9f3d48e59dd013ff027376c19cf5463","score":"0.7441075","text":"def username(self) -> str:\n return pulumi.get(self, \"username\")","title":""},{"docid":"b9f3d48e59dd013ff027376c19cf5463","score":"0.7441075","text":"def username(self) -> str:\n return pulumi.get(self, \"username\")","title":""},{"docid":"b9f3d48e59dd013ff027376c19cf5463","score":"0.7441075","text":"def username(self) -> str:\n return pulumi.get(self, \"username\")","title":""},{"docid":"b9f3d48e59dd013ff027376c19cf5463","score":"0.7441075","text":"def username(self) -> str:\n return pulumi.get(self, \"username\")","title":""},{"docid":"b9f3d48e59dd013ff027376c19cf5463","score":"0.7441075","text":"def username(self) -> str:\n return pulumi.get(self, \"username\")","title":""},{"docid":"b9f3d48e59dd013ff027376c19cf5463","score":"0.7441075","text":"def username(self) -> str:\n return pulumi.get(self, \"username\")","title":""},{"docid":"b9f3d48e59dd013ff027376c19cf5463","score":"0.7441075","text":"def username(self) -> str:\n return pulumi.get(self, \"username\")","title":""},{"docid":"b9f3d48e59dd013ff027376c19cf5463","score":"0.7441075","text":"def username(self) -> str:\n return pulumi.get(self, \"username\")","title":""},{"docid":"b9f3d48e59dd013ff027376c19cf5463","score":"0.7441075","text":"def username(self) -> str:\n return pulumi.get(self, \"username\")","title":""},{"docid":"6bf051bacff7fd330da9e23971b9e9bd","score":"0.7416411","text":"def getUserName(self):\n return None","title":""},{"docid":"358cec07721aedec626d26dbed2f5ef0","score":"0.7408613","text":"def get_user_name(self, userid):\n name_info = 'firstname, lastname, username'\n name = self.sql('select {} from ssismdl_user where id = {}'.format(name_info, userid))()[0]\n firstname, lastname, username = name\n email = '<a href=\"mailto:{username}@ssis-suzhou.net\">email</a>'.format(**dict(username=username))\n name = \"{} {}\".format(firstname, lastname, email)\n self.verbose and print(\"Name: {}\".format(name))\n return name","title":""},{"docid":"26e3e3c3c5930dfca3927a8f61de932b","score":"0.74084187","text":"def get_user_name(user):\n return user['UserName']","title":""},{"docid":"3a2847773cee2d5bde79266cddb6eac3","score":"0.7404665","text":"def display_name(self):\n user_name = self.username if self.username else \"Unknown user\"\n user_full_name = (\n (\n (self.first_name + \" \" if self.first_name else \"\")\n + (self.last_name if self.last_name else \"\")\n )\n if (self.first_name or self.last_name)\n else user_name\n )\n return user_full_name.strip()","title":""},{"docid":"7f763fff6792411d207e88ff82c2a8b6","score":"0.7379036","text":"def full_name(self) -> str:\n if self.name or self.family_name:\n return f\"{self.name or ''} {self.family_name or ''}\".strip()\n return self.username","title":""},{"docid":"69087b9ac9c8487d332b74092bd688e8","score":"0.7371651","text":"def user_name(self, obj):\n return \"{} <{}>\".format(obj.user.get_full_name(), obj.user.email)","title":""},{"docid":"45cf3421ab1d4e80ad1c24358fe7164e","score":"0.7344011","text":"def getusername(self, id):\n user = self.getuser(id)\n return (\"%s %s (%s)\" % (\n user[1],\n user[2],\n user[0]))","title":""},{"docid":"2546486f561693145982afcb28541217","score":"0.7343832","text":"def get_name(self):\r\n return self.profile().get('name','none')","title":""},{"docid":"da23b9d09c536e1c114314e540202478","score":"0.7303339","text":"def user_profile_name(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"user_profile_name\")","title":""},{"docid":"92d8eb5409e1443d39a743937025d14f","score":"0.72995764","text":"def get_name_from_user(user_name):\n cur.execute(\"\"\"SELECT nombre, apellido_1, apellido_2\n FROM personas\n WHERE usuario = %s\"\"\", (user_name,))\n name = cur.fetchone()\n return name","title":""},{"docid":"791fd5981ffa92f0157b683762792762","score":"0.72916895","text":"def _get_username(self):\n return self.generator.get_username()","title":""},{"docid":"56ad4eaa5e2a324172fcd7bbc4d6dbce","score":"0.7282299","text":"def username(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"username\")","title":""},{"docid":"56ad4eaa5e2a324172fcd7bbc4d6dbce","score":"0.7282299","text":"def username(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"username\")","title":""},{"docid":"a0575d47090a001929f97914106bb611","score":"0.72822803","text":"def username(self) -> Optional[str]:\n return pulumi.get(self, \"username\")","title":""},{"docid":"a0575d47090a001929f97914106bb611","score":"0.72822803","text":"def username(self) -> Optional[str]:\n return pulumi.get(self, \"username\")","title":""},{"docid":"a0575d47090a001929f97914106bb611","score":"0.72822803","text":"def username(self) -> Optional[str]:\n return pulumi.get(self, \"username\")","title":""},{"docid":"a0575d47090a001929f97914106bb611","score":"0.72822803","text":"def username(self) -> Optional[str]:\n return pulumi.get(self, \"username\")","title":""},{"docid":"a0575d47090a001929f97914106bb611","score":"0.72822803","text":"def username(self) -> Optional[str]:\n return pulumi.get(self, \"username\")","title":""},{"docid":"a0575d47090a001929f97914106bb611","score":"0.72822803","text":"def username(self) -> Optional[str]:\n return pulumi.get(self, \"username\")","title":""},{"docid":"a0575d47090a001929f97914106bb611","score":"0.72822803","text":"def username(self) -> Optional[str]:\n return pulumi.get(self, \"username\")","title":""},{"docid":"a0575d47090a001929f97914106bb611","score":"0.72822803","text":"def username(self) -> Optional[str]:\n return pulumi.get(self, \"username\")","title":""},{"docid":"6c9a5e356b2681216a5795b4b2a7659e","score":"0.72795564","text":"def get_full_name_or_username(self):\n user = self.user\n if user.first_name or user.last_name:\n # We will return this as translated string. Maybe there are some\n # countries that first display the last name.\n name = _(\"%(first_name)s %(last_name)s\") % \\\n {'first_name': user.first_name,\n 'last_name': user.last_name}\n else:\n # Fallback to the username if usernames are used\n if not userena_settings.USERENA_WITHOUT_USERNAMES:\n name = \"%(username)s\" % {'username': user.username}\n else:\n name = \"%(email)s\" % {'email': user.email}\n return name.strip()","title":""},{"docid":"17dfb8a4b7531df0223bcbe642857878","score":"0.7236698","text":"def get_username(self) -> str:\n return self._oidc.user_getfield('preferred_username')","title":""},{"docid":"5c6e0ccc4ededb62e83d1eaba9bd7fbe","score":"0.7226644","text":"def get_name(self, id):\n vals = self.c.execute(\"SELECT name FROM users WHERE id = '%s'\" % id)\n names = [x for x in vals]\n if len(names) == 1:\n return str(names[0][0])\n return None","title":""},{"docid":"1767f2b141950170cce6808003b36ad5","score":"0.7222824","text":"def personaname(self):\n return self._personaname","title":""},{"docid":"7682487e28b513b989054b2b83199b10","score":"0.7221455","text":"def username(self) -> str:\n return self._username","title":""},{"docid":"0ce38b2426152ddcaa5191345460f073","score":"0.7219045","text":"def getUsername():\n return username","title":""},{"docid":"ecde48856f59d3ef8b25a8d47c631fbf","score":"0.72167003","text":"def get_user_display(user) -> str:\n full_name = user.get_full_name()\n return full_name if len(full_name.strip()) else user.username","title":""},{"docid":"879819bc5bafdd12ea338949b0a03a42","score":"0.7216359","text":"def get_username(self):\n raise NotImplementedError","title":""},{"docid":"8a2de4d87c37eeae572b3865c415b327","score":"0.72132367","text":"def get_full_name(self):\n\t\treturn self.user.first_name + \" \" + self.user.last_name","title":""},{"docid":"1e6c43cf4bf93b2cac85c3d93abb7fda","score":"0.72052646","text":"def get_username(self):\n username = self.get_value('username')\n if username:\n return username\n else:\n return getpass.getuser()\n \n if self.cf.has_option(self.heading, 'username'):\n return self.cf.get(self.heading, 'username')","title":""},{"docid":"bdfaaf54d71d0d8eac9e9b78040b62bc","score":"0.71873975","text":"def _getUserName(self):\n return getpass.getuser()","title":""},{"docid":"a85da6a45e001af42805dbf54255224a","score":"0.71776205","text":"def username(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"username\")","title":""},{"docid":"a85da6a45e001af42805dbf54255224a","score":"0.71776205","text":"def username(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"username\")","title":""},{"docid":"a85da6a45e001af42805dbf54255224a","score":"0.71776205","text":"def username(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"username\")","title":""},{"docid":"5e0e7bc71f8ba11560030703db15123b","score":"0.71574616","text":"async def get_full_name(self) -> Optional[str]:\n return pwd.getpwnam(self.user).pw_gecos.split(\",\")[0]","title":""},{"docid":"52f98cd6daf6d43607be397056304b2e","score":"0.7153635","text":"def name(self):\n return self.given_name + ' ' + self.surname","title":""}],"string":"[\n {\n \"docid\": \"d3e35db1a2327055baa7cf8197681877\",\n \"score\": \"0.85153365\",\n \"text\": \"def get_name(self):\\n return self._user.name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd962d543d325b6cb86da8222ad995fe\",\n \"score\": \"0.84088993\",\n \"text\": \"def get_user_name(self):\\n user = User.by_id(self.user_id)\\n return user.name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd962d543d325b6cb86da8222ad995fe\",\n \"score\": \"0.84088993\",\n \"text\": \"def get_user_name(self):\\n user = User.by_id(self.user_id)\\n return user.name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c38c7a91662edff55079ca9d841f1a65\",\n \"score\": \"0.83776337\",\n \"text\": \"def user_name(self) -> str:\\n return pulumi.get(self, \\\"user_name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7bab5c0a5b6f6e241debda84af97c71\",\n \"score\": \"0.83414084\",\n \"text\": \"def get_user_name(self):\\n return self._user_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe817f7455c01924b62a4c06ff8cf370\",\n \"score\": \"0.8308692\",\n \"text\": \"def get_user_name(self):\\n return self.user.get_full_name()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75ffdb0315385c149937b055f51ad796\",\n \"score\": \"0.82164556\",\n \"text\": \"def user_name(self) -> Optional[str]:\\n return pulumi.get(self, \\\"user_name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd78d8a314967636155e57271b078b67\",\n \"score\": \"0.81838244\",\n \"text\": \"def user_name(self) -> str:\\n return self._user_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1a78600d0b4f612c20b3e5cbaed86e7\",\n \"score\": \"0.8176067\",\n \"text\": \"def name(self):\\n if self.user is not None:\\n return self.user.get_full_name()\\n else:\\n return self.user_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"487b7448bc34fa92e472a685658555ed\",\n \"score\": \"0.81500876\",\n \"text\": \"def username(self):\\n name = self['user'].get('name', None)\\n if name:\\n return name\\n else:\\n return self['user'].get('username', None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7afbe8548d6c6124d2b87297dfa8928\",\n \"score\": \"0.81100714\",\n \"text\": \"def get_full_name(self):\\n return self.username\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7afbe8548d6c6124d2b87297dfa8928\",\n \"score\": \"0.81100714\",\n \"text\": \"def get_full_name(self):\\n return self.username\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7afbe8548d6c6124d2b87297dfa8928\",\n \"score\": \"0.81100714\",\n \"text\": \"def get_full_name(self):\\n return self.username\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5120174d6c1af71c91b2dc402a16ab5\",\n \"score\": \"0.8067261\",\n \"text\": \"def user_name(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"user_name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5120174d6c1af71c91b2dc402a16ab5\",\n \"score\": \"0.8067261\",\n \"text\": \"def user_name(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"user_name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5120174d6c1af71c91b2dc402a16ab5\",\n \"score\": \"0.8067261\",\n \"text\": \"def user_name(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"user_name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5120174d6c1af71c91b2dc402a16ab5\",\n \"score\": \"0.8067261\",\n \"text\": \"def user_name(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"user_name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5120174d6c1af71c91b2dc402a16ab5\",\n \"score\": \"0.8067261\",\n \"text\": \"def user_name(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"user_name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5120174d6c1af71c91b2dc402a16ab5\",\n \"score\": \"0.8067261\",\n \"text\": \"def user_name(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"user_name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5120174d6c1af71c91b2dc402a16ab5\",\n \"score\": \"0.8067261\",\n \"text\": \"def user_name(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"user_name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5120174d6c1af71c91b2dc402a16ab5\",\n \"score\": \"0.8067261\",\n \"text\": \"def user_name(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"user_name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5120174d6c1af71c91b2dc402a16ab5\",\n \"score\": \"0.8067261\",\n \"text\": \"def user_name(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"user_name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5120174d6c1af71c91b2dc402a16ab5\",\n \"score\": \"0.8067261\",\n \"text\": \"def user_name(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"user_name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5120174d6c1af71c91b2dc402a16ab5\",\n \"score\": \"0.8067261\",\n \"text\": \"def user_name(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"user_name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2eed35af81cd943dae63c5ad4b1b6f24\",\n \"score\": \"0.80647093\",\n \"text\": \"def user_name(self):\\n return self._user_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e195f343b3f09c9f7c3d90450c15f36c\",\n \"score\": \"0.80296254\",\n \"text\": \"def user_name(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"user_name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e195f343b3f09c9f7c3d90450c15f36c\",\n \"score\": \"0.80296254\",\n \"text\": \"def user_name(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"user_name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17c744ece37ee95e4966af26d33877e1\",\n \"score\": \"0.8010694\",\n \"text\": \"def name(self) -> Optional[str]:\\n return self._user\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11ef98b342879ff22bc36ee8764234c4\",\n \"score\": \"0.8006749\",\n \"text\": \"def user_name(self) -> pulumi.Output[Optional[str]]:\\n return pulumi.get(self, \\\"user_name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11ef98b342879ff22bc36ee8764234c4\",\n \"score\": \"0.8006749\",\n \"text\": \"def user_name(self) -> pulumi.Output[Optional[str]]:\\n return pulumi.get(self, \\\"user_name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2209fa258ff098919b22ccc5670c9c73\",\n \"score\": \"0.7958158\",\n \"text\": \"def get_full_name(self):\\n return six.text_type(self.get_username())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b7e2af122fcd9bdbd43f77500e4a5d2\",\n \"score\": \"0.780032\",\n \"text\": \"def get_username():\\n return user\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3aafb6a92f942cc513ec0d3a01dc742\",\n \"score\": \"0.777432\",\n \"text\": \"def getUserName(self):\\n return self.getId()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee064f3d24b3ab0154b6c10107fa98b1\",\n \"score\": \"0.7714885\",\n \"text\": \"def get_username(self) -> str:\\n try:\\n user = self.user.username\\n except ObjectDoesNotExist:\\n user = \\\"\\\"\\n return user\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0a5f5c8881b0276540c99ac64b6318b\",\n \"score\": \"0.7699066\",\n \"text\": \"def full_name(self):\\n return self.user.get_full_name()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3caacaad65f672faf9d1a77a9d60675f\",\n \"score\": \"0.76468724\",\n \"text\": \"def _get_user_name(**kwargs):\\n return kwargs['user'].user_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3fa275510b41c0afd0dca7c00638721\",\n \"score\": \"0.7643679\",\n \"text\": \"def getUserName():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbf8426ada05e08426d5efbbaab67419\",\n \"score\": \"0.76348865\",\n \"text\": \"def given_name(self):\\n return self.getattr('given_name')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"425379f12160c4d8be11e9515768ffea\",\n \"score\": \"0.7619373\",\n \"text\": \"def username(self):\\n return self.person.usernames[0] if self.person and len(self.person.usernames) > 0 else None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92e04997314ffdf726ce2ee97ac1f6a2\",\n \"score\": \"0.7575072\",\n \"text\": \"def getUserName(self):\\n return self.context.getUserName()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9cc1066737f4579f46b8c123d7f734e5\",\n \"score\": \"0.75701725\",\n \"text\": \"def user_name(self):\\r\\n return self.update.effective_user.full_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7bb843d9693ecc4234421ed1920cfe1d\",\n \"score\": \"0.75541955\",\n \"text\": \"def get_full_name(self):\\n full_name = '%s %s' % (self.first_name, self.last_name)\\n return full_name.strip() or self.username\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7730103ff724d43f61b294833b2801ee\",\n \"score\": \"0.75505465\",\n \"text\": \"def user_profile_name(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"user_profile_name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e817303567f35fd453c9bf2b94dcecb\",\n \"score\": \"0.7534759\",\n \"text\": \"def full_name(self) -> str:\\n if self.name or self.family_name:\\n return f\\\"{self.name or ''} {self.family_name or ''}\\\".strip()\\n return self.username\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91e255011c16a0b6118a9ef0019dc136\",\n \"score\": \"0.7512216\",\n \"text\": \"def get_short_name(self):\\n return self.username\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91e255011c16a0b6118a9ef0019dc136\",\n \"score\": \"0.7512216\",\n \"text\": \"def get_short_name(self):\\n return self.username\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91e255011c16a0b6118a9ef0019dc136\",\n \"score\": \"0.7512216\",\n \"text\": \"def get_short_name(self):\\n return self.username\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91e255011c16a0b6118a9ef0019dc136\",\n \"score\": \"0.7512216\",\n \"text\": \"def get_short_name(self):\\n return self.username\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91e255011c16a0b6118a9ef0019dc136\",\n \"score\": \"0.7512216\",\n \"text\": \"def get_short_name(self):\\n return self.username\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91e255011c16a0b6118a9ef0019dc136\",\n \"score\": \"0.7512216\",\n \"text\": \"def get_short_name(self):\\n return self.username\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91e255011c16a0b6118a9ef0019dc136\",\n \"score\": \"0.7512216\",\n \"text\": \"def get_short_name(self):\\n return self.username\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0060f4227c0e0906f8da6878e69e5274\",\n \"score\": \"0.7488508\",\n \"text\": \"def get_user_name(self, user_id: str) -> str:\\n return self.SqlData.get_user_name(user_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7516dd39e058547ce68b2a17044d0820\",\n \"score\": \"0.7442402\",\n \"text\": \"def user_profile_name(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"user_profile_name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9f3d48e59dd013ff027376c19cf5463\",\n \"score\": \"0.7441075\",\n \"text\": \"def username(self) -> str:\\n return pulumi.get(self, \\\"username\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9f3d48e59dd013ff027376c19cf5463\",\n \"score\": \"0.7441075\",\n \"text\": \"def username(self) -> str:\\n return pulumi.get(self, \\\"username\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9f3d48e59dd013ff027376c19cf5463\",\n \"score\": \"0.7441075\",\n \"text\": \"def username(self) -> str:\\n return pulumi.get(self, \\\"username\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9f3d48e59dd013ff027376c19cf5463\",\n \"score\": \"0.7441075\",\n \"text\": \"def username(self) -> str:\\n return pulumi.get(self, \\\"username\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9f3d48e59dd013ff027376c19cf5463\",\n \"score\": \"0.7441075\",\n \"text\": \"def username(self) -> str:\\n return pulumi.get(self, \\\"username\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9f3d48e59dd013ff027376c19cf5463\",\n \"score\": \"0.7441075\",\n \"text\": \"def username(self) -> str:\\n return pulumi.get(self, \\\"username\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9f3d48e59dd013ff027376c19cf5463\",\n \"score\": \"0.7441075\",\n \"text\": \"def username(self) -> str:\\n return pulumi.get(self, \\\"username\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9f3d48e59dd013ff027376c19cf5463\",\n \"score\": \"0.7441075\",\n \"text\": \"def username(self) -> str:\\n return pulumi.get(self, \\\"username\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9f3d48e59dd013ff027376c19cf5463\",\n \"score\": \"0.7441075\",\n \"text\": \"def username(self) -> str:\\n return pulumi.get(self, \\\"username\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9f3d48e59dd013ff027376c19cf5463\",\n \"score\": \"0.7441075\",\n \"text\": \"def username(self) -> str:\\n return pulumi.get(self, \\\"username\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6bf051bacff7fd330da9e23971b9e9bd\",\n \"score\": \"0.7416411\",\n \"text\": \"def getUserName(self):\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"358cec07721aedec626d26dbed2f5ef0\",\n \"score\": \"0.7408613\",\n \"text\": \"def get_user_name(self, userid):\\n name_info = 'firstname, lastname, username'\\n name = self.sql('select {} from ssismdl_user where id = {}'.format(name_info, userid))()[0]\\n firstname, lastname, username = name\\n email = '<a href=\\\"mailto:{username}@ssis-suzhou.net\\\">email</a>'.format(**dict(username=username))\\n name = \\\"{} {}\\\".format(firstname, lastname, email)\\n self.verbose and print(\\\"Name: {}\\\".format(name))\\n return name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26e3e3c3c5930dfca3927a8f61de932b\",\n \"score\": \"0.74084187\",\n \"text\": \"def get_user_name(user):\\n return user['UserName']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a2847773cee2d5bde79266cddb6eac3\",\n \"score\": \"0.7404665\",\n \"text\": \"def display_name(self):\\n user_name = self.username if self.username else \\\"Unknown user\\\"\\n user_full_name = (\\n (\\n (self.first_name + \\\" \\\" if self.first_name else \\\"\\\")\\n + (self.last_name if self.last_name else \\\"\\\")\\n )\\n if (self.first_name or self.last_name)\\n else user_name\\n )\\n return user_full_name.strip()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f763fff6792411d207e88ff82c2a8b6\",\n \"score\": \"0.7379036\",\n \"text\": \"def full_name(self) -> str:\\n if self.name or self.family_name:\\n return f\\\"{self.name or ''} {self.family_name or ''}\\\".strip()\\n return self.username\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69087b9ac9c8487d332b74092bd688e8\",\n \"score\": \"0.7371651\",\n \"text\": \"def user_name(self, obj):\\n return \\\"{} <{}>\\\".format(obj.user.get_full_name(), obj.user.email)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45cf3421ab1d4e80ad1c24358fe7164e\",\n \"score\": \"0.7344011\",\n \"text\": \"def getusername(self, id):\\n user = self.getuser(id)\\n return (\\\"%s %s (%s)\\\" % (\\n user[1],\\n user[2],\\n user[0]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2546486f561693145982afcb28541217\",\n \"score\": \"0.7343832\",\n \"text\": \"def get_name(self):\\r\\n return self.profile().get('name','none')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da23b9d09c536e1c114314e540202478\",\n \"score\": \"0.7303339\",\n \"text\": \"def user_profile_name(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"user_profile_name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92d8eb5409e1443d39a743937025d14f\",\n \"score\": \"0.72995764\",\n \"text\": \"def get_name_from_user(user_name):\\n cur.execute(\\\"\\\"\\\"SELECT nombre, apellido_1, apellido_2\\n FROM personas\\n WHERE usuario = %s\\\"\\\"\\\", (user_name,))\\n name = cur.fetchone()\\n return name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"791fd5981ffa92f0157b683762792762\",\n \"score\": \"0.72916895\",\n \"text\": \"def _get_username(self):\\n return self.generator.get_username()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56ad4eaa5e2a324172fcd7bbc4d6dbce\",\n \"score\": \"0.7282299\",\n \"text\": \"def username(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"username\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56ad4eaa5e2a324172fcd7bbc4d6dbce\",\n \"score\": \"0.7282299\",\n \"text\": \"def username(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"username\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0575d47090a001929f97914106bb611\",\n \"score\": \"0.72822803\",\n \"text\": \"def username(self) -> Optional[str]:\\n return pulumi.get(self, \\\"username\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0575d47090a001929f97914106bb611\",\n \"score\": \"0.72822803\",\n \"text\": \"def username(self) -> Optional[str]:\\n return pulumi.get(self, \\\"username\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0575d47090a001929f97914106bb611\",\n \"score\": \"0.72822803\",\n \"text\": \"def username(self) -> Optional[str]:\\n return pulumi.get(self, \\\"username\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0575d47090a001929f97914106bb611\",\n \"score\": \"0.72822803\",\n \"text\": \"def username(self) -> Optional[str]:\\n return pulumi.get(self, \\\"username\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0575d47090a001929f97914106bb611\",\n \"score\": \"0.72822803\",\n \"text\": \"def username(self) -> Optional[str]:\\n return pulumi.get(self, \\\"username\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0575d47090a001929f97914106bb611\",\n \"score\": \"0.72822803\",\n \"text\": \"def username(self) -> Optional[str]:\\n return pulumi.get(self, \\\"username\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0575d47090a001929f97914106bb611\",\n \"score\": \"0.72822803\",\n \"text\": \"def username(self) -> Optional[str]:\\n return pulumi.get(self, \\\"username\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0575d47090a001929f97914106bb611\",\n \"score\": \"0.72822803\",\n \"text\": \"def username(self) -> Optional[str]:\\n return pulumi.get(self, \\\"username\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c9a5e356b2681216a5795b4b2a7659e\",\n \"score\": \"0.72795564\",\n \"text\": \"def get_full_name_or_username(self):\\n user = self.user\\n if user.first_name or user.last_name:\\n # We will return this as translated string. Maybe there are some\\n # countries that first display the last name.\\n name = _(\\\"%(first_name)s %(last_name)s\\\") % \\\\\\n {'first_name': user.first_name,\\n 'last_name': user.last_name}\\n else:\\n # Fallback to the username if usernames are used\\n if not userena_settings.USERENA_WITHOUT_USERNAMES:\\n name = \\\"%(username)s\\\" % {'username': user.username}\\n else:\\n name = \\\"%(email)s\\\" % {'email': user.email}\\n return name.strip()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17dfb8a4b7531df0223bcbe642857878\",\n \"score\": \"0.7236698\",\n \"text\": \"def get_username(self) -> str:\\n return self._oidc.user_getfield('preferred_username')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c6e0ccc4ededb62e83d1eaba9bd7fbe\",\n \"score\": \"0.7226644\",\n \"text\": \"def get_name(self, id):\\n vals = self.c.execute(\\\"SELECT name FROM users WHERE id = '%s'\\\" % id)\\n names = [x for x in vals]\\n if len(names) == 1:\\n return str(names[0][0])\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1767f2b141950170cce6808003b36ad5\",\n \"score\": \"0.7222824\",\n \"text\": \"def personaname(self):\\n return self._personaname\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7682487e28b513b989054b2b83199b10\",\n \"score\": \"0.7221455\",\n \"text\": \"def username(self) -> str:\\n return self._username\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ce38b2426152ddcaa5191345460f073\",\n \"score\": \"0.7219045\",\n \"text\": \"def getUsername():\\n return username\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecde48856f59d3ef8b25a8d47c631fbf\",\n \"score\": \"0.72167003\",\n \"text\": \"def get_user_display(user) -> str:\\n full_name = user.get_full_name()\\n return full_name if len(full_name.strip()) else user.username\",\n \"title\": \"\"\n },\n {\n \"docid\": \"879819bc5bafdd12ea338949b0a03a42\",\n \"score\": \"0.7216359\",\n \"text\": \"def get_username(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a2de4d87c37eeae572b3865c415b327\",\n \"score\": \"0.72132367\",\n \"text\": \"def get_full_name(self):\\n\\t\\treturn self.user.first_name + \\\" \\\" + self.user.last_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e6c43cf4bf93b2cac85c3d93abb7fda\",\n \"score\": \"0.72052646\",\n \"text\": \"def get_username(self):\\n username = self.get_value('username')\\n if username:\\n return username\\n else:\\n return getpass.getuser()\\n \\n if self.cf.has_option(self.heading, 'username'):\\n return self.cf.get(self.heading, 'username')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bdfaaf54d71d0d8eac9e9b78040b62bc\",\n \"score\": \"0.71873975\",\n \"text\": \"def _getUserName(self):\\n return getpass.getuser()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a85da6a45e001af42805dbf54255224a\",\n \"score\": \"0.71776205\",\n \"text\": \"def username(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"username\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a85da6a45e001af42805dbf54255224a\",\n \"score\": \"0.71776205\",\n \"text\": \"def username(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"username\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a85da6a45e001af42805dbf54255224a\",\n \"score\": \"0.71776205\",\n \"text\": \"def username(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"username\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e0e7bc71f8ba11560030703db15123b\",\n \"score\": \"0.71574616\",\n \"text\": \"async def get_full_name(self) -> Optional[str]:\\n return pwd.getpwnam(self.user).pw_gecos.split(\\\",\\\")[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52f98cd6daf6d43607be397056304b2e\",\n \"score\": \"0.7153635\",\n \"text\": \"def name(self):\\n return self.given_name + ' ' + self.surname\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":885,"cells":{"query_id":{"kind":"string","value":"7b22a96ecec5b39ecad79489fa76e28b"},"query":{"kind":"string","value":"Given a dictionary and a set of valid keys, return a dictionary which only contains keys found in `valid`."},"positive_passages":{"kind":"list like","value":[{"docid":"08a3ed9cbce96c06a4e9829a12d1274d","score":"0.65921986","text":"def _strip_keys(obj, valid):\n return {key: obj[key] for key in obj if key in valid}","title":""}],"string":"[\n {\n \"docid\": \"08a3ed9cbce96c06a4e9829a12d1274d\",\n \"score\": \"0.65921986\",\n \"text\": \"def _strip_keys(obj, valid):\\n return {key: obj[key] for key in obj if key in valid}\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"e7640a96ac8c550bd84f13f830e1a7f1","score":"0.6878941","text":"def subset_dict(dictionary, keys):\n\n result = dictionary.copy()\n for original_key in dictionary:\n if original_key not in keys:\n del result[original_key]\n\n return result","title":""},{"docid":"2f25e2e7be737b9ccce4a27f3aed3633","score":"0.6434246","text":"def check_keys(dict_, keys):\n\n for k in keys:\n if k not in dict_:\n return False\n\n return True","title":""},{"docid":"0e649c27571867ce1d5f10139409b167","score":"0.6394572","text":"def dict_contains_only(dct, allowed, allow_mpp=True):\n\n for key in dct.keys():\n if allow_mpp and key.startswith(\"mpp-\"):\n continue\n if key in allowed:\n continue\n return False\n return True","title":""},{"docid":"04c826035c6321df2b2e7c312f1485b9","score":"0.634918","text":"def filter_dict(data, *keys):\n d = {}\n for k in keys:\n val = data.get(k)\n if val:\n d[k] = val\n return d","title":""},{"docid":"1426726677cda6202e28cf687a776626","score":"0.63116944","text":"def dict_has_keys(d: Mapping, *keys):\n dk = d.keys()\n for k in keys:\n if k not in dk:\n logging.info(f'key {k} is not included in the dict')\n return False\n return True","title":""},{"docid":"2157fce2288d15155a0c8ecc6ef1f189","score":"0.620142","text":"def _validate_dict_keys(dict_to_check, required_keys, optional_keys):\n assert set(required_keys) <= set(dict_to_check.keys()), (\n 'Missing keys: %s' % dict_to_check)\n assert set(dict_to_check.keys()) <= set(required_keys + optional_keys), (\n 'Extra keys: %s' % dict_to_check)","title":""},{"docid":"f48beab27f1c77267bee696bbb538858","score":"0.61690384","text":"def prune_dict(self, d, keys):\n return {k: v for k, v in d.items() if k not in keys}","title":""},{"docid":"1732dad975dbd5d6f76bf511199ef0e1","score":"0.6159179","text":"def sub_dict(d, keep):\n return {\n key: value\n for key, value in d.items()\n if key in keep\n }","title":""},{"docid":"a0b375d6ef4be10e3386dfd7cdcc3ae2","score":"0.61275613","text":"def filter_kwargs(kwargs, list_of_keys, out=False):\n new_kwargs = {}\n for key in kwargs:\n if (key not in list_of_keys and out) or \\\n (key in list_of_keys and not out):\n new_kwargs[key] = kwargs[key]\n return new_kwargs","title":""},{"docid":"d34977374fd9ed515cb8cf80ee88dbb1","score":"0.6120651","text":"def _filter_dict(dict_to_filter, subset):\n if dict_to_filter is None or not subset:\n return dict_to_filter\n\n return {\n k: d for k, d in dict_to_filter.iteritems()\n if _dict_has_subset(d, subset)\n }","title":""},{"docid":"4d532239a0f944d508ca4c2b631bdd24","score":"0.6110579","text":"def _filter_dict(old_dict, cb):\n return {k: v for k, v in old_dict.items() if cb(k)}","title":""},{"docid":"213d24a0a00b9c504b54deb028ee0d25","score":"0.609966","text":"def subdict(d, expected_dict):\n return {k: v for k, v in d.items() if k in expected_dict}","title":""},{"docid":"3e3eedb235d1c0210d083fb559027ff3","score":"0.6074622","text":"def reduced_params(params, unwanted_keys):\n if not isinstance(unwanted_keys, set):\n unwanted_keys = set(unwanted_keys)\n\n return {k: params[k] for k in params.keys() - unwanted_keys}","title":""},{"docid":"e8a7152d190ae52b29dbab694d07a7f7","score":"0.6065296","text":"def filterkeys(predicate, dict_):\n predicate = bool if predicate is None else ensure_callable(predicate)\n ensure_mapping(dict_)\n return dict_.__class__((k, v) for k, v in iteritems(dict_) if predicate(k))","title":""},{"docid":"a79e5473fad817dbae7055be04ff4b95","score":"0.60485023","text":"def dict_without_keys(d, *omitkeys):\n return {key: d[key] for key in d.keys() if key not in omitkeys}","title":""},{"docid":"c342b009c2758d7defe243b23cceacec","score":"0.60253656","text":"def dictconfig_filter_keys(d: DictConfig, fn: Optional[Callable] = None) -> DictConfig:\n if fn is None:\n fn = lambda _: True\n return DictConfig(\n {\n k: dictconfig_filter_keys(v, fn) if isinstance(v, DictConfig) else v\n for k, v in d.items()\n if fn(k)\n }\n )","title":""},{"docid":"d96681beb8eba4d9fcc7e7396482cb13","score":"0.59987354","text":"def select_keys(d, keys):\n return {key: d.get(key) for key in keys}","title":""},{"docid":"f6e9e7636ac33a2a65b519bfed88933e","score":"0.5987169","text":"def select_keys(mapping, keys, **kwargs):\n # type: (Mapping[str, Any], Iterable[str], **Any) -> Dict[str, Any]\n result = {key: mapping[key] for key in keys if key in mapping}\n result.update(**kwargs)\n return result","title":""},{"docid":"7c7a36acd96421dbc8c9e1cda8cce62d","score":"0.5970194","text":"def pick(keys, dict):\n picked_dict = {}\n for k in dict.keys():\n if k in keys:\n picked_dict[k] = dict[k]\n return picked_dict","title":""},{"docid":"f268ae385399533ab489bed5047bf48f","score":"0.59593","text":"def pick(base: Dict[str, Any], *keys: str) -> Dict[str, Any]:\n\n return {key: base[key] for key in keys if key in base and base[key] is not None}","title":""},{"docid":"ff82ea51153f55f0b2808313c9103e34","score":"0.5865604","text":"def subdict(d, keys):\r\n return dict((k, d[k]) for k in keys if k in d)","title":""},{"docid":"aa64b369fe97d509343743ff3a9d88a3","score":"0.58567107","text":"def extract_keys(d: dict[str, Any], keys: Iterable[str]) -> dict[str, Any]:\n\n return {k: d[k] for k in keys}","title":""},{"docid":"1bf6ed268c4faa02881ebfb07950b046","score":"0.5846738","text":"def remove_dict_keys(d, keys):\n return {k: deepcopy(v) for k, v in d.items() if k not in keys}","title":""},{"docid":"43bb2f5e9da9e96b00b106d8d397b6bc","score":"0.58441204","text":"def find_keys(form, keys):\n assert isinstance(form, dict) and isinstance(keys, list)\n data = form.copy()\n # add keys if they are missing\n for key in keys:\n if key not in data:\n data[key] = None\n # ensure there are not any extra keys\n if len(keys) != len(form.keys()):\n raise KeyError('invalid fields')","title":""},{"docid":"50e9fbf1d3b0b9c9cbdf3424f9508c55","score":"0.5831708","text":"def find_all_by_key(d, key, ignore_keys=None):\n if ignore_keys is None:\n ignore_keys = default_ignore_keys\n\n acc = []\n for g in dict_generator(d):\n # Ignore paths 'g' in 'd' that contain ignored keys\n if len([e for e in g if e in ignore_keys]) == 0 and key in g:\n acc.append(g)\n return acc","title":""},{"docid":"73b81e49ca9337532d6ae39978953472","score":"0.581862","text":"def get_valid_request_keys(keys, type_map):\n return set(key for key in keys if key in type_map)","title":""},{"docid":"73b1754f706b3208af57aee1e96a7455","score":"0.57385033","text":"def clean_empty_keyvalues_from_dict(d):\n if not isinstance(d, (dict, list)):\n return d\n if isinstance(d, list):\n return [v for v in (clean_empty_keyvalues_from_dict(v) for v in d) if v]\n return {k: v for k, v in ((k, clean_empty_keyvalues_from_dict(v)) for k, v in d.items()) if v}","title":""},{"docid":"4c89053a6d3cbc3b1829c27f0c32629c","score":"0.57359403","text":"def check_valid_keys(in_dict):\n\n valid_keys = set([\n 'Vertical_Coordinate',\n 'Duration',\n 'Source',\n 'LeadTime',\n 'property',\n 'Procedure'\n ])\n\n for k in list(in_dict.keys()):\n try: \n assert k in valid_keys\n except AssertionError:\n logging.error( str(k) + \" is not a valid key\")\n logging.error(\"valid keys are:\\n\" + \",\\n\".join(valid_keys))\n raise","title":""},{"docid":"d328b797ac3ab2f79d27869813df3876","score":"0.5717519","text":"def key_intersection(*dicts: dict) -> dict:\n return functools.reduce(lambda a, b: a & b.keys(), dicts)","title":""},{"docid":"074145eb6044d7c1b71e2ce4060e39e7","score":"0.56937224","text":"def _cleanse_dict(original):\n return dict((k, v) for k, v in six.iteritems(original) if \"_pass\" not in k)","title":""},{"docid":"6ac550213b0c34260682eb7ccc031f7c","score":"0.56740266","text":"def starfilteritems(predicate, dict_):\n ensure_mapping(dict_)\n\n if predicate is None:\n predicate = lambda k, v: all((k, v))\n else:\n ensure_callable(predicate)\n\n return dict_.__class__((k, v) for k, v in iteritems(dict_)\n if predicate(k, v))","title":""},{"docid":"b12cf0c1b2747e055924f8f32cace65f","score":"0.5632604","text":"def subdict_by_filter(dct, func, remove_original=False):\n keylist = list(filter(func, dct))\n subdict = dict((k, dct[k]) for k in keylist)\n if remove_original:\n for k in keylist:\n del dct[k]\n return subdict","title":""},{"docid":"2ed2d9c70d56fc7c7db23728303e961c","score":"0.56311554","text":"def items_with_keys(d, keys):\n for key, value in d.items():\n if key in keys:\n yield key, value","title":""},{"docid":"45493e8b93e530c038b6bd96eced3701","score":"0.5623356","text":"def required(mapping, keys):\n assert(isinstance(mapping,collections.Mapping)), \"Not a mapping.\"\n for key in keys:\n if key not in mapping:\n raise KeyError(key)","title":""},{"docid":"de6bc6de2bbbe76ec101c5e2b60b77f8","score":"0.55767024","text":"def remove_from_key_rd_dicts( key_rd_dict, key_rd_dicts ):\n k = key_rd_dict.keys()[ 0 ]\n v = key_rd_dict[ k ]\n clean_key_rd_dicts = []\n for krd_dict in key_rd_dicts:\n key = krd_dict.keys()[ 0 ]\n val = krd_dict[ key ]\n if key == k and val == v:\n continue\n clean_key_rd_dicts.append( krd_dict )\n return clean_key_rd_dicts","title":""},{"docid":"0869e192529d967740251290936cce04","score":"0.5546951","text":"def verify(self, dictionary):\n if isinstance(dictionary, dict):\n if len(dictionary) == len(self.get_accepted_keys()):\n for k, v in dictionary.items():\n if k in self.get_accepted_keys():\n pass\n else:\n return False\n return True\n return False","title":""},{"docid":"f874b92cb8c20d46e2d7ca5d813854cd","score":"0.5545825","text":"def all_keys_known(dictionary, known_keys, logger=None):\n\n unknown_keys = [k for k in dictionary if k not in known_keys]\n\n if unknown_keys:\n if logger:\n logger.error('The following keys are unknown: {}.\\n'.\n format(','.join([\"'{}'\".format(x) for x in unknown_keys])))\n else:\n print('ERROR: The following keys are unknown: {}.\\n'.\n format(','.join([\"'{}'\".format(x) for x in unknown_keys])))\n return False\n else:\n return True","title":""},{"docid":"a14d5a4dd8c93321feea705847663f3f","score":"0.5537621","text":"def prune_dict(d, predicate):\n\n keys = [k for k, v in d.items() if predicate(k, v)]\n for k in keys:\n del d[k]","title":""},{"docid":"b8b36c84915644b8961b4011bf12cf08","score":"0.55073327","text":"def assert_keys_exist(keys: List[Hashable], dictionary: Dict) -> None:\n\n # iterate over every key and ensure that it is contained in the dictionary\n for key in keys:\n assert key in dictionary, f\"Missing key {key} in {dictionary}.\"","title":""},{"docid":"c0fedde16a2faa2c283379804a34f456","score":"0.5491811","text":"def dictionary_compress(\n dictionary_to_compress: dict[str, Any],\n keys_to_keep: set,\n) -> dict[str, Any]:\n return {dict_key: dictionary_to_compress[dict_key] for dict_key in keys_to_keep}","title":""},{"docid":"86e639be526f92b4fcfdeeea3164a0af","score":"0.54584986","text":"def filteritems(predicate, dict_):\n predicate = all if predicate is None else ensure_callable(predicate)\n ensure_mapping(dict_)\n return dict_.__class__(ifilter(predicate, iteritems(dict_)))","title":""},{"docid":"cf3876a6c5e316db3acc3f322d307d22","score":"0.5456094","text":"def dic_remove_entries(in_dic, filter_dic):\n # Checks.\n assert in_dic, \"given dictionary in_dic empty\"\n assert filter_dic, \"given dictionary filter_dic empty\"\n # Filter.\n for filter_id in filter_dic:\n if filter_id in in_dic:\n del in_dic[filter_id]\n return in_dic","title":""},{"docid":"3f71eb0df9839861f28868172fccf88c","score":"0.54447484","text":"def test_in_dict_false(self):\n my_dict = {'A': 3, 'B': 44, 'C': 8}\n self.assertFalse(in_dict(my_dict, 'D'))","title":""},{"docid":"68deb2ca92c36a3739bfb0fa04499042","score":"0.544466","text":"def asselect(item:Dict, keys:List, *, excludes:List=None) -> Dict:\n keys = keys or askeys(item)\n keys = [\n key for key in keys\n if key not in (excludes or [])\n ]\n return {\n key:item[key] for key in keys if key in item\n }","title":""},{"docid":"b1919774647b1bbef1b3e3bdd2ba5f71","score":"0.5439396","text":"def contains(hidict, keys):\n assert 'Mary' in hidict\n assert ('Mary', 'likes', 'lot') in hidict\n assert ('Mary', 'likes') in hidict\n assert ('Marvin', 'likes') not in hidict\n assert ('Mary', 'likes', 'lot', 'Bob') in hidict","title":""},{"docid":"8eb2537d16f184a17fc857ab189e7c2c","score":"0.5386315","text":"def ensure_dict_key(\n in_dict: Dict, keys: str, delimiter: str = DEFAULT_TARGET_DELIM\n) -> Dict:\n if delimiter in keys:\n a_keys = keys.split(delimiter)\n else:\n a_keys = [keys]\n dict_pointer = in_dict\n while a_keys:\n current_key = a_keys.pop(0)\n if current_key not in dict_pointer or not isinstance(\n dict_pointer[current_key], dict\n ):\n dict_pointer[current_key] = {}\n dict_pointer = dict_pointer[current_key]\n return in_dict","title":""},{"docid":"6831771ee081a117fddea51781e3edc5","score":"0.5372264","text":"def in_key_rd_dicts( key_rd_dict, key_rd_dicts ):\n k = key_rd_dict.keys()[ 0 ]\n v = key_rd_dict[ k ]\n for key_rd_dict in key_rd_dicts:\n for key, val in key_rd_dict.items():\n if key == k and val == v:\n return True\n return False","title":""},{"docid":"7be4cf0471ddbe45b473f4cf8d71b81d","score":"0.5337741","text":"def subset(self, keys):\r\n sub = self.__class__()\r\n\r\n self_keys = set(self.store.keys())\r\n subset_keys = self_keys.intersection(keys)\r\n removed_keys = self_keys.difference(subset_keys)\r\n\r\n sub.store = {k: self.store[k] for k in subset_keys}\r\n for loc, sorted_items in six.iteritems(self.sorted):\r\n loc_keys = copy(self.sorted[loc][0])\r\n loc_values = copy(self.sorted[loc][1])\r\n for k in removed_keys:\r\n i = loc_keys.index(k)\r\n del loc_keys[i]\r\n del loc_values[i]\r\n sub.sorted[loc] = (loc_keys, loc_values)\r\n return sub","title":""},{"docid":"b08e17b9a12c048b775e4da2793e5177","score":"0.53296703","text":"def filter_options(options, desired_key_set):\n return filter_dict_none_values(filter_dict_by_key_set(options, desired_key_set))","title":""},{"docid":"c7bc5848b14770deb327f218ed0e6654","score":"0.53145516","text":"def validateMapKeys(dataDict):\n neededKeys = set(['DN', 'ID', 'LOGIN', 'NAME', 'ROLES'])\n for entry in dataDict:\n if not all(elem in list(entry.keys()) for elem in neededKeys):\n print(\"Needed keys not found in response: %s\" % (neededKeys - set(entry.keys())), file=sys.stderr)\n print(\"It needs to be reported to the CRIC developers!\", file=sys.stderr)\n exit(3)\n for key in set(entry.keys()) - neededKeys:\n entry.pop(key)","title":""},{"docid":"cf906482d9714d895053739c1c9e06d8","score":"0.5309691","text":"def _dict_has_subset(dict_to_check, subset):\n if not subset: return True\n\n for k, v in subset.iteritems():\n if dict_to_check.get(k) is None:\n return False\n elif (dict_to_check[k] != v and str(dict_to_check[k]) != str(v)):\n return False\n return True","title":""},{"docid":"b14f87772a0a671310f5e9c876b7b9a6","score":"0.530853","text":"def dict_by(keys: Set[str], f: Callable[[str], Any]) -> Dict[str, Any]:\n return {k: f(k) for k in keys}","title":""},{"docid":"cf954a6442363c9df7930e89ab61e9f6","score":"0.5306642","text":"def required_keys(obj, keys, sentinel=object()):\n missing_keys = list(filter(lambda k: deepgetattr(obj, k, sentinel) == sentinel, keys))\n\n if len(missing_keys):\n raise ValueError(f\"Required parameters: {', '.join(missing_keys)}\")","title":""},{"docid":"1a97847e127751da4f4870b4d6e4f25d","score":"0.52912146","text":"def _prune_dict(dict_, on_keys, for_values, ignore_paths, path):\r\n pruned = {}\r\n for k, v in dict_.items():\r\n if k in on_keys and v in for_values:\r\n # current `obj` matches pruning criteria: do *not* return it\r\n return None\r\n elif v:\r\n # recursively prune current key's value\r\n path.append(k)\r\n pruned_v = prune_obj(\r\n obj=v,\r\n on_keys=on_keys,\r\n for_values=for_values,\r\n ignore_paths=ignore_paths,\r\n path=path,\r\n )\r\n if pruned_v:\r\n pruned[k] = pruned_v\r\n path.pop()\r\n else:\r\n # catch and return empty value; it may be meaningful\r\n pruned[k] = v\r\n return pruned","title":""},{"docid":"4b809f58bb3b2a5293c42b9cc7dceeb0","score":"0.5271548","text":"def _has_keys(self, keys, d):\n for key in keys:\n if not key in d:\n # self.logger.debug(\"Key missing %s: %s\" % (key, d))\n self.logger.error(\"ERROR: Key is missing %s\" % key)\n return False\n for key in d.keys():\n if not key in keys:\n self.logger.error(\"ERROR: Dictionary has an extra key %s\" % key)\n return False\n\n return True","title":""},{"docid":"55ca1310041ff5f53723f40f7e2f486b","score":"0.527063","text":"def _filter_params(cls, params):\n return dict((k, v) for (k, v) in params.iteritems() if k in cls.fields)","title":""},{"docid":"340fb9e33920a625570cfd0e2dcf5b7c","score":"0.52670044","text":"def _filter_hidden(dictionary):\n # type: (Attrs) -> Attrs\n return {k: v for k, v in dictionary.viewitems() if not k.startswith('_')}","title":""},{"docid":"b30ec460dfe07c420997ebb40ec343e6","score":"0.5258602","text":"def get_missing_keys(data, required_keys):\n return [ key for key in required_keys if not key in data ]","title":""},{"docid":"abaccd5e85651db13509bfa7c678edfe","score":"0.525216","text":"def assert_keys(form, keys):\n assert isinstance(form, dict) and isinstance(keys, list)\n # ensure all keys are valid\n for key in keys:\n if key not in form:\n raise KeyError('invalid fields')\n # error if number of keys is off\n if len(keys) != len(form.keys()):\n raise KeyError('missing required fields')","title":""},{"docid":"c97ae36364db9e6852feed064bd6a099","score":"0.52318764","text":"def filter_dictionaries(*args, **kwargs):\n filtered = []\n\n for item in args[0]:\n for key in kwargs.keys():\n if key in item:\n if item[key] == kwargs[key]:\n filtered.append(item)\n\n return filtered","title":""},{"docid":"d0de68f5fab679484834bfef3be06375","score":"0.52050763","text":"def validate_keys(data, required_keys, option_keys=None):\n key_set = set(data.keys())\n required_set = set(required_keys)\n is_ok = (required_set - key_set) == set()\n if is_ok and option_keys:\n option_set = set(option_keys)\n is_ok = (key_set - required_set - option_set) == set()\n return is_ok","title":""},{"docid":"8e8d7dca6efeb8ce993d56478d3ac638","score":"0.52008206","text":"def filter_df_dict_col(df, col, keys):\n \n sdf = extract_dict(df, col)\n f = partial(filter_dict, keys = keys)\n return list(map(f, sdf))","title":""},{"docid":"734a6061adb381fa769fe7a63c499513","score":"0.5193562","text":"def empty(key, dict):\n if key in dict.keys():\n if dict[key]:\n return False\n return True","title":""},{"docid":"bd9fa2ef496c2571d3864cd683e11e86","score":"0.5190214","text":"def dict_update_verify(dict_in, dict_add):\n for key, value_add in dict_add.items():\n if key not in dict_in:\n dict_in[key] = value_add\n else:\n if isinstance(value_add, np.ndarray):\n if np.any(dict_in[key] != value_add):\n msg = \"dict value mismatch for key = %s\" % key\n raise RuntimeError(msg)\n elif dict_in[key] != value_add:\n msg = \"dict value mismatch for key = %s\" % key\n raise RuntimeError(msg)\n return dict_in","title":""},{"docid":"76babaddbb564f86f440dfb8c218ec12","score":"0.51874703","text":"def get_multi(self, keys):\n dictionary = self.dictionary\n pairs = ((key, dictionary[key]) for key in keys if key in dictionary)\n now = datetime.datetime.now\n return dict((key, value) for key, (value, exp) in pairs\n if not exp or exp > now())","title":""},{"docid":"a155ed61c099edac4da75344b4bac7b4","score":"0.5182667","text":"def _copy_dic(orig_dic, excluded_keys=None):\n if excluded_keys is None:\n excluded_keys = []\n dic = {}\n for k, v in orig_dic.items():\n if k not in excluded_keys:\n dic[k] = copy(v)\n return dic","title":""},{"docid":"2d1c0df3e74a05050e9255d902f81a00","score":"0.5174145","text":"def required(mandatory):\n def decorator(f):\n @wraps(f)\n def wrapper(*dicts):\n for key in mandatory:\n if key not in dicts[1]:\n raise ValueError('Key \"%s\" is missing from argument' % (\n key))\n return f(*dicts)\n return wrapper\n return decorator","title":""},{"docid":"a64a8e8debe47821ae3975110eb89469","score":"0.5167925","text":"def injective(d):\n v = d.values()\n v_set = set(v)\n return len(v) == len(v_set)","title":""},{"docid":"043b9a174791d1a270f53f78848ab951","score":"0.51627827","text":"def _remove_matching_keys(d: Dict[str, Any], regex: str):\n compiled_regex = re.compile(regex)\n keys_to_delete = [k for k in d.keys() if compiled_regex.match(k)]\n for k in keys_to_delete:\n del d[k]","title":""},{"docid":"33e4c6e62c86c7c0505f2450046a2c83","score":"0.5162098","text":"def copy_dict(d, *keys):\n return {key: d[key] for key in keys}","title":""},{"docid":"b31f393c7eb3cd64b7b14e9cd4e694f3","score":"0.51574826","text":"def hide_keys(dictionary, keys_to_hide, new_value='xxx'):\n _new_dict = {}\n for key, value in dictionary.items():\n _new_dict[key] = new_value if key in keys_to_hide else value\n return _new_dict","title":""},{"docid":"c1959b3f36762c168cb2f1db4716e645","score":"0.51493233","text":"def _check_dictionary(evaluation_dict):\n\n missing_keys = list(set(REQUIRED_KEYS) - set(evaluation_dict.keys()))\n if len(missing_keys) == 0:\n return\n\n error_string = (\n '\\n{0:s}\\nKeys listed above were expected, but not found, in '\n 'dictionary.'\n ).format(str(missing_keys))\n\n raise ValueError(error_string)","title":""},{"docid":"a67dc6b9f3cd06d6687753529f74c6de","score":"0.5140313","text":"def fill_dict_defaults(d, required_keys=None, opt_keys=None, noleft=True):\n if required_keys is None:\n required_keys = []\n if opt_keys is None:\n opt_keys = {}\n if d is None:\n if not required_keys:\n if opt_keys is None:\n raise TypeError(\"`d` and `opt_keys` are both None.\")\n return opt_keys.copy()\n else:\n raise ValueError(\"`d` is None, but `required_keys` is not empty.\")\n\n d = d.copy()\n out = {}\n # Set required keys\n for key in required_keys:\n if key in d:\n out[key] = d.pop(key)\n else:\n raise KeyError(\"Dict is missing required key '{}'.\".format(key))\n # Set optional values, if key not given\n for key, val in opt_keys.items():\n out[key] = d.pop(key, val)\n # Extra keys are left in d: Complain, drop or use them\n if d:\n if noleft not in [True, \"drop\", \"use\"]:\n raise ValueError(\"`noleft` can be one of `True`, 'drop', 'use'.\")\n if noleft is True:\n raise KeyError(\"Leftover keys ['{}'].\".format(\n arr2str(list(d.keys()), sep=\"', '\")))\n elif noleft == \"use\":\n for key, val in d.items():\n out[key] = val\n else: # Drop\n pass\n\n return out","title":""},{"docid":"5ad1e57ad3e2ea148615e282fb402a1d","score":"0.5138393","text":"def _psd_params_checker(params):\n if params is None:\n return dict()\n elif not isinstance(params, dict):\n raise ValueError('The parameter `psd_params` has type %s. Expected '\n 'dict instead.' % type(params))\n else:\n expected_keys = ['welch_n_fft', 'welch_n_per_seg', 'welch_n_overlap']\n valid_keys = list()\n for n in params:\n if n not in expected_keys:\n raise ValueError('The key %s in `psd_params` is not valid and '\n 'will be ignored. Valid keys are: %s' %\n (n, str(expected_keys)))\n else:\n valid_keys.append(n)\n valid_params = {n: params[n] for n in valid_keys}\n return valid_params","title":""},{"docid":"2510d3ab94dff02c9cb91562cb265a53","score":"0.51352537","text":"def omegaconf_filter_keys(d, fn=None):\n if fn is None:\n fn = lambda _: True\n if is_list(d):\n return ListConfig([omegaconf_filter_keys(v, fn) for v in d])\n elif is_dict(d):\n return DictConfig(\n {k: omegaconf_filter_keys(v, fn) for k, v in d.items() if fn(k)}\n )\n else:\n return d","title":""},{"docid":"1849691f22caa21cd0acea88c4eedc22","score":"0.5133167","text":"def request_keys(keys):\n\n @decorator\n def _request_keys(fn, self, *args):\n json_keys = self.json.keys()\n for each in keys:\n if each not in json_keys:\n raise ResponseException('keys not in list: %s, %s' % (json_keys, keys))\n return fn(self, *args)\n\n return _request_keys","title":""},{"docid":"61007ab96bd217cdad93e12d65d4d498","score":"0.51303834","text":"def subdict(d, keys):\r\n return dict((k, d[k]) for k in keys)","title":""},{"docid":"09bfec63d144c94c17ee74027a85ff77","score":"0.51295996","text":"def _select_allowed_items(item_dict, allow_patterns, disallow_patterns):\n items_selected = {}\n for item in item_dict:\n select_item = False\n if allow_patterns:\n if allow_patterns[0] is None:\n select_item = True\n else:\n for pattern in allow_patterns:\n if re.search(pattern, item):\n select_item = True\n break\n if select_item:\n if disallow_patterns and (disallow_patterns[0] is not None):\n for pattern in disallow_patterns:\n if re.search(pattern, item):\n select_item = False\n break\n if select_item:\n items_selected[item] = copy.deepcopy(item_dict[item])\n\n return items_selected","title":""},{"docid":"4c39f381e3ec927f5cda5471c78dba2e","score":"0.51236546","text":"def sub_dicts_eq(keys: set, *objs: object) -> bool:\n if not objs:\n return True\n\n eq_subset = partial(select_keys, keys)\n return all_eq(*map(eq_subset, objs))","title":""},{"docid":"1f1bf07b1bfc16c4b6c995ba21e4d91a","score":"0.5111044","text":"def _verify_config_dict(self, valid, config, dev_os, key_path=None):\n if not key_path:\n key_path = []\n for key, value in valid.items():\n self._verify_config_key(key, value, valid, config, dev_os, key_path)","title":""},{"docid":"6672ce2a7c6adb84bce68e0d0d6f9f59","score":"0.5104399","text":"def test_not_in(self):\n cd = TestDict('test')\n cd.clear()\n assert len(list(cd.keys())) == 0\n assert 'bob' not in cd","title":""},{"docid":"6367b8b3e6903129ca3a8de674533fb9","score":"0.5091958","text":"def m(dict, *kv):\r\n for (k, v) in kv:\r\n if v:\r\n dict[k] = v\r\n return dict","title":""},{"docid":"2376ea068dfecb1826bfb95edf1fb9fd","score":"0.5080572","text":"def contains(d1, d2):\n if any((not hasattr(d1, 'keys'), not hasattr(d2, 'keys'))):\n return d1 == d2\n return all([contains(d1[k], d2[k]) if k in d1.keys() else False for k in d2.keys()])","title":""},{"docid":"f0b1cba0e240a292d44caa0fef113a76","score":"0.50755644","text":"def validate_dict_key(input_dict, invalid_chr, suggested_chr):\n invalid_key_list = []\n for k, v in input_dict.items():\n if isinstance(v, dict):\n if isinstance(k, str) and invalid_chr in k:\n invalid_key_list.append(k)\n validate_dict_key(v, invalid_chr, suggested_chr)\n else:\n if isinstance(k, str) and invalid_chr in k:\n invalid_key_list.append(k)\n if invalid_key_list:\n raise RuntimeError(\n \"Sadly our database can't digest periods in \"\n \"dictionary keys. We have found a number of \"\n \"entries in your spreadsheet that will \"\n \"violate this. these are listed below:\\n{}\\n\"\n \"As annoying as this is, we suggest you change \"\n \"the sample name to remove the {} characters,\"\n \" for example, you could replace {} with {} in \"\n \"your spreadsheet.\".format(\n invalid_key_list, invalid_chr, invalid_chr, suggested_chr\n )\n )","title":""},{"docid":"a598e36e7681eb8494dc29c157357398","score":"0.5070648","text":"def sortedDictKeys(d, onlykeys=[], reverse=False):\n if onlykeys != []:\n keys = intersect(d.keys(), onlykeys)\n else:\n keys = list(d)\n keys.sort()\n if reverse:\n keys.reverse()\n return keys","title":""},{"docid":"041195d415668321ca296024ccc6bec9","score":"0.5058789","text":"def in_dict(a_dict, a_key):\n return a_key in a_dict","title":""},{"docid":"4a9e63c7273b4b0c7b772a8dadd9cd76","score":"0.5049973","text":"def filtered(self):\n return {k: v for k, v in self.__dict__.items() if not\n k.startswith('_')}","title":""},{"docid":"b326f18f31ab19b7bd3230871ef97964","score":"0.5045093","text":"def validate_dictionary_keys(self, dict_key, dict_value, message):\n if dict_key not in [*dict_value]:\n raise serializers.ValidationError(message)","title":""},{"docid":"2bb6aa15130b9f75c60a92feec4f23d6","score":"0.5041604","text":"def _check_keys(dict):\n for key in dict:\n if isinstance(dict[key], spio.matlab.mio5_params.mat_struct):\n dict[key] = _todict(dict[key])\n return dict","title":""},{"docid":"5238708e8bbaa80d7b7aa4c53f4fe52f","score":"0.50398624","text":"def ensure_dict(d: Mapping[K, V], *, copy: bool = False) -> dict[K, V]:\n if type(d) is dict:\n return d.copy() if copy else d\n try:\n layers = d.layers # type: ignore\n except AttributeError:\n return dict(d)\n\n result = {}\n for layer in toolz.unique(layers.values(), key=id):\n result.update(layer)\n return result","title":""},{"docid":"e4a5ace2cfd82469042c3f7f53efbedb","score":"0.5024665","text":"def validate_dictionary(cls: NamedTuple, dic: dict):\n valid_dic = {}\n fields_list_lower = [k.lower() for k in cls._fields]\n for k in dic.keys():\n index = fields_list_lower.index(k.lower()) # Compare all in lower case. Avoid Caps sensitive.\n safe_key = cls._fields[index]\n\n if cls._field_types.get(safe_key) == int:\n valid_dic[safe_key] = int(dic.get(k))\n elif cls._field_types.get(safe_key) == float:\n valid_dic[safe_key] = float(dic.get(k))\n elif cls._field_types.get(safe_key) == bool:\n if hasattr(dic.get(k), \"capitalize\"):\n valid_dic[safe_key] = True if dic.get(k).capitalize() == \"True\" else False\n else:\n valid_dic[safe_key] = dic.get(k)\n elif cls._field_types.get(safe_key) == str:\n if dic.get(k) == \"null\":\n valid_dic[safe_key] = None\n else:\n valid_dic[safe_key] = str(dic.get(k))\n else:\n valid_dic[safe_key] = dic.get(k)\n return valid_dic","title":""},{"docid":"22a005eb4db709c788936655741d6038","score":"0.5021864","text":"def _validate_train_parameters(parameters: dict, keys: dict):\n\n for k, v in keys.items():\n if k not in parameters:\n raise RuntimeError(f'Missing key \"{k}\" in parameters for training')\n if isinstance(v, list):\n for i in v:\n if i not in parameters[k]:\n raise RuntimeError(f'Missing key \"{k}.{i}\" in parameters for training')\n if isinstance(v, dict):\n _validate_train_parameters(parameters[k], v)","title":""},{"docid":"140f406d9f3725e9dd0e9cd617701fc5","score":"0.50195307","text":"def filtervalues(predicate, dict_):\n predicate = bool if predicate is None else ensure_callable(predicate)\n ensure_mapping(dict_)\n return dict_.__class__((k, v) for k, v in iteritems(dict_) if predicate(v))","title":""},{"docid":"02c3febd223dfff63524f5d8ee92af53","score":"0.501811","text":"def make_query(i, required_keys=None):\r\n query = {}\r\n for k, v in i.items():\r\n if k.startswith('_'):\r\n continue\r\n if required_keys and k not in required_keys:\r\n continue\r\n if v == '':\r\n v = None\r\n query[k] = v\r\n return query","title":""},{"docid":"4cb2b6dfc2060d31e1114d7e7dbf3505","score":"0.50152856","text":"def slice_parset(d, key):\n return dict((k, v) for k, v in list(d.items()) if k.startswith(key))","title":""},{"docid":"9ae063870b6c2dd8df7c8f9bfaff4828","score":"0.5014754","text":"def filter_lower_case_keys(dict):\n return {key: value for key, value in dict.items() if key.islower()}","title":""},{"docid":"c051be1e9177e25deec8344973268e9d","score":"0.5013101","text":"def safeget(dct, keys):\n for key in keys:\n try:\n dct = dct[key]\n except KeyError:\n return None\n return dct","title":""},{"docid":"c051be1e9177e25deec8344973268e9d","score":"0.5013101","text":"def safeget(dct, keys):\n for key in keys:\n try:\n dct = dct[key]\n except KeyError:\n return None\n return dct","title":""},{"docid":"5ed3d217e97733da5f3ae642b5bbdff1","score":"0.50121284","text":"def filter_only_exposed(data, exposed_keys, additional_information_function=None):\n\n if data is None:\n return None\n\n new_data = [None] * len(data)\n if type(data) == list:\n for i, d in enumerate(data):\n new_data[i] = filter_only_exposed(d, exposed_keys)\n return new_data\n\n new_item = {}\n for k, v in data.items():\n if k in exposed_keys:\n new_item[k] = v\n\n if additional_information_function is not None:\n additional_information_function(new_item)\n\n return new_item\n # return {k: v for k, v in data.items() if k in exposed_keys}","title":""},{"docid":"0bca5e2d8892abeeba98457c132981c9","score":"0.50117964","text":"def _check_keys(d):\n for key in d:\n elem = d[key]\n if isinstance(elem,\n scipy.io.matlab.mio5_params.mat_struct):\n d[key] = _todict(elem)\n elif _has_struct(elem):\n d[key] = _tolist(elem)\n return d","title":""}],"string":"[\n {\n \"docid\": \"e7640a96ac8c550bd84f13f830e1a7f1\",\n \"score\": \"0.6878941\",\n \"text\": \"def subset_dict(dictionary, keys):\\n\\n result = dictionary.copy()\\n for original_key in dictionary:\\n if original_key not in keys:\\n del result[original_key]\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f25e2e7be737b9ccce4a27f3aed3633\",\n \"score\": \"0.6434246\",\n \"text\": \"def check_keys(dict_, keys):\\n\\n for k in keys:\\n if k not in dict_:\\n return False\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e649c27571867ce1d5f10139409b167\",\n \"score\": \"0.6394572\",\n \"text\": \"def dict_contains_only(dct, allowed, allow_mpp=True):\\n\\n for key in dct.keys():\\n if allow_mpp and key.startswith(\\\"mpp-\\\"):\\n continue\\n if key in allowed:\\n continue\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04c826035c6321df2b2e7c312f1485b9\",\n \"score\": \"0.634918\",\n \"text\": \"def filter_dict(data, *keys):\\n d = {}\\n for k in keys:\\n val = data.get(k)\\n if val:\\n d[k] = val\\n return d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1426726677cda6202e28cf687a776626\",\n \"score\": \"0.63116944\",\n \"text\": \"def dict_has_keys(d: Mapping, *keys):\\n dk = d.keys()\\n for k in keys:\\n if k not in dk:\\n logging.info(f'key {k} is not included in the dict')\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2157fce2288d15155a0c8ecc6ef1f189\",\n \"score\": \"0.620142\",\n \"text\": \"def _validate_dict_keys(dict_to_check, required_keys, optional_keys):\\n assert set(required_keys) <= set(dict_to_check.keys()), (\\n 'Missing keys: %s' % dict_to_check)\\n assert set(dict_to_check.keys()) <= set(required_keys + optional_keys), (\\n 'Extra keys: %s' % dict_to_check)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f48beab27f1c77267bee696bbb538858\",\n \"score\": \"0.61690384\",\n \"text\": \"def prune_dict(self, d, keys):\\n return {k: v for k, v in d.items() if k not in keys}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1732dad975dbd5d6f76bf511199ef0e1\",\n \"score\": \"0.6159179\",\n \"text\": \"def sub_dict(d, keep):\\n return {\\n key: value\\n for key, value in d.items()\\n if key in keep\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0b375d6ef4be10e3386dfd7cdcc3ae2\",\n \"score\": \"0.61275613\",\n \"text\": \"def filter_kwargs(kwargs, list_of_keys, out=False):\\n new_kwargs = {}\\n for key in kwargs:\\n if (key not in list_of_keys and out) or \\\\\\n (key in list_of_keys and not out):\\n new_kwargs[key] = kwargs[key]\\n return new_kwargs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d34977374fd9ed515cb8cf80ee88dbb1\",\n \"score\": \"0.6120651\",\n \"text\": \"def _filter_dict(dict_to_filter, subset):\\n if dict_to_filter is None or not subset:\\n return dict_to_filter\\n\\n return {\\n k: d for k, d in dict_to_filter.iteritems()\\n if _dict_has_subset(d, subset)\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d532239a0f944d508ca4c2b631bdd24\",\n \"score\": \"0.6110579\",\n \"text\": \"def _filter_dict(old_dict, cb):\\n return {k: v for k, v in old_dict.items() if cb(k)}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"213d24a0a00b9c504b54deb028ee0d25\",\n \"score\": \"0.609966\",\n \"text\": \"def subdict(d, expected_dict):\\n return {k: v for k, v in d.items() if k in expected_dict}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e3eedb235d1c0210d083fb559027ff3\",\n \"score\": \"0.6074622\",\n \"text\": \"def reduced_params(params, unwanted_keys):\\n if not isinstance(unwanted_keys, set):\\n unwanted_keys = set(unwanted_keys)\\n\\n return {k: params[k] for k in params.keys() - unwanted_keys}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8a7152d190ae52b29dbab694d07a7f7\",\n \"score\": \"0.6065296\",\n \"text\": \"def filterkeys(predicate, dict_):\\n predicate = bool if predicate is None else ensure_callable(predicate)\\n ensure_mapping(dict_)\\n return dict_.__class__((k, v) for k, v in iteritems(dict_) if predicate(k))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a79e5473fad817dbae7055be04ff4b95\",\n \"score\": \"0.60485023\",\n \"text\": \"def dict_without_keys(d, *omitkeys):\\n return {key: d[key] for key in d.keys() if key not in omitkeys}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c342b009c2758d7defe243b23cceacec\",\n \"score\": \"0.60253656\",\n \"text\": \"def dictconfig_filter_keys(d: DictConfig, fn: Optional[Callable] = None) -> DictConfig:\\n if fn is None:\\n fn = lambda _: True\\n return DictConfig(\\n {\\n k: dictconfig_filter_keys(v, fn) if isinstance(v, DictConfig) else v\\n for k, v in d.items()\\n if fn(k)\\n }\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d96681beb8eba4d9fcc7e7396482cb13\",\n \"score\": \"0.59987354\",\n \"text\": \"def select_keys(d, keys):\\n return {key: d.get(key) for key in keys}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6e9e7636ac33a2a65b519bfed88933e\",\n \"score\": \"0.5987169\",\n \"text\": \"def select_keys(mapping, keys, **kwargs):\\n # type: (Mapping[str, Any], Iterable[str], **Any) -> Dict[str, Any]\\n result = {key: mapping[key] for key in keys if key in mapping}\\n result.update(**kwargs)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c7a36acd96421dbc8c9e1cda8cce62d\",\n \"score\": \"0.5970194\",\n \"text\": \"def pick(keys, dict):\\n picked_dict = {}\\n for k in dict.keys():\\n if k in keys:\\n picked_dict[k] = dict[k]\\n return picked_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f268ae385399533ab489bed5047bf48f\",\n \"score\": \"0.59593\",\n \"text\": \"def pick(base: Dict[str, Any], *keys: str) -> Dict[str, Any]:\\n\\n return {key: base[key] for key in keys if key in base and base[key] is not None}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff82ea51153f55f0b2808313c9103e34\",\n \"score\": \"0.5865604\",\n \"text\": \"def subdict(d, keys):\\r\\n return dict((k, d[k]) for k in keys if k in d)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa64b369fe97d509343743ff3a9d88a3\",\n \"score\": \"0.58567107\",\n \"text\": \"def extract_keys(d: dict[str, Any], keys: Iterable[str]) -> dict[str, Any]:\\n\\n return {k: d[k] for k in keys}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1bf6ed268c4faa02881ebfb07950b046\",\n \"score\": \"0.5846738\",\n \"text\": \"def remove_dict_keys(d, keys):\\n return {k: deepcopy(v) for k, v in d.items() if k not in keys}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43bb2f5e9da9e96b00b106d8d397b6bc\",\n \"score\": \"0.58441204\",\n \"text\": \"def find_keys(form, keys):\\n assert isinstance(form, dict) and isinstance(keys, list)\\n data = form.copy()\\n # add keys if they are missing\\n for key in keys:\\n if key not in data:\\n data[key] = None\\n # ensure there are not any extra keys\\n if len(keys) != len(form.keys()):\\n raise KeyError('invalid fields')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50e9fbf1d3b0b9c9cbdf3424f9508c55\",\n \"score\": \"0.5831708\",\n \"text\": \"def find_all_by_key(d, key, ignore_keys=None):\\n if ignore_keys is None:\\n ignore_keys = default_ignore_keys\\n\\n acc = []\\n for g in dict_generator(d):\\n # Ignore paths 'g' in 'd' that contain ignored keys\\n if len([e for e in g if e in ignore_keys]) == 0 and key in g:\\n acc.append(g)\\n return acc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73b81e49ca9337532d6ae39978953472\",\n \"score\": \"0.581862\",\n \"text\": \"def get_valid_request_keys(keys, type_map):\\n return set(key for key in keys if key in type_map)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73b1754f706b3208af57aee1e96a7455\",\n \"score\": \"0.57385033\",\n \"text\": \"def clean_empty_keyvalues_from_dict(d):\\n if not isinstance(d, (dict, list)):\\n return d\\n if isinstance(d, list):\\n return [v for v in (clean_empty_keyvalues_from_dict(v) for v in d) if v]\\n return {k: v for k, v in ((k, clean_empty_keyvalues_from_dict(v)) for k, v in d.items()) if v}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c89053a6d3cbc3b1829c27f0c32629c\",\n \"score\": \"0.57359403\",\n \"text\": \"def check_valid_keys(in_dict):\\n\\n valid_keys = set([\\n 'Vertical_Coordinate',\\n 'Duration',\\n 'Source',\\n 'LeadTime',\\n 'property',\\n 'Procedure'\\n ])\\n\\n for k in list(in_dict.keys()):\\n try: \\n assert k in valid_keys\\n except AssertionError:\\n logging.error( str(k) + \\\" is not a valid key\\\")\\n logging.error(\\\"valid keys are:\\\\n\\\" + \\\",\\\\n\\\".join(valid_keys))\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d328b797ac3ab2f79d27869813df3876\",\n \"score\": \"0.5717519\",\n \"text\": \"def key_intersection(*dicts: dict) -> dict:\\n return functools.reduce(lambda a, b: a & b.keys(), dicts)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"074145eb6044d7c1b71e2ce4060e39e7\",\n \"score\": \"0.56937224\",\n \"text\": \"def _cleanse_dict(original):\\n return dict((k, v) for k, v in six.iteritems(original) if \\\"_pass\\\" not in k)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ac550213b0c34260682eb7ccc031f7c\",\n \"score\": \"0.56740266\",\n \"text\": \"def starfilteritems(predicate, dict_):\\n ensure_mapping(dict_)\\n\\n if predicate is None:\\n predicate = lambda k, v: all((k, v))\\n else:\\n ensure_callable(predicate)\\n\\n return dict_.__class__((k, v) for k, v in iteritems(dict_)\\n if predicate(k, v))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b12cf0c1b2747e055924f8f32cace65f\",\n \"score\": \"0.5632604\",\n \"text\": \"def subdict_by_filter(dct, func, remove_original=False):\\n keylist = list(filter(func, dct))\\n subdict = dict((k, dct[k]) for k in keylist)\\n if remove_original:\\n for k in keylist:\\n del dct[k]\\n return subdict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ed2d9c70d56fc7c7db23728303e961c\",\n \"score\": \"0.56311554\",\n \"text\": \"def items_with_keys(d, keys):\\n for key, value in d.items():\\n if key in keys:\\n yield key, value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45493e8b93e530c038b6bd96eced3701\",\n \"score\": \"0.5623356\",\n \"text\": \"def required(mapping, keys):\\n assert(isinstance(mapping,collections.Mapping)), \\\"Not a mapping.\\\"\\n for key in keys:\\n if key not in mapping:\\n raise KeyError(key)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de6bc6de2bbbe76ec101c5e2b60b77f8\",\n \"score\": \"0.55767024\",\n \"text\": \"def remove_from_key_rd_dicts( key_rd_dict, key_rd_dicts ):\\n k = key_rd_dict.keys()[ 0 ]\\n v = key_rd_dict[ k ]\\n clean_key_rd_dicts = []\\n for krd_dict in key_rd_dicts:\\n key = krd_dict.keys()[ 0 ]\\n val = krd_dict[ key ]\\n if key == k and val == v:\\n continue\\n clean_key_rd_dicts.append( krd_dict )\\n return clean_key_rd_dicts\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0869e192529d967740251290936cce04\",\n \"score\": \"0.5546951\",\n \"text\": \"def verify(self, dictionary):\\n if isinstance(dictionary, dict):\\n if len(dictionary) == len(self.get_accepted_keys()):\\n for k, v in dictionary.items():\\n if k in self.get_accepted_keys():\\n pass\\n else:\\n return False\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f874b92cb8c20d46e2d7ca5d813854cd\",\n \"score\": \"0.5545825\",\n \"text\": \"def all_keys_known(dictionary, known_keys, logger=None):\\n\\n unknown_keys = [k for k in dictionary if k not in known_keys]\\n\\n if unknown_keys:\\n if logger:\\n logger.error('The following keys are unknown: {}.\\\\n'.\\n format(','.join([\\\"'{}'\\\".format(x) for x in unknown_keys])))\\n else:\\n print('ERROR: The following keys are unknown: {}.\\\\n'.\\n format(','.join([\\\"'{}'\\\".format(x) for x in unknown_keys])))\\n return False\\n else:\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a14d5a4dd8c93321feea705847663f3f\",\n \"score\": \"0.5537621\",\n \"text\": \"def prune_dict(d, predicate):\\n\\n keys = [k for k, v in d.items() if predicate(k, v)]\\n for k in keys:\\n del d[k]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8b36c84915644b8961b4011bf12cf08\",\n \"score\": \"0.55073327\",\n \"text\": \"def assert_keys_exist(keys: List[Hashable], dictionary: Dict) -> None:\\n\\n # iterate over every key and ensure that it is contained in the dictionary\\n for key in keys:\\n assert key in dictionary, f\\\"Missing key {key} in {dictionary}.\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0fedde16a2faa2c283379804a34f456\",\n \"score\": \"0.5491811\",\n \"text\": \"def dictionary_compress(\\n dictionary_to_compress: dict[str, Any],\\n keys_to_keep: set,\\n) -> dict[str, Any]:\\n return {dict_key: dictionary_to_compress[dict_key] for dict_key in keys_to_keep}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86e639be526f92b4fcfdeeea3164a0af\",\n \"score\": \"0.54584986\",\n \"text\": \"def filteritems(predicate, dict_):\\n predicate = all if predicate is None else ensure_callable(predicate)\\n ensure_mapping(dict_)\\n return dict_.__class__(ifilter(predicate, iteritems(dict_)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf3876a6c5e316db3acc3f322d307d22\",\n \"score\": \"0.5456094\",\n \"text\": \"def dic_remove_entries(in_dic, filter_dic):\\n # Checks.\\n assert in_dic, \\\"given dictionary in_dic empty\\\"\\n assert filter_dic, \\\"given dictionary filter_dic empty\\\"\\n # Filter.\\n for filter_id in filter_dic:\\n if filter_id in in_dic:\\n del in_dic[filter_id]\\n return in_dic\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f71eb0df9839861f28868172fccf88c\",\n \"score\": \"0.54447484\",\n \"text\": \"def test_in_dict_false(self):\\n my_dict = {'A': 3, 'B': 44, 'C': 8}\\n self.assertFalse(in_dict(my_dict, 'D'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68deb2ca92c36a3739bfb0fa04499042\",\n \"score\": \"0.544466\",\n \"text\": \"def asselect(item:Dict, keys:List, *, excludes:List=None) -> Dict:\\n keys = keys or askeys(item)\\n keys = [\\n key for key in keys\\n if key not in (excludes or [])\\n ]\\n return {\\n key:item[key] for key in keys if key in item\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1919774647b1bbef1b3e3bdd2ba5f71\",\n \"score\": \"0.5439396\",\n \"text\": \"def contains(hidict, keys):\\n assert 'Mary' in hidict\\n assert ('Mary', 'likes', 'lot') in hidict\\n assert ('Mary', 'likes') in hidict\\n assert ('Marvin', 'likes') not in hidict\\n assert ('Mary', 'likes', 'lot', 'Bob') in hidict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8eb2537d16f184a17fc857ab189e7c2c\",\n \"score\": \"0.5386315\",\n \"text\": \"def ensure_dict_key(\\n in_dict: Dict, keys: str, delimiter: str = DEFAULT_TARGET_DELIM\\n) -> Dict:\\n if delimiter in keys:\\n a_keys = keys.split(delimiter)\\n else:\\n a_keys = [keys]\\n dict_pointer = in_dict\\n while a_keys:\\n current_key = a_keys.pop(0)\\n if current_key not in dict_pointer or not isinstance(\\n dict_pointer[current_key], dict\\n ):\\n dict_pointer[current_key] = {}\\n dict_pointer = dict_pointer[current_key]\\n return in_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6831771ee081a117fddea51781e3edc5\",\n \"score\": \"0.5372264\",\n \"text\": \"def in_key_rd_dicts( key_rd_dict, key_rd_dicts ):\\n k = key_rd_dict.keys()[ 0 ]\\n v = key_rd_dict[ k ]\\n for key_rd_dict in key_rd_dicts:\\n for key, val in key_rd_dict.items():\\n if key == k and val == v:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7be4cf0471ddbe45b473f4cf8d71b81d\",\n \"score\": \"0.5337741\",\n \"text\": \"def subset(self, keys):\\r\\n sub = self.__class__()\\r\\n\\r\\n self_keys = set(self.store.keys())\\r\\n subset_keys = self_keys.intersection(keys)\\r\\n removed_keys = self_keys.difference(subset_keys)\\r\\n\\r\\n sub.store = {k: self.store[k] for k in subset_keys}\\r\\n for loc, sorted_items in six.iteritems(self.sorted):\\r\\n loc_keys = copy(self.sorted[loc][0])\\r\\n loc_values = copy(self.sorted[loc][1])\\r\\n for k in removed_keys:\\r\\n i = loc_keys.index(k)\\r\\n del loc_keys[i]\\r\\n del loc_values[i]\\r\\n sub.sorted[loc] = (loc_keys, loc_values)\\r\\n return sub\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b08e17b9a12c048b775e4da2793e5177\",\n \"score\": \"0.53296703\",\n \"text\": \"def filter_options(options, desired_key_set):\\n return filter_dict_none_values(filter_dict_by_key_set(options, desired_key_set))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7bc5848b14770deb327f218ed0e6654\",\n \"score\": \"0.53145516\",\n \"text\": \"def validateMapKeys(dataDict):\\n neededKeys = set(['DN', 'ID', 'LOGIN', 'NAME', 'ROLES'])\\n for entry in dataDict:\\n if not all(elem in list(entry.keys()) for elem in neededKeys):\\n print(\\\"Needed keys not found in response: %s\\\" % (neededKeys - set(entry.keys())), file=sys.stderr)\\n print(\\\"It needs to be reported to the CRIC developers!\\\", file=sys.stderr)\\n exit(3)\\n for key in set(entry.keys()) - neededKeys:\\n entry.pop(key)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf906482d9714d895053739c1c9e06d8\",\n \"score\": \"0.5309691\",\n \"text\": \"def _dict_has_subset(dict_to_check, subset):\\n if not subset: return True\\n\\n for k, v in subset.iteritems():\\n if dict_to_check.get(k) is None:\\n return False\\n elif (dict_to_check[k] != v and str(dict_to_check[k]) != str(v)):\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b14f87772a0a671310f5e9c876b7b9a6\",\n \"score\": \"0.530853\",\n \"text\": \"def dict_by(keys: Set[str], f: Callable[[str], Any]) -> Dict[str, Any]:\\n return {k: f(k) for k in keys}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf954a6442363c9df7930e89ab61e9f6\",\n \"score\": \"0.5306642\",\n \"text\": \"def required_keys(obj, keys, sentinel=object()):\\n missing_keys = list(filter(lambda k: deepgetattr(obj, k, sentinel) == sentinel, keys))\\n\\n if len(missing_keys):\\n raise ValueError(f\\\"Required parameters: {', '.join(missing_keys)}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a97847e127751da4f4870b4d6e4f25d\",\n \"score\": \"0.52912146\",\n \"text\": \"def _prune_dict(dict_, on_keys, for_values, ignore_paths, path):\\r\\n pruned = {}\\r\\n for k, v in dict_.items():\\r\\n if k in on_keys and v in for_values:\\r\\n # current `obj` matches pruning criteria: do *not* return it\\r\\n return None\\r\\n elif v:\\r\\n # recursively prune current key's value\\r\\n path.append(k)\\r\\n pruned_v = prune_obj(\\r\\n obj=v,\\r\\n on_keys=on_keys,\\r\\n for_values=for_values,\\r\\n ignore_paths=ignore_paths,\\r\\n path=path,\\r\\n )\\r\\n if pruned_v:\\r\\n pruned[k] = pruned_v\\r\\n path.pop()\\r\\n else:\\r\\n # catch and return empty value; it may be meaningful\\r\\n pruned[k] = v\\r\\n return pruned\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b809f58bb3b2a5293c42b9cc7dceeb0\",\n \"score\": \"0.5271548\",\n \"text\": \"def _has_keys(self, keys, d):\\n for key in keys:\\n if not key in d:\\n # self.logger.debug(\\\"Key missing %s: %s\\\" % (key, d))\\n self.logger.error(\\\"ERROR: Key is missing %s\\\" % key)\\n return False\\n for key in d.keys():\\n if not key in keys:\\n self.logger.error(\\\"ERROR: Dictionary has an extra key %s\\\" % key)\\n return False\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55ca1310041ff5f53723f40f7e2f486b\",\n \"score\": \"0.527063\",\n \"text\": \"def _filter_params(cls, params):\\n return dict((k, v) for (k, v) in params.iteritems() if k in cls.fields)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"340fb9e33920a625570cfd0e2dcf5b7c\",\n \"score\": \"0.52670044\",\n \"text\": \"def _filter_hidden(dictionary):\\n # type: (Attrs) -> Attrs\\n return {k: v for k, v in dictionary.viewitems() if not k.startswith('_')}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b30ec460dfe07c420997ebb40ec343e6\",\n \"score\": \"0.5258602\",\n \"text\": \"def get_missing_keys(data, required_keys):\\n return [ key for key in required_keys if not key in data ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abaccd5e85651db13509bfa7c678edfe\",\n \"score\": \"0.525216\",\n \"text\": \"def assert_keys(form, keys):\\n assert isinstance(form, dict) and isinstance(keys, list)\\n # ensure all keys are valid\\n for key in keys:\\n if key not in form:\\n raise KeyError('invalid fields')\\n # error if number of keys is off\\n if len(keys) != len(form.keys()):\\n raise KeyError('missing required fields')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c97ae36364db9e6852feed064bd6a099\",\n \"score\": \"0.52318764\",\n \"text\": \"def filter_dictionaries(*args, **kwargs):\\n filtered = []\\n\\n for item in args[0]:\\n for key in kwargs.keys():\\n if key in item:\\n if item[key] == kwargs[key]:\\n filtered.append(item)\\n\\n return filtered\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0de68f5fab679484834bfef3be06375\",\n \"score\": \"0.52050763\",\n \"text\": \"def validate_keys(data, required_keys, option_keys=None):\\n key_set = set(data.keys())\\n required_set = set(required_keys)\\n is_ok = (required_set - key_set) == set()\\n if is_ok and option_keys:\\n option_set = set(option_keys)\\n is_ok = (key_set - required_set - option_set) == set()\\n return is_ok\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e8d7dca6efeb8ce993d56478d3ac638\",\n \"score\": \"0.52008206\",\n \"text\": \"def filter_df_dict_col(df, col, keys):\\n \\n sdf = extract_dict(df, col)\\n f = partial(filter_dict, keys = keys)\\n return list(map(f, sdf))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"734a6061adb381fa769fe7a63c499513\",\n \"score\": \"0.5193562\",\n \"text\": \"def empty(key, dict):\\n if key in dict.keys():\\n if dict[key]:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd9fa2ef496c2571d3864cd683e11e86\",\n \"score\": \"0.5190214\",\n \"text\": \"def dict_update_verify(dict_in, dict_add):\\n for key, value_add in dict_add.items():\\n if key not in dict_in:\\n dict_in[key] = value_add\\n else:\\n if isinstance(value_add, np.ndarray):\\n if np.any(dict_in[key] != value_add):\\n msg = \\\"dict value mismatch for key = %s\\\" % key\\n raise RuntimeError(msg)\\n elif dict_in[key] != value_add:\\n msg = \\\"dict value mismatch for key = %s\\\" % key\\n raise RuntimeError(msg)\\n return dict_in\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76babaddbb564f86f440dfb8c218ec12\",\n \"score\": \"0.51874703\",\n \"text\": \"def get_multi(self, keys):\\n dictionary = self.dictionary\\n pairs = ((key, dictionary[key]) for key in keys if key in dictionary)\\n now = datetime.datetime.now\\n return dict((key, value) for key, (value, exp) in pairs\\n if not exp or exp > now())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a155ed61c099edac4da75344b4bac7b4\",\n \"score\": \"0.5182667\",\n \"text\": \"def _copy_dic(orig_dic, excluded_keys=None):\\n if excluded_keys is None:\\n excluded_keys = []\\n dic = {}\\n for k, v in orig_dic.items():\\n if k not in excluded_keys:\\n dic[k] = copy(v)\\n return dic\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d1c0df3e74a05050e9255d902f81a00\",\n \"score\": \"0.5174145\",\n \"text\": \"def required(mandatory):\\n def decorator(f):\\n @wraps(f)\\n def wrapper(*dicts):\\n for key in mandatory:\\n if key not in dicts[1]:\\n raise ValueError('Key \\\"%s\\\" is missing from argument' % (\\n key))\\n return f(*dicts)\\n return wrapper\\n return decorator\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a64a8e8debe47821ae3975110eb89469\",\n \"score\": \"0.5167925\",\n \"text\": \"def injective(d):\\n v = d.values()\\n v_set = set(v)\\n return len(v) == len(v_set)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"043b9a174791d1a270f53f78848ab951\",\n \"score\": \"0.51627827\",\n \"text\": \"def _remove_matching_keys(d: Dict[str, Any], regex: str):\\n compiled_regex = re.compile(regex)\\n keys_to_delete = [k for k in d.keys() if compiled_regex.match(k)]\\n for k in keys_to_delete:\\n del d[k]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33e4c6e62c86c7c0505f2450046a2c83\",\n \"score\": \"0.5162098\",\n \"text\": \"def copy_dict(d, *keys):\\n return {key: d[key] for key in keys}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b31f393c7eb3cd64b7b14e9cd4e694f3\",\n \"score\": \"0.51574826\",\n \"text\": \"def hide_keys(dictionary, keys_to_hide, new_value='xxx'):\\n _new_dict = {}\\n for key, value in dictionary.items():\\n _new_dict[key] = new_value if key in keys_to_hide else value\\n return _new_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1959b3f36762c168cb2f1db4716e645\",\n \"score\": \"0.51493233\",\n \"text\": \"def _check_dictionary(evaluation_dict):\\n\\n missing_keys = list(set(REQUIRED_KEYS) - set(evaluation_dict.keys()))\\n if len(missing_keys) == 0:\\n return\\n\\n error_string = (\\n '\\\\n{0:s}\\\\nKeys listed above were expected, but not found, in '\\n 'dictionary.'\\n ).format(str(missing_keys))\\n\\n raise ValueError(error_string)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a67dc6b9f3cd06d6687753529f74c6de\",\n \"score\": \"0.5140313\",\n \"text\": \"def fill_dict_defaults(d, required_keys=None, opt_keys=None, noleft=True):\\n if required_keys is None:\\n required_keys = []\\n if opt_keys is None:\\n opt_keys = {}\\n if d is None:\\n if not required_keys:\\n if opt_keys is None:\\n raise TypeError(\\\"`d` and `opt_keys` are both None.\\\")\\n return opt_keys.copy()\\n else:\\n raise ValueError(\\\"`d` is None, but `required_keys` is not empty.\\\")\\n\\n d = d.copy()\\n out = {}\\n # Set required keys\\n for key in required_keys:\\n if key in d:\\n out[key] = d.pop(key)\\n else:\\n raise KeyError(\\\"Dict is missing required key '{}'.\\\".format(key))\\n # Set optional values, if key not given\\n for key, val in opt_keys.items():\\n out[key] = d.pop(key, val)\\n # Extra keys are left in d: Complain, drop or use them\\n if d:\\n if noleft not in [True, \\\"drop\\\", \\\"use\\\"]:\\n raise ValueError(\\\"`noleft` can be one of `True`, 'drop', 'use'.\\\")\\n if noleft is True:\\n raise KeyError(\\\"Leftover keys ['{}'].\\\".format(\\n arr2str(list(d.keys()), sep=\\\"', '\\\")))\\n elif noleft == \\\"use\\\":\\n for key, val in d.items():\\n out[key] = val\\n else: # Drop\\n pass\\n\\n return out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ad1e57ad3e2ea148615e282fb402a1d\",\n \"score\": \"0.5138393\",\n \"text\": \"def _psd_params_checker(params):\\n if params is None:\\n return dict()\\n elif not isinstance(params, dict):\\n raise ValueError('The parameter `psd_params` has type %s. Expected '\\n 'dict instead.' % type(params))\\n else:\\n expected_keys = ['welch_n_fft', 'welch_n_per_seg', 'welch_n_overlap']\\n valid_keys = list()\\n for n in params:\\n if n not in expected_keys:\\n raise ValueError('The key %s in `psd_params` is not valid and '\\n 'will be ignored. Valid keys are: %s' %\\n (n, str(expected_keys)))\\n else:\\n valid_keys.append(n)\\n valid_params = {n: params[n] for n in valid_keys}\\n return valid_params\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2510d3ab94dff02c9cb91562cb265a53\",\n \"score\": \"0.51352537\",\n \"text\": \"def omegaconf_filter_keys(d, fn=None):\\n if fn is None:\\n fn = lambda _: True\\n if is_list(d):\\n return ListConfig([omegaconf_filter_keys(v, fn) for v in d])\\n elif is_dict(d):\\n return DictConfig(\\n {k: omegaconf_filter_keys(v, fn) for k, v in d.items() if fn(k)}\\n )\\n else:\\n return d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1849691f22caa21cd0acea88c4eedc22\",\n \"score\": \"0.5133167\",\n \"text\": \"def request_keys(keys):\\n\\n @decorator\\n def _request_keys(fn, self, *args):\\n json_keys = self.json.keys()\\n for each in keys:\\n if each not in json_keys:\\n raise ResponseException('keys not in list: %s, %s' % (json_keys, keys))\\n return fn(self, *args)\\n\\n return _request_keys\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61007ab96bd217cdad93e12d65d4d498\",\n \"score\": \"0.51303834\",\n \"text\": \"def subdict(d, keys):\\r\\n return dict((k, d[k]) for k in keys)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09bfec63d144c94c17ee74027a85ff77\",\n \"score\": \"0.51295996\",\n \"text\": \"def _select_allowed_items(item_dict, allow_patterns, disallow_patterns):\\n items_selected = {}\\n for item in item_dict:\\n select_item = False\\n if allow_patterns:\\n if allow_patterns[0] is None:\\n select_item = True\\n else:\\n for pattern in allow_patterns:\\n if re.search(pattern, item):\\n select_item = True\\n break\\n if select_item:\\n if disallow_patterns and (disallow_patterns[0] is not None):\\n for pattern in disallow_patterns:\\n if re.search(pattern, item):\\n select_item = False\\n break\\n if select_item:\\n items_selected[item] = copy.deepcopy(item_dict[item])\\n\\n return items_selected\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c39f381e3ec927f5cda5471c78dba2e\",\n \"score\": \"0.51236546\",\n \"text\": \"def sub_dicts_eq(keys: set, *objs: object) -> bool:\\n if not objs:\\n return True\\n\\n eq_subset = partial(select_keys, keys)\\n return all_eq(*map(eq_subset, objs))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f1bf07b1bfc16c4b6c995ba21e4d91a\",\n \"score\": \"0.5111044\",\n \"text\": \"def _verify_config_dict(self, valid, config, dev_os, key_path=None):\\n if not key_path:\\n key_path = []\\n for key, value in valid.items():\\n self._verify_config_key(key, value, valid, config, dev_os, key_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6672ce2a7c6adb84bce68e0d0d6f9f59\",\n \"score\": \"0.5104399\",\n \"text\": \"def test_not_in(self):\\n cd = TestDict('test')\\n cd.clear()\\n assert len(list(cd.keys())) == 0\\n assert 'bob' not in cd\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6367b8b3e6903129ca3a8de674533fb9\",\n \"score\": \"0.5091958\",\n \"text\": \"def m(dict, *kv):\\r\\n for (k, v) in kv:\\r\\n if v:\\r\\n dict[k] = v\\r\\n return dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2376ea068dfecb1826bfb95edf1fb9fd\",\n \"score\": \"0.5080572\",\n \"text\": \"def contains(d1, d2):\\n if any((not hasattr(d1, 'keys'), not hasattr(d2, 'keys'))):\\n return d1 == d2\\n return all([contains(d1[k], d2[k]) if k in d1.keys() else False for k in d2.keys()])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0b1cba0e240a292d44caa0fef113a76\",\n \"score\": \"0.50755644\",\n \"text\": \"def validate_dict_key(input_dict, invalid_chr, suggested_chr):\\n invalid_key_list = []\\n for k, v in input_dict.items():\\n if isinstance(v, dict):\\n if isinstance(k, str) and invalid_chr in k:\\n invalid_key_list.append(k)\\n validate_dict_key(v, invalid_chr, suggested_chr)\\n else:\\n if isinstance(k, str) and invalid_chr in k:\\n invalid_key_list.append(k)\\n if invalid_key_list:\\n raise RuntimeError(\\n \\\"Sadly our database can't digest periods in \\\"\\n \\\"dictionary keys. We have found a number of \\\"\\n \\\"entries in your spreadsheet that will \\\"\\n \\\"violate this. these are listed below:\\\\n{}\\\\n\\\"\\n \\\"As annoying as this is, we suggest you change \\\"\\n \\\"the sample name to remove the {} characters,\\\"\\n \\\" for example, you could replace {} with {} in \\\"\\n \\\"your spreadsheet.\\\".format(\\n invalid_key_list, invalid_chr, invalid_chr, suggested_chr\\n )\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a598e36e7681eb8494dc29c157357398\",\n \"score\": \"0.5070648\",\n \"text\": \"def sortedDictKeys(d, onlykeys=[], reverse=False):\\n if onlykeys != []:\\n keys = intersect(d.keys(), onlykeys)\\n else:\\n keys = list(d)\\n keys.sort()\\n if reverse:\\n keys.reverse()\\n return keys\",\n \"title\": \"\"\n },\n {\n \"docid\": \"041195d415668321ca296024ccc6bec9\",\n \"score\": \"0.5058789\",\n \"text\": \"def in_dict(a_dict, a_key):\\n return a_key in a_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a9e63c7273b4b0c7b772a8dadd9cd76\",\n \"score\": \"0.5049973\",\n \"text\": \"def filtered(self):\\n return {k: v for k, v in self.__dict__.items() if not\\n k.startswith('_')}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b326f18f31ab19b7bd3230871ef97964\",\n \"score\": \"0.5045093\",\n \"text\": \"def validate_dictionary_keys(self, dict_key, dict_value, message):\\n if dict_key not in [*dict_value]:\\n raise serializers.ValidationError(message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2bb6aa15130b9f75c60a92feec4f23d6\",\n \"score\": \"0.5041604\",\n \"text\": \"def _check_keys(dict):\\n for key in dict:\\n if isinstance(dict[key], spio.matlab.mio5_params.mat_struct):\\n dict[key] = _todict(dict[key])\\n return dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5238708e8bbaa80d7b7aa4c53f4fe52f\",\n \"score\": \"0.50398624\",\n \"text\": \"def ensure_dict(d: Mapping[K, V], *, copy: bool = False) -> dict[K, V]:\\n if type(d) is dict:\\n return d.copy() if copy else d\\n try:\\n layers = d.layers # type: ignore\\n except AttributeError:\\n return dict(d)\\n\\n result = {}\\n for layer in toolz.unique(layers.values(), key=id):\\n result.update(layer)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4a5ace2cfd82469042c3f7f53efbedb\",\n \"score\": \"0.5024665\",\n \"text\": \"def validate_dictionary(cls: NamedTuple, dic: dict):\\n valid_dic = {}\\n fields_list_lower = [k.lower() for k in cls._fields]\\n for k in dic.keys():\\n index = fields_list_lower.index(k.lower()) # Compare all in lower case. Avoid Caps sensitive.\\n safe_key = cls._fields[index]\\n\\n if cls._field_types.get(safe_key) == int:\\n valid_dic[safe_key] = int(dic.get(k))\\n elif cls._field_types.get(safe_key) == float:\\n valid_dic[safe_key] = float(dic.get(k))\\n elif cls._field_types.get(safe_key) == bool:\\n if hasattr(dic.get(k), \\\"capitalize\\\"):\\n valid_dic[safe_key] = True if dic.get(k).capitalize() == \\\"True\\\" else False\\n else:\\n valid_dic[safe_key] = dic.get(k)\\n elif cls._field_types.get(safe_key) == str:\\n if dic.get(k) == \\\"null\\\":\\n valid_dic[safe_key] = None\\n else:\\n valid_dic[safe_key] = str(dic.get(k))\\n else:\\n valid_dic[safe_key] = dic.get(k)\\n return valid_dic\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22a005eb4db709c788936655741d6038\",\n \"score\": \"0.5021864\",\n \"text\": \"def _validate_train_parameters(parameters: dict, keys: dict):\\n\\n for k, v in keys.items():\\n if k not in parameters:\\n raise RuntimeError(f'Missing key \\\"{k}\\\" in parameters for training')\\n if isinstance(v, list):\\n for i in v:\\n if i not in parameters[k]:\\n raise RuntimeError(f'Missing key \\\"{k}.{i}\\\" in parameters for training')\\n if isinstance(v, dict):\\n _validate_train_parameters(parameters[k], v)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"140f406d9f3725e9dd0e9cd617701fc5\",\n \"score\": \"0.50195307\",\n \"text\": \"def filtervalues(predicate, dict_):\\n predicate = bool if predicate is None else ensure_callable(predicate)\\n ensure_mapping(dict_)\\n return dict_.__class__((k, v) for k, v in iteritems(dict_) if predicate(v))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02c3febd223dfff63524f5d8ee92af53\",\n \"score\": \"0.501811\",\n \"text\": \"def make_query(i, required_keys=None):\\r\\n query = {}\\r\\n for k, v in i.items():\\r\\n if k.startswith('_'):\\r\\n continue\\r\\n if required_keys and k not in required_keys:\\r\\n continue\\r\\n if v == '':\\r\\n v = None\\r\\n query[k] = v\\r\\n return query\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4cb2b6dfc2060d31e1114d7e7dbf3505\",\n \"score\": \"0.50152856\",\n \"text\": \"def slice_parset(d, key):\\n return dict((k, v) for k, v in list(d.items()) if k.startswith(key))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ae063870b6c2dd8df7c8f9bfaff4828\",\n \"score\": \"0.5014754\",\n \"text\": \"def filter_lower_case_keys(dict):\\n return {key: value for key, value in dict.items() if key.islower()}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c051be1e9177e25deec8344973268e9d\",\n \"score\": \"0.5013101\",\n \"text\": \"def safeget(dct, keys):\\n for key in keys:\\n try:\\n dct = dct[key]\\n except KeyError:\\n return None\\n return dct\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c051be1e9177e25deec8344973268e9d\",\n \"score\": \"0.5013101\",\n \"text\": \"def safeget(dct, keys):\\n for key in keys:\\n try:\\n dct = dct[key]\\n except KeyError:\\n return None\\n return dct\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ed3d217e97733da5f3ae642b5bbdff1\",\n \"score\": \"0.50121284\",\n \"text\": \"def filter_only_exposed(data, exposed_keys, additional_information_function=None):\\n\\n if data is None:\\n return None\\n\\n new_data = [None] * len(data)\\n if type(data) == list:\\n for i, d in enumerate(data):\\n new_data[i] = filter_only_exposed(d, exposed_keys)\\n return new_data\\n\\n new_item = {}\\n for k, v in data.items():\\n if k in exposed_keys:\\n new_item[k] = v\\n\\n if additional_information_function is not None:\\n additional_information_function(new_item)\\n\\n return new_item\\n # return {k: v for k, v in data.items() if k in exposed_keys}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0bca5e2d8892abeeba98457c132981c9\",\n \"score\": \"0.50117964\",\n \"text\": \"def _check_keys(d):\\n for key in d:\\n elem = d[key]\\n if isinstance(elem,\\n scipy.io.matlab.mio5_params.mat_struct):\\n d[key] = _todict(elem)\\n elif _has_struct(elem):\\n d[key] = _tolist(elem)\\n return d\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":886,"cells":{"query_id":{"kind":"string","value":"c35652b26ac9b90ab52b8a7de68039e5"},"query":{"kind":"string","value":"Returns true if there is any boolean True in A"},"positive_passages":{"kind":"list like","value":[{"docid":"488f7cc7ab70b194a5fd819ee1942cf5","score":"0.7278255","text":"def any_(A):\n return A.any()","title":""}],"string":"[\n {\n \"docid\": \"488f7cc7ab70b194a5fd819ee1942cf5\",\n \"score\": \"0.7278255\",\n \"text\": \"def any_(A):\\n return A.any()\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"2b72524cb32c8aea61e7fb5e34fc692e","score":"0.7511237","text":"def __nonzero__(self):\n return any([bool(i) for i in self])","title":""},{"docid":"fb85a89afbc2a2508032f356aedf04dd","score":"0.6771524","text":"def all(self):\n if not all(dt == 'bool' for nm, dt, null in self.sdbtype.full_rep):\n raise TypeError(\"any() only valid for boolean arrays\")\n\n return self.aggregate(*('min(%s)' % att for att in self.att_names)) > 0","title":""},{"docid":"f5618de45b1d16b2131d917353c10e46","score":"0.6705862","text":"def any_is_true(generator):\n for flag in generator:\n if flag:\n return True\n return False","title":""},{"docid":"cff9184c82b0cbe4a8107643bc3d01fc","score":"0.6680552","text":"def any(self):\n if not all(dt == 'bool' for nm, dt, null in self.sdbtype.full_rep):\n raise TypeError(\"any() only valid for boolean arrays\")\n\n return self.aggregate(*('max(%s)' % att for att in self.att_names)) > 0","title":""},{"docid":"f9b5b9992a3f73589b6f089f7e9f9ed4","score":"0.6645404","text":"def any(iterable):\n for element in iterable:\n if element:\n return True\n return False","title":""},{"docid":"bbdc733a9b2b85a56adceb165cf8f9ff","score":"0.6489734","text":"def _all_zero(self, A):\n for x in A:\n if x != 0:\n return False\n return True","title":""},{"docid":"1882810dab2052f67be27989a6775c22","score":"0.6469668","text":"def isTrue(list_truth_values):\n if list_truth_values == []:\n return False\n else:\n return reduce(operator.and_, list_truth_values, True)","title":""},{"docid":"24afb54142bf181356aa23fb935ee4ee","score":"0.6432742","text":"def countTruth(boolFunc, aList):\n return len(filter(boolFunc, aList))","title":""},{"docid":"cf039d499d5169151daa5fcc8eb6a465","score":"0.6395526","text":"def all(iterable):\n for element in iterable:\n if not element:\n return False\n return True","title":""},{"docid":"1d2df2ca5f4477ee4f16a4cf25b428dd","score":"0.6394134","text":"def __bool__(self):\n return self.__nonzero__()","title":""},{"docid":"1d2df2ca5f4477ee4f16a4cf25b428dd","score":"0.6394134","text":"def __bool__(self):\n return self.__nonzero__()","title":""},{"docid":"1d2df2ca5f4477ee4f16a4cf25b428dd","score":"0.6394134","text":"def __bool__(self):\n return self.__nonzero__()","title":""},{"docid":"c3106680aee5be84134835e6dce9f792","score":"0.637476","text":"def any(iterable):\n for element in iterable:\n if element:\n return True\n return False","title":""},{"docid":"fdbe3d0d711045e004d137ab9c8f70b9","score":"0.6328298","text":"def all(iterable):\n for element in iterable:\n if not element:\n return False\n return True","title":""},{"docid":"fdbe3d0d711045e004d137ab9c8f70b9","score":"0.6328298","text":"def all(iterable):\n for element in iterable:\n if not element:\n return False\n return True","title":""},{"docid":"1d46d699c7d8ed8b9308eba468f47d64","score":"0.6289138","text":"def any(iterable):\n return True","title":""},{"docid":"41fe42accd705585f565e5f7e34f19c4","score":"0.6273393","text":"def is_all_true(self) -> bool:\r\n return self.pixels_in_mask == 0","title":""},{"docid":"95fe78761d542a3d4b9b9563fa539f1f","score":"0.6267928","text":"def myall(iterable):\n \"\"\" Returns True only if all the elements in the iterable are truthy, else returns False \"\"\"\n for item in iterable:\n if not item:\n return False\n return True","title":""},{"docid":"946d3a23a06fc92fa0904852376fbe8d","score":"0.62348896","text":"def isTrue():","title":""},{"docid":"6f9d25b10913227ccb731cfcbf72820e","score":"0.6222977","text":"def __bool__(self):\n\t\treturn self._N != 0","title":""},{"docid":"5b976b6fe901e03f6f6e33ed02213f41","score":"0.6157532","text":"def __nonzero__(self):\n raise ValueError(\n f\"The truth value of a {self.__class__.__name__} is ambiguous. \"\n + \"Use a.empty, a.bool(), a.item(), a.any() or a.all().\"\n )","title":""},{"docid":"6ae58a47fa8333fb529bdce97b6a6040","score":"0.6095795","text":"def __nonzero__(self):\n return bool(self._list)","title":""},{"docid":"424696b24f9220eb47bc881718a218ca","score":"0.6068682","text":"def turns_true_with(self, var, val):\n for a, v in var.atom_values(val):\n if self.unsatisfied and v in self.atom_index2literals[a.index]:\n return True\n return False","title":""},{"docid":"ba40a9e74948dadf36e45605c7486863","score":"0.60676944","text":"def is_true(self, elem_list):\n raise NotImplementedError()","title":""},{"docid":"ae01a7daf6faa679ee9b5aa981f8061b","score":"0.6043491","text":"def has_any(self):\n\n return len(self.get_all()) > 0","title":""},{"docid":"a1cafe758ab9eaf5b82e78be7eba1a46","score":"0.602879","text":"def __bool__(self):\n return not self.isempty()","title":""},{"docid":"71f2e74ab8f1e111aea9916360816c46","score":"0.6024775","text":"def checkState(self, nparray):\n if all(i >= 0 for i in nparray):\n return True\n else:\n return False","title":""},{"docid":"4b8ad2859a11de0940bd8b8d7de83b4a","score":"0.60147244","text":"def true(shape):\n return full(shape, True, dtype=bool)","title":""},{"docid":"af7c18ff321aadd7657aa6df465728f6","score":"0.6014013","text":"def has_single_liaisons(liaisons):\n return not any(i == j == 1 for i, j in zip(liaisons, liaisons[1:]))","title":""},{"docid":"3e267c350aafc3ee07acd208233660ec","score":"0.5997192","text":"def goal_test(self):\n return all(goal in self.initial for goal in self.goals)","title":""},{"docid":"8df81cb23cfc9b10e1ee5f23f987947e","score":"0.5991744","text":"def consistent_all(self, A):\n assert len(A) <= self.num_vars\n for i in range(len(A)):\n if not self.consistent_other(i, A):\n return False\n return True","title":""},{"docid":"ddd97887e8da591e40d7b370d53dc5b1","score":"0.5980589","text":"def state(self):\r\n return bool(self.state_true_count() != 0)","title":""},{"docid":"e40f150f519022d984c57f270edee7d1","score":"0.5933973","text":"def is_flag_set(flag):\n return any_flags_set(flag)","title":""},{"docid":"fa4f1d91c3ae06d16a66e475038a8a50","score":"0.59121734","text":"def get_bool_result(self) -> bool:\n return all(passage_result.result for passage_result in self._results)","title":""},{"docid":"3c3764e759ffb1c330eb653ebe8a237c","score":"0.5902867","text":"def __bool__(self) -> bool:\n return bool(self.and_ or self.or_ or self.not_)","title":""},{"docid":"b2ae1a2bdeaf33179f4f4cbd49b708e3","score":"0.587973","text":"def All(self):\n return self.include is None and not self.exclude","title":""},{"docid":"9414da4b2e3d6a368eaaac47fb076e09","score":"0.5870207","text":"def __nonzero__(self):\r\n return bool(self.eval())","title":""},{"docid":"e601585602baa8caab41b84858fdc466","score":"0.5855268","text":"def truth(self, *args) -> bool:","title":""},{"docid":"7c07288a31dd6eae0cdeebde63a2e56c","score":"0.5852711","text":"def __bool__(self):\n\n return self._n > 0","title":""},{"docid":"0a33e2305d91617fee5721271286c4b8","score":"0.58477974","text":"def __nonzero__(self):\n return bool(self.oids)","title":""},{"docid":"3d5aee59fb22da88853db14bc61b788c","score":"0.5837937","text":"def every(predicate, seq):\n for x in seq:\n if not predicate(x): return False\n return True","title":""},{"docid":"35b8087b3792fd05875f1f49b0d4cf4c","score":"0.5829817","text":"def __bool__(self):\n return not self.is_empty()","title":""},{"docid":"9e6c07a55c5faf04680e50487c6ec970","score":"0.5828629","text":"def _booleanAnd(self, elem):\n return self.lhs.value(elem) and self.rhs.value(elem)","title":""},{"docid":"f0b87c7cd5f1e7dd30decff3eb754a3a","score":"0.5824575","text":"def isAny(self,test):\n for x in np.nditer(self.t, op_flags=['readonly']):\n if test(x):\n return True\n return False","title":""},{"docid":"9060dc708d83dcd615203e979437faf0","score":"0.5823648","text":"def __bool__(self):\n return bool(self._residues)","title":""},{"docid":"c2d63835ff0182dcfa10dc23a6a5e464","score":"0.5762349","text":"def test_any(self, *iterable):\r\n for expr in iterable:\r\n if self.test(expr):\r\n return True\r\n return False","title":""},{"docid":"c2d63835ff0182dcfa10dc23a6a5e464","score":"0.5762349","text":"def test_any(self, *iterable):\r\n for expr in iterable:\r\n if self.test(expr):\r\n return True\r\n return False","title":""},{"docid":"2822850d6dbdc5e1c923e166b2b988b9","score":"0.5760255","text":"def g1(a, b): \r\n if a == True and b == True:\r\n return False\r\n else:\r\n return True","title":""},{"docid":"1e276a8c88efcdac9aaa9745a986b98b","score":"0.5757271","text":"def n_all(iterable):\n logger.info(\"In n_all..\")\n\n for element in iterable:\n if element is None:\n return False\n return True","title":""},{"docid":"db07b44c03dc35781454ffab9854d94f","score":"0.5752071","text":"def any(self, *names):\n return any(self.__contains__(name) for name in names)","title":""},{"docid":"3001bc030b5fb011873755ec90f8080c","score":"0.5747407","text":"def isSatisfiable(self):\n bools = [c.isSatisfiable() for c in self.constraints.values()]\n return all(bools)","title":""},{"docid":"a633ceb0eaa8eda76fed10222bbf50fc","score":"0.57309043","text":"def __nonzero__(self):\n return all([self.project])","title":""},{"docid":"f0f283510fbe4f8b90122dc7b4a951ee","score":"0.57272243","text":"def any_(obj):\n return any(obj)","title":""},{"docid":"baae751515a73f4ccec86f1c88361e6b","score":"0.5723565","text":"def boolean_TTcondition(belong_to_training):\n return any([not b for b in belong_to_training])","title":""},{"docid":"5f38d4e6f8bf7ba0395fd1443599f19e","score":"0.571831","text":"def all(self, predicate):\n\n return self.filter(lambda v: not predicate(v)).some().map(lambda b: not b)","title":""},{"docid":"d3d8919da610803f7fd44b594b785b9e","score":"0.57161695","text":"def all_(obj):\n return all(obj)","title":""},{"docid":"2f2bf9c872ff12a1aff0810d01b08c9a","score":"0.5712127","text":"def any_flags_set(*desired_flags):\n active_flags = get_flags()\n return any(flag in active_flags for flag in desired_flags)","title":""},{"docid":"3e6428d6d93ba716b88b91ecd89e91b0","score":"0.5710732","text":"def isActive(self):\n if self.alwaysActive:\n return True\n activeVariables = sum(\n [v.active if v is not None else True for v in self.listOfVariables]\n )\n return activeVariables > 0","title":""},{"docid":"9697bb42daa2f41a9a94cdd3072aa984","score":"0.5706596","text":"def all_filled(self):\n has_none = np.sum(np.array([v is None for v in self.cache.values()]))\n return False if has_none > 0 else True\n # check_df = self.df.isnull()*1\n # return False if check_df.sum(axis=1).sum(axis=0) > 0 else True","title":""},{"docid":"0f7d8be7496e258186da55df526814c9","score":"0.57042426","text":"def truth(a): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"38b3a260b25ec92e5bd1ec01c2527b7d","score":"0.5698759","text":"def some(self, func):\n return any(func(value) for value in self.args)","title":""},{"docid":"e6a438574ac5a97f5f8d7c127261d4db","score":"0.56779104","text":"def check(a, x):\n return x in a","title":""},{"docid":"b93bf921f2e785d2658b02bcdd3d9e3a","score":"0.56776375","text":"def __bool__(self):\n\n return bool(self.__terms)","title":""},{"docid":"8e7ab4918b2b78d416e826b272d7e25e","score":"0.56757563","text":"def __nonzero__(self):\r\n return True","title":""},{"docid":"615f7313e2852e5e7ae8b7c0a8357fc7","score":"0.5667287","text":"def is_detecting_anything(self):\n nn = lambda x:x is not None\n return len(filter(nn, self._prev_level))>0 or len(filter(nn, self._minmax))>0","title":""},{"docid":"8d046af264f51c584f7a8cdf26be5a52","score":"0.5660552","text":"def _HasChanges(args, flags):\n return any(FlagIsExplicitlySet(args, flag) for flag in flags)","title":""},{"docid":"cd373b5a146d6dd6a473d2b882812dfb","score":"0.5654258","text":"def is_anulada(self):\n if self.estado == self.__class__.ANULADA or \\\n any(self.historial_set.filter(estado=Historial.RECHAZADA)):\n return True\n return False","title":""},{"docid":"9bb30c2e568193806c115c14387ee862","score":"0.56444037","text":"def __bool__(self):\n return self.taxonomy.exists and self.alignment.exists","title":""},{"docid":"4b6773c803f2a1cec0cf524f9284ed66","score":"0.5640802","text":"def Any(self):\n return self.include is None or len(self.include)","title":""},{"docid":"cf4b8d7f9b168a4afab79df819ffdea3","score":"0.5636154","text":"def __bool__(self) -> bool:\n return len(self) > 0","title":""},{"docid":"7d91173891b285433d9143c96ee7e68b","score":"0.56340426","text":"def all_by(pred: Pred, seq: Seq) -> bool:\n pred = to_callable(pred)\n return all(map(pred, seq))","title":""},{"docid":"31cc69a554dd40bb8dc0f188c9f509fe","score":"0.5616245","text":"def is_detecting_anything(self):\n nn = lambda x:x is not None\n return len(list(filter(nn, self._prev_level)))>0 or len(list(filter(nn, self._minmax)))>0","title":""},{"docid":"ae1b83317bce7e2bd9517719296bd605","score":"0.5614786","text":"def __bool__(self):\n return bool(self.points)","title":""},{"docid":"6675f32f31e9448c338ebe707efe5316","score":"0.5613473","text":"def scheme_truep(val):\n return val is not False","title":""},{"docid":"aab0ccd73f3d2b8a42aee91f82d47e3e","score":"0.5611146","text":"def empty(self):\n return False if self.q[self.flag] else True","title":""},{"docid":"a6fe27a4d84857083f2b21cd9a82e9fd","score":"0.5605707","text":"def any(self, name):\n self._validate_column_name(name)\n dtype = self.get_dtype(name)\n if dtype != 'boolean':\n msg = ('all() can only be called on a boolean column; you '\n 'provided column of dtype={}').format(dtype)\n raise TypeError(msg)\n return self._df[self._df[name].isin([True])].count() > 0","title":""},{"docid":"3a0a8848277f48ec91f1e68790d45671","score":"0.5600929","text":"def any(fn, *args):\n seen = False\n while True:\n if not fn(*args): break\n seen = True\n return seen","title":""},{"docid":"efbe3362777d87bac43564311ad8d2ba","score":"0.5595323","text":"def aggregate(values: Series) -> bool:\n return values.fillna(False).astype(bool).all()","title":""},{"docid":"62485e3299c4cb4b38d5c34a0178a23a","score":"0.5590884","text":"def has_fullHouse(self):\n return((self.has_3ofAKind()) and (self.has_pair()))","title":""},{"docid":"af07cc52b13e1f0b5678c51862590772","score":"0.5587857","text":"def boolean_TTcondition(belong_to_training):\n return all(belong_to_training)","title":""},{"docid":"36d18cb080c523a46a3bf84926da6cbd","score":"0.55823314","text":"def __bool__(self) -> bool:\n return any((self.excluded_ids, self.excluded_auth_ids, self.excluded_auth_roles,\n self.eligible_ids, self.eligible_auth_ids, self.eligible_auth_roles))","title":""},{"docid":"0a08ff0da660d3b83cd99474d0b628a1","score":"0.55779356","text":"def __nonzero__(self): #pragma: no cover\n return bool(self())","title":""},{"docid":"38c1741f966a47a1ecc2956ed266e080","score":"0.5576794","text":"def all_flags_set(*desired_flags):\n active_flags = get_flags()\n return all(flag in active_flags for flag in desired_flags)","title":""},{"docid":"fc2d1d6fc7d6e67914ffe0505d969f56","score":"0.5574398","text":"def __nonzero__ (self) :\n if len(self) :\n return True\n return False","title":""},{"docid":"8a50b68e8d479a6190f3b8581a88bbe3","score":"0.55677193","text":"def g3(a, b): \r\n return not (a and b)","title":""},{"docid":"982727ab34038ee4d54492ede864c2e1","score":"0.55671394","text":"def resolved(self):\n # type: () -> bool\n accumulator = True\n for item in self:\n accumulator = bool(accumulator and item.resolved)\n return accumulator","title":""},{"docid":"333e2257ead7717dc67108fd43e6c341","score":"0.55662954","text":"def __nonzero__(self):\n return True","title":""},{"docid":"333e2257ead7717dc67108fd43e6c341","score":"0.55662954","text":"def __nonzero__(self):\n return True","title":""},{"docid":"758bdf5408b794effcb3b911f2c9a778","score":"0.5563776","text":"def check_condition(self):\n pop_fitness_list = np.array([ind.fitness for ind in self.population], dtype=int)\n if np.any(pop_fitness_list == 0):\n return True","title":""},{"docid":"3bfe9eb4380a8aed4b1d3a9bac5c56ee","score":"0.55602986","text":"def statement3(my_set: set[int],\n my_p: Callable[[int], bool],\n my_q: Callable[[int], bool]) -> bool:\n return all([my_p(x) and my_q(x) for x in my_set])","title":""},{"docid":"96c069147f4ad8c2a00fd6aabfb9177e","score":"0.5560026","text":"def __nonzero__(self):\n return bool(self.products)","title":""},{"docid":"2e4629259d346a71c44f09e79e648ca1","score":"0.55586714","text":"def match_any(iterable, predicate):\n for x in iterable:\n if predicate(x):\n return True\n return False","title":""},{"docid":"87c49ed0aa20b6439f7401897adc91c9","score":"0.5557968","text":"def __nonzero__(self):\n return self.values.__nonzero__()","title":""},{"docid":"9bd0616260f3598a67338ecfb921ee5e","score":"0.5556244","text":"def visit_true(self, *_):\n\n return True","title":""},{"docid":"8ed902c1739dd1ed086dffc196346b73","score":"0.555507","text":"def isSet(self):\n return self._isSet","title":""},{"docid":"b5d5bc4c628c2c5f7f56c4699447421f","score":"0.55547386","text":"def containsAny(str, set):\n return True in []","title":""},{"docid":"5e14d820740a0c68dd6e143f506cac45","score":"0.5550138","text":"def passed(self):\n values = [self.state[k] for k in self.tests]\n passes = [s['pass'] for s in values if s['pass'] is not None]\n if passes:\n return all(passes)\n return False","title":""},{"docid":"549207deced4879d432bf1f8b5be7e24","score":"0.5545806","text":"def aggregate(values: Series) -> bool:\n return values.fillna(False).astype(bool).any()","title":""},{"docid":"0d9449c1173b1c482c8cda85d6757291","score":"0.5544268","text":"def __bool__(self):\n self._cache.extend(self._itr)\n return bool(self._cache)","title":""},{"docid":"8492f34b210fc3f2afb0728c8d0eac4a","score":"0.5543846","text":"def every(pred, coll):\n pred = _make_pred(pred)\n\n for e in coll:\n if not pred(e):\n return False\n\n return True","title":""}],"string":"[\n {\n \"docid\": \"2b72524cb32c8aea61e7fb5e34fc692e\",\n \"score\": \"0.7511237\",\n \"text\": \"def __nonzero__(self):\\n return any([bool(i) for i in self])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb85a89afbc2a2508032f356aedf04dd\",\n \"score\": \"0.6771524\",\n \"text\": \"def all(self):\\n if not all(dt == 'bool' for nm, dt, null in self.sdbtype.full_rep):\\n raise TypeError(\\\"any() only valid for boolean arrays\\\")\\n\\n return self.aggregate(*('min(%s)' % att for att in self.att_names)) > 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5618de45b1d16b2131d917353c10e46\",\n \"score\": \"0.6705862\",\n \"text\": \"def any_is_true(generator):\\n for flag in generator:\\n if flag:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cff9184c82b0cbe4a8107643bc3d01fc\",\n \"score\": \"0.6680552\",\n \"text\": \"def any(self):\\n if not all(dt == 'bool' for nm, dt, null in self.sdbtype.full_rep):\\n raise TypeError(\\\"any() only valid for boolean arrays\\\")\\n\\n return self.aggregate(*('max(%s)' % att for att in self.att_names)) > 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9b5b9992a3f73589b6f089f7e9f9ed4\",\n \"score\": \"0.6645404\",\n \"text\": \"def any(iterable):\\n for element in iterable:\\n if element:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbdc733a9b2b85a56adceb165cf8f9ff\",\n \"score\": \"0.6489734\",\n \"text\": \"def _all_zero(self, A):\\n for x in A:\\n if x != 0:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1882810dab2052f67be27989a6775c22\",\n \"score\": \"0.6469668\",\n \"text\": \"def isTrue(list_truth_values):\\n if list_truth_values == []:\\n return False\\n else:\\n return reduce(operator.and_, list_truth_values, True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24afb54142bf181356aa23fb935ee4ee\",\n \"score\": \"0.6432742\",\n \"text\": \"def countTruth(boolFunc, aList):\\n return len(filter(boolFunc, aList))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf039d499d5169151daa5fcc8eb6a465\",\n \"score\": \"0.6395526\",\n \"text\": \"def all(iterable):\\n for element in iterable:\\n if not element:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d2df2ca5f4477ee4f16a4cf25b428dd\",\n \"score\": \"0.6394134\",\n \"text\": \"def __bool__(self):\\n return self.__nonzero__()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d2df2ca5f4477ee4f16a4cf25b428dd\",\n \"score\": \"0.6394134\",\n \"text\": \"def __bool__(self):\\n return self.__nonzero__()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d2df2ca5f4477ee4f16a4cf25b428dd\",\n \"score\": \"0.6394134\",\n \"text\": \"def __bool__(self):\\n return self.__nonzero__()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3106680aee5be84134835e6dce9f792\",\n \"score\": \"0.637476\",\n \"text\": \"def any(iterable):\\n for element in iterable:\\n if element:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fdbe3d0d711045e004d137ab9c8f70b9\",\n \"score\": \"0.6328298\",\n \"text\": \"def all(iterable):\\n for element in iterable:\\n if not element:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fdbe3d0d711045e004d137ab9c8f70b9\",\n \"score\": \"0.6328298\",\n \"text\": \"def all(iterable):\\n for element in iterable:\\n if not element:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d46d699c7d8ed8b9308eba468f47d64\",\n \"score\": \"0.6289138\",\n \"text\": \"def any(iterable):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41fe42accd705585f565e5f7e34f19c4\",\n \"score\": \"0.6273393\",\n \"text\": \"def is_all_true(self) -> bool:\\r\\n return self.pixels_in_mask == 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95fe78761d542a3d4b9b9563fa539f1f\",\n \"score\": \"0.6267928\",\n \"text\": \"def myall(iterable):\\n \\\"\\\"\\\" Returns True only if all the elements in the iterable are truthy, else returns False \\\"\\\"\\\"\\n for item in iterable:\\n if not item:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"946d3a23a06fc92fa0904852376fbe8d\",\n \"score\": \"0.62348896\",\n \"text\": \"def isTrue():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f9d25b10913227ccb731cfcbf72820e\",\n \"score\": \"0.6222977\",\n \"text\": \"def __bool__(self):\\n\\t\\treturn self._N != 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b976b6fe901e03f6f6e33ed02213f41\",\n \"score\": \"0.6157532\",\n \"text\": \"def __nonzero__(self):\\n raise ValueError(\\n f\\\"The truth value of a {self.__class__.__name__} is ambiguous. \\\"\\n + \\\"Use a.empty, a.bool(), a.item(), a.any() or a.all().\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ae58a47fa8333fb529bdce97b6a6040\",\n \"score\": \"0.6095795\",\n \"text\": \"def __nonzero__(self):\\n return bool(self._list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"424696b24f9220eb47bc881718a218ca\",\n \"score\": \"0.6068682\",\n \"text\": \"def turns_true_with(self, var, val):\\n for a, v in var.atom_values(val):\\n if self.unsatisfied and v in self.atom_index2literals[a.index]:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba40a9e74948dadf36e45605c7486863\",\n \"score\": \"0.60676944\",\n \"text\": \"def is_true(self, elem_list):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae01a7daf6faa679ee9b5aa981f8061b\",\n \"score\": \"0.6043491\",\n \"text\": \"def has_any(self):\\n\\n return len(self.get_all()) > 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1cafe758ab9eaf5b82e78be7eba1a46\",\n \"score\": \"0.602879\",\n \"text\": \"def __bool__(self):\\n return not self.isempty()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71f2e74ab8f1e111aea9916360816c46\",\n \"score\": \"0.6024775\",\n \"text\": \"def checkState(self, nparray):\\n if all(i >= 0 for i in nparray):\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b8ad2859a11de0940bd8b8d7de83b4a\",\n \"score\": \"0.60147244\",\n \"text\": \"def true(shape):\\n return full(shape, True, dtype=bool)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af7c18ff321aadd7657aa6df465728f6\",\n \"score\": \"0.6014013\",\n \"text\": \"def has_single_liaisons(liaisons):\\n return not any(i == j == 1 for i, j in zip(liaisons, liaisons[1:]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e267c350aafc3ee07acd208233660ec\",\n \"score\": \"0.5997192\",\n \"text\": \"def goal_test(self):\\n return all(goal in self.initial for goal in self.goals)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8df81cb23cfc9b10e1ee5f23f987947e\",\n \"score\": \"0.5991744\",\n \"text\": \"def consistent_all(self, A):\\n assert len(A) <= self.num_vars\\n for i in range(len(A)):\\n if not self.consistent_other(i, A):\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddd97887e8da591e40d7b370d53dc5b1\",\n \"score\": \"0.5980589\",\n \"text\": \"def state(self):\\r\\n return bool(self.state_true_count() != 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e40f150f519022d984c57f270edee7d1\",\n \"score\": \"0.5933973\",\n \"text\": \"def is_flag_set(flag):\\n return any_flags_set(flag)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa4f1d91c3ae06d16a66e475038a8a50\",\n \"score\": \"0.59121734\",\n \"text\": \"def get_bool_result(self) -> bool:\\n return all(passage_result.result for passage_result in self._results)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c3764e759ffb1c330eb653ebe8a237c\",\n \"score\": \"0.5902867\",\n \"text\": \"def __bool__(self) -> bool:\\n return bool(self.and_ or self.or_ or self.not_)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2ae1a2bdeaf33179f4f4cbd49b708e3\",\n \"score\": \"0.587973\",\n \"text\": \"def All(self):\\n return self.include is None and not self.exclude\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9414da4b2e3d6a368eaaac47fb076e09\",\n \"score\": \"0.5870207\",\n \"text\": \"def __nonzero__(self):\\r\\n return bool(self.eval())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e601585602baa8caab41b84858fdc466\",\n \"score\": \"0.5855268\",\n \"text\": \"def truth(self, *args) -> bool:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c07288a31dd6eae0cdeebde63a2e56c\",\n \"score\": \"0.5852711\",\n \"text\": \"def __bool__(self):\\n\\n return self._n > 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a33e2305d91617fee5721271286c4b8\",\n \"score\": \"0.58477974\",\n \"text\": \"def __nonzero__(self):\\n return bool(self.oids)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d5aee59fb22da88853db14bc61b788c\",\n \"score\": \"0.5837937\",\n \"text\": \"def every(predicate, seq):\\n for x in seq:\\n if not predicate(x): return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35b8087b3792fd05875f1f49b0d4cf4c\",\n \"score\": \"0.5829817\",\n \"text\": \"def __bool__(self):\\n return not self.is_empty()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e6c07a55c5faf04680e50487c6ec970\",\n \"score\": \"0.5828629\",\n \"text\": \"def _booleanAnd(self, elem):\\n return self.lhs.value(elem) and self.rhs.value(elem)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0b87c7cd5f1e7dd30decff3eb754a3a\",\n \"score\": \"0.5824575\",\n \"text\": \"def isAny(self,test):\\n for x in np.nditer(self.t, op_flags=['readonly']):\\n if test(x):\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9060dc708d83dcd615203e979437faf0\",\n \"score\": \"0.5823648\",\n \"text\": \"def __bool__(self):\\n return bool(self._residues)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2d63835ff0182dcfa10dc23a6a5e464\",\n \"score\": \"0.5762349\",\n \"text\": \"def test_any(self, *iterable):\\r\\n for expr in iterable:\\r\\n if self.test(expr):\\r\\n return True\\r\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2d63835ff0182dcfa10dc23a6a5e464\",\n \"score\": \"0.5762349\",\n \"text\": \"def test_any(self, *iterable):\\r\\n for expr in iterable:\\r\\n if self.test(expr):\\r\\n return True\\r\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2822850d6dbdc5e1c923e166b2b988b9\",\n \"score\": \"0.5760255\",\n \"text\": \"def g1(a, b): \\r\\n if a == True and b == True:\\r\\n return False\\r\\n else:\\r\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e276a8c88efcdac9aaa9745a986b98b\",\n \"score\": \"0.5757271\",\n \"text\": \"def n_all(iterable):\\n logger.info(\\\"In n_all..\\\")\\n\\n for element in iterable:\\n if element is None:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db07b44c03dc35781454ffab9854d94f\",\n \"score\": \"0.5752071\",\n \"text\": \"def any(self, *names):\\n return any(self.__contains__(name) for name in names)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3001bc030b5fb011873755ec90f8080c\",\n \"score\": \"0.5747407\",\n \"text\": \"def isSatisfiable(self):\\n bools = [c.isSatisfiable() for c in self.constraints.values()]\\n return all(bools)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a633ceb0eaa8eda76fed10222bbf50fc\",\n \"score\": \"0.57309043\",\n \"text\": \"def __nonzero__(self):\\n return all([self.project])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0f283510fbe4f8b90122dc7b4a951ee\",\n \"score\": \"0.57272243\",\n \"text\": \"def any_(obj):\\n return any(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"baae751515a73f4ccec86f1c88361e6b\",\n \"score\": \"0.5723565\",\n \"text\": \"def boolean_TTcondition(belong_to_training):\\n return any([not b for b in belong_to_training])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f38d4e6f8bf7ba0395fd1443599f19e\",\n \"score\": \"0.571831\",\n \"text\": \"def all(self, predicate):\\n\\n return self.filter(lambda v: not predicate(v)).some().map(lambda b: not b)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3d8919da610803f7fd44b594b785b9e\",\n \"score\": \"0.57161695\",\n \"text\": \"def all_(obj):\\n return all(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f2bf9c872ff12a1aff0810d01b08c9a\",\n \"score\": \"0.5712127\",\n \"text\": \"def any_flags_set(*desired_flags):\\n active_flags = get_flags()\\n return any(flag in active_flags for flag in desired_flags)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e6428d6d93ba716b88b91ecd89e91b0\",\n \"score\": \"0.5710732\",\n \"text\": \"def isActive(self):\\n if self.alwaysActive:\\n return True\\n activeVariables = sum(\\n [v.active if v is not None else True for v in self.listOfVariables]\\n )\\n return activeVariables > 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9697bb42daa2f41a9a94cdd3072aa984\",\n \"score\": \"0.5706596\",\n \"text\": \"def all_filled(self):\\n has_none = np.sum(np.array([v is None for v in self.cache.values()]))\\n return False if has_none > 0 else True\\n # check_df = self.df.isnull()*1\\n # return False if check_df.sum(axis=1).sum(axis=0) > 0 else True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f7d8be7496e258186da55df526814c9\",\n \"score\": \"0.57042426\",\n \"text\": \"def truth(a): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38b3a260b25ec92e5bd1ec01c2527b7d\",\n \"score\": \"0.5698759\",\n \"text\": \"def some(self, func):\\n return any(func(value) for value in self.args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6a438574ac5a97f5f8d7c127261d4db\",\n \"score\": \"0.56779104\",\n \"text\": \"def check(a, x):\\n return x in a\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b93bf921f2e785d2658b02bcdd3d9e3a\",\n \"score\": \"0.56776375\",\n \"text\": \"def __bool__(self):\\n\\n return bool(self.__terms)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e7ab4918b2b78d416e826b272d7e25e\",\n \"score\": \"0.56757563\",\n \"text\": \"def __nonzero__(self):\\r\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"615f7313e2852e5e7ae8b7c0a8357fc7\",\n \"score\": \"0.5667287\",\n \"text\": \"def is_detecting_anything(self):\\n nn = lambda x:x is not None\\n return len(filter(nn, self._prev_level))>0 or len(filter(nn, self._minmax))>0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d046af264f51c584f7a8cdf26be5a52\",\n \"score\": \"0.5660552\",\n \"text\": \"def _HasChanges(args, flags):\\n return any(FlagIsExplicitlySet(args, flag) for flag in flags)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd373b5a146d6dd6a473d2b882812dfb\",\n \"score\": \"0.5654258\",\n \"text\": \"def is_anulada(self):\\n if self.estado == self.__class__.ANULADA or \\\\\\n any(self.historial_set.filter(estado=Historial.RECHAZADA)):\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bb30c2e568193806c115c14387ee862\",\n \"score\": \"0.56444037\",\n \"text\": \"def __bool__(self):\\n return self.taxonomy.exists and self.alignment.exists\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b6773c803f2a1cec0cf524f9284ed66\",\n \"score\": \"0.5640802\",\n \"text\": \"def Any(self):\\n return self.include is None or len(self.include)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf4b8d7f9b168a4afab79df819ffdea3\",\n \"score\": \"0.5636154\",\n \"text\": \"def __bool__(self) -> bool:\\n return len(self) > 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d91173891b285433d9143c96ee7e68b\",\n \"score\": \"0.56340426\",\n \"text\": \"def all_by(pred: Pred, seq: Seq) -> bool:\\n pred = to_callable(pred)\\n return all(map(pred, seq))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31cc69a554dd40bb8dc0f188c9f509fe\",\n \"score\": \"0.5616245\",\n \"text\": \"def is_detecting_anything(self):\\n nn = lambda x:x is not None\\n return len(list(filter(nn, self._prev_level)))>0 or len(list(filter(nn, self._minmax)))>0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae1b83317bce7e2bd9517719296bd605\",\n \"score\": \"0.5614786\",\n \"text\": \"def __bool__(self):\\n return bool(self.points)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6675f32f31e9448c338ebe707efe5316\",\n \"score\": \"0.5613473\",\n \"text\": \"def scheme_truep(val):\\n return val is not False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aab0ccd73f3d2b8a42aee91f82d47e3e\",\n \"score\": \"0.5611146\",\n \"text\": \"def empty(self):\\n return False if self.q[self.flag] else True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6fe27a4d84857083f2b21cd9a82e9fd\",\n \"score\": \"0.5605707\",\n \"text\": \"def any(self, name):\\n self._validate_column_name(name)\\n dtype = self.get_dtype(name)\\n if dtype != 'boolean':\\n msg = ('all() can only be called on a boolean column; you '\\n 'provided column of dtype={}').format(dtype)\\n raise TypeError(msg)\\n return self._df[self._df[name].isin([True])].count() > 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a0a8848277f48ec91f1e68790d45671\",\n \"score\": \"0.5600929\",\n \"text\": \"def any(fn, *args):\\n seen = False\\n while True:\\n if not fn(*args): break\\n seen = True\\n return seen\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efbe3362777d87bac43564311ad8d2ba\",\n \"score\": \"0.5595323\",\n \"text\": \"def aggregate(values: Series) -> bool:\\n return values.fillna(False).astype(bool).all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62485e3299c4cb4b38d5c34a0178a23a\",\n \"score\": \"0.5590884\",\n \"text\": \"def has_fullHouse(self):\\n return((self.has_3ofAKind()) and (self.has_pair()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af07cc52b13e1f0b5678c51862590772\",\n \"score\": \"0.5587857\",\n \"text\": \"def boolean_TTcondition(belong_to_training):\\n return all(belong_to_training)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36d18cb080c523a46a3bf84926da6cbd\",\n \"score\": \"0.55823314\",\n \"text\": \"def __bool__(self) -> bool:\\n return any((self.excluded_ids, self.excluded_auth_ids, self.excluded_auth_roles,\\n self.eligible_ids, self.eligible_auth_ids, self.eligible_auth_roles))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a08ff0da660d3b83cd99474d0b628a1\",\n \"score\": \"0.55779356\",\n \"text\": \"def __nonzero__(self): #pragma: no cover\\n return bool(self())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38c1741f966a47a1ecc2956ed266e080\",\n \"score\": \"0.5576794\",\n \"text\": \"def all_flags_set(*desired_flags):\\n active_flags = get_flags()\\n return all(flag in active_flags for flag in desired_flags)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc2d1d6fc7d6e67914ffe0505d969f56\",\n \"score\": \"0.5574398\",\n \"text\": \"def __nonzero__ (self) :\\n if len(self) :\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a50b68e8d479a6190f3b8581a88bbe3\",\n \"score\": \"0.55677193\",\n \"text\": \"def g3(a, b): \\r\\n return not (a and b)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"982727ab34038ee4d54492ede864c2e1\",\n \"score\": \"0.55671394\",\n \"text\": \"def resolved(self):\\n # type: () -> bool\\n accumulator = True\\n for item in self:\\n accumulator = bool(accumulator and item.resolved)\\n return accumulator\",\n \"title\": \"\"\n },\n {\n \"docid\": \"333e2257ead7717dc67108fd43e6c341\",\n \"score\": \"0.55662954\",\n \"text\": \"def __nonzero__(self):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"333e2257ead7717dc67108fd43e6c341\",\n \"score\": \"0.55662954\",\n \"text\": \"def __nonzero__(self):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"758bdf5408b794effcb3b911f2c9a778\",\n \"score\": \"0.5563776\",\n \"text\": \"def check_condition(self):\\n pop_fitness_list = np.array([ind.fitness for ind in self.population], dtype=int)\\n if np.any(pop_fitness_list == 0):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bfe9eb4380a8aed4b1d3a9bac5c56ee\",\n \"score\": \"0.55602986\",\n \"text\": \"def statement3(my_set: set[int],\\n my_p: Callable[[int], bool],\\n my_q: Callable[[int], bool]) -> bool:\\n return all([my_p(x) and my_q(x) for x in my_set])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96c069147f4ad8c2a00fd6aabfb9177e\",\n \"score\": \"0.5560026\",\n \"text\": \"def __nonzero__(self):\\n return bool(self.products)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e4629259d346a71c44f09e79e648ca1\",\n \"score\": \"0.55586714\",\n \"text\": \"def match_any(iterable, predicate):\\n for x in iterable:\\n if predicate(x):\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87c49ed0aa20b6439f7401897adc91c9\",\n \"score\": \"0.5557968\",\n \"text\": \"def __nonzero__(self):\\n return self.values.__nonzero__()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bd0616260f3598a67338ecfb921ee5e\",\n \"score\": \"0.5556244\",\n \"text\": \"def visit_true(self, *_):\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ed902c1739dd1ed086dffc196346b73\",\n \"score\": \"0.555507\",\n \"text\": \"def isSet(self):\\n return self._isSet\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5d5bc4c628c2c5f7f56c4699447421f\",\n \"score\": \"0.55547386\",\n \"text\": \"def containsAny(str, set):\\n return True in []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e14d820740a0c68dd6e143f506cac45\",\n \"score\": \"0.5550138\",\n \"text\": \"def passed(self):\\n values = [self.state[k] for k in self.tests]\\n passes = [s['pass'] for s in values if s['pass'] is not None]\\n if passes:\\n return all(passes)\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"549207deced4879d432bf1f8b5be7e24\",\n \"score\": \"0.5545806\",\n \"text\": \"def aggregate(values: Series) -> bool:\\n return values.fillna(False).astype(bool).any()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d9449c1173b1c482c8cda85d6757291\",\n \"score\": \"0.5544268\",\n \"text\": \"def __bool__(self):\\n self._cache.extend(self._itr)\\n return bool(self._cache)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8492f34b210fc3f2afb0728c8d0eac4a\",\n \"score\": \"0.5543846\",\n \"text\": \"def every(pred, coll):\\n pred = _make_pred(pred)\\n\\n for e in coll:\\n if not pred(e):\\n return False\\n\\n return True\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":887,"cells":{"query_id":{"kind":"string","value":"3471ed748751f1988b094a1dd75394f2"},"query":{"kind":"string","value":"Allows to get Piano C chord with predetermined octave, harmony and duration"},"positive_passages":{"kind":"list like","value":[{"docid":"19446f336a41d59941282262ac238341","score":"0.844064","text":"def create_c_chord(self, octave, duration, harmony):\n\n result = None\n\n if str(harmony) == \"major\":\n c_note = AudioSegment.from_file(self.path_to_directory + \"/samples/piano/\"+str(octave)+\"/c.wav\")\n temp1 = c_note.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave)+\"/g.wav\"))\n temp2 = temp1.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave+1)+\"/c.wav\"))\n temp3 = temp2.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave+1)+\"/e.wav\"))\n result = temp3.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave+1)+\"/g.wav\"))\n elif str(harmony) == \"minor\":\n c_note = AudioSegment.from_file(self.path_to_directory + \"/samples/piano/\"+str(octave)+\"/c.wav\")\n temp1 = c_note.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave)+\"/g.wav\"))\n temp2 = temp1.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave+1)+\"/c.wav\"))\n temp3 = temp2.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave+1)+\"/d_sharp.wav\"))\n result = temp3.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave+1)+\"/g.wav\"))\n\n return Piano.calculate_duration(result, duration, self.tempo)","title":""}],"string":"[\n {\n \"docid\": \"19446f336a41d59941282262ac238341\",\n \"score\": \"0.844064\",\n \"text\": \"def create_c_chord(self, octave, duration, harmony):\\n\\n result = None\\n\\n if str(harmony) == \\\"major\\\":\\n c_note = AudioSegment.from_file(self.path_to_directory + \\\"/samples/piano/\\\"+str(octave)+\\\"/c.wav\\\")\\n temp1 = c_note.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave)+\\\"/g.wav\\\"))\\n temp2 = temp1.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave+1)+\\\"/c.wav\\\"))\\n temp3 = temp2.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave+1)+\\\"/e.wav\\\"))\\n result = temp3.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave+1)+\\\"/g.wav\\\"))\\n elif str(harmony) == \\\"minor\\\":\\n c_note = AudioSegment.from_file(self.path_to_directory + \\\"/samples/piano/\\\"+str(octave)+\\\"/c.wav\\\")\\n temp1 = c_note.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave)+\\\"/g.wav\\\"))\\n temp2 = temp1.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave+1)+\\\"/c.wav\\\"))\\n temp3 = temp2.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave+1)+\\\"/d_sharp.wav\\\"))\\n result = temp3.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave+1)+\\\"/g.wav\\\"))\\n\\n return Piano.calculate_duration(result, duration, self.tempo)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"07e77d4f3665179daa18aa85ae1ad833","score":"0.84081155","text":"def create_c_chord(self, octave, duration, harmony):\n\n result = None\n\n if str(harmony) == \"major\":\n c_note = AudioSegment.from_file(self.path_to_directory + \"/samples/guitar/\"+str(octave)+\"/c.wav\")\n temp1 = c_note.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave)+\"/g.wav\"))\n temp2 = temp1.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave+1)+\"/c.wav\"))\n temp3 = temp2.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave+1)+\"/e.wav\"))\n result = temp3.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave+1)+\"/g.wav\"))\n elif str(harmony) == \"minor\":\n c_note = AudioSegment.from_file(self.path_to_directory + \"/samples/guitar/\"+str(octave)+\"/c.wav\")\n temp1 = c_note.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave)+\"/g.wav\"))\n temp2 = temp1.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave+1)+\"/c.wav\"))\n temp3 = temp2.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave+1)+\"/d_sharp.wav\"))\n result = temp3.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave+1)+\"/g.wav\"))\n\n return Piano.calculate_duration(result, duration, self.tempo)","title":""},{"docid":"289be3303d8348b0f45eea19c1805114","score":"0.78927076","text":"def create_a_chord(self, octave, duration, harmony):\n\n result = None\n\n if str(harmony) == \"major\":\n temp = AudioSegment.from_file(self.path_to_directory + \"/samples/piano/\"+str(octave - 1)+\"/a.wav\")\n temp1 = temp.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave)+\"/e.wav\"))\n temp2 = temp1.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave)+\"/a.wav\"))\n temp3 = temp2.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave + 1)+\"/c_sharp.wav\"))\n temp4 = temp3.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave + 1)+\"/e.wav\"))\n result = temp4.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave+1)+\"/a.wav\"))\n elif str(harmony) == \"minor\":\n temp = AudioSegment.from_file(self.path_to_directory + \"/samples/piano/\"+str(octave - 1)+\"/a.wav\")\n temp1 = temp.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave)+\"/e.wav\"))\n temp2 = temp1.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave)+\"/a.wav\"))\n temp3 = temp2.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave + 1)+\"/c.wav\"))\n temp4 = temp3.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave + 1)+\"/e.wav\"))\n result = temp4.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave + 1)+\"/a.wav\"))\n\n return Piano.calculate_duration(result, duration, self.tempo)","title":""},{"docid":"d39678cb5bfeb4f5c8bb7db41d4ad3e9","score":"0.7787941","text":"def create_c_sharp_chord(self, octave, duration, harmony):\n\n result = None\n\n if str(harmony) == \"major\":\n c_note = AudioSegment.from_file(self.path_to_directory + \"/samples/guitar/\"+str(octave)+\"/c_sharp.wav\")\n temp1 = c_note.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave)+\"/g_sharp.wav\"))\n temp2 = temp1.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave+1)+\"/c_sharp.wav\"))\n temp3 = temp2.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave+1)+\"/f.wav\"))\n result = temp3.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave+1)+\"/g_sharp.wav\"))\n elif str(harmony) == \"minor\":\n c_note = AudioSegment.from_file(self.path_to_directory + \"/samples/guitar/\"+str(octave)+\"/c_sharp.wav\")\n temp1 = c_note.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave)+\"/g_sharp.wav\"))\n temp2 = temp1.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave+1)+\"/c_sharp.wav\"))\n temp3 = temp2.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave+1)+\"/e.wav\"))\n result = temp3.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave+1)+\"/g_sharp.wav\"))\n\n return Piano.calculate_duration(result, duration, self.tempo)","title":""},{"docid":"32340495e3ac8d5c7baab1fbec59b91d","score":"0.7779683","text":"def create_a_chord(self, octave, duration, harmony):\n\n result = None\n\n if str(harmony) == \"major\":\n temp = AudioSegment.from_file(self.path_to_directory + \"/samples/guitar/\"+str(octave - 1)+\"/a.wav\")\n temp1 = temp.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave)+\"/e.wav\"))\n temp2 = temp1.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave)+\"/a.wav\"))\n temp3 = temp2.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave + 1)+\"/c_sharp.wav\"))\n temp4 = temp3.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave + 1)+\"/e.wav\"))\n result = temp4.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave+1)+\"/a.wav\"))\n elif str(harmony) == \"minor\":\n temp = AudioSegment.from_file(self.path_to_directory + \"/samples/guitar/\"+str(octave - 1)+\"/a.wav\")\n temp1 = temp.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave)+\"/e.wav\"))\n temp2 = temp1.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave)+\"/a.wav\"))\n temp3 = temp2.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave + 1)+\"/c.wav\"))\n temp4 = temp3.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave + 1)+\"/e.wav\"))\n result = temp4.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave + 1)+\"/a.wav\"))\n\n return Piano.calculate_duration(result, duration, self.tempo)","title":""},{"docid":"67b79292611f31802ce7a9a41e5a8bb0","score":"0.77772343","text":"def create_c_sharp_chord(self, octave, duration, harmony):\n\n result = None\n\n if str(harmony) == \"major\":\n c_note = AudioSegment.from_file(self.path_to_directory + \"/samples/piano/\"+str(octave)+\"/c_sharp.wav\")\n temp1 = c_note.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave)+\"/g_sharp.wav\"))\n temp2 = temp1.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave+1)+\"/c_sharp.wav\"))\n temp3 = temp2.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave+1)+\"/f.wav\"))\n result = temp3.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave+1)+\"/g_sharp.wav\"))\n elif str(harmony) == \"minor\":\n c_note = AudioSegment.from_file(self.path_to_directory + \"/samples/piano/\"+str(octave)+\"/c_sharp.wav\")\n temp1 = c_note.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave)+\"/g_sharp.wav\"))\n temp2 = temp1.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave+1)+\"/c_sharp.wav\"))\n temp3 = temp2.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave+1)+\"/e.wav\"))\n result = temp3.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave+1)+\"/g_sharp.wav\"))\n\n return Piano.calculate_duration(result, duration, self.tempo)","title":""},{"docid":"52fd98755430851a0d8618e034e240cb","score":"0.7711506","text":"def create_d_chord(self, octave, duration, harmony):\n\n result = None\n\n if str(harmony) == \"major\":\n temp = AudioSegment.from_file(self.path_to_directory + \"/samples/piano/\"+str(octave)+\"/d.wav\")\n temp1 = temp.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave)+\"/a.wav\"))\n temp2 = temp1.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave+1)+\"/d.wav\"))\n result = temp2.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave+1)+\"/f_sharp.wav\"))\n elif str(harmony) == \"minor\":\n temp = AudioSegment.from_file(self.path_to_directory + \"/samples/piano/\"+str(octave)+\"/d.wav\")\n temp1 = temp.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave)+\"/a.wav\"))\n temp2 = temp1.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave+1)+\"/d.wav\"))\n result = temp2.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave+1)+\"/f.wav\"))\n\n return Piano.calculate_duration(result, duration, self.tempo)","title":""},{"docid":"2e205bc4cdf05f5347855f8352a9ac99","score":"0.7625333","text":"def create_g_chord(self, octave, duration, harmony):\n\n result = None\n\n if str(harmony) == \"major\":\n temp = AudioSegment.from_file(self.path_to_directory + \"/samples/piano/\"+str(octave - 1)+\"/g.wav\")\n temp1 = temp.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave)+\"/d.wav\"))\n temp2 = temp1.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave)+\"/g.wav\"))\n temp3 = temp2.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave)+\"/h.wav\"))\n temp4 = temp3.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave + 1)+\"/d.wav\"))\n result = temp4.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave+1)+\"/g.wav\"))\n elif str(harmony) == \"minor\":\n temp = AudioSegment.from_file(self.path_to_directory + \"/samples/piano/\"+str(octave - 1)+\"/g.wav\")\n temp1 = temp.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave)+\"/d.wav\"))\n temp2 = temp1.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave)+\"/g.wav\"))\n temp3 = temp2.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave)+\"/a_sharp.wav\"))\n temp4 = temp3.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave + 1)+\"/d.wav\"))\n result = temp4.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave+1)+\"/g.wav\"))\n\n return Piano.calculate_duration(result, duration, self.tempo)","title":""},{"docid":"7e59019251d97da65f465a714681b46a","score":"0.76127225","text":"def create_d_chord(self, octave, duration, harmony):\n\n result = None\n\n if str(harmony) == \"major\":\n temp = AudioSegment.from_file(self.path_to_directory + \"/samples/guitar/\"+str(octave)+\"/d.wav\")\n temp1 = temp.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave)+\"/a.wav\"))\n temp2 = temp1.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave+1)+\"/d.wav\"))\n result = temp2.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave+1)+\"/f_sharp.wav\"))\n elif str(harmony) == \"minor\":\n temp = AudioSegment.from_file(self.path_to_directory + \"/samples/guitar/\"+str(octave)+\"/d.wav\")\n temp1 = temp.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave)+\"/a.wav\"))\n temp2 = temp1.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave+1)+\"/d.wav\"))\n result = temp2.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave+1)+\"/f.wav\"))\n\n return Piano.calculate_duration(result, duration, self.tempo)","title":""},{"docid":"11ba1d5992a328281ce7c884262a2a61","score":"0.75451344","text":"def create_e_chord(self, octave, duration, harmony):\n\n result = None\n\n if str(harmony) == \"major\":\n temp = AudioSegment.from_file(self.path_to_directory + \"/samples/piano/\"+str(octave - 1)+\"/e.wav\")\n temp1 = temp.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave - 1)+\"/h.wav\"))\n temp2 = temp1.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave)+\"/e.wav\"))\n temp3 = temp2.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave)+\"/g_sharp.wav\"))\n temp4 = temp3.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave)+\"/h.wav\"))\n result = temp4.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave+1)+\"/e.wav\"))\n elif str(harmony) == \"minor\":\n temp = AudioSegment.from_file(self.path_to_directory + \"/samples/piano/\"+str(octave - 1)+\"/e.wav\")\n temp1 = temp.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave - 1)+\"/h.wav\"))\n temp2 = temp1.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave)+\"/e.wav\"))\n temp3 = temp2.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave)+\"/g.wav\"))\n temp4 = temp3.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave)+\"/h.wav\"))\n result = temp4.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave+1)+\"/e.wav\"))\n\n return Piano.calculate_duration(result, duration, self.tempo)","title":""},{"docid":"2c039e7b8735ebc1efce7520cd9159d0","score":"0.75377893","text":"def create_h_chord(self, octave, duration, harmony):\n\n result = None\n\n if str(harmony) == \"major\":\n temp = AudioSegment.from_file(self.path_to_directory + \"/samples/piano/\"+str(octave - 1)+\"/h.wav\")\n temp1 = temp.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave)+\"/f_sharp.wav\"))\n temp2 = temp1.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave)+\"/h.wav\"))\n temp3 = temp2.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave + 1)+\"/d_sharp.wav\"))\n result = temp3.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave + 1)+\"/f_sharp.wav\"))\n elif str(harmony) == \"minor\":\n temp = AudioSegment.from_file(self.path_to_directory + \"/samples/piano/\"+str(octave - 1)+\"/h.wav\")\n temp1 = temp.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave)+\"/f_sharp.wav\"))\n temp2 = temp1.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave)+\"/h.wav\"))\n temp3 = temp2.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave + 1)+\"/d.wav\"))\n result = temp3.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave + 1)+\"/f_sharp.wav\"))\n\n return Piano.calculate_duration(result, duration, self.tempo)","title":""},{"docid":"85dfd1b3577cb1ddd83ca07cda1fcee8","score":"0.7478326","text":"def create_g_chord(self, octave, duration, harmony):\n\n result = None\n\n if str(harmony) == \"major\":\n temp = AudioSegment.from_file(self.path_to_directory + \"/samples/guitar/\"+str(octave - 1)+\"/g.wav\")\n temp1 = temp.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave)+\"/d.wav\"))\n temp2 = temp1.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave)+\"/g.wav\"))\n temp3 = temp2.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave)+\"/h.wav\"))\n temp4 = temp3.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave + 1)+\"/d.wav\"))\n result = temp4.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave+1)+\"/g.wav\"))\n elif str(harmony) == \"minor\":\n temp = AudioSegment.from_file(self.path_to_directory + \"/samples/guitar/\"+str(octave - 1)+\"/g.wav\")\n temp1 = temp.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave)+\"/d.wav\"))\n temp2 = temp1.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave)+\"/g.wav\"))\n temp3 = temp2.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave)+\"/a_sharp.wav\"))\n temp4 = temp3.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave + 1)+\"/d.wav\"))\n result = temp4.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave+1)+\"/g.wav\"))\n\n return Piano.calculate_duration(result, duration, self.tempo)","title":""},{"docid":"52f6fb736ee4755c988dc5be1eedde38","score":"0.7460553","text":"def create_h_chord(self, octave, duration, harmony):\n\n result = None\n\n if str(harmony) == \"major\":\n temp = AudioSegment.from_file(self.path_to_directory + \"/samples/guitar/\"+str(octave - 1)+\"/h.wav\")\n temp1 = temp.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave)+\"/f_sharp.wav\"))\n temp2 = temp1.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave)+\"/h.wav\"))\n temp3 = temp2.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave + 1)+\"/d_sharp.wav\"))\n result = temp3.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave + 1)+\"/f_sharp.wav\"))\n elif str(harmony) == \"minor\":\n temp = AudioSegment.from_file(self.path_to_directory + \"/samples/guitar/\"+str(octave - 1)+\"/h.wav\")\n temp1 = temp.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave)+\"/f_sharp.wav\"))\n temp2 = temp1.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave)+\"/h.wav\"))\n temp3 = temp2.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave + 1)+\"/d.wav\"))\n result = temp3.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave + 1)+\"/f_sharp.wav\"))\n\n return Piano.calculate_duration(result, duration, self.tempo)","title":""},{"docid":"27ac38cd0ed8ad9cb94916e2e4437a75","score":"0.7444743","text":"def create_e_chord(self, octave, duration, harmony):\n\n result = None\n\n if str(harmony) == \"major\":\n temp = AudioSegment.from_file(self.path_to_directory + \"/samples/guitar/\"+str(octave - 1)+\"/e.wav\")\n temp1 = temp.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave - 1)+\"/h.wav\"))\n temp2 = temp1.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave)+\"/e.wav\"))\n temp3 = temp2.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave)+\"/g_sharp.wav\"))\n temp4 = temp3.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave)+\"/h.wav\"))\n result = temp4.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave+1)+\"/e.wav\"))\n elif str(harmony) == \"minor\":\n temp = AudioSegment.from_file(self.path_to_directory + \"/samples/guitar/\"+str(octave - 1)+\"/e.wav\")\n temp1 = temp.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave - 1)+\"/h.wav\"))\n temp2 = temp1.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave)+\"/e.wav\"))\n temp3 = temp2.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave)+\"/g.wav\"))\n temp4 = temp3.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave)+\"/h.wav\"))\n result = temp4.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave+1)+\"/e.wav\"))\n\n return Piano.calculate_duration(result, duration, self.tempo)","title":""},{"docid":"1e3f1cbc0378b3297dd61b3e04fbb436","score":"0.72458655","text":"def create_f_chord(self, octave, duration, harmony):\n\n result = None\n\n if str(harmony) == \"major\":\n temp = AudioSegment.from_file(self.path_to_directory + \"/samples/piano/\"+str(octave - 1)+\"/f.wav\")\n temp1 = temp.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave)+\"/c.wav\"))\n temp2 = temp1.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave)+\"/f.wav\"))\n temp3 = temp2.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave)+\"/a.wav\"))\n temp4 = temp3.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave + 1)+\"/c.wav\"))\n result = temp4.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave+1)+\"/f.wav\"))\n elif str(harmony) == \"minor\":\n temp = AudioSegment.from_file(self.path_to_directory + \"/samples/piano/\"+str(octave - 1)+\"/f.wav\")\n temp1 = temp.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave)+\"/c.wav\"))\n temp2 = temp1.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave)+\"/f.wav\"))\n temp3 = temp2.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave)+\"/g_sharp.wav\"))\n temp4 = temp3.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave + 1)+\"/c.wav\"))\n result = temp4.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave+1)+\"/f.wav\"))\n\n return Piano.calculate_duration(result, duration, self.tempo)","title":""},{"docid":"2f5ffde54a36090a9c0dab6072b72736","score":"0.7195761","text":"def get_default_chord(self, chord, octave, duration, harmony):\n\n result = None\n\n if chord == \"c\":\n result = self.create_c_chord(octave, duration, harmony)\n elif chord == \"c_sharp\":\n result = self.create_c_sharp_chord(octave, duration, harmony)\n elif chord == \"d\":\n result = self.create_d_chord(octave, duration, harmony)\n elif chord == \"d_sharp\":\n result = self.create_d_sharp_chord(octave, duration, harmony)\n elif chord == \"e\":\n result = self.create_e_chord(octave, duration, harmony)\n elif chord == \"f\":\n result = self.create_f_chord(octave, duration, harmony)\n elif chord == \"f_sharp\":\n result = self.create_f_sharp_chord(octave, duration, harmony)\n elif chord == \"g\":\n result = self.create_g_chord(octave, duration, harmony)\n elif chord == \"g_sharp\":\n result = self.create_g_sharp_chord(octave, duration, harmony)\n elif chord == \"a\":\n result = self.create_a_chord(octave, duration, harmony)\n elif chord == \"a_sharp\":\n result = self.create_a_sharp_chord(octave, duration, harmony)\n elif chord == \"h\":\n result = self.create_h_chord(octave, duration, harmony)\n\n return result","title":""},{"docid":"2f5ffde54a36090a9c0dab6072b72736","score":"0.7195761","text":"def get_default_chord(self, chord, octave, duration, harmony):\n\n result = None\n\n if chord == \"c\":\n result = self.create_c_chord(octave, duration, harmony)\n elif chord == \"c_sharp\":\n result = self.create_c_sharp_chord(octave, duration, harmony)\n elif chord == \"d\":\n result = self.create_d_chord(octave, duration, harmony)\n elif chord == \"d_sharp\":\n result = self.create_d_sharp_chord(octave, duration, harmony)\n elif chord == \"e\":\n result = self.create_e_chord(octave, duration, harmony)\n elif chord == \"f\":\n result = self.create_f_chord(octave, duration, harmony)\n elif chord == \"f_sharp\":\n result = self.create_f_sharp_chord(octave, duration, harmony)\n elif chord == \"g\":\n result = self.create_g_chord(octave, duration, harmony)\n elif chord == \"g_sharp\":\n result = self.create_g_sharp_chord(octave, duration, harmony)\n elif chord == \"a\":\n result = self.create_a_chord(octave, duration, harmony)\n elif chord == \"a_sharp\":\n result = self.create_a_sharp_chord(octave, duration, harmony)\n elif chord == \"h\":\n result = self.create_h_chord(octave, duration, harmony)\n\n return result","title":""},{"docid":"e6bc55b276a23ddc65fe9cf99a8f3543","score":"0.714988","text":"def create_f_chord(self, octave, duration, harmony):\n\n result = None\n\n if str(harmony) == \"major\":\n temp = AudioSegment.from_file(self.path_to_directory + \"/samples/guitar/\"+str(octave - 1)+\"/f.wav\")\n temp1 = temp.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave)+\"/c.wav\"))\n temp2 = temp1.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave)+\"/f.wav\"))\n temp3 = temp2.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave)+\"/a.wav\"))\n temp4 = temp3.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave + 1)+\"/c.wav\"))\n result = temp4.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave+1)+\"/f.wav\"))\n elif str(harmony) == \"minor\":\n temp = AudioSegment.from_file(self.path_to_directory + \"/samples/guitar/\"+str(octave - 1)+\"/f.wav\")\n temp1 = temp.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave)+\"/c.wav\"))\n temp2 = temp1.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave)+\"/f.wav\"))\n temp3 = temp2.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave)+\"/g_sharp.wav\"))\n temp4 = temp3.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave + 1)+\"/c.wav\"))\n result = temp4.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave+1)+\"/f.wav\"))\n\n return Piano.calculate_duration(result, duration, self.tempo)","title":""},{"docid":"06e9c698f54795034de1f480b85cab0a","score":"0.7058978","text":"def create_a_sharp_chord(self, octave, duration, harmony):\n\n result = None\n\n if str(harmony) == \"major\":\n temp = AudioSegment.from_file(self.path_to_directory + \"/samples/piano/\"+str(octave - 1)+\"/a_sharp.wav\")\n temp1 = temp.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave)+\"/f.wav\"))\n temp2 = temp1.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave)+\"/a_sharp.wav\"))\n temp3 = temp2.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave + 1)+\"/d.wav\"))\n temp4 = temp3.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave + 1)+\"/f.wav\"))\n result = temp4.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave + 1)+\"/a_sharp.wav\"))\n elif str(harmony) == \"minor\":\n temp = AudioSegment.from_file(self.path_to_directory + \"/samples/piano/\"+str(octave - 1)+\"/a_sharp.wav\")\n temp1 = temp.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave)+\"/f.wav\"))\n temp2 = temp1.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave)+\"/a_sharp.wav\"))\n temp3 = temp2.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave + 1)+\"/c_sharp.wav\"))\n temp4 = temp3.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave + 1)+\"/f.wav\"))\n result = temp4.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave + 1)+\"/a_sharp.wav\"))\n\n return Piano.calculate_duration(result, duration, self.tempo)","title":""},{"docid":"f26cbf54727772749225061c103bfdb3","score":"0.7057801","text":"def create_g_sharp_chord(self, octave, duration, harmony):\n\n result = None\n\n if str(harmony) == \"major\":\n temp = AudioSegment.from_file(self.path_to_directory + \"/samples/piano/\"+str(octave - 1)+\"/g_sharp.wav\")\n temp1 = temp.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave)+\"/d_sharp.wav\"))\n temp2 = temp1.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave)+\"/g_sharp.wav\"))\n temp3 = temp2.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave + 1)+\"/c.wav\"))\n temp4 = temp3.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave + 1)+\"/d_sharp.wav\"))\n result = temp4.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave + 1)+\"/g_sharp.wav\"))\n elif str(harmony) == \"minor\":\n temp = AudioSegment.from_file(self.path_to_directory + \"/samples/piano/\"+str(octave - 1)+\"/g_sharp.wav\")\n temp1 = temp.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave)+\"/d_sharp.wav\"))\n temp2 = temp1.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave)+\"/g_sharp.wav\"))\n temp3 = temp2.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave)+\"/h.wav\"))\n temp4 = temp3.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave + 1)+\"/d_sharp.wav\"))\n result = temp4.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave + 1)+\"/g_sharp.wav\"))\n\n return Piano.calculate_duration(result, duration, self.tempo)","title":""},{"docid":"82937ac07d95a22e0509d49f5578f09c","score":"0.703113","text":"def create_a_sharp_chord(self, octave, duration, harmony):\n\n result = None\n\n if str(harmony) == \"major\":\n temp = AudioSegment.from_file(self.path_to_directory + \"/samples/guitar/\"+str(octave - 1)+\"/a_sharp.wav\")\n temp1 = temp.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave)+\"/f.wav\"))\n temp2 = temp1.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave)+\"/a_sharp.wav\"))\n temp3 = temp2.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave + 1)+\"/d.wav\"))\n temp4 = temp3.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave + 1)+\"/f.wav\"))\n result = temp4.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave + 1)+\"/a_sharp.wav\"))\n elif str(harmony) == \"minor\":\n temp = AudioSegment.from_file(self.path_to_directory + \"/samples/guitar/\"+str(octave - 1)+\"/a_sharp.wav\")\n temp1 = temp.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave)+\"/f.wav\"))\n temp2 = temp1.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave)+\"/a_sharp.wav\"))\n temp3 = temp2.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave + 1)+\"/c_sharp.wav\"))\n temp4 = temp3.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave + 1)+\"/f.wav\"))\n result = temp4.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave + 1)+\"/a_sharp.wav\"))\n\n return Piano.calculate_duration(result, duration, self.tempo)","title":""},{"docid":"5e2dcef98de0c9547d51e4c5feda11fb","score":"0.7019466","text":"def create_g_sharp_chord(self, octave, duration, harmony):\n\n result = None\n\n if str(harmony) == \"major\":\n temp = AudioSegment.from_file(self.path_to_directory + \"/samples/guitar/\"+str(octave - 1)+\"/g_sharp.wav\")\n temp1 = temp.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave)+\"/d_sharp.wav\"))\n temp2 = temp1.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave)+\"/g_sharp.wav\"))\n temp3 = temp2.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave + 1)+\"/c.wav\"))\n temp4 = temp3.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave + 1)+\"/d_sharp.wav\"))\n result = temp4.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave + 1)+\"/g_sharp.wav\"))\n elif str(harmony) == \"minor\":\n temp = AudioSegment.from_file(self.path_to_directory + \"/samples/guitar/\"+str(octave - 1)+\"/g_sharp.wav\")\n temp1 = temp.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave)+\"/d_sharp.wav\"))\n temp2 = temp1.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave)+\"/g_sharp.wav\"))\n temp3 = temp2.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave)+\"/h.wav\"))\n temp4 = temp3.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave + 1)+\"/d_sharp.wav\"))\n result = temp4.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave + 1)+\"/g_sharp.wav\"))\n\n return Piano.calculate_duration(result, duration, self.tempo)","title":""},{"docid":"2010714d4a28a28881eb9858934b9cce","score":"0.69897336","text":"def create_d_sharp_chord(self, octave, duration, harmony):\n\n result = None\n\n if str(harmony) == \"major\":\n temp = AudioSegment.from_file(self.path_to_directory + \"/samples/piano/\"+str(octave)+\"/d_sharp.wav\")\n temp1 = temp.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave)+\"/a_sharp.wav\"))\n temp2 = temp1.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave+1)+\"/d_sharp.wav\"))\n result = temp2.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave+1)+\"/g.wav\"))\n elif str(harmony) == \"minor\":\n temp = AudioSegment.from_file(self.path_to_directory + \"/samples/piano/\"+str(octave)+\"/d_sharp.wav\")\n temp1 = temp.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave)+\"/a_sharp.wav\"))\n temp2 = temp1.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave+1)+\"/d_sharp.wav\"))\n result = temp2.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave+1)+\"/f_sharp.wav\"))\n\n return Piano.calculate_duration(result, duration, self.tempo)","title":""},{"docid":"30d9411fd6bcb3057e680eb6f6b0ec02","score":"0.697627","text":"def create_d_sharp_chord(self, octave, duration, harmony):\n\n result = None\n\n if str(harmony) == \"major\":\n temp = AudioSegment.from_file(self.path_to_directory + \"/samples/guitar/\"+str(octave)+\"/d_sharp.wav\")\n temp1 = temp.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave)+\"/a_sharp.wav\"))\n temp2 = temp1.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave+1)+\"/d_sharp.wav\"))\n result = temp2.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave+1)+\"/g.wav\"))\n elif str(harmony) == \"minor\":\n temp = AudioSegment.from_file(self.path_to_directory + \"/samples/guitar/\"+str(octave)+\"/d_sharp.wav\")\n temp1 = temp.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave)+\"/a_sharp.wav\"))\n temp2 = temp1.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave+1)+\"/d_sharp.wav\"))\n result = temp2.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave+1)+\"/f_sharp.wav\"))\n\n return Piano.calculate_duration(result, duration, self.tempo)","title":""},{"docid":"083ed1ba8549f291d403729620d1eaf4","score":"0.6800724","text":"def create_f_sharp_chord(self, octave, duration, harmony):\n\n result = None\n\n if str(harmony) == \"major\":\n temp = AudioSegment.from_file(self.path_to_directory + \"/samples/piano/\"+str(octave - 1)+\"/f_sharp.wav\")\n temp1 = temp.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave)+\"/c_sharp.wav\"))\n temp2 = temp1.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave)+\"/f_sharp.wav\"))\n temp3 = temp2.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave)+\"/a_sharp.wav\"))\n temp4 = temp3.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave + 1)+\"/c_sharp.wav\"))\n result = temp4.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave+1)+\"/f_sharp.wav\"))\n elif str(harmony) == \"minor\":\n temp = AudioSegment.from_file(self.path_to_directory + \"/samples/piano/\"+str(octave - 1)+\"/f_sharp.wav\")\n temp1 = temp.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave)+\"/c_sharp.wav\"))\n temp2 = temp1.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave)+\"/f_sharp.wav\"))\n temp3 = temp2.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave)+\"/a.wav\"))\n temp4 = temp3.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave + 1)+\"/c_sharp.wav\"))\n result = temp4.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/piano/\"+str(octave + 1)+\"/f_sharp.wav\"))\n\n return Piano.calculate_duration(result, duration, self.tempo)","title":""},{"docid":"158ff374dddf9f64aa35e8426a3896e8","score":"0.6761683","text":"def create_f_sharp_chord(self, octave, duration, harmony):\n result = None\n\n if str(harmony) == \"major\":\n temp = AudioSegment.from_file(self.path_to_directory + \"/samples/guitar/\"+str(octave - 1)+\"/f_sharp.wav\")\n temp1 = temp.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave)+\"/c_sharp.wav\"))\n temp2 = temp1.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave)+\"/f_sharp.wav\"))\n temp3 = temp2.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave)+\"/a_sharp.wav\"))\n temp4 = temp3.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave + 1)+\"/c_sharp.wav\"))\n result = temp4.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave+1)+\"/f_sharp.wav\"))\n elif str(harmony) == \"minor\":\n temp = AudioSegment.from_file(self.path_to_directory + \"/samples/guitar/\"+str(octave - 1)+\"/f_sharp.wav\")\n temp1 = temp.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave)+\"/c_sharp.wav\"))\n temp2 = temp1.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave)+\"/f_sharp.wav\"))\n temp3 = temp2.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave)+\"/a.wav\"))\n temp4 = temp3.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave + 1)+\"/c_sharp.wav\"))\n result = temp4.overlay(AudioSegment.from_file(\n self.path_to_directory + \"/samples/guitar/\"+str(octave + 1)+\"/f_sharp.wav\"))\n\n return Piano.calculate_duration(result, duration, self.tempo)","title":""},{"docid":"c8ec1f7589fa34f4983d85c2a72bf744","score":"0.6690171","text":"def music21_to_chord_duration(p, key):\n p_chords = p.chordify()\n p_chords_o = p_chords.flat.getElementsByClass('Chord')\n chord_list = []\n chord_function_list = []\n duration_list = []\n for ch in p_chords_o:\n duration_list.append(ch.duration.quarterLength)\n ch.closedPosition(forceOctave=4, inPlace=True)\n rn = roman.romanNumeralFromChord(ch, key)\n rp = rn.pitches\n rp_names = \",\".join([pi.name + pi.unicodeNameWithOctave[-1] for pi in rp])\n chord_list.append(rp_names)\n chord_function_list.append(rn.figure)\n return chord_list, chord_function_list, duration_list","title":""},{"docid":"46eccdf61ddcb855167f27fd282a92ce","score":"0.6621113","text":"def get_not_default_chord(self, notes_, duration):\n\n # Split each elements of input array by symbol,\n # In result we get array of array of string.\n # Each elements of its array is array which contain two elements:\n # first element ([0][0]) - name of note. Second ([0][1]) - octave of corresponding note\n notes = [[]]\n for item in notes_:\n temp = item.split(\",\")\n notes.append(temp)\n\n del notes[0] # Delete first element, because its empty\n i = 2 # i = 2, because before start loop, we concatenate first and second elements of array\n\n result = AudioSegment.from_file(self.path_to_directory + \"/samples/piano/\" + notes[0][1] + \"/\" +\n notes[0][0] + \".wav\")\n result = result.overlay(AudioSegment.from_file(self.path_to_directory + \"/samples/piano/\" + notes[1][1] +\n \"/\" + notes[1][0] + \".wav\"))\n\n while i < len(notes):\n # Concatenate current and previous element of array in parallel mode\n result = result.overlay(AudioSegment.from_file(self.path_to_directory + \"/samples/piano/\" +\n notes[i][1] + \"/\" + notes[i][0] + \".wav\"))\n i += 1\n\n return Piano.calculate_duration(result, duration, self.tempo)","title":""},{"docid":"fd7518c2200a8daf57ced9e6c4116aa4","score":"0.66190565","text":"def generate_chord():\n A = generate_note(440,1)\n C = generate_note(532.25,1)\n E = generate_note(659.25,1)\n B = generate_note(493.88,1)\n D = generate_note(587.33,1)\n F = generate_note(698.46,1)\n G = generate_note(783.99,1)\n ACE = A.wave+C.wave+E.wave\n CEG = C.wave+E.wave+G.wave\n ACE = Signal(44100,ACE)\n CEG = Signal(44100,CEG)\n ACE.write_file('chord1')\n CEG.write_file('chordCEG')\n zeros = np.zeros(len(ACE.wave))\n mary = np.append(E.wave,D.wave)\n mary = np.append(mary,[C.wave,D.wave,E.wave,E.wave,E.wave,D.wave,D.wave,D.wave,E.wave,E.wave,E.wave])\n mary = Signal(44100,mary)\n mary.write_file('chord2')\n pass","title":""},{"docid":"70805818eceeb3ab6bcc3b72a3961142","score":"0.661035","text":"def get_not_default_chord(self, notes_, duration):\n\n # Split each elements of input array by symbol ,\n # In result we get array of array of string.\n # Each elements of its array is array which contain two elements:\n # first element ([0][0]) - name of note. Second ([0][1]) - octave of corresponding note\n notes = [[]]\n for item in notes_:\n temp = item.split(\",\")\n notes.append(temp)\n\n del notes[0] # Delete first element, because its empty\n i = 2 # i = 2, because before start loop, we concatenate first and second elements of array\n\n result = AudioSegment.from_file(self.path_to_directory + \"/samples/piano/\" + notes[0][1] + \"/\" +\n notes[0][0] + \".wav\")\n result = result.overlay(AudioSegment.from_file(self.path_to_directory + \"/samples/piano/\" + notes[1][1] +\n \"/\" + notes[1][0] + \".wav\"))\n\n while i < len(notes):\n # Concatenate current and previous element of array in parallel mode\n result = result.overlay(AudioSegment.from_file(self.path_to_directory + \"/samples/piano/\" +\n notes[i][1] + \"/\" + notes[i][0] + \".wav\"))\n i += 1\n\n return Piano.calculate_duration(result, duration, self.tempo)","title":""},{"docid":"8349e9c16cea3a078017575741c1874d","score":"0.63813186","text":"def playChord(self, notes=('C6', 'E6', 'G6'), durations=0.3):\n\n freqs = [self._pitch_to_freq(p) for p in notes]\n if not isinstance(durations, list):\n durations = [durations]*len(freqs)\n\n for f, d in zip(freqs, durations):\n self.beeper.beep(d, f)","title":""},{"docid":"c1c4d32843a62e482dac86c4830f2e78","score":"0.62916815","text":"def getChordSymbol(line):\n\n\t# FIXME : Shouldn't assume sharp note names, but need CO to be extended to allow nameless root notes.\n\tnotes = ['C','C#','D','D#','E','F','F#','G','G#','A','A#','B']\n\t\n\ttry:\n\t\t[crotchetnum, timestamp, rootnum, ctype] = line.split(',')\n\texcept Exception,e :\n\t\tprint \"Exception ! Trying to split line : \"+line\n\t\traise\n\n\trootnote = notes[int(rootnum)]\n\t\n\tshorthandmap = {'major' : 'maj', 'minor' : 'min', 'sus4' : 'sus4', 'dim' : 'dim', 'sus9' : '(1,5,9)'}\n\t\n\t\t\n\treturn rootnote+':'+shorthandmap[ctype.strip()]","title":""},{"docid":"86568ba7ddc3f665d2fc45f4b0f931f7","score":"0.6118687","text":"def chord(chord):\n if len(chord) == 1:\n root=degrees[chord]\n note_list = [root,root+4,root+7]\n elif chord[-1] =='m':\n root=degrees[chord[0]]\n note_list = [root,root+3,root+7]\n return note_list","title":""},{"docid":"fba7e57ab8ababd57034367e2031b295","score":"0.6068974","text":"def play_chord(self, chord):\r\n #Settings are optimized for chords with a long release time\r\n vol = random.randint(30, 70)\r\n self.output.note_on(note, vol, 6)\r\n self.output.note_on(note + chord[0], 50 + vol, 6)\r\n self.output.note_on(note + chord[1], 50 + vol, 6)\r\n if len(chord) > 2:\r\n self.output.note_on(note + chord[2], 50)\r\n \r\n a = random.choice([-5, 0, 7, 12])\r\n b = random.randint(1,3)\r\n \r\n time.sleep(random.randint(1,3))\r\n self.output.note_on(note + a, 50)\r\n time.sleep(random.randint(1,3))\r\n \r\n self.output.note_off(note)\r\n self.output.note_off(note + chord[0])\r\n self.output.note_off(note + chord[1])\r\n self.output.note_off(note + a)\r\n self.output.note_off(note + b)\r\n \r\n if len(chord) > 2:\r\n self.output.note_off(note + chord[2])","title":""},{"docid":"d81baffd411c23e5284c05a59bf79ecd","score":"0.5919639","text":"def get_note(self, note, octave, duration):\n\n result = None\n\n if note == \"c\":\n result = AudioSegment.from_file(self.path_to_directory + \"/samples/guitar/\" + str(octave) + \"/c.wav\")\n elif note == \"c_sharp\":\n result = AudioSegment.from_file(self.path_to_directory + \"/samples/guitar/\" + str(octave) + \"/c_sharp.wav\")\n elif note == \"d\":\n result = AudioSegment.from_file(self.path_to_directory + \"/samples/guitar/\" + str(octave) + \"/d.wav\")\n elif note == \"d_sharp\":\n result = AudioSegment.from_file(self.path_to_directory + \"/samples/guitar/\" + str(octave) + \"/d_sharp.wav\")\n elif note == \"e\":\n result = AudioSegment.from_file(self.path_to_directory + \"/samples/guitar/\" + str(octave) + \"/e.wav\")\n elif note == \"f\":\n result = AudioSegment.from_file(self.path_to_directory + \"/samples/guitar/\" + str(octave) + \"/f.wav\")\n elif note == \"f_sharp\":\n result = AudioSegment.from_file(self.path_to_directory + \"/samples/guitar/\" + str(octave) + \"/f_sharp.wav\")\n elif note == \"g\":\n result = AudioSegment.from_file(self.path_to_directory + \"/samples/guitar/\" + str(octave) + \"/g.wav\")\n elif note == \"g_sharp\":\n result = AudioSegment.from_file(self.path_to_directory + \"/samples/guitar/\" + str(octave) + \"/g_sharp.wav\")\n elif note == \"a\":\n result = AudioSegment.from_file(self.path_to_directory + \"/samples/guitar/\" + str(octave) + \"/a.wav\")\n elif note == \"a_sharp\":\n result = AudioSegment.from_file(self.path_to_directory + \"/samples/guitar/\" + str(octave) + \"/a_sharp.wav\")\n elif note == \"h\":\n result = AudioSegment.from_file(self.path_to_directory + \"/samples/guitar/\" + str(octave) + \"/h.wav\")\n elif note == \"silence\":\n result = AudioSegment.from_file(self.path_to_directory + \"/samples/guitar/silence.wav\")\n\n return Piano.calculate_duration(result, duration, self.tempo)","title":""},{"docid":"85dff3cedbaf2755a7b2411487701937","score":"0.58418316","text":"def get_playlist(self, chord):\r\n \r\n steps = self.profile_manager.logic.bar_division\r\n pulses = random.randint(1,5)\r\n rhythm_list = self.rhythm_gen.bjorklund(pulses, steps)\r\n \r\n for index, item in enumerate(rhythm_list):\r\n if item == 0:\r\n rhythm_list[index] = \"0\"\r\n else:\r\n rhythm_list[index] = self.get_rand_note()\r\n \r\n \"SYNTAX: CHN-NOTE-LEN-VEL eg. 1-50-16-65\"\r\n \r\n return rhythm_list\r\n \r\n \r\n \"\"\"\" TEMPORARY TEST LISTS\r\n lsts = [\r\n [0, 0, \"1-C3-16-60\", 0],\r\n [\"2-D5-4-77\", \"3-D2-8-60\", 0, 0],\r\n [0, 0, 0, \"3-F2-16-80\"],\r\n [0, \"1-C1-2-65\", 0, 0]\r\n ]\r\n \"\"\"","title":""},{"docid":"871706cb668eb9f60a6036b000e6220e","score":"0.58189493","text":"def cMaj():\n playNote(60,250,90) #play C note\n playNote(64,250,90) #play E note\n playNote(69,250,90) #play G note ","title":""},{"docid":"3d64be4aae5f30cc5d88ee51e1e2e492","score":"0.5816853","text":"def get_note(self, note, octave, duration):\n\n result = None\n\n if note == \"c\":\n result = AudioSegment.from_file(self.path_to_directory + \"/samples/piano/\" + str(octave) + \"/c.wav\")\n elif note == \"c_sharp\":\n result = AudioSegment.from_file(self.path_to_directory + \"/samples/piano/\" + str(octave) + \"/c_sharp.wav\")\n elif note == \"d\":\n result = AudioSegment.from_file(self.path_to_directory + \"/samples/piano/\" + str(octave) + \"/d.wav\")\n elif note == \"d_sharp\":\n result = AudioSegment.from_file(self.path_to_directory + \"/samples/piano/\" + str(octave) + \"/d_sharp.wav\")\n elif note == \"e\":\n result = AudioSegment.from_file(self.path_to_directory + \"/samples/piano/\" + str(octave) + \"/e.wav\")\n elif note == \"f\":\n result = AudioSegment.from_file(self.path_to_directory + \"/samples/piano/\" + str(octave) + \"/f.wav\")\n elif note == \"f_sharp\":\n result = AudioSegment.from_file(self.path_to_directory + \"/samples/piano/\" + str(octave) + \"/f_sharp.wav\")\n elif note == \"g\":\n result = AudioSegment.from_file(self.path_to_directory + \"/samples/piano/\" + str(octave) + \"/g.wav\")\n elif note == \"g_sharp\":\n result = AudioSegment.from_file(self.path_to_directory + \"/samples/piano/\" + str(octave) + \"/g_sharp.wav\")\n elif note == \"a\":\n result = AudioSegment.from_file(self.path_to_directory + \"/samples/piano/\" + str(octave) + \"/a.wav\")\n elif note == \"a_sharp\":\n result = AudioSegment.from_file(self.path_to_directory + \"/samples/piano/\" + str(octave) + \"/a_sharp.wav\")\n elif note == \"h\":\n result = AudioSegment.from_file(self.path_to_directory + \"/samples/piano/\" + str(octave) + \"/h.wav\")\n elif note == \"silence\":\n result = AudioSegment.from_file(self.path_to_directory + \"/samples/piano/silence.wav\")\n\n return Piano.calculate_duration(result, duration, self.tempo)","title":""},{"docid":"47a78ad056e110ac2a54e3ca5addd87b","score":"0.58123404","text":"def modal_chord_html(request):\n indexes = {\n 'Ab': 0,\n 'A': 1,\n 'Bb':2,\n 'B':3,\n 'Cb':3,\n 'C':4,\n 'C#':5,\n 'Db':5,\n 'D': 6,\n 'D#': 7,\n 'Eb': 7,\n 'E':8,\n 'F':9,\n 'F#':10,\n 'Gb':10,\n 'G':11,\n 'G#':0\n }\n ccli = int(request.GET.get('ccli'))\n transpose_final_key = request.GET.get('transpose_value_str')\n song = Song.objects.get(ccli=int(ccli))\n chord_stream = song.chords\n if not chord_stream:\n return\n lines = chord_stream.split('\\n')\n \n html = ''\n emphasis = False\n for line in lines:\n #handle directives first\n if '{' in line:\n if '{title:' in line:\n html += '<h3>'+line[7:-1]+'</h3>'\n continue\n elif '{st:' in line:\n html += '<h5>'+line[4:-1]+'</h5>'\n continue\n elif '{key:' in line:\n original_key = line[5:-1]\n continue\n elif '{start_of_chorus}' in line:\n emphasis = True\n continue\n elif '{end_of_chorus}' in line:\n emphasis = False\n continue\n #no directive in line\n else:\n if '[' in line:\n separations = []\n separations = line.split('[')\n chords = []\n lyrics = []\n # go through each section and get the chord and lyric pairings\n for section in separations:\n if section == '':\n continue\n #if no chord, still append nothing to chords\n elif ']' not in section:\n lyrics.append(section)\n chords.append('')\n continue \n chordlyric = section.split(']')\n chords.append(chordlyric[0])\n lyrics.append(chordlyric[1])\n html += '<table>'\n if emphasis:\n html += '<tr class=\"emphasis\">'\n else:\n html += '<tr>'\n\n transposed_chords = []\n \n if 'm' in original_key:\n # minor = True\n original_root = original_key[:-1]\n final_root = transpose_final_key[:-1]\n else:\n # minor = False\n original_root = original_key\n final_root = transpose_final_key\n original_key_index = indexes[original_root]\n final_key_index = indexes[final_root]\n \n # original_key_index = indexes[original_key]\n # final_key_index = indexes[transpose_final_key]\n transpose_step = final_key_index - original_key_index\n \n for chord in chords:\n if chord == '':\n transposed_chords.append('')\n continue\n transposed_chord = transpose(chord, transpose_step, original_key, transpose_final_key)\n transposed_chords.append(transposed_chord)\n\n for chord in transposed_chords:\n html += '<td>'+chord+'</td>'\n \n if emphasis:\n html += '</tr><tr class=\"emphasis\">'\n else:\n html += '</tr><tr>'\n \n for lyric in lyrics:\n html += '<td>'+lyric+'</td>'\n \n html += '</tr></table>'\n\n else:\n html += '<br>' + line\n #adding another br so last line doesn't get cut off\n html += '<br>'\n return HttpResponse(html)","title":""},{"docid":"061cc36dc706558abe7c498a16e65d54","score":"0.5729549","text":"def get_chord(symbol, base_note):\n return [triad_notes[symbol][i] + base_note for i in range(len(triad_notes[symbol]))]","title":""},{"docid":"a46203395044e9a769570a140ba2749d","score":"0.5715923","text":"def _chord_callback(data_value) -> List[InputMediaPhoto]:\n caption = data_value.split('-')[1]\n chord = caption.replace('#', 'x').lower()\n print('Chord:', caption)\n return ChordsParser.get_chord(chord, caption)","title":""},{"docid":"7d458fe38a5c776227158fc4841374ca","score":"0.5696006","text":"def get_stream_from_chord_prog(chord_prog, key):\n #print(chord_prog)\n stream_chord_prog = stream.Stream()\n quintet = get_voice_led_quintet(chord_prog, key) #gets tuple of eight notes for bassline (except not lowered an octave). its actually a quintet with five parts. We only want 2 of the parts\n counter = 0\n for scale_figure in chord_prog:\n comp_chord = roman.RomanNumeral(scale_figure, key) # this creates a music21 object from the scale degree\n chord_pitches_list = list(comp_chord.pitches) #We want to add bass note to chord object, however, chord object is tuple and tuple can't be manipulated (tuples are immutable)\n #bass_note = comp_chord.bass() #gets lowest note from 'chord'\n bass_note = note.Note(quintet[0][counter])\n bass_note.octave = bass_note.octave - 2 #<<<#lowers the octave by TWO, making the note lower\n chord_pitches_list.append(bass_note) #<<made list of tuple and added bass note to it\n\n high_note = note.Note(quintet[3][counter])\n high_note.octave = high_note.octave + 1 #<<<#raises the octave by ONE, making the note higher\n chord_pitches_list.append(high_note) #<<chord pitches list is the list that is about to be added the strea,\n\n chord_pitches_tuple = tuple(chord_pitches_list) #turn edited version of chord into a tuple\n comp_chord = chord.Chord(chord_pitches_tuple) #make chord object out of tuple\n comp_chord.duration.quarterLength = 4.0 #this turns the chord into actual notes with a duration so it can be added onto the music21 stream\n stream_chord_prog.append(comp_chord)\n counter += 1\n\n return stream_chord_prog","title":""},{"docid":"9dcde27d9a2a275056e51427148c7906","score":"0.562131","text":"def quarter_chord(self) -> cas.DM:\n return 0.75 * self.xyz_le + 0.25 * self.xyz_te()","title":""},{"docid":"aff8e790f0f6f856298309f26fc3bd49","score":"0.5526221","text":"def __init__(self, start, end, direction):\n self.start = start\n self.end = end\n self.direction = direction\n v = 0.5 * (end-start) # half oriented chord\n angle = phase(v) - direction\n while angle > pi:\n angle -= 2*pi\n while angle <= -pi:\n angle += 2*pi\n self.angle = angle # signed angle between arc and chord\n self.center = 0.5*(start+end) + 1j*v / tan(angle) # center of circle\n absv = abs(v)\n self.l = 2 * absv # length of chord\n if angle >= 0:\n self.radius = absv / sin(angle) # radius\n self.alpha = phase(v) - pi/2 - angle # start angle\n self.beta = self.alpha + 2*angle # end angle\n else:\n self.radius = -absv / sin(angle)\n self.alpha = phase(v) + pi/2 - angle\n self.beta = self.alpha + 2*angle\n self.end_direction = direction + 2*angle # direction of arc in end point","title":""},{"docid":"8ae05cd98e134758259a70e4e5ee2060","score":"0.55210674","text":"def construct_harmony(curr_loop):\n\n # Constructing the shell chords\n prev_chord = None\n prev_voice = None\n\n for i in range(curr_loop.measures):\n curr_chord = shell_chord(curr_loop, i, prev_chord)\n curr_voice = get_voicing(curr_chord, curr_loop, prev_voice)\n\n prev_chord = curr_chord\n prev_voice = curr_voice\n\n for curr_note in curr_loop.harm['shell'].measures[i].notes:\n curr_note.value = {'chord': curr_chord, 'voicing': curr_voice}\n\n # Decorating with passing chords\n curr_loop.harm['passing'] = curr_loop.harm['shell'].extend().collapse(curr_loop)\n for curr_measure in curr_loop.harm['passing'].measures:\n passing_chords(curr_measure, curr_loop)\n\n # Constructing the rhythmic attack/sustain Frame\n temp_harm = curr_loop.harm['rhythm'].extend()\n for curr_note in temp_harm.measures[0].notes:\n if curr_note.index == 0:\n curr_note.value = 'A'\n elif curr_loop.harm['passing'][curr_note.index].value == curr_loop.harm['passing'][curr_note.index-1].value:\n curr_note.value = 'S'\n else:\n curr_note.value = 'A'\n curr_loop.harm['rhythm'] = temp_harm.collapse(curr_loop)\n\n # Constructing the final compressed harmony Frame\n curr_loop.harm['final'] = curr_loop.harm['passing'].extend().collapse(curr_loop)\n curr_loop.harm['final'].compress()","title":""},{"docid":"2a7d3c49a27b321e3cf280951b3215c6","score":"0.5509813","text":"def choose_chord(self):\r\n chords = [[3, 7], [4, 7], [3, 6], [5, 7], [5, 12], [4, 9, 12], [3, 7, 10]]\r\n \r\n return random.choice(chords)","title":""},{"docid":"43cd0dc91a1315d2f20bc3d871c6ab4f","score":"0.5481143","text":"def getChordScale(self, n):","title":""},{"docid":"5558a2277d2339f694775f65a6add6ec","score":"0.54629415","text":"def calculate_chord(radius, arc_degrees):\n\n # Calculate the arc_degrees in radians.\n # We need this because sin() expects it.\n arc_radians = radians(arc_degrees)\n\n # Calculate the chord.\n return radius * (2 * sin(arc_radians / 2)) # km","title":""},{"docid":"48a9d4bc105a52d8a938b5a3cae4e7a2","score":"0.54548794","text":"def make_interval_chord_midi(chords, key):\n result = []\n c_transposed = transpose(chords, key, 'C')\n\n for chord in c_transposed:\n if chord[0] == 'H':\n result.append('-1')\n continue\n\n if len(chord) == 1 or (chord[1] != '#' and chord[1] != 'b'):\n name_index = 1\n if chord[0] == 'F':\n mode = 'b'\n else:\n mode = '#'\n else:\n name_index = 2\n mode = chord[1]\n\n interval = calc_interval('C', chord[:name_index])\n\n if interval in vocab_value:\n result_chord = vocab_interval[interval]\n else:\n if mode == '#':\n interval -= 0.5\n else:\n interval += 0.5\n result_chord = vocab_interval[interval] + mode\n\n result.append(result_chord + chord[name_index:])\n\n return result","title":""},{"docid":"6fdb35b740d2c9a54ad87d270553c841","score":"0.5394407","text":"def CA_measures(self):\n for i in range(self.length):\n chord = self.chrd_prg[i%len(self.chrd_prg)]\n rmeasure = Measure(chord=chord, note_lengths=self.note_lengths)\n rmeasure.CA_notes()\n rmeasure.CA_bass()\n self.add_measure(rmeasure)","title":""},{"docid":"776bb12e6e05e3812aae727bd4f29c7c","score":"0.5390063","text":"def make_chord_from_intervals(intervals, starting_pitch=0):\n chord = [starting_pitch]\n index = 0\n for num_half_steps in intervals:\n chord.append(mod_12(chord[index] + num_half_steps))\n index += 1\n return tuple(chord)","title":""},{"docid":"4efe77093d3599cee0b16ecad7718972","score":"0.53892785","text":"def build_chromatic_scale():\n # letter names for note entries\n letter_names = [\"C\", \"D\", \"E\", \"F\", \"G\", \"A\", \"B\"]\n # symbolic accidental names for notes, \"\" is for a diatonic note without accidental\n accidental_names = [\"bb\", \"b\", \"\", \"#\", \"##\"]\n # safe accidental name variants\n accidental_safe_name = {\"bb\": \"ff\", \"b\": \"f\", \"\": \"\", \"#\": \"s\", \"##\": \"ss\"}\n # lowest octave name is 00 instead of -1.\n octave_names = [\"00\", \"0\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\"]\n # semi-tone shifts applied to diatonic notes within the octave\n letter_steps = [0, 2, 4, 5, 7, 9, 11]\n # semi-tone shift applied to diatonic notes for notes with accidentals\n accidental_steps = [-2, -1, 0, 1, 2]\n # init table with midi keys, each holding a note array and frequency.\n # the note array will hold all chromatic note names for the midi key.\n table = {key: [['','','','',''], 440.0 * 2 ** ((key-69.0)/12)] for key in range(128)}\n # iterate all the octave for midi key numbers 0-127.\n for octave_index, octave_name in enumerate(octave_names):\n # starting key number for notes in the octave\n octave_midi = octave_index * 12\n # iterate all the diatonic letter names for notes.\n for letter_index, letter_name in enumerate(letter_names):\n # the midi key number of the diatonic note\n letter_midi = octave_midi + letter_steps[letter_index]\n # iterate the accidentals and create all possible note names for the letter\n for accidental_index, accidental_name in enumerate(accidental_names):\n # get the semitone amount to shift the letter note by\n accidental_step = accidental_steps[accidental_index]\n # calculate the midi key number for the note\n note_midi = letter_midi + accidental_step\n # stop notes outside of midi range (there are only a few)\n if 0 <= note_midi <= 127:\n accidental_name = accidental_names[accidental_index]\n # create the official note name\n note_name1 = letter_name + accidental_name + octave_name\n # create the Pitch for the official name.\n note_pitch = Pitch(note_name1)\n # create variants (lower case letter names, safe accidental names)\n note_name2 = letter_name.lower() + accidental_name + octave_name\n note_name3 = letter_name + accidental_safe_name[accidental_name] + octave_name\n note_name4 = letter_name.lower() + accidental_safe_name[accidental_name] + octave_name\n # fetch the midi data from the table\n midi_data = table[note_midi]\n # add the note to the note array\n ##midi_data[0][accidental_index] = note_name1\n midi_data[0][accidental_index] = note_pitch\n # get the frequency from the midi data and add it to the note data.\n note_freq = table[note_midi][1]\n # add the hash entry for the note name\n ##table[note_name1] = [note_midi, note_freq]\n table[note_name1] = [note_midi, note_freq, note_pitch]\n # add the variants (lower case letter, safe accidentals)\n table[note_name2] = table[note_name1]\n table[note_name3] = table[note_name1]\n table[note_name4] = table[note_name1]\n # add entries for musical rests\n r = Pitch()\n table['R'] = [-1, 0.0, r]\n table['r'] = table['R']\n #table[-1] = [[r,r,r,r,r], 0.0]\n return table","title":""},{"docid":"bd37a3c965c17b03739e99eb400ad848","score":"0.5384938","text":"def convertChordsToNotes(s):","title":""},{"docid":"58c3ff2226003166ac27f2589249276d","score":"0.53816175","text":"def circle(\r\n piece,\r\n pitch_type='tpc',\r\n measures=None, # need documentation\r\n log=False,\r\n vocabulary={0:'C', 1:'Db', 2:'D', 3:'Eb', 4:'E', 5:'F', 6:'Gb', 7:'G', 8:'Ab', 9:'A', 10:'Bb', 11:'B'},\r\n pitch_class_display=False,\r\n colorbar=True,\r\n duration=False,\r\n fifths=True,\r\n figsize=[7, 4],\r\n top=None,\r\n rotation=0,\r\n clockwise=True,\r\n cmap='Blues',\r\n nan_color=None,\r\n show=False,\r\n **kwargs):\r\n #settings\r\n df = get_df_short(piece, vocabulary=vocabulary, pitch_type=pitch_type, measures=measures, duration=duration)\r\n\r\n #color map\r\n cmap = matplotlib.cm.get_cmap(cmap)\r\n color_note = []\r\n\r\n #dataFrame for the plot if tpc\r\n df_tpc_pie = pd.DataFrame(columns=['note', 'part', 'pc'])\r\n\r\n #put top in the right form\r\n if pd.isnull(top) == False:\r\n if is_tpc(top) and pitch_class_display:\r\n top = get_pc(top)\r\n if is_pc(top) and not pitch_class_display:\r\n top = vocabulary[int(top)]\r\n\r\n #remember position of data in Series\r\n s_pos = pd.Series()\r\n count = 0\r\n part = 0\r\n letter = 'nan'\r\n s_fifth = pd.Series()\r\n \r\n fig = plt.figure(figsize=figsize)\r\n if not show:\r\n plt.close(fig)\r\n ax = fig.add_subplot(111, aspect='equal')\r\n \r\n #Set the order in function of fifth\r\n if fifths:\r\n s_tpc_format = pd.Series((0, 7, 2, 9, 4, 11, 6, 1, 8, 3, 10, 5))\r\n else:\r\n s_tpc_format = pd.Series((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11))\r\n\r\n #for plot if pitch_class_display\r\n s_twelve_ones = pd.Series((1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), index=s_tpc_format)\r\n\r\n #if it show the tpc values\r\n if pitch_class_display == False:\r\n #put the right values in 'number'\r\n if duration:\r\n df_data = df.copy()\r\n df_data.rename(columns={'duration': 'number'},inplace=True)\r\n else:\r\n df_data = df.copy()\r\n df_data.rename(columns={'nb': 'number'},inplace=True)\r\n\r\n #Normalize the values for the colors\r\n max_value = df_data['number'].max()\r\n min_value = df_data['number'].min()\r\n if log:\r\n norm = matplotlib.colors.LogNorm(vmin=min_value, vmax=max_value)\r\n else:\r\n norm = matplotlib.colors.Normalize(0, vmax=max_value)\r\n \r\n #for chromatic order\r\n if fifths == False:\r\n\r\n #for each pitch class values\r\n for i in range(12):\r\n\r\n #if a pitch class is represented in the data\r\n if df_data['pc'].isin([s_tpc_format[i]]).any():\r\n count = 0\r\n s_pos.drop(s_pos.index, inplace=True)\r\n \r\n #count how much time there is tpc values for a same pitch class\r\n for j in range(df_data['pc'].isin([s_tpc_format[i]]).shape[0]):\r\n if df_data['pc'].isin([s_tpc_format[i]])[j]:\r\n s_pos.at[count] = j\r\n count = count + 1\r\n\r\n #devide the pie part and set color\r\n for j in range(count):\r\n part = 1/count\r\n letter = df_data.at[s_pos.at[j], 'step']\r\n\r\n #write the notes\r\n letter = put_flat_sharp(letter, df_data.at[s_pos.at[j], 'acc'])\r\n\r\n #register the informations\r\n df_tpc_pie = df_tpc_pie.append({'note':letter, 'part':part},\r\n ignore_index=True)\r\n color_note.append(cmap(norm(df_data.at[s_pos.at[j], 'number'])))\r\n\r\n #if the pitch class do no appear in the piece\r\n else:\r\n letter = vocabulary[s_tpc_format[i]]\r\n\r\n df_tpc_pie = df_tpc_pie.append({'note':letter, 'part':1}, ignore_index=True)\r\n if pd.isnull(nan_color):\r\n color_note.append(cmap(0))\r\n else:\r\n color_note.append(nan_color)\r\n else:\r\n #get the fifth numbers of the notes\r\n for i in range(df_data.shape[0]):\r\n s_fifth.at[i] = get_fifth_nb(df_data.at[i, 'tpc'])\r\n df_data['fifth'] = s_fifth\r\n\r\n #create df_tpc_pie and get the colours\r\n for i in range(df_data['fifth'].max()-df_data['fifth'].min()+1):\r\n #the part are equal for the moment\r\n df_tpc_pie.at[i, 'part'] = 1\r\n df_tpc_pie.at[i, 'note'] = get_fifth_note(i + df_data['fifth'].min())\r\n df_tpc_pie.at[i, 'pc'] = get_pc(df_tpc_pie.at[i, 'note'])\r\n \r\n if df_data['fifth'].isin([i + df_data['fifth'].min()]).any():\r\n #get the colour for the note who has the good fifth number\r\n color_note.append(cmap(norm(df_data['number'][df_data['fifth']==(i + df_data['fifth'].min())].iat[0])))\r\n elif df_data['fifth'].isin([i + df_data['fifth'].min()]).any() == False and pd.isnull(nan_color) == False:\r\n color_note.append(nan_color)\r\n else:\r\n color_note.append(cmap(0))\r\n\r\n #if clockwise invert the order of the data to be displayed clockwise, inverse also the index\r\n if clockwise:\r\n df_tpc_pie = df_tpc_pie.iloc[::-1]\r\n color_note = list(reversed(color_note))\r\n\r\n #calculate the angle for the topPitchClass to be at the top\r\n if pd.isnull(top) == False and fifths == False and df_tpc_pie['note'].isin([top]).any() == True:\r\n if clockwise:\r\n rotation = rotation + 90 + df_tpc_pie.at[0, 'part'] * 15\r\n else:\r\n rotation = rotation + 90 - df_tpc_pie.at[0, 'part'] * 15\r\n for i in range(df_tpc_pie.shape[0]):\r\n if top == df_tpc_pie.at[i, 'note']:\r\n if df_tpc_pie.at[i, 'part'] != 1:\r\n if clockwise:\r\n rotation = rotation - 15*df_tpc_pie.at[i, 'part']\r\n else:\r\n rotation = rotation + 15*df_tpc_pie.at[i, 'part']\r\n break\r\n else:\r\n if clockwise:\r\n rotation = rotation + 30*df_tpc_pie.at[i, 'part']\r\n else:\r\n rotation = rotation - 30*df_tpc_pie.at[i, 'part']\r\n\r\n #put the top note at the top\r\n if pd.isnull(top) == False and fifths == True and df_tpc_pie['note'].isin([top]).any() == True:\r\n if clockwise:\r\n rotation = rotation + 90 + 180/df_tpc_pie.shape[0]\r\n else:\r\n rotation = rotation + 90 - 180/df_tpc_pie.shape[0]\r\n for i in range (df_tpc_pie.shape[0]):\r\n if df_tpc_pie.at[i, 'note'] == top:\r\n break\r\n else:\r\n #the sens of reading depend on the orientation\r\n if clockwise:\r\n rotation = rotation + 360/df_tpc_pie.shape[0]\r\n else:\r\n rotation = rotation - 360/df_tpc_pie.shape[0]\r\n \r\n\r\n #put nice sharps and flats\r\n for i in range(df_tpc_pie.shape[0]):\r\n df_tpc_pie.at[i, 'note'] = df_tpc_pie.at[i, 'note'].replace('b', r'$\\flat$')\\\r\n .replace('#', r'$\\sharp$')\r\n \r\n #plot the piechart with index 'tpc'\r\n df_tpc_pie.index = df_tpc_pie['note']\r\n \r\n #do the pie chart\r\n ax.pie(labels=df_tpc_pie.index, x=df_tpc_pie['part'], colors=color_note, startangle=rotation, **kwargs)\r\n\r\n #if asked plot the colorbar left of the piechart\r\n if colorbar:\r\n ax2 = fig.add_subplot(1, 10, 1)\r\n cb1 = matplotlib.colorbar.ColorbarBase(ax2, cmap=cmap, norm=norm, orientation='vertical')\r\n \r\n #display with the pc values\r\n else:\r\n #put the right values in 'number'\r\n if duration:\r\n df_data = pd.concat(\r\n [df['pc'], df['duration']],\r\n axis=1,\r\n keys=['pc', 'number'])\r\n else:\r\n df_data = pd.concat(\r\n [df['pc'], df['nb']],\r\n axis=1,\r\n keys=['pc', 'number'])\r\n\r\n #Normalize the values for the colors\r\n max_value = df_data['number'].max()\r\n min_value = df_data['number'].min()\r\n if log:\r\n norm = matplotlib.colors.LogNorm(vmin=min_value, vmax=max_value)\r\n else:\r\n norm = matplotlib.colors.Normalize(0, vmax=max_value)\r\n \r\n #set data df_data\r\n df_data = (df_data.groupby('pc')).sum()\r\n df_data = df_data.reindex(s_tpc_format)\r\n df_data.fillna(0, inplace=True)\r\n\r\n #set colors\r\n for i in range(0, 12):\r\n if df_data.iat[i, 0] != 0:\r\n color_note.append(cmap(norm(df_data.iat[i, 0])))\r\n else:\r\n if pd.isnull(nan_color):\r\n color_note.append(cmap(0))\r\n else:\r\n color_note.append(nan_color)\r\n\r\n #if clockwise invert the order of the data to be displayed clockwise\r\n if clockwise:\r\n s_twelve_ones = s_twelve_ones.iloc[::-1]\r\n color_note = list(reversed(color_note))\r\n\r\n #calculate the angle for the topPitchClass to be at the top\r\n if pd.isnull(top) == False:\r\n for i in range(s_tpc_format.shape[0]):\r\n if top == (s_twelve_ones.index)[i]:\r\n rotation = rotation + 75 - i * 30\r\n break\r\n ax.pie(labels=s_twelve_ones.index, x=s_twelve_ones, colors=color_note, startangle=rotation, **kwargs)\r\n\r\n #if asked plot the colorbar left of the piechart\r\n if colorbar:\r\n ax2 = fig.add_subplot(1, 10, 1)\r\n cb1 = matplotlib.colorbar.ColorbarBase(ax2, cmap=cmap, norm=norm, orientation='vertical')\r\n return fig","title":""},{"docid":"96bc891fd8a531831923c45c61e834bb","score":"0.53810406","text":"def getChordScaleDegreesAsChord(self, n, degrees):","title":""},{"docid":"d11eeb501fc43303b16778ecb8c6fe5d","score":"0.5331633","text":"def create_chord_from_random_notes_in_key(self, minNum=2, maxNum=6):\n nl = [np.random.choice(self.get_notes_in_key()) for i in range(np.random.randint(minNum,maxNum))]\n ch = chord.Chord(nl)\n return ch","title":""},{"docid":"a964c7bae35ea170ff9130b53cf248bc","score":"0.5285635","text":"def CA_bass(self):\n chords = chord(self.chord)\n bass = Note(pitch = chords, duration = 4)\n self.add_bass(bass)","title":""},{"docid":"d50341a0dffe5291a72c4650ae36f284","score":"0.5267212","text":"def __illustrate__(self):\n import abjad\n chord = abjad.Chord(self, abjad.Duration(1))\n voice = abjad.Voice([chord])\n staff = abjad.Staff([voice])\n score = abjad.Score([staff])\n lilypond_file = abjad.LilyPondFile.new(score)\n return lilypond_file","title":""},{"docid":"6079cb601f8d09ad748995c4ec408565","score":"0.52628016","text":"def psychoAcousticDistance(chord1, chord2):","title":""},{"docid":"34c00e6afb804011f5175562ae4c91c6","score":"0.52496254","text":"def generate_melody(key, progression, progression_repeats, major=True):\n out = []\n for _ in range(progression_repeats):\n time_used = 0.0 # Number of measures that have been generated so far\n for i, chord in enumerate(progression):\n all_tones = generate_scale(key, 2, major)\n chord_tones = get_chord(chord, note_number(key+'2'))\n chord_tones.extend([x+12 for x in chord_tones])\n non_chord_tones = list(set(all_tones[:-1]) - set(chord_tones))\n non_chord_tones.extend([x+12 for x in non_chord_tones])\n last_played = None\n\n # Generate a sequence of notes to fill a measure for this chord\n while time_used < i + 1:\n note_vals = [(0.125, 2), (0.25, 4), (0.375, 2), (0.5, 2), (0.75, 1), (1.0, 1), (1.25, 0.5), (1.5, 0.25)]\n # Only allow note lengths that will fit into the len(progression)\n # measures for this chord progression (i.e. don't allow spill\n # of notes into different repetitions of the progression)\n possible_note_vals = [x for x, p in note_vals if time_used + x <= len(progression)]\n note_vals_prob = [p for x, p in note_vals if time_used + x <= len(progression)]\n note_vals_prob = [x*1.0/sum(note_vals_prob) for x in note_vals_prob]\n # Choose a note length\n note_val = numpy.random.choice(possible_note_vals, p=note_vals_prob)\n # Choose the set of note numbers we could pick from (either\n # the chord tones or non-chord tones)\n select_from = non_chord_tones if int(randint(0, 10)/10.0) else chord_tones\n # Incentivize choosing notes that are close to the previously\n # played note so that we aren't just jumping all over the\n # place and sounding terribly random\n NEARBY_INCENTIVE = 2.0\n select_from_probabilities = [ (36 - int(math.fabs(last_played - x)))**NEARBY_INCENTIVE if last_played else 1 for x in select_from ]\n select_from_probabilities = [ x * 1.0 / sum(select_from_probabilities) for x in select_from_probabilities ]\n out.append((numpy.random.choice(select_from, p=select_from_probabilities), 80, note_val))\n last_played = out[-1][0]\n out.extend([None for x in range(int(note_val/0.125)-1)])\n time_used += note_val\n assert len(out) == 8 * len(progression) * progression_repeats\n return out","title":""},{"docid":"c28fe18c4f71af211ccda3a21b8b6d9d","score":"0.51965153","text":"def CA_notes(self):\n beat = 0\n while beat < self.length:\n if beat+1 in self.str_beat:\n pitch = [random.choice(chord(self.chord))]\n else:\n pitch = [random.choice(notes)]\n duration = random.choices(list(self.note_lengths.keys()),list(self.note_lengths.values()))\n beat += duration[0]\n if beat > self.length:\n beat -= duration[0]\n duration = self.length - beat\n duration = [duration]\n beat += duration[0]\n rnote = Note(pitch, duration[0])\n self.add_note(rnote)\n assert beat == self.length","title":""},{"docid":"f8b9d3dd93127f378765ea1056cd6190","score":"0.5183889","text":"def cpsmidi(cps: float) -> float:\n return 69 + 12 * np.log2(cps / 440.0)","title":""},{"docid":"cd25ace712ad5321dca786806750271e","score":"0.51812875","text":"def octave(self, octave = 0):\r\n return Note(self.pitch + octave * 12)","title":""},{"docid":"015a1c60b8980ac36f7c09d7f8e35bbb","score":"0.5160207","text":"def __init__(self, start_time: float, end_time: float, all_chords_list: list):\n self.all_chords_list = all_chords_list\n self.start_time = start_time\n self.end_time = end_time\n self.duration = end_time - start_time\n self.notes = []\n self.pitches = set()\n self.pitch_classes = set()\n self.chroma = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n self.most_likely_chord = None\n self.most_likely_chord_score = -10","title":""},{"docid":"b763695c4cd07303bca85b1cf84fb6c8","score":"0.5141266","text":"def _parse_chord(self, chord_word):\n if chord_word != '' and chord_word[0].islower():\n res = chord_word[0].upper()\n res += 'm'\n return res\n elif (chord_word == '*'):\n return ''\n else:\n return chord_word","title":""},{"docid":"5cc9fb7de89c0c6c6c46537d169b04df","score":"0.5115718","text":"def translate_to_chord(state_prog):\n list = ['C', 'D', 'E', 'F', 'G', 'A', 'B']\n result = []\n for state in state_prog:\n word = \"\"\n for i, p in enumerate(state):\n if i == 0:\n word += list[int(p) - 1]\n else:\n word += p\n result.append(word)\n\n return result","title":""},{"docid":"26ad0028b519146c0d63597fbea1cebd","score":"0.50975525","text":"def chord_spaced_parameters(points):\n cds = chord_lengths(points)\n d = sum(cds)\n uk = [0.] + cds + [1.]\n return [sum(uk[0:i + 1]) / d for i in range(len(points))]","title":""},{"docid":"dc4d18b36a9a2afb394fc596894976a3","score":"0.507723","text":"def composing(chord_pair_dict):\n full_song = []\n full_song.append(choose_theme_a(chord_pair_dict))\n full_song.append(choose_theme_b(chord_pair_dict))\n full_song.append(choose_theme_a(chord_pair_dict))\n stream_full_song = get_stream_from_chord_prog(full_song)\n stream_full_song = get_voice_led_stream(stream_full_song) #stub\n return","title":""},{"docid":"49a15ef0f1320ba06eff446899d5502d","score":"0.50548774","text":"def random_note_in_chord(self, index_):\n\n\t\t# select random note from chord\n\t\tif self.chords[index_] == -1:\n\t\t\tsemitones_above_tonic = self.chords[index_]\n\t\t\treturn note.Rest(quarterLength=1)\n\n\t\telse:\n\t\t\tpossible_notes = self.chords[index_].split('/')\n\t\t\tselect_idx = np.random.randint(len(possible_notes))\n\t\t\tsemitones_above_tonic = int(possible_notes[select_idx])\n\t\t\t\n\t\t\t# return note\n\t\t\ttonic = self.key.getTonic()\n\t\t\tpitch_ = tonic.transpose(semitones_above_tonic)\n\n\t\t\t# select random obctave within vocal range\n\t\t\tlow, high = self.vocal_range\n\n\t\t\t#lowest octave\n\t\t\tlowest_oct = pitch_.transposeAboveTarget(low).octave\n\n\t\t\t# highest octave\n\t\t\thighest_oct = pitch_.transposeBelowTarget(high).octave\n\n\t\t\t# bug sometimes arises where low higher than high\n\t\t\tif lowest_oct >= highest_oct:\n\t\t\t\tlowest_oct = low.octave\n\n\t\t\t# highest + 1 as numpy.random.randint is exclusive of high \n\t\t\toct_ = np.random.randint(low=lowest_oct, high=highest_oct + 1)\n\t\t\tpitch_.octave = oct_\n\n\t\t\treturn note.Note(pitch_, quarterLength=1)","title":""},{"docid":"0ab8fa8b931568248078ce7174654edb","score":"0.5049438","text":"def __init__(self, midi_pitch):\n self.midi_pitch = midi_pitch\n self.letter = PITCHES[midi_pitch % 12]\n self.octave = midi_pitch // 12","title":""},{"docid":"ffb0ff6cc669fcd46cdf994b9b40334e","score":"0.5039541","text":"def choose_random_chord(chord_pair_dict):\n chord_prog = []\n first_chord = statistics\n #getting list of ALL chords and storing in \"chord_options\"\n chord_options = []\n for chord_pair in chord_pair_dict:\n chord_pair = chord_pair.split(\" -> \")\n print(chord_pair, chord_pair[0])\n chord_options.append(chord_pair[0])\n #getting \"corresponding_chord_probability\"\n corresponding_chord_probability = []\n total = 0\n for chord_pair in chord_pair_dict:\n total += chord_pair_dict[chord_pair] #this loop gets total number of chords in song\n\n for chord_num in chord_pair_dict:\n chance = chord_pair_dict[chord_num]/total #for every chord in the dictionary, find chance of occurence and add to \"corresponding_chord_probability\"\n corresponding_chord_probability.append(chance) #for each option that COULD follow, append the chance of it following\n print(\"chord options\", chord_options)\n print(\"corresponging chord probability\", corresponding_chord_probability)\n chosen_chord = numpy.random.choice(chord_options, 1, p=corresponding_chord_probability)[0]\n return(chosen_chord)","title":""},{"docid":"9c144faab4b43d19ba6114750580c9f4","score":"0.5008513","text":"def chord_recognition_all(X, ann_matrix, p=0.15, filt_len=None, filt_type='mean'):\n if filt_len is not None:\n if filt_type == 'mean':\n X, Fs_X = libfmp.c3.smooth_downsample_feature_sequence(X, Fs=1, filt_len=filt_len, down_sampling=1)\n if filt_type == 'median':\n X, Fs_X = libfmp.c3.median_downsample_feature_sequence(X, Fs=1, filt_len=filt_len, down_sampling=1)\n # Template-based chord recogntion\n chord_sim, chord_Tem = libfmp.c5.chord_recognition_template(X, norm_sim='1')\n result_Tem = libfmp.c5.compute_eval_measures(ann_matrix, chord_Tem)\n # HMM-based chord recogntion\n A = libfmp.c5.uniform_transition_matrix(p=p)\n C = 1 / 24 * np.ones((1, 24))\n B_O = chord_sim\n chord_HMM, _, _, _ = libfmp.c5.viterbi_log_likelihood(A, C, B_O)\n result_HMM = libfmp.c5.compute_eval_measures(ann_matrix, chord_HMM)\n return result_Tem, result_HMM, chord_Tem, chord_HMM, chord_sim","title":""},{"docid":"972e0e04e35f36f7f6b25bdcfe598511","score":"0.49731722","text":"def zelda_Song_of_Time():\r\n play_note(NOTE_A4, 0.5)\r\n play_note(NOTE_D4, 1.0)\r\n play_note(NOTE_F4, 0.5)\r\n play_note(NOTE_A4, 0.5)\r\n play_note(NOTE_D4, 1.0)\r\n play_note(NOTE_F4, 0.5)\r\n play_note(NOTE_A4, 0.25)\r\n play_note(NOTE_C5, 0.25)\r\n play_note(NOTE_B4, 0.5)\r\n play_note(NOTE_G4, 0.5)\r\n play_note(NOTE_F4, 0.25)\r\n play_note(NOTE_G4, 0.25)\r\n play_note(NOTE_A4, 0.5)\r\n play_note(NOTE_D4, 0.5)\r\n play_note(NOTE_C4, 0.25)\r\n play_note(NOTE_E4, 0.25)\r\n play_note(NOTE_D4, 1.5)\r\n PWM.stop(piezo_pin)\r\n PWM.cleanup()","title":""},{"docid":"b570cd43bb7be4a2e735e7d3bc480f69","score":"0.49640536","text":"def GetCd(alpha, h):\r\n\r\n if not isinstance(h, numbers.Number):\r\n raise ValueError('Wrong arg type passed to GetCd() for h. Must be'\r\n + ' int or float')\r\n\r\n return AC['parasiteDrag'] + (16 * h / AC['span']) ** 2 / (1 + (16 * h / AC['span']) ** 2) * \\\r\n GetCl(alpha) ** 2 / (AC['AR'] * AC['e'] * math.pi)","title":""},{"docid":"98f54016ff21e87547a477e4f003fdd9","score":"0.4963949","text":"def test_2q_cphase_circuit():\n prog = Program(CPHASE(0.1, 1, 2))\n\n expected = r\"\"\"\n \\begin{tikzcd}\n \\lstick{\\ket{q_{1}}} & \\gate[wires=2]{CPHASE(0.1)} & \\qw \\\\\n \\lstick{\\ket{q_{2}}} & \\qw & \\qw\n \\end{tikzcd}\n \"\"\".strip().split()\n\n actual = to_latex(prog).split()\n start_idx = actual.index(\"\\\\begin{tikzcd}\")\n assert expected == actual[start_idx : start_idx + len(expected)]","title":""},{"docid":"3ed974bd52d951f3331b9b9d489796c2","score":"0.49583477","text":"def gen_cp(duration, h):\n cp_times = []\n cp_time = 0\n while cp_time < duration:\n dwell_time = np.random.exponential(1. / h)\n cp_time += dwell_time\n if cp_time < duration:\n cp_times += [cp_time]\n\n return np.array(cp_times)","title":""},{"docid":"44db732dff5bcfd63247895fd5a077f7","score":"0.49443159","text":"def tada():\n note(440, 0.3)\n note(440, 0.1)\n note(440, 0.1)\n note(880, 0.8)","title":""},{"docid":"f229b430a33defcfc903c00d5e065ac8","score":"0.4930778","text":"def load_chords(jams_path, leadsheet_version):\n try:\n with open(jams_path, \"r\") as fhandle:\n jam = jams.load(fhandle)\n except FileNotFoundError:\n raise FileNotFoundError(\"jams_path {} does not exist\".format(jams_path))\n\n if leadsheet_version:\n anno = jam.search(namespace=\"chord\")[0]\n else:\n anno = jam.search(namespace=\"chord\")[1]\n intervals, values = anno.to_interval_values()\n return annotations.ChordData(intervals, \"s\", values, \"jams\")","title":""},{"docid":"39be2b4eec200d450cfb9d168e48466a","score":"0.4928727","text":"def make_interval_chord(chords, key):\n result = []\n c_transposed = transpose(chords, key, 'C')\n\n for chord in c_transposed:\n if len(chord) == 1 or (chord[1] != '#' and chord[1] != 'b'):\n name_index = 1\n if chord[0] == 'F':\n mode = 'b'\n else:\n mode = '#'\n else:\n name_index = 2\n mode = chord[1]\n\n interval = calc_interval('C', chord[:name_index])\n\n if interval in vocab_value:\n result_chord = vocab_interval[interval]\n else:\n if mode == '#':\n interval -= 0.5\n else:\n interval += 0.5\n result_chord = vocab_interval[interval] + mode\n\n result.append(result_chord + chord[name_index:])\n\n return result","title":""},{"docid":"7e5956a1bd6d2453f993953413ac4f28","score":"0.49157333","text":"def make_banju_16bit(chord_prog, qlen, owd):\n part = stream.Part()\n\n #print(chord_prog)\n\n # Setting for the path and sample files\n os.chdir(owd)\n current_dir = os.getcwd()\n sample_dir = current_dir + '/banju/16bit'\n os.chdir(sample_dir)\n file_names = [file for file in os.listdir(sample_dir) if file != \".DS_Store\"]\n file_to_open = random.choice(file_names)\n # file_to_open = '16bit/chord_001.txt'\\\n file = open(file_to_open, 'r', encoding=\"utf-8\")\n\n file2 = file.read().replace(' ', '')\n banju_list = [int(f) for f in file2.split(',')]\n# Done\n on_list = []\n\n #print('banju is ', banju_list)\n\n for i, banju in enumerate(banju_list):\n if (banju == -1):\n on_list.append(-1)\n continue\n isOn = [1 if 8 & banju else 0, 1 if 4 & banju else 0, 1 if 2 & banju else 0, 1 if 1 & banju else 0]\n on_list.append(isOn)\n\n print('banju is ', on_list)\n\n for i, chord in enumerate(chord_prog): # C, Dm, G7, etc....\n offset = i * 4\n if i > qlen / 4:\n break\n c = harmony.ChordSymbol(chord)\n previous = []\n for j, is_on in enumerate(on_list): # [1, 0, 0, 1]\n if is_on == -1:\n for note_temp in previous:\n note_temp.quarterLength += 0.25\n else:\n previous.clear()\n for k, on in enumerate(is_on):\n if on:\n if k < len(c.pitches):\n note_temp = note.Note(c.pitches[k], quarterLength=0.25)\n part.insert(offset + j * 0.25, note_temp)\n previous.append(note_temp)\n elif k == 3 and k == len(c.pitches):\n note_temp = note.Note(c.pitches[0], quarterLength=0.25)\n note_temp.octave += 1\n part.insert(offset + j * 0.25, note_temp)\n previous.append(note_temp)\n\n part.insert(instrument.AcousticGuitar())\n # part.show('text', addEndTimes=True)\n return part","title":""},{"docid":"ce345cf9dcd8bab5100db2bb9d7fd9c5","score":"0.4902935","text":"def make_banju_8bit(chord_prog, qlen):\n part = stream.Part()\n\n print(chord_prog)\n\n # Setting for the path and sample files\n current_dir = os.getcwd()\n sample_dir = current_dir + '/banju'\n os.chdir(sample_dir)\n file_names = [file for file in os.listdir(sample_dir)]\n # file_to_open = random.choice(file_names)\n file_to_open = '8bit/condense_001.txt'\n file = open(file_to_open, 'r', encoding=\"utf-8\")\n\n file2 = file.readline().replace(' ', '')\n banju_list = [int(f) for f in file2.split(',')]\n\n on_list = []\n\n print(banju_list)\n\n for i, banju in enumerate(banju_list):\n if (banju == -1):\n on_list.append(-1)\n continue\n isOn = [1 if 8 & banju else 0, 1 if 4 & banju else 0, 1 if 2 & banju else 0, 1 if 1 & banju else 0]\n on_list.append(isOn)\n\n print(on_list)\n\n for i, chord in enumerate(chord_prog): # C, Dm, G7, etc....\n offset = i * 4\n if i > qlen / 4:\n break\n c = harmony.ChordSymbol(chord)\n previous = []\n for j, is_on in enumerate(on_list): # [1, 0, 0, 1]\n if is_on == -1:\n for note_temp in previous:\n note_temp.quarterLength += 0.5\n else:\n previous.clear()\n for k, on in enumerate(is_on):\n if on:\n if k < len(c.pitches):\n note_temp = note.Note(c.pitches[k], quarterLength=0.5)\n part.insert(offset + j * 0.5, note_temp)\n previous.append(note_temp)\n elif k == 3 and k == len(c.pitches):\n note_temp = note.Note(c.pitches[0], quarterLength=0.5)\n note_temp.octave += 1\n part.insert(offset + j * 0.5, note_temp)\n previous.append(note_temp)\n\n part.show('text', addEndTimes=True)\n return part","title":""},{"docid":"7ab93364f3deb75a42b8bba4c4e59291","score":"0.48928526","text":"def scale(notes=(\"c\", \"d\", \"e\", \"f\", \"g\", \"a\", \"b\", \"c5\")):\r\n def f(res, name, tempo):\r\n \"\"\" \r\n res: list, list of output states of the qc. \r\n return: Song object.\r\n \"\"\"\r\n s = Song(name=name, tempo=tempo)\r\n duration = 1\r\n for x in res:\r\n note = notes[int(x, base=2) % len(notes)]\r\n s.addNote(note, duration)\r\n return s\r\n return f","title":""},{"docid":"8d8d6b0b0a66d6fabb50e9fd77169942","score":"0.48915246","text":"def chordify(self, rules: Rules = TriadBaroque()) -> Stream:\n stream = Stream()\n prev_chord: Chord = rules.first_chord(self.key, self.input_notes[0])\n stream.append(prev_chord)\n for note in self.input_notes[1:]:\n prev_chord = rules.next_chord(self.key, prev_chord, note)\n stream.append(prev_chord)\n stream.append(rules.end_cadence(self.key, prev_chord))\n\n return stream.flat","title":""},{"docid":"6de14a221f19f476ba6b705027d11dc5","score":"0.48873156","text":"def calc_modes_circ(chi=3., Lmbda=0.5e-6, d_t=0.7):\n chi_rads = (chi/3600.)*np.pi/180.\n M = (np.pi * chi_rads * d_t/(4. * Lmbda))**2.\n\n return M","title":""},{"docid":"5f7b5b0e9e85eda088a6d272adc19663","score":"0.4881925","text":"def chord_lengths(points):\n return [distance_point_point(a, b) for a, b in zip(points[:-1], points[1:])]","title":""},{"docid":"be0e93569ae41144411db714f6b6f287","score":"0.48799253","text":"def csch(x):\n return 1 / sinh(x)","title":""},{"docid":"9b0dedb3d5fa209d8d27be9a975d32e2","score":"0.48784468","text":"def circle(x, z, r, t, duration):\n y_circle = r * math.cos(2 * math.pi * (1 / duration) * t)\n z_circle =+ r * math.sin(2 * math.pi * (1 / duration) * t)\n alphas = computeIK(x, y_circle, z_circle + z)\n return alphas","title":""},{"docid":"f75801a816527c88b3ddfef45517fdd1","score":"0.48645824","text":"def text_to_sample_midi(input_name):\n assert 'txt' in input_name\n\n input_file = open(input_name, 'r')\n # if input_name[1] == 'b' or input_name[1] == '#':\n # key = input_name[0:2]\n # else:\n # key = input_name[0]\n key = 'C'\n sample = []\n\n for line in input_file:\n sample += line.strip().split(',')\n\n return make_interval_chord(sample, key)","title":""},{"docid":"7fb7b733c54da9310a54f4620a4d4b6c","score":"0.48508376","text":"def __init__(self,length=8,chrd_prg=['C','Am','F','G'],note_lengths={1:1,.5:1}):\n self.length = length\n self.measures = []\n self.chrd_prg = chrd_prg\n self.note_lengths=note_lengths","title":""},{"docid":"381c1cb27b7230523a5a730a0fef184b","score":"0.48507717","text":"def __init__(self, dir_path, data=[]):\r\n self.measures = [] #measure information\r\n self.pitch = [] # pitch information\r\n #transpose the key of every song to C Major\r\n #key must be unchangable, so use 34 as the never reachable index\r\n #not nessary assign value by such a clumsy way. while the clumy way also has its advantages... \r\n self.pitch_dictionary = {globalConstant.note[0] : 0, globalConstant.note[1] : 1, globalConstant.note[2] : 34, globalConstant.note[3] : 0,\r\n globalConstant.note[4] : 2, globalConstant.note[5] : 3, globalConstant.note[6] : 1, globalConstant.note[7] : 2,\r\n globalConstant.note[8] : 4, globalConstant.note[9] : 5, globalConstant.note[10] : 3, globalConstant.note[11] : 4,\r\n globalConstant.note[12] : 5, globalConstant.note[13] : 6, globalConstant.note[14] : 4, globalConstant.note[15] : 5,\r\n globalConstant.note[16] : 7, globalConstant.note[17] : 8, globalConstant.note[18] : 6, globalConstant.note[19] : 7,\r\n globalConstant.note[20] : 9, globalConstant.note[21] : 10, globalConstant.note[22] : 8, globalConstant.note[23] : 9,\r\n globalConstant.note[24] : 10, globalConstant.note[25] : 11, globalConstant.note[26] : 9, globalConstant.note[27] : 10,\r\n globalConstant.note[28] : 12, globalConstant.note[29] : 13, globalConstant.note[30] : 11, globalConstant.note[31] : 12,\r\n globalConstant.note[32] : 14, globalConstant.note[33] : 15, globalConstant.note[34] : 13, globalConstant.note[35] : 14,\r\n globalConstant.note[36] : 16, globalConstant.note[37] : 17, globalConstant.note[38] : 15, globalConstant.note[39] : 16,\r\n globalConstant.note[40] : 17, globalConstant.note[41] : 18, globalConstant.note[42] : 16, globalConstant.note[43] : 17,\r\n globalConstant.note[44] : 19, globalConstant.note[45] : 20, globalConstant.note[46] : 18, globalConstant.note[47] : 19,\r\n globalConstant.note[48] : 21, globalConstant.note[49] : 22, globalConstant.note[50] : 20, globalConstant.note[51] : 21,\r\n globalConstant.note[52] : 22, globalConstant.note[53] : 23, globalConstant.note[54] : 21, globalConstant.note[55] : 22,\r\n globalConstant.note[56] : 24, globalConstant.note[57] : 25, globalConstant.note[58] : 23, globalConstant.note[59] : 24,\r\n globalConstant.note[60] : 26, globalConstant.note[61] : 27, globalConstant.note[62] : 25, globalConstant.note[63] : 26,\r\n globalConstant.note[64] : 28, globalConstant.note[65] : 29, globalConstant.note[66] : 27, globalConstant.note[67] : 28,\r\n globalConstant.note[68] : 29, globalConstant.note[69] : 30, globalConstant.note[70] : 28, globalConstant.note[71] : 29,\r\n globalConstant.note[72] : 31, globalConstant.note[73] : 32, globalConstant.note[74] : 30, globalConstant.note[75] : 31}\r\n\r\n \r\n #list first, then convert it to numpy array. see ListToNumpyArrayTest.py.\r\n self.duration = [] # duration information\r\n \r\n self.duration_dictionary = {globalConstant.index0 : 0, \r\n globalConstant.index1 : 1,\r\n globalConstant.index2 : 2,\r\n globalConstant.index3 : 3,\r\n globalConstant.index4 : 4,\r\n globalConstant.index5 : 5, \r\n globalConstant.index6 : 6,\r\n globalConstant.index7 : 7,\r\n globalConstant.index8 : 8,\r\n globalConstant.index9 : 9,\r\n globalConstant.index10 : 10,\r\n globalConstant.index11 : 11,\r\n globalConstant.index12 : 12,\r\n globalConstant.index13 : 13,\r\n globalConstant.index14 : 14,\r\n globalConstant.index15 : 15,\r\n globalConstant.index16 : 16,\r\n globalConstant.index17 : 17,\r\n globalConstant.index18 : 18,\r\n globalConstant.index19 : 19,\r\n globalConstant.index20 : 20,\r\n globalConstant.index21 : 21,\r\n globalConstant.index22 : 22 \r\n }\r\n self.dir_path = dir_path; #path of directionary\r\n self.data = data #ABC files information\r","title":""},{"docid":"34b5a740693ba353d6a41eebd2a0c9fe","score":"0.4847936","text":"def choose_theme_b(chord_pair_dict):\n chord_prog = []\n first_chord = choose_random_chord(chord_pair_dict)\n chord_prog.append(first_chord)\n current_chord_num = first_chord\n print(current_chord_num)\n markov_chain(chord_pair_dict, chord_prog, current_chord_num, 7) #adds seven more chords to chord prog..\n return chord_prog","title":""},{"docid":"8c6dfe478812ecd7d5b786b4cb49af63","score":"0.48426485","text":"def CA_Lnote(self):\n pitch = [random.choice(chord(self.chord))]\n duration = 4\n lnote = Note(pitch, duration)\n self.add_note(lnote)","title":""},{"docid":"e3055c4af12716cfeef4a1dad68dac89","score":"0.4837407","text":"def cyril(prev_play, count=[0]):\n count[0] += 1\n choices = [\"P\", \"S\", \"S\", \"R\", \"P\"]\n return choices[count[0] % len(choices)]","title":""},{"docid":"388116c2a4cd96ac66d21d323593b2da","score":"0.4821277","text":"def apply(self, reverse_harmony=True, time_interval=None, transcription=True, results_sample_size=200):\n from tonalmodel.interval import Interval, IntervalType\n\n self.__reverse_harmony = reverse_harmony\n self.__time_interval = time_interval if time_interval is not None else \\\n NumericInterval(Fraction(0), self.score.line.duration.duration)\n\n reduced_line, first_position, duration = self.score.line.sub_line(self.time_interval)\n reduced_reversed_line = reduced_line.clone()\n reduced_reversed_line.reverse()\n\n reduced_hct = self.score.hct.sub_hct(NumericInterval(first_position.position,\n first_position.position + duration.duration))\n\n reduced_reversed_hct = reduced_hct.reverse()\n\n # If reversing harmony OR if reversing harmony and not doing transposition\n # These cases do not require transcription:\n # a) reverse harmony and no transcription - return reversed line + reversed harmony\n # b) no reverse harmony and no transcription - return reversed_line + original hct\n # c) reverse harmony and transcription - return reversed_line and reversed harmony.\n if reverse_harmony or not transcription:\n return reduced_reversed_line, reduced_reversed_hct if reverse_harmony else reduced_hct\n\n # Case: not reversing harmony AND requiring a transposition\n # Must specify reversed line here, but ANALYZE against reversed_hct, which is the original harmony\n # making the correct analysis of the original melody.\n t_ht = THarmonicTranscription(reduced_reversed_line, reduced_reversed_hct)\n\n # Find lowest tone:\n notes = reduced_reversed_line.get_all_notes()\n note_index, _ = min(enumerate(notes), key=lambda n: n[1].diatonic_pitch.chromatic_distance)\n lowest_pitch = notes[note_index].diatonic_pitch\n\n # Adapt reversed melody to original harmony.\n tag_map = gen_tag_map(notes[0], reduced_hct.hc_list()[0])\n results = t_ht.apply(reduced_hct,\n lowest_pitch,\n tag_map, t_ht.height + 6, results_sample_size,\n tunnel_half_interval=Interval(4, IntervalType.Perfect))\n\n filtered_results = MinContourFilter(reduced_reversed_line, results.pitch_results)\n scored_filtered_results = filtered_results.scored_results\n\n if len(scored_filtered_results) == 0:\n return None, None\n\n return scored_filtered_results[0][0], reduced_hct","title":""},{"docid":"07a69fc354feb7ccf0633762182acb30","score":"0.48089764","text":"def play_progression(*args):\n for x in args:\n for note in get_chord(x, note_number('C5')):\n play(note, 80, 1)\n time.sleep(1)","title":""},{"docid":"e9b08c83eedbf7655d3db62859897a50","score":"0.48085463","text":"def randomChord(self,rangeVal=[-12,12],inKey=True,nowStart = True):\n\t\tnewNotes = []\n\t\tfor i in range(len(self.notes)):\n\t\t\tmaxval,minval = rangeVal\n\t\t\tshiftVal = mapValue(random(),0,1,minval,maxval)\n\t\t\tnewNotes.append(self.notes[i]+shiftVal)\n\t\t\n\t\tif inKey == True:\n\t\t\tnewNotes = enforceKey(newNotes,self.key)\n\t\tif self.octaveRange is not None:\n\t\t\tnewNotes = enforceOctave(newNotes,self.octaveRange)\n\n\t\tself.newNotes(newNotes, nowStart = nowStart)","title":""},{"docid":"d2ebd20d1112e6c1c57c64ba85a0d1dc","score":"0.4772306","text":"def CA_Lbass(self):\n chords = chord(self.chord)\n bass = Note(pitch = chords, duration = 4)\n self.add_bass(bass)","title":""},{"docid":"f56399ea6fb4b7b6d9a65cbed364c319","score":"0.4768541","text":"def chords_of_scale(root_name: str, root_acci: Accidental, chord_type: str) -> list[list[str]]:\n result_list = chord_scale_namer(ChordOrScale.Scale, root_name, root_acci, chord_type, 7)\n chords_list = []\n i = 0\n while i < 7:\n current_chord = [result_list[i], result_list[(i + 2) % 7], result_list[(i + 4) % 7]]\n chords_list.append(current_chord)\n i += 1\n if chord_type == \"nat_minor\":\n if 'bb' in chords_list[4][1]:\n chords_list[4][1] = chords_list[4][1][0] + 'b'\n elif 'b' in chords_list[4][1]:\n chords_list[4][1] = chords_list[4][1][0]\n elif '#' in chords_list[4][1]:\n chords_list[4][1] = chords_list[4][1][0] + 'x'\n else:\n chords_list[4][1] = chords_list[4][1][0] + '#'\n # ^raised 7th for V chord in minor scale adjustment\n return chords_list","title":""},{"docid":"4ff064658ace8b1cc9964e7c8dbd3143","score":"0.47653127","text":"def makeCone(dir, ksi, length, resolution, d1, angle, reverse=False):\n print d1, angle\n retval = []\n if reverse:\n rot = makeAxisRotation(dir, math.pi*2./resolution)\n else:\n rot = makeAxisRotation(dir, -math.pi*2./resolution)\n\n for i in range(0, resolution):\n p1 = ksi*d1\n p2 = dir*length + ksi*(d1+length*math.tan(angle))\n\n for i in range(0,len(retval)):\n retval[i] = np.dot(rot, retval[i])\n\n retval.append(p1)\n retval.append(p2)\n\n return retval","title":""},{"docid":"774899192b2b1534655dae5aa31551df","score":"0.47606248","text":"def parse_chord_sequence(chord_sequence):\n chord_list = chord_sequence.split(\"|\")\n note_list = []\n note_frqs = []\n for i in range(len(chord_list)):\n chord_list[i] = chord_list[i].strip()\n chord_list[i] = chord_list[i].split('_')\n chord_notes = notes.chord_notes(*chord_list[i])\n note_list.append(chord_notes)\n return note_list","title":""},{"docid":"8f69e5ff3cf66b63a51b7e53e9c6b646","score":"0.47594944","text":"def create_chord_template(request):\n all_songs = Song.objects.all()\n for song in all_songs:\n if song.chords:\n continue\n ccli = song.ccli\n title = song.title\n f = open('D:/dropbox/django/songs_chordpro/'+str(ccli)+'.cho', 'w')\n title_line = '{title:'+title+'}\\n'\n authors_list = []\n for author in song.authors.all():\n authors_list.append(author.full_name)\n publisher_list = []\n publication_date = song.publication_year\n for publisher in song.publisher.all():\n publisher_list.append(publisher.name)\n authors_line = '{st:Words and Music by '+ ', '.join(authors_list)+'}\\n'\n if publication_date == 1111:\n publishers_line = '{st:Public Domain}\\n'\n else:\n publishers_line = '{st:Copyright '+str(song.publication_year)+' '+', '.join(publisher_list)+'}\\n'\n ccli_line = '{st:CCLI ' + str(ccli) + '}'\n f.write(title_line.encode('utf8'))\n if len(authors_list) != 0:\n f.write(authors_line.encode('utf8'))\n f.write(publishers_line)\n f.write(ccli_line)\n f.close()\n\n return HttpResponseRedirect(reverse('songs.views.success'))","title":""}],"string":"[\n {\n \"docid\": \"07e77d4f3665179daa18aa85ae1ad833\",\n \"score\": \"0.84081155\",\n \"text\": \"def create_c_chord(self, octave, duration, harmony):\\n\\n result = None\\n\\n if str(harmony) == \\\"major\\\":\\n c_note = AudioSegment.from_file(self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave)+\\\"/c.wav\\\")\\n temp1 = c_note.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave)+\\\"/g.wav\\\"))\\n temp2 = temp1.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave+1)+\\\"/c.wav\\\"))\\n temp3 = temp2.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave+1)+\\\"/e.wav\\\"))\\n result = temp3.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave+1)+\\\"/g.wav\\\"))\\n elif str(harmony) == \\\"minor\\\":\\n c_note = AudioSegment.from_file(self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave)+\\\"/c.wav\\\")\\n temp1 = c_note.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave)+\\\"/g.wav\\\"))\\n temp2 = temp1.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave+1)+\\\"/c.wav\\\"))\\n temp3 = temp2.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave+1)+\\\"/d_sharp.wav\\\"))\\n result = temp3.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave+1)+\\\"/g.wav\\\"))\\n\\n return Piano.calculate_duration(result, duration, self.tempo)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"289be3303d8348b0f45eea19c1805114\",\n \"score\": \"0.78927076\",\n \"text\": \"def create_a_chord(self, octave, duration, harmony):\\n\\n result = None\\n\\n if str(harmony) == \\\"major\\\":\\n temp = AudioSegment.from_file(self.path_to_directory + \\\"/samples/piano/\\\"+str(octave - 1)+\\\"/a.wav\\\")\\n temp1 = temp.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave)+\\\"/e.wav\\\"))\\n temp2 = temp1.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave)+\\\"/a.wav\\\"))\\n temp3 = temp2.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave + 1)+\\\"/c_sharp.wav\\\"))\\n temp4 = temp3.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave + 1)+\\\"/e.wav\\\"))\\n result = temp4.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave+1)+\\\"/a.wav\\\"))\\n elif str(harmony) == \\\"minor\\\":\\n temp = AudioSegment.from_file(self.path_to_directory + \\\"/samples/piano/\\\"+str(octave - 1)+\\\"/a.wav\\\")\\n temp1 = temp.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave)+\\\"/e.wav\\\"))\\n temp2 = temp1.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave)+\\\"/a.wav\\\"))\\n temp3 = temp2.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave + 1)+\\\"/c.wav\\\"))\\n temp4 = temp3.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave + 1)+\\\"/e.wav\\\"))\\n result = temp4.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave + 1)+\\\"/a.wav\\\"))\\n\\n return Piano.calculate_duration(result, duration, self.tempo)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d39678cb5bfeb4f5c8bb7db41d4ad3e9\",\n \"score\": \"0.7787941\",\n \"text\": \"def create_c_sharp_chord(self, octave, duration, harmony):\\n\\n result = None\\n\\n if str(harmony) == \\\"major\\\":\\n c_note = AudioSegment.from_file(self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave)+\\\"/c_sharp.wav\\\")\\n temp1 = c_note.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave)+\\\"/g_sharp.wav\\\"))\\n temp2 = temp1.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave+1)+\\\"/c_sharp.wav\\\"))\\n temp3 = temp2.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave+1)+\\\"/f.wav\\\"))\\n result = temp3.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave+1)+\\\"/g_sharp.wav\\\"))\\n elif str(harmony) == \\\"minor\\\":\\n c_note = AudioSegment.from_file(self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave)+\\\"/c_sharp.wav\\\")\\n temp1 = c_note.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave)+\\\"/g_sharp.wav\\\"))\\n temp2 = temp1.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave+1)+\\\"/c_sharp.wav\\\"))\\n temp3 = temp2.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave+1)+\\\"/e.wav\\\"))\\n result = temp3.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave+1)+\\\"/g_sharp.wav\\\"))\\n\\n return Piano.calculate_duration(result, duration, self.tempo)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32340495e3ac8d5c7baab1fbec59b91d\",\n \"score\": \"0.7779683\",\n \"text\": \"def create_a_chord(self, octave, duration, harmony):\\n\\n result = None\\n\\n if str(harmony) == \\\"major\\\":\\n temp = AudioSegment.from_file(self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave - 1)+\\\"/a.wav\\\")\\n temp1 = temp.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave)+\\\"/e.wav\\\"))\\n temp2 = temp1.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave)+\\\"/a.wav\\\"))\\n temp3 = temp2.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave + 1)+\\\"/c_sharp.wav\\\"))\\n temp4 = temp3.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave + 1)+\\\"/e.wav\\\"))\\n result = temp4.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave+1)+\\\"/a.wav\\\"))\\n elif str(harmony) == \\\"minor\\\":\\n temp = AudioSegment.from_file(self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave - 1)+\\\"/a.wav\\\")\\n temp1 = temp.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave)+\\\"/e.wav\\\"))\\n temp2 = temp1.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave)+\\\"/a.wav\\\"))\\n temp3 = temp2.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave + 1)+\\\"/c.wav\\\"))\\n temp4 = temp3.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave + 1)+\\\"/e.wav\\\"))\\n result = temp4.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave + 1)+\\\"/a.wav\\\"))\\n\\n return Piano.calculate_duration(result, duration, self.tempo)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67b79292611f31802ce7a9a41e5a8bb0\",\n \"score\": \"0.77772343\",\n \"text\": \"def create_c_sharp_chord(self, octave, duration, harmony):\\n\\n result = None\\n\\n if str(harmony) == \\\"major\\\":\\n c_note = AudioSegment.from_file(self.path_to_directory + \\\"/samples/piano/\\\"+str(octave)+\\\"/c_sharp.wav\\\")\\n temp1 = c_note.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave)+\\\"/g_sharp.wav\\\"))\\n temp2 = temp1.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave+1)+\\\"/c_sharp.wav\\\"))\\n temp3 = temp2.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave+1)+\\\"/f.wav\\\"))\\n result = temp3.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave+1)+\\\"/g_sharp.wav\\\"))\\n elif str(harmony) == \\\"minor\\\":\\n c_note = AudioSegment.from_file(self.path_to_directory + \\\"/samples/piano/\\\"+str(octave)+\\\"/c_sharp.wav\\\")\\n temp1 = c_note.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave)+\\\"/g_sharp.wav\\\"))\\n temp2 = temp1.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave+1)+\\\"/c_sharp.wav\\\"))\\n temp3 = temp2.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave+1)+\\\"/e.wav\\\"))\\n result = temp3.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave+1)+\\\"/g_sharp.wav\\\"))\\n\\n return Piano.calculate_duration(result, duration, self.tempo)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52fd98755430851a0d8618e034e240cb\",\n \"score\": \"0.7711506\",\n \"text\": \"def create_d_chord(self, octave, duration, harmony):\\n\\n result = None\\n\\n if str(harmony) == \\\"major\\\":\\n temp = AudioSegment.from_file(self.path_to_directory + \\\"/samples/piano/\\\"+str(octave)+\\\"/d.wav\\\")\\n temp1 = temp.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave)+\\\"/a.wav\\\"))\\n temp2 = temp1.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave+1)+\\\"/d.wav\\\"))\\n result = temp2.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave+1)+\\\"/f_sharp.wav\\\"))\\n elif str(harmony) == \\\"minor\\\":\\n temp = AudioSegment.from_file(self.path_to_directory + \\\"/samples/piano/\\\"+str(octave)+\\\"/d.wav\\\")\\n temp1 = temp.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave)+\\\"/a.wav\\\"))\\n temp2 = temp1.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave+1)+\\\"/d.wav\\\"))\\n result = temp2.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave+1)+\\\"/f.wav\\\"))\\n\\n return Piano.calculate_duration(result, duration, self.tempo)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e205bc4cdf05f5347855f8352a9ac99\",\n \"score\": \"0.7625333\",\n \"text\": \"def create_g_chord(self, octave, duration, harmony):\\n\\n result = None\\n\\n if str(harmony) == \\\"major\\\":\\n temp = AudioSegment.from_file(self.path_to_directory + \\\"/samples/piano/\\\"+str(octave - 1)+\\\"/g.wav\\\")\\n temp1 = temp.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave)+\\\"/d.wav\\\"))\\n temp2 = temp1.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave)+\\\"/g.wav\\\"))\\n temp3 = temp2.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave)+\\\"/h.wav\\\"))\\n temp4 = temp3.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave + 1)+\\\"/d.wav\\\"))\\n result = temp4.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave+1)+\\\"/g.wav\\\"))\\n elif str(harmony) == \\\"minor\\\":\\n temp = AudioSegment.from_file(self.path_to_directory + \\\"/samples/piano/\\\"+str(octave - 1)+\\\"/g.wav\\\")\\n temp1 = temp.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave)+\\\"/d.wav\\\"))\\n temp2 = temp1.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave)+\\\"/g.wav\\\"))\\n temp3 = temp2.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave)+\\\"/a_sharp.wav\\\"))\\n temp4 = temp3.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave + 1)+\\\"/d.wav\\\"))\\n result = temp4.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave+1)+\\\"/g.wav\\\"))\\n\\n return Piano.calculate_duration(result, duration, self.tempo)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e59019251d97da65f465a714681b46a\",\n \"score\": \"0.76127225\",\n \"text\": \"def create_d_chord(self, octave, duration, harmony):\\n\\n result = None\\n\\n if str(harmony) == \\\"major\\\":\\n temp = AudioSegment.from_file(self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave)+\\\"/d.wav\\\")\\n temp1 = temp.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave)+\\\"/a.wav\\\"))\\n temp2 = temp1.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave+1)+\\\"/d.wav\\\"))\\n result = temp2.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave+1)+\\\"/f_sharp.wav\\\"))\\n elif str(harmony) == \\\"minor\\\":\\n temp = AudioSegment.from_file(self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave)+\\\"/d.wav\\\")\\n temp1 = temp.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave)+\\\"/a.wav\\\"))\\n temp2 = temp1.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave+1)+\\\"/d.wav\\\"))\\n result = temp2.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave+1)+\\\"/f.wav\\\"))\\n\\n return Piano.calculate_duration(result, duration, self.tempo)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11ba1d5992a328281ce7c884262a2a61\",\n \"score\": \"0.75451344\",\n \"text\": \"def create_e_chord(self, octave, duration, harmony):\\n\\n result = None\\n\\n if str(harmony) == \\\"major\\\":\\n temp = AudioSegment.from_file(self.path_to_directory + \\\"/samples/piano/\\\"+str(octave - 1)+\\\"/e.wav\\\")\\n temp1 = temp.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave - 1)+\\\"/h.wav\\\"))\\n temp2 = temp1.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave)+\\\"/e.wav\\\"))\\n temp3 = temp2.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave)+\\\"/g_sharp.wav\\\"))\\n temp4 = temp3.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave)+\\\"/h.wav\\\"))\\n result = temp4.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave+1)+\\\"/e.wav\\\"))\\n elif str(harmony) == \\\"minor\\\":\\n temp = AudioSegment.from_file(self.path_to_directory + \\\"/samples/piano/\\\"+str(octave - 1)+\\\"/e.wav\\\")\\n temp1 = temp.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave - 1)+\\\"/h.wav\\\"))\\n temp2 = temp1.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave)+\\\"/e.wav\\\"))\\n temp3 = temp2.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave)+\\\"/g.wav\\\"))\\n temp4 = temp3.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave)+\\\"/h.wav\\\"))\\n result = temp4.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave+1)+\\\"/e.wav\\\"))\\n\\n return Piano.calculate_duration(result, duration, self.tempo)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c039e7b8735ebc1efce7520cd9159d0\",\n \"score\": \"0.75377893\",\n \"text\": \"def create_h_chord(self, octave, duration, harmony):\\n\\n result = None\\n\\n if str(harmony) == \\\"major\\\":\\n temp = AudioSegment.from_file(self.path_to_directory + \\\"/samples/piano/\\\"+str(octave - 1)+\\\"/h.wav\\\")\\n temp1 = temp.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave)+\\\"/f_sharp.wav\\\"))\\n temp2 = temp1.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave)+\\\"/h.wav\\\"))\\n temp3 = temp2.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave + 1)+\\\"/d_sharp.wav\\\"))\\n result = temp3.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave + 1)+\\\"/f_sharp.wav\\\"))\\n elif str(harmony) == \\\"minor\\\":\\n temp = AudioSegment.from_file(self.path_to_directory + \\\"/samples/piano/\\\"+str(octave - 1)+\\\"/h.wav\\\")\\n temp1 = temp.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave)+\\\"/f_sharp.wav\\\"))\\n temp2 = temp1.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave)+\\\"/h.wav\\\"))\\n temp3 = temp2.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave + 1)+\\\"/d.wav\\\"))\\n result = temp3.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave + 1)+\\\"/f_sharp.wav\\\"))\\n\\n return Piano.calculate_duration(result, duration, self.tempo)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85dfd1b3577cb1ddd83ca07cda1fcee8\",\n \"score\": \"0.7478326\",\n \"text\": \"def create_g_chord(self, octave, duration, harmony):\\n\\n result = None\\n\\n if str(harmony) == \\\"major\\\":\\n temp = AudioSegment.from_file(self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave - 1)+\\\"/g.wav\\\")\\n temp1 = temp.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave)+\\\"/d.wav\\\"))\\n temp2 = temp1.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave)+\\\"/g.wav\\\"))\\n temp3 = temp2.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave)+\\\"/h.wav\\\"))\\n temp4 = temp3.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave + 1)+\\\"/d.wav\\\"))\\n result = temp4.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave+1)+\\\"/g.wav\\\"))\\n elif str(harmony) == \\\"minor\\\":\\n temp = AudioSegment.from_file(self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave - 1)+\\\"/g.wav\\\")\\n temp1 = temp.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave)+\\\"/d.wav\\\"))\\n temp2 = temp1.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave)+\\\"/g.wav\\\"))\\n temp3 = temp2.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave)+\\\"/a_sharp.wav\\\"))\\n temp4 = temp3.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave + 1)+\\\"/d.wav\\\"))\\n result = temp4.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave+1)+\\\"/g.wav\\\"))\\n\\n return Piano.calculate_duration(result, duration, self.tempo)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52f6fb736ee4755c988dc5be1eedde38\",\n \"score\": \"0.7460553\",\n \"text\": \"def create_h_chord(self, octave, duration, harmony):\\n\\n result = None\\n\\n if str(harmony) == \\\"major\\\":\\n temp = AudioSegment.from_file(self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave - 1)+\\\"/h.wav\\\")\\n temp1 = temp.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave)+\\\"/f_sharp.wav\\\"))\\n temp2 = temp1.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave)+\\\"/h.wav\\\"))\\n temp3 = temp2.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave + 1)+\\\"/d_sharp.wav\\\"))\\n result = temp3.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave + 1)+\\\"/f_sharp.wav\\\"))\\n elif str(harmony) == \\\"minor\\\":\\n temp = AudioSegment.from_file(self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave - 1)+\\\"/h.wav\\\")\\n temp1 = temp.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave)+\\\"/f_sharp.wav\\\"))\\n temp2 = temp1.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave)+\\\"/h.wav\\\"))\\n temp3 = temp2.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave + 1)+\\\"/d.wav\\\"))\\n result = temp3.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave + 1)+\\\"/f_sharp.wav\\\"))\\n\\n return Piano.calculate_duration(result, duration, self.tempo)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27ac38cd0ed8ad9cb94916e2e4437a75\",\n \"score\": \"0.7444743\",\n \"text\": \"def create_e_chord(self, octave, duration, harmony):\\n\\n result = None\\n\\n if str(harmony) == \\\"major\\\":\\n temp = AudioSegment.from_file(self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave - 1)+\\\"/e.wav\\\")\\n temp1 = temp.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave - 1)+\\\"/h.wav\\\"))\\n temp2 = temp1.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave)+\\\"/e.wav\\\"))\\n temp3 = temp2.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave)+\\\"/g_sharp.wav\\\"))\\n temp4 = temp3.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave)+\\\"/h.wav\\\"))\\n result = temp4.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave+1)+\\\"/e.wav\\\"))\\n elif str(harmony) == \\\"minor\\\":\\n temp = AudioSegment.from_file(self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave - 1)+\\\"/e.wav\\\")\\n temp1 = temp.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave - 1)+\\\"/h.wav\\\"))\\n temp2 = temp1.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave)+\\\"/e.wav\\\"))\\n temp3 = temp2.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave)+\\\"/g.wav\\\"))\\n temp4 = temp3.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave)+\\\"/h.wav\\\"))\\n result = temp4.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave+1)+\\\"/e.wav\\\"))\\n\\n return Piano.calculate_duration(result, duration, self.tempo)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e3f1cbc0378b3297dd61b3e04fbb436\",\n \"score\": \"0.72458655\",\n \"text\": \"def create_f_chord(self, octave, duration, harmony):\\n\\n result = None\\n\\n if str(harmony) == \\\"major\\\":\\n temp = AudioSegment.from_file(self.path_to_directory + \\\"/samples/piano/\\\"+str(octave - 1)+\\\"/f.wav\\\")\\n temp1 = temp.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave)+\\\"/c.wav\\\"))\\n temp2 = temp1.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave)+\\\"/f.wav\\\"))\\n temp3 = temp2.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave)+\\\"/a.wav\\\"))\\n temp4 = temp3.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave + 1)+\\\"/c.wav\\\"))\\n result = temp4.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave+1)+\\\"/f.wav\\\"))\\n elif str(harmony) == \\\"minor\\\":\\n temp = AudioSegment.from_file(self.path_to_directory + \\\"/samples/piano/\\\"+str(octave - 1)+\\\"/f.wav\\\")\\n temp1 = temp.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave)+\\\"/c.wav\\\"))\\n temp2 = temp1.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave)+\\\"/f.wav\\\"))\\n temp3 = temp2.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave)+\\\"/g_sharp.wav\\\"))\\n temp4 = temp3.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave + 1)+\\\"/c.wav\\\"))\\n result = temp4.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave+1)+\\\"/f.wav\\\"))\\n\\n return Piano.calculate_duration(result, duration, self.tempo)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f5ffde54a36090a9c0dab6072b72736\",\n \"score\": \"0.7195761\",\n \"text\": \"def get_default_chord(self, chord, octave, duration, harmony):\\n\\n result = None\\n\\n if chord == \\\"c\\\":\\n result = self.create_c_chord(octave, duration, harmony)\\n elif chord == \\\"c_sharp\\\":\\n result = self.create_c_sharp_chord(octave, duration, harmony)\\n elif chord == \\\"d\\\":\\n result = self.create_d_chord(octave, duration, harmony)\\n elif chord == \\\"d_sharp\\\":\\n result = self.create_d_sharp_chord(octave, duration, harmony)\\n elif chord == \\\"e\\\":\\n result = self.create_e_chord(octave, duration, harmony)\\n elif chord == \\\"f\\\":\\n result = self.create_f_chord(octave, duration, harmony)\\n elif chord == \\\"f_sharp\\\":\\n result = self.create_f_sharp_chord(octave, duration, harmony)\\n elif chord == \\\"g\\\":\\n result = self.create_g_chord(octave, duration, harmony)\\n elif chord == \\\"g_sharp\\\":\\n result = self.create_g_sharp_chord(octave, duration, harmony)\\n elif chord == \\\"a\\\":\\n result = self.create_a_chord(octave, duration, harmony)\\n elif chord == \\\"a_sharp\\\":\\n result = self.create_a_sharp_chord(octave, duration, harmony)\\n elif chord == \\\"h\\\":\\n result = self.create_h_chord(octave, duration, harmony)\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f5ffde54a36090a9c0dab6072b72736\",\n \"score\": \"0.7195761\",\n \"text\": \"def get_default_chord(self, chord, octave, duration, harmony):\\n\\n result = None\\n\\n if chord == \\\"c\\\":\\n result = self.create_c_chord(octave, duration, harmony)\\n elif chord == \\\"c_sharp\\\":\\n result = self.create_c_sharp_chord(octave, duration, harmony)\\n elif chord == \\\"d\\\":\\n result = self.create_d_chord(octave, duration, harmony)\\n elif chord == \\\"d_sharp\\\":\\n result = self.create_d_sharp_chord(octave, duration, harmony)\\n elif chord == \\\"e\\\":\\n result = self.create_e_chord(octave, duration, harmony)\\n elif chord == \\\"f\\\":\\n result = self.create_f_chord(octave, duration, harmony)\\n elif chord == \\\"f_sharp\\\":\\n result = self.create_f_sharp_chord(octave, duration, harmony)\\n elif chord == \\\"g\\\":\\n result = self.create_g_chord(octave, duration, harmony)\\n elif chord == \\\"g_sharp\\\":\\n result = self.create_g_sharp_chord(octave, duration, harmony)\\n elif chord == \\\"a\\\":\\n result = self.create_a_chord(octave, duration, harmony)\\n elif chord == \\\"a_sharp\\\":\\n result = self.create_a_sharp_chord(octave, duration, harmony)\\n elif chord == \\\"h\\\":\\n result = self.create_h_chord(octave, duration, harmony)\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6bc55b276a23ddc65fe9cf99a8f3543\",\n \"score\": \"0.714988\",\n \"text\": \"def create_f_chord(self, octave, duration, harmony):\\n\\n result = None\\n\\n if str(harmony) == \\\"major\\\":\\n temp = AudioSegment.from_file(self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave - 1)+\\\"/f.wav\\\")\\n temp1 = temp.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave)+\\\"/c.wav\\\"))\\n temp2 = temp1.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave)+\\\"/f.wav\\\"))\\n temp3 = temp2.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave)+\\\"/a.wav\\\"))\\n temp4 = temp3.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave + 1)+\\\"/c.wav\\\"))\\n result = temp4.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave+1)+\\\"/f.wav\\\"))\\n elif str(harmony) == \\\"minor\\\":\\n temp = AudioSegment.from_file(self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave - 1)+\\\"/f.wav\\\")\\n temp1 = temp.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave)+\\\"/c.wav\\\"))\\n temp2 = temp1.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave)+\\\"/f.wav\\\"))\\n temp3 = temp2.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave)+\\\"/g_sharp.wav\\\"))\\n temp4 = temp3.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave + 1)+\\\"/c.wav\\\"))\\n result = temp4.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave+1)+\\\"/f.wav\\\"))\\n\\n return Piano.calculate_duration(result, duration, self.tempo)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06e9c698f54795034de1f480b85cab0a\",\n \"score\": \"0.7058978\",\n \"text\": \"def create_a_sharp_chord(self, octave, duration, harmony):\\n\\n result = None\\n\\n if str(harmony) == \\\"major\\\":\\n temp = AudioSegment.from_file(self.path_to_directory + \\\"/samples/piano/\\\"+str(octave - 1)+\\\"/a_sharp.wav\\\")\\n temp1 = temp.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave)+\\\"/f.wav\\\"))\\n temp2 = temp1.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave)+\\\"/a_sharp.wav\\\"))\\n temp3 = temp2.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave + 1)+\\\"/d.wav\\\"))\\n temp4 = temp3.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave + 1)+\\\"/f.wav\\\"))\\n result = temp4.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave + 1)+\\\"/a_sharp.wav\\\"))\\n elif str(harmony) == \\\"minor\\\":\\n temp = AudioSegment.from_file(self.path_to_directory + \\\"/samples/piano/\\\"+str(octave - 1)+\\\"/a_sharp.wav\\\")\\n temp1 = temp.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave)+\\\"/f.wav\\\"))\\n temp2 = temp1.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave)+\\\"/a_sharp.wav\\\"))\\n temp3 = temp2.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave + 1)+\\\"/c_sharp.wav\\\"))\\n temp4 = temp3.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave + 1)+\\\"/f.wav\\\"))\\n result = temp4.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave + 1)+\\\"/a_sharp.wav\\\"))\\n\\n return Piano.calculate_duration(result, duration, self.tempo)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f26cbf54727772749225061c103bfdb3\",\n \"score\": \"0.7057801\",\n \"text\": \"def create_g_sharp_chord(self, octave, duration, harmony):\\n\\n result = None\\n\\n if str(harmony) == \\\"major\\\":\\n temp = AudioSegment.from_file(self.path_to_directory + \\\"/samples/piano/\\\"+str(octave - 1)+\\\"/g_sharp.wav\\\")\\n temp1 = temp.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave)+\\\"/d_sharp.wav\\\"))\\n temp2 = temp1.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave)+\\\"/g_sharp.wav\\\"))\\n temp3 = temp2.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave + 1)+\\\"/c.wav\\\"))\\n temp4 = temp3.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave + 1)+\\\"/d_sharp.wav\\\"))\\n result = temp4.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave + 1)+\\\"/g_sharp.wav\\\"))\\n elif str(harmony) == \\\"minor\\\":\\n temp = AudioSegment.from_file(self.path_to_directory + \\\"/samples/piano/\\\"+str(octave - 1)+\\\"/g_sharp.wav\\\")\\n temp1 = temp.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave)+\\\"/d_sharp.wav\\\"))\\n temp2 = temp1.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave)+\\\"/g_sharp.wav\\\"))\\n temp3 = temp2.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave)+\\\"/h.wav\\\"))\\n temp4 = temp3.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave + 1)+\\\"/d_sharp.wav\\\"))\\n result = temp4.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave + 1)+\\\"/g_sharp.wav\\\"))\\n\\n return Piano.calculate_duration(result, duration, self.tempo)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82937ac07d95a22e0509d49f5578f09c\",\n \"score\": \"0.703113\",\n \"text\": \"def create_a_sharp_chord(self, octave, duration, harmony):\\n\\n result = None\\n\\n if str(harmony) == \\\"major\\\":\\n temp = AudioSegment.from_file(self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave - 1)+\\\"/a_sharp.wav\\\")\\n temp1 = temp.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave)+\\\"/f.wav\\\"))\\n temp2 = temp1.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave)+\\\"/a_sharp.wav\\\"))\\n temp3 = temp2.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave + 1)+\\\"/d.wav\\\"))\\n temp4 = temp3.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave + 1)+\\\"/f.wav\\\"))\\n result = temp4.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave + 1)+\\\"/a_sharp.wav\\\"))\\n elif str(harmony) == \\\"minor\\\":\\n temp = AudioSegment.from_file(self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave - 1)+\\\"/a_sharp.wav\\\")\\n temp1 = temp.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave)+\\\"/f.wav\\\"))\\n temp2 = temp1.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave)+\\\"/a_sharp.wav\\\"))\\n temp3 = temp2.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave + 1)+\\\"/c_sharp.wav\\\"))\\n temp4 = temp3.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave + 1)+\\\"/f.wav\\\"))\\n result = temp4.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave + 1)+\\\"/a_sharp.wav\\\"))\\n\\n return Piano.calculate_duration(result, duration, self.tempo)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e2dcef98de0c9547d51e4c5feda11fb\",\n \"score\": \"0.7019466\",\n \"text\": \"def create_g_sharp_chord(self, octave, duration, harmony):\\n\\n result = None\\n\\n if str(harmony) == \\\"major\\\":\\n temp = AudioSegment.from_file(self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave - 1)+\\\"/g_sharp.wav\\\")\\n temp1 = temp.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave)+\\\"/d_sharp.wav\\\"))\\n temp2 = temp1.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave)+\\\"/g_sharp.wav\\\"))\\n temp3 = temp2.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave + 1)+\\\"/c.wav\\\"))\\n temp4 = temp3.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave + 1)+\\\"/d_sharp.wav\\\"))\\n result = temp4.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave + 1)+\\\"/g_sharp.wav\\\"))\\n elif str(harmony) == \\\"minor\\\":\\n temp = AudioSegment.from_file(self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave - 1)+\\\"/g_sharp.wav\\\")\\n temp1 = temp.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave)+\\\"/d_sharp.wav\\\"))\\n temp2 = temp1.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave)+\\\"/g_sharp.wav\\\"))\\n temp3 = temp2.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave)+\\\"/h.wav\\\"))\\n temp4 = temp3.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave + 1)+\\\"/d_sharp.wav\\\"))\\n result = temp4.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave + 1)+\\\"/g_sharp.wav\\\"))\\n\\n return Piano.calculate_duration(result, duration, self.tempo)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2010714d4a28a28881eb9858934b9cce\",\n \"score\": \"0.69897336\",\n \"text\": \"def create_d_sharp_chord(self, octave, duration, harmony):\\n\\n result = None\\n\\n if str(harmony) == \\\"major\\\":\\n temp = AudioSegment.from_file(self.path_to_directory + \\\"/samples/piano/\\\"+str(octave)+\\\"/d_sharp.wav\\\")\\n temp1 = temp.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave)+\\\"/a_sharp.wav\\\"))\\n temp2 = temp1.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave+1)+\\\"/d_sharp.wav\\\"))\\n result = temp2.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave+1)+\\\"/g.wav\\\"))\\n elif str(harmony) == \\\"minor\\\":\\n temp = AudioSegment.from_file(self.path_to_directory + \\\"/samples/piano/\\\"+str(octave)+\\\"/d_sharp.wav\\\")\\n temp1 = temp.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave)+\\\"/a_sharp.wav\\\"))\\n temp2 = temp1.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave+1)+\\\"/d_sharp.wav\\\"))\\n result = temp2.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave+1)+\\\"/f_sharp.wav\\\"))\\n\\n return Piano.calculate_duration(result, duration, self.tempo)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30d9411fd6bcb3057e680eb6f6b0ec02\",\n \"score\": \"0.697627\",\n \"text\": \"def create_d_sharp_chord(self, octave, duration, harmony):\\n\\n result = None\\n\\n if str(harmony) == \\\"major\\\":\\n temp = AudioSegment.from_file(self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave)+\\\"/d_sharp.wav\\\")\\n temp1 = temp.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave)+\\\"/a_sharp.wav\\\"))\\n temp2 = temp1.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave+1)+\\\"/d_sharp.wav\\\"))\\n result = temp2.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave+1)+\\\"/g.wav\\\"))\\n elif str(harmony) == \\\"minor\\\":\\n temp = AudioSegment.from_file(self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave)+\\\"/d_sharp.wav\\\")\\n temp1 = temp.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave)+\\\"/a_sharp.wav\\\"))\\n temp2 = temp1.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave+1)+\\\"/d_sharp.wav\\\"))\\n result = temp2.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave+1)+\\\"/f_sharp.wav\\\"))\\n\\n return Piano.calculate_duration(result, duration, self.tempo)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"083ed1ba8549f291d403729620d1eaf4\",\n \"score\": \"0.6800724\",\n \"text\": \"def create_f_sharp_chord(self, octave, duration, harmony):\\n\\n result = None\\n\\n if str(harmony) == \\\"major\\\":\\n temp = AudioSegment.from_file(self.path_to_directory + \\\"/samples/piano/\\\"+str(octave - 1)+\\\"/f_sharp.wav\\\")\\n temp1 = temp.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave)+\\\"/c_sharp.wav\\\"))\\n temp2 = temp1.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave)+\\\"/f_sharp.wav\\\"))\\n temp3 = temp2.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave)+\\\"/a_sharp.wav\\\"))\\n temp4 = temp3.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave + 1)+\\\"/c_sharp.wav\\\"))\\n result = temp4.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave+1)+\\\"/f_sharp.wav\\\"))\\n elif str(harmony) == \\\"minor\\\":\\n temp = AudioSegment.from_file(self.path_to_directory + \\\"/samples/piano/\\\"+str(octave - 1)+\\\"/f_sharp.wav\\\")\\n temp1 = temp.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave)+\\\"/c_sharp.wav\\\"))\\n temp2 = temp1.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave)+\\\"/f_sharp.wav\\\"))\\n temp3 = temp2.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave)+\\\"/a.wav\\\"))\\n temp4 = temp3.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave + 1)+\\\"/c_sharp.wav\\\"))\\n result = temp4.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/piano/\\\"+str(octave + 1)+\\\"/f_sharp.wav\\\"))\\n\\n return Piano.calculate_duration(result, duration, self.tempo)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"158ff374dddf9f64aa35e8426a3896e8\",\n \"score\": \"0.6761683\",\n \"text\": \"def create_f_sharp_chord(self, octave, duration, harmony):\\n result = None\\n\\n if str(harmony) == \\\"major\\\":\\n temp = AudioSegment.from_file(self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave - 1)+\\\"/f_sharp.wav\\\")\\n temp1 = temp.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave)+\\\"/c_sharp.wav\\\"))\\n temp2 = temp1.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave)+\\\"/f_sharp.wav\\\"))\\n temp3 = temp2.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave)+\\\"/a_sharp.wav\\\"))\\n temp4 = temp3.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave + 1)+\\\"/c_sharp.wav\\\"))\\n result = temp4.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave+1)+\\\"/f_sharp.wav\\\"))\\n elif str(harmony) == \\\"minor\\\":\\n temp = AudioSegment.from_file(self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave - 1)+\\\"/f_sharp.wav\\\")\\n temp1 = temp.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave)+\\\"/c_sharp.wav\\\"))\\n temp2 = temp1.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave)+\\\"/f_sharp.wav\\\"))\\n temp3 = temp2.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave)+\\\"/a.wav\\\"))\\n temp4 = temp3.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave + 1)+\\\"/c_sharp.wav\\\"))\\n result = temp4.overlay(AudioSegment.from_file(\\n self.path_to_directory + \\\"/samples/guitar/\\\"+str(octave + 1)+\\\"/f_sharp.wav\\\"))\\n\\n return Piano.calculate_duration(result, duration, self.tempo)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8ec1f7589fa34f4983d85c2a72bf744\",\n \"score\": \"0.6690171\",\n \"text\": \"def music21_to_chord_duration(p, key):\\n p_chords = p.chordify()\\n p_chords_o = p_chords.flat.getElementsByClass('Chord')\\n chord_list = []\\n chord_function_list = []\\n duration_list = []\\n for ch in p_chords_o:\\n duration_list.append(ch.duration.quarterLength)\\n ch.closedPosition(forceOctave=4, inPlace=True)\\n rn = roman.romanNumeralFromChord(ch, key)\\n rp = rn.pitches\\n rp_names = \\\",\\\".join([pi.name + pi.unicodeNameWithOctave[-1] for pi in rp])\\n chord_list.append(rp_names)\\n chord_function_list.append(rn.figure)\\n return chord_list, chord_function_list, duration_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46eccdf61ddcb855167f27fd282a92ce\",\n \"score\": \"0.6621113\",\n \"text\": \"def get_not_default_chord(self, notes_, duration):\\n\\n # Split each elements of input array by symbol,\\n # In result we get array of array of string.\\n # Each elements of its array is array which contain two elements:\\n # first element ([0][0]) - name of note. Second ([0][1]) - octave of corresponding note\\n notes = [[]]\\n for item in notes_:\\n temp = item.split(\\\",\\\")\\n notes.append(temp)\\n\\n del notes[0] # Delete first element, because its empty\\n i = 2 # i = 2, because before start loop, we concatenate first and second elements of array\\n\\n result = AudioSegment.from_file(self.path_to_directory + \\\"/samples/piano/\\\" + notes[0][1] + \\\"/\\\" +\\n notes[0][0] + \\\".wav\\\")\\n result = result.overlay(AudioSegment.from_file(self.path_to_directory + \\\"/samples/piano/\\\" + notes[1][1] +\\n \\\"/\\\" + notes[1][0] + \\\".wav\\\"))\\n\\n while i < len(notes):\\n # Concatenate current and previous element of array in parallel mode\\n result = result.overlay(AudioSegment.from_file(self.path_to_directory + \\\"/samples/piano/\\\" +\\n notes[i][1] + \\\"/\\\" + notes[i][0] + \\\".wav\\\"))\\n i += 1\\n\\n return Piano.calculate_duration(result, duration, self.tempo)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd7518c2200a8daf57ced9e6c4116aa4\",\n \"score\": \"0.66190565\",\n \"text\": \"def generate_chord():\\n A = generate_note(440,1)\\n C = generate_note(532.25,1)\\n E = generate_note(659.25,1)\\n B = generate_note(493.88,1)\\n D = generate_note(587.33,1)\\n F = generate_note(698.46,1)\\n G = generate_note(783.99,1)\\n ACE = A.wave+C.wave+E.wave\\n CEG = C.wave+E.wave+G.wave\\n ACE = Signal(44100,ACE)\\n CEG = Signal(44100,CEG)\\n ACE.write_file('chord1')\\n CEG.write_file('chordCEG')\\n zeros = np.zeros(len(ACE.wave))\\n mary = np.append(E.wave,D.wave)\\n mary = np.append(mary,[C.wave,D.wave,E.wave,E.wave,E.wave,D.wave,D.wave,D.wave,E.wave,E.wave,E.wave])\\n mary = Signal(44100,mary)\\n mary.write_file('chord2')\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70805818eceeb3ab6bcc3b72a3961142\",\n \"score\": \"0.661035\",\n \"text\": \"def get_not_default_chord(self, notes_, duration):\\n\\n # Split each elements of input array by symbol ,\\n # In result we get array of array of string.\\n # Each elements of its array is array which contain two elements:\\n # first element ([0][0]) - name of note. Second ([0][1]) - octave of corresponding note\\n notes = [[]]\\n for item in notes_:\\n temp = item.split(\\\",\\\")\\n notes.append(temp)\\n\\n del notes[0] # Delete first element, because its empty\\n i = 2 # i = 2, because before start loop, we concatenate first and second elements of array\\n\\n result = AudioSegment.from_file(self.path_to_directory + \\\"/samples/piano/\\\" + notes[0][1] + \\\"/\\\" +\\n notes[0][0] + \\\".wav\\\")\\n result = result.overlay(AudioSegment.from_file(self.path_to_directory + \\\"/samples/piano/\\\" + notes[1][1] +\\n \\\"/\\\" + notes[1][0] + \\\".wav\\\"))\\n\\n while i < len(notes):\\n # Concatenate current and previous element of array in parallel mode\\n result = result.overlay(AudioSegment.from_file(self.path_to_directory + \\\"/samples/piano/\\\" +\\n notes[i][1] + \\\"/\\\" + notes[i][0] + \\\".wav\\\"))\\n i += 1\\n\\n return Piano.calculate_duration(result, duration, self.tempo)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8349e9c16cea3a078017575741c1874d\",\n \"score\": \"0.63813186\",\n \"text\": \"def playChord(self, notes=('C6', 'E6', 'G6'), durations=0.3):\\n\\n freqs = [self._pitch_to_freq(p) for p in notes]\\n if not isinstance(durations, list):\\n durations = [durations]*len(freqs)\\n\\n for f, d in zip(freqs, durations):\\n self.beeper.beep(d, f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1c4d32843a62e482dac86c4830f2e78\",\n \"score\": \"0.62916815\",\n \"text\": \"def getChordSymbol(line):\\n\\n\\t# FIXME : Shouldn't assume sharp note names, but need CO to be extended to allow nameless root notes.\\n\\tnotes = ['C','C#','D','D#','E','F','F#','G','G#','A','A#','B']\\n\\t\\n\\ttry:\\n\\t\\t[crotchetnum, timestamp, rootnum, ctype] = line.split(',')\\n\\texcept Exception,e :\\n\\t\\tprint \\\"Exception ! Trying to split line : \\\"+line\\n\\t\\traise\\n\\n\\trootnote = notes[int(rootnum)]\\n\\t\\n\\tshorthandmap = {'major' : 'maj', 'minor' : 'min', 'sus4' : 'sus4', 'dim' : 'dim', 'sus9' : '(1,5,9)'}\\n\\t\\n\\t\\t\\n\\treturn rootnote+':'+shorthandmap[ctype.strip()]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86568ba7ddc3f665d2fc45f4b0f931f7\",\n \"score\": \"0.6118687\",\n \"text\": \"def chord(chord):\\n if len(chord) == 1:\\n root=degrees[chord]\\n note_list = [root,root+4,root+7]\\n elif chord[-1] =='m':\\n root=degrees[chord[0]]\\n note_list = [root,root+3,root+7]\\n return note_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fba7e57ab8ababd57034367e2031b295\",\n \"score\": \"0.6068974\",\n \"text\": \"def play_chord(self, chord):\\r\\n #Settings are optimized for chords with a long release time\\r\\n vol = random.randint(30, 70)\\r\\n self.output.note_on(note, vol, 6)\\r\\n self.output.note_on(note + chord[0], 50 + vol, 6)\\r\\n self.output.note_on(note + chord[1], 50 + vol, 6)\\r\\n if len(chord) > 2:\\r\\n self.output.note_on(note + chord[2], 50)\\r\\n \\r\\n a = random.choice([-5, 0, 7, 12])\\r\\n b = random.randint(1,3)\\r\\n \\r\\n time.sleep(random.randint(1,3))\\r\\n self.output.note_on(note + a, 50)\\r\\n time.sleep(random.randint(1,3))\\r\\n \\r\\n self.output.note_off(note)\\r\\n self.output.note_off(note + chord[0])\\r\\n self.output.note_off(note + chord[1])\\r\\n self.output.note_off(note + a)\\r\\n self.output.note_off(note + b)\\r\\n \\r\\n if len(chord) > 2:\\r\\n self.output.note_off(note + chord[2])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d81baffd411c23e5284c05a59bf79ecd\",\n \"score\": \"0.5919639\",\n \"text\": \"def get_note(self, note, octave, duration):\\n\\n result = None\\n\\n if note == \\\"c\\\":\\n result = AudioSegment.from_file(self.path_to_directory + \\\"/samples/guitar/\\\" + str(octave) + \\\"/c.wav\\\")\\n elif note == \\\"c_sharp\\\":\\n result = AudioSegment.from_file(self.path_to_directory + \\\"/samples/guitar/\\\" + str(octave) + \\\"/c_sharp.wav\\\")\\n elif note == \\\"d\\\":\\n result = AudioSegment.from_file(self.path_to_directory + \\\"/samples/guitar/\\\" + str(octave) + \\\"/d.wav\\\")\\n elif note == \\\"d_sharp\\\":\\n result = AudioSegment.from_file(self.path_to_directory + \\\"/samples/guitar/\\\" + str(octave) + \\\"/d_sharp.wav\\\")\\n elif note == \\\"e\\\":\\n result = AudioSegment.from_file(self.path_to_directory + \\\"/samples/guitar/\\\" + str(octave) + \\\"/e.wav\\\")\\n elif note == \\\"f\\\":\\n result = AudioSegment.from_file(self.path_to_directory + \\\"/samples/guitar/\\\" + str(octave) + \\\"/f.wav\\\")\\n elif note == \\\"f_sharp\\\":\\n result = AudioSegment.from_file(self.path_to_directory + \\\"/samples/guitar/\\\" + str(octave) + \\\"/f_sharp.wav\\\")\\n elif note == \\\"g\\\":\\n result = AudioSegment.from_file(self.path_to_directory + \\\"/samples/guitar/\\\" + str(octave) + \\\"/g.wav\\\")\\n elif note == \\\"g_sharp\\\":\\n result = AudioSegment.from_file(self.path_to_directory + \\\"/samples/guitar/\\\" + str(octave) + \\\"/g_sharp.wav\\\")\\n elif note == \\\"a\\\":\\n result = AudioSegment.from_file(self.path_to_directory + \\\"/samples/guitar/\\\" + str(octave) + \\\"/a.wav\\\")\\n elif note == \\\"a_sharp\\\":\\n result = AudioSegment.from_file(self.path_to_directory + \\\"/samples/guitar/\\\" + str(octave) + \\\"/a_sharp.wav\\\")\\n elif note == \\\"h\\\":\\n result = AudioSegment.from_file(self.path_to_directory + \\\"/samples/guitar/\\\" + str(octave) + \\\"/h.wav\\\")\\n elif note == \\\"silence\\\":\\n result = AudioSegment.from_file(self.path_to_directory + \\\"/samples/guitar/silence.wav\\\")\\n\\n return Piano.calculate_duration(result, duration, self.tempo)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85dff3cedbaf2755a7b2411487701937\",\n \"score\": \"0.58418316\",\n \"text\": \"def get_playlist(self, chord):\\r\\n \\r\\n steps = self.profile_manager.logic.bar_division\\r\\n pulses = random.randint(1,5)\\r\\n rhythm_list = self.rhythm_gen.bjorklund(pulses, steps)\\r\\n \\r\\n for index, item in enumerate(rhythm_list):\\r\\n if item == 0:\\r\\n rhythm_list[index] = \\\"0\\\"\\r\\n else:\\r\\n rhythm_list[index] = self.get_rand_note()\\r\\n \\r\\n \\\"SYNTAX: CHN-NOTE-LEN-VEL eg. 1-50-16-65\\\"\\r\\n \\r\\n return rhythm_list\\r\\n \\r\\n \\r\\n \\\"\\\"\\\"\\\" TEMPORARY TEST LISTS\\r\\n lsts = [\\r\\n [0, 0, \\\"1-C3-16-60\\\", 0],\\r\\n [\\\"2-D5-4-77\\\", \\\"3-D2-8-60\\\", 0, 0],\\r\\n [0, 0, 0, \\\"3-F2-16-80\\\"],\\r\\n [0, \\\"1-C1-2-65\\\", 0, 0]\\r\\n ]\\r\\n \\\"\\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"871706cb668eb9f60a6036b000e6220e\",\n \"score\": \"0.58189493\",\n \"text\": \"def cMaj():\\n playNote(60,250,90) #play C note\\n playNote(64,250,90) #play E note\\n playNote(69,250,90) #play G note \",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d64be4aae5f30cc5d88ee51e1e2e492\",\n \"score\": \"0.5816853\",\n \"text\": \"def get_note(self, note, octave, duration):\\n\\n result = None\\n\\n if note == \\\"c\\\":\\n result = AudioSegment.from_file(self.path_to_directory + \\\"/samples/piano/\\\" + str(octave) + \\\"/c.wav\\\")\\n elif note == \\\"c_sharp\\\":\\n result = AudioSegment.from_file(self.path_to_directory + \\\"/samples/piano/\\\" + str(octave) + \\\"/c_sharp.wav\\\")\\n elif note == \\\"d\\\":\\n result = AudioSegment.from_file(self.path_to_directory + \\\"/samples/piano/\\\" + str(octave) + \\\"/d.wav\\\")\\n elif note == \\\"d_sharp\\\":\\n result = AudioSegment.from_file(self.path_to_directory + \\\"/samples/piano/\\\" + str(octave) + \\\"/d_sharp.wav\\\")\\n elif note == \\\"e\\\":\\n result = AudioSegment.from_file(self.path_to_directory + \\\"/samples/piano/\\\" + str(octave) + \\\"/e.wav\\\")\\n elif note == \\\"f\\\":\\n result = AudioSegment.from_file(self.path_to_directory + \\\"/samples/piano/\\\" + str(octave) + \\\"/f.wav\\\")\\n elif note == \\\"f_sharp\\\":\\n result = AudioSegment.from_file(self.path_to_directory + \\\"/samples/piano/\\\" + str(octave) + \\\"/f_sharp.wav\\\")\\n elif note == \\\"g\\\":\\n result = AudioSegment.from_file(self.path_to_directory + \\\"/samples/piano/\\\" + str(octave) + \\\"/g.wav\\\")\\n elif note == \\\"g_sharp\\\":\\n result = AudioSegment.from_file(self.path_to_directory + \\\"/samples/piano/\\\" + str(octave) + \\\"/g_sharp.wav\\\")\\n elif note == \\\"a\\\":\\n result = AudioSegment.from_file(self.path_to_directory + \\\"/samples/piano/\\\" + str(octave) + \\\"/a.wav\\\")\\n elif note == \\\"a_sharp\\\":\\n result = AudioSegment.from_file(self.path_to_directory + \\\"/samples/piano/\\\" + str(octave) + \\\"/a_sharp.wav\\\")\\n elif note == \\\"h\\\":\\n result = AudioSegment.from_file(self.path_to_directory + \\\"/samples/piano/\\\" + str(octave) + \\\"/h.wav\\\")\\n elif note == \\\"silence\\\":\\n result = AudioSegment.from_file(self.path_to_directory + \\\"/samples/piano/silence.wav\\\")\\n\\n return Piano.calculate_duration(result, duration, self.tempo)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47a78ad056e110ac2a54e3ca5addd87b\",\n \"score\": \"0.58123404\",\n \"text\": \"def modal_chord_html(request):\\n indexes = {\\n 'Ab': 0,\\n 'A': 1,\\n 'Bb':2,\\n 'B':3,\\n 'Cb':3,\\n 'C':4,\\n 'C#':5,\\n 'Db':5,\\n 'D': 6,\\n 'D#': 7,\\n 'Eb': 7,\\n 'E':8,\\n 'F':9,\\n 'F#':10,\\n 'Gb':10,\\n 'G':11,\\n 'G#':0\\n }\\n ccli = int(request.GET.get('ccli'))\\n transpose_final_key = request.GET.get('transpose_value_str')\\n song = Song.objects.get(ccli=int(ccli))\\n chord_stream = song.chords\\n if not chord_stream:\\n return\\n lines = chord_stream.split('\\\\n')\\n \\n html = ''\\n emphasis = False\\n for line in lines:\\n #handle directives first\\n if '{' in line:\\n if '{title:' in line:\\n html += '<h3>'+line[7:-1]+'</h3>'\\n continue\\n elif '{st:' in line:\\n html += '<h5>'+line[4:-1]+'</h5>'\\n continue\\n elif '{key:' in line:\\n original_key = line[5:-1]\\n continue\\n elif '{start_of_chorus}' in line:\\n emphasis = True\\n continue\\n elif '{end_of_chorus}' in line:\\n emphasis = False\\n continue\\n #no directive in line\\n else:\\n if '[' in line:\\n separations = []\\n separations = line.split('[')\\n chords = []\\n lyrics = []\\n # go through each section and get the chord and lyric pairings\\n for section in separations:\\n if section == '':\\n continue\\n #if no chord, still append nothing to chords\\n elif ']' not in section:\\n lyrics.append(section)\\n chords.append('')\\n continue \\n chordlyric = section.split(']')\\n chords.append(chordlyric[0])\\n lyrics.append(chordlyric[1])\\n html += '<table>'\\n if emphasis:\\n html += '<tr class=\\\"emphasis\\\">'\\n else:\\n html += '<tr>'\\n\\n transposed_chords = []\\n \\n if 'm' in original_key:\\n # minor = True\\n original_root = original_key[:-1]\\n final_root = transpose_final_key[:-1]\\n else:\\n # minor = False\\n original_root = original_key\\n final_root = transpose_final_key\\n original_key_index = indexes[original_root]\\n final_key_index = indexes[final_root]\\n \\n # original_key_index = indexes[original_key]\\n # final_key_index = indexes[transpose_final_key]\\n transpose_step = final_key_index - original_key_index\\n \\n for chord in chords:\\n if chord == '':\\n transposed_chords.append('')\\n continue\\n transposed_chord = transpose(chord, transpose_step, original_key, transpose_final_key)\\n transposed_chords.append(transposed_chord)\\n\\n for chord in transposed_chords:\\n html += '<td>'+chord+'</td>'\\n \\n if emphasis:\\n html += '</tr><tr class=\\\"emphasis\\\">'\\n else:\\n html += '</tr><tr>'\\n \\n for lyric in lyrics:\\n html += '<td>'+lyric+'</td>'\\n \\n html += '</tr></table>'\\n\\n else:\\n html += '<br>' + line\\n #adding another br so last line doesn't get cut off\\n html += '<br>'\\n return HttpResponse(html)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"061cc36dc706558abe7c498a16e65d54\",\n \"score\": \"0.5729549\",\n \"text\": \"def get_chord(symbol, base_note):\\n return [triad_notes[symbol][i] + base_note for i in range(len(triad_notes[symbol]))]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a46203395044e9a769570a140ba2749d\",\n \"score\": \"0.5715923\",\n \"text\": \"def _chord_callback(data_value) -> List[InputMediaPhoto]:\\n caption = data_value.split('-')[1]\\n chord = caption.replace('#', 'x').lower()\\n print('Chord:', caption)\\n return ChordsParser.get_chord(chord, caption)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d458fe38a5c776227158fc4841374ca\",\n \"score\": \"0.5696006\",\n \"text\": \"def get_stream_from_chord_prog(chord_prog, key):\\n #print(chord_prog)\\n stream_chord_prog = stream.Stream()\\n quintet = get_voice_led_quintet(chord_prog, key) #gets tuple of eight notes for bassline (except not lowered an octave). its actually a quintet with five parts. We only want 2 of the parts\\n counter = 0\\n for scale_figure in chord_prog:\\n comp_chord = roman.RomanNumeral(scale_figure, key) # this creates a music21 object from the scale degree\\n chord_pitches_list = list(comp_chord.pitches) #We want to add bass note to chord object, however, chord object is tuple and tuple can't be manipulated (tuples are immutable)\\n #bass_note = comp_chord.bass() #gets lowest note from 'chord'\\n bass_note = note.Note(quintet[0][counter])\\n bass_note.octave = bass_note.octave - 2 #<<<#lowers the octave by TWO, making the note lower\\n chord_pitches_list.append(bass_note) #<<made list of tuple and added bass note to it\\n\\n high_note = note.Note(quintet[3][counter])\\n high_note.octave = high_note.octave + 1 #<<<#raises the octave by ONE, making the note higher\\n chord_pitches_list.append(high_note) #<<chord pitches list is the list that is about to be added the strea,\\n\\n chord_pitches_tuple = tuple(chord_pitches_list) #turn edited version of chord into a tuple\\n comp_chord = chord.Chord(chord_pitches_tuple) #make chord object out of tuple\\n comp_chord.duration.quarterLength = 4.0 #this turns the chord into actual notes with a duration so it can be added onto the music21 stream\\n stream_chord_prog.append(comp_chord)\\n counter += 1\\n\\n return stream_chord_prog\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9dcde27d9a2a275056e51427148c7906\",\n \"score\": \"0.562131\",\n \"text\": \"def quarter_chord(self) -> cas.DM:\\n return 0.75 * self.xyz_le + 0.25 * self.xyz_te()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aff8e790f0f6f856298309f26fc3bd49\",\n \"score\": \"0.5526221\",\n \"text\": \"def __init__(self, start, end, direction):\\n self.start = start\\n self.end = end\\n self.direction = direction\\n v = 0.5 * (end-start) # half oriented chord\\n angle = phase(v) - direction\\n while angle > pi:\\n angle -= 2*pi\\n while angle <= -pi:\\n angle += 2*pi\\n self.angle = angle # signed angle between arc and chord\\n self.center = 0.5*(start+end) + 1j*v / tan(angle) # center of circle\\n absv = abs(v)\\n self.l = 2 * absv # length of chord\\n if angle >= 0:\\n self.radius = absv / sin(angle) # radius\\n self.alpha = phase(v) - pi/2 - angle # start angle\\n self.beta = self.alpha + 2*angle # end angle\\n else:\\n self.radius = -absv / sin(angle)\\n self.alpha = phase(v) + pi/2 - angle\\n self.beta = self.alpha + 2*angle\\n self.end_direction = direction + 2*angle # direction of arc in end point\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ae05cd98e134758259a70e4e5ee2060\",\n \"score\": \"0.55210674\",\n \"text\": \"def construct_harmony(curr_loop):\\n\\n # Constructing the shell chords\\n prev_chord = None\\n prev_voice = None\\n\\n for i in range(curr_loop.measures):\\n curr_chord = shell_chord(curr_loop, i, prev_chord)\\n curr_voice = get_voicing(curr_chord, curr_loop, prev_voice)\\n\\n prev_chord = curr_chord\\n prev_voice = curr_voice\\n\\n for curr_note in curr_loop.harm['shell'].measures[i].notes:\\n curr_note.value = {'chord': curr_chord, 'voicing': curr_voice}\\n\\n # Decorating with passing chords\\n curr_loop.harm['passing'] = curr_loop.harm['shell'].extend().collapse(curr_loop)\\n for curr_measure in curr_loop.harm['passing'].measures:\\n passing_chords(curr_measure, curr_loop)\\n\\n # Constructing the rhythmic attack/sustain Frame\\n temp_harm = curr_loop.harm['rhythm'].extend()\\n for curr_note in temp_harm.measures[0].notes:\\n if curr_note.index == 0:\\n curr_note.value = 'A'\\n elif curr_loop.harm['passing'][curr_note.index].value == curr_loop.harm['passing'][curr_note.index-1].value:\\n curr_note.value = 'S'\\n else:\\n curr_note.value = 'A'\\n curr_loop.harm['rhythm'] = temp_harm.collapse(curr_loop)\\n\\n # Constructing the final compressed harmony Frame\\n curr_loop.harm['final'] = curr_loop.harm['passing'].extend().collapse(curr_loop)\\n curr_loop.harm['final'].compress()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a7d3c49a27b321e3cf280951b3215c6\",\n \"score\": \"0.5509813\",\n \"text\": \"def choose_chord(self):\\r\\n chords = [[3, 7], [4, 7], [3, 6], [5, 7], [5, 12], [4, 9, 12], [3, 7, 10]]\\r\\n \\r\\n return random.choice(chords)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43cd0dc91a1315d2f20bc3d871c6ab4f\",\n \"score\": \"0.5481143\",\n \"text\": \"def getChordScale(self, n):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5558a2277d2339f694775f65a6add6ec\",\n \"score\": \"0.54629415\",\n \"text\": \"def calculate_chord(radius, arc_degrees):\\n\\n # Calculate the arc_degrees in radians.\\n # We need this because sin() expects it.\\n arc_radians = radians(arc_degrees)\\n\\n # Calculate the chord.\\n return radius * (2 * sin(arc_radians / 2)) # km\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48a9d4bc105a52d8a938b5a3cae4e7a2\",\n \"score\": \"0.54548794\",\n \"text\": \"def make_interval_chord_midi(chords, key):\\n result = []\\n c_transposed = transpose(chords, key, 'C')\\n\\n for chord in c_transposed:\\n if chord[0] == 'H':\\n result.append('-1')\\n continue\\n\\n if len(chord) == 1 or (chord[1] != '#' and chord[1] != 'b'):\\n name_index = 1\\n if chord[0] == 'F':\\n mode = 'b'\\n else:\\n mode = '#'\\n else:\\n name_index = 2\\n mode = chord[1]\\n\\n interval = calc_interval('C', chord[:name_index])\\n\\n if interval in vocab_value:\\n result_chord = vocab_interval[interval]\\n else:\\n if mode == '#':\\n interval -= 0.5\\n else:\\n interval += 0.5\\n result_chord = vocab_interval[interval] + mode\\n\\n result.append(result_chord + chord[name_index:])\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fdb35b740d2c9a54ad87d270553c841\",\n \"score\": \"0.5394407\",\n \"text\": \"def CA_measures(self):\\n for i in range(self.length):\\n chord = self.chrd_prg[i%len(self.chrd_prg)]\\n rmeasure = Measure(chord=chord, note_lengths=self.note_lengths)\\n rmeasure.CA_notes()\\n rmeasure.CA_bass()\\n self.add_measure(rmeasure)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"776bb12e6e05e3812aae727bd4f29c7c\",\n \"score\": \"0.5390063\",\n \"text\": \"def make_chord_from_intervals(intervals, starting_pitch=0):\\n chord = [starting_pitch]\\n index = 0\\n for num_half_steps in intervals:\\n chord.append(mod_12(chord[index] + num_half_steps))\\n index += 1\\n return tuple(chord)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4efe77093d3599cee0b16ecad7718972\",\n \"score\": \"0.53892785\",\n \"text\": \"def build_chromatic_scale():\\n # letter names for note entries\\n letter_names = [\\\"C\\\", \\\"D\\\", \\\"E\\\", \\\"F\\\", \\\"G\\\", \\\"A\\\", \\\"B\\\"]\\n # symbolic accidental names for notes, \\\"\\\" is for a diatonic note without accidental\\n accidental_names = [\\\"bb\\\", \\\"b\\\", \\\"\\\", \\\"#\\\", \\\"##\\\"]\\n # safe accidental name variants\\n accidental_safe_name = {\\\"bb\\\": \\\"ff\\\", \\\"b\\\": \\\"f\\\", \\\"\\\": \\\"\\\", \\\"#\\\": \\\"s\\\", \\\"##\\\": \\\"ss\\\"}\\n # lowest octave name is 00 instead of -1.\\n octave_names = [\\\"00\\\", \\\"0\\\", \\\"1\\\", \\\"2\\\", \\\"3\\\", \\\"4\\\", \\\"5\\\", \\\"6\\\", \\\"7\\\", \\\"8\\\", \\\"9\\\"]\\n # semi-tone shifts applied to diatonic notes within the octave\\n letter_steps = [0, 2, 4, 5, 7, 9, 11]\\n # semi-tone shift applied to diatonic notes for notes with accidentals\\n accidental_steps = [-2, -1, 0, 1, 2]\\n # init table with midi keys, each holding a note array and frequency.\\n # the note array will hold all chromatic note names for the midi key.\\n table = {key: [['','','','',''], 440.0 * 2 ** ((key-69.0)/12)] for key in range(128)}\\n # iterate all the octave for midi key numbers 0-127.\\n for octave_index, octave_name in enumerate(octave_names):\\n # starting key number for notes in the octave\\n octave_midi = octave_index * 12\\n # iterate all the diatonic letter names for notes.\\n for letter_index, letter_name in enumerate(letter_names):\\n # the midi key number of the diatonic note\\n letter_midi = octave_midi + letter_steps[letter_index]\\n # iterate the accidentals and create all possible note names for the letter\\n for accidental_index, accidental_name in enumerate(accidental_names):\\n # get the semitone amount to shift the letter note by\\n accidental_step = accidental_steps[accidental_index]\\n # calculate the midi key number for the note\\n note_midi = letter_midi + accidental_step\\n # stop notes outside of midi range (there are only a few)\\n if 0 <= note_midi <= 127:\\n accidental_name = accidental_names[accidental_index]\\n # create the official note name\\n note_name1 = letter_name + accidental_name + octave_name\\n # create the Pitch for the official name.\\n note_pitch = Pitch(note_name1)\\n # create variants (lower case letter names, safe accidental names)\\n note_name2 = letter_name.lower() + accidental_name + octave_name\\n note_name3 = letter_name + accidental_safe_name[accidental_name] + octave_name\\n note_name4 = letter_name.lower() + accidental_safe_name[accidental_name] + octave_name\\n # fetch the midi data from the table\\n midi_data = table[note_midi]\\n # add the note to the note array\\n ##midi_data[0][accidental_index] = note_name1\\n midi_data[0][accidental_index] = note_pitch\\n # get the frequency from the midi data and add it to the note data.\\n note_freq = table[note_midi][1]\\n # add the hash entry for the note name\\n ##table[note_name1] = [note_midi, note_freq]\\n table[note_name1] = [note_midi, note_freq, note_pitch]\\n # add the variants (lower case letter, safe accidentals)\\n table[note_name2] = table[note_name1]\\n table[note_name3] = table[note_name1]\\n table[note_name4] = table[note_name1]\\n # add entries for musical rests\\n r = Pitch()\\n table['R'] = [-1, 0.0, r]\\n table['r'] = table['R']\\n #table[-1] = [[r,r,r,r,r], 0.0]\\n return table\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd37a3c965c17b03739e99eb400ad848\",\n \"score\": \"0.5384938\",\n \"text\": \"def convertChordsToNotes(s):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58c3ff2226003166ac27f2589249276d\",\n \"score\": \"0.53816175\",\n \"text\": \"def circle(\\r\\n piece,\\r\\n pitch_type='tpc',\\r\\n measures=None, # need documentation\\r\\n log=False,\\r\\n vocabulary={0:'C', 1:'Db', 2:'D', 3:'Eb', 4:'E', 5:'F', 6:'Gb', 7:'G', 8:'Ab', 9:'A', 10:'Bb', 11:'B'},\\r\\n pitch_class_display=False,\\r\\n colorbar=True,\\r\\n duration=False,\\r\\n fifths=True,\\r\\n figsize=[7, 4],\\r\\n top=None,\\r\\n rotation=0,\\r\\n clockwise=True,\\r\\n cmap='Blues',\\r\\n nan_color=None,\\r\\n show=False,\\r\\n **kwargs):\\r\\n #settings\\r\\n df = get_df_short(piece, vocabulary=vocabulary, pitch_type=pitch_type, measures=measures, duration=duration)\\r\\n\\r\\n #color map\\r\\n cmap = matplotlib.cm.get_cmap(cmap)\\r\\n color_note = []\\r\\n\\r\\n #dataFrame for the plot if tpc\\r\\n df_tpc_pie = pd.DataFrame(columns=['note', 'part', 'pc'])\\r\\n\\r\\n #put top in the right form\\r\\n if pd.isnull(top) == False:\\r\\n if is_tpc(top) and pitch_class_display:\\r\\n top = get_pc(top)\\r\\n if is_pc(top) and not pitch_class_display:\\r\\n top = vocabulary[int(top)]\\r\\n\\r\\n #remember position of data in Series\\r\\n s_pos = pd.Series()\\r\\n count = 0\\r\\n part = 0\\r\\n letter = 'nan'\\r\\n s_fifth = pd.Series()\\r\\n \\r\\n fig = plt.figure(figsize=figsize)\\r\\n if not show:\\r\\n plt.close(fig)\\r\\n ax = fig.add_subplot(111, aspect='equal')\\r\\n \\r\\n #Set the order in function of fifth\\r\\n if fifths:\\r\\n s_tpc_format = pd.Series((0, 7, 2, 9, 4, 11, 6, 1, 8, 3, 10, 5))\\r\\n else:\\r\\n s_tpc_format = pd.Series((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11))\\r\\n\\r\\n #for plot if pitch_class_display\\r\\n s_twelve_ones = pd.Series((1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), index=s_tpc_format)\\r\\n\\r\\n #if it show the tpc values\\r\\n if pitch_class_display == False:\\r\\n #put the right values in 'number'\\r\\n if duration:\\r\\n df_data = df.copy()\\r\\n df_data.rename(columns={'duration': 'number'},inplace=True)\\r\\n else:\\r\\n df_data = df.copy()\\r\\n df_data.rename(columns={'nb': 'number'},inplace=True)\\r\\n\\r\\n #Normalize the values for the colors\\r\\n max_value = df_data['number'].max()\\r\\n min_value = df_data['number'].min()\\r\\n if log:\\r\\n norm = matplotlib.colors.LogNorm(vmin=min_value, vmax=max_value)\\r\\n else:\\r\\n norm = matplotlib.colors.Normalize(0, vmax=max_value)\\r\\n \\r\\n #for chromatic order\\r\\n if fifths == False:\\r\\n\\r\\n #for each pitch class values\\r\\n for i in range(12):\\r\\n\\r\\n #if a pitch class is represented in the data\\r\\n if df_data['pc'].isin([s_tpc_format[i]]).any():\\r\\n count = 0\\r\\n s_pos.drop(s_pos.index, inplace=True)\\r\\n \\r\\n #count how much time there is tpc values for a same pitch class\\r\\n for j in range(df_data['pc'].isin([s_tpc_format[i]]).shape[0]):\\r\\n if df_data['pc'].isin([s_tpc_format[i]])[j]:\\r\\n s_pos.at[count] = j\\r\\n count = count + 1\\r\\n\\r\\n #devide the pie part and set color\\r\\n for j in range(count):\\r\\n part = 1/count\\r\\n letter = df_data.at[s_pos.at[j], 'step']\\r\\n\\r\\n #write the notes\\r\\n letter = put_flat_sharp(letter, df_data.at[s_pos.at[j], 'acc'])\\r\\n\\r\\n #register the informations\\r\\n df_tpc_pie = df_tpc_pie.append({'note':letter, 'part':part},\\r\\n ignore_index=True)\\r\\n color_note.append(cmap(norm(df_data.at[s_pos.at[j], 'number'])))\\r\\n\\r\\n #if the pitch class do no appear in the piece\\r\\n else:\\r\\n letter = vocabulary[s_tpc_format[i]]\\r\\n\\r\\n df_tpc_pie = df_tpc_pie.append({'note':letter, 'part':1}, ignore_index=True)\\r\\n if pd.isnull(nan_color):\\r\\n color_note.append(cmap(0))\\r\\n else:\\r\\n color_note.append(nan_color)\\r\\n else:\\r\\n #get the fifth numbers of the notes\\r\\n for i in range(df_data.shape[0]):\\r\\n s_fifth.at[i] = get_fifth_nb(df_data.at[i, 'tpc'])\\r\\n df_data['fifth'] = s_fifth\\r\\n\\r\\n #create df_tpc_pie and get the colours\\r\\n for i in range(df_data['fifth'].max()-df_data['fifth'].min()+1):\\r\\n #the part are equal for the moment\\r\\n df_tpc_pie.at[i, 'part'] = 1\\r\\n df_tpc_pie.at[i, 'note'] = get_fifth_note(i + df_data['fifth'].min())\\r\\n df_tpc_pie.at[i, 'pc'] = get_pc(df_tpc_pie.at[i, 'note'])\\r\\n \\r\\n if df_data['fifth'].isin([i + df_data['fifth'].min()]).any():\\r\\n #get the colour for the note who has the good fifth number\\r\\n color_note.append(cmap(norm(df_data['number'][df_data['fifth']==(i + df_data['fifth'].min())].iat[0])))\\r\\n elif df_data['fifth'].isin([i + df_data['fifth'].min()]).any() == False and pd.isnull(nan_color) == False:\\r\\n color_note.append(nan_color)\\r\\n else:\\r\\n color_note.append(cmap(0))\\r\\n\\r\\n #if clockwise invert the order of the data to be displayed clockwise, inverse also the index\\r\\n if clockwise:\\r\\n df_tpc_pie = df_tpc_pie.iloc[::-1]\\r\\n color_note = list(reversed(color_note))\\r\\n\\r\\n #calculate the angle for the topPitchClass to be at the top\\r\\n if pd.isnull(top) == False and fifths == False and df_tpc_pie['note'].isin([top]).any() == True:\\r\\n if clockwise:\\r\\n rotation = rotation + 90 + df_tpc_pie.at[0, 'part'] * 15\\r\\n else:\\r\\n rotation = rotation + 90 - df_tpc_pie.at[0, 'part'] * 15\\r\\n for i in range(df_tpc_pie.shape[0]):\\r\\n if top == df_tpc_pie.at[i, 'note']:\\r\\n if df_tpc_pie.at[i, 'part'] != 1:\\r\\n if clockwise:\\r\\n rotation = rotation - 15*df_tpc_pie.at[i, 'part']\\r\\n else:\\r\\n rotation = rotation + 15*df_tpc_pie.at[i, 'part']\\r\\n break\\r\\n else:\\r\\n if clockwise:\\r\\n rotation = rotation + 30*df_tpc_pie.at[i, 'part']\\r\\n else:\\r\\n rotation = rotation - 30*df_tpc_pie.at[i, 'part']\\r\\n\\r\\n #put the top note at the top\\r\\n if pd.isnull(top) == False and fifths == True and df_tpc_pie['note'].isin([top]).any() == True:\\r\\n if clockwise:\\r\\n rotation = rotation + 90 + 180/df_tpc_pie.shape[0]\\r\\n else:\\r\\n rotation = rotation + 90 - 180/df_tpc_pie.shape[0]\\r\\n for i in range (df_tpc_pie.shape[0]):\\r\\n if df_tpc_pie.at[i, 'note'] == top:\\r\\n break\\r\\n else:\\r\\n #the sens of reading depend on the orientation\\r\\n if clockwise:\\r\\n rotation = rotation + 360/df_tpc_pie.shape[0]\\r\\n else:\\r\\n rotation = rotation - 360/df_tpc_pie.shape[0]\\r\\n \\r\\n\\r\\n #put nice sharps and flats\\r\\n for i in range(df_tpc_pie.shape[0]):\\r\\n df_tpc_pie.at[i, 'note'] = df_tpc_pie.at[i, 'note'].replace('b', r'$\\\\flat$')\\\\\\r\\n .replace('#', r'$\\\\sharp$')\\r\\n \\r\\n #plot the piechart with index 'tpc'\\r\\n df_tpc_pie.index = df_tpc_pie['note']\\r\\n \\r\\n #do the pie chart\\r\\n ax.pie(labels=df_tpc_pie.index, x=df_tpc_pie['part'], colors=color_note, startangle=rotation, **kwargs)\\r\\n\\r\\n #if asked plot the colorbar left of the piechart\\r\\n if colorbar:\\r\\n ax2 = fig.add_subplot(1, 10, 1)\\r\\n cb1 = matplotlib.colorbar.ColorbarBase(ax2, cmap=cmap, norm=norm, orientation='vertical')\\r\\n \\r\\n #display with the pc values\\r\\n else:\\r\\n #put the right values in 'number'\\r\\n if duration:\\r\\n df_data = pd.concat(\\r\\n [df['pc'], df['duration']],\\r\\n axis=1,\\r\\n keys=['pc', 'number'])\\r\\n else:\\r\\n df_data = pd.concat(\\r\\n [df['pc'], df['nb']],\\r\\n axis=1,\\r\\n keys=['pc', 'number'])\\r\\n\\r\\n #Normalize the values for the colors\\r\\n max_value = df_data['number'].max()\\r\\n min_value = df_data['number'].min()\\r\\n if log:\\r\\n norm = matplotlib.colors.LogNorm(vmin=min_value, vmax=max_value)\\r\\n else:\\r\\n norm = matplotlib.colors.Normalize(0, vmax=max_value)\\r\\n \\r\\n #set data df_data\\r\\n df_data = (df_data.groupby('pc')).sum()\\r\\n df_data = df_data.reindex(s_tpc_format)\\r\\n df_data.fillna(0, inplace=True)\\r\\n\\r\\n #set colors\\r\\n for i in range(0, 12):\\r\\n if df_data.iat[i, 0] != 0:\\r\\n color_note.append(cmap(norm(df_data.iat[i, 0])))\\r\\n else:\\r\\n if pd.isnull(nan_color):\\r\\n color_note.append(cmap(0))\\r\\n else:\\r\\n color_note.append(nan_color)\\r\\n\\r\\n #if clockwise invert the order of the data to be displayed clockwise\\r\\n if clockwise:\\r\\n s_twelve_ones = s_twelve_ones.iloc[::-1]\\r\\n color_note = list(reversed(color_note))\\r\\n\\r\\n #calculate the angle for the topPitchClass to be at the top\\r\\n if pd.isnull(top) == False:\\r\\n for i in range(s_tpc_format.shape[0]):\\r\\n if top == (s_twelve_ones.index)[i]:\\r\\n rotation = rotation + 75 - i * 30\\r\\n break\\r\\n ax.pie(labels=s_twelve_ones.index, x=s_twelve_ones, colors=color_note, startangle=rotation, **kwargs)\\r\\n\\r\\n #if asked plot the colorbar left of the piechart\\r\\n if colorbar:\\r\\n ax2 = fig.add_subplot(1, 10, 1)\\r\\n cb1 = matplotlib.colorbar.ColorbarBase(ax2, cmap=cmap, norm=norm, orientation='vertical')\\r\\n return fig\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96bc891fd8a531831923c45c61e834bb\",\n \"score\": \"0.53810406\",\n \"text\": \"def getChordScaleDegreesAsChord(self, n, degrees):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d11eeb501fc43303b16778ecb8c6fe5d\",\n \"score\": \"0.5331633\",\n \"text\": \"def create_chord_from_random_notes_in_key(self, minNum=2, maxNum=6):\\n nl = [np.random.choice(self.get_notes_in_key()) for i in range(np.random.randint(minNum,maxNum))]\\n ch = chord.Chord(nl)\\n return ch\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a964c7bae35ea170ff9130b53cf248bc\",\n \"score\": \"0.5285635\",\n \"text\": \"def CA_bass(self):\\n chords = chord(self.chord)\\n bass = Note(pitch = chords, duration = 4)\\n self.add_bass(bass)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d50341a0dffe5291a72c4650ae36f284\",\n \"score\": \"0.5267212\",\n \"text\": \"def __illustrate__(self):\\n import abjad\\n chord = abjad.Chord(self, abjad.Duration(1))\\n voice = abjad.Voice([chord])\\n staff = abjad.Staff([voice])\\n score = abjad.Score([staff])\\n lilypond_file = abjad.LilyPondFile.new(score)\\n return lilypond_file\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6079cb601f8d09ad748995c4ec408565\",\n \"score\": \"0.52628016\",\n \"text\": \"def psychoAcousticDistance(chord1, chord2):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34c00e6afb804011f5175562ae4c91c6\",\n \"score\": \"0.52496254\",\n \"text\": \"def generate_melody(key, progression, progression_repeats, major=True):\\n out = []\\n for _ in range(progression_repeats):\\n time_used = 0.0 # Number of measures that have been generated so far\\n for i, chord in enumerate(progression):\\n all_tones = generate_scale(key, 2, major)\\n chord_tones = get_chord(chord, note_number(key+'2'))\\n chord_tones.extend([x+12 for x in chord_tones])\\n non_chord_tones = list(set(all_tones[:-1]) - set(chord_tones))\\n non_chord_tones.extend([x+12 for x in non_chord_tones])\\n last_played = None\\n\\n # Generate a sequence of notes to fill a measure for this chord\\n while time_used < i + 1:\\n note_vals = [(0.125, 2), (0.25, 4), (0.375, 2), (0.5, 2), (0.75, 1), (1.0, 1), (1.25, 0.5), (1.5, 0.25)]\\n # Only allow note lengths that will fit into the len(progression)\\n # measures for this chord progression (i.e. don't allow spill\\n # of notes into different repetitions of the progression)\\n possible_note_vals = [x for x, p in note_vals if time_used + x <= len(progression)]\\n note_vals_prob = [p for x, p in note_vals if time_used + x <= len(progression)]\\n note_vals_prob = [x*1.0/sum(note_vals_prob) for x in note_vals_prob]\\n # Choose a note length\\n note_val = numpy.random.choice(possible_note_vals, p=note_vals_prob)\\n # Choose the set of note numbers we could pick from (either\\n # the chord tones or non-chord tones)\\n select_from = non_chord_tones if int(randint(0, 10)/10.0) else chord_tones\\n # Incentivize choosing notes that are close to the previously\\n # played note so that we aren't just jumping all over the\\n # place and sounding terribly random\\n NEARBY_INCENTIVE = 2.0\\n select_from_probabilities = [ (36 - int(math.fabs(last_played - x)))**NEARBY_INCENTIVE if last_played else 1 for x in select_from ]\\n select_from_probabilities = [ x * 1.0 / sum(select_from_probabilities) for x in select_from_probabilities ]\\n out.append((numpy.random.choice(select_from, p=select_from_probabilities), 80, note_val))\\n last_played = out[-1][0]\\n out.extend([None for x in range(int(note_val/0.125)-1)])\\n time_used += note_val\\n assert len(out) == 8 * len(progression) * progression_repeats\\n return out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c28fe18c4f71af211ccda3a21b8b6d9d\",\n \"score\": \"0.51965153\",\n \"text\": \"def CA_notes(self):\\n beat = 0\\n while beat < self.length:\\n if beat+1 in self.str_beat:\\n pitch = [random.choice(chord(self.chord))]\\n else:\\n pitch = [random.choice(notes)]\\n duration = random.choices(list(self.note_lengths.keys()),list(self.note_lengths.values()))\\n beat += duration[0]\\n if beat > self.length:\\n beat -= duration[0]\\n duration = self.length - beat\\n duration = [duration]\\n beat += duration[0]\\n rnote = Note(pitch, duration[0])\\n self.add_note(rnote)\\n assert beat == self.length\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8b9d3dd93127f378765ea1056cd6190\",\n \"score\": \"0.5183889\",\n \"text\": \"def cpsmidi(cps: float) -> float:\\n return 69 + 12 * np.log2(cps / 440.0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd25ace712ad5321dca786806750271e\",\n \"score\": \"0.51812875\",\n \"text\": \"def octave(self, octave = 0):\\r\\n return Note(self.pitch + octave * 12)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"015a1c60b8980ac36f7c09d7f8e35bbb\",\n \"score\": \"0.5160207\",\n \"text\": \"def __init__(self, start_time: float, end_time: float, all_chords_list: list):\\n self.all_chords_list = all_chords_list\\n self.start_time = start_time\\n self.end_time = end_time\\n self.duration = end_time - start_time\\n self.notes = []\\n self.pitches = set()\\n self.pitch_classes = set()\\n self.chroma = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\\n self.most_likely_chord = None\\n self.most_likely_chord_score = -10\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b763695c4cd07303bca85b1cf84fb6c8\",\n \"score\": \"0.5141266\",\n \"text\": \"def _parse_chord(self, chord_word):\\n if chord_word != '' and chord_word[0].islower():\\n res = chord_word[0].upper()\\n res += 'm'\\n return res\\n elif (chord_word == '*'):\\n return ''\\n else:\\n return chord_word\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5cc9fb7de89c0c6c6c46537d169b04df\",\n \"score\": \"0.5115718\",\n \"text\": \"def translate_to_chord(state_prog):\\n list = ['C', 'D', 'E', 'F', 'G', 'A', 'B']\\n result = []\\n for state in state_prog:\\n word = \\\"\\\"\\n for i, p in enumerate(state):\\n if i == 0:\\n word += list[int(p) - 1]\\n else:\\n word += p\\n result.append(word)\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26ad0028b519146c0d63597fbea1cebd\",\n \"score\": \"0.50975525\",\n \"text\": \"def chord_spaced_parameters(points):\\n cds = chord_lengths(points)\\n d = sum(cds)\\n uk = [0.] + cds + [1.]\\n return [sum(uk[0:i + 1]) / d for i in range(len(points))]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc4d18b36a9a2afb394fc596894976a3\",\n \"score\": \"0.507723\",\n \"text\": \"def composing(chord_pair_dict):\\n full_song = []\\n full_song.append(choose_theme_a(chord_pair_dict))\\n full_song.append(choose_theme_b(chord_pair_dict))\\n full_song.append(choose_theme_a(chord_pair_dict))\\n stream_full_song = get_stream_from_chord_prog(full_song)\\n stream_full_song = get_voice_led_stream(stream_full_song) #stub\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49a15ef0f1320ba06eff446899d5502d\",\n \"score\": \"0.50548774\",\n \"text\": \"def random_note_in_chord(self, index_):\\n\\n\\t\\t# select random note from chord\\n\\t\\tif self.chords[index_] == -1:\\n\\t\\t\\tsemitones_above_tonic = self.chords[index_]\\n\\t\\t\\treturn note.Rest(quarterLength=1)\\n\\n\\t\\telse:\\n\\t\\t\\tpossible_notes = self.chords[index_].split('/')\\n\\t\\t\\tselect_idx = np.random.randint(len(possible_notes))\\n\\t\\t\\tsemitones_above_tonic = int(possible_notes[select_idx])\\n\\t\\t\\t\\n\\t\\t\\t# return note\\n\\t\\t\\ttonic = self.key.getTonic()\\n\\t\\t\\tpitch_ = tonic.transpose(semitones_above_tonic)\\n\\n\\t\\t\\t# select random obctave within vocal range\\n\\t\\t\\tlow, high = self.vocal_range\\n\\n\\t\\t\\t#lowest octave\\n\\t\\t\\tlowest_oct = pitch_.transposeAboveTarget(low).octave\\n\\n\\t\\t\\t# highest octave\\n\\t\\t\\thighest_oct = pitch_.transposeBelowTarget(high).octave\\n\\n\\t\\t\\t# bug sometimes arises where low higher than high\\n\\t\\t\\tif lowest_oct >= highest_oct:\\n\\t\\t\\t\\tlowest_oct = low.octave\\n\\n\\t\\t\\t# highest + 1 as numpy.random.randint is exclusive of high \\n\\t\\t\\toct_ = np.random.randint(low=lowest_oct, high=highest_oct + 1)\\n\\t\\t\\tpitch_.octave = oct_\\n\\n\\t\\t\\treturn note.Note(pitch_, quarterLength=1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ab8fa8b931568248078ce7174654edb\",\n \"score\": \"0.5049438\",\n \"text\": \"def __init__(self, midi_pitch):\\n self.midi_pitch = midi_pitch\\n self.letter = PITCHES[midi_pitch % 12]\\n self.octave = midi_pitch // 12\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffb0ff6cc669fcd46cdf994b9b40334e\",\n \"score\": \"0.5039541\",\n \"text\": \"def choose_random_chord(chord_pair_dict):\\n chord_prog = []\\n first_chord = statistics\\n #getting list of ALL chords and storing in \\\"chord_options\\\"\\n chord_options = []\\n for chord_pair in chord_pair_dict:\\n chord_pair = chord_pair.split(\\\" -> \\\")\\n print(chord_pair, chord_pair[0])\\n chord_options.append(chord_pair[0])\\n #getting \\\"corresponding_chord_probability\\\"\\n corresponding_chord_probability = []\\n total = 0\\n for chord_pair in chord_pair_dict:\\n total += chord_pair_dict[chord_pair] #this loop gets total number of chords in song\\n\\n for chord_num in chord_pair_dict:\\n chance = chord_pair_dict[chord_num]/total #for every chord in the dictionary, find chance of occurence and add to \\\"corresponding_chord_probability\\\"\\n corresponding_chord_probability.append(chance) #for each option that COULD follow, append the chance of it following\\n print(\\\"chord options\\\", chord_options)\\n print(\\\"corresponging chord probability\\\", corresponding_chord_probability)\\n chosen_chord = numpy.random.choice(chord_options, 1, p=corresponding_chord_probability)[0]\\n return(chosen_chord)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c144faab4b43d19ba6114750580c9f4\",\n \"score\": \"0.5008513\",\n \"text\": \"def chord_recognition_all(X, ann_matrix, p=0.15, filt_len=None, filt_type='mean'):\\n if filt_len is not None:\\n if filt_type == 'mean':\\n X, Fs_X = libfmp.c3.smooth_downsample_feature_sequence(X, Fs=1, filt_len=filt_len, down_sampling=1)\\n if filt_type == 'median':\\n X, Fs_X = libfmp.c3.median_downsample_feature_sequence(X, Fs=1, filt_len=filt_len, down_sampling=1)\\n # Template-based chord recogntion\\n chord_sim, chord_Tem = libfmp.c5.chord_recognition_template(X, norm_sim='1')\\n result_Tem = libfmp.c5.compute_eval_measures(ann_matrix, chord_Tem)\\n # HMM-based chord recogntion\\n A = libfmp.c5.uniform_transition_matrix(p=p)\\n C = 1 / 24 * np.ones((1, 24))\\n B_O = chord_sim\\n chord_HMM, _, _, _ = libfmp.c5.viterbi_log_likelihood(A, C, B_O)\\n result_HMM = libfmp.c5.compute_eval_measures(ann_matrix, chord_HMM)\\n return result_Tem, result_HMM, chord_Tem, chord_HMM, chord_sim\",\n \"title\": \"\"\n },\n {\n \"docid\": \"972e0e04e35f36f7f6b25bdcfe598511\",\n \"score\": \"0.49731722\",\n \"text\": \"def zelda_Song_of_Time():\\r\\n play_note(NOTE_A4, 0.5)\\r\\n play_note(NOTE_D4, 1.0)\\r\\n play_note(NOTE_F4, 0.5)\\r\\n play_note(NOTE_A4, 0.5)\\r\\n play_note(NOTE_D4, 1.0)\\r\\n play_note(NOTE_F4, 0.5)\\r\\n play_note(NOTE_A4, 0.25)\\r\\n play_note(NOTE_C5, 0.25)\\r\\n play_note(NOTE_B4, 0.5)\\r\\n play_note(NOTE_G4, 0.5)\\r\\n play_note(NOTE_F4, 0.25)\\r\\n play_note(NOTE_G4, 0.25)\\r\\n play_note(NOTE_A4, 0.5)\\r\\n play_note(NOTE_D4, 0.5)\\r\\n play_note(NOTE_C4, 0.25)\\r\\n play_note(NOTE_E4, 0.25)\\r\\n play_note(NOTE_D4, 1.5)\\r\\n PWM.stop(piezo_pin)\\r\\n PWM.cleanup()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b570cd43bb7be4a2e735e7d3bc480f69\",\n \"score\": \"0.49640536\",\n \"text\": \"def GetCd(alpha, h):\\r\\n\\r\\n if not isinstance(h, numbers.Number):\\r\\n raise ValueError('Wrong arg type passed to GetCd() for h. Must be'\\r\\n + ' int or float')\\r\\n\\r\\n return AC['parasiteDrag'] + (16 * h / AC['span']) ** 2 / (1 + (16 * h / AC['span']) ** 2) * \\\\\\r\\n GetCl(alpha) ** 2 / (AC['AR'] * AC['e'] * math.pi)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98f54016ff21e87547a477e4f003fdd9\",\n \"score\": \"0.4963949\",\n \"text\": \"def test_2q_cphase_circuit():\\n prog = Program(CPHASE(0.1, 1, 2))\\n\\n expected = r\\\"\\\"\\\"\\n \\\\begin{tikzcd}\\n \\\\lstick{\\\\ket{q_{1}}} & \\\\gate[wires=2]{CPHASE(0.1)} & \\\\qw \\\\\\\\\\n \\\\lstick{\\\\ket{q_{2}}} & \\\\qw & \\\\qw\\n \\\\end{tikzcd}\\n \\\"\\\"\\\".strip().split()\\n\\n actual = to_latex(prog).split()\\n start_idx = actual.index(\\\"\\\\\\\\begin{tikzcd}\\\")\\n assert expected == actual[start_idx : start_idx + len(expected)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ed974bd52d951f3331b9b9d489796c2\",\n \"score\": \"0.49583477\",\n \"text\": \"def gen_cp(duration, h):\\n cp_times = []\\n cp_time = 0\\n while cp_time < duration:\\n dwell_time = np.random.exponential(1. / h)\\n cp_time += dwell_time\\n if cp_time < duration:\\n cp_times += [cp_time]\\n\\n return np.array(cp_times)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44db732dff5bcfd63247895fd5a077f7\",\n \"score\": \"0.49443159\",\n \"text\": \"def tada():\\n note(440, 0.3)\\n note(440, 0.1)\\n note(440, 0.1)\\n note(880, 0.8)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f229b430a33defcfc903c00d5e065ac8\",\n \"score\": \"0.4930778\",\n \"text\": \"def load_chords(jams_path, leadsheet_version):\\n try:\\n with open(jams_path, \\\"r\\\") as fhandle:\\n jam = jams.load(fhandle)\\n except FileNotFoundError:\\n raise FileNotFoundError(\\\"jams_path {} does not exist\\\".format(jams_path))\\n\\n if leadsheet_version:\\n anno = jam.search(namespace=\\\"chord\\\")[0]\\n else:\\n anno = jam.search(namespace=\\\"chord\\\")[1]\\n intervals, values = anno.to_interval_values()\\n return annotations.ChordData(intervals, \\\"s\\\", values, \\\"jams\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39be2b4eec200d450cfb9d168e48466a\",\n \"score\": \"0.4928727\",\n \"text\": \"def make_interval_chord(chords, key):\\n result = []\\n c_transposed = transpose(chords, key, 'C')\\n\\n for chord in c_transposed:\\n if len(chord) == 1 or (chord[1] != '#' and chord[1] != 'b'):\\n name_index = 1\\n if chord[0] == 'F':\\n mode = 'b'\\n else:\\n mode = '#'\\n else:\\n name_index = 2\\n mode = chord[1]\\n\\n interval = calc_interval('C', chord[:name_index])\\n\\n if interval in vocab_value:\\n result_chord = vocab_interval[interval]\\n else:\\n if mode == '#':\\n interval -= 0.5\\n else:\\n interval += 0.5\\n result_chord = vocab_interval[interval] + mode\\n\\n result.append(result_chord + chord[name_index:])\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e5956a1bd6d2453f993953413ac4f28\",\n \"score\": \"0.49157333\",\n \"text\": \"def make_banju_16bit(chord_prog, qlen, owd):\\n part = stream.Part()\\n\\n #print(chord_prog)\\n\\n # Setting for the path and sample files\\n os.chdir(owd)\\n current_dir = os.getcwd()\\n sample_dir = current_dir + '/banju/16bit'\\n os.chdir(sample_dir)\\n file_names = [file for file in os.listdir(sample_dir) if file != \\\".DS_Store\\\"]\\n file_to_open = random.choice(file_names)\\n # file_to_open = '16bit/chord_001.txt'\\\\\\n file = open(file_to_open, 'r', encoding=\\\"utf-8\\\")\\n\\n file2 = file.read().replace(' ', '')\\n banju_list = [int(f) for f in file2.split(',')]\\n# Done\\n on_list = []\\n\\n #print('banju is ', banju_list)\\n\\n for i, banju in enumerate(banju_list):\\n if (banju == -1):\\n on_list.append(-1)\\n continue\\n isOn = [1 if 8 & banju else 0, 1 if 4 & banju else 0, 1 if 2 & banju else 0, 1 if 1 & banju else 0]\\n on_list.append(isOn)\\n\\n print('banju is ', on_list)\\n\\n for i, chord in enumerate(chord_prog): # C, Dm, G7, etc....\\n offset = i * 4\\n if i > qlen / 4:\\n break\\n c = harmony.ChordSymbol(chord)\\n previous = []\\n for j, is_on in enumerate(on_list): # [1, 0, 0, 1]\\n if is_on == -1:\\n for note_temp in previous:\\n note_temp.quarterLength += 0.25\\n else:\\n previous.clear()\\n for k, on in enumerate(is_on):\\n if on:\\n if k < len(c.pitches):\\n note_temp = note.Note(c.pitches[k], quarterLength=0.25)\\n part.insert(offset + j * 0.25, note_temp)\\n previous.append(note_temp)\\n elif k == 3 and k == len(c.pitches):\\n note_temp = note.Note(c.pitches[0], quarterLength=0.25)\\n note_temp.octave += 1\\n part.insert(offset + j * 0.25, note_temp)\\n previous.append(note_temp)\\n\\n part.insert(instrument.AcousticGuitar())\\n # part.show('text', addEndTimes=True)\\n return part\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce345cf9dcd8bab5100db2bb9d7fd9c5\",\n \"score\": \"0.4902935\",\n \"text\": \"def make_banju_8bit(chord_prog, qlen):\\n part = stream.Part()\\n\\n print(chord_prog)\\n\\n # Setting for the path and sample files\\n current_dir = os.getcwd()\\n sample_dir = current_dir + '/banju'\\n os.chdir(sample_dir)\\n file_names = [file for file in os.listdir(sample_dir)]\\n # file_to_open = random.choice(file_names)\\n file_to_open = '8bit/condense_001.txt'\\n file = open(file_to_open, 'r', encoding=\\\"utf-8\\\")\\n\\n file2 = file.readline().replace(' ', '')\\n banju_list = [int(f) for f in file2.split(',')]\\n\\n on_list = []\\n\\n print(banju_list)\\n\\n for i, banju in enumerate(banju_list):\\n if (banju == -1):\\n on_list.append(-1)\\n continue\\n isOn = [1 if 8 & banju else 0, 1 if 4 & banju else 0, 1 if 2 & banju else 0, 1 if 1 & banju else 0]\\n on_list.append(isOn)\\n\\n print(on_list)\\n\\n for i, chord in enumerate(chord_prog): # C, Dm, G7, etc....\\n offset = i * 4\\n if i > qlen / 4:\\n break\\n c = harmony.ChordSymbol(chord)\\n previous = []\\n for j, is_on in enumerate(on_list): # [1, 0, 0, 1]\\n if is_on == -1:\\n for note_temp in previous:\\n note_temp.quarterLength += 0.5\\n else:\\n previous.clear()\\n for k, on in enumerate(is_on):\\n if on:\\n if k < len(c.pitches):\\n note_temp = note.Note(c.pitches[k], quarterLength=0.5)\\n part.insert(offset + j * 0.5, note_temp)\\n previous.append(note_temp)\\n elif k == 3 and k == len(c.pitches):\\n note_temp = note.Note(c.pitches[0], quarterLength=0.5)\\n note_temp.octave += 1\\n part.insert(offset + j * 0.5, note_temp)\\n previous.append(note_temp)\\n\\n part.show('text', addEndTimes=True)\\n return part\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ab93364f3deb75a42b8bba4c4e59291\",\n \"score\": \"0.48928526\",\n \"text\": \"def scale(notes=(\\\"c\\\", \\\"d\\\", \\\"e\\\", \\\"f\\\", \\\"g\\\", \\\"a\\\", \\\"b\\\", \\\"c5\\\")):\\r\\n def f(res, name, tempo):\\r\\n \\\"\\\"\\\" \\r\\n res: list, list of output states of the qc. \\r\\n return: Song object.\\r\\n \\\"\\\"\\\"\\r\\n s = Song(name=name, tempo=tempo)\\r\\n duration = 1\\r\\n for x in res:\\r\\n note = notes[int(x, base=2) % len(notes)]\\r\\n s.addNote(note, duration)\\r\\n return s\\r\\n return f\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d8d6b0b0a66d6fabb50e9fd77169942\",\n \"score\": \"0.48915246\",\n \"text\": \"def chordify(self, rules: Rules = TriadBaroque()) -> Stream:\\n stream = Stream()\\n prev_chord: Chord = rules.first_chord(self.key, self.input_notes[0])\\n stream.append(prev_chord)\\n for note in self.input_notes[1:]:\\n prev_chord = rules.next_chord(self.key, prev_chord, note)\\n stream.append(prev_chord)\\n stream.append(rules.end_cadence(self.key, prev_chord))\\n\\n return stream.flat\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6de14a221f19f476ba6b705027d11dc5\",\n \"score\": \"0.48873156\",\n \"text\": \"def calc_modes_circ(chi=3., Lmbda=0.5e-6, d_t=0.7):\\n chi_rads = (chi/3600.)*np.pi/180.\\n M = (np.pi * chi_rads * d_t/(4. * Lmbda))**2.\\n\\n return M\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f7b5b0e9e85eda088a6d272adc19663\",\n \"score\": \"0.4881925\",\n \"text\": \"def chord_lengths(points):\\n return [distance_point_point(a, b) for a, b in zip(points[:-1], points[1:])]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be0e93569ae41144411db714f6b6f287\",\n \"score\": \"0.48799253\",\n \"text\": \"def csch(x):\\n return 1 / sinh(x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b0dedb3d5fa209d8d27be9a975d32e2\",\n \"score\": \"0.48784468\",\n \"text\": \"def circle(x, z, r, t, duration):\\n y_circle = r * math.cos(2 * math.pi * (1 / duration) * t)\\n z_circle =+ r * math.sin(2 * math.pi * (1 / duration) * t)\\n alphas = computeIK(x, y_circle, z_circle + z)\\n return alphas\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f75801a816527c88b3ddfef45517fdd1\",\n \"score\": \"0.48645824\",\n \"text\": \"def text_to_sample_midi(input_name):\\n assert 'txt' in input_name\\n\\n input_file = open(input_name, 'r')\\n # if input_name[1] == 'b' or input_name[1] == '#':\\n # key = input_name[0:2]\\n # else:\\n # key = input_name[0]\\n key = 'C'\\n sample = []\\n\\n for line in input_file:\\n sample += line.strip().split(',')\\n\\n return make_interval_chord(sample, key)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fb7b733c54da9310a54f4620a4d4b6c\",\n \"score\": \"0.48508376\",\n \"text\": \"def __init__(self,length=8,chrd_prg=['C','Am','F','G'],note_lengths={1:1,.5:1}):\\n self.length = length\\n self.measures = []\\n self.chrd_prg = chrd_prg\\n self.note_lengths=note_lengths\",\n \"title\": \"\"\n },\n {\n \"docid\": \"381c1cb27b7230523a5a730a0fef184b\",\n \"score\": \"0.48507717\",\n \"text\": \"def __init__(self, dir_path, data=[]):\\r\\n self.measures = [] #measure information\\r\\n self.pitch = [] # pitch information\\r\\n #transpose the key of every song to C Major\\r\\n #key must be unchangable, so use 34 as the never reachable index\\r\\n #not nessary assign value by such a clumsy way. while the clumy way also has its advantages... \\r\\n self.pitch_dictionary = {globalConstant.note[0] : 0, globalConstant.note[1] : 1, globalConstant.note[2] : 34, globalConstant.note[3] : 0,\\r\\n globalConstant.note[4] : 2, globalConstant.note[5] : 3, globalConstant.note[6] : 1, globalConstant.note[7] : 2,\\r\\n globalConstant.note[8] : 4, globalConstant.note[9] : 5, globalConstant.note[10] : 3, globalConstant.note[11] : 4,\\r\\n globalConstant.note[12] : 5, globalConstant.note[13] : 6, globalConstant.note[14] : 4, globalConstant.note[15] : 5,\\r\\n globalConstant.note[16] : 7, globalConstant.note[17] : 8, globalConstant.note[18] : 6, globalConstant.note[19] : 7,\\r\\n globalConstant.note[20] : 9, globalConstant.note[21] : 10, globalConstant.note[22] : 8, globalConstant.note[23] : 9,\\r\\n globalConstant.note[24] : 10, globalConstant.note[25] : 11, globalConstant.note[26] : 9, globalConstant.note[27] : 10,\\r\\n globalConstant.note[28] : 12, globalConstant.note[29] : 13, globalConstant.note[30] : 11, globalConstant.note[31] : 12,\\r\\n globalConstant.note[32] : 14, globalConstant.note[33] : 15, globalConstant.note[34] : 13, globalConstant.note[35] : 14,\\r\\n globalConstant.note[36] : 16, globalConstant.note[37] : 17, globalConstant.note[38] : 15, globalConstant.note[39] : 16,\\r\\n globalConstant.note[40] : 17, globalConstant.note[41] : 18, globalConstant.note[42] : 16, globalConstant.note[43] : 17,\\r\\n globalConstant.note[44] : 19, globalConstant.note[45] : 20, globalConstant.note[46] : 18, globalConstant.note[47] : 19,\\r\\n globalConstant.note[48] : 21, globalConstant.note[49] : 22, globalConstant.note[50] : 20, globalConstant.note[51] : 21,\\r\\n globalConstant.note[52] : 22, globalConstant.note[53] : 23, globalConstant.note[54] : 21, globalConstant.note[55] : 22,\\r\\n globalConstant.note[56] : 24, globalConstant.note[57] : 25, globalConstant.note[58] : 23, globalConstant.note[59] : 24,\\r\\n globalConstant.note[60] : 26, globalConstant.note[61] : 27, globalConstant.note[62] : 25, globalConstant.note[63] : 26,\\r\\n globalConstant.note[64] : 28, globalConstant.note[65] : 29, globalConstant.note[66] : 27, globalConstant.note[67] : 28,\\r\\n globalConstant.note[68] : 29, globalConstant.note[69] : 30, globalConstant.note[70] : 28, globalConstant.note[71] : 29,\\r\\n globalConstant.note[72] : 31, globalConstant.note[73] : 32, globalConstant.note[74] : 30, globalConstant.note[75] : 31}\\r\\n\\r\\n \\r\\n #list first, then convert it to numpy array. see ListToNumpyArrayTest.py.\\r\\n self.duration = [] # duration information\\r\\n \\r\\n self.duration_dictionary = {globalConstant.index0 : 0, \\r\\n globalConstant.index1 : 1,\\r\\n globalConstant.index2 : 2,\\r\\n globalConstant.index3 : 3,\\r\\n globalConstant.index4 : 4,\\r\\n globalConstant.index5 : 5, \\r\\n globalConstant.index6 : 6,\\r\\n globalConstant.index7 : 7,\\r\\n globalConstant.index8 : 8,\\r\\n globalConstant.index9 : 9,\\r\\n globalConstant.index10 : 10,\\r\\n globalConstant.index11 : 11,\\r\\n globalConstant.index12 : 12,\\r\\n globalConstant.index13 : 13,\\r\\n globalConstant.index14 : 14,\\r\\n globalConstant.index15 : 15,\\r\\n globalConstant.index16 : 16,\\r\\n globalConstant.index17 : 17,\\r\\n globalConstant.index18 : 18,\\r\\n globalConstant.index19 : 19,\\r\\n globalConstant.index20 : 20,\\r\\n globalConstant.index21 : 21,\\r\\n globalConstant.index22 : 22 \\r\\n }\\r\\n self.dir_path = dir_path; #path of directionary\\r\\n self.data = data #ABC files information\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34b5a740693ba353d6a41eebd2a0c9fe\",\n \"score\": \"0.4847936\",\n \"text\": \"def choose_theme_b(chord_pair_dict):\\n chord_prog = []\\n first_chord = choose_random_chord(chord_pair_dict)\\n chord_prog.append(first_chord)\\n current_chord_num = first_chord\\n print(current_chord_num)\\n markov_chain(chord_pair_dict, chord_prog, current_chord_num, 7) #adds seven more chords to chord prog..\\n return chord_prog\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c6dfe478812ecd7d5b786b4cb49af63\",\n \"score\": \"0.48426485\",\n \"text\": \"def CA_Lnote(self):\\n pitch = [random.choice(chord(self.chord))]\\n duration = 4\\n lnote = Note(pitch, duration)\\n self.add_note(lnote)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3055c4af12716cfeef4a1dad68dac89\",\n \"score\": \"0.4837407\",\n \"text\": \"def cyril(prev_play, count=[0]):\\n count[0] += 1\\n choices = [\\\"P\\\", \\\"S\\\", \\\"S\\\", \\\"R\\\", \\\"P\\\"]\\n return choices[count[0] % len(choices)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"388116c2a4cd96ac66d21d323593b2da\",\n \"score\": \"0.4821277\",\n \"text\": \"def apply(self, reverse_harmony=True, time_interval=None, transcription=True, results_sample_size=200):\\n from tonalmodel.interval import Interval, IntervalType\\n\\n self.__reverse_harmony = reverse_harmony\\n self.__time_interval = time_interval if time_interval is not None else \\\\\\n NumericInterval(Fraction(0), self.score.line.duration.duration)\\n\\n reduced_line, first_position, duration = self.score.line.sub_line(self.time_interval)\\n reduced_reversed_line = reduced_line.clone()\\n reduced_reversed_line.reverse()\\n\\n reduced_hct = self.score.hct.sub_hct(NumericInterval(first_position.position,\\n first_position.position + duration.duration))\\n\\n reduced_reversed_hct = reduced_hct.reverse()\\n\\n # If reversing harmony OR if reversing harmony and not doing transposition\\n # These cases do not require transcription:\\n # a) reverse harmony and no transcription - return reversed line + reversed harmony\\n # b) no reverse harmony and no transcription - return reversed_line + original hct\\n # c) reverse harmony and transcription - return reversed_line and reversed harmony.\\n if reverse_harmony or not transcription:\\n return reduced_reversed_line, reduced_reversed_hct if reverse_harmony else reduced_hct\\n\\n # Case: not reversing harmony AND requiring a transposition\\n # Must specify reversed line here, but ANALYZE against reversed_hct, which is the original harmony\\n # making the correct analysis of the original melody.\\n t_ht = THarmonicTranscription(reduced_reversed_line, reduced_reversed_hct)\\n\\n # Find lowest tone:\\n notes = reduced_reversed_line.get_all_notes()\\n note_index, _ = min(enumerate(notes), key=lambda n: n[1].diatonic_pitch.chromatic_distance)\\n lowest_pitch = notes[note_index].diatonic_pitch\\n\\n # Adapt reversed melody to original harmony.\\n tag_map = gen_tag_map(notes[0], reduced_hct.hc_list()[0])\\n results = t_ht.apply(reduced_hct,\\n lowest_pitch,\\n tag_map, t_ht.height + 6, results_sample_size,\\n tunnel_half_interval=Interval(4, IntervalType.Perfect))\\n\\n filtered_results = MinContourFilter(reduced_reversed_line, results.pitch_results)\\n scored_filtered_results = filtered_results.scored_results\\n\\n if len(scored_filtered_results) == 0:\\n return None, None\\n\\n return scored_filtered_results[0][0], reduced_hct\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07a69fc354feb7ccf0633762182acb30\",\n \"score\": \"0.48089764\",\n \"text\": \"def play_progression(*args):\\n for x in args:\\n for note in get_chord(x, note_number('C5')):\\n play(note, 80, 1)\\n time.sleep(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9b08c83eedbf7655d3db62859897a50\",\n \"score\": \"0.48085463\",\n \"text\": \"def randomChord(self,rangeVal=[-12,12],inKey=True,nowStart = True):\\n\\t\\tnewNotes = []\\n\\t\\tfor i in range(len(self.notes)):\\n\\t\\t\\tmaxval,minval = rangeVal\\n\\t\\t\\tshiftVal = mapValue(random(),0,1,minval,maxval)\\n\\t\\t\\tnewNotes.append(self.notes[i]+shiftVal)\\n\\t\\t\\n\\t\\tif inKey == True:\\n\\t\\t\\tnewNotes = enforceKey(newNotes,self.key)\\n\\t\\tif self.octaveRange is not None:\\n\\t\\t\\tnewNotes = enforceOctave(newNotes,self.octaveRange)\\n\\n\\t\\tself.newNotes(newNotes, nowStart = nowStart)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2ebd20d1112e6c1c57c64ba85a0d1dc\",\n \"score\": \"0.4772306\",\n \"text\": \"def CA_Lbass(self):\\n chords = chord(self.chord)\\n bass = Note(pitch = chords, duration = 4)\\n self.add_bass(bass)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f56399ea6fb4b7b6d9a65cbed364c319\",\n \"score\": \"0.4768541\",\n \"text\": \"def chords_of_scale(root_name: str, root_acci: Accidental, chord_type: str) -> list[list[str]]:\\n result_list = chord_scale_namer(ChordOrScale.Scale, root_name, root_acci, chord_type, 7)\\n chords_list = []\\n i = 0\\n while i < 7:\\n current_chord = [result_list[i], result_list[(i + 2) % 7], result_list[(i + 4) % 7]]\\n chords_list.append(current_chord)\\n i += 1\\n if chord_type == \\\"nat_minor\\\":\\n if 'bb' in chords_list[4][1]:\\n chords_list[4][1] = chords_list[4][1][0] + 'b'\\n elif 'b' in chords_list[4][1]:\\n chords_list[4][1] = chords_list[4][1][0]\\n elif '#' in chords_list[4][1]:\\n chords_list[4][1] = chords_list[4][1][0] + 'x'\\n else:\\n chords_list[4][1] = chords_list[4][1][0] + '#'\\n # ^raised 7th for V chord in minor scale adjustment\\n return chords_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ff064658ace8b1cc9964e7c8dbd3143\",\n \"score\": \"0.47653127\",\n \"text\": \"def makeCone(dir, ksi, length, resolution, d1, angle, reverse=False):\\n print d1, angle\\n retval = []\\n if reverse:\\n rot = makeAxisRotation(dir, math.pi*2./resolution)\\n else:\\n rot = makeAxisRotation(dir, -math.pi*2./resolution)\\n\\n for i in range(0, resolution):\\n p1 = ksi*d1\\n p2 = dir*length + ksi*(d1+length*math.tan(angle))\\n\\n for i in range(0,len(retval)):\\n retval[i] = np.dot(rot, retval[i])\\n\\n retval.append(p1)\\n retval.append(p2)\\n\\n return retval\",\n \"title\": \"\"\n },\n {\n \"docid\": \"774899192b2b1534655dae5aa31551df\",\n \"score\": \"0.47606248\",\n \"text\": \"def parse_chord_sequence(chord_sequence):\\n chord_list = chord_sequence.split(\\\"|\\\")\\n note_list = []\\n note_frqs = []\\n for i in range(len(chord_list)):\\n chord_list[i] = chord_list[i].strip()\\n chord_list[i] = chord_list[i].split('_')\\n chord_notes = notes.chord_notes(*chord_list[i])\\n note_list.append(chord_notes)\\n return note_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f69e5ff3cf66b63a51b7e53e9c6b646\",\n \"score\": \"0.47594944\",\n \"text\": \"def create_chord_template(request):\\n all_songs = Song.objects.all()\\n for song in all_songs:\\n if song.chords:\\n continue\\n ccli = song.ccli\\n title = song.title\\n f = open('D:/dropbox/django/songs_chordpro/'+str(ccli)+'.cho', 'w')\\n title_line = '{title:'+title+'}\\\\n'\\n authors_list = []\\n for author in song.authors.all():\\n authors_list.append(author.full_name)\\n publisher_list = []\\n publication_date = song.publication_year\\n for publisher in song.publisher.all():\\n publisher_list.append(publisher.name)\\n authors_line = '{st:Words and Music by '+ ', '.join(authors_list)+'}\\\\n'\\n if publication_date == 1111:\\n publishers_line = '{st:Public Domain}\\\\n'\\n else:\\n publishers_line = '{st:Copyright '+str(song.publication_year)+' '+', '.join(publisher_list)+'}\\\\n'\\n ccli_line = '{st:CCLI ' + str(ccli) + '}'\\n f.write(title_line.encode('utf8'))\\n if len(authors_list) != 0:\\n f.write(authors_line.encode('utf8'))\\n f.write(publishers_line)\\n f.write(ccli_line)\\n f.close()\\n\\n return HttpResponseRedirect(reverse('songs.views.success'))\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":888,"cells":{"query_id":{"kind":"string","value":"ccdbd001147d5b9402f617c20d9e2566"},"query":{"kind":"string","value":"MC approximation of individual Generalized GaussNewton/Fisher diagonal."},"positive_passages":{"kind":"list like","value":[{"docid":"e09aba5e7a974d75ea6a635b3bb67e70","score":"0.0","text":"def diag_ggn_mc_batch(self, mc_samples: int) -> List[Tensor]:\n return","title":""}],"string":"[\n {\n \"docid\": \"e09aba5e7a974d75ea6a635b3bb67e70\",\n \"score\": \"0.0\",\n \"text\": \"def diag_ggn_mc_batch(self, mc_samples: int) -> List[Tensor]:\\n return\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"83fc45443edde46c289583e7f26e160b","score":"0.6200017","text":"def _mmd_g_(self):\r\n # calculate pairwise distance\r\n dist_gg, dist_gd, dist_dd = get_squared_dist(\r\n self.score_gen, self.score_data, z_score=False, do_summary=self.do_summary)\r\n\r\n # mmd\r\n self.loss_gen = mixture_mmd_g(\r\n dist_gg, dist_gd, dist_dd, self.batch_size, sigma=self.sigma,\r\n name='mmd_g', do_summary=self.do_summary)\r\n self.loss_dis = -self.loss_gen\r\n if self.dis_penalty is not None:\r\n self.loss_dis = self.loss_dis + self.dis_penalty","title":""},{"docid":"639d3d6e37a4ce1aa0ffa25764cd4ec2","score":"0.6191505","text":"def gfp_dynamics_1cc_(F,V,Cm1,beta,dtsim):\n G = np.zeros_like(F)\n G[0] = Cm1*V[0]\n for k in range(len(F)-1):\n G[k+1] = G[k]+(V[k]*F[k]-beta*G[k])*dtsim\n return G,G[-1]/V[-1]","title":""},{"docid":"d46119625b9ce37cf39481a6c2753ed5","score":"0.61492175","text":"def gfp_dynamics_1cc(F,V,Cm1,beta,dtsim):\n G = np.zeros_like(F)\n G[0] = Cm1/2\n for k in range(len(F)-1):\n G[k+1] = G[k]+(V[k]*F[k]-beta*G[k])*dtsim\n return G,G[-1]","title":""},{"docid":"c86dd1ad31830cb2cc980e6175180280","score":"0.6106971","text":"def gmm_mllr_diag_cov(X, gmm, niter=10):\n\n # remove illed gaussians\n logprob=gmm.score_samples(X)\n pcompx=gmm.predict_proba(X)\n ###logprob,pcompx = gmm.eval(X)\n psum = np.sum(pcompx, axis=0)\n ill_g = (psum == 0);\n if any(ill_g):\n valid = psum > 0\n gmm.means_ = gmm.means_[valid,:]\n gmm.weights_ = gmm.weights_[valid]\n gmm.weights_ = gmm.weights_/sum(gmm.weights_)\n gmm.covariances_ = gmm.covariances_[valid]\n ###logprob,pcompx = gmm.eval(X)\n logprob=gmm.score_samples(X)\n pcompx=gmm.predict_proba(X)\n\n # calculate G and Z \n C = len(gmm.weights_)\n T,dim = X.shape\n W = np.empty([dim,dim+1])\n G = np.zeros([dim,dim+1,dim+1])\n # 1. first calculate D[0,...,C) and Z\n D = np.zeros([C,dim+1,dim+1])\n V = np.zeros([C,dim,dim])\n Z = np.zeros([dim,dim+1])\n for c in range(0,C):\n mu = gmm.means_[c]\n sigma = np.diag(gmm.covariances_[c])\n sigma_inv = np.linalg.inv(sigma)\n p = pcompx[:,c]\n\n xi = np.empty_like(mu)\n xi[:] = mu\n xi = np.insert(xi,0,1)\n xi = np.reshape(xi, [len(xi),1])\n D[c] = xi.dot(xi.T)\n V[c] = np.sum(p)*sigma_inv\n for t in range(0,T):\n xt = np.reshape(X[t],[len(X[t]),1])\n Z += p[t]*sigma_inv.dot(xt).dot(xi.T)\n \n # 2. now calculate G\n for i in range(0,dim): \n for c in range(0,C): # tie all Gaussians\n G[i] += V[c,i,i]*D[c]\n try: \n G_i_inv = np.linalg.inv(G[i])\n except:\n print('G is nearly singular and pseudo-inverse is used.')\n G_i_inv = np.linalg.pinv(G[i])\n z_i = np.reshape(Z[i],[dim+1,1])\n W[i] = G_i_inv.dot(z_i)[:,0]\n \n # transform means\n for c in range(0,C):\n xi = np.insert(gmm.means_[c],0,1)\n xi = np.reshape(xi,[len(xi),1])\n gmm.means_[c] = W.dot(xi)[:,0]\n \n # remove non positive definite matrices\n ###logprob,pcompx = gmm.eval(X)\n logprob=gmm.score_samples(X)\n pcompx=gmm.predict_proba(X)\n psum = np.sum(pcompx, axis=0)\n ill = (psum == 0);\n if np.any(ill):\n valid = (ill == 0)\n gmm.means_ = gmm.means_[valid]\n gmm.weights_ = gmm.weights_[valid]\n gmm.weights_ = gmm.weights_/sum(gmm.weights_)\n gmm.covariances_ = gmm.covariances_[valid]\n K = gmm.means_.shape[0]\n return gmm","title":""},{"docid":"73e96a26603d715bb00dbb56cf565df3","score":"0.59942466","text":"def gmm(X, k, epsilon=0.0001):\n\n n = X.shape[0]\n d = X.shape[1]\n mu = k_means(X, k) #np.zeros((k, X.shape[1]))\n sigma = np.ones((k, d))\n pi = np.ones((k, 1)) / k\n\n\n while 1:\n\n r = np.zeros((n, k))\n\n # E step\n for i in range(n):\n for c in range(k):\n r[i,c] = pi[c] * gauss.pdf(X[i], mean=mu[c], cov=sigma[c]) / np.sum([pi[j] * gauss.pdf(X[i], mean=mu[j], cov=sigma[j]) for j in range(k)])\n # M step\n \n new_mu = np.zeros((k, d))\n new_sigma = np.ones((k, d))\n new_pi = np.ones((k, 1))\n\n for c in range(k):\n new_pi[c] = np.sum(r[i,c] for i in range(n)) / n\n new_mu[c] = np.sum(r[i,c] * X[i] for i in range(n)) / (n * new_pi[c])\n new_sigma[c] = np.sum(r[i,c] * (X[i] - new_mu[c]) ** 2 for i in range(n)) / (n * new_pi[c])\n # print(sigma[c])\n new_sigma[c] = np.clip(new_sigma[c], epsilon, float('inf'))\n # print(new_sigma[c])\n \n\n if la.norm(new_pi - pi) < epsilon:\n break\n\n if la.norm(new_sigma - sigma) < epsilon:\n break\n\n if la.norm(new_mu - mu) < epsilon:\n break\n\n pi = new_pi\n sigma = new_sigma\n mu = new_mu\n\n return mu, sigma, pi","title":""},{"docid":"94487e0e6c94de3c624b972d87681643","score":"0.5977705","text":"def grl_equilibration(fish):\n dim = fish.shape[0]\n eqi = np.eye(dim)\n for i in range(dim): \n if i%6==0:\n eqi[i,i] = 1e4\n return eqi.dot(fish.dot(eqi)) #creates a copy. ","title":""},{"docid":"db963bb8c3c5dff9224c4a962bc446e5","score":"0.5919506","text":"def get_hessian_g(x: numpy.array) -> numpy.array:\r\n # 2A\r\n #print(\"Inside Hessian of G: \\n\")\r\n #print(\"x.size \" + str(x.size))\r\n\r\n #print(numpy.eye(x.size))\r\n return numpy.eye(x.size)","title":""},{"docid":"53f8b7ea3d6ec625e8ed9f96083dccab","score":"0.5821681","text":"def g(t, x):\t\n M = np.dot(np.dot(R.T, np.diag(P[t, :])), R)\n return np.dot(x.T, np.dot(M, x))","title":""},{"docid":"554b1e9c98d6dd061e314f851f559183","score":"0.5801351","text":"def hessian_posteriori_matrices(xm,m,Q,sm2,H_mat):\n ########################################################\n # GRADIENT AND HESSIAN OF B AND b\n ########################################################\n def B00_x(x):\n if x=='e': sd=1\n else: sd=0\n dQ = H_mat['Q_{}'.format(x)][0,0]\n Q0=Q[0,0]; den=Q0+sm2\n return ((sd*Q0+sm2*dQ)*den-sm2*Q0*(dQ+sd))/den**2\n def B00_xy(x,y):\n if x=='e': dxe=1\n else: dxe=0\n if y=='e': dye=1\n else: dye=0\n dQx = H_mat['Q_{}'.format(x)][0,0]\n dQy = H_mat['Q_{}'.format(y)][0,0]\n dQxy = H_mat['Q_{}{}'.format(x,y)][0,0]\n Q00=Q[0,0]\n return (-2*dxe*dye*Q00**2 + (2*dQy*dxe + 2*dQx*dye)*Q00*sm2 +\\\n (-2*dQx*dQy + dQxy*Q00)*sm2**2 + dQxy*sm2**3)/(Q00 + sm2)**3\n def B01_x(x):\n if x=='e': dxe=1\n else: dxe=0\n dQ = H_mat['Q_{}'.format(x)]\n dQ00x = dQ[0,0];dQ01x = dQ[0,1]\n Q00=Q[0,0]; Q01=Q[0,1]\n return (dQ01x*sm2*(Q00 + sm2) + (-(dQ00x*sm2) + Q00*dxe)*Q01)/(Q00 + sm2)**2\n def B01_xy(x,y):\n if x=='e': dxe=1\n else: dxe=0\n if y=='e': dye=1\n else: dye=0\n Q00=Q[0,0];Q01=Q[0,1]\n dQx = H_mat['Q_{}'.format(x)]\n dQy = H_mat['Q_{}'.format(y)]\n dQxy = H_mat['Q_{}{}'.format(x,y)]\n dQ00x = dQx[0,0];dQ00y = dQy[0,0]\n dQ01x = dQx[0,1];dQ01y = dQy[0,1]\n dQ00xy= dQxy[0,0];dQ01xy= dQxy[0,1];dQ11xy= dQxy[1,1]\n\n return (Q01*((-(dQ00y*dxe) - (dQ00x + 2*dxe)*dye)*Q00 + (dQ00y*(2*dQ00x + dxe) + dQ00x*dye - dQ00xy*Q00)*sm2 -\\\n dQ00xy*sm2**2) + (Q00 + sm2)*((dQ01y*dxe + dQ01x*dye)*Q00 - (dQ00y*dQ01x + dQ00x*dQ01y - dQ01xy*Q00)*sm2 +\\\n dQ01xy*sm2**2))/(Q00 + sm2)**3\n def B11_x(x):\n if x=='e': dxe=1\n else: dxe=0\n Q00=Q[0,0];Q01=Q[0,1]\n dQx = H_mat['Q_{}'.format(x)]\n dQ00x=dQx[0,0]; dQ01x=dQx[0,1]; dQ11x=dQx[1,1]\n return dQ11x + ((dQ00x + dxe)*Q01**2)/(Q00 + sm2)**2 - (2*dQ01x*Q01)/(Q00 + sm2)\n def B11_xy(x,y):\n if x=='e': dxe=1\n else: dxe=0\n if y=='e': dye=1\n else: dye=0\n Q00=Q[0,0];Q01=Q[0,1]\n dQx = H_mat['Q_{}'.format(x)]\n dQy = H_mat['Q_{}'.format(y)]\n dQxy = H_mat['Q_{}{}'.format(x,y)]\n dQ00x = dQx[0,0];dQ00y = dQy[0,0]\n dQ01x = dQx[0,1];dQ01y = dQy[0,1]\n dQ00xy= dQxy[0,0];dQ01xy= dQxy[0,1];dQ11xy= dQxy[1,1]\n return dQ11xy - (2*(dQ00x + dxe)*(dQ00y + dye)*Q01**2)/(Q00 + sm2)**3 +\\\n (2*dQ01y*(dQ00x + dxe)*Q01)/(Q00 + sm2)**2 +\\\n (2*dQ01x*(dQ00y + dye)*Q01)/(Q00 + sm2)**2 + (dQ00xy*Q01**2)/(Q00 + sm2)**2 -\\\n (2*dQ01x*dQ01y)/(Q00 + sm2) - (2*dQ01xy*Q01)/(Q00 + sm2)\n def b0_x(x):\n if x=='e': dxe=1\n else: dxe=0\n dQ00x = H_mat['Q_{}'.format(x)][0,0]\n dm00x = H_mat['m_{}'.format(x)][0,0]\n Q00=Q[0,0];m00=m[0,0]\n return (Q00*(dm00x*sm2 + dxe*(m00 - xm)) + sm2*(dm00x*sm2 + dQ00x*(-m00 + xm)))/(Q00 + sm2)**2\n def b0_xy(x,y):\n if x=='e': dxe=1\n else: dxe=0\n if y=='e': dye=1\n else: dye=0\n dQ00x = H_mat['Q_{}'.format(x)][0,0]\n dQ00xy = H_mat['Q_{}{}'.format(x,y)][0,0]\n dQ00y = H_mat['Q_{}'.format(y)][0,0]\n dm00x = H_mat['m_{}'.format(x)][0,0]\n dm00y = H_mat['m_{}'.format(y)][0,0]\n dm00xy = H_mat['m_{}{}'.format(x,y)][0,0]\n Q00=Q[0,0];m00=m[0,0]\n return -(((dQ00y + dye)*(Q00 + sm2)*(dxe*m00 + dm00x*sm2 + dQ00x*xm) -\\\n (Q00 + sm2)**2*(dm00y*dxe + dm00x*dye + dm00xy*sm2 + dQ00xy*xm) +\\\n (dQ00x + dxe)*(Q00 + sm2)*(dye*m00 + dm00y*sm2 + dQ00y*xm) -\\\n 2*(dQ00x + dxe)*(dQ00y + dye)*(m00*sm2 + Q00*xm) +\\\n dQ00xy*(Q00 + sm2)*(m00*sm2 + Q00*xm))/(Q00 + sm2)**3)\n def b1_x(x):\n if x=='e': dxe=1\n else: dxe=0\n dQx = H_mat['Q_{}'.format(x)]\n dQ00x = dQx[0,0]\n dQ01x = dQx[0,1]\n dmx = H_mat['m_{}'.format(x)]\n dm10x = dmx[1,0];dm00x = dmx[0,0]\n Q00=Q[0,0];m00=m[0,0]\n Q01=Q[0,1];m10=m[1,0]\n return dm10x - (dm00x*Q01)/(Q00 + sm2) -\\\n ((dQ00x + dxe)*Q01*(-m00 + xm))/(Q00 + sm2)**2 + (dQ01x*(-m00 + xm))/(Q00 + sm2)\n def b1_xy(x,y):\n if x=='e': dxe=1\n else: dxe=0\n if y=='e': dye=1\n else: dye=0\n Q00=Q[0,0];Q01=Q[0,1];m00=m[0,0]\n dQx = H_mat['Q_{}'.format(x)]\n dQy = H_mat['Q_{}'.format(y)]\n dQxy = H_mat['Q_{}{}'.format(x,y)]\n dQ00x = dQx[0,0];dQ00y = dQy[0,0]\n dQ01x = dQx[0,1];dQ01y = dQy[0,1]\n dQ00xy= dQxy[0,0];dQ01xy= dQxy[0,1];dQ11xy= dQxy[1,1]\n dmx = H_mat['m_{}'.format(x)]\n dmy = H_mat['m_{}'.format(y)]\n dmxy = H_mat['m_{}{}'.format(x,y)]\n dm10x = dmx[1,0];dm00x = dmx[0,0]\n dm10y = dmy[1,0];dm00y = dmy[0,0]\n dm10xy = dmxy[1,0];dm00xy = dmxy[0,0]\n return -((-(dm00y*(dQ00x + dxe)*Q01*(Q00 + sm2)) -\\\n dm00x*(dQ00y + dye)*Q01*(Q00 + sm2) + dm00y*dQ01x*(Q00 + sm2)**2 +\\\n dm00x*dQ01y*(Q00 + sm2)**2 + dm00xy*Q01*(Q00 + sm2)**2 -\\\n dm10xy*(Q00 + sm2)**3 - 2*(dQ00x + dxe)*(dQ00y + dye)*Q01*(-m00 + xm) +\\\n dQ01y*(dQ00x + dxe)*(Q00 + sm2)*(-m00 + xm) +\\\n dQ01x*(dQ00y + dye)*(Q00 + sm2)*(-m00 + xm) +\\\n dQ00xy*Q01*(Q00 + sm2)*(-m00 + xm) - dQ01xy*(Q00 + sm2)**2*(-m00 + xm))/\\\n (Q00 + sm2)**3)\n ########################################################\n # FILL THE MATRIX\n ########################################################\n ret = {}\n build_vec = lambda x,y: np.array([[x],[y]])\n build_mat= lambda x,y,z: np.array([[x,y],[y,z]])\n for k in ['m','g','s','e']:\n ret['b_{}'.format(k)] = build_vec(b0_x(k),b1_x(k))\n ret['B_{}'.format(k)] = build_mat(B00_x(k),B01_x(k),B11_x(k))\n for j in ['m','g','s','e']:\n ret['b_{}{}'.format(k,j)] = build_vec(b0_xy(k,j),b1_xy(k,j))\n ret['B_{}{}'.format(k,j)] =\\\n build_mat(B00_xy(k,j),B01_xy(k,j),B11_xy(k,j))\n return ret","title":""},{"docid":"1cceb30e5b3ae0d78a0beb9ef32b0a2a","score":"0.57995254","text":"def apply_G(self):\n\n mach_f = self.vec['u']('M_f')\n mach = self.vec['p']('M')\n\n mach_f[0] = mach[0]","title":""},{"docid":"8a5c7fe3c13bc7629d5f225cf7f657ba","score":"0.56645346","text":"def ME(X, Yd, m = 4, gating = 'linear', add_bias = False):\n # Dimensions:\n # wexp (ns x ne x m)\n # wgat (m x ne)\n # yexp (N, ns, m)\n # ygat (N x m)\n # g = ygat = (N x m)\n # p (N x m)\n # h (N x m) ?\n\n # Add column with 1s for the bias\n if add_bias: # add column of 1s in X matrix for bias\n X = np.insert(X, 0, 1, axis=1)\n if Yd.ndim == 1: # convert row vector to column vector\n Yd = np.array([Yd]).T\n N, ne = X.shape # number of instances and features\n eps = np.finfo(float).eps # small number to avoid nan and division by zero\n\n ns = Yd.shape[1] # number of ME outputs\n var = np.ones(m) # adaptative variance\n wexp = np.random.rand(ns, ne, m) # expert weight\n wgat = np.random.rand(m, ne) # linear gating weight\n ygat = softmax(X @ wgat.T) # gating output (a priori probability)\n yexp = calc_saida_esp(X, wexp) # expert output\n\n j = random.sample(list(np.arange(N)), N)\n alpha = 1/m * np.ones((m,1)) # gaussian gating parameter\n gamma = X[j[:m],:] # gaussian gating parameter\n sigma = np.zeros((ne, ne, m)) # gaussian gating parameter\n for i in range(m):\n sigma[:,:,i] = np.eye(ne, ne) # gaussian gating parameter\n\n\n p = calc_prob_exp(yexp, Yd, var) # P(y|X,theta)\n p_xv = np.zeros((N, m)) # P(X|v)\n lik_old = 0\n lik_new = likelihood(ygat, p)\n it = 0\n maxiter = 100\n while abs(lik_new - lik_old) > 1e-3 and it < maxiter:\n #print('lik = ', lik_new, \"diff: \", lik_new - lik_old)\n # Linear gating\n if gating == 'linear':\n # Step E - Estimate H\n p = calc_prob_exp(yexp, Yd, var) # P(y|X,theta)\n h = prob_h(ygat, p)\n # Step M: optimize gating and expert outputs\n for i in range(m):\n wexp[:,:,i], var[i] = atualiza_exp(X, Yd, h[:, i], var[i], wexp[:,:,i], ns)\n wgat = atualiza_gat_linear(X, h, wgat) # gating weights\n ygat = softmax(X @ wgat.T) # gating output\n yexp = calc_saida_esp(X, wexp) # expert output\n\n lik_old = lik_new\n lik_new = likelihood(ygat, p)\n # Gaussian gating\n else:\n # Step E:\n ygat = prob(X, alpha, gamma, sigma)\n p = calc_prob_exp(yexp, Yd, var)\n h = alpha.T * ygat * p / np.sum(eps + alpha.T * ygat * p, axis = 1, keepdims=True)\n # Step M: optimize gating and expert outputs\n for i in range(m):\n wexp[:,:,i], var[i] = atualiza_exp(X, Yd, h[:, i], var[i], wexp[:,:,i], ns)\n alpha = np.mean(h, axis =0, keepdims = True).T\n gamma = h.T @ X / np.sum(h + eps, axis=0, keepdims=True).T\n for i in range(m):\n dif = X - gamma[i]\n sigma[:,:,i] = (dif / np.sum(h[:,i]) * h[:,i:i+1]).T @ dif\n\n #ygat = gating_output(X, p_xv, alpha) # gating output\n yexp = calc_saida_esp(X, wexp) # expert output\n\n lik_old = lik_new\n lik_new = likelihood(ygat, p)\n\n if it > 15 and gating == 'gaussian':\n if lik_new < lik_old:\n print(\"Doesn't converge.\")\n elif lik_new < lik_old + 5:\n print(\"Converge.\")\n break\n\n #print('lik = ', lik_new, \"diff: \", abs(lik_new - lik_old))\n it += 1\n\n return wexp, wgat, var, alpha, gamma, sigma","title":""},{"docid":"be8b005853c3b8c29bfb455785dd8a69","score":"0.56465757","text":"def hamiltonian(self):","title":""},{"docid":"8f0e0a35b50bd2f3e97f0e513370247a","score":"0.56284153","text":"def metric_matrix(dmat):\n dmat = numpy.array(dmat)\n\n natms = len(dmat)\n\n dcvec = distances_from_center(dmat)\n\n gmat = numpy.eye(natms)\n\n for i, j in itertools.product(range(natms), range(natms)):\n gmat[i, j] = (dcvec[i]**2 + dcvec[j]**2 - dmat[i, j]**2)/2.\n\n return gmat","title":""},{"docid":"88545d3d9f21157bf68a8876c3c19bf4","score":"0.56258357","text":"def mvhermgauss(H, D):\n gh_x, gh_w = hermgauss(H)\n x = np.array(list(itertools.product(*(gh_x,) * D))) # H**DxD\n w = np.prod(np.array(list(itertools.product(*(gh_w,) * D))), 1) # H**D\n return x, w","title":""},{"docid":"08f4d863cd671cf58fcc5d4a69fba2ae","score":"0.56042755","text":"def g_multivariate_normal(x,M):\n return .5*np.dot(x,M+M.T)","title":""},{"docid":"e26d7a88f63aa5f497434c78e3d27a38","score":"0.5598046","text":"def apply_G(self):\n\n mach_i = self.vec['u']('M_i')\n mach = self.vec['p']('M')\n\n mach_i[0] = mach[0]","title":""},{"docid":"495799d796cd50b6cd63888bbd3d31a9","score":"0.5594839","text":"def calc_M_1(m_s,x_1): \r\n \r\n M_1 = np.zeros((m_s.shape[0], m_s.shape[1], m_s.shape[2]))\r\n \r\n for l in range(m_s.shape[0]):\r\n M_1[l] = m_s[l] @ np.diagflat(x_1[0])\r\n\r\n return(M_1)","title":""},{"docid":"517e0cd15d3df58cdba05955b49ea97b","score":"0.5592457","text":"def gfp_dynamics(F,V,Cm1,beta,dtsim):\n G = []\n n=0\n for f,v in zip(F,V):\n if n==0: cm1 = Cm1\n g,cm1 = gfp_dynamics_1cc(f,v,cm1,beta,dtsim)\n G.append(g)\n n+=1\n return G","title":""},{"docid":"8ab0882572fcbb0246a08fe0fec490ea","score":"0.5567783","text":"def gauss_dimensions_cov(dimensions=range(2,100,20), total_points=200,\n num_experiments=100, d=10):\n k = 2\n if not d:\n d = dimensions[0]\n table = np.zeros((num_experiments*len(dimensions), 6))\n count = 0\n\n for D in dimensions:\n for l in range(num_experiments):\n\n # generate data\n m1 = np.zeros(D)\n m2 = np.concatenate((np.ones(d), np.zeros(D-d)))\n s1 = np.eye(D)\n # from uniform 1, 5\n s2_1 = np.array([1.367, 3.175, 3.247, 4.403, 1.249, 1.969, 4.035, 4.237, 2.813, 3.637])\n s2 = np.diag(np.concatenate((s2_1, np.ones(D-d))))\n n1, n2 = np.random.multinomial(total_points, [0.5,0.5])\n X, z = data.multivariate_normal([m1, m2], [s1, s2], [n1, n2])\n\n rho = lambda x, y: np.linalg.norm(x-y)\n G = eclust.kernel_matrix(X, rho)\n \n # can change the number of times we execute each experiment\n # and initialization method as well\n table[count, 0] = D\n table[count, 1] = run_clustering.energy_hartigan(k, X, G, z, \n init=\"k-means++\", run_times=5)\n table[count, 2] = run_clustering.energy_lloyd(k, X, G, z, \n init=\"k-means++\", run_times=5)\n table[count, 3] = run_clustering.spectral(k, X, G, z, \n run_times=5)\n table[count, 4] = run_clustering.kmeans(k, X, z, \n init=\"k-means++\", run_times=5)\n table[count, 5] = run_clustering.gmm(k, X, z, \n init=\"kmeans\", run_times=5)\n count += 1\n\n return table","title":""},{"docid":"0166b5aae8cbec6fc919803d44db863e","score":"0.55659634","text":"def newson(m,e_g):\r\n mf = f(m,e_g)\r\n mdf = df(m,e_g)\r\n a = e_g - (mf/mdf)\r\n return a","title":""},{"docid":"09be79630f3ccb94f0c345fea17749e4","score":"0.55326855","text":"def calc_g2():","title":""},{"docid":"af61f4ce204e3ce6dfa2b9e220df9e80","score":"0.55179346","text":"def gmm(X, k):\n gauss = sklearn.mixture.GaussianMixture(n_components=k).fit(X)\n pi = gauss.weights_\n m = gauss.means_\n S = gauss.covariances_\n clss = gauss.predict(X)\n bic = gauss.bic(X)\n return pi, m, S, clss, bic","title":""},{"docid":"c706dfe3e1d3b1e1680155eba5e60899","score":"0.550719","text":"def gowers_matrix(D):\n assert_square(D)\n\n n = float(D.shape[0])\n o = np.ones((int(n), 1))\n I = np.identity(int(n)) - (1/n)*o.dot(o.T)\n A = -0.5*(np.square(D))\n G = I.dot(A).dot(I)\n\n return(G)","title":""},{"docid":"929c187924254c36095672f32dfe4846","score":"0.5504482","text":"def gmm(X, k):\n\n Gaussian = sklearn.mixture.GaussianMixture(n_components=k)\n params = Gaussian.fit(X)\n clss = Gaussian.predict(X)\n pi = params.weights_\n m = params.means_\n S = params.covariances_\n bic = Gaussian.bic(X)\n\n return pi, m, S, clss, bic","title":""},{"docid":"eea487ca95e5fd139c2ec23e7f1e2ef6","score":"0.54867804","text":"def gmm_map_qb(X, gmm, rho=.1, epsilon=1, niter=10):\n\n # init\n ###logprob,pcompx = gmm.eval(X)\n logprob=gmm.score_samples(X)\n pcompx=gmm.predict_proba(X)\n psum = np.sum(pcompx, axis=0) #(18)\n # remove illed gaussians\n ill_g = (psum == 0);\n if any(ill_g):\n valid = psum > 0\n gmm.means_ = gmm.means_[valid,:]\n gmm.weights_ = gmm.weights_[valid]\n gmm.weights_ = gmm.weights_/sum(gmm.weights_)\n gmm.covariances_ = gmm.covariances_[valid]\n\n ###logprob,pcompx = gmm.eval(X)\n logprob=gmm.score_samples(X)\n pcompx=gmm.predict_proba(X)\n\n psum = np.sum(pcompx, axis=0) #(18)\n K,nDim = gmm.means_.shape\n tau = psum*epsilon #(22)\n tau_update = tau\n nu = 1 + tau #(23)\n nu_update = nu\n alpha = nDim + tau #(24)\n alpha_update = alpha\n mu = np.empty([K,nDim])\n mu_update = np.empty([K,nDim])\n yu = np.empty([K,nDim,nDim])\n yu_update = np.empty([K,nDim,nDim])\n for k in range(0,K):\n mu[k] = gmm.means_[k] #(25)\n yu[k] = tau[k] * gmm.covariances_[k] #(26)\n\n # EM iterations \n s = np.empty([K,nDim,nDim])\n N = X.shape[0]\n for iter in range(0, niter):\n print('iter=',iter)\n print(np.sum(gmm.weights_))\n #plot_gmm(gmm,X)\n # E-step: posterior probabilities\n ###logprob, pcompx = gmm.eval(X)\n logprob=gmm.score_samples(X)\n pcompx=gmm.predict_proba(X)\n \n # remove illed gaussians\n psum = np.sum(pcompx, axis=0) # (18)\n #print psum\n #raw_input()\n ill_g = (psum == 0);\n #print ill_g\n if any(ill_g):\n valid = psum > 0\n gmm.means_ = gmm.means_[valid,:]\n gmm.weights_ = gmm.weights_[valid]\n gmm.weights_ = gmm.weights_/sum(gmm.weights_)\n gmm.covariances_ = gmm.covariances_[valid]\n mu = mu[valid]\n mu_update = mu_update[valid]\n yu = yu[valid]\n yu_update = yu_update[valid]\n tau = tau[valid]\n tau_update = tau_update[valid]\n alpha = alpha[valid]\n alpha_update = alpha_update[valid]\n nu = nu[valid]\n nu_update = nu_update[valid]\n K = gmm.means_.shape[0]\n continue\n\n # M-step, eqs. from KimLoizou'10\n # Hyper-parameters\n psum = np.sum(pcompx, axis=0) # (18)\n #print np.sum(pcompx,axis=1)\n #print 'psum',psum\n #print gmm.weights_\n #raw_input()\n x_expected = np.dot(pcompx.T, X)/np.tile(psum[np.newaxis].T,(1,nDim)) #(19)\n #print 'x_expected',x_expected\n for k in range(0,K):\n #raw_input()\n # (20)\n s[k] = np.dot((X-np.tile(x_expected[k],(N,1))).T, (X-np.tile(x_expected[k],(N,1)))*np.tile(pcompx[:,k][np.newaxis].T,(1,nDim)))\n # (15)\n #print 'yu[k]',yu[k] \n #print 'x_expected - mu', (x_expected-mu).T \n #yu_update[k] = rho*yu[k] + s[k] + rho*tau[k]*psum[k]/(rho*tau[k]+psum[k])*np.dot((x_expected-mu).T, x_expected-mu) \n yu[k] = rho*yu[k] + s[k] + rho*tau[k]*psum[k]/(rho*tau[k]+psum[k])*np.dot((x_expected[k,:]-mu[k,:]).T, x_expected[k,:]-mu[k,:]) \n #print 'yu[k],after',yu[k] \n \n # (21)\n beta = psum/(rho*tau+psum)\n # mu, eq (14)\n #print beta.shape\n #print mu.shape\n #print x_expected.shape\n #mu_update = np.tile(beta[np.newaxis].T,(1,nDim))*x_expected + np.tile((1-beta)[np.newaxis].T,(1,nDim))*mu\n mu = np.tile(beta[np.newaxis].T,(1,nDim))*x_expected + np.tile((1-beta)[np.newaxis].T,(1,nDim))*mu\n # tau, eq (11)\n #tau_update = rho*tau + psum\n tau = rho*tau + psum\n # alpha, eq (12)\n #alpha_update = rho*(alpha-nDim) + nDim + psum \n alpha = rho*(alpha-nDim) + nDim + psum \n #print 'alpha',alpha\n # nu, eq (13)\n #nu_update = rho*(nu-1) + 1 + psum\n nu = rho*(nu-1) + 1 + psum\n \n # GMM parameters\n # weight, (27)\n #gmm.weights_ = (nu_update-1)/np.sum(nu_update-1)\n gmm.weights_ = (nu-1)/np.sum(nu-1)\n ill = (gmm.weights_ == 0)\n print(np.sum(gmm.weights_))\n # mean, (28)\n #gmm.means_ = mu_update \n gmm.means_ = mu \n # sigma, (29)\n for k in range(0,K):\n #if alpha_update[k] != nDim:\n if alpha[k] != nDim:\n #gmm.covars_[k] = yu_update[k]/(alpha_update[k]-nDim)\n gmm.covariances_[k] = yu[k]/(alpha[k]-nDim)\n else:\n #gmm.covars_[k] = yu_update[k]/tau_update[k]\n gmm.covariances_[k] = yu[k]/tau[k]\n try:\n np.linalg.cholesky(gmm.covariances_[k])\n except:\n ill[k] = 1\n print('cov_%d not positive definite' % k)\n \n # remove non positive definite matrices\n if np.any(ill):\n valid = (ill == 0)\n gmm.means_ = gmm.means_[valid]\n gmm.weights_ = gmm.weights_[valid]\n gmm.weights_ = gmm.weights_/sum(gmm.weights_)\n gmm.covariances_ = gmm.covariances_[valid]\n mu = mu[valid]\n mu_update = mu_update[valid]\n yu = yu[valid]\n yu_update = yu_update[valid]\n tau = tau[valid]\n tau_update = tau_update[valid]\n alpha = alpha[valid]\n alpha_update = alpha_update[valid]\n nu = nu[valid]\n nu_update = nu_update[valid]\n K = gmm.means_.shape[0]\n return gmm","title":""},{"docid":"735d0d019bd2cc33e9df61617c1f321f","score":"0.546018","text":"def apply_G(self):\n\n tmin = self.vec['u']('Tmin')\n tau = self.vec['p']('tau')\n\n fmax = numpy.max(self.min - tau)\n tmin[0] = fmax + 1/self.rho * \\\n numpy.log(numpy.sum(numpy.exp(self.rho*(self.min - tau - fmax))))","title":""},{"docid":"eba553876d226a66113689776e46a30f","score":"0.54464203","text":"def c_m(self):\n c_m = (1.5 * np.matmul(\n np.swapaxes(from_6x6_to_21x1(self.d_sq), -1, -2),\n from_6x6_to_21x1(E_shear)) / np.matmul(\n np.swapaxes(from_6x6_to_21x1(self.d_sq), -1, -2),\n from_6x6_to_21x1(E_iso)))[..., 0, 0]\n return c_m","title":""},{"docid":"9c304d79bfef84afa830ef8277572c50","score":"0.54338884","text":"def diag_ggn_mc(self, mc_samples: int) -> List[Tensor]:\n return","title":""},{"docid":"f3a3e77730d932ce9e0ae5b7cc720a8f","score":"0.542774","text":"def hessian_log_likelihood(xm,m,Q,sm2,H_mat):\n def G(x):\n if x=='e': dxe=1\n else: dxe=0\n dQ00x = H_mat['Q_{}'.format(x)][0,0]\n dm00x = H_mat['m_{}'.format(x)][0,0]\n Q00=Q[0,0];m00=m[0,0]\n return (-((dQ00x + dxe)*(Q00 + sm2)) + 2*dm00x*(Q00 + sm2)*(-m00 + xm)\\\n + (dQ00x + dxe)*(-m00 + xm)**2)/(2.*(Q00 + sm2)**2)\n def H(x,y):\n if x=='e': dxe=1\n else: dxe=0\n if y=='e': dye=1\n else: dye=0\n dQ00x = H_mat['Q_{}'.format(x)][0,0]\n dQ00xy = H_mat['Q_{}{}'.format(x,y)][0,0]\n dQ00y = H_mat['Q_{}'.format(y)][0,0]\n dm00x = H_mat['m_{}'.format(x)][0,0]\n dm00y = H_mat['m_{}'.format(y)][0,0]\n dm00xy = H_mat['m_{}{}'.format(x,y)][0,0]\n Q00=Q[0,0];m00=m[0,0]\n return ((dQ00x + dxe)*(dQ00y + dye)*(Q00 + sm2) - 2*dm00x*dm00y*(Q00 + sm2)**2 -\\\n dQ00xy*(Q00 + sm2)**2 - 2*dm00y*(dQ00x + dxe)*(Q00 + sm2)*(-m00 + xm) -\\\n 2*dm00x*(dQ00y + dye)*(Q00 + sm2)*(-m00 + xm) + 2*dm00xy*(Q00 + sm2)**2*(-m00 + xm) -\\\n 2*(dQ00x + dxe)*(dQ00y + dye)*(-m00 + xm)**2 + dQ00xy*(Q00 + sm2)*(-m00 + xm)**2)/\\\n (2.*(Q00 + sm2)**3)\n grad = np.array([G(x) for x in ['m','g','s','e']])[:,None]\n hess = np.array([H(x,y) for y in ['m','g','s','e'] for x in\\\n ['m','g','s','e']]).reshape(4,4)\n return grad, hess","title":""},{"docid":"9facf115a2c7ba815a36137cfe4175d0","score":"0.5423068","text":"def create_bigG(G, A, M):\n GA = np.dot(G, A)\n n_c , n_t = M.shape\n _ , n_s = G.shape\n m_p = A.shape[1]//n_s\n row = []\n col = []\n data = []\n for i in range(n_t):\n for j in range(n_c):\n row.append([j+n_c*i]*n_s*m_p)\n col.append([n_s*i+k for k in range(n_s*m_p)])\n data.append(GA[j, :])\n data = np.array(data).reshape(-1)\n row = np.array(row).reshape(-1)\n col = np.array(col).reshape(-1)\n return coo_matrix((data, (row, col)), shape=(n_t*n_c, n_s*(n_t + m_p - 1)))","title":""},{"docid":"891744071be3c09ef6f85a5cefa4e603","score":"0.5418729","text":"def __init__(self, Vh, gamma, delta, locations, m_true, Theta = None, pen = 1e1, order=2, rel_tol=1e-12, max_iter=1000):\n assert delta != 0. or pen != 0, \"Intrinsic Gaussian Prior are not supported\"\n self.Vh = Vh\n \n trial = dl.TrialFunction(Vh)\n test = dl.TestFunction(Vh)\n \n if Theta == None:\n varfL = dl.inner(dl.nabla_grad(trial), dl.nabla_grad(test))*dl.dx\n else:\n varfL = dl.inner(Theta*dl.grad(trial), dl.grad(test))*dl.dx\n varfM = dl.inner(trial,test)*dl.dx\n \n self.M = dl.assemble(varfM)\n if dlversion() <= (1,6,0):\n self.Msolver = dl.PETScKrylovSolver(\"cg\", \"jacobi\")\n else:\n self.Msolver = dl.PETScKrylovSolver(self.Vh.mesh().mpi_comm(), \"cg\", \"jacobi\")\n self.Msolver.set_operator(self.M)\n self.Msolver.parameters[\"maximum_iterations\"] = max_iter\n self.Msolver.parameters[\"relative_tolerance\"] = rel_tol\n self.Msolver.parameters[\"error_on_nonconvergence\"] = True\n self.Msolver.parameters[\"nonzero_initial_guess\"] = False\n \n #mfun = Mollifier(gamma/delta, dl.inv(Theta), order, locations)\n mfun = dl.Expression(code_Mollifier, degree = Vh.ufl_element().degree()+2)\n mfun.l = gamma/delta\n mfun.o = order\n mfun.theta0 = 1./Theta.theta0\n mfun.theta1 = 1./Theta.theta1\n mfun.alpha = Theta.alpha\n for ii in range(locations.shape[0]):\n mfun.addLocation(locations[ii,0], locations[ii,1])\n \n varfmo = mfun*dl.inner(trial,test)*dl.dx\n MO = dl.assemble(pen*varfmo)\n \n self.A = dl.assemble(gamma*varfL+delta*varfM + pen*varfmo)\n \n if dlversion() <= (1,6,0):\n self.Asolver = dl.PETScKrylovSolver(\"cg\", amg_method())\n else:\n self.Asolver = dl.PETScKrylovSolver(self.Vh.mesh().mpi_comm(), \"cg\", amg_method())\n self.Asolver.set_operator(self.A)\n self.Asolver.parameters[\"maximum_iterations\"] = max_iter\n self.Asolver.parameters[\"relative_tolerance\"] = rel_tol\n self.Asolver.parameters[\"error_on_nonconvergence\"] = True\n self.Asolver.parameters[\"nonzero_initial_guess\"] = False\n \n old_qr = dl.parameters[\"form_compiler\"][\"quadrature_degree\"]\n dl.parameters[\"form_compiler\"][\"quadrature_degree\"] = -1\n qdegree = 2*Vh._ufl_element.degree()\n metadata = {\"quadrature_degree\" : qdegree}\n \n if dlversion() >= (2017,1,0):\n representation_old = dl.parameters[\"form_compiler\"][\"representation\"]\n dl.parameters[\"form_compiler\"][\"representation\"] = \"quadrature\"\n \n if dlversion() <= (1,6,0):\n Qh = dl.FunctionSpace(Vh.mesh(), 'Quadrature', qdegree)\n else:\n element = dl.FiniteElement(\"Quadrature\", Vh.mesh().ufl_cell(), qdegree, quad_scheme=\"default\")\n Qh = dl.FunctionSpace(Vh.mesh(), element)\n \n ph = dl.TrialFunction(Qh)\n qh = dl.TestFunction(Qh)\n Mqh = dl.assemble(ph*qh*dl.dx(metadata=metadata))\n ones = dl.interpolate(dl.Constant(1.), Qh).vector()\n dMqh = Mqh*ones\n Mqh.zero()\n dMqh.set_local( ones.get_local() / np.sqrt(dMqh.get_local() ) )\n Mqh.set_diagonal(dMqh)\n MixedM = dl.assemble(ph*test*dl.dx(metadata=metadata))\n self.sqrtM = MatMatMult(MixedM, Mqh)\n \n dl.parameters[\"form_compiler\"][\"quadrature_degree\"] = old_qr\n \n if dlversion() >= (2017,1,0):\n dl.parameters[\"form_compiler\"][\"representation\"] = representation_old\n \n self.R = _BilaplacianR(self.A, self.Msolver) \n self.Rsolver = _BilaplacianRsolver(self.Asolver, self.M)\n \n rhs = dl.Vector()\n self.mean = dl.Vector()\n self.init_vector(rhs, 0)\n self.init_vector(self.mean, 0)\n \n MO.mult(m_true, rhs)\n self.Asolver.solve(self.mean, rhs)","title":""},{"docid":"2447e339ec4250b8692a4b9a6c0dd0e2","score":"0.54172355","text":"def test_newtons_method():\n\n\tH = zeros((2,2))\n\tg = zeros((2,2)) # first column, gradient of first direct. derv., second second direct. derv, should be the same\n\n\tfor n in range(2):\n\t\t# directional derivatives to evaluate columns of the hessian\n\t\tq1 = array([[13.]])\n\t\tq2 = array([[17.]])\n\t\tze = array([[0.]])\n\n\t\tq1dot = array([[1.*(n==0)]])\n\t\tq2dot = array([[1.*(n==1)]])\n\n\t\tzedot = array([[0.]])\n\n\t\tcg = CGraph()\n\t\tFq1 = Function(Mtc(q1, q1dot))\n\t\tFq2 = Function(Mtc(q2, q2dot))\n\t\tFze = Function(Mtc(ze, zedot))\n\t\tFC = Function([[Fq1*Fq1,Fze],[Fze,Fq2*Fq2]])\n\t\tFPhi = FC.trace()\n\n\t\tPhibar = array([[1.]])\n\t\tPhibardot = array([[0.]])\n\t\t\n\t\tcg.independentFunctionList = [Fq1,Fq2]\n\t\tcg.dependentFunctionList = [FPhi]\n\t\tcg.reverse([Mtc(Phibar, Phibardot)])\n\t\t\n\t\t\n\t\tg[0,n] = Fq1.xbar.X[0,0]\n\t\tg[1,n] = Fq2.xbar.X[0,0]\n\t\tH[0,n] = Fq1.xbar.Xdot[0,0]\n\t\tH[1,n] = Fq2.xbar.Xdot[0,0]\n\n\n\n\t# compute new search direction\n\tdelta_q = numpy.linalg.solve(H,-g[:,0])\n\tq_plus = [13.,17.] + delta_q\n\tassert numpy.prod(q_plus == [0.,0.])","title":""},{"docid":"98e52c056e4b86de92fbff0b7b08ca45","score":"0.53999287","text":"def g_func( self, lmnAB, lmnCD, rstAB, rstCD, ijk, ang_A, ang_B, PA, PB, gammaAB, ang_C, ang_D, QC, QD, gammaCD, PQ):\n delta = 1/(4.0*gammaAB) + 1/(4.0*gammaCD)\n \n return ( ((-1) ** lmnAB)\n * self.theta_func(lmnAB,ang_A,ang_B,PA,PB,rstAB,gammaAB)\n * self.theta_func(lmnCD,ang_C,ang_D,QC,QD,rstCD,gammaCD)\n * ((-1) ** ijk)\n * ((2*delta) ** (2*(rstAB + rstCD)))\n * math.factorial(lmnAB + lmnCD - 2*rstAB - 2*rstCD)\n * (delta ** ijk)\n * (PQ ** (lmnAB + lmnCD - 2*(rstAB + rstCD + ijk)))\n / ((4*delta) ** (lmnAB + lmnCD))\n / math.factorial(ijk)\n / math.factorial(lmnAB + lmnCD - 2*(rstAB + rstCD + ijk)) )","title":""},{"docid":"48ad6e5314a8360a90b0ed8e00b2716a","score":"0.53907347","text":"def efficient_full_exact_ACDM(self, X, n_iter):\n calc_KL_time = 0\n e = np.e \n B_size = len(self.B_)\n Z = len(self.S_)\n total_step = n_iter*B_size\n # Initialization of Theta\n theta_prime_tilde = dict()\n theta_prime_hat = dict()\n for phi in self.B_:\n theta_prime_tilde[phi] = 0.0\n theta_prime_hat[phi] = 0.0\n\n # Initialization of U\n U_hat = dict() \n U_prime_hat = dict() \n U_prime_tilde = dict()\n for x in self.S_:\n U_hat[x] = 0\n U_prime_hat[x] = 0.0\n U_prime_tilde[x] = 0.0\n # Initialization of Coefficients\n alpha = [0] * (total_step+1)\n gamma = [0] * (total_step+2)\n c_hat = [0] * (total_step+2)\n gamma[-1] =1/(2.0) \n c_hat[-1] = 3.0 + 2*math.sqrt(2.000) \n for k in range(total_step+1):\n gamma_k_before = gamma[k-1]\n gamma_k = math.sqrt(1.0/(4.0)+gamma_k_before**2)+1.0/(2.0)\n gamma[k] = gamma_k\n alpha[k] =1/(gamma_k)\n c_hat[k] = (1-alpha[k]) * c_hat[k-1]\n # Caching \n e_U_hat_dict = dict() \n for x in self.S_:\n e_U_hat_dict[x] = 1.0\n # Beginning of Iteration\n start = time.time()\n for iter_ in range(n_iter):\n for index in range(B_size):\n k = iter_*B_size + index\n phi = random.choice(self.B_)\n if self.etahat_[phi] >= 1.0 or self.etahat_[phi]== 0.0:\n continue\n # Caching\n c_hat_k = c_hat[k]\n c_hat_k_after = c_hat[k+1]\n alpha_k_after = alpha[k+1]\n gamma_k = gamma[k]\n\n # calculate G, Z\n G = 0 \n for x in self.Ssub_[phi]:\n G += e_U_hat_dict[x]\n eta_ = G/Z\n # update tilde_theta_prime, hat_theta_prime\n nabla = 4.0*(self.etahat_[phi] - eta_)\n nabla_exact = np.log((1.0 -eta_)* self.etahat_[phi]/(eta_*(1-self.etahat_[phi])))\n\n delta_tilde = nabla_exact/len(X)\n delta_hat = nabla_exact/len(X)\n step1 = gamma_k/B_size * delta_tilde\n step2 = 1.0/c_hat_k_after*((alpha_k_after-(1-c_hat_k_after))*gamma_k/B_size*delta_tilde+(1-alpha_k_after)*delta_hat)\n\n\n theta_prime_tilde[phi] += step1\n theta_prime_hat[phi] += step2 \n for x in self.Ssub_[phi]:\n U_prime_tilde[x] += step1\n U_prime_hat[x] += step2\n U_hat_temp = (1-c_hat_k_after)*U_prime_tilde[x]+c_hat_k_after*U_prime_hat[x]\n e_U_hat_temp = e ** U_hat_temp\n Z += e_U_hat_temp - e_U_hat_dict[x]\n e_U_hat_dict[x] = e_U_hat_temp\n\n update_start_time = time.time()\n for x in self.B_:\n self.theta_[x] = (1-c_hat_k_after)*theta_prime_tilde[x]+c_hat_k_after*theta_prime_hat[x]\n self.theta_[phi] += 4*(self.etahat_[phi]-eta_)\n self.compute_theta_perp()\n self.compute_P()\n kl=self.compute_KL()\n calc_KL_time += time.time() - update_start_time \n print(iter_ ,\":\", \"KL divergence: \", f'{kl:.16f}' ,\" time : %4.2f\"% (time.time()-calc_KL_time-start),flush=True)","title":""},{"docid":"2b71317599315baa15abd0d8007dc05c","score":"0.5383931","text":"def __init__(self, Vh, gamma, delta, Theta = None, mean=None, rel_tol=1e-12, max_iter=1000):\n assert delta != 0., \"Intrinsic Gaussian Prior are not supported\"\n self.Vh = Vh\n\n trial = dl.TrialFunction(Vh)\n test = dl.TestFunction(Vh)\n \n if Theta == None:\n varfL = dl.inner(dl.nabla_grad(trial), dl.nabla_grad(test))*dl.dx\n else:\n varfL = dl.inner( Theta*dl.grad(trial), dl.grad(test))*dl.dx\n \n varfM = dl.inner(trial,test)*dl.dx\n \n self.M = dl.assemble(varfM)\n if dlversion() <= (1,6,0):\n self.Msolver = dl.PETScKrylovSolver(\"cg\", \"jacobi\")\n else:\n self.Msolver = dl.PETScKrylovSolver(self.Vh.mesh().mpi_comm(), \"cg\", \"jacobi\")\n self.Msolver.set_operator(self.M)\n self.Msolver.parameters[\"maximum_iterations\"] = max_iter\n self.Msolver.parameters[\"relative_tolerance\"] = rel_tol\n self.Msolver.parameters[\"error_on_nonconvergence\"] = True\n self.Msolver.parameters[\"nonzero_initial_guess\"] = False\n \n self.A = dl.assemble(gamma*varfL + delta*varfM) \n if dlversion() <= (1,6,0):\n self.Asolver = dl.PETScKrylovSolver(\"cg\", amg_method())\n else:\n self.Asolver = dl.PETScKrylovSolver(self.Vh.mesh().mpi_comm(), \"cg\", amg_method())\n self.Asolver.set_operator(self.A)\n self.Asolver.parameters[\"maximum_iterations\"] = max_iter\n self.Asolver.parameters[\"relative_tolerance\"] = rel_tol\n self.Asolver.parameters[\"error_on_nonconvergence\"] = True\n self.Asolver.parameters[\"nonzero_initial_guess\"] = False\n \n old_qr = dl.parameters[\"form_compiler\"][\"quadrature_degree\"]\n dl.parameters[\"form_compiler\"][\"quadrature_degree\"] = -1\n qdegree = 2*Vh._ufl_element.degree()\n metadata = {\"quadrature_degree\" : qdegree}\n\n if dlversion() >= (2017,1,0):\n representation_old = dl.parameters[\"form_compiler\"][\"representation\"]\n dl.parameters[\"form_compiler\"][\"representation\"] = \"quadrature\"\n \n if dlversion() <= (1,6,0):\n Qh = dl.FunctionSpace(Vh.mesh(), 'Quadrature', qdegree)\n else:\n element = dl.FiniteElement(\"Quadrature\", Vh.mesh().ufl_cell(), qdegree, quad_scheme=\"default\")\n Qh = dl.FunctionSpace(Vh.mesh(), element)\n \n ph = dl.TrialFunction(Qh)\n qh = dl.TestFunction(Qh)\n Mqh = dl.assemble(ph*qh*dl.dx(metadata=metadata))\n ones = dl.interpolate(dl.Constant(1.), Qh).vector()\n dMqh = Mqh*ones\n Mqh.zero()\n dMqh.set_local( ones.get_local() / np.sqrt(dMqh.get_local() ) )\n Mqh.set_diagonal(dMqh)\n MixedM = dl.assemble(ph*test*dl.dx(metadata=metadata))\n self.sqrtM = MatMatMult(MixedM, Mqh)\n\n dl.parameters[\"form_compiler\"][\"quadrature_degree\"] = old_qr\n \n if dlversion() >= (2017,1,0):\n dl.parameters[\"form_compiler\"][\"representation\"] = representation_old\n \n self.R = _BilaplacianR(self.A, self.Msolver) \n self.Rsolver = _BilaplacianRsolver(self.Asolver, self.M)\n \n self.mean = mean\n \n if self.mean is None:\n self.mean = dl.Vector()\n self.init_vector(self.mean, 0)","title":""},{"docid":"a3365b18e9f481a59257890844aae335","score":"0.53832597","text":"def _g(self, w):\n p_w = self._p_w(w)\n return (self.p_data_x[None, :, None] * p_w[None, :, :] * self.feature\n ).sum(axis=(1, 2)) - self.E_feature","title":""},{"docid":"7ab7fc8bf2f63fe190d89a5e3852222d","score":"0.5356697","text":"def optimise_gaussian(self, filename = \"test\"):\n\n # first do minimisation with MMFF94 and dihedrals contrainted\n # this will hopefully fix clashes\n mp = AllChem.MMFFGetMoleculeProperties(self.molecule, mmffVariant=\"MMFF94\")\n ff = AllChem.MMFFGetMoleculeForceField(self.molecule, mp)\n\n current_angles = self.get_dihedrals()\n N = 0\n for angle in current_angles:\n ff.MMFFAddTorsionConstraint(angle[0], angle[1], angle[2], angle[3], False, angle[4], angle[4], 500000.0)\n N += 1\n\n ff.Minimize(maxIts=500,forceTol=0.0001,energyTol=1e-06)\n\n # do gaussian opt\n self.molecule.SetProp(\"_Name\", filename)\n Gmethod = \"pm6\"\n output = qmgaussian.gaussian([self.molecule], method=\"opt \" + Gmethod, charge=0, mult=1, charged_fragments=True, cpus=1, keepFiles=True)\n\n if output[0] is not None:\n atoms = output[0]['atomicPos']\n\n energykey = 'g16:(' + Gmethod + ')_energy'\n self.energy = float(output[0][energykey])\n self.energy = self.energy * 627.15 # converts from hartree to kcal/mol\n conf = self.molecule.GetConformer()\n N = 0\n # This assumes that the atom ordering is the same!!!!\n for xyz in atoms: \n conf.SetAtomPosition(N, xyz)\n N += 1\n elif output[0] is None:\n # gaussian failed, set energy to a high number, this is kind of a hack\n self.energy = 9999.0","title":""},{"docid":"13132ac0f9ad0d8857ca54eed5036ac8","score":"0.5354696","text":"def hessian_1cc(W,mlam,gamma,sl2,sm2,dt,s,S,grad_matS,rescale,sd2):\n ##### likelihood and gradient at initial conditions\n gll,hll = hessian_log_likelihood(W[0,0],s,S,sm2,grad_matS)\n #### Initialize parameters for recurrence\n F, A, a = parameters(gamma,dt,mlam,sl2)\n grad_param = hessian_parameters(gamma,dt,mlam,sl2)\n ##### P(z_0|x_0^m)\n b,B = posteriori_matrices(W[0,0],s,S,sm2)\n grad_mat_b = hessian_posteriori_matrices(W[0,0],s,S,sm2,grad_matS)\n for j in range(1,W.shape[1]):\n ###### P(z_{t+dt}|D_t) = N(m,Q))\n m,Q = new_mean_cov(b,B,F,A,a)\n grad_mat_Q = hessian_new_mean_cov(b,B,F,A,a,grad_param,grad_mat_b)\n ##### P(z_{t+dt}|D_{t+dt}) = N(b',B')\n b,B = posteriori_matrices(W[0,j],m,Q,sm2)\n grad_mat_b = hessian_posteriori_matrices(W[0,j],m,Q,sm2,grad_mat_Q)\n ##### Likelihood\n G,H = hessian_log_likelihood(W[0,j],m,Q,sm2,grad_mat_Q)\n gll+=G; hll=+H\n # Predict for daughter cell\n m,Q = new_mean_cov(b,B,F,A,a)\n grad_mat_Q = hessian_new_mean_cov(b,B,F,A,a,grad_param,grad_mat_b)\n # Find next cell initial conditions (9% asym div)\n s, S, grad_matS = hess_cell_division_likelihood(m,Q,grad_mat_Q,sd2,rescale)\n return hll, gll, s, S, grad_matS","title":""},{"docid":"7469fac99123f049f1b2e136e5eedca0","score":"0.53455836","text":"def rand_mmd_g(dist_all, batch_size, omega=0.5, max_iter=0, name='mmd', do_summary=False, scope_prefix=''):\r\n with tf.name_scope(name):\r\n m = tf.constant(batch_size, tf.float32)\r\n\r\n def kernel(b):\r\n return tf.exp(-dist_all * b)\r\n\r\n def f(b):\r\n k = kernel(b)\r\n e_k = matrix_mean_wo_diagonal(k, 2 * m)\r\n return e_k - omega, k\r\n\r\n def df(k):\r\n kd = -k * dist_all # gradient of exp(-d*w)\r\n e_kd = matrix_mean_wo_diagonal(kd, 2 * m)\r\n return e_kd\r\n\r\n # initialize sigma as the geometric mean of all pairwise distances\r\n dist_mean = matrix_mean_wo_diagonal(dist_all, 2 * m)\r\n beta = -tf.log(omega) / (dist_mean + FLAGS.EPSI) # beta = 1/2/sigma\r\n # if max_iter is larger than one, do newton's update\r\n if max_iter > 0:\r\n beta, _ = tf.while_loop(\r\n cond=lambda _1, i: i < max_iter,\r\n body=lambda b, i: newton_root(b, f, df, step=i),\r\n loop_vars=(beta, tf.constant(0, dtype=tf.int32)))\r\n\r\n k_all = kernel(beta)\r\n k_xx = k_all[0:batch_size, 0:batch_size]\r\n k_xy_0 = k_all[0:batch_size, batch_size:]\r\n k_xy_1 = k_all[batch_size:, 0:batch_size]\r\n k_yy = k_all[batch_size:, batch_size:]\r\n\r\n e_kxx = matrix_mean_wo_diagonal(k_xx, m)\r\n e_kxy_0 = matrix_mean_wo_diagonal(k_xy_0, m)\r\n e_kxy_1 = matrix_mean_wo_diagonal(k_xy_1, m)\r\n e_kyy = matrix_mean_wo_diagonal(k_yy, m)\r\n\r\n if do_summary:\r\n with tf.name_scope(None): # return to root scope to avoid scope overlap \r\n tf.summary.scalar(scope_prefix + name + '/kxx', e_kxx)\r\n tf.summary.scalar(scope_prefix + name + '/kyy', e_kyy)\r\n tf.summary.scalar(scope_prefix + name + '/kxy_0', e_kxy_0)\r\n tf.summary.scalar(scope_prefix + name + '/kxy_1', e_kxy_1)\r\n # tf.summary.scalar(scope_prefix + name + 'omega', omega)\r\n\r\n return e_kxx + e_kyy - e_kxy_0 - e_kxy_1","title":""},{"docid":"d4a475014918f6668221cd055be938a4","score":"0.5344345","text":"def _compute_gauss_pdf_gradients(x, mu, sigma):\n sigma_matrix = torch.diag(sigma)\n distr = torch.distributions.MultivariateNormal(mu, sigma_matrix)\n return distr.log_prob(x).exp()","title":""},{"docid":"fca994135a04f0b3e09ec041daf5d4d5","score":"0.53440166","text":"def _mixed_norm_solver_bcd(\n M,\n G,\n alpha,\n lipschitz_constant,\n maxit=200,\n tol=1e-8,\n verbose=None,\n init=None,\n n_orient=1,\n dgap_freq=10,\n use_accel=True,\n K=5,\n):\n _, n_times = M.shape\n _, n_sources = G.shape\n n_positions = n_sources // n_orient\n\n if init is None:\n X = np.zeros((n_sources, n_times))\n R = M.copy()\n else:\n X = init\n R = M - np.dot(G, X)\n\n E = [] # track primal objective function\n highest_d_obj = -np.inf\n active_set = np.zeros(n_sources, dtype=bool) # start with full AS\n\n alpha_lc = alpha / lipschitz_constant\n\n if use_accel:\n last_K_X = np.empty((K + 1, n_sources, n_times))\n U = np.zeros((K, n_sources * n_times))\n\n # First make G fortran for faster access to blocks of columns\n G = np.asfortranarray(G)\n # Ensure these are correct for dgemm\n assert R.dtype == np.float64\n assert G.dtype == np.float64\n one_ovr_lc = 1.0 / lipschitz_constant\n\n # assert that all the multiplied matrices are fortran contiguous\n assert X.T.flags.f_contiguous\n assert R.T.flags.f_contiguous\n assert G.flags.f_contiguous\n # storing list of contiguous arrays\n list_G_j_c = []\n for j in range(n_positions):\n idx = slice(j * n_orient, (j + 1) * n_orient)\n list_G_j_c.append(np.ascontiguousarray(G[:, idx]))\n\n for i in range(maxit):\n _bcd(G, X, R, active_set, one_ovr_lc, n_orient, alpha_lc, list_G_j_c)\n\n if (i + 1) % dgap_freq == 0:\n _, p_obj, d_obj, _ = dgap_l21(\n M, G, X[active_set], active_set, alpha, n_orient\n )\n highest_d_obj = max(d_obj, highest_d_obj)\n gap = p_obj - highest_d_obj\n E.append(p_obj)\n logger.debug(\n \"Iteration %d :: p_obj %f :: dgap %f :: n_active %d\"\n % (i + 1, p_obj, gap, np.sum(active_set) / n_orient)\n )\n\n if gap < tol:\n logger.debug(\"Convergence reached ! (gap: %s < %s)\" % (gap, tol))\n break\n\n # using Anderson acceleration of the primal variable for faster\n # convergence\n if use_accel:\n last_K_X[i % (K + 1)] = X\n\n if i % (K + 1) == K:\n for k in range(K):\n U[k] = last_K_X[k + 1].ravel() - last_K_X[k].ravel()\n C = U @ U.T\n # at least on ARM64 we can't rely on np.linalg.solve to\n # reliably raise LinAlgError here, so use SVD instead\n # equivalent to:\n # z = np.linalg.solve(C, np.ones(K))\n u, s, _ = np.linalg.svd(C, hermitian=True)\n if s[-1] <= 1e-6 * s[0] or not np.isfinite(s).all():\n logger.debug(\"Iteration %d: LinAlg Error\" % (i + 1))\n continue\n z = ((u * 1 / s) @ u.T).sum(0)\n c = z / z.sum()\n X_acc = np.sum(last_K_X[:-1] * c[:, None, None], axis=0)\n _grp_norm2_acc = groups_norm2(X_acc, n_orient)\n active_set_acc = _grp_norm2_acc != 0\n if n_orient > 1:\n active_set_acc = np.kron(\n active_set_acc, np.ones(n_orient, dtype=bool)\n )\n p_obj = _primal_l21(M, G, X[active_set], active_set, alpha, n_orient)[0]\n p_obj_acc = _primal_l21(\n M, G, X_acc[active_set_acc], active_set_acc, alpha, n_orient\n )[0]\n if p_obj_acc < p_obj:\n X = X_acc\n active_set = active_set_acc\n R = M - G[:, active_set] @ X[active_set]\n\n X = X[active_set]\n\n return X, active_set, E","title":""},{"docid":"178de04b2af5641740265bd3c7a8bc3c","score":"0.5335637","text":"def g(q, xi):\n return flx.eigenvalue(q)-xi","title":""},{"docid":"3c034f9bd66b94fa7d3e4ceebf409aeb","score":"0.5334269","text":"def hamiltonian_diag_1d(mu,V,deltax):\n\n\n #constructs kinetic energy operator\n T=kinetic_op_1d(mu,deltax,V.size)\n #constructs hamiltonian\n H=hamiltonian_1d(T,V)\n #performs diagonalization\n eigen_val,eigen_vec=solve_eigenstates(H)\n\n return eigen_val,eigen_vec","title":""},{"docid":"3efdf2a40d5b6d307da83456e17bdd6e","score":"0.53294677","text":"def diag_ggn(self) -> List[Tensor]:\n return","title":""},{"docid":"4a742599d07d57c1603fa2403b352556","score":"0.5328856","text":"def Gxyz(lA,mA,nA,lB,mB,nB,lC,mC,nC,lD,mD,nD,a,b,c,d,RA,RB,RC,RD):\n with loops.Scope() as s:\n s.lA,s.mA,s.nA,s.lB,s.mB,s.nB,s.lC,s.mC,s.nC,s.lD,s.mD,s.nD,s.a,s.b,s.c,s.d,s.RA,s.RB,s.RC,s.RD = lA,mA,nA,lB,mB,nB,lC,mC,nC,lD,mD,nD,a,b,c,d,RA,RB,RC,RD\n s.gP = s.a + s.b\n s.gQ = s.c + s.d\n s.delta = 1/(4*s.gP) + 1/(4*s.gQ)\n s.RP = gaussian_product(s.a,s.b,s.RA,s.RB)\n s.RQ = gaussian_product(s.c,s.d,s.RC,s.RD)\n s.ABsq = np.dot(s.RA-s.RB,s.RA-s.RB)\n s.CDsq = np.dot(s.RC-s.RD,s.RC-s.RD)\n s.PQsq = np.dot(s.RP-s.RQ,s.RP-s.RQ)\n s.boysarg = s.PQsq / (4 * s.delta)\n\n s.Gxyz = 0.0\n s.l = lA + lB \n s.r = np.floor(s.l/2) \n s.lp = lC + lD \n s.rp = np.floor(s.lp/2) \n s.i = np.floor((s.l + s.lp - 2 * s.r - 2 * s.rp) / 2) \n\n for _ in s.while_range(lambda: s.l > -1):\n s.r = np.floor(s.l/2)\n for _ in s.while_range(lambda: s.r > -1):\n s.lp = lC + lD \n for _ in s.while_range(lambda: s.lp > -1):\n s.rp = np.floor(s.lp/2)\n for _ in s.while_range(lambda: s.rp > -1):\n #s.i = np.floor((s.l + s.lp - 2 * s.r - 2 * s.rp) / 2) # Compiles forever until OOM\n s.i = np.floor((s.l - 2 * s.r + s.lp - 2 * s.rp) / 2) # This works, compiles fine since loop variables are added in order\n for _ in s.while_range(lambda: s.i > -1):\n gx = gi(s.l,s.lp,s.r,s.rp,s.i, s.lA,s.lB,s.RA[0],s.RB[0],s.RP[0],s.gP, s.lC,s.lD,s.RC[0],s.RD[0],s.RQ[0],s.gQ)\n nu = s.l - 2 * s.r + s.lp - 2 * s.rp - s.i #THIS ORDER MATTERS (UGH)\n F = boys(nu, s.boysarg)\n s.Gxyz += F * gx\n\n\n #s.nu = s.l + s.lp - 2 * (s.r+s.rp)-s.i\n #nu = s.l - 2 * s.r + s.lp - 2 * s.rp - s.i\n #F = boys(s.nu, s.boysarg)\n #F = 0.5 * (s.boysarg + 1e-11)**(-(s.nu + 0.5)) * jax.lax.igamma(s.nu + 0.5, s.boysarg + 1e-11) * np.exp(jax.lax.lgamma(s.nu + 0.5))\n\n # WORKS: fake boys function acucmulation\n #nu = s.l - 2 * s.r + s.lp - 2 * s.rp - s.i #THIS ORDER MATTERS (UGH)\n #F = 0.5 * (s.boysarg + 1e-11)**(-(nu + 0.5)) * jax.lax.igamma(nu + 0.5, s.boysarg + 1e-11) * np.exp(jax.lax.lgamma(nu + 0.5))\n #s.Gxyz += F\n\n\n # This works ( thou shalt not add s.l + s.lp\n #nu = s.l - 2 * s.r + s.lp - 2 * s.rp - s.i #THIS ORDER MATTERS (UGH)\n #F = boys(nu, s.boysarg)\n #s.Gxyz += F\n\n s.i -= 1\n s.rp -= 1\n s.lp -= 1\n s.r -= 1\n s.l -= 1\n\n return s.Gxyz\n\n\n\n #Na = N(a,lA,mA,nA)\n #Nb = N(b,lB,mB,nB)\n #Nc = N(c,lC,mC,nC)\n #Nd = N(d,lD,mD,nD)\n\n #Gxyz *= Na * Nb * Nc * Nd","title":""},{"docid":"89987b761534239f9d8c38765aa0e849","score":"0.53207606","text":"def _calc_m(x0, xt, tau=1):\n\n # These represent the C(tau) and C(0) covariance matrices\n # Note: x is an anomaly vector, no division by N-1 because it's undone\n # in the inversion anyways\n\n # Division by number of samples ignored due to inverse\n x0x0 = np.dot(x0.T, x0)\n x0xt = np.dot(xt.T, x0)\n\n # Calculate the mapping term G_tau\n G = np.dot(x0xt, pinv(x0x0))\n\n # Calculate the forcing matrix to check that all modes are damped\n Geigs = eigvals(G)\n Leigs = (1. / tau) * np.log(Geigs)\n\n if np.any(Leigs.real >= 0):\n logger.debug('L eigenvalues: \\n' + str(Leigs))\n raise ValueError(\n 'Positive eigenvalues detected in forecast matrix L.')\n\n return G","title":""},{"docid":"7d6513d5e0985d8f627c0a32a832a6a6","score":"0.5317741","text":"def calcgaussian( self, frequency=None ):\n if frequency :\n diff = frequency - self.B\n else :\n diff = self.frequencies - self.B\n return exp( -0.5 * diff * diff * self.C2 )","title":""},{"docid":"f6392db40c17f5f1e25f1e0aba7ad643","score":"0.5317538","text":"def test_diagonalizing_gates(self):\n base = qml.Hadamard(0)\n diag_gate = Adjoint(base).diagonalizing_gates()[0]\n\n assert isinstance(diag_gate, qml.RY)\n assert qml.math.allclose(diag_gate.data[0], -np.pi / 4)","title":""},{"docid":"21697a506e1ca5131df2e9f1930bfd38","score":"0.5314103","text":"def _calc_MC(self):\r\n _ = self; T, K, rf_r, net_r, sCP = _.T, _.K, _.rf_r, _.net_r, _.signCP\r\n _ = self.ref; S, vol, q = _.S0, _.vol, _.q\r\n _ = self.px_spec; n, m, keep_hist, rng_seed, rho = _.nsteps, _.npaths, _.keep_hist, _.rng_seed, _.rho\r\n _ = self.px_spec.ref2; S2, vol2, q2 = _.S0, _.vol, _.q\r\n _ = self._LT_specs(); u, d, p, df, dt = _['u'], _['d'], _['p'], _['df_dt'], _['dt']\r\n\r\n px = list()\r\n numpy.random.seed(rng_seed)\r\n\r\n for path in range(0,m):\r\n\r\n ## Generate correlated Wiener Processes\r\n u = numpy.random.normal(size=n)\r\n v = numpy.random.normal(size=n)\r\n v = rho * u + math.sqrt(1 - rho**2) * v\r\n u = u * math.sqrt(dt)\r\n v = v * math.sqrt(dt)\r\n\r\n ## Simulate the paths\r\n s1, s2, mu1, mu2 = [S], [S2], (rf_r - q)*dt, (rf_r - q2)*dt\r\n\r\n for t in range(0, len(u)):\r\n s1.append(s1[-1] * (mu1 + vol * u[t]) + s1[-1])\r\n s2.append(s2[-1] * (mu2 + vol2 * v[t]) + s2[-1])\r\n\r\n val = np.maximum(sCP * (s2[-1] - s1[-1] - K), 0) * math.exp(-rf_r * T) # Calculate the Payoff\r\n px.append((val))\r\n\r\n self.px_spec.add(px=float(np.mean(px)))\r\n\r\n return self","title":""},{"docid":"03b47414233ac7b3883179ec28ad283b","score":"0.5308294","text":"def make_G(V, q):\n # Shape to 1x1 matrix (scalar)\n V = Matrix([V])\n\n # Return jacobian as a column vector\n return V.jacobian(q).T","title":""},{"docid":"c382a5bb452ac01e1c8a73b504bda258","score":"0.5307989","text":"def gmm_cluster(data_set, cls_num):\n iter_count = 0\n MAX_ITER_COUNT = 25\n sample_num = data_set.shape[0]\n sample_dim = data_set.shape[1]\n data_set_T = numpy.transpose(data_set)\n\n alphas = numpy.asarray([1.0 / cls_num] * cls_num).T\n means = numpy.asarray(select_means(data_set, cls_num))\n covariances = numpy.asarray([numpy.cov(data_set_T) for i in range(0, cls_num)])\n\n gammas = numpy.asarray(\n [[alphas[j] for j in range(0, cls_num)] for i in range(0, sample_num)]\n )\n\n while iter_count < MAX_ITER_COUNT:\n # E step: calculate the probability matrix \"gammas\" based on current means, covariances and alphas.\n p = numpy.asarray(\n [\n [\n gauss(\n data_set_T[:, i].reshape(sample_dim, 1),\n means[j].reshape(sample_dim, 1),\n covariances[j],\n )[0, 0]\n for j in range(0, cls_num)\n ]\n for i in range(0, sample_num)\n ]\n )\n \n gammas = numpy.asarray(\n [\n numpy.multiply(p[i], alphas) / numpy.dot(p[i].T, alphas)\n for i in range(0, sample_num)\n ]\n )\n\n # M step: update means, covariances and alphas based on new \"gammas\".\n means = numpy.asarray(\n [\n (numpy.dot(data_set_T, gammas[:, i]) / numpy.sum(gammas[:, i])).reshape(sample_dim, 1) \n for i in range(0, cls_num)\n ]\n )\n\n covariances = numpy.asarray(\n [\n numpy.dot(\n numpy.multiply(data_set_T - means[i], gammas[:, i]),\n (data_set_T - means[i]).T,\n )\n / numpy.sum(gammas[:, i])\n for i in range(0, cls_num)\n ]\n )\n alphas = numpy.asarray([numpy.sum(gammas[:, i]) / sample_num for i in range(0, cls_num)])\n iter_count += 1\n\n return means, gammas","title":""},{"docid":"316575b1ced6a4e975d7572f1d6eb2ce","score":"0.53045654","text":"def method(m,points,data):\n n = len(data)-1\n u,s,g,h = [],[],[],[]\n if m>n:\n m = n\n print \"The highest power is adjusted to: \" + str(n)\n for i in range(m+1):\n u.append([None]*(n+2))\n u[i][n+1] = 0\n s.append(0) \n g.append(0) \n h.append(0) \n for i in range(n+1):\n u[0][i] = 1\n stmp = u[0][i]**2\n s[0] += stmp\n g[0] += points[i]*stmp\n u[0][n+1] += u[0][i]*data[i]\n g[0] = g[0]/s[0]\n u[0][n+1] = u[0][n+1]/s[0]\n for i in range(n+1): \n u[1][i] = points[i]*u[0][i]-g[0]*u[0][i]\n s[1] += u[1][i]**2\n g[1] += points[i]*u[1][i]**2\n h[1] += points[i]*u[1][i]*u[0][i]\n u[1][n+1] += u[1][i]*data[i]\n g[1] = g[1]/s[1]\n h[1] = h[1]/s[0]\n u[1][n+1] = u[1][n+1]/s[1] \n if m>=2:\n for i in range(1,m):\n for j in range(n+1):\n u[i+1][j] = points[j]*u[i][j]-g[i]*u[i][j]-h[i]*u[i-1][j]\n s[i+1] += u[i+1][j]**2\n g[i+1] += points[j]*u[i+1][j]**2\n h[i+1] += points[j]*u[i+1][j]*u[i][j]\n u[i+1][n+1] += u[i+1][j]*data[j]\n g[i+1] = g[i+1]/s[i+1]\n h[i+1] = h[i+1]/s[i]\n u[i+1][n+1] = u[i+1][n+1]/s[i+1] \n return u","title":""},{"docid":"a6a5542365503202a32777702b724d32","score":"0.5300515","text":"def compute_gating_distances(self):\n\t\t# For Gibbs\n\t\tgating_distances_evals = pdist(self.x, metric=self.gating_kern)\n\t\tself.gating_distances = squareform(gating_distances_evals)\n\t\tself.gating_distances_to_rest = np.sum(self.gating_distances, axis=1)\n\n\t\t# For HMH\n\t\tgating_log_distances_1st_incomplete = pdist(self.x, metric=self.gating_log_kern_1st_incomplete)\n\t\tgating_log_distances_2nd_incomplete = pdist(self.x, metric=self.gating_log_kern_2nd_incomplete)\n\t\t\n\t\tgating_log_distances_1st_evals = gating_log_distances_1st_incomplete * gating_distances_evals\n\t\tgating_log_distances_2nd_evals = gating_log_distances_2nd_incomplete * gating_distances_evals\n\t\t\n\t\tself.gating_log_distances_1st = squareform(gating_log_distances_1st_evals)\n\t\tself.gating_log_distances_2nd = squareform(gating_log_distances_2nd_evals)\n\t\tself.gating_log_distances_1st_to_rest = np.sum(self.gating_log_distances_1st, axis=1)\n\t\tself.gating_log_distances_2nd_to_rest = np.sum(self.gating_log_distances_2nd, axis=1)","title":""},{"docid":"09f2212a9ff3ed2438a87dbbc5aaf4e5","score":"0.5298525","text":"def gauss():\n from Disk import Disk\n from Mersenne import Mersenne\n\n # inputs\n N = 10**5\n box = [(0,1), (0,1)]\n # the point cloud generator\n cloud = Mersenne()\n # the region of integration\n disk = Disk(center=(0,0), radius=1)\n\n # the integration algorithm\n interior = 0\n for i in range(N):\n point = cloud.point(box)\n if disk.interior(point):\n interior += 1\n # print the estimate of π\n print(\"π: {:.8f}\".format(4*interior/N))\n return","title":""},{"docid":"5635d8eaff8d1cbb10b60a522719cb95","score":"0.529164","text":"def calcGenCost(self):\n df = self.df_0.copy(deep=True)\n\n #pre-processing:\n #adjust coal fuel prices by the \"coal_dol_per_mmbtu\" input\n df.loc[df.fuel_type=='coal', 'fuel_price' + str(self.time)] = scipy.maximum(0, df.loc[df.fuel_type=='coal', 'fuel_price' + str(self.time)] + self.coal_dol_per_mmbtu)\n #adjust coal capacity by the \"coal_capacity_derate\" input\n df.loc[df.fuel_type=='coal', 'mw' + str(self.time)] = df.loc[df.fuel_type=='coal', 'mw' + str(self.time)] * (1.0 - self.coal_capacity_derate)\n #calculate the generation cost:\n df['fuel_cost'] = df['heat_rate' + str(self.time)] * df['fuel_price' + str(self.time)]\n df['co2_cost'] = df['co2' + str(self.time)] * self.co2_dol_per_kg\n df['so2_cost'] = df['so2' + str(self.time)] * self.so2_dol_per_kg\n df['nox_cost'] = df['nox' + str(self.time)] * self.nox_dol_per_kg\n df['gen_cost'] = scipy.maximum(0.01, df.fuel_cost + df.co2_cost + df.so2_cost + df.nox_cost + df.vom)\n #add a zero generator so that the bid stack goes all the way down to zero. This is important for calculating information for the marginal generator when the marginal generator is the first one in the bid stack.\n df['dmg_easiur'] = df['dmg' + str(self.time)]\n #if self.initialization:\n df = df.append(df.loc[0]*0)\n df = df.append(df.iloc[-1])\n #self.initialization = False\n df.sort_values('gen_cost', inplace=True)\n #move coal_0 and ngcc_0 to the front of the merit order regardless of their gen cost\n coal_0_ind = df[df.orispl_unit=='coal_0'].index[0]\n ngcc_0_ind = df[df.orispl_unit=='ngcc_0'].index[0]\n df = pandas.concat([df.iloc[[0],:], df[df.orispl_unit=='coal_0'], df[df.orispl_unit=='ngcc_0'], df.drop([0, coal_0_ind, ngcc_0_ind], axis=0)], axis=0)\n df.reset_index(drop=True, inplace=True)\n df['demand'] = df['mw' + str(self.time)].cumsum()\n df.loc[len(df)-1, 'demand'] = df.loc[len(df)-1, 'demand'] + 1000000 #creates a very large generator at the end of the merit order so that demand cannot be greater than supply\n df['f'] = df['demand']\n df['s'] = scipy.append(0, scipy.array(df.f[0:-1]))\n df['a'] = scipy.maximum(df.s - df.min_out*(1/0.10), 1.0)\n #add a very large demand for the last row\n self.df = df","title":""},{"docid":"608b239b715ae75924ded17a7364d352","score":"0.52880335","text":"def gibbs_step(self, C, Gamma, Sigma_det, log_pc, idx = None):\n if idx is None:\n idx = np.random.randint(self.d) \n u = self.X_td[:, idx][:, None]\n idx_val = C[idx]\n if idx_val:\n Gamma_1 = Gamma\n Sigma_det_1 = Sigma_det\n log_p1 = log_pc\n C[idx] = 0\n key = tuple(C.tolist())\n if key in self._cache:\n self._cache_hits += 1\n Gamma_0, Sigma_det_0, log_p0 = self._cache[key]\n else:\n Gamma_0, Sigma_det_0 = sherman_morrison_update(\n Gamma, Sigma_det, -u, u)\n log_p0 = self.log_p(C, Gamma_0, Sigma_det_0)\n self._cache_insert(key, (Gamma_0, Sigma_det_0, log_p0))\n else:\n Gamma_0 = Gamma\n Sigma_det_0 = Sigma_det\n log_p0 = log_pc\n C[idx] = 1\n key = tuple(C.tolist())\n if key in self._cache:\n self._cache_hits += 1\n Gamma_1, Sigma_det_1, log_p1 = self._cache[key]\n else:\n Gamma_1, Sigma_det_1 = sherman_morrison_update(\n Gamma, Sigma_det, u, u)\n log_p1 = self.log_p(C, Gamma_1, Sigma_det_1)\n self._cache_insert(key, (Gamma_1, Sigma_det_1, log_p1))\n\n Z = logsumexp(log_p0, log_p1)\n with warnings.catch_warnings():\n warnings.simplefilter('error')\n p0 = np.exp(log_p0 - Z)\n\n if np.random.rand() > p0:\n # we set it to 1\n C[idx] = 1\n Gamma = Gamma_1\n Sigma_det = Sigma_det_1\n log_pc = log_p1\n\n else:\n # we set it to 0\n C[idx] = 0\n Gamma = Gamma_0\n Sigma_det = Sigma_det_0\n log_pc = log_p0\n self.likelihoods.append(log_pc)\n return C, Gamma, Sigma_det, log_pc","title":""},{"docid":"456990deb35189fdd95089e95703ed43","score":"0.52843225","text":"def get_gmm(self,gr_charge, ex_charge, FG_elstat, struc, index, E01, \r\n int2cart, freq, red_mass, order=2, approx=1.1, CoarseGrain='C'):\r\n \r\n dR_Hmm,dR_env_Hmm = self.get_SingleDefect_derivation(gr_charge, ex_charge, FG_elstat, struc, index, E01, order=2, approx=1.1)\r\n \r\n # freq is actualy wavenumber (= frequency/speed_of_light).\r\n # therefore angluar frequency omega in atomic units = 100/(2*Rydberg_inf) wavenumber in [cm-1]\r\n omega_au = freq/conversion_facs_energy[\"1/cm\"]\r\n RedMass_au = red_mass/conversion_facs_mass\r\n # in atomic units hbar = 1.0, m_e = 1.0, elementary_charge = 1.0, 1/(4*pi*eps_0) = 1.0, speed_of_light = 137 ( fine-structure constant)\r\n \r\n # pick only carbon atoms from eigenvectors of normal modes (assume that fluorine atoms doesn't influent the result) - in needed\r\n if CoarseGrain in [\"C\",\"plane\"] :\r\n indxC = np.where(np.array(struc.at_type) == 'C')\r\n index = np.zeros((len(indxC),3),dtype='i8')\r\n for ii in range(3):\r\n index[:,ii] = index*3+ii\r\n index.reshape(3*len(indxC))\r\n int2cart_loc = int2cart[index,:]\r\n else:\r\n int2cart_loc = int2cart.copy()\r\n \r\n g_mm = np.dot(int2cart_loc.T,dR_Hmm) + np.dot(int2cart.T,dR_env_Hmm)\r\n g_mm = g_mm/(np.sqrt(omega_au*omega_au*omega_au))\r\n g_mm = g_mm/(2*np.sqrt(RedMass_au))\r\n \r\n return g_mm","title":""},{"docid":"2c440c76b9936635298b4bb1cc941f6e","score":"0.5281879","text":"def compute_G(self):\n \"\"\" YOUR CODE HERE\"\"\"\n G = 0\n raise NotImplementedError\n\n return G","title":""},{"docid":"5d0418f4c70674079dcac873d1f89d15","score":"0.528127","text":"def MMD2H_Gaussian(kernel,gm,samples):\n N = len(samples)\n \n EE = EE_Gaussian(gm,kernel)\n \n crossed_term = 0\n for sample in samples:\n crossed_term += E_Gaussian(np.array([sample]), gm, kernel)\n\n gram_term = 0\n for sample_n in samples:\n for sample_m in samples:\n gram_term += kernel.pdf(sample_n,sample_m)\n\n return EE - 2*crossed_term/N + gram_term/N**2","title":""},{"docid":"df345ab6b0854133a35e504f993f2047","score":"0.5279673","text":"def _rand_g_(self):\r\n # calculate pairwise distance\r\n dist_gg, dist_gd, dist_dd = get_squared_dist(\r\n self.score_gen, self.score_data, z_score=False, do_summary=self.do_summary)\r\n\r\n # mmd\r\n with tf.name_scope('rand_g'):\r\n omega = tf.random_uniform([], self.omega_range[0], self.omega_range[1], dtype=tf.float32) \\\r\n if isinstance(self.omega_range, (list, tuple)) else self.omega_range\r\n loss_gr = rand_mmd_g_xy(\r\n dist_gg, dist_gd, dist_dd, self.batch_size, omega=omega,\r\n max_iter=3, name='mmd_gr', do_summary=self.do_summary, scope_prefix='rand_g/')\r\n loss_gn = rand_mmd_g_xn(\r\n self.score_gen, self.ref_normal, self.batch_size, self.num_scores, dist_xx=dist_gg, omega=omega,\r\n max_iter=3, name='mmd_gn', do_summary=self.do_summary, scope_prefix='rand_g/')\r\n loss_rn = rand_mmd_g_xn(\r\n self.score_data, self.ref_normal, self.batch_size, self.num_scores, dist_xx=dist_dd, omega=omega,\r\n max_iter=3, name='mmd_rn', do_summary=self.do_summary, scope_prefix='rand_g/')\r\n # final loss\r\n self.loss_gen = loss_gr\r\n self.loss_dis = loss_rn - loss_gr\r\n\r\n # self.debug_register = [omega, loss_gr, loss_gn, loss_rn]\r\n if self.do_summary:\r\n with tf.name_scope(None): # return to root scope to avoid scope overlap \r\n tf.summary.scalar('rand_g/omega', omega)\r\n tf.summary.scalar('GANLoss/gr', loss_gr)\r\n tf.summary.scalar('GANLoss/gn', loss_gn)\r\n tf.summary.scalar('GANLoss/rn', loss_rn)","title":""},{"docid":"7c8d34f3de667ebed5b0cd6412c5bd80","score":"0.5276958","text":"def g_reuss(self):\n return 15. / (8. * self.inv[:3, :3].trace() -\n 4. * np.triu(self.inv[:3, :3]).sum() +\n 3. * self.inv[3:, 3:].trace())","title":""},{"docid":"139e5a880bf42ab96f1e97c12d0b786d","score":"0.5272041","text":"def gauss(self, mu, sigma):\n pass","title":""},{"docid":"e952e878765b02e7efa8a78b9d3a3ee1","score":"0.5262486","text":"def _KLD_diag_gaussians(mu, log_sigma, prior_mu, prior_log_sigma):\n return prior_log_sigma - log_sigma + 0.5 * (tf.exp(2 * log_sigma)\n + tf.square(mu - prior_mu)) * tf.exp(-2. * prior_log_sigma) - 0.5","title":""},{"docid":"39784702b7ef84286fe174b59114d798","score":"0.5260531","text":"def _g(self, RD):\n return 1.0 / math.sqrt(1.0 + 3.0 * math.pow(RD, 2.0) / math.pow(math.pi, 2.0))","title":""},{"docid":"91b5829f4ec2d8af3d847ca0afb9c897","score":"0.5248102","text":"def initG_uniform_old(C, commlangdistros, innov = 0):\n\n g = commlangdistros[0,:].size\n c = commlangdistros[:,0].size\n n = C[:,0].size\n Gassertions(C, commlangdistros, g)\n \n G = matlib.zeros((n,g))\n for i in range(0,c):\n for h in range(0,C[:,0].size):\n if C[h,i] == 1:\n G[h] = commlangdistros[i,:]\n return G","title":""},{"docid":"579e396569e9e92d66a631ad60cc4537","score":"0.5235529","text":"def gaussian_product_diag(d1, d2):\n inv_cov1 = 1 / np.diag(d1.cov)\n inv_cov2 = 1 / np.diag(d2.cov)\n sigma = 1 / (inv_cov1 + inv_cov2)\n mu = sigma * (inv_cov1*d1.mean + inv_cov2*d2.mean) \n return Gaussian(*mu, *sigma)","title":""},{"docid":"9834df3259e407b7b7bba15b13995dc3","score":"0.5230722","text":"def calcgaussianp( self, frequency=None ):\n if frequency :\n diff = frequency - self.Bp\n else :\n diff = self.frequencies - self.Bp\n return exp( -0.5 * diff * diff * self.C2p )","title":""},{"docid":"5201a4f88595c5589a9e007ba199474c","score":"0.52282745","text":"def shell_Green_grid_Arnoldi_Mmn_step(n,k, invchi, rgrid,rsqrgrid,rdiffgrid, RgMgrid, ImMgrid, unitMvecs, Gmat, plotVectors=False):\n #first, begin by orthogonalizing and normalizing unitMvecs[-1]\n #use relation U = V^{-1} - G\n \"\"\"\n see comment for analogous method for N waves, shell_Green_grid_Arnoldi_Nmn_step\n coef1 = Gmat[-1,-1]\n unitMvecs[-1] -= coef1*unitMvecs[-2]\n \n if Gmat.shape[0]>1: #since G has symmetric Arnoldi representation (so tridiagonal), G*M_j has non-zero overlap with M_j and M_{j-1}\n coef2 = Gmat[-2,-1]\n unitMvecs[-1] -= coef2*unitMvecs[-3]\n \n unitMvecs[-1][:] = np.real(unitMvecs[-1][:])\n \"\"\"\n vecnum = Gmat.shape[0]\n for i in range(vecnum):\n coef = rgrid_Mmn_vdot(unitMvecs[i], unitMvecs[-1], rsqrgrid,rdiffgrid)\n unitMvecs[-1] -= coef*unitMvecs[i]\n unitMvecs[-1][:] = np.real(unitMvecs[-1][:])\n \n norm = np.sqrt(rgrid_Mmn_normsqr(unitMvecs[-1], rsqrgrid,rdiffgrid))\n unitMvecs[-1] /= norm\n \n if plotVectors:\n rgrid_Mmn_plot(unitMvecs[-1], rgrid)\n \n #get new vector\n newvecM = shell_Green_grid_Mmn_vec(n,k, rsqrgrid,rdiffgrid, RgMgrid,ImMgrid, unitMvecs[-1])\n \n newvecM[:] = np.real(newvecM)\n \n newGmat = np.zeros((Gmat.shape[0]+1,Gmat.shape[1]+1), dtype=np.complex)\n newGmat[:-1,:-1] = Gmat[:,:]\n \n newGmat[-1,-1] = rgrid_Mmn_vdot(unitMvecs[-1], newvecM, rsqrgrid,rdiffgrid)\n newGmat[-2,-1] = rgrid_Mmn_vdot(unitMvecs[-2], newvecM, rsqrgrid,rdiffgrid)\n newGmat[-1,-2] = newGmat[-2,-1]\n \n unitMvecs.append(newvecM) #append to end of unitMvecs for next round of iteration\n return newGmat","title":""},{"docid":"821ba4e50a8fbfaea368b31eb7d0b02c","score":"0.52278423","text":"def pm(v,g):\r\n p = np.zeros_like(v)\r\n p[:,0] = v[:,0]*g; p[:,1] = v[:,1]*g; p[:,2] = v[:,2]*g\r\n return p","title":""},{"docid":"2f7309efc8ce4006ec33e0e04b05c45f","score":"0.5226854","text":"def initG_indicator_old(C, commlangdistros, innov = 0):\n\n g = commlangdistros[0,:].size\n c = commlangdistros[:,0].size\n n = C[:,0].size\n Gassertions(C, commlangdistros, g)\n \n G = matlib.zeros((n,g))\n\n for comm in range(0,c):\n lowerbound = 0\n rangedict = {}\n for i in range(0,(commlangdistros[comm,:].size)):\n prob = commlangdistros[comm,:].item(i)\n if prob == 0:\n continue\n rangedict[(lowerbound, lowerbound + prob)] = i\n lowerbound += prob\n for h in range(0,C[:,0].size):\n if C[h,comm] == 1:\n rando = random.random()\n for grange, grammar in rangedict.iteritems():\n if grange[0] <= rando and rando < grange[1]:\n if random.random() < innov:\n G[h,random.randint(0,g-1)] = 1\n else:\n G[h,grammar] = 1\n return G","title":""},{"docid":"cd8416dc1d2cb6a79dfc7673b1add71c","score":"0.52182436","text":"def NonDiagonalElements(self,n=0,m=1):\n Hnm = sp.zeros(self.size,self.size)\n for i in range(0,self.size):\n for j in range(0,self.size):\n Hnm[i,j] = 1/self.T*sp.integrate(self.Vt[i,j]*sp.exp(-sp.I*(m-n)*self.G*t),(t,0,self.T),conds='none')\n return(Hnm)","title":""},{"docid":"07b025ce908e487bd58063a1260f8a9e","score":"0.521736","text":"def apply_G(self):\n\n pvec = self.vec['p']\n uvec = self.vec['u']\n\n speed = pvec('v') * 1e2\n dist = pvec('x') * 1e6\n gamma = pvec('gamma') * 1e-1\n time = uvec('time')\n\n time_temp = ((dist[1:] - dist[0:-1]) /\n (((speed[1:] + speed[0:-1])/2) *\n numpy.cos((gamma[1:] + gamma[0:-1])/2)))\n time[0] = numpy.sum(time_temp)/1e4","title":""},{"docid":"8800c004fa23a5a5fdee4e422efb623f","score":"0.5215661","text":"def gaussian(x,mu,fwhm):\n \n c = fwhm/(2* np.sqrt(2*np.log(2)))\n return np.exp(-1*((x-mu)**2/(2*c**2)))","title":""},{"docid":"94d8016e105380d54b280b1a52e67a3b","score":"0.52119476","text":"def gauss_upper_diag_tridiag(M):\n M[0]/=M[0][0]\n for i in range(1,len(M[:,0])):\n M[i]-=M[i-1]*M[i,i-1]\n M[i]/=M[i][i]\n return M","title":""},{"docid":"85c74a6d2fce1d009f1411e600eb8fc1","score":"0.5210143","text":"def generate():\n\n m = np.array([2., 5., 5., 2.])\n temp = 2.0 * (np.random.rand(4, 4)) # to random-generate S\n S = np.dot(temp, temp.T) # make sure S is positive semidefinite, symmetric\n d, U = np.linalg.eig(S)\n L = np.diagflat(d)\n A = np.dot(U, np.sqrt(L))\n X = np.random.randn(4, 1000)\n Y = np.dot(A,X) + np.tile(m.reshape(4,1), 1000) \n return S, m, Y","title":""},{"docid":"7273e003b5fb28fd61827833dbe8f464","score":"0.5208473","text":"def _calc_m(x0, xt, tau=1):\n \n # These represent the C(tau) and C(0) covariance matrices\n # Note: x is an anomaly vector, no division by N-1 because it's undone\n # in the inversion anyways\n \n x0x0 = np.dot(x0.T, x0)\n x0xt = np.dot(xt.T, x0)\n\n # Calculate the mapping term G_tau\n G = np.dot(x0xt, pinv(x0x0))\n\n # Calculate the forcing matrix to check that all modes are damped\n Geigs = eigvals(G)\n Leigs = (1./tau) * np.log(Geigs)\n\n if np.any(Leigs.real >= 0):\n logger.debug('L eigenvalues: \\n' + str(Leigs))\n raise ValueError('Positive eigenvalues detected in forecast matrix L.')\n\n return G","title":""},{"docid":"6102fccc3a1df6d52a7b25e0f71aea4f","score":"0.52029395","text":"def gsl_eigen_genherm_alloc(*args, **kwargs):\n return _gslwrap.gsl_eigen_genherm_alloc(*args, **kwargs)","title":""},{"docid":"1abb2b137688ce80ceaffc51a5d512f5","score":"0.5202235","text":"def efficient_ACDM(self, X, n_iter):\n calc_KL_time = 0\n e = np.e \n B_size = len(self.B_)\n Z = len(self.S_)\n total_step = n_iter*B_size\n # Initialization of Theta\n theta_prime_tilde = dict()\n theta_prime_hat = dict()\n for phi in self.B_:\n theta_prime_tilde[phi] = 0.0\n theta_prime_hat[phi] = 0.0\n\n # Initialization of U\n U_hat = dict() \n U_prime_hat = dict() \n U_prime_tilde = dict()\n for x in self.S_:\n U_hat[x] = 0\n U_prime_hat[x] = 0.0\n U_prime_tilde[x] = 0.0\n # Initialization of Coefficients\n alpha = [0] * (total_step+1)\n gamma = [0] * (total_step+2)\n c_hat = [0] * (total_step+2)\n gamma[-1] =1/(2.0) \n c_hat[-1] = 3.0 + 2*math.sqrt(2.000) \n for k in range(total_step+1):\n gamma_k_before = gamma[k-1]\n gamma_k = math.sqrt(1.0/(4.0)+gamma_k_before**2)+1.0/(2.0)\n gamma[k] = gamma_k\n alpha[k] =1/(gamma_k )\n c_hat[k] = (1-alpha[k]) * c_hat[k-1]\n # Caching \n e_U_hat_dict = dict() \n for x in self.S_:\n e_U_hat_dict[x] = 1.0\n # Beginning of Iteration\n start = time.time()\n for iter_ in range(n_iter):\n for index in range(B_size):\n k = iter_*B_size + index\n phi = random.choice(self.B_)\n # Caching\n c_hat_k = c_hat[k]\n c_hat_k_after = c_hat[k+1]\n alpha_k_after = alpha[k+1]\n gamma_k = gamma[k]\n\n # calculate G, Z\n G = 0 \n for x in self.Ssub_[phi]:\n G += e_U_hat_dict[x]\n eta_ = G/Z\n # update tilde_theta_prime, hat_theta_prime\n nabla = (self.etahat_[phi] - eta_)\n step1 = 4.0 * gamma_k/B_size\n step2 = 4.0/c_hat_k_after*(1-alpha_k_after-(alpha_k_after+1-c_hat_k_after)*gamma_k/B_size)\n\n theta_prime_tilde[phi] += step1 * nabla\n theta_prime_hat[phi] += step2 * nabla\n for x in self.Ssub_[phi]:\n U_prime_tilde[x] += step1 * nabla\n U_prime_hat[x] += step2 * nabla\n U_hat_temp = (1-c_hat_k_after)*U_prime_tilde[x]+c_hat_k_after*U_prime_hat[x]\n e_U_hat_temp = e ** U_hat_temp\n Z += e_U_hat_temp - e_U_hat_dict[x]\n e_U_hat_dict[x] = e_U_hat_temp\n\n update_start_time = time.time()\n for x in self.B_:\n self.theta_[x] = (1-c_hat_k_after)*theta_prime_tilde[x]+c_hat_k_after*theta_prime_hat[x]\n self.theta_[phi] += 4*(self.etahat_[phi]-eta_)\n self.compute_theta_perp()\n self.compute_P()\n kl=self.compute_KL()\n calc_KL_time += time.time() - update_start_time \n print(iter_ ,\":\", \"KL divergence: \", f'{kl:.16f}' ,\" time : %4.2f\"% (time.time()-calc_KL_time-start),flush=True)","title":""},{"docid":"f63554c2bb826513c5dea1ca73b00c83","score":"0.5196821","text":"def GS_classical(A):\n\n m, n = A.shape\n\n Q = np.zeros((m, n), dtype=A.dtype)\n R = np.zeros((n, n), dtype=A.dtype)\n\n for j in range(n):\n R[:j-1,j] = np.dot(Q[:,:j-1].T.conj(), A[:,j])\n Q[:,j] = A[:, j] - np.dot(Q[:,:j-1], R[:j-1,j])\n R[j,j] = np.linalg.norm(Q[:,j])\n Q[:,j] /= R[j,j]\n\n return Q, R","title":""},{"docid":"15d3fc1df384b92cb613df958ec1fa5a","score":"0.51892686","text":"def gmm_pdf(X, centroids, ccov, mc, individual=False):\n if individual:\n pdf = np.zeros((len(X), len(centroids)))\n for i in range(len(centroids)):\n pdf[:,i] = mulnormpdf(X, centroids[i], ccov[i]) * mc[i]\n return pdf\n else:\n pdf = None\n for i in range(len(centroids)):\n pdfadd = mulnormpdf(X, centroids[i], ccov[i]) * mc[i]\n if pdf==None:\n pdf = pdfadd\n else:\n pdf = pdf + pdfadd\n return pdf","title":""},{"docid":"d31c54b12c1baf7755080f3f45d71dc6","score":"0.51869303","text":"def gauss(self, mu, sigma):\n pass","title":""},{"docid":"d31c54b12c1baf7755080f3f45d71dc6","score":"0.51869303","text":"def gauss(self, mu, sigma):\n pass","title":""},{"docid":"d31c54b12c1baf7755080f3f45d71dc6","score":"0.51869303","text":"def gauss(self, mu, sigma):\n pass","title":""},{"docid":"fb29cc8a52060aec3c186eedb3104457","score":"0.51843107","text":"def gauss_pdf_mat(x):\n sub = x - self.mean\n r0 = inv_cov * sub\n exponent = -0.5 * numpy.sum(sub.A * r0.A, axis=0)\n if (numpy.shape(exponent) != (x.shape[1],)):\n raise AssertionError(\"exponent has the wrong shape, should be (%d,), but is (%d,)\" % x.shape[1], exponent.shape[0])\n g = const * (numpy.e ** exponent)\n return g","title":""},{"docid":"445110a070b4c7d159d0c08e5b1db617","score":"0.5181291","text":"def marginal_covar_matrix(self, g, beta, gamma, logphi, alpha, eval_gradient=False):\n # reshape g\n g = g.reshape(self.dim.R, self.dim.N).T # reshape \n g = np.column_stack((np.ones(self.dim.N), g)) # and append a col. of 1s\n\n # get the struct matrices\n struct_mats = np.array([sum(brd*Ld for brd, Ld in zip(br, self.basis_mats))\n for br in beta])\n # Add an axis for n=0,...,N-1\n At = struct_mats[None, :, :, :] * g[..., None, None]\n At = At.sum(1)\n\n # Get the additive noise error term\n #Gamma = np.diag(np.concatenate([gk*np.ones(self.dim.N) for gk in gamma]))\n Gamma = np.kron(np.eye(self.dim.N), np.diag(gamma))\n\n # Get the initial state covariance matrix\n C = np.kron(np.ones((self.dim.N, self.dim.N)),\n np.diag(alpha))\n \n ####\n # Construct the transformation matrix K\n W = self._weight_matrix\n K = sum(sparse.kron(sparse.kron(W[:, i][:, None],\n sparse.eye(1, self.dim.N, i)),\n At[i, ...])\n for i in range(self.dim.N))\n I = sparse.eye(self.dim.K)\n\n eifix = sparse.eye(1, self.dim.N, self.ifix)\n K += sum(sparse.kron(sparse.kron(sparse.eye(1, self.dim.N, i).transpose(),\n eifix),\n I)\n for i in range(self.dim.N))\n #_K = np.array(K.todense())\n if eval_gradient:\n dCdg = [np.zeros((self.dim.N*self.dim.K, self.dim.N*self.dim.K))]*self.dim.N*self.dim.R \n\n #_dCdg = np.zeros((self.dim.N*self.dim.R,\n # self.dim.N*self.dim.K,\n # self.dim.N*self.dim.K))\n\n # Gradient of the operator K wrt g_rn\n dK = [sparse.kron(sparse.kron(W[:, n][:, None],\n sparse.eye(1, self.dim.N, n)),\n struct_mats[r+1])\n for r in range(self.dim.R)\n for n in range(self.dim.N)]\n #_dK = np.array([np.kron(W[:, n][:, None], struct_mats[r+1])\n # for r in range(self.dim.R)\n # for n in range(self.dim.N)])\n\n dCdgamma = np.zeros((self.dim.N*self.dim.K,\n self.dim.N*self.dim.K,\n self.dim.K))\n dCdalpha = [np.kron(np.ones((self.dim.N, self.dim.N)),\n np.diag(np.eye(N=1, M=self.dim.K, k=k).ravel()))\n for k in range(self.dim.K)]\n\n for m in range(self.order):\n\n dCdg = np.array([K.dot(K.dot(dC_grn).T) for dC_grn in dCdg]) \n for i, dK_grn in enumerate(dK):\n kcdk = K.dot(dK_grn.dot(C).T)\n dCdg[i] += kcdk + kcdk.T\n\n # update gradient of C wrt to g_{rn}\n #CKt = K.dot(C).T\n #print(K.shape, _dCdg.shape)\n #expr = _K.dot(_dCdg)\n #print(expr.shape)\n #assert(False)\n #expr = dK.dot(CKt.reshape(self.dim.N, self.dim.K, self.dim.N*self.dim.K)).sum(2)\n #expr += expr.transpose(0, 2, 1)\n #expr2 = K.dot(K.dot(dCdg).T)\n #dCdg = expr + expr2\n #dCdg = [K.dot(dK_grn.dot(C).T) + \\\n # dK_grn.dot(K.dot(C).T) + \\\n # K.dot(K.dot(dC_grn).T)\n # for dK_grn, dC_grn in zip(dK, dCdg)]\n\n # update gradient of C wrt to Gamma\n dCdgamma = np.dstack((K.dot(K.dot(dCdgamma[..., i]).T)\n for i in range(dCdgamma.shape[-1])))\n\n diag_ek = np.zeros((self.dim.K, self.dim.K))\n for k in range(self.dim.K):\n diag_ek[k, k] = 1.\n dCdgamma[..., k] += np.kron(np.eye(self.dim.N), diag_ek)\n diag_ek[k, k] = 0.\n\n # update gradient of C wrt to alpha\n dCdalpha = [K.dot(K.dot(dCdak).T) for dCdak in dCdalpha]\n \n C = K.dot(K.dot(C).T) + Gamma\n\n #print(\"...done.\")\n dCdg = np.dstack(dCdg)\n return (C,\n dCdg,\n dCdgamma,\n np.dstack(dCdalpha))\n\n else:\n for m in range(self.order):\n C = K.dot(K.dot(C).T) + Gamma \n return C","title":""},{"docid":"8f1f8a13fd6db519eedebdda1733c583","score":"0.51747596","text":"def build_generalized_fock_matrix(Num_MO,\n h_MO,\n g_MO,\n one_rdm,\n two_rdm ):\n F = np.zeros((Num_MO, Num_MO))\n for m in range(Num_MO):\n for n in range(Num_MO):\n for q in range(Num_MO):\n F[m,n] += one_rdm[m, q] * h_MO[n,q]\n for r in range(Num_MO):\n for s in range(Num_MO):\n F[m,n] += two_rdm[m,q,r,s] * g_MO[n,q,r,s]\n return F","title":""},{"docid":"11a0ad90c0475b7c3d80acbe06eca254","score":"0.5172434","text":"def testQg(self):\n msg = self.case_name\n self.case.sort_generators() # ext2int\n _, _, gn = self.opf._remove_isolated(self.case)\n Qg = self.opf._get_qgen_var(gn, self.case.base_mva)\n\n mpQg0 = mmread(join(DATA_DIR, self.case_name, \"opf\", \"Qg0.mtx\"))\n mpQmin = mmread(join(DATA_DIR, self.case_name, \"opf\", \"Qmin.mtx\"))\n mpQmax = mmread(join(DATA_DIR, self.case_name, \"opf\", \"Qmax.mtx\"))\n\n self.assertTrue(mfeq1(Qg.v0, mpQg0.flatten()), msg)\n self.assertTrue(mfeq1(Qg.vl, mpQmin.flatten()), msg)\n# self.assertTrue(mfeq1(Qg.vu, mpQmax.flatten()), msg)\n self.assertTrue(mfeq1(Qg.vu, mpQmax.flatten()), msg)","title":""},{"docid":"8e9ccae31f4dc6e4dfa70f81f1598ea1","score":"0.5170576","text":"def apply_G(self):\n\n alt_f = self.vec['u']('h_f')\n alt = self.vec['p']('h')\n\n alt_f[0] = alt[0]","title":""},{"docid":"5d4405672a59c838bc935b1da26d2f99","score":"0.5170253","text":"def mh_gaussian(init,ys,ts,iters,fi=None):\n print(\"Running Metropolis Algorithm with Gaussian proposals.\")\n D = len(init)\n samples = np.zeros((iters,D))\n my_dist = dist(init)\n # initialize state and log-likelihood\n state = init.copy()\n Lp_state = my_dist.log_likelihood(ys,ts)\n accepts = 0.\n\n cov = (0.1)**2 * np.eye(D)*1./D\n for i in np.arange(0, iters):\n if fi is not None:\n write_samp(fi,state)\n # propose a new state\n prop = (np.random.multivariate_normal(state.ravel(), cov))\n move_p = np.log(scipy.stats.multivariate_normal(state,cov).pdf(prop))\n rev_p = np.log(scipy.stats.multivariate_normal(prop,cov).pdf(state))\n my_dist.set_params(prop)\n Lp_prop = my_dist.log_likelihood(ys,ts)\n rand = np.random.rand()\n\n if np.log(rand) < min(1,((Lp_prop+rev_p) - (Lp_state+move_p))):\n print (\"acct bc %s < %s (iter %s)\"%(np.log(rand),(Lp_prop-Lp_state),i))\n accepts += 1\n state = prop.copy()\n print state\n Lp_state = Lp_prop\n else:\n my_dist.set_params(state)\n samples[i] = state.copy()\n\n print 'Acceptance ratio', accepts/iters\n return samples","title":""},{"docid":"f500066ec71462760a1d0c385aad5461","score":"0.5167478","text":"def gsl_eigen_genhermv_alloc(*args, **kwargs):\n return _gslwrap.gsl_eigen_genhermv_alloc(*args, **kwargs)","title":""},{"docid":"0d6a3c40227d11bf75b9113f674b3f6d","score":"0.51667595","text":"def G ( x , y , z , u , v , w ) :\n result = 0.0 \n result += x * x * y + x * y * y \n result += z * z * u + z * u * u \n result += v * v * w + v * w * w \n result += x * z * w + x * u * v \n ## result += y * z * w + y * u * w ## <-- typo in E.Byckling & K.Kajantie\n result += y * z * v + y * u * w ## <-- correct line \n result -= x * y * ( z + u + v + w ) \n result -= z * u * ( x + y + v + w ) \n result -= v * w * ( x + y + z + u )\n ##\n return result","title":""},{"docid":"2f13202e74890870c8cace5e32b59e68","score":"0.51664","text":"def gauss_f(d, fwhm=0.529177):\n sigma = fwhm / (2 * np.sqrt(2 * np.log(2)))\n d2 = d * d\n return np.exp(-1 / (2 * sigma**2) * d2)","title":""},{"docid":"f6218ad79a806807a819ee2c6b26d12f","score":"0.5164684","text":"def calc_F(H, G, m=None):\n assert_square(H)\n assert_square(G)\n\n n = H.shape[0]\n I = np.identity(n)\n IG = I-G\n\n if m:\n F = (np.trace(H.dot(G).dot(H)) / (m-1)) / (np.trace(IG.dot(G).dot(IG)) / (n-m))\n else:\n F = (np.trace(H.dot(G).dot(H))) / np.trace(IG.dot(G).dot(IG))\n\n return F","title":""},{"docid":"47f3dcdeb2f2505d5b8da196d6868f5a","score":"0.5158682","text":"def generate_global_matrix_1D(request):\n\n gmsh_buffer, cell_type = request.param\n\n diff = 0.1\n vel = -1.0\n r = vel/diff\n\n with tempfile.NamedTemporaryFile(suffix='.msh') as temp:\n temp.write(gmsh_buffer.encode('utf-8'))\n temp.flush()\n points, cells, point_data, cell_data, field_data = meshio.read(temp.name)\n\n _points = np.array(points[:, 0], dtype='double', order='F')\n _cells = np.array(cells[cell_type] + 1, dtype='int32', order='F')\n # NOTE: Always change 0-based to 1-based indexing\n\n num_cells, num_pts_per_cell, num_pts = (\n _cells.shape[0],\n _cells.shape[1],\n _points.shape[0]\n )\n\n # NOTE: Can't do this anymore because fixture is parametrized\n # # Sanity checks on matrix sizes\n # assert num_cells == 52\n # assert num_pts_per_cell == 2\n # assert num_pts == 53\n\n # Zero Ie array\n A = np.zeros(\n (num_pts, num_pts),\n dtype='double',\n order='F'\n )\n\n # Initialize function\n f = helpers.set_assemble1D_c_args(\n num_cells,\n num_pts_per_cell,\n num_pts\n )\n\n print('\\nCalling: ', f.__name__, '\\n With N = ', num_pts)\n # Calculate Global stiffness matrix `A`\n f(\n num_cells,\n num_pts_per_cell,\n num_pts,\n _points,\n _cells,\n np.float64(diff),\n np.float(vel),\n A\n )\n\n # A 1D line in Gmsh notation will always have end points as pt 0 and 1\n left_list = [0]\n right_list = [1]\n\n # Set boundary condtions in Ie matrix\n for ii in left_list + right_list:\n A[ii,:] = 0.; A[ii,ii] = 1.\n\n # Print condition number of `A`\n print(' Cond(A) = ', np.linalg.cond(A), '\\n')\n\n # Set boundary condtions in RHS vector\n b = np.zeros((num_pts,))\n for ii in right_list:\n b[ii] = 1.\n\n x = np.linalg.solve(A, b)\n\n return x, r, _points","title":""},{"docid":"982a3700e8d93dda58ba51fadea2b726","score":"0.5156181","text":"def _TFmdm_set_cosm(self):\n omega_lambda = self.q0\n omega_matter = self.m0\n omega_lambda_z = self.qz\n omega_matter_z = self.mz\n theta_cmb = self.theta_cmb\n redshift = self.z\n h2 = self.h*self.h\n omhh = self.m0*h2\n obhh = self.b0*h2\n onhh = self.n0*h2\n f_baryon = self.b0/self.m0\n f_hdm = self.n0/self.m0\n f_cdm = 1.0 - f_baryon - f_hdm\n f_cb = f_cdm + f_baryon\n f_bnu = f_baryon + f_hdm\n num_degen_hdm = self.nnu\n# equality scale (Eqn 1 in EH99)\n z_equality = 25000.0*omhh/theta_cmb**4 # Actually 1+z_eq \n k_equality = 0.0746*omhh/theta_cmb**2\n# drag epoch and sound horizon (Eqn 2)\n z_drag_b1 = 0.313*pow(omhh,-0.419)*(1+0.607*pow(omhh,0.674))\n z_drag_b2 = 0.238*pow(omhh,0.223)\n z_drag = 1291*pow(omhh,0.251)/(1.0+0.659*pow(omhh,0.828))*\\\n (1.0+z_drag_b1*pow(obhh,z_drag_b2))\n# (Eqn 3)\n y_drag = z_equality/(1.0+z_drag)\n# (Eqn 4)\n sound_horizon_fit = 44.5*np.log(9.83/omhh)/np.sqrt(1.0+10.0*np.power(obhh,0.75))\n# Set up for the free-streaming & infall growth function */\n# (Eqn 11)\n p_c = 0.25*(5.0-np.sqrt(1+24.0*f_cdm))\n p_cb = 0.25*(5.0-np.sqrt(1+24.0*f_cb))\n# (Eqn 10)\n# growth_k0 is used for calculating the free streaming scale\n growth_k0 = (z_equality/(1.0+redshift)*2.5*\n omega_matter_z/(pow(omega_matter_z,4.0/7.0)-\n omega_lambda_z+(1.0+omega_matter_z/2.0)*\n (1.0+omega_lambda_z/70.0)))\n# for scale-independent growth of power spectrum \n# (same as Density.growth_factor)\n# growth_to_z0 = (z_equality*2.5*omega_matter/\n# (pow(omega_matter,4.0/7.0)-\n# omega_lambda +\n# (1.0+omega_matter/2.0)*(1.0+omega_lambda/70.0)))\n# growth_to_z0 = growth_k0/growth_to_z0\n\n# Compute small-scale suppression \n# (Eqn 15)\n alpha_nu = (f_cdm/f_cb*(5.0-2.*(p_c+p_cb))/(5.-4.*p_cb)*\n pow(1+y_drag,p_cb-p_c)*\n (1+f_bnu*(-0.553+0.126*f_bnu*f_bnu))/\n (1-0.193*np.sqrt(f_hdm*num_degen_hdm)+\n 0.169*f_hdm*pow(num_degen_hdm,0.2))*\n (1+(p_c-p_cb)/2*(1+1/(3.-4.*p_c)/(7.-4.*p_cb))/(1+y_drag))\n )\n alpha_gamma = np.sqrt(alpha_nu)\n# (Eqn 21)\n beta_c = 1/(1-0.949*f_bnu)\n# Done setting scalar variables\n return(omhh, f_hdm, f_cb, growth_k0, p_cb, \n alpha_gamma, sound_horizon_fit, beta_c)","title":""},{"docid":"abbe4f6aecc94299d8d616b1db8be9bd","score":"0.5147316","text":"def calculateGMag(x):\n g_vector = np.array([x['g_x'], x['g_y'], x['g_z']])\n return np.sqrt(g_vector.dot(g_vector))","title":""},{"docid":"374289d702f40c9b3e42bf5dd38c2470","score":"0.51468635","text":"def initG_indicator(hindices, commlangdistros, innov = 0):\n\n g = commlangdistros[0,:].size\n c = commlangdistros[:,0].size\n n = hindices[0].shape[1]\n# print \"n\", hindices[0].shape[1]\n# Gassertions(C, commlangdistros, g)\n \n G = matlib.zeros((n,g))\n\n\n randmat = nprandom.rand(G.shape[0],1)\n\n for b in range(0,n):\n if randmat[b] < innov:\n G[b,random.randint(0,g-1)] = 1\n else:\n comm = np.asarray(hindices[1])[0][b]\n distr = np.asarray(commlangdistros[comm,:])[0]\n G[b,:] = nprandom.multinomial(1, distr)\n\n return G","title":""},{"docid":"57531a2ef18270590abf01d4616a5f79","score":"0.51447994","text":"def test_MonteCarlo():\n\tfrom diffusion_model import energy\t#use diffusion model energy function as test for functionality of MC minimisation\n\timport nose.tools\n\tfrom nose.tools import assert_equal\n\tfrom nose.tools import assert_true\n\tfrom random import randint\n\timport random\n\timport matplotlib.pyplot as plt\n\t\n\t#minimum energy state doesn't change at absolute zero\n\tfor n in range (3): #try three times\n\t\tdensity=[1.0,1.0,1.0,1.0]\n\t\tdensity=MonteCarlo (\"diffusion_model\",\"energy\",density,0)\n\t\tassert_equal(density,[1.0,1.0,1.0,1.0])\n\t\n\t#energy state lowers until minimum energy state is reached at absolute zero or iterated 100 times\n\tdensity=range(10)\n\tfor i in range (10):\n\t\tdensity[i]=random.randint(1,10) #fill density with random integers\n\tif max(density)-min(density)<=1:\n\t\tdensity_backup=density[:]\n\t\tMonteCarlo(\"diffusion_model\",\"energy\",density,0)\n\t\tassert_true(density=density_backup)\n\telse:\n\t\tE0=energy(density)\n\t\tx=100\n\t\tMCEnergy=[]\n\t\twhile (max(density)-min(density)>=2) and x>0: #if distribution is non-uniform energy minimum is not reached\n\t\t\tMCEnergy.append(energy(density))\n\t\t\tdensity=MonteCarlo(\"diffusion_model\",\"energy\",density,0)\n\t\t\tx-=1\n\t\tprint \"energy changed by \", MCEnergy\n\t\tplt.plot(MCEnergy)\n\t\tplt.show()\n\tassert_true(E0>energy(density))","title":""},{"docid":"2e650b8a358b8258ff4bbeef4cd13aeb","score":"0.51432973","text":"def evaluate_mog(self, x, y):\n\t\tf = np.zeros(x.shape)\n\t\tparams = self.pos.copy()\n\t\tparams[:,2] = np.exp(params[:,2])\n\t\tparams[:,3] = np.exp(params[:,3])\n\t\tparams[:,4] = np.exp(0.5*params[:,4])\n\n\t\tfor i in range(self.num):\n\t\t\tgaussian = Gaussian(params=params[i, :])\n\t\t\tf += gaussian.evaluate(x, y)\n\t\treturn f","title":""},{"docid":"147355d6af861a805ab2b384ef15cdaf","score":"0.5142504","text":"def locmat(nodes,de): #local stiffness (jacobian) and force (residual) over the reference element\r\n# print(nodes.shape)\r\n# print(de.shape)\r\n Xi=np.tile(GP.xi,OrdGauss)\r\n Eta=np.repeat(GP.xi,OrdGauss)\r\n dof=de.reshape(de.size,-1,1).repeat(len(Xi),axis=-1) #arranging dof for (dot) product with B (len(Xi) and not len(GP.xi)) !!!\r\n Wg=np.outer(GP.wght,GP.wght).flatten() \r\n Nshp=np.kron(np.eye(geom.nDim).reshape(geom.nDim,geom.nDim,-1),B.Ns(Xi,Eta)) #kron has to be taken on nDim (and not OrdGauss)\r\n# print(np.array(B.Ns(Xi,Eta)).shape)\r\n gDshpL=np.array(B.dN(Xi,Eta)).reshape(geom.nDim,int(Eltype[1]),-1) #local derivatives\r\n Je=np.einsum('ilk,lj->ijk',gDshpL,nodes.reshape(geom.nDim,-1).T) #computing the jacobian\r\n detJ=(Je[0,0,:]*Je[1,1,:]-Je[0,1,:]*Je[1,0,:])\r\n# print(detJ)\r\n# Jeinv=1/detJ*np.array([[Je[1,1,:],-Je[0,1,:]],[-Je[1,0,:],Je[0,0,:]]]) #avoid computing inverse on a loop (--check ?)\r\n Jeinv=1/detJ*np.einsum('ijk->jik',Je)\r\n gDshpG=np.einsum('ilk,ljk->ijk',Jeinv,gDshpL) #global derivatives \r\n Bmat=np.kron(np.eye(geom.nDim).reshape(geom.nDim,geom.nDim,-1),gDshpG) \r\n# B1=Bmat.copy()\r\n gradU=np.einsum('ilk,ljk->ijk',Bmat,dof) #remember that gradU is never symmetric !!!\r\n# print(gradU)\r\n \"\"\"\r\n Computing the deformation gradient (F12,F11,F22).T = B*de, and first piola (S) --> (S12,S11,S22), \r\n Multiplying by the Gauss-weights, and calculating the element residual\r\n \"\"\"\r\n F=gradU+np.array([1,0,0,1.]).reshape(-1,1,1).repeat(len(Xi),axis=-1) #computing F as (F11,F11,F22) shape: 3x1xNGP\r\n detF=F[0]*F[3]-F[1]*F[2] \r\n WpI1=dWdIi.DWDI1(*F)\r\n WpJ=dWdIi.DWDJ(*F)\r\n WppI1=dWdIi.D2WDI12(*F)\r\n WppJ=dWdIi.D2WDJ2(*F)\r\n Finv=np.array([F[3],-F[1],-F[1],F[0]])/detF #avoid computing inverse on the loop for the deformation gradient \r\n# Helpful variables: \r\n S=WpI1*2*F+(WpJ*detF).reshape(1,1,-1)*Finv[np.array([0,2,1,3],int)] #notice the swap of axes for transpose\r\n fac=Wg*detJ*geom.thck\r\n S*=fac #multiplying S by the determinant of the jacobian, thickness, and gauss-weights\r\n res=np.einsum('lik,ljk->ij',Bmat,S) #double contraction along axis 1 and 2 (of B)\r\n \"\"\"\r\n Computing the Consistent Tangent:D= B^T *C *B <-- Cijkl, check notes\r\n Cijkl = 4*W''_(I1) Fij Fkl + 2 W'_(I1) delik deljl + J**2*W''_(J) F-1ji F-1lk +J*W'_(J) F-1ji F-1lk - J W'_(J) F-1jk F-1li \r\n F = (F11,F12,f21,F22).T\r\n \"\"\"\r\n F11=F[0];F12=F[1];F21=F[2];F22=F[3]\r\n #This C does not have minor symmetry (relates S to F) , only major symmetry \r\n \r\n C1111=4*WppI1*F11*F11+2*WpI1+detF**2*WppJ*Finv[0]**2 #scalar addition to multi-dimensional array (--check??) \r\n C1112=4*WppI1*F11*F12+detF**2*WppJ*Finv[0]*Finv[2]\r\n C1121=4*WppI1*F11*F21+detF**2*WppJ*Finv[0]*Finv[1]\r\n C1122=4*WppI1*F11*F22+detF**2*WppJ*Finv[0]*Finv[3]+detF*WpJ*Finv[0]*Finv[3]-detF*WpJ*Finv[1]*Finv[2]\r\n C1212=4*WppI1*F12*F12+2*WpI1+detF**2*WppJ*Finv[2]**2\r\n C1221=4*WppI1*F12*F21+detF**2*WppJ*Finv[2]*Finv[1]+detF*WpJ*(Finv[2]*Finv[1] -Finv[3]*Finv[0])\r\n C1222=4*WppI1*F12*F22+detF**2*WppJ*Finv[2]*Finv[3]\r\n C2121=4*WppI1*F21*F21+2*WpI1+detF**2*WppJ*Finv[1]**2\r\n C2122=4*WppI1*F21*F22+detF**2*WppJ*Finv[2]*Finv[3]+detF*WpJ*(Finv[2]*Finv[3] -Finv[3]*Finv[1])\r\n C2222=4*WppI1*F22*F22+2*WpI1+detF**2*WppJ*Finv[3]**2\r\n \r\n C1111=C1111.flatten()\r\n C1112=C1112.flatten()\r\n C1121=C1121.flatten()\r\n C1122=C1122.flatten()\r\n C1212=C1212.flatten()\r\n C1221=C1221.flatten()\r\n C1222=C1222.flatten()\r\n C2121=C2121.flatten()\r\n C2122=C2122.flatten()\r\n C2222=C2222.flatten()\r\n \r\n C=np.array([[C1111,C1112,C1121,C1122],\r\n [C1112,C1212,C1221,C1222],\r\n [C1121,C1221,C2121,C2122],\r\n [C1122,C1222,C2122,C2222]])\r\n \r\n D=np.einsum('lik,lpk,pjk->ij',Bmat,C,Bmat) #Check the multiplication once for a simple case!\r\n# print(res)\r\n return {'K':D,\r\n 'F':res.flatten(),\r\n 'Stress':S,\r\n 'DefGrad':F,\r\n 'InptGlobal':np.einsum('ilj,l->ij',Nshp,nodes),\r\n 'NGP':len(Xi)}","title":""},{"docid":"42e165342059e3872fbe57292d7dca3b","score":"0.5142341","text":"def Me(self) -> np.ndarray:\n ip, iw = self.gauss\n psi = self.psi(ip)\n dpsi = self.dpsi(ip)\n J = self.J(dpsi)\n dJ = self.dJ(J)\n\n Me = np.zeros((self._NODES * self._DOFS, self._NODES * self._DOFS), dtype=_FLOAT)\n\n for i in range(ip.shape[1]):\n N = self.N(psi, i)\n Me += self.material.rho * (N.T @ N) * dJ[i] * iw[i]\n\n return Me","title":""}],"string":"[\n {\n \"docid\": \"83fc45443edde46c289583e7f26e160b\",\n \"score\": \"0.6200017\",\n \"text\": \"def _mmd_g_(self):\\r\\n # calculate pairwise distance\\r\\n dist_gg, dist_gd, dist_dd = get_squared_dist(\\r\\n self.score_gen, self.score_data, z_score=False, do_summary=self.do_summary)\\r\\n\\r\\n # mmd\\r\\n self.loss_gen = mixture_mmd_g(\\r\\n dist_gg, dist_gd, dist_dd, self.batch_size, sigma=self.sigma,\\r\\n name='mmd_g', do_summary=self.do_summary)\\r\\n self.loss_dis = -self.loss_gen\\r\\n if self.dis_penalty is not None:\\r\\n self.loss_dis = self.loss_dis + self.dis_penalty\",\n \"title\": \"\"\n },\n {\n \"docid\": \"639d3d6e37a4ce1aa0ffa25764cd4ec2\",\n \"score\": \"0.6191505\",\n \"text\": \"def gfp_dynamics_1cc_(F,V,Cm1,beta,dtsim):\\n G = np.zeros_like(F)\\n G[0] = Cm1*V[0]\\n for k in range(len(F)-1):\\n G[k+1] = G[k]+(V[k]*F[k]-beta*G[k])*dtsim\\n return G,G[-1]/V[-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d46119625b9ce37cf39481a6c2753ed5\",\n \"score\": \"0.61492175\",\n \"text\": \"def gfp_dynamics_1cc(F,V,Cm1,beta,dtsim):\\n G = np.zeros_like(F)\\n G[0] = Cm1/2\\n for k in range(len(F)-1):\\n G[k+1] = G[k]+(V[k]*F[k]-beta*G[k])*dtsim\\n return G,G[-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c86dd1ad31830cb2cc980e6175180280\",\n \"score\": \"0.6106971\",\n \"text\": \"def gmm_mllr_diag_cov(X, gmm, niter=10):\\n\\n # remove illed gaussians\\n logprob=gmm.score_samples(X)\\n pcompx=gmm.predict_proba(X)\\n ###logprob,pcompx = gmm.eval(X)\\n psum = np.sum(pcompx, axis=0)\\n ill_g = (psum == 0);\\n if any(ill_g):\\n valid = psum > 0\\n gmm.means_ = gmm.means_[valid,:]\\n gmm.weights_ = gmm.weights_[valid]\\n gmm.weights_ = gmm.weights_/sum(gmm.weights_)\\n gmm.covariances_ = gmm.covariances_[valid]\\n ###logprob,pcompx = gmm.eval(X)\\n logprob=gmm.score_samples(X)\\n pcompx=gmm.predict_proba(X)\\n\\n # calculate G and Z \\n C = len(gmm.weights_)\\n T,dim = X.shape\\n W = np.empty([dim,dim+1])\\n G = np.zeros([dim,dim+1,dim+1])\\n # 1. first calculate D[0,...,C) and Z\\n D = np.zeros([C,dim+1,dim+1])\\n V = np.zeros([C,dim,dim])\\n Z = np.zeros([dim,dim+1])\\n for c in range(0,C):\\n mu = gmm.means_[c]\\n sigma = np.diag(gmm.covariances_[c])\\n sigma_inv = np.linalg.inv(sigma)\\n p = pcompx[:,c]\\n\\n xi = np.empty_like(mu)\\n xi[:] = mu\\n xi = np.insert(xi,0,1)\\n xi = np.reshape(xi, [len(xi),1])\\n D[c] = xi.dot(xi.T)\\n V[c] = np.sum(p)*sigma_inv\\n for t in range(0,T):\\n xt = np.reshape(X[t],[len(X[t]),1])\\n Z += p[t]*sigma_inv.dot(xt).dot(xi.T)\\n \\n # 2. now calculate G\\n for i in range(0,dim): \\n for c in range(0,C): # tie all Gaussians\\n G[i] += V[c,i,i]*D[c]\\n try: \\n G_i_inv = np.linalg.inv(G[i])\\n except:\\n print('G is nearly singular and pseudo-inverse is used.')\\n G_i_inv = np.linalg.pinv(G[i])\\n z_i = np.reshape(Z[i],[dim+1,1])\\n W[i] = G_i_inv.dot(z_i)[:,0]\\n \\n # transform means\\n for c in range(0,C):\\n xi = np.insert(gmm.means_[c],0,1)\\n xi = np.reshape(xi,[len(xi),1])\\n gmm.means_[c] = W.dot(xi)[:,0]\\n \\n # remove non positive definite matrices\\n ###logprob,pcompx = gmm.eval(X)\\n logprob=gmm.score_samples(X)\\n pcompx=gmm.predict_proba(X)\\n psum = np.sum(pcompx, axis=0)\\n ill = (psum == 0);\\n if np.any(ill):\\n valid = (ill == 0)\\n gmm.means_ = gmm.means_[valid]\\n gmm.weights_ = gmm.weights_[valid]\\n gmm.weights_ = gmm.weights_/sum(gmm.weights_)\\n gmm.covariances_ = gmm.covariances_[valid]\\n K = gmm.means_.shape[0]\\n return gmm\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73e96a26603d715bb00dbb56cf565df3\",\n \"score\": \"0.59942466\",\n \"text\": \"def gmm(X, k, epsilon=0.0001):\\n\\n n = X.shape[0]\\n d = X.shape[1]\\n mu = k_means(X, k) #np.zeros((k, X.shape[1]))\\n sigma = np.ones((k, d))\\n pi = np.ones((k, 1)) / k\\n\\n\\n while 1:\\n\\n r = np.zeros((n, k))\\n\\n # E step\\n for i in range(n):\\n for c in range(k):\\n r[i,c] = pi[c] * gauss.pdf(X[i], mean=mu[c], cov=sigma[c]) / np.sum([pi[j] * gauss.pdf(X[i], mean=mu[j], cov=sigma[j]) for j in range(k)])\\n # M step\\n \\n new_mu = np.zeros((k, d))\\n new_sigma = np.ones((k, d))\\n new_pi = np.ones((k, 1))\\n\\n for c in range(k):\\n new_pi[c] = np.sum(r[i,c] for i in range(n)) / n\\n new_mu[c] = np.sum(r[i,c] * X[i] for i in range(n)) / (n * new_pi[c])\\n new_sigma[c] = np.sum(r[i,c] * (X[i] - new_mu[c]) ** 2 for i in range(n)) / (n * new_pi[c])\\n # print(sigma[c])\\n new_sigma[c] = np.clip(new_sigma[c], epsilon, float('inf'))\\n # print(new_sigma[c])\\n \\n\\n if la.norm(new_pi - pi) < epsilon:\\n break\\n\\n if la.norm(new_sigma - sigma) < epsilon:\\n break\\n\\n if la.norm(new_mu - mu) < epsilon:\\n break\\n\\n pi = new_pi\\n sigma = new_sigma\\n mu = new_mu\\n\\n return mu, sigma, pi\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94487e0e6c94de3c624b972d87681643\",\n \"score\": \"0.5977705\",\n \"text\": \"def grl_equilibration(fish):\\n dim = fish.shape[0]\\n eqi = np.eye(dim)\\n for i in range(dim): \\n if i%6==0:\\n eqi[i,i] = 1e4\\n return eqi.dot(fish.dot(eqi)) #creates a copy. \",\n \"title\": \"\"\n },\n {\n \"docid\": \"db963bb8c3c5dff9224c4a962bc446e5\",\n \"score\": \"0.5919506\",\n \"text\": \"def get_hessian_g(x: numpy.array) -> numpy.array:\\r\\n # 2A\\r\\n #print(\\\"Inside Hessian of G: \\\\n\\\")\\r\\n #print(\\\"x.size \\\" + str(x.size))\\r\\n\\r\\n #print(numpy.eye(x.size))\\r\\n return numpy.eye(x.size)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53f8b7ea3d6ec625e8ed9f96083dccab\",\n \"score\": \"0.5821681\",\n \"text\": \"def g(t, x):\\t\\n M = np.dot(np.dot(R.T, np.diag(P[t, :])), R)\\n return np.dot(x.T, np.dot(M, x))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"554b1e9c98d6dd061e314f851f559183\",\n \"score\": \"0.5801351\",\n \"text\": \"def hessian_posteriori_matrices(xm,m,Q,sm2,H_mat):\\n ########################################################\\n # GRADIENT AND HESSIAN OF B AND b\\n ########################################################\\n def B00_x(x):\\n if x=='e': sd=1\\n else: sd=0\\n dQ = H_mat['Q_{}'.format(x)][0,0]\\n Q0=Q[0,0]; den=Q0+sm2\\n return ((sd*Q0+sm2*dQ)*den-sm2*Q0*(dQ+sd))/den**2\\n def B00_xy(x,y):\\n if x=='e': dxe=1\\n else: dxe=0\\n if y=='e': dye=1\\n else: dye=0\\n dQx = H_mat['Q_{}'.format(x)][0,0]\\n dQy = H_mat['Q_{}'.format(y)][0,0]\\n dQxy = H_mat['Q_{}{}'.format(x,y)][0,0]\\n Q00=Q[0,0]\\n return (-2*dxe*dye*Q00**2 + (2*dQy*dxe + 2*dQx*dye)*Q00*sm2 +\\\\\\n (-2*dQx*dQy + dQxy*Q00)*sm2**2 + dQxy*sm2**3)/(Q00 + sm2)**3\\n def B01_x(x):\\n if x=='e': dxe=1\\n else: dxe=0\\n dQ = H_mat['Q_{}'.format(x)]\\n dQ00x = dQ[0,0];dQ01x = dQ[0,1]\\n Q00=Q[0,0]; Q01=Q[0,1]\\n return (dQ01x*sm2*(Q00 + sm2) + (-(dQ00x*sm2) + Q00*dxe)*Q01)/(Q00 + sm2)**2\\n def B01_xy(x,y):\\n if x=='e': dxe=1\\n else: dxe=0\\n if y=='e': dye=1\\n else: dye=0\\n Q00=Q[0,0];Q01=Q[0,1]\\n dQx = H_mat['Q_{}'.format(x)]\\n dQy = H_mat['Q_{}'.format(y)]\\n dQxy = H_mat['Q_{}{}'.format(x,y)]\\n dQ00x = dQx[0,0];dQ00y = dQy[0,0]\\n dQ01x = dQx[0,1];dQ01y = dQy[0,1]\\n dQ00xy= dQxy[0,0];dQ01xy= dQxy[0,1];dQ11xy= dQxy[1,1]\\n\\n return (Q01*((-(dQ00y*dxe) - (dQ00x + 2*dxe)*dye)*Q00 + (dQ00y*(2*dQ00x + dxe) + dQ00x*dye - dQ00xy*Q00)*sm2 -\\\\\\n dQ00xy*sm2**2) + (Q00 + sm2)*((dQ01y*dxe + dQ01x*dye)*Q00 - (dQ00y*dQ01x + dQ00x*dQ01y - dQ01xy*Q00)*sm2 +\\\\\\n dQ01xy*sm2**2))/(Q00 + sm2)**3\\n def B11_x(x):\\n if x=='e': dxe=1\\n else: dxe=0\\n Q00=Q[0,0];Q01=Q[0,1]\\n dQx = H_mat['Q_{}'.format(x)]\\n dQ00x=dQx[0,0]; dQ01x=dQx[0,1]; dQ11x=dQx[1,1]\\n return dQ11x + ((dQ00x + dxe)*Q01**2)/(Q00 + sm2)**2 - (2*dQ01x*Q01)/(Q00 + sm2)\\n def B11_xy(x,y):\\n if x=='e': dxe=1\\n else: dxe=0\\n if y=='e': dye=1\\n else: dye=0\\n Q00=Q[0,0];Q01=Q[0,1]\\n dQx = H_mat['Q_{}'.format(x)]\\n dQy = H_mat['Q_{}'.format(y)]\\n dQxy = H_mat['Q_{}{}'.format(x,y)]\\n dQ00x = dQx[0,0];dQ00y = dQy[0,0]\\n dQ01x = dQx[0,1];dQ01y = dQy[0,1]\\n dQ00xy= dQxy[0,0];dQ01xy= dQxy[0,1];dQ11xy= dQxy[1,1]\\n return dQ11xy - (2*(dQ00x + dxe)*(dQ00y + dye)*Q01**2)/(Q00 + sm2)**3 +\\\\\\n (2*dQ01y*(dQ00x + dxe)*Q01)/(Q00 + sm2)**2 +\\\\\\n (2*dQ01x*(dQ00y + dye)*Q01)/(Q00 + sm2)**2 + (dQ00xy*Q01**2)/(Q00 + sm2)**2 -\\\\\\n (2*dQ01x*dQ01y)/(Q00 + sm2) - (2*dQ01xy*Q01)/(Q00 + sm2)\\n def b0_x(x):\\n if x=='e': dxe=1\\n else: dxe=0\\n dQ00x = H_mat['Q_{}'.format(x)][0,0]\\n dm00x = H_mat['m_{}'.format(x)][0,0]\\n Q00=Q[0,0];m00=m[0,0]\\n return (Q00*(dm00x*sm2 + dxe*(m00 - xm)) + sm2*(dm00x*sm2 + dQ00x*(-m00 + xm)))/(Q00 + sm2)**2\\n def b0_xy(x,y):\\n if x=='e': dxe=1\\n else: dxe=0\\n if y=='e': dye=1\\n else: dye=0\\n dQ00x = H_mat['Q_{}'.format(x)][0,0]\\n dQ00xy = H_mat['Q_{}{}'.format(x,y)][0,0]\\n dQ00y = H_mat['Q_{}'.format(y)][0,0]\\n dm00x = H_mat['m_{}'.format(x)][0,0]\\n dm00y = H_mat['m_{}'.format(y)][0,0]\\n dm00xy = H_mat['m_{}{}'.format(x,y)][0,0]\\n Q00=Q[0,0];m00=m[0,0]\\n return -(((dQ00y + dye)*(Q00 + sm2)*(dxe*m00 + dm00x*sm2 + dQ00x*xm) -\\\\\\n (Q00 + sm2)**2*(dm00y*dxe + dm00x*dye + dm00xy*sm2 + dQ00xy*xm) +\\\\\\n (dQ00x + dxe)*(Q00 + sm2)*(dye*m00 + dm00y*sm2 + dQ00y*xm) -\\\\\\n 2*(dQ00x + dxe)*(dQ00y + dye)*(m00*sm2 + Q00*xm) +\\\\\\n dQ00xy*(Q00 + sm2)*(m00*sm2 + Q00*xm))/(Q00 + sm2)**3)\\n def b1_x(x):\\n if x=='e': dxe=1\\n else: dxe=0\\n dQx = H_mat['Q_{}'.format(x)]\\n dQ00x = dQx[0,0]\\n dQ01x = dQx[0,1]\\n dmx = H_mat['m_{}'.format(x)]\\n dm10x = dmx[1,0];dm00x = dmx[0,0]\\n Q00=Q[0,0];m00=m[0,0]\\n Q01=Q[0,1];m10=m[1,0]\\n return dm10x - (dm00x*Q01)/(Q00 + sm2) -\\\\\\n ((dQ00x + dxe)*Q01*(-m00 + xm))/(Q00 + sm2)**2 + (dQ01x*(-m00 + xm))/(Q00 + sm2)\\n def b1_xy(x,y):\\n if x=='e': dxe=1\\n else: dxe=0\\n if y=='e': dye=1\\n else: dye=0\\n Q00=Q[0,0];Q01=Q[0,1];m00=m[0,0]\\n dQx = H_mat['Q_{}'.format(x)]\\n dQy = H_mat['Q_{}'.format(y)]\\n dQxy = H_mat['Q_{}{}'.format(x,y)]\\n dQ00x = dQx[0,0];dQ00y = dQy[0,0]\\n dQ01x = dQx[0,1];dQ01y = dQy[0,1]\\n dQ00xy= dQxy[0,0];dQ01xy= dQxy[0,1];dQ11xy= dQxy[1,1]\\n dmx = H_mat['m_{}'.format(x)]\\n dmy = H_mat['m_{}'.format(y)]\\n dmxy = H_mat['m_{}{}'.format(x,y)]\\n dm10x = dmx[1,0];dm00x = dmx[0,0]\\n dm10y = dmy[1,0];dm00y = dmy[0,0]\\n dm10xy = dmxy[1,0];dm00xy = dmxy[0,0]\\n return -((-(dm00y*(dQ00x + dxe)*Q01*(Q00 + sm2)) -\\\\\\n dm00x*(dQ00y + dye)*Q01*(Q00 + sm2) + dm00y*dQ01x*(Q00 + sm2)**2 +\\\\\\n dm00x*dQ01y*(Q00 + sm2)**2 + dm00xy*Q01*(Q00 + sm2)**2 -\\\\\\n dm10xy*(Q00 + sm2)**3 - 2*(dQ00x + dxe)*(dQ00y + dye)*Q01*(-m00 + xm) +\\\\\\n dQ01y*(dQ00x + dxe)*(Q00 + sm2)*(-m00 + xm) +\\\\\\n dQ01x*(dQ00y + dye)*(Q00 + sm2)*(-m00 + xm) +\\\\\\n dQ00xy*Q01*(Q00 + sm2)*(-m00 + xm) - dQ01xy*(Q00 + sm2)**2*(-m00 + xm))/\\\\\\n (Q00 + sm2)**3)\\n ########################################################\\n # FILL THE MATRIX\\n ########################################################\\n ret = {}\\n build_vec = lambda x,y: np.array([[x],[y]])\\n build_mat= lambda x,y,z: np.array([[x,y],[y,z]])\\n for k in ['m','g','s','e']:\\n ret['b_{}'.format(k)] = build_vec(b0_x(k),b1_x(k))\\n ret['B_{}'.format(k)] = build_mat(B00_x(k),B01_x(k),B11_x(k))\\n for j in ['m','g','s','e']:\\n ret['b_{}{}'.format(k,j)] = build_vec(b0_xy(k,j),b1_xy(k,j))\\n ret['B_{}{}'.format(k,j)] =\\\\\\n build_mat(B00_xy(k,j),B01_xy(k,j),B11_xy(k,j))\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1cceb30e5b3ae0d78a0beb9ef32b0a2a\",\n \"score\": \"0.57995254\",\n \"text\": \"def apply_G(self):\\n\\n mach_f = self.vec['u']('M_f')\\n mach = self.vec['p']('M')\\n\\n mach_f[0] = mach[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a5c7fe3c13bc7629d5f225cf7f657ba\",\n \"score\": \"0.56645346\",\n \"text\": \"def ME(X, Yd, m = 4, gating = 'linear', add_bias = False):\\n # Dimensions:\\n # wexp (ns x ne x m)\\n # wgat (m x ne)\\n # yexp (N, ns, m)\\n # ygat (N x m)\\n # g = ygat = (N x m)\\n # p (N x m)\\n # h (N x m) ?\\n\\n # Add column with 1s for the bias\\n if add_bias: # add column of 1s in X matrix for bias\\n X = np.insert(X, 0, 1, axis=1)\\n if Yd.ndim == 1: # convert row vector to column vector\\n Yd = np.array([Yd]).T\\n N, ne = X.shape # number of instances and features\\n eps = np.finfo(float).eps # small number to avoid nan and division by zero\\n\\n ns = Yd.shape[1] # number of ME outputs\\n var = np.ones(m) # adaptative variance\\n wexp = np.random.rand(ns, ne, m) # expert weight\\n wgat = np.random.rand(m, ne) # linear gating weight\\n ygat = softmax(X @ wgat.T) # gating output (a priori probability)\\n yexp = calc_saida_esp(X, wexp) # expert output\\n\\n j = random.sample(list(np.arange(N)), N)\\n alpha = 1/m * np.ones((m,1)) # gaussian gating parameter\\n gamma = X[j[:m],:] # gaussian gating parameter\\n sigma = np.zeros((ne, ne, m)) # gaussian gating parameter\\n for i in range(m):\\n sigma[:,:,i] = np.eye(ne, ne) # gaussian gating parameter\\n\\n\\n p = calc_prob_exp(yexp, Yd, var) # P(y|X,theta)\\n p_xv = np.zeros((N, m)) # P(X|v)\\n lik_old = 0\\n lik_new = likelihood(ygat, p)\\n it = 0\\n maxiter = 100\\n while abs(lik_new - lik_old) > 1e-3 and it < maxiter:\\n #print('lik = ', lik_new, \\\"diff: \\\", lik_new - lik_old)\\n # Linear gating\\n if gating == 'linear':\\n # Step E - Estimate H\\n p = calc_prob_exp(yexp, Yd, var) # P(y|X,theta)\\n h = prob_h(ygat, p)\\n # Step M: optimize gating and expert outputs\\n for i in range(m):\\n wexp[:,:,i], var[i] = atualiza_exp(X, Yd, h[:, i], var[i], wexp[:,:,i], ns)\\n wgat = atualiza_gat_linear(X, h, wgat) # gating weights\\n ygat = softmax(X @ wgat.T) # gating output\\n yexp = calc_saida_esp(X, wexp) # expert output\\n\\n lik_old = lik_new\\n lik_new = likelihood(ygat, p)\\n # Gaussian gating\\n else:\\n # Step E:\\n ygat = prob(X, alpha, gamma, sigma)\\n p = calc_prob_exp(yexp, Yd, var)\\n h = alpha.T * ygat * p / np.sum(eps + alpha.T * ygat * p, axis = 1, keepdims=True)\\n # Step M: optimize gating and expert outputs\\n for i in range(m):\\n wexp[:,:,i], var[i] = atualiza_exp(X, Yd, h[:, i], var[i], wexp[:,:,i], ns)\\n alpha = np.mean(h, axis =0, keepdims = True).T\\n gamma = h.T @ X / np.sum(h + eps, axis=0, keepdims=True).T\\n for i in range(m):\\n dif = X - gamma[i]\\n sigma[:,:,i] = (dif / np.sum(h[:,i]) * h[:,i:i+1]).T @ dif\\n\\n #ygat = gating_output(X, p_xv, alpha) # gating output\\n yexp = calc_saida_esp(X, wexp) # expert output\\n\\n lik_old = lik_new\\n lik_new = likelihood(ygat, p)\\n\\n if it > 15 and gating == 'gaussian':\\n if lik_new < lik_old:\\n print(\\\"Doesn't converge.\\\")\\n elif lik_new < lik_old + 5:\\n print(\\\"Converge.\\\")\\n break\\n\\n #print('lik = ', lik_new, \\\"diff: \\\", abs(lik_new - lik_old))\\n it += 1\\n\\n return wexp, wgat, var, alpha, gamma, sigma\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be8b005853c3b8c29bfb455785dd8a69\",\n \"score\": \"0.56465757\",\n \"text\": \"def hamiltonian(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f0e0a35b50bd2f3e97f0e513370247a\",\n \"score\": \"0.56284153\",\n \"text\": \"def metric_matrix(dmat):\\n dmat = numpy.array(dmat)\\n\\n natms = len(dmat)\\n\\n dcvec = distances_from_center(dmat)\\n\\n gmat = numpy.eye(natms)\\n\\n for i, j in itertools.product(range(natms), range(natms)):\\n gmat[i, j] = (dcvec[i]**2 + dcvec[j]**2 - dmat[i, j]**2)/2.\\n\\n return gmat\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88545d3d9f21157bf68a8876c3c19bf4\",\n \"score\": \"0.56258357\",\n \"text\": \"def mvhermgauss(H, D):\\n gh_x, gh_w = hermgauss(H)\\n x = np.array(list(itertools.product(*(gh_x,) * D))) # H**DxD\\n w = np.prod(np.array(list(itertools.product(*(gh_w,) * D))), 1) # H**D\\n return x, w\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08f4d863cd671cf58fcc5d4a69fba2ae\",\n \"score\": \"0.56042755\",\n \"text\": \"def g_multivariate_normal(x,M):\\n return .5*np.dot(x,M+M.T)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e26d7a88f63aa5f497434c78e3d27a38\",\n \"score\": \"0.5598046\",\n \"text\": \"def apply_G(self):\\n\\n mach_i = self.vec['u']('M_i')\\n mach = self.vec['p']('M')\\n\\n mach_i[0] = mach[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"495799d796cd50b6cd63888bbd3d31a9\",\n \"score\": \"0.5594839\",\n \"text\": \"def calc_M_1(m_s,x_1): \\r\\n \\r\\n M_1 = np.zeros((m_s.shape[0], m_s.shape[1], m_s.shape[2]))\\r\\n \\r\\n for l in range(m_s.shape[0]):\\r\\n M_1[l] = m_s[l] @ np.diagflat(x_1[0])\\r\\n\\r\\n return(M_1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"517e0cd15d3df58cdba05955b49ea97b\",\n \"score\": \"0.5592457\",\n \"text\": \"def gfp_dynamics(F,V,Cm1,beta,dtsim):\\n G = []\\n n=0\\n for f,v in zip(F,V):\\n if n==0: cm1 = Cm1\\n g,cm1 = gfp_dynamics_1cc(f,v,cm1,beta,dtsim)\\n G.append(g)\\n n+=1\\n return G\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ab0882572fcbb0246a08fe0fec490ea\",\n \"score\": \"0.5567783\",\n \"text\": \"def gauss_dimensions_cov(dimensions=range(2,100,20), total_points=200,\\n num_experiments=100, d=10):\\n k = 2\\n if not d:\\n d = dimensions[0]\\n table = np.zeros((num_experiments*len(dimensions), 6))\\n count = 0\\n\\n for D in dimensions:\\n for l in range(num_experiments):\\n\\n # generate data\\n m1 = np.zeros(D)\\n m2 = np.concatenate((np.ones(d), np.zeros(D-d)))\\n s1 = np.eye(D)\\n # from uniform 1, 5\\n s2_1 = np.array([1.367, 3.175, 3.247, 4.403, 1.249, 1.969, 4.035, 4.237, 2.813, 3.637])\\n s2 = np.diag(np.concatenate((s2_1, np.ones(D-d))))\\n n1, n2 = np.random.multinomial(total_points, [0.5,0.5])\\n X, z = data.multivariate_normal([m1, m2], [s1, s2], [n1, n2])\\n\\n rho = lambda x, y: np.linalg.norm(x-y)\\n G = eclust.kernel_matrix(X, rho)\\n \\n # can change the number of times we execute each experiment\\n # and initialization method as well\\n table[count, 0] = D\\n table[count, 1] = run_clustering.energy_hartigan(k, X, G, z, \\n init=\\\"k-means++\\\", run_times=5)\\n table[count, 2] = run_clustering.energy_lloyd(k, X, G, z, \\n init=\\\"k-means++\\\", run_times=5)\\n table[count, 3] = run_clustering.spectral(k, X, G, z, \\n run_times=5)\\n table[count, 4] = run_clustering.kmeans(k, X, z, \\n init=\\\"k-means++\\\", run_times=5)\\n table[count, 5] = run_clustering.gmm(k, X, z, \\n init=\\\"kmeans\\\", run_times=5)\\n count += 1\\n\\n return table\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0166b5aae8cbec6fc919803d44db863e\",\n \"score\": \"0.55659634\",\n \"text\": \"def newson(m,e_g):\\r\\n mf = f(m,e_g)\\r\\n mdf = df(m,e_g)\\r\\n a = e_g - (mf/mdf)\\r\\n return a\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09be79630f3ccb94f0c345fea17749e4\",\n \"score\": \"0.55326855\",\n \"text\": \"def calc_g2():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af61f4ce204e3ce6dfa2b9e220df9e80\",\n \"score\": \"0.55179346\",\n \"text\": \"def gmm(X, k):\\n gauss = sklearn.mixture.GaussianMixture(n_components=k).fit(X)\\n pi = gauss.weights_\\n m = gauss.means_\\n S = gauss.covariances_\\n clss = gauss.predict(X)\\n bic = gauss.bic(X)\\n return pi, m, S, clss, bic\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c706dfe3e1d3b1e1680155eba5e60899\",\n \"score\": \"0.550719\",\n \"text\": \"def gowers_matrix(D):\\n assert_square(D)\\n\\n n = float(D.shape[0])\\n o = np.ones((int(n), 1))\\n I = np.identity(int(n)) - (1/n)*o.dot(o.T)\\n A = -0.5*(np.square(D))\\n G = I.dot(A).dot(I)\\n\\n return(G)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"929c187924254c36095672f32dfe4846\",\n \"score\": \"0.5504482\",\n \"text\": \"def gmm(X, k):\\n\\n Gaussian = sklearn.mixture.GaussianMixture(n_components=k)\\n params = Gaussian.fit(X)\\n clss = Gaussian.predict(X)\\n pi = params.weights_\\n m = params.means_\\n S = params.covariances_\\n bic = Gaussian.bic(X)\\n\\n return pi, m, S, clss, bic\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eea487ca95e5fd139c2ec23e7f1e2ef6\",\n \"score\": \"0.54867804\",\n \"text\": \"def gmm_map_qb(X, gmm, rho=.1, epsilon=1, niter=10):\\n\\n # init\\n ###logprob,pcompx = gmm.eval(X)\\n logprob=gmm.score_samples(X)\\n pcompx=gmm.predict_proba(X)\\n psum = np.sum(pcompx, axis=0) #(18)\\n # remove illed gaussians\\n ill_g = (psum == 0);\\n if any(ill_g):\\n valid = psum > 0\\n gmm.means_ = gmm.means_[valid,:]\\n gmm.weights_ = gmm.weights_[valid]\\n gmm.weights_ = gmm.weights_/sum(gmm.weights_)\\n gmm.covariances_ = gmm.covariances_[valid]\\n\\n ###logprob,pcompx = gmm.eval(X)\\n logprob=gmm.score_samples(X)\\n pcompx=gmm.predict_proba(X)\\n\\n psum = np.sum(pcompx, axis=0) #(18)\\n K,nDim = gmm.means_.shape\\n tau = psum*epsilon #(22)\\n tau_update = tau\\n nu = 1 + tau #(23)\\n nu_update = nu\\n alpha = nDim + tau #(24)\\n alpha_update = alpha\\n mu = np.empty([K,nDim])\\n mu_update = np.empty([K,nDim])\\n yu = np.empty([K,nDim,nDim])\\n yu_update = np.empty([K,nDim,nDim])\\n for k in range(0,K):\\n mu[k] = gmm.means_[k] #(25)\\n yu[k] = tau[k] * gmm.covariances_[k] #(26)\\n\\n # EM iterations \\n s = np.empty([K,nDim,nDim])\\n N = X.shape[0]\\n for iter in range(0, niter):\\n print('iter=',iter)\\n print(np.sum(gmm.weights_))\\n #plot_gmm(gmm,X)\\n # E-step: posterior probabilities\\n ###logprob, pcompx = gmm.eval(X)\\n logprob=gmm.score_samples(X)\\n pcompx=gmm.predict_proba(X)\\n \\n # remove illed gaussians\\n psum = np.sum(pcompx, axis=0) # (18)\\n #print psum\\n #raw_input()\\n ill_g = (psum == 0);\\n #print ill_g\\n if any(ill_g):\\n valid = psum > 0\\n gmm.means_ = gmm.means_[valid,:]\\n gmm.weights_ = gmm.weights_[valid]\\n gmm.weights_ = gmm.weights_/sum(gmm.weights_)\\n gmm.covariances_ = gmm.covariances_[valid]\\n mu = mu[valid]\\n mu_update = mu_update[valid]\\n yu = yu[valid]\\n yu_update = yu_update[valid]\\n tau = tau[valid]\\n tau_update = tau_update[valid]\\n alpha = alpha[valid]\\n alpha_update = alpha_update[valid]\\n nu = nu[valid]\\n nu_update = nu_update[valid]\\n K = gmm.means_.shape[0]\\n continue\\n\\n # M-step, eqs. from KimLoizou'10\\n # Hyper-parameters\\n psum = np.sum(pcompx, axis=0) # (18)\\n #print np.sum(pcompx,axis=1)\\n #print 'psum',psum\\n #print gmm.weights_\\n #raw_input()\\n x_expected = np.dot(pcompx.T, X)/np.tile(psum[np.newaxis].T,(1,nDim)) #(19)\\n #print 'x_expected',x_expected\\n for k in range(0,K):\\n #raw_input()\\n # (20)\\n s[k] = np.dot((X-np.tile(x_expected[k],(N,1))).T, (X-np.tile(x_expected[k],(N,1)))*np.tile(pcompx[:,k][np.newaxis].T,(1,nDim)))\\n # (15)\\n #print 'yu[k]',yu[k] \\n #print 'x_expected - mu', (x_expected-mu).T \\n #yu_update[k] = rho*yu[k] + s[k] + rho*tau[k]*psum[k]/(rho*tau[k]+psum[k])*np.dot((x_expected-mu).T, x_expected-mu) \\n yu[k] = rho*yu[k] + s[k] + rho*tau[k]*psum[k]/(rho*tau[k]+psum[k])*np.dot((x_expected[k,:]-mu[k,:]).T, x_expected[k,:]-mu[k,:]) \\n #print 'yu[k],after',yu[k] \\n \\n # (21)\\n beta = psum/(rho*tau+psum)\\n # mu, eq (14)\\n #print beta.shape\\n #print mu.shape\\n #print x_expected.shape\\n #mu_update = np.tile(beta[np.newaxis].T,(1,nDim))*x_expected + np.tile((1-beta)[np.newaxis].T,(1,nDim))*mu\\n mu = np.tile(beta[np.newaxis].T,(1,nDim))*x_expected + np.tile((1-beta)[np.newaxis].T,(1,nDim))*mu\\n # tau, eq (11)\\n #tau_update = rho*tau + psum\\n tau = rho*tau + psum\\n # alpha, eq (12)\\n #alpha_update = rho*(alpha-nDim) + nDim + psum \\n alpha = rho*(alpha-nDim) + nDim + psum \\n #print 'alpha',alpha\\n # nu, eq (13)\\n #nu_update = rho*(nu-1) + 1 + psum\\n nu = rho*(nu-1) + 1 + psum\\n \\n # GMM parameters\\n # weight, (27)\\n #gmm.weights_ = (nu_update-1)/np.sum(nu_update-1)\\n gmm.weights_ = (nu-1)/np.sum(nu-1)\\n ill = (gmm.weights_ == 0)\\n print(np.sum(gmm.weights_))\\n # mean, (28)\\n #gmm.means_ = mu_update \\n gmm.means_ = mu \\n # sigma, (29)\\n for k in range(0,K):\\n #if alpha_update[k] != nDim:\\n if alpha[k] != nDim:\\n #gmm.covars_[k] = yu_update[k]/(alpha_update[k]-nDim)\\n gmm.covariances_[k] = yu[k]/(alpha[k]-nDim)\\n else:\\n #gmm.covars_[k] = yu_update[k]/tau_update[k]\\n gmm.covariances_[k] = yu[k]/tau[k]\\n try:\\n np.linalg.cholesky(gmm.covariances_[k])\\n except:\\n ill[k] = 1\\n print('cov_%d not positive definite' % k)\\n \\n # remove non positive definite matrices\\n if np.any(ill):\\n valid = (ill == 0)\\n gmm.means_ = gmm.means_[valid]\\n gmm.weights_ = gmm.weights_[valid]\\n gmm.weights_ = gmm.weights_/sum(gmm.weights_)\\n gmm.covariances_ = gmm.covariances_[valid]\\n mu = mu[valid]\\n mu_update = mu_update[valid]\\n yu = yu[valid]\\n yu_update = yu_update[valid]\\n tau = tau[valid]\\n tau_update = tau_update[valid]\\n alpha = alpha[valid]\\n alpha_update = alpha_update[valid]\\n nu = nu[valid]\\n nu_update = nu_update[valid]\\n K = gmm.means_.shape[0]\\n return gmm\",\n \"title\": \"\"\n },\n {\n \"docid\": \"735d0d019bd2cc33e9df61617c1f321f\",\n \"score\": \"0.546018\",\n \"text\": \"def apply_G(self):\\n\\n tmin = self.vec['u']('Tmin')\\n tau = self.vec['p']('tau')\\n\\n fmax = numpy.max(self.min - tau)\\n tmin[0] = fmax + 1/self.rho * \\\\\\n numpy.log(numpy.sum(numpy.exp(self.rho*(self.min - tau - fmax))))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eba553876d226a66113689776e46a30f\",\n \"score\": \"0.54464203\",\n \"text\": \"def c_m(self):\\n c_m = (1.5 * np.matmul(\\n np.swapaxes(from_6x6_to_21x1(self.d_sq), -1, -2),\\n from_6x6_to_21x1(E_shear)) / np.matmul(\\n np.swapaxes(from_6x6_to_21x1(self.d_sq), -1, -2),\\n from_6x6_to_21x1(E_iso)))[..., 0, 0]\\n return c_m\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c304d79bfef84afa830ef8277572c50\",\n \"score\": \"0.54338884\",\n \"text\": \"def diag_ggn_mc(self, mc_samples: int) -> List[Tensor]:\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3a3e77730d932ce9e0ae5b7cc720a8f\",\n \"score\": \"0.542774\",\n \"text\": \"def hessian_log_likelihood(xm,m,Q,sm2,H_mat):\\n def G(x):\\n if x=='e': dxe=1\\n else: dxe=0\\n dQ00x = H_mat['Q_{}'.format(x)][0,0]\\n dm00x = H_mat['m_{}'.format(x)][0,0]\\n Q00=Q[0,0];m00=m[0,0]\\n return (-((dQ00x + dxe)*(Q00 + sm2)) + 2*dm00x*(Q00 + sm2)*(-m00 + xm)\\\\\\n + (dQ00x + dxe)*(-m00 + xm)**2)/(2.*(Q00 + sm2)**2)\\n def H(x,y):\\n if x=='e': dxe=1\\n else: dxe=0\\n if y=='e': dye=1\\n else: dye=0\\n dQ00x = H_mat['Q_{}'.format(x)][0,0]\\n dQ00xy = H_mat['Q_{}{}'.format(x,y)][0,0]\\n dQ00y = H_mat['Q_{}'.format(y)][0,0]\\n dm00x = H_mat['m_{}'.format(x)][0,0]\\n dm00y = H_mat['m_{}'.format(y)][0,0]\\n dm00xy = H_mat['m_{}{}'.format(x,y)][0,0]\\n Q00=Q[0,0];m00=m[0,0]\\n return ((dQ00x + dxe)*(dQ00y + dye)*(Q00 + sm2) - 2*dm00x*dm00y*(Q00 + sm2)**2 -\\\\\\n dQ00xy*(Q00 + sm2)**2 - 2*dm00y*(dQ00x + dxe)*(Q00 + sm2)*(-m00 + xm) -\\\\\\n 2*dm00x*(dQ00y + dye)*(Q00 + sm2)*(-m00 + xm) + 2*dm00xy*(Q00 + sm2)**2*(-m00 + xm) -\\\\\\n 2*(dQ00x + dxe)*(dQ00y + dye)*(-m00 + xm)**2 + dQ00xy*(Q00 + sm2)*(-m00 + xm)**2)/\\\\\\n (2.*(Q00 + sm2)**3)\\n grad = np.array([G(x) for x in ['m','g','s','e']])[:,None]\\n hess = np.array([H(x,y) for y in ['m','g','s','e'] for x in\\\\\\n ['m','g','s','e']]).reshape(4,4)\\n return grad, hess\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9facf115a2c7ba815a36137cfe4175d0\",\n \"score\": \"0.5423068\",\n \"text\": \"def create_bigG(G, A, M):\\n GA = np.dot(G, A)\\n n_c , n_t = M.shape\\n _ , n_s = G.shape\\n m_p = A.shape[1]//n_s\\n row = []\\n col = []\\n data = []\\n for i in range(n_t):\\n for j in range(n_c):\\n row.append([j+n_c*i]*n_s*m_p)\\n col.append([n_s*i+k for k in range(n_s*m_p)])\\n data.append(GA[j, :])\\n data = np.array(data).reshape(-1)\\n row = np.array(row).reshape(-1)\\n col = np.array(col).reshape(-1)\\n return coo_matrix((data, (row, col)), shape=(n_t*n_c, n_s*(n_t + m_p - 1)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"891744071be3c09ef6f85a5cefa4e603\",\n \"score\": \"0.5418729\",\n \"text\": \"def __init__(self, Vh, gamma, delta, locations, m_true, Theta = None, pen = 1e1, order=2, rel_tol=1e-12, max_iter=1000):\\n assert delta != 0. or pen != 0, \\\"Intrinsic Gaussian Prior are not supported\\\"\\n self.Vh = Vh\\n \\n trial = dl.TrialFunction(Vh)\\n test = dl.TestFunction(Vh)\\n \\n if Theta == None:\\n varfL = dl.inner(dl.nabla_grad(trial), dl.nabla_grad(test))*dl.dx\\n else:\\n varfL = dl.inner(Theta*dl.grad(trial), dl.grad(test))*dl.dx\\n varfM = dl.inner(trial,test)*dl.dx\\n \\n self.M = dl.assemble(varfM)\\n if dlversion() <= (1,6,0):\\n self.Msolver = dl.PETScKrylovSolver(\\\"cg\\\", \\\"jacobi\\\")\\n else:\\n self.Msolver = dl.PETScKrylovSolver(self.Vh.mesh().mpi_comm(), \\\"cg\\\", \\\"jacobi\\\")\\n self.Msolver.set_operator(self.M)\\n self.Msolver.parameters[\\\"maximum_iterations\\\"] = max_iter\\n self.Msolver.parameters[\\\"relative_tolerance\\\"] = rel_tol\\n self.Msolver.parameters[\\\"error_on_nonconvergence\\\"] = True\\n self.Msolver.parameters[\\\"nonzero_initial_guess\\\"] = False\\n \\n #mfun = Mollifier(gamma/delta, dl.inv(Theta), order, locations)\\n mfun = dl.Expression(code_Mollifier, degree = Vh.ufl_element().degree()+2)\\n mfun.l = gamma/delta\\n mfun.o = order\\n mfun.theta0 = 1./Theta.theta0\\n mfun.theta1 = 1./Theta.theta1\\n mfun.alpha = Theta.alpha\\n for ii in range(locations.shape[0]):\\n mfun.addLocation(locations[ii,0], locations[ii,1])\\n \\n varfmo = mfun*dl.inner(trial,test)*dl.dx\\n MO = dl.assemble(pen*varfmo)\\n \\n self.A = dl.assemble(gamma*varfL+delta*varfM + pen*varfmo)\\n \\n if dlversion() <= (1,6,0):\\n self.Asolver = dl.PETScKrylovSolver(\\\"cg\\\", amg_method())\\n else:\\n self.Asolver = dl.PETScKrylovSolver(self.Vh.mesh().mpi_comm(), \\\"cg\\\", amg_method())\\n self.Asolver.set_operator(self.A)\\n self.Asolver.parameters[\\\"maximum_iterations\\\"] = max_iter\\n self.Asolver.parameters[\\\"relative_tolerance\\\"] = rel_tol\\n self.Asolver.parameters[\\\"error_on_nonconvergence\\\"] = True\\n self.Asolver.parameters[\\\"nonzero_initial_guess\\\"] = False\\n \\n old_qr = dl.parameters[\\\"form_compiler\\\"][\\\"quadrature_degree\\\"]\\n dl.parameters[\\\"form_compiler\\\"][\\\"quadrature_degree\\\"] = -1\\n qdegree = 2*Vh._ufl_element.degree()\\n metadata = {\\\"quadrature_degree\\\" : qdegree}\\n \\n if dlversion() >= (2017,1,0):\\n representation_old = dl.parameters[\\\"form_compiler\\\"][\\\"representation\\\"]\\n dl.parameters[\\\"form_compiler\\\"][\\\"representation\\\"] = \\\"quadrature\\\"\\n \\n if dlversion() <= (1,6,0):\\n Qh = dl.FunctionSpace(Vh.mesh(), 'Quadrature', qdegree)\\n else:\\n element = dl.FiniteElement(\\\"Quadrature\\\", Vh.mesh().ufl_cell(), qdegree, quad_scheme=\\\"default\\\")\\n Qh = dl.FunctionSpace(Vh.mesh(), element)\\n \\n ph = dl.TrialFunction(Qh)\\n qh = dl.TestFunction(Qh)\\n Mqh = dl.assemble(ph*qh*dl.dx(metadata=metadata))\\n ones = dl.interpolate(dl.Constant(1.), Qh).vector()\\n dMqh = Mqh*ones\\n Mqh.zero()\\n dMqh.set_local( ones.get_local() / np.sqrt(dMqh.get_local() ) )\\n Mqh.set_diagonal(dMqh)\\n MixedM = dl.assemble(ph*test*dl.dx(metadata=metadata))\\n self.sqrtM = MatMatMult(MixedM, Mqh)\\n \\n dl.parameters[\\\"form_compiler\\\"][\\\"quadrature_degree\\\"] = old_qr\\n \\n if dlversion() >= (2017,1,0):\\n dl.parameters[\\\"form_compiler\\\"][\\\"representation\\\"] = representation_old\\n \\n self.R = _BilaplacianR(self.A, self.Msolver) \\n self.Rsolver = _BilaplacianRsolver(self.Asolver, self.M)\\n \\n rhs = dl.Vector()\\n self.mean = dl.Vector()\\n self.init_vector(rhs, 0)\\n self.init_vector(self.mean, 0)\\n \\n MO.mult(m_true, rhs)\\n self.Asolver.solve(self.mean, rhs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2447e339ec4250b8692a4b9a6c0dd0e2\",\n \"score\": \"0.54172355\",\n \"text\": \"def test_newtons_method():\\n\\n\\tH = zeros((2,2))\\n\\tg = zeros((2,2)) # first column, gradient of first direct. derv., second second direct. derv, should be the same\\n\\n\\tfor n in range(2):\\n\\t\\t# directional derivatives to evaluate columns of the hessian\\n\\t\\tq1 = array([[13.]])\\n\\t\\tq2 = array([[17.]])\\n\\t\\tze = array([[0.]])\\n\\n\\t\\tq1dot = array([[1.*(n==0)]])\\n\\t\\tq2dot = array([[1.*(n==1)]])\\n\\n\\t\\tzedot = array([[0.]])\\n\\n\\t\\tcg = CGraph()\\n\\t\\tFq1 = Function(Mtc(q1, q1dot))\\n\\t\\tFq2 = Function(Mtc(q2, q2dot))\\n\\t\\tFze = Function(Mtc(ze, zedot))\\n\\t\\tFC = Function([[Fq1*Fq1,Fze],[Fze,Fq2*Fq2]])\\n\\t\\tFPhi = FC.trace()\\n\\n\\t\\tPhibar = array([[1.]])\\n\\t\\tPhibardot = array([[0.]])\\n\\t\\t\\n\\t\\tcg.independentFunctionList = [Fq1,Fq2]\\n\\t\\tcg.dependentFunctionList = [FPhi]\\n\\t\\tcg.reverse([Mtc(Phibar, Phibardot)])\\n\\t\\t\\n\\t\\t\\n\\t\\tg[0,n] = Fq1.xbar.X[0,0]\\n\\t\\tg[1,n] = Fq2.xbar.X[0,0]\\n\\t\\tH[0,n] = Fq1.xbar.Xdot[0,0]\\n\\t\\tH[1,n] = Fq2.xbar.Xdot[0,0]\\n\\n\\n\\n\\t# compute new search direction\\n\\tdelta_q = numpy.linalg.solve(H,-g[:,0])\\n\\tq_plus = [13.,17.] + delta_q\\n\\tassert numpy.prod(q_plus == [0.,0.])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98e52c056e4b86de92fbff0b7b08ca45\",\n \"score\": \"0.53999287\",\n \"text\": \"def g_func( self, lmnAB, lmnCD, rstAB, rstCD, ijk, ang_A, ang_B, PA, PB, gammaAB, ang_C, ang_D, QC, QD, gammaCD, PQ):\\n delta = 1/(4.0*gammaAB) + 1/(4.0*gammaCD)\\n \\n return ( ((-1) ** lmnAB)\\n * self.theta_func(lmnAB,ang_A,ang_B,PA,PB,rstAB,gammaAB)\\n * self.theta_func(lmnCD,ang_C,ang_D,QC,QD,rstCD,gammaCD)\\n * ((-1) ** ijk)\\n * ((2*delta) ** (2*(rstAB + rstCD)))\\n * math.factorial(lmnAB + lmnCD - 2*rstAB - 2*rstCD)\\n * (delta ** ijk)\\n * (PQ ** (lmnAB + lmnCD - 2*(rstAB + rstCD + ijk)))\\n / ((4*delta) ** (lmnAB + lmnCD))\\n / math.factorial(ijk)\\n / math.factorial(lmnAB + lmnCD - 2*(rstAB + rstCD + ijk)) )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48ad6e5314a8360a90b0ed8e00b2716a\",\n \"score\": \"0.53907347\",\n \"text\": \"def efficient_full_exact_ACDM(self, X, n_iter):\\n calc_KL_time = 0\\n e = np.e \\n B_size = len(self.B_)\\n Z = len(self.S_)\\n total_step = n_iter*B_size\\n # Initialization of Theta\\n theta_prime_tilde = dict()\\n theta_prime_hat = dict()\\n for phi in self.B_:\\n theta_prime_tilde[phi] = 0.0\\n theta_prime_hat[phi] = 0.0\\n\\n # Initialization of U\\n U_hat = dict() \\n U_prime_hat = dict() \\n U_prime_tilde = dict()\\n for x in self.S_:\\n U_hat[x] = 0\\n U_prime_hat[x] = 0.0\\n U_prime_tilde[x] = 0.0\\n # Initialization of Coefficients\\n alpha = [0] * (total_step+1)\\n gamma = [0] * (total_step+2)\\n c_hat = [0] * (total_step+2)\\n gamma[-1] =1/(2.0) \\n c_hat[-1] = 3.0 + 2*math.sqrt(2.000) \\n for k in range(total_step+1):\\n gamma_k_before = gamma[k-1]\\n gamma_k = math.sqrt(1.0/(4.0)+gamma_k_before**2)+1.0/(2.0)\\n gamma[k] = gamma_k\\n alpha[k] =1/(gamma_k)\\n c_hat[k] = (1-alpha[k]) * c_hat[k-1]\\n # Caching \\n e_U_hat_dict = dict() \\n for x in self.S_:\\n e_U_hat_dict[x] = 1.0\\n # Beginning of Iteration\\n start = time.time()\\n for iter_ in range(n_iter):\\n for index in range(B_size):\\n k = iter_*B_size + index\\n phi = random.choice(self.B_)\\n if self.etahat_[phi] >= 1.0 or self.etahat_[phi]== 0.0:\\n continue\\n # Caching\\n c_hat_k = c_hat[k]\\n c_hat_k_after = c_hat[k+1]\\n alpha_k_after = alpha[k+1]\\n gamma_k = gamma[k]\\n\\n # calculate G, Z\\n G = 0 \\n for x in self.Ssub_[phi]:\\n G += e_U_hat_dict[x]\\n eta_ = G/Z\\n # update tilde_theta_prime, hat_theta_prime\\n nabla = 4.0*(self.etahat_[phi] - eta_)\\n nabla_exact = np.log((1.0 -eta_)* self.etahat_[phi]/(eta_*(1-self.etahat_[phi])))\\n\\n delta_tilde = nabla_exact/len(X)\\n delta_hat = nabla_exact/len(X)\\n step1 = gamma_k/B_size * delta_tilde\\n step2 = 1.0/c_hat_k_after*((alpha_k_after-(1-c_hat_k_after))*gamma_k/B_size*delta_tilde+(1-alpha_k_after)*delta_hat)\\n\\n\\n theta_prime_tilde[phi] += step1\\n theta_prime_hat[phi] += step2 \\n for x in self.Ssub_[phi]:\\n U_prime_tilde[x] += step1\\n U_prime_hat[x] += step2\\n U_hat_temp = (1-c_hat_k_after)*U_prime_tilde[x]+c_hat_k_after*U_prime_hat[x]\\n e_U_hat_temp = e ** U_hat_temp\\n Z += e_U_hat_temp - e_U_hat_dict[x]\\n e_U_hat_dict[x] = e_U_hat_temp\\n\\n update_start_time = time.time()\\n for x in self.B_:\\n self.theta_[x] = (1-c_hat_k_after)*theta_prime_tilde[x]+c_hat_k_after*theta_prime_hat[x]\\n self.theta_[phi] += 4*(self.etahat_[phi]-eta_)\\n self.compute_theta_perp()\\n self.compute_P()\\n kl=self.compute_KL()\\n calc_KL_time += time.time() - update_start_time \\n print(iter_ ,\\\":\\\", \\\"KL divergence: \\\", f'{kl:.16f}' ,\\\" time : %4.2f\\\"% (time.time()-calc_KL_time-start),flush=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b71317599315baa15abd0d8007dc05c\",\n \"score\": \"0.5383931\",\n \"text\": \"def __init__(self, Vh, gamma, delta, Theta = None, mean=None, rel_tol=1e-12, max_iter=1000):\\n assert delta != 0., \\\"Intrinsic Gaussian Prior are not supported\\\"\\n self.Vh = Vh\\n\\n trial = dl.TrialFunction(Vh)\\n test = dl.TestFunction(Vh)\\n \\n if Theta == None:\\n varfL = dl.inner(dl.nabla_grad(trial), dl.nabla_grad(test))*dl.dx\\n else:\\n varfL = dl.inner( Theta*dl.grad(trial), dl.grad(test))*dl.dx\\n \\n varfM = dl.inner(trial,test)*dl.dx\\n \\n self.M = dl.assemble(varfM)\\n if dlversion() <= (1,6,0):\\n self.Msolver = dl.PETScKrylovSolver(\\\"cg\\\", \\\"jacobi\\\")\\n else:\\n self.Msolver = dl.PETScKrylovSolver(self.Vh.mesh().mpi_comm(), \\\"cg\\\", \\\"jacobi\\\")\\n self.Msolver.set_operator(self.M)\\n self.Msolver.parameters[\\\"maximum_iterations\\\"] = max_iter\\n self.Msolver.parameters[\\\"relative_tolerance\\\"] = rel_tol\\n self.Msolver.parameters[\\\"error_on_nonconvergence\\\"] = True\\n self.Msolver.parameters[\\\"nonzero_initial_guess\\\"] = False\\n \\n self.A = dl.assemble(gamma*varfL + delta*varfM) \\n if dlversion() <= (1,6,0):\\n self.Asolver = dl.PETScKrylovSolver(\\\"cg\\\", amg_method())\\n else:\\n self.Asolver = dl.PETScKrylovSolver(self.Vh.mesh().mpi_comm(), \\\"cg\\\", amg_method())\\n self.Asolver.set_operator(self.A)\\n self.Asolver.parameters[\\\"maximum_iterations\\\"] = max_iter\\n self.Asolver.parameters[\\\"relative_tolerance\\\"] = rel_tol\\n self.Asolver.parameters[\\\"error_on_nonconvergence\\\"] = True\\n self.Asolver.parameters[\\\"nonzero_initial_guess\\\"] = False\\n \\n old_qr = dl.parameters[\\\"form_compiler\\\"][\\\"quadrature_degree\\\"]\\n dl.parameters[\\\"form_compiler\\\"][\\\"quadrature_degree\\\"] = -1\\n qdegree = 2*Vh._ufl_element.degree()\\n metadata = {\\\"quadrature_degree\\\" : qdegree}\\n\\n if dlversion() >= (2017,1,0):\\n representation_old = dl.parameters[\\\"form_compiler\\\"][\\\"representation\\\"]\\n dl.parameters[\\\"form_compiler\\\"][\\\"representation\\\"] = \\\"quadrature\\\"\\n \\n if dlversion() <= (1,6,0):\\n Qh = dl.FunctionSpace(Vh.mesh(), 'Quadrature', qdegree)\\n else:\\n element = dl.FiniteElement(\\\"Quadrature\\\", Vh.mesh().ufl_cell(), qdegree, quad_scheme=\\\"default\\\")\\n Qh = dl.FunctionSpace(Vh.mesh(), element)\\n \\n ph = dl.TrialFunction(Qh)\\n qh = dl.TestFunction(Qh)\\n Mqh = dl.assemble(ph*qh*dl.dx(metadata=metadata))\\n ones = dl.interpolate(dl.Constant(1.), Qh).vector()\\n dMqh = Mqh*ones\\n Mqh.zero()\\n dMqh.set_local( ones.get_local() / np.sqrt(dMqh.get_local() ) )\\n Mqh.set_diagonal(dMqh)\\n MixedM = dl.assemble(ph*test*dl.dx(metadata=metadata))\\n self.sqrtM = MatMatMult(MixedM, Mqh)\\n\\n dl.parameters[\\\"form_compiler\\\"][\\\"quadrature_degree\\\"] = old_qr\\n \\n if dlversion() >= (2017,1,0):\\n dl.parameters[\\\"form_compiler\\\"][\\\"representation\\\"] = representation_old\\n \\n self.R = _BilaplacianR(self.A, self.Msolver) \\n self.Rsolver = _BilaplacianRsolver(self.Asolver, self.M)\\n \\n self.mean = mean\\n \\n if self.mean is None:\\n self.mean = dl.Vector()\\n self.init_vector(self.mean, 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3365b18e9f481a59257890844aae335\",\n \"score\": \"0.53832597\",\n \"text\": \"def _g(self, w):\\n p_w = self._p_w(w)\\n return (self.p_data_x[None, :, None] * p_w[None, :, :] * self.feature\\n ).sum(axis=(1, 2)) - self.E_feature\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ab7fc8bf2f63fe190d89a5e3852222d\",\n \"score\": \"0.5356697\",\n \"text\": \"def optimise_gaussian(self, filename = \\\"test\\\"):\\n\\n # first do minimisation with MMFF94 and dihedrals contrainted\\n # this will hopefully fix clashes\\n mp = AllChem.MMFFGetMoleculeProperties(self.molecule, mmffVariant=\\\"MMFF94\\\")\\n ff = AllChem.MMFFGetMoleculeForceField(self.molecule, mp)\\n\\n current_angles = self.get_dihedrals()\\n N = 0\\n for angle in current_angles:\\n ff.MMFFAddTorsionConstraint(angle[0], angle[1], angle[2], angle[3], False, angle[4], angle[4], 500000.0)\\n N += 1\\n\\n ff.Minimize(maxIts=500,forceTol=0.0001,energyTol=1e-06)\\n\\n # do gaussian opt\\n self.molecule.SetProp(\\\"_Name\\\", filename)\\n Gmethod = \\\"pm6\\\"\\n output = qmgaussian.gaussian([self.molecule], method=\\\"opt \\\" + Gmethod, charge=0, mult=1, charged_fragments=True, cpus=1, keepFiles=True)\\n\\n if output[0] is not None:\\n atoms = output[0]['atomicPos']\\n\\n energykey = 'g16:(' + Gmethod + ')_energy'\\n self.energy = float(output[0][energykey])\\n self.energy = self.energy * 627.15 # converts from hartree to kcal/mol\\n conf = self.molecule.GetConformer()\\n N = 0\\n # This assumes that the atom ordering is the same!!!!\\n for xyz in atoms: \\n conf.SetAtomPosition(N, xyz)\\n N += 1\\n elif output[0] is None:\\n # gaussian failed, set energy to a high number, this is kind of a hack\\n self.energy = 9999.0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13132ac0f9ad0d8857ca54eed5036ac8\",\n \"score\": \"0.5354696\",\n \"text\": \"def hessian_1cc(W,mlam,gamma,sl2,sm2,dt,s,S,grad_matS,rescale,sd2):\\n ##### likelihood and gradient at initial conditions\\n gll,hll = hessian_log_likelihood(W[0,0],s,S,sm2,grad_matS)\\n #### Initialize parameters for recurrence\\n F, A, a = parameters(gamma,dt,mlam,sl2)\\n grad_param = hessian_parameters(gamma,dt,mlam,sl2)\\n ##### P(z_0|x_0^m)\\n b,B = posteriori_matrices(W[0,0],s,S,sm2)\\n grad_mat_b = hessian_posteriori_matrices(W[0,0],s,S,sm2,grad_matS)\\n for j in range(1,W.shape[1]):\\n ###### P(z_{t+dt}|D_t) = N(m,Q))\\n m,Q = new_mean_cov(b,B,F,A,a)\\n grad_mat_Q = hessian_new_mean_cov(b,B,F,A,a,grad_param,grad_mat_b)\\n ##### P(z_{t+dt}|D_{t+dt}) = N(b',B')\\n b,B = posteriori_matrices(W[0,j],m,Q,sm2)\\n grad_mat_b = hessian_posteriori_matrices(W[0,j],m,Q,sm2,grad_mat_Q)\\n ##### Likelihood\\n G,H = hessian_log_likelihood(W[0,j],m,Q,sm2,grad_mat_Q)\\n gll+=G; hll=+H\\n # Predict for daughter cell\\n m,Q = new_mean_cov(b,B,F,A,a)\\n grad_mat_Q = hessian_new_mean_cov(b,B,F,A,a,grad_param,grad_mat_b)\\n # Find next cell initial conditions (9% asym div)\\n s, S, grad_matS = hess_cell_division_likelihood(m,Q,grad_mat_Q,sd2,rescale)\\n return hll, gll, s, S, grad_matS\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7469fac99123f049f1b2e136e5eedca0\",\n \"score\": \"0.53455836\",\n \"text\": \"def rand_mmd_g(dist_all, batch_size, omega=0.5, max_iter=0, name='mmd', do_summary=False, scope_prefix=''):\\r\\n with tf.name_scope(name):\\r\\n m = tf.constant(batch_size, tf.float32)\\r\\n\\r\\n def kernel(b):\\r\\n return tf.exp(-dist_all * b)\\r\\n\\r\\n def f(b):\\r\\n k = kernel(b)\\r\\n e_k = matrix_mean_wo_diagonal(k, 2 * m)\\r\\n return e_k - omega, k\\r\\n\\r\\n def df(k):\\r\\n kd = -k * dist_all # gradient of exp(-d*w)\\r\\n e_kd = matrix_mean_wo_diagonal(kd, 2 * m)\\r\\n return e_kd\\r\\n\\r\\n # initialize sigma as the geometric mean of all pairwise distances\\r\\n dist_mean = matrix_mean_wo_diagonal(dist_all, 2 * m)\\r\\n beta = -tf.log(omega) / (dist_mean + FLAGS.EPSI) # beta = 1/2/sigma\\r\\n # if max_iter is larger than one, do newton's update\\r\\n if max_iter > 0:\\r\\n beta, _ = tf.while_loop(\\r\\n cond=lambda _1, i: i < max_iter,\\r\\n body=lambda b, i: newton_root(b, f, df, step=i),\\r\\n loop_vars=(beta, tf.constant(0, dtype=tf.int32)))\\r\\n\\r\\n k_all = kernel(beta)\\r\\n k_xx = k_all[0:batch_size, 0:batch_size]\\r\\n k_xy_0 = k_all[0:batch_size, batch_size:]\\r\\n k_xy_1 = k_all[batch_size:, 0:batch_size]\\r\\n k_yy = k_all[batch_size:, batch_size:]\\r\\n\\r\\n e_kxx = matrix_mean_wo_diagonal(k_xx, m)\\r\\n e_kxy_0 = matrix_mean_wo_diagonal(k_xy_0, m)\\r\\n e_kxy_1 = matrix_mean_wo_diagonal(k_xy_1, m)\\r\\n e_kyy = matrix_mean_wo_diagonal(k_yy, m)\\r\\n\\r\\n if do_summary:\\r\\n with tf.name_scope(None): # return to root scope to avoid scope overlap \\r\\n tf.summary.scalar(scope_prefix + name + '/kxx', e_kxx)\\r\\n tf.summary.scalar(scope_prefix + name + '/kyy', e_kyy)\\r\\n tf.summary.scalar(scope_prefix + name + '/kxy_0', e_kxy_0)\\r\\n tf.summary.scalar(scope_prefix + name + '/kxy_1', e_kxy_1)\\r\\n # tf.summary.scalar(scope_prefix + name + 'omega', omega)\\r\\n\\r\\n return e_kxx + e_kyy - e_kxy_0 - e_kxy_1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4a475014918f6668221cd055be938a4\",\n \"score\": \"0.5344345\",\n \"text\": \"def _compute_gauss_pdf_gradients(x, mu, sigma):\\n sigma_matrix = torch.diag(sigma)\\n distr = torch.distributions.MultivariateNormal(mu, sigma_matrix)\\n return distr.log_prob(x).exp()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fca994135a04f0b3e09ec041daf5d4d5\",\n \"score\": \"0.53440166\",\n \"text\": \"def _mixed_norm_solver_bcd(\\n M,\\n G,\\n alpha,\\n lipschitz_constant,\\n maxit=200,\\n tol=1e-8,\\n verbose=None,\\n init=None,\\n n_orient=1,\\n dgap_freq=10,\\n use_accel=True,\\n K=5,\\n):\\n _, n_times = M.shape\\n _, n_sources = G.shape\\n n_positions = n_sources // n_orient\\n\\n if init is None:\\n X = np.zeros((n_sources, n_times))\\n R = M.copy()\\n else:\\n X = init\\n R = M - np.dot(G, X)\\n\\n E = [] # track primal objective function\\n highest_d_obj = -np.inf\\n active_set = np.zeros(n_sources, dtype=bool) # start with full AS\\n\\n alpha_lc = alpha / lipschitz_constant\\n\\n if use_accel:\\n last_K_X = np.empty((K + 1, n_sources, n_times))\\n U = np.zeros((K, n_sources * n_times))\\n\\n # First make G fortran for faster access to blocks of columns\\n G = np.asfortranarray(G)\\n # Ensure these are correct for dgemm\\n assert R.dtype == np.float64\\n assert G.dtype == np.float64\\n one_ovr_lc = 1.0 / lipschitz_constant\\n\\n # assert that all the multiplied matrices are fortran contiguous\\n assert X.T.flags.f_contiguous\\n assert R.T.flags.f_contiguous\\n assert G.flags.f_contiguous\\n # storing list of contiguous arrays\\n list_G_j_c = []\\n for j in range(n_positions):\\n idx = slice(j * n_orient, (j + 1) * n_orient)\\n list_G_j_c.append(np.ascontiguousarray(G[:, idx]))\\n\\n for i in range(maxit):\\n _bcd(G, X, R, active_set, one_ovr_lc, n_orient, alpha_lc, list_G_j_c)\\n\\n if (i + 1) % dgap_freq == 0:\\n _, p_obj, d_obj, _ = dgap_l21(\\n M, G, X[active_set], active_set, alpha, n_orient\\n )\\n highest_d_obj = max(d_obj, highest_d_obj)\\n gap = p_obj - highest_d_obj\\n E.append(p_obj)\\n logger.debug(\\n \\\"Iteration %d :: p_obj %f :: dgap %f :: n_active %d\\\"\\n % (i + 1, p_obj, gap, np.sum(active_set) / n_orient)\\n )\\n\\n if gap < tol:\\n logger.debug(\\\"Convergence reached ! (gap: %s < %s)\\\" % (gap, tol))\\n break\\n\\n # using Anderson acceleration of the primal variable for faster\\n # convergence\\n if use_accel:\\n last_K_X[i % (K + 1)] = X\\n\\n if i % (K + 1) == K:\\n for k in range(K):\\n U[k] = last_K_X[k + 1].ravel() - last_K_X[k].ravel()\\n C = U @ U.T\\n # at least on ARM64 we can't rely on np.linalg.solve to\\n # reliably raise LinAlgError here, so use SVD instead\\n # equivalent to:\\n # z = np.linalg.solve(C, np.ones(K))\\n u, s, _ = np.linalg.svd(C, hermitian=True)\\n if s[-1] <= 1e-6 * s[0] or not np.isfinite(s).all():\\n logger.debug(\\\"Iteration %d: LinAlg Error\\\" % (i + 1))\\n continue\\n z = ((u * 1 / s) @ u.T).sum(0)\\n c = z / z.sum()\\n X_acc = np.sum(last_K_X[:-1] * c[:, None, None], axis=0)\\n _grp_norm2_acc = groups_norm2(X_acc, n_orient)\\n active_set_acc = _grp_norm2_acc != 0\\n if n_orient > 1:\\n active_set_acc = np.kron(\\n active_set_acc, np.ones(n_orient, dtype=bool)\\n )\\n p_obj = _primal_l21(M, G, X[active_set], active_set, alpha, n_orient)[0]\\n p_obj_acc = _primal_l21(\\n M, G, X_acc[active_set_acc], active_set_acc, alpha, n_orient\\n )[0]\\n if p_obj_acc < p_obj:\\n X = X_acc\\n active_set = active_set_acc\\n R = M - G[:, active_set] @ X[active_set]\\n\\n X = X[active_set]\\n\\n return X, active_set, E\",\n \"title\": \"\"\n },\n {\n \"docid\": \"178de04b2af5641740265bd3c7a8bc3c\",\n \"score\": \"0.5335637\",\n \"text\": \"def g(q, xi):\\n return flx.eigenvalue(q)-xi\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c034f9bd66b94fa7d3e4ceebf409aeb\",\n \"score\": \"0.5334269\",\n \"text\": \"def hamiltonian_diag_1d(mu,V,deltax):\\n\\n\\n #constructs kinetic energy operator\\n T=kinetic_op_1d(mu,deltax,V.size)\\n #constructs hamiltonian\\n H=hamiltonian_1d(T,V)\\n #performs diagonalization\\n eigen_val,eigen_vec=solve_eigenstates(H)\\n\\n return eigen_val,eigen_vec\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3efdf2a40d5b6d307da83456e17bdd6e\",\n \"score\": \"0.53294677\",\n \"text\": \"def diag_ggn(self) -> List[Tensor]:\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a742599d07d57c1603fa2403b352556\",\n \"score\": \"0.5328856\",\n \"text\": \"def Gxyz(lA,mA,nA,lB,mB,nB,lC,mC,nC,lD,mD,nD,a,b,c,d,RA,RB,RC,RD):\\n with loops.Scope() as s:\\n s.lA,s.mA,s.nA,s.lB,s.mB,s.nB,s.lC,s.mC,s.nC,s.lD,s.mD,s.nD,s.a,s.b,s.c,s.d,s.RA,s.RB,s.RC,s.RD = lA,mA,nA,lB,mB,nB,lC,mC,nC,lD,mD,nD,a,b,c,d,RA,RB,RC,RD\\n s.gP = s.a + s.b\\n s.gQ = s.c + s.d\\n s.delta = 1/(4*s.gP) + 1/(4*s.gQ)\\n s.RP = gaussian_product(s.a,s.b,s.RA,s.RB)\\n s.RQ = gaussian_product(s.c,s.d,s.RC,s.RD)\\n s.ABsq = np.dot(s.RA-s.RB,s.RA-s.RB)\\n s.CDsq = np.dot(s.RC-s.RD,s.RC-s.RD)\\n s.PQsq = np.dot(s.RP-s.RQ,s.RP-s.RQ)\\n s.boysarg = s.PQsq / (4 * s.delta)\\n\\n s.Gxyz = 0.0\\n s.l = lA + lB \\n s.r = np.floor(s.l/2) \\n s.lp = lC + lD \\n s.rp = np.floor(s.lp/2) \\n s.i = np.floor((s.l + s.lp - 2 * s.r - 2 * s.rp) / 2) \\n\\n for _ in s.while_range(lambda: s.l > -1):\\n s.r = np.floor(s.l/2)\\n for _ in s.while_range(lambda: s.r > -1):\\n s.lp = lC + lD \\n for _ in s.while_range(lambda: s.lp > -1):\\n s.rp = np.floor(s.lp/2)\\n for _ in s.while_range(lambda: s.rp > -1):\\n #s.i = np.floor((s.l + s.lp - 2 * s.r - 2 * s.rp) / 2) # Compiles forever until OOM\\n s.i = np.floor((s.l - 2 * s.r + s.lp - 2 * s.rp) / 2) # This works, compiles fine since loop variables are added in order\\n for _ in s.while_range(lambda: s.i > -1):\\n gx = gi(s.l,s.lp,s.r,s.rp,s.i, s.lA,s.lB,s.RA[0],s.RB[0],s.RP[0],s.gP, s.lC,s.lD,s.RC[0],s.RD[0],s.RQ[0],s.gQ)\\n nu = s.l - 2 * s.r + s.lp - 2 * s.rp - s.i #THIS ORDER MATTERS (UGH)\\n F = boys(nu, s.boysarg)\\n s.Gxyz += F * gx\\n\\n\\n #s.nu = s.l + s.lp - 2 * (s.r+s.rp)-s.i\\n #nu = s.l - 2 * s.r + s.lp - 2 * s.rp - s.i\\n #F = boys(s.nu, s.boysarg)\\n #F = 0.5 * (s.boysarg + 1e-11)**(-(s.nu + 0.5)) * jax.lax.igamma(s.nu + 0.5, s.boysarg + 1e-11) * np.exp(jax.lax.lgamma(s.nu + 0.5))\\n\\n # WORKS: fake boys function acucmulation\\n #nu = s.l - 2 * s.r + s.lp - 2 * s.rp - s.i #THIS ORDER MATTERS (UGH)\\n #F = 0.5 * (s.boysarg + 1e-11)**(-(nu + 0.5)) * jax.lax.igamma(nu + 0.5, s.boysarg + 1e-11) * np.exp(jax.lax.lgamma(nu + 0.5))\\n #s.Gxyz += F\\n\\n\\n # This works ( thou shalt not add s.l + s.lp\\n #nu = s.l - 2 * s.r + s.lp - 2 * s.rp - s.i #THIS ORDER MATTERS (UGH)\\n #F = boys(nu, s.boysarg)\\n #s.Gxyz += F\\n\\n s.i -= 1\\n s.rp -= 1\\n s.lp -= 1\\n s.r -= 1\\n s.l -= 1\\n\\n return s.Gxyz\\n\\n\\n\\n #Na = N(a,lA,mA,nA)\\n #Nb = N(b,lB,mB,nB)\\n #Nc = N(c,lC,mC,nC)\\n #Nd = N(d,lD,mD,nD)\\n\\n #Gxyz *= Na * Nb * Nc * Nd\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89987b761534239f9d8c38765aa0e849\",\n \"score\": \"0.53207606\",\n \"text\": \"def _calc_m(x0, xt, tau=1):\\n\\n # These represent the C(tau) and C(0) covariance matrices\\n # Note: x is an anomaly vector, no division by N-1 because it's undone\\n # in the inversion anyways\\n\\n # Division by number of samples ignored due to inverse\\n x0x0 = np.dot(x0.T, x0)\\n x0xt = np.dot(xt.T, x0)\\n\\n # Calculate the mapping term G_tau\\n G = np.dot(x0xt, pinv(x0x0))\\n\\n # Calculate the forcing matrix to check that all modes are damped\\n Geigs = eigvals(G)\\n Leigs = (1. / tau) * np.log(Geigs)\\n\\n if np.any(Leigs.real >= 0):\\n logger.debug('L eigenvalues: \\\\n' + str(Leigs))\\n raise ValueError(\\n 'Positive eigenvalues detected in forecast matrix L.')\\n\\n return G\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d6513d5e0985d8f627c0a32a832a6a6\",\n \"score\": \"0.5317741\",\n \"text\": \"def calcgaussian( self, frequency=None ):\\n if frequency :\\n diff = frequency - self.B\\n else :\\n diff = self.frequencies - self.B\\n return exp( -0.5 * diff * diff * self.C2 )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6392db40c17f5f1e25f1e0aba7ad643\",\n \"score\": \"0.5317538\",\n \"text\": \"def test_diagonalizing_gates(self):\\n base = qml.Hadamard(0)\\n diag_gate = Adjoint(base).diagonalizing_gates()[0]\\n\\n assert isinstance(diag_gate, qml.RY)\\n assert qml.math.allclose(diag_gate.data[0], -np.pi / 4)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21697a506e1ca5131df2e9f1930bfd38\",\n \"score\": \"0.5314103\",\n \"text\": \"def _calc_MC(self):\\r\\n _ = self; T, K, rf_r, net_r, sCP = _.T, _.K, _.rf_r, _.net_r, _.signCP\\r\\n _ = self.ref; S, vol, q = _.S0, _.vol, _.q\\r\\n _ = self.px_spec; n, m, keep_hist, rng_seed, rho = _.nsteps, _.npaths, _.keep_hist, _.rng_seed, _.rho\\r\\n _ = self.px_spec.ref2; S2, vol2, q2 = _.S0, _.vol, _.q\\r\\n _ = self._LT_specs(); u, d, p, df, dt = _['u'], _['d'], _['p'], _['df_dt'], _['dt']\\r\\n\\r\\n px = list()\\r\\n numpy.random.seed(rng_seed)\\r\\n\\r\\n for path in range(0,m):\\r\\n\\r\\n ## Generate correlated Wiener Processes\\r\\n u = numpy.random.normal(size=n)\\r\\n v = numpy.random.normal(size=n)\\r\\n v = rho * u + math.sqrt(1 - rho**2) * v\\r\\n u = u * math.sqrt(dt)\\r\\n v = v * math.sqrt(dt)\\r\\n\\r\\n ## Simulate the paths\\r\\n s1, s2, mu1, mu2 = [S], [S2], (rf_r - q)*dt, (rf_r - q2)*dt\\r\\n\\r\\n for t in range(0, len(u)):\\r\\n s1.append(s1[-1] * (mu1 + vol * u[t]) + s1[-1])\\r\\n s2.append(s2[-1] * (mu2 + vol2 * v[t]) + s2[-1])\\r\\n\\r\\n val = np.maximum(sCP * (s2[-1] - s1[-1] - K), 0) * math.exp(-rf_r * T) # Calculate the Payoff\\r\\n px.append((val))\\r\\n\\r\\n self.px_spec.add(px=float(np.mean(px)))\\r\\n\\r\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03b47414233ac7b3883179ec28ad283b\",\n \"score\": \"0.5308294\",\n \"text\": \"def make_G(V, q):\\n # Shape to 1x1 matrix (scalar)\\n V = Matrix([V])\\n\\n # Return jacobian as a column vector\\n return V.jacobian(q).T\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c382a5bb452ac01e1c8a73b504bda258\",\n \"score\": \"0.5307989\",\n \"text\": \"def gmm_cluster(data_set, cls_num):\\n iter_count = 0\\n MAX_ITER_COUNT = 25\\n sample_num = data_set.shape[0]\\n sample_dim = data_set.shape[1]\\n data_set_T = numpy.transpose(data_set)\\n\\n alphas = numpy.asarray([1.0 / cls_num] * cls_num).T\\n means = numpy.asarray(select_means(data_set, cls_num))\\n covariances = numpy.asarray([numpy.cov(data_set_T) for i in range(0, cls_num)])\\n\\n gammas = numpy.asarray(\\n [[alphas[j] for j in range(0, cls_num)] for i in range(0, sample_num)]\\n )\\n\\n while iter_count < MAX_ITER_COUNT:\\n # E step: calculate the probability matrix \\\"gammas\\\" based on current means, covariances and alphas.\\n p = numpy.asarray(\\n [\\n [\\n gauss(\\n data_set_T[:, i].reshape(sample_dim, 1),\\n means[j].reshape(sample_dim, 1),\\n covariances[j],\\n )[0, 0]\\n for j in range(0, cls_num)\\n ]\\n for i in range(0, sample_num)\\n ]\\n )\\n \\n gammas = numpy.asarray(\\n [\\n numpy.multiply(p[i], alphas) / numpy.dot(p[i].T, alphas)\\n for i in range(0, sample_num)\\n ]\\n )\\n\\n # M step: update means, covariances and alphas based on new \\\"gammas\\\".\\n means = numpy.asarray(\\n [\\n (numpy.dot(data_set_T, gammas[:, i]) / numpy.sum(gammas[:, i])).reshape(sample_dim, 1) \\n for i in range(0, cls_num)\\n ]\\n )\\n\\n covariances = numpy.asarray(\\n [\\n numpy.dot(\\n numpy.multiply(data_set_T - means[i], gammas[:, i]),\\n (data_set_T - means[i]).T,\\n )\\n / numpy.sum(gammas[:, i])\\n for i in range(0, cls_num)\\n ]\\n )\\n alphas = numpy.asarray([numpy.sum(gammas[:, i]) / sample_num for i in range(0, cls_num)])\\n iter_count += 1\\n\\n return means, gammas\",\n \"title\": \"\"\n },\n {\n \"docid\": \"316575b1ced6a4e975d7572f1d6eb2ce\",\n \"score\": \"0.53045654\",\n \"text\": \"def method(m,points,data):\\n n = len(data)-1\\n u,s,g,h = [],[],[],[]\\n if m>n:\\n m = n\\n print \\\"The highest power is adjusted to: \\\" + str(n)\\n for i in range(m+1):\\n u.append([None]*(n+2))\\n u[i][n+1] = 0\\n s.append(0) \\n g.append(0) \\n h.append(0) \\n for i in range(n+1):\\n u[0][i] = 1\\n stmp = u[0][i]**2\\n s[0] += stmp\\n g[0] += points[i]*stmp\\n u[0][n+1] += u[0][i]*data[i]\\n g[0] = g[0]/s[0]\\n u[0][n+1] = u[0][n+1]/s[0]\\n for i in range(n+1): \\n u[1][i] = points[i]*u[0][i]-g[0]*u[0][i]\\n s[1] += u[1][i]**2\\n g[1] += points[i]*u[1][i]**2\\n h[1] += points[i]*u[1][i]*u[0][i]\\n u[1][n+1] += u[1][i]*data[i]\\n g[1] = g[1]/s[1]\\n h[1] = h[1]/s[0]\\n u[1][n+1] = u[1][n+1]/s[1] \\n if m>=2:\\n for i in range(1,m):\\n for j in range(n+1):\\n u[i+1][j] = points[j]*u[i][j]-g[i]*u[i][j]-h[i]*u[i-1][j]\\n s[i+1] += u[i+1][j]**2\\n g[i+1] += points[j]*u[i+1][j]**2\\n h[i+1] += points[j]*u[i+1][j]*u[i][j]\\n u[i+1][n+1] += u[i+1][j]*data[j]\\n g[i+1] = g[i+1]/s[i+1]\\n h[i+1] = h[i+1]/s[i]\\n u[i+1][n+1] = u[i+1][n+1]/s[i+1] \\n return u\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6a5542365503202a32777702b724d32\",\n \"score\": \"0.5300515\",\n \"text\": \"def compute_gating_distances(self):\\n\\t\\t# For Gibbs\\n\\t\\tgating_distances_evals = pdist(self.x, metric=self.gating_kern)\\n\\t\\tself.gating_distances = squareform(gating_distances_evals)\\n\\t\\tself.gating_distances_to_rest = np.sum(self.gating_distances, axis=1)\\n\\n\\t\\t# For HMH\\n\\t\\tgating_log_distances_1st_incomplete = pdist(self.x, metric=self.gating_log_kern_1st_incomplete)\\n\\t\\tgating_log_distances_2nd_incomplete = pdist(self.x, metric=self.gating_log_kern_2nd_incomplete)\\n\\t\\t\\n\\t\\tgating_log_distances_1st_evals = gating_log_distances_1st_incomplete * gating_distances_evals\\n\\t\\tgating_log_distances_2nd_evals = gating_log_distances_2nd_incomplete * gating_distances_evals\\n\\t\\t\\n\\t\\tself.gating_log_distances_1st = squareform(gating_log_distances_1st_evals)\\n\\t\\tself.gating_log_distances_2nd = squareform(gating_log_distances_2nd_evals)\\n\\t\\tself.gating_log_distances_1st_to_rest = np.sum(self.gating_log_distances_1st, axis=1)\\n\\t\\tself.gating_log_distances_2nd_to_rest = np.sum(self.gating_log_distances_2nd, axis=1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09f2212a9ff3ed2438a87dbbc5aaf4e5\",\n \"score\": \"0.5298525\",\n \"text\": \"def gauss():\\n from Disk import Disk\\n from Mersenne import Mersenne\\n\\n # inputs\\n N = 10**5\\n box = [(0,1), (0,1)]\\n # the point cloud generator\\n cloud = Mersenne()\\n # the region of integration\\n disk = Disk(center=(0,0), radius=1)\\n\\n # the integration algorithm\\n interior = 0\\n for i in range(N):\\n point = cloud.point(box)\\n if disk.interior(point):\\n interior += 1\\n # print the estimate of π\\n print(\\\"π: {:.8f}\\\".format(4*interior/N))\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5635d8eaff8d1cbb10b60a522719cb95\",\n \"score\": \"0.529164\",\n \"text\": \"def calcGenCost(self):\\n df = self.df_0.copy(deep=True)\\n\\n #pre-processing:\\n #adjust coal fuel prices by the \\\"coal_dol_per_mmbtu\\\" input\\n df.loc[df.fuel_type=='coal', 'fuel_price' + str(self.time)] = scipy.maximum(0, df.loc[df.fuel_type=='coal', 'fuel_price' + str(self.time)] + self.coal_dol_per_mmbtu)\\n #adjust coal capacity by the \\\"coal_capacity_derate\\\" input\\n df.loc[df.fuel_type=='coal', 'mw' + str(self.time)] = df.loc[df.fuel_type=='coal', 'mw' + str(self.time)] * (1.0 - self.coal_capacity_derate)\\n #calculate the generation cost:\\n df['fuel_cost'] = df['heat_rate' + str(self.time)] * df['fuel_price' + str(self.time)]\\n df['co2_cost'] = df['co2' + str(self.time)] * self.co2_dol_per_kg\\n df['so2_cost'] = df['so2' + str(self.time)] * self.so2_dol_per_kg\\n df['nox_cost'] = df['nox' + str(self.time)] * self.nox_dol_per_kg\\n df['gen_cost'] = scipy.maximum(0.01, df.fuel_cost + df.co2_cost + df.so2_cost + df.nox_cost + df.vom)\\n #add a zero generator so that the bid stack goes all the way down to zero. This is important for calculating information for the marginal generator when the marginal generator is the first one in the bid stack.\\n df['dmg_easiur'] = df['dmg' + str(self.time)]\\n #if self.initialization:\\n df = df.append(df.loc[0]*0)\\n df = df.append(df.iloc[-1])\\n #self.initialization = False\\n df.sort_values('gen_cost', inplace=True)\\n #move coal_0 and ngcc_0 to the front of the merit order regardless of their gen cost\\n coal_0_ind = df[df.orispl_unit=='coal_0'].index[0]\\n ngcc_0_ind = df[df.orispl_unit=='ngcc_0'].index[0]\\n df = pandas.concat([df.iloc[[0],:], df[df.orispl_unit=='coal_0'], df[df.orispl_unit=='ngcc_0'], df.drop([0, coal_0_ind, ngcc_0_ind], axis=0)], axis=0)\\n df.reset_index(drop=True, inplace=True)\\n df['demand'] = df['mw' + str(self.time)].cumsum()\\n df.loc[len(df)-1, 'demand'] = df.loc[len(df)-1, 'demand'] + 1000000 #creates a very large generator at the end of the merit order so that demand cannot be greater than supply\\n df['f'] = df['demand']\\n df['s'] = scipy.append(0, scipy.array(df.f[0:-1]))\\n df['a'] = scipy.maximum(df.s - df.min_out*(1/0.10), 1.0)\\n #add a very large demand for the last row\\n self.df = df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"608b239b715ae75924ded17a7364d352\",\n \"score\": \"0.52880335\",\n \"text\": \"def gibbs_step(self, C, Gamma, Sigma_det, log_pc, idx = None):\\n if idx is None:\\n idx = np.random.randint(self.d) \\n u = self.X_td[:, idx][:, None]\\n idx_val = C[idx]\\n if idx_val:\\n Gamma_1 = Gamma\\n Sigma_det_1 = Sigma_det\\n log_p1 = log_pc\\n C[idx] = 0\\n key = tuple(C.tolist())\\n if key in self._cache:\\n self._cache_hits += 1\\n Gamma_0, Sigma_det_0, log_p0 = self._cache[key]\\n else:\\n Gamma_0, Sigma_det_0 = sherman_morrison_update(\\n Gamma, Sigma_det, -u, u)\\n log_p0 = self.log_p(C, Gamma_0, Sigma_det_0)\\n self._cache_insert(key, (Gamma_0, Sigma_det_0, log_p0))\\n else:\\n Gamma_0 = Gamma\\n Sigma_det_0 = Sigma_det\\n log_p0 = log_pc\\n C[idx] = 1\\n key = tuple(C.tolist())\\n if key in self._cache:\\n self._cache_hits += 1\\n Gamma_1, Sigma_det_1, log_p1 = self._cache[key]\\n else:\\n Gamma_1, Sigma_det_1 = sherman_morrison_update(\\n Gamma, Sigma_det, u, u)\\n log_p1 = self.log_p(C, Gamma_1, Sigma_det_1)\\n self._cache_insert(key, (Gamma_1, Sigma_det_1, log_p1))\\n\\n Z = logsumexp(log_p0, log_p1)\\n with warnings.catch_warnings():\\n warnings.simplefilter('error')\\n p0 = np.exp(log_p0 - Z)\\n\\n if np.random.rand() > p0:\\n # we set it to 1\\n C[idx] = 1\\n Gamma = Gamma_1\\n Sigma_det = Sigma_det_1\\n log_pc = log_p1\\n\\n else:\\n # we set it to 0\\n C[idx] = 0\\n Gamma = Gamma_0\\n Sigma_det = Sigma_det_0\\n log_pc = log_p0\\n self.likelihoods.append(log_pc)\\n return C, Gamma, Sigma_det, log_pc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"456990deb35189fdd95089e95703ed43\",\n \"score\": \"0.52843225\",\n \"text\": \"def get_gmm(self,gr_charge, ex_charge, FG_elstat, struc, index, E01, \\r\\n int2cart, freq, red_mass, order=2, approx=1.1, CoarseGrain='C'):\\r\\n \\r\\n dR_Hmm,dR_env_Hmm = self.get_SingleDefect_derivation(gr_charge, ex_charge, FG_elstat, struc, index, E01, order=2, approx=1.1)\\r\\n \\r\\n # freq is actualy wavenumber (= frequency/speed_of_light).\\r\\n # therefore angluar frequency omega in atomic units = 100/(2*Rydberg_inf) wavenumber in [cm-1]\\r\\n omega_au = freq/conversion_facs_energy[\\\"1/cm\\\"]\\r\\n RedMass_au = red_mass/conversion_facs_mass\\r\\n # in atomic units hbar = 1.0, m_e = 1.0, elementary_charge = 1.0, 1/(4*pi*eps_0) = 1.0, speed_of_light = 137 ( fine-structure constant)\\r\\n \\r\\n # pick only carbon atoms from eigenvectors of normal modes (assume that fluorine atoms doesn't influent the result) - in needed\\r\\n if CoarseGrain in [\\\"C\\\",\\\"plane\\\"] :\\r\\n indxC = np.where(np.array(struc.at_type) == 'C')\\r\\n index = np.zeros((len(indxC),3),dtype='i8')\\r\\n for ii in range(3):\\r\\n index[:,ii] = index*3+ii\\r\\n index.reshape(3*len(indxC))\\r\\n int2cart_loc = int2cart[index,:]\\r\\n else:\\r\\n int2cart_loc = int2cart.copy()\\r\\n \\r\\n g_mm = np.dot(int2cart_loc.T,dR_Hmm) + np.dot(int2cart.T,dR_env_Hmm)\\r\\n g_mm = g_mm/(np.sqrt(omega_au*omega_au*omega_au))\\r\\n g_mm = g_mm/(2*np.sqrt(RedMass_au))\\r\\n \\r\\n return g_mm\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c440c76b9936635298b4bb1cc941f6e\",\n \"score\": \"0.5281879\",\n \"text\": \"def compute_G(self):\\n \\\"\\\"\\\" YOUR CODE HERE\\\"\\\"\\\"\\n G = 0\\n raise NotImplementedError\\n\\n return G\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d0418f4c70674079dcac873d1f89d15\",\n \"score\": \"0.528127\",\n \"text\": \"def MMD2H_Gaussian(kernel,gm,samples):\\n N = len(samples)\\n \\n EE = EE_Gaussian(gm,kernel)\\n \\n crossed_term = 0\\n for sample in samples:\\n crossed_term += E_Gaussian(np.array([sample]), gm, kernel)\\n\\n gram_term = 0\\n for sample_n in samples:\\n for sample_m in samples:\\n gram_term += kernel.pdf(sample_n,sample_m)\\n\\n return EE - 2*crossed_term/N + gram_term/N**2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df345ab6b0854133a35e504f993f2047\",\n \"score\": \"0.5279673\",\n \"text\": \"def _rand_g_(self):\\r\\n # calculate pairwise distance\\r\\n dist_gg, dist_gd, dist_dd = get_squared_dist(\\r\\n self.score_gen, self.score_data, z_score=False, do_summary=self.do_summary)\\r\\n\\r\\n # mmd\\r\\n with tf.name_scope('rand_g'):\\r\\n omega = tf.random_uniform([], self.omega_range[0], self.omega_range[1], dtype=tf.float32) \\\\\\r\\n if isinstance(self.omega_range, (list, tuple)) else self.omega_range\\r\\n loss_gr = rand_mmd_g_xy(\\r\\n dist_gg, dist_gd, dist_dd, self.batch_size, omega=omega,\\r\\n max_iter=3, name='mmd_gr', do_summary=self.do_summary, scope_prefix='rand_g/')\\r\\n loss_gn = rand_mmd_g_xn(\\r\\n self.score_gen, self.ref_normal, self.batch_size, self.num_scores, dist_xx=dist_gg, omega=omega,\\r\\n max_iter=3, name='mmd_gn', do_summary=self.do_summary, scope_prefix='rand_g/')\\r\\n loss_rn = rand_mmd_g_xn(\\r\\n self.score_data, self.ref_normal, self.batch_size, self.num_scores, dist_xx=dist_dd, omega=omega,\\r\\n max_iter=3, name='mmd_rn', do_summary=self.do_summary, scope_prefix='rand_g/')\\r\\n # final loss\\r\\n self.loss_gen = loss_gr\\r\\n self.loss_dis = loss_rn - loss_gr\\r\\n\\r\\n # self.debug_register = [omega, loss_gr, loss_gn, loss_rn]\\r\\n if self.do_summary:\\r\\n with tf.name_scope(None): # return to root scope to avoid scope overlap \\r\\n tf.summary.scalar('rand_g/omega', omega)\\r\\n tf.summary.scalar('GANLoss/gr', loss_gr)\\r\\n tf.summary.scalar('GANLoss/gn', loss_gn)\\r\\n tf.summary.scalar('GANLoss/rn', loss_rn)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c8d34f3de667ebed5b0cd6412c5bd80\",\n \"score\": \"0.5276958\",\n \"text\": \"def g_reuss(self):\\n return 15. / (8. * self.inv[:3, :3].trace() -\\n 4. * np.triu(self.inv[:3, :3]).sum() +\\n 3. * self.inv[3:, 3:].trace())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"139e5a880bf42ab96f1e97c12d0b786d\",\n \"score\": \"0.5272041\",\n \"text\": \"def gauss(self, mu, sigma):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e952e878765b02e7efa8a78b9d3a3ee1\",\n \"score\": \"0.5262486\",\n \"text\": \"def _KLD_diag_gaussians(mu, log_sigma, prior_mu, prior_log_sigma):\\n return prior_log_sigma - log_sigma + 0.5 * (tf.exp(2 * log_sigma)\\n + tf.square(mu - prior_mu)) * tf.exp(-2. * prior_log_sigma) - 0.5\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39784702b7ef84286fe174b59114d798\",\n \"score\": \"0.5260531\",\n \"text\": \"def _g(self, RD):\\n return 1.0 / math.sqrt(1.0 + 3.0 * math.pow(RD, 2.0) / math.pow(math.pi, 2.0))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91b5829f4ec2d8af3d847ca0afb9c897\",\n \"score\": \"0.5248102\",\n \"text\": \"def initG_uniform_old(C, commlangdistros, innov = 0):\\n\\n g = commlangdistros[0,:].size\\n c = commlangdistros[:,0].size\\n n = C[:,0].size\\n Gassertions(C, commlangdistros, g)\\n \\n G = matlib.zeros((n,g))\\n for i in range(0,c):\\n for h in range(0,C[:,0].size):\\n if C[h,i] == 1:\\n G[h] = commlangdistros[i,:]\\n return G\",\n \"title\": \"\"\n },\n {\n \"docid\": \"579e396569e9e92d66a631ad60cc4537\",\n \"score\": \"0.5235529\",\n \"text\": \"def gaussian_product_diag(d1, d2):\\n inv_cov1 = 1 / np.diag(d1.cov)\\n inv_cov2 = 1 / np.diag(d2.cov)\\n sigma = 1 / (inv_cov1 + inv_cov2)\\n mu = sigma * (inv_cov1*d1.mean + inv_cov2*d2.mean) \\n return Gaussian(*mu, *sigma)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9834df3259e407b7b7bba15b13995dc3\",\n \"score\": \"0.5230722\",\n \"text\": \"def calcgaussianp( self, frequency=None ):\\n if frequency :\\n diff = frequency - self.Bp\\n else :\\n diff = self.frequencies - self.Bp\\n return exp( -0.5 * diff * diff * self.C2p )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5201a4f88595c5589a9e007ba199474c\",\n \"score\": \"0.52282745\",\n \"text\": \"def shell_Green_grid_Arnoldi_Mmn_step(n,k, invchi, rgrid,rsqrgrid,rdiffgrid, RgMgrid, ImMgrid, unitMvecs, Gmat, plotVectors=False):\\n #first, begin by orthogonalizing and normalizing unitMvecs[-1]\\n #use relation U = V^{-1} - G\\n \\\"\\\"\\\"\\n see comment for analogous method for N waves, shell_Green_grid_Arnoldi_Nmn_step\\n coef1 = Gmat[-1,-1]\\n unitMvecs[-1] -= coef1*unitMvecs[-2]\\n \\n if Gmat.shape[0]>1: #since G has symmetric Arnoldi representation (so tridiagonal), G*M_j has non-zero overlap with M_j and M_{j-1}\\n coef2 = Gmat[-2,-1]\\n unitMvecs[-1] -= coef2*unitMvecs[-3]\\n \\n unitMvecs[-1][:] = np.real(unitMvecs[-1][:])\\n \\\"\\\"\\\"\\n vecnum = Gmat.shape[0]\\n for i in range(vecnum):\\n coef = rgrid_Mmn_vdot(unitMvecs[i], unitMvecs[-1], rsqrgrid,rdiffgrid)\\n unitMvecs[-1] -= coef*unitMvecs[i]\\n unitMvecs[-1][:] = np.real(unitMvecs[-1][:])\\n \\n norm = np.sqrt(rgrid_Mmn_normsqr(unitMvecs[-1], rsqrgrid,rdiffgrid))\\n unitMvecs[-1] /= norm\\n \\n if plotVectors:\\n rgrid_Mmn_plot(unitMvecs[-1], rgrid)\\n \\n #get new vector\\n newvecM = shell_Green_grid_Mmn_vec(n,k, rsqrgrid,rdiffgrid, RgMgrid,ImMgrid, unitMvecs[-1])\\n \\n newvecM[:] = np.real(newvecM)\\n \\n newGmat = np.zeros((Gmat.shape[0]+1,Gmat.shape[1]+1), dtype=np.complex)\\n newGmat[:-1,:-1] = Gmat[:,:]\\n \\n newGmat[-1,-1] = rgrid_Mmn_vdot(unitMvecs[-1], newvecM, rsqrgrid,rdiffgrid)\\n newGmat[-2,-1] = rgrid_Mmn_vdot(unitMvecs[-2], newvecM, rsqrgrid,rdiffgrid)\\n newGmat[-1,-2] = newGmat[-2,-1]\\n \\n unitMvecs.append(newvecM) #append to end of unitMvecs for next round of iteration\\n return newGmat\",\n \"title\": \"\"\n },\n {\n \"docid\": \"821ba4e50a8fbfaea368b31eb7d0b02c\",\n \"score\": \"0.52278423\",\n \"text\": \"def pm(v,g):\\r\\n p = np.zeros_like(v)\\r\\n p[:,0] = v[:,0]*g; p[:,1] = v[:,1]*g; p[:,2] = v[:,2]*g\\r\\n return p\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f7309efc8ce4006ec33e0e04b05c45f\",\n \"score\": \"0.5226854\",\n \"text\": \"def initG_indicator_old(C, commlangdistros, innov = 0):\\n\\n g = commlangdistros[0,:].size\\n c = commlangdistros[:,0].size\\n n = C[:,0].size\\n Gassertions(C, commlangdistros, g)\\n \\n G = matlib.zeros((n,g))\\n\\n for comm in range(0,c):\\n lowerbound = 0\\n rangedict = {}\\n for i in range(0,(commlangdistros[comm,:].size)):\\n prob = commlangdistros[comm,:].item(i)\\n if prob == 0:\\n continue\\n rangedict[(lowerbound, lowerbound + prob)] = i\\n lowerbound += prob\\n for h in range(0,C[:,0].size):\\n if C[h,comm] == 1:\\n rando = random.random()\\n for grange, grammar in rangedict.iteritems():\\n if grange[0] <= rando and rando < grange[1]:\\n if random.random() < innov:\\n G[h,random.randint(0,g-1)] = 1\\n else:\\n G[h,grammar] = 1\\n return G\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd8416dc1d2cb6a79dfc7673b1add71c\",\n \"score\": \"0.52182436\",\n \"text\": \"def NonDiagonalElements(self,n=0,m=1):\\n Hnm = sp.zeros(self.size,self.size)\\n for i in range(0,self.size):\\n for j in range(0,self.size):\\n Hnm[i,j] = 1/self.T*sp.integrate(self.Vt[i,j]*sp.exp(-sp.I*(m-n)*self.G*t),(t,0,self.T),conds='none')\\n return(Hnm)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07b025ce908e487bd58063a1260f8a9e\",\n \"score\": \"0.521736\",\n \"text\": \"def apply_G(self):\\n\\n pvec = self.vec['p']\\n uvec = self.vec['u']\\n\\n speed = pvec('v') * 1e2\\n dist = pvec('x') * 1e6\\n gamma = pvec('gamma') * 1e-1\\n time = uvec('time')\\n\\n time_temp = ((dist[1:] - dist[0:-1]) /\\n (((speed[1:] + speed[0:-1])/2) *\\n numpy.cos((gamma[1:] + gamma[0:-1])/2)))\\n time[0] = numpy.sum(time_temp)/1e4\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8800c004fa23a5a5fdee4e422efb623f\",\n \"score\": \"0.5215661\",\n \"text\": \"def gaussian(x,mu,fwhm):\\n \\n c = fwhm/(2* np.sqrt(2*np.log(2)))\\n return np.exp(-1*((x-mu)**2/(2*c**2)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94d8016e105380d54b280b1a52e67a3b\",\n \"score\": \"0.52119476\",\n \"text\": \"def gauss_upper_diag_tridiag(M):\\n M[0]/=M[0][0]\\n for i in range(1,len(M[:,0])):\\n M[i]-=M[i-1]*M[i,i-1]\\n M[i]/=M[i][i]\\n return M\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85c74a6d2fce1d009f1411e600eb8fc1\",\n \"score\": \"0.5210143\",\n \"text\": \"def generate():\\n\\n m = np.array([2., 5., 5., 2.])\\n temp = 2.0 * (np.random.rand(4, 4)) # to random-generate S\\n S = np.dot(temp, temp.T) # make sure S is positive semidefinite, symmetric\\n d, U = np.linalg.eig(S)\\n L = np.diagflat(d)\\n A = np.dot(U, np.sqrt(L))\\n X = np.random.randn(4, 1000)\\n Y = np.dot(A,X) + np.tile(m.reshape(4,1), 1000) \\n return S, m, Y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7273e003b5fb28fd61827833dbe8f464\",\n \"score\": \"0.5208473\",\n \"text\": \"def _calc_m(x0, xt, tau=1):\\n \\n # These represent the C(tau) and C(0) covariance matrices\\n # Note: x is an anomaly vector, no division by N-1 because it's undone\\n # in the inversion anyways\\n \\n x0x0 = np.dot(x0.T, x0)\\n x0xt = np.dot(xt.T, x0)\\n\\n # Calculate the mapping term G_tau\\n G = np.dot(x0xt, pinv(x0x0))\\n\\n # Calculate the forcing matrix to check that all modes are damped\\n Geigs = eigvals(G)\\n Leigs = (1./tau) * np.log(Geigs)\\n\\n if np.any(Leigs.real >= 0):\\n logger.debug('L eigenvalues: \\\\n' + str(Leigs))\\n raise ValueError('Positive eigenvalues detected in forecast matrix L.')\\n\\n return G\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6102fccc3a1df6d52a7b25e0f71aea4f\",\n \"score\": \"0.52029395\",\n \"text\": \"def gsl_eigen_genherm_alloc(*args, **kwargs):\\n return _gslwrap.gsl_eigen_genherm_alloc(*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1abb2b137688ce80ceaffc51a5d512f5\",\n \"score\": \"0.5202235\",\n \"text\": \"def efficient_ACDM(self, X, n_iter):\\n calc_KL_time = 0\\n e = np.e \\n B_size = len(self.B_)\\n Z = len(self.S_)\\n total_step = n_iter*B_size\\n # Initialization of Theta\\n theta_prime_tilde = dict()\\n theta_prime_hat = dict()\\n for phi in self.B_:\\n theta_prime_tilde[phi] = 0.0\\n theta_prime_hat[phi] = 0.0\\n\\n # Initialization of U\\n U_hat = dict() \\n U_prime_hat = dict() \\n U_prime_tilde = dict()\\n for x in self.S_:\\n U_hat[x] = 0\\n U_prime_hat[x] = 0.0\\n U_prime_tilde[x] = 0.0\\n # Initialization of Coefficients\\n alpha = [0] * (total_step+1)\\n gamma = [0] * (total_step+2)\\n c_hat = [0] * (total_step+2)\\n gamma[-1] =1/(2.0) \\n c_hat[-1] = 3.0 + 2*math.sqrt(2.000) \\n for k in range(total_step+1):\\n gamma_k_before = gamma[k-1]\\n gamma_k = math.sqrt(1.0/(4.0)+gamma_k_before**2)+1.0/(2.0)\\n gamma[k] = gamma_k\\n alpha[k] =1/(gamma_k )\\n c_hat[k] = (1-alpha[k]) * c_hat[k-1]\\n # Caching \\n e_U_hat_dict = dict() \\n for x in self.S_:\\n e_U_hat_dict[x] = 1.0\\n # Beginning of Iteration\\n start = time.time()\\n for iter_ in range(n_iter):\\n for index in range(B_size):\\n k = iter_*B_size + index\\n phi = random.choice(self.B_)\\n # Caching\\n c_hat_k = c_hat[k]\\n c_hat_k_after = c_hat[k+1]\\n alpha_k_after = alpha[k+1]\\n gamma_k = gamma[k]\\n\\n # calculate G, Z\\n G = 0 \\n for x in self.Ssub_[phi]:\\n G += e_U_hat_dict[x]\\n eta_ = G/Z\\n # update tilde_theta_prime, hat_theta_prime\\n nabla = (self.etahat_[phi] - eta_)\\n step1 = 4.0 * gamma_k/B_size\\n step2 = 4.0/c_hat_k_after*(1-alpha_k_after-(alpha_k_after+1-c_hat_k_after)*gamma_k/B_size)\\n\\n theta_prime_tilde[phi] += step1 * nabla\\n theta_prime_hat[phi] += step2 * nabla\\n for x in self.Ssub_[phi]:\\n U_prime_tilde[x] += step1 * nabla\\n U_prime_hat[x] += step2 * nabla\\n U_hat_temp = (1-c_hat_k_after)*U_prime_tilde[x]+c_hat_k_after*U_prime_hat[x]\\n e_U_hat_temp = e ** U_hat_temp\\n Z += e_U_hat_temp - e_U_hat_dict[x]\\n e_U_hat_dict[x] = e_U_hat_temp\\n\\n update_start_time = time.time()\\n for x in self.B_:\\n self.theta_[x] = (1-c_hat_k_after)*theta_prime_tilde[x]+c_hat_k_after*theta_prime_hat[x]\\n self.theta_[phi] += 4*(self.etahat_[phi]-eta_)\\n self.compute_theta_perp()\\n self.compute_P()\\n kl=self.compute_KL()\\n calc_KL_time += time.time() - update_start_time \\n print(iter_ ,\\\":\\\", \\\"KL divergence: \\\", f'{kl:.16f}' ,\\\" time : %4.2f\\\"% (time.time()-calc_KL_time-start),flush=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f63554c2bb826513c5dea1ca73b00c83\",\n \"score\": \"0.5196821\",\n \"text\": \"def GS_classical(A):\\n\\n m, n = A.shape\\n\\n Q = np.zeros((m, n), dtype=A.dtype)\\n R = np.zeros((n, n), dtype=A.dtype)\\n\\n for j in range(n):\\n R[:j-1,j] = np.dot(Q[:,:j-1].T.conj(), A[:,j])\\n Q[:,j] = A[:, j] - np.dot(Q[:,:j-1], R[:j-1,j])\\n R[j,j] = np.linalg.norm(Q[:,j])\\n Q[:,j] /= R[j,j]\\n\\n return Q, R\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15d3fc1df384b92cb613df958ec1fa5a\",\n \"score\": \"0.51892686\",\n \"text\": \"def gmm_pdf(X, centroids, ccov, mc, individual=False):\\n if individual:\\n pdf = np.zeros((len(X), len(centroids)))\\n for i in range(len(centroids)):\\n pdf[:,i] = mulnormpdf(X, centroids[i], ccov[i]) * mc[i]\\n return pdf\\n else:\\n pdf = None\\n for i in range(len(centroids)):\\n pdfadd = mulnormpdf(X, centroids[i], ccov[i]) * mc[i]\\n if pdf==None:\\n pdf = pdfadd\\n else:\\n pdf = pdf + pdfadd\\n return pdf\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d31c54b12c1baf7755080f3f45d71dc6\",\n \"score\": \"0.51869303\",\n \"text\": \"def gauss(self, mu, sigma):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d31c54b12c1baf7755080f3f45d71dc6\",\n \"score\": \"0.51869303\",\n \"text\": \"def gauss(self, mu, sigma):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d31c54b12c1baf7755080f3f45d71dc6\",\n \"score\": \"0.51869303\",\n \"text\": \"def gauss(self, mu, sigma):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb29cc8a52060aec3c186eedb3104457\",\n \"score\": \"0.51843107\",\n \"text\": \"def gauss_pdf_mat(x):\\n sub = x - self.mean\\n r0 = inv_cov * sub\\n exponent = -0.5 * numpy.sum(sub.A * r0.A, axis=0)\\n if (numpy.shape(exponent) != (x.shape[1],)):\\n raise AssertionError(\\\"exponent has the wrong shape, should be (%d,), but is (%d,)\\\" % x.shape[1], exponent.shape[0])\\n g = const * (numpy.e ** exponent)\\n return g\",\n \"title\": \"\"\n },\n {\n \"docid\": \"445110a070b4c7d159d0c08e5b1db617\",\n \"score\": \"0.5181291\",\n \"text\": \"def marginal_covar_matrix(self, g, beta, gamma, logphi, alpha, eval_gradient=False):\\n # reshape g\\n g = g.reshape(self.dim.R, self.dim.N).T # reshape \\n g = np.column_stack((np.ones(self.dim.N), g)) # and append a col. of 1s\\n\\n # get the struct matrices\\n struct_mats = np.array([sum(brd*Ld for brd, Ld in zip(br, self.basis_mats))\\n for br in beta])\\n # Add an axis for n=0,...,N-1\\n At = struct_mats[None, :, :, :] * g[..., None, None]\\n At = At.sum(1)\\n\\n # Get the additive noise error term\\n #Gamma = np.diag(np.concatenate([gk*np.ones(self.dim.N) for gk in gamma]))\\n Gamma = np.kron(np.eye(self.dim.N), np.diag(gamma))\\n\\n # Get the initial state covariance matrix\\n C = np.kron(np.ones((self.dim.N, self.dim.N)),\\n np.diag(alpha))\\n \\n ####\\n # Construct the transformation matrix K\\n W = self._weight_matrix\\n K = sum(sparse.kron(sparse.kron(W[:, i][:, None],\\n sparse.eye(1, self.dim.N, i)),\\n At[i, ...])\\n for i in range(self.dim.N))\\n I = sparse.eye(self.dim.K)\\n\\n eifix = sparse.eye(1, self.dim.N, self.ifix)\\n K += sum(sparse.kron(sparse.kron(sparse.eye(1, self.dim.N, i).transpose(),\\n eifix),\\n I)\\n for i in range(self.dim.N))\\n #_K = np.array(K.todense())\\n if eval_gradient:\\n dCdg = [np.zeros((self.dim.N*self.dim.K, self.dim.N*self.dim.K))]*self.dim.N*self.dim.R \\n\\n #_dCdg = np.zeros((self.dim.N*self.dim.R,\\n # self.dim.N*self.dim.K,\\n # self.dim.N*self.dim.K))\\n\\n # Gradient of the operator K wrt g_rn\\n dK = [sparse.kron(sparse.kron(W[:, n][:, None],\\n sparse.eye(1, self.dim.N, n)),\\n struct_mats[r+1])\\n for r in range(self.dim.R)\\n for n in range(self.dim.N)]\\n #_dK = np.array([np.kron(W[:, n][:, None], struct_mats[r+1])\\n # for r in range(self.dim.R)\\n # for n in range(self.dim.N)])\\n\\n dCdgamma = np.zeros((self.dim.N*self.dim.K,\\n self.dim.N*self.dim.K,\\n self.dim.K))\\n dCdalpha = [np.kron(np.ones((self.dim.N, self.dim.N)),\\n np.diag(np.eye(N=1, M=self.dim.K, k=k).ravel()))\\n for k in range(self.dim.K)]\\n\\n for m in range(self.order):\\n\\n dCdg = np.array([K.dot(K.dot(dC_grn).T) for dC_grn in dCdg]) \\n for i, dK_grn in enumerate(dK):\\n kcdk = K.dot(dK_grn.dot(C).T)\\n dCdg[i] += kcdk + kcdk.T\\n\\n # update gradient of C wrt to g_{rn}\\n #CKt = K.dot(C).T\\n #print(K.shape, _dCdg.shape)\\n #expr = _K.dot(_dCdg)\\n #print(expr.shape)\\n #assert(False)\\n #expr = dK.dot(CKt.reshape(self.dim.N, self.dim.K, self.dim.N*self.dim.K)).sum(2)\\n #expr += expr.transpose(0, 2, 1)\\n #expr2 = K.dot(K.dot(dCdg).T)\\n #dCdg = expr + expr2\\n #dCdg = [K.dot(dK_grn.dot(C).T) + \\\\\\n # dK_grn.dot(K.dot(C).T) + \\\\\\n # K.dot(K.dot(dC_grn).T)\\n # for dK_grn, dC_grn in zip(dK, dCdg)]\\n\\n # update gradient of C wrt to Gamma\\n dCdgamma = np.dstack((K.dot(K.dot(dCdgamma[..., i]).T)\\n for i in range(dCdgamma.shape[-1])))\\n\\n diag_ek = np.zeros((self.dim.K, self.dim.K))\\n for k in range(self.dim.K):\\n diag_ek[k, k] = 1.\\n dCdgamma[..., k] += np.kron(np.eye(self.dim.N), diag_ek)\\n diag_ek[k, k] = 0.\\n\\n # update gradient of C wrt to alpha\\n dCdalpha = [K.dot(K.dot(dCdak).T) for dCdak in dCdalpha]\\n \\n C = K.dot(K.dot(C).T) + Gamma\\n\\n #print(\\\"...done.\\\")\\n dCdg = np.dstack(dCdg)\\n return (C,\\n dCdg,\\n dCdgamma,\\n np.dstack(dCdalpha))\\n\\n else:\\n for m in range(self.order):\\n C = K.dot(K.dot(C).T) + Gamma \\n return C\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f1f8a13fd6db519eedebdda1733c583\",\n \"score\": \"0.51747596\",\n \"text\": \"def build_generalized_fock_matrix(Num_MO,\\n h_MO,\\n g_MO,\\n one_rdm,\\n two_rdm ):\\n F = np.zeros((Num_MO, Num_MO))\\n for m in range(Num_MO):\\n for n in range(Num_MO):\\n for q in range(Num_MO):\\n F[m,n] += one_rdm[m, q] * h_MO[n,q]\\n for r in range(Num_MO):\\n for s in range(Num_MO):\\n F[m,n] += two_rdm[m,q,r,s] * g_MO[n,q,r,s]\\n return F\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11a0ad90c0475b7c3d80acbe06eca254\",\n \"score\": \"0.5172434\",\n \"text\": \"def testQg(self):\\n msg = self.case_name\\n self.case.sort_generators() # ext2int\\n _, _, gn = self.opf._remove_isolated(self.case)\\n Qg = self.opf._get_qgen_var(gn, self.case.base_mva)\\n\\n mpQg0 = mmread(join(DATA_DIR, self.case_name, \\\"opf\\\", \\\"Qg0.mtx\\\"))\\n mpQmin = mmread(join(DATA_DIR, self.case_name, \\\"opf\\\", \\\"Qmin.mtx\\\"))\\n mpQmax = mmread(join(DATA_DIR, self.case_name, \\\"opf\\\", \\\"Qmax.mtx\\\"))\\n\\n self.assertTrue(mfeq1(Qg.v0, mpQg0.flatten()), msg)\\n self.assertTrue(mfeq1(Qg.vl, mpQmin.flatten()), msg)\\n# self.assertTrue(mfeq1(Qg.vu, mpQmax.flatten()), msg)\\n self.assertTrue(mfeq1(Qg.vu, mpQmax.flatten()), msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e9ccae31f4dc6e4dfa70f81f1598ea1\",\n \"score\": \"0.5170576\",\n \"text\": \"def apply_G(self):\\n\\n alt_f = self.vec['u']('h_f')\\n alt = self.vec['p']('h')\\n\\n alt_f[0] = alt[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d4405672a59c838bc935b1da26d2f99\",\n \"score\": \"0.5170253\",\n \"text\": \"def mh_gaussian(init,ys,ts,iters,fi=None):\\n print(\\\"Running Metropolis Algorithm with Gaussian proposals.\\\")\\n D = len(init)\\n samples = np.zeros((iters,D))\\n my_dist = dist(init)\\n # initialize state and log-likelihood\\n state = init.copy()\\n Lp_state = my_dist.log_likelihood(ys,ts)\\n accepts = 0.\\n\\n cov = (0.1)**2 * np.eye(D)*1./D\\n for i in np.arange(0, iters):\\n if fi is not None:\\n write_samp(fi,state)\\n # propose a new state\\n prop = (np.random.multivariate_normal(state.ravel(), cov))\\n move_p = np.log(scipy.stats.multivariate_normal(state,cov).pdf(prop))\\n rev_p = np.log(scipy.stats.multivariate_normal(prop,cov).pdf(state))\\n my_dist.set_params(prop)\\n Lp_prop = my_dist.log_likelihood(ys,ts)\\n rand = np.random.rand()\\n\\n if np.log(rand) < min(1,((Lp_prop+rev_p) - (Lp_state+move_p))):\\n print (\\\"acct bc %s < %s (iter %s)\\\"%(np.log(rand),(Lp_prop-Lp_state),i))\\n accepts += 1\\n state = prop.copy()\\n print state\\n Lp_state = Lp_prop\\n else:\\n my_dist.set_params(state)\\n samples[i] = state.copy()\\n\\n print 'Acceptance ratio', accepts/iters\\n return samples\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f500066ec71462760a1d0c385aad5461\",\n \"score\": \"0.5167478\",\n \"text\": \"def gsl_eigen_genhermv_alloc(*args, **kwargs):\\n return _gslwrap.gsl_eigen_genhermv_alloc(*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d6a3c40227d11bf75b9113f674b3f6d\",\n \"score\": \"0.51667595\",\n \"text\": \"def G ( x , y , z , u , v , w ) :\\n result = 0.0 \\n result += x * x * y + x * y * y \\n result += z * z * u + z * u * u \\n result += v * v * w + v * w * w \\n result += x * z * w + x * u * v \\n ## result += y * z * w + y * u * w ## <-- typo in E.Byckling & K.Kajantie\\n result += y * z * v + y * u * w ## <-- correct line \\n result -= x * y * ( z + u + v + w ) \\n result -= z * u * ( x + y + v + w ) \\n result -= v * w * ( x + y + z + u )\\n ##\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f13202e74890870c8cace5e32b59e68\",\n \"score\": \"0.51664\",\n \"text\": \"def gauss_f(d, fwhm=0.529177):\\n sigma = fwhm / (2 * np.sqrt(2 * np.log(2)))\\n d2 = d * d\\n return np.exp(-1 / (2 * sigma**2) * d2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6218ad79a806807a819ee2c6b26d12f\",\n \"score\": \"0.5164684\",\n \"text\": \"def calc_F(H, G, m=None):\\n assert_square(H)\\n assert_square(G)\\n\\n n = H.shape[0]\\n I = np.identity(n)\\n IG = I-G\\n\\n if m:\\n F = (np.trace(H.dot(G).dot(H)) / (m-1)) / (np.trace(IG.dot(G).dot(IG)) / (n-m))\\n else:\\n F = (np.trace(H.dot(G).dot(H))) / np.trace(IG.dot(G).dot(IG))\\n\\n return F\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47f3dcdeb2f2505d5b8da196d6868f5a\",\n \"score\": \"0.5158682\",\n \"text\": \"def generate_global_matrix_1D(request):\\n\\n gmsh_buffer, cell_type = request.param\\n\\n diff = 0.1\\n vel = -1.0\\n r = vel/diff\\n\\n with tempfile.NamedTemporaryFile(suffix='.msh') as temp:\\n temp.write(gmsh_buffer.encode('utf-8'))\\n temp.flush()\\n points, cells, point_data, cell_data, field_data = meshio.read(temp.name)\\n\\n _points = np.array(points[:, 0], dtype='double', order='F')\\n _cells = np.array(cells[cell_type] + 1, dtype='int32', order='F')\\n # NOTE: Always change 0-based to 1-based indexing\\n\\n num_cells, num_pts_per_cell, num_pts = (\\n _cells.shape[0],\\n _cells.shape[1],\\n _points.shape[0]\\n )\\n\\n # NOTE: Can't do this anymore because fixture is parametrized\\n # # Sanity checks on matrix sizes\\n # assert num_cells == 52\\n # assert num_pts_per_cell == 2\\n # assert num_pts == 53\\n\\n # Zero Ie array\\n A = np.zeros(\\n (num_pts, num_pts),\\n dtype='double',\\n order='F'\\n )\\n\\n # Initialize function\\n f = helpers.set_assemble1D_c_args(\\n num_cells,\\n num_pts_per_cell,\\n num_pts\\n )\\n\\n print('\\\\nCalling: ', f.__name__, '\\\\n With N = ', num_pts)\\n # Calculate Global stiffness matrix `A`\\n f(\\n num_cells,\\n num_pts_per_cell,\\n num_pts,\\n _points,\\n _cells,\\n np.float64(diff),\\n np.float(vel),\\n A\\n )\\n\\n # A 1D line in Gmsh notation will always have end points as pt 0 and 1\\n left_list = [0]\\n right_list = [1]\\n\\n # Set boundary condtions in Ie matrix\\n for ii in left_list + right_list:\\n A[ii,:] = 0.; A[ii,ii] = 1.\\n\\n # Print condition number of `A`\\n print(' Cond(A) = ', np.linalg.cond(A), '\\\\n')\\n\\n # Set boundary condtions in RHS vector\\n b = np.zeros((num_pts,))\\n for ii in right_list:\\n b[ii] = 1.\\n\\n x = np.linalg.solve(A, b)\\n\\n return x, r, _points\",\n \"title\": \"\"\n },\n {\n \"docid\": \"982a3700e8d93dda58ba51fadea2b726\",\n \"score\": \"0.5156181\",\n \"text\": \"def _TFmdm_set_cosm(self):\\n omega_lambda = self.q0\\n omega_matter = self.m0\\n omega_lambda_z = self.qz\\n omega_matter_z = self.mz\\n theta_cmb = self.theta_cmb\\n redshift = self.z\\n h2 = self.h*self.h\\n omhh = self.m0*h2\\n obhh = self.b0*h2\\n onhh = self.n0*h2\\n f_baryon = self.b0/self.m0\\n f_hdm = self.n0/self.m0\\n f_cdm = 1.0 - f_baryon - f_hdm\\n f_cb = f_cdm + f_baryon\\n f_bnu = f_baryon + f_hdm\\n num_degen_hdm = self.nnu\\n# equality scale (Eqn 1 in EH99)\\n z_equality = 25000.0*omhh/theta_cmb**4 # Actually 1+z_eq \\n k_equality = 0.0746*omhh/theta_cmb**2\\n# drag epoch and sound horizon (Eqn 2)\\n z_drag_b1 = 0.313*pow(omhh,-0.419)*(1+0.607*pow(omhh,0.674))\\n z_drag_b2 = 0.238*pow(omhh,0.223)\\n z_drag = 1291*pow(omhh,0.251)/(1.0+0.659*pow(omhh,0.828))*\\\\\\n (1.0+z_drag_b1*pow(obhh,z_drag_b2))\\n# (Eqn 3)\\n y_drag = z_equality/(1.0+z_drag)\\n# (Eqn 4)\\n sound_horizon_fit = 44.5*np.log(9.83/omhh)/np.sqrt(1.0+10.0*np.power(obhh,0.75))\\n# Set up for the free-streaming & infall growth function */\\n# (Eqn 11)\\n p_c = 0.25*(5.0-np.sqrt(1+24.0*f_cdm))\\n p_cb = 0.25*(5.0-np.sqrt(1+24.0*f_cb))\\n# (Eqn 10)\\n# growth_k0 is used for calculating the free streaming scale\\n growth_k0 = (z_equality/(1.0+redshift)*2.5*\\n omega_matter_z/(pow(omega_matter_z,4.0/7.0)-\\n omega_lambda_z+(1.0+omega_matter_z/2.0)*\\n (1.0+omega_lambda_z/70.0)))\\n# for scale-independent growth of power spectrum \\n# (same as Density.growth_factor)\\n# growth_to_z0 = (z_equality*2.5*omega_matter/\\n# (pow(omega_matter,4.0/7.0)-\\n# omega_lambda +\\n# (1.0+omega_matter/2.0)*(1.0+omega_lambda/70.0)))\\n# growth_to_z0 = growth_k0/growth_to_z0\\n\\n# Compute small-scale suppression \\n# (Eqn 15)\\n alpha_nu = (f_cdm/f_cb*(5.0-2.*(p_c+p_cb))/(5.-4.*p_cb)*\\n pow(1+y_drag,p_cb-p_c)*\\n (1+f_bnu*(-0.553+0.126*f_bnu*f_bnu))/\\n (1-0.193*np.sqrt(f_hdm*num_degen_hdm)+\\n 0.169*f_hdm*pow(num_degen_hdm,0.2))*\\n (1+(p_c-p_cb)/2*(1+1/(3.-4.*p_c)/(7.-4.*p_cb))/(1+y_drag))\\n )\\n alpha_gamma = np.sqrt(alpha_nu)\\n# (Eqn 21)\\n beta_c = 1/(1-0.949*f_bnu)\\n# Done setting scalar variables\\n return(omhh, f_hdm, f_cb, growth_k0, p_cb, \\n alpha_gamma, sound_horizon_fit, beta_c)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abbe4f6aecc94299d8d616b1db8be9bd\",\n \"score\": \"0.5147316\",\n \"text\": \"def calculateGMag(x):\\n g_vector = np.array([x['g_x'], x['g_y'], x['g_z']])\\n return np.sqrt(g_vector.dot(g_vector))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"374289d702f40c9b3e42bf5dd38c2470\",\n \"score\": \"0.51468635\",\n \"text\": \"def initG_indicator(hindices, commlangdistros, innov = 0):\\n\\n g = commlangdistros[0,:].size\\n c = commlangdistros[:,0].size\\n n = hindices[0].shape[1]\\n# print \\\"n\\\", hindices[0].shape[1]\\n# Gassertions(C, commlangdistros, g)\\n \\n G = matlib.zeros((n,g))\\n\\n\\n randmat = nprandom.rand(G.shape[0],1)\\n\\n for b in range(0,n):\\n if randmat[b] < innov:\\n G[b,random.randint(0,g-1)] = 1\\n else:\\n comm = np.asarray(hindices[1])[0][b]\\n distr = np.asarray(commlangdistros[comm,:])[0]\\n G[b,:] = nprandom.multinomial(1, distr)\\n\\n return G\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57531a2ef18270590abf01d4616a5f79\",\n \"score\": \"0.51447994\",\n \"text\": \"def test_MonteCarlo():\\n\\tfrom diffusion_model import energy\\t#use diffusion model energy function as test for functionality of MC minimisation\\n\\timport nose.tools\\n\\tfrom nose.tools import assert_equal\\n\\tfrom nose.tools import assert_true\\n\\tfrom random import randint\\n\\timport random\\n\\timport matplotlib.pyplot as plt\\n\\t\\n\\t#minimum energy state doesn't change at absolute zero\\n\\tfor n in range (3): #try three times\\n\\t\\tdensity=[1.0,1.0,1.0,1.0]\\n\\t\\tdensity=MonteCarlo (\\\"diffusion_model\\\",\\\"energy\\\",density,0)\\n\\t\\tassert_equal(density,[1.0,1.0,1.0,1.0])\\n\\t\\n\\t#energy state lowers until minimum energy state is reached at absolute zero or iterated 100 times\\n\\tdensity=range(10)\\n\\tfor i in range (10):\\n\\t\\tdensity[i]=random.randint(1,10) #fill density with random integers\\n\\tif max(density)-min(density)<=1:\\n\\t\\tdensity_backup=density[:]\\n\\t\\tMonteCarlo(\\\"diffusion_model\\\",\\\"energy\\\",density,0)\\n\\t\\tassert_true(density=density_backup)\\n\\telse:\\n\\t\\tE0=energy(density)\\n\\t\\tx=100\\n\\t\\tMCEnergy=[]\\n\\t\\twhile (max(density)-min(density)>=2) and x>0: #if distribution is non-uniform energy minimum is not reached\\n\\t\\t\\tMCEnergy.append(energy(density))\\n\\t\\t\\tdensity=MonteCarlo(\\\"diffusion_model\\\",\\\"energy\\\",density,0)\\n\\t\\t\\tx-=1\\n\\t\\tprint \\\"energy changed by \\\", MCEnergy\\n\\t\\tplt.plot(MCEnergy)\\n\\t\\tplt.show()\\n\\tassert_true(E0>energy(density))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e650b8a358b8258ff4bbeef4cd13aeb\",\n \"score\": \"0.51432973\",\n \"text\": \"def evaluate_mog(self, x, y):\\n\\t\\tf = np.zeros(x.shape)\\n\\t\\tparams = self.pos.copy()\\n\\t\\tparams[:,2] = np.exp(params[:,2])\\n\\t\\tparams[:,3] = np.exp(params[:,3])\\n\\t\\tparams[:,4] = np.exp(0.5*params[:,4])\\n\\n\\t\\tfor i in range(self.num):\\n\\t\\t\\tgaussian = Gaussian(params=params[i, :])\\n\\t\\t\\tf += gaussian.evaluate(x, y)\\n\\t\\treturn f\",\n \"title\": \"\"\n },\n {\n \"docid\": \"147355d6af861a805ab2b384ef15cdaf\",\n \"score\": \"0.5142504\",\n \"text\": \"def locmat(nodes,de): #local stiffness (jacobian) and force (residual) over the reference element\\r\\n# print(nodes.shape)\\r\\n# print(de.shape)\\r\\n Xi=np.tile(GP.xi,OrdGauss)\\r\\n Eta=np.repeat(GP.xi,OrdGauss)\\r\\n dof=de.reshape(de.size,-1,1).repeat(len(Xi),axis=-1) #arranging dof for (dot) product with B (len(Xi) and not len(GP.xi)) !!!\\r\\n Wg=np.outer(GP.wght,GP.wght).flatten() \\r\\n Nshp=np.kron(np.eye(geom.nDim).reshape(geom.nDim,geom.nDim,-1),B.Ns(Xi,Eta)) #kron has to be taken on nDim (and not OrdGauss)\\r\\n# print(np.array(B.Ns(Xi,Eta)).shape)\\r\\n gDshpL=np.array(B.dN(Xi,Eta)).reshape(geom.nDim,int(Eltype[1]),-1) #local derivatives\\r\\n Je=np.einsum('ilk,lj->ijk',gDshpL,nodes.reshape(geom.nDim,-1).T) #computing the jacobian\\r\\n detJ=(Je[0,0,:]*Je[1,1,:]-Je[0,1,:]*Je[1,0,:])\\r\\n# print(detJ)\\r\\n# Jeinv=1/detJ*np.array([[Je[1,1,:],-Je[0,1,:]],[-Je[1,0,:],Je[0,0,:]]]) #avoid computing inverse on a loop (--check ?)\\r\\n Jeinv=1/detJ*np.einsum('ijk->jik',Je)\\r\\n gDshpG=np.einsum('ilk,ljk->ijk',Jeinv,gDshpL) #global derivatives \\r\\n Bmat=np.kron(np.eye(geom.nDim).reshape(geom.nDim,geom.nDim,-1),gDshpG) \\r\\n# B1=Bmat.copy()\\r\\n gradU=np.einsum('ilk,ljk->ijk',Bmat,dof) #remember that gradU is never symmetric !!!\\r\\n# print(gradU)\\r\\n \\\"\\\"\\\"\\r\\n Computing the deformation gradient (F12,F11,F22).T = B*de, and first piola (S) --> (S12,S11,S22), \\r\\n Multiplying by the Gauss-weights, and calculating the element residual\\r\\n \\\"\\\"\\\"\\r\\n F=gradU+np.array([1,0,0,1.]).reshape(-1,1,1).repeat(len(Xi),axis=-1) #computing F as (F11,F11,F22) shape: 3x1xNGP\\r\\n detF=F[0]*F[3]-F[1]*F[2] \\r\\n WpI1=dWdIi.DWDI1(*F)\\r\\n WpJ=dWdIi.DWDJ(*F)\\r\\n WppI1=dWdIi.D2WDI12(*F)\\r\\n WppJ=dWdIi.D2WDJ2(*F)\\r\\n Finv=np.array([F[3],-F[1],-F[1],F[0]])/detF #avoid computing inverse on the loop for the deformation gradient \\r\\n# Helpful variables: \\r\\n S=WpI1*2*F+(WpJ*detF).reshape(1,1,-1)*Finv[np.array([0,2,1,3],int)] #notice the swap of axes for transpose\\r\\n fac=Wg*detJ*geom.thck\\r\\n S*=fac #multiplying S by the determinant of the jacobian, thickness, and gauss-weights\\r\\n res=np.einsum('lik,ljk->ij',Bmat,S) #double contraction along axis 1 and 2 (of B)\\r\\n \\\"\\\"\\\"\\r\\n Computing the Consistent Tangent:D= B^T *C *B <-- Cijkl, check notes\\r\\n Cijkl = 4*W''_(I1) Fij Fkl + 2 W'_(I1) delik deljl + J**2*W''_(J) F-1ji F-1lk +J*W'_(J) F-1ji F-1lk - J W'_(J) F-1jk F-1li \\r\\n F = (F11,F12,f21,F22).T\\r\\n \\\"\\\"\\\"\\r\\n F11=F[0];F12=F[1];F21=F[2];F22=F[3]\\r\\n #This C does not have minor symmetry (relates S to F) , only major symmetry \\r\\n \\r\\n C1111=4*WppI1*F11*F11+2*WpI1+detF**2*WppJ*Finv[0]**2 #scalar addition to multi-dimensional array (--check??) \\r\\n C1112=4*WppI1*F11*F12+detF**2*WppJ*Finv[0]*Finv[2]\\r\\n C1121=4*WppI1*F11*F21+detF**2*WppJ*Finv[0]*Finv[1]\\r\\n C1122=4*WppI1*F11*F22+detF**2*WppJ*Finv[0]*Finv[3]+detF*WpJ*Finv[0]*Finv[3]-detF*WpJ*Finv[1]*Finv[2]\\r\\n C1212=4*WppI1*F12*F12+2*WpI1+detF**2*WppJ*Finv[2]**2\\r\\n C1221=4*WppI1*F12*F21+detF**2*WppJ*Finv[2]*Finv[1]+detF*WpJ*(Finv[2]*Finv[1] -Finv[3]*Finv[0])\\r\\n C1222=4*WppI1*F12*F22+detF**2*WppJ*Finv[2]*Finv[3]\\r\\n C2121=4*WppI1*F21*F21+2*WpI1+detF**2*WppJ*Finv[1]**2\\r\\n C2122=4*WppI1*F21*F22+detF**2*WppJ*Finv[2]*Finv[3]+detF*WpJ*(Finv[2]*Finv[3] -Finv[3]*Finv[1])\\r\\n C2222=4*WppI1*F22*F22+2*WpI1+detF**2*WppJ*Finv[3]**2\\r\\n \\r\\n C1111=C1111.flatten()\\r\\n C1112=C1112.flatten()\\r\\n C1121=C1121.flatten()\\r\\n C1122=C1122.flatten()\\r\\n C1212=C1212.flatten()\\r\\n C1221=C1221.flatten()\\r\\n C1222=C1222.flatten()\\r\\n C2121=C2121.flatten()\\r\\n C2122=C2122.flatten()\\r\\n C2222=C2222.flatten()\\r\\n \\r\\n C=np.array([[C1111,C1112,C1121,C1122],\\r\\n [C1112,C1212,C1221,C1222],\\r\\n [C1121,C1221,C2121,C2122],\\r\\n [C1122,C1222,C2122,C2222]])\\r\\n \\r\\n D=np.einsum('lik,lpk,pjk->ij',Bmat,C,Bmat) #Check the multiplication once for a simple case!\\r\\n# print(res)\\r\\n return {'K':D,\\r\\n 'F':res.flatten(),\\r\\n 'Stress':S,\\r\\n 'DefGrad':F,\\r\\n 'InptGlobal':np.einsum('ilj,l->ij',Nshp,nodes),\\r\\n 'NGP':len(Xi)}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42e165342059e3872fbe57292d7dca3b\",\n \"score\": \"0.5142341\",\n \"text\": \"def Me(self) -> np.ndarray:\\n ip, iw = self.gauss\\n psi = self.psi(ip)\\n dpsi = self.dpsi(ip)\\n J = self.J(dpsi)\\n dJ = self.dJ(J)\\n\\n Me = np.zeros((self._NODES * self._DOFS, self._NODES * self._DOFS), dtype=_FLOAT)\\n\\n for i in range(ip.shape[1]):\\n N = self.N(psi, i)\\n Me += self.material.rho * (N.T @ N) * dJ[i] * iw[i]\\n\\n return Me\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":889,"cells":{"query_id":{"kind":"string","value":"7f5f715f96636816652301188d4a7d08"},"query":{"kind":"string","value":"Return the linear transformation of `y` by `x` or `x` by `y` when one or both of `x` and `y` is a LinearTransform instance"},"positive_passages":{"kind":"list like","value":[{"docid":"8e445e1df5116820f63b2719a02595de","score":"0.5793339","text":"def dot_shape(x, y):\n if isinstance(x, LinearTransform):\n return dot_shape_from_shape(x, tuple(y.shape))\n elif isinstance(y, LinearTransform):\n return dot_shape_from_shape(tuple(x.shape), y)\n else:\n raise TypeError('One of x or y should be a LinearTransform')","title":""}],"string":"[\n {\n \"docid\": \"8e445e1df5116820f63b2719a02595de\",\n \"score\": \"0.5793339\",\n \"text\": \"def dot_shape(x, y):\\n if isinstance(x, LinearTransform):\\n return dot_shape_from_shape(x, tuple(y.shape))\\n elif isinstance(y, LinearTransform):\\n return dot_shape_from_shape(tuple(x.shape), y)\\n else:\\n raise TypeError('One of x or y should be a LinearTransform')\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"345bd03e2d81a8f108ff75220e9a3080","score":"0.66478336","text":"def transform(self, x, y):\n \n return numpy.array((x, y, 1)).dot(self._matrix.T)[0:2]","title":""},{"docid":"fa8bde99f2c683e4d0c5f040c3610a54","score":"0.61888194","text":"def _transform(self, X, y=None):\n Xt = X\n if y is None:\n for _, name, transform in self._iter():\n Xt = transform.transform(Xt)\n return Xt\n else:\n yt = y\n for _, name, transform in self._iter():\n if self._skip_transform(transform):\n continue\n if isinstance(transform, YTransformer):\n yt = transform.fit_transform(yt, Xt)\n elif isinstance(transform, XAndYTransformer) \\\n or isinstance(transform, XOrYTransformer):\n Xt, yt = transform.fit_transform(Xt, yt)\n else:\n Xt = transform.transform(Xt)\n return Xt, yt","title":""},{"docid":"604a70a828d7f86e7da3af8a59043cc7","score":"0.6155586","text":"def dot(x, y):\n if isinstance(x, LinearTransform):\n return x.rmul(y)\n elif isinstance(y, LinearTransform):\n return y.lmul(x)\n else:\n return theano.dot(x,y)","title":""},{"docid":"d1e2e76ac248b15f3fe52ba49d9c2e2d","score":"0.61270404","text":"def transl2(x, y=None):\n\n if np.isscalar(x):\n T = np.identity(3)\n T[:2, 2] = [x, y]\n return T\n elif argcheck.isvector(x, 2):\n T = np.identity(3)\n T[:2, 2] = argcheck.getvector(x, 2)\n return T\n elif argcheck.ismatrix(x, (3, 3)):\n return x[:2, 2]\n else:\n ValueError('bad argument')","title":""},{"docid":"17af9d93b6e6ebaca1b4ce1fc3f65f1f","score":"0.6072614","text":"def map_linear(self, x, x1, x2, y1, y2):\n m = (y2 - y1) / (x2 - x1)\n return y1 + m * (x - x1)","title":""},{"docid":"036bcff5e071f20c9213b74f2f95e346","score":"0.60661876","text":"def transl2(x=None, y=None):\n if type(x) is np.matrix:\n if common.ishomog(x, [3, 3]):\n return x[:2, 2]\n elif type(x) is list:\n if len(x) == 2:\n temp = np.matrix([[x[0]], [x[1]]])\n temp = np.concatenate((np.eye(2), temp), axis=1)\n return np.concatenate((temp, np.matrix([[0, 0, 1]])), axis=0)\n elif x is not None and y is not None:\n t = np.matrix([[x], [y]])\n return rt2tr(np.eye(2), t)","title":""},{"docid":"6519c308d44c63d4e0ade99223bafac4","score":"0.59372556","text":"def fit_transform(self, X, y):\n return self.fit(X, y).transform(X)","title":""},{"docid":"6519c308d44c63d4e0ade99223bafac4","score":"0.59372556","text":"def fit_transform(self, X, y):\n return self.fit(X, y).transform(X)","title":""},{"docid":"0b416f951b48cd1db0012a9df8ab197e","score":"0.5922566","text":"def fit_transform(self, X, y):\n return self.fit(X, y).transform(X, y)","title":""},{"docid":"79661e5a747e4bd148302284edfd07c3","score":"0.58838165","text":"def _linear(x):\n return x","title":""},{"docid":"1dc8bd313822acf238fdcb062104679f","score":"0.58141077","text":"def fit_transform(self, X, y=None, **trans_kwargs):\n return self.fit(X, y).transform(X, **trans_kwargs)","title":""},{"docid":"21a11d91aefdcc52a0cbd3d1c5b3e051","score":"0.57625264","text":"def translate(self, x, y):\n raise NotImplementedError","title":""},{"docid":"fafba04d80dffdced53979a3bdfc98f7","score":"0.57254046","text":"def coefToTransformation(xcoef, ycoef):\n return np.linalg.inv(np.array([[xcoef[1], xcoef[2], xcoef[0]],\n [ycoef[1], ycoef[2], ycoef[0]],\n [0, 0, 1]]))","title":""},{"docid":"65d80324de6d127517d0343b86f84baf","score":"0.56842816","text":"def fit_transform(self, X, y=None, **fit_params):\n return self.fit(X, y).transform(X)","title":""},{"docid":"545129a28728ae0b31e0bdc87be5b323","score":"0.56673855","text":"def fit_transform(self, X, y=None):\n self.fit(X, y)\n return self.transform(X)","title":""},{"docid":"1bf532328d6bb21597494a37e774d3de","score":"0.56484675","text":"def _spatial_transform(transform, x, y):\n x = x - transform.origin[0]\n y = y - transform.origin[1]\n\n theta = -transform.rotation * np.pi/180\n x, y = (x * np.cos(theta) - y * np.sin(theta),\n y * np.cos(theta) + x * np.sin(theta))\n\n if transform.magnification is not None:\n x = x / transform.magnification\n y = y / transform.magnification\n\n if transform.x_reflection:\n y = -y\n\n return x, y","title":""},{"docid":"74540150c323740a2011e48dd61d4a52","score":"0.56308955","text":"def fit_transform(self, X, y=None, **inputs):\n return self.fit(X, y=y, **inputs).transform(X, y)","title":""},{"docid":"f5fba4bb25605c43d9cc0788ccfb4f82","score":"0.5621015","text":"def comp4983_lin_reg_fit(x: np.ndarray, y: np.ndarray):\n return np.dot(np.dot(np.linalg.inv(np.dot(x.transpose(), x)), x.transpose()), y)","title":""},{"docid":"80ed02b562471a2c5adb13f5e5c378ab","score":"0.5608641","text":"def linear(x):\n return x","title":""},{"docid":"80ed02b562471a2c5adb13f5e5c378ab","score":"0.5608641","text":"def linear(x):\n return x","title":""},{"docid":"92a290b37fdc25d18dfcfd39d993300c","score":"0.56079733","text":"def translate(self, x, y, transformFrame=True):\n return self.transform(Transform(1, 0, 0, 1, x, y), transformFrame=transformFrame)","title":""},{"docid":"7216cf900cd91b7ba23e44b662e4d12c","score":"0.56032175","text":"def fit_transform(self, x, y, p=[]):\n\n self.fit(x, y, p)\n\n return self.transform(x)","title":""},{"docid":"ea6a01624c35ec9e687596895f204028","score":"0.5596607","text":"def transform(self, X, y=...):\n ...","title":""},{"docid":"f1c4872bc15c9b6606a681bfedbe0f92","score":"0.55920976","text":"def transform(self,\r\n x: np.ndarray,\r\n y: Optional[np.ndarray] = None) -> Union[np.ndarray, Tuple[np.ndarray, np.ndarray]]:\r\n\r\n if self.input_vector:\r\n x = x.reshape(-1, 1)\r\n\r\n x_new = []\r\n for i in x[:, 0]:\r\n values_list = list(map(self.label_dict.get, i.split(self.sep)[-self.order:]))\r\n while len(values_list) < self.order:\r\n values_list = [self.label_dict[self.r_just_string]] + values_list\r\n x_new.append(values_list)\r\n\r\n if y is None:\r\n if self.return_vector:\r\n return np.array(x_new).ravel()\r\n else:\r\n return np.array(x_new)\r\n else:\r\n return np.array(x_new), np.vectorize(self.label_dict.get)(y)","title":""},{"docid":"e4647c3dc32232fc4b2581c2b4b16862","score":"0.5582409","text":"def conv( x, y, trans_matrix=None ):\n\n\tif trans_matrix:\n\t\txt = trans_matrix[0][0] * x + trans_matrix[0][1] * y + trans_matrix[0][2]\n\t\tyt = trans_matrix[1][0] * x + trans_matrix[1][1] * y + trans_matrix[1][2]\n\t\treturn xt, yt\n\telse:\n\t\treturn x, y","title":""},{"docid":"cfac40ece33cfbf69f870fe945be1a82","score":"0.5582098","text":"def _fit_transform_one(transformer,\n X,\n y,\n weight,\n message_clsname='',\n message=None,\n **fit_params):\n with _print_elapsed_time(message_clsname, message):\n if hasattr(transformer, 'fit_transform'):\n res = transformer.fit_transform(X, y, **fit_params)\n else:\n res = transformer.fit(X, y, **fit_params).transform(X)\n\n if (isinstance(transformer, XAndYTransformer) or isinstance(transformer, XOrYTransformer)) and weight is None:\n return *res, transformer\n elif (isinstance(transformer, XAndYTransformer) or isinstance(transformer, XOrYTransformer)) and weight is not None:\n res = res * weight\n return *res, transformer\n elif weight is None:\n return res, transformer\n else:\n return res * weight, transformer","title":""},{"docid":"4fe97b03cab4543dbe8b4affee73223f","score":"0.55807245","text":"def linear_func(xq: float, x0: float, x1: float, y0: float, y1: float):\n slope = (y1 - y0) / (x1 - x0)\n return (slope * (xq - x0)) + y0","title":""},{"docid":"2a2bf193d825d471d0ef415e7e27bea9","score":"0.5578561","text":"def transform(self, x, y, wcsID):\n\n self.commit()\n if wcsID == 0: return (x, y, wcsID)\n\n # Since transformation is defined by a direct linear (or log) mapping\n # between two rectangular windows, apply the usual linear\n # interpolation.\n\n # log scale does not affect the w numbers at all, a plot\n # ranging from 10 to 10,000 will have wx1,wx2 = (10,10000),\n # not (1,4)\n\n return (self.transform1d(coord=x,dimension='x',wcsID=wcsID),\n self.transform1d(coord=y,dimension='y',wcsID=wcsID),\n wcsID)","title":""},{"docid":"6efff56618ab65237a4402c413928f0d","score":"0.5553774","text":"def fit_transform(self, X, y=None, **fit_params):\n # fit method of arity 2 (supervised transformation)\n return self.fit(X, y, **fit_params).transform(X)","title":""},{"docid":"8d77c1600b995b4c67e2bb81116ad5bd","score":"0.55082804","text":"def fit_transform(self, X, y=None, **fit_params):\n # non-optimized default implementation; override when a better\n # method is possible for a given clustering algorithm\n if y is None:\n # fit method of arity 1 (unsupervised transformation)\n return self.fit(X, **fit_params).transform(X)\n else:\n # fit method of arity 2 (supervised transformation)\n return self.fit(X, y, **fit_params).transform(X)","title":""},{"docid":"3e2a01f266c56af4eeaba1d718578fd6","score":"0.54977244","text":"def fit_transform(self, X, y=None, **fit_params):\r\n # non-optimized default implementation; override when a better\r\n # method is possible for a given clustering algorithm\r\n if y is None:\r\n # fit method of arity 1 (unsupervised transformation)\r\n return self.fit(X, **fit_params).transform(X)\r\n else:\r\n # fit method of arity 2 (supervised transformation)\r\n return self.fit(X, y, **fit_params).transform(X)","title":""},{"docid":"f6c1910b2a7b382ee630ae23e9a134b4","score":"0.54848564","text":"def translate(x):\n\n x = T.as_tensor_variable(x)\n\n m = T.eye(4, 4)\n m = T.set_subtensor(m[0,3], x[0])\n m = T.set_subtensor(m[1,3], x[1])\n m = T.set_subtensor(m[2,3], x[2])\n\n mInv = T.eye(4, 4)\n mInv = T.set_subtensor(mInv[0,3], -x[0])\n mInv = T.set_subtensor(mInv[1,3], -x[1])\n mInv = T.set_subtensor(mInv[2,3], -x[2])\n\n return Transform(m, mInv)","title":""},{"docid":"6346997d9b8902c970b07fd5be330f36","score":"0.5482218","text":"def linear(x, p0, p1):\r\n return p1*x + p0","title":""},{"docid":"16c2cd29ac51f63fbf99a763173e5596","score":"0.5462316","text":"def fit_transform(self, X, y=None, **fit_params):\n self.fit(X, y, **fit_params)\n return self.transform(X)","title":""},{"docid":"16c2cd29ac51f63fbf99a763173e5596","score":"0.5462316","text":"def fit_transform(self, X, y=None, **fit_params):\n self.fit(X, y, **fit_params)\n return self.transform(X)","title":""},{"docid":"9422ce42921eb3fc8c3738d5d69412f6","score":"0.54554415","text":"def get_x_to_y(x,y):\n return ROOT.TVector3(y.X()-x.X(),y.Y()-x.Y(),y.Z()-x.Z())","title":""},{"docid":"0ea8b310040ea2a81cc13606ed6d6b2a","score":"0.54348564","text":"def linear_transformation(arg0, arg1=None, arg2=None, side='left'):\n from sage.matrix.constructor import matrix\n from sage.modules.module import is_VectorSpace\n from sage.modules.free_module import VectorSpace\n from sage.categories.homset import Hom\n from sage.symbolic.ring import SymbolicRing\n from sage.modules.vector_callable_symbolic_dense import Vector_callable_symbolic_dense\n from inspect import isfunction\n\n if not side in ['left', 'right']:\n raise ValueError(\"side must be 'left' or 'right', not {0}\".format(side))\n if not (is_Matrix(arg0) or is_VectorSpace(arg0)):\n raise TypeError('first argument must be a matrix or a vector space, not {0}'.format(arg0))\n if is_Matrix(arg0):\n R = arg0.base_ring()\n if not R.is_field():\n try:\n R = R.fraction_field()\n except (NotImplementedError, TypeError):\n msg = 'matrix must have entries from a field, or a ring with a fraction field, not {0}'\n raise TypeError(msg.format(R))\n if side == 'right':\n arg0 = arg0.transpose()\n side = 'left'\n arg2 = arg0\n arg0 = VectorSpace(R, arg2.nrows())\n arg1 = VectorSpace(R, arg2.ncols())\n elif is_VectorSpace(arg0):\n if not is_VectorSpace(arg1):\n msg = 'if first argument is a vector space, then second argument must be a vector space, not {0}'\n raise TypeError(msg.format(arg1))\n if arg0.base_ring() != arg1.base_ring():\n msg = 'vector spaces must have the same field of scalars, not {0} and {1}'\n raise TypeError(msg.format(arg0.base_ring(), arg1.base_ring()))\n\n # Now arg0 = domain D, arg1 = codomain C, and\n # both are vector spaces with common field of scalars\n # use these to make a VectorSpaceHomSpace\n # arg2 might be a matrix that began in arg0\n D = arg0\n C = arg1\n H = Hom(D, C, category=None)\n\n # Examine arg2 as the \"rule\" for the linear transformation\n # Pass on matrices, Python functions and lists to homspace call\n # Convert symbolic function here, to a matrix\n if is_Matrix(arg2):\n if side == 'right':\n arg2 = arg2.transpose()\n elif isinstance(arg2, (list, tuple)):\n pass\n elif isfunction(arg2):\n pass\n elif isinstance(arg2, Vector_callable_symbolic_dense):\n args = arg2.parent().base_ring()._arguments\n exprs = arg2.change_ring(SymbolicRing())\n m = len(args)\n n = len(exprs)\n if m != D.degree():\n raise ValueError('symbolic function has the wrong number of inputs for domain')\n if n != C.degree():\n raise ValueError('symbolic function has the wrong number of outputs for codomain')\n arg2 = [[e.coefficient(a) for e in exprs] for a in args]\n try:\n arg2 = matrix(D.base_ring(), m, n, arg2)\n except TypeError as e:\n msg = 'symbolic function must be linear in all the inputs:\\n' + e.args[0]\n raise ValueError(msg)\n # have matrix with respect to standard bases, now consider user bases\n images = [v*arg2 for v in D.basis()]\n try:\n arg2 = matrix([C.coordinates(C(a)) for a in images])\n except (ArithmeticError, TypeError) as e:\n msg = 'some image of the function is not in the codomain, because\\n' + e.args[0]\n raise ArithmeticError(msg)\n else:\n msg = 'third argument must be a matrix, function, or list of images, not {0}'\n raise TypeError(msg.format(arg2))\n\n # arg2 now compatible with homspace H call method\n # __init__ will check matrix sizes versus domain/codomain dimensions\n return H(arg2)","title":""},{"docid":"b6f43f7d78d90d037f01998225679f50","score":"0.5431147","text":"def htranslation2(x=0, y=0):\n return sp.Matrix([[1, 0, x],\n [0, 1, y],\n [0, 0, 1]])","title":""},{"docid":"cb0ea76e8b38d8df6d6311237c47164f","score":"0.54303443","text":"def asLinearOperator(X, parallel=False):\n if not parallel: \n def matvec(v): \n return X.dot(v)\n \n def rmatvec(v): \n return X.T.dot(v)\n \n def matmat(V): \n return X.dot(V)\n \n def rmatmat(V): \n return X.T.dot(V)\n else:\n def matvec(v): \n return X.pdot(v)\n \n def rmatvec(v): \n return X.T.pdot(v)\n \n def matmat(V): \n return X.pdot(V)\n \n def rmatmat(V): \n return X.T.pdot(V)\n \n return GeneralLinearOperator(X.shape, matvec, rmatvec, matmat, rmatmat, X.dtype)","title":""},{"docid":"e1947e5e2a9321b4092295a0df3b53fe","score":"0.5429983","text":"def mmd_linear(X, Y):\r\n delta = X.mean(0) - Y.mean(0)\r\n return delta.dot(delta.T)","title":""},{"docid":"698e86b357b2602de9f485298490b1c9","score":"0.5415955","text":"def transform(self, x):\n return self._transform(x, inverse=False)","title":""},{"docid":"99a7b47409b93c7660d9f9a6ce9246a4","score":"0.53800523","text":"def linear_regression(x, y):\r\n model = LinearRegression()\r\n return get_theta_vector(model.fit(x, y))","title":""},{"docid":"b2e9316c55d257b005dbdb14e6c7d566","score":"0.5355075","text":"def warp_xy(x, y, old_crs, new_crs):\n if old_crs == new_crs:\n return x,y\n\n old_crs_proj = pyproj.Proj(old_crs)\n new_crs_proj = pyproj.Proj(new_crs)\n return pyproj.transform(old_crs_proj, new_crs_proj, x,y)","title":""},{"docid":"3c2842af3a7cb509a3768f515091206e","score":"0.5347562","text":"def linear_regression(self, dataset):\n\n x,y = dataset\n # solution (explain how to achieve this analitical expression)\n w = np.dot(np.linalg.inv(np.dot(x.T,x)), np.dot(x.T,y))\n return w","title":""},{"docid":"c2adc562449d6978109120f28938e605","score":"0.53445894","text":"def dot_shape_from_shape(x, y):\n if isinstance(x, LinearTransform):\n if type(y) != tuple:\n raise TypeError('y should be tuple', y)\n return x.col_shape() + x.split_right_shape(y, False)[1]\n elif isinstance(y, LinearTransform):\n if type(x) != tuple:\n raise TypeError('x should be tuple', x)\n return y.split_left_shape(x, False)[0] + y.row_shape()\n else:\n raise TypeError('One of x or y should be a LinearTransform')","title":""},{"docid":"08bafeda826a360b52decb448a38d781","score":"0.5338421","text":"def translate(self, x=0, y=0):\n fx, fy = self.functions\n return self.func((fx + x, fy + y), self.limits)","title":""},{"docid":"01ced9b525c98325de3dcd6ec6c91c72","score":"0.5335345","text":"def transl(x=None, y=None, z=None):\n if type(x) is np.matrix:\n if common.ishomog(x, [4, 4]):\n return x[:3, 2]\n elif type(x) is list:\n if len(x) == 3:\n temp = np.matrix([[x[0]], [x[1]], [x[2]]])\n temp = np.concatenate((np.eye(3), temp), axis=1)\n return np.concatenate((temp, np.matrix([[0, 0, 0, 1]])), axis=0)\n # todo trajectory case\n elif x is not None and y is not None and z is not None:\n t = np.matrix([[x], [y], [z]])\n return rt2tr(np.eye(3), t)\n else:\n raise AttributeError(\"Invalid arguments\")","title":""},{"docid":"d5179424098e0ecb03a92769a1ee4603","score":"0.5329425","text":"def transform(self, y, X=None, **kwargs):\n check_is_fitted(self, \"lam1_\")\n\n lam1 = self.lam1_\n lam2 = self.lam2_\n\n y, exog = self._check_y_X(y, X)\n y += lam2\n\n neg_mask = y <= 0.\n if neg_mask.any():\n action = self.neg_action\n msg = \"Negative or zero values present in y\"\n if action == \"raise\":\n raise ValueError(msg)\n elif action == \"warn\":\n warnings.warn(msg, UserWarning)\n y[neg_mask] = self.floor\n\n if lam1 == 0:\n return np.log(y), exog\n return (y ** lam1 - 1) / lam1, exog","title":""},{"docid":"b1075bd44587e2002f2faefff4e7a92f","score":"0.5313699","text":"def fit_transform(self, X: Any, y: Any = None) -> np.ndarray:\n self.fit(X, y)\n\n return self.transform(X, y)","title":""},{"docid":"2cc63fc6a334092265052c1a16c153c5","score":"0.5301931","text":"def linear_interpolation(y1, x1, y2, x2, v11, v12, v21, v22, p_y, p_x):\n # x interpolation\n if x1 == x2:\n p1 = v11\n p2 = v21\n else:\n diffx = (x2 - x1)\n rat1 = (x2 - p_x) / diffx\n rat2 = (p_x - x1) / diffx\n\n p1 = v11 * rat1 + v12 * rat2\n p2 = v21 * rat1 + v22 * rat2\n\n # y interpolation\n if y1 == y2:\n p = p1\n else:\n diffy = (y2 - y1)\n rat1 = (y2 - p_y) / diffy\n rat2 = (p_y - y1) / diffy\n\n p = p1 * rat1 + p2 * rat2\n\n return p","title":""},{"docid":"a8c422513293361f99988172e3a50ed5","score":"0.5298343","text":"def r_linear_regression(x, y):\n x_dim = x.shape[1]\n for i in range(x_dim):\n xr_i = FloatVector(x[:, i])\n robjects.globalenv[\"x%d\" % i] = xr_i\n yr = FloatVector(y.view(-1))\n robjects.globalenv[\"y\"] = yr\n formula = \"y~\" + \"+\".join(\"x%d\" % i_ for i_ in range(x_dim))\n robjects.r(\"fit <- lm(%s)\" % formula)\n robjects.r(\"lin_reg_out <- coef(fit)\")\n lin_reg_out = robjects.r[\"lin_reg_out\"]\n intercept = float(lin_reg_out[0])\n weights = torch.DoubleTensor(lin_reg_out[1:])\n weights[torch.isnan(weights)] = 0.0\n return LinearFunction(intercept, weights)","title":""},{"docid":"eec4fcef1023921d928f77ea9c345a54","score":"0.52963626","text":"def invert_point(x, y, transform):\r\n return transform.inverted().transform_point((x, y))","title":""},{"docid":"a09d400451f0063cecb358b31cedf907","score":"0.5287271","text":"def transform(self, X, y=None):\n return X.todense()","title":""},{"docid":"86a01aaef5e853c0351baef90de71199","score":"0.52816355","text":"def fromTranslate(self, p_float, p_float_1): # real signature unknown; restored from __doc__\r\n return QTransform","title":""},{"docid":"39ca3e0cf14af46ab1e7cb650dd3efd2","score":"0.52799237","text":"def regressionLine(x, y):\n x2 = [float(i**2) for i in x]\n y2 = [float(i**2) for i in y]\n xy = [x[i]*y[i] for i in xrange(0,len(x))]\n a = (sum(x2)*sum(y) - sum(x)*sum(xy))/(len(x)*sum(x2) - sum(x)**2)\n b = (len(x)*sum(xy) - sum(x)*sum(y))/(len(x)*sum(x2) - sum(x)**2)\n return (round(a,4),round(b,4))","title":""},{"docid":"125c1e93354f846e9bbea0a0c2d624bb","score":"0.52734256","text":"def linterp(x1, x2, y1, y2, x):\n return tf.div_no_nan((x * (y2 - y1) + y1 * x2 - x1 * y2), (x2 - x1))","title":""},{"docid":"06ed52f661f42d7a629443f3e0e5b0f2","score":"0.5272565","text":"def lin_interp(X, x1, y1, x2, y2):\n\n m = (y2 - y1) / (x2 - x1)\n Y = m * (X - x1) + y1\n return Y","title":""},{"docid":"8f8611dc2081d7c076624c5a9d048ed9","score":"0.52725315","text":"def translate(self, x, y):\n p = self.points.copy()\n p[0::2] = p[0::2] + x\n p[1::2] = p[1::2] + y\n\n return self.copy(p)","title":""},{"docid":"b017400b3741aafd12495a76c706f6d6","score":"0.52689767","text":"def linear_regression_0(x, y):\n n = len(x)\n sum_x = sum(x)\n sum_y = sum(y)\n sum_x2 = sum(x**2)\n sum_xy = sum(x*y)\n delta = n*sum_x2-sum_x**2\n m = (n*sum_xy-sum_x*sum_y)/delta\n c = (sum_x2*sum_y-sum_x*sum_xy)/delta\n return m, c","title":""},{"docid":"83a7d325af13e1e85b55f13c3a7fe475","score":"0.5267445","text":"def rotate_to_source(linear: Dict[Tuple[str, int], ITeg],\n target_vars: List[TegVar],\n source_vars: List[TegVar]) -> List[ITeg]:\n rotation = []\n num_vars = len(target_vars)\n exprs = [linear[(s_var.name, s_var.uid)] for s_var in source_vars]\n for source_index in range(num_vars):\n if source_index == 0:\n rotation.append(sum((Const(1) if i == 0 else Const(-1)) * exprs[i] * target_vars[i] for i in range(num_vars)))\n elif source_index < len(linear):\n i = source_index\n inverse_rotation = sum(((Const(1) if i == j else Const(0))\n - (exprs[i] * exprs[j]) / (1 + exprs[0])) * target_vars[j]\n for j in range(1, num_vars))\n rotation.append(inverse_rotation + exprs[i] * target_vars[0])\n else:\n raise ValueError(f'Requested source coordinate index: {source_index} is invalid.')\n\n return rotation","title":""},{"docid":"ea6aa49b3001a80ad302ba7a4f612122","score":"0.5250189","text":"def fit_linear_regression(X: np.array, y: np.array) -> (np.array, np.array):\r\n w = np.linalg.pinv(X).T @ y\r\n s = svd(X, compute_uv=False)\r\n return w,s","title":""},{"docid":"740ee0c68e59906e81db190c4acaf793","score":"0.5248054","text":"def linear(params, x, data=None):\n\ta = params['slope'].value\n\tb = params['offs'].value\n\tmodel = a*x + b\n\tif data is None:\n\t\treturn model\n\telse:\n\t\treturn model-data","title":""},{"docid":"179f1d49f1096918f085b67f5aebd4dc","score":"0.52468514","text":"def invert_transform(self, Y, X=None):","title":""},{"docid":"29277c7db8d402cbf45f543542f92fe2","score":"0.52465487","text":"def to_linear_transformation(self, reset=False):\n tf = np.dot(self.B * self.D, self.B.T)\n if reset:\n self.reset()\n return tf","title":""},{"docid":"a6a2f6543f0ca5b521f5e3b474fc898d","score":"0.5240029","text":"def transform(x,y,theta):\n s = np.sin(theta)\n c = np.cos(theta)\n return np.matrix([[c,-s,x],[s,c,y],[0,0,1]])","title":""},{"docid":"4c84ec28b33b33a292a996a1cb857821","score":"0.52322036","text":"def linear_interpolation (x1: float,\n x2: float,\n y1: float,\n y2: float,\n x3: float) -> float:\n\n f_linear = interp1d([x1, x2], [y1, y2])\n y3 = float(f_linear(x3))\n\n return y3","title":""},{"docid":"b69a4c9ac7eecf77bd74790c76afda85","score":"0.522647","text":"def _make_transform(rxy, gxy, bxy, wxy):\n xy = np.array([rxy, gxy, bxy]).transpose()\n xyz = np.append(xy, (1 - xy[0] - xy[1])[np.newaxis, :], axis = 0)\n wxyz = np.append(wxy, 1 - wxy[0] - wxy[1])\n wXYZ = wxyz / wxyz[1]\n I = np.linalg.inv(xyz)\n scale = np.inner(I, wXYZ)\n M = xyz * scale[np.newaxis, :]\n I *= (1 / scale)[:, np.newaxis]\n return M, I","title":""},{"docid":"9ea196ef6e48af2af09fe39c1a20b478","score":"0.52213454","text":"def forward(x: Any, y: Any) -> Any:\n if isinstance(x, torch.Tensor) or isinstance(y, torch.Tensor):\n out = torch.mul(x, y)\n else:\n out = x * y\n return out","title":""},{"docid":"ba23f58b15766521c74a4fb8e1ca8f83","score":"0.52193224","text":"def transformPoint(x, y, from_srs, to_srs):\n coordXform = osr.CoordinateTransformation(from_srs, to_srs)\n yRound = round(y, 8)\n xRound = round(x, 8)\n\n result = coordXform.TransformPoint(xRound, yRound)\n\n gx = result[0]\n gy = result[1]\n\n return gx, gy","title":""},{"docid":"9de4b9d0af4f1ba17fd554d549867fbe","score":"0.52089655","text":"def lin_interp(x, x0, x1, y0, y1):\n y = y0 + (y1-y0)*(x-x0)/(x1-x0)\n \n return y","title":""},{"docid":"e9c6fb973a55d4f73c88d913af5a508e","score":"0.5193385","text":"def translate_matrix((a,b,c,d,e,f), (x,y)):\n return (a,b,c,d,x*a+y*c+e,x*b+y*d+f)","title":""},{"docid":"c8ff3dfc15a06975def89dd6a9b70d98","score":"0.5190883","text":"def to_linear_transformation(self, reset=False):\n tf = self.C**0.5\n if reset:\n self.reset()\n return tf","title":""},{"docid":"3111e1e27da9a1e1fec617edc752cee7","score":"0.5185958","text":"def Mxform(x1,y1,x2,y2):\n return Jones.toMueller([[dot(x2,x1), dot(x2, y1)], [dot(y2,x1), dot(y2,y1)]])","title":""},{"docid":"070323b9c290f430a524142f5e3f3c59","score":"0.51817906","text":"def fit_transform(self, X, y, verbose=False, fit_kwargs=None):\n self._fit(X, y, verbose=verbose, fit_kwargs=fit_kwargs)\n return self._transform(verbose=verbose)","title":""},{"docid":"964cb557be4d061a161ec47a3a145d45","score":"0.517799","text":"def line_equation(x1, y1, x2, y2):\n if x2 == x1:\n return (1, 0, -x1) # special case for vertical line\n m = (y2 - y1) / (x2 - x1)\n a = m\n b = -1\n c = -m * x1 + y1\n return (a, b, c)","title":""},{"docid":"ae1fef3a1872e39f54f55b16e8d47235","score":"0.5173923","text":"def linear_transformation(self, A):\n assert A.shape[0] == self.N \n return MultivariateNormal(A.T @ self.u, A.T @ self.C @ A)","title":""},{"docid":"3da223ba2015997747c6a3746f0c1a3b","score":"0.5141163","text":"def fit_transform(self, X, y=None, sample_weight=None, sample_cost=None):\n\n\t\treturn self.fit(X, y=y, sample_weight=sample_weight, \n\t\t\tsample_cost=sample_cost).transform(X, y=y, \n\t\t\tsample_weight=sample_weight)","title":""},{"docid":"88e623c49a4e0960bcbbd8965aa05f88","score":"0.5136432","text":"def linear(self):\n return self._linear","title":""},{"docid":"671f5c82a80f429ac3943baf9fd52347","score":"0.5136224","text":"def inverse_transform(self, x):\n return self._transform(x, inverse=True)","title":""},{"docid":"f833a7fe581a9b9864a1fb3d1857a59e","score":"0.5126777","text":"def linear_interpolator(xs, ys):\n\n def return_function(xp):\n \"\"\" xp is the x value to find the interpolation \"\"\"\n for i in range(len(xs)-1):\n # Define interpolating range for this iteration\n x0 = xs[i]\n x1 = xs[i+1]\n if not (x0 <= xp <= x1):\n continue\n y0 = ys[i]\n y1 = ys[i+1]\n xd = (xp - x0)/float(x1 - x0)\n yp = y0*(1-xd) + y1*xd\n return yp\n return None\n return return_function","title":""},{"docid":"46253b212b9c308d8b70a28749c3d07f","score":"0.5123316","text":"def least_squares(y, tx):\r\n a = tx.T.dot(tx)\r\n b = tx.T.dot(y)\r\n return np.linalg.solve(a, b)","title":""},{"docid":"96be74a8ac7fbefb74414b20b0dedcc5","score":"0.51219106","text":"def lmodel(x, a, b):\n y = a + b*x\n return y","title":""},{"docid":"49d0422787a01e260b9e2bd7a859f466","score":"0.51181537","text":"def inverse_transform(self, y, X=None):\n check_is_fitted(self, \"lam1_\")\n\n lam1 = self.lam1_\n lam2 = self.lam2_\n\n y, exog = self._check_y_X(y, X)\n if lam1 == 0:\n return np.exp(y) - lam2, exog\n\n numer = y * lam1 # remove denominator\n numer += 1. # add 1 back to it\n de_exp = numer ** (1. / lam1) # de-exponentiate\n return de_exp - lam2, exog","title":""},{"docid":"3ceb65633c739ba2cb2c7f2aae090821","score":"0.511662","text":"def __solve_linear_syste(self, A, b):\n dx = np.linalg.solve(A, b)\n return dx","title":""},{"docid":"bbe72778f5cc04b44b4e015afda61e21","score":"0.51158327","text":"def fit_line(x, y):\n slope, intercept, r, p, stderr = sps.linregress(x, y)\n return slope, intercept, r**2","title":""},{"docid":"486f6e0f7ded4508cbca25577e86ee94","score":"0.511329","text":"def linear_fit(y, x, m=None, method='robust'):\n Model = RLM if method == 'robust' else OLS\n if m is None:\n X = np.empty((len(y), 2))\n X[:, 0] = x\n X[:, 1] = 1\n res = Model(y, X).fit()\n return res.params\n else:\n X = np.ones(len(y))\n res = Model(np.array(y) - m * np.array(x), X).fit()\n return m, res.params[0]","title":""},{"docid":"098ed8e16c52479f34c4336cb344fc9f","score":"0.51080406","text":"def transform_y(self, y):\n self.logging('transforming y...')\n y_new = y.copy()\n for t in self.transformers:\n self.logging('-> {}'.format(t.name))\n y_new = t.transform_y(y_new)\n return y_new","title":""},{"docid":"3258c1239ecea3bbee91751c83173069","score":"0.5103071","text":"def fit_transform(self, X, y=None, **params):\n self.fit(X, **params)\n\n X_transformed= self.X_transformed\n \n return X_transformed","title":""},{"docid":"6f025c51b493eed3eed5d41868615391","score":"0.5101539","text":"def translation(img, x, y):\n\n rows, cols = _get_rows_cols(img)\n M = np.float32([[1 ,0 , int(x)], [0, 1, int(y)]])\n return cv2.warpAffine(img, M, (cols, rows))","title":""},{"docid":"266d6591390d0be464af05bf43df5b73","score":"0.50921667","text":"def linear_regression(x, y, uncert_x=None, uncert_y=None):\n # if only one uncertainty is given, calculate analytically\n if uncert_x is None:\n return linear_regression_1(x, y, uncert_y)\n elif uncert_y is None:\n # as linear_regression_1 assumes uncertainties in y, switch the axes\n m, um, c, uc, corr, chisq = linear_regression_1(y,x,uncert_x)\n sigma_c = sqrt(uc**2/m**2+c**2*um**2/m**4-c/m**3*corr*um*uc)\n return 1/m, um/m**2, -c/m, sigma_c, corr, chisq\n \n # For a first guess, assume a slope around 1 and weight both uncertainties\n # equal. Calculate initial values analytically.\n uncert_sum = uncert_x + uncert_y\n m0, um0, c0, uc0, corr0, chisq0 = linear_regression_1(x, y, uncert_sum)\n\n def f(B, x):\n return B[0]*x + B[1]\n \n model = scipy.odr.Model(f)\n data = scipy.odr.RealData(x, y, sx=uncert_x, sy=uncert_y)\n odr = scipy.odr.ODR(data, model, beta0=[m0, c0])\n output = odr.run()\n ndof = len(x)-2\n chiq = output.res_var*ndof\n sigma_m = sqrt(output.cov_beta[0,0])\n sigma_c = sqrt(output.cov_beta[1,1])\n corr = output.cov_beta[0,1] /sigma_m /sigma_c\n \n return (output.beta[0], sigma_m, output.beta[1], sigma_c, corr, chiq)","title":""},{"docid":"8b4f9cfaafae4561d55ff075f493cdd0","score":"0.5088912","text":"def piecewise_linear(x: float)->float:\n if x < -1:\n return -1\n if x > 1:\n return 1\n return x","title":""},{"docid":"398a2b6e104450ea556165a3edbd6972","score":"0.50806266","text":"def linefit(x1, y1, x2, y2):\n if x2 == x1:\n m = Inf\n b = NaN\n else:\n m = old_div((y2 - y1), (x2 - x1))\n b = y1 - m * x1\n return m, b","title":""},{"docid":"67fc09d123264b9b24fcc268b9496f0c","score":"0.50646925","text":"def linear(target, X, A1='', A2=''):\n A = _parse_args(target=target, key=A1, default=0.0)\n B = _parse_args(target=target, key=A2, default=0.0)\n X = target[X]\n\n r = A * X + B\n S1 = A\n S2 = B\n values = {'S1': S1, 'S2': S2, 'rate': r}\n return values","title":""},{"docid":"3ae36658d69a2948e9b75bb716e6c99f","score":"0.5064198","text":"def translate(self, p_float, p_float_1): # real signature unknown; restored from __doc__\r\n return QTransform","title":""},{"docid":"b0a645104402136fb7da09f3f8dc7a5c","score":"0.5058161","text":"def compute_linear_regression_line(a0, a1):\n return f'{a0} + {a1}x'","title":""},{"docid":"8f128f9f226d8717014d847a65f6cb0b","score":"0.50547147","text":"def fit_transform(self, X, y=...):\n ...","title":""},{"docid":"7c14700936f5936326d580b38a7a4b8d","score":"0.5054449","text":"def fit_transform(self, X, y=None, **fit_params):\n\n\n results = self._parallel_func(X, y, fit_params, _fit_transform_one)\n if not results:\n # All transformers are None\n return np.zeros((X.shape[0], 0))\n\n Xs, transformers = zip(*results)\n self._update_transformer_list(transformers)\n\n Xs = self._filter_skips(Xs)\n return self._custom_hstack(Xs)","title":""},{"docid":"695fabf3004a581ea06dae99e9b42625","score":"0.50521016","text":"def L(x, y, x0, y0):\n return P3.F(x0,y0) + P3.F_x(x0,y0)*(x-x0) + P3.F_y(x0,y0)*(y-y0)","title":""},{"docid":"c9806f2d3426c4bdb5fe2970f534bba6","score":"0.5050443","text":"def _linear_predictor(self, X):\n check_is_fitted(self)\n X = self._validate_data(\n X,\n accept_sparse=[\"csr\", \"csc\", \"coo\"],\n dtype=[np.float64, np.float32],\n ensure_2d=True,\n allow_nd=False,\n reset=False,\n )\n return X @ self.coef_ + self.intercept_","title":""},{"docid":"8a2542682a4aea3e3b5c763e9cc151f9","score":"0.5047879","text":"def lerp(x, x0, x1, y0, y1):\n t = (x-x0)/(x1 - x0)\n return (1 - t) * y0 + t * y1","title":""}],"string":"[\n {\n \"docid\": \"345bd03e2d81a8f108ff75220e9a3080\",\n \"score\": \"0.66478336\",\n \"text\": \"def transform(self, x, y):\\n \\n return numpy.array((x, y, 1)).dot(self._matrix.T)[0:2]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa8bde99f2c683e4d0c5f040c3610a54\",\n \"score\": \"0.61888194\",\n \"text\": \"def _transform(self, X, y=None):\\n Xt = X\\n if y is None:\\n for _, name, transform in self._iter():\\n Xt = transform.transform(Xt)\\n return Xt\\n else:\\n yt = y\\n for _, name, transform in self._iter():\\n if self._skip_transform(transform):\\n continue\\n if isinstance(transform, YTransformer):\\n yt = transform.fit_transform(yt, Xt)\\n elif isinstance(transform, XAndYTransformer) \\\\\\n or isinstance(transform, XOrYTransformer):\\n Xt, yt = transform.fit_transform(Xt, yt)\\n else:\\n Xt = transform.transform(Xt)\\n return Xt, yt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"604a70a828d7f86e7da3af8a59043cc7\",\n \"score\": \"0.6155586\",\n \"text\": \"def dot(x, y):\\n if isinstance(x, LinearTransform):\\n return x.rmul(y)\\n elif isinstance(y, LinearTransform):\\n return y.lmul(x)\\n else:\\n return theano.dot(x,y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1e2e76ac248b15f3fe52ba49d9c2e2d\",\n \"score\": \"0.61270404\",\n \"text\": \"def transl2(x, y=None):\\n\\n if np.isscalar(x):\\n T = np.identity(3)\\n T[:2, 2] = [x, y]\\n return T\\n elif argcheck.isvector(x, 2):\\n T = np.identity(3)\\n T[:2, 2] = argcheck.getvector(x, 2)\\n return T\\n elif argcheck.ismatrix(x, (3, 3)):\\n return x[:2, 2]\\n else:\\n ValueError('bad argument')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17af9d93b6e6ebaca1b4ce1fc3f65f1f\",\n \"score\": \"0.6072614\",\n \"text\": \"def map_linear(self, x, x1, x2, y1, y2):\\n m = (y2 - y1) / (x2 - x1)\\n return y1 + m * (x - x1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"036bcff5e071f20c9213b74f2f95e346\",\n \"score\": \"0.60661876\",\n \"text\": \"def transl2(x=None, y=None):\\n if type(x) is np.matrix:\\n if common.ishomog(x, [3, 3]):\\n return x[:2, 2]\\n elif type(x) is list:\\n if len(x) == 2:\\n temp = np.matrix([[x[0]], [x[1]]])\\n temp = np.concatenate((np.eye(2), temp), axis=1)\\n return np.concatenate((temp, np.matrix([[0, 0, 1]])), axis=0)\\n elif x is not None and y is not None:\\n t = np.matrix([[x], [y]])\\n return rt2tr(np.eye(2), t)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6519c308d44c63d4e0ade99223bafac4\",\n \"score\": \"0.59372556\",\n \"text\": \"def fit_transform(self, X, y):\\n return self.fit(X, y).transform(X)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6519c308d44c63d4e0ade99223bafac4\",\n \"score\": \"0.59372556\",\n \"text\": \"def fit_transform(self, X, y):\\n return self.fit(X, y).transform(X)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b416f951b48cd1db0012a9df8ab197e\",\n \"score\": \"0.5922566\",\n \"text\": \"def fit_transform(self, X, y):\\n return self.fit(X, y).transform(X, y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79661e5a747e4bd148302284edfd07c3\",\n \"score\": \"0.58838165\",\n \"text\": \"def _linear(x):\\n return x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1dc8bd313822acf238fdcb062104679f\",\n \"score\": \"0.58141077\",\n \"text\": \"def fit_transform(self, X, y=None, **trans_kwargs):\\n return self.fit(X, y).transform(X, **trans_kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21a11d91aefdcc52a0cbd3d1c5b3e051\",\n \"score\": \"0.57625264\",\n \"text\": \"def translate(self, x, y):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fafba04d80dffdced53979a3bdfc98f7\",\n \"score\": \"0.57254046\",\n \"text\": \"def coefToTransformation(xcoef, ycoef):\\n return np.linalg.inv(np.array([[xcoef[1], xcoef[2], xcoef[0]],\\n [ycoef[1], ycoef[2], ycoef[0]],\\n [0, 0, 1]]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65d80324de6d127517d0343b86f84baf\",\n \"score\": \"0.56842816\",\n \"text\": \"def fit_transform(self, X, y=None, **fit_params):\\n return self.fit(X, y).transform(X)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"545129a28728ae0b31e0bdc87be5b323\",\n \"score\": \"0.56673855\",\n \"text\": \"def fit_transform(self, X, y=None):\\n self.fit(X, y)\\n return self.transform(X)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1bf532328d6bb21597494a37e774d3de\",\n \"score\": \"0.56484675\",\n \"text\": \"def _spatial_transform(transform, x, y):\\n x = x - transform.origin[0]\\n y = y - transform.origin[1]\\n\\n theta = -transform.rotation * np.pi/180\\n x, y = (x * np.cos(theta) - y * np.sin(theta),\\n y * np.cos(theta) + x * np.sin(theta))\\n\\n if transform.magnification is not None:\\n x = x / transform.magnification\\n y = y / transform.magnification\\n\\n if transform.x_reflection:\\n y = -y\\n\\n return x, y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74540150c323740a2011e48dd61d4a52\",\n \"score\": \"0.56308955\",\n \"text\": \"def fit_transform(self, X, y=None, **inputs):\\n return self.fit(X, y=y, **inputs).transform(X, y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5fba4bb25605c43d9cc0788ccfb4f82\",\n \"score\": \"0.5621015\",\n \"text\": \"def comp4983_lin_reg_fit(x: np.ndarray, y: np.ndarray):\\n return np.dot(np.dot(np.linalg.inv(np.dot(x.transpose(), x)), x.transpose()), y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80ed02b562471a2c5adb13f5e5c378ab\",\n \"score\": \"0.5608641\",\n \"text\": \"def linear(x):\\n return x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80ed02b562471a2c5adb13f5e5c378ab\",\n \"score\": \"0.5608641\",\n \"text\": \"def linear(x):\\n return x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92a290b37fdc25d18dfcfd39d993300c\",\n \"score\": \"0.56079733\",\n \"text\": \"def translate(self, x, y, transformFrame=True):\\n return self.transform(Transform(1, 0, 0, 1, x, y), transformFrame=transformFrame)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7216cf900cd91b7ba23e44b662e4d12c\",\n \"score\": \"0.56032175\",\n \"text\": \"def fit_transform(self, x, y, p=[]):\\n\\n self.fit(x, y, p)\\n\\n return self.transform(x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea6a01624c35ec9e687596895f204028\",\n \"score\": \"0.5596607\",\n \"text\": \"def transform(self, X, y=...):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1c4872bc15c9b6606a681bfedbe0f92\",\n \"score\": \"0.55920976\",\n \"text\": \"def transform(self,\\r\\n x: np.ndarray,\\r\\n y: Optional[np.ndarray] = None) -> Union[np.ndarray, Tuple[np.ndarray, np.ndarray]]:\\r\\n\\r\\n if self.input_vector:\\r\\n x = x.reshape(-1, 1)\\r\\n\\r\\n x_new = []\\r\\n for i in x[:, 0]:\\r\\n values_list = list(map(self.label_dict.get, i.split(self.sep)[-self.order:]))\\r\\n while len(values_list) < self.order:\\r\\n values_list = [self.label_dict[self.r_just_string]] + values_list\\r\\n x_new.append(values_list)\\r\\n\\r\\n if y is None:\\r\\n if self.return_vector:\\r\\n return np.array(x_new).ravel()\\r\\n else:\\r\\n return np.array(x_new)\\r\\n else:\\r\\n return np.array(x_new), np.vectorize(self.label_dict.get)(y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4647c3dc32232fc4b2581c2b4b16862\",\n \"score\": \"0.5582409\",\n \"text\": \"def conv( x, y, trans_matrix=None ):\\n\\n\\tif trans_matrix:\\n\\t\\txt = trans_matrix[0][0] * x + trans_matrix[0][1] * y + trans_matrix[0][2]\\n\\t\\tyt = trans_matrix[1][0] * x + trans_matrix[1][1] * y + trans_matrix[1][2]\\n\\t\\treturn xt, yt\\n\\telse:\\n\\t\\treturn x, y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cfac40ece33cfbf69f870fe945be1a82\",\n \"score\": \"0.5582098\",\n \"text\": \"def _fit_transform_one(transformer,\\n X,\\n y,\\n weight,\\n message_clsname='',\\n message=None,\\n **fit_params):\\n with _print_elapsed_time(message_clsname, message):\\n if hasattr(transformer, 'fit_transform'):\\n res = transformer.fit_transform(X, y, **fit_params)\\n else:\\n res = transformer.fit(X, y, **fit_params).transform(X)\\n\\n if (isinstance(transformer, XAndYTransformer) or isinstance(transformer, XOrYTransformer)) and weight is None:\\n return *res, transformer\\n elif (isinstance(transformer, XAndYTransformer) or isinstance(transformer, XOrYTransformer)) and weight is not None:\\n res = res * weight\\n return *res, transformer\\n elif weight is None:\\n return res, transformer\\n else:\\n return res * weight, transformer\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fe97b03cab4543dbe8b4affee73223f\",\n \"score\": \"0.55807245\",\n \"text\": \"def linear_func(xq: float, x0: float, x1: float, y0: float, y1: float):\\n slope = (y1 - y0) / (x1 - x0)\\n return (slope * (xq - x0)) + y0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a2bf193d825d471d0ef415e7e27bea9\",\n \"score\": \"0.5578561\",\n \"text\": \"def transform(self, x, y, wcsID):\\n\\n self.commit()\\n if wcsID == 0: return (x, y, wcsID)\\n\\n # Since transformation is defined by a direct linear (or log) mapping\\n # between two rectangular windows, apply the usual linear\\n # interpolation.\\n\\n # log scale does not affect the w numbers at all, a plot\\n # ranging from 10 to 10,000 will have wx1,wx2 = (10,10000),\\n # not (1,4)\\n\\n return (self.transform1d(coord=x,dimension='x',wcsID=wcsID),\\n self.transform1d(coord=y,dimension='y',wcsID=wcsID),\\n wcsID)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6efff56618ab65237a4402c413928f0d\",\n \"score\": \"0.5553774\",\n \"text\": \"def fit_transform(self, X, y=None, **fit_params):\\n # fit method of arity 2 (supervised transformation)\\n return self.fit(X, y, **fit_params).transform(X)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d77c1600b995b4c67e2bb81116ad5bd\",\n \"score\": \"0.55082804\",\n \"text\": \"def fit_transform(self, X, y=None, **fit_params):\\n # non-optimized default implementation; override when a better\\n # method is possible for a given clustering algorithm\\n if y is None:\\n # fit method of arity 1 (unsupervised transformation)\\n return self.fit(X, **fit_params).transform(X)\\n else:\\n # fit method of arity 2 (supervised transformation)\\n return self.fit(X, y, **fit_params).transform(X)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e2a01f266c56af4eeaba1d718578fd6\",\n \"score\": \"0.54977244\",\n \"text\": \"def fit_transform(self, X, y=None, **fit_params):\\r\\n # non-optimized default implementation; override when a better\\r\\n # method is possible for a given clustering algorithm\\r\\n if y is None:\\r\\n # fit method of arity 1 (unsupervised transformation)\\r\\n return self.fit(X, **fit_params).transform(X)\\r\\n else:\\r\\n # fit method of arity 2 (supervised transformation)\\r\\n return self.fit(X, y, **fit_params).transform(X)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6c1910b2a7b382ee630ae23e9a134b4\",\n \"score\": \"0.54848564\",\n \"text\": \"def translate(x):\\n\\n x = T.as_tensor_variable(x)\\n\\n m = T.eye(4, 4)\\n m = T.set_subtensor(m[0,3], x[0])\\n m = T.set_subtensor(m[1,3], x[1])\\n m = T.set_subtensor(m[2,3], x[2])\\n\\n mInv = T.eye(4, 4)\\n mInv = T.set_subtensor(mInv[0,3], -x[0])\\n mInv = T.set_subtensor(mInv[1,3], -x[1])\\n mInv = T.set_subtensor(mInv[2,3], -x[2])\\n\\n return Transform(m, mInv)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6346997d9b8902c970b07fd5be330f36\",\n \"score\": \"0.5482218\",\n \"text\": \"def linear(x, p0, p1):\\r\\n return p1*x + p0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16c2cd29ac51f63fbf99a763173e5596\",\n \"score\": \"0.5462316\",\n \"text\": \"def fit_transform(self, X, y=None, **fit_params):\\n self.fit(X, y, **fit_params)\\n return self.transform(X)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16c2cd29ac51f63fbf99a763173e5596\",\n \"score\": \"0.5462316\",\n \"text\": \"def fit_transform(self, X, y=None, **fit_params):\\n self.fit(X, y, **fit_params)\\n return self.transform(X)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9422ce42921eb3fc8c3738d5d69412f6\",\n \"score\": \"0.54554415\",\n \"text\": \"def get_x_to_y(x,y):\\n return ROOT.TVector3(y.X()-x.X(),y.Y()-x.Y(),y.Z()-x.Z())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ea8b310040ea2a81cc13606ed6d6b2a\",\n \"score\": \"0.54348564\",\n \"text\": \"def linear_transformation(arg0, arg1=None, arg2=None, side='left'):\\n from sage.matrix.constructor import matrix\\n from sage.modules.module import is_VectorSpace\\n from sage.modules.free_module import VectorSpace\\n from sage.categories.homset import Hom\\n from sage.symbolic.ring import SymbolicRing\\n from sage.modules.vector_callable_symbolic_dense import Vector_callable_symbolic_dense\\n from inspect import isfunction\\n\\n if not side in ['left', 'right']:\\n raise ValueError(\\\"side must be 'left' or 'right', not {0}\\\".format(side))\\n if not (is_Matrix(arg0) or is_VectorSpace(arg0)):\\n raise TypeError('first argument must be a matrix or a vector space, not {0}'.format(arg0))\\n if is_Matrix(arg0):\\n R = arg0.base_ring()\\n if not R.is_field():\\n try:\\n R = R.fraction_field()\\n except (NotImplementedError, TypeError):\\n msg = 'matrix must have entries from a field, or a ring with a fraction field, not {0}'\\n raise TypeError(msg.format(R))\\n if side == 'right':\\n arg0 = arg0.transpose()\\n side = 'left'\\n arg2 = arg0\\n arg0 = VectorSpace(R, arg2.nrows())\\n arg1 = VectorSpace(R, arg2.ncols())\\n elif is_VectorSpace(arg0):\\n if not is_VectorSpace(arg1):\\n msg = 'if first argument is a vector space, then second argument must be a vector space, not {0}'\\n raise TypeError(msg.format(arg1))\\n if arg0.base_ring() != arg1.base_ring():\\n msg = 'vector spaces must have the same field of scalars, not {0} and {1}'\\n raise TypeError(msg.format(arg0.base_ring(), arg1.base_ring()))\\n\\n # Now arg0 = domain D, arg1 = codomain C, and\\n # both are vector spaces with common field of scalars\\n # use these to make a VectorSpaceHomSpace\\n # arg2 might be a matrix that began in arg0\\n D = arg0\\n C = arg1\\n H = Hom(D, C, category=None)\\n\\n # Examine arg2 as the \\\"rule\\\" for the linear transformation\\n # Pass on matrices, Python functions and lists to homspace call\\n # Convert symbolic function here, to a matrix\\n if is_Matrix(arg2):\\n if side == 'right':\\n arg2 = arg2.transpose()\\n elif isinstance(arg2, (list, tuple)):\\n pass\\n elif isfunction(arg2):\\n pass\\n elif isinstance(arg2, Vector_callable_symbolic_dense):\\n args = arg2.parent().base_ring()._arguments\\n exprs = arg2.change_ring(SymbolicRing())\\n m = len(args)\\n n = len(exprs)\\n if m != D.degree():\\n raise ValueError('symbolic function has the wrong number of inputs for domain')\\n if n != C.degree():\\n raise ValueError('symbolic function has the wrong number of outputs for codomain')\\n arg2 = [[e.coefficient(a) for e in exprs] for a in args]\\n try:\\n arg2 = matrix(D.base_ring(), m, n, arg2)\\n except TypeError as e:\\n msg = 'symbolic function must be linear in all the inputs:\\\\n' + e.args[0]\\n raise ValueError(msg)\\n # have matrix with respect to standard bases, now consider user bases\\n images = [v*arg2 for v in D.basis()]\\n try:\\n arg2 = matrix([C.coordinates(C(a)) for a in images])\\n except (ArithmeticError, TypeError) as e:\\n msg = 'some image of the function is not in the codomain, because\\\\n' + e.args[0]\\n raise ArithmeticError(msg)\\n else:\\n msg = 'third argument must be a matrix, function, or list of images, not {0}'\\n raise TypeError(msg.format(arg2))\\n\\n # arg2 now compatible with homspace H call method\\n # __init__ will check matrix sizes versus domain/codomain dimensions\\n return H(arg2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6f43f7d78d90d037f01998225679f50\",\n \"score\": \"0.5431147\",\n \"text\": \"def htranslation2(x=0, y=0):\\n return sp.Matrix([[1, 0, x],\\n [0, 1, y],\\n [0, 0, 1]])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb0ea76e8b38d8df6d6311237c47164f\",\n \"score\": \"0.54303443\",\n \"text\": \"def asLinearOperator(X, parallel=False):\\n if not parallel: \\n def matvec(v): \\n return X.dot(v)\\n \\n def rmatvec(v): \\n return X.T.dot(v)\\n \\n def matmat(V): \\n return X.dot(V)\\n \\n def rmatmat(V): \\n return X.T.dot(V)\\n else:\\n def matvec(v): \\n return X.pdot(v)\\n \\n def rmatvec(v): \\n return X.T.pdot(v)\\n \\n def matmat(V): \\n return X.pdot(V)\\n \\n def rmatmat(V): \\n return X.T.pdot(V)\\n \\n return GeneralLinearOperator(X.shape, matvec, rmatvec, matmat, rmatmat, X.dtype)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1947e5e2a9321b4092295a0df3b53fe\",\n \"score\": \"0.5429983\",\n \"text\": \"def mmd_linear(X, Y):\\r\\n delta = X.mean(0) - Y.mean(0)\\r\\n return delta.dot(delta.T)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"698e86b357b2602de9f485298490b1c9\",\n \"score\": \"0.5415955\",\n \"text\": \"def transform(self, x):\\n return self._transform(x, inverse=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99a7b47409b93c7660d9f9a6ce9246a4\",\n \"score\": \"0.53800523\",\n \"text\": \"def linear_regression(x, y):\\r\\n model = LinearRegression()\\r\\n return get_theta_vector(model.fit(x, y))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2e9316c55d257b005dbdb14e6c7d566\",\n \"score\": \"0.5355075\",\n \"text\": \"def warp_xy(x, y, old_crs, new_crs):\\n if old_crs == new_crs:\\n return x,y\\n\\n old_crs_proj = pyproj.Proj(old_crs)\\n new_crs_proj = pyproj.Proj(new_crs)\\n return pyproj.transform(old_crs_proj, new_crs_proj, x,y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c2842af3a7cb509a3768f515091206e\",\n \"score\": \"0.5347562\",\n \"text\": \"def linear_regression(self, dataset):\\n\\n x,y = dataset\\n # solution (explain how to achieve this analitical expression)\\n w = np.dot(np.linalg.inv(np.dot(x.T,x)), np.dot(x.T,y))\\n return w\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2adc562449d6978109120f28938e605\",\n \"score\": \"0.53445894\",\n \"text\": \"def dot_shape_from_shape(x, y):\\n if isinstance(x, LinearTransform):\\n if type(y) != tuple:\\n raise TypeError('y should be tuple', y)\\n return x.col_shape() + x.split_right_shape(y, False)[1]\\n elif isinstance(y, LinearTransform):\\n if type(x) != tuple:\\n raise TypeError('x should be tuple', x)\\n return y.split_left_shape(x, False)[0] + y.row_shape()\\n else:\\n raise TypeError('One of x or y should be a LinearTransform')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08bafeda826a360b52decb448a38d781\",\n \"score\": \"0.5338421\",\n \"text\": \"def translate(self, x=0, y=0):\\n fx, fy = self.functions\\n return self.func((fx + x, fy + y), self.limits)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01ced9b525c98325de3dcd6ec6c91c72\",\n \"score\": \"0.5335345\",\n \"text\": \"def transl(x=None, y=None, z=None):\\n if type(x) is np.matrix:\\n if common.ishomog(x, [4, 4]):\\n return x[:3, 2]\\n elif type(x) is list:\\n if len(x) == 3:\\n temp = np.matrix([[x[0]], [x[1]], [x[2]]])\\n temp = np.concatenate((np.eye(3), temp), axis=1)\\n return np.concatenate((temp, np.matrix([[0, 0, 0, 1]])), axis=0)\\n # todo trajectory case\\n elif x is not None and y is not None and z is not None:\\n t = np.matrix([[x], [y], [z]])\\n return rt2tr(np.eye(3), t)\\n else:\\n raise AttributeError(\\\"Invalid arguments\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5179424098e0ecb03a92769a1ee4603\",\n \"score\": \"0.5329425\",\n \"text\": \"def transform(self, y, X=None, **kwargs):\\n check_is_fitted(self, \\\"lam1_\\\")\\n\\n lam1 = self.lam1_\\n lam2 = self.lam2_\\n\\n y, exog = self._check_y_X(y, X)\\n y += lam2\\n\\n neg_mask = y <= 0.\\n if neg_mask.any():\\n action = self.neg_action\\n msg = \\\"Negative or zero values present in y\\\"\\n if action == \\\"raise\\\":\\n raise ValueError(msg)\\n elif action == \\\"warn\\\":\\n warnings.warn(msg, UserWarning)\\n y[neg_mask] = self.floor\\n\\n if lam1 == 0:\\n return np.log(y), exog\\n return (y ** lam1 - 1) / lam1, exog\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1075bd44587e2002f2faefff4e7a92f\",\n \"score\": \"0.5313699\",\n \"text\": \"def fit_transform(self, X: Any, y: Any = None) -> np.ndarray:\\n self.fit(X, y)\\n\\n return self.transform(X, y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2cc63fc6a334092265052c1a16c153c5\",\n \"score\": \"0.5301931\",\n \"text\": \"def linear_interpolation(y1, x1, y2, x2, v11, v12, v21, v22, p_y, p_x):\\n # x interpolation\\n if x1 == x2:\\n p1 = v11\\n p2 = v21\\n else:\\n diffx = (x2 - x1)\\n rat1 = (x2 - p_x) / diffx\\n rat2 = (p_x - x1) / diffx\\n\\n p1 = v11 * rat1 + v12 * rat2\\n p2 = v21 * rat1 + v22 * rat2\\n\\n # y interpolation\\n if y1 == y2:\\n p = p1\\n else:\\n diffy = (y2 - y1)\\n rat1 = (y2 - p_y) / diffy\\n rat2 = (p_y - y1) / diffy\\n\\n p = p1 * rat1 + p2 * rat2\\n\\n return p\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8c422513293361f99988172e3a50ed5\",\n \"score\": \"0.5298343\",\n \"text\": \"def r_linear_regression(x, y):\\n x_dim = x.shape[1]\\n for i in range(x_dim):\\n xr_i = FloatVector(x[:, i])\\n robjects.globalenv[\\\"x%d\\\" % i] = xr_i\\n yr = FloatVector(y.view(-1))\\n robjects.globalenv[\\\"y\\\"] = yr\\n formula = \\\"y~\\\" + \\\"+\\\".join(\\\"x%d\\\" % i_ for i_ in range(x_dim))\\n robjects.r(\\\"fit <- lm(%s)\\\" % formula)\\n robjects.r(\\\"lin_reg_out <- coef(fit)\\\")\\n lin_reg_out = robjects.r[\\\"lin_reg_out\\\"]\\n intercept = float(lin_reg_out[0])\\n weights = torch.DoubleTensor(lin_reg_out[1:])\\n weights[torch.isnan(weights)] = 0.0\\n return LinearFunction(intercept, weights)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eec4fcef1023921d928f77ea9c345a54\",\n \"score\": \"0.52963626\",\n \"text\": \"def invert_point(x, y, transform):\\r\\n return transform.inverted().transform_point((x, y))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a09d400451f0063cecb358b31cedf907\",\n \"score\": \"0.5287271\",\n \"text\": \"def transform(self, X, y=None):\\n return X.todense()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86a01aaef5e853c0351baef90de71199\",\n \"score\": \"0.52816355\",\n \"text\": \"def fromTranslate(self, p_float, p_float_1): # real signature unknown; restored from __doc__\\r\\n return QTransform\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39ca3e0cf14af46ab1e7cb650dd3efd2\",\n \"score\": \"0.52799237\",\n \"text\": \"def regressionLine(x, y):\\n x2 = [float(i**2) for i in x]\\n y2 = [float(i**2) for i in y]\\n xy = [x[i]*y[i] for i in xrange(0,len(x))]\\n a = (sum(x2)*sum(y) - sum(x)*sum(xy))/(len(x)*sum(x2) - sum(x)**2)\\n b = (len(x)*sum(xy) - sum(x)*sum(y))/(len(x)*sum(x2) - sum(x)**2)\\n return (round(a,4),round(b,4))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"125c1e93354f846e9bbea0a0c2d624bb\",\n \"score\": \"0.52734256\",\n \"text\": \"def linterp(x1, x2, y1, y2, x):\\n return tf.div_no_nan((x * (y2 - y1) + y1 * x2 - x1 * y2), (x2 - x1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06ed52f661f42d7a629443f3e0e5b0f2\",\n \"score\": \"0.5272565\",\n \"text\": \"def lin_interp(X, x1, y1, x2, y2):\\n\\n m = (y2 - y1) / (x2 - x1)\\n Y = m * (X - x1) + y1\\n return Y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f8611dc2081d7c076624c5a9d048ed9\",\n \"score\": \"0.52725315\",\n \"text\": \"def translate(self, x, y):\\n p = self.points.copy()\\n p[0::2] = p[0::2] + x\\n p[1::2] = p[1::2] + y\\n\\n return self.copy(p)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b017400b3741aafd12495a76c706f6d6\",\n \"score\": \"0.52689767\",\n \"text\": \"def linear_regression_0(x, y):\\n n = len(x)\\n sum_x = sum(x)\\n sum_y = sum(y)\\n sum_x2 = sum(x**2)\\n sum_xy = sum(x*y)\\n delta = n*sum_x2-sum_x**2\\n m = (n*sum_xy-sum_x*sum_y)/delta\\n c = (sum_x2*sum_y-sum_x*sum_xy)/delta\\n return m, c\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83a7d325af13e1e85b55f13c3a7fe475\",\n \"score\": \"0.5267445\",\n \"text\": \"def rotate_to_source(linear: Dict[Tuple[str, int], ITeg],\\n target_vars: List[TegVar],\\n source_vars: List[TegVar]) -> List[ITeg]:\\n rotation = []\\n num_vars = len(target_vars)\\n exprs = [linear[(s_var.name, s_var.uid)] for s_var in source_vars]\\n for source_index in range(num_vars):\\n if source_index == 0:\\n rotation.append(sum((Const(1) if i == 0 else Const(-1)) * exprs[i] * target_vars[i] for i in range(num_vars)))\\n elif source_index < len(linear):\\n i = source_index\\n inverse_rotation = sum(((Const(1) if i == j else Const(0))\\n - (exprs[i] * exprs[j]) / (1 + exprs[0])) * target_vars[j]\\n for j in range(1, num_vars))\\n rotation.append(inverse_rotation + exprs[i] * target_vars[0])\\n else:\\n raise ValueError(f'Requested source coordinate index: {source_index} is invalid.')\\n\\n return rotation\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea6aa49b3001a80ad302ba7a4f612122\",\n \"score\": \"0.5250189\",\n \"text\": \"def fit_linear_regression(X: np.array, y: np.array) -> (np.array, np.array):\\r\\n w = np.linalg.pinv(X).T @ y\\r\\n s = svd(X, compute_uv=False)\\r\\n return w,s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"740ee0c68e59906e81db190c4acaf793\",\n \"score\": \"0.5248054\",\n \"text\": \"def linear(params, x, data=None):\\n\\ta = params['slope'].value\\n\\tb = params['offs'].value\\n\\tmodel = a*x + b\\n\\tif data is None:\\n\\t\\treturn model\\n\\telse:\\n\\t\\treturn model-data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"179f1d49f1096918f085b67f5aebd4dc\",\n \"score\": \"0.52468514\",\n \"text\": \"def invert_transform(self, Y, X=None):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29277c7db8d402cbf45f543542f92fe2\",\n \"score\": \"0.52465487\",\n \"text\": \"def to_linear_transformation(self, reset=False):\\n tf = np.dot(self.B * self.D, self.B.T)\\n if reset:\\n self.reset()\\n return tf\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6a2f6543f0ca5b521f5e3b474fc898d\",\n \"score\": \"0.5240029\",\n \"text\": \"def transform(x,y,theta):\\n s = np.sin(theta)\\n c = np.cos(theta)\\n return np.matrix([[c,-s,x],[s,c,y],[0,0,1]])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c84ec28b33b33a292a996a1cb857821\",\n \"score\": \"0.52322036\",\n \"text\": \"def linear_interpolation (x1: float,\\n x2: float,\\n y1: float,\\n y2: float,\\n x3: float) -> float:\\n\\n f_linear = interp1d([x1, x2], [y1, y2])\\n y3 = float(f_linear(x3))\\n\\n return y3\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b69a4c9ac7eecf77bd74790c76afda85\",\n \"score\": \"0.522647\",\n \"text\": \"def _make_transform(rxy, gxy, bxy, wxy):\\n xy = np.array([rxy, gxy, bxy]).transpose()\\n xyz = np.append(xy, (1 - xy[0] - xy[1])[np.newaxis, :], axis = 0)\\n wxyz = np.append(wxy, 1 - wxy[0] - wxy[1])\\n wXYZ = wxyz / wxyz[1]\\n I = np.linalg.inv(xyz)\\n scale = np.inner(I, wXYZ)\\n M = xyz * scale[np.newaxis, :]\\n I *= (1 / scale)[:, np.newaxis]\\n return M, I\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ea196ef6e48af2af09fe39c1a20b478\",\n \"score\": \"0.52213454\",\n \"text\": \"def forward(x: Any, y: Any) -> Any:\\n if isinstance(x, torch.Tensor) or isinstance(y, torch.Tensor):\\n out = torch.mul(x, y)\\n else:\\n out = x * y\\n return out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba23f58b15766521c74a4fb8e1ca8f83\",\n \"score\": \"0.52193224\",\n \"text\": \"def transformPoint(x, y, from_srs, to_srs):\\n coordXform = osr.CoordinateTransformation(from_srs, to_srs)\\n yRound = round(y, 8)\\n xRound = round(x, 8)\\n\\n result = coordXform.TransformPoint(xRound, yRound)\\n\\n gx = result[0]\\n gy = result[1]\\n\\n return gx, gy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9de4b9d0af4f1ba17fd554d549867fbe\",\n \"score\": \"0.52089655\",\n \"text\": \"def lin_interp(x, x0, x1, y0, y1):\\n y = y0 + (y1-y0)*(x-x0)/(x1-x0)\\n \\n return y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9c6fb973a55d4f73c88d913af5a508e\",\n \"score\": \"0.5193385\",\n \"text\": \"def translate_matrix((a,b,c,d,e,f), (x,y)):\\n return (a,b,c,d,x*a+y*c+e,x*b+y*d+f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8ff3dfc15a06975def89dd6a9b70d98\",\n \"score\": \"0.5190883\",\n \"text\": \"def to_linear_transformation(self, reset=False):\\n tf = self.C**0.5\\n if reset:\\n self.reset()\\n return tf\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3111e1e27da9a1e1fec617edc752cee7\",\n \"score\": \"0.5185958\",\n \"text\": \"def Mxform(x1,y1,x2,y2):\\n return Jones.toMueller([[dot(x2,x1), dot(x2, y1)], [dot(y2,x1), dot(y2,y1)]])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"070323b9c290f430a524142f5e3f3c59\",\n \"score\": \"0.51817906\",\n \"text\": \"def fit_transform(self, X, y, verbose=False, fit_kwargs=None):\\n self._fit(X, y, verbose=verbose, fit_kwargs=fit_kwargs)\\n return self._transform(verbose=verbose)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"964cb557be4d061a161ec47a3a145d45\",\n \"score\": \"0.517799\",\n \"text\": \"def line_equation(x1, y1, x2, y2):\\n if x2 == x1:\\n return (1, 0, -x1) # special case for vertical line\\n m = (y2 - y1) / (x2 - x1)\\n a = m\\n b = -1\\n c = -m * x1 + y1\\n return (a, b, c)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae1fef3a1872e39f54f55b16e8d47235\",\n \"score\": \"0.5173923\",\n \"text\": \"def linear_transformation(self, A):\\n assert A.shape[0] == self.N \\n return MultivariateNormal(A.T @ self.u, A.T @ self.C @ A)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3da223ba2015997747c6a3746f0c1a3b\",\n \"score\": \"0.5141163\",\n \"text\": \"def fit_transform(self, X, y=None, sample_weight=None, sample_cost=None):\\n\\n\\t\\treturn self.fit(X, y=y, sample_weight=sample_weight, \\n\\t\\t\\tsample_cost=sample_cost).transform(X, y=y, \\n\\t\\t\\tsample_weight=sample_weight)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88e623c49a4e0960bcbbd8965aa05f88\",\n \"score\": \"0.5136432\",\n \"text\": \"def linear(self):\\n return self._linear\",\n \"title\": \"\"\n },\n {\n \"docid\": \"671f5c82a80f429ac3943baf9fd52347\",\n \"score\": \"0.5136224\",\n \"text\": \"def inverse_transform(self, x):\\n return self._transform(x, inverse=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f833a7fe581a9b9864a1fb3d1857a59e\",\n \"score\": \"0.5126777\",\n \"text\": \"def linear_interpolator(xs, ys):\\n\\n def return_function(xp):\\n \\\"\\\"\\\" xp is the x value to find the interpolation \\\"\\\"\\\"\\n for i in range(len(xs)-1):\\n # Define interpolating range for this iteration\\n x0 = xs[i]\\n x1 = xs[i+1]\\n if not (x0 <= xp <= x1):\\n continue\\n y0 = ys[i]\\n y1 = ys[i+1]\\n xd = (xp - x0)/float(x1 - x0)\\n yp = y0*(1-xd) + y1*xd\\n return yp\\n return None\\n return return_function\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46253b212b9c308d8b70a28749c3d07f\",\n \"score\": \"0.5123316\",\n \"text\": \"def least_squares(y, tx):\\r\\n a = tx.T.dot(tx)\\r\\n b = tx.T.dot(y)\\r\\n return np.linalg.solve(a, b)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96be74a8ac7fbefb74414b20b0dedcc5\",\n \"score\": \"0.51219106\",\n \"text\": \"def lmodel(x, a, b):\\n y = a + b*x\\n return y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49d0422787a01e260b9e2bd7a859f466\",\n \"score\": \"0.51181537\",\n \"text\": \"def inverse_transform(self, y, X=None):\\n check_is_fitted(self, \\\"lam1_\\\")\\n\\n lam1 = self.lam1_\\n lam2 = self.lam2_\\n\\n y, exog = self._check_y_X(y, X)\\n if lam1 == 0:\\n return np.exp(y) - lam2, exog\\n\\n numer = y * lam1 # remove denominator\\n numer += 1. # add 1 back to it\\n de_exp = numer ** (1. / lam1) # de-exponentiate\\n return de_exp - lam2, exog\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ceb65633c739ba2cb2c7f2aae090821\",\n \"score\": \"0.511662\",\n \"text\": \"def __solve_linear_syste(self, A, b):\\n dx = np.linalg.solve(A, b)\\n return dx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbe72778f5cc04b44b4e015afda61e21\",\n \"score\": \"0.51158327\",\n \"text\": \"def fit_line(x, y):\\n slope, intercept, r, p, stderr = sps.linregress(x, y)\\n return slope, intercept, r**2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"486f6e0f7ded4508cbca25577e86ee94\",\n \"score\": \"0.511329\",\n \"text\": \"def linear_fit(y, x, m=None, method='robust'):\\n Model = RLM if method == 'robust' else OLS\\n if m is None:\\n X = np.empty((len(y), 2))\\n X[:, 0] = x\\n X[:, 1] = 1\\n res = Model(y, X).fit()\\n return res.params\\n else:\\n X = np.ones(len(y))\\n res = Model(np.array(y) - m * np.array(x), X).fit()\\n return m, res.params[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"098ed8e16c52479f34c4336cb344fc9f\",\n \"score\": \"0.51080406\",\n \"text\": \"def transform_y(self, y):\\n self.logging('transforming y...')\\n y_new = y.copy()\\n for t in self.transformers:\\n self.logging('-> {}'.format(t.name))\\n y_new = t.transform_y(y_new)\\n return y_new\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3258c1239ecea3bbee91751c83173069\",\n \"score\": \"0.5103071\",\n \"text\": \"def fit_transform(self, X, y=None, **params):\\n self.fit(X, **params)\\n\\n X_transformed= self.X_transformed\\n \\n return X_transformed\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f025c51b493eed3eed5d41868615391\",\n \"score\": \"0.5101539\",\n \"text\": \"def translation(img, x, y):\\n\\n rows, cols = _get_rows_cols(img)\\n M = np.float32([[1 ,0 , int(x)], [0, 1, int(y)]])\\n return cv2.warpAffine(img, M, (cols, rows))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"266d6591390d0be464af05bf43df5b73\",\n \"score\": \"0.50921667\",\n \"text\": \"def linear_regression(x, y, uncert_x=None, uncert_y=None):\\n # if only one uncertainty is given, calculate analytically\\n if uncert_x is None:\\n return linear_regression_1(x, y, uncert_y)\\n elif uncert_y is None:\\n # as linear_regression_1 assumes uncertainties in y, switch the axes\\n m, um, c, uc, corr, chisq = linear_regression_1(y,x,uncert_x)\\n sigma_c = sqrt(uc**2/m**2+c**2*um**2/m**4-c/m**3*corr*um*uc)\\n return 1/m, um/m**2, -c/m, sigma_c, corr, chisq\\n \\n # For a first guess, assume a slope around 1 and weight both uncertainties\\n # equal. Calculate initial values analytically.\\n uncert_sum = uncert_x + uncert_y\\n m0, um0, c0, uc0, corr0, chisq0 = linear_regression_1(x, y, uncert_sum)\\n\\n def f(B, x):\\n return B[0]*x + B[1]\\n \\n model = scipy.odr.Model(f)\\n data = scipy.odr.RealData(x, y, sx=uncert_x, sy=uncert_y)\\n odr = scipy.odr.ODR(data, model, beta0=[m0, c0])\\n output = odr.run()\\n ndof = len(x)-2\\n chiq = output.res_var*ndof\\n sigma_m = sqrt(output.cov_beta[0,0])\\n sigma_c = sqrt(output.cov_beta[1,1])\\n corr = output.cov_beta[0,1] /sigma_m /sigma_c\\n \\n return (output.beta[0], sigma_m, output.beta[1], sigma_c, corr, chiq)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b4f9cfaafae4561d55ff075f493cdd0\",\n \"score\": \"0.5088912\",\n \"text\": \"def piecewise_linear(x: float)->float:\\n if x < -1:\\n return -1\\n if x > 1:\\n return 1\\n return x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"398a2b6e104450ea556165a3edbd6972\",\n \"score\": \"0.50806266\",\n \"text\": \"def linefit(x1, y1, x2, y2):\\n if x2 == x1:\\n m = Inf\\n b = NaN\\n else:\\n m = old_div((y2 - y1), (x2 - x1))\\n b = y1 - m * x1\\n return m, b\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67fc09d123264b9b24fcc268b9496f0c\",\n \"score\": \"0.50646925\",\n \"text\": \"def linear(target, X, A1='', A2=''):\\n A = _parse_args(target=target, key=A1, default=0.0)\\n B = _parse_args(target=target, key=A2, default=0.0)\\n X = target[X]\\n\\n r = A * X + B\\n S1 = A\\n S2 = B\\n values = {'S1': S1, 'S2': S2, 'rate': r}\\n return values\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ae36658d69a2948e9b75bb716e6c99f\",\n \"score\": \"0.5064198\",\n \"text\": \"def translate(self, p_float, p_float_1): # real signature unknown; restored from __doc__\\r\\n return QTransform\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0a645104402136fb7da09f3f8dc7a5c\",\n \"score\": \"0.5058161\",\n \"text\": \"def compute_linear_regression_line(a0, a1):\\n return f'{a0} + {a1}x'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f128f9f226d8717014d847a65f6cb0b\",\n \"score\": \"0.50547147\",\n \"text\": \"def fit_transform(self, X, y=...):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c14700936f5936326d580b38a7a4b8d\",\n \"score\": \"0.5054449\",\n \"text\": \"def fit_transform(self, X, y=None, **fit_params):\\n\\n\\n results = self._parallel_func(X, y, fit_params, _fit_transform_one)\\n if not results:\\n # All transformers are None\\n return np.zeros((X.shape[0], 0))\\n\\n Xs, transformers = zip(*results)\\n self._update_transformer_list(transformers)\\n\\n Xs = self._filter_skips(Xs)\\n return self._custom_hstack(Xs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"695fabf3004a581ea06dae99e9b42625\",\n \"score\": \"0.50521016\",\n \"text\": \"def L(x, y, x0, y0):\\n return P3.F(x0,y0) + P3.F_x(x0,y0)*(x-x0) + P3.F_y(x0,y0)*(y-y0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9806f2d3426c4bdb5fe2970f534bba6\",\n \"score\": \"0.5050443\",\n \"text\": \"def _linear_predictor(self, X):\\n check_is_fitted(self)\\n X = self._validate_data(\\n X,\\n accept_sparse=[\\\"csr\\\", \\\"csc\\\", \\\"coo\\\"],\\n dtype=[np.float64, np.float32],\\n ensure_2d=True,\\n allow_nd=False,\\n reset=False,\\n )\\n return X @ self.coef_ + self.intercept_\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a2542682a4aea3e3b5c763e9cc151f9\",\n \"score\": \"0.5047879\",\n \"text\": \"def lerp(x, x0, x1, y0, y1):\\n t = (x-x0)/(x1 - x0)\\n return (1 - t) * y0 + t * y1\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":890,"cells":{"query_id":{"kind":"string","value":"78b9154d02d7e2e82a6670697ecad797"},"query":{"kind":"string","value":"Called when app is resized."},"positive_passages":{"kind":"list like","value":[{"docid":"54a5013ed2c7440077ce98fbcf991d1d","score":"0.65565294","text":"def on_resize_parent(self,event):\n #print(\"parent event size=\"+str(event.width)+\" X \"+str(event.height))\n self.canvas_width = event.width\n self.canvas_height = event.height\n self.canvas.config(width=self.canvas_width, height=self.canvas_height)\n self.show_image()","title":""}],"string":"[\n {\n \"docid\": \"54a5013ed2c7440077ce98fbcf991d1d\",\n \"score\": \"0.65565294\",\n \"text\": \"def on_resize_parent(self,event):\\n #print(\\\"parent event size=\\\"+str(event.width)+\\\" X \\\"+str(event.height))\\n self.canvas_width = event.width\\n self.canvas_height = event.height\\n self.canvas.config(width=self.canvas_width, height=self.canvas_height)\\n self.show_image()\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"cf95f21cd7739a672db6e5c661a5ca90","score":"0.8178788","text":"def onResize(self, window, width, height):\n pass","title":""},{"docid":"e1743c098b05d52132a2b8d8e82d29e0","score":"0.80237764","text":"def _resize(self, event):\n pass","title":""},{"docid":"545f61a7170492b00c7bae3c814618dd","score":"0.78258556","text":"def resizeEvent(self, event):\n\n # -- Call the function which handles the tab mode switching\n self.setTabMode(self.tabMode)\n\n # -- Store the current window size in our scribble settings\n self.storeWidgetGeometry()","title":""},{"docid":"bd179e6627dd9fd92de561925238420b","score":"0.77364415","text":"def resizeEvent(self, event):\n QtGui.QMainWindow.resizeEvent(self, event)\n QtGui.QApplication.processEvents()","title":""},{"docid":"ac0dea63a46529b934e56916f8c9309a","score":"0.7684432","text":"def resizeEvent(self,event):\n self.grab_default_dimensions()","title":""},{"docid":"17f72fd5c27658d73eb945ad02c4cdd2","score":"0.7643923","text":"def on_resize( self, width, height ):\r\n # we don't do anything by default\r\n pass","title":""},{"docid":"395879c1edefadb20a47477d3ebc5fcf","score":"0.7602799","text":"def resizeEvent(self, new_size):\n\n self.app.settings['mainwindow_w'] = new_size.size().width()\n self.app.settings['mainwindow_h'] = new_size.size().height()","title":""},{"docid":"d3ab0786d88fe417a524eba3b9f10b5f","score":"0.75728744","text":"def on_resize(self, event):\n self.context.set_viewport(0, 0, event.size[0], event.size[1])\n for visual in self.visuals:\n visual.on_resize(event.size)\n self.update()","title":""},{"docid":"04f643a929684c613ea1b8e7c1a789c7","score":"0.7476696","text":"def resizeEvent(self, event):\n \n # perform the default resizing\n super(PlotCanvas, self).resizeEvent(event)\n # fix the layout\n self.fixLayout()","title":""},{"docid":"2b9f6f33fcb706563753028a1405e87c","score":"0.7400655","text":"def on_resize(self, width, height):\n # label\n self.label.y = height - 10\n G.eventhandler.call(\"user:window:resize\", width, height)","title":""},{"docid":"86663dd22bc723587e2991dcc7a363e9","score":"0.7394615","text":"def resizeEvent(self, event):\n super(QContainer, self).resizeEvent(event)\n self.resized.emit()","title":""},{"docid":"77da8abdda864503551d76db56f268ec","score":"0.73922807","text":"def resize(self, event):\n print(\"Screen.resize()\")\n if event:\n self.width = event.width\n self.height = event.height\n self.draw_grid()","title":""},{"docid":"fd1aac8efc588442d8eae6a14af92cd0","score":"0.7337735","text":"def _on_parent_resized(self):\n # resize overlay\n self.resize(self.parentWidget().size())","title":""},{"docid":"5aee5937ac70940fc2a79f9b5e0fa13e","score":"0.73191726","text":"def _auto_resize(self, window):\n if self.get_size() != self.winsize:\n self.winsize = self.get_size()\n self._app.emit(\"widget-layout-changed\", self)","title":""},{"docid":"4235b3631ae0d04bf3a84d0e3cfe6ddb","score":"0.7298679","text":"def _onResize(self, event):\n if 'gtk2' in wx.PlatformInfo or 'gtk3' in wx.PlatformInfo:\n self._doResize()\n else:\n wx.CallAfter(self._doResize)\n event.Skip()","title":""},{"docid":"ca8e0ae1cb9207a3f3477b323a2e6200","score":"0.72599375","text":"def on_resize_event(self, event):\n self.width = event.width\n self.height = event.height\n log.debug(\"New size 'ManageMyDico' : {}x{}\".format(self.width, self.height))","title":""},{"docid":"e73a4b3bbb1509305aa68f94765cfd0d","score":"0.7212062","text":"def resizeEvent(self, event):\n super(XImageSlider, self).resizeEvent(event)\n \n self.recalculate()","title":""},{"docid":"a98d5a2f3464f593aeb9e2bf44a645ea","score":"0.71496797","text":"def on_resize_parent(self, event):\n\n self.statusbar.config(width = event.width, height = 2)","title":""},{"docid":"636e8c6f36b13ac8feccd1f284874bf3","score":"0.71427923","text":"def resizeEvent(self, event):\n # update the image scale\n self._update_pixmap_size()\n super(ShotWidget, self).resizeEvent(event)","title":""},{"docid":"aa6fa9b49d0a61d993c93d3ce6bf5201","score":"0.7137341","text":"def handle_resize(event):\n # grabbing the newely resized windows size\n scale_size = event.dict['size']\n\n # save these scaling factors globally as they affect the global screen\n # rendering\n global scale_x\n global scale_y\n\n # grabbing new scaling factor values\n (x_o, y_o) = size_o\n (x_1, y_1) = scale_size\n # calculating new scaling factor values\n scale_x = (x_1 / x_o)\n scale_y = (y_1 / y_o)","title":""},{"docid":"098b2f0cfede484173fe38e39e73931a","score":"0.7097699","text":"def resizeEvent(self, event: QtGui.QResizeEvent) -> None:\n\n self.views[0].resize()\n\n super(ViewMain, self).resizeEvent(event)","title":""},{"docid":"74ebbd543242d6d43e9b3dcf535a9cd5","score":"0.7042027","text":"def resize(self, event):\r\n padding = 4 # not sure why this is required, but if I don't include it it growths continually\r\n self.size = max(min(event.width - padding, event.height - padding), 100)\r\n self.update_indicator()","title":""},{"docid":"d3c12195a44d7ea84a07a701c3e48b3a","score":"0.6989664","text":"def on_resize_parentx(self, event):\n self.statusbar.config(width = event.width, height = 2)","title":""},{"docid":"00e63848501c41f29fd637bfc5ee3351","score":"0.69684976","text":"def OnSize( self, evt ):\n if not self.IsMaximized():\n self.preMaxSize = self.GetSize()","title":""},{"docid":"7af3e4f793d485e1fd1caf0c52c66287","score":"0.6942225","text":"def _updateSizes(self) -> None:\n print(f'QmlOffscreenRenderer._updateSizes: {self.size}')\n width, height = self.size.toTuple()\n self._window.setGeometry(0, 0, width, height)\n self.rootItem.setWidth(width)\n self.rootItem.setHeight(height)","title":""},{"docid":"aff5fc4c09588e1a7408797b2018d98c","score":"0.69375074","text":"def resizeEvent(self, event):\n super(Editor, self).resizeEvent(event)\n self.resize_signal.emit(event)","title":""},{"docid":"1b90d7c48229884795b49280a3a47ced","score":"0.69124967","text":"def resizeEvent(self, event):\r\n QtGui.QSplitter.resizeEvent(self, event)\r\n\r\n parent = self.parent()\r\n if (not self._initialized and parent and\r\n (self.isVisible() or isinstance(parent, QtGui.QMainWindow))):\r\n self._initialized = True\r\n self._resize_items()","title":""},{"docid":"4660be854a6f5d1cc04138bf2f8d2078","score":"0.68822205","text":"def resizeEvent(self, event):\r\n QDialog.resizeEvent(self, event)\r\n self.size_change.emit(self.size())","title":""},{"docid":"f15e715bcac281544615f13e4abc649d","score":"0.68478733","text":"def handle_key_resize_event(self):\n self.read_screen_size()\n if self.prompt_for_screen_size():\n self.redraw()\n else:\n self.keep_running = False","title":""},{"docid":"d530ec8c5bd73b76e222741a9a4e8b03","score":"0.6823641","text":"def componentResized(self, e):\n gCanvas.initOffscreenImage()\n if gCanvas.isShowing():\n gCanvas.repaint()","title":""},{"docid":"8cf8c9857378022ef38587717e3777e6","score":"0.68174404","text":"def resizeEvent(self, event):\n super(QResizingGroupBox, self).resizeEvent(event)\n self.resized.emit()","title":""},{"docid":"39f12a918c809ca410f776b0a36c6a91","score":"0.68159217","text":"def on_resize(self, event):\n width, height = self.GetClientSize()\n self.buffer = wx.Bitmap(width, height)\n self.update_bitmap()","title":""},{"docid":"c09bdf83637867959da01096cd97b590","score":"0.67809826","text":"def OnSize(self,event):\n\t\tself.Refresh()","title":""},{"docid":"c19bb33feebe718e73108c0b8960463f","score":"0.677057","text":"def resizeEvent(self, event):\n if not self._page:\n return\n\n self.resizePolygonsToPixmap()","title":""},{"docid":"aeba497f1fb2b5a7b5d04667ecd2a172","score":"0.6724207","text":"def canvasSizeChanged(self, app):\n\n self.x = int(self.xFrac * app.width) - self.width // 2\n self.y = int(self.yFrac * app.height) - self.height // 2","title":""},{"docid":"ad92c8ca2670bf39ae2f78d573eebbec","score":"0.6718328","text":"def on_resize(self, width, height):\n # label\n self.label.y = height - 10\n # reticle\n if self.reticle:\n self.reticle.delete()\n x, y = self.width // 2, self.height // 2\n n = 10\n self.reticle = pyglet.graphics.vertex_list(4, ('v2i', (x - n, y, x + n, y, x, y - n, x, y + n)))\n\n if G.inventoryhandler.should_game_freeze():\n G.inventoryhandler.send_event(\"on_resize\", width, height)","title":""},{"docid":"16d79c51fa64494c89aa8ba635cdafe9","score":"0.6717966","text":"def _tk_resize(self,event):\n w = self._window.winfo_width()-self._canvas._dw\n h = self._window.winfo_height()-self._canvas._dh\n self._canvas._lastw = self._canvas._currw\n self._canvas._lasth = self._canvas._currh\n self._canvas._currw = w\n self._canvas._currh = h\n dw = self._canvas._currw-self._canvas._lastw\n dh = self._canvas._currh-self._canvas._lasth\n self._canvas.config(width=w,height=h)\n if dw or dh:\n self._canvas.move('all',dw/2.0,dh/2.0)\n _Context.Instance().refresh()","title":""},{"docid":"08ee0081ae2e0bf7f48c7945a48f2469","score":"0.668153","text":"def resizeEvent(self, event):\n self.rubberBand.resize(self.size())","title":""},{"docid":"eeeac7b25cdcfed60e28199d5017eff8","score":"0.6678866","text":"def resizeEvent(self, event):\n\n self.settings.setValue(\"geometry\", self.saveGeometry())\n\n super().resizeEvent(event)","title":""},{"docid":"4bd6d87d7ac96ca7e0c292a5062731b4","score":"0.66657233","text":"def resize_if_necessary(self):\n self.toplinesizer.Layout() # misschien is dit bij wx variant nodig terwijl het bij de qt\n # variant vanzelf gebeurt?","title":""},{"docid":"98fdfcb7a638d72fc060ce5922fdafd3","score":"0.66656166","text":"def _resize(self, event):\n old_width, old_height = self.mne.fig_size_px\n new_width, new_height = self._get_size_px()\n new_margins = _calc_new_margins(\n self, old_width, old_height, new_width, new_height\n )\n self.subplots_adjust(**new_margins)\n # zen mode bookkeeping\n self.mne.zen_w *= old_width / new_width\n self.mne.zen_h *= old_height / new_height\n self.mne.fig_size_px = (new_width, new_height)\n self.canvas.draw_idle()","title":""},{"docid":"516dc86dd08c1e76c44aa5bdb407c9d9","score":"0.6665127","text":"def OnSize(self, event):\n\n self.Invalidate()","title":""},{"docid":"8308f12be2971d528cc016adc2d3f0e5","score":"0.6586651","text":"def on_resize(self, width, height):\n\n glViewport(0, 0, width, height)\n glMatrixMode(GL_PROJECTION)\n glLoadIdentity()\n gluPerspective(60., float(width)/height, 0.05, 1000)\n glMatrixMode(GL_MODELVIEW)\n return True","title":""},{"docid":"767c0ccd83762b649a2abf0867c1c0ea","score":"0.65726393","text":"def onFrameSizeUpdate(self, frameWidth, frameHeight):\n\n pass","title":""},{"docid":"60087bfb733c4f0c559c49d433812d22","score":"0.65127563","text":"def _resize_and_draw(self, event=None):\n self.draw(self.canvas.winfo_width(), self.canvas.winfo_height())","title":""},{"docid":"2ef79f855c9cf3b6702343152576f51e","score":"0.6495703","text":"def OnResize(self, *unused):\n g = self.parent.geometry()\n if g != self._geometry and self.player:\n u, v = self.player.video_get_size() # often (0, 0)\n if v > 0 and u > 0:\n # get window size and position\n g, x, y = g.split('+')\n w, h = g.split('x')\n if u > v: # ... for landscape\n # adjust the window height\n h = round(float(w) * v / u)\n else: # ... for portrait\n # adjust the window width\n w = round(float(h) * u / v)\n self.parent.geometry(\"%sx%s+%s+%s\" % (w, h, x, y))\n self._geometry = self.parent.geometry() # actual","title":""},{"docid":"4536929c84f174970f4b31c8fba5f4e9","score":"0.64822936","text":"def x_resize(self, event):\n self._translator.resize(self.width, self.height)\n self.width = self.winfo_width()\n self.height = self.winfo_height()\n self._translator = CoordinateTranslator(\n self.width, self.height, len(self.data.get_temperature()))\n self._controller.update_plot()","title":""},{"docid":"b8517a32cd49493e53876a3de6cce31a","score":"0.6458348","text":"def on_resize_parentx(self,event):\n ##print(\"parent event size=\"+str(event.width)+\" X \"+str(event.height))\n self.canvas_width = event.width\n self.canvas.config(width=self.canvas_width)\n self.show_image()","title":""},{"docid":"b2ff4d0dc4d8619c11b2c60574bc7a6e","score":"0.6424668","text":"def on_resize(self, width, height):\n super(GUI, self).on_resize(width, height)\n self.slack_slider.width = width - self.GUI_PADDING * 2\n self.beta_slider.width = width - self.GUI_PADDING * 2\n\n height -= self.GUI_HEIGHT\n if height <= 0:\n return\n\n drawing_width, drawing_height = 400, 400\n if drawing_height == 0 or drawing_height == 0:\n return\n display_aspect = width / float(height)\n drawing_aspect = drawing_width / float(drawing_height)\n if drawing_aspect > display_aspect:\n self.drawing_width = width\n self.drawing_height = width / drawing_aspect\n else:\n self.drawing_height = height\n self.drawing_width = height * drawing_aspect\n self.drawing_x = (width - self.drawing_width) / 2\n self.drawing_y = (height - self.drawing_height) / 2 + self.GUI_HEIGHT","title":""},{"docid":"3e7cb0fe569ed3d32a6b0fc2d77fda15","score":"0.641843","text":"def resizeEvent(self, event):\n width = event.size().width()\n self.tb_width = (0.5 * width) - 50\n self.table.setColumnWidth(0, 0.30 * self.tb_width)\n self.table.setColumnWidth(1, 0.40 * self.tb_width)\n self.table.setColumnWidth(2, 0.15 * self.tb_width)\n self.table.setColumnWidth(3, 0.15 * self.tb_width)\n\n # Important don't delete it\n QMdiSubWindow.resizeEvent(self, event)","title":""},{"docid":"79348bf47a0d3b8d5228dc1ae55a6d79","score":"0.6415301","text":"def _gVideoResizeEvent(self, event):\n if self.imgdisplayed is not None:\n self._displayimage(self.imgdisplayed)\n QtGui.QGraphicsView.resizeEvent(self.ui.gVideo, event)","title":""},{"docid":"740b878ca5f067022bc8d1cfba8d3edd","score":"0.64054227","text":"def _resize(self, event):\n h,w = Graph.reset(self)\n w_spacing = w/len(self._tf)\n Graph.draw_axes(h, w, 1)\n self._draw_connections(h, w_spacing) \n self._draw_points(h, w_spacing)\n print(\"Graph Displayed\") # Confirmation","title":""},{"docid":"7381a0437c1d6e9fa2ba36e43780d253","score":"0.640094","text":"def _size ( self, event ):\r\n super( ThemedControl, self )._size( event )\r\n self.updated = True","title":""},{"docid":"319a1a9d95fd7186cfe24bd94ba0752d","score":"0.6357787","text":"def _gVideoResizeEvent(self, event):\n if self.imgdisplayed is not None:\n self._displayimage(self.imgdisplayed, -1.0, -1.0)\n QtGui.QGraphicsView.resizeEvent(self.ui.gVideo, event)","title":""},{"docid":"81339b4eaa40284baa751d36b35cda75","score":"0.63368255","text":"def resize(self, size: QSize) -> None:\n print(f'QmlOffscreenRenderer.resize: {self.size} → {size}, {self.initialized}')\n if not self.initialized or self.size == size:\n return\n\n self.size = size\n\n if self.rootItem and self.ctx.makeCurrent():\n self._destroyFrameBuffer()\n self._createFrameBuffer()\n self._updateSizes()","title":""},{"docid":"10c13849592b0ef2fc55b5fa3c6f8058","score":"0.63337874","text":"def resize(self, event):\n new_width = event.width - 30\n self.set_notebook_width(new_width)\n self.set_scroll_region(event.height - 30)","title":""},{"docid":"c7ac4bf604238ace97a58964a8f8ccf9","score":"0.6289703","text":"def resizeEvent(self, event):\n dim = event.size()\n self.height1 = dim.height()\n self.width1 = dim.width()\n wscale = self.width1 / 800\n hscale = self.height1 / 600\n tmpy = 80 * hscale\n tmpx = 170 * wscale\n self.genderBox.setGeometry(tmpx, tmpy, 470, 35)\n tmpy = 10\n tmpx = self.width1 - 160\n self.helpBttn.setGeometry(tmpx, tmpy, 150, 50)\n tmpy = 20\n tmpwidth = self.width1 - 20\n self.titleLbl.setGeometry(10, 30, tmpwidth, 40)\n tmpx = 110 * wscale\n tmpy = 160 * hscale\n tmpwidth = 600 * wscale\n tmpheight = 300 * hscale\n self.declList.setGeometry(tmpx, tmpy, tmpwidth, tmpheight)\n tmpy = self.height1 - 70\n tmpx = 10\n self.cancelBttn.setGeometry(tmpx, tmpy, 150, 50)\n tmpx = self.width1 - 170\n self.height1 - 70\n self.acceptBttn.setGeometry(tmpx, tmpy, 150, 50)\n tmpos = self.pos()\n self.winx = tmpos.x()\n self.winy = tmpos.y()\n self.geometry = list([self.winx, self.winy, self.width1, self.height1])","title":""},{"docid":"eeb02c61fed2e23771a400b03222b812","score":"0.6271389","text":"def size_changed(self, size_x, size_y):\n size_x = max(1, size_x)\n size_y = max(1, size_y)\n self.__size_x = size_x\n self.__size_y = size_y\n self.display.SetSize(size_x, size_y)\n self.render()","title":""},{"docid":"fc3b393ea6e0d1df9caf41ec780d9a44","score":"0.6241969","text":"def resize(self):\n self.msg += \"Resizing figure, deleting keys from layout\\n\"\n for key in [\"width\", \"height\", \"autosize\", \"margin\"]:\n try:\n del self.plotly_fig[\"layout\"][key]\n except (KeyError, AttributeError):\n pass","title":""},{"docid":"6e236d9341a5c1f2e7623b3439e95367","score":"0.6215719","text":"def update_sizes(self) -> None:\n if setup.screen_size.changed():\n self.re_setup_menu()\n self.background_x_mult = setup.screen_size.get_width() * c.BACKGROUND_X_SCALER\n self.background_y_mult = setup.screen_size.get_height() * c.BACKGROUND_Y_SCALER\n self.menu_y = int(setup.screen_size.get_height() / 3.5)\n\n self.view = pygame.Rect((0, 0), (setup.screen_size.get_width(), setup.screen_size.get_height()))\n\n size_delta = (int(self.background_rect.width*self.background_x_mult), int(self.background_rect.height*self.background_y_mult))\n self.background = pygame.transform.scale(self.background, size_delta)","title":""},{"docid":"bc5ab447eed83314bd2a1f28b38351f2","score":"0.6205133","text":"def resizedGfx(self):\n self.w = int(self.canvas.clientWidth)\n self.h = int(self.canvas.clientHeight)","title":""},{"docid":"e90d9ef5892444d26549747e86a1306c","score":"0.6190203","text":"def resizeEvent(self, event):\r\n QtGui.QTableView.resizeEvent(self, event)\r\n\r\n parent = self.parent()\r\n if (not self._initial_size and parent and\r\n (self.isVisible() or isinstance(parent, QtGui.QMainWindow))):\r\n self._initial_size = True\r\n self.resizeColumnsToContents()","title":""},{"docid":"146c985e1a08e8f8bec8dd591483fc63","score":"0.61643076","text":"def on_layout_requested(self, event):\n self.size_hint_updated()","title":""},{"docid":"6ebff4e313aad81d4f722cd26f90b81f","score":"0.61550546","text":"def on_size_change(self,event):\n self.panel_width,self.panel_height = event.GetSize()\n print (\"Width =\",self.panel_width,\"Height =\",self.panel_height)\n w = self.panel_width\n h = self.panel_height\n try:\n self.fields['graph0'].im_size_show = (int((w-384)/2),int(h/5))\n self.fields['graph1'].im_size_show = (int((w-384)/2),int(h/5))\n self.fields['graph2'].im_size_show = (int((w-384)/2),int(h/5))\n self.fields['graph3'].im_size_show = (int((w-384)/2),int(h))\n except:\n pass\n\n #self.panel.SetSizer(self.sizer_main)\n #self.sizer_main.Fit(self)\n self.Layout()\n self.panel.Layout()\n #self.panel.Fit()","title":""},{"docid":"b8499e141555fb4866c20c6646573b4b","score":"0.61459696","text":"def resizeEvent(self, event):\r\n\r\n QtGui.QTableView.resizeEvent(self, event)\r\n\r\n if self._editor.auto_size:\r\n self.resizeColumnsToContents()\r\n self.resizeRowsToContents()\r\n\r\n else:\r\n parent = self.parent()\r\n if (not self._initial_size and parent and\r\n (self.isVisible() or isinstance(parent, QtGui.QMainWindow))):\r\n self._initial_size = True\r\n if self._editor.auto_size:\r\n self.resizeColumnsToContents()\r\n self.resizeRowsToContents()","title":""},{"docid":"ef846209dbfd3d9fc5a9324419e1385e","score":"0.61013967","text":"def resize(self, new_rows, new_cols):\n\t\tnew_x_res = self.resolution[0] * new_rows / self.rows\n\t\tnew_y_res = self.resolution[1] * new_cols / self.cols\n\n\t\tself.resolution = [new_x_res, new_y_res]\n\t\tself.window.resize(*self.resolution)","title":""},{"docid":"b3c1d66ccc21a2c314042f141d9a18a2","score":"0.6077875","text":"def resize(self, width, height):\n pass","title":""},{"docid":"045f094872f47dd66c8bbe6863c9d1b2","score":"0.6064716","text":"def on_resizeButton_clicked(self):\n for column in range(self.callTrace.columnCount()):\n self.callTrace.resizeColumnToContents(column)","title":""},{"docid":"0ad3a84f114e68b65e97b694f3ea8d3f","score":"0.60419774","text":"def on_drag_end(self, widget, context):\n self.pane_resize()","title":""},{"docid":"fe81904f286db52a086c18e29e441e1f","score":"0.6028545","text":"def display_size_update(self, rect_):\r\n self.screen_size = rect_","title":""},{"docid":"d34e6c579919a174d447f27bde2081c3","score":"0.60209614","text":"def _onCanvasFrameDimension(self, event):\n canvas = self.getCanvas()\n frame = self.getCanvasFrame()\n\n canvasHeight = event.height\n canvasWidth = event.width\n\n # Get the new canvas dimensions\n if canvasWidth <= frame.winfo_reqwidth():\n canvasWidth = frame.winfo_reqwidth()\n if canvasHeight <= frame.winfo_reqheight():\n canvasHeight = frame.winfo_reqheight()\n\n if self.__canvasFrameDimension == \"both\":\n canvas.itemconfig(self.canvasWindow, height=canvasHeight,\n width=canvasWidth)\n elif self.__canvasFrameDimension == \"x\":\n canvas.itemconfig(self.canvasWindow, width=canvasWidth)\n elif self.__canvasFrameDimension == \"y\":\n canvas.itemconfig(self.canvasWindow, height=canvasHeight)","title":""},{"docid":"906ff98be964101bb8aa857191ea7757","score":"0.60031354","text":"def menuBarResize(self, val='True', **kwargs):\n \n pass","title":""},{"docid":"20391e7e2e6acd0158cee5dfd73ba9b8","score":"0.5998202","text":"def add_resize_listener_func(self, func):\n self._resize_listeners.append(func)","title":""},{"docid":"2be01e634f952434a009ec775473f7ea","score":"0.59910166","text":"def window_configure(self, *args):\n\n self.window_size = args[0].get_size()\n self.apply_dynamic_scaling_all(self.window_size[0], self.window_size[1]) # todo: add std. win. width and height\n self.apply_dynamic_font_all(self.default_font_factor, self.window_size[1])","title":""},{"docid":"936e2e4a94e861497dcd9000a29bd1b4","score":"0.59852576","text":"def resize_empty_screen(self, wid, hig):\n self.SetSize(wid, hig)","title":""},{"docid":"8f391f9884e7abedf1493413596def64","score":"0.5976079","text":"def showEvent(self, event):\r\n QtGui.QSplitter.showEvent(self, event)\r\n if not self._initialized:\r\n self._initialized = True\r\n self._resize_items()","title":""},{"docid":"7486d222cf4bbf74876fa53899794e7b","score":"0.5961208","text":"def check_resize(game, event):\n # get new screen width and height\n w, h = event.dict['size']\n # place aspect ratios in settings\n w_ratio = 16\n h_ratio = 9\n #find how many pixels in new screen size\n w_pxls = w / w_ratio\n h_pxls = h / h_ratio\n\n # ensure aspect ratio is maintained\n if w_pxls < h_pxls:\n w = int(h_pxls * w_ratio)\n elif h_pxls < w_pxls:\n h = int(w_pxls * h_ratio)\n if w <= 400 and h <= 225:\n print('screen lower than minimum')\n w = 400\n h = 225\n\n print('w: ' + str(w))\n print('h: ' + str(h))\n game.resize_percentage = round(w / game.DISPLAY_W, 2)\n print('rp: ' + str(game.resize_percentage))\n game.DISPLAY_H = h\n game.DISPLAY_W = w\n game.resize_displays()\n game.mid_h = h / 2\n game.mid_w = w / 2\n game.resize_fonts()\n\n\n game.ball.resize()\n game.bat.resize()\n game.obstacle.resize()\n game.scoreboard.resize()\n\n game.menu.resize_main()\n game.main_menu.resize()\n game.options.resize()\n game.credits.resize()\n game.create_user.resize()\n game.select_user.resize()\n game.game_over_screen.resize()","title":""},{"docid":"0d242c0e99915717ea4f76fb5b7b4e15","score":"0.59186184","text":"def reset_original_size(self):\n self.set_view()\n self.emit_signal_if_scene_rect_changed() # sends signal","title":""},{"docid":"0c8630dda03334cb6916818e98635056","score":"0.5907763","text":"def check_resize(event):\n global size, size_orig\n global width, w_orig, w_offset\n global height, h_orig, h_offset\n global RESIZE\n size_orig = event.size\n w_orig = event.w\n h_orig = event.h\n width = min(event.w, int(event.h * 1920 / 1080))\n height = int(width * 1080 / 1920)\n size = (width, height)\n h_offset = int((h_orig - height) / 2)\n w_offset = int((w_orig - width) / 2)\n RESIZE = True","title":""},{"docid":"4d2bb11adfe92cce282971765ac05a60","score":"0.5893002","text":"def window_size(self, window_size):\n self.render_window.SetSize(window_size[0], window_size[1])\n self._window_size_unset = False\n self.render()","title":""},{"docid":"6494aaadc5f569c3e9736fc7623f3f2e","score":"0.58919287","text":"def invalidate(self):\n super(QDockFrameLayout, self).invalidate()\n self._size_hint = QSize()\n self._min_size = QSize()\n self._max_size = QSize()","title":""},{"docid":"9f52d62edab650da2834913db11606ab","score":"0.5888416","text":"def resize(self, widget):\r\n width = widget.width\r\n height = widget.height\r\n \r\n if self.dimension is BOTH or self.dimension is HORIZONTAL:\r\n width = self.getWidth(widget)\r\n if self.dimension is BOTH or self.dimension is VERTICAL:\r\n height = self.getHeight(widget)\r\n \r\n widget.resizeTo(width, height)","title":""},{"docid":"78c29e227ed00cd37234280ebe2c7ac7","score":"0.58856606","text":"def hideEvent(self, event):\n\n # -- Store the current window size in our scribble settings\n self.storeWidgetGeometry()","title":""},{"docid":"093526c047552ec8af2d110045808bbe","score":"0.58665204","text":"def _on_size_allocate(self, widget, event, data=None):\n\n x, y, width, height = self.get_allocation()\n if width > self.edit_region_width:\n margin = (width - self.edit_region_width) / 2\n self.set_left_margin(margin)\n self.set_right_margin(margin)","title":""},{"docid":"36013738a4943a38612fd19c3cb76be4","score":"0.5863162","text":"def setFrameDimensions(self):\n if self.__canvasFrameDimension is not None:\n self.getCanvas().bind('<Configure>', self._onCanvasFrameDimension)","title":""},{"docid":"6964f69afe6d40cf51480088047c1831","score":"0.58570856","text":"def resizeEvent(self, resize_event):\n\n if self.image is not None:\n if self._oversize:\n scaled_image_size = resize_event.size()\n else:\n scaled_image_size = resize_event.size().boundedTo(self.image.size())\n\n self.scaled_image = self.image.scaled(scaled_image_size, QtCore.Qt.KeepAspectRatio, QtCore.Qt.SmoothTransformation)","title":""},{"docid":"6144815fbe4f933a0cfc9a95064007f0","score":"0.5837666","text":"def configureWindow(self):\r\n self.master.title(self.title)\r\n self.master.geometry(\"{0}x{1}+0+0\".format(self.master.winfo_screenwidth()\r\n ,self.master.winfo_screenheight()))\r\n self.master.resizable(width=False, height=False)","title":""},{"docid":"216ba54cd53ccb9a634cd6f26f522054","score":"0.583537","text":"def scaleFitWindow(self):\r\n e = 2.0 # So that no scrollbars are generated.\r\n w1 = self.dock.width() - e\r\n h1 = self.dock.height() - e\r\n a1 = w1/ h1\r\n # Calculate a new scale value based on the pixmap's aspect ratio.\r\n w2 = self.canvas.pixmap.width() - 0.0\r\n h2 = self.canvas.pixmap.height() - 0.0\r\n a2 = w2 / h2\r\n #logging.info('dock_w:%d h:%d \\n canvas_w:%d h:%d'%(w1,h1,w2,h2))\r\n return w1 / w2 if a2 >= a1 else h1 / h2","title":""},{"docid":"62c465f439e45456f9ec470d28561e28","score":"0.5824469","text":"def _doResize(self):\n\n if not self: # avoid a PyDeadObject error\n return\n\n if self.GetSize().height < 32:\n return # avoid an endless update bug when the height is small.\n\n numCols = self.GetColumnCount()\n if numCols == 0:\n return # Nothing to resize.\n\n col_widths = [(self._GetMinColWidth(col)\n if col in self._autosize_columns\n else self.GetColumnWidth(col))\n for col in range(numCols)]\n\n # We're showing the vertical scrollbar -> allow for scrollbar width\n # NOTE: on GTK, the scrollbar is included in the client size, but on\n # Windows it is not included\n listWidth = self.GetClientSize().width\n\n if sum(col_widths) < listWidth:\n col_widths[self._resizeCol] += listWidth - sum(col_widths)\n\n for col, width in enumerate(col_widths):\n self.SetColumnWidth(col, width)","title":""},{"docid":"af7f28a236c5d321a09ca6faa0012492","score":"0.5818246","text":"def _size ( self, event ):\r\n control = self.control\r\n if control is not None:\r\n control.Layout()\r\n control.Refresh()","title":""},{"docid":"d9bdf44eaf481aa6685448cd86b742ba","score":"0.5809648","text":"def resize(width, height):\n wnd().resizeTo(width, height)","title":""},{"docid":"0125be69f72d9d9da29e7e6eff6b42cb","score":"0.5807572","text":"def getAutoResize(self, **kwargs):\n \n pass","title":""},{"docid":"c3984a6b1f6a93902ba951db08806507","score":"0.57903713","text":"def shell_icon_size_changed(self, icon_size):\n pass","title":""},{"docid":"8f7774ce3f335cbfed4d675e2565b698","score":"0.5752382","text":"def resizeEvent(self, event: QResizeEvent):\n text = self.normal_text()\n\n if self.get_text_width(text, 0.7) > self.label.frameGeometry().width():\n text = self.medium_text()\n\n if self.get_text_width(text, 0.5) > self.label.frameGeometry().width():\n text = self.small_text()\n\n self.label.setText(text)\n\n self.detailed_view.resizeEvent(event)","title":""},{"docid":"5e327a221a51c66eb1801ae08f2f0522","score":"0.574986","text":"def ammolist_resize(self, event):\n\n if not self.AmmoList.GetColumnCount():\n return\n\n column_width = self.AmmoList.GetClientSize()[0] - 4\n self.AmmoList.SetColumnWidth(0, column_width / 3)\n self.AmmoList.SetColumnWidth(1, column_width / 3)\n self.AmmoList.SetColumnWidth(2, column_width / 3)","title":""},{"docid":"d4a9b701667845babad538989c65ff66","score":"0.5746227","text":"def set_window_size(self, width, height):\n print(width, height)\n gl.glViewport(0, 0, width, height)\n self.bind_size(width, height)","title":""},{"docid":"9df84fc420cd08c543ff35dbcde025e9","score":"0.5743378","text":"def sizeHint(self):\n return QtCore.QSize(800, 600)","title":""},{"docid":"be1b27a1596479a0f7f3018a30000d30","score":"0.5731725","text":"def iconSizeChanged(self, *args, **kwargs): # real signature unknown\n pass","title":""},{"docid":"c49d481da4e0a8bbabeed1bf1864c85e","score":"0.5697398","text":"def resize_ui_components(self, PICK):\n self.set_column_widths_log_entry_tab()\n self.set_column_widths_event_tab()\n self.set_column_widths_vector_view_tab()","title":""},{"docid":"88281e9f972587e010976ecb5f654e27","score":"0.56946164","text":"def resizeEvent(self, event):\r\n if self._editor.factory.word_wrap:\r\n for i in range(self.topLevelItemCount()):\r\n mi = self.indexFromItem(self.topLevelItem(i))\r\n id = self.itemDelegate(mi)\r\n id.sizeHintChanged.emit(mi)\r\n super(self.__class__, self).resizeEvent(event)","title":""}],"string":"[\n {\n \"docid\": \"cf95f21cd7739a672db6e5c661a5ca90\",\n \"score\": \"0.8178788\",\n \"text\": \"def onResize(self, window, width, height):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1743c098b05d52132a2b8d8e82d29e0\",\n \"score\": \"0.80237764\",\n \"text\": \"def _resize(self, event):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"545f61a7170492b00c7bae3c814618dd\",\n \"score\": \"0.78258556\",\n \"text\": \"def resizeEvent(self, event):\\n\\n # -- Call the function which handles the tab mode switching\\n self.setTabMode(self.tabMode)\\n\\n # -- Store the current window size in our scribble settings\\n self.storeWidgetGeometry()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd179e6627dd9fd92de561925238420b\",\n \"score\": \"0.77364415\",\n \"text\": \"def resizeEvent(self, event):\\n QtGui.QMainWindow.resizeEvent(self, event)\\n QtGui.QApplication.processEvents()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac0dea63a46529b934e56916f8c9309a\",\n \"score\": \"0.7684432\",\n \"text\": \"def resizeEvent(self,event):\\n self.grab_default_dimensions()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17f72fd5c27658d73eb945ad02c4cdd2\",\n \"score\": \"0.7643923\",\n \"text\": \"def on_resize( self, width, height ):\\r\\n # we don't do anything by default\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"395879c1edefadb20a47477d3ebc5fcf\",\n \"score\": \"0.7602799\",\n \"text\": \"def resizeEvent(self, new_size):\\n\\n self.app.settings['mainwindow_w'] = new_size.size().width()\\n self.app.settings['mainwindow_h'] = new_size.size().height()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3ab0786d88fe417a524eba3b9f10b5f\",\n \"score\": \"0.75728744\",\n \"text\": \"def on_resize(self, event):\\n self.context.set_viewport(0, 0, event.size[0], event.size[1])\\n for visual in self.visuals:\\n visual.on_resize(event.size)\\n self.update()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04f643a929684c613ea1b8e7c1a789c7\",\n \"score\": \"0.7476696\",\n \"text\": \"def resizeEvent(self, event):\\n \\n # perform the default resizing\\n super(PlotCanvas, self).resizeEvent(event)\\n # fix the layout\\n self.fixLayout()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b9f6f33fcb706563753028a1405e87c\",\n \"score\": \"0.7400655\",\n \"text\": \"def on_resize(self, width, height):\\n # label\\n self.label.y = height - 10\\n G.eventhandler.call(\\\"user:window:resize\\\", width, height)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86663dd22bc723587e2991dcc7a363e9\",\n \"score\": \"0.7394615\",\n \"text\": \"def resizeEvent(self, event):\\n super(QContainer, self).resizeEvent(event)\\n self.resized.emit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77da8abdda864503551d76db56f268ec\",\n \"score\": \"0.73922807\",\n \"text\": \"def resize(self, event):\\n print(\\\"Screen.resize()\\\")\\n if event:\\n self.width = event.width\\n self.height = event.height\\n self.draw_grid()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd1aac8efc588442d8eae6a14af92cd0\",\n \"score\": \"0.7337735\",\n \"text\": \"def _on_parent_resized(self):\\n # resize overlay\\n self.resize(self.parentWidget().size())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5aee5937ac70940fc2a79f9b5e0fa13e\",\n \"score\": \"0.73191726\",\n \"text\": \"def _auto_resize(self, window):\\n if self.get_size() != self.winsize:\\n self.winsize = self.get_size()\\n self._app.emit(\\\"widget-layout-changed\\\", self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4235b3631ae0d04bf3a84d0e3cfe6ddb\",\n \"score\": \"0.7298679\",\n \"text\": \"def _onResize(self, event):\\n if 'gtk2' in wx.PlatformInfo or 'gtk3' in wx.PlatformInfo:\\n self._doResize()\\n else:\\n wx.CallAfter(self._doResize)\\n event.Skip()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca8e0ae1cb9207a3f3477b323a2e6200\",\n \"score\": \"0.72599375\",\n \"text\": \"def on_resize_event(self, event):\\n self.width = event.width\\n self.height = event.height\\n log.debug(\\\"New size 'ManageMyDico' : {}x{}\\\".format(self.width, self.height))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e73a4b3bbb1509305aa68f94765cfd0d\",\n \"score\": \"0.7212062\",\n \"text\": \"def resizeEvent(self, event):\\n super(XImageSlider, self).resizeEvent(event)\\n \\n self.recalculate()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a98d5a2f3464f593aeb9e2bf44a645ea\",\n \"score\": \"0.71496797\",\n \"text\": \"def on_resize_parent(self, event):\\n\\n self.statusbar.config(width = event.width, height = 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"636e8c6f36b13ac8feccd1f284874bf3\",\n \"score\": \"0.71427923\",\n \"text\": \"def resizeEvent(self, event):\\n # update the image scale\\n self._update_pixmap_size()\\n super(ShotWidget, self).resizeEvent(event)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa6fa9b49d0a61d993c93d3ce6bf5201\",\n \"score\": \"0.7137341\",\n \"text\": \"def handle_resize(event):\\n # grabbing the newely resized windows size\\n scale_size = event.dict['size']\\n\\n # save these scaling factors globally as they affect the global screen\\n # rendering\\n global scale_x\\n global scale_y\\n\\n # grabbing new scaling factor values\\n (x_o, y_o) = size_o\\n (x_1, y_1) = scale_size\\n # calculating new scaling factor values\\n scale_x = (x_1 / x_o)\\n scale_y = (y_1 / y_o)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"098b2f0cfede484173fe38e39e73931a\",\n \"score\": \"0.7097699\",\n \"text\": \"def resizeEvent(self, event: QtGui.QResizeEvent) -> None:\\n\\n self.views[0].resize()\\n\\n super(ViewMain, self).resizeEvent(event)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74ebbd543242d6d43e9b3dcf535a9cd5\",\n \"score\": \"0.7042027\",\n \"text\": \"def resize(self, event):\\r\\n padding = 4 # not sure why this is required, but if I don't include it it growths continually\\r\\n self.size = max(min(event.width - padding, event.height - padding), 100)\\r\\n self.update_indicator()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3c12195a44d7ea84a07a701c3e48b3a\",\n \"score\": \"0.6989664\",\n \"text\": \"def on_resize_parentx(self, event):\\n self.statusbar.config(width = event.width, height = 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00e63848501c41f29fd637bfc5ee3351\",\n \"score\": \"0.69684976\",\n \"text\": \"def OnSize( self, evt ):\\n if not self.IsMaximized():\\n self.preMaxSize = self.GetSize()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7af3e4f793d485e1fd1caf0c52c66287\",\n \"score\": \"0.6942225\",\n \"text\": \"def _updateSizes(self) -> None:\\n print(f'QmlOffscreenRenderer._updateSizes: {self.size}')\\n width, height = self.size.toTuple()\\n self._window.setGeometry(0, 0, width, height)\\n self.rootItem.setWidth(width)\\n self.rootItem.setHeight(height)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aff5fc4c09588e1a7408797b2018d98c\",\n \"score\": \"0.69375074\",\n \"text\": \"def resizeEvent(self, event):\\n super(Editor, self).resizeEvent(event)\\n self.resize_signal.emit(event)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b90d7c48229884795b49280a3a47ced\",\n \"score\": \"0.69124967\",\n \"text\": \"def resizeEvent(self, event):\\r\\n QtGui.QSplitter.resizeEvent(self, event)\\r\\n\\r\\n parent = self.parent()\\r\\n if (not self._initialized and parent and\\r\\n (self.isVisible() or isinstance(parent, QtGui.QMainWindow))):\\r\\n self._initialized = True\\r\\n self._resize_items()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4660be854a6f5d1cc04138bf2f8d2078\",\n \"score\": \"0.68822205\",\n \"text\": \"def resizeEvent(self, event):\\r\\n QDialog.resizeEvent(self, event)\\r\\n self.size_change.emit(self.size())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f15e715bcac281544615f13e4abc649d\",\n \"score\": \"0.68478733\",\n \"text\": \"def handle_key_resize_event(self):\\n self.read_screen_size()\\n if self.prompt_for_screen_size():\\n self.redraw()\\n else:\\n self.keep_running = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d530ec8c5bd73b76e222741a9a4e8b03\",\n \"score\": \"0.6823641\",\n \"text\": \"def componentResized(self, e):\\n gCanvas.initOffscreenImage()\\n if gCanvas.isShowing():\\n gCanvas.repaint()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cf8c9857378022ef38587717e3777e6\",\n \"score\": \"0.68174404\",\n \"text\": \"def resizeEvent(self, event):\\n super(QResizingGroupBox, self).resizeEvent(event)\\n self.resized.emit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39f12a918c809ca410f776b0a36c6a91\",\n \"score\": \"0.68159217\",\n \"text\": \"def on_resize(self, event):\\n width, height = self.GetClientSize()\\n self.buffer = wx.Bitmap(width, height)\\n self.update_bitmap()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c09bdf83637867959da01096cd97b590\",\n \"score\": \"0.67809826\",\n \"text\": \"def OnSize(self,event):\\n\\t\\tself.Refresh()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c19bb33feebe718e73108c0b8960463f\",\n \"score\": \"0.677057\",\n \"text\": \"def resizeEvent(self, event):\\n if not self._page:\\n return\\n\\n self.resizePolygonsToPixmap()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aeba497f1fb2b5a7b5d04667ecd2a172\",\n \"score\": \"0.6724207\",\n \"text\": \"def canvasSizeChanged(self, app):\\n\\n self.x = int(self.xFrac * app.width) - self.width // 2\\n self.y = int(self.yFrac * app.height) - self.height // 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad92c8ca2670bf39ae2f78d573eebbec\",\n \"score\": \"0.6718328\",\n \"text\": \"def on_resize(self, width, height):\\n # label\\n self.label.y = height - 10\\n # reticle\\n if self.reticle:\\n self.reticle.delete()\\n x, y = self.width // 2, self.height // 2\\n n = 10\\n self.reticle = pyglet.graphics.vertex_list(4, ('v2i', (x - n, y, x + n, y, x, y - n, x, y + n)))\\n\\n if G.inventoryhandler.should_game_freeze():\\n G.inventoryhandler.send_event(\\\"on_resize\\\", width, height)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16d79c51fa64494c89aa8ba635cdafe9\",\n \"score\": \"0.6717966\",\n \"text\": \"def _tk_resize(self,event):\\n w = self._window.winfo_width()-self._canvas._dw\\n h = self._window.winfo_height()-self._canvas._dh\\n self._canvas._lastw = self._canvas._currw\\n self._canvas._lasth = self._canvas._currh\\n self._canvas._currw = w\\n self._canvas._currh = h\\n dw = self._canvas._currw-self._canvas._lastw\\n dh = self._canvas._currh-self._canvas._lasth\\n self._canvas.config(width=w,height=h)\\n if dw or dh:\\n self._canvas.move('all',dw/2.0,dh/2.0)\\n _Context.Instance().refresh()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08ee0081ae2e0bf7f48c7945a48f2469\",\n \"score\": \"0.668153\",\n \"text\": \"def resizeEvent(self, event):\\n self.rubberBand.resize(self.size())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eeeac7b25cdcfed60e28199d5017eff8\",\n \"score\": \"0.6678866\",\n \"text\": \"def resizeEvent(self, event):\\n\\n self.settings.setValue(\\\"geometry\\\", self.saveGeometry())\\n\\n super().resizeEvent(event)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4bd6d87d7ac96ca7e0c292a5062731b4\",\n \"score\": \"0.66657233\",\n \"text\": \"def resize_if_necessary(self):\\n self.toplinesizer.Layout() # misschien is dit bij wx variant nodig terwijl het bij de qt\\n # variant vanzelf gebeurt?\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98fdfcb7a638d72fc060ce5922fdafd3\",\n \"score\": \"0.66656166\",\n \"text\": \"def _resize(self, event):\\n old_width, old_height = self.mne.fig_size_px\\n new_width, new_height = self._get_size_px()\\n new_margins = _calc_new_margins(\\n self, old_width, old_height, new_width, new_height\\n )\\n self.subplots_adjust(**new_margins)\\n # zen mode bookkeeping\\n self.mne.zen_w *= old_width / new_width\\n self.mne.zen_h *= old_height / new_height\\n self.mne.fig_size_px = (new_width, new_height)\\n self.canvas.draw_idle()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"516dc86dd08c1e76c44aa5bdb407c9d9\",\n \"score\": \"0.6665127\",\n \"text\": \"def OnSize(self, event):\\n\\n self.Invalidate()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8308f12be2971d528cc016adc2d3f0e5\",\n \"score\": \"0.6586651\",\n \"text\": \"def on_resize(self, width, height):\\n\\n glViewport(0, 0, width, height)\\n glMatrixMode(GL_PROJECTION)\\n glLoadIdentity()\\n gluPerspective(60., float(width)/height, 0.05, 1000)\\n glMatrixMode(GL_MODELVIEW)\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"767c0ccd83762b649a2abf0867c1c0ea\",\n \"score\": \"0.65726393\",\n \"text\": \"def onFrameSizeUpdate(self, frameWidth, frameHeight):\\n\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60087bfb733c4f0c559c49d433812d22\",\n \"score\": \"0.65127563\",\n \"text\": \"def _resize_and_draw(self, event=None):\\n self.draw(self.canvas.winfo_width(), self.canvas.winfo_height())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ef79f855c9cf3b6702343152576f51e\",\n \"score\": \"0.6495703\",\n \"text\": \"def OnResize(self, *unused):\\n g = self.parent.geometry()\\n if g != self._geometry and self.player:\\n u, v = self.player.video_get_size() # often (0, 0)\\n if v > 0 and u > 0:\\n # get window size and position\\n g, x, y = g.split('+')\\n w, h = g.split('x')\\n if u > v: # ... for landscape\\n # adjust the window height\\n h = round(float(w) * v / u)\\n else: # ... for portrait\\n # adjust the window width\\n w = round(float(h) * u / v)\\n self.parent.geometry(\\\"%sx%s+%s+%s\\\" % (w, h, x, y))\\n self._geometry = self.parent.geometry() # actual\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4536929c84f174970f4b31c8fba5f4e9\",\n \"score\": \"0.64822936\",\n \"text\": \"def x_resize(self, event):\\n self._translator.resize(self.width, self.height)\\n self.width = self.winfo_width()\\n self.height = self.winfo_height()\\n self._translator = CoordinateTranslator(\\n self.width, self.height, len(self.data.get_temperature()))\\n self._controller.update_plot()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8517a32cd49493e53876a3de6cce31a\",\n \"score\": \"0.6458348\",\n \"text\": \"def on_resize_parentx(self,event):\\n ##print(\\\"parent event size=\\\"+str(event.width)+\\\" X \\\"+str(event.height))\\n self.canvas_width = event.width\\n self.canvas.config(width=self.canvas_width)\\n self.show_image()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2ff4d0dc4d8619c11b2c60574bc7a6e\",\n \"score\": \"0.6424668\",\n \"text\": \"def on_resize(self, width, height):\\n super(GUI, self).on_resize(width, height)\\n self.slack_slider.width = width - self.GUI_PADDING * 2\\n self.beta_slider.width = width - self.GUI_PADDING * 2\\n\\n height -= self.GUI_HEIGHT\\n if height <= 0:\\n return\\n\\n drawing_width, drawing_height = 400, 400\\n if drawing_height == 0 or drawing_height == 0:\\n return\\n display_aspect = width / float(height)\\n drawing_aspect = drawing_width / float(drawing_height)\\n if drawing_aspect > display_aspect:\\n self.drawing_width = width\\n self.drawing_height = width / drawing_aspect\\n else:\\n self.drawing_height = height\\n self.drawing_width = height * drawing_aspect\\n self.drawing_x = (width - self.drawing_width) / 2\\n self.drawing_y = (height - self.drawing_height) / 2 + self.GUI_HEIGHT\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e7cb0fe569ed3d32a6b0fc2d77fda15\",\n \"score\": \"0.641843\",\n \"text\": \"def resizeEvent(self, event):\\n width = event.size().width()\\n self.tb_width = (0.5 * width) - 50\\n self.table.setColumnWidth(0, 0.30 * self.tb_width)\\n self.table.setColumnWidth(1, 0.40 * self.tb_width)\\n self.table.setColumnWidth(2, 0.15 * self.tb_width)\\n self.table.setColumnWidth(3, 0.15 * self.tb_width)\\n\\n # Important don't delete it\\n QMdiSubWindow.resizeEvent(self, event)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79348bf47a0d3b8d5228dc1ae55a6d79\",\n \"score\": \"0.6415301\",\n \"text\": \"def _gVideoResizeEvent(self, event):\\n if self.imgdisplayed is not None:\\n self._displayimage(self.imgdisplayed)\\n QtGui.QGraphicsView.resizeEvent(self.ui.gVideo, event)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"740b878ca5f067022bc8d1cfba8d3edd\",\n \"score\": \"0.64054227\",\n \"text\": \"def _resize(self, event):\\n h,w = Graph.reset(self)\\n w_spacing = w/len(self._tf)\\n Graph.draw_axes(h, w, 1)\\n self._draw_connections(h, w_spacing) \\n self._draw_points(h, w_spacing)\\n print(\\\"Graph Displayed\\\") # Confirmation\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7381a0437c1d6e9fa2ba36e43780d253\",\n \"score\": \"0.640094\",\n \"text\": \"def _size ( self, event ):\\r\\n super( ThemedControl, self )._size( event )\\r\\n self.updated = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"319a1a9d95fd7186cfe24bd94ba0752d\",\n \"score\": \"0.6357787\",\n \"text\": \"def _gVideoResizeEvent(self, event):\\n if self.imgdisplayed is not None:\\n self._displayimage(self.imgdisplayed, -1.0, -1.0)\\n QtGui.QGraphicsView.resizeEvent(self.ui.gVideo, event)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81339b4eaa40284baa751d36b35cda75\",\n \"score\": \"0.63368255\",\n \"text\": \"def resize(self, size: QSize) -> None:\\n print(f'QmlOffscreenRenderer.resize: {self.size} → {size}, {self.initialized}')\\n if not self.initialized or self.size == size:\\n return\\n\\n self.size = size\\n\\n if self.rootItem and self.ctx.makeCurrent():\\n self._destroyFrameBuffer()\\n self._createFrameBuffer()\\n self._updateSizes()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10c13849592b0ef2fc55b5fa3c6f8058\",\n \"score\": \"0.63337874\",\n \"text\": \"def resize(self, event):\\n new_width = event.width - 30\\n self.set_notebook_width(new_width)\\n self.set_scroll_region(event.height - 30)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7ac4bf604238ace97a58964a8f8ccf9\",\n \"score\": \"0.6289703\",\n \"text\": \"def resizeEvent(self, event):\\n dim = event.size()\\n self.height1 = dim.height()\\n self.width1 = dim.width()\\n wscale = self.width1 / 800\\n hscale = self.height1 / 600\\n tmpy = 80 * hscale\\n tmpx = 170 * wscale\\n self.genderBox.setGeometry(tmpx, tmpy, 470, 35)\\n tmpy = 10\\n tmpx = self.width1 - 160\\n self.helpBttn.setGeometry(tmpx, tmpy, 150, 50)\\n tmpy = 20\\n tmpwidth = self.width1 - 20\\n self.titleLbl.setGeometry(10, 30, tmpwidth, 40)\\n tmpx = 110 * wscale\\n tmpy = 160 * hscale\\n tmpwidth = 600 * wscale\\n tmpheight = 300 * hscale\\n self.declList.setGeometry(tmpx, tmpy, tmpwidth, tmpheight)\\n tmpy = self.height1 - 70\\n tmpx = 10\\n self.cancelBttn.setGeometry(tmpx, tmpy, 150, 50)\\n tmpx = self.width1 - 170\\n self.height1 - 70\\n self.acceptBttn.setGeometry(tmpx, tmpy, 150, 50)\\n tmpos = self.pos()\\n self.winx = tmpos.x()\\n self.winy = tmpos.y()\\n self.geometry = list([self.winx, self.winy, self.width1, self.height1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eeb02c61fed2e23771a400b03222b812\",\n \"score\": \"0.6271389\",\n \"text\": \"def size_changed(self, size_x, size_y):\\n size_x = max(1, size_x)\\n size_y = max(1, size_y)\\n self.__size_x = size_x\\n self.__size_y = size_y\\n self.display.SetSize(size_x, size_y)\\n self.render()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc3b393ea6e0d1df9caf41ec780d9a44\",\n \"score\": \"0.6241969\",\n \"text\": \"def resize(self):\\n self.msg += \\\"Resizing figure, deleting keys from layout\\\\n\\\"\\n for key in [\\\"width\\\", \\\"height\\\", \\\"autosize\\\", \\\"margin\\\"]:\\n try:\\n del self.plotly_fig[\\\"layout\\\"][key]\\n except (KeyError, AttributeError):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e236d9341a5c1f2e7623b3439e95367\",\n \"score\": \"0.6215719\",\n \"text\": \"def update_sizes(self) -> None:\\n if setup.screen_size.changed():\\n self.re_setup_menu()\\n self.background_x_mult = setup.screen_size.get_width() * c.BACKGROUND_X_SCALER\\n self.background_y_mult = setup.screen_size.get_height() * c.BACKGROUND_Y_SCALER\\n self.menu_y = int(setup.screen_size.get_height() / 3.5)\\n\\n self.view = pygame.Rect((0, 0), (setup.screen_size.get_width(), setup.screen_size.get_height()))\\n\\n size_delta = (int(self.background_rect.width*self.background_x_mult), int(self.background_rect.height*self.background_y_mult))\\n self.background = pygame.transform.scale(self.background, size_delta)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc5ab447eed83314bd2a1f28b38351f2\",\n \"score\": \"0.6205133\",\n \"text\": \"def resizedGfx(self):\\n self.w = int(self.canvas.clientWidth)\\n self.h = int(self.canvas.clientHeight)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e90d9ef5892444d26549747e86a1306c\",\n \"score\": \"0.6190203\",\n \"text\": \"def resizeEvent(self, event):\\r\\n QtGui.QTableView.resizeEvent(self, event)\\r\\n\\r\\n parent = self.parent()\\r\\n if (not self._initial_size and parent and\\r\\n (self.isVisible() or isinstance(parent, QtGui.QMainWindow))):\\r\\n self._initial_size = True\\r\\n self.resizeColumnsToContents()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"146c985e1a08e8f8bec8dd591483fc63\",\n \"score\": \"0.61643076\",\n \"text\": \"def on_layout_requested(self, event):\\n self.size_hint_updated()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ebff4e313aad81d4f722cd26f90b81f\",\n \"score\": \"0.61550546\",\n \"text\": \"def on_size_change(self,event):\\n self.panel_width,self.panel_height = event.GetSize()\\n print (\\\"Width =\\\",self.panel_width,\\\"Height =\\\",self.panel_height)\\n w = self.panel_width\\n h = self.panel_height\\n try:\\n self.fields['graph0'].im_size_show = (int((w-384)/2),int(h/5))\\n self.fields['graph1'].im_size_show = (int((w-384)/2),int(h/5))\\n self.fields['graph2'].im_size_show = (int((w-384)/2),int(h/5))\\n self.fields['graph3'].im_size_show = (int((w-384)/2),int(h))\\n except:\\n pass\\n\\n #self.panel.SetSizer(self.sizer_main)\\n #self.sizer_main.Fit(self)\\n self.Layout()\\n self.panel.Layout()\\n #self.panel.Fit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8499e141555fb4866c20c6646573b4b\",\n \"score\": \"0.61459696\",\n \"text\": \"def resizeEvent(self, event):\\r\\n\\r\\n QtGui.QTableView.resizeEvent(self, event)\\r\\n\\r\\n if self._editor.auto_size:\\r\\n self.resizeColumnsToContents()\\r\\n self.resizeRowsToContents()\\r\\n\\r\\n else:\\r\\n parent = self.parent()\\r\\n if (not self._initial_size and parent and\\r\\n (self.isVisible() or isinstance(parent, QtGui.QMainWindow))):\\r\\n self._initial_size = True\\r\\n if self._editor.auto_size:\\r\\n self.resizeColumnsToContents()\\r\\n self.resizeRowsToContents()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef846209dbfd3d9fc5a9324419e1385e\",\n \"score\": \"0.61013967\",\n \"text\": \"def resize(self, new_rows, new_cols):\\n\\t\\tnew_x_res = self.resolution[0] * new_rows / self.rows\\n\\t\\tnew_y_res = self.resolution[1] * new_cols / self.cols\\n\\n\\t\\tself.resolution = [new_x_res, new_y_res]\\n\\t\\tself.window.resize(*self.resolution)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3c1d66ccc21a2c314042f141d9a18a2\",\n \"score\": \"0.6077875\",\n \"text\": \"def resize(self, width, height):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"045f094872f47dd66c8bbe6863c9d1b2\",\n \"score\": \"0.6064716\",\n \"text\": \"def on_resizeButton_clicked(self):\\n for column in range(self.callTrace.columnCount()):\\n self.callTrace.resizeColumnToContents(column)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ad3a84f114e68b65e97b694f3ea8d3f\",\n \"score\": \"0.60419774\",\n \"text\": \"def on_drag_end(self, widget, context):\\n self.pane_resize()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe81904f286db52a086c18e29e441e1f\",\n \"score\": \"0.6028545\",\n \"text\": \"def display_size_update(self, rect_):\\r\\n self.screen_size = rect_\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d34e6c579919a174d447f27bde2081c3\",\n \"score\": \"0.60209614\",\n \"text\": \"def _onCanvasFrameDimension(self, event):\\n canvas = self.getCanvas()\\n frame = self.getCanvasFrame()\\n\\n canvasHeight = event.height\\n canvasWidth = event.width\\n\\n # Get the new canvas dimensions\\n if canvasWidth <= frame.winfo_reqwidth():\\n canvasWidth = frame.winfo_reqwidth()\\n if canvasHeight <= frame.winfo_reqheight():\\n canvasHeight = frame.winfo_reqheight()\\n\\n if self.__canvasFrameDimension == \\\"both\\\":\\n canvas.itemconfig(self.canvasWindow, height=canvasHeight,\\n width=canvasWidth)\\n elif self.__canvasFrameDimension == \\\"x\\\":\\n canvas.itemconfig(self.canvasWindow, width=canvasWidth)\\n elif self.__canvasFrameDimension == \\\"y\\\":\\n canvas.itemconfig(self.canvasWindow, height=canvasHeight)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"906ff98be964101bb8aa857191ea7757\",\n \"score\": \"0.60031354\",\n \"text\": \"def menuBarResize(self, val='True', **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20391e7e2e6acd0158cee5dfd73ba9b8\",\n \"score\": \"0.5998202\",\n \"text\": \"def add_resize_listener_func(self, func):\\n self._resize_listeners.append(func)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2be01e634f952434a009ec775473f7ea\",\n \"score\": \"0.59910166\",\n \"text\": \"def window_configure(self, *args):\\n\\n self.window_size = args[0].get_size()\\n self.apply_dynamic_scaling_all(self.window_size[0], self.window_size[1]) # todo: add std. win. width and height\\n self.apply_dynamic_font_all(self.default_font_factor, self.window_size[1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"936e2e4a94e861497dcd9000a29bd1b4\",\n \"score\": \"0.59852576\",\n \"text\": \"def resize_empty_screen(self, wid, hig):\\n self.SetSize(wid, hig)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f391f9884e7abedf1493413596def64\",\n \"score\": \"0.5976079\",\n \"text\": \"def showEvent(self, event):\\r\\n QtGui.QSplitter.showEvent(self, event)\\r\\n if not self._initialized:\\r\\n self._initialized = True\\r\\n self._resize_items()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7486d222cf4bbf74876fa53899794e7b\",\n \"score\": \"0.5961208\",\n \"text\": \"def check_resize(game, event):\\n # get new screen width and height\\n w, h = event.dict['size']\\n # place aspect ratios in settings\\n w_ratio = 16\\n h_ratio = 9\\n #find how many pixels in new screen size\\n w_pxls = w / w_ratio\\n h_pxls = h / h_ratio\\n\\n # ensure aspect ratio is maintained\\n if w_pxls < h_pxls:\\n w = int(h_pxls * w_ratio)\\n elif h_pxls < w_pxls:\\n h = int(w_pxls * h_ratio)\\n if w <= 400 and h <= 225:\\n print('screen lower than minimum')\\n w = 400\\n h = 225\\n\\n print('w: ' + str(w))\\n print('h: ' + str(h))\\n game.resize_percentage = round(w / game.DISPLAY_W, 2)\\n print('rp: ' + str(game.resize_percentage))\\n game.DISPLAY_H = h\\n game.DISPLAY_W = w\\n game.resize_displays()\\n game.mid_h = h / 2\\n game.mid_w = w / 2\\n game.resize_fonts()\\n\\n\\n game.ball.resize()\\n game.bat.resize()\\n game.obstacle.resize()\\n game.scoreboard.resize()\\n\\n game.menu.resize_main()\\n game.main_menu.resize()\\n game.options.resize()\\n game.credits.resize()\\n game.create_user.resize()\\n game.select_user.resize()\\n game.game_over_screen.resize()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d242c0e99915717ea4f76fb5b7b4e15\",\n \"score\": \"0.59186184\",\n \"text\": \"def reset_original_size(self):\\n self.set_view()\\n self.emit_signal_if_scene_rect_changed() # sends signal\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c8630dda03334cb6916818e98635056\",\n \"score\": \"0.5907763\",\n \"text\": \"def check_resize(event):\\n global size, size_orig\\n global width, w_orig, w_offset\\n global height, h_orig, h_offset\\n global RESIZE\\n size_orig = event.size\\n w_orig = event.w\\n h_orig = event.h\\n width = min(event.w, int(event.h * 1920 / 1080))\\n height = int(width * 1080 / 1920)\\n size = (width, height)\\n h_offset = int((h_orig - height) / 2)\\n w_offset = int((w_orig - width) / 2)\\n RESIZE = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d2bb11adfe92cce282971765ac05a60\",\n \"score\": \"0.5893002\",\n \"text\": \"def window_size(self, window_size):\\n self.render_window.SetSize(window_size[0], window_size[1])\\n self._window_size_unset = False\\n self.render()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6494aaadc5f569c3e9736fc7623f3f2e\",\n \"score\": \"0.58919287\",\n \"text\": \"def invalidate(self):\\n super(QDockFrameLayout, self).invalidate()\\n self._size_hint = QSize()\\n self._min_size = QSize()\\n self._max_size = QSize()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f52d62edab650da2834913db11606ab\",\n \"score\": \"0.5888416\",\n \"text\": \"def resize(self, widget):\\r\\n width = widget.width\\r\\n height = widget.height\\r\\n \\r\\n if self.dimension is BOTH or self.dimension is HORIZONTAL:\\r\\n width = self.getWidth(widget)\\r\\n if self.dimension is BOTH or self.dimension is VERTICAL:\\r\\n height = self.getHeight(widget)\\r\\n \\r\\n widget.resizeTo(width, height)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78c29e227ed00cd37234280ebe2c7ac7\",\n \"score\": \"0.58856606\",\n \"text\": \"def hideEvent(self, event):\\n\\n # -- Store the current window size in our scribble settings\\n self.storeWidgetGeometry()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"093526c047552ec8af2d110045808bbe\",\n \"score\": \"0.58665204\",\n \"text\": \"def _on_size_allocate(self, widget, event, data=None):\\n\\n x, y, width, height = self.get_allocation()\\n if width > self.edit_region_width:\\n margin = (width - self.edit_region_width) / 2\\n self.set_left_margin(margin)\\n self.set_right_margin(margin)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36013738a4943a38612fd19c3cb76be4\",\n \"score\": \"0.5863162\",\n \"text\": \"def setFrameDimensions(self):\\n if self.__canvasFrameDimension is not None:\\n self.getCanvas().bind('<Configure>', self._onCanvasFrameDimension)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6964f69afe6d40cf51480088047c1831\",\n \"score\": \"0.58570856\",\n \"text\": \"def resizeEvent(self, resize_event):\\n\\n if self.image is not None:\\n if self._oversize:\\n scaled_image_size = resize_event.size()\\n else:\\n scaled_image_size = resize_event.size().boundedTo(self.image.size())\\n\\n self.scaled_image = self.image.scaled(scaled_image_size, QtCore.Qt.KeepAspectRatio, QtCore.Qt.SmoothTransformation)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6144815fbe4f933a0cfc9a95064007f0\",\n \"score\": \"0.5837666\",\n \"text\": \"def configureWindow(self):\\r\\n self.master.title(self.title)\\r\\n self.master.geometry(\\\"{0}x{1}+0+0\\\".format(self.master.winfo_screenwidth()\\r\\n ,self.master.winfo_screenheight()))\\r\\n self.master.resizable(width=False, height=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"216ba54cd53ccb9a634cd6f26f522054\",\n \"score\": \"0.583537\",\n \"text\": \"def scaleFitWindow(self):\\r\\n e = 2.0 # So that no scrollbars are generated.\\r\\n w1 = self.dock.width() - e\\r\\n h1 = self.dock.height() - e\\r\\n a1 = w1/ h1\\r\\n # Calculate a new scale value based on the pixmap's aspect ratio.\\r\\n w2 = self.canvas.pixmap.width() - 0.0\\r\\n h2 = self.canvas.pixmap.height() - 0.0\\r\\n a2 = w2 / h2\\r\\n #logging.info('dock_w:%d h:%d \\\\n canvas_w:%d h:%d'%(w1,h1,w2,h2))\\r\\n return w1 / w2 if a2 >= a1 else h1 / h2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62c465f439e45456f9ec470d28561e28\",\n \"score\": \"0.5824469\",\n \"text\": \"def _doResize(self):\\n\\n if not self: # avoid a PyDeadObject error\\n return\\n\\n if self.GetSize().height < 32:\\n return # avoid an endless update bug when the height is small.\\n\\n numCols = self.GetColumnCount()\\n if numCols == 0:\\n return # Nothing to resize.\\n\\n col_widths = [(self._GetMinColWidth(col)\\n if col in self._autosize_columns\\n else self.GetColumnWidth(col))\\n for col in range(numCols)]\\n\\n # We're showing the vertical scrollbar -> allow for scrollbar width\\n # NOTE: on GTK, the scrollbar is included in the client size, but on\\n # Windows it is not included\\n listWidth = self.GetClientSize().width\\n\\n if sum(col_widths) < listWidth:\\n col_widths[self._resizeCol] += listWidth - sum(col_widths)\\n\\n for col, width in enumerate(col_widths):\\n self.SetColumnWidth(col, width)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af7f28a236c5d321a09ca6faa0012492\",\n \"score\": \"0.5818246\",\n \"text\": \"def _size ( self, event ):\\r\\n control = self.control\\r\\n if control is not None:\\r\\n control.Layout()\\r\\n control.Refresh()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9bdf44eaf481aa6685448cd86b742ba\",\n \"score\": \"0.5809648\",\n \"text\": \"def resize(width, height):\\n wnd().resizeTo(width, height)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0125be69f72d9d9da29e7e6eff6b42cb\",\n \"score\": \"0.5807572\",\n \"text\": \"def getAutoResize(self, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3984a6b1f6a93902ba951db08806507\",\n \"score\": \"0.57903713\",\n \"text\": \"def shell_icon_size_changed(self, icon_size):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f7774ce3f335cbfed4d675e2565b698\",\n \"score\": \"0.5752382\",\n \"text\": \"def resizeEvent(self, event: QResizeEvent):\\n text = self.normal_text()\\n\\n if self.get_text_width(text, 0.7) > self.label.frameGeometry().width():\\n text = self.medium_text()\\n\\n if self.get_text_width(text, 0.5) > self.label.frameGeometry().width():\\n text = self.small_text()\\n\\n self.label.setText(text)\\n\\n self.detailed_view.resizeEvent(event)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e327a221a51c66eb1801ae08f2f0522\",\n \"score\": \"0.574986\",\n \"text\": \"def ammolist_resize(self, event):\\n\\n if not self.AmmoList.GetColumnCount():\\n return\\n\\n column_width = self.AmmoList.GetClientSize()[0] - 4\\n self.AmmoList.SetColumnWidth(0, column_width / 3)\\n self.AmmoList.SetColumnWidth(1, column_width / 3)\\n self.AmmoList.SetColumnWidth(2, column_width / 3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4a9b701667845babad538989c65ff66\",\n \"score\": \"0.5746227\",\n \"text\": \"def set_window_size(self, width, height):\\n print(width, height)\\n gl.glViewport(0, 0, width, height)\\n self.bind_size(width, height)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9df84fc420cd08c543ff35dbcde025e9\",\n \"score\": \"0.5743378\",\n \"text\": \"def sizeHint(self):\\n return QtCore.QSize(800, 600)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be1b27a1596479a0f7f3018a30000d30\",\n \"score\": \"0.5731725\",\n \"text\": \"def iconSizeChanged(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c49d481da4e0a8bbabeed1bf1864c85e\",\n \"score\": \"0.5697398\",\n \"text\": \"def resize_ui_components(self, PICK):\\n self.set_column_widths_log_entry_tab()\\n self.set_column_widths_event_tab()\\n self.set_column_widths_vector_view_tab()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88281e9f972587e010976ecb5f654e27\",\n \"score\": \"0.56946164\",\n \"text\": \"def resizeEvent(self, event):\\r\\n if self._editor.factory.word_wrap:\\r\\n for i in range(self.topLevelItemCount()):\\r\\n mi = self.indexFromItem(self.topLevelItem(i))\\r\\n id = self.itemDelegate(mi)\\r\\n id.sizeHintChanged.emit(mi)\\r\\n super(self.__class__, self).resizeEvent(event)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":891,"cells":{"query_id":{"kind":"string","value":"a926b398d55b0d419a47a274dabe7003"},"query":{"kind":"string","value":"Return distbelief conditional_gradient values. Return values been generated from the distbelief conditional_gradient unittest, running with a learning rate of 0.1 and a lambda_ of 0.1. These values record how a parameter vector of size 10, initialized with 0.0, gets updated with 10 consecutive conditional_gradient steps. It uses random gradients."},"positive_passages":{"kind":"list like","value":[{"docid":"28f73ae6b769b9399f6857f05c9b9ce7","score":"0.0","text":"def _dbParamsCG01(self):\n db_grad = [[]] * 10\n db_out = [[]] * 10\n # pylint: disable=line-too-long\n db_grad[0] = [\n 0.00096264342, 0.17914793, 0.93945462, 0.41396621, 0.53037018,\n 0.93197989, 0.78648776, 0.50036013, 0.55345792, 0.96722615\n ]\n db_out[0] = [\n -4.1555551e-05, -7.7334875e-03, -4.0554531e-02, -1.7870162e-02,\n -2.2895107e-02, -4.0231861e-02, -3.3951234e-02, -2.1599628e-02,\n -2.3891762e-02, -4.1753378e-02\n ]\n db_grad[1] = [\n 0.17075552, 0.88821375, 0.20873757, 0.25236958, 0.57578111,\n 0.15312378, 0.5513742, 0.94687688, 0.16012503, 0.22159521\n ]\n db_out[1] = [\n -0.00961733, -0.0507779, -0.01580694, -0.01599489, -0.03470477,\n -0.01264373, -0.03443632, -0.05546713, -0.01140388, -0.01665068\n ]\n db_grad[2] = [\n 0.35077485, 0.47304362, 0.44412705, 0.44368884, 0.078527533,\n 0.81223965, 0.31168157, 0.43203235, 0.16792089, 0.24644311\n ]\n db_out[2] = [\n -0.02462724, -0.03699233, -0.03154434, -0.03153357, -0.00876844,\n -0.05606323, -0.02447166, -0.03469437, -0.0124694, -0.01829169\n ]\n db_grad[3] = [\n 0.9694621, 0.75035888, 0.28171822, 0.83813518, 0.53807181,\n 0.3728098, 0.81454384, 0.03848977, 0.89759839, 0.93665648\n ]\n db_out[3] = [\n -0.04124615, -0.03371741, -0.0144246, -0.03668303, -0.02240246,\n -0.02052062, -0.03503307, -0.00500922, -0.03715545, -0.0393002\n ]\n db_grad[4] = [\n 0.38578293, 0.8536852, 0.88722926, 0.66276771, 0.13678469,\n 0.94036359, 0.69107032, 0.81897682, 0.5433259, 0.67860287\n ]\n db_out[4] = [\n -0.01979208, -0.0380417, -0.03747472, -0.0305847, -0.00779536,\n -0.04024222, -0.03156913, -0.0337613, -0.02578116, -0.03148952\n ]\n db_grad[5] = [\n 0.27885768, 0.76100707, 0.24625534, 0.81354135, 0.18959245,\n 0.48038563, 0.84163809, 0.41172323, 0.83259648, 0.44941229\n ]\n db_out[5] = [\n -0.01555188, -0.04084422, -0.01573331, -0.04265549, -0.01000746,\n -0.02740575, -0.04412147, -0.02341569, -0.0431026, -0.02502293\n ]\n db_grad[6] = [\n 0.27233034, 0.056316052, 0.5039115, 0.24105175, 0.35697976,\n 0.75913221, 0.73577434, 0.16014607, 0.57500273, 0.071136251\n ]\n db_out[6] = [\n -0.01890448, -0.00767214, -0.03367592, -0.01962219, -0.02374279,\n -0.05110247, -0.05128598, -0.01254396, -0.04094185, -0.00703416\n ]\n db_grad[7] = [\n 0.58697265, 0.2494842, 0.08106143, 0.39954534, 0.15892942,\n 0.12683646, 0.74053431, 0.16033, 0.66625422, 0.73515922\n ]\n db_out[7] = [\n -0.03772914, -0.01599993, -0.00831695, -0.02635719, -0.01207801,\n -0.01285448, -0.05034328, -0.01104364, -0.04477356, -0.04558991\n ]\n db_grad[8] = [\n 0.8215279, 0.41994119, 0.95172721, 0.68000203, 0.79439718,\n 0.43384039, 0.55561525, 0.22567581, 0.93331909, 0.29438227\n ]\n db_out[8] = [\n -0.03919835, -0.01970845, -0.04187151, -0.03195836, -0.03546333,\n -0.01999326, -0.02899324, -0.01083582, -0.04472339, -0.01725317\n ]\n db_grad[9] = [\n 0.68297005, 0.67758518, 0.1748755, 0.13266537, 0.70697063,\n 0.055731893, 0.68593478, 0.50580865, 0.12602448, 0.093537711\n ]\n db_out[9] = [\n -0.04510314, -0.04282944, -0.0147322, -0.0111956, -0.04617687,\n -0.00535998, -0.0442614, -0.03158399, -0.01207165, -0.00736567\n ]\n # pylint: enable=line-too-long\n return db_grad, db_out","title":""}],"string":"[\n {\n \"docid\": \"28f73ae6b769b9399f6857f05c9b9ce7\",\n \"score\": \"0.0\",\n \"text\": \"def _dbParamsCG01(self):\\n db_grad = [[]] * 10\\n db_out = [[]] * 10\\n # pylint: disable=line-too-long\\n db_grad[0] = [\\n 0.00096264342, 0.17914793, 0.93945462, 0.41396621, 0.53037018,\\n 0.93197989, 0.78648776, 0.50036013, 0.55345792, 0.96722615\\n ]\\n db_out[0] = [\\n -4.1555551e-05, -7.7334875e-03, -4.0554531e-02, -1.7870162e-02,\\n -2.2895107e-02, -4.0231861e-02, -3.3951234e-02, -2.1599628e-02,\\n -2.3891762e-02, -4.1753378e-02\\n ]\\n db_grad[1] = [\\n 0.17075552, 0.88821375, 0.20873757, 0.25236958, 0.57578111,\\n 0.15312378, 0.5513742, 0.94687688, 0.16012503, 0.22159521\\n ]\\n db_out[1] = [\\n -0.00961733, -0.0507779, -0.01580694, -0.01599489, -0.03470477,\\n -0.01264373, -0.03443632, -0.05546713, -0.01140388, -0.01665068\\n ]\\n db_grad[2] = [\\n 0.35077485, 0.47304362, 0.44412705, 0.44368884, 0.078527533,\\n 0.81223965, 0.31168157, 0.43203235, 0.16792089, 0.24644311\\n ]\\n db_out[2] = [\\n -0.02462724, -0.03699233, -0.03154434, -0.03153357, -0.00876844,\\n -0.05606323, -0.02447166, -0.03469437, -0.0124694, -0.01829169\\n ]\\n db_grad[3] = [\\n 0.9694621, 0.75035888, 0.28171822, 0.83813518, 0.53807181,\\n 0.3728098, 0.81454384, 0.03848977, 0.89759839, 0.93665648\\n ]\\n db_out[3] = [\\n -0.04124615, -0.03371741, -0.0144246, -0.03668303, -0.02240246,\\n -0.02052062, -0.03503307, -0.00500922, -0.03715545, -0.0393002\\n ]\\n db_grad[4] = [\\n 0.38578293, 0.8536852, 0.88722926, 0.66276771, 0.13678469,\\n 0.94036359, 0.69107032, 0.81897682, 0.5433259, 0.67860287\\n ]\\n db_out[4] = [\\n -0.01979208, -0.0380417, -0.03747472, -0.0305847, -0.00779536,\\n -0.04024222, -0.03156913, -0.0337613, -0.02578116, -0.03148952\\n ]\\n db_grad[5] = [\\n 0.27885768, 0.76100707, 0.24625534, 0.81354135, 0.18959245,\\n 0.48038563, 0.84163809, 0.41172323, 0.83259648, 0.44941229\\n ]\\n db_out[5] = [\\n -0.01555188, -0.04084422, -0.01573331, -0.04265549, -0.01000746,\\n -0.02740575, -0.04412147, -0.02341569, -0.0431026, -0.02502293\\n ]\\n db_grad[6] = [\\n 0.27233034, 0.056316052, 0.5039115, 0.24105175, 0.35697976,\\n 0.75913221, 0.73577434, 0.16014607, 0.57500273, 0.071136251\\n ]\\n db_out[6] = [\\n -0.01890448, -0.00767214, -0.03367592, -0.01962219, -0.02374279,\\n -0.05110247, -0.05128598, -0.01254396, -0.04094185, -0.00703416\\n ]\\n db_grad[7] = [\\n 0.58697265, 0.2494842, 0.08106143, 0.39954534, 0.15892942,\\n 0.12683646, 0.74053431, 0.16033, 0.66625422, 0.73515922\\n ]\\n db_out[7] = [\\n -0.03772914, -0.01599993, -0.00831695, -0.02635719, -0.01207801,\\n -0.01285448, -0.05034328, -0.01104364, -0.04477356, -0.04558991\\n ]\\n db_grad[8] = [\\n 0.8215279, 0.41994119, 0.95172721, 0.68000203, 0.79439718,\\n 0.43384039, 0.55561525, 0.22567581, 0.93331909, 0.29438227\\n ]\\n db_out[8] = [\\n -0.03919835, -0.01970845, -0.04187151, -0.03195836, -0.03546333,\\n -0.01999326, -0.02899324, -0.01083582, -0.04472339, -0.01725317\\n ]\\n db_grad[9] = [\\n 0.68297005, 0.67758518, 0.1748755, 0.13266537, 0.70697063,\\n 0.055731893, 0.68593478, 0.50580865, 0.12602448, 0.093537711\\n ]\\n db_out[9] = [\\n -0.04510314, -0.04282944, -0.0147322, -0.0111956, -0.04617687,\\n -0.00535998, -0.0442614, -0.03158399, -0.01207165, -0.00736567\\n ]\\n # pylint: enable=line-too-long\\n return db_grad, db_out\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"36c653aeb3b687e085e9534cdbd33142","score":"0.608587","text":"def value_gradient():\n # sess.run(calculated) to calculate value of state\n state = tf.placeholder(\"float\",[None,4])\n w1 = tf.get_variable(\"w1\",[4,10])\n b1 = tf.get_variable(\"b1\",[10])\n h1 = tf.nn.relu(tf.matmul(state,w1) + b1)\n w2 = tf.get_variable(\"w2\",[10,1])\n b2 = tf.get_variable(\"b2\",[1])\n calculated = tf.matmul(h1,w2) + b2\n\n # sess.run(optimizer) to update the value of a state\n newvals = tf.placeholder(\"float\",[None,1])\n diffs = calculated - newvals\n loss = tf.nn.l2_loss(diffs)\n optimizer = tf.train.AdamOptimizer(0.1).minimize(loss)","title":""},{"docid":"7031d5b8e1d0033983741d80f26d07ae","score":"0.5833809","text":"def gradient(self, y_true, y_pred):\n predictions = np.clip(y_pred, self.epsilon, 1. - self.epsilon)\n return - y_true / predictions","title":""},{"docid":"fd4d2d98de481b90a7100fc253e4a2b2","score":"0.5808814","text":"def test_kernel_gradient():\n gamma = 0.1\n x, x_ref = X[0], X[1]\n my = rbf_kernel_gradient(gamma, x[np.newaxis, :], x_ref[np.newaxis, :])[:, 0]\n numerical = []\n for i in range(D):\n h = np.zeros_like(x)\n h[i] += 1e-3\n x_f, x_b = x+h, x-h\n partial_gd = (rbf_kernel(gamma, x_f[np.newaxis, :], x_ref[np.newaxis, :]) - \\\n rbf_kernel(gamma, x_b[np.newaxis, :], x_ref[np.newaxis, :]))[0, 0] / 2e-3\n numerical.append(partial_gd)\n assert_array_almost_equal(np.array(numerical), my, 6)","title":""},{"docid":"67a580bb89366d8230a68aaf7a1a3f10","score":"0.57066965","text":"def _betainc_custom_gradient(a, b, x):\n return _betainc_naive(a, b, x)","title":""},{"docid":"f3a420da11bbc458ec19d7a5832d36e5","score":"0.5667132","text":"def get_gradient(lambdas, data, index_info):\n\n model = get_train_model(lambdas, index_info)\n transition_grad = get_transition_gradient(model, data)\n feature_grad = get_feature_gradient(model, data)\n gradient = np.concatenate((transition_grad, feature_grad))\n\n return gradient * -1","title":""},{"docid":"ced6a91e2de5e96ab5686cf1e17a9f48","score":"0.56448185","text":"def compute_value_gradient(self, real_value):\r\n\r\n return (real_value - self.value) * RatesNeuralNetwork.sigmoid(self.value)","title":""},{"docid":"22d5dbec41e21f1e125bbc72de08df30","score":"0.5629934","text":"def value_gradient(self):\n\n # lower and upper bound brightnesss\n lower_blue = np.array([0, 0, 170])\n upper_blue = np.array([255, 255, 255])\n light_hsv = cv2.inRange(self.hsv, lower_blue, upper_blue)\n\n # lower and upper bound darkness\n lower_blue = np.array([0, 0, 0])\n upper_blue = np.array([255, 255, 80])\n dark_hsv = cv2.inRange(self.hsv, lower_blue, upper_blue)\n\n light = cv2.countNonZero(light_hsv)\n dark = cv2.countNonZero(dark_hsv)\n height, width, _ = self.img.shape\n size = height*width\n return light/size, dark/size","title":""},{"docid":"07b1030573e2d14f0b6489fc7c95cd05","score":"0.56227076","text":"def test_gradient_param(self, setup):\n sess, tri, trinp, test_points = setup\n\n # Disable project\n trinp.project = True\n tri.project = True\n\n x = tf.placeholder(tf.float64, [1, 2])\n\n true_gradient = trinp.parameter_derivative(test_points)\n true_gradient = np.array(true_gradient.todense())\n\n y = tri(x)\n grad_tf = tf.gradients(y, tri.parameters)[0]\n dense_gradient = np.zeros(true_gradient[0].shape, dtype=np.float)\n\n for i, test in enumerate(test_points):\n gradient = sess.run(grad_tf, feed_dict={x: test[None, :]})\n dense_gradient[:] = 0.\n dense_gradient[gradient.indices] = gradient.values[:, 0]\n assert_allclose(dense_gradient, true_gradient[i])","title":""},{"docid":"3fe49c4cb4f3f79bad0731182a1867f5","score":"0.5619189","text":"def fairness_parameter_gradient(self, **optimization_target_args):\n pass","title":""},{"docid":"fa53087e1e82789100184e0617bff0db","score":"0.5583061","text":"def model_parameter_gradient(self, **optimization_target_args):\n gradient = -self.utility_function.gradient(**optimization_target_args)\n fairness_gradient = self.fairness_function.gradient(**optimization_target_args)\n\n if self.error_delta == 0.0:\n grad_fairness = self.fairness_rate * fairness_gradient\n else:\n lambda1, lambda2 = self.fairness_rate\n grad_fairness = lambda1 * -fairness_gradient + lambda2 * fairness_gradient\n\n gradient += grad_fairness\n return gradient","title":""},{"docid":"c1afe73e4a20aa221683b000e737ee7d","score":"0.55646396","text":"def computeGradients(X,Y,Ws,bs,lambdaValue):\n sizeD = X.shape[0]\n netSize = len(Ws)\n assert netSize>=2, \"network size must be greater than 2\"\n\n #forward pass\n scores,hiddenlayers,P = evaluateClassifier(X,Ws,bs)\n\n #backward pass\n dWs = []\n dbs = []\n\n #gradient on final scores\n dscores = P#N*K\n dscores -=Y#N*K\n dscores /= sizeD\n\n #from last score to\n for i in range(netSize-1):\n dWi = np.dot(hiddenlayers[netSize-i-2].T,dscores)#netSize must be greater than 2\n dbi = np.sum(dscores,axis=0,keepdims=False)\n dh = np.dot(dscores,Ws[netSize-i-1].T)\n dh[hiddenlayers[netSize-i-2]<=0] = 0\n dscores = dh\n dWs.append(dWi)\n dbs.append(dbi)\n\n #backpropagate to W1 and b1\n dW1 = np.dot(X.T,dscores)\n db1 = np.sum(dscores,axis=0,keepdims=False)\n dWs.append(dW1)\n dbs.append(db1)\n\n # reverse dWs and dbs\n dWs.reverse()\n dbs.reverse()\n\n #reg grad for Ws\n for i in range(netSize):\n dWs[i] += lambdaValue*Ws[i]\n return dWs,dbs","title":""},{"docid":"5afac6e58dd09cb6076be6bcf6052b41","score":"0.554397","text":"def policy_gradient():\n # Placeholders for state and prefered action\n state = tf.placeholder(\"state\",[None,4])\n actions = tf.placeholder(\"float\",[None,2])\n # Variable for parameters 4 inputs, 2 output(one-hot)\n params = tf.get_variable(\"policy_parameters\",[4,2])\n # Layer\n y = tf.matmul(state,params)\n # Output\n probabilities = tf.nn.softmax(y)\n # Multiply probabilitie for each action by the prefered action.\n good_probabilities = tf.reduce_sum(tf.mul(probabilities, actions), axis=[1])\n # maximize the log probability\n log_probabilities = tf.log(good_probabilities)\n loss = -tf.reduce_sum(log_probabilities)\n optimizer = tf.train.AdamOptimizer(0.01).minimize(loss)","title":""},{"docid":"8012067aa8c5f69a256803137263d0a0","score":"0.55414635","text":"def gradient(self, y_true, y_pred):\n pass","title":""},{"docid":"f266d75e7aad7f0883cf8b62b51c3b05","score":"0.5537623","text":"def helper_gradient(function,gradient,sample,\n eps=np.sqrt(np.finfo(float).eps)):\n value = function(sample)\n num_dims = sample.shape[0]\n num_qoi = value.shape[0]\n grad = np.empty((num_dims,num_qoi),float)\n for i in range(num_dims):\n perturbed_sample = sample.copy()\n perturbed_sample[i] += eps\n perturbed_value = function(perturbed_sample)\n grad[i,:] = (perturbed_value-value)/eps\n assert np.allclose(gradient(sample).squeeze(),grad.squeeze(),atol=eps*10)","title":""},{"docid":"0c60b4a00911bc4807ef1222965caa6a","score":"0.5501193","text":"def _define_gradients(self):\n dWax = np.zeros_like(self.Wax)\n dWaa = np.zeros_like(self.Waa)\n dWya = np.zeros_like(self.Wya)\n\n db = np.zeros_like(self.b)\n dby = np.zeros_like(self.by)\n\n da_next = np.zeros_like(self.hidden_list[0])\n\n return dWax, dWaa, dWya, db, dby, da_next","title":""},{"docid":"6156756e6cff93fe5010aa5e1f279b18","score":"0.5468707","text":"def model_parameter_gradient(self, **optimization_target_args):\n gradient = -self.utility_function.gradient(**optimization_target_args)\n\n if self.fairness_rate > 0:\n fairness = self.fairness_function(**optimization_target_args)\n fairness_gradient = self.fairness_function.gradient(**optimization_target_args)\n\n grad_fairness = self.fairness_rate * fairness * fairness_gradient\n gradient += grad_fairness\n\n return gradient","title":""},{"docid":"bf8ecef147b19e5ac6123fd2df6f4593","score":"0.54677916","text":"def build_loss_and_gradients(self, var_list):\n # Form dictionary in order to replace conditioning on prior or\n # observed variable with conditioning on a specific value.\n scope = tf.get_default_graph().unique_name(\"inference\")\n dict_swap = {z: qz.value()\n for z, qz in six.iteritems(self.latent_vars)}\n for x, qx in six.iteritems(self.data):\n if isinstance(x, ed.RandomVariable):\n if isinstance(qx, ed.RandomVariable):\n dict_swap[x] = qx.value()\n else:\n dict_swap[x] = qx\n\n p_log_prob = 0.0\n for z in six.iterkeys(self.latent_vars):\n z_copy = copy(z, dict_swap, scope=scope)\n p_log_prob += tf.reduce_sum(\n self.scale.get(z, 1.0) * z_copy.log_prob(dict_swap[z]))\n\n for x in six.iterkeys(self.data):\n if isinstance(x, ed.RandomVariable):\n if dict_swap:\n x_copy = copy(x, dict_swap, scope=scope)\n else:\n x_copy = x\n p_log_prob += tf.reduce_sum(\n self.scale.get(x, 1.0) * x_copy.log_prob(dict_swap[x]))\n\n reg_penalty = tf.reduce_sum(tf.losses.get_regularization_losses(scope=self.scope))\n loss = -p_log_prob + reg_penalty\n\n grads = tf.gradients(loss, var_list)\n grads_and_vars = list(zip(grads, var_list))\n return loss, grads_and_vars","title":""},{"docid":"35beed0bd8bd7829dd314d8ea1eb0e8d","score":"0.5445362","text":"def feature_learning_gradient_based(self, v, eta, fps, tau_s, numSamples, f_regularize=lambda C: 0.):\n # Data contribution\n if isinstance(self.sig_obs, (float, np.floating)):\n sig_obs = self.sig_obs * np.ones(self.D * self.K)\n else:\n sig_obs = self.sig_obs\n dCdt_data = (sig_obs**(-2))[:,None] * ( np.outer(v, self.mu) - self.C @ ( np.outer(self.mu, self.mu) + self.Sig ) )\n dCdt_regularizer = (sig_obs**(-2))[:,None] * 1/numSamples * f_regularize(self.C)\n dC = eta / (fps * numSamples * tau_s) * ( dCdt_data + dCdt_regularizer )\n return self.C + dC","title":""},{"docid":"efc3fdb00750d5c83aa83bfb5c41f217","score":"0.54450005","text":"def pgradient(self):\n configs = self._configscurrent\n nconf, nelec = configs.configs.shape[:2]\n na, nb = len(self.a_basis), len(self.b_basis)\n nup, ndown = self._mol.nelec\n\n # order of spin channel: upup,updown,downdown\n d_all, ij = configs.dist.dist_matrix(configs.configs)\n r_all = np.linalg.norm(d_all, axis=-1)\n bvalues = np.stack([b.value(d_all, r_all) for b in self.b_basis], axis=-1)\n inds = tuple(zip(*ij))\n b_2d_values = np.zeros((nelec, nelec, nconf, nb))\n for s, shape in enumerate([(nup, nup), (nup, ndown), (ndown, ndown)]):\n b_2d_values[inds] = bvalues.swapaxes(0, 1)\n\n a = self.a_values\n up, down = slice(0, nup), slice(nup, None)\n c_ders = np.zeros((nconf, self._mol.natm, na, na, nb, 3))\n einstr = \"inIk,jnIl,ijnm->nIklm\"\n c_ders[..., 0] = np.einsum(einstr, a[up], a[up], b_2d_values[up, up])\n c_ders[..., 1] = np.einsum(einstr, a[up], a[down], b_2d_values[up, down])\n c_ders[..., 2] = np.einsum(einstr, a[down], a[down], b_2d_values[down, down])\n c_ders += c_ders.swapaxes(2, 3)\n\n return {\"ccoeff\": 0.5 * c_ders}","title":""},{"docid":"9e6b747e7ef5976647562955abcba7f6","score":"0.5442318","text":"def infer(self, potential, iterations, cf=True):\n energy = tf.zeros(shape=(10,), dtype=self.dtype)\n for i in tf.range(iterations):\n if cf:\n grd, energy = self.gradient_cf(potential)\n else:\n with tf.GradientTape(watch_accessed_variables=False) as tape:\n tape.watch(self.trainable_variables) # only watch mu, sigma and rou\n energy = self.bethe_free_energy(potential)\n grd = tape.gradient(energy, self.trainable_variables)\n\n # umax = tf.reduce_max(tf.abs(grd[0]), axis=[1, 2, 3, 4, 5], keepdims=True)\n # omax = tf.reduce_max(tf.abs(grd[1]), axis=[1, 2, 3, 4, 5], keepdims=True)\n # pmax = tf.reduce_max(tf.abs(grd[2]), axis=[1, 2, 3, 4, 5], keepdims=True)\n # self.mu.assign(tf.minimum(tf.maximum(self.mu - grd[0] * (self.lr / umax), -1.), 1.))\n # self.sigma.assign(tf.minimum(tf.maximum(self.sigma - grd[1] * (self.lr / omax), 0.01), 10.))\n # self.rou.assign(tf.minimum(tf.maximum(self.rou - grd[2] * (self.lr / pmax), -0.99), 0.99))\n self.optimizer.apply_gradients(zip(grd, self.trainable_variables))\n\n if tf.equal(tf.math.mod(i, 50), 0) or tf.equal(i + 1, iterations):\n tf.print(tf.strings.format('iter: {} dmu = {}, dsigma = {}, drou = {}, dw={}, Energy = {}', (\n i, tf.reduce_mean(tf.abs(grd[0])), tf.reduce_mean(tf.abs(grd[1])), tf.reduce_mean(tf.abs(grd[2])),\n tf.reduce_mean(tf.abs(grd[3])), tf.reduce_mean(energy))))\n return energy","title":""},{"docid":"67398bc9cb64005c9b5a40cfdbc7bd67","score":"0.54416275","text":"def _estimate_gradient(self, points, values, prediction_points):\n if self.surrogate is not None:\n self.surrogate.fit(prediction_points, values)\n self.surrogate.optimizations_number = 1\n prediction_function = self.surrogate.predict\n else:\n from scipy.interpolate import LinearNDInterpolator\n\n prediction_function = LinearNDInterpolator(\n prediction_points, values, fill_value=0\n ).__call__\n\n gradient_values = gradient(\n point=prediction_points,\n runmodel_object=prediction_function,\n order=\"first\",\n df_step=self.step_size,\n )\n return gradient_values","title":""},{"docid":"c132d4bff77dd4a495f9cb09f8ac34ae","score":"0.543683","text":"def fairness_parameter_gradient(self, **optimization_target_args):\n if self.error_delta == 0.0:\n return self.fairness_function(**optimization_target_args)\n else:\n return np.array([-self.fairness_function(**optimization_target_args) - self._error_delta,\n self.fairness_function(**optimization_target_args) - self._error_delta]).squeeze()","title":""},{"docid":"9e277bc497446bf29085892181028e77","score":"0.5363549","text":"def f_gradient(x, y, lbd, km):\r\n\r\n temp_vector = km.dot(x)\r\n n = len(y)\r\n\r\n grad_value = np.diag(-y).dot(km.dot(exp(-y * temp_vector)/(1 + exp(-y * temp_vector))))/n + lbd * temp_vector\r\n\r\n return grad_value","title":""},{"docid":"580e5dfccc2eb296337da3f5f6efcb90","score":"0.5363145","text":"def get_gradient(self, label, sample):\n self.predict(sample)\n self.calc_delta(label)\n self.calc_gradient()","title":""},{"docid":"cb0fd286ba82d3765bc2abc48a773452","score":"0.53611887","text":"def gradient(self):\n gradient = np.zeros(len(self.variables))\n \n if self.state1 < self.nStates-1:\n gradient[self.state1] = self.transitCount / self.pi1\n else:\n for state in range((self.nStates-1)):\n gradient[state] = -self.transitCount/self.pi1\n \n gradient[self.bivariateGradInd] = self.transitCount/self.theta\n return gradient","title":""},{"docid":"a3da846e3a548fd4dc973da5835bfa5d","score":"0.5356187","text":"def process_gradient(self):\n return","title":""},{"docid":"f4e3f5cd8f010ccfa9e57486a2596872","score":"0.53547686","text":"def critic_z_gradient_penalty(self, batch_size, y_true, y_pred):\n alpha = tf.keras.backend.random_uniform((batch_size, 1, 1))\n interpolated = (alpha * y_true) + ((1 - alpha) * y_pred)\n\n with tf.GradientTape() as gp_tape:\n gp_tape.watch(interpolated)\n # 1. Get the discriminator output for this interpolated image.\n pred = self.critic_z(interpolated)\n\n # 2. Calculate the gradients w.r.t to this interpolated image.\n grads = gp_tape.gradient(pred, [interpolated])[0]\n norm = tf.sqrt(tf.reduce_sum(tf.square(grads), axis=[1, 2]))\n gp = tf.reduce_mean((1.0 - norm) ** 2)\n\n return gp","title":""},{"docid":"00f9b0658a2fcd08f59a893ea6573524","score":"0.5348646","text":"def gradient(self, params):\n return 10 * (params - 3)","title":""},{"docid":"3b7a96aee3fd64bf99edcd1ae8eb693c","score":"0.5343812","text":"def test_gradient(self):\n discretization = GridWorld([[0, 1], [0, 1]], [2, 2])\n delaunay = _Triangulation(discretization)\n\n points = np.array([[0, 0],\n [1, 0],\n [0, 1],\n [1, 1]], dtype=np.int)\n nodes = delaunay.discretization.state_to_index(points)\n\n # Simplex with node values:\n # 3 - 1\n # | \\ |\n # 1 - 2\n # --> x\n\n values = np.zeros(delaunay.nindex)\n values[nodes] = [1, 2, 3, 1]\n\n test_points = np.array([[0.01, 0.01],\n [0.99, 0.99]])\n\n true_grad = np.array([[1, 2], [-2, -1]])\n\n # Construct true H (gradient as function of values)\n true_H = np.zeros((2 * delaunay.input_dim, delaunay.nindex))\n\n true_H[0, nodes[[0, 1]]] = [-1, 1]\n true_H[1, nodes[[0, 2]]] = [-1, 1]\n true_H[2, nodes[[2, 3]]] = [-1, 1]\n true_H[3, nodes[[1, 3]]] = [-1, 1]\n\n # Evaluate gradient with and without values\n H = delaunay.gradient_parameter_derivative(test_points).toarray()\n delaunay.parameters = values\n grad = delaunay.gradient(test_points)\n\n # Compare\n assert_allclose(grad, true_grad)\n assert_allclose(H, true_H)\n assert_allclose(true_grad,\n H.dot(values).reshape(-1, delaunay.input_dim))","title":""},{"docid":"22bf60ac4a91286535593dc3b2dce3fc","score":"0.5337247","text":"def grad_descent (x, y, learning): \n\n w = np.zeros(22)\n converage=1\n\n for runs in range(30):\n gradient = grad(w, x, y, 0)\n w = w - (learning * gradient)\n normalg= np.linalg.norm(gradient)\n print(\"normalg: \", normalg)\n if np.isinf(normalg):\n print(\"normalg goes to inf, goning to break the loop.\")\n break\n if normalg <= converage:\n print(\"normalg <= converage!!!\")\n break\n iteration_list.append(runs)\n print(\"w: \",w )\n print(\"i:\", runs)\n return w","title":""},{"docid":"5df40f294c48e7d39fce90ba15a62722","score":"0.528242","text":"def policy_gradient(state, weight):\n # probabilities of actions\n probs = policy(state, weight)\n\n # random action\n action = np.random.choice(len(probs[0]), p=probs[0])\n\n # softmax gradient\n # probs shape (1, 2)\n # s shape (2, 1)\n s = probs.reshape(-1, 1)\n\n # softmax matrix\n softmax = np.diagflat(s) - s @ s.T\n\n # Take the obs for the action taken\n dsoftmax = softmax[action, :]\n\n # Derivative of natural logarithm\n dlog = dsoftmax / probs[0, action]\n\n gradient = state.T @ dlog[None, :]\n\n return action, gradient","title":""},{"docid":"3eda0ad4deca20d60958f3d62c082e99","score":"0.52776885","text":"def bias_gradient_statistics(model, layer_name=None):\n\n return parameter_statistics(model, 'b', 'grad', layer_name)","title":""},{"docid":"409e5c93318ab5cd95ee10d1445fd0e0","score":"0.5273136","text":"def variables_gradient(self, loss: tf.Tensor, state: np.ndarray, label: tf.Tensor) -> List:\n variables = self.get_variables()\n losses = []\n for i, var in enumerate(variables):\n new_vars = copy.copy(variables)\n new_vars[i] = tf.add(var, self.g_epsilon)\n self.set_variables(new_vars)\n new_loss = self.state_to_loss(state, label)\n losses.append(new_loss)\n\n self.set_variables(variables)\n dy = [tf.subtract(x, loss) for x in losses]\n grads = [tf.divide([y], self.g_epsilon) for y in dy]\n return grads","title":""},{"docid":"0ca1475f01f1768b94cb23be1bd875a9","score":"0.5272091","text":"def _conditional_bias(forecast, reference, dim='svd', **metric_kwargs):\n acc = _pearson_r(forecast, reference, dim=dim)\n conditional_bias = acc - _std_ratio(forecast, reference, dim=dim) ** -1\n return conditional_bias","title":""},{"docid":"4ea2a2bd926445a55193d5d1f2fcb84e","score":"0.52589333","text":"def rand_free_conf(self):\n uniform_val = np.random.uniform(0, 1, self.state_dim)\n node = self.threshold[:, 0]*(1-uniform_val) + self.threshold[:, 1]*uniform_val\n return node","title":""},{"docid":"e456a997c8b1cfcf541bec152faa4691","score":"0.52534866","text":"def _gradient_penalty(self, real_data, generated_data):\n discriminator = self\n batch_size = real_data.size()[0]\n\n # Calculate interpolation\n alpha = torch.rand(batch_size, 1)\n alpha = alpha.expand_as(real_data)\n if self.use_cuda:\n alpha = alpha.cuda()\n interpolated = alpha * real_data.data + (1 - alpha) * generated_data.data\n interpolated = Variable(interpolated, requires_grad=True)\n if self.use_cuda:\n interpolated = interpolated.cuda()\n\n # Calculate probability of interpolated examples\n prob_interpolated = discriminator(interpolated)\n\n # Calculate gradients of probabilities with respect to examples\n gradients = torch_grad(outputs=prob_interpolated, inputs=interpolated,\n grad_outputs=torch.ones(\n prob_interpolated.size()).cuda() if self.use_cuda else torch.ones(\n prob_interpolated.size()),\n create_graph=True, retain_graph=True)[0]\n\n # Gradients have shape (batch_size, num_channels, img_width, img_height),\n # so flatten to easily take norm per example in batch\n gradients = gradients.view(batch_size, -1)\n\n # Derivatives of the gradient close to 0 can cause problems because of\n # the square root, so manually calculate norm and add epsilon\n\n # gradients_norm = torch.sqrt(torch.sum(gradients ** 2, dim=1) + 1e-12)\n\n # Return gradient penalty\n return self.LAMBDA * ((gradients.norm(2, dim=1) - 1) ** 2).mean()","title":""},{"docid":"2fa793f6c99f14ebefee4834dcfe421f","score":"0.5252034","text":"def test_kernel_gradient_on_hyper_params():\n gamma = 5\n K, my = rbf_kernel(gamma, X, X, gradient_on_gamma=True)\n gamma_ = gamma + 1e-3\n K_ = rbf_kernel(gamma_, X, X)\n numeric = (K_ - K) / 1e-3\n assert_array_almost_equal(numeric, my, 3)","title":""},{"docid":"50e0b85cbcdfdfc945fdb92344c28f88","score":"0.5250229","text":"def compute_gradient(theta, X, y, lambda_):\n h = sigmoid(X @ theta)\n l2 = np.append([0], ((lambda_ * theta[1:]) / len(y)))\n return (X.T @ (h - y)) / len(y) + l2","title":""},{"docid":"77d5f45933aaebe24ee64fda6ce0b4f2","score":"0.5237509","text":"def gradient_duels_gen(self, num_feat, num_datapoints):\n #n = 14 # number of datapoints\n #x1 = np.linspace(self.Gridmin1, self.Gridmax1, n)[:, None] # Operating temp.\n x1 = np.arange(self.Gridmin1, self.Gridmax1, self.temp_diff)[:,None]\n \n # sampling from indexes\n indexes = np.arange(x1.shape[0])\n \n ind_samp1 = np.random.choice(indexes, size = 1)\n x_samp1_01 = x1[ind_samp1]\n \n ind_samp2_all1 = np.random.choice(indexes, size = num_datapoints)\n x_samp21 = x1[ind_samp2_all1]\n x_samp1_n1 = x_samp21[:-1]\n x_samp11 = np.append(x_samp1_01, x_samp1_n1)[:,None]\n \n u1, u1_der = objfunc.beta_utility_gen(x_samp11, self.l,\n self.a, self.b)\n u2, u2_der = objfunc.beta_utility_gen(x_samp21, self.l,\n self.a, self.b) \n \n u2_der = u2_der[:,0] \n y_der = np.zeros(u2_der.shape[0])\n y_der[u2_der > 0] = 1.\n \n y_der[u2_der < 0] = -1.\n\n return (x_samp11, x_samp21, y_der, u1, u2)","title":""},{"docid":"988f0d739c253b24475a9d8fc0657b62","score":"0.5225748","text":"def compute_gradient_penalty(D, real_samples, fake_samples,index):\n # Random weight term for interpolation between real and fake samples\n # sample shape:(time_length, batch_size*scene_traj_num, dim) = (81, 128, 2)\n real_samples = real_samples.permute(1,2,0)\n fake_samples = fake_samples.permute(1,2,0)\n # (batch_size*scene_traj_num,2,time_length)\n alpha = Tensor(np.random.random((real_samples.size(0), 1, 1)))\n # Get random interpolation between real and fake samples\n interpolates = (alpha * real_samples + ((1 - alpha) * fake_samples)).requires_grad_(True)\n\n # scene_vel = diff(interpolates, 1)\n # scene_acc = diff(interpolates, 2)\n # interpolates = torch.cat((interpolates, scene_vel, scene_acc), 1)\n interpolates = interpolates.permute(2, 0, 1)\n\n d_interpolates = D(interpolates,index)\n out_shape = real_samples.size(0)\n fake = Variable(Tensor(out_shape,1).fill_(1.0), requires_grad=False)\n # Get gradient w.r.t. interpolates\n # with torch.autograd.detect_anomaly():\n gradients = autograd.grad(\n outputs=d_interpolates,\n inputs=interpolates,\n grad_outputs=fake,\n create_graph=True,\n retain_graph=True,\n only_inputs=True,\n )[0]\n gradients = gradients.contiguous().view(gradients.size(0), -1)\n # b = torch.nn.utils.clip_grad_norm_(gradients, 10)\n # GP\n # gp = ((gradients.norm(2, dim=1) - 1) ** 2).mean()\n # LP\n # gp = (torch.clamp(gradients.norm(2, dim=1) - 1, 0, float(\"inf\")) ** 2).mean()\n # wgan-div\n gp = (gradients.norm(2, dim=1) ** 6).mean()\n return gp","title":""},{"docid":"3d34d0cb30407fa060d532a72478ce85","score":"0.5211051","text":"def variables_gradient_exact(self, state: np.ndarray, label: tf.Tensor) -> List:\n variables = self.get_variables()\n grads = []\n for i, var in enumerate(variables):\n new_vars_plus = copy.copy(variables)\n new_vars_minus = copy.copy(variables)\n\n new_vars_plus[i] = tf.add(var, np.pi/2)\n new_vars_minus[i] = tf.subtract(var, np.pi/2)\n\n self.set_variables(new_vars_plus)\n loss_plus = self.state_to_loss(state, label)\n\n self.set_variables(new_vars_minus)\n loss_minus = self.state_to_loss(state, label)\n grad = tf.cast(tf.subtract(loss_plus, loss_minus), tf.float32)\n grads.append(grad)\n\n self.set_variables(variables)\n return grads","title":""},{"docid":"45b69162d2419fb6a54b9823f2be7448","score":"0.5210655","text":"def compute_gradient_penalty(D, real_samples, fake_samples):\n batch_size = real_samples.size(0)\n lamda = 10 \n alpha = torch.rand(batch_size,1)\n \n #Slicing only a size same as real_samples to avoid conflict with different train batch sizes \n #fake_samples = fake_samples[:batch_size, :]\n \n #print('alpha {} real {} fake {}'.format(alpha.size(), real_samples.size(), fake_samples.size()))\n if cuda:\n alpha=alpha.cuda()\n\n interpolates = (alpha * real_samples.data) + ((1 - alpha) * fake_samples.data) #.requires_grad_(True)\n interpolates = autograd.Variable(interpolates, requires_grad=True)\n if cuda:\n interpolates=interpolates.cuda()\n \n d_interpolates = D(interpolates)\n d_interpolates = d_interpolates.view(-1,1) ### newly added line\n \n fake = Variable(Tensor(real_samples.shape[0], 1).fill_(1.0), requires_grad = True)\n \n # Get gradient w.r.t. interpolates\n gradients = autograd.grad(\n outputs=d_interpolates,\n inputs=interpolates,\n grad_outputs=fake,\n create_graph=True, \n retain_graph=True,\n only_inputs=True,\n )[0]\n \n gradient_penalty = (((gradients.norm(2, dim=1) - 1) ** 2)* lamda).mean()\n return gradient_penalty","title":""},{"docid":"1c65cb52ea2592836b56d16ed0b3b758","score":"0.5209864","text":"def test_fully_observed_gradient(self):\n weights = np.zeros(8 + 32)\n learner = Learner(MatrixBeliefPropagator)\n self.set_up_learner(learner, latent=False)\n learner.start_time = time.time()\n learner.set_regularization(0.0, 1.0)\n gradient_error = check_grad(learner.subgrad_obj, learner.subgrad_grad, weights)\n\n # numerical_grad = approx_fprime(weights, learner.subgrad_obj, 1e-4)\n # analytical_grad = learner.subgrad_grad(weights)\n # plt.plot(numerical_grad, 'r')\n # plt.plot(analytical_grad, 'b')\n # plt.show()\n\n print(\"Gradient error: %f\" % gradient_error)\n assert gradient_error < 1e-1, \"Gradient is wrong\"","title":""},{"docid":"1a08f918a59011eae30f07b4f69cb3a9","score":"0.5208682","text":"def get_gradients(model, epsilon, delta=0.01, max_norm=1.0):\n scale = np.sqrt(2 * np.log(1.25 / delta)) / epsilon\n\n # Compute the gradient norm\n total_norm = 0.0\n for p in model.parameters():\n if p.requires_grad:\n total_norm += p.grad.data.norm(2).item() ** 2\n total_norm = total_norm ** (1. / 2)\n\n # Clip and add noise\n gradients = []\n for p in model.parameters():\n if p.requires_grad:\n grad = p.grad.data.clone()\n grad /= max(1.0, total_norm / max_norm)\n grad += torch.normal(\n torch.zeros(p.grad.data.size()), \n torch.zeros(p.grad.data.size()) + scale * max_norm\n )\n gradients.append(grad)\n return gradients","title":""},{"docid":"12caef42fe697a2e7c051fb69f59993b","score":"0.52036786","text":"def return_weights(X, b, d, mincols):\n max_iter = 100\n threshold= .00001\n w= np.empty((1,X.shape[1]))\n w.fill(1)\n p_1 = 1/(1+b*d)\n n = X.shape[0]\n E_old = 1\n for i in np.arange(0, max_iter):\n d = pairwise_distances(X,X, metric = weighted_euclidean, **{'weights':w})\n grad_w = np.empty((1,X.shape[1]))\n part_pq = -b/((1+b*d)**2)\n p = 1/(1+b*d)\n E = (2/(n*(n-1))) * np.sum(np.triu(.5*((p*(1-p_1) + p_1*(1-p))), 1))\n if E_old - E < threshold:\n break\n E_old = E\n part_eq = (1-2*p_1)\n w_valid = np.where(w > 0)[1]\n\n if w_valid.shape[0] == mincols:\n break\n\n for j in w_valid:\n d_w = pairwise_distances(X, X, metric = single_delta, **{'F':j})\n part_w = w[0, j]*(d_w)**2 / d\n part_w = np.triu(part_w, 1)\n grad_w_j = 1/(n*(n-1)) * part_eq * part_pq * part_w\n grad_w_j = np.triu(grad_w_j, 1)\n grad_w[ 0, j] = np.nansum(grad_w_j)\n grad_w = grad_w * 50\n w = w-grad_w\n w = w.clip(min=0)\n #if i %10 == 0 and i > 0:\n #print(\"Iteration {} Finished\".format(i))\n #print(\"Weights : {} \".format(w))\n #print(\"Function Improvement : {}\".format(E_old - E))\n\n wmax = np.max(w)\n w = w / wmax\n print(\"{} Iterations Required\".format(i))\n return w","title":""},{"docid":"e72ad7b7d0ac7f4666cf382e59af9690","score":"0.51941454","text":"def evaluate_gradient(self, locations: np.ndarray) -> np.ndarray:\n v1 = self.geological_feature_a.evaluate_gradient(locations)\n # v1 /= np.linalg.norm(v1,axis=1)[:,None]\n v2 = self.geological_feature_b.evaluate_gradient(locations)\n # v2 /= np.linalg.norm(v2,axis=1)[:,None]\n return np.cross(v1, v2, axisa=1, axisb=1)","title":""},{"docid":"ce5b823ebb84c377b232eb5b7e699c5a","score":"0.5193387","text":"def compute_gradient(outputs, perturbed_outputs, perturbation_values):\n\n if len(perturbed_outputs.shape) == 1:\n perturbed_outputs.shape = (len(perturbed_outputs), 1)\n (srom_size, dim) = perturbed_outputs.shape\n\n if len(outputs) != srom_size:\n raise ValueError(\"# output samples must match perturbed outputs\")\n\n if len(perturbation_values) != dim:\n raise ValueError(\"length of perturbation_values must match \"\n \"dimension\")\n\n gradients = np.zeros((srom_size, dim))\n\n for i in range(dim):\n grad = ((perturbed_outputs[:, i] - outputs.flatten())\n / perturbation_values[i])\n gradients[:, i] = grad\n\n return gradients","title":""},{"docid":"1449140da04e71a188a0876ebc27713a","score":"0.519283","text":"def test_magnetic_pressure_gradient(DummyStellarator):\n eq = Equilibrium.load(\n load_from=str(DummyStellarator[\"output_path\"]), file_format=\"hdf5\"\n )\n\n # partial derivatives wrt rho\n num_rho = 110\n grid = LinearGrid(NFP=eq.NFP, rho=num_rho)\n drho = grid.nodes[1, 0]\n data = eq.compute([\"|B|\", \"grad(|B|^2)_rho\"], grid=grid)\n B2_r = np.convolve(data[\"|B|\"] ** 2, FD_COEF_1_4, \"same\") / drho\n np.testing.assert_allclose(\n data[\"grad(|B|^2)_rho\"][3:-2],\n B2_r[3:-2],\n rtol=1e-3,\n atol=1e-3 * np.nanmean(np.abs(data[\"grad(|B|^2)_rho\"])),\n )\n\n # partial derivative wrt theta\n num_theta = 90\n grid = LinearGrid(NFP=eq.NFP, theta=num_theta)\n dtheta = grid.nodes[1, 1]\n data = eq.compute([\"|B|\", \"grad(|B|^2)_theta\"], grid=grid)\n B2_t = np.convolve(data[\"|B|\"] ** 2, FD_COEF_1_4, \"same\") / dtheta\n np.testing.assert_allclose(\n data[\"grad(|B|^2)_theta\"][2:-2],\n B2_t[2:-2],\n rtol=1e-2,\n atol=1e-2 * np.nanmean(np.abs(data[\"grad(|B|^2)_theta\"])),\n )\n\n # partial derivative wrt zeta\n num_zeta = 90\n grid = LinearGrid(NFP=eq.NFP, zeta=num_zeta)\n dzeta = grid.nodes[1, 2]\n data = eq.compute([\"|B|\", \"grad(|B|^2)_zeta\"], grid=grid)\n B2_z = np.convolve(data[\"|B|\"] ** 2, FD_COEF_1_4, \"same\") / dzeta\n np.testing.assert_allclose(\n data[\"grad(|B|^2)_zeta\"][2:-2],\n B2_z[2:-2],\n rtol=1e-2,\n atol=1e-2 * np.mean(np.abs(data[\"grad(|B|^2)_zeta\"])),\n )","title":""},{"docid":"5a3d4847f2b628169a86a692ba0ce8fb","score":"0.5191244","text":"def _gradient(self, _x, _y):\n ### YOUR CODE HERE\n n_features,*other = _x.shape\n v = np.matmul(self.W, _x)\n q = self.softmax(v)\n sfmx = np.reshape(q-_y,(self.k,1))\n _g = np.matmul(sfmx, np.reshape(_x,(n_features,1)).T)\n return _g\n ### END YOUR CODE","title":""},{"docid":"0bfd6fa164ac8dd7f861558a5348f7ef","score":"0.51905155","text":"def test_gradient(self):\n weights = np.zeros(8 + 32)\n learner = Learner(MatrixBeliefPropagator)\n self.set_up_learner(learner, latent=True)\n learner.start_time = time.time()\n learner.set_regularization(0.0, 1.0)\n gradient_error = check_grad(learner.subgrad_obj, learner.subgrad_grad, weights)\n\n # numerical_grad = approx_fprime(weights, learner.subgrad_obj, 1e-4)\n # analytical_grad = learner.subgrad_grad(weights)\n # plt.plot(numerical_grad, 'r')\n # plt.plot(analytical_grad, 'b')\n # plt.show()\n\n print(\"Gradient error: %e\" % gradient_error)\n assert gradient_error < 1e-1, \"Gradient is wrong\"","title":""},{"docid":"a4aa9b2483cb517c0034ebd127de8257","score":"0.5185588","text":"def rosenbrock_criterion_and_gradient(params):\n return rosenbrock_scalar_criterion(params), rosenbrock_gradient(params)","title":""},{"docid":"e05fa8987a1fb67e9d06882bafe9dd49","score":"0.5179254","text":"def perceptron_gradient(i):\n inputs = data[i]\n input_len = len(inputs['tokens'])\n gold_labels = inputs['gold_tags']\n features = Features(inputs, feature_names)\n\n def score(cur_tag, pre_tag, i):\n return parameters.dot_product(features.compute_features(cur_tag, pre_tag, i))\n\n tags = decode(input_len, tagset, score)\n fvector = compute_features(tags, input_len, features) # Add the predicted features\n #print('Input:', inputs) # helpful for debugging\n #print(\"Predicted Feature Vector:\", fvector.fdict)\n #print(\"Predicted Score:\", parameters.dot_product(fvector))\n fvector.times_plus_equal(-1, compute_features(gold_labels, input_len, features)) # Subtract the features for the gold labels\n #print(\"Gold Labels Feature Vector: \", compute_features(gold_labels, input_len, features).fdict)\n #print(\"Gold Labels Score:\", parameters.dot_product(compute_features(gold_labels, input_len, features)))\n return fvector","title":""},{"docid":"457ddb880d430d707902245cb167d2fa","score":"0.5176523","text":"def gradient_descent(features, values, theta, alpha, num_iterations):\n\n # Write code here that performs num_iterations updates to the elements of theta.\n # times. Every time you compute the cost for a given list of thetas, append it \n # to cost_history.\n # See the Instructor notes for hints. \n \n m = len(values)\n cost_history = []\n\n for i in range(num_iterations):\n predicted_values = np.dot(features, theta)\n theta = theta - alpha / m * np.dot((predicted_values - values), features)\n \n cost = compute_cost(features, values, theta)\n cost_history.append(cost)\n\n return theta, pd.Series(cost_history) # leave this line for the grader","title":""},{"docid":"7223d2c960671fd35aa3482f74ffce63","score":"0.51736283","text":"def gradient(self, predicted: Tensor, actual: Tensor) -> Tensor:\n raise NotImplementedError","title":""},{"docid":"ce6f4331a827f4983971f8c4707036bc","score":"0.516938","text":"def compute_gradient_penalty(self,D, real_samples, fake_samples):\n # Random weight term for interpolation between real and fake samples\n # Get random interpolation between real and fake samples\n interpolates = self.interpolate(real_samples,fake_samples).requires_grad_(True)\n d_interpolates = D(interpolates)\n# fake = nn.Parameter(nn.Tensor(real_samples.shape[0], 1).fill_(1.0), requires_grad=False)\n # Get gradient w.r.t. interpolates\n gradients = grad(\n outputs=d_interpolates,\n inputs=interpolates,\n grad_outputs=torch.ones_like(d_interpolates).to(self.device),\n create_graph=True,\n retain_graph=True,\n only_inputs=True,\n )[0]\n gradients = gradients.view(gradients.size(0), -1)\n gradient_penalty = ((gradients.norm(2, dim=1) - 1) ** 2).mean()\n return gradient_penalty","title":""},{"docid":"f4bd3107a8f569724bdda5e24c90d7d3","score":"0.51625127","text":"def get_gradient(f, r, h=1e-4, normalize=True):\n N = r.size//3\n grad = np.zeros(r.shape)\n for i in range(N):\n r_copy = r.copy()\n x, y, z = r[i]\n variations = np.array([[x+h, y, z],\n [x-h, y, z],\n [x, y+h, z],\n [x, y-h, z],\n [x, y, z+h],\n [x, y, z-h]])\n varied_potentials = []\n for j in variations:\n r_copy[i] = j\n varied_potentials.append(f(r_copy))\n dx = (varied_potentials[0] - varied_potentials[1])/(2*h)\n dy = (varied_potentials[2] - varied_potentials[3])/(2*h)\n dz = (varied_potentials[4] - varied_potentials[5])/(2*h)\n grad[i] = [dx, dy, dz]\n if normalize:\n try:\n grad = grad/(abs(np.max(grad)))\n except FloatingPointError as e:\n print(np.max(grad))\n print(grad)\n raise e\n return grad","title":""},{"docid":"8878e0f5fd1e720a383922db0b5bd042","score":"0.51243705","text":"def compute_gradient_for_all(self):\r\n\r\n # Compute theta * x (for clarification, * is DOT PRODUCT)\r\n sigma = self.theta.dot(self.x.T)\r\n\r\n # Sigmoid the result of theta * x\r\n sigmoid_v = np.vectorize(self.sigmoid) # So we can use our sigmoid()-method on vectors\r\n sigma = sigmoid_v(sigma) # Sigmoid all values\r\n\r\n # Subtract all values by corresponding label in the labels vector (self.y)\r\n sigma -= self.y\r\n\r\n # Now we must multiply by Xk^i\r\n sigma = self.x.T.dot(sigma)\r\n\r\n for k in range(self.FEATURES):\r\n self.gradient[k] = sigma[k] / self.TRAINING_DATAPOINTS","title":""},{"docid":"057a2981152acc851edc93a94c793277","score":"0.5124171","text":"def compute_gradient_penalty(D, real_samples, fake_samples):\n # Random weight term for interpolation between real fake samples\n alpha = torch.cuda.FloatTensor(np.random.random((real_samples.size(0), 1, 1, 1)))\n # Get random interpolation between real and fake samples\n interpolates = (alpha * real_samples + ((1 - alpha) * fake_samples)).reauires_grad_(True)\n validity = D(interpolates)\n fake = autograd.Variable(torch.cuda.FloatTensor(np.ones(validity.shape)), requires_grad=False)\n # Get gradient w.r.t interpolates\n gradients = autograd.grad(outputs=validity, inputs=interpolates,\n grad_outputs=fake, create_graph=True, retain_graph=True,\n only_inputs=True)[0]\n gradients = gradients.view(gradients.size(0), -1)\n # gradients_penalty = ((gradients.norm(2, dim=1) - 1)**2).mean()\n gradients_penalty = (torch.clamp(gradients.norm(2, dim=1) - 1., min=0.)).mean()\n return gradients_penalty","title":""},{"docid":"fff856b440e782122aa6ab2e56f95726","score":"0.51237553","text":"def infer(self, potential, iterations, cf=True):\n energy = tf.zeros(shape=(10,), dtype=self.dtype)\n for i in tf.range(iterations):\n if cf:\n grd, energy = self.gradient_cf(potential)\n else:\n with tf.GradientTape(watch_accessed_variables=False) as tape:\n tape.watch(self.trainable_variables)\n energy = self.bethe_free_energy(potential)\n grd = tape.gradient(energy, self.trainable_variables)\n\n self.optimizer.apply_gradients(zip(grd, self.trainable_variables))\n\n if tf.equal(tf.math.mod(i, 50), 0) or tf.equal(i + 1, iterations):\n tf.print(tf.strings.format('iter: {} dmu = {}, dsigma = {}, dw={}, Energy = {}', (\n i, tf.reduce_mean(tf.abs(grd[0])), tf.reduce_mean(tf.abs(grd[1])), tf.reduce_mean(tf.abs(grd[2])),\n tf.reduce_mean(energy))))\n return energy","title":""},{"docid":"fc8669f470a231bcfd3c41a84ae3899f","score":"0.51141465","text":"def __gradient_descend(self, batch, labels):\n\n # current activations vectors, weights matrices and biases vectors\n a_1, a_2, a_3, a_4, w_lj, w_ji, w_ik, b_2, b_3, b_4 = self.nn\n\n # temporary biases vectors and weights matrices\n b_2t, b_3t, b_4t = numpy.zeros(self.l_2), numpy.zeros(self.l_3), numpy.zeros(L_4)\n w_lj_t, w_ji_t, w_ik_t = numpy.zeros([L_1, self.l_2]), numpy.zeros([self.l_2, self.l_3]), \\\n numpy.zeros([self.l_3, L_4]),\n\n # for every image in mini-batch we calculate gradient descent step approximation, and then average them\n for sample in range(len(batch)):\n\n # compute desired output based on label\n count += 1\n tgt = numpy.zeros(10)\n tgt[labels[sample]] = 1.0\n self.evaluate_img(batch[sample])\n\n # partial derivatives for biases (in vector form)\n a = 2 * (a_4 - tgt) * a_4 * (1 - a_4)\n b = (2 * (a_4 - tgt) * a_4 * (1 - a_4)).dot(w_ik.T) * (a_3 * (1 - a_3))\n c = (2 * (a_4 - tgt) * a_4 * (1 - a_4)).dot(w_ik.T) * (a_3 * (1 - a_3)).dot(w_ji.T) * (a_2 * (1 - a_2))\n\n b_2t += c\n b_3t += b\n b_4t += a\n\n # partial derivative for weight is just bias vector dot activation vector because of the Chain Rule\n w_lj_t += c.reshape((-1, 1)).dot([a_1]).T\n w_ji_t += b.reshape((-1, 1)).dot([a_2]).T\n w_ik_t += a.reshape((-1, 1)).dot([a_3]).T\n\n # update weights in NN internal state\n for x in zip(range(4, 10), [w_lj_t, w_ji_t, w_ik_t, b_2t, b_3t, b_4t]):\n self.nn[x[0]] -= x[1] / len(batch) * self.eta","title":""},{"docid":"72a3b094c4177afd136c739f28a73a71","score":"0.51052195","text":"def critic_gradient(self):\n cinput = self.critic_Q.input\n coutput = self.critic_Q.output\n\n # compute the gradient of the action with q value, dq/da.\n action_grads = K.gradients(coutput, cinput[1])\n\n return K.function([cinput[0], cinput[1]], action_grads)","title":""},{"docid":"fecbfd37f5231cf150330d3d27230c31","score":"0.51039535","text":"def getChargeGradients(self):\n grad_en, grad_orbs = self.getMOgradients() \n # occupation numbers, 2 - doubly occupied, 0 - virtual orbital\n f = self.dftb.getOccupation()\n # density matrix depends only on occupied orbitals\n orbs = self.dftb.getKSCoefficients()\n occ_indx = np.where(f > 0.0)[0]\n CdCdp = np.tensordot(grad_orbs[:,:,occ_indx], orbs[:,occ_indx], axes=(2,1))\n # gradients of density matrix\n gradP = 2 * (CdCdp + np.swapaxes(CdCdp, 1,2))\n # overlap matrix \n S = self.dftb.S\n # gradients of overlap matrix\n gradS = self.gradS\n \n # number of atoms\n atomlist = self.dftb.getGeometry()\n valorbs = self.dftb.valorbs\n Nat = len(atomlist)\n # charge gradients\n dQdp = np.zeros((3*Nat,Nat))\n\n # difference density matrix relative to reference density matrix\n PmP0 = self.dftb.P-self.dftb.P0\n # iterate over atoms\n mu = 0\n for i,(Zi,posi) in enumerate(atomlist):\n # iterate over orbitals on center i\n for (ni,li,mi) in valorbs[Zi]:\n # iterate over atoms\n nu = 0\n for j,(Zj,posj) in enumerate(atomlist):\n # iterate over orbitals on center j\n for (nj,lj,mj) in valorbs[Zj]:\n dQdp[:,i] += gradP[:,mu,nu] * S[mu,nu] + PmP0[mu,nu] * gradS[:,mu,nu]\n nu += 1\n mu += 1\n\n return dQdp","title":""},{"docid":"c0bb567bc45fafc45796d754a1aa57ba","score":"0.5099989","text":"def _erfcx_custom_gradient(x):\n return _erfcx_naive(x)","title":""},{"docid":"b603ba87dcb9a489b65272a10241c643","score":"0.50907755","text":"def pc_conditional_sampler(rng, score_state, labels):\n # Initial sample\n rng, step_rng = random.split(rng)\n x = sde.prior_sampling(step_rng, shape)\n\n timesteps = jnp.linspace(sde.T, eps, sde.N)\n\n def loop_body(i, val):\n rng, x, x_mean = val\n t = timesteps[i]\n vec_t = jnp.ones(shape[0]) * t\n rng, step_rng = random.split(rng)\n x, x_mean = conditional_corrector_update_fn(step_rng, score_state, x, vec_t, labels)\n rng, step_rng = random.split(rng)\n x, x_mean = conditional_predictor_update_fn(step_rng, score_state, x, vec_t, labels)\n return rng, x, x_mean\n\n _, x, x_mean = jax.lax.fori_loop(0, sde.N, loop_body, (rng, x, x))\n return inverse_scaler(x_mean if denoise else x)","title":""},{"docid":"fcf1793f515421aee8d64d02107897dc","score":"0.5087558","text":"def compute_gradient_penalty(self, D, real_samples, fake_samples):\r\n # Random weight term for interpolation between real and fake samples\r\n alpha = self.Tensor(np.random.random((real_samples.size(0), 1, 1)))\r\n # Get random interpolation between real and fake samples\r\n interpolates = (alpha * real_samples + ((1 - alpha) * fake_samples)).requires_grad_(True)\r\n d_interpolates = D(interpolates)\r\n fake = Variable(self.Tensor(real_samples.shape[0], 1).fill_(1.0), requires_grad=False)\r\n # Get gradient w.r.t. interpolates\r\n gradients = torch.autograd.grad(\r\n outputs=d_interpolates,\r\n inputs=interpolates,\r\n grad_outputs=fake,\r\n create_graph=True,\r\n retain_graph=True,\r\n only_inputs=True,\r\n )[0]\r\n gradients = gradients.view(gradients.size(0), -1)\r\n gradient_penalty = ((gradients.norm(2, dim=1) - 1) ** 2).mean()\r\n return gradient_penalty","title":""},{"docid":"d8e718e02490ca062c045e12062ea40e","score":"0.5060968","text":"def test_conditional_backward():\n width = 5\n height = 7\n z_size = 2\n batch_size = 3\n\n model = ConditionalCPPN(\n ConditionalModelConfig(width=width,\n height=height,\n n_units_xyr=3,\n n_hidden_units=[\n 10,\n 10,\n ],\n z_size=z_size,\n in_width=64,\n in_height=64,\n in_channel=1,\n use_batch_norm=False))\n model.zerograds()\n\n # create inputs: inputs is dict whose key is batch index, and value is tuple of (x, z) for each index\n x, z, inputs = gen_input_batch(batch_size, width, height, z_size)\n c = chainer.Variable(get_dammy_input(batch_size, 64, 64, 1)) # init dammy conditional input\n\n # forward prop\n y = model.forward(x, z, c)\n\n # taking loss at only first image\n t = get_dammy_output(batch_size, width, height)\n loss = F.mean_squared_error(y[0], t[0])\n\n g_x, g_z = chainer.grad((loss, ), inputs[0])\n g_c = chainer.grad((loss, ), (c, ))[0].data\n\n assert g_c[0].sum() != 0.0, f\"gradient of c is zero\"\n assert g_x.data.sum() != 0.0, f\"gradient of x is zero\"\n assert g_z.data.sum() != 0.0, f\"gradient of z is zero\"\n\n g_x, g_z = chainer.grad((loss, ), inputs[1])\n assert g_c[1].sum() == 0.0, f\"gradient of c is zero\"\n assert g_x.data.sum() == 0.0, f\"gradient of x is zero\"\n assert g_z.data.sum() == 0.0, f\"gradient of z is zero\"\n\n g_x, g_z = chainer.grad((loss, ), inputs[2])\n assert g_c[2].sum() == 0.0, f\"gradient of c is zero\"\n assert g_x.data.sum() == 0.0, f\"gradient of x is zero\"\n assert g_z.data.sum() == 0.0, f\"gradient of z is zero\"","title":""},{"docid":"54de286220e9ff41169c25d92ff536f9","score":"0.5057102","text":"def _CreateGradientProcess(self):\n # We compute the scalar value gradient\n if self.domain_size == 2:\n self.local_gradient = KratosMultiphysics.ComputeNodalGradientProcess2D(self.main_model_part, self.scalar_variable, self.gradient_variable, KratosMultiphysics.NODAL_AREA)\n else:\n self.local_gradient = KratosMultiphysics.ComputeNodalGradientProcess3D(self.main_model_part, self.scalar_variable, self.gradient_variable, KratosMultiphysics.NODAL_AREA)","title":""},{"docid":"846640f7c1bf03bc54edd095d18c52d5","score":"0.5051862","text":"def process_gradient(engine, learning_rate, parameters, grad_clip):\n with tf.GradientTape() as tape:\n engine.optical_system.update()\n engine.clear_ray_history()\n engine.ray_trace(3)\n #output = tf.stack(\n # [engine.finished_rays[\"y_end\"], engine.finished_rays[\"z_end\"]],\n # axis=1\n #)\n #goal = engine.finished_rays[\"rank\"]\n #goal *= - magnification\n #error = tf.squared_difference(output - goal)\n error = engine.finished_rays[\"y_end\"]**2 + engine.finished_rays[\"z_end\"]**2\n \n grad = tape.gradient(error, parameters)\n \n grad = tf.where(tf.math.is_finite(grad), grad, tf.zeros_like(grad))\n grad = tf.clip_by_value(grad, -grad_clip, grad_clip)\n grad *= learning_rate\n \n return grad, tf.reduce_sum(error)","title":""},{"docid":"dd62e32fee294b780a644352244c2d78","score":"0.50498974","text":"def compute_gradient_penalty(D, real_samples, fake_samples):\n # Random weight term for interpolation between real and fake samples\n cuda = True if torch.cuda.is_available() else False\n Tensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor\n alpha = Tensor(np.random.random((real_samples.size(0), 1, 1, 1)))\n # Get random interpolation between real and fake samples\n interpolates = (alpha * real_samples + ((1 - alpha)\n * fake_samples)).requires_grad_(True)\n d_interpolates = D(interpolates)\n fake = Variable(Tensor(real_samples.shape[0], 1, 62, 62).fill_(\n 1.0), requires_grad=False)\n# torch.Size([8, 6, 224, 224])\n# torch.Size([8, 1, 26, 26])\n # print(real_samples.shape)\n # print(real_samples.size(0))\n # print(alpha.shape)\n # print(interpolates.shape)\n # print(d_interpolates.shape)\n # Get gradient w.r.t. interpolates\n gradients = autograd.grad(\n outputs=d_interpolates,\n inputs=interpolates,\n grad_outputs=fake,\n create_graph=True,\n retain_graph=True,\n only_inputs=True,\n )[0]\n gradients = gradients.view(gradients.size(0), -1)\n gradient_penalty = ((gradients.norm(2, dim=1) - 1) ** 2).mean()\n return gradient_penalty","title":""},{"docid":"602855e85cf779a0938208dbb92d9a37","score":"0.5044244","text":"def make_gradient(bcs: Boundaries) -> Callable:\n assert isinstance(bcs.grid, PolarGrid)\n bcs.check_value_rank(0)\n\n # calculate preliminary quantities\n dim_r = bcs.grid.shape[0]\n r_min, _ = bcs.grid.axes_bounds[0]\n dr = bcs.grid.discretization[0]\n scale_r = 1 / (2 * dr)\n \n # prepare boundary values\n boundary = bcs[0]\n value_lower_bc = boundary.low.get_virtual_point_evaluator()\n value_upper_bc = boundary.high.get_virtual_point_evaluator()\n \n @jit_allocate_out(out_shape=(2, dim_r))\n def gradient(arr, out=None):\n \"\"\" apply gradient operator to array `arr` \"\"\"\n # no-flux at the origin \n i = 0\n if r_min == 0:\n out[0, i] = (arr[1] - arr[0]) * scale_r\n else:\n arr_r_l = value_lower_bc(arr, (i,))\n out[0, i] = (arr[1] - arr_r_l) * scale_r \n out[1, i] = 0 # no angular dependence by definition\n \n for i in range(1, dim_r - 1): # iterate inner radial points\n out[0, i] = (arr[i + 1] - arr[i - 1]) * scale_r\n out[1, i] = 0 # no angular dependence by definition\n\n i = dim_r - 1\n arr_r_h = value_upper_bc(arr, (i,))\n out[0, i] = (arr_r_h - arr[i - 1]) * scale_r\n out[1, i] = 0 # no angular dependence by definition\n \n return out\n \n return gradient # type: ignore","title":""},{"docid":"58a378a2efe7033c47901d9629542330","score":"0.50441283","text":"def calculate_gradient(self):\n\n self.Foliations['G_x'] = np.sin(np.deg2rad(self.Foliations[\"dip\"])) * \\\n np.sin(np.deg2rad(self.Foliations[\"azimuth\"])) * self.Foliations[\"polarity\"]\n self.Foliations['G_y'] = np.sin(np.deg2rad(self.Foliations[\"dip\"])) * \\\n np.cos(np.deg2rad(self.Foliations[\"azimuth\"])) * self.Foliations[\"polarity\"]\n self.Foliations['G_z'] = np.cos(np.deg2rad(self.Foliations[\"dip\"])) * self.Foliations[\"polarity\"]","title":""},{"docid":"fcd074383a3528488950dad3bf0eaf37","score":"0.50408494","text":"def model_parameter_gradient(self, **optimization_target_args):\n pass","title":""},{"docid":"2d4494f9feea3ceabee0fcf820960aa4","score":"0.5034822","text":"def gradient_descent(features, values, theta, alpha, num_iterations):\n \n m = len(values)\n cost_history = []\n\n for i in range(num_iterations):\n predicted_values = np.dot(features, theta) \n residuals = values - predicted_values\n\n # record cost\n sum_of_square_residuals = np.square(residuals).sum()\n cost = sum_of_square_residuals / (2*m)\n cost_history.append(cost)\n\n # update theta\n theta = theta + (alpha/m) * np.dot(residuals, features)\n\n return theta, pandas.Series(cost_history)","title":""},{"docid":"81bd5fed29e2e72239ea253dd9935f52","score":"0.502759","text":"def body_fn(w, should_continue, seed):\n beta_seed, unif_seed, next_seed = samplers.split_seed(seed, n=3)\n z = beta.sample(sample_shape=sample_batch_shape, seed=beta_seed)\n # set_shape needed here because of b/139013403\n tensorshape_util.set_shape(z, w.shape)\n w = tf.where(should_continue,\n (1. - (1. + b) * z) / (1. - (1. - b) * z),\n w)\n if not self.allow_nan_stats:\n w = tf.debugging.check_numerics(w, 'w')\n unif = samplers.uniform(\n sample_batch_shape, seed=unif_seed, dtype=self.dtype)\n # set_shape needed here because of b/139013403\n tensorshape_util.set_shape(unif, w.shape)\n should_continue = should_continue & (\n concentration * w + dim * tf.math.log1p(-x * w) - c <\n # Use log1p(-unif) to prevent log(0) and ensure that log(1) is\n # possible.\n tf.math.log1p(-unif))\n return w, should_continue, next_seed","title":""},{"docid":"b1d765ec711865c886d7c3435cf5d785","score":"0.50275344","text":"def _apply_critic_gradients(self, gradient, trainable_variables):\n self._critic_optimizer.apply_gradients(zip(gradient, trainable_variables))","title":""},{"docid":"fabd42e07a79dc815d895028d488c682","score":"0.5025568","text":"def gradients(self, states, actions):\n\n return self.sess.run(self.action_grads, feed_dict={\n self.state: states,\n self.action: actions\n })[0]","title":""},{"docid":"fd29def8876c8ee3aa7afa3b3f0f9155","score":"0.50216705","text":"def gradient_descent(features, values, theta, alpha, num_iterations):\r\n \r\n m = len(values)\r\n cost_history = []\r\n\r\n for i in range(num_iterations):\r\n # your code here\r\n predicted_values = np.dot(features, theta)\r\n theta = theta + (alpha/m)*np.dot((values - predicted_values),features)\r\n cost_history.append(compute_cost(features, values, theta))\r\n \r\n return theta, pandas.Series(cost_history)","title":""},{"docid":"1351bde491c56bcfc90f4ddf3d9e3b4a","score":"0.50214595","text":"def gradient_descent(features, values, theta, alpha, num_iterations):\n\n\t# Write code here that performs num_iterations updates to the elements of theta.\n\t# times. Every time you compute the cost for a given list of thetas, append it \n\t# to cost_history.\n\t# See the Instructor notes for hints. \n\t\n\tcost_history = [] \n\t# print features.shape\n\t# print features.shape[0] #1157\n\t# print features.shape[1] #3\n\tm = features.shape[0]\n\t# print \"features=\", features\n\t# print \"values=\", values\n\t# print \"theta=\", theta\n\t# print \"alpha=\", alpha\n\t# print \"num_iterations=\", num_iterations\n\n\t###########################\n\t### YOUR CODE GOES HERE ###\n\t###########################\n\t# print features.shape\n\t# print len(theta)\n\t# print range(1, features.shape[1])\n\tfor k in range(num_iterations): \n\t\tcost_history.append(compute_cost(features, values, theta))\n\t\t# print \"latest_cost=\", latest_cost\n\t\t# print \"np.unique(np.dot(features, theta))=\", np.unique(np.dot(features, theta)) \n\t\t# print \"np.dot(features, theta)=\", np.dot(features, theta)\n\t\t# print \"values - np.dot(features, theta)=\", values - np.dot(features, theta)\n\t\t# print \"theta=\", theta\n\t\terrors = np.dot(features, theta)\n\t\ttheta = theta + alpha/m * np.dot((values - errors), features)\n\n\treturn theta, pandas.Series(cost_history) # leave this line for the grader","title":""},{"docid":"1b33a5e227bbc454b6924f7b8528327b","score":"0.50211924","text":"def gradient_descent(features, values, theta, alpha, num_iterations):\n m = len(values)\n cost_history = []\n\n for i in range(num_iterations):\n predicted = np.dot(features,theta)\n pred = np.dot((values-predicted),features)\n theta = theta + (alpha/m)*pred\n cost = compute_cost(features, values, theta)\n cost_history.append(cost)\n return theta, pandas.Series(cost_history)","title":""},{"docid":"cf350017e9e8e8fe8e5814d573caf556","score":"0.50205314","text":"def get_gradient(x, x_start, y, theta, bias, fair_direction, regularizer):\n prob = 1/(1+np.exp(-np.sum(x * theta)-bias))\n # scalar = 2 * regularizer * np.sum(fair_direction * (x - x_start))\n # grad = (prob - y) * theta + scalar * fair_direction\n # return grad / np.linalg.norm(grad)\n\n # prob = 1/(1+np.exp(-np.dot(x, theta)-bias))\n # return (prob - y) * theta + 2 * regularizer * np.array([(x[0] - x_start[0]), 0], dtype = 'float64')\n\n return (prob - y) * np.array([0, theta[1]], dtype='float64')","title":""},{"docid":"de85929aafdc1de2ed6eb5b251cf4d14","score":"0.5020356","text":"def _rbf_func(x):\n N, D = x.shape\n diff = x.unsqueeze(1) - x.unsqueeze(0) # [N, N, D]\n dist_sq = torch.sum(diff**2, -1) # [N, N]\n h, _ = torch.median(dist_sq.view(-1), dim=0)\n if h == 0.:\n h = torch.ones_like(h)\n else:\n h = h / max(np.log(N), 1.)\n\n kappa = torch.exp(-dist_sq / h) # [N, N]\n kappa_grad = -2 * kappa.unsqueeze(-1) * diff / h # [N, N, D]\n return kappa, kappa_grad","title":""},{"docid":"5608ef800e443e4a702b23512a8141d3","score":"0.501444","text":"def reparameterized_target_gradient(self):\n x = self.get_macrocycle_parameters()\n reparameterized_f_g = self.target_gradient()\n repar = self.reparameterize()\n\n if len(repar) != 0:\n for i in range(self.nmp):\n if repar[i].reparamed:\n reparameterized_f_g[1][i] *= (x[i]+repar[i].offset)\n\n return reparameterized_f_g # dont need to do anything to f","title":""},{"docid":"a9cc7766eb443f43a3cfa4130b669bdb","score":"0.50086755","text":"def gradient_descent(self, training_data, epochs, eta, test_data):\n\n num_test = len(test_data)\n for _ in xrange(epochs):\n random.shuffle(training_data)\n self.update_params(training_data, eta)\n temp_test_accu = 1.0 * self.evaluate(test_data)/num_test\n return temp_test_accu, self.lamb_init, self.bias_init, self.weight_init","title":""},{"docid":"a2a229127c2af5d0ba98c283eb49abf9","score":"0.50034535","text":"def critic_x_gradient_penalty(self, batch_size, y_true, y_pred):\n alpha = tf.keras.backend.random_uniform((batch_size, 1, 1))\n interpolated = (alpha * y_true) + ((1 - alpha) * y_pred)\n\n with tf.GradientTape() as gp_tape:\n gp_tape.watch(interpolated)\n # 1. Get the discriminator output for this interpolated image.\n pred = self.critic_x(interpolated)\n\n grads = gp_tape.gradient(pred, [interpolated])[0]\n norm = tf.sqrt(tf.reduce_sum(tf.square(grads), axis=[1, 2]))\n gp = tf.reduce_mean((norm - 1.0) ** 2)\n\n return gp","title":""},{"docid":"379a4cf4b3a55c785eebd6b7e9b4c11a","score":"0.49968395","text":"def gradients(self):\n return self.optimiser.compute_gradients(self.loss)","title":""},{"docid":"8dce001a3688eed678fbab2dbcebc269","score":"0.49932095","text":"def gradientDescent(params,features,learning_rate,expectedValues):\n error = 0\n newParams = list(params)\n\n # PREVIOUS Implementation\n # for param in range(len(params)):\n # sumErrors = 0 # Keeps an accumulate of the errors\n # acc = 0 # coefficient value\n # for instance in range(len(features)):\n # yhat = calculateHyp(params,features.iloc[instance])\n # error = yhat - expectedValues.iloc[instance]\n # acc = acc + (error * features.iloc[instance,param]) # Calculate sumatory of gradient descent formula\n # # acc = acc + (learning_rate * (expectedValues.iloc[instance] - yhat) * yhat * (1 - yhat) * )\n # newParams[param] = params[param] - learning_rate * (1/len(features) * acc) # Here is the formula taught for gradient descent, acc is the value obtained from the sumatory\n \n # Optimized Version\n acc = 0\n yHat = calculateHyp(params,features)\n error = yHat - expectedValues\n acc = numpy.dot(error,features) # numpy takes care of all of this by calculating the dot product, thus getting the five parameters\n\n newParams = params - learning_rate * (1 / len(features) * acc)\n return newParams","title":""},{"docid":"8261c2b33fd88366f9eb22dff8c5f7cb","score":"0.4992119","text":"def run_gradient_descent_iteration(X, Y, theta, alpha, lambda_factor, temp_parameter):\n # print('X = ', X)\n # print('Y = ', Y)\n # print('theta = ', theta)\n # print('alpha = ', alpha)\n # print('lambda_factor = ', lambda_factor)\n # print('temp_parameter = ', temp_parameter)\n\n p = np.array([]).astype(float)\n p = compute_probabilities( X, theta, temp_parameter )\n\n # print(\"p = \", p)\n\n row_ind = Y.copy() #tendrá ? filas\n col_ind = np.array(range(len(Y))) \n data = np.array( [ 1 for i in range( len(Y) ) ] ) \n y_equal_label = sparse.coo_matrix((data, (row_ind, col_ind)),shape=(len(theta),len(Y)))\n\n # print(\"y_equal_label = \",y_equal_label.toarray() )\n\n term1 = np.array([]).astype(float)\n term1 = np.dot (\n y_equal_label.toarray().astype(float) - p , X\n )\n\n # print(\"term1 = \", term1)\n\n term2 = np.array([]).astype(float)\n term2 = lambda_factor * theta\n\n # print(\"term2 = \", term2)\n\n derivative = np.array([]).astype(float)\n derivative = term1 * (-1) / (temp_parameter *len(X)) + lambda_factor * term2\n \n # print(\"derivative = \", derivative)\n\n updated_theta = np.array([]).astype(float)\n updated_theta = theta - alpha * derivative \n\n\n return updated_theta#, \"Este es mi output\"\n # raise NotImplementedError","title":""},{"docid":"c5aa21a30400d72c5953e83b607bc41d","score":"0.49900797","text":"def step(self, closure=None):\n loss = None\n if closure is not None:\n loss = closure()\n\n for group in self.param_groups:\n weight_decay = group['weight_decay']\n momentum = group['momentum']\n threshold = group['threshold']\n lr = group['lr']\n \n device = group['params'][0].grad.device\n grad_norm = torch.norm(torch.stack([torch.norm(p.grad.detach()).to(device) \\\n for p in group['params'] if p.grad is not None]))\n if grad_norm.isnan() or grad_norm.isinf():\n raise RuntimeError(f'The total norm for gradients from is non-finite, so it cannot be clipped.')\n\n if grad_norm > threshold:\n actual_lr = lr * threshold / (grad_norm + 1e-8)\n else:\n actual_lr = lr\n \n for p in group['params']:\n if p.grad is None:\n continue\n d_p = p.grad.data\n if weight_decay != 0:\n d_p.add_(p.data, alpha=weight_decay)\n param_state = self.state[p]\n if 'momentum_buffer' not in param_state:\n buf = param_state['momentum_buffer'] = torch.zeros_like(p.data)\n else:\n buf = param_state['momentum_buffer']\n buf.mul_(momentum).add_(d_p, alpha=actual_lr)\n p.data.add_(-buf)\n\n return loss","title":""},{"docid":"fe0eed9a27ce50d493baabc187d16361","score":"0.4989592","text":"def compute_gradient_penalty(D, real_samples, fake_samples):\n\n # Random weight term for interpolation between real and fake samples\n alpha = Tensor(np.random.random((real_samples.size(0), 1, 1, 1)))\n # Get random interpolation between real and fake samples\n interpolates = (alpha * real_samples + ((1 - alpha) * fake_samples)).requires_grad_(True)\n d_interpolates = D(interpolates).reshape(-1, 1)\n fake = Variable(Tensor(real_samples.shape[0], 1).fill_(1.0), requires_grad=False)\n # Get gradient w.r.t. interpolates\n gradients = autograd.grad(\n outputs=d_interpolates,\n inputs=interpolates,\n grad_outputs=fake,\n create_graph=True,\n retain_graph=True,\n only_inputs=True,\n )[0]\n gradients = gradients.view(gradients.size(0), -1)\n gradient_penalty = ((gradients.norm(2, dim=1) - 1) ** 2).mean()\n return gradient_penalty","title":""},{"docid":"656f848354a5f71361fb277ae72896fb","score":"0.49882525","text":"def gradient(C0,x,y):\r\n #concentration = C0/(4*np.pi*d*D*duT)*np.exp(-(x-dis2targ)**2/(400*D*duT*50)) #depends on diffusion conditions along x-axis\r\n concentration = C0 * np.exp(-(x-dis2targ)**2/(4*Dt)) #/(4*np.pi*Dt)**0.5\r\n return concentration","title":""},{"docid":"64bda9d814679b4fd9497f6ad66b3088","score":"0.49871475","text":"def cell_gradient_BC(self):\n if getattr(self, \"_cell_gradient_BC\", None) is None:\n BC = self.set_cell_gradient_BC(self._cell_gradient_BC_list)\n n = self.vnC\n if self.dim == 1:\n G = _ddxCellGradBC(n[0], BC[0])\n elif self.dim == 2:\n G1 = sp.kron(speye(n[1]), _ddxCellGradBC(n[0], BC[0]))\n G2 = sp.kron(_ddxCellGradBC(n[1], BC[1]), speye(n[0]))\n G = sp.block_diag((G1, G2), format=\"csr\")\n elif self.dim == 3:\n G1 = kron3(speye(n[2]), speye(n[1]), _ddxCellGradBC(n[0], BC[0]))\n G2 = kron3(speye(n[2]), _ddxCellGradBC(n[1], BC[1]), speye(n[0]))\n G3 = kron3(_ddxCellGradBC(n[2], BC[2]), speye(n[1]), speye(n[0]))\n G = sp.block_diag((G1, G2, G3), format=\"csr\")\n # Compute areas of cell faces & volumes\n S = self.face_areas\n V = (\n self.aveCC2F * self.cell_volumes\n ) # Average volume between adjacent cells\n self._cell_gradient_BC = sdiag(S / V) * G\n return self._cell_gradient_BC","title":""},{"docid":"6024f484385b52ef55c712ddf276848c","score":"0.498429","text":"def CG(b, x0, TOLERANCE=1.0e-10, MAX_ITERATIONS=100):\r\n\r\n # Initializations\r\n x = x0\r\n d = -tf.gradient(Loss, x)\r\n r0 = b - finite_differences(x,d)\r\n\r\n\r\n # Start iterations\r\n for i in range(MAX_ITERATIONS):\r\n a = float(np.dot(d.T, r0) / np.dot(d.T, finite_differences(x, d)))\r\n x = x - a * gradient(x)\r\n\r\n ri = r0 - np.dot(finite_differences(x, d), d)\r\n\r\n # print i, np.linalg.norm(ri)\r\n # Checks stopping condition\r\n if np.linalg.norm(ri) < TOLERANCE:\r\n return x\r\n\r\n # Otherwise go on to find new direction\r\n b = float(np.dot(gradient(x).T, finite_differences(x, d)))\r\n d = - gradient(x) + b * d\r\n r0 = ri\r\n return x","title":""},{"docid":"13869890bc96c80cf348ea4b1d2e17e6","score":"0.49820924","text":"def conditional_predictor_update_fn(rng, state, x, t, labels):\n score_fn = mutils.get_score_fn(sde, score_model, state.params_ema, state.model_state, train=False,\n continuous=continuous)\n\n def total_grad_fn(x, t):\n ve_noise_scale = sde.marginal_prob(x, t)[1]\n return score_fn(x, t) + classifier_grad_fn(x, ve_noise_scale, labels)\n\n if predictor is None:\n predictor_obj = NonePredictor(sde, total_grad_fn, probability_flow)\n else:\n predictor_obj = predictor(sde, total_grad_fn, probability_flow)\n return predictor_obj.update_fn(rng, x, t)","title":""},{"docid":"9df0f4f63c89d6f519ac3c7557073791","score":"0.49804133","text":"def gradient(X, Y, Y_predicted):\n Error = Y_predicted - Y # Calculate error\n dW = np.dot(X.T, Error) # Compute derivative of error w.r.t weight, i.e., (target - output) * x\n db = np.sum(Error) # Compute derivative of error w.r.t bias\n return dW, db # return derivative of weight and bias","title":""},{"docid":"a11a27d91183dc76912739a7982ded3d","score":"0.49787378","text":"def evaluate_gradient(self, evaluation_points, property_array):\n if property_array.shape[0] != self.n_nodes:\n logger.error(\"Property array does not match grid\")\n raise ValueError(\"cannot assign {} vlaues to array of shape {}\".format(\n property_array.shape[0], self.n_nodes))\n \n idc, inside = self.position_to_cell_corners(evaluation_points)\n T = np.zeros((idc.shape[0], 3, 8))\n T[inside, :, :] = self.get_element_gradient_for_location(evaluation_points[inside, :])[1]\n # indices = np.array([self.position_to_cell_index(evaluation_points)])\n # idc = self.global_indicies(indices.swapaxes(0,1))\n # print(idc)\n T[inside, 0, :] *= property_array[idc[inside, :]]\n T[inside, 1, :] *= property_array[idc[inside, :]]\n T[inside, 2, :] *= property_array[idc[inside, :]]\n return np.array(\n [np.sum(T[:, 0, :], axis=1), np.sum(T[:, 1, :], axis=1) ,\n np.sum(T[:, 2, :], axis=1) ]).T","title":""},{"docid":"66298dbd5963d8e321f1f2c765037516","score":"0.497799","text":"def gradient_descent(features, values, theta, alpha, num_iterations):\n \n m = len(values)\n cost_history = []\n\n X = np.matrix(features)\n Y = np.matrix(values).transpose()\n for i in range(num_iterations):\n TH = np.matrix(theta).transpose()\n XTY = X*TH-Y\n cost = compute_cost(features, values, theta)\n cost_history.append(cost)\n TH = TH - alpha/m * (X.transpose()*XTY)\n theta = np.array(TH.transpose())[0]\n return theta, pd.Series(cost_history)","title":""},{"docid":"6c690b72f44c533cdd6a01526716c14f","score":"0.49740028","text":"def gradient_check_internal(f, x, h=1e-4, threshold=1e-5):\n rndstate = random.getstate()\n random.setstate(rndstate)\n np.random.seed(0)\n fx, grad = f(x) # Evaluate function value at original point\n\n # Iterate over all indices in x\n it = np.nditer(x, flags=['multi_index'], op_flags=['readwrite'])\n result = []\n while not it.finished:\n ix = it.multi_index\n\n x_step = x.copy()\n x_step[ix] += h / 2\n random.setstate(rndstate)\n np.random.seed(0)\n fx_pos, _ = f(x_step)\n\n x_step = x.copy()\n x_step[ix] -= h / 2\n random.setstate(rndstate)\n np.random.seed(0)\n fx_neg, _ = f(x_step)\n\n numgrad = (fx_pos - fx_neg) / h\n\n reldiff = abs(numgrad - grad[ix]) / max(1, abs(numgrad), abs(grad[ix]))\n\n if reldiff > threshold:\n result.append((ix, grad[ix], numgrad))\n\n it.iternext() # Step to next dimension\n\n return result","title":""},{"docid":"c50502964387b0c3def84180d9c11b65","score":"0.49720913","text":"def hyperparam_gradients(self, batch):\n X, y, side_info = batch\n batch_factor = self.n / np.shape(X)[-1]\n\n m_u, S_u = [self.dist_params['qu'][key] for key in ['mean', 'var']]\n m_w = self.get_expected_w()\n\n def get_derivations(h):\n \"\"\"\n This debug function numerically derivates the ELBO w.r.t. the hyperparameters\n with (ELBO(x + h) - ELBO(x))/h, where x is the hyperparameter\n :param h: change for the derivation\n :return: gradients w.r.t. the h.params\n \"\"\"\n old_kp = self.kernel_params\n old_kernel = self.kernel\n res = {}\n for key in sorted(self.kernel_params.keys()) + [None]:\n if key is not None and self.kernel_params[key] is None:\n # This means that this Kernel is not used\n continue\n new_kps = old_kp.copy()\n\n if key is not None:\n new_kps[key] += h\n else:\n key = 'old'\n self.kernel_params = new_kps\n self.kernel = kernels.Kernel_Handler(**new_kps)\n self.update_kernel()\n c = {}\n if self.omega_gradients:\n self.local_params = self.update_local_params(X, y, side_info=side_info)\n c['dc'] = np.array(self.local_params)\n Theta = np.diag(self.theta(c['dc']))\n elbo = self.calc_ELBO(X, y, side_info)\n c['L'] = sum(elbo)\n c['L1'] = elbo[0]\n c['KL_u'] = elbo[1]\n c['KL_w'] = elbo[2]\n c['Kmm'] = self.Kmm\n c['Knm'] = self.get_Kmn(X, side_info).T\n if side_info is None:\n c['Knn'] = self.kernel(X.T)\n else:\n c['Knn'] = self.kernel(X.T, side_X=side_info.T)\n c['Kmminv'] = self.Kmm_inv\n c['KnmKmminv'] = np.dot(c['Knm'], self.Kmm_inv)\n c['KnmKmminvKmn'] = multi_dot([c['Knm'], self.Kmm_inv, c['Knm'].T])\n c['ykmu'] = multi_dot([y.T, c['Knm'], self.Kmm_inv, m_u])\n c['trthk'] = np.trace(multi_dot([Theta, c['Knn'] - multi_dot([c['Knm'], self.Kmm_inv, c['Knm'].T])]))\n c['trkmntknmsu'] = np.trace(multi_dot([self.Kmm_inv, c['Knm'].T, Theta, c['Knm'], self.Kmm_inv, S_u]))\n c['mk+xwth'] = self.sh.multi_dot([(multi_dot([c['Knm'], self.Kmm_inv, m_u]) + self.sh.dot(X.T, m_w)).T,\n Theta,\n self.sh.multi_dot([c['Knm'], self.Kmm_inv, m_u]) + self.sh.dot(X.T,\n m_w)])\n c['trks'] = np.trace(np.dot(self.Kmm_inv, S_u))\n c['mkm'] = multi_dot([m_u.T, self.Kmm_inv, m_u])\n c['logk'] = self.sh.logdet(self.Kmm)\n c['theta_diag'] = np.array(self.theta(c['dc']))\n c['KL_omega'] = np.array((sum(\n [ci / 4 * np.tanh(ci / 2) for i, ci in enumerate(c['dc'])]))\n - sum([core.math.logcosh(ci / 2) for ci in c['dc']]))\n\n res[key] = c\n self.kernel = old_kernel\n return {var_key: {key: (res[var_key][key] - res['old'][key]) / h for key in res['old']} for var_key in res}\n\n cs = self.local_params\n Theta = self.sh.diag(self.theta(cs))\n\n #Getting the kernel gradients\n if side_info is not None:\n dKmms = self.kernel.gradients(self.Z.T, side_X=self.Z_side.T)\n dKnms = self.kernel.gradients(X.T, self.Z.T, side_X=side_info.T, side_Y=self.Z_side.T)\n dKnns = self.kernel.gradients(X.T, side_X=side_info.T)\n Kmn_prime, Kmn = self.get_Kmn_prime_Kmn(X, side_info=side_info)\n else:\n dKmms = self.kernel.gradients(self.Z.T)\n dKnms = self.kernel.gradients(X.T, self.Z.T)\n dKnns = self.kernel.gradients(X.T)\n Kmn_prime, Kmn = self.get_Kmn_prime_Kmn(X)\n dkmminvs = {key: - multi_dot([self.Kmm_inv, dKmms[key], self.Kmm_inv]) for key in dKnns}\n dknmkmminvs = {\n key: np.dot(dKnms[key], self.Kmm_inv) - multi_dot([Kmn.T, self.Kmm_inv, dKmms[key], self.Kmm_inv])\n for key in dKnns}\n dknmkmminvkmns = {key: 2 * multi_dot([dKnms[key], self.Kmm_inv, Kmn])\n - multi_dot([Kmn.T, self.Kmm_inv, dKmms[key], self.Kmm_inv, Kmn])\n for key in dKnns}\n tanhs = [np.tanh(c / 2) for c in cs]\n if self.omega_gradients:\n dcs = self.local_parameter_gradients(dKnms, dkmminvs,\n {key: np.diag(dKnns[key]) - np.diag(dknmkmminvkmns[key]) for key in\n dKnns},\n X, Kmn, Kmn_prime=Kmn_prime)\n\n dtheta_diag = self.theta_gradients(cs)\n dTheta = {key: np.asarray([dcs[key][i] * dtheta_diag[i] for i in range(len(cs))]) for key in dKnns}\n K_tilde = self.get_K_tilde(X, Kmn, side_info=side_info)\n\n derivations = {}\n c_diffs = {}\n m_eps = multi_dot([Kmn.T, self.Kmm_inv, m_u])\n Xw = self.sh.dot(X.T, m_w)\n xwkmu = m_eps + Xw\n\n # Calculating the gradients, w.r.t. the different hyperparameters\n for key in dKnns:\n if self.epoch_count < self.ls_head_start and key in ['rbf', 'lin']:\n continue\n\n if core.math.debug():\n c = {}\n c['ykmu'] = multi_dot([y.T, dknmkmminvs[key], m_u])\n c['trthk'] = self.sh.trace(self.sh.dot(Theta, dKnns[key])) \\\n - self.sh.trace(self.sh.dot(Theta, dknmkmminvkmns[key]))\n c['trkmntknmsu'] = self.sh.trace(2 * self.sh.multi_dot([Kmn_prime, Theta, dknmkmminvs[key], S_u]))\n c['mk+xwth'] = 2 * self.sh.to_dense(self.sh.multi_dot([xwkmu.T,\n Theta, dknmkmminvs[key], m_u]))\n c['KL_omega'] = 0\n if self.omega_gradients:\n c['trthk'] += self.sh.trace(self.sh.multiply(dTheta[key], K_tilde))\n c['trkmntknmsu'] += self.sh.trace(\n self.sh.multi_dot([self.sh.multiply(Kmn_prime, dTheta[key]), Kmn_prime.T, S_u]))\n c['mk+xwth'] += self.sh.to_dense(self.sh.dot(self.sh.multiply(xwkmu.T, dTheta[key]), xwkmu))\n c['KL_omega'] = -sum([dcs[key][i] * (.25 * tanhs[i] * (1 + ci / 2 * tanhs[i]) - ci / 8)\n for i, ci in enumerate(cs)])\n\n c['trks'] = np.trace(multi_dot([dkmminvs[key], S_u]))\n c['mkm'] = multi_dot([m_u.T, dkmminvs[key], m_u])\n c['logk'] = np.trace(np.dot(self.Kmm_inv, dKmms[key]))\n\n derivations[key] = multi_dot([y.T, dknmkmminvs[key], m_u])\n derivations[key] -= self.sh.trace_dot(Theta, dKnns[key])\n derivations[key] += self.sh.trace_dot(Theta, dknmkmminvkmns[key])\n\n derivations[key] -= self.sh.trace(2 * self.sh.multi_dot([Kmn_prime, Theta, dknmkmminvs[key], S_u]))\n derivations[key] -= (2 * self.sh.to_dense(self.sh.multi_dot([xwkmu.T,\n Theta, dknmkmminvs[key], m_u])))\n if self.omega_gradients:\n derivations[key] -= self.sh.trace(\n self.sh.multi_dot([np.multiply(Kmn_prime, dTheta[key]), Kmn_prime.T, S_u]))\n derivations[key] -= np.sum(np.multiply(dTheta[key], np.diag(K_tilde)))\n derivations[key] -= self.sh.to_dense(self.sh.dot(self.sh.multiply(m_eps.T, dTheta[key]), m_eps))\n derivations[key] -= 2 * self.sh.to_dense(self.sh.dot(self.sh.multiply(m_eps.T, dTheta[key]), Xw))\n derivations[key] -= self.get_expected_wXThetaXw(Xw, X, dTheta[key])\n derivations[key] *= 1 / 2\n if self.omega_gradients:\n derivations[key] -= sum([dcs[key][i] * (.25 * tanhs[i] * (1 + ci / 2 * tanhs[i]) - ci / 8)\n for i, ci in enumerate(cs)])\n\n derivations[key] *= batch_factor\n if core.math.debug(): c['L1'] = derivations[key].copy()\n derivations[key] -= 1 / 2 * self.sh.trace_dot(dkmminvs[key], S_u)\n derivations[key] -= 1 / 2 * multi_dot([m_u.T, dkmminvs[key], m_u])\n derivations[key] -= 1 / 2 * self.sh.trace_dot(self.Kmm_inv, dKmms[key])\n derivations[key] = self.sh.to_dense(derivations[key])\n if core.math.debug():\n c['KL_u'] = derivations[key] - c['L1']\n c_diffs[key] = c\n\n if core.math.debug():\n # Checking if the calculated and numerical gradients are similar\n h = 1E-3\n real_devt = get_derivations(h)\n print()\n for key in c_diffs:\n print(key, real_devt[key]['L'], derivations[key],\n '--------------------------------', derivations[key] / real_devt[key]['L'], 'diff:',\n abs(derivations[key] - real_devt[key]['L']))\n if not 0.97 < derivations[key] / real_devt[key]['L'] < 1.03 \\\n and abs(derivations[key] - real_devt[key]['L']) > 1E-4:\n print('AYAYAY calc -real - calc/real')\n for key2 in c_diffs[key]:\n if not (abs(real_devt[key][key2] - c_diffs[key][key2]) < 1E-10\n or 0.95 < real_devt[key][key2] / c_diffs[key][key2] < 1.05):\n print(key2, real_devt[key][key2], c_diffs[key][key2], c_diffs[key][key2] /\n self.sh.to_dense(real_devt[key][key2]))\n\n return derivations","title":""},{"docid":"972e69d8dd2bab3cecf7147b30feaec4","score":"0.49636534","text":"def _gradient_clipping(self, grad, var, non_zero, eps=1e-3, threshold=1e-2):\n\n norm_grad = array_ops.ones_like(var) * non_zero(self._axis_aware_euclidian_norm(grad))\n norm_var = math_ops.maximum(self._axis_aware_euclidian_norm(var), eps)\n dynamic_threshold = threshold * (norm_var / norm_grad)\n return array_ops.where(dynamic_threshold < 1., grad * dynamic_threshold, grad)","title":""},{"docid":"671f29a0aa721b80af35f52b1629b216","score":"0.4958247","text":"def get_activations_and_gradient(self, z):\n a = self.activate(z)\n return a, np.multiply(a, 1 - a)","title":""},{"docid":"a1e1dea42461e12a2d26f1ccf9d469cd","score":"0.495605","text":"def do_reinforce_modified_policy_gradient_bias_subtracted(self, iterations=200, batch_size=1500, gamma=0.9,\n learning_rate=3e-4, init_variance=0.01):\n average_returns = [0] * iterations\n optimizer = torch.optim.Adam(self.policy_network.parameters(), lr=learning_rate)\n\n variance = torch.nn.parameter.Parameter(torch.ones(2) * init_variance) # for two dimensions\n\n b = 0 # Subtraction co-efficient\n std_dev_b = 1.0 # Division co-efficient\n\n for iteration in range(iterations): # Updated once per loop\n net_reward = 0.0\n episodes = 0\n loss = 0.0\n step = 0\n prev_state = self.env.reset()\n\n G_tau = []\n current_trajectory = {\"log_probabilities\": [], \"rewards\": []}\n\n while step < batch_size: # collection of data for batch size (say 500 steps)\n\n mean = self.policy_network(prev_state) # Forward pass\n\n assert mean.shape == (2,), f\"{mean.shape}\"\n assert -2.00 <= sum(mean) <= 2.00, f\"{sum(mean)}\"\n\n covariance = torch.diag(abs(variance) + 1e-3 * torch.ones(2))\n # to avoid covariance going to zero! or negative\n probable_actions = MultivariateNormal(loc=mean, covariance_matrix=covariance)\n action = probable_actions.sample() # Choose a 2-D vector for action\n\n current_state, reward, done, _ = self.env.step(action)\n\n current_trajectory[\"log_probabilities\"].append(probable_actions.log_prob(action))\n current_trajectory[\"rewards\"].append(reward)\n\n prev_state = current_state\n\n if done or step == batch_size - 1: # episode terminated\n T = len(current_trajectory[\"rewards\"]) # Total steps\n # For calculating b\n for t in range(T):\n discounted_reward = gamma ** (T - t) * current_trajectory[\"rewards\"][t]\n G_tau.append(discounted_reward)\n\n discounted_reward = sum(current_trajectory[\"rewards\"][t] * (gamma ** t) for t in\n range(len(current_trajectory[\"rewards\"])))\n\n objective_over_all_episodes = 0.0\n for t in range(T):\n summed_future_reward = 0.0\n for t_bar in range(t, T):\n summed_future_reward += (gamma ** (t_bar - t)) * (current_trajectory[\"rewards\"][t_bar])\n objective_over_all_episodes += current_trajectory[\"log_probabilities\"][t] * ((summed_future_reward - b)/std_dev_b)\n\n loss += objective_over_all_episodes\n net_reward += discounted_reward\n current_trajectory = {\"log_probabilities\": [],\n \"rewards\": []} # Resetting for collecting next episode data\n prev_state = self.env.reset()\n episodes += 1\n\n step += 1\n\n # End of say, 500 steps, update policy\n optimizer.zero_grad() # Clear gradients, to prevent accumulation\n loss = -1 * loss * (1 / episodes)\n # Objective function, -1 => Gradient Ascent (Torch does descent by default)\n loss.backward() # Calculate gradient\n optimizer.step() # Update neural net (aka policy)\n average_returns[iteration] = net_reward / episodes # For plotting later\n\n assert len(G_tau) == batch_size, f\"{batch_size, len(G_tau)}\"\n b = sum(G_tau) / batch_size\n std_dev_b = np.std(G_tau)\n\n # # DEBUG\n print(f\"Iteration: {iteration}, \"\n f\"Average Return : {average_returns[iteration]}, \"\n f\"Episodes: {episodes}\")\n print(f\"Avg loss : {loss}\")\n\n self.plot_average_returns(average_returns, title=f\"Average Returns | Batch Size: {batch_size}\")","title":""}],"string":"[\n {\n \"docid\": \"36c653aeb3b687e085e9534cdbd33142\",\n \"score\": \"0.608587\",\n \"text\": \"def value_gradient():\\n # sess.run(calculated) to calculate value of state\\n state = tf.placeholder(\\\"float\\\",[None,4])\\n w1 = tf.get_variable(\\\"w1\\\",[4,10])\\n b1 = tf.get_variable(\\\"b1\\\",[10])\\n h1 = tf.nn.relu(tf.matmul(state,w1) + b1)\\n w2 = tf.get_variable(\\\"w2\\\",[10,1])\\n b2 = tf.get_variable(\\\"b2\\\",[1])\\n calculated = tf.matmul(h1,w2) + b2\\n\\n # sess.run(optimizer) to update the value of a state\\n newvals = tf.placeholder(\\\"float\\\",[None,1])\\n diffs = calculated - newvals\\n loss = tf.nn.l2_loss(diffs)\\n optimizer = tf.train.AdamOptimizer(0.1).minimize(loss)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7031d5b8e1d0033983741d80f26d07ae\",\n \"score\": \"0.5833809\",\n \"text\": \"def gradient(self, y_true, y_pred):\\n predictions = np.clip(y_pred, self.epsilon, 1. - self.epsilon)\\n return - y_true / predictions\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd4d2d98de481b90a7100fc253e4a2b2\",\n \"score\": \"0.5808814\",\n \"text\": \"def test_kernel_gradient():\\n gamma = 0.1\\n x, x_ref = X[0], X[1]\\n my = rbf_kernel_gradient(gamma, x[np.newaxis, :], x_ref[np.newaxis, :])[:, 0]\\n numerical = []\\n for i in range(D):\\n h = np.zeros_like(x)\\n h[i] += 1e-3\\n x_f, x_b = x+h, x-h\\n partial_gd = (rbf_kernel(gamma, x_f[np.newaxis, :], x_ref[np.newaxis, :]) - \\\\\\n rbf_kernel(gamma, x_b[np.newaxis, :], x_ref[np.newaxis, :]))[0, 0] / 2e-3\\n numerical.append(partial_gd)\\n assert_array_almost_equal(np.array(numerical), my, 6)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67a580bb89366d8230a68aaf7a1a3f10\",\n \"score\": \"0.57066965\",\n \"text\": \"def _betainc_custom_gradient(a, b, x):\\n return _betainc_naive(a, b, x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3a420da11bbc458ec19d7a5832d36e5\",\n \"score\": \"0.5667132\",\n \"text\": \"def get_gradient(lambdas, data, index_info):\\n\\n model = get_train_model(lambdas, index_info)\\n transition_grad = get_transition_gradient(model, data)\\n feature_grad = get_feature_gradient(model, data)\\n gradient = np.concatenate((transition_grad, feature_grad))\\n\\n return gradient * -1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ced6a91e2de5e96ab5686cf1e17a9f48\",\n \"score\": \"0.56448185\",\n \"text\": \"def compute_value_gradient(self, real_value):\\r\\n\\r\\n return (real_value - self.value) * RatesNeuralNetwork.sigmoid(self.value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22d5dbec41e21f1e125bbc72de08df30\",\n \"score\": \"0.5629934\",\n \"text\": \"def value_gradient(self):\\n\\n # lower and upper bound brightnesss\\n lower_blue = np.array([0, 0, 170])\\n upper_blue = np.array([255, 255, 255])\\n light_hsv = cv2.inRange(self.hsv, lower_blue, upper_blue)\\n\\n # lower and upper bound darkness\\n lower_blue = np.array([0, 0, 0])\\n upper_blue = np.array([255, 255, 80])\\n dark_hsv = cv2.inRange(self.hsv, lower_blue, upper_blue)\\n\\n light = cv2.countNonZero(light_hsv)\\n dark = cv2.countNonZero(dark_hsv)\\n height, width, _ = self.img.shape\\n size = height*width\\n return light/size, dark/size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07b1030573e2d14f0b6489fc7c95cd05\",\n \"score\": \"0.56227076\",\n \"text\": \"def test_gradient_param(self, setup):\\n sess, tri, trinp, test_points = setup\\n\\n # Disable project\\n trinp.project = True\\n tri.project = True\\n\\n x = tf.placeholder(tf.float64, [1, 2])\\n\\n true_gradient = trinp.parameter_derivative(test_points)\\n true_gradient = np.array(true_gradient.todense())\\n\\n y = tri(x)\\n grad_tf = tf.gradients(y, tri.parameters)[0]\\n dense_gradient = np.zeros(true_gradient[0].shape, dtype=np.float)\\n\\n for i, test in enumerate(test_points):\\n gradient = sess.run(grad_tf, feed_dict={x: test[None, :]})\\n dense_gradient[:] = 0.\\n dense_gradient[gradient.indices] = gradient.values[:, 0]\\n assert_allclose(dense_gradient, true_gradient[i])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fe49c4cb4f3f79bad0731182a1867f5\",\n \"score\": \"0.5619189\",\n \"text\": \"def fairness_parameter_gradient(self, **optimization_target_args):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa53087e1e82789100184e0617bff0db\",\n \"score\": \"0.5583061\",\n \"text\": \"def model_parameter_gradient(self, **optimization_target_args):\\n gradient = -self.utility_function.gradient(**optimization_target_args)\\n fairness_gradient = self.fairness_function.gradient(**optimization_target_args)\\n\\n if self.error_delta == 0.0:\\n grad_fairness = self.fairness_rate * fairness_gradient\\n else:\\n lambda1, lambda2 = self.fairness_rate\\n grad_fairness = lambda1 * -fairness_gradient + lambda2 * fairness_gradient\\n\\n gradient += grad_fairness\\n return gradient\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1afe73e4a20aa221683b000e737ee7d\",\n \"score\": \"0.55646396\",\n \"text\": \"def computeGradients(X,Y,Ws,bs,lambdaValue):\\n sizeD = X.shape[0]\\n netSize = len(Ws)\\n assert netSize>=2, \\\"network size must be greater than 2\\\"\\n\\n #forward pass\\n scores,hiddenlayers,P = evaluateClassifier(X,Ws,bs)\\n\\n #backward pass\\n dWs = []\\n dbs = []\\n\\n #gradient on final scores\\n dscores = P#N*K\\n dscores -=Y#N*K\\n dscores /= sizeD\\n\\n #from last score to\\n for i in range(netSize-1):\\n dWi = np.dot(hiddenlayers[netSize-i-2].T,dscores)#netSize must be greater than 2\\n dbi = np.sum(dscores,axis=0,keepdims=False)\\n dh = np.dot(dscores,Ws[netSize-i-1].T)\\n dh[hiddenlayers[netSize-i-2]<=0] = 0\\n dscores = dh\\n dWs.append(dWi)\\n dbs.append(dbi)\\n\\n #backpropagate to W1 and b1\\n dW1 = np.dot(X.T,dscores)\\n db1 = np.sum(dscores,axis=0,keepdims=False)\\n dWs.append(dW1)\\n dbs.append(db1)\\n\\n # reverse dWs and dbs\\n dWs.reverse()\\n dbs.reverse()\\n\\n #reg grad for Ws\\n for i in range(netSize):\\n dWs[i] += lambdaValue*Ws[i]\\n return dWs,dbs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5afac6e58dd09cb6076be6bcf6052b41\",\n \"score\": \"0.554397\",\n \"text\": \"def policy_gradient():\\n # Placeholders for state and prefered action\\n state = tf.placeholder(\\\"state\\\",[None,4])\\n actions = tf.placeholder(\\\"float\\\",[None,2])\\n # Variable for parameters 4 inputs, 2 output(one-hot)\\n params = tf.get_variable(\\\"policy_parameters\\\",[4,2])\\n # Layer\\n y = tf.matmul(state,params)\\n # Output\\n probabilities = tf.nn.softmax(y)\\n # Multiply probabilitie for each action by the prefered action.\\n good_probabilities = tf.reduce_sum(tf.mul(probabilities, actions), axis=[1])\\n # maximize the log probability\\n log_probabilities = tf.log(good_probabilities)\\n loss = -tf.reduce_sum(log_probabilities)\\n optimizer = tf.train.AdamOptimizer(0.01).minimize(loss)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8012067aa8c5f69a256803137263d0a0\",\n \"score\": \"0.55414635\",\n \"text\": \"def gradient(self, y_true, y_pred):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f266d75e7aad7f0883cf8b62b51c3b05\",\n \"score\": \"0.5537623\",\n \"text\": \"def helper_gradient(function,gradient,sample,\\n eps=np.sqrt(np.finfo(float).eps)):\\n value = function(sample)\\n num_dims = sample.shape[0]\\n num_qoi = value.shape[0]\\n grad = np.empty((num_dims,num_qoi),float)\\n for i in range(num_dims):\\n perturbed_sample = sample.copy()\\n perturbed_sample[i] += eps\\n perturbed_value = function(perturbed_sample)\\n grad[i,:] = (perturbed_value-value)/eps\\n assert np.allclose(gradient(sample).squeeze(),grad.squeeze(),atol=eps*10)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c60b4a00911bc4807ef1222965caa6a\",\n \"score\": \"0.5501193\",\n \"text\": \"def _define_gradients(self):\\n dWax = np.zeros_like(self.Wax)\\n dWaa = np.zeros_like(self.Waa)\\n dWya = np.zeros_like(self.Wya)\\n\\n db = np.zeros_like(self.b)\\n dby = np.zeros_like(self.by)\\n\\n da_next = np.zeros_like(self.hidden_list[0])\\n\\n return dWax, dWaa, dWya, db, dby, da_next\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6156756e6cff93fe5010aa5e1f279b18\",\n \"score\": \"0.5468707\",\n \"text\": \"def model_parameter_gradient(self, **optimization_target_args):\\n gradient = -self.utility_function.gradient(**optimization_target_args)\\n\\n if self.fairness_rate > 0:\\n fairness = self.fairness_function(**optimization_target_args)\\n fairness_gradient = self.fairness_function.gradient(**optimization_target_args)\\n\\n grad_fairness = self.fairness_rate * fairness * fairness_gradient\\n gradient += grad_fairness\\n\\n return gradient\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf8ecef147b19e5ac6123fd2df6f4593\",\n \"score\": \"0.54677916\",\n \"text\": \"def build_loss_and_gradients(self, var_list):\\n # Form dictionary in order to replace conditioning on prior or\\n # observed variable with conditioning on a specific value.\\n scope = tf.get_default_graph().unique_name(\\\"inference\\\")\\n dict_swap = {z: qz.value()\\n for z, qz in six.iteritems(self.latent_vars)}\\n for x, qx in six.iteritems(self.data):\\n if isinstance(x, ed.RandomVariable):\\n if isinstance(qx, ed.RandomVariable):\\n dict_swap[x] = qx.value()\\n else:\\n dict_swap[x] = qx\\n\\n p_log_prob = 0.0\\n for z in six.iterkeys(self.latent_vars):\\n z_copy = copy(z, dict_swap, scope=scope)\\n p_log_prob += tf.reduce_sum(\\n self.scale.get(z, 1.0) * z_copy.log_prob(dict_swap[z]))\\n\\n for x in six.iterkeys(self.data):\\n if isinstance(x, ed.RandomVariable):\\n if dict_swap:\\n x_copy = copy(x, dict_swap, scope=scope)\\n else:\\n x_copy = x\\n p_log_prob += tf.reduce_sum(\\n self.scale.get(x, 1.0) * x_copy.log_prob(dict_swap[x]))\\n\\n reg_penalty = tf.reduce_sum(tf.losses.get_regularization_losses(scope=self.scope))\\n loss = -p_log_prob + reg_penalty\\n\\n grads = tf.gradients(loss, var_list)\\n grads_and_vars = list(zip(grads, var_list))\\n return loss, grads_and_vars\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35beed0bd8bd7829dd314d8ea1eb0e8d\",\n \"score\": \"0.5445362\",\n \"text\": \"def feature_learning_gradient_based(self, v, eta, fps, tau_s, numSamples, f_regularize=lambda C: 0.):\\n # Data contribution\\n if isinstance(self.sig_obs, (float, np.floating)):\\n sig_obs = self.sig_obs * np.ones(self.D * self.K)\\n else:\\n sig_obs = self.sig_obs\\n dCdt_data = (sig_obs**(-2))[:,None] * ( np.outer(v, self.mu) - self.C @ ( np.outer(self.mu, self.mu) + self.Sig ) )\\n dCdt_regularizer = (sig_obs**(-2))[:,None] * 1/numSamples * f_regularize(self.C)\\n dC = eta / (fps * numSamples * tau_s) * ( dCdt_data + dCdt_regularizer )\\n return self.C + dC\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efc3fdb00750d5c83aa83bfb5c41f217\",\n \"score\": \"0.54450005\",\n \"text\": \"def pgradient(self):\\n configs = self._configscurrent\\n nconf, nelec = configs.configs.shape[:2]\\n na, nb = len(self.a_basis), len(self.b_basis)\\n nup, ndown = self._mol.nelec\\n\\n # order of spin channel: upup,updown,downdown\\n d_all, ij = configs.dist.dist_matrix(configs.configs)\\n r_all = np.linalg.norm(d_all, axis=-1)\\n bvalues = np.stack([b.value(d_all, r_all) for b in self.b_basis], axis=-1)\\n inds = tuple(zip(*ij))\\n b_2d_values = np.zeros((nelec, nelec, nconf, nb))\\n for s, shape in enumerate([(nup, nup), (nup, ndown), (ndown, ndown)]):\\n b_2d_values[inds] = bvalues.swapaxes(0, 1)\\n\\n a = self.a_values\\n up, down = slice(0, nup), slice(nup, None)\\n c_ders = np.zeros((nconf, self._mol.natm, na, na, nb, 3))\\n einstr = \\\"inIk,jnIl,ijnm->nIklm\\\"\\n c_ders[..., 0] = np.einsum(einstr, a[up], a[up], b_2d_values[up, up])\\n c_ders[..., 1] = np.einsum(einstr, a[up], a[down], b_2d_values[up, down])\\n c_ders[..., 2] = np.einsum(einstr, a[down], a[down], b_2d_values[down, down])\\n c_ders += c_ders.swapaxes(2, 3)\\n\\n return {\\\"ccoeff\\\": 0.5 * c_ders}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e6b747e7ef5976647562955abcba7f6\",\n \"score\": \"0.5442318\",\n \"text\": \"def infer(self, potential, iterations, cf=True):\\n energy = tf.zeros(shape=(10,), dtype=self.dtype)\\n for i in tf.range(iterations):\\n if cf:\\n grd, energy = self.gradient_cf(potential)\\n else:\\n with tf.GradientTape(watch_accessed_variables=False) as tape:\\n tape.watch(self.trainable_variables) # only watch mu, sigma and rou\\n energy = self.bethe_free_energy(potential)\\n grd = tape.gradient(energy, self.trainable_variables)\\n\\n # umax = tf.reduce_max(tf.abs(grd[0]), axis=[1, 2, 3, 4, 5], keepdims=True)\\n # omax = tf.reduce_max(tf.abs(grd[1]), axis=[1, 2, 3, 4, 5], keepdims=True)\\n # pmax = tf.reduce_max(tf.abs(grd[2]), axis=[1, 2, 3, 4, 5], keepdims=True)\\n # self.mu.assign(tf.minimum(tf.maximum(self.mu - grd[0] * (self.lr / umax), -1.), 1.))\\n # self.sigma.assign(tf.minimum(tf.maximum(self.sigma - grd[1] * (self.lr / omax), 0.01), 10.))\\n # self.rou.assign(tf.minimum(tf.maximum(self.rou - grd[2] * (self.lr / pmax), -0.99), 0.99))\\n self.optimizer.apply_gradients(zip(grd, self.trainable_variables))\\n\\n if tf.equal(tf.math.mod(i, 50), 0) or tf.equal(i + 1, iterations):\\n tf.print(tf.strings.format('iter: {} dmu = {}, dsigma = {}, drou = {}, dw={}, Energy = {}', (\\n i, tf.reduce_mean(tf.abs(grd[0])), tf.reduce_mean(tf.abs(grd[1])), tf.reduce_mean(tf.abs(grd[2])),\\n tf.reduce_mean(tf.abs(grd[3])), tf.reduce_mean(energy))))\\n return energy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67398bc9cb64005c9b5a40cfdbc7bd67\",\n \"score\": \"0.54416275\",\n \"text\": \"def _estimate_gradient(self, points, values, prediction_points):\\n if self.surrogate is not None:\\n self.surrogate.fit(prediction_points, values)\\n self.surrogate.optimizations_number = 1\\n prediction_function = self.surrogate.predict\\n else:\\n from scipy.interpolate import LinearNDInterpolator\\n\\n prediction_function = LinearNDInterpolator(\\n prediction_points, values, fill_value=0\\n ).__call__\\n\\n gradient_values = gradient(\\n point=prediction_points,\\n runmodel_object=prediction_function,\\n order=\\\"first\\\",\\n df_step=self.step_size,\\n )\\n return gradient_values\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c132d4bff77dd4a495f9cb09f8ac34ae\",\n \"score\": \"0.543683\",\n \"text\": \"def fairness_parameter_gradient(self, **optimization_target_args):\\n if self.error_delta == 0.0:\\n return self.fairness_function(**optimization_target_args)\\n else:\\n return np.array([-self.fairness_function(**optimization_target_args) - self._error_delta,\\n self.fairness_function(**optimization_target_args) - self._error_delta]).squeeze()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e277bc497446bf29085892181028e77\",\n \"score\": \"0.5363549\",\n \"text\": \"def f_gradient(x, y, lbd, km):\\r\\n\\r\\n temp_vector = km.dot(x)\\r\\n n = len(y)\\r\\n\\r\\n grad_value = np.diag(-y).dot(km.dot(exp(-y * temp_vector)/(1 + exp(-y * temp_vector))))/n + lbd * temp_vector\\r\\n\\r\\n return grad_value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"580e5dfccc2eb296337da3f5f6efcb90\",\n \"score\": \"0.5363145\",\n \"text\": \"def get_gradient(self, label, sample):\\n self.predict(sample)\\n self.calc_delta(label)\\n self.calc_gradient()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb0fd286ba82d3765bc2abc48a773452\",\n \"score\": \"0.53611887\",\n \"text\": \"def gradient(self):\\n gradient = np.zeros(len(self.variables))\\n \\n if self.state1 < self.nStates-1:\\n gradient[self.state1] = self.transitCount / self.pi1\\n else:\\n for state in range((self.nStates-1)):\\n gradient[state] = -self.transitCount/self.pi1\\n \\n gradient[self.bivariateGradInd] = self.transitCount/self.theta\\n return gradient\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3da846e3a548fd4dc973da5835bfa5d\",\n \"score\": \"0.5356187\",\n \"text\": \"def process_gradient(self):\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4e3f5cd8f010ccfa9e57486a2596872\",\n \"score\": \"0.53547686\",\n \"text\": \"def critic_z_gradient_penalty(self, batch_size, y_true, y_pred):\\n alpha = tf.keras.backend.random_uniform((batch_size, 1, 1))\\n interpolated = (alpha * y_true) + ((1 - alpha) * y_pred)\\n\\n with tf.GradientTape() as gp_tape:\\n gp_tape.watch(interpolated)\\n # 1. Get the discriminator output for this interpolated image.\\n pred = self.critic_z(interpolated)\\n\\n # 2. Calculate the gradients w.r.t to this interpolated image.\\n grads = gp_tape.gradient(pred, [interpolated])[0]\\n norm = tf.sqrt(tf.reduce_sum(tf.square(grads), axis=[1, 2]))\\n gp = tf.reduce_mean((1.0 - norm) ** 2)\\n\\n return gp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00f9b0658a2fcd08f59a893ea6573524\",\n \"score\": \"0.5348646\",\n \"text\": \"def gradient(self, params):\\n return 10 * (params - 3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b7a96aee3fd64bf99edcd1ae8eb693c\",\n \"score\": \"0.5343812\",\n \"text\": \"def test_gradient(self):\\n discretization = GridWorld([[0, 1], [0, 1]], [2, 2])\\n delaunay = _Triangulation(discretization)\\n\\n points = np.array([[0, 0],\\n [1, 0],\\n [0, 1],\\n [1, 1]], dtype=np.int)\\n nodes = delaunay.discretization.state_to_index(points)\\n\\n # Simplex with node values:\\n # 3 - 1\\n # | \\\\ |\\n # 1 - 2\\n # --> x\\n\\n values = np.zeros(delaunay.nindex)\\n values[nodes] = [1, 2, 3, 1]\\n\\n test_points = np.array([[0.01, 0.01],\\n [0.99, 0.99]])\\n\\n true_grad = np.array([[1, 2], [-2, -1]])\\n\\n # Construct true H (gradient as function of values)\\n true_H = np.zeros((2 * delaunay.input_dim, delaunay.nindex))\\n\\n true_H[0, nodes[[0, 1]]] = [-1, 1]\\n true_H[1, nodes[[0, 2]]] = [-1, 1]\\n true_H[2, nodes[[2, 3]]] = [-1, 1]\\n true_H[3, nodes[[1, 3]]] = [-1, 1]\\n\\n # Evaluate gradient with and without values\\n H = delaunay.gradient_parameter_derivative(test_points).toarray()\\n delaunay.parameters = values\\n grad = delaunay.gradient(test_points)\\n\\n # Compare\\n assert_allclose(grad, true_grad)\\n assert_allclose(H, true_H)\\n assert_allclose(true_grad,\\n H.dot(values).reshape(-1, delaunay.input_dim))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22bf60ac4a91286535593dc3b2dce3fc\",\n \"score\": \"0.5337247\",\n \"text\": \"def grad_descent (x, y, learning): \\n\\n w = np.zeros(22)\\n converage=1\\n\\n for runs in range(30):\\n gradient = grad(w, x, y, 0)\\n w = w - (learning * gradient)\\n normalg= np.linalg.norm(gradient)\\n print(\\\"normalg: \\\", normalg)\\n if np.isinf(normalg):\\n print(\\\"normalg goes to inf, goning to break the loop.\\\")\\n break\\n if normalg <= converage:\\n print(\\\"normalg <= converage!!!\\\")\\n break\\n iteration_list.append(runs)\\n print(\\\"w: \\\",w )\\n print(\\\"i:\\\", runs)\\n return w\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5df40f294c48e7d39fce90ba15a62722\",\n \"score\": \"0.528242\",\n \"text\": \"def policy_gradient(state, weight):\\n # probabilities of actions\\n probs = policy(state, weight)\\n\\n # random action\\n action = np.random.choice(len(probs[0]), p=probs[0])\\n\\n # softmax gradient\\n # probs shape (1, 2)\\n # s shape (2, 1)\\n s = probs.reshape(-1, 1)\\n\\n # softmax matrix\\n softmax = np.diagflat(s) - s @ s.T\\n\\n # Take the obs for the action taken\\n dsoftmax = softmax[action, :]\\n\\n # Derivative of natural logarithm\\n dlog = dsoftmax / probs[0, action]\\n\\n gradient = state.T @ dlog[None, :]\\n\\n return action, gradient\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3eda0ad4deca20d60958f3d62c082e99\",\n \"score\": \"0.52776885\",\n \"text\": \"def bias_gradient_statistics(model, layer_name=None):\\n\\n return parameter_statistics(model, 'b', 'grad', layer_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"409e5c93318ab5cd95ee10d1445fd0e0\",\n \"score\": \"0.5273136\",\n \"text\": \"def variables_gradient(self, loss: tf.Tensor, state: np.ndarray, label: tf.Tensor) -> List:\\n variables = self.get_variables()\\n losses = []\\n for i, var in enumerate(variables):\\n new_vars = copy.copy(variables)\\n new_vars[i] = tf.add(var, self.g_epsilon)\\n self.set_variables(new_vars)\\n new_loss = self.state_to_loss(state, label)\\n losses.append(new_loss)\\n\\n self.set_variables(variables)\\n dy = [tf.subtract(x, loss) for x in losses]\\n grads = [tf.divide([y], self.g_epsilon) for y in dy]\\n return grads\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ca1475f01f1768b94cb23be1bd875a9\",\n \"score\": \"0.5272091\",\n \"text\": \"def _conditional_bias(forecast, reference, dim='svd', **metric_kwargs):\\n acc = _pearson_r(forecast, reference, dim=dim)\\n conditional_bias = acc - _std_ratio(forecast, reference, dim=dim) ** -1\\n return conditional_bias\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ea2a2bd926445a55193d5d1f2fcb84e\",\n \"score\": \"0.52589333\",\n \"text\": \"def rand_free_conf(self):\\n uniform_val = np.random.uniform(0, 1, self.state_dim)\\n node = self.threshold[:, 0]*(1-uniform_val) + self.threshold[:, 1]*uniform_val\\n return node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e456a997c8b1cfcf541bec152faa4691\",\n \"score\": \"0.52534866\",\n \"text\": \"def _gradient_penalty(self, real_data, generated_data):\\n discriminator = self\\n batch_size = real_data.size()[0]\\n\\n # Calculate interpolation\\n alpha = torch.rand(batch_size, 1)\\n alpha = alpha.expand_as(real_data)\\n if self.use_cuda:\\n alpha = alpha.cuda()\\n interpolated = alpha * real_data.data + (1 - alpha) * generated_data.data\\n interpolated = Variable(interpolated, requires_grad=True)\\n if self.use_cuda:\\n interpolated = interpolated.cuda()\\n\\n # Calculate probability of interpolated examples\\n prob_interpolated = discriminator(interpolated)\\n\\n # Calculate gradients of probabilities with respect to examples\\n gradients = torch_grad(outputs=prob_interpolated, inputs=interpolated,\\n grad_outputs=torch.ones(\\n prob_interpolated.size()).cuda() if self.use_cuda else torch.ones(\\n prob_interpolated.size()),\\n create_graph=True, retain_graph=True)[0]\\n\\n # Gradients have shape (batch_size, num_channels, img_width, img_height),\\n # so flatten to easily take norm per example in batch\\n gradients = gradients.view(batch_size, -1)\\n\\n # Derivatives of the gradient close to 0 can cause problems because of\\n # the square root, so manually calculate norm and add epsilon\\n\\n # gradients_norm = torch.sqrt(torch.sum(gradients ** 2, dim=1) + 1e-12)\\n\\n # Return gradient penalty\\n return self.LAMBDA * ((gradients.norm(2, dim=1) - 1) ** 2).mean()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fa793f6c99f14ebefee4834dcfe421f\",\n \"score\": \"0.5252034\",\n \"text\": \"def test_kernel_gradient_on_hyper_params():\\n gamma = 5\\n K, my = rbf_kernel(gamma, X, X, gradient_on_gamma=True)\\n gamma_ = gamma + 1e-3\\n K_ = rbf_kernel(gamma_, X, X)\\n numeric = (K_ - K) / 1e-3\\n assert_array_almost_equal(numeric, my, 3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50e0b85cbcdfdfc945fdb92344c28f88\",\n \"score\": \"0.5250229\",\n \"text\": \"def compute_gradient(theta, X, y, lambda_):\\n h = sigmoid(X @ theta)\\n l2 = np.append([0], ((lambda_ * theta[1:]) / len(y)))\\n return (X.T @ (h - y)) / len(y) + l2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77d5f45933aaebe24ee64fda6ce0b4f2\",\n \"score\": \"0.5237509\",\n \"text\": \"def gradient_duels_gen(self, num_feat, num_datapoints):\\n #n = 14 # number of datapoints\\n #x1 = np.linspace(self.Gridmin1, self.Gridmax1, n)[:, None] # Operating temp.\\n x1 = np.arange(self.Gridmin1, self.Gridmax1, self.temp_diff)[:,None]\\n \\n # sampling from indexes\\n indexes = np.arange(x1.shape[0])\\n \\n ind_samp1 = np.random.choice(indexes, size = 1)\\n x_samp1_01 = x1[ind_samp1]\\n \\n ind_samp2_all1 = np.random.choice(indexes, size = num_datapoints)\\n x_samp21 = x1[ind_samp2_all1]\\n x_samp1_n1 = x_samp21[:-1]\\n x_samp11 = np.append(x_samp1_01, x_samp1_n1)[:,None]\\n \\n u1, u1_der = objfunc.beta_utility_gen(x_samp11, self.l,\\n self.a, self.b)\\n u2, u2_der = objfunc.beta_utility_gen(x_samp21, self.l,\\n self.a, self.b) \\n \\n u2_der = u2_der[:,0] \\n y_der = np.zeros(u2_der.shape[0])\\n y_der[u2_der > 0] = 1.\\n \\n y_der[u2_der < 0] = -1.\\n\\n return (x_samp11, x_samp21, y_der, u1, u2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"988f0d739c253b24475a9d8fc0657b62\",\n \"score\": \"0.5225748\",\n \"text\": \"def compute_gradient_penalty(D, real_samples, fake_samples,index):\\n # Random weight term for interpolation between real and fake samples\\n # sample shape:(time_length, batch_size*scene_traj_num, dim) = (81, 128, 2)\\n real_samples = real_samples.permute(1,2,0)\\n fake_samples = fake_samples.permute(1,2,0)\\n # (batch_size*scene_traj_num,2,time_length)\\n alpha = Tensor(np.random.random((real_samples.size(0), 1, 1)))\\n # Get random interpolation between real and fake samples\\n interpolates = (alpha * real_samples + ((1 - alpha) * fake_samples)).requires_grad_(True)\\n\\n # scene_vel = diff(interpolates, 1)\\n # scene_acc = diff(interpolates, 2)\\n # interpolates = torch.cat((interpolates, scene_vel, scene_acc), 1)\\n interpolates = interpolates.permute(2, 0, 1)\\n\\n d_interpolates = D(interpolates,index)\\n out_shape = real_samples.size(0)\\n fake = Variable(Tensor(out_shape,1).fill_(1.0), requires_grad=False)\\n # Get gradient w.r.t. interpolates\\n # with torch.autograd.detect_anomaly():\\n gradients = autograd.grad(\\n outputs=d_interpolates,\\n inputs=interpolates,\\n grad_outputs=fake,\\n create_graph=True,\\n retain_graph=True,\\n only_inputs=True,\\n )[0]\\n gradients = gradients.contiguous().view(gradients.size(0), -1)\\n # b = torch.nn.utils.clip_grad_norm_(gradients, 10)\\n # GP\\n # gp = ((gradients.norm(2, dim=1) - 1) ** 2).mean()\\n # LP\\n # gp = (torch.clamp(gradients.norm(2, dim=1) - 1, 0, float(\\\"inf\\\")) ** 2).mean()\\n # wgan-div\\n gp = (gradients.norm(2, dim=1) ** 6).mean()\\n return gp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d34d0cb30407fa060d532a72478ce85\",\n \"score\": \"0.5211051\",\n \"text\": \"def variables_gradient_exact(self, state: np.ndarray, label: tf.Tensor) -> List:\\n variables = self.get_variables()\\n grads = []\\n for i, var in enumerate(variables):\\n new_vars_plus = copy.copy(variables)\\n new_vars_minus = copy.copy(variables)\\n\\n new_vars_plus[i] = tf.add(var, np.pi/2)\\n new_vars_minus[i] = tf.subtract(var, np.pi/2)\\n\\n self.set_variables(new_vars_plus)\\n loss_plus = self.state_to_loss(state, label)\\n\\n self.set_variables(new_vars_minus)\\n loss_minus = self.state_to_loss(state, label)\\n grad = tf.cast(tf.subtract(loss_plus, loss_minus), tf.float32)\\n grads.append(grad)\\n\\n self.set_variables(variables)\\n return grads\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45b69162d2419fb6a54b9823f2be7448\",\n \"score\": \"0.5210655\",\n \"text\": \"def compute_gradient_penalty(D, real_samples, fake_samples):\\n batch_size = real_samples.size(0)\\n lamda = 10 \\n alpha = torch.rand(batch_size,1)\\n \\n #Slicing only a size same as real_samples to avoid conflict with different train batch sizes \\n #fake_samples = fake_samples[:batch_size, :]\\n \\n #print('alpha {} real {} fake {}'.format(alpha.size(), real_samples.size(), fake_samples.size()))\\n if cuda:\\n alpha=alpha.cuda()\\n\\n interpolates = (alpha * real_samples.data) + ((1 - alpha) * fake_samples.data) #.requires_grad_(True)\\n interpolates = autograd.Variable(interpolates, requires_grad=True)\\n if cuda:\\n interpolates=interpolates.cuda()\\n \\n d_interpolates = D(interpolates)\\n d_interpolates = d_interpolates.view(-1,1) ### newly added line\\n \\n fake = Variable(Tensor(real_samples.shape[0], 1).fill_(1.0), requires_grad = True)\\n \\n # Get gradient w.r.t. interpolates\\n gradients = autograd.grad(\\n outputs=d_interpolates,\\n inputs=interpolates,\\n grad_outputs=fake,\\n create_graph=True, \\n retain_graph=True,\\n only_inputs=True,\\n )[0]\\n \\n gradient_penalty = (((gradients.norm(2, dim=1) - 1) ** 2)* lamda).mean()\\n return gradient_penalty\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c65cb52ea2592836b56d16ed0b3b758\",\n \"score\": \"0.5209864\",\n \"text\": \"def test_fully_observed_gradient(self):\\n weights = np.zeros(8 + 32)\\n learner = Learner(MatrixBeliefPropagator)\\n self.set_up_learner(learner, latent=False)\\n learner.start_time = time.time()\\n learner.set_regularization(0.0, 1.0)\\n gradient_error = check_grad(learner.subgrad_obj, learner.subgrad_grad, weights)\\n\\n # numerical_grad = approx_fprime(weights, learner.subgrad_obj, 1e-4)\\n # analytical_grad = learner.subgrad_grad(weights)\\n # plt.plot(numerical_grad, 'r')\\n # plt.plot(analytical_grad, 'b')\\n # plt.show()\\n\\n print(\\\"Gradient error: %f\\\" % gradient_error)\\n assert gradient_error < 1e-1, \\\"Gradient is wrong\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a08f918a59011eae30f07b4f69cb3a9\",\n \"score\": \"0.5208682\",\n \"text\": \"def get_gradients(model, epsilon, delta=0.01, max_norm=1.0):\\n scale = np.sqrt(2 * np.log(1.25 / delta)) / epsilon\\n\\n # Compute the gradient norm\\n total_norm = 0.0\\n for p in model.parameters():\\n if p.requires_grad:\\n total_norm += p.grad.data.norm(2).item() ** 2\\n total_norm = total_norm ** (1. / 2)\\n\\n # Clip and add noise\\n gradients = []\\n for p in model.parameters():\\n if p.requires_grad:\\n grad = p.grad.data.clone()\\n grad /= max(1.0, total_norm / max_norm)\\n grad += torch.normal(\\n torch.zeros(p.grad.data.size()), \\n torch.zeros(p.grad.data.size()) + scale * max_norm\\n )\\n gradients.append(grad)\\n return gradients\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12caef42fe697a2e7c051fb69f59993b\",\n \"score\": \"0.52036786\",\n \"text\": \"def return_weights(X, b, d, mincols):\\n max_iter = 100\\n threshold= .00001\\n w= np.empty((1,X.shape[1]))\\n w.fill(1)\\n p_1 = 1/(1+b*d)\\n n = X.shape[0]\\n E_old = 1\\n for i in np.arange(0, max_iter):\\n d = pairwise_distances(X,X, metric = weighted_euclidean, **{'weights':w})\\n grad_w = np.empty((1,X.shape[1]))\\n part_pq = -b/((1+b*d)**2)\\n p = 1/(1+b*d)\\n E = (2/(n*(n-1))) * np.sum(np.triu(.5*((p*(1-p_1) + p_1*(1-p))), 1))\\n if E_old - E < threshold:\\n break\\n E_old = E\\n part_eq = (1-2*p_1)\\n w_valid = np.where(w > 0)[1]\\n\\n if w_valid.shape[0] == mincols:\\n break\\n\\n for j in w_valid:\\n d_w = pairwise_distances(X, X, metric = single_delta, **{'F':j})\\n part_w = w[0, j]*(d_w)**2 / d\\n part_w = np.triu(part_w, 1)\\n grad_w_j = 1/(n*(n-1)) * part_eq * part_pq * part_w\\n grad_w_j = np.triu(grad_w_j, 1)\\n grad_w[ 0, j] = np.nansum(grad_w_j)\\n grad_w = grad_w * 50\\n w = w-grad_w\\n w = w.clip(min=0)\\n #if i %10 == 0 and i > 0:\\n #print(\\\"Iteration {} Finished\\\".format(i))\\n #print(\\\"Weights : {} \\\".format(w))\\n #print(\\\"Function Improvement : {}\\\".format(E_old - E))\\n\\n wmax = np.max(w)\\n w = w / wmax\\n print(\\\"{} Iterations Required\\\".format(i))\\n return w\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e72ad7b7d0ac7f4666cf382e59af9690\",\n \"score\": \"0.51941454\",\n \"text\": \"def evaluate_gradient(self, locations: np.ndarray) -> np.ndarray:\\n v1 = self.geological_feature_a.evaluate_gradient(locations)\\n # v1 /= np.linalg.norm(v1,axis=1)[:,None]\\n v2 = self.geological_feature_b.evaluate_gradient(locations)\\n # v2 /= np.linalg.norm(v2,axis=1)[:,None]\\n return np.cross(v1, v2, axisa=1, axisb=1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce5b823ebb84c377b232eb5b7e699c5a\",\n \"score\": \"0.5193387\",\n \"text\": \"def compute_gradient(outputs, perturbed_outputs, perturbation_values):\\n\\n if len(perturbed_outputs.shape) == 1:\\n perturbed_outputs.shape = (len(perturbed_outputs), 1)\\n (srom_size, dim) = perturbed_outputs.shape\\n\\n if len(outputs) != srom_size:\\n raise ValueError(\\\"# output samples must match perturbed outputs\\\")\\n\\n if len(perturbation_values) != dim:\\n raise ValueError(\\\"length of perturbation_values must match \\\"\\n \\\"dimension\\\")\\n\\n gradients = np.zeros((srom_size, dim))\\n\\n for i in range(dim):\\n grad = ((perturbed_outputs[:, i] - outputs.flatten())\\n / perturbation_values[i])\\n gradients[:, i] = grad\\n\\n return gradients\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1449140da04e71a188a0876ebc27713a\",\n \"score\": \"0.519283\",\n \"text\": \"def test_magnetic_pressure_gradient(DummyStellarator):\\n eq = Equilibrium.load(\\n load_from=str(DummyStellarator[\\\"output_path\\\"]), file_format=\\\"hdf5\\\"\\n )\\n\\n # partial derivatives wrt rho\\n num_rho = 110\\n grid = LinearGrid(NFP=eq.NFP, rho=num_rho)\\n drho = grid.nodes[1, 0]\\n data = eq.compute([\\\"|B|\\\", \\\"grad(|B|^2)_rho\\\"], grid=grid)\\n B2_r = np.convolve(data[\\\"|B|\\\"] ** 2, FD_COEF_1_4, \\\"same\\\") / drho\\n np.testing.assert_allclose(\\n data[\\\"grad(|B|^2)_rho\\\"][3:-2],\\n B2_r[3:-2],\\n rtol=1e-3,\\n atol=1e-3 * np.nanmean(np.abs(data[\\\"grad(|B|^2)_rho\\\"])),\\n )\\n\\n # partial derivative wrt theta\\n num_theta = 90\\n grid = LinearGrid(NFP=eq.NFP, theta=num_theta)\\n dtheta = grid.nodes[1, 1]\\n data = eq.compute([\\\"|B|\\\", \\\"grad(|B|^2)_theta\\\"], grid=grid)\\n B2_t = np.convolve(data[\\\"|B|\\\"] ** 2, FD_COEF_1_4, \\\"same\\\") / dtheta\\n np.testing.assert_allclose(\\n data[\\\"grad(|B|^2)_theta\\\"][2:-2],\\n B2_t[2:-2],\\n rtol=1e-2,\\n atol=1e-2 * np.nanmean(np.abs(data[\\\"grad(|B|^2)_theta\\\"])),\\n )\\n\\n # partial derivative wrt zeta\\n num_zeta = 90\\n grid = LinearGrid(NFP=eq.NFP, zeta=num_zeta)\\n dzeta = grid.nodes[1, 2]\\n data = eq.compute([\\\"|B|\\\", \\\"grad(|B|^2)_zeta\\\"], grid=grid)\\n B2_z = np.convolve(data[\\\"|B|\\\"] ** 2, FD_COEF_1_4, \\\"same\\\") / dzeta\\n np.testing.assert_allclose(\\n data[\\\"grad(|B|^2)_zeta\\\"][2:-2],\\n B2_z[2:-2],\\n rtol=1e-2,\\n atol=1e-2 * np.mean(np.abs(data[\\\"grad(|B|^2)_zeta\\\"])),\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a3d4847f2b628169a86a692ba0ce8fb\",\n \"score\": \"0.5191244\",\n \"text\": \"def _gradient(self, _x, _y):\\n ### YOUR CODE HERE\\n n_features,*other = _x.shape\\n v = np.matmul(self.W, _x)\\n q = self.softmax(v)\\n sfmx = np.reshape(q-_y,(self.k,1))\\n _g = np.matmul(sfmx, np.reshape(_x,(n_features,1)).T)\\n return _g\\n ### END YOUR CODE\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0bfd6fa164ac8dd7f861558a5348f7ef\",\n \"score\": \"0.51905155\",\n \"text\": \"def test_gradient(self):\\n weights = np.zeros(8 + 32)\\n learner = Learner(MatrixBeliefPropagator)\\n self.set_up_learner(learner, latent=True)\\n learner.start_time = time.time()\\n learner.set_regularization(0.0, 1.0)\\n gradient_error = check_grad(learner.subgrad_obj, learner.subgrad_grad, weights)\\n\\n # numerical_grad = approx_fprime(weights, learner.subgrad_obj, 1e-4)\\n # analytical_grad = learner.subgrad_grad(weights)\\n # plt.plot(numerical_grad, 'r')\\n # plt.plot(analytical_grad, 'b')\\n # plt.show()\\n\\n print(\\\"Gradient error: %e\\\" % gradient_error)\\n assert gradient_error < 1e-1, \\\"Gradient is wrong\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4aa9b2483cb517c0034ebd127de8257\",\n \"score\": \"0.5185588\",\n \"text\": \"def rosenbrock_criterion_and_gradient(params):\\n return rosenbrock_scalar_criterion(params), rosenbrock_gradient(params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e05fa8987a1fb67e9d06882bafe9dd49\",\n \"score\": \"0.5179254\",\n \"text\": \"def perceptron_gradient(i):\\n inputs = data[i]\\n input_len = len(inputs['tokens'])\\n gold_labels = inputs['gold_tags']\\n features = Features(inputs, feature_names)\\n\\n def score(cur_tag, pre_tag, i):\\n return parameters.dot_product(features.compute_features(cur_tag, pre_tag, i))\\n\\n tags = decode(input_len, tagset, score)\\n fvector = compute_features(tags, input_len, features) # Add the predicted features\\n #print('Input:', inputs) # helpful for debugging\\n #print(\\\"Predicted Feature Vector:\\\", fvector.fdict)\\n #print(\\\"Predicted Score:\\\", parameters.dot_product(fvector))\\n fvector.times_plus_equal(-1, compute_features(gold_labels, input_len, features)) # Subtract the features for the gold labels\\n #print(\\\"Gold Labels Feature Vector: \\\", compute_features(gold_labels, input_len, features).fdict)\\n #print(\\\"Gold Labels Score:\\\", parameters.dot_product(compute_features(gold_labels, input_len, features)))\\n return fvector\",\n \"title\": \"\"\n },\n {\n \"docid\": \"457ddb880d430d707902245cb167d2fa\",\n \"score\": \"0.5176523\",\n \"text\": \"def gradient_descent(features, values, theta, alpha, num_iterations):\\n\\n # Write code here that performs num_iterations updates to the elements of theta.\\n # times. Every time you compute the cost for a given list of thetas, append it \\n # to cost_history.\\n # See the Instructor notes for hints. \\n \\n m = len(values)\\n cost_history = []\\n\\n for i in range(num_iterations):\\n predicted_values = np.dot(features, theta)\\n theta = theta - alpha / m * np.dot((predicted_values - values), features)\\n \\n cost = compute_cost(features, values, theta)\\n cost_history.append(cost)\\n\\n return theta, pd.Series(cost_history) # leave this line for the grader\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7223d2c960671fd35aa3482f74ffce63\",\n \"score\": \"0.51736283\",\n \"text\": \"def gradient(self, predicted: Tensor, actual: Tensor) -> Tensor:\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce6f4331a827f4983971f8c4707036bc\",\n \"score\": \"0.516938\",\n \"text\": \"def compute_gradient_penalty(self,D, real_samples, fake_samples):\\n # Random weight term for interpolation between real and fake samples\\n # Get random interpolation between real and fake samples\\n interpolates = self.interpolate(real_samples,fake_samples).requires_grad_(True)\\n d_interpolates = D(interpolates)\\n# fake = nn.Parameter(nn.Tensor(real_samples.shape[0], 1).fill_(1.0), requires_grad=False)\\n # Get gradient w.r.t. interpolates\\n gradients = grad(\\n outputs=d_interpolates,\\n inputs=interpolates,\\n grad_outputs=torch.ones_like(d_interpolates).to(self.device),\\n create_graph=True,\\n retain_graph=True,\\n only_inputs=True,\\n )[0]\\n gradients = gradients.view(gradients.size(0), -1)\\n gradient_penalty = ((gradients.norm(2, dim=1) - 1) ** 2).mean()\\n return gradient_penalty\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4bd3107a8f569724bdda5e24c90d7d3\",\n \"score\": \"0.51625127\",\n \"text\": \"def get_gradient(f, r, h=1e-4, normalize=True):\\n N = r.size//3\\n grad = np.zeros(r.shape)\\n for i in range(N):\\n r_copy = r.copy()\\n x, y, z = r[i]\\n variations = np.array([[x+h, y, z],\\n [x-h, y, z],\\n [x, y+h, z],\\n [x, y-h, z],\\n [x, y, z+h],\\n [x, y, z-h]])\\n varied_potentials = []\\n for j in variations:\\n r_copy[i] = j\\n varied_potentials.append(f(r_copy))\\n dx = (varied_potentials[0] - varied_potentials[1])/(2*h)\\n dy = (varied_potentials[2] - varied_potentials[3])/(2*h)\\n dz = (varied_potentials[4] - varied_potentials[5])/(2*h)\\n grad[i] = [dx, dy, dz]\\n if normalize:\\n try:\\n grad = grad/(abs(np.max(grad)))\\n except FloatingPointError as e:\\n print(np.max(grad))\\n print(grad)\\n raise e\\n return grad\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8878e0f5fd1e720a383922db0b5bd042\",\n \"score\": \"0.51243705\",\n \"text\": \"def compute_gradient_for_all(self):\\r\\n\\r\\n # Compute theta * x (for clarification, * is DOT PRODUCT)\\r\\n sigma = self.theta.dot(self.x.T)\\r\\n\\r\\n # Sigmoid the result of theta * x\\r\\n sigmoid_v = np.vectorize(self.sigmoid) # So we can use our sigmoid()-method on vectors\\r\\n sigma = sigmoid_v(sigma) # Sigmoid all values\\r\\n\\r\\n # Subtract all values by corresponding label in the labels vector (self.y)\\r\\n sigma -= self.y\\r\\n\\r\\n # Now we must multiply by Xk^i\\r\\n sigma = self.x.T.dot(sigma)\\r\\n\\r\\n for k in range(self.FEATURES):\\r\\n self.gradient[k] = sigma[k] / self.TRAINING_DATAPOINTS\",\n \"title\": \"\"\n },\n {\n \"docid\": \"057a2981152acc851edc93a94c793277\",\n \"score\": \"0.5124171\",\n \"text\": \"def compute_gradient_penalty(D, real_samples, fake_samples):\\n # Random weight term for interpolation between real fake samples\\n alpha = torch.cuda.FloatTensor(np.random.random((real_samples.size(0), 1, 1, 1)))\\n # Get random interpolation between real and fake samples\\n interpolates = (alpha * real_samples + ((1 - alpha) * fake_samples)).reauires_grad_(True)\\n validity = D(interpolates)\\n fake = autograd.Variable(torch.cuda.FloatTensor(np.ones(validity.shape)), requires_grad=False)\\n # Get gradient w.r.t interpolates\\n gradients = autograd.grad(outputs=validity, inputs=interpolates,\\n grad_outputs=fake, create_graph=True, retain_graph=True,\\n only_inputs=True)[0]\\n gradients = gradients.view(gradients.size(0), -1)\\n # gradients_penalty = ((gradients.norm(2, dim=1) - 1)**2).mean()\\n gradients_penalty = (torch.clamp(gradients.norm(2, dim=1) - 1., min=0.)).mean()\\n return gradients_penalty\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fff856b440e782122aa6ab2e56f95726\",\n \"score\": \"0.51237553\",\n \"text\": \"def infer(self, potential, iterations, cf=True):\\n energy = tf.zeros(shape=(10,), dtype=self.dtype)\\n for i in tf.range(iterations):\\n if cf:\\n grd, energy = self.gradient_cf(potential)\\n else:\\n with tf.GradientTape(watch_accessed_variables=False) as tape:\\n tape.watch(self.trainable_variables)\\n energy = self.bethe_free_energy(potential)\\n grd = tape.gradient(energy, self.trainable_variables)\\n\\n self.optimizer.apply_gradients(zip(grd, self.trainable_variables))\\n\\n if tf.equal(tf.math.mod(i, 50), 0) or tf.equal(i + 1, iterations):\\n tf.print(tf.strings.format('iter: {} dmu = {}, dsigma = {}, dw={}, Energy = {}', (\\n i, tf.reduce_mean(tf.abs(grd[0])), tf.reduce_mean(tf.abs(grd[1])), tf.reduce_mean(tf.abs(grd[2])),\\n tf.reduce_mean(energy))))\\n return energy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc8669f470a231bcfd3c41a84ae3899f\",\n \"score\": \"0.51141465\",\n \"text\": \"def __gradient_descend(self, batch, labels):\\n\\n # current activations vectors, weights matrices and biases vectors\\n a_1, a_2, a_3, a_4, w_lj, w_ji, w_ik, b_2, b_3, b_4 = self.nn\\n\\n # temporary biases vectors and weights matrices\\n b_2t, b_3t, b_4t = numpy.zeros(self.l_2), numpy.zeros(self.l_3), numpy.zeros(L_4)\\n w_lj_t, w_ji_t, w_ik_t = numpy.zeros([L_1, self.l_2]), numpy.zeros([self.l_2, self.l_3]), \\\\\\n numpy.zeros([self.l_3, L_4]),\\n\\n # for every image in mini-batch we calculate gradient descent step approximation, and then average them\\n for sample in range(len(batch)):\\n\\n # compute desired output based on label\\n count += 1\\n tgt = numpy.zeros(10)\\n tgt[labels[sample]] = 1.0\\n self.evaluate_img(batch[sample])\\n\\n # partial derivatives for biases (in vector form)\\n a = 2 * (a_4 - tgt) * a_4 * (1 - a_4)\\n b = (2 * (a_4 - tgt) * a_4 * (1 - a_4)).dot(w_ik.T) * (a_3 * (1 - a_3))\\n c = (2 * (a_4 - tgt) * a_4 * (1 - a_4)).dot(w_ik.T) * (a_3 * (1 - a_3)).dot(w_ji.T) * (a_2 * (1 - a_2))\\n\\n b_2t += c\\n b_3t += b\\n b_4t += a\\n\\n # partial derivative for weight is just bias vector dot activation vector because of the Chain Rule\\n w_lj_t += c.reshape((-1, 1)).dot([a_1]).T\\n w_ji_t += b.reshape((-1, 1)).dot([a_2]).T\\n w_ik_t += a.reshape((-1, 1)).dot([a_3]).T\\n\\n # update weights in NN internal state\\n for x in zip(range(4, 10), [w_lj_t, w_ji_t, w_ik_t, b_2t, b_3t, b_4t]):\\n self.nn[x[0]] -= x[1] / len(batch) * self.eta\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72a3b094c4177afd136c739f28a73a71\",\n \"score\": \"0.51052195\",\n \"text\": \"def critic_gradient(self):\\n cinput = self.critic_Q.input\\n coutput = self.critic_Q.output\\n\\n # compute the gradient of the action with q value, dq/da.\\n action_grads = K.gradients(coutput, cinput[1])\\n\\n return K.function([cinput[0], cinput[1]], action_grads)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fecbfd37f5231cf150330d3d27230c31\",\n \"score\": \"0.51039535\",\n \"text\": \"def getChargeGradients(self):\\n grad_en, grad_orbs = self.getMOgradients() \\n # occupation numbers, 2 - doubly occupied, 0 - virtual orbital\\n f = self.dftb.getOccupation()\\n # density matrix depends only on occupied orbitals\\n orbs = self.dftb.getKSCoefficients()\\n occ_indx = np.where(f > 0.0)[0]\\n CdCdp = np.tensordot(grad_orbs[:,:,occ_indx], orbs[:,occ_indx], axes=(2,1))\\n # gradients of density matrix\\n gradP = 2 * (CdCdp + np.swapaxes(CdCdp, 1,2))\\n # overlap matrix \\n S = self.dftb.S\\n # gradients of overlap matrix\\n gradS = self.gradS\\n \\n # number of atoms\\n atomlist = self.dftb.getGeometry()\\n valorbs = self.dftb.valorbs\\n Nat = len(atomlist)\\n # charge gradients\\n dQdp = np.zeros((3*Nat,Nat))\\n\\n # difference density matrix relative to reference density matrix\\n PmP0 = self.dftb.P-self.dftb.P0\\n # iterate over atoms\\n mu = 0\\n for i,(Zi,posi) in enumerate(atomlist):\\n # iterate over orbitals on center i\\n for (ni,li,mi) in valorbs[Zi]:\\n # iterate over atoms\\n nu = 0\\n for j,(Zj,posj) in enumerate(atomlist):\\n # iterate over orbitals on center j\\n for (nj,lj,mj) in valorbs[Zj]:\\n dQdp[:,i] += gradP[:,mu,nu] * S[mu,nu] + PmP0[mu,nu] * gradS[:,mu,nu]\\n nu += 1\\n mu += 1\\n\\n return dQdp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0bb567bc45fafc45796d754a1aa57ba\",\n \"score\": \"0.5099989\",\n \"text\": \"def _erfcx_custom_gradient(x):\\n return _erfcx_naive(x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b603ba87dcb9a489b65272a10241c643\",\n \"score\": \"0.50907755\",\n \"text\": \"def pc_conditional_sampler(rng, score_state, labels):\\n # Initial sample\\n rng, step_rng = random.split(rng)\\n x = sde.prior_sampling(step_rng, shape)\\n\\n timesteps = jnp.linspace(sde.T, eps, sde.N)\\n\\n def loop_body(i, val):\\n rng, x, x_mean = val\\n t = timesteps[i]\\n vec_t = jnp.ones(shape[0]) * t\\n rng, step_rng = random.split(rng)\\n x, x_mean = conditional_corrector_update_fn(step_rng, score_state, x, vec_t, labels)\\n rng, step_rng = random.split(rng)\\n x, x_mean = conditional_predictor_update_fn(step_rng, score_state, x, vec_t, labels)\\n return rng, x, x_mean\\n\\n _, x, x_mean = jax.lax.fori_loop(0, sde.N, loop_body, (rng, x, x))\\n return inverse_scaler(x_mean if denoise else x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcf1793f515421aee8d64d02107897dc\",\n \"score\": \"0.5087558\",\n \"text\": \"def compute_gradient_penalty(self, D, real_samples, fake_samples):\\r\\n # Random weight term for interpolation between real and fake samples\\r\\n alpha = self.Tensor(np.random.random((real_samples.size(0), 1, 1)))\\r\\n # Get random interpolation between real and fake samples\\r\\n interpolates = (alpha * real_samples + ((1 - alpha) * fake_samples)).requires_grad_(True)\\r\\n d_interpolates = D(interpolates)\\r\\n fake = Variable(self.Tensor(real_samples.shape[0], 1).fill_(1.0), requires_grad=False)\\r\\n # Get gradient w.r.t. interpolates\\r\\n gradients = torch.autograd.grad(\\r\\n outputs=d_interpolates,\\r\\n inputs=interpolates,\\r\\n grad_outputs=fake,\\r\\n create_graph=True,\\r\\n retain_graph=True,\\r\\n only_inputs=True,\\r\\n )[0]\\r\\n gradients = gradients.view(gradients.size(0), -1)\\r\\n gradient_penalty = ((gradients.norm(2, dim=1) - 1) ** 2).mean()\\r\\n return gradient_penalty\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8e718e02490ca062c045e12062ea40e\",\n \"score\": \"0.5060968\",\n \"text\": \"def test_conditional_backward():\\n width = 5\\n height = 7\\n z_size = 2\\n batch_size = 3\\n\\n model = ConditionalCPPN(\\n ConditionalModelConfig(width=width,\\n height=height,\\n n_units_xyr=3,\\n n_hidden_units=[\\n 10,\\n 10,\\n ],\\n z_size=z_size,\\n in_width=64,\\n in_height=64,\\n in_channel=1,\\n use_batch_norm=False))\\n model.zerograds()\\n\\n # create inputs: inputs is dict whose key is batch index, and value is tuple of (x, z) for each index\\n x, z, inputs = gen_input_batch(batch_size, width, height, z_size)\\n c = chainer.Variable(get_dammy_input(batch_size, 64, 64, 1)) # init dammy conditional input\\n\\n # forward prop\\n y = model.forward(x, z, c)\\n\\n # taking loss at only first image\\n t = get_dammy_output(batch_size, width, height)\\n loss = F.mean_squared_error(y[0], t[0])\\n\\n g_x, g_z = chainer.grad((loss, ), inputs[0])\\n g_c = chainer.grad((loss, ), (c, ))[0].data\\n\\n assert g_c[0].sum() != 0.0, f\\\"gradient of c is zero\\\"\\n assert g_x.data.sum() != 0.0, f\\\"gradient of x is zero\\\"\\n assert g_z.data.sum() != 0.0, f\\\"gradient of z is zero\\\"\\n\\n g_x, g_z = chainer.grad((loss, ), inputs[1])\\n assert g_c[1].sum() == 0.0, f\\\"gradient of c is zero\\\"\\n assert g_x.data.sum() == 0.0, f\\\"gradient of x is zero\\\"\\n assert g_z.data.sum() == 0.0, f\\\"gradient of z is zero\\\"\\n\\n g_x, g_z = chainer.grad((loss, ), inputs[2])\\n assert g_c[2].sum() == 0.0, f\\\"gradient of c is zero\\\"\\n assert g_x.data.sum() == 0.0, f\\\"gradient of x is zero\\\"\\n assert g_z.data.sum() == 0.0, f\\\"gradient of z is zero\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54de286220e9ff41169c25d92ff536f9\",\n \"score\": \"0.5057102\",\n \"text\": \"def _CreateGradientProcess(self):\\n # We compute the scalar value gradient\\n if self.domain_size == 2:\\n self.local_gradient = KratosMultiphysics.ComputeNodalGradientProcess2D(self.main_model_part, self.scalar_variable, self.gradient_variable, KratosMultiphysics.NODAL_AREA)\\n else:\\n self.local_gradient = KratosMultiphysics.ComputeNodalGradientProcess3D(self.main_model_part, self.scalar_variable, self.gradient_variable, KratosMultiphysics.NODAL_AREA)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"846640f7c1bf03bc54edd095d18c52d5\",\n \"score\": \"0.5051862\",\n \"text\": \"def process_gradient(engine, learning_rate, parameters, grad_clip):\\n with tf.GradientTape() as tape:\\n engine.optical_system.update()\\n engine.clear_ray_history()\\n engine.ray_trace(3)\\n #output = tf.stack(\\n # [engine.finished_rays[\\\"y_end\\\"], engine.finished_rays[\\\"z_end\\\"]],\\n # axis=1\\n #)\\n #goal = engine.finished_rays[\\\"rank\\\"]\\n #goal *= - magnification\\n #error = tf.squared_difference(output - goal)\\n error = engine.finished_rays[\\\"y_end\\\"]**2 + engine.finished_rays[\\\"z_end\\\"]**2\\n \\n grad = tape.gradient(error, parameters)\\n \\n grad = tf.where(tf.math.is_finite(grad), grad, tf.zeros_like(grad))\\n grad = tf.clip_by_value(grad, -grad_clip, grad_clip)\\n grad *= learning_rate\\n \\n return grad, tf.reduce_sum(error)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd62e32fee294b780a644352244c2d78\",\n \"score\": \"0.50498974\",\n \"text\": \"def compute_gradient_penalty(D, real_samples, fake_samples):\\n # Random weight term for interpolation between real and fake samples\\n cuda = True if torch.cuda.is_available() else False\\n Tensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor\\n alpha = Tensor(np.random.random((real_samples.size(0), 1, 1, 1)))\\n # Get random interpolation between real and fake samples\\n interpolates = (alpha * real_samples + ((1 - alpha)\\n * fake_samples)).requires_grad_(True)\\n d_interpolates = D(interpolates)\\n fake = Variable(Tensor(real_samples.shape[0], 1, 62, 62).fill_(\\n 1.0), requires_grad=False)\\n# torch.Size([8, 6, 224, 224])\\n# torch.Size([8, 1, 26, 26])\\n # print(real_samples.shape)\\n # print(real_samples.size(0))\\n # print(alpha.shape)\\n # print(interpolates.shape)\\n # print(d_interpolates.shape)\\n # Get gradient w.r.t. interpolates\\n gradients = autograd.grad(\\n outputs=d_interpolates,\\n inputs=interpolates,\\n grad_outputs=fake,\\n create_graph=True,\\n retain_graph=True,\\n only_inputs=True,\\n )[0]\\n gradients = gradients.view(gradients.size(0), -1)\\n gradient_penalty = ((gradients.norm(2, dim=1) - 1) ** 2).mean()\\n return gradient_penalty\",\n \"title\": \"\"\n },\n {\n \"docid\": \"602855e85cf779a0938208dbb92d9a37\",\n \"score\": \"0.5044244\",\n \"text\": \"def make_gradient(bcs: Boundaries) -> Callable:\\n assert isinstance(bcs.grid, PolarGrid)\\n bcs.check_value_rank(0)\\n\\n # calculate preliminary quantities\\n dim_r = bcs.grid.shape[0]\\n r_min, _ = bcs.grid.axes_bounds[0]\\n dr = bcs.grid.discretization[0]\\n scale_r = 1 / (2 * dr)\\n \\n # prepare boundary values\\n boundary = bcs[0]\\n value_lower_bc = boundary.low.get_virtual_point_evaluator()\\n value_upper_bc = boundary.high.get_virtual_point_evaluator()\\n \\n @jit_allocate_out(out_shape=(2, dim_r))\\n def gradient(arr, out=None):\\n \\\"\\\"\\\" apply gradient operator to array `arr` \\\"\\\"\\\"\\n # no-flux at the origin \\n i = 0\\n if r_min == 0:\\n out[0, i] = (arr[1] - arr[0]) * scale_r\\n else:\\n arr_r_l = value_lower_bc(arr, (i,))\\n out[0, i] = (arr[1] - arr_r_l) * scale_r \\n out[1, i] = 0 # no angular dependence by definition\\n \\n for i in range(1, dim_r - 1): # iterate inner radial points\\n out[0, i] = (arr[i + 1] - arr[i - 1]) * scale_r\\n out[1, i] = 0 # no angular dependence by definition\\n\\n i = dim_r - 1\\n arr_r_h = value_upper_bc(arr, (i,))\\n out[0, i] = (arr_r_h - arr[i - 1]) * scale_r\\n out[1, i] = 0 # no angular dependence by definition\\n \\n return out\\n \\n return gradient # type: ignore\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58a378a2efe7033c47901d9629542330\",\n \"score\": \"0.50441283\",\n \"text\": \"def calculate_gradient(self):\\n\\n self.Foliations['G_x'] = np.sin(np.deg2rad(self.Foliations[\\\"dip\\\"])) * \\\\\\n np.sin(np.deg2rad(self.Foliations[\\\"azimuth\\\"])) * self.Foliations[\\\"polarity\\\"]\\n self.Foliations['G_y'] = np.sin(np.deg2rad(self.Foliations[\\\"dip\\\"])) * \\\\\\n np.cos(np.deg2rad(self.Foliations[\\\"azimuth\\\"])) * self.Foliations[\\\"polarity\\\"]\\n self.Foliations['G_z'] = np.cos(np.deg2rad(self.Foliations[\\\"dip\\\"])) * self.Foliations[\\\"polarity\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcd074383a3528488950dad3bf0eaf37\",\n \"score\": \"0.50408494\",\n \"text\": \"def model_parameter_gradient(self, **optimization_target_args):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d4494f9feea3ceabee0fcf820960aa4\",\n \"score\": \"0.5034822\",\n \"text\": \"def gradient_descent(features, values, theta, alpha, num_iterations):\\n \\n m = len(values)\\n cost_history = []\\n\\n for i in range(num_iterations):\\n predicted_values = np.dot(features, theta) \\n residuals = values - predicted_values\\n\\n # record cost\\n sum_of_square_residuals = np.square(residuals).sum()\\n cost = sum_of_square_residuals / (2*m)\\n cost_history.append(cost)\\n\\n # update theta\\n theta = theta + (alpha/m) * np.dot(residuals, features)\\n\\n return theta, pandas.Series(cost_history)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81bd5fed29e2e72239ea253dd9935f52\",\n \"score\": \"0.502759\",\n \"text\": \"def body_fn(w, should_continue, seed):\\n beta_seed, unif_seed, next_seed = samplers.split_seed(seed, n=3)\\n z = beta.sample(sample_shape=sample_batch_shape, seed=beta_seed)\\n # set_shape needed here because of b/139013403\\n tensorshape_util.set_shape(z, w.shape)\\n w = tf.where(should_continue,\\n (1. - (1. + b) * z) / (1. - (1. - b) * z),\\n w)\\n if not self.allow_nan_stats:\\n w = tf.debugging.check_numerics(w, 'w')\\n unif = samplers.uniform(\\n sample_batch_shape, seed=unif_seed, dtype=self.dtype)\\n # set_shape needed here because of b/139013403\\n tensorshape_util.set_shape(unif, w.shape)\\n should_continue = should_continue & (\\n concentration * w + dim * tf.math.log1p(-x * w) - c <\\n # Use log1p(-unif) to prevent log(0) and ensure that log(1) is\\n # possible.\\n tf.math.log1p(-unif))\\n return w, should_continue, next_seed\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1d765ec711865c886d7c3435cf5d785\",\n \"score\": \"0.50275344\",\n \"text\": \"def _apply_critic_gradients(self, gradient, trainable_variables):\\n self._critic_optimizer.apply_gradients(zip(gradient, trainable_variables))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fabd42e07a79dc815d895028d488c682\",\n \"score\": \"0.5025568\",\n \"text\": \"def gradients(self, states, actions):\\n\\n return self.sess.run(self.action_grads, feed_dict={\\n self.state: states,\\n self.action: actions\\n })[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd29def8876c8ee3aa7afa3b3f0f9155\",\n \"score\": \"0.50216705\",\n \"text\": \"def gradient_descent(features, values, theta, alpha, num_iterations):\\r\\n \\r\\n m = len(values)\\r\\n cost_history = []\\r\\n\\r\\n for i in range(num_iterations):\\r\\n # your code here\\r\\n predicted_values = np.dot(features, theta)\\r\\n theta = theta + (alpha/m)*np.dot((values - predicted_values),features)\\r\\n cost_history.append(compute_cost(features, values, theta))\\r\\n \\r\\n return theta, pandas.Series(cost_history)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1351bde491c56bcfc90f4ddf3d9e3b4a\",\n \"score\": \"0.50214595\",\n \"text\": \"def gradient_descent(features, values, theta, alpha, num_iterations):\\n\\n\\t# Write code here that performs num_iterations updates to the elements of theta.\\n\\t# times. Every time you compute the cost for a given list of thetas, append it \\n\\t# to cost_history.\\n\\t# See the Instructor notes for hints. \\n\\t\\n\\tcost_history = [] \\n\\t# print features.shape\\n\\t# print features.shape[0] #1157\\n\\t# print features.shape[1] #3\\n\\tm = features.shape[0]\\n\\t# print \\\"features=\\\", features\\n\\t# print \\\"values=\\\", values\\n\\t# print \\\"theta=\\\", theta\\n\\t# print \\\"alpha=\\\", alpha\\n\\t# print \\\"num_iterations=\\\", num_iterations\\n\\n\\t###########################\\n\\t### YOUR CODE GOES HERE ###\\n\\t###########################\\n\\t# print features.shape\\n\\t# print len(theta)\\n\\t# print range(1, features.shape[1])\\n\\tfor k in range(num_iterations): \\n\\t\\tcost_history.append(compute_cost(features, values, theta))\\n\\t\\t# print \\\"latest_cost=\\\", latest_cost\\n\\t\\t# print \\\"np.unique(np.dot(features, theta))=\\\", np.unique(np.dot(features, theta)) \\n\\t\\t# print \\\"np.dot(features, theta)=\\\", np.dot(features, theta)\\n\\t\\t# print \\\"values - np.dot(features, theta)=\\\", values - np.dot(features, theta)\\n\\t\\t# print \\\"theta=\\\", theta\\n\\t\\terrors = np.dot(features, theta)\\n\\t\\ttheta = theta + alpha/m * np.dot((values - errors), features)\\n\\n\\treturn theta, pandas.Series(cost_history) # leave this line for the grader\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b33a5e227bbc454b6924f7b8528327b\",\n \"score\": \"0.50211924\",\n \"text\": \"def gradient_descent(features, values, theta, alpha, num_iterations):\\n m = len(values)\\n cost_history = []\\n\\n for i in range(num_iterations):\\n predicted = np.dot(features,theta)\\n pred = np.dot((values-predicted),features)\\n theta = theta + (alpha/m)*pred\\n cost = compute_cost(features, values, theta)\\n cost_history.append(cost)\\n return theta, pandas.Series(cost_history)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf350017e9e8e8fe8e5814d573caf556\",\n \"score\": \"0.50205314\",\n \"text\": \"def get_gradient(x, x_start, y, theta, bias, fair_direction, regularizer):\\n prob = 1/(1+np.exp(-np.sum(x * theta)-bias))\\n # scalar = 2 * regularizer * np.sum(fair_direction * (x - x_start))\\n # grad = (prob - y) * theta + scalar * fair_direction\\n # return grad / np.linalg.norm(grad)\\n\\n # prob = 1/(1+np.exp(-np.dot(x, theta)-bias))\\n # return (prob - y) * theta + 2 * regularizer * np.array([(x[0] - x_start[0]), 0], dtype = 'float64')\\n\\n return (prob - y) * np.array([0, theta[1]], dtype='float64')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de85929aafdc1de2ed6eb5b251cf4d14\",\n \"score\": \"0.5020356\",\n \"text\": \"def _rbf_func(x):\\n N, D = x.shape\\n diff = x.unsqueeze(1) - x.unsqueeze(0) # [N, N, D]\\n dist_sq = torch.sum(diff**2, -1) # [N, N]\\n h, _ = torch.median(dist_sq.view(-1), dim=0)\\n if h == 0.:\\n h = torch.ones_like(h)\\n else:\\n h = h / max(np.log(N), 1.)\\n\\n kappa = torch.exp(-dist_sq / h) # [N, N]\\n kappa_grad = -2 * kappa.unsqueeze(-1) * diff / h # [N, N, D]\\n return kappa, kappa_grad\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5608ef800e443e4a702b23512a8141d3\",\n \"score\": \"0.501444\",\n \"text\": \"def reparameterized_target_gradient(self):\\n x = self.get_macrocycle_parameters()\\n reparameterized_f_g = self.target_gradient()\\n repar = self.reparameterize()\\n\\n if len(repar) != 0:\\n for i in range(self.nmp):\\n if repar[i].reparamed:\\n reparameterized_f_g[1][i] *= (x[i]+repar[i].offset)\\n\\n return reparameterized_f_g # dont need to do anything to f\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9cc7766eb443f43a3cfa4130b669bdb\",\n \"score\": \"0.50086755\",\n \"text\": \"def gradient_descent(self, training_data, epochs, eta, test_data):\\n\\n num_test = len(test_data)\\n for _ in xrange(epochs):\\n random.shuffle(training_data)\\n self.update_params(training_data, eta)\\n temp_test_accu = 1.0 * self.evaluate(test_data)/num_test\\n return temp_test_accu, self.lamb_init, self.bias_init, self.weight_init\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2a229127c2af5d0ba98c283eb49abf9\",\n \"score\": \"0.50034535\",\n \"text\": \"def critic_x_gradient_penalty(self, batch_size, y_true, y_pred):\\n alpha = tf.keras.backend.random_uniform((batch_size, 1, 1))\\n interpolated = (alpha * y_true) + ((1 - alpha) * y_pred)\\n\\n with tf.GradientTape() as gp_tape:\\n gp_tape.watch(interpolated)\\n # 1. Get the discriminator output for this interpolated image.\\n pred = self.critic_x(interpolated)\\n\\n grads = gp_tape.gradient(pred, [interpolated])[0]\\n norm = tf.sqrt(tf.reduce_sum(tf.square(grads), axis=[1, 2]))\\n gp = tf.reduce_mean((norm - 1.0) ** 2)\\n\\n return gp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"379a4cf4b3a55c785eebd6b7e9b4c11a\",\n \"score\": \"0.49968395\",\n \"text\": \"def gradients(self):\\n return self.optimiser.compute_gradients(self.loss)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8dce001a3688eed678fbab2dbcebc269\",\n \"score\": \"0.49932095\",\n \"text\": \"def gradientDescent(params,features,learning_rate,expectedValues):\\n error = 0\\n newParams = list(params)\\n\\n # PREVIOUS Implementation\\n # for param in range(len(params)):\\n # sumErrors = 0 # Keeps an accumulate of the errors\\n # acc = 0 # coefficient value\\n # for instance in range(len(features)):\\n # yhat = calculateHyp(params,features.iloc[instance])\\n # error = yhat - expectedValues.iloc[instance]\\n # acc = acc + (error * features.iloc[instance,param]) # Calculate sumatory of gradient descent formula\\n # # acc = acc + (learning_rate * (expectedValues.iloc[instance] - yhat) * yhat * (1 - yhat) * )\\n # newParams[param] = params[param] - learning_rate * (1/len(features) * acc) # Here is the formula taught for gradient descent, acc is the value obtained from the sumatory\\n \\n # Optimized Version\\n acc = 0\\n yHat = calculateHyp(params,features)\\n error = yHat - expectedValues\\n acc = numpy.dot(error,features) # numpy takes care of all of this by calculating the dot product, thus getting the five parameters\\n\\n newParams = params - learning_rate * (1 / len(features) * acc)\\n return newParams\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8261c2b33fd88366f9eb22dff8c5f7cb\",\n \"score\": \"0.4992119\",\n \"text\": \"def run_gradient_descent_iteration(X, Y, theta, alpha, lambda_factor, temp_parameter):\\n # print('X = ', X)\\n # print('Y = ', Y)\\n # print('theta = ', theta)\\n # print('alpha = ', alpha)\\n # print('lambda_factor = ', lambda_factor)\\n # print('temp_parameter = ', temp_parameter)\\n\\n p = np.array([]).astype(float)\\n p = compute_probabilities( X, theta, temp_parameter )\\n\\n # print(\\\"p = \\\", p)\\n\\n row_ind = Y.copy() #tendrá ? filas\\n col_ind = np.array(range(len(Y))) \\n data = np.array( [ 1 for i in range( len(Y) ) ] ) \\n y_equal_label = sparse.coo_matrix((data, (row_ind, col_ind)),shape=(len(theta),len(Y)))\\n\\n # print(\\\"y_equal_label = \\\",y_equal_label.toarray() )\\n\\n term1 = np.array([]).astype(float)\\n term1 = np.dot (\\n y_equal_label.toarray().astype(float) - p , X\\n )\\n\\n # print(\\\"term1 = \\\", term1)\\n\\n term2 = np.array([]).astype(float)\\n term2 = lambda_factor * theta\\n\\n # print(\\\"term2 = \\\", term2)\\n\\n derivative = np.array([]).astype(float)\\n derivative = term1 * (-1) / (temp_parameter *len(X)) + lambda_factor * term2\\n \\n # print(\\\"derivative = \\\", derivative)\\n\\n updated_theta = np.array([]).astype(float)\\n updated_theta = theta - alpha * derivative \\n\\n\\n return updated_theta#, \\\"Este es mi output\\\"\\n # raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5aa21a30400d72c5953e83b607bc41d\",\n \"score\": \"0.49900797\",\n \"text\": \"def step(self, closure=None):\\n loss = None\\n if closure is not None:\\n loss = closure()\\n\\n for group in self.param_groups:\\n weight_decay = group['weight_decay']\\n momentum = group['momentum']\\n threshold = group['threshold']\\n lr = group['lr']\\n \\n device = group['params'][0].grad.device\\n grad_norm = torch.norm(torch.stack([torch.norm(p.grad.detach()).to(device) \\\\\\n for p in group['params'] if p.grad is not None]))\\n if grad_norm.isnan() or grad_norm.isinf():\\n raise RuntimeError(f'The total norm for gradients from is non-finite, so it cannot be clipped.')\\n\\n if grad_norm > threshold:\\n actual_lr = lr * threshold / (grad_norm + 1e-8)\\n else:\\n actual_lr = lr\\n \\n for p in group['params']:\\n if p.grad is None:\\n continue\\n d_p = p.grad.data\\n if weight_decay != 0:\\n d_p.add_(p.data, alpha=weight_decay)\\n param_state = self.state[p]\\n if 'momentum_buffer' not in param_state:\\n buf = param_state['momentum_buffer'] = torch.zeros_like(p.data)\\n else:\\n buf = param_state['momentum_buffer']\\n buf.mul_(momentum).add_(d_p, alpha=actual_lr)\\n p.data.add_(-buf)\\n\\n return loss\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe0eed9a27ce50d493baabc187d16361\",\n \"score\": \"0.4989592\",\n \"text\": \"def compute_gradient_penalty(D, real_samples, fake_samples):\\n\\n # Random weight term for interpolation between real and fake samples\\n alpha = Tensor(np.random.random((real_samples.size(0), 1, 1, 1)))\\n # Get random interpolation between real and fake samples\\n interpolates = (alpha * real_samples + ((1 - alpha) * fake_samples)).requires_grad_(True)\\n d_interpolates = D(interpolates).reshape(-1, 1)\\n fake = Variable(Tensor(real_samples.shape[0], 1).fill_(1.0), requires_grad=False)\\n # Get gradient w.r.t. interpolates\\n gradients = autograd.grad(\\n outputs=d_interpolates,\\n inputs=interpolates,\\n grad_outputs=fake,\\n create_graph=True,\\n retain_graph=True,\\n only_inputs=True,\\n )[0]\\n gradients = gradients.view(gradients.size(0), -1)\\n gradient_penalty = ((gradients.norm(2, dim=1) - 1) ** 2).mean()\\n return gradient_penalty\",\n \"title\": \"\"\n },\n {\n \"docid\": \"656f848354a5f71361fb277ae72896fb\",\n \"score\": \"0.49882525\",\n \"text\": \"def gradient(C0,x,y):\\r\\n #concentration = C0/(4*np.pi*d*D*duT)*np.exp(-(x-dis2targ)**2/(400*D*duT*50)) #depends on diffusion conditions along x-axis\\r\\n concentration = C0 * np.exp(-(x-dis2targ)**2/(4*Dt)) #/(4*np.pi*Dt)**0.5\\r\\n return concentration\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64bda9d814679b4fd9497f6ad66b3088\",\n \"score\": \"0.49871475\",\n \"text\": \"def cell_gradient_BC(self):\\n if getattr(self, \\\"_cell_gradient_BC\\\", None) is None:\\n BC = self.set_cell_gradient_BC(self._cell_gradient_BC_list)\\n n = self.vnC\\n if self.dim == 1:\\n G = _ddxCellGradBC(n[0], BC[0])\\n elif self.dim == 2:\\n G1 = sp.kron(speye(n[1]), _ddxCellGradBC(n[0], BC[0]))\\n G2 = sp.kron(_ddxCellGradBC(n[1], BC[1]), speye(n[0]))\\n G = sp.block_diag((G1, G2), format=\\\"csr\\\")\\n elif self.dim == 3:\\n G1 = kron3(speye(n[2]), speye(n[1]), _ddxCellGradBC(n[0], BC[0]))\\n G2 = kron3(speye(n[2]), _ddxCellGradBC(n[1], BC[1]), speye(n[0]))\\n G3 = kron3(_ddxCellGradBC(n[2], BC[2]), speye(n[1]), speye(n[0]))\\n G = sp.block_diag((G1, G2, G3), format=\\\"csr\\\")\\n # Compute areas of cell faces & volumes\\n S = self.face_areas\\n V = (\\n self.aveCC2F * self.cell_volumes\\n ) # Average volume between adjacent cells\\n self._cell_gradient_BC = sdiag(S / V) * G\\n return self._cell_gradient_BC\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6024f484385b52ef55c712ddf276848c\",\n \"score\": \"0.498429\",\n \"text\": \"def CG(b, x0, TOLERANCE=1.0e-10, MAX_ITERATIONS=100):\\r\\n\\r\\n # Initializations\\r\\n x = x0\\r\\n d = -tf.gradient(Loss, x)\\r\\n r0 = b - finite_differences(x,d)\\r\\n\\r\\n\\r\\n # Start iterations\\r\\n for i in range(MAX_ITERATIONS):\\r\\n a = float(np.dot(d.T, r0) / np.dot(d.T, finite_differences(x, d)))\\r\\n x = x - a * gradient(x)\\r\\n\\r\\n ri = r0 - np.dot(finite_differences(x, d), d)\\r\\n\\r\\n # print i, np.linalg.norm(ri)\\r\\n # Checks stopping condition\\r\\n if np.linalg.norm(ri) < TOLERANCE:\\r\\n return x\\r\\n\\r\\n # Otherwise go on to find new direction\\r\\n b = float(np.dot(gradient(x).T, finite_differences(x, d)))\\r\\n d = - gradient(x) + b * d\\r\\n r0 = ri\\r\\n return x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13869890bc96c80cf348ea4b1d2e17e6\",\n \"score\": \"0.49820924\",\n \"text\": \"def conditional_predictor_update_fn(rng, state, x, t, labels):\\n score_fn = mutils.get_score_fn(sde, score_model, state.params_ema, state.model_state, train=False,\\n continuous=continuous)\\n\\n def total_grad_fn(x, t):\\n ve_noise_scale = sde.marginal_prob(x, t)[1]\\n return score_fn(x, t) + classifier_grad_fn(x, ve_noise_scale, labels)\\n\\n if predictor is None:\\n predictor_obj = NonePredictor(sde, total_grad_fn, probability_flow)\\n else:\\n predictor_obj = predictor(sde, total_grad_fn, probability_flow)\\n return predictor_obj.update_fn(rng, x, t)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9df0f4f63c89d6f519ac3c7557073791\",\n \"score\": \"0.49804133\",\n \"text\": \"def gradient(X, Y, Y_predicted):\\n Error = Y_predicted - Y # Calculate error\\n dW = np.dot(X.T, Error) # Compute derivative of error w.r.t weight, i.e., (target - output) * x\\n db = np.sum(Error) # Compute derivative of error w.r.t bias\\n return dW, db # return derivative of weight and bias\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a11a27d91183dc76912739a7982ded3d\",\n \"score\": \"0.49787378\",\n \"text\": \"def evaluate_gradient(self, evaluation_points, property_array):\\n if property_array.shape[0] != self.n_nodes:\\n logger.error(\\\"Property array does not match grid\\\")\\n raise ValueError(\\\"cannot assign {} vlaues to array of shape {}\\\".format(\\n property_array.shape[0], self.n_nodes))\\n \\n idc, inside = self.position_to_cell_corners(evaluation_points)\\n T = np.zeros((idc.shape[0], 3, 8))\\n T[inside, :, :] = self.get_element_gradient_for_location(evaluation_points[inside, :])[1]\\n # indices = np.array([self.position_to_cell_index(evaluation_points)])\\n # idc = self.global_indicies(indices.swapaxes(0,1))\\n # print(idc)\\n T[inside, 0, :] *= property_array[idc[inside, :]]\\n T[inside, 1, :] *= property_array[idc[inside, :]]\\n T[inside, 2, :] *= property_array[idc[inside, :]]\\n return np.array(\\n [np.sum(T[:, 0, :], axis=1), np.sum(T[:, 1, :], axis=1) ,\\n np.sum(T[:, 2, :], axis=1) ]).T\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66298dbd5963d8e321f1f2c765037516\",\n \"score\": \"0.497799\",\n \"text\": \"def gradient_descent(features, values, theta, alpha, num_iterations):\\n \\n m = len(values)\\n cost_history = []\\n\\n X = np.matrix(features)\\n Y = np.matrix(values).transpose()\\n for i in range(num_iterations):\\n TH = np.matrix(theta).transpose()\\n XTY = X*TH-Y\\n cost = compute_cost(features, values, theta)\\n cost_history.append(cost)\\n TH = TH - alpha/m * (X.transpose()*XTY)\\n theta = np.array(TH.transpose())[0]\\n return theta, pd.Series(cost_history)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c690b72f44c533cdd6a01526716c14f\",\n \"score\": \"0.49740028\",\n \"text\": \"def gradient_check_internal(f, x, h=1e-4, threshold=1e-5):\\n rndstate = random.getstate()\\n random.setstate(rndstate)\\n np.random.seed(0)\\n fx, grad = f(x) # Evaluate function value at original point\\n\\n # Iterate over all indices in x\\n it = np.nditer(x, flags=['multi_index'], op_flags=['readwrite'])\\n result = []\\n while not it.finished:\\n ix = it.multi_index\\n\\n x_step = x.copy()\\n x_step[ix] += h / 2\\n random.setstate(rndstate)\\n np.random.seed(0)\\n fx_pos, _ = f(x_step)\\n\\n x_step = x.copy()\\n x_step[ix] -= h / 2\\n random.setstate(rndstate)\\n np.random.seed(0)\\n fx_neg, _ = f(x_step)\\n\\n numgrad = (fx_pos - fx_neg) / h\\n\\n reldiff = abs(numgrad - grad[ix]) / max(1, abs(numgrad), abs(grad[ix]))\\n\\n if reldiff > threshold:\\n result.append((ix, grad[ix], numgrad))\\n\\n it.iternext() # Step to next dimension\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c50502964387b0c3def84180d9c11b65\",\n \"score\": \"0.49720913\",\n \"text\": \"def hyperparam_gradients(self, batch):\\n X, y, side_info = batch\\n batch_factor = self.n / np.shape(X)[-1]\\n\\n m_u, S_u = [self.dist_params['qu'][key] for key in ['mean', 'var']]\\n m_w = self.get_expected_w()\\n\\n def get_derivations(h):\\n \\\"\\\"\\\"\\n This debug function numerically derivates the ELBO w.r.t. the hyperparameters\\n with (ELBO(x + h) - ELBO(x))/h, where x is the hyperparameter\\n :param h: change for the derivation\\n :return: gradients w.r.t. the h.params\\n \\\"\\\"\\\"\\n old_kp = self.kernel_params\\n old_kernel = self.kernel\\n res = {}\\n for key in sorted(self.kernel_params.keys()) + [None]:\\n if key is not None and self.kernel_params[key] is None:\\n # This means that this Kernel is not used\\n continue\\n new_kps = old_kp.copy()\\n\\n if key is not None:\\n new_kps[key] += h\\n else:\\n key = 'old'\\n self.kernel_params = new_kps\\n self.kernel = kernels.Kernel_Handler(**new_kps)\\n self.update_kernel()\\n c = {}\\n if self.omega_gradients:\\n self.local_params = self.update_local_params(X, y, side_info=side_info)\\n c['dc'] = np.array(self.local_params)\\n Theta = np.diag(self.theta(c['dc']))\\n elbo = self.calc_ELBO(X, y, side_info)\\n c['L'] = sum(elbo)\\n c['L1'] = elbo[0]\\n c['KL_u'] = elbo[1]\\n c['KL_w'] = elbo[2]\\n c['Kmm'] = self.Kmm\\n c['Knm'] = self.get_Kmn(X, side_info).T\\n if side_info is None:\\n c['Knn'] = self.kernel(X.T)\\n else:\\n c['Knn'] = self.kernel(X.T, side_X=side_info.T)\\n c['Kmminv'] = self.Kmm_inv\\n c['KnmKmminv'] = np.dot(c['Knm'], self.Kmm_inv)\\n c['KnmKmminvKmn'] = multi_dot([c['Knm'], self.Kmm_inv, c['Knm'].T])\\n c['ykmu'] = multi_dot([y.T, c['Knm'], self.Kmm_inv, m_u])\\n c['trthk'] = np.trace(multi_dot([Theta, c['Knn'] - multi_dot([c['Knm'], self.Kmm_inv, c['Knm'].T])]))\\n c['trkmntknmsu'] = np.trace(multi_dot([self.Kmm_inv, c['Knm'].T, Theta, c['Knm'], self.Kmm_inv, S_u]))\\n c['mk+xwth'] = self.sh.multi_dot([(multi_dot([c['Knm'], self.Kmm_inv, m_u]) + self.sh.dot(X.T, m_w)).T,\\n Theta,\\n self.sh.multi_dot([c['Knm'], self.Kmm_inv, m_u]) + self.sh.dot(X.T,\\n m_w)])\\n c['trks'] = np.trace(np.dot(self.Kmm_inv, S_u))\\n c['mkm'] = multi_dot([m_u.T, self.Kmm_inv, m_u])\\n c['logk'] = self.sh.logdet(self.Kmm)\\n c['theta_diag'] = np.array(self.theta(c['dc']))\\n c['KL_omega'] = np.array((sum(\\n [ci / 4 * np.tanh(ci / 2) for i, ci in enumerate(c['dc'])]))\\n - sum([core.math.logcosh(ci / 2) for ci in c['dc']]))\\n\\n res[key] = c\\n self.kernel = old_kernel\\n return {var_key: {key: (res[var_key][key] - res['old'][key]) / h for key in res['old']} for var_key in res}\\n\\n cs = self.local_params\\n Theta = self.sh.diag(self.theta(cs))\\n\\n #Getting the kernel gradients\\n if side_info is not None:\\n dKmms = self.kernel.gradients(self.Z.T, side_X=self.Z_side.T)\\n dKnms = self.kernel.gradients(X.T, self.Z.T, side_X=side_info.T, side_Y=self.Z_side.T)\\n dKnns = self.kernel.gradients(X.T, side_X=side_info.T)\\n Kmn_prime, Kmn = self.get_Kmn_prime_Kmn(X, side_info=side_info)\\n else:\\n dKmms = self.kernel.gradients(self.Z.T)\\n dKnms = self.kernel.gradients(X.T, self.Z.T)\\n dKnns = self.kernel.gradients(X.T)\\n Kmn_prime, Kmn = self.get_Kmn_prime_Kmn(X)\\n dkmminvs = {key: - multi_dot([self.Kmm_inv, dKmms[key], self.Kmm_inv]) for key in dKnns}\\n dknmkmminvs = {\\n key: np.dot(dKnms[key], self.Kmm_inv) - multi_dot([Kmn.T, self.Kmm_inv, dKmms[key], self.Kmm_inv])\\n for key in dKnns}\\n dknmkmminvkmns = {key: 2 * multi_dot([dKnms[key], self.Kmm_inv, Kmn])\\n - multi_dot([Kmn.T, self.Kmm_inv, dKmms[key], self.Kmm_inv, Kmn])\\n for key in dKnns}\\n tanhs = [np.tanh(c / 2) for c in cs]\\n if self.omega_gradients:\\n dcs = self.local_parameter_gradients(dKnms, dkmminvs,\\n {key: np.diag(dKnns[key]) - np.diag(dknmkmminvkmns[key]) for key in\\n dKnns},\\n X, Kmn, Kmn_prime=Kmn_prime)\\n\\n dtheta_diag = self.theta_gradients(cs)\\n dTheta = {key: np.asarray([dcs[key][i] * dtheta_diag[i] for i in range(len(cs))]) for key in dKnns}\\n K_tilde = self.get_K_tilde(X, Kmn, side_info=side_info)\\n\\n derivations = {}\\n c_diffs = {}\\n m_eps = multi_dot([Kmn.T, self.Kmm_inv, m_u])\\n Xw = self.sh.dot(X.T, m_w)\\n xwkmu = m_eps + Xw\\n\\n # Calculating the gradients, w.r.t. the different hyperparameters\\n for key in dKnns:\\n if self.epoch_count < self.ls_head_start and key in ['rbf', 'lin']:\\n continue\\n\\n if core.math.debug():\\n c = {}\\n c['ykmu'] = multi_dot([y.T, dknmkmminvs[key], m_u])\\n c['trthk'] = self.sh.trace(self.sh.dot(Theta, dKnns[key])) \\\\\\n - self.sh.trace(self.sh.dot(Theta, dknmkmminvkmns[key]))\\n c['trkmntknmsu'] = self.sh.trace(2 * self.sh.multi_dot([Kmn_prime, Theta, dknmkmminvs[key], S_u]))\\n c['mk+xwth'] = 2 * self.sh.to_dense(self.sh.multi_dot([xwkmu.T,\\n Theta, dknmkmminvs[key], m_u]))\\n c['KL_omega'] = 0\\n if self.omega_gradients:\\n c['trthk'] += self.sh.trace(self.sh.multiply(dTheta[key], K_tilde))\\n c['trkmntknmsu'] += self.sh.trace(\\n self.sh.multi_dot([self.sh.multiply(Kmn_prime, dTheta[key]), Kmn_prime.T, S_u]))\\n c['mk+xwth'] += self.sh.to_dense(self.sh.dot(self.sh.multiply(xwkmu.T, dTheta[key]), xwkmu))\\n c['KL_omega'] = -sum([dcs[key][i] * (.25 * tanhs[i] * (1 + ci / 2 * tanhs[i]) - ci / 8)\\n for i, ci in enumerate(cs)])\\n\\n c['trks'] = np.trace(multi_dot([dkmminvs[key], S_u]))\\n c['mkm'] = multi_dot([m_u.T, dkmminvs[key], m_u])\\n c['logk'] = np.trace(np.dot(self.Kmm_inv, dKmms[key]))\\n\\n derivations[key] = multi_dot([y.T, dknmkmminvs[key], m_u])\\n derivations[key] -= self.sh.trace_dot(Theta, dKnns[key])\\n derivations[key] += self.sh.trace_dot(Theta, dknmkmminvkmns[key])\\n\\n derivations[key] -= self.sh.trace(2 * self.sh.multi_dot([Kmn_prime, Theta, dknmkmminvs[key], S_u]))\\n derivations[key] -= (2 * self.sh.to_dense(self.sh.multi_dot([xwkmu.T,\\n Theta, dknmkmminvs[key], m_u])))\\n if self.omega_gradients:\\n derivations[key] -= self.sh.trace(\\n self.sh.multi_dot([np.multiply(Kmn_prime, dTheta[key]), Kmn_prime.T, S_u]))\\n derivations[key] -= np.sum(np.multiply(dTheta[key], np.diag(K_tilde)))\\n derivations[key] -= self.sh.to_dense(self.sh.dot(self.sh.multiply(m_eps.T, dTheta[key]), m_eps))\\n derivations[key] -= 2 * self.sh.to_dense(self.sh.dot(self.sh.multiply(m_eps.T, dTheta[key]), Xw))\\n derivations[key] -= self.get_expected_wXThetaXw(Xw, X, dTheta[key])\\n derivations[key] *= 1 / 2\\n if self.omega_gradients:\\n derivations[key] -= sum([dcs[key][i] * (.25 * tanhs[i] * (1 + ci / 2 * tanhs[i]) - ci / 8)\\n for i, ci in enumerate(cs)])\\n\\n derivations[key] *= batch_factor\\n if core.math.debug(): c['L1'] = derivations[key].copy()\\n derivations[key] -= 1 / 2 * self.sh.trace_dot(dkmminvs[key], S_u)\\n derivations[key] -= 1 / 2 * multi_dot([m_u.T, dkmminvs[key], m_u])\\n derivations[key] -= 1 / 2 * self.sh.trace_dot(self.Kmm_inv, dKmms[key])\\n derivations[key] = self.sh.to_dense(derivations[key])\\n if core.math.debug():\\n c['KL_u'] = derivations[key] - c['L1']\\n c_diffs[key] = c\\n\\n if core.math.debug():\\n # Checking if the calculated and numerical gradients are similar\\n h = 1E-3\\n real_devt = get_derivations(h)\\n print()\\n for key in c_diffs:\\n print(key, real_devt[key]['L'], derivations[key],\\n '--------------------------------', derivations[key] / real_devt[key]['L'], 'diff:',\\n abs(derivations[key] - real_devt[key]['L']))\\n if not 0.97 < derivations[key] / real_devt[key]['L'] < 1.03 \\\\\\n and abs(derivations[key] - real_devt[key]['L']) > 1E-4:\\n print('AYAYAY calc -real - calc/real')\\n for key2 in c_diffs[key]:\\n if not (abs(real_devt[key][key2] - c_diffs[key][key2]) < 1E-10\\n or 0.95 < real_devt[key][key2] / c_diffs[key][key2] < 1.05):\\n print(key2, real_devt[key][key2], c_diffs[key][key2], c_diffs[key][key2] /\\n self.sh.to_dense(real_devt[key][key2]))\\n\\n return derivations\",\n \"title\": \"\"\n },\n {\n \"docid\": \"972e69d8dd2bab3cecf7147b30feaec4\",\n \"score\": \"0.49636534\",\n \"text\": \"def _gradient_clipping(self, grad, var, non_zero, eps=1e-3, threshold=1e-2):\\n\\n norm_grad = array_ops.ones_like(var) * non_zero(self._axis_aware_euclidian_norm(grad))\\n norm_var = math_ops.maximum(self._axis_aware_euclidian_norm(var), eps)\\n dynamic_threshold = threshold * (norm_var / norm_grad)\\n return array_ops.where(dynamic_threshold < 1., grad * dynamic_threshold, grad)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"671f29a0aa721b80af35f52b1629b216\",\n \"score\": \"0.4958247\",\n \"text\": \"def get_activations_and_gradient(self, z):\\n a = self.activate(z)\\n return a, np.multiply(a, 1 - a)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1e1dea42461e12a2d26f1ccf9d469cd\",\n \"score\": \"0.495605\",\n \"text\": \"def do_reinforce_modified_policy_gradient_bias_subtracted(self, iterations=200, batch_size=1500, gamma=0.9,\\n learning_rate=3e-4, init_variance=0.01):\\n average_returns = [0] * iterations\\n optimizer = torch.optim.Adam(self.policy_network.parameters(), lr=learning_rate)\\n\\n variance = torch.nn.parameter.Parameter(torch.ones(2) * init_variance) # for two dimensions\\n\\n b = 0 # Subtraction co-efficient\\n std_dev_b = 1.0 # Division co-efficient\\n\\n for iteration in range(iterations): # Updated once per loop\\n net_reward = 0.0\\n episodes = 0\\n loss = 0.0\\n step = 0\\n prev_state = self.env.reset()\\n\\n G_tau = []\\n current_trajectory = {\\\"log_probabilities\\\": [], \\\"rewards\\\": []}\\n\\n while step < batch_size: # collection of data for batch size (say 500 steps)\\n\\n mean = self.policy_network(prev_state) # Forward pass\\n\\n assert mean.shape == (2,), f\\\"{mean.shape}\\\"\\n assert -2.00 <= sum(mean) <= 2.00, f\\\"{sum(mean)}\\\"\\n\\n covariance = torch.diag(abs(variance) + 1e-3 * torch.ones(2))\\n # to avoid covariance going to zero! or negative\\n probable_actions = MultivariateNormal(loc=mean, covariance_matrix=covariance)\\n action = probable_actions.sample() # Choose a 2-D vector for action\\n\\n current_state, reward, done, _ = self.env.step(action)\\n\\n current_trajectory[\\\"log_probabilities\\\"].append(probable_actions.log_prob(action))\\n current_trajectory[\\\"rewards\\\"].append(reward)\\n\\n prev_state = current_state\\n\\n if done or step == batch_size - 1: # episode terminated\\n T = len(current_trajectory[\\\"rewards\\\"]) # Total steps\\n # For calculating b\\n for t in range(T):\\n discounted_reward = gamma ** (T - t) * current_trajectory[\\\"rewards\\\"][t]\\n G_tau.append(discounted_reward)\\n\\n discounted_reward = sum(current_trajectory[\\\"rewards\\\"][t] * (gamma ** t) for t in\\n range(len(current_trajectory[\\\"rewards\\\"])))\\n\\n objective_over_all_episodes = 0.0\\n for t in range(T):\\n summed_future_reward = 0.0\\n for t_bar in range(t, T):\\n summed_future_reward += (gamma ** (t_bar - t)) * (current_trajectory[\\\"rewards\\\"][t_bar])\\n objective_over_all_episodes += current_trajectory[\\\"log_probabilities\\\"][t] * ((summed_future_reward - b)/std_dev_b)\\n\\n loss += objective_over_all_episodes\\n net_reward += discounted_reward\\n current_trajectory = {\\\"log_probabilities\\\": [],\\n \\\"rewards\\\": []} # Resetting for collecting next episode data\\n prev_state = self.env.reset()\\n episodes += 1\\n\\n step += 1\\n\\n # End of say, 500 steps, update policy\\n optimizer.zero_grad() # Clear gradients, to prevent accumulation\\n loss = -1 * loss * (1 / episodes)\\n # Objective function, -1 => Gradient Ascent (Torch does descent by default)\\n loss.backward() # Calculate gradient\\n optimizer.step() # Update neural net (aka policy)\\n average_returns[iteration] = net_reward / episodes # For plotting later\\n\\n assert len(G_tau) == batch_size, f\\\"{batch_size, len(G_tau)}\\\"\\n b = sum(G_tau) / batch_size\\n std_dev_b = np.std(G_tau)\\n\\n # # DEBUG\\n print(f\\\"Iteration: {iteration}, \\\"\\n f\\\"Average Return : {average_returns[iteration]}, \\\"\\n f\\\"Episodes: {episodes}\\\")\\n print(f\\\"Avg loss : {loss}\\\")\\n\\n self.plot_average_returns(average_returns, title=f\\\"Average Returns | Batch Size: {batch_size}\\\")\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":892,"cells":{"query_id":{"kind":"string","value":"32f78e2a0364d8bb29944dbe2da16fc5"},"query":{"kind":"string","value":"Tracker for next neuron id in the given genome"},"positive_passages":{"kind":"list like","value":[{"docid":"dedcb910b62f8b3963721ac1eaea7b52","score":"0.8468444","text":"def next_nid(genome):\n nid = genome['last_neuron'] + 1\n genome['last_neuron'] = nid\n return nid","title":""}],"string":"[\n {\n \"docid\": \"dedcb910b62f8b3963721ac1eaea7b52\",\n \"score\": \"0.8468444\",\n \"text\": \"def next_nid(genome):\\n nid = genome['last_neuron'] + 1\\n genome['last_neuron'] = nid\\n return nid\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"fcf8bdb7177722960dff82a8755c738f","score":"0.59789985","text":"def next_id(self):\n nid = self._next_id\n self._next_id = self._next_id + 1\n return nid","title":""},{"docid":"a3d6cfa9e8896e9291b73b372ea92d75","score":"0.59274477","text":"def nextId(self):\r\n \r\n pass","title":""},{"docid":"5432564591795c1acdb3b5fadad12f5f","score":"0.58401716","text":"def get_next_unique_id(self) -> str:","title":""},{"docid":"5a201d394e803f8d5555fefda93fcf8e","score":"0.5815166","text":"def get_next_id(self) -> int:\r\n pass","title":""},{"docid":"711a98274259de153db0e69fbce70661","score":"0.58012456","text":"def next_mcid(self):\n\tresult = self.next()\n\n\tif self.count == 0:\n\t self.save()\n\t self.count = self.iterations\n\telse:\n\t self.count -= 1\n\n\treturn result","title":""},{"docid":"94646c06b3aa9b19d8b7d24cd3e875c4","score":"0.5765719","text":"def _get_next_id(self):\n if self.sensors:\n next_id = max(self.sensors.keys()) + 1\n else:\n next_id = 1\n if next_id <= 254:\n return next_id","title":""},{"docid":"2647d544e0f3efa7aa62c2668a674069","score":"0.5743264","text":"def next_generation(self):\n\t\tchild_pop = [copy.deepcopy(self.elite_ind)]\n\t\t\n\t\t# Perform tournament selection.\n\t\tfor i in range(len(self.genomes)-1):\n\t\t\ttourn = random.sample(self.genomes,2)\n\t\t\tif tourn[0]['fitness'] < tourn[1]['fitness']:\n\t\t\t\tchild_pop.append(copy.deepcopy(tourn[0]))\n\t\t\telse:\n\t\t\t\tchild_pop.append(copy.deepcopy(tourn[1]))\n\t\t\tchild_pop[-1]['id'] = self.child_id\n\t\t\tself.child_id += 1\n\n\t\t\n\t\t#Crossover\n\t\tchild_pop = single_point_crossover(child_pop, cross_over_prob)\n\n\t\t# Mutate genes in the child genomes.\n\t\tchild_pop = random_value_mutation(child_pop, mutation_prob)\n\t\t\n\n\t\tself.genomes = child_pop\n\t\tself.id_map = {k:v for k,v in zip([x['id'] for x in self.genomes],[i for i in range(self.pop_size)])}","title":""},{"docid":"6f38b7e6e15a59f8cf539fcbbeeafe5a","score":"0.5736271","text":"def _get_next_id(self):\n # type: () -> int\n ret = self._next_id\n self._next_id = ret + 1\n return ret","title":""},{"docid":"37634bb616b66f045df9680feeae7e15","score":"0.5727054","text":"def _get_next_id(self) -> int:\n ret = self._next_id\n self._next_id = ret + 1\n return ret","title":""},{"docid":"40a299ce12e16024815467a07dfb0c1b","score":"0.5615426","text":"def _next_id():\n global iid\n with iid_clock:\n result = iid\n iid = iid + 1\n return result","title":""},{"docid":"56aa3c0e549c48cbda6d3f2f8fbffd99","score":"0.556686","text":"def _next_id(self):\n self._id += 1\n return self._id","title":""},{"docid":"85adb3359737c232f867186583565de6","score":"0.55654854","text":"def get_next_id(self):\n return None","title":""},{"docid":"85adb3359737c232f867186583565de6","score":"0.55654854","text":"def get_next_id(self):\n return None","title":""},{"docid":"99f4a68fa89f59e504394e9a4acedeff","score":"0.55591255","text":"def get_next_individual(self):\n pass","title":""},{"docid":"4dae28d305f3ef0ea65bae77858d9abb","score":"0.5550575","text":"def get_new_network_id(db):","title":""},{"docid":"280681a733c378915ac4d33e3ebd1648","score":"0.5548189","text":"def next_id(cls, sess):\n current = sess.query(func.max(cls.id)).one()[0] or -1\n return current + 1","title":""},{"docid":"13de5cd23b40fdb5369784b153b638ca","score":"0.55082417","text":"def init_individual():\n genome = create_genome()\n\n # Create i/p and o/p neurons\n nid = 0\n for i in range(INPUTS):\n neuron = create_neuron(layer=Layer.INPUT)\n neuron['id'] = nid\n genome['neurons'][nid] = neuron\n genome['ip_neurons'].append(nid)\n nid += 1\n\n for i in range(OUTPUTS):\n neuron = create_neuron(layer=Layer.OUTPUT)\n neuron['id'] = nid\n genome['neurons'][nid] = neuron\n genome['op_neurons'].append(nid)\n nid += 1\n\n for i in range(BIAS):\n neuron = create_neuron(layer=Layer.BIAS)\n neuron['id'] = nid\n genome['neurons'][nid] = neuron\n genome['bias_neurons'].append(nid)\n nid += 1\n\n genome['last_neuron'] = nid - 1\n # Create a gene for every ip, op pair\n innov_no = 0\n for i in range(INPUTS):\n for j in range(OUTPUTS):\n gene = create_gene(innov_no=innov_no)\n gene['ip'] = genome['ip_neurons'][i]\n gene['op'] = genome['op_neurons'][j]\n gene['wt'] = random.random() * 2 - 1\n genome['genes'][innov_no] = gene\n #genome['genes'][(gene['ip'], gene['op'])] = gene\n innov_no += 1\n\n for i in range(BIAS):\n for j in range(OUTPUTS):\n gene = create_gene(innov_no=innov_no)\n gene['ip'] = genome['bias_neurons'][i]\n gene['op'] = genome['op_neurons'][j]\n gene['wt'] = random.random() * 2 - 1\n genome['genes'][innov_no] = gene\n #genome['genes'][(gene['ip'], gene['op'])] = gene\n innov_no += 1\n\n return genome","title":""},{"docid":"6ab163bda76c30d4fb577da427063129","score":"0.5506506","text":"def next_uid(self) -> int:\n ...","title":""},{"docid":"04dcabceebe67b1184031249ec9bf6fe","score":"0.55025584","text":"def assign_id(self):\n ev_data, pie, prev_dict = self.reset_env()\n for frame_id in range(1+self.stepSize, self.ds.image_count+1, self.stepSize): # Upto last but one\n image_id = str(frame_id).zfill(6)\n cur_dict = ev_data[image_id] # Current frame is detection, previous frame is groundtruth\n cur_dict = self.two_frame_reid(pie, cur_dict, prev_dict)\n ev_data[image_id] = cur_dict\n self.save_frame(image_id, cur_dict)\n # for the next iteration\n prev_dict = cur_dict\n # Save the updated ev\n #self.visualize_reid(ev_data)","title":""},{"docid":"4fab2187bcf913e5a373a86b857b6013","score":"0.54925704","text":"def _nextId(self):\n res = self._nextIdInt\n self._nextIdInt += 1\n return res","title":""},{"docid":"78792f9e9ec173a6dc2d13b056224ec6","score":"0.54902375","text":"def get_next_id(self):\n self.id_ctr += 1\n return self.id_ctr","title":""},{"docid":"45fb7a4beff8057d754747832d6a843c","score":"0.5488286","text":"def get_next_id(self):\n if len(self.sensors):\n next_id = max(self.sensors.keys()) + 1\n else:\n next_id = 1\n if next_id <= 254:\n return next_id\n return None","title":""},{"docid":"1e1e282ff41437918e540a75aac6ac3c","score":"0.5481234","text":"def get_next_id(self):\n self.next_id += 1\n return self.next_id - 1","title":""},{"docid":"0157f55109d7523a4b7573bf07dbac57","score":"0.54343045","text":"def _get_node_id(self):\n pointer = self.last_node_id\n self.last_node_id += 1\n return pointer","title":""},{"docid":"bc1be17843be1dccad868dc950fa7d99","score":"0.5433168","text":"def _get_next_id(self):\n if self.sensors:\n next_id = max(self.sensors.keys()) + 1\n else:\n next_id = 1\n if next_id <= self.const.MAX_NODE_ID:\n return next_id\n return None","title":""},{"docid":"e9d4cc5e07b1fefbecae8c2e7fa22a7e","score":"0.5424672","text":"def next_id(self):\n return (max(self.instances) + 1) if self.instances else 1","title":""},{"docid":"b5960fcb28ee9baf6e6d20b1175fd29c","score":"0.5406518","text":"def get_next_id(cls):\n cls.next_id += 1\n return cls.next_id","title":""},{"docid":"22b6e640104f2c81be927c7c4cf7b0b1","score":"0.53898156","text":"def advance_next_gen(self) -> None:\n leader_board = self.get_leader_board()\n breeders = [self.players[int(i)] for i in leader_board[\"model\"]]\n players = self.breed(breeders)\n self.players = players\n\n # Metadata\n self.gen_number += 1","title":""},{"docid":"03cf49241e23b0384dc2c7a02066c22a","score":"0.5383871","text":"def _next_id(self):\n DbMO._ID += 1\n return DbMO._ID","title":""},{"docid":"d20f8767c6444e5f810dc16287d39715","score":"0.5378135","text":"def get_next_id(cls):\n cls.current_id += 1\n return cls.current_id","title":""},{"docid":"9ce299df45bb495ff00db4703239920b","score":"0.5376336","text":"def get_next_id(self):\n\n self.current_id += 1\n\n return self.current_id","title":""},{"docid":"f8598f6b6218c3b380d8cdf211c7fc8f","score":"0.5354867","text":"def node_id_get():\n NMLayer1.nodeID += 1\n return NMLayer1.nodeID","title":""},{"docid":"179595cbf2b794c8a2d5cd4ed3059107","score":"0.532394","text":"def get_next_announcement_id(self, origin_node_id):\n logger.debug(\"Getting next announcement ID.\")\n\n # Let's not hit the disk if we don't have to\n if self.next_picture_id is not None:\n logger.debug(\"Updating announcement ID in memory.\")\n rtn = self.next_announcement_id\n self.next_announcement_id += 1\n return \"%d-%d\"%(rtn, origin_node_id)\n\n logger.debug(\"Updating announcement ID from disk.\")\n # Query the database to see what the last id was?\n SQL = \"SELECT announcement_id FROM db_announcements WHERE \" \\\n \"origin_node_id = ?\"\n results = self._sql(SQL, [origin_node_id]).fetchall()\n\n # If it's empty, this is a new database\n if not results:\n self.next_announcement_id = 2\n return \"1-%d\"%origin_node_id\n else:\n id_list = [int(r[0].split(\"-\")[0]) for r in results]\n id_list.sort(reverse=True)\n current_id = id_list[0]\n rtn = current_id+1\n self.next_announcement_id = rtn+1\n return \"%d-%d\"%(rtn, origin_node_id)","title":""},{"docid":"a5d7b171a0ccd22d2f731fbf440dce62","score":"0.5291159","text":"def _get_next_id(self):\n\n current_id = self._last_id + 1\n self._last_id = current_id\n\n return current_id","title":""},{"docid":"bb473350bb521071a5773b184cb0ae7c","score":"0.52792424","text":"def step(self):\n _neighbor_iter = self.model.grid.iter_neighbors((self._row, self._col), True)\n neighbors_opinion = Counter(n.get_state() for n in _neighbor_iter)\n # Following is a a tuple (attribute, occurrences)\n polled_opinions = neighbors_opinion.most_common()\n tied_opinions = []\n for neighbor in polled_opinions:\n if neighbor[1] == polled_opinions[0][1]:\n tied_opinions.append(neighbor)\n\n self._next_state = self.random.choice(tied_opinions)[0]","title":""},{"docid":"0aeebf2c450d1bdc2a5f4de753136927","score":"0.5275587","text":"def graph_id_get():\n NMLayer1.graphID += 1\n return NMLayer1.graphID","title":""},{"docid":"fe1a070f7af027a79fc380cd57455364","score":"0.5274608","text":"def next_member(band):","title":""},{"docid":"1b435f0ea70680e927323e8a8e52024c","score":"0.52724135","text":"def next_object_id():\n id = Transition._next_id\n Transition._next_id += 1\n return id","title":""},{"docid":"2c14ccac2b4a55feb0ceba1ff506da38","score":"0.5267359","text":"def __next(self, number=1):\n idd = self.uqsetting.lastid + number\n self.uqsetting.lastid = idd\n return idd","title":""},{"docid":"1e9e156b738ac594654b9a7b5feab3ca","score":"0.52543956","text":"def get_next_person_id(self, origin_node_id):\n logger.debug(\"Getting next person ID.\")\n \n # Let's not hit the disk if we don't have to\n if self.next_person_id is not None:\n logger.debug(\"Updating person ID in memory.\")\n rtn = self.next_person_id\n self.next_person_id += 1\n return \"%d-%d\"%(rtn, origin_node_id)\n \n logger.debug(\"Updating person ID from disk.\")\n \n SQL = \"SELECT person_id FROM db_person_bio WHERE origin_node_id = ?\"\n results = self._sql(SQL, [origin_node_id]).fetchall()\n \n # If it's empty, this is a new database\n if not results:\n self.next_person_id = 2\n return \"1-%d\"%origin_node_id\n else:\n id_list = [int(r[0].split(\"-\")[0]) for r in results]\n id_list.sort(reverse=True)\n current_id = id_list[0]\n rtn = current_id+1\n self.next_person_id = rtn+1\n return \"%d-%d\"%(rtn, origin_node_id)","title":""},{"docid":"5f19933e84ff7e22fc91507cbd1c4d64","score":"0.5252095","text":"def _generate_vertex_id() -> int:\r\n result = DefaultMultiDirectedHyperGraph.next_id\r\n DefaultMultiDirectedHyperGraph.next_id += 1\r\n return result","title":""},{"docid":"55155342d5584b32a3bab379c31f802b","score":"0.52378625","text":"def _get_next_id(self, event):\n return self.redis.incr(self.NEXT_ID_KEY % event)","title":""},{"docid":"54dcdd5df73aa78b8e26ab740521c9db","score":"0.52281964","text":"def get_next_id(self, table, start_id=0):\n all_ids = self.lookup_all('id', table)\n next_id = start_id\n while next_id in all_ids:\n next_id += 1\n return next_id","title":""},{"docid":"c94dcd05ec60aacd8976f504a9ba3203","score":"0.52199024","text":"def update_identity_pool(self):\n for camID, stracker_state in self.stracker_states.items():\n while len(stracker_state.tracklets_finished) > 0:\n tracklet = stracker_state.tracklets_finished.pop()\n if tracklet.globalID >= 0:\n # Tracklet has been re-identified\n identity = self.identity_pool[tracklet.globalID]\n\n # Update Identity features using this tracklet with randomly chosen features\n # recent_detections = tracklet.detection_history[-len(tracklet.feature_history):]\n # p = np.array([detection.score for _, detection in recent_detections])\n # p = p / np.sum(p)\n # inds = np.random.choice(range(len(tracklet.feature_history)), self.n_feature_samples, replace=False,\n # p=p)\n # new_features = np.stack([tracklet.feature_history[ind][1]['openreid'].copy() for ind in inds])\n\n # New update method: Shrink the feature history size by HCC\n if tracklet.sample_features is None:\n tracklet.update_sample_features()\n identity.features = np.vstack((identity.features, tracklet.sample_features))\n\n else:\n # Tracklet hasn't been re-identified yet, start a new identity\n if tracklet.time_lived >= self.n_feature_samples:\n self.max_id += 1\n tracklet.globalID = self.max_id\n self.identity_pool[self.max_id] = Identity(self.max_id, camID, tracklet.id, tracklet,\n n_feature_samples=self.n_feature_samples)\n self.logger.info('Initiating global ID #{} from tracklet (cam = {}, localID = {})'.format(\n self.max_id, camID, tracklet.id\n ))","title":""},{"docid":"ea4626a2d452b395943068c33104d404","score":"0.52120024","text":"def next_reg_sequence(self):\r\n self.registration_sequence += 1\r\n return self.registration_sequence","title":""},{"docid":"8ba9d425a2db5575bb097c4e1e4acc50","score":"0.5199645","text":"def next_job_id():\n return state.next_job_id()","title":""},{"docid":"f2f7b97476b9561deb3ed77db3545d87","score":"0.5194709","text":"def _get_unique_node_id(self):\n self.__unique_node_counter += 1\n return f\"unique_{self.__unique_node_counter}\"","title":""},{"docid":"bf579b94d1a8187ba1aa97b5db6aeaae","score":"0.51920646","text":"def nextcid(self):\n self.cidseq += 1\n return self.cidseq","title":""},{"docid":"f9d3ba22ad1f2860a6293b32e2df36ab","score":"0.5183412","text":"def gen_id(self):\n self._id_counter += 1\n return self._id_counter","title":""},{"docid":"3b68605cc45a6c8f40db4b24ac26dd0a","score":"0.51816994","text":"def run(self, id_to_follow):\n id_is_in_msg = True\n max_lost_iterations = 30\n curr_lost_iterations = 0\n dist_to_human = 0.6\n last_time = rospy.Time.now()\n while not rospy.is_shutdown() and id_is_in_msg:\n if self.new_msg:\n self.new_msg = False\n found_id_in_msg = False\n for person in self.last_pipol_msg.peopleSet:\n #print \"person is: \" + str(person)\n if person.targetId == id_to_follow:\n found_id_in_msg = True\n # Create nav goal to there if its the id we want\n# ps = createGoal(person.x, person.y, dist_to_human)\n ps = PoseStamped()\n ps.header.frame_id = \"base_link\"\n ps.header.stamp = rospy.Time.now()\n ps.pose.position.x = person.x - dist_to_human\n ps.pose.position.y = person.y\n # Compute orientation from the current robot pose to the point\n #ps.pose.orientation = compute_orientation(PoseStamped(), ps)\n ps.pose.orientation = Quaternion(0.0, 0.0, 0.0, 1.0)\n #Publish\n if rospy.Time.now() - last_time > rospy.Duration(1.0):\n self.move_base_pub.publish(ps)\n last_time = rospy.Time.now()\n# else:\n# print \"person.targetId != id_to_follow\"\n# print str(person.targetId) + \" != \" + str(id_to_follow)\n if not found_id_in_msg: # If we lost the id stop\n curr_lost_iterations += 1\n if curr_lost_iterations > max_lost_iterations:\n id_is_in_msg = False\n else:\n rospy.sleep(0.1)\n \n rospy.logerr(\"We lost the id! Stopping the tracking!\")","title":""},{"docid":"aa552f86d18ae478191f448abe5a1326","score":"0.51689225","text":"def _next_street(self):\n if self._street == RIVER:\n self._current = TERMINAL_ID\n return\n\n for p in self._players:\n if p.has_folded or p.is_allin:\n self._current = TERMINAL_ID\n return\n\n # TODO add deal cards\n self._street += 1\n self._current = PLAYER_1_ID\n return","title":""},{"docid":"f6f3b025b8c166d4e99fcd5adb8b026c","score":"0.51657444","text":"def get_node_id(self):\n with self.lock:\n node_id = self.next_id\n self.next_id += 1\n return node_id","title":""},{"docid":"5b52a056d10c857aef389bf1f0b88e80","score":"0.5165147","text":"def tell(self, x, y):\n # print(f\"in tell {x}, {y}\")\n self.seen_count[x] += 1\n (snr,) = y\n self.seen_snr[x] = float(snr)\n if snr > 500:\n target = 10\n else:\n target = 1\n if self.seen_count[x] == 1:\n self.next_point = (x + 1) % self.num_samples\n else:\n self.next_point = self.agent(x, max(target - self.seen_count[x], 0))","title":""},{"docid":"3d3e97d517d5f3393639a494a87af7c1","score":"0.5161672","text":"def _GetNextFlowId(self):\n ret = self._next_flow_id\n self._next_flow_id += 1\n return ret","title":""},{"docid":"37666ccebcf2af0117760392db375409","score":"0.5156098","text":"def nextId(self):\n\tself.nextid += 1\n\treturn (\"_%d\" % self.nextid)","title":""},{"docid":"32eacb0541e8aa5a03504e250c5d4f7f","score":"0.51528364","text":"def _get_gml_id(self):\n self._gml_id += 1\n return 'gml{0}'.format(self._gml_id)","title":""},{"docid":"56d3e58a9e5cd9e22e5d3ff09bf6ebad","score":"0.51495326","text":"def _generateEventID ():\r\n global _nextEventID\r\n _nextEventID += 1\r\n return _nextEventID","title":""},{"docid":"83ca8f62cd700450785e6ac89cfebd0b","score":"0.51428884","text":"def tag ( self ):\n\n self.id = Node.Ni\n Node.Ni += 1","title":""},{"docid":"fb61f81742c00bacb7e014c3bc64b3fc","score":"0.51401377","text":"def test_next_id_property(stw, donor1, donor2):\n assert stw.next_id == 1\n\n \"\"\"given a blank donor collection\n when two donors are added and next_id called\n integer above max is returned\"\"\"\n stw.create_donor(donor2)\n assert stw.next_id == 1\n\n stw.create_donor(donor1)\n assert stw.next_id == 3","title":""},{"docid":"fb61f81742c00bacb7e014c3bc64b3fc","score":"0.51401377","text":"def test_next_id_property(stw, donor1, donor2):\n assert stw.next_id == 1\n\n \"\"\"given a blank donor collection\n when two donors are added and next_id called\n integer above max is returned\"\"\"\n stw.create_donor(donor2)\n assert stw.next_id == 1\n\n stw.create_donor(donor1)\n assert stw.next_id == 3","title":""},{"docid":"a96c9af225c3136f234a4392c3fab238","score":"0.51401204","text":"def _next_intermediate_id(self):\r\n # Note the format of intermediate uncertain-number UIDs \r\n # differs from elementary uncertain-number UIDs (changed in v1.3.7).\r\n # GTC code only tests UIDs for order and equality, it does not make \r\n # use of the internal structure of a UID. However, previously an elementary \r\n # UID and an intermediate UID could be equal, although they were intended \r\n # to identify different types of node object. Now intermediate UIDs are \r\n # 3-tuples and elementary UIDs 2-tuples, so there can be no confusion.\r\n self._intermediate_id_counter += 1\r\n return (\r\n self._id,\r\n self._intermediate_id_counter,\r\n 0\r\n )","title":""},{"docid":"b45ca4ea35000ddd5f4531a00408b6e4","score":"0.51346064","text":"def next_neuron(self, simulation_step, area):\n\t\tself.i += 1\n\t\treturn TrivialNeuron(self.i - 1)","title":""},{"docid":"c0eccd2428da7a89d470b499fb7bc226","score":"0.5133675","text":"def next_obs_ph(self):\n pass","title":""},{"docid":"a5cb1171a68a34ccdeb6f9951a96d236","score":"0.5128901","text":"def increase_gen(self):\n\n self.current_gen += 1","title":""},{"docid":"75bc712f5d6ccfd6b1d6ab3e0efcc687","score":"0.51256377","text":"def nextId(self):\n if self.__nextId__ is False:\n # Request the next urn\n self.__prev__, self.__nextId__ = self.getPrevNextUrn(reference=self.urn.reference)\n return self.__nextId__","title":""},{"docid":"af3e1dc9407c2b953b4d0a7750aa723a","score":"0.511992","text":"async def _generate_next_id(self):\n async with self._lock:\n next_id = self._next_id\n self._next_id += 1\n return next_id","title":""},{"docid":"211d168409c03e9a8a34c82d31630a67","score":"0.51158303","text":"def generatePointID(self):\n self.idGenerator += 1\n return self.idGenerator","title":""},{"docid":"d44cc0aa03b18297f21e5ef69fa5f760","score":"0.5108937","text":"def __init__(self):\n self.current_id = 0\n self.current_gen = 1","title":""},{"docid":"c5eb56d14addb404548c42491e8bc074","score":"0.5105979","text":"def fetch_next_txids():","title":""},{"docid":"fee7d6608c83137df6f817eb95c05cf5","score":"0.5101974","text":"def acquire_ident(self):\n ident = self.next_id\n self.next_id += 2\n return ident","title":""},{"docid":"8905666531a722e34d7ed16338db1fb8","score":"0.50991255","text":"def next_dataset(self):","title":""},{"docid":"6e9ecd758ca49ec64f7de656f6bacd6a","score":"0.50908786","text":"def step(self,automaton):\n self.nextLocation = self.getBestLocation(automaton)","title":""},{"docid":"90ca01cecc8bd87433929e40e538eff7","score":"0.5081366","text":"def _unique_id():\n result = _unique_id.next_id\n _unique_id.next_id += 1\n return result","title":""},{"docid":"4a578f09cf120889702534894f5080e4","score":"0.50787044","text":"def getnextid(session, qry):\n nextid = qry.one()\n session.commit()\n data = nextid[0]\n if data is not None:\n return data + 1\n else:\n return 1","title":""},{"docid":"53c120172dd5444f830fcbdfb8c1a3a2","score":"0.50754595","text":"def generate_new_trader_id(self):\n _id = self.get_num_records(self.table) + 1\n print(_id)\n return _id","title":""},{"docid":"1cf44098498cad7fdad42786e09d57de","score":"0.50678444","text":"def next(self, session_id):\n \n self.dbc.find_update_increment(1, session_id)","title":""},{"docid":"65f104cf45cf2bfd2b17ef859adfa70e","score":"0.50670964","text":"def next_track(self, message):\n pass","title":""},{"docid":"a593d09ffe7e6f04ad41c6366dcc56f4","score":"0.50659955","text":"def _next_image(self, image_id, action):\n return self._all_graph[image_id][action]","title":""},{"docid":"13197ba22271b8d1791464c5b6c7a39d","score":"0.5048989","text":"def get_next_picture_id(self, origin_node_id):\n logger.debug(\"Getting next picture ID.\")\n \n # Let's not hit the disk if we don't have to\n if self.next_picture_id is not None:\n logger.debug(\"Updating picture ID in memory.\")\n rtn = self.next_picture_id\n self.next_picture_id += 1\n return \"%d-%d\"%(rtn, origin_node_id)\n \n logger.debug(\"Updating picture ID from disk.\")\n # Query the database to see what the last id was?\n SQL = \"SELECT picture_id FROM db_pictures WHERE origin_node_id = ?\"\n results = self._sql(SQL, [origin_node_id]).fetchall()\n \n # If it's empty, this is a new database\n if not results:\n self.next_picture_id = 2\n return \"1-%d\"%origin_node_id\n else:\n id_list = [int(r[0].split(\"-\")[0]) for r in results]\n id_list.sort(reverse=True)\n current_id = id_list[0]\n rtn = current_id+1\n self.next_picture_id = rtn+1\n return \"%d-%d\"%(rtn, origin_node_id)","title":""},{"docid":"5fbb5f21aaaaf4b336db9f8af2db7b95","score":"0.5043903","text":"def db_next_hid( self ):\n conn = object_session( self ).connection()\n table = self.table\n trans = conn.begin()\n try:\n next_hid = select( [table.c.hid_counter], table.c.id == self.id, for_update=True ).scalar()\n table.update( table.c.id == self.id ).execute( hid_counter = ( next_hid + 1 ) )\n trans.commit()\n return next_hid\n except:\n trans.rollback()\n raise","title":""},{"docid":"4cfbb0c28553f6f613b2309a6430b022","score":"0.5041089","text":"def train_one_generation(s):\n s.fitness_computation_on_population()\n s.create_roulette_wheel()\n s.select_chromosomes_from_roulette_wheel()\n s.track_best_chromosomes()\n s.track_performance_over_generations()\n s.generate_next_generation()\n s.generation += 1","title":""},{"docid":"ec5d2a9b96693755e1b7ae31fa2aebd1","score":"0.5040065","text":"def next_station(self):\n if self.__connected:\n try:\n data = f\"{self.__line_number} {self.__station + 1} {self.__id}\"\n self.__send_to_server(data)\n self.__station += 1\n if self.__station in self.__stations.keys():\n self.__total_people_count += self.__stations[self.__station]\n except:\n self.__connected = False\n self.asking_user_to_reconnect = False\n\n if int(self.__station) + 1 > Bus.MAX_STATION: # stop the bus client when he reaches his final station\n self.stop = True\n self.__gui.display_finished()\n return\n else:\n print(\"user trying to send update the server about change in the station but i am offline.\")","title":""},{"docid":"2e89662d09ae9601c638a0c4416bec0b","score":"0.5038427","text":"def _get_next_entrust_no(self):\n return str(self.seq_gen.get_next('entrust_no'))","title":""},{"docid":"f307b129bdb0994e8f8c147081bf79b6","score":"0.5036329","text":"def next_id(self):\n return self._next_id.value","title":""},{"docid":"9ab93c3fe0b2d893c69feac9f7ec64b2","score":"0.50349325","text":"def alloc_id(self):\n self._nextid += 1\n return self._nextid","title":""},{"docid":"9ab93c3fe0b2d893c69feac9f7ec64b2","score":"0.50349325","text":"def alloc_id(self):\n self._nextid += 1\n return self._nextid","title":""},{"docid":"56ed7336019bd5d31f65dfd835ae0066","score":"0.5033949","text":"async def get_next_id(db: asyncpg.pool.Pool):\n query = \"\"\"\n SELECT id FROM giveaways ORDER BY id DESC LIMIT 1\n \"\"\"\n res = await db.fetch(query)\n if not res:\n return 0\n res = res[0]['id']\n return res + 1","title":""},{"docid":"4fb2ebafc2c77352ded5966611649cdf","score":"0.5028199","text":"def findCounterExample(self):\n\t\twhile True:\n\t\t\tcurrent_state_h = self.resetExploration()\n\n\t\t\tfor ep in range(STEPS_PER_EPISODE):\n\t\t\t\ta = int(random.random()//(1/(len(self.world.map.availableActions(self.world.map.current)))))\n\t\t\t\t(r_h,r_m,next_state_h,next_state_m,obs) = self.executeOneStepExploration(current_state_h,a)\n\n\t\t\t\tif obs != 'null':\n\t\t\t\t\tself.observation_seq.append(obs)\n\t\t\t\t\tself.reward_trace.append(r_m)\n\n\t\t\t\tif self.isCounterExample(r_h,r_m):\n\t\t\t\t\tself.nuof_counter_examples += 1\n\t\t\t\t\treturn None\n\t\t\t\telse:\n\t\t\t\t\tcurrent_state_h = next_state_h\n\t\t\t\t\tself.world.map.current = next_state_m","title":""},{"docid":"5d66b23ab5cbefdff261d26a989c9d7b","score":"0.5024713","text":"def eval_genomes(genomes, config):\n global gen\n #win = WIN\n gen += 1\n\n # start by creating lists holding the genome itself, the\n # neural network associated with the genome and the\n # bird object that uses that network to play\n nets = []\n birds = []\n ge = []\n for genome_id, genome in genomes:\n index = 0\n genome.fitness = 0 # start with fitness level of 0\n net = neat.nn.FeedForwardNetwork.create(genome, config)\n nets.append(net)\n birds.append(Bird(230,350))\n birds[index].index = index\n ge.append(genome)\n\n base = Base(FLOOR)\n pipes = [Pipe(700)]\n score = 0\n\n run = True\n while run and len(birds) > 0:\n\n pipe_ind = 0\n if len(birds) > 0:\n if len(pipes) > 1 and birds[0].x > pipes[0].x: # determine whether to use the first or second\n pipe_ind = 1\n\n top_pipe = pipes[pipe_ind].height\n bot_pipe = pipes[pipe_ind].bottom\n pipe = pipes[pipe_ind]\n\n #decide_birds_parallel(nets, birds, pipe)\n\n for x, bird in enumerate(birds):\n ge[x].fitness += 0.1\n decide_birds(nets, birds, bird, top_pipe, bot_pipe)\n\n base.move()\n\n rem = []\n add_pipe = False\n for pipe in pipes:\n pipe.move()\n # check for collision\n for bird in birds:\n if pipe.collide(bird):\n ge[birds.index(bird)].fitness -= 1\n nets.pop(birds.index(bird))\n ge.pop(birds.index(bird))\n birds.pop(birds.index(bird))\n\n if pipe.x < 0:\n rem.append(pipe)\n\n if not pipe.passed and pipe.x < bird.x:\n pipe.passed = True\n add_pipe = True\n\n if add_pipe:\n score += 1\n # can add this line to give more reward for passing through a pipe (not required)\n for genome in ge:\n genome.fitness += 5\n pipes.append(Pipe(WIN_WIDTH))\n\n for r in rem:\n pipes.remove(r)\n\n for bird in birds:\n if bird.y - 10 >= FLOOR or bird.y < -50:\n nets.pop(birds.index(bird))\n ge.pop(birds.index(bird))\n birds.pop(birds.index(bird))\n\n # break if score gets large enough\n if score > 20:\n pickle.dump(nets[0], open(\"best.pickle\", \"wb\"))\n break","title":""},{"docid":"46919608a184fdb5a8bb3678bfdb487c","score":"0.5022995","text":"def next_index():\n random_index = 0\n while random_index in prev:\n random_index = r.randint(0, len(taunts) - 1)\n prev[0] = prev[1]\n prev[1] = random_index\n return random_index","title":""},{"docid":"06111e3f96a3e41a4f5974d0e12635bc","score":"0.5019364","text":"def nextNet(self):\n self.cIndex += 1\n if self.cIndex < len(self.generation.pop):\n self.updateCurrentNet()\n else:\n print '-' * 30\n log = \"GENERATION {}, AVERAGE {}\"\n print log.format(self.generation.generationNumber, self.generation.averageFitness())\n print '#' * 30\n self.generation.generateNextPop()\n self.cIndex = 0\n self.updateCurrentNet()","title":""},{"docid":"65d224f19df66218edaabea8d12d20ec","score":"0.5015222","text":"def take_id(self):\n result = self.id\n self.id += 1\n return result","title":""},{"docid":"7ac5a1c01050d3d728929b048205f9a6","score":"0.5014364","text":"def receive_connection(self, origin_neuron, axon):\n\t\tself.ingoing += 1","title":""},{"docid":"f6d465a727dd3deda8e4da13d6606442","score":"0.5013583","text":"def relabel_id(self, g, old, new):\n self.id_to_node[g.nodes[new]['mob_id']] = new","title":""},{"docid":"feb058a52296401b50aba8d8829d4828","score":"0.501338","text":"def pick_next_cell(self):\n maximal_connectivity = self.get_maximal_connectivity()\n assert(maximal_connectivity > 1)\n not_yet_visited_cells = np.logical_and( self.already_inspected_cells == False, self.actual_connectivity_tested == False )\n possible_indices = np.where( np.logical_and(self.connectivity_vector == maximal_connectivity,\n not_yet_visited_cells ) )\n next_frame_id = self.mesh_one.elements[possible_indices[0][0]].id_in_frame\n\n return next_frame_id","title":""},{"docid":"2de9345060555d20e1eba7c446e43429","score":"0.5010251","text":"def get_id():\n new_id = ID_COUNTER.count\n ID_COUNTER.count += 1\n return new_id","title":""},{"docid":"6820a33a0a3c66bcdaa7af87d378fbb7","score":"0.500999","text":"def tracker():\n pass","title":""},{"docid":"89ce60f1ef54d88310b0aa2b7a9f3785","score":"0.5008181","text":"def visit (self):\n self.count += 1","title":""},{"docid":"520825c61e25b15f491698d63d1ffe7d","score":"0.5003811","text":"def _get_next_tree_id(self):\r\n qs = self.get_queryset()\r\n max_tree_id = list(qs.aggregate(Max(self.tree_id_attr)).values())[0]\r\n max_tree_id = max_tree_id or 0\r\n return max_tree_id + 1","title":""},{"docid":"22f4040859d3cc90afc9eb835faebd15","score":"0.50011396","text":"def next_id(self):\n for n in itertools.count(start=len(self._all) + 1):\n id_str = \"{}{}{}\".format(LAYER_ID, core.Node.ID_SEPARATOR, n)\n try:\n self._root.by_id(id_str)\n except KeyError:\n return id_str","title":""}],"string":"[\n {\n \"docid\": \"fcf8bdb7177722960dff82a8755c738f\",\n \"score\": \"0.59789985\",\n \"text\": \"def next_id(self):\\n nid = self._next_id\\n self._next_id = self._next_id + 1\\n return nid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3d6cfa9e8896e9291b73b372ea92d75\",\n \"score\": \"0.59274477\",\n \"text\": \"def nextId(self):\\r\\n \\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5432564591795c1acdb3b5fadad12f5f\",\n \"score\": \"0.58401716\",\n \"text\": \"def get_next_unique_id(self) -> str:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a201d394e803f8d5555fefda93fcf8e\",\n \"score\": \"0.5815166\",\n \"text\": \"def get_next_id(self) -> int:\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"711a98274259de153db0e69fbce70661\",\n \"score\": \"0.58012456\",\n \"text\": \"def next_mcid(self):\\n\\tresult = self.next()\\n\\n\\tif self.count == 0:\\n\\t self.save()\\n\\t self.count = self.iterations\\n\\telse:\\n\\t self.count -= 1\\n\\n\\treturn result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94646c06b3aa9b19d8b7d24cd3e875c4\",\n \"score\": \"0.5765719\",\n \"text\": \"def _get_next_id(self):\\n if self.sensors:\\n next_id = max(self.sensors.keys()) + 1\\n else:\\n next_id = 1\\n if next_id <= 254:\\n return next_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2647d544e0f3efa7aa62c2668a674069\",\n \"score\": \"0.5743264\",\n \"text\": \"def next_generation(self):\\n\\t\\tchild_pop = [copy.deepcopy(self.elite_ind)]\\n\\t\\t\\n\\t\\t# Perform tournament selection.\\n\\t\\tfor i in range(len(self.genomes)-1):\\n\\t\\t\\ttourn = random.sample(self.genomes,2)\\n\\t\\t\\tif tourn[0]['fitness'] < tourn[1]['fitness']:\\n\\t\\t\\t\\tchild_pop.append(copy.deepcopy(tourn[0]))\\n\\t\\t\\telse:\\n\\t\\t\\t\\tchild_pop.append(copy.deepcopy(tourn[1]))\\n\\t\\t\\tchild_pop[-1]['id'] = self.child_id\\n\\t\\t\\tself.child_id += 1\\n\\n\\t\\t\\n\\t\\t#Crossover\\n\\t\\tchild_pop = single_point_crossover(child_pop, cross_over_prob)\\n\\n\\t\\t# Mutate genes in the child genomes.\\n\\t\\tchild_pop = random_value_mutation(child_pop, mutation_prob)\\n\\t\\t\\n\\n\\t\\tself.genomes = child_pop\\n\\t\\tself.id_map = {k:v for k,v in zip([x['id'] for x in self.genomes],[i for i in range(self.pop_size)])}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f38b7e6e15a59f8cf539fcbbeeafe5a\",\n \"score\": \"0.5736271\",\n \"text\": \"def _get_next_id(self):\\n # type: () -> int\\n ret = self._next_id\\n self._next_id = ret + 1\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37634bb616b66f045df9680feeae7e15\",\n \"score\": \"0.5727054\",\n \"text\": \"def _get_next_id(self) -> int:\\n ret = self._next_id\\n self._next_id = ret + 1\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40a299ce12e16024815467a07dfb0c1b\",\n \"score\": \"0.5615426\",\n \"text\": \"def _next_id():\\n global iid\\n with iid_clock:\\n result = iid\\n iid = iid + 1\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56aa3c0e549c48cbda6d3f2f8fbffd99\",\n \"score\": \"0.556686\",\n \"text\": \"def _next_id(self):\\n self._id += 1\\n return self._id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85adb3359737c232f867186583565de6\",\n \"score\": \"0.55654854\",\n \"text\": \"def get_next_id(self):\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85adb3359737c232f867186583565de6\",\n \"score\": \"0.55654854\",\n \"text\": \"def get_next_id(self):\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99f4a68fa89f59e504394e9a4acedeff\",\n \"score\": \"0.55591255\",\n \"text\": \"def get_next_individual(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4dae28d305f3ef0ea65bae77858d9abb\",\n \"score\": \"0.5550575\",\n \"text\": \"def get_new_network_id(db):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"280681a733c378915ac4d33e3ebd1648\",\n \"score\": \"0.5548189\",\n \"text\": \"def next_id(cls, sess):\\n current = sess.query(func.max(cls.id)).one()[0] or -1\\n return current + 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13de5cd23b40fdb5369784b153b638ca\",\n \"score\": \"0.55082417\",\n \"text\": \"def init_individual():\\n genome = create_genome()\\n\\n # Create i/p and o/p neurons\\n nid = 0\\n for i in range(INPUTS):\\n neuron = create_neuron(layer=Layer.INPUT)\\n neuron['id'] = nid\\n genome['neurons'][nid] = neuron\\n genome['ip_neurons'].append(nid)\\n nid += 1\\n\\n for i in range(OUTPUTS):\\n neuron = create_neuron(layer=Layer.OUTPUT)\\n neuron['id'] = nid\\n genome['neurons'][nid] = neuron\\n genome['op_neurons'].append(nid)\\n nid += 1\\n\\n for i in range(BIAS):\\n neuron = create_neuron(layer=Layer.BIAS)\\n neuron['id'] = nid\\n genome['neurons'][nid] = neuron\\n genome['bias_neurons'].append(nid)\\n nid += 1\\n\\n genome['last_neuron'] = nid - 1\\n # Create a gene for every ip, op pair\\n innov_no = 0\\n for i in range(INPUTS):\\n for j in range(OUTPUTS):\\n gene = create_gene(innov_no=innov_no)\\n gene['ip'] = genome['ip_neurons'][i]\\n gene['op'] = genome['op_neurons'][j]\\n gene['wt'] = random.random() * 2 - 1\\n genome['genes'][innov_no] = gene\\n #genome['genes'][(gene['ip'], gene['op'])] = gene\\n innov_no += 1\\n\\n for i in range(BIAS):\\n for j in range(OUTPUTS):\\n gene = create_gene(innov_no=innov_no)\\n gene['ip'] = genome['bias_neurons'][i]\\n gene['op'] = genome['op_neurons'][j]\\n gene['wt'] = random.random() * 2 - 1\\n genome['genes'][innov_no] = gene\\n #genome['genes'][(gene['ip'], gene['op'])] = gene\\n innov_no += 1\\n\\n return genome\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ab163bda76c30d4fb577da427063129\",\n \"score\": \"0.5506506\",\n \"text\": \"def next_uid(self) -> int:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04dcabceebe67b1184031249ec9bf6fe\",\n \"score\": \"0.55025584\",\n \"text\": \"def assign_id(self):\\n ev_data, pie, prev_dict = self.reset_env()\\n for frame_id in range(1+self.stepSize, self.ds.image_count+1, self.stepSize): # Upto last but one\\n image_id = str(frame_id).zfill(6)\\n cur_dict = ev_data[image_id] # Current frame is detection, previous frame is groundtruth\\n cur_dict = self.two_frame_reid(pie, cur_dict, prev_dict)\\n ev_data[image_id] = cur_dict\\n self.save_frame(image_id, cur_dict)\\n # for the next iteration\\n prev_dict = cur_dict\\n # Save the updated ev\\n #self.visualize_reid(ev_data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fab2187bcf913e5a373a86b857b6013\",\n \"score\": \"0.54925704\",\n \"text\": \"def _nextId(self):\\n res = self._nextIdInt\\n self._nextIdInt += 1\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78792f9e9ec173a6dc2d13b056224ec6\",\n \"score\": \"0.54902375\",\n \"text\": \"def get_next_id(self):\\n self.id_ctr += 1\\n return self.id_ctr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45fb7a4beff8057d754747832d6a843c\",\n \"score\": \"0.5488286\",\n \"text\": \"def get_next_id(self):\\n if len(self.sensors):\\n next_id = max(self.sensors.keys()) + 1\\n else:\\n next_id = 1\\n if next_id <= 254:\\n return next_id\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e1e282ff41437918e540a75aac6ac3c\",\n \"score\": \"0.5481234\",\n \"text\": \"def get_next_id(self):\\n self.next_id += 1\\n return self.next_id - 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0157f55109d7523a4b7573bf07dbac57\",\n \"score\": \"0.54343045\",\n \"text\": \"def _get_node_id(self):\\n pointer = self.last_node_id\\n self.last_node_id += 1\\n return pointer\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc1be17843be1dccad868dc950fa7d99\",\n \"score\": \"0.5433168\",\n \"text\": \"def _get_next_id(self):\\n if self.sensors:\\n next_id = max(self.sensors.keys()) + 1\\n else:\\n next_id = 1\\n if next_id <= self.const.MAX_NODE_ID:\\n return next_id\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9d4cc5e07b1fefbecae8c2e7fa22a7e\",\n \"score\": \"0.5424672\",\n \"text\": \"def next_id(self):\\n return (max(self.instances) + 1) if self.instances else 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5960fcb28ee9baf6e6d20b1175fd29c\",\n \"score\": \"0.5406518\",\n \"text\": \"def get_next_id(cls):\\n cls.next_id += 1\\n return cls.next_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22b6e640104f2c81be927c7c4cf7b0b1\",\n \"score\": \"0.53898156\",\n \"text\": \"def advance_next_gen(self) -> None:\\n leader_board = self.get_leader_board()\\n breeders = [self.players[int(i)] for i in leader_board[\\\"model\\\"]]\\n players = self.breed(breeders)\\n self.players = players\\n\\n # Metadata\\n self.gen_number += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03cf49241e23b0384dc2c7a02066c22a\",\n \"score\": \"0.5383871\",\n \"text\": \"def _next_id(self):\\n DbMO._ID += 1\\n return DbMO._ID\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d20f8767c6444e5f810dc16287d39715\",\n \"score\": \"0.5378135\",\n \"text\": \"def get_next_id(cls):\\n cls.current_id += 1\\n return cls.current_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ce299df45bb495ff00db4703239920b\",\n \"score\": \"0.5376336\",\n \"text\": \"def get_next_id(self):\\n\\n self.current_id += 1\\n\\n return self.current_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8598f6b6218c3b380d8cdf211c7fc8f\",\n \"score\": \"0.5354867\",\n \"text\": \"def node_id_get():\\n NMLayer1.nodeID += 1\\n return NMLayer1.nodeID\",\n \"title\": \"\"\n },\n {\n \"docid\": \"179595cbf2b794c8a2d5cd4ed3059107\",\n \"score\": \"0.532394\",\n \"text\": \"def get_next_announcement_id(self, origin_node_id):\\n logger.debug(\\\"Getting next announcement ID.\\\")\\n\\n # Let's not hit the disk if we don't have to\\n if self.next_picture_id is not None:\\n logger.debug(\\\"Updating announcement ID in memory.\\\")\\n rtn = self.next_announcement_id\\n self.next_announcement_id += 1\\n return \\\"%d-%d\\\"%(rtn, origin_node_id)\\n\\n logger.debug(\\\"Updating announcement ID from disk.\\\")\\n # Query the database to see what the last id was?\\n SQL = \\\"SELECT announcement_id FROM db_announcements WHERE \\\" \\\\\\n \\\"origin_node_id = ?\\\"\\n results = self._sql(SQL, [origin_node_id]).fetchall()\\n\\n # If it's empty, this is a new database\\n if not results:\\n self.next_announcement_id = 2\\n return \\\"1-%d\\\"%origin_node_id\\n else:\\n id_list = [int(r[0].split(\\\"-\\\")[0]) for r in results]\\n id_list.sort(reverse=True)\\n current_id = id_list[0]\\n rtn = current_id+1\\n self.next_announcement_id = rtn+1\\n return \\\"%d-%d\\\"%(rtn, origin_node_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5d7b171a0ccd22d2f731fbf440dce62\",\n \"score\": \"0.5291159\",\n \"text\": \"def _get_next_id(self):\\n\\n current_id = self._last_id + 1\\n self._last_id = current_id\\n\\n return current_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb473350bb521071a5773b184cb0ae7c\",\n \"score\": \"0.52792424\",\n \"text\": \"def step(self):\\n _neighbor_iter = self.model.grid.iter_neighbors((self._row, self._col), True)\\n neighbors_opinion = Counter(n.get_state() for n in _neighbor_iter)\\n # Following is a a tuple (attribute, occurrences)\\n polled_opinions = neighbors_opinion.most_common()\\n tied_opinions = []\\n for neighbor in polled_opinions:\\n if neighbor[1] == polled_opinions[0][1]:\\n tied_opinions.append(neighbor)\\n\\n self._next_state = self.random.choice(tied_opinions)[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0aeebf2c450d1bdc2a5f4de753136927\",\n \"score\": \"0.5275587\",\n \"text\": \"def graph_id_get():\\n NMLayer1.graphID += 1\\n return NMLayer1.graphID\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe1a070f7af027a79fc380cd57455364\",\n \"score\": \"0.5274608\",\n \"text\": \"def next_member(band):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b435f0ea70680e927323e8a8e52024c\",\n \"score\": \"0.52724135\",\n \"text\": \"def next_object_id():\\n id = Transition._next_id\\n Transition._next_id += 1\\n return id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c14ccac2b4a55feb0ceba1ff506da38\",\n \"score\": \"0.5267359\",\n \"text\": \"def __next(self, number=1):\\n idd = self.uqsetting.lastid + number\\n self.uqsetting.lastid = idd\\n return idd\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e9e156b738ac594654b9a7b5feab3ca\",\n \"score\": \"0.52543956\",\n \"text\": \"def get_next_person_id(self, origin_node_id):\\n logger.debug(\\\"Getting next person ID.\\\")\\n \\n # Let's not hit the disk if we don't have to\\n if self.next_person_id is not None:\\n logger.debug(\\\"Updating person ID in memory.\\\")\\n rtn = self.next_person_id\\n self.next_person_id += 1\\n return \\\"%d-%d\\\"%(rtn, origin_node_id)\\n \\n logger.debug(\\\"Updating person ID from disk.\\\")\\n \\n SQL = \\\"SELECT person_id FROM db_person_bio WHERE origin_node_id = ?\\\"\\n results = self._sql(SQL, [origin_node_id]).fetchall()\\n \\n # If it's empty, this is a new database\\n if not results:\\n self.next_person_id = 2\\n return \\\"1-%d\\\"%origin_node_id\\n else:\\n id_list = [int(r[0].split(\\\"-\\\")[0]) for r in results]\\n id_list.sort(reverse=True)\\n current_id = id_list[0]\\n rtn = current_id+1\\n self.next_person_id = rtn+1\\n return \\\"%d-%d\\\"%(rtn, origin_node_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f19933e84ff7e22fc91507cbd1c4d64\",\n \"score\": \"0.5252095\",\n \"text\": \"def _generate_vertex_id() -> int:\\r\\n result = DefaultMultiDirectedHyperGraph.next_id\\r\\n DefaultMultiDirectedHyperGraph.next_id += 1\\r\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55155342d5584b32a3bab379c31f802b\",\n \"score\": \"0.52378625\",\n \"text\": \"def _get_next_id(self, event):\\n return self.redis.incr(self.NEXT_ID_KEY % event)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54dcdd5df73aa78b8e26ab740521c9db\",\n \"score\": \"0.52281964\",\n \"text\": \"def get_next_id(self, table, start_id=0):\\n all_ids = self.lookup_all('id', table)\\n next_id = start_id\\n while next_id in all_ids:\\n next_id += 1\\n return next_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c94dcd05ec60aacd8976f504a9ba3203\",\n \"score\": \"0.52199024\",\n \"text\": \"def update_identity_pool(self):\\n for camID, stracker_state in self.stracker_states.items():\\n while len(stracker_state.tracklets_finished) > 0:\\n tracklet = stracker_state.tracklets_finished.pop()\\n if tracklet.globalID >= 0:\\n # Tracklet has been re-identified\\n identity = self.identity_pool[tracklet.globalID]\\n\\n # Update Identity features using this tracklet with randomly chosen features\\n # recent_detections = tracklet.detection_history[-len(tracklet.feature_history):]\\n # p = np.array([detection.score for _, detection in recent_detections])\\n # p = p / np.sum(p)\\n # inds = np.random.choice(range(len(tracklet.feature_history)), self.n_feature_samples, replace=False,\\n # p=p)\\n # new_features = np.stack([tracklet.feature_history[ind][1]['openreid'].copy() for ind in inds])\\n\\n # New update method: Shrink the feature history size by HCC\\n if tracklet.sample_features is None:\\n tracklet.update_sample_features()\\n identity.features = np.vstack((identity.features, tracklet.sample_features))\\n\\n else:\\n # Tracklet hasn't been re-identified yet, start a new identity\\n if tracklet.time_lived >= self.n_feature_samples:\\n self.max_id += 1\\n tracklet.globalID = self.max_id\\n self.identity_pool[self.max_id] = Identity(self.max_id, camID, tracklet.id, tracklet,\\n n_feature_samples=self.n_feature_samples)\\n self.logger.info('Initiating global ID #{} from tracklet (cam = {}, localID = {})'.format(\\n self.max_id, camID, tracklet.id\\n ))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea4626a2d452b395943068c33104d404\",\n \"score\": \"0.52120024\",\n \"text\": \"def next_reg_sequence(self):\\r\\n self.registration_sequence += 1\\r\\n return self.registration_sequence\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ba9d425a2db5575bb097c4e1e4acc50\",\n \"score\": \"0.5199645\",\n \"text\": \"def next_job_id():\\n return state.next_job_id()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2f7b97476b9561deb3ed77db3545d87\",\n \"score\": \"0.5194709\",\n \"text\": \"def _get_unique_node_id(self):\\n self.__unique_node_counter += 1\\n return f\\\"unique_{self.__unique_node_counter}\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf579b94d1a8187ba1aa97b5db6aeaae\",\n \"score\": \"0.51920646\",\n \"text\": \"def nextcid(self):\\n self.cidseq += 1\\n return self.cidseq\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9d3ba22ad1f2860a6293b32e2df36ab\",\n \"score\": \"0.5183412\",\n \"text\": \"def gen_id(self):\\n self._id_counter += 1\\n return self._id_counter\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b68605cc45a6c8f40db4b24ac26dd0a\",\n \"score\": \"0.51816994\",\n \"text\": \"def run(self, id_to_follow):\\n id_is_in_msg = True\\n max_lost_iterations = 30\\n curr_lost_iterations = 0\\n dist_to_human = 0.6\\n last_time = rospy.Time.now()\\n while not rospy.is_shutdown() and id_is_in_msg:\\n if self.new_msg:\\n self.new_msg = False\\n found_id_in_msg = False\\n for person in self.last_pipol_msg.peopleSet:\\n #print \\\"person is: \\\" + str(person)\\n if person.targetId == id_to_follow:\\n found_id_in_msg = True\\n # Create nav goal to there if its the id we want\\n# ps = createGoal(person.x, person.y, dist_to_human)\\n ps = PoseStamped()\\n ps.header.frame_id = \\\"base_link\\\"\\n ps.header.stamp = rospy.Time.now()\\n ps.pose.position.x = person.x - dist_to_human\\n ps.pose.position.y = person.y\\n # Compute orientation from the current robot pose to the point\\n #ps.pose.orientation = compute_orientation(PoseStamped(), ps)\\n ps.pose.orientation = Quaternion(0.0, 0.0, 0.0, 1.0)\\n #Publish\\n if rospy.Time.now() - last_time > rospy.Duration(1.0):\\n self.move_base_pub.publish(ps)\\n last_time = rospy.Time.now()\\n# else:\\n# print \\\"person.targetId != id_to_follow\\\"\\n# print str(person.targetId) + \\\" != \\\" + str(id_to_follow)\\n if not found_id_in_msg: # If we lost the id stop\\n curr_lost_iterations += 1\\n if curr_lost_iterations > max_lost_iterations:\\n id_is_in_msg = False\\n else:\\n rospy.sleep(0.1)\\n \\n rospy.logerr(\\\"We lost the id! Stopping the tracking!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa552f86d18ae478191f448abe5a1326\",\n \"score\": \"0.51689225\",\n \"text\": \"def _next_street(self):\\n if self._street == RIVER:\\n self._current = TERMINAL_ID\\n return\\n\\n for p in self._players:\\n if p.has_folded or p.is_allin:\\n self._current = TERMINAL_ID\\n return\\n\\n # TODO add deal cards\\n self._street += 1\\n self._current = PLAYER_1_ID\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6f3b025b8c166d4e99fcd5adb8b026c\",\n \"score\": \"0.51657444\",\n \"text\": \"def get_node_id(self):\\n with self.lock:\\n node_id = self.next_id\\n self.next_id += 1\\n return node_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b52a056d10c857aef389bf1f0b88e80\",\n \"score\": \"0.5165147\",\n \"text\": \"def tell(self, x, y):\\n # print(f\\\"in tell {x}, {y}\\\")\\n self.seen_count[x] += 1\\n (snr,) = y\\n self.seen_snr[x] = float(snr)\\n if snr > 500:\\n target = 10\\n else:\\n target = 1\\n if self.seen_count[x] == 1:\\n self.next_point = (x + 1) % self.num_samples\\n else:\\n self.next_point = self.agent(x, max(target - self.seen_count[x], 0))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d3e97d517d5f3393639a494a87af7c1\",\n \"score\": \"0.5161672\",\n \"text\": \"def _GetNextFlowId(self):\\n ret = self._next_flow_id\\n self._next_flow_id += 1\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37666ccebcf2af0117760392db375409\",\n \"score\": \"0.5156098\",\n \"text\": \"def nextId(self):\\n\\tself.nextid += 1\\n\\treturn (\\\"_%d\\\" % self.nextid)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32eacb0541e8aa5a03504e250c5d4f7f\",\n \"score\": \"0.51528364\",\n \"text\": \"def _get_gml_id(self):\\n self._gml_id += 1\\n return 'gml{0}'.format(self._gml_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56d3e58a9e5cd9e22e5d3ff09bf6ebad\",\n \"score\": \"0.51495326\",\n \"text\": \"def _generateEventID ():\\r\\n global _nextEventID\\r\\n _nextEventID += 1\\r\\n return _nextEventID\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83ca8f62cd700450785e6ac89cfebd0b\",\n \"score\": \"0.51428884\",\n \"text\": \"def tag ( self ):\\n\\n self.id = Node.Ni\\n Node.Ni += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb61f81742c00bacb7e014c3bc64b3fc\",\n \"score\": \"0.51401377\",\n \"text\": \"def test_next_id_property(stw, donor1, donor2):\\n assert stw.next_id == 1\\n\\n \\\"\\\"\\\"given a blank donor collection\\n when two donors are added and next_id called\\n integer above max is returned\\\"\\\"\\\"\\n stw.create_donor(donor2)\\n assert stw.next_id == 1\\n\\n stw.create_donor(donor1)\\n assert stw.next_id == 3\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb61f81742c00bacb7e014c3bc64b3fc\",\n \"score\": \"0.51401377\",\n \"text\": \"def test_next_id_property(stw, donor1, donor2):\\n assert stw.next_id == 1\\n\\n \\\"\\\"\\\"given a blank donor collection\\n when two donors are added and next_id called\\n integer above max is returned\\\"\\\"\\\"\\n stw.create_donor(donor2)\\n assert stw.next_id == 1\\n\\n stw.create_donor(donor1)\\n assert stw.next_id == 3\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a96c9af225c3136f234a4392c3fab238\",\n \"score\": \"0.51401204\",\n \"text\": \"def _next_intermediate_id(self):\\r\\n # Note the format of intermediate uncertain-number UIDs \\r\\n # differs from elementary uncertain-number UIDs (changed in v1.3.7).\\r\\n # GTC code only tests UIDs for order and equality, it does not make \\r\\n # use of the internal structure of a UID. However, previously an elementary \\r\\n # UID and an intermediate UID could be equal, although they were intended \\r\\n # to identify different types of node object. Now intermediate UIDs are \\r\\n # 3-tuples and elementary UIDs 2-tuples, so there can be no confusion.\\r\\n self._intermediate_id_counter += 1\\r\\n return (\\r\\n self._id,\\r\\n self._intermediate_id_counter,\\r\\n 0\\r\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b45ca4ea35000ddd5f4531a00408b6e4\",\n \"score\": \"0.51346064\",\n \"text\": \"def next_neuron(self, simulation_step, area):\\n\\t\\tself.i += 1\\n\\t\\treturn TrivialNeuron(self.i - 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0eccd2428da7a89d470b499fb7bc226\",\n \"score\": \"0.5133675\",\n \"text\": \"def next_obs_ph(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5cb1171a68a34ccdeb6f9951a96d236\",\n \"score\": \"0.5128901\",\n \"text\": \"def increase_gen(self):\\n\\n self.current_gen += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75bc712f5d6ccfd6b1d6ab3e0efcc687\",\n \"score\": \"0.51256377\",\n \"text\": \"def nextId(self):\\n if self.__nextId__ is False:\\n # Request the next urn\\n self.__prev__, self.__nextId__ = self.getPrevNextUrn(reference=self.urn.reference)\\n return self.__nextId__\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af3e1dc9407c2b953b4d0a7750aa723a\",\n \"score\": \"0.511992\",\n \"text\": \"async def _generate_next_id(self):\\n async with self._lock:\\n next_id = self._next_id\\n self._next_id += 1\\n return next_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"211d168409c03e9a8a34c82d31630a67\",\n \"score\": \"0.51158303\",\n \"text\": \"def generatePointID(self):\\n self.idGenerator += 1\\n return self.idGenerator\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d44cc0aa03b18297f21e5ef69fa5f760\",\n \"score\": \"0.5108937\",\n \"text\": \"def __init__(self):\\n self.current_id = 0\\n self.current_gen = 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5eb56d14addb404548c42491e8bc074\",\n \"score\": \"0.5105979\",\n \"text\": \"def fetch_next_txids():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fee7d6608c83137df6f817eb95c05cf5\",\n \"score\": \"0.5101974\",\n \"text\": \"def acquire_ident(self):\\n ident = self.next_id\\n self.next_id += 2\\n return ident\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8905666531a722e34d7ed16338db1fb8\",\n \"score\": \"0.50991255\",\n \"text\": \"def next_dataset(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e9ecd758ca49ec64f7de656f6bacd6a\",\n \"score\": \"0.50908786\",\n \"text\": \"def step(self,automaton):\\n self.nextLocation = self.getBestLocation(automaton)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90ca01cecc8bd87433929e40e538eff7\",\n \"score\": \"0.5081366\",\n \"text\": \"def _unique_id():\\n result = _unique_id.next_id\\n _unique_id.next_id += 1\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a578f09cf120889702534894f5080e4\",\n \"score\": \"0.50787044\",\n \"text\": \"def getnextid(session, qry):\\n nextid = qry.one()\\n session.commit()\\n data = nextid[0]\\n if data is not None:\\n return data + 1\\n else:\\n return 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53c120172dd5444f830fcbdfb8c1a3a2\",\n \"score\": \"0.50754595\",\n \"text\": \"def generate_new_trader_id(self):\\n _id = self.get_num_records(self.table) + 1\\n print(_id)\\n return _id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1cf44098498cad7fdad42786e09d57de\",\n \"score\": \"0.50678444\",\n \"text\": \"def next(self, session_id):\\n \\n self.dbc.find_update_increment(1, session_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65f104cf45cf2bfd2b17ef859adfa70e\",\n \"score\": \"0.50670964\",\n \"text\": \"def next_track(self, message):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a593d09ffe7e6f04ad41c6366dcc56f4\",\n \"score\": \"0.50659955\",\n \"text\": \"def _next_image(self, image_id, action):\\n return self._all_graph[image_id][action]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13197ba22271b8d1791464c5b6c7a39d\",\n \"score\": \"0.5048989\",\n \"text\": \"def get_next_picture_id(self, origin_node_id):\\n logger.debug(\\\"Getting next picture ID.\\\")\\n \\n # Let's not hit the disk if we don't have to\\n if self.next_picture_id is not None:\\n logger.debug(\\\"Updating picture ID in memory.\\\")\\n rtn = self.next_picture_id\\n self.next_picture_id += 1\\n return \\\"%d-%d\\\"%(rtn, origin_node_id)\\n \\n logger.debug(\\\"Updating picture ID from disk.\\\")\\n # Query the database to see what the last id was?\\n SQL = \\\"SELECT picture_id FROM db_pictures WHERE origin_node_id = ?\\\"\\n results = self._sql(SQL, [origin_node_id]).fetchall()\\n \\n # If it's empty, this is a new database\\n if not results:\\n self.next_picture_id = 2\\n return \\\"1-%d\\\"%origin_node_id\\n else:\\n id_list = [int(r[0].split(\\\"-\\\")[0]) for r in results]\\n id_list.sort(reverse=True)\\n current_id = id_list[0]\\n rtn = current_id+1\\n self.next_picture_id = rtn+1\\n return \\\"%d-%d\\\"%(rtn, origin_node_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5fbb5f21aaaaf4b336db9f8af2db7b95\",\n \"score\": \"0.5043903\",\n \"text\": \"def db_next_hid( self ):\\n conn = object_session( self ).connection()\\n table = self.table\\n trans = conn.begin()\\n try:\\n next_hid = select( [table.c.hid_counter], table.c.id == self.id, for_update=True ).scalar()\\n table.update( table.c.id == self.id ).execute( hid_counter = ( next_hid + 1 ) )\\n trans.commit()\\n return next_hid\\n except:\\n trans.rollback()\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4cfbb0c28553f6f613b2309a6430b022\",\n \"score\": \"0.5041089\",\n \"text\": \"def train_one_generation(s):\\n s.fitness_computation_on_population()\\n s.create_roulette_wheel()\\n s.select_chromosomes_from_roulette_wheel()\\n s.track_best_chromosomes()\\n s.track_performance_over_generations()\\n s.generate_next_generation()\\n s.generation += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec5d2a9b96693755e1b7ae31fa2aebd1\",\n \"score\": \"0.5040065\",\n \"text\": \"def next_station(self):\\n if self.__connected:\\n try:\\n data = f\\\"{self.__line_number} {self.__station + 1} {self.__id}\\\"\\n self.__send_to_server(data)\\n self.__station += 1\\n if self.__station in self.__stations.keys():\\n self.__total_people_count += self.__stations[self.__station]\\n except:\\n self.__connected = False\\n self.asking_user_to_reconnect = False\\n\\n if int(self.__station) + 1 > Bus.MAX_STATION: # stop the bus client when he reaches his final station\\n self.stop = True\\n self.__gui.display_finished()\\n return\\n else:\\n print(\\\"user trying to send update the server about change in the station but i am offline.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e89662d09ae9601c638a0c4416bec0b\",\n \"score\": \"0.5038427\",\n \"text\": \"def _get_next_entrust_no(self):\\n return str(self.seq_gen.get_next('entrust_no'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f307b129bdb0994e8f8c147081bf79b6\",\n \"score\": \"0.5036329\",\n \"text\": \"def next_id(self):\\n return self._next_id.value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ab93c3fe0b2d893c69feac9f7ec64b2\",\n \"score\": \"0.50349325\",\n \"text\": \"def alloc_id(self):\\n self._nextid += 1\\n return self._nextid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ab93c3fe0b2d893c69feac9f7ec64b2\",\n \"score\": \"0.50349325\",\n \"text\": \"def alloc_id(self):\\n self._nextid += 1\\n return self._nextid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56ed7336019bd5d31f65dfd835ae0066\",\n \"score\": \"0.5033949\",\n \"text\": \"async def get_next_id(db: asyncpg.pool.Pool):\\n query = \\\"\\\"\\\"\\n SELECT id FROM giveaways ORDER BY id DESC LIMIT 1\\n \\\"\\\"\\\"\\n res = await db.fetch(query)\\n if not res:\\n return 0\\n res = res[0]['id']\\n return res + 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fb2ebafc2c77352ded5966611649cdf\",\n \"score\": \"0.5028199\",\n \"text\": \"def findCounterExample(self):\\n\\t\\twhile True:\\n\\t\\t\\tcurrent_state_h = self.resetExploration()\\n\\n\\t\\t\\tfor ep in range(STEPS_PER_EPISODE):\\n\\t\\t\\t\\ta = int(random.random()//(1/(len(self.world.map.availableActions(self.world.map.current)))))\\n\\t\\t\\t\\t(r_h,r_m,next_state_h,next_state_m,obs) = self.executeOneStepExploration(current_state_h,a)\\n\\n\\t\\t\\t\\tif obs != 'null':\\n\\t\\t\\t\\t\\tself.observation_seq.append(obs)\\n\\t\\t\\t\\t\\tself.reward_trace.append(r_m)\\n\\n\\t\\t\\t\\tif self.isCounterExample(r_h,r_m):\\n\\t\\t\\t\\t\\tself.nuof_counter_examples += 1\\n\\t\\t\\t\\t\\treturn None\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tcurrent_state_h = next_state_h\\n\\t\\t\\t\\t\\tself.world.map.current = next_state_m\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d66b23ab5cbefdff261d26a989c9d7b\",\n \"score\": \"0.5024713\",\n \"text\": \"def eval_genomes(genomes, config):\\n global gen\\n #win = WIN\\n gen += 1\\n\\n # start by creating lists holding the genome itself, the\\n # neural network associated with the genome and the\\n # bird object that uses that network to play\\n nets = []\\n birds = []\\n ge = []\\n for genome_id, genome in genomes:\\n index = 0\\n genome.fitness = 0 # start with fitness level of 0\\n net = neat.nn.FeedForwardNetwork.create(genome, config)\\n nets.append(net)\\n birds.append(Bird(230,350))\\n birds[index].index = index\\n ge.append(genome)\\n\\n base = Base(FLOOR)\\n pipes = [Pipe(700)]\\n score = 0\\n\\n run = True\\n while run and len(birds) > 0:\\n\\n pipe_ind = 0\\n if len(birds) > 0:\\n if len(pipes) > 1 and birds[0].x > pipes[0].x: # determine whether to use the first or second\\n pipe_ind = 1\\n\\n top_pipe = pipes[pipe_ind].height\\n bot_pipe = pipes[pipe_ind].bottom\\n pipe = pipes[pipe_ind]\\n\\n #decide_birds_parallel(nets, birds, pipe)\\n\\n for x, bird in enumerate(birds):\\n ge[x].fitness += 0.1\\n decide_birds(nets, birds, bird, top_pipe, bot_pipe)\\n\\n base.move()\\n\\n rem = []\\n add_pipe = False\\n for pipe in pipes:\\n pipe.move()\\n # check for collision\\n for bird in birds:\\n if pipe.collide(bird):\\n ge[birds.index(bird)].fitness -= 1\\n nets.pop(birds.index(bird))\\n ge.pop(birds.index(bird))\\n birds.pop(birds.index(bird))\\n\\n if pipe.x < 0:\\n rem.append(pipe)\\n\\n if not pipe.passed and pipe.x < bird.x:\\n pipe.passed = True\\n add_pipe = True\\n\\n if add_pipe:\\n score += 1\\n # can add this line to give more reward for passing through a pipe (not required)\\n for genome in ge:\\n genome.fitness += 5\\n pipes.append(Pipe(WIN_WIDTH))\\n\\n for r in rem:\\n pipes.remove(r)\\n\\n for bird in birds:\\n if bird.y - 10 >= FLOOR or bird.y < -50:\\n nets.pop(birds.index(bird))\\n ge.pop(birds.index(bird))\\n birds.pop(birds.index(bird))\\n\\n # break if score gets large enough\\n if score > 20:\\n pickle.dump(nets[0], open(\\\"best.pickle\\\", \\\"wb\\\"))\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46919608a184fdb5a8bb3678bfdb487c\",\n \"score\": \"0.5022995\",\n \"text\": \"def next_index():\\n random_index = 0\\n while random_index in prev:\\n random_index = r.randint(0, len(taunts) - 1)\\n prev[0] = prev[1]\\n prev[1] = random_index\\n return random_index\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06111e3f96a3e41a4f5974d0e12635bc\",\n \"score\": \"0.5019364\",\n \"text\": \"def nextNet(self):\\n self.cIndex += 1\\n if self.cIndex < len(self.generation.pop):\\n self.updateCurrentNet()\\n else:\\n print '-' * 30\\n log = \\\"GENERATION {}, AVERAGE {}\\\"\\n print log.format(self.generation.generationNumber, self.generation.averageFitness())\\n print '#' * 30\\n self.generation.generateNextPop()\\n self.cIndex = 0\\n self.updateCurrentNet()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65d224f19df66218edaabea8d12d20ec\",\n \"score\": \"0.5015222\",\n \"text\": \"def take_id(self):\\n result = self.id\\n self.id += 1\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ac5a1c01050d3d728929b048205f9a6\",\n \"score\": \"0.5014364\",\n \"text\": \"def receive_connection(self, origin_neuron, axon):\\n\\t\\tself.ingoing += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6d465a727dd3deda8e4da13d6606442\",\n \"score\": \"0.5013583\",\n \"text\": \"def relabel_id(self, g, old, new):\\n self.id_to_node[g.nodes[new]['mob_id']] = new\",\n \"title\": \"\"\n },\n {\n \"docid\": \"feb058a52296401b50aba8d8829d4828\",\n \"score\": \"0.501338\",\n \"text\": \"def pick_next_cell(self):\\n maximal_connectivity = self.get_maximal_connectivity()\\n assert(maximal_connectivity > 1)\\n not_yet_visited_cells = np.logical_and( self.already_inspected_cells == False, self.actual_connectivity_tested == False )\\n possible_indices = np.where( np.logical_and(self.connectivity_vector == maximal_connectivity,\\n not_yet_visited_cells ) )\\n next_frame_id = self.mesh_one.elements[possible_indices[0][0]].id_in_frame\\n\\n return next_frame_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2de9345060555d20e1eba7c446e43429\",\n \"score\": \"0.5010251\",\n \"text\": \"def get_id():\\n new_id = ID_COUNTER.count\\n ID_COUNTER.count += 1\\n return new_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6820a33a0a3c66bcdaa7af87d378fbb7\",\n \"score\": \"0.500999\",\n \"text\": \"def tracker():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89ce60f1ef54d88310b0aa2b7a9f3785\",\n \"score\": \"0.5008181\",\n \"text\": \"def visit (self):\\n self.count += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"520825c61e25b15f491698d63d1ffe7d\",\n \"score\": \"0.5003811\",\n \"text\": \"def _get_next_tree_id(self):\\r\\n qs = self.get_queryset()\\r\\n max_tree_id = list(qs.aggregate(Max(self.tree_id_attr)).values())[0]\\r\\n max_tree_id = max_tree_id or 0\\r\\n return max_tree_id + 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22f4040859d3cc90afc9eb835faebd15\",\n \"score\": \"0.50011396\",\n \"text\": \"def next_id(self):\\n for n in itertools.count(start=len(self._all) + 1):\\n id_str = \\\"{}{}{}\\\".format(LAYER_ID, core.Node.ID_SEPARATOR, n)\\n try:\\n self._root.by_id(id_str)\\n except KeyError:\\n return id_str\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":893,"cells":{"query_id":{"kind":"string","value":"a32121a9940e96988968f7fa32f121dc"},"query":{"kind":"string","value":"Load training data from file in ``.npz`` format."},"positive_passages":{"kind":"list like","value":[{"docid":"f8d684abd15e1b6a8abcd32d14e978da","score":"0.0","text":"def load_training_data(file, validation_split=0, axes=None, n_images=None, verbose=False):\n\n f = np.load(file)\n X, Y = f['X'], f['Y']\n if axes is None:\n axes = f['axes']\n axes = axes_check_and_normalize(axes)\n\n # assert X.shape == Y.shape\n assert X.ndim == Y.ndim\n assert len(axes) == X.ndim\n assert 'C' in axes\n if n_images is None:\n n_images = X.shape[0]\n assert X.shape[0] == Y.shape[0]\n assert 0 < n_images <= X.shape[0]\n assert 0 <= validation_split < 1\n\n X, Y = X[:n_images], Y[:n_images]\n channel = axes_dict(axes)['C']\n\n if validation_split > 0:\n n_val = int(round(n_images * validation_split))\n n_train = n_images - n_val\n assert 0 < n_val and 0 < n_train\n X_t, Y_t = X[-n_val:], Y[-n_val:]\n X, Y = X[:n_train], Y[:n_train]\n assert X.shape[0] == n_train and X_t.shape[0] == n_val\n X_t = move_channel_for_backend(X_t,channel=channel)\n Y_t = move_channel_for_backend(Y_t,channel=channel)\n\n X = move_channel_for_backend(X,channel=channel)\n Y = move_channel_for_backend(Y,channel=channel)\n\n axes = axes.replace('C','') # remove channel\n if backend_channels_last():\n axes = axes+'C'\n else:\n axes = axes[:1]+'C'+axes[1:]\n\n data_val = (X_t,Y_t) if validation_split > 0 else None\n\n if verbose:\n ax = axes_dict(axes)\n n_train, n_val = len(X), len(X_t) if validation_split>0 else 0\n image_size = tuple( X.shape[ax[a]] for a in axes if a in 'TZYX' )\n n_dim = len(image_size)\n n_channel_in, n_channel_out = X.shape[ax['C']], Y.shape[ax['C']]\n\n print('number of training images:\\t', n_train)\n print('number of validation images:\\t', n_val)\n print('image size (%dD):\\t\\t'%n_dim, image_size)\n print('axes:\\t\\t\\t\\t', axes)\n print('channels in / out:\\t\\t', n_channel_in, '/', n_channel_out)\n\n return (X,Y), data_val, axes","title":""}],"string":"[\n {\n \"docid\": \"f8d684abd15e1b6a8abcd32d14e978da\",\n \"score\": \"0.0\",\n \"text\": \"def load_training_data(file, validation_split=0, axes=None, n_images=None, verbose=False):\\n\\n f = np.load(file)\\n X, Y = f['X'], f['Y']\\n if axes is None:\\n axes = f['axes']\\n axes = axes_check_and_normalize(axes)\\n\\n # assert X.shape == Y.shape\\n assert X.ndim == Y.ndim\\n assert len(axes) == X.ndim\\n assert 'C' in axes\\n if n_images is None:\\n n_images = X.shape[0]\\n assert X.shape[0] == Y.shape[0]\\n assert 0 < n_images <= X.shape[0]\\n assert 0 <= validation_split < 1\\n\\n X, Y = X[:n_images], Y[:n_images]\\n channel = axes_dict(axes)['C']\\n\\n if validation_split > 0:\\n n_val = int(round(n_images * validation_split))\\n n_train = n_images - n_val\\n assert 0 < n_val and 0 < n_train\\n X_t, Y_t = X[-n_val:], Y[-n_val:]\\n X, Y = X[:n_train], Y[:n_train]\\n assert X.shape[0] == n_train and X_t.shape[0] == n_val\\n X_t = move_channel_for_backend(X_t,channel=channel)\\n Y_t = move_channel_for_backend(Y_t,channel=channel)\\n\\n X = move_channel_for_backend(X,channel=channel)\\n Y = move_channel_for_backend(Y,channel=channel)\\n\\n axes = axes.replace('C','') # remove channel\\n if backend_channels_last():\\n axes = axes+'C'\\n else:\\n axes = axes[:1]+'C'+axes[1:]\\n\\n data_val = (X_t,Y_t) if validation_split > 0 else None\\n\\n if verbose:\\n ax = axes_dict(axes)\\n n_train, n_val = len(X), len(X_t) if validation_split>0 else 0\\n image_size = tuple( X.shape[ax[a]] for a in axes if a in 'TZYX' )\\n n_dim = len(image_size)\\n n_channel_in, n_channel_out = X.shape[ax['C']], Y.shape[ax['C']]\\n\\n print('number of training images:\\\\t', n_train)\\n print('number of validation images:\\\\t', n_val)\\n print('image size (%dD):\\\\t\\\\t'%n_dim, image_size)\\n print('axes:\\\\t\\\\t\\\\t\\\\t', axes)\\n print('channels in / out:\\\\t\\\\t', n_channel_in, '/', n_channel_out)\\n\\n return (X,Y), data_val, axes\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"ace84a1c87b92c64a54709a4a12c05a0","score":"0.72628057","text":"def load_npy(self, filename):\n self.set_data(np.load(filename))","title":""},{"docid":"7b2e39750ff5265c68e2283630227104","score":"0.70330673","text":"def load(self, filename: str) -> None:\n with np.load(filename + \".npz\") as data:\n self._data = data['data']\n self._labels = data['labels']","title":""},{"docid":"80721b5885f107b7052ad18eebe80629","score":"0.6879551","text":"def load_data(self):\n try:\n data = np.load(\"pregen_dataset.npz\")\n except:\n raise Exception(\"There is no pregen_dataset.npz to load in this folder!\")\n self.N = data[\"N\"]\n self.x_1d = data[\"x\"]\n self.y_1d = data[\"y\"]\n self.z_1d = data[\"z\"]\n self.shape = data[\"shape\"]\n self.terrain = data[\"terrain\"]","title":""},{"docid":"2e3e2a22c18986c95a54977869e82c9d","score":"0.68498284","text":"def load_npz(npz_file):\n data = np.load(npz_file, encoding=\"latin1\")\n return data['arr_0']","title":""},{"docid":"b3db8401bdcd57a1cb5d2817ddcf6568","score":"0.6806034","text":"def load_npz(file, obj, path='', strict=True):\n with numpy.load(file) as f:\n d = NpzDeserializer(f, path=path, strict=strict)\n d.load(obj)","title":""},{"docid":"2a01fc0f0ff22fcd0aacf1a8e98a44e4","score":"0.6738641","text":"def load_model(self, filename):\n [self.num_layers, self.sizes, self.weights, self.biases] = np.load(\n filename, allow_pickle=True)","title":""},{"docid":"17d67c9b95add5d73d297ced46d0333d","score":"0.6724067","text":"def load_data(path='mnist.npz'):\n \n # 这是原本的路径\n '''path = get_file(path,\n origin='https://s3.amazonaws.com/img-datasets/mnist.npz',\n file_hash='8a61469f7ea1b51cbae51d4f78837e45')'''\n \n # 这是我修改后保存数据的路径\n path = 'G:/desktop/1/mnist.npz'\n \n f = np.load(path)\n x_train, y_train = f['x_train'], f['y_train']\n x_test, y_test = f['x_test'], f['y_test']\n f.close()\n return (x_train, y_train), (x_test, y_test)","title":""},{"docid":"cd773c7908f4fa0c1db1dddea829ec72","score":"0.6700202","text":"def load_npz(filepath):\n d = np.load(filepath)\n return d['Xtr'], d['Xte'], d['ytr'], d['yte'], d['Ztr'], d['Zte']","title":""},{"docid":"0ca55a0695d91ea4fbad798158c2e647","score":"0.66919774","text":"def load_npz(file_name):\n if not file_name.endswith('.npz'):\n file_name += '.npz'\n with np.load(file_name) as loader:\n loader = dict(loader)\n adj_matrix = sp.csr_matrix((loader['adj_data'], loader['adj_indices'],\n loader['adj_indptr']), shape=loader['adj_shape'])\n\n if 'attr_data' in loader:\n attr_matrix = sp.csr_matrix((loader['attr_data'], loader['attr_indices'],\n loader['attr_indptr']), shape=loader['attr_shape'])\n else:\n attr_matrix = None\n\n labels = loader.get('labels')\n\n return adj_matrix, attr_matrix, labels","title":""},{"docid":"2a19c99fc850a03c304dcc53ac4aeb8a","score":"0.6670107","text":"def loadnpy(filename):\n return np.load(filename)","title":""},{"docid":"6a6f4918998a0185fd54080bd3e24ca0","score":"0.66639733","text":"def load_neuraldata(filename):\r\n data = np.load(filename)[()];\r\n return np.array(data)","title":""},{"docid":"032f79ff45f83d05dcb873441ca0b45f","score":"0.66138434","text":"def load_data(path='mnist.npz'):\n f = np.load(path)\n x_train, y_train = f['x_train'], f['y_train']\n x_test, y_test = f['x_test'], f['y_test']\n f.close()\n return (x_train, y_train), (x_test, y_test)","title":""},{"docid":"e42cf43aaa617c575ebcb3df1e1782c3","score":"0.65952045","text":"def load_model(self, file_name):\n self.w_nodes = np.loadtxt(file_name)","title":""},{"docid":"c1fb319a6fbec5bd1beb14469babac78","score":"0.6530733","text":"def test_load_file(self):\n loader = Loader('./tests/example.npz')\n loader.load_file()\n self.assertIsNotNone(loader.data)","title":""},{"docid":"615af61615ccfe8a9bb26c0f059872ad","score":"0.6502738","text":"def load_data(file_name):\n f = gzip.open(file_name, 'rb')\n training_data, validation_data, test_data = cPickle.load(f)\n f.close()\n return (training_data, validation_data, test_data)","title":""},{"docid":"56c6fb07374646eabd1f85788818d592","score":"0.64150727","text":"def loadFile(self, filename):\n f = open(filename, 'r')\n dataset = f.readlines()\n for data in dataset[1:]:\n d = data.split()\n self.lexiVector[d[0]] = np.array(d[1:], dtype=float)","title":""},{"docid":"fd1f65995732d3d772c34efedf6388ce","score":"0.64106315","text":"def load_dat(filename):\n fh = open(filename, \"rb\")\n spam = fh.read(12)\n _, _, sampSize, _ = unpack(\">IIHH\", spam)\n veclen = int(sampSize / 4)\n fh.seek(12, 0)\n dat = np.fromfile(fh, dtype=np.float32)\n dat = dat.reshape(int(len(dat) / veclen), veclen)\n dat = dat.byteswap()\n fh.close()\n return dat","title":""},{"docid":"7653420abf9d9c75ed95c3ac15d4eff3","score":"0.63710654","text":"def loadTrainingData(filename, images, facePoints=None, delim=None, offset=None): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"575420bb93113e982d44dd375352af96","score":"0.6348388","text":"def read_npzdata(folder, file, *arg):\n #import pdb; pdb.set_trace()\n full_path = os.path.join(folder, file)\n fold.file_exists(full_path)\n npz_data = np.load(full_path)\n \n parameters = []\n for param in arg:\n param_read = npz_data[param]\n parameters.append(param_read)\n \n del npz_data, param_read\n return parameters","title":""},{"docid":"b2a271732c9d2a6e42c3b6f875e5a569","score":"0.633575","text":"def load_data(filename):\n with open(filename) as training_file:\n # Split the lines on commas and convert data to floats.\n data = np.array([list(map(float, line.split(','))) for line in training_file.readlines()])\n # Extract label from dataset and return.\n return np.transpose(data[:, :-1]), np.array([data[:, -1]])","title":""},{"docid":"6fc5abb82acf3c8a5ed2016cbf82d5b7","score":"0.6335043","text":"def _load_npz(fname):\n npz = np.load(fname, allow_pickle=True)\n return (npz['info'].tolist(), npz['data'], npz['blinks'], \n npz['saccades'], npz['messages'], \n tuple(npz['ch_names']), tuple(npz['eye_names']))","title":""},{"docid":"14f396d185668f51919034415eff9289","score":"0.6321453","text":"def fromfilename(cls, filename):\n data = pickle.load(open(filename, 'rb'))\n data.data = np.load(filename + '_data.npy')\n data.randoms = np.load(filename + '_randoms.npy')\n return data","title":""},{"docid":"1b82281dcf6942f347bf3d898d432140","score":"0.6290441","text":"def load(filename) :\n # determine filename\n dir = os.path.dirname(__file__)\n f = os.path.join(dir, '', filename)\n\n # load data\n with open(f, 'r') as fid :\n data = np.loadtxt(fid, delimiter=\",\")\n\n return data","title":""},{"docid":"ba67897a34f3249ce8f2cda7ac7361a3","score":"0.6229675","text":"def load_data(data_file):\n print('loading data ...')\n f = gzip.open(data_file, 'rb')\n train_set, valid_set, test_set = load_pickle(f)\n f.close()\n\n train_set_x, train_set_y = make_numpy_array(train_set)\n valid_set_x, valid_set_y = make_numpy_array(valid_set)\n test_set_x, test_set_y = make_numpy_array(test_set)\n\n return [(train_set_x, train_set_y), (valid_set_x, valid_set_y), (test_set_x, test_set_y)]","title":""},{"docid":"bbf663ea51019c667153f44e40cd648a","score":"0.6216395","text":"def load_z(self):\n self.z = self.read_var(self.zvar)\n self.test_shape(self.zvar, self.z.shape, 2)","title":""},{"docid":"db1a06499b5ab8896a2966449680898f","score":"0.61855006","text":"def load_npy(self, path):\n self.matrix = np.load(os.path.join(path, \"vectors.npy\"))\n # self.load_with_alpha(0.6)\n self.vocabulary = Vocabulary_simple()\n self.vocabulary.load(path)\n self.name += os.path.basename(os.path.normpath(path))","title":""},{"docid":"059e3c3596be62da7eb85430098a3f98","score":"0.6182488","text":"def load(self, f):\n # see if there are header rows\n with possibly_open_file(f, 'rb') as g:\n header_lines = 0\n for i, line in enumerate(g):\n try:\n nums = [float(tok) for tok in\n re.split('\\s|,', line.decode('utf-8'))\n if len(tok)]\n if len(nums) >= 2:\n header_lines = i\n break\n except ValueError:\n continue\n\n self.data = np.loadtxt(f, unpack=True, skiprows=header_lines)\n\n if hasattr(f, 'read'):\n fname = f.name\n else:\n fname = f\n\n self.filename = fname\n self.name = os.path.splitext(os.path.basename(fname))[0]","title":""},{"docid":"52b7fdd7c14db900284bc41b428334f3","score":"0.6151441","text":"def load(self, filename):\n\n return np.loadtxt(filename, dtype='int32', skiprows=1)","title":""},{"docid":"27a4b2ad7e04fdbd2aa151197a44c794","score":"0.6146876","text":"def _load_npz(self, path):\n npzfile = np.load(path)\n return dict(npzfile)","title":""},{"docid":"d36f26a108bf6d4ac18731a826f892e0","score":"0.61197513","text":"def read_dataset(filename):","title":""},{"docid":"db0ec23fa5238767ed1209db8b0787d4","score":"0.6106835","text":"def load(filename):\n filepath = reader_filepath(sample, filename, pathfunc)\n return np.load(filepath, allow_pickle=allow_pickle)","title":""},{"docid":"fa32036f63f638d39900c1c427cee9e8","score":"0.6099805","text":"def load(self, file_name):\n super(NeuralNet, self).load(file_name)\n self._model = models.load_model(file_name)\n read_list = joblib.load('{}_nn'.format(file_name))\n self._stopped_epoch = read_list[0]\n self._inference_model = None\n self._inference_batch_size = None","title":""},{"docid":"7781e744a66c65540be2ade22a3050d0","score":"0.60986054","text":"def load_data(self) -> None:\n if self.extension == \"parquet\":\n train = pd.read_parquet(self.paths[0])\n validation = pd.read_parquet(self.paths[1])\n test = pd.read_parquet(self.paths[2])\n elif self.extension == \"csv\":\n train = pd.read_csv(self.paths[0])\n validation = pd.read_csv(self.paths[1])\n test = pd.read_csv(self.paths[2])\n\n self.data = [train, validation, test]","title":""},{"docid":"486288060f31ca8f7409e3bc944f4eb6","score":"0.60809344","text":"def loadz(filepath):\n y = np.load(filepath)\n return coo_matrix((y['data'],(y['row'],y['col'])),shape=y['shape'])","title":""},{"docid":"e802d90ca7dfd06842c6411fe0dddbd8","score":"0.6066158","text":"def loadz(file):\r\n y = np.load(file)\r\n return coo_matrix((y['data'],(y['row'],y['col'])),shape=y['shape'])","title":""},{"docid":"94d5aa51a262e460cdf91ef35932adfe","score":"0.6058451","text":"def laod(cls, file):\n data = np.load(file)\n return cls(data)","title":""},{"docid":"72c436c14565634415b71f48f4be5c72","score":"0.6052672","text":"def loadz(file):\n y = np.load(file)\n return coo_matrix((y['data'],(y['row'],y['col'])),shape=y['shape'])","title":""},{"docid":"d99af401d5ab8e21152bd62dc29d876d","score":"0.6047507","text":"def load_data():\n f = gzip.open('mnist.pkl.gz', 'rb')\n training_data, validation_data, test_data = cPickle.load(f,encoding='bytes')\n f.close()\n return (training_data, validation_data, test_data)","title":""},{"docid":"bacb4ffb5016017493f02e756fe43fd0","score":"0.6047478","text":"def load_training_data(data_file: str) -> list:\n # NOTE: torch expects float data;\n # default numpy.loadtxt reads as float64,\n # so specify dtype=numpy.single\n raw = numpy.loadtxt(data_file, dtype=numpy.single, delimiter=',')\n data = list()\n for i in range(raw.shape[0]):\n data.append((raw[i][1:].tolist(), [raw[i][0]]))\n return data","title":""},{"docid":"dc3a9ab8373050b343cde23889da46de","score":"0.60469246","text":"def load(file: str) -> PointData:\n data = np.load(file)\n point = data['point']\n point_data_data = data['point_data']\n\n point_data = PointData(point, point_data_data)\n return point_data","title":""},{"docid":"857a010f8123fad97e19a4750cbef747","score":"0.60373116","text":"def load(cls, filepath, name=None):\n if isinstance(filepath, np.lib.npyio.NpzFile):\n npfile = filepath\n else:\n npfile = np.load(filepath, allow_pickle=True)\n arrs = dict(npfile.items())\n state = arrs.pop(BrainBase.STATE).item()\n if name is not None:\n state[BrainBase.NAME] = name\n return cls.fromState(state, arrs=arrs)","title":""},{"docid":"4d830a93d1a478acfede48538a4b4940","score":"0.6033458","text":"def load_npy(name):\n\twith open(name, \"rb\") as fr:\n\t\treturn np.load(fr)","title":""},{"docid":"df92d4ef1e2151c831440b54f4384728","score":"0.60331804","text":"def load_data():\n f = gzip.open('mnist.pkl.gz', 'rb')\n train_set, valid_set, test_set = pickle.load(f, encoding='latin')\n f.close()\n return train_set, valid_set, test_set","title":""},{"docid":"2310924c4c5f13215ff44a224ee4d39e","score":"0.6031938","text":"def load_training_data():\n\n train_data_numpy = np.empty((NUM_TRAINING_SAMPLES,) + tuple(GEOMETRY.sinogram_shape))\n train_labels_numpy = np.empty((NUM_TRAINING_SAMPLES,) + tuple(GEOMETRY.volume_shape))\n i = 0\n for index in TRAIN_INDEX:\n train_data_file = '../data_preprocessing/sinograms/sinogram_' + str(index) + '.npy'\n train_data_numpy[i, :, :, :] = np.load(train_data_file)[:GEOMETRY.number_of_projections, :, :]\n train_label_file = '../data_preprocessing/recon_360/recon_' + str(index) + '.npy'\n train_labels_numpy[i, :, :, :] = np.load(train_label_file)\n i = i + 1\n\n return train_data_numpy, train_labels_numpy","title":""},{"docid":"ecda2166afacf6eff60eb0fab315f04a","score":"0.60268885","text":"def loadpklz(filename):\n import gzip\n import pickle\n with gzip.open(filename, 'rb') as f:\n obj = pickle.load(f)\n return obj","title":""},{"docid":"3ef256b86a224cc8cffb86b6bcdbbd3f","score":"0.6021615","text":"def load_experiment(filename):\r\n data = np.load(filename)[()];\r\n return np.array(data)","title":""},{"docid":"c105f55ab89203b6bf1a0f216afd7038","score":"0.60150373","text":"def load_data(input_file):\n data = np.genfromtxt(input_file, delimiter=',', skip_header=0, names=None)\n return data","title":""},{"docid":"6aebd43cd6aaff39d95ea9f24ed45d24","score":"0.600216","text":"def load_data(filename):\n file = open(filename, \"r\")\n lines = file.readlines()\n\n data = []\n for line in lines:\n data.append(line.split(\"\\n\")[0].split(\"\\t\"))\n\n return np.array(data, dtype=float)","title":""},{"docid":"ae7e8e7ad585c106b79125af464021c7","score":"0.59909415","text":"def read_model_data(model, filename):\n filename = os.path.join('models/', '%s.%s' % (filename, PARAM_EXTENSION))\n with open(filename, 'r') as f:\n data = pickle.load(f)\n\n savemat('dir_est_weights_raw.mat', {'data': data})\n\n lasagne.layers.set_all_param_values(model, data)","title":""},{"docid":"c37811897f738402f426a77f5a09bb08","score":"0.599009","text":"def load_model(self, filename):\n model = np.load(f\"models/{filename}\", allow_pickle=True)\n self.beta = model[\"beta\"].reshape(-1, 1)","title":""},{"docid":"05abe7a3b4f714d2f51298f149738005","score":"0.5985719","text":"def import_spyview_dat(data_dir, filename):\n with open(os.path.join(data_dir, filename)) as f:\n dat = np.loadtxt(f)\n return dat","title":""},{"docid":"14d02e680fda012bc52728947ea891cf","score":"0.59768766","text":"def load_data():\n f = gzip.open('mnist.pkl.gz', 'rb')\n training_data, validation_data, test_data = pickle.load(f, encoding=\"latin1\")\n f.close()\n return (training_data, validation_data, test_data)","title":""},{"docid":"208fe2a5225fe658ec1ceada52a3cd3e","score":"0.5968667","text":"def load_data():\r\n f = gzip.open('mnist.pkl.gz', 'rb')\r\n training_data, validation_data, test_data = pickle.load(f, encoding=\"latin1\")\r\n f.close()\r\n return (training_data, validation_data, test_data)","title":""},{"docid":"1c760dc8e7129b5052d64bd9a7f5bd99","score":"0.596175","text":"def from_file(cls,data,dist='rice',**kwargs):\n\n kwargs.update(unpack=False)\n data = np.loadtxt(data,**kwargs)\n\n return cls(data,dist=dist)","title":""},{"docid":"7bc07adea956cb3ffe7425ad81a92b4b","score":"0.5960422","text":"def load_model(self, weight_file): \n\t\tself.w = np.load(weight_file)","title":""},{"docid":"1639d1ac2515c942689c0eb7ebfeb4ea","score":"0.5933849","text":"def load(self, file):\n if isinstance(file, basestring):\n with open(file, \"rb\") as file:\n self.load(file)\n else:\n loaded = load(file)\n self.model = loaded[0]\n self.priorCount = loaded[1]\n self.countPerFeature = loaded[2]","title":""},{"docid":"4b3a6605369c364262917a78bacba547","score":"0.593033","text":"def load_data():\n f = gzip.open('../data/mnist.pkl.gz', 'rb')\n training_data, validation_data, test_data = cPickle.load(f)\n f.close()\n return (training_data, validation_data, test_data)","title":""},{"docid":"2ee123f1c040051be2fbb70289ff7f9e","score":"0.59225124","text":"def load_data():\n\n # Get the data.\n train_data_filename = maybe_download('train-images-idx3-ubyte.gz')\n train_labels_filename = maybe_download('train-labels-idx1-ubyte.gz')\n test_data_filename = maybe_download('t10k-images-idx3-ubyte.gz')\n test_labels_filename = maybe_download('t10k-labels-idx1-ubyte.gz')\n\n # Extract it into numpy arrays.\n train_data = extract_data(train_data_filename, FLAGS.train_size + FLAGS.validation_size)\n train_labels = extract_labels(train_labels_filename, FLAGS.train_size + FLAGS.validation_size)\n test_data = extract_data(test_data_filename, FLAGS.test_size)\n test_labels = extract_labels(test_labels_filename, FLAGS.test_size)\n\n validation_data = train_data[:FLAGS.validation_size, ...]\n validation_labels = train_labels[:FLAGS.validation_size]\n train_data = train_data[FLAGS.validation_size:, ...]\n train_labels = train_labels[FLAGS.validation_size:]\n\n return train_data, train_labels, validation_data, validation_labels, test_data, test_labels","title":""},{"docid":"6323602c8d12dfa5f36e2121f7cc31be","score":"0.59224015","text":"def load_train_idx():\n with open('traindata.pik', 'r') as f:\n traindata = pik.load(f)\n \n return traindata","title":""},{"docid":"8607a80e8cdd7460f35cbcd55f1ce353","score":"0.5921994","text":"def load_dataset(filename):\n with open(filename, 'rb') as f:\n return pickle.load(f)","title":""},{"docid":"bc107e65e57376dd5776990c8db7056b","score":"0.591993","text":"def load_checkpoint(self, file_path):\n checkpoint = torch.load(file_path)\n model = RNNClassifier(\n checkpoint[\"input_size\"],\n checkpoint[\"hidden_size\"],\n checkpoint[\"output_size\"],\n checkpoint[\"n_layers\"],\n )\n model.load_state_dict(checkpoint[\"state_dict\"])\n super().leverage_model(model)","title":""},{"docid":"624b307a6ac4b0cc7a039d01770b85ab","score":"0.59192276","text":"def load_data(self):\n self.data = self.read_var(self.datavar)\n self.test_shape(self.datavar, self.data.shape, 2)","title":""},{"docid":"2608962fc6fd854fdec3f32eb31e4e10","score":"0.5918931","text":"def load_cached_data(filename='/tmp/smallnorb.pkl.gz'):\n\n with gzip.open(filename, 'r') as file:\n train_data, test_data, train_labels, test_labels, train_info, test_info = \\\n cPickle.load(file)\n\n return train_data, test_data, train_labels, test_labels, train_info, test_info","title":""},{"docid":"ecc039a44fd96899259dfc34630abe27","score":"0.59123504","text":"def load_state(self, file):\n tmp_A = sp.load(file)\n\n self.D[0] = 1\n for n in xrange(self.N):\n self.D[n + 1] = tmp_A[n + 1].shape[2]\n self._init_arrays()\n self.A = tmp_A","title":""},{"docid":"6a71c000542fcf0545fc50d3f0773d1b","score":"0.59035015","text":"def load_file(filename):","title":""},{"docid":"eeee226d2aeec56ee8580b0b5c49a6b5","score":"0.5896797","text":"def load(exr_path):\n from time import time\n from subprocess import call\n\n # Convert to .npz\n npz_f = '/tmp/%s_t%s.npz' % \\\n (basename(exr_path).replace('.exr', ''), time())\n call(['python2',\n '%s/../../commandline/exr2npz.py' % dirname(abspath(__file__)),\n exr_path,\n npz_f])\n\n # Load this .npz\n data = np.load(npz_f)\n return data","title":""},{"docid":"da161d2355a03244fc70e86199b4893e","score":"0.5891124","text":"def load_data(fn):\n\tdata = []\n\twith open(FILE_DIR + fn, \"r\") as f:\n\n\t\tfor line in f.readlines():\n\n\t\t\tsample = line.split()\n\n\t\t\t# Strip punctuation from sentence\n\t\t\tfor i in range(len(sample)):\n\n\t\t\t\tsample[i] = \"\".join(\n\t\t\t\t\tl for l in sample[i] if l not in string.punctuation)\n\n\t\t\tdata.append((sample[:-1], int(sample[-1])))\n\n\treturn np.array(data)","title":""},{"docid":"6dca2ec325b48c4d370cace3b9d48e4f","score":"0.5890306","text":"def load_vectors(file_name):\n fin = io.open(file_name, 'r', encoding='utf-8', newline='\\n', errors='ignore')\n # The first line contains the number of rows (n) and the dimensionality (d)\n n, d = map(int, fin.readline().split())\n data = dict()\n for line in fin:\n tokens = line.rstrip().split(' ')\n data[tokens[0]] = map(float, tokens[1:])\n fin.close()\n return data","title":""},{"docid":"f71ae65ce70481e859fbc2f28e428d8d","score":"0.58823687","text":"def read_training_data():\n data_file = open('../RPCRunner/data/data', 'rb')\n labels_file = open('../RPCRunner/data/labels', 'rb')\n labels = np.loadtxt(labels_file, dtype=np.int8)\n data = np.fromstring(np.array([data_file.read(650) for i in labels]),\n dtype=np.uint8)\n return np.reshape(data, (-1, 650)), labels","title":""},{"docid":"75b5b71358a496bcfdb84c7b28d92b28","score":"0.58816165","text":"def load_data(self,filename=\"data.pickle\"):\n with open(filename,\"r\") as f:\n self.feature_vectors,self.classifications,self.keys = pickle.load(f)","title":""},{"docid":"ce4c53f05e7764f08259748bad35d17c","score":"0.5881304","text":"def load(cls, name, file_, **kwargs):\n dict_data = np.load(file_)\n return cls.from_dict(name, dict_data, **kwargs)","title":""},{"docid":"8f2553b3cd84c4269780b655966f3a38","score":"0.5879019","text":"def load(cls,path):\n fpath=Path(path)\n prm=loads(open(fpath+'prm.dat','r'))\n data=dict(np.load(fpath+'data.npz'))\n results=dict(np.load(fpath+'results.npz'))\n return cls(data=data,results=results,parameters=prm)","title":""},{"docid":"1b4ea148bd9c9522995b340647fcb100","score":"0.5878805","text":"def load_model(self, weight_file): \n\n\t\tself.w = np.load(weight_file)","title":""},{"docid":"0e47e854901d749ecd0073c16742bbdd","score":"0.5869626","text":"def load_binary_data(filename, dtype=np.float32):\n f = open(filename, \"rb\")\n data = f.read()\n f.close()\n _data = np.fromstring(data, dtype)\n if sys.byteorder == 'big':\n _data = _data.byteswap()\n return _data","title":""},{"docid":"d97b26bade4261bf6630570a2d167c41","score":"0.5863161","text":"def load_pkl(file):\n f = open(file, 'rb')\n data = pickle.load(f)\n f.close()\n return data","title":""},{"docid":"b646d37a7d7cdc25a2e436037d455497","score":"0.5855168","text":"def dataLoader(file):\n data = []\n with open(file) as file:\n for line in file:\n data.append(line.strip().split(\" \"))\n file.close()\n return np.array(data)","title":""},{"docid":"4555609604f3f468467ccb76c64a5b78","score":"0.58483803","text":"def _load_training_data(self):\n self._save_training_data()","title":""},{"docid":"75c93b7a546dc78fd7d5bf1baf192e9a","score":"0.584714","text":"def load_data(path, kind='train'):\n labels_path = os.path.join(path, '%s-labels-idx1-ubyte.gz' % kind)\n images_path = os.path.join(path, '%s-images-idx3-ubyte.gz' % kind)\n with gzip.open(labels_path, 'rb') as lbpath:\n labels = np.frombuffer(lbpath.read(), dtype=np.uint8, offset=8)\n with gzip.open(images_path, 'rb') as imgpath:\n images = np.frombuffer(imgpath.read(), dtype=np.uint8, offset=16).reshape(len(labels), 784)\n return images, labels","title":""},{"docid":"77099e667ba7de507acd0ec1d609a32e","score":"0.5845702","text":"def load_data():\n data_file = \"./data/mnist.pkl.gz\"\n # Note: it is around 17MB\n check_and_download(data_file,\n \"https://github.com/mnielsen/neural-networks-and-deep-learning/raw/master/data/mnist.pkl.gz\")\n f = gzip.open(data_file, 'rb')\n training_data, validation_data, test_data = pickle.load(\n f, encoding=\"latin1\")\n f.close()\n return training_data, validation_data, test_data","title":""},{"docid":"d4083e04b3cdfeff7ff87e7012e69b3e","score":"0.5833193","text":"def read_data_pkl(data_file):\n with open(data_file, \"rb\") as fp:\n data = pkl.load(fp)\n return data","title":""},{"docid":"d6a63f9e0d156ca44eacada673598541","score":"0.5832519","text":"def load_data(path):\n kwargs = {'num_workers': 1, 'pin_memory': True, 'drop_last': True} if args.cuda else {'drop_last': True}\n\n # normalize = transforms.Normalize((0.957, 0.647, 0.349), (0.080, 0.148, 0.153))\n # normalize = transforms.Normalize((0.640, 0.435, 0.240, 0.440), (0.475, 0.342, 0.214, 0.380))\n train_transform = transforms.Compose([\n transforms.Resize(args.input_size),\n transforms.RandomCrop(args.output_size),\n # transforms.RandomHorizontalFlip(),\n # transforms.ToTensor(),\n # normalize,\n ])\n center_transform = transforms.Compose([\n transforms.Resize(args.input_size),\n transforms.CenterCrop(args.output_size),\n # transforms.ToTensor(),\n # normalize,\n ])\n # train_set = Dataset(class_num=2, data_path=os.path.join(path, 'train.txt'),\n if args.data_repeat:\n train_set = Dataset(class_num=2, data_path=os.path.join(path, 'train_new.txt'),\n file_path=path, grayscale=False, p=0.5,transform=train_transform)\n else:\n train_set = Dataset(class_num=2, data_path=os.path.join(path, 'train.txt'),\n file_path=path, grayscale=False, p=0.5,transform=train_transform)\n train_loader = torch.utils.data.DataLoader(train_set,\n batch_size=args.batch_size, shuffle=True, **kwargs)\n test_set = Dataset(class_num=2, data_path=os.path.join(path, 'test.txt'),\n file_path=path, grayscale=False, transform=center_transform)\n test_loader = torch.utils.data.DataLoader(test_set,\n batch_size=args.test_batch_size, shuffle=False, **kwargs)\n return train_loader, test_loader","title":""},{"docid":"5a77584e7d869ab0144d148d41370502","score":"0.5830943","text":"def read_dta_file(filename):\n data = np.fromfile(filename, dtype='>f8')\n return data","title":""},{"docid":"1074bf89110f4eb577e00cbf26d558d4","score":"0.5827046","text":"def load_training_file(file):\n with open(file) as f:\n haiku = f.read()\n return haiku","title":""},{"docid":"ceae20025d93e4cf5c43e2feca116183","score":"0.582278","text":"def load_poses(file_name):\n\tpose_file = os.path.join(poses_path+ file_name)\n\tposes = []\n\t# Read and parse the poses\n\twith open(pose_file, 'r') as f:\n\t\tfor line in f.readlines():\n\t\t\tT = np.fromstring(line, dtype=float, sep=' ')\n\t\t\tT = T.reshape(3, 4)\n\t\t\tposes.append(T)\n\t\t\t# print T\n\tposes_array = np.array(poses)\n\treturn poses_array","title":""},{"docid":"4d1ecbb0a854744c276050008f2a1bf2","score":"0.5818108","text":"def train(self, train_file, verbose=False):\n f = open(train_file, 'r')\n f.close()","title":""},{"docid":"68a580887e4de92a770a702ebf25f687","score":"0.58161604","text":"def read_train_data_f(filename2read, dim_p, h_prev, h_post, idx_f_use, data_size, sp_x=False, sp_y=False, is_npz=False):\n idx_f_use = make_numpy_array(idx_f_use, keep_1dim=True)\n len_filename = len(filename2read)\n _data_size = 0\n _data_size_list = []\n for nidx_d in range(0, len_filename):\n filename2read_sel = filename2read[nidx_d]\n data_read_tmp = np.load(filename2read_sel, allow_pickle=True)\n f_train_in = data_read_tmp['data_f'] if is_npz else data_read_tmp[()]['data_f']\n _data_size = _data_size + f_train_in.shape[0]\n _data_size_list.append(f_train_in.shape[0])\n\n _ratio = data_size / float(_data_size)\n data_size_list = [int(x * _ratio) for x in _data_size_list]\n data_size_list[-1] = data_size - np.sum(data_size_list[0:-1])\n\n dim_x, dim_y = dim_p * h_prev, dim_p * h_post\n dim_x_3 = 3 * h_prev\n if sp_y:\n dim_y_3 = 3 * (h_post + 1)\n else:\n dim_y_3 = 3 * (h_post + 2)\n dim_f = idx_f_use.shape[0] if len(idx_f_use) > 0 else 0\n\n idx_xin_tmp, idx_yin_tmp = np.arange(0, dim_x_3), np.arange(0, dim_y_3)\n if sp_y:\n idx_y0 = np.arange(0, dim_p * h_post)\n idx_y1 = np.arange(dim_p, dim_p * (h_post + 1))\n else:\n idx_y0 = np.arange(dim_p, dim_p * (h_post + 1))\n idx_y1 = np.arange(dim_p * 2, dim_p * (h_post + 2))\n\n h_prev_ref, h_post_ref = dim_x_3, dim_y_3\n\n x_train = np.zeros((data_size, dim_x), dtype=np.float32)\n y0_train = np.zeros((data_size, dim_y), dtype=np.float32)\n y1_train = np.zeros((data_size, dim_y), dtype=np.float32)\n f_train = np.zeros((data_size, dim_f), dtype=np.float32) if dim_f > 0 else []\n\n cnt_data = 0\n idx_sel_list = []\n for nidx_d in range(0, len_filename):\n filename2read_sel = filename2read[nidx_d]\n data_read_tmp = np.load(filename2read_sel, allow_pickle=True)\n\n if is_npz:\n x_train_in = data_read_tmp['data_x_sp'] if sp_x else data_read_tmp['data_x']\n y_train_in = data_read_tmp['data_y_sp'] if sp_y else data_read_tmp['data_y']\n else:\n x_train_in = data_read_tmp[()]['data_x_sp'] if sp_x else data_read_tmp[()]['data_x']\n y_train_in = data_read_tmp[()]['data_y_sp'] if sp_y else data_read_tmp[()]['data_y']\n\n if nidx_d == 0:\n h_prev_ref, h_post_ref = x_train_in.shape[1], y_train_in.shape[1]\n\n if dim_p == 2:\n idx_xin_tmp = np.setdiff1d(idx_xin_tmp, np.arange(2, h_prev_ref, 3))\n idx_yin_tmp = np.setdiff1d(idx_yin_tmp, np.arange(2, h_post_ref, 3))\n\n x_train_in = x_train_in[:, idx_xin_tmp]\n y_train_in = y_train_in[:, idx_yin_tmp]\n\n if dim_f > 0:\n f_train_in = data_read_tmp['data_f'] if is_npz else data_read_tmp[()]['data_f']\n f_train_in = f_train_in[:, idx_f_use]\n\n # Update\n size_before = int(x_train_in.shape[0])\n idx_rand_tmp_ = np.random.permutation(size_before)\n size_after = int(data_size_list[nidx_d])\n idx_rand_tmp = idx_rand_tmp_[np.arange(0, size_after)]\n idx_sel_list.append(idx_rand_tmp)\n\n idx_update_tmp = np.arange(cnt_data, cnt_data + size_after)\n x_train[idx_update_tmp, :] = x_train_in[idx_rand_tmp, :]\n y_train_in_tmp = y_train_in[idx_rand_tmp, :]\n\n y0_train[idx_update_tmp, :] = y_train_in_tmp[:, idx_y0]\n y1_train[idx_update_tmp, :] = y_train_in_tmp[:, idx_y1]\n\n if dim_f > 0:\n f_train[idx_update_tmp, :] = f_train_in[idx_rand_tmp, :]\n\n cnt_data = cnt_data + size_after\n\n idx_update = np.arange(0, cnt_data)\n x_train = x_train[idx_update, :]\n y0_train = y0_train[idx_update, :]\n y1_train = y1_train[idx_update, :]\n\n if dim_f > 0:\n f_train = f_train[idx_update, :]\n else:\n f_train = []\n\n return x_train, y0_train, y1_train, f_train, idx_sel_list","title":""},{"docid":"dd56c0d88fd9b1f4f836a40e37582211","score":"0.5786443","text":"def load(cls, filename):\n print('Loading', filename)\n \n data = np.load(filename)\n \n dataset = Dataset(data['input_interval'], \n data['prediction_interval'], \n data['categories'].tolist())\n \n dataset.train_set = data['train_set_input'], data['train_set_label']\n dataset.test_set = data['test_set_input'], data['test_set_label']\n \n dataset.generation_done = True\n \n return dataset","title":""},{"docid":"14124338f8ba0286bfb477eb25df46da","score":"0.5786158","text":"def load_data(fp, ts):\n\n # have timestamp?\n if ts:\n dt = np.dtype([\n ('event', np.int, 2),\n ('score', np.float),\n ('event_feature', np.dtype([('timestamp', np.int)]))\n ])\n else:\n dt = np.dtype([\n ('event', np.int, 2),\n ('score', np.float)\n ])\n\n # load training data\n x = np.genfromtxt(fname=fp, delimiter='\\t', dtype=dt)\n\n # close file\n if fp is not sys.stdin:\n fp.close()\n\n return x","title":""},{"docid":"1d21555dfa6d658f565f8dbde1082828","score":"0.5783919","text":"def load(self,file):\n self.set_defaults()\n if \"+\" in file:\n files = file.split(\"+\")\n else:\n files = [file]\n for file in files:\n if \".pymodel\" in file:\n with open(file,\"r\") as stream:\n obj = cPickle.load(stream)\n if type(obj)==LineRecognizer:\n for k,v in obj.__dict__:\n self.__dict__[k] = v\n else:\n self.cmodel = obj\n elif \".cmodel\" in file:\n self.cmodel = ocropy.load_IModel(file)\n elif \".csize\" in file:\n self.linemodel = SimpleLineModel()\n self.linemodel.load(file)\n else:\n raise Exception(\"unknown extension\")","title":""},{"docid":"bf45305415b0d697663c8429126a09ff","score":"0.5781997","text":"def load_data(filename):\n if \"images\" in filename:\n offset = IMAGE_OFFSET\n length = IMAGE_SIZE * IMAGE_SIZE\n else:\n offset = LABEL_OFFSET\n length = 1\n\n filepath = os.sep.join((DATA_DIR, filename))\n if not os.path.exists(filepath):\n print(\"Downloading {}\".format(filename))\n urllib.request.urlretrieve(URL+filename, filepath)\n print(\"Done!\")\n with gzip.open(filepath, \"rb\") as file_:\n data = np.frombuffer(file_.read(), np.uint8, offset=offset)\n return data.reshape(-1, length)","title":""},{"docid":"e03bace59271a35937392a1ca7ab59ad","score":"0.5781892","text":"def load_data(self):\n np.random.seed(1234)\n self.input_data = np.random.rand(self.config.n_samples, self.config.n_features)\n self.input_labels = np.ones((self.config.n_samples,), dtype=np.int32)","title":""},{"docid":"3204e624fdd38b8b664d02c05cef657b","score":"0.5778889","text":"def load_labels(filename):\n with gzip.open(filename, 'rb') as f:\n magic, _ = struct.unpack('>ii', f.read(8))\n if magic != MNIST_LABEL:\n raise ValueError(\"Error reading labels\")\n array = np.frombuffer(f.read(), dtype='uint8')\n array = array.reshape(array.size, 1)\n return array","title":""},{"docid":"ae777a5694f99a2088335ac9080037fa","score":"0.57754767","text":"def read_npy(fname, **kwargs):\n if path.splitext(fname)[-1].lower() == \".npy\":\n data = np.load(fname)\n color = kwargs.get('color', None)\n if color is not None:\n data = retrieve_color_section(data, color)\n return data\n else:\n return None","title":""},{"docid":"5d16b85d0b79c7dcf82438956931c838","score":"0.5772619","text":"def load_data_file(self, filename, decimal_point='.'):\n # self.raw_data = pd.DataFrame.from_csv(filename, sep=';')\n self.raw_data = pd.read_csv(filename, sep=';', index_col = 0, \n parse_dates = True, decimal=decimal_point)\n self.cpi_predictions = self.find_data(['cpi'])\n self.cpi_jae_predictions = self.find_data(['jae','xe'], \n exclude_cols=['CPI-jae'])\n self.gap_predictions = self.find_data(['gap'], \n exclude_cols=['Produksjonsgap'])","title":""},{"docid":"7bd9c735a737d7cb6df08ba85fb8bd5c","score":"0.5759066","text":"def initialize_data_from_file(self, filepath):\n with open(filepath, 'r') as f:\n for line in f:\n terms = line.strip().split(',')\n self.examples.append(Feature(terms))","title":""},{"docid":"a9620849daf21bb0d109b2e5fe298b24","score":"0.5751678","text":"def read_compas(filepath='../data/compas_preprocessed.npz',\n random_state=42):\n if filepath.endswith('ready.npz') and os.path.exists(filepath):\n return load_npz(filepath)\n\n d = np.load(filepath)\n X, y, Z = d['X'], d['y'], d['Z']\n\n n = X.shape[0] # Number of examples\n\n # Get train test split\n tr_idx, te_idx = _get_train_test_split(n, train_frac, random_state)\n Xtr, Xte, ytr, yte, Ztr, Zte = _apply_train_test_split(X, y, Z,\n tr_idx, te_idx)\n\n # Whiten feature data\n Xtr, Xte = _whiten_data(Xtr, Xte)\n\n # Center sensitive data\n Ztr, Zte = _center_data(Ztr, Zte)\n\n # Add intercept\n Xtr, Xte = _add_intercept(Xtr, Xte)\n\n # Labels are already 0/1\n\n return Xtr, Xte, ytr, yte, Ztr, Zte","title":""},{"docid":"606f32d24c8b666a456f3342a12b7ce2","score":"0.5751383","text":"def load_data(filename):\n\tud_graph = grew.graph(filename)\n\treturn ud_graph","title":""},{"docid":"ccfe99bf810b14318106e6c15e9d8067","score":"0.5750405","text":"def load_train_data(self):\n for r in self.train_data:\n self.train_label.append(r.pop(0))\n self.train_data = np.array(self.train_data)","title":""},{"docid":"ffab2a5c5e62ed614e7b545cadc2d134","score":"0.5749849","text":"def extract_features(filename, train_dir):\n print('Extracting', filename)\n with open(train_dir+filename) as file:\n data = numpy.genfromtxt(file, delimiter=',')\n num_datapoints = len(data)\n rows = 1\n cols = len(data[0])\n data = data.reshape(num_datapoints, rows, cols, 1)\n return data","title":""},{"docid":"900ab0d0f5568fe4e2492c84d2464f77","score":"0.574869","text":"def save_training_data(file, X, Y, axes):\n isinstance(file,(Path,string_types)) or _raise(ValueError())\n file = Path(file).with_suffix('.npz')\n file.parent.mkdir(parents=True,exist_ok=True)\n\n axes = axes_check_and_normalize(axes)\n len(axes) == X.ndim or _raise(ValueError())\n np.savez(str(file), X=X, Y=Y, axes=axes)","title":""},{"docid":"e5cffd8d644bcd015251bb977bf4327f","score":"0.57472396","text":"def load_test_data(model_name, dir_name):\n print(\"Loading test data...\")\n filename = GRU_DATA_DIRECTORY + dir_name + '/' + model_name + '_test_data.npz'\n npzfile = np.load(filename)\n return npzfile[\"test_data\"]","title":""}],"string":"[\n {\n \"docid\": \"ace84a1c87b92c64a54709a4a12c05a0\",\n \"score\": \"0.72628057\",\n \"text\": \"def load_npy(self, filename):\\n self.set_data(np.load(filename))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b2e39750ff5265c68e2283630227104\",\n \"score\": \"0.70330673\",\n \"text\": \"def load(self, filename: str) -> None:\\n with np.load(filename + \\\".npz\\\") as data:\\n self._data = data['data']\\n self._labels = data['labels']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80721b5885f107b7052ad18eebe80629\",\n \"score\": \"0.6879551\",\n \"text\": \"def load_data(self):\\n try:\\n data = np.load(\\\"pregen_dataset.npz\\\")\\n except:\\n raise Exception(\\\"There is no pregen_dataset.npz to load in this folder!\\\")\\n self.N = data[\\\"N\\\"]\\n self.x_1d = data[\\\"x\\\"]\\n self.y_1d = data[\\\"y\\\"]\\n self.z_1d = data[\\\"z\\\"]\\n self.shape = data[\\\"shape\\\"]\\n self.terrain = data[\\\"terrain\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e3e2a22c18986c95a54977869e82c9d\",\n \"score\": \"0.68498284\",\n \"text\": \"def load_npz(npz_file):\\n data = np.load(npz_file, encoding=\\\"latin1\\\")\\n return data['arr_0']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3db8401bdcd57a1cb5d2817ddcf6568\",\n \"score\": \"0.6806034\",\n \"text\": \"def load_npz(file, obj, path='', strict=True):\\n with numpy.load(file) as f:\\n d = NpzDeserializer(f, path=path, strict=strict)\\n d.load(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a01fc0f0ff22fcd0aacf1a8e98a44e4\",\n \"score\": \"0.6738641\",\n \"text\": \"def load_model(self, filename):\\n [self.num_layers, self.sizes, self.weights, self.biases] = np.load(\\n filename, allow_pickle=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17d67c9b95add5d73d297ced46d0333d\",\n \"score\": \"0.6724067\",\n \"text\": \"def load_data(path='mnist.npz'):\\n \\n # 这是原本的路径\\n '''path = get_file(path,\\n origin='https://s3.amazonaws.com/img-datasets/mnist.npz',\\n file_hash='8a61469f7ea1b51cbae51d4f78837e45')'''\\n \\n # 这是我修改后保存数据的路径\\n path = 'G:/desktop/1/mnist.npz'\\n \\n f = np.load(path)\\n x_train, y_train = f['x_train'], f['y_train']\\n x_test, y_test = f['x_test'], f['y_test']\\n f.close()\\n return (x_train, y_train), (x_test, y_test)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd773c7908f4fa0c1db1dddea829ec72\",\n \"score\": \"0.6700202\",\n \"text\": \"def load_npz(filepath):\\n d = np.load(filepath)\\n return d['Xtr'], d['Xte'], d['ytr'], d['yte'], d['Ztr'], d['Zte']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ca55a0695d91ea4fbad798158c2e647\",\n \"score\": \"0.66919774\",\n \"text\": \"def load_npz(file_name):\\n if not file_name.endswith('.npz'):\\n file_name += '.npz'\\n with np.load(file_name) as loader:\\n loader = dict(loader)\\n adj_matrix = sp.csr_matrix((loader['adj_data'], loader['adj_indices'],\\n loader['adj_indptr']), shape=loader['adj_shape'])\\n\\n if 'attr_data' in loader:\\n attr_matrix = sp.csr_matrix((loader['attr_data'], loader['attr_indices'],\\n loader['attr_indptr']), shape=loader['attr_shape'])\\n else:\\n attr_matrix = None\\n\\n labels = loader.get('labels')\\n\\n return adj_matrix, attr_matrix, labels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a19c99fc850a03c304dcc53ac4aeb8a\",\n \"score\": \"0.6670107\",\n \"text\": \"def loadnpy(filename):\\n return np.load(filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a6f4918998a0185fd54080bd3e24ca0\",\n \"score\": \"0.66639733\",\n \"text\": \"def load_neuraldata(filename):\\r\\n data = np.load(filename)[()];\\r\\n return np.array(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"032f79ff45f83d05dcb873441ca0b45f\",\n \"score\": \"0.66138434\",\n \"text\": \"def load_data(path='mnist.npz'):\\n f = np.load(path)\\n x_train, y_train = f['x_train'], f['y_train']\\n x_test, y_test = f['x_test'], f['y_test']\\n f.close()\\n return (x_train, y_train), (x_test, y_test)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e42cf43aaa617c575ebcb3df1e1782c3\",\n \"score\": \"0.65952045\",\n \"text\": \"def load_model(self, file_name):\\n self.w_nodes = np.loadtxt(file_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1fb319a6fbec5bd1beb14469babac78\",\n \"score\": \"0.6530733\",\n \"text\": \"def test_load_file(self):\\n loader = Loader('./tests/example.npz')\\n loader.load_file()\\n self.assertIsNotNone(loader.data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"615af61615ccfe8a9bb26c0f059872ad\",\n \"score\": \"0.6502738\",\n \"text\": \"def load_data(file_name):\\n f = gzip.open(file_name, 'rb')\\n training_data, validation_data, test_data = cPickle.load(f)\\n f.close()\\n return (training_data, validation_data, test_data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56c6fb07374646eabd1f85788818d592\",\n \"score\": \"0.64150727\",\n \"text\": \"def loadFile(self, filename):\\n f = open(filename, 'r')\\n dataset = f.readlines()\\n for data in dataset[1:]:\\n d = data.split()\\n self.lexiVector[d[0]] = np.array(d[1:], dtype=float)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd1f65995732d3d772c34efedf6388ce\",\n \"score\": \"0.64106315\",\n \"text\": \"def load_dat(filename):\\n fh = open(filename, \\\"rb\\\")\\n spam = fh.read(12)\\n _, _, sampSize, _ = unpack(\\\">IIHH\\\", spam)\\n veclen = int(sampSize / 4)\\n fh.seek(12, 0)\\n dat = np.fromfile(fh, dtype=np.float32)\\n dat = dat.reshape(int(len(dat) / veclen), veclen)\\n dat = dat.byteswap()\\n fh.close()\\n return dat\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7653420abf9d9c75ed95c3ac15d4eff3\",\n \"score\": \"0.63710654\",\n \"text\": \"def loadTrainingData(filename, images, facePoints=None, delim=None, offset=None): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"575420bb93113e982d44dd375352af96\",\n \"score\": \"0.6348388\",\n \"text\": \"def read_npzdata(folder, file, *arg):\\n #import pdb; pdb.set_trace()\\n full_path = os.path.join(folder, file)\\n fold.file_exists(full_path)\\n npz_data = np.load(full_path)\\n \\n parameters = []\\n for param in arg:\\n param_read = npz_data[param]\\n parameters.append(param_read)\\n \\n del npz_data, param_read\\n return parameters\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2a271732c9d2a6e42c3b6f875e5a569\",\n \"score\": \"0.633575\",\n \"text\": \"def load_data(filename):\\n with open(filename) as training_file:\\n # Split the lines on commas and convert data to floats.\\n data = np.array([list(map(float, line.split(','))) for line in training_file.readlines()])\\n # Extract label from dataset and return.\\n return np.transpose(data[:, :-1]), np.array([data[:, -1]])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fc5abb82acf3c8a5ed2016cbf82d5b7\",\n \"score\": \"0.6335043\",\n \"text\": \"def _load_npz(fname):\\n npz = np.load(fname, allow_pickle=True)\\n return (npz['info'].tolist(), npz['data'], npz['blinks'], \\n npz['saccades'], npz['messages'], \\n tuple(npz['ch_names']), tuple(npz['eye_names']))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14f396d185668f51919034415eff9289\",\n \"score\": \"0.6321453\",\n \"text\": \"def fromfilename(cls, filename):\\n data = pickle.load(open(filename, 'rb'))\\n data.data = np.load(filename + '_data.npy')\\n data.randoms = np.load(filename + '_randoms.npy')\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b82281dcf6942f347bf3d898d432140\",\n \"score\": \"0.6290441\",\n \"text\": \"def load(filename) :\\n # determine filename\\n dir = os.path.dirname(__file__)\\n f = os.path.join(dir, '', filename)\\n\\n # load data\\n with open(f, 'r') as fid :\\n data = np.loadtxt(fid, delimiter=\\\",\\\")\\n\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba67897a34f3249ce8f2cda7ac7361a3\",\n \"score\": \"0.6229675\",\n \"text\": \"def load_data(data_file):\\n print('loading data ...')\\n f = gzip.open(data_file, 'rb')\\n train_set, valid_set, test_set = load_pickle(f)\\n f.close()\\n\\n train_set_x, train_set_y = make_numpy_array(train_set)\\n valid_set_x, valid_set_y = make_numpy_array(valid_set)\\n test_set_x, test_set_y = make_numpy_array(test_set)\\n\\n return [(train_set_x, train_set_y), (valid_set_x, valid_set_y), (test_set_x, test_set_y)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbf663ea51019c667153f44e40cd648a\",\n \"score\": \"0.6216395\",\n \"text\": \"def load_z(self):\\n self.z = self.read_var(self.zvar)\\n self.test_shape(self.zvar, self.z.shape, 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db1a06499b5ab8896a2966449680898f\",\n \"score\": \"0.61855006\",\n \"text\": \"def load_npy(self, path):\\n self.matrix = np.load(os.path.join(path, \\\"vectors.npy\\\"))\\n # self.load_with_alpha(0.6)\\n self.vocabulary = Vocabulary_simple()\\n self.vocabulary.load(path)\\n self.name += os.path.basename(os.path.normpath(path))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"059e3c3596be62da7eb85430098a3f98\",\n \"score\": \"0.6182488\",\n \"text\": \"def load(self, f):\\n # see if there are header rows\\n with possibly_open_file(f, 'rb') as g:\\n header_lines = 0\\n for i, line in enumerate(g):\\n try:\\n nums = [float(tok) for tok in\\n re.split('\\\\s|,', line.decode('utf-8'))\\n if len(tok)]\\n if len(nums) >= 2:\\n header_lines = i\\n break\\n except ValueError:\\n continue\\n\\n self.data = np.loadtxt(f, unpack=True, skiprows=header_lines)\\n\\n if hasattr(f, 'read'):\\n fname = f.name\\n else:\\n fname = f\\n\\n self.filename = fname\\n self.name = os.path.splitext(os.path.basename(fname))[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52b7fdd7c14db900284bc41b428334f3\",\n \"score\": \"0.6151441\",\n \"text\": \"def load(self, filename):\\n\\n return np.loadtxt(filename, dtype='int32', skiprows=1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27a4b2ad7e04fdbd2aa151197a44c794\",\n \"score\": \"0.6146876\",\n \"text\": \"def _load_npz(self, path):\\n npzfile = np.load(path)\\n return dict(npzfile)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d36f26a108bf6d4ac18731a826f892e0\",\n \"score\": \"0.61197513\",\n \"text\": \"def read_dataset(filename):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db0ec23fa5238767ed1209db8b0787d4\",\n \"score\": \"0.6106835\",\n \"text\": \"def load(filename):\\n filepath = reader_filepath(sample, filename, pathfunc)\\n return np.load(filepath, allow_pickle=allow_pickle)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa32036f63f638d39900c1c427cee9e8\",\n \"score\": \"0.6099805\",\n \"text\": \"def load(self, file_name):\\n super(NeuralNet, self).load(file_name)\\n self._model = models.load_model(file_name)\\n read_list = joblib.load('{}_nn'.format(file_name))\\n self._stopped_epoch = read_list[0]\\n self._inference_model = None\\n self._inference_batch_size = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7781e744a66c65540be2ade22a3050d0\",\n \"score\": \"0.60986054\",\n \"text\": \"def load_data(self) -> None:\\n if self.extension == \\\"parquet\\\":\\n train = pd.read_parquet(self.paths[0])\\n validation = pd.read_parquet(self.paths[1])\\n test = pd.read_parquet(self.paths[2])\\n elif self.extension == \\\"csv\\\":\\n train = pd.read_csv(self.paths[0])\\n validation = pd.read_csv(self.paths[1])\\n test = pd.read_csv(self.paths[2])\\n\\n self.data = [train, validation, test]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"486288060f31ca8f7409e3bc944f4eb6\",\n \"score\": \"0.60809344\",\n \"text\": \"def loadz(filepath):\\n y = np.load(filepath)\\n return coo_matrix((y['data'],(y['row'],y['col'])),shape=y['shape'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e802d90ca7dfd06842c6411fe0dddbd8\",\n \"score\": \"0.6066158\",\n \"text\": \"def loadz(file):\\r\\n y = np.load(file)\\r\\n return coo_matrix((y['data'],(y['row'],y['col'])),shape=y['shape'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94d5aa51a262e460cdf91ef35932adfe\",\n \"score\": \"0.6058451\",\n \"text\": \"def laod(cls, file):\\n data = np.load(file)\\n return cls(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72c436c14565634415b71f48f4be5c72\",\n \"score\": \"0.6052672\",\n \"text\": \"def loadz(file):\\n y = np.load(file)\\n return coo_matrix((y['data'],(y['row'],y['col'])),shape=y['shape'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d99af401d5ab8e21152bd62dc29d876d\",\n \"score\": \"0.6047507\",\n \"text\": \"def load_data():\\n f = gzip.open('mnist.pkl.gz', 'rb')\\n training_data, validation_data, test_data = cPickle.load(f,encoding='bytes')\\n f.close()\\n return (training_data, validation_data, test_data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bacb4ffb5016017493f02e756fe43fd0\",\n \"score\": \"0.6047478\",\n \"text\": \"def load_training_data(data_file: str) -> list:\\n # NOTE: torch expects float data;\\n # default numpy.loadtxt reads as float64,\\n # so specify dtype=numpy.single\\n raw = numpy.loadtxt(data_file, dtype=numpy.single, delimiter=',')\\n data = list()\\n for i in range(raw.shape[0]):\\n data.append((raw[i][1:].tolist(), [raw[i][0]]))\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc3a9ab8373050b343cde23889da46de\",\n \"score\": \"0.60469246\",\n \"text\": \"def load(file: str) -> PointData:\\n data = np.load(file)\\n point = data['point']\\n point_data_data = data['point_data']\\n\\n point_data = PointData(point, point_data_data)\\n return point_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"857a010f8123fad97e19a4750cbef747\",\n \"score\": \"0.60373116\",\n \"text\": \"def load(cls, filepath, name=None):\\n if isinstance(filepath, np.lib.npyio.NpzFile):\\n npfile = filepath\\n else:\\n npfile = np.load(filepath, allow_pickle=True)\\n arrs = dict(npfile.items())\\n state = arrs.pop(BrainBase.STATE).item()\\n if name is not None:\\n state[BrainBase.NAME] = name\\n return cls.fromState(state, arrs=arrs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d830a93d1a478acfede48538a4b4940\",\n \"score\": \"0.6033458\",\n \"text\": \"def load_npy(name):\\n\\twith open(name, \\\"rb\\\") as fr:\\n\\t\\treturn np.load(fr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df92d4ef1e2151c831440b54f4384728\",\n \"score\": \"0.60331804\",\n \"text\": \"def load_data():\\n f = gzip.open('mnist.pkl.gz', 'rb')\\n train_set, valid_set, test_set = pickle.load(f, encoding='latin')\\n f.close()\\n return train_set, valid_set, test_set\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2310924c4c5f13215ff44a224ee4d39e\",\n \"score\": \"0.6031938\",\n \"text\": \"def load_training_data():\\n\\n train_data_numpy = np.empty((NUM_TRAINING_SAMPLES,) + tuple(GEOMETRY.sinogram_shape))\\n train_labels_numpy = np.empty((NUM_TRAINING_SAMPLES,) + tuple(GEOMETRY.volume_shape))\\n i = 0\\n for index in TRAIN_INDEX:\\n train_data_file = '../data_preprocessing/sinograms/sinogram_' + str(index) + '.npy'\\n train_data_numpy[i, :, :, :] = np.load(train_data_file)[:GEOMETRY.number_of_projections, :, :]\\n train_label_file = '../data_preprocessing/recon_360/recon_' + str(index) + '.npy'\\n train_labels_numpy[i, :, :, :] = np.load(train_label_file)\\n i = i + 1\\n\\n return train_data_numpy, train_labels_numpy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecda2166afacf6eff60eb0fab315f04a\",\n \"score\": \"0.60268885\",\n \"text\": \"def loadpklz(filename):\\n import gzip\\n import pickle\\n with gzip.open(filename, 'rb') as f:\\n obj = pickle.load(f)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ef256b86a224cc8cffb86b6bcdbbd3f\",\n \"score\": \"0.6021615\",\n \"text\": \"def load_experiment(filename):\\r\\n data = np.load(filename)[()];\\r\\n return np.array(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c105f55ab89203b6bf1a0f216afd7038\",\n \"score\": \"0.60150373\",\n \"text\": \"def load_data(input_file):\\n data = np.genfromtxt(input_file, delimiter=',', skip_header=0, names=None)\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6aebd43cd6aaff39d95ea9f24ed45d24\",\n \"score\": \"0.600216\",\n \"text\": \"def load_data(filename):\\n file = open(filename, \\\"r\\\")\\n lines = file.readlines()\\n\\n data = []\\n for line in lines:\\n data.append(line.split(\\\"\\\\n\\\")[0].split(\\\"\\\\t\\\"))\\n\\n return np.array(data, dtype=float)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae7e8e7ad585c106b79125af464021c7\",\n \"score\": \"0.59909415\",\n \"text\": \"def read_model_data(model, filename):\\n filename = os.path.join('models/', '%s.%s' % (filename, PARAM_EXTENSION))\\n with open(filename, 'r') as f:\\n data = pickle.load(f)\\n\\n savemat('dir_est_weights_raw.mat', {'data': data})\\n\\n lasagne.layers.set_all_param_values(model, data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c37811897f738402f426a77f5a09bb08\",\n \"score\": \"0.599009\",\n \"text\": \"def load_model(self, filename):\\n model = np.load(f\\\"models/{filename}\\\", allow_pickle=True)\\n self.beta = model[\\\"beta\\\"].reshape(-1, 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05abe7a3b4f714d2f51298f149738005\",\n \"score\": \"0.5985719\",\n \"text\": \"def import_spyview_dat(data_dir, filename):\\n with open(os.path.join(data_dir, filename)) as f:\\n dat = np.loadtxt(f)\\n return dat\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14d02e680fda012bc52728947ea891cf\",\n \"score\": \"0.59768766\",\n \"text\": \"def load_data():\\n f = gzip.open('mnist.pkl.gz', 'rb')\\n training_data, validation_data, test_data = pickle.load(f, encoding=\\\"latin1\\\")\\n f.close()\\n return (training_data, validation_data, test_data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"208fe2a5225fe658ec1ceada52a3cd3e\",\n \"score\": \"0.5968667\",\n \"text\": \"def load_data():\\r\\n f = gzip.open('mnist.pkl.gz', 'rb')\\r\\n training_data, validation_data, test_data = pickle.load(f, encoding=\\\"latin1\\\")\\r\\n f.close()\\r\\n return (training_data, validation_data, test_data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c760dc8e7129b5052d64bd9a7f5bd99\",\n \"score\": \"0.596175\",\n \"text\": \"def from_file(cls,data,dist='rice',**kwargs):\\n\\n kwargs.update(unpack=False)\\n data = np.loadtxt(data,**kwargs)\\n\\n return cls(data,dist=dist)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7bc07adea956cb3ffe7425ad81a92b4b\",\n \"score\": \"0.5960422\",\n \"text\": \"def load_model(self, weight_file): \\n\\t\\tself.w = np.load(weight_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1639d1ac2515c942689c0eb7ebfeb4ea\",\n \"score\": \"0.5933849\",\n \"text\": \"def load(self, file):\\n if isinstance(file, basestring):\\n with open(file, \\\"rb\\\") as file:\\n self.load(file)\\n else:\\n loaded = load(file)\\n self.model = loaded[0]\\n self.priorCount = loaded[1]\\n self.countPerFeature = loaded[2]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b3a6605369c364262917a78bacba547\",\n \"score\": \"0.593033\",\n \"text\": \"def load_data():\\n f = gzip.open('../data/mnist.pkl.gz', 'rb')\\n training_data, validation_data, test_data = cPickle.load(f)\\n f.close()\\n return (training_data, validation_data, test_data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ee123f1c040051be2fbb70289ff7f9e\",\n \"score\": \"0.59225124\",\n \"text\": \"def load_data():\\n\\n # Get the data.\\n train_data_filename = maybe_download('train-images-idx3-ubyte.gz')\\n train_labels_filename = maybe_download('train-labels-idx1-ubyte.gz')\\n test_data_filename = maybe_download('t10k-images-idx3-ubyte.gz')\\n test_labels_filename = maybe_download('t10k-labels-idx1-ubyte.gz')\\n\\n # Extract it into numpy arrays.\\n train_data = extract_data(train_data_filename, FLAGS.train_size + FLAGS.validation_size)\\n train_labels = extract_labels(train_labels_filename, FLAGS.train_size + FLAGS.validation_size)\\n test_data = extract_data(test_data_filename, FLAGS.test_size)\\n test_labels = extract_labels(test_labels_filename, FLAGS.test_size)\\n\\n validation_data = train_data[:FLAGS.validation_size, ...]\\n validation_labels = train_labels[:FLAGS.validation_size]\\n train_data = train_data[FLAGS.validation_size:, ...]\\n train_labels = train_labels[FLAGS.validation_size:]\\n\\n return train_data, train_labels, validation_data, validation_labels, test_data, test_labels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6323602c8d12dfa5f36e2121f7cc31be\",\n \"score\": \"0.59224015\",\n \"text\": \"def load_train_idx():\\n with open('traindata.pik', 'r') as f:\\n traindata = pik.load(f)\\n \\n return traindata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8607a80e8cdd7460f35cbcd55f1ce353\",\n \"score\": \"0.5921994\",\n \"text\": \"def load_dataset(filename):\\n with open(filename, 'rb') as f:\\n return pickle.load(f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc107e65e57376dd5776990c8db7056b\",\n \"score\": \"0.591993\",\n \"text\": \"def load_checkpoint(self, file_path):\\n checkpoint = torch.load(file_path)\\n model = RNNClassifier(\\n checkpoint[\\\"input_size\\\"],\\n checkpoint[\\\"hidden_size\\\"],\\n checkpoint[\\\"output_size\\\"],\\n checkpoint[\\\"n_layers\\\"],\\n )\\n model.load_state_dict(checkpoint[\\\"state_dict\\\"])\\n super().leverage_model(model)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"624b307a6ac4b0cc7a039d01770b85ab\",\n \"score\": \"0.59192276\",\n \"text\": \"def load_data(self):\\n self.data = self.read_var(self.datavar)\\n self.test_shape(self.datavar, self.data.shape, 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2608962fc6fd854fdec3f32eb31e4e10\",\n \"score\": \"0.5918931\",\n \"text\": \"def load_cached_data(filename='/tmp/smallnorb.pkl.gz'):\\n\\n with gzip.open(filename, 'r') as file:\\n train_data, test_data, train_labels, test_labels, train_info, test_info = \\\\\\n cPickle.load(file)\\n\\n return train_data, test_data, train_labels, test_labels, train_info, test_info\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecc039a44fd96899259dfc34630abe27\",\n \"score\": \"0.59123504\",\n \"text\": \"def load_state(self, file):\\n tmp_A = sp.load(file)\\n\\n self.D[0] = 1\\n for n in xrange(self.N):\\n self.D[n + 1] = tmp_A[n + 1].shape[2]\\n self._init_arrays()\\n self.A = tmp_A\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a71c000542fcf0545fc50d3f0773d1b\",\n \"score\": \"0.59035015\",\n \"text\": \"def load_file(filename):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eeee226d2aeec56ee8580b0b5c49a6b5\",\n \"score\": \"0.5896797\",\n \"text\": \"def load(exr_path):\\n from time import time\\n from subprocess import call\\n\\n # Convert to .npz\\n npz_f = '/tmp/%s_t%s.npz' % \\\\\\n (basename(exr_path).replace('.exr', ''), time())\\n call(['python2',\\n '%s/../../commandline/exr2npz.py' % dirname(abspath(__file__)),\\n exr_path,\\n npz_f])\\n\\n # Load this .npz\\n data = np.load(npz_f)\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da161d2355a03244fc70e86199b4893e\",\n \"score\": \"0.5891124\",\n \"text\": \"def load_data(fn):\\n\\tdata = []\\n\\twith open(FILE_DIR + fn, \\\"r\\\") as f:\\n\\n\\t\\tfor line in f.readlines():\\n\\n\\t\\t\\tsample = line.split()\\n\\n\\t\\t\\t# Strip punctuation from sentence\\n\\t\\t\\tfor i in range(len(sample)):\\n\\n\\t\\t\\t\\tsample[i] = \\\"\\\".join(\\n\\t\\t\\t\\t\\tl for l in sample[i] if l not in string.punctuation)\\n\\n\\t\\t\\tdata.append((sample[:-1], int(sample[-1])))\\n\\n\\treturn np.array(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6dca2ec325b48c4d370cace3b9d48e4f\",\n \"score\": \"0.5890306\",\n \"text\": \"def load_vectors(file_name):\\n fin = io.open(file_name, 'r', encoding='utf-8', newline='\\\\n', errors='ignore')\\n # The first line contains the number of rows (n) and the dimensionality (d)\\n n, d = map(int, fin.readline().split())\\n data = dict()\\n for line in fin:\\n tokens = line.rstrip().split(' ')\\n data[tokens[0]] = map(float, tokens[1:])\\n fin.close()\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f71ae65ce70481e859fbc2f28e428d8d\",\n \"score\": \"0.58823687\",\n \"text\": \"def read_training_data():\\n data_file = open('../RPCRunner/data/data', 'rb')\\n labels_file = open('../RPCRunner/data/labels', 'rb')\\n labels = np.loadtxt(labels_file, dtype=np.int8)\\n data = np.fromstring(np.array([data_file.read(650) for i in labels]),\\n dtype=np.uint8)\\n return np.reshape(data, (-1, 650)), labels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75b5b71358a496bcfdb84c7b28d92b28\",\n \"score\": \"0.58816165\",\n \"text\": \"def load_data(self,filename=\\\"data.pickle\\\"):\\n with open(filename,\\\"r\\\") as f:\\n self.feature_vectors,self.classifications,self.keys = pickle.load(f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce4c53f05e7764f08259748bad35d17c\",\n \"score\": \"0.5881304\",\n \"text\": \"def load(cls, name, file_, **kwargs):\\n dict_data = np.load(file_)\\n return cls.from_dict(name, dict_data, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f2553b3cd84c4269780b655966f3a38\",\n \"score\": \"0.5879019\",\n \"text\": \"def load(cls,path):\\n fpath=Path(path)\\n prm=loads(open(fpath+'prm.dat','r'))\\n data=dict(np.load(fpath+'data.npz'))\\n results=dict(np.load(fpath+'results.npz'))\\n return cls(data=data,results=results,parameters=prm)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b4ea148bd9c9522995b340647fcb100\",\n \"score\": \"0.5878805\",\n \"text\": \"def load_model(self, weight_file): \\n\\n\\t\\tself.w = np.load(weight_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e47e854901d749ecd0073c16742bbdd\",\n \"score\": \"0.5869626\",\n \"text\": \"def load_binary_data(filename, dtype=np.float32):\\n f = open(filename, \\\"rb\\\")\\n data = f.read()\\n f.close()\\n _data = np.fromstring(data, dtype)\\n if sys.byteorder == 'big':\\n _data = _data.byteswap()\\n return _data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d97b26bade4261bf6630570a2d167c41\",\n \"score\": \"0.5863161\",\n \"text\": \"def load_pkl(file):\\n f = open(file, 'rb')\\n data = pickle.load(f)\\n f.close()\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b646d37a7d7cdc25a2e436037d455497\",\n \"score\": \"0.5855168\",\n \"text\": \"def dataLoader(file):\\n data = []\\n with open(file) as file:\\n for line in file:\\n data.append(line.strip().split(\\\" \\\"))\\n file.close()\\n return np.array(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4555609604f3f468467ccb76c64a5b78\",\n \"score\": \"0.58483803\",\n \"text\": \"def _load_training_data(self):\\n self._save_training_data()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75c93b7a546dc78fd7d5bf1baf192e9a\",\n \"score\": \"0.584714\",\n \"text\": \"def load_data(path, kind='train'):\\n labels_path = os.path.join(path, '%s-labels-idx1-ubyte.gz' % kind)\\n images_path = os.path.join(path, '%s-images-idx3-ubyte.gz' % kind)\\n with gzip.open(labels_path, 'rb') as lbpath:\\n labels = np.frombuffer(lbpath.read(), dtype=np.uint8, offset=8)\\n with gzip.open(images_path, 'rb') as imgpath:\\n images = np.frombuffer(imgpath.read(), dtype=np.uint8, offset=16).reshape(len(labels), 784)\\n return images, labels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77099e667ba7de507acd0ec1d609a32e\",\n \"score\": \"0.5845702\",\n \"text\": \"def load_data():\\n data_file = \\\"./data/mnist.pkl.gz\\\"\\n # Note: it is around 17MB\\n check_and_download(data_file,\\n \\\"https://github.com/mnielsen/neural-networks-and-deep-learning/raw/master/data/mnist.pkl.gz\\\")\\n f = gzip.open(data_file, 'rb')\\n training_data, validation_data, test_data = pickle.load(\\n f, encoding=\\\"latin1\\\")\\n f.close()\\n return training_data, validation_data, test_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4083e04b3cdfeff7ff87e7012e69b3e\",\n \"score\": \"0.5833193\",\n \"text\": \"def read_data_pkl(data_file):\\n with open(data_file, \\\"rb\\\") as fp:\\n data = pkl.load(fp)\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6a63f9e0d156ca44eacada673598541\",\n \"score\": \"0.5832519\",\n \"text\": \"def load_data(path):\\n kwargs = {'num_workers': 1, 'pin_memory': True, 'drop_last': True} if args.cuda else {'drop_last': True}\\n\\n # normalize = transforms.Normalize((0.957, 0.647, 0.349), (0.080, 0.148, 0.153))\\n # normalize = transforms.Normalize((0.640, 0.435, 0.240, 0.440), (0.475, 0.342, 0.214, 0.380))\\n train_transform = transforms.Compose([\\n transforms.Resize(args.input_size),\\n transforms.RandomCrop(args.output_size),\\n # transforms.RandomHorizontalFlip(),\\n # transforms.ToTensor(),\\n # normalize,\\n ])\\n center_transform = transforms.Compose([\\n transforms.Resize(args.input_size),\\n transforms.CenterCrop(args.output_size),\\n # transforms.ToTensor(),\\n # normalize,\\n ])\\n # train_set = Dataset(class_num=2, data_path=os.path.join(path, 'train.txt'),\\n if args.data_repeat:\\n train_set = Dataset(class_num=2, data_path=os.path.join(path, 'train_new.txt'),\\n file_path=path, grayscale=False, p=0.5,transform=train_transform)\\n else:\\n train_set = Dataset(class_num=2, data_path=os.path.join(path, 'train.txt'),\\n file_path=path, grayscale=False, p=0.5,transform=train_transform)\\n train_loader = torch.utils.data.DataLoader(train_set,\\n batch_size=args.batch_size, shuffle=True, **kwargs)\\n test_set = Dataset(class_num=2, data_path=os.path.join(path, 'test.txt'),\\n file_path=path, grayscale=False, transform=center_transform)\\n test_loader = torch.utils.data.DataLoader(test_set,\\n batch_size=args.test_batch_size, shuffle=False, **kwargs)\\n return train_loader, test_loader\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a77584e7d869ab0144d148d41370502\",\n \"score\": \"0.5830943\",\n \"text\": \"def read_dta_file(filename):\\n data = np.fromfile(filename, dtype='>f8')\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1074bf89110f4eb577e00cbf26d558d4\",\n \"score\": \"0.5827046\",\n \"text\": \"def load_training_file(file):\\n with open(file) as f:\\n haiku = f.read()\\n return haiku\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ceae20025d93e4cf5c43e2feca116183\",\n \"score\": \"0.582278\",\n \"text\": \"def load_poses(file_name):\\n\\tpose_file = os.path.join(poses_path+ file_name)\\n\\tposes = []\\n\\t# Read and parse the poses\\n\\twith open(pose_file, 'r') as f:\\n\\t\\tfor line in f.readlines():\\n\\t\\t\\tT = np.fromstring(line, dtype=float, sep=' ')\\n\\t\\t\\tT = T.reshape(3, 4)\\n\\t\\t\\tposes.append(T)\\n\\t\\t\\t# print T\\n\\tposes_array = np.array(poses)\\n\\treturn poses_array\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d1ecbb0a854744c276050008f2a1bf2\",\n \"score\": \"0.5818108\",\n \"text\": \"def train(self, train_file, verbose=False):\\n f = open(train_file, 'r')\\n f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68a580887e4de92a770a702ebf25f687\",\n \"score\": \"0.58161604\",\n \"text\": \"def read_train_data_f(filename2read, dim_p, h_prev, h_post, idx_f_use, data_size, sp_x=False, sp_y=False, is_npz=False):\\n idx_f_use = make_numpy_array(idx_f_use, keep_1dim=True)\\n len_filename = len(filename2read)\\n _data_size = 0\\n _data_size_list = []\\n for nidx_d in range(0, len_filename):\\n filename2read_sel = filename2read[nidx_d]\\n data_read_tmp = np.load(filename2read_sel, allow_pickle=True)\\n f_train_in = data_read_tmp['data_f'] if is_npz else data_read_tmp[()]['data_f']\\n _data_size = _data_size + f_train_in.shape[0]\\n _data_size_list.append(f_train_in.shape[0])\\n\\n _ratio = data_size / float(_data_size)\\n data_size_list = [int(x * _ratio) for x in _data_size_list]\\n data_size_list[-1] = data_size - np.sum(data_size_list[0:-1])\\n\\n dim_x, dim_y = dim_p * h_prev, dim_p * h_post\\n dim_x_3 = 3 * h_prev\\n if sp_y:\\n dim_y_3 = 3 * (h_post + 1)\\n else:\\n dim_y_3 = 3 * (h_post + 2)\\n dim_f = idx_f_use.shape[0] if len(idx_f_use) > 0 else 0\\n\\n idx_xin_tmp, idx_yin_tmp = np.arange(0, dim_x_3), np.arange(0, dim_y_3)\\n if sp_y:\\n idx_y0 = np.arange(0, dim_p * h_post)\\n idx_y1 = np.arange(dim_p, dim_p * (h_post + 1))\\n else:\\n idx_y0 = np.arange(dim_p, dim_p * (h_post + 1))\\n idx_y1 = np.arange(dim_p * 2, dim_p * (h_post + 2))\\n\\n h_prev_ref, h_post_ref = dim_x_3, dim_y_3\\n\\n x_train = np.zeros((data_size, dim_x), dtype=np.float32)\\n y0_train = np.zeros((data_size, dim_y), dtype=np.float32)\\n y1_train = np.zeros((data_size, dim_y), dtype=np.float32)\\n f_train = np.zeros((data_size, dim_f), dtype=np.float32) if dim_f > 0 else []\\n\\n cnt_data = 0\\n idx_sel_list = []\\n for nidx_d in range(0, len_filename):\\n filename2read_sel = filename2read[nidx_d]\\n data_read_tmp = np.load(filename2read_sel, allow_pickle=True)\\n\\n if is_npz:\\n x_train_in = data_read_tmp['data_x_sp'] if sp_x else data_read_tmp['data_x']\\n y_train_in = data_read_tmp['data_y_sp'] if sp_y else data_read_tmp['data_y']\\n else:\\n x_train_in = data_read_tmp[()]['data_x_sp'] if sp_x else data_read_tmp[()]['data_x']\\n y_train_in = data_read_tmp[()]['data_y_sp'] if sp_y else data_read_tmp[()]['data_y']\\n\\n if nidx_d == 0:\\n h_prev_ref, h_post_ref = x_train_in.shape[1], y_train_in.shape[1]\\n\\n if dim_p == 2:\\n idx_xin_tmp = np.setdiff1d(idx_xin_tmp, np.arange(2, h_prev_ref, 3))\\n idx_yin_tmp = np.setdiff1d(idx_yin_tmp, np.arange(2, h_post_ref, 3))\\n\\n x_train_in = x_train_in[:, idx_xin_tmp]\\n y_train_in = y_train_in[:, idx_yin_tmp]\\n\\n if dim_f > 0:\\n f_train_in = data_read_tmp['data_f'] if is_npz else data_read_tmp[()]['data_f']\\n f_train_in = f_train_in[:, idx_f_use]\\n\\n # Update\\n size_before = int(x_train_in.shape[0])\\n idx_rand_tmp_ = np.random.permutation(size_before)\\n size_after = int(data_size_list[nidx_d])\\n idx_rand_tmp = idx_rand_tmp_[np.arange(0, size_after)]\\n idx_sel_list.append(idx_rand_tmp)\\n\\n idx_update_tmp = np.arange(cnt_data, cnt_data + size_after)\\n x_train[idx_update_tmp, :] = x_train_in[idx_rand_tmp, :]\\n y_train_in_tmp = y_train_in[idx_rand_tmp, :]\\n\\n y0_train[idx_update_tmp, :] = y_train_in_tmp[:, idx_y0]\\n y1_train[idx_update_tmp, :] = y_train_in_tmp[:, idx_y1]\\n\\n if dim_f > 0:\\n f_train[idx_update_tmp, :] = f_train_in[idx_rand_tmp, :]\\n\\n cnt_data = cnt_data + size_after\\n\\n idx_update = np.arange(0, cnt_data)\\n x_train = x_train[idx_update, :]\\n y0_train = y0_train[idx_update, :]\\n y1_train = y1_train[idx_update, :]\\n\\n if dim_f > 0:\\n f_train = f_train[idx_update, :]\\n else:\\n f_train = []\\n\\n return x_train, y0_train, y1_train, f_train, idx_sel_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd56c0d88fd9b1f4f836a40e37582211\",\n \"score\": \"0.5786443\",\n \"text\": \"def load(cls, filename):\\n print('Loading', filename)\\n \\n data = np.load(filename)\\n \\n dataset = Dataset(data['input_interval'], \\n data['prediction_interval'], \\n data['categories'].tolist())\\n \\n dataset.train_set = data['train_set_input'], data['train_set_label']\\n dataset.test_set = data['test_set_input'], data['test_set_label']\\n \\n dataset.generation_done = True\\n \\n return dataset\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14124338f8ba0286bfb477eb25df46da\",\n \"score\": \"0.5786158\",\n \"text\": \"def load_data(fp, ts):\\n\\n # have timestamp?\\n if ts:\\n dt = np.dtype([\\n ('event', np.int, 2),\\n ('score', np.float),\\n ('event_feature', np.dtype([('timestamp', np.int)]))\\n ])\\n else:\\n dt = np.dtype([\\n ('event', np.int, 2),\\n ('score', np.float)\\n ])\\n\\n # load training data\\n x = np.genfromtxt(fname=fp, delimiter='\\\\t', dtype=dt)\\n\\n # close file\\n if fp is not sys.stdin:\\n fp.close()\\n\\n return x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d21555dfa6d658f565f8dbde1082828\",\n \"score\": \"0.5783919\",\n \"text\": \"def load(self,file):\\n self.set_defaults()\\n if \\\"+\\\" in file:\\n files = file.split(\\\"+\\\")\\n else:\\n files = [file]\\n for file in files:\\n if \\\".pymodel\\\" in file:\\n with open(file,\\\"r\\\") as stream:\\n obj = cPickle.load(stream)\\n if type(obj)==LineRecognizer:\\n for k,v in obj.__dict__:\\n self.__dict__[k] = v\\n else:\\n self.cmodel = obj\\n elif \\\".cmodel\\\" in file:\\n self.cmodel = ocropy.load_IModel(file)\\n elif \\\".csize\\\" in file:\\n self.linemodel = SimpleLineModel()\\n self.linemodel.load(file)\\n else:\\n raise Exception(\\\"unknown extension\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf45305415b0d697663c8429126a09ff\",\n \"score\": \"0.5781997\",\n \"text\": \"def load_data(filename):\\n if \\\"images\\\" in filename:\\n offset = IMAGE_OFFSET\\n length = IMAGE_SIZE * IMAGE_SIZE\\n else:\\n offset = LABEL_OFFSET\\n length = 1\\n\\n filepath = os.sep.join((DATA_DIR, filename))\\n if not os.path.exists(filepath):\\n print(\\\"Downloading {}\\\".format(filename))\\n urllib.request.urlretrieve(URL+filename, filepath)\\n print(\\\"Done!\\\")\\n with gzip.open(filepath, \\\"rb\\\") as file_:\\n data = np.frombuffer(file_.read(), np.uint8, offset=offset)\\n return data.reshape(-1, length)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e03bace59271a35937392a1ca7ab59ad\",\n \"score\": \"0.5781892\",\n \"text\": \"def load_data(self):\\n np.random.seed(1234)\\n self.input_data = np.random.rand(self.config.n_samples, self.config.n_features)\\n self.input_labels = np.ones((self.config.n_samples,), dtype=np.int32)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3204e624fdd38b8b664d02c05cef657b\",\n \"score\": \"0.5778889\",\n \"text\": \"def load_labels(filename):\\n with gzip.open(filename, 'rb') as f:\\n magic, _ = struct.unpack('>ii', f.read(8))\\n if magic != MNIST_LABEL:\\n raise ValueError(\\\"Error reading labels\\\")\\n array = np.frombuffer(f.read(), dtype='uint8')\\n array = array.reshape(array.size, 1)\\n return array\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae777a5694f99a2088335ac9080037fa\",\n \"score\": \"0.57754767\",\n \"text\": \"def read_npy(fname, **kwargs):\\n if path.splitext(fname)[-1].lower() == \\\".npy\\\":\\n data = np.load(fname)\\n color = kwargs.get('color', None)\\n if color is not None:\\n data = retrieve_color_section(data, color)\\n return data\\n else:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d16b85d0b79c7dcf82438956931c838\",\n \"score\": \"0.5772619\",\n \"text\": \"def load_data_file(self, filename, decimal_point='.'):\\n # self.raw_data = pd.DataFrame.from_csv(filename, sep=';')\\n self.raw_data = pd.read_csv(filename, sep=';', index_col = 0, \\n parse_dates = True, decimal=decimal_point)\\n self.cpi_predictions = self.find_data(['cpi'])\\n self.cpi_jae_predictions = self.find_data(['jae','xe'], \\n exclude_cols=['CPI-jae'])\\n self.gap_predictions = self.find_data(['gap'], \\n exclude_cols=['Produksjonsgap'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7bd9c735a737d7cb6df08ba85fb8bd5c\",\n \"score\": \"0.5759066\",\n \"text\": \"def initialize_data_from_file(self, filepath):\\n with open(filepath, 'r') as f:\\n for line in f:\\n terms = line.strip().split(',')\\n self.examples.append(Feature(terms))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9620849daf21bb0d109b2e5fe298b24\",\n \"score\": \"0.5751678\",\n \"text\": \"def read_compas(filepath='../data/compas_preprocessed.npz',\\n random_state=42):\\n if filepath.endswith('ready.npz') and os.path.exists(filepath):\\n return load_npz(filepath)\\n\\n d = np.load(filepath)\\n X, y, Z = d['X'], d['y'], d['Z']\\n\\n n = X.shape[0] # Number of examples\\n\\n # Get train test split\\n tr_idx, te_idx = _get_train_test_split(n, train_frac, random_state)\\n Xtr, Xte, ytr, yte, Ztr, Zte = _apply_train_test_split(X, y, Z,\\n tr_idx, te_idx)\\n\\n # Whiten feature data\\n Xtr, Xte = _whiten_data(Xtr, Xte)\\n\\n # Center sensitive data\\n Ztr, Zte = _center_data(Ztr, Zte)\\n\\n # Add intercept\\n Xtr, Xte = _add_intercept(Xtr, Xte)\\n\\n # Labels are already 0/1\\n\\n return Xtr, Xte, ytr, yte, Ztr, Zte\",\n \"title\": \"\"\n },\n {\n \"docid\": \"606f32d24c8b666a456f3342a12b7ce2\",\n \"score\": \"0.5751383\",\n \"text\": \"def load_data(filename):\\n\\tud_graph = grew.graph(filename)\\n\\treturn ud_graph\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ccfe99bf810b14318106e6c15e9d8067\",\n \"score\": \"0.5750405\",\n \"text\": \"def load_train_data(self):\\n for r in self.train_data:\\n self.train_label.append(r.pop(0))\\n self.train_data = np.array(self.train_data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffab2a5c5e62ed614e7b545cadc2d134\",\n \"score\": \"0.5749849\",\n \"text\": \"def extract_features(filename, train_dir):\\n print('Extracting', filename)\\n with open(train_dir+filename) as file:\\n data = numpy.genfromtxt(file, delimiter=',')\\n num_datapoints = len(data)\\n rows = 1\\n cols = len(data[0])\\n data = data.reshape(num_datapoints, rows, cols, 1)\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"900ab0d0f5568fe4e2492c84d2464f77\",\n \"score\": \"0.574869\",\n \"text\": \"def save_training_data(file, X, Y, axes):\\n isinstance(file,(Path,string_types)) or _raise(ValueError())\\n file = Path(file).with_suffix('.npz')\\n file.parent.mkdir(parents=True,exist_ok=True)\\n\\n axes = axes_check_and_normalize(axes)\\n len(axes) == X.ndim or _raise(ValueError())\\n np.savez(str(file), X=X, Y=Y, axes=axes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5cffd8d644bcd015251bb977bf4327f\",\n \"score\": \"0.57472396\",\n \"text\": \"def load_test_data(model_name, dir_name):\\n print(\\\"Loading test data...\\\")\\n filename = GRU_DATA_DIRECTORY + dir_name + '/' + model_name + '_test_data.npz'\\n npzfile = np.load(filename)\\n return npzfile[\\\"test_data\\\"]\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":894,"cells":{"query_id":{"kind":"string","value":"55581e6316965b25df6fc0a49bab86fe"},"query":{"kind":"string","value":"Check pickling of an object across another process. python is the path to the python interpreter (defaults to sys.executable) Set verbose=True to print the unpickled object in the other process."},"positive_passages":{"kind":"list like","value":[{"docid":"03fa3ae7b2e67f43a813be7bd9c17298","score":"0.63948804","text":"def check(obj, *args, **kwds):\n # == undocumented ==\n # python -- the string path or executable name of the selected python\n # verbose -- if True, be verbose about printing warning messages\n # all other args and kwds are passed to dill.dumps #FIXME: ignore on load\n verbose = kwds.pop('verbose', False)\n python = kwds.pop('python', None)\n if python is None:\n import sys\n python = sys.executable\n # type check\n isinstance(python, str)\n import subprocess\n fail = True\n try:\n _obj = dumps(obj, *args, **kwds)\n fail = False\n finally:\n if fail and verbose:\n print(\"DUMP FAILED\")\n #FIXME: fails if python interpreter path contains spaces\n # Use the following instead (which also processes the 'ignore' keyword):\n # ignore = kwds.pop('ignore', None)\n # unpickle = \"dill.loads(%s, ignore=%s)\"%(repr(_obj), repr(ignore))\n # cmd = [python, \"-c\", \"import dill; print(%s)\"%unpickle]\n # msg = \"SUCCESS\" if not subprocess.call(cmd) else \"LOAD FAILED\"\n msg = \"%s -c import dill; print(dill.loads(%s))\" % (python, repr(_obj))\n msg = \"SUCCESS\" if not subprocess.call(msg.split(None,2)) else \"LOAD FAILED\"\n if verbose:\n print(msg)\n return","title":""}],"string":"[\n {\n \"docid\": \"03fa3ae7b2e67f43a813be7bd9c17298\",\n \"score\": \"0.63948804\",\n \"text\": \"def check(obj, *args, **kwds):\\n # == undocumented ==\\n # python -- the string path or executable name of the selected python\\n # verbose -- if True, be verbose about printing warning messages\\n # all other args and kwds are passed to dill.dumps #FIXME: ignore on load\\n verbose = kwds.pop('verbose', False)\\n python = kwds.pop('python', None)\\n if python is None:\\n import sys\\n python = sys.executable\\n # type check\\n isinstance(python, str)\\n import subprocess\\n fail = True\\n try:\\n _obj = dumps(obj, *args, **kwds)\\n fail = False\\n finally:\\n if fail and verbose:\\n print(\\\"DUMP FAILED\\\")\\n #FIXME: fails if python interpreter path contains spaces\\n # Use the following instead (which also processes the 'ignore' keyword):\\n # ignore = kwds.pop('ignore', None)\\n # unpickle = \\\"dill.loads(%s, ignore=%s)\\\"%(repr(_obj), repr(ignore))\\n # cmd = [python, \\\"-c\\\", \\\"import dill; print(%s)\\\"%unpickle]\\n # msg = \\\"SUCCESS\\\" if not subprocess.call(cmd) else \\\"LOAD FAILED\\\"\\n msg = \\\"%s -c import dill; print(dill.loads(%s))\\\" % (python, repr(_obj))\\n msg = \\\"SUCCESS\\\" if not subprocess.call(msg.split(None,2)) else \\\"LOAD FAILED\\\"\\n if verbose:\\n print(msg)\\n return\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"be5a0d70305d6329b2a939b438987978","score":"0.57015604","text":"def is_picklable(obj):\n try:\n pickle.dumps(obj)\n except Exception:\n return False\n return True","title":""},{"docid":"f261160b7dd7376478f85f0b1a0037bc","score":"0.554075","text":"def test_pickle():\n pass","title":""},{"docid":"7727c93a2da3438d527808186874317f","score":"0.5514787","text":"def _try_to_pickle(obj):\n pickle.dumps(obj)","title":""},{"docid":"b19488131b14b85ec8157ee1f75d034b","score":"0.5501831","text":"def check():\n try_it = pickle.load(open(\"data/pickleLilEvery.pkl\", 'rb'))\n print(try_it)","title":""},{"docid":"aedb80a1e4d83b69e3c6544676598ab4","score":"0.55003035","text":"def check_pickable(obj):\n # TESTME!\n temp_file = join(const.TEMP_DIR, str(timestamp()) + \"tmp\")\n try:\n pick_dump(obj, temp_file)\n except:\n return False\n finally:\n try:\n removefile(temp_file)\n except Exception as e:\n log.error(\"The file %s could not be removed: %s\",\n temp_file, e)\n return True","title":""},{"docid":"fa8bb88cdee9b89335723b70f787eef4","score":"0.5398693","text":"def test_pickle() -> None:\n pack_unpack_test(\n Decimal(42),\n use_pickle=True,\n )","title":""},{"docid":"0c7473e624a360822433760d27e76291","score":"0.5341918","text":"def load_obj_pickle(adress, name, load_type=None, load_way=None):\n import cPickle\n\n print 'loading data...', adress + name + '.cpkl'\n\n if load_type == 'serial' or load_type == None:\n if load_way == None or load_way == 'cpickle':\n with open(adress + name + '.cpkl', 'rb') as f:\n return cPickle.load(f)","title":""},{"docid":"3972f5540230fb1dbb5c5ef30791cc20","score":"0.5327268","text":"def pickles(obj,exact=False,safe=False,**kwds):\n if safe: exceptions = (Exception,) # RuntimeError, ValueError\n else:\n exceptions = (TypeError, AssertionError, NotImplementedError, PicklingError, UnpicklingError)\n try:\n pik = copy(obj, **kwds)\n #FIXME: should check types match first, then check content if \"exact\"\n try:\n #FIXME: should be \"(pik == obj).all()\" for numpy comparison, though that'll fail if shapes differ\n result = bool(pik.all() == obj.all())\n except (AttributeError, TypeError):\n warnings.filterwarnings('ignore')\n result = pik == obj\n warnings.resetwarnings()\n if hasattr(result, 'toarray'): # for unusual types like sparse matrix\n result = result.toarray().all()\n if result: return True\n if not exact:\n result = type(pik) == type(obj)\n if result: return result\n # class instances might have been dumped with byref=False\n return repr(type(pik)) == repr(type(obj)) #XXX: InstanceType?\n return False\n except exceptions:\n return False","title":""},{"docid":"a8417e7002339d6400ade2ba7d06fc43","score":"0.53073394","text":"def test_can_pickle(self):\n settings = UploadSettings(None, FakeDataServiceApi(), None, ProjectNameOrId.create_from_name('mouse'), None)\n params = ('one', 'two', 'three')\n context = UploadContext(settings, params, multiprocessing.Manager().Queue(), 12)\n pickle.dumps(context)","title":""},{"docid":"f777cad13eb4904124f6a91ec22a7088","score":"0.5282426","text":"def test_pickle_round_trip(tmpdir, acetone):\n with tmpdir.as_cwd():\n acetone.pickle(state=\"test\")\n mols = unpickle()\n pickle_mol = mols[\"test\"]\n for atom in acetone.atoms:\n pickle_atom = pickle_mol.get_atom_with_name(atom.atom_name)\n assert pickle_atom.__dict__ == atom.__dict__\n assert acetone.bonds == pickle_mol.bonds\n assert acetone.angles == pickle_mol.angles\n assert acetone.dihedrals == pickle_mol.dihedrals","title":""},{"docid":"a51c893e14f8495e1e28bf6d0156475a","score":"0.52675366","text":"def test_save_load_pickle(self):\n pickle.loads(pickle.dumps(self.V))\n return True","title":""},{"docid":"f7431ee7caa0c5cd6ca7bf2fe3ff3ed2","score":"0.52216977","text":"def test_pickle(self):\n obj = pickle.dumps(self.config)\n config2 = pickle.loads(obj)\n self.assertEqual(self.config.db.sqlite_path, config2.db.sqlite_path)\n self.assertEqual(self.config.server.port, config2.server.port)","title":""},{"docid":"497f7bcc480f85ebeac11610ba95b49d","score":"0.52023524","text":"def jarIt(file,obj):\n file = open(file,'w')\n cPickle.dump(obj,file)\n print \"pickled \",obj\n file.close()\n del file\n return","title":""},{"docid":"e0e31a8ac5ec574a861b250bf7488b5a","score":"0.5148917","text":"def test_store_object_can_be_serialized_by_pickle():\n pickle.dump(_get_store(\"https://example.com\"), io.BytesIO())\n pickle.dump(_get_store(\"databricks\"), io.BytesIO())\n # pickle.dump(_get_store(f\"sqlite:///{tmpdir.strpath}/mlflow.db\"), io.BytesIO())\n # This throws `AttributeError: Can't pickle local object 'create_engine.<locals>.connect'`","title":""},{"docid":"81dbd9931acc68c6916659d12628ae72","score":"0.51201713","text":"def manage_pickle(path, fun, args=None, verbose=True) :\n\n\t# Test if the file already exists\n\tif os.path.exists(path) :\n\t\t# The file exists: load with pickle, and return\n\t\tif verbose==True:\n\t\t\tprint(\"manage_pickle: the file '{}' has been loaded\"\n\t\t\t\t.format(path))\n\t\twith open(path, 'rb') as file:\n\t\t\treturn pickle.load(file)\n\telse:\n\t\tif args==None: result = fun()\n\t\telse: result = fun(args)\n\t\twith open(path, 'wb') as file:\n\t\t\tpickler = pickle.Pickler(file)\n\t\t\tpickler.dump(result)\n\t\treturn result","title":""},{"docid":"f725c6c115d4e1559ece22523001ca33","score":"0.50983566","text":"def test_repr_using_jsonpickle():\n thing = ObjWithJsonPickleRepr()\n thing.child = ObjWithJsonPickleRepr()\n thing.child.parent = thing\n encoded = jsonpickle.encode(thing)\n decoded = jsonpickle.decode(encoded)\n assert id(decoded) == id(decoded.child.parent)","title":""},{"docid":"34a919347f2a8859995cc4f97dca79b6","score":"0.50923824","text":"def test_pickle(self, trigger, jitter):\n trigger.jitter = jitter\n data = pickle.dumps(trigger, 2)\n trigger2 = pickle.loads(data)\n\n for attr in BaseCombiningTrigger.__slots__:\n assert repr(getattr(trigger2, attr)) == repr(getattr(trigger, attr))","title":""},{"docid":"34a919347f2a8859995cc4f97dca79b6","score":"0.50923824","text":"def test_pickle(self, trigger, jitter):\n trigger.jitter = jitter\n data = pickle.dumps(trigger, 2)\n trigger2 = pickle.loads(data)\n\n for attr in BaseCombiningTrigger.__slots__:\n assert repr(getattr(trigger2, attr)) == repr(getattr(trigger, attr))","title":""},{"docid":"e25eff53c700776937033b01e54d0baa","score":"0.50833565","text":"def test_double_pickle(tmpdir, acetone):\n with tmpdir.as_cwd():\n acetone.pickle(state=\"input\")\n # remove all coords\n acetone.coords[\"input\"] = []\n acetone.pickle(state=\"after\")\n\n # now check we have both states\n mols = unpickle()\n assert \"after\" in mols\n assert \"input\" in mols","title":""},{"docid":"7f1eeb74bb049a15354626157337988c","score":"0.5041047","text":"def test_pickle(self):\n data1 = {1, 2, 3}\n data2 = {4, 5}\n logoutputpickle = io.BytesIO()\n pickle.dump(data1, logoutputpickle)\n pickle.dump(data2, logoutputpickle)\n instream = io.BytesIO(logoutputpickle.getvalue())\n self.assertEquals (data1, pickle.load(instream))\n self.assertEquals (data2, pickle.load(instream))","title":""},{"docid":"41675bb2c22d538adccda0d279e7e1ed","score":"0.5021402","text":"def load_python_obj(file_name:str) -> Any:\n with open(file_name,'rb') as f:\n obj = pickle.load(f)\n return obj","title":""},{"docid":"4ed860389b3110898ca3f0d9ebc10c2a","score":"0.5017657","text":"def test_pickle():\n utils.pickle_save(d, 'd')\n test = utils.pickle_load('d')\n\n subprocess.check_call(['rm', 'd.pkl'])\n\n assert isinstance(d, dict)\n assert test['entry1'] == 'test'\n assert test['entry2'] == 'also_test'","title":""},{"docid":"a22301d7508879c587d61f9b136dfc33","score":"0.5012499","text":"def test_pickle(level_name):\n observations = [\"RGBD\"]\n config = {\"width\": \"640\", \"height\": \"480\", \"botCount\": \"2\", \"random_seed\": \"42\"}\n renderer = \"hardware\"\n\n env_1 = deepmind_lab.Lab(level_name, observations, config=config, renderer=renderer)\n env_1 = DmLabCompatibilityV0(env_1)\n\n env_2 = pickle.loads(pickle.dumps(env_1))\n\n obs_1, info_1 = env_1.reset()\n obs_2, info_2 = env_2.reset()\n assert data_equivalence(obs_1, obs_2)\n assert data_equivalence(info_1, info_2)\n for _ in range(100):\n actions = env_1.action_space.sample()\n obs_1, reward_1, term_1, trunc_1, info_1 = env_1.step(actions)\n obs_2, reward_2, term_2, trunc_2, info_2 = env_2.step(actions)\n # assert data_equivalence(obs_1, obs_2)\n assert reward_1 == reward_2\n assert term_1 == term_2 and trunc_1 == trunc_2\n assert data_equivalence(info_1, info_2)\n\n env_1.close()\n env_2.close()","title":""},{"docid":"00ac57b69eedb9e322441fcefa9198a7","score":"0.5003634","text":"def test_pickle_simple():\n t = task(actions=[\"echo\"], name=\"t\")\n pkl = pickle.dumps(t)\n t2 = pickle.loads(pkl)\n assert t2.actions == [\"echo\"]","title":""},{"docid":"932e57724b5c81e71d8f6e9777dc100b","score":"0.500185","text":"def pickleme(obj, filename, protocol=2):\n with open(filename, 'w') as f:\n cPickle.dump(obj, f, protocol=protocol)","title":""},{"docid":"f1deb625d50a9ce49d9a817bf92127ab","score":"0.49529862","text":"def saveToPickleFile(python_object, path_to_file='object.pkl'):\n out_file = open(path_to_file, 'wb')\n pkl.dump(python_object, out_file)\n out_file.close()","title":""},{"docid":"9ec1c3ca17254909c67fd95a957e2296","score":"0.49479043","text":"def test_pickling(simulation_factory, two_particle_snapshot_factory):\n sim = simulation_factory(two_particle_snapshot_factory())\n mc = hoomd.hpmc.integrate.Sphere(d=0.1, a=0.1)\n mc.shape['A'] = dict(diameter=1.1)\n mc.shape['B'] = dict(diameter=1.3)\n sim.operations.integrator = mc\n\n cl = hoomd.hpmc.update.Clusters(trigger=hoomd.trigger.Periodic(5),\n pivot_move_ratio=0.1)\n operation_pickling_check(cl, sim)","title":""},{"docid":"a88f96038af3edd13950cf60e602c0cd","score":"0.49454793","text":"def test_pickle(self):\n x,s = self.create_testdata()\n X = Container()\n X.add(x)\n task = mmCCZI()\n task.fit(X)\n with tempfile.TemporaryFile(mode='w+b') as tf:\n cPickle.dump(task, tf)","title":""},{"docid":"fa4cd65f92a716bbe641988333a3e24f","score":"0.4927952","text":"def test_pickle():\n\n dct = ODict([\n (\"c\", 3),\n (\"d\", 4),\n ])\n data = pickle.dumps(dct)\n dct2 = pickle.loads(data)\n assert dct == dct2","title":""},{"docid":"15f47236e456522515d6ae0c33db045a","score":"0.49261117","text":"def save_obj_pickle(obj, adress, name, save_type='single_file'):\n\n print 'saving pickle data...', adress + name\n grow(adress)\n if save_type == 'single_file':\n with open(adress + name + '.cpkl', 'wb') as f:\n cPickle.dump(obj, f, cPickle.HIGHEST_PROTOCOL)\n\n #\"\"\"\n # Serial divided\n if save_type == 'serial_divided':\n os.system(\"rm \" + adress + \"/*.cpkl\" + \"> /dev/null 2>&1\")\n beg = time.time()\n for dic in obj:\n # separating test_dic of xs_dic\n if dic['name'] == 'xs_dic':\n with open(adress + name + '/' + dic['name'] + '.cpkl', 'wb') as f:\n cPickle.dump(dic, f, cPickle.HIGHEST_PROTOCOL)\n else:\n # separating each test\n for test in dic['test'].keys():\n with open(adress + name + '/' + dic['name'] + '_' + test + '.cpkl', 'wb') as f:\n cPickle.dump(dic['test'][test], f, cPickle.HIGHEST_PROTOCOL)\n print time.time() - beg # 0.51","title":""},{"docid":"80269120c76a9348b8811add40e150b8","score":"0.49125978","text":"def zodb_pickle(obj):\n f = BytesIO()\n p = PersistentPickler(_persistent_id, f, _protocol)\n klass = obj.__class__\n assert not hasattr(obj, '__getinitargs__'), \"not ready for constructors\"\n args = None\n\n mod = getattr(klass, '__module__', None)\n if mod is not None:\n klass = mod, klass.__name__\n\n state = obj.__getstate__()\n\n p.dump((klass, args))\n p.dump(state)\n return f.getvalue()","title":""},{"docid":"bde011e49d12df0fe6742652a1b73838","score":"0.49102893","text":"def MeasurePickle(python, options, extra_args):\n bm_path = Relative(\"performance/bm_pickle.py\")\n return MeasureGeneric(python, options, bm_path, extra_args=extra_args)","title":""},{"docid":"44d6940d10ee29c368d91669198158cf","score":"0.4906187","text":"def load(self, pickled_blackbox) -> None:\n loaded_data = None\n\n try:\n loaded_data = pickle.loads(pickled_blackbox)\n except pickle.UnpicklingError as e:\n loaded_data = None\n print(\"UnpicklingError: \", str(e))\n\n if loaded_data:\n try:\n self.models = loaded_data[\"models\"]\n self.scaler = loaded_data[\"scaler\"]\n self.verbose = loaded_data[\"verbose\"]\n except KeyError:\n print(\"Keys not found in pickle!\")","title":""},{"docid":"6a888fe0bf1136a22b79f39d24addedc","score":"0.49028245","text":"def test_pickle_and_unpickle_no_usage(token_response):\n pickled_versions = [\n pickle.dumps(token_response, protocol=n)\n for n in range(pickle.HIGHEST_PROTOCOL + 1)\n ]\n unpickled_versions = [pickle.loads(x) for x in pickled_versions]\n for x in unpickled_versions:\n assert x.by_resource_server == token_response.by_resource_server","title":""},{"docid":"9f9c0f1f5bc3aeb5971596ed5461043f","score":"0.4900738","text":"def test_builtin_realizations(name, protocol):\n # get class instance\n original = getattr(cosmology, name)\n\n # pickle and unpickle\n f = pickle.dumps(original, protocol=protocol)\n unpickled = pickle.loads(f)\n\n # test equality\n assert unpickled == original\n assert unpickled.meta == original.meta","title":""},{"docid":"e3c53598aa52428cf0f1954efb520e30","score":"0.48850453","text":"def test_is_pickleable(self, obs_dim, action_dim):\n env = MetaRLEnv(DummyBoxEnv(obs_dim=obs_dim, action_dim=action_dim))\n with mock.patch(('metarl.tf.policies.'\n 'continuous_mlp_policy.MLPModel'),\n new=SimpleMLPModel):\n policy = ContinuousMLPPolicy(env_spec=env.spec)\n\n env.reset()\n obs, _, _, _ = env.step(1)\n\n with tf.compat.v1.variable_scope('ContinuousMLPPolicy/MLPModel',\n reuse=True):\n return_var = tf.compat.v1.get_variable('return_var')\n # assign it to all one\n return_var.load(tf.ones_like(return_var).eval())\n output1 = self.sess.run(\n policy.model.outputs,\n feed_dict={policy.model.input: [obs.flatten()]})\n\n p = pickle.dumps(policy)\n with tf.compat.v1.Session(graph=tf.Graph()) as sess:\n policy_pickled = pickle.loads(p)\n output2 = sess.run(\n policy_pickled.model.outputs,\n feed_dict={policy_pickled.model.input: [obs.flatten()]})\n assert np.array_equal(output1, output2)","title":""},{"docid":"627be76d59515ae06cab73dd5b567cc5","score":"0.48549765","text":"def unpickle_something(pickle_filename):\n if not os.path.isfile(pickle_filename):\n return f\"{pickle_filename} doesn't exist!\"\n with open(pickle_filename, 'rb') as file:\n something_unpickled = pickle.load(file)\n print(f\"Loaded '{pickle_filename}'\")\n return something_unpickled","title":""},{"docid":"0a93746c6048116f345d7eef7890244e","score":"0.48459494","text":"def _dump(obj, fname) :\n\ttry :\n\t\tf = open(fname, \"w\")\n\t\tpickle.dump(obj, f, _Pickle_Protocol)\n\t\tf.flush()\n\tfinally :\n\t\tf.close()","title":""},{"docid":"0f17921d510c2f8674dc0d4f74a7be9c","score":"0.4827176","text":"def test_1234_pickle():\n __location__ = os.path.realpath(\n os.path.join(os.getcwd(), os.path.dirname(__file__))\n )\n with open(os.path.join(__location__, \"spec-1234.json\")) as f:\n d = json.loads(f.read())\n spec = CombinatorialSpecification.from_dict(d)\n spec.count_objects_of_size(10)\n s = pickle.dumps(spec)\n new_spec = pickle.loads(s)\n assert new_spec == spec","title":""},{"docid":"8903443b7e1967093113856f7a2763b2","score":"0.48209557","text":"def unpickleObject(fname):\n f = open(os.path.expanduser(fname),'rb')\n try:\n obj = cPickle.load(f)\n except:\n f.close()\n raise\n return obj","title":""},{"docid":"92969e44078803a08f86567d27e6cf82","score":"0.48031557","text":"def test_pickle(self, timezone):\n\n trigger = CronTrigger(year=2016, month='5-6', day='20-28', hour=7, minute=25, second='*',\n timezone=timezone)\n data = pickle.dumps(trigger, 2)\n trigger2 = pickle.loads(data)\n\n for attr in CronTrigger.__slots__:\n assert getattr(trigger2, attr) == getattr(trigger, attr)","title":""},{"docid":"a309e8a098b5040f4ee223353e3794e2","score":"0.47961402","text":"def readFromPickleFile(path_to_file='object.pkl'):\n in_file = open(path_to_file, 'rb')\n python_object = pkl.load(in_file)\n return python_object","title":""},{"docid":"86856721b4bc23c7c7775359132ceed4","score":"0.47818154","text":"def big_dump(file_path, object_to_dump):\n SimPickler.dump(file_path+\".new\", object_to_dump)\n shutil.move(file_path+\".new\", file_path)","title":""},{"docid":"bef54f4987ef06f223c65aea0eb56521","score":"0.4778086","text":"def worker_load(adress):\n with open(adress, 'rb') as f:\n return cPickle.load(f)","title":""},{"docid":"b4b5afa73652769a0de4886561fc09ba","score":"0.47728184","text":"def load_object(filename):\n\twith open(filename, \"rb\") as f:\n\t\ttry:\n\t\t\treturn pickle.load(f)\n\t\texcept EOFError:\n\t\t\tpass","title":""},{"docid":"3378626c3f14906f335a248c94ea59d5","score":"0.476833","text":"def mp_with_object():\n g = object()\n return MetaProcessor(g), g, ext","title":""},{"docid":"b35efe333d9e09761e50fb0f93137b84","score":"0.47649705","text":"def pickleObject(obj,fname):\n f = open(os.path.expanduser(fname),'wb')\n cPickle.dump(obj,f,1)","title":""},{"docid":"fdd350400703e1b1bdc6de7f5186c25e","score":"0.475868","text":"def is_picklable(self, event):\n try:\n pickle.dumps(event)\n except Exception as e:\n message = \"(event - %s) is not picklable, Reason: %s\" % (\n event.identify(), e)\n assert False, message","title":""},{"docid":"520a35af30c7254cac14ab7f67c463b1","score":"0.47565234","text":"def evaluate_result(pickled_result):\n # its a pickled object with four parts\n code, result, stdout, stderr = pickle.loads(pickled_result)\n # just dump the stderr and stdout\n sys.stderr.write(stderr)\n sys.stdout.write(stdout)\n # code indicates whether we had an exception or not\n if code:\n return result\n else:\n raise result","title":""},{"docid":"9912c01da21e04d133f2fb5730bd4c85","score":"0.47526616","text":"def dump(file_path, object_to_dump):\n\n if(os.path.exists(file_path)):\n os.remove(file_path)\n\n output_pkl = open(file_path, 'wb')\n pickle.dump(object_to_dump,output_pkl)\n output_pkl.close()","title":""},{"docid":"d4c0bc1f80923506249b523dc6ecb1ec","score":"0.47362074","text":"def picklable_wrapper(obj):\n try:\n pickle.dumps(obj)\n return obj\n except:\n return PicklableWrapper(obj)","title":""},{"docid":"ca30c336bd12ccfdeddb39e9f8362a8f","score":"0.47143298","text":"def load_pickle(filename):\n print(f\"Loaded from utils/{filename}\")\n with open(\"utils/\"+filename, 'rb') as f:\n return pickle.load(f)","title":""},{"docid":"6533f4212d27e3a2af01ea5549b32097","score":"0.4710462","text":"def writepickle(obj, filepath, verbose=False, protocol = -1):\n\tif os.path.splitext(filepath)[1] == \".gz\":\n\t\tpkl_file = gzip.open(filepath, 'wb')\n\telse:\n\t\tpkl_file = open(filepath, 'wb')\n\t\n\tpickle.dump(obj, pkl_file, protocol)\n\tpkl_file.close()\n\tif verbose: print \"Wrote %s\" % filepath","title":""},{"docid":"17d5b989a5f284f5dbdaaa1fb0580307","score":"0.47098103","text":"def dump(obj, file, protocol=None, byref=None, fmode=None, recurse=None, **kwds):#, strictio=None):\n from .settings import settings\n protocol = settings['protocol'] if protocol is None else int(protocol)\n _kwds = kwds.copy()\n _kwds.update(dict(byref=byref, fmode=fmode, recurse=recurse))\n Pickler(file, protocol, **_kwds).dump(obj)\n return","title":""},{"docid":"505f818acd73142c2d5080e426591872","score":"0.470276","text":"def __init__(self, do_pickle: bool = False) -> None:\n super().__init__()\n self.do_pickle = do_pickle","title":""},{"docid":"505f818acd73142c2d5080e426591872","score":"0.470276","text":"def __init__(self, do_pickle: bool = False) -> None:\n super().__init__()\n self.do_pickle = do_pickle","title":""},{"docid":"892b80348acd0372b1ee525b33f4ee1e","score":"0.47005093","text":"def __init__(self, lookupdir):\n lookupdir = lookupdir.strip('/')\n self.__pickle_dir__ = lookupdir\n self.__full_dir__ = '%s/%s' % (os.getcwd(), lookupdir)\n sys.path.append(self.__pickle_dir__)\n self.check_dir()\n \"\"\"\n okay, dir exists\n \"\"\"\n try:\n self.__pickle_files__ = os.listdir(self.__pickle_dir__)\n except:\n self.__pickle_files__ = []\n\n for filename in self.__pickle_files__:\n if filename.endswith('.pick'):\n self.process_import(filename)","title":""},{"docid":"81031b1d5e000b19dde895207f017740","score":"0.46926653","text":"def get_pickleable_etype(cls, loads=..., dumps=...):\n ...","title":""},{"docid":"5d4a1c0bde43a81a33f768ffb9824d9a","score":"0.46892667","text":"def load_object(self, value):\n if value is None:\n return None\n return pickle.loads(value)","title":""},{"docid":"c761528771917e6bdae60d52752776df","score":"0.46856233","text":"def load_obj(name):\n with open(name, 'rb') as f:\n return pickle.load(f)","title":""},{"docid":"cd9aefa76418517c4519c64eeb6fea6b","score":"0.46820268","text":"def loadPickle(name, picklepath=datapath, num=None, red=0):\n \n if red:\n if num == None:\n # Check if there is more than one\n flist = filelist(picklepath)\n if (name + '_red_1.pkl') in flist:\n print 'LOADPICKLE: Warning! There is more than one pickle file for this object! Make sure it is the right one!'\n f = open(picklepath+name+'_red.pkl', 'rb')\n pickle = cPickle.load(f)\n f.close()\n elif num != None:\n f = open(picklepath+name+'_red_'+numCheck(num)+'.pkl', 'rb')\n pickle = cPickle.load(f)\n f.close()\n return pickle\n else:\n if num == None:\n # Check if there is more than one\n flist = filelist(picklepath)\n if (name + '_obs_1.pkl') in flist:\n print 'LOADPICKLE: Warning! There is more than one pickle file for this object! Make sure it is the right one!'\n f = open(picklepath+name+'_obs.pkl', 'rb')\n pickle = cPickle.load(f)\n f.close()\n elif num != None:\n f = open(picklepath+name+'_obs_'+numCheck(num)+'.pkl', 'rb')\n pickle = cPickle.load(f)\n f.close()\n return pickle","title":""},{"docid":"30a11a0a7f623d25c621367d2c967a97","score":"0.46734425","text":"def loadVariable(filename='objs.pkl',verbose=False):\r\n with open(filename, 'rb') as f:\r\n variable = pickle.load(f)\r\n if(verbose):\r\n print(\"It has been successfully loaded in \"+filename)\r\n return variable","title":""},{"docid":"ab72955193d7bf1c6b15a278695094f1","score":"0.4672109","text":"def test_clone(self):\n obj = self.binning\n self.assertEqual(obj, obj.clone())","title":""},{"docid":"ab72955193d7bf1c6b15a278695094f1","score":"0.4672109","text":"def test_clone(self):\n obj = self.binning\n self.assertEqual(obj, obj.clone())","title":""},{"docid":"00e26ab19fde795ff1322b9c73737d51","score":"0.4671238","text":"def pickle_load(filename: str) -> object:\n with open(f'../pickles/{filename}.pickle', 'rb') as f:\n print(f'Loading {filename}.pickle')\n return pickle.load(f)","title":""},{"docid":"615ad0e3d0e622cb14882227bcc326d0","score":"0.46633083","text":"def save_pickle(filename, obj):\n with open(\"utils/\"+filename, 'wb') as f:\n pickle.dump(obj, f)\n print(f\"Saved to utils/{filename}\")","title":""},{"docid":"cf9ab55fcdfa40fae8d9ccef76c0b033","score":"0.46621472","text":"def test_parse_pickle(self):\n data = {'key': 'value'}\n raw = pickle.dumps(data)\n res = self.encoder_pickle.parse(raw)\n assert res['key'] == 'value'","title":""},{"docid":"a60edf50a1fbceede21f61dda780ed38","score":"0.4647641","text":"def write_pickle(self, obj, protocol= -1):\n dump_pickle(obj, self.file, protocol)","title":""},{"docid":"e7465d4b306995a8668e9c105406711a","score":"0.46459946","text":"def test_pickle(frozen_thawed_angle: AngleClass) -> None:\n Angle = frozen_thawed_angle\n test_data = 38.0, 257.125, 0.0\n\n orig = Angle(test_data)\n pick = pickle.dumps(orig)\n thaw = pickle.loads(pick)\n\n assert orig is not thaw\n assert orig == thaw\n\n # Ensure both produce the same pickle - so they can be interchanged.\n cy_pick = pickle.dumps(getattr(vec_mod, 'Cy_' + Angle.__name__)(test_data))\n py_pick = pickle.dumps(getattr(vec_mod, 'Py_' + Angle.__name__)(test_data))\n\n assert cy_pick == py_pick == pick","title":""},{"docid":"c12d392929beb2d3cffebdcb947c61cb","score":"0.46389517","text":"def find_pickleable_exception(exc, loads=..., dumps=...):\n ...","title":""},{"docid":"e4c9c079c48017b82ceb1a87b34abfec","score":"0.46337512","text":"def test_prevents_core_dump(self):\n instance = self.test_instance\n instance.open()\n self.mock_module_daemon.prevent_core_dump.assert_called_with()","title":""},{"docid":"a9325060d11afe2981900175a96b8962","score":"0.46261737","text":"def dumpit(obj, fname):\n pklname = fname + '.pkl'\n with open(os.path.join('tmp2', pklname), 'wb') as f:\n pickle.dump(obj, f, -1)","title":""},{"docid":"974b32305ff7c728624b3af7ee1f983e","score":"0.46063992","text":"def test_pickle_current(self):\r\n dump = pickle.dumps(NodeSet(\"foo[1-100]\"))\r\n self.assertNotEqual(dump, None)\r\n nodeset = pickle.loads(dump)\r\n self.assertEqual(nodeset, NodeSet(\"foo[1-100]\"))\r\n self.assertEqual(str(nodeset), \"foo[1-100]\")\r\n self.assertEqual(nodeset[0], \"foo1\")\r\n self.assertEqual(nodeset[1], \"foo2\")\r\n self.assertEqual(nodeset[-1], \"foo100\")","title":""},{"docid":"decadd7795fb0a5737abc7654935e473","score":"0.46051767","text":"def readpickle(filepath, verbose=False):\n\tif os.path.splitext(filepath)[1] == \".gz\":\n\t\tpkl_file = gzip.open(filepath,'rb')\n\telse:\n\t\tpkl_file = open(filepath, 'rb')\n\tobj = pickle.load(pkl_file)\n\tpkl_file.close()\n\tif verbose: print \"Read %s\" % filepath\n\treturn obj","title":""},{"docid":"f68f7aad5632a77af3002be65879818d","score":"0.4602073","text":"def save_python_obj(file_name:str,obj:Any) -> None:\n with open(file_name,'wb') as f:\n pickle.dump(obj,f,protocol=4)","title":""},{"docid":"27963cddebbe7962e7e4a5a84563c335","score":"0.4594329","text":"def read_object(filename):\n with open(filename, 'rb', buffering=2000000) as f:\n obj = pickle.load(f)\n return obj","title":""},{"docid":"36b190e2a74d18a6ab3ff7f2b5fd4087","score":"0.459425","text":"def loads_either(cloudpickle_out, pickle_out):\n if CLOUDPICKLE_AVAILABLE and cloudpickle_out is not None:\n return cloudpickle.loads(cloudpickle_out)\n elif pickle_out is not None:\n return pickle.loads(pickle_out)\n else:\n raise ValueError('Not able to load the pickled object')","title":""},{"docid":"ecb3a73a86d89304e7c94fedc6ca3f8a","score":"0.45935196","text":"def pick_dump(obj, path):\n with open(path, \"wb\") as f:\n pick.dump(obj, f)","title":""},{"docid":"64d48446781f45c2f3cde3123010fc45","score":"0.45796266","text":"def _PickleBenchmark(base_python, changed_python, options, extra_args):\n return SimpleBenchmark(MeasurePickle,\n base_python, changed_python, options, extra_args)","title":""},{"docid":"84aa5d19309c1e5ab875b0d2d9d4adbd","score":"0.45681998","text":"def pickleChecker():\n PICKLE_FOLDER = \"../../pickles/\"\n PICKLE_PATH_X = Path(PICKLE_FOLDER + 'x_hand.npy')\n PICKLE_PATH_Y = Path(PICKLE_FOLDER + 'y_hand.npy')\n try:\n X = np.load(PICKLE_PATH_X)\n Y = np.load(PICKLE_PATH_Y)\n return X, Y\n except IOError as ioe:\n print(ioe)\n return ioe","title":""},{"docid":"8aa2f106fd0f272d3f6ad3b73cf0576d","score":"0.45653313","text":"def pickle_dump(obj, f, ext='.pkl'):\n if isinstance(f, Path):\n f = f.as_posix()\n \n with open(f+ext, 'wb') as file:\n return cloudpickle.dump(obj=obj, file=file, protocol=pickle.HIGHEST_PROTOCOL)","title":""},{"docid":"a76dac2185a0cf73735e3efd9518493a","score":"0.4564233","text":"def test_thing_with_fd():\n fd = open(__file__, 'r')\n fd.close()\n obj = Thing(fd)\n jsonstr = jsonpickle.encode(obj)\n newobj = jsonpickle.decode(jsonstr)\n assert newobj.name is None","title":""},{"docid":"55203861360f9fc3fbfd0a1e2227d85b","score":"0.4557539","text":"def pickle_dumps(obj: Any, error_msg: str):\n try:\n return pickle.dumps(obj)\n except TypeError as e:\n sio = io.StringIO()\n inspect_serializability(obj, print_file=sio)\n msg = f\"{error_msg}:\\n{sio.getvalue()}\"\n raise TypeError(msg) from e","title":""},{"docid":"523740e83de61ba54e2cc2cbce22659c","score":"0.45540762","text":"def test_omits_prevent_core_dump_if_prevent_core_false(self):\n instance = self.test_instance\n instance.prevent_core = False\n instance.open()\n self.assertFalse(self.mock_module_daemon.prevent_core_dump.called)","title":""},{"docid":"497f21f0f4a7926c810884623178d1e3","score":"0.4553845","text":"def register_pickle():\n def pickle_dumps(obj, dumper=pickle.dumps):\n return dumper(obj, protocol=pickle_protocol)\n\n registry.register('pickle', pickle_dumps, unpickle,\n content_type='application/x-python-serialize',\n content_encoding='binary')","title":""},{"docid":"88b92510d640a6e5b96a9e1dc044da7a","score":"0.4553397","text":"def load_obj(path, filename):\n file_path = os.path.join(path, filename)\n with open(file_path, 'rb') as handle:\n return pickle.load(handle)","title":""},{"docid":"d68319a44208d8f0598e609497cc7bb4","score":"0.45527503","text":"def pickle_obj(obj: Any, outfile: str = \"temp_dir/temp.p\") -> None:\n with open(outfile, \"wb\") as fp:\n pickle.dump(obj, fp)","title":""},{"docid":"2ae208e023dc8a4972a817cda2dc53d5","score":"0.45501328","text":"def test_pickle(self, timezone):\n trigger = DateTrigger(date(2016, 4, 3), timezone=timezone)\n data = pickle.dumps(trigger, 2)\n trigger2 = pickle.loads(data)\n assert trigger2.run_date == trigger.run_date","title":""},{"docid":"2dd135372283e36681574f34f142d653","score":"0.45471847","text":"def test4CrossInheritanceObject(self):\n from derived import Derived, DerivedCustomReduce\n\n d1 = Derived()\n attr = 7\n d2 = DerivedCustomReduce(attr)\n\n # Generic __reduce__ should raise an exception\n self.assertRaises(IOError, pickle.dumps, d1)\n\n # Custom __reduce__ should allow pickling\n d2_2 = pickle.loads(pickle.dumps(d2))\n self.assertEqual(d2_2.attr, attr)","title":""},{"docid":"e2da4fe72f69feb81018663390a259e1","score":"0.4539921","text":"def test3PickleFacadeCheck(self):\n\n def get_root_facade():\n return ROOT\n\n facade = pickle.loads(pickle.dumps(get_root_facade()))\n\n # Check attributes of the unserialized facade\n self.assertEqual(facade.__name__, ROOT.__name__)\n self.assertEqual(facade.__file__, ROOT.__file__)","title":""},{"docid":"123b5f3a3a4cb2174ca1041864fb71f4","score":"0.45366675","text":"def read_pickle_object(pickle_file_name):\n with open(pickle_file_name, 'rb') as inn:\n m = pickle.load(inn)\n return m","title":""},{"docid":"9ffd4fcb430cf8d133eace520f9732df","score":"0.45343965","text":"def syncPickle(self,token,obj):\r\n hashValue = hashlib.md5(token.encode(\"utf8\")).hexdigest()\r\n objectpath = self.doc.module_path+\"/objects/\"\r\n \r\n if not os.path.isdir(objectpath):\r\n os.makedirs(objectpath)\r\n logger.warning(\"[Sync pickle] make folder: %s \"%objectpath)\r\n \r\n with open(\"{}{}.pkl\".format(objectpath,hashValue),\"wb\") as pfile:\r\n pickle.dump(obj,pfile)\r\n pfile.close()\r\n logger.info(\"[Sync pickle] pickled %s \"%hashValue)\r\n \r\n # if exists, append to pickle file\r","title":""},{"docid":"13dae520758b46974b5b558c43e9140e","score":"0.45284578","text":"def test_comp_pickle_save():\n data = ['test', 'list']\n filename = 'test_comp_pkl_save'\n comp_pickle_save(data, filename)\n\n assert os.path.isfile(filename) is True","title":""},{"docid":"d19bf2704921a1b0315c41c84a5cd390","score":"0.45246783","text":"def loadPickle(filename):\n file = open(filename, 'rb')\n obj = pickle.load(file)\n file.close()\n #logging.debug(\"Loaded \" + filename + \" with object \" + str(type(obj)))\n\n return obj","title":""},{"docid":"8eec84ec8fc7df78915a8f9e6b3b2c3e","score":"0.45126012","text":"def clone_via_serialize(obj):\n s = cPickle.dumps(obj, get_pickle_protocol())\n return cPickle.loads(s)","title":""},{"docid":"ab8b7b7dcc21890f118e2cfef56c1bfc","score":"0.45121163","text":"def no_match(py_obj, h_group, call_id=0, **kwargs):\n pickled_obj = pickle.dumps(py_obj)\n d = h_group.create_dataset('data_%i' % call_id, data=[pickled_obj])\n d.attrs[\"type\"] = [b'pickle']\n\n warnings.warn(\"%s type not understood, data have been serialized\" % type(py_obj),\n SerializedWarning)","title":""},{"docid":"a95b87f99b0e50c66ee999c88a37f0a8","score":"0.4506766","text":"def load_obj(name):\n with open(name + '.pkl', 'rb') as f:\n return pickle.load(f)","title":""},{"docid":"a95b87f99b0e50c66ee999c88a37f0a8","score":"0.4506766","text":"def load_obj(name):\n with open(name + '.pkl', 'rb') as f:\n return pickle.load(f)","title":""},{"docid":"763b1e654318eee177e0aead9dfc665a","score":"0.4494884","text":"def _to_picklefile(self, outprefix, verbose=False):\r\n if verbose:\r\n s = \"Exporting cross-correlations in binary format to file: {}.pickle\"\r\n print(s.format(outprefix))\r\n\r\n f = psutils.openandbackup(outprefix + '.pickle', mode='wb')\r\n pickle.dump(self, f, protocol=2)\r\n f.close()","title":""},{"docid":"576efd210816e2c291cd3aff1983414a","score":"0.44890103","text":"def load():\n\n with open(\"example.pickle\", \"rb\") as f:\n return pickle.load(file=f)","title":""}],"string":"[\n {\n \"docid\": \"be5a0d70305d6329b2a939b438987978\",\n \"score\": \"0.57015604\",\n \"text\": \"def is_picklable(obj):\\n try:\\n pickle.dumps(obj)\\n except Exception:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f261160b7dd7376478f85f0b1a0037bc\",\n \"score\": \"0.554075\",\n \"text\": \"def test_pickle():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7727c93a2da3438d527808186874317f\",\n \"score\": \"0.5514787\",\n \"text\": \"def _try_to_pickle(obj):\\n pickle.dumps(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b19488131b14b85ec8157ee1f75d034b\",\n \"score\": \"0.5501831\",\n \"text\": \"def check():\\n try_it = pickle.load(open(\\\"data/pickleLilEvery.pkl\\\", 'rb'))\\n print(try_it)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aedb80a1e4d83b69e3c6544676598ab4\",\n \"score\": \"0.55003035\",\n \"text\": \"def check_pickable(obj):\\n # TESTME!\\n temp_file = join(const.TEMP_DIR, str(timestamp()) + \\\"tmp\\\")\\n try:\\n pick_dump(obj, temp_file)\\n except:\\n return False\\n finally:\\n try:\\n removefile(temp_file)\\n except Exception as e:\\n log.error(\\\"The file %s could not be removed: %s\\\",\\n temp_file, e)\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa8bb88cdee9b89335723b70f787eef4\",\n \"score\": \"0.5398693\",\n \"text\": \"def test_pickle() -> None:\\n pack_unpack_test(\\n Decimal(42),\\n use_pickle=True,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c7473e624a360822433760d27e76291\",\n \"score\": \"0.5341918\",\n \"text\": \"def load_obj_pickle(adress, name, load_type=None, load_way=None):\\n import cPickle\\n\\n print 'loading data...', adress + name + '.cpkl'\\n\\n if load_type == 'serial' or load_type == None:\\n if load_way == None or load_way == 'cpickle':\\n with open(adress + name + '.cpkl', 'rb') as f:\\n return cPickle.load(f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3972f5540230fb1dbb5c5ef30791cc20\",\n \"score\": \"0.5327268\",\n \"text\": \"def pickles(obj,exact=False,safe=False,**kwds):\\n if safe: exceptions = (Exception,) # RuntimeError, ValueError\\n else:\\n exceptions = (TypeError, AssertionError, NotImplementedError, PicklingError, UnpicklingError)\\n try:\\n pik = copy(obj, **kwds)\\n #FIXME: should check types match first, then check content if \\\"exact\\\"\\n try:\\n #FIXME: should be \\\"(pik == obj).all()\\\" for numpy comparison, though that'll fail if shapes differ\\n result = bool(pik.all() == obj.all())\\n except (AttributeError, TypeError):\\n warnings.filterwarnings('ignore')\\n result = pik == obj\\n warnings.resetwarnings()\\n if hasattr(result, 'toarray'): # for unusual types like sparse matrix\\n result = result.toarray().all()\\n if result: return True\\n if not exact:\\n result = type(pik) == type(obj)\\n if result: return result\\n # class instances might have been dumped with byref=False\\n return repr(type(pik)) == repr(type(obj)) #XXX: InstanceType?\\n return False\\n except exceptions:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8417e7002339d6400ade2ba7d06fc43\",\n \"score\": \"0.53073394\",\n \"text\": \"def test_can_pickle(self):\\n settings = UploadSettings(None, FakeDataServiceApi(), None, ProjectNameOrId.create_from_name('mouse'), None)\\n params = ('one', 'two', 'three')\\n context = UploadContext(settings, params, multiprocessing.Manager().Queue(), 12)\\n pickle.dumps(context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f777cad13eb4904124f6a91ec22a7088\",\n \"score\": \"0.5282426\",\n \"text\": \"def test_pickle_round_trip(tmpdir, acetone):\\n with tmpdir.as_cwd():\\n acetone.pickle(state=\\\"test\\\")\\n mols = unpickle()\\n pickle_mol = mols[\\\"test\\\"]\\n for atom in acetone.atoms:\\n pickle_atom = pickle_mol.get_atom_with_name(atom.atom_name)\\n assert pickle_atom.__dict__ == atom.__dict__\\n assert acetone.bonds == pickle_mol.bonds\\n assert acetone.angles == pickle_mol.angles\\n assert acetone.dihedrals == pickle_mol.dihedrals\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a51c893e14f8495e1e28bf6d0156475a\",\n \"score\": \"0.52675366\",\n \"text\": \"def test_save_load_pickle(self):\\n pickle.loads(pickle.dumps(self.V))\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7431ee7caa0c5cd6ca7bf2fe3ff3ed2\",\n \"score\": \"0.52216977\",\n \"text\": \"def test_pickle(self):\\n obj = pickle.dumps(self.config)\\n config2 = pickle.loads(obj)\\n self.assertEqual(self.config.db.sqlite_path, config2.db.sqlite_path)\\n self.assertEqual(self.config.server.port, config2.server.port)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"497f7bcc480f85ebeac11610ba95b49d\",\n \"score\": \"0.52023524\",\n \"text\": \"def jarIt(file,obj):\\n file = open(file,'w')\\n cPickle.dump(obj,file)\\n print \\\"pickled \\\",obj\\n file.close()\\n del file\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0e31a8ac5ec574a861b250bf7488b5a\",\n \"score\": \"0.5148917\",\n \"text\": \"def test_store_object_can_be_serialized_by_pickle():\\n pickle.dump(_get_store(\\\"https://example.com\\\"), io.BytesIO())\\n pickle.dump(_get_store(\\\"databricks\\\"), io.BytesIO())\\n # pickle.dump(_get_store(f\\\"sqlite:///{tmpdir.strpath}/mlflow.db\\\"), io.BytesIO())\\n # This throws `AttributeError: Can't pickle local object 'create_engine.<locals>.connect'`\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81dbd9931acc68c6916659d12628ae72\",\n \"score\": \"0.51201713\",\n \"text\": \"def manage_pickle(path, fun, args=None, verbose=True) :\\n\\n\\t# Test if the file already exists\\n\\tif os.path.exists(path) :\\n\\t\\t# The file exists: load with pickle, and return\\n\\t\\tif verbose==True:\\n\\t\\t\\tprint(\\\"manage_pickle: the file '{}' has been loaded\\\"\\n\\t\\t\\t\\t.format(path))\\n\\t\\twith open(path, 'rb') as file:\\n\\t\\t\\treturn pickle.load(file)\\n\\telse:\\n\\t\\tif args==None: result = fun()\\n\\t\\telse: result = fun(args)\\n\\t\\twith open(path, 'wb') as file:\\n\\t\\t\\tpickler = pickle.Pickler(file)\\n\\t\\t\\tpickler.dump(result)\\n\\t\\treturn result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f725c6c115d4e1559ece22523001ca33\",\n \"score\": \"0.50983566\",\n \"text\": \"def test_repr_using_jsonpickle():\\n thing = ObjWithJsonPickleRepr()\\n thing.child = ObjWithJsonPickleRepr()\\n thing.child.parent = thing\\n encoded = jsonpickle.encode(thing)\\n decoded = jsonpickle.decode(encoded)\\n assert id(decoded) == id(decoded.child.parent)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34a919347f2a8859995cc4f97dca79b6\",\n \"score\": \"0.50923824\",\n \"text\": \"def test_pickle(self, trigger, jitter):\\n trigger.jitter = jitter\\n data = pickle.dumps(trigger, 2)\\n trigger2 = pickle.loads(data)\\n\\n for attr in BaseCombiningTrigger.__slots__:\\n assert repr(getattr(trigger2, attr)) == repr(getattr(trigger, attr))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34a919347f2a8859995cc4f97dca79b6\",\n \"score\": \"0.50923824\",\n \"text\": \"def test_pickle(self, trigger, jitter):\\n trigger.jitter = jitter\\n data = pickle.dumps(trigger, 2)\\n trigger2 = pickle.loads(data)\\n\\n for attr in BaseCombiningTrigger.__slots__:\\n assert repr(getattr(trigger2, attr)) == repr(getattr(trigger, attr))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e25eff53c700776937033b01e54d0baa\",\n \"score\": \"0.50833565\",\n \"text\": \"def test_double_pickle(tmpdir, acetone):\\n with tmpdir.as_cwd():\\n acetone.pickle(state=\\\"input\\\")\\n # remove all coords\\n acetone.coords[\\\"input\\\"] = []\\n acetone.pickle(state=\\\"after\\\")\\n\\n # now check we have both states\\n mols = unpickle()\\n assert \\\"after\\\" in mols\\n assert \\\"input\\\" in mols\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f1eeb74bb049a15354626157337988c\",\n \"score\": \"0.5041047\",\n \"text\": \"def test_pickle(self):\\n data1 = {1, 2, 3}\\n data2 = {4, 5}\\n logoutputpickle = io.BytesIO()\\n pickle.dump(data1, logoutputpickle)\\n pickle.dump(data2, logoutputpickle)\\n instream = io.BytesIO(logoutputpickle.getvalue())\\n self.assertEquals (data1, pickle.load(instream))\\n self.assertEquals (data2, pickle.load(instream))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41675bb2c22d538adccda0d279e7e1ed\",\n \"score\": \"0.5021402\",\n \"text\": \"def load_python_obj(file_name:str) -> Any:\\n with open(file_name,'rb') as f:\\n obj = pickle.load(f)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ed860389b3110898ca3f0d9ebc10c2a\",\n \"score\": \"0.5017657\",\n \"text\": \"def test_pickle():\\n utils.pickle_save(d, 'd')\\n test = utils.pickle_load('d')\\n\\n subprocess.check_call(['rm', 'd.pkl'])\\n\\n assert isinstance(d, dict)\\n assert test['entry1'] == 'test'\\n assert test['entry2'] == 'also_test'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a22301d7508879c587d61f9b136dfc33\",\n \"score\": \"0.5012499\",\n \"text\": \"def test_pickle(level_name):\\n observations = [\\\"RGBD\\\"]\\n config = {\\\"width\\\": \\\"640\\\", \\\"height\\\": \\\"480\\\", \\\"botCount\\\": \\\"2\\\", \\\"random_seed\\\": \\\"42\\\"}\\n renderer = \\\"hardware\\\"\\n\\n env_1 = deepmind_lab.Lab(level_name, observations, config=config, renderer=renderer)\\n env_1 = DmLabCompatibilityV0(env_1)\\n\\n env_2 = pickle.loads(pickle.dumps(env_1))\\n\\n obs_1, info_1 = env_1.reset()\\n obs_2, info_2 = env_2.reset()\\n assert data_equivalence(obs_1, obs_2)\\n assert data_equivalence(info_1, info_2)\\n for _ in range(100):\\n actions = env_1.action_space.sample()\\n obs_1, reward_1, term_1, trunc_1, info_1 = env_1.step(actions)\\n obs_2, reward_2, term_2, trunc_2, info_2 = env_2.step(actions)\\n # assert data_equivalence(obs_1, obs_2)\\n assert reward_1 == reward_2\\n assert term_1 == term_2 and trunc_1 == trunc_2\\n assert data_equivalence(info_1, info_2)\\n\\n env_1.close()\\n env_2.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00ac57b69eedb9e322441fcefa9198a7\",\n \"score\": \"0.5003634\",\n \"text\": \"def test_pickle_simple():\\n t = task(actions=[\\\"echo\\\"], name=\\\"t\\\")\\n pkl = pickle.dumps(t)\\n t2 = pickle.loads(pkl)\\n assert t2.actions == [\\\"echo\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"932e57724b5c81e71d8f6e9777dc100b\",\n \"score\": \"0.500185\",\n \"text\": \"def pickleme(obj, filename, protocol=2):\\n with open(filename, 'w') as f:\\n cPickle.dump(obj, f, protocol=protocol)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1deb625d50a9ce49d9a817bf92127ab\",\n \"score\": \"0.49529862\",\n \"text\": \"def saveToPickleFile(python_object, path_to_file='object.pkl'):\\n out_file = open(path_to_file, 'wb')\\n pkl.dump(python_object, out_file)\\n out_file.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ec1c3ca17254909c67fd95a957e2296\",\n \"score\": \"0.49479043\",\n \"text\": \"def test_pickling(simulation_factory, two_particle_snapshot_factory):\\n sim = simulation_factory(two_particle_snapshot_factory())\\n mc = hoomd.hpmc.integrate.Sphere(d=0.1, a=0.1)\\n mc.shape['A'] = dict(diameter=1.1)\\n mc.shape['B'] = dict(diameter=1.3)\\n sim.operations.integrator = mc\\n\\n cl = hoomd.hpmc.update.Clusters(trigger=hoomd.trigger.Periodic(5),\\n pivot_move_ratio=0.1)\\n operation_pickling_check(cl, sim)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a88f96038af3edd13950cf60e602c0cd\",\n \"score\": \"0.49454793\",\n \"text\": \"def test_pickle(self):\\n x,s = self.create_testdata()\\n X = Container()\\n X.add(x)\\n task = mmCCZI()\\n task.fit(X)\\n with tempfile.TemporaryFile(mode='w+b') as tf:\\n cPickle.dump(task, tf)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa4cd65f92a716bbe641988333a3e24f\",\n \"score\": \"0.4927952\",\n \"text\": \"def test_pickle():\\n\\n dct = ODict([\\n (\\\"c\\\", 3),\\n (\\\"d\\\", 4),\\n ])\\n data = pickle.dumps(dct)\\n dct2 = pickle.loads(data)\\n assert dct == dct2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15f47236e456522515d6ae0c33db045a\",\n \"score\": \"0.49261117\",\n \"text\": \"def save_obj_pickle(obj, adress, name, save_type='single_file'):\\n\\n print 'saving pickle data...', adress + name\\n grow(adress)\\n if save_type == 'single_file':\\n with open(adress + name + '.cpkl', 'wb') as f:\\n cPickle.dump(obj, f, cPickle.HIGHEST_PROTOCOL)\\n\\n #\\\"\\\"\\\"\\n # Serial divided\\n if save_type == 'serial_divided':\\n os.system(\\\"rm \\\" + adress + \\\"/*.cpkl\\\" + \\\"> /dev/null 2>&1\\\")\\n beg = time.time()\\n for dic in obj:\\n # separating test_dic of xs_dic\\n if dic['name'] == 'xs_dic':\\n with open(adress + name + '/' + dic['name'] + '.cpkl', 'wb') as f:\\n cPickle.dump(dic, f, cPickle.HIGHEST_PROTOCOL)\\n else:\\n # separating each test\\n for test in dic['test'].keys():\\n with open(adress + name + '/' + dic['name'] + '_' + test + '.cpkl', 'wb') as f:\\n cPickle.dump(dic['test'][test], f, cPickle.HIGHEST_PROTOCOL)\\n print time.time() - beg # 0.51\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80269120c76a9348b8811add40e150b8\",\n \"score\": \"0.49125978\",\n \"text\": \"def zodb_pickle(obj):\\n f = BytesIO()\\n p = PersistentPickler(_persistent_id, f, _protocol)\\n klass = obj.__class__\\n assert not hasattr(obj, '__getinitargs__'), \\\"not ready for constructors\\\"\\n args = None\\n\\n mod = getattr(klass, '__module__', None)\\n if mod is not None:\\n klass = mod, klass.__name__\\n\\n state = obj.__getstate__()\\n\\n p.dump((klass, args))\\n p.dump(state)\\n return f.getvalue()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bde011e49d12df0fe6742652a1b73838\",\n \"score\": \"0.49102893\",\n \"text\": \"def MeasurePickle(python, options, extra_args):\\n bm_path = Relative(\\\"performance/bm_pickle.py\\\")\\n return MeasureGeneric(python, options, bm_path, extra_args=extra_args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44d6940d10ee29c368d91669198158cf\",\n \"score\": \"0.4906187\",\n \"text\": \"def load(self, pickled_blackbox) -> None:\\n loaded_data = None\\n\\n try:\\n loaded_data = pickle.loads(pickled_blackbox)\\n except pickle.UnpicklingError as e:\\n loaded_data = None\\n print(\\\"UnpicklingError: \\\", str(e))\\n\\n if loaded_data:\\n try:\\n self.models = loaded_data[\\\"models\\\"]\\n self.scaler = loaded_data[\\\"scaler\\\"]\\n self.verbose = loaded_data[\\\"verbose\\\"]\\n except KeyError:\\n print(\\\"Keys not found in pickle!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a888fe0bf1136a22b79f39d24addedc\",\n \"score\": \"0.49028245\",\n \"text\": \"def test_pickle_and_unpickle_no_usage(token_response):\\n pickled_versions = [\\n pickle.dumps(token_response, protocol=n)\\n for n in range(pickle.HIGHEST_PROTOCOL + 1)\\n ]\\n unpickled_versions = [pickle.loads(x) for x in pickled_versions]\\n for x in unpickled_versions:\\n assert x.by_resource_server == token_response.by_resource_server\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f9c0f1f5bc3aeb5971596ed5461043f\",\n \"score\": \"0.4900738\",\n \"text\": \"def test_builtin_realizations(name, protocol):\\n # get class instance\\n original = getattr(cosmology, name)\\n\\n # pickle and unpickle\\n f = pickle.dumps(original, protocol=protocol)\\n unpickled = pickle.loads(f)\\n\\n # test equality\\n assert unpickled == original\\n assert unpickled.meta == original.meta\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3c53598aa52428cf0f1954efb520e30\",\n \"score\": \"0.48850453\",\n \"text\": \"def test_is_pickleable(self, obs_dim, action_dim):\\n env = MetaRLEnv(DummyBoxEnv(obs_dim=obs_dim, action_dim=action_dim))\\n with mock.patch(('metarl.tf.policies.'\\n 'continuous_mlp_policy.MLPModel'),\\n new=SimpleMLPModel):\\n policy = ContinuousMLPPolicy(env_spec=env.spec)\\n\\n env.reset()\\n obs, _, _, _ = env.step(1)\\n\\n with tf.compat.v1.variable_scope('ContinuousMLPPolicy/MLPModel',\\n reuse=True):\\n return_var = tf.compat.v1.get_variable('return_var')\\n # assign it to all one\\n return_var.load(tf.ones_like(return_var).eval())\\n output1 = self.sess.run(\\n policy.model.outputs,\\n feed_dict={policy.model.input: [obs.flatten()]})\\n\\n p = pickle.dumps(policy)\\n with tf.compat.v1.Session(graph=tf.Graph()) as sess:\\n policy_pickled = pickle.loads(p)\\n output2 = sess.run(\\n policy_pickled.model.outputs,\\n feed_dict={policy_pickled.model.input: [obs.flatten()]})\\n assert np.array_equal(output1, output2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"627be76d59515ae06cab73dd5b567cc5\",\n \"score\": \"0.48549765\",\n \"text\": \"def unpickle_something(pickle_filename):\\n if not os.path.isfile(pickle_filename):\\n return f\\\"{pickle_filename} doesn't exist!\\\"\\n with open(pickle_filename, 'rb') as file:\\n something_unpickled = pickle.load(file)\\n print(f\\\"Loaded '{pickle_filename}'\\\")\\n return something_unpickled\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a93746c6048116f345d7eef7890244e\",\n \"score\": \"0.48459494\",\n \"text\": \"def _dump(obj, fname) :\\n\\ttry :\\n\\t\\tf = open(fname, \\\"w\\\")\\n\\t\\tpickle.dump(obj, f, _Pickle_Protocol)\\n\\t\\tf.flush()\\n\\tfinally :\\n\\t\\tf.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f17921d510c2f8674dc0d4f74a7be9c\",\n \"score\": \"0.4827176\",\n \"text\": \"def test_1234_pickle():\\n __location__ = os.path.realpath(\\n os.path.join(os.getcwd(), os.path.dirname(__file__))\\n )\\n with open(os.path.join(__location__, \\\"spec-1234.json\\\")) as f:\\n d = json.loads(f.read())\\n spec = CombinatorialSpecification.from_dict(d)\\n spec.count_objects_of_size(10)\\n s = pickle.dumps(spec)\\n new_spec = pickle.loads(s)\\n assert new_spec == spec\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8903443b7e1967093113856f7a2763b2\",\n \"score\": \"0.48209557\",\n \"text\": \"def unpickleObject(fname):\\n f = open(os.path.expanduser(fname),'rb')\\n try:\\n obj = cPickle.load(f)\\n except:\\n f.close()\\n raise\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92969e44078803a08f86567d27e6cf82\",\n \"score\": \"0.48031557\",\n \"text\": \"def test_pickle(self, timezone):\\n\\n trigger = CronTrigger(year=2016, month='5-6', day='20-28', hour=7, minute=25, second='*',\\n timezone=timezone)\\n data = pickle.dumps(trigger, 2)\\n trigger2 = pickle.loads(data)\\n\\n for attr in CronTrigger.__slots__:\\n assert getattr(trigger2, attr) == getattr(trigger, attr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a309e8a098b5040f4ee223353e3794e2\",\n \"score\": \"0.47961402\",\n \"text\": \"def readFromPickleFile(path_to_file='object.pkl'):\\n in_file = open(path_to_file, 'rb')\\n python_object = pkl.load(in_file)\\n return python_object\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86856721b4bc23c7c7775359132ceed4\",\n \"score\": \"0.47818154\",\n \"text\": \"def big_dump(file_path, object_to_dump):\\n SimPickler.dump(file_path+\\\".new\\\", object_to_dump)\\n shutil.move(file_path+\\\".new\\\", file_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bef54f4987ef06f223c65aea0eb56521\",\n \"score\": \"0.4778086\",\n \"text\": \"def worker_load(adress):\\n with open(adress, 'rb') as f:\\n return cPickle.load(f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4b5afa73652769a0de4886561fc09ba\",\n \"score\": \"0.47728184\",\n \"text\": \"def load_object(filename):\\n\\twith open(filename, \\\"rb\\\") as f:\\n\\t\\ttry:\\n\\t\\t\\treturn pickle.load(f)\\n\\t\\texcept EOFError:\\n\\t\\t\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3378626c3f14906f335a248c94ea59d5\",\n \"score\": \"0.476833\",\n \"text\": \"def mp_with_object():\\n g = object()\\n return MetaProcessor(g), g, ext\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b35efe333d9e09761e50fb0f93137b84\",\n \"score\": \"0.47649705\",\n \"text\": \"def pickleObject(obj,fname):\\n f = open(os.path.expanduser(fname),'wb')\\n cPickle.dump(obj,f,1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fdd350400703e1b1bdc6de7f5186c25e\",\n \"score\": \"0.475868\",\n \"text\": \"def is_picklable(self, event):\\n try:\\n pickle.dumps(event)\\n except Exception as e:\\n message = \\\"(event - %s) is not picklable, Reason: %s\\\" % (\\n event.identify(), e)\\n assert False, message\",\n \"title\": \"\"\n },\n {\n \"docid\": \"520a35af30c7254cac14ab7f67c463b1\",\n \"score\": \"0.47565234\",\n \"text\": \"def evaluate_result(pickled_result):\\n # its a pickled object with four parts\\n code, result, stdout, stderr = pickle.loads(pickled_result)\\n # just dump the stderr and stdout\\n sys.stderr.write(stderr)\\n sys.stdout.write(stdout)\\n # code indicates whether we had an exception or not\\n if code:\\n return result\\n else:\\n raise result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9912c01da21e04d133f2fb5730bd4c85\",\n \"score\": \"0.47526616\",\n \"text\": \"def dump(file_path, object_to_dump):\\n\\n if(os.path.exists(file_path)):\\n os.remove(file_path)\\n\\n output_pkl = open(file_path, 'wb')\\n pickle.dump(object_to_dump,output_pkl)\\n output_pkl.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4c0bc1f80923506249b523dc6ecb1ec\",\n \"score\": \"0.47362074\",\n \"text\": \"def picklable_wrapper(obj):\\n try:\\n pickle.dumps(obj)\\n return obj\\n except:\\n return PicklableWrapper(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca30c336bd12ccfdeddb39e9f8362a8f\",\n \"score\": \"0.47143298\",\n \"text\": \"def load_pickle(filename):\\n print(f\\\"Loaded from utils/{filename}\\\")\\n with open(\\\"utils/\\\"+filename, 'rb') as f:\\n return pickle.load(f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6533f4212d27e3a2af01ea5549b32097\",\n \"score\": \"0.4710462\",\n \"text\": \"def writepickle(obj, filepath, verbose=False, protocol = -1):\\n\\tif os.path.splitext(filepath)[1] == \\\".gz\\\":\\n\\t\\tpkl_file = gzip.open(filepath, 'wb')\\n\\telse:\\n\\t\\tpkl_file = open(filepath, 'wb')\\n\\t\\n\\tpickle.dump(obj, pkl_file, protocol)\\n\\tpkl_file.close()\\n\\tif verbose: print \\\"Wrote %s\\\" % filepath\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17d5b989a5f284f5dbdaaa1fb0580307\",\n \"score\": \"0.47098103\",\n \"text\": \"def dump(obj, file, protocol=None, byref=None, fmode=None, recurse=None, **kwds):#, strictio=None):\\n from .settings import settings\\n protocol = settings['protocol'] if protocol is None else int(protocol)\\n _kwds = kwds.copy()\\n _kwds.update(dict(byref=byref, fmode=fmode, recurse=recurse))\\n Pickler(file, protocol, **_kwds).dump(obj)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"505f818acd73142c2d5080e426591872\",\n \"score\": \"0.470276\",\n \"text\": \"def __init__(self, do_pickle: bool = False) -> None:\\n super().__init__()\\n self.do_pickle = do_pickle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"505f818acd73142c2d5080e426591872\",\n \"score\": \"0.470276\",\n \"text\": \"def __init__(self, do_pickle: bool = False) -> None:\\n super().__init__()\\n self.do_pickle = do_pickle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"892b80348acd0372b1ee525b33f4ee1e\",\n \"score\": \"0.47005093\",\n \"text\": \"def __init__(self, lookupdir):\\n lookupdir = lookupdir.strip('/')\\n self.__pickle_dir__ = lookupdir\\n self.__full_dir__ = '%s/%s' % (os.getcwd(), lookupdir)\\n sys.path.append(self.__pickle_dir__)\\n self.check_dir()\\n \\\"\\\"\\\"\\n okay, dir exists\\n \\\"\\\"\\\"\\n try:\\n self.__pickle_files__ = os.listdir(self.__pickle_dir__)\\n except:\\n self.__pickle_files__ = []\\n\\n for filename in self.__pickle_files__:\\n if filename.endswith('.pick'):\\n self.process_import(filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81031b1d5e000b19dde895207f017740\",\n \"score\": \"0.46926653\",\n \"text\": \"def get_pickleable_etype(cls, loads=..., dumps=...):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d4a1c0bde43a81a33f768ffb9824d9a\",\n \"score\": \"0.46892667\",\n \"text\": \"def load_object(self, value):\\n if value is None:\\n return None\\n return pickle.loads(value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c761528771917e6bdae60d52752776df\",\n \"score\": \"0.46856233\",\n \"text\": \"def load_obj(name):\\n with open(name, 'rb') as f:\\n return pickle.load(f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd9aefa76418517c4519c64eeb6fea6b\",\n \"score\": \"0.46820268\",\n \"text\": \"def loadPickle(name, picklepath=datapath, num=None, red=0):\\n \\n if red:\\n if num == None:\\n # Check if there is more than one\\n flist = filelist(picklepath)\\n if (name + '_red_1.pkl') in flist:\\n print 'LOADPICKLE: Warning! There is more than one pickle file for this object! Make sure it is the right one!'\\n f = open(picklepath+name+'_red.pkl', 'rb')\\n pickle = cPickle.load(f)\\n f.close()\\n elif num != None:\\n f = open(picklepath+name+'_red_'+numCheck(num)+'.pkl', 'rb')\\n pickle = cPickle.load(f)\\n f.close()\\n return pickle\\n else:\\n if num == None:\\n # Check if there is more than one\\n flist = filelist(picklepath)\\n if (name + '_obs_1.pkl') in flist:\\n print 'LOADPICKLE: Warning! There is more than one pickle file for this object! Make sure it is the right one!'\\n f = open(picklepath+name+'_obs.pkl', 'rb')\\n pickle = cPickle.load(f)\\n f.close()\\n elif num != None:\\n f = open(picklepath+name+'_obs_'+numCheck(num)+'.pkl', 'rb')\\n pickle = cPickle.load(f)\\n f.close()\\n return pickle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30a11a0a7f623d25c621367d2c967a97\",\n \"score\": \"0.46734425\",\n \"text\": \"def loadVariable(filename='objs.pkl',verbose=False):\\r\\n with open(filename, 'rb') as f:\\r\\n variable = pickle.load(f)\\r\\n if(verbose):\\r\\n print(\\\"It has been successfully loaded in \\\"+filename)\\r\\n return variable\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab72955193d7bf1c6b15a278695094f1\",\n \"score\": \"0.4672109\",\n \"text\": \"def test_clone(self):\\n obj = self.binning\\n self.assertEqual(obj, obj.clone())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab72955193d7bf1c6b15a278695094f1\",\n \"score\": \"0.4672109\",\n \"text\": \"def test_clone(self):\\n obj = self.binning\\n self.assertEqual(obj, obj.clone())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00e26ab19fde795ff1322b9c73737d51\",\n \"score\": \"0.4671238\",\n \"text\": \"def pickle_load(filename: str) -> object:\\n with open(f'../pickles/{filename}.pickle', 'rb') as f:\\n print(f'Loading {filename}.pickle')\\n return pickle.load(f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"615ad0e3d0e622cb14882227bcc326d0\",\n \"score\": \"0.46633083\",\n \"text\": \"def save_pickle(filename, obj):\\n with open(\\\"utils/\\\"+filename, 'wb') as f:\\n pickle.dump(obj, f)\\n print(f\\\"Saved to utils/{filename}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf9ab55fcdfa40fae8d9ccef76c0b033\",\n \"score\": \"0.46621472\",\n \"text\": \"def test_parse_pickle(self):\\n data = {'key': 'value'}\\n raw = pickle.dumps(data)\\n res = self.encoder_pickle.parse(raw)\\n assert res['key'] == 'value'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a60edf50a1fbceede21f61dda780ed38\",\n \"score\": \"0.4647641\",\n \"text\": \"def write_pickle(self, obj, protocol= -1):\\n dump_pickle(obj, self.file, protocol)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7465d4b306995a8668e9c105406711a\",\n \"score\": \"0.46459946\",\n \"text\": \"def test_pickle(frozen_thawed_angle: AngleClass) -> None:\\n Angle = frozen_thawed_angle\\n test_data = 38.0, 257.125, 0.0\\n\\n orig = Angle(test_data)\\n pick = pickle.dumps(orig)\\n thaw = pickle.loads(pick)\\n\\n assert orig is not thaw\\n assert orig == thaw\\n\\n # Ensure both produce the same pickle - so they can be interchanged.\\n cy_pick = pickle.dumps(getattr(vec_mod, 'Cy_' + Angle.__name__)(test_data))\\n py_pick = pickle.dumps(getattr(vec_mod, 'Py_' + Angle.__name__)(test_data))\\n\\n assert cy_pick == py_pick == pick\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c12d392929beb2d3cffebdcb947c61cb\",\n \"score\": \"0.46389517\",\n \"text\": \"def find_pickleable_exception(exc, loads=..., dumps=...):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4c9c079c48017b82ceb1a87b34abfec\",\n \"score\": \"0.46337512\",\n \"text\": \"def test_prevents_core_dump(self):\\n instance = self.test_instance\\n instance.open()\\n self.mock_module_daemon.prevent_core_dump.assert_called_with()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9325060d11afe2981900175a96b8962\",\n \"score\": \"0.46261737\",\n \"text\": \"def dumpit(obj, fname):\\n pklname = fname + '.pkl'\\n with open(os.path.join('tmp2', pklname), 'wb') as f:\\n pickle.dump(obj, f, -1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"974b32305ff7c728624b3af7ee1f983e\",\n \"score\": \"0.46063992\",\n \"text\": \"def test_pickle_current(self):\\r\\n dump = pickle.dumps(NodeSet(\\\"foo[1-100]\\\"))\\r\\n self.assertNotEqual(dump, None)\\r\\n nodeset = pickle.loads(dump)\\r\\n self.assertEqual(nodeset, NodeSet(\\\"foo[1-100]\\\"))\\r\\n self.assertEqual(str(nodeset), \\\"foo[1-100]\\\")\\r\\n self.assertEqual(nodeset[0], \\\"foo1\\\")\\r\\n self.assertEqual(nodeset[1], \\\"foo2\\\")\\r\\n self.assertEqual(nodeset[-1], \\\"foo100\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"decadd7795fb0a5737abc7654935e473\",\n \"score\": \"0.46051767\",\n \"text\": \"def readpickle(filepath, verbose=False):\\n\\tif os.path.splitext(filepath)[1] == \\\".gz\\\":\\n\\t\\tpkl_file = gzip.open(filepath,'rb')\\n\\telse:\\n\\t\\tpkl_file = open(filepath, 'rb')\\n\\tobj = pickle.load(pkl_file)\\n\\tpkl_file.close()\\n\\tif verbose: print \\\"Read %s\\\" % filepath\\n\\treturn obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f68f7aad5632a77af3002be65879818d\",\n \"score\": \"0.4602073\",\n \"text\": \"def save_python_obj(file_name:str,obj:Any) -> None:\\n with open(file_name,'wb') as f:\\n pickle.dump(obj,f,protocol=4)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27963cddebbe7962e7e4a5a84563c335\",\n \"score\": \"0.4594329\",\n \"text\": \"def read_object(filename):\\n with open(filename, 'rb', buffering=2000000) as f:\\n obj = pickle.load(f)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36b190e2a74d18a6ab3ff7f2b5fd4087\",\n \"score\": \"0.459425\",\n \"text\": \"def loads_either(cloudpickle_out, pickle_out):\\n if CLOUDPICKLE_AVAILABLE and cloudpickle_out is not None:\\n return cloudpickle.loads(cloudpickle_out)\\n elif pickle_out is not None:\\n return pickle.loads(pickle_out)\\n else:\\n raise ValueError('Not able to load the pickled object')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecb3a73a86d89304e7c94fedc6ca3f8a\",\n \"score\": \"0.45935196\",\n \"text\": \"def pick_dump(obj, path):\\n with open(path, \\\"wb\\\") as f:\\n pick.dump(obj, f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64d48446781f45c2f3cde3123010fc45\",\n \"score\": \"0.45796266\",\n \"text\": \"def _PickleBenchmark(base_python, changed_python, options, extra_args):\\n return SimpleBenchmark(MeasurePickle,\\n base_python, changed_python, options, extra_args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84aa5d19309c1e5ab875b0d2d9d4adbd\",\n \"score\": \"0.45681998\",\n \"text\": \"def pickleChecker():\\n PICKLE_FOLDER = \\\"../../pickles/\\\"\\n PICKLE_PATH_X = Path(PICKLE_FOLDER + 'x_hand.npy')\\n PICKLE_PATH_Y = Path(PICKLE_FOLDER + 'y_hand.npy')\\n try:\\n X = np.load(PICKLE_PATH_X)\\n Y = np.load(PICKLE_PATH_Y)\\n return X, Y\\n except IOError as ioe:\\n print(ioe)\\n return ioe\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8aa2f106fd0f272d3f6ad3b73cf0576d\",\n \"score\": \"0.45653313\",\n \"text\": \"def pickle_dump(obj, f, ext='.pkl'):\\n if isinstance(f, Path):\\n f = f.as_posix()\\n \\n with open(f+ext, 'wb') as file:\\n return cloudpickle.dump(obj=obj, file=file, protocol=pickle.HIGHEST_PROTOCOL)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a76dac2185a0cf73735e3efd9518493a\",\n \"score\": \"0.4564233\",\n \"text\": \"def test_thing_with_fd():\\n fd = open(__file__, 'r')\\n fd.close()\\n obj = Thing(fd)\\n jsonstr = jsonpickle.encode(obj)\\n newobj = jsonpickle.decode(jsonstr)\\n assert newobj.name is None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55203861360f9fc3fbfd0a1e2227d85b\",\n \"score\": \"0.4557539\",\n \"text\": \"def pickle_dumps(obj: Any, error_msg: str):\\n try:\\n return pickle.dumps(obj)\\n except TypeError as e:\\n sio = io.StringIO()\\n inspect_serializability(obj, print_file=sio)\\n msg = f\\\"{error_msg}:\\\\n{sio.getvalue()}\\\"\\n raise TypeError(msg) from e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"523740e83de61ba54e2cc2cbce22659c\",\n \"score\": \"0.45540762\",\n \"text\": \"def test_omits_prevent_core_dump_if_prevent_core_false(self):\\n instance = self.test_instance\\n instance.prevent_core = False\\n instance.open()\\n self.assertFalse(self.mock_module_daemon.prevent_core_dump.called)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"497f21f0f4a7926c810884623178d1e3\",\n \"score\": \"0.4553845\",\n \"text\": \"def register_pickle():\\n def pickle_dumps(obj, dumper=pickle.dumps):\\n return dumper(obj, protocol=pickle_protocol)\\n\\n registry.register('pickle', pickle_dumps, unpickle,\\n content_type='application/x-python-serialize',\\n content_encoding='binary')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88b92510d640a6e5b96a9e1dc044da7a\",\n \"score\": \"0.4553397\",\n \"text\": \"def load_obj(path, filename):\\n file_path = os.path.join(path, filename)\\n with open(file_path, 'rb') as handle:\\n return pickle.load(handle)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d68319a44208d8f0598e609497cc7bb4\",\n \"score\": \"0.45527503\",\n \"text\": \"def pickle_obj(obj: Any, outfile: str = \\\"temp_dir/temp.p\\\") -> None:\\n with open(outfile, \\\"wb\\\") as fp:\\n pickle.dump(obj, fp)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ae208e023dc8a4972a817cda2dc53d5\",\n \"score\": \"0.45501328\",\n \"text\": \"def test_pickle(self, timezone):\\n trigger = DateTrigger(date(2016, 4, 3), timezone=timezone)\\n data = pickle.dumps(trigger, 2)\\n trigger2 = pickle.loads(data)\\n assert trigger2.run_date == trigger.run_date\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2dd135372283e36681574f34f142d653\",\n \"score\": \"0.45471847\",\n \"text\": \"def test4CrossInheritanceObject(self):\\n from derived import Derived, DerivedCustomReduce\\n\\n d1 = Derived()\\n attr = 7\\n d2 = DerivedCustomReduce(attr)\\n\\n # Generic __reduce__ should raise an exception\\n self.assertRaises(IOError, pickle.dumps, d1)\\n\\n # Custom __reduce__ should allow pickling\\n d2_2 = pickle.loads(pickle.dumps(d2))\\n self.assertEqual(d2_2.attr, attr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2da4fe72f69feb81018663390a259e1\",\n \"score\": \"0.4539921\",\n \"text\": \"def test3PickleFacadeCheck(self):\\n\\n def get_root_facade():\\n return ROOT\\n\\n facade = pickle.loads(pickle.dumps(get_root_facade()))\\n\\n # Check attributes of the unserialized facade\\n self.assertEqual(facade.__name__, ROOT.__name__)\\n self.assertEqual(facade.__file__, ROOT.__file__)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"123b5f3a3a4cb2174ca1041864fb71f4\",\n \"score\": \"0.45366675\",\n \"text\": \"def read_pickle_object(pickle_file_name):\\n with open(pickle_file_name, 'rb') as inn:\\n m = pickle.load(inn)\\n return m\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ffd4fcb430cf8d133eace520f9732df\",\n \"score\": \"0.45343965\",\n \"text\": \"def syncPickle(self,token,obj):\\r\\n hashValue = hashlib.md5(token.encode(\\\"utf8\\\")).hexdigest()\\r\\n objectpath = self.doc.module_path+\\\"/objects/\\\"\\r\\n \\r\\n if not os.path.isdir(objectpath):\\r\\n os.makedirs(objectpath)\\r\\n logger.warning(\\\"[Sync pickle] make folder: %s \\\"%objectpath)\\r\\n \\r\\n with open(\\\"{}{}.pkl\\\".format(objectpath,hashValue),\\\"wb\\\") as pfile:\\r\\n pickle.dump(obj,pfile)\\r\\n pfile.close()\\r\\n logger.info(\\\"[Sync pickle] pickled %s \\\"%hashValue)\\r\\n \\r\\n # if exists, append to pickle file\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13dae520758b46974b5b558c43e9140e\",\n \"score\": \"0.45284578\",\n \"text\": \"def test_comp_pickle_save():\\n data = ['test', 'list']\\n filename = 'test_comp_pkl_save'\\n comp_pickle_save(data, filename)\\n\\n assert os.path.isfile(filename) is True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d19bf2704921a1b0315c41c84a5cd390\",\n \"score\": \"0.45246783\",\n \"text\": \"def loadPickle(filename):\\n file = open(filename, 'rb')\\n obj = pickle.load(file)\\n file.close()\\n #logging.debug(\\\"Loaded \\\" + filename + \\\" with object \\\" + str(type(obj)))\\n\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8eec84ec8fc7df78915a8f9e6b3b2c3e\",\n \"score\": \"0.45126012\",\n \"text\": \"def clone_via_serialize(obj):\\n s = cPickle.dumps(obj, get_pickle_protocol())\\n return cPickle.loads(s)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab8b7b7dcc21890f118e2cfef56c1bfc\",\n \"score\": \"0.45121163\",\n \"text\": \"def no_match(py_obj, h_group, call_id=0, **kwargs):\\n pickled_obj = pickle.dumps(py_obj)\\n d = h_group.create_dataset('data_%i' % call_id, data=[pickled_obj])\\n d.attrs[\\\"type\\\"] = [b'pickle']\\n\\n warnings.warn(\\\"%s type not understood, data have been serialized\\\" % type(py_obj),\\n SerializedWarning)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a95b87f99b0e50c66ee999c88a37f0a8\",\n \"score\": \"0.4506766\",\n \"text\": \"def load_obj(name):\\n with open(name + '.pkl', 'rb') as f:\\n return pickle.load(f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a95b87f99b0e50c66ee999c88a37f0a8\",\n \"score\": \"0.4506766\",\n \"text\": \"def load_obj(name):\\n with open(name + '.pkl', 'rb') as f:\\n return pickle.load(f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"763b1e654318eee177e0aead9dfc665a\",\n \"score\": \"0.4494884\",\n \"text\": \"def _to_picklefile(self, outprefix, verbose=False):\\r\\n if verbose:\\r\\n s = \\\"Exporting cross-correlations in binary format to file: {}.pickle\\\"\\r\\n print(s.format(outprefix))\\r\\n\\r\\n f = psutils.openandbackup(outprefix + '.pickle', mode='wb')\\r\\n pickle.dump(self, f, protocol=2)\\r\\n f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"576efd210816e2c291cd3aff1983414a\",\n \"score\": \"0.44890103\",\n \"text\": \"def load():\\n\\n with open(\\\"example.pickle\\\", \\\"rb\\\") as f:\\n return pickle.load(file=f)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":895,"cells":{"query_id":{"kind":"string","value":"cfba5384395dd909785d5d1aa4c50363"},"query":{"kind":"string","value":"Load recurrent neural network from h5.file"},"positive_passages":{"kind":"list like","value":[{"docid":"091e5e3e7c9e117ec65308a08f85f7cb","score":"0.60439736","text":"def load_RNN(self, model=None):\n\n if model != None:\n return models.load_model(model)","title":""}],"string":"[\n {\n \"docid\": \"091e5e3e7c9e117ec65308a08f85f7cb\",\n \"score\": \"0.60439736\",\n \"text\": \"def load_RNN(self, model=None):\\n\\n if model != None:\\n return models.load_model(model)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"a472291e19ca01e1c6c4f112fa1d57ec","score":"0.7449959","text":"def load(path: str):\n with h5py.File(path, 'r') as net_file:\n net = TensorNetwork(backend=net_file[\"backend\"][()])\n nodes = list(net_file[\"nodes\"].keys())\n edges = list(net_file[\"edges\"].keys())\n\n for node_name in nodes:\n node_data = net_file[\"nodes/\" + node_name]\n node_type = get_component(node_data['type'][()])\n node_type._load_node(net, node_data)\n\n nodes_dict = {node.name: node for node in net.nodes_set}\n\n for edge in edges:\n edge_data = net_file[\"edges/\" + edge]\n Edge._load_edge(edge_data, nodes_dict)\n return net","title":""},{"docid":"7ee11035c32d78a0774d9aafc2156b52","score":"0.6647814","text":"def load(filepath, load_weights=True):\n # TODO: support loading LambdaLayer that includes parametric self defined function with outside variables\n M = utils.load_hdf5_graph(filepath=filepath, load_weights=load_weights)\n return M","title":""},{"docid":"1d2a45d91cd2380dc051484acca2d50b","score":"0.6638091","text":"def load_model(self):\n self.online_network.total_model.load_weights(\"models/{}.h5\".format(self.name))\n self.target_network.total_model.load_weights(\"models/{}.h5\".format(self.name))\n # load memory\n self.memory.samples = []\n self.memory.read_from_file(\"{}{}.mem\".format(self.save_big_path, self.name))\n # load steps and epsilon\n mylogfile = open(\"models/{}.csv\".format(self.name), 'r')\n firstRow = mylogfile.readline()\n fieldnames = firstRow.strip('\\n').split(\";\")\n self.steps = int(fieldnames[0])\n self.epsilon = float(fieldnames[1])","title":""},{"docid":"29183a0011a8b619fefeac47d13cca0c","score":"0.6504803","text":"def load(cls, filename):\n data = np.load(filename, allow_pickle=True)\n neurons = [w.shape[0] for w in data[\"weights\"]]\n neurons.append(data[\"weights\"][-1].shape[1])\n network = cls(neurons)\n network.weights = data[\"weights\"]\n network.biases = data[\"biases\"]\n return network","title":""},{"docid":"0a27968244c4e77e7422b8fe518551d1","score":"0.6485048","text":"def load_example(file_path):\n data = h5py.File(file_path, 'r')\n rgb_images = list(data['image'])\n depth_images = list(data['depth_image'])\n frame_indices = np.arange(len(rgb_images))\n gripper_status = list(data['gripper'])\n action_status = list(data['label'])\n gripper_action_goal_idx = []\n\n # print(\"gripper \",gripper_status)\n # print(\"frames \", frame_indices)\n\n # generate new action labels and goal action indices\n gripper_action_label, gripper_action_goal_idx = generate_gripper_action_label(data, action_status, gripper_status, gripper_action_goal_idx)\n\n rgb_images = ConvertImageListToNumpy(np.squeeze(rgb_images), format='list')\n depth_images = ConvertImageListToNumpy(np.squeeze(depth_images), format='list')\n numpy_data = {\n 'rgb_images': rgb_images,\n 'depth_images': depth_images,\n 'frame_indices': frame_indices,\n 'gripper_status': gripper_status,\n 'action_status': action_status,\n 'gripper_action_label': gripper_action_label,\n 'gripper_action_goal_idx': gripper_action_goal_idx\n }\n\n return data, numpy_data","title":""},{"docid":"b132f70c1944a3fb40d32205e40ecc67","score":"0.64657694","text":"def load_model(self, filename):\n if \"/\" in filename:\n basename = \"/\".join(filename.split(\"/\")[:-1]) + \"/\" + filename.split(\"/\")[-1].split(\".\")[0]\n else:\n basename = filename.split(\".\")[0]\n serializers.load_hdf5(filename + '.weights', self)","title":""},{"docid":"b0eb3eb7df6ea26390f936d3b13e8978","score":"0.64656603","text":"def reload_net(path):\n return keras.models.load_model(os.path.normpath(path))","title":""},{"docid":"22be45c34475cfec1667433e2a31deac","score":"0.64272606","text":"def load_model(self, model_dir: str) -> \"OneClassNeuralNetwork\":\n params = np.load(f'{model_dir}/params.npz')\n w = params['w']\n V = params['V']\n nu = params['nu'].tolist()\n model = load_model(f'{model_dir}/model.h5',\n custom_objects={'custom_hinge': self.custom_ocnn_loss(nu, w, V)})\n return model","title":""},{"docid":"a9fcacf30761b31a91c3aa57c955d8e4","score":"0.6398747","text":"def load(path):\r\n net = torch.load(path)\r\n return net","title":""},{"docid":"cc9057976e1fd6334d78a3616bef8eec","score":"0.63440007","text":"def load_model(self, filename):\n summary = torch.load(filename)\n gen = Generator(self.args)\n gen.load_state_dict(summary['gnet'])","title":""},{"docid":"abd314ad5cc9da2e0a547244052e1387","score":"0.62680495","text":"def load_h5(fname):\n with h5py.File(fname, 'r') as h5f:\n return h5f['data'][:]","title":""},{"docid":"cceb570ec15af08c764ea25d1849252a","score":"0.62357044","text":"def load(self, file_name):\n with self.graph.as_default():\n saver = tf.train.Saver(tf.trainable_variables())\n saver.restore(self.session, self.io_nn.model_dir_checkpoints + file_name + '.ckpt')\n print(\"Loaded network from file \" + file_name)","title":""},{"docid":"0e2585576168677c2cfba83b27970410","score":"0.6230742","text":"def read_data(self):\n file = h5py.File(self.fname, 'r')\n \n self.x_train = np.float32(file['x_train'][:])\n self.y_train = np.float32(file['y_train'][:, 0])\n self.x_test = np.float32(file['x_test'][:])\n self.y_test = np.float32(file['y_test'][:, 0])\n \n file.close()","title":""},{"docid":"bd7bc8ac8f3b1d36a9e565230c42ddfa","score":"0.62071514","text":"def load(cls, filename):\n data = np.load(filename)\n network = cls(data[\"neuron_counts\"])\n network.W[...] = data[\"W\"]\n network.b[...] = data[\"b\"]\n index = 0\n for cell in network.cells:\n for p in cell.parameters():\n p[...] = data[\"arr_\" + str(index)]\n index += 1\n return network","title":""},{"docid":"ceca592c1b2d4eb082fb8efc3fcd179f","score":"0.62018853","text":"def load_network(path, separator=\";,\"):\n\n layers = []\n weights = []\n biases = []\n\n file = open(path, \"r\")\n\n layers_line = file.readline()\n\n for size in layers_line.split(separator[0]):\n layers.append(int(size))\n\n for k in range(len(layers) - 1):\n weight = numpy.zeros((layers[k+1], layers[k]), dtype=float)\n line = file.readline()\n nodes = line.split(separator[0])\n\n for i in range(len(nodes)):\n node = nodes[i].split(separator[1])\n\n for j in range(len(node)):\n weight[i, j] = float(node[j])\n\n weights.append(weight)\n\n for k in range(len(layers) - 1):\n bias = numpy.zeros((layers[k+1], 1), dtype=float)\n line = file.readline()\n nodes = line.split(separator[0])\n\n for i in range(len(nodes)):\n bias[i, 0] = float(nodes[i])\n\n biases.append(bias)\n\n network = MultiPerceptron(layers)\n network.set_weights_and_biases(weights, biases)\n\n return network","title":""},{"docid":"4cba79333cbabfb4dd6d42374806bd87","score":"0.6201638","text":"def model_from_file(cls, model_path, input_shape, n_dense_neurons=1000,\n dropout_ratio=0.5, pool_size=(2, 2), activation_name='sigmoid',\n depth=4, n_base_filters=16, n_classes=2,\n batch_normalization=False, min_receptive_field_size=None):\n net_name = os.path.splitext(os.path.basename(model_path))[0]\n net = cls(input_shape, net_name, n_dense_neurons=n_dense_neurons,\n dropout_ratio=dropout_ratio, pool_size=pool_size,\n activation_name=activation_name, depth=depth,\n n_base_filters=n_base_filters, n_classes=n_classes,\n batch_normalization=batch_normalization,\n min_receptive_field_size=min_receptive_field_size)\n net.model = net._get_model()\n net.model.load_weights(model_path)\n return net","title":""},{"docid":"237fc295d7127bdbf31d2a606a593214","score":"0.6157838","text":"def load_network(filepath):\n\n with open(filepath, 'rb') as input_f:\n network = pickle.load(input_f)\n\n return network","title":""},{"docid":"bf653656691fd5022fc48c0783ea3b27","score":"0.61289394","text":"def load_model(self, filename):\n summary = torch.load(filename)\n discriminator = Discriminator(self.args)\n discriminator.load_state_dict(summary['dnet'])","title":""},{"docid":"f444d81bef1d15e11564c7d21f871464","score":"0.6128591","text":"def load_from_file(model_fname):\n log_info(\"Loading reranker from %s...\" % model_fname)\n with file_stream(model_fname, 'rb', encoding=None) as fh:\n data = pickle.load(fh)\n ret = RerankingClassifier(cfg=data['cfg'])\n ret.load_all_settings(data)\n\n # re-build TF graph and restore the TF session\n tf_session_fname = os.path.abspath(re.sub(r'(.pickle)?(.gz)?$', '.tfsess', model_fname))\n ret._init_neural_network()\n ret.saver.restore(ret.session, tf_session_fname)\n return ret","title":""},{"docid":"58f777ba64a0883012697793bf3e9c37","score":"0.6106363","text":"def load(self, file_name):\n with self.graph.as_default():\n saver = tf.train.Saver()\n saver.restore(self.session, io.tf_save_path + file_name + '.ckpt')\n print \"Loaded network from file \" + file_name","title":""},{"docid":"b4ccc7a477a89c3a24efa7aa8a05f7bc","score":"0.6103693","text":"def load_model(model_weights_path):\n\n model = models.resnet50(pretrained=False)\n for param in model.parameters():\n param.requires_grad = False\n in_fea = model.fc.in_features\n model.fc = nn.Linear(in_fea, 120)\n model_weights = torch.load(model_weights_path, map_location=torch.device('cpu'))\n model.load_state_dict(model_weights)\n return model","title":""},{"docid":"cec903bb713cc8fd2fb2a89f08498220","score":"0.61029655","text":"def load_network_from_file(self, model_path = \"model.bin\") -> str:\n # TODO: change assert to Raise.\n assert os.path.exists(model_path), \"File does not exist. You can download network with download_network()\"\n self.network.load_state_dict(torch.load(model_path, map_location = torch.device(self.device)))\n self.network.device = self.device\n return f'Weights for network loaded from {model_path}'","title":""},{"docid":"152d8dff1732ec69c7223ce4caf9f8c8","score":"0.60978454","text":"def load_model(self,filename):\r\n file_name_model = \"best_DNN_models\\\\\" + filename + '.json'\r\n file_name_weights = \"best_DNN_models\\\\\" + filename + '.h5'\r\n print(\"Loading model from: \" + file_name_model)\r\n json_file = open(file_name_model, 'r')\r\n loaded_model_json = json_file.read()\r\n json_file.close()\r\n loaded_model = model_from_json(loaded_model_json)\r\n loaded_model.load_weights(file_name_weights)\r\n self.model = loaded_model\r\n print(\"Model loaded successfully!\")","title":""},{"docid":"74290cd0501ece4fbb1377c9f46397d9","score":"0.60837543","text":"def load(file):\n model_file=open(file,'rb') \n nn= pickle.load(model_file)\n model_file.close()\n return nn","title":""},{"docid":"3c609b96fd6939402b2ae44091b2c130","score":"0.60827225","text":"def load_model_from_h5(run_hyperparameters: Dict, model_filepath: str) -> keras.Model:\n custom_objects = _get_custom_objects(run_hyperparameters[\"metrics\"])\n return keras.models.load_model(model_filepath, custom_objects=custom_objects)","title":""},{"docid":"f93bd5b97a5442c08a4249157074e87b","score":"0.6067554","text":"def __init__(self, model_name, anchor_box_ball=(5, 5), anchor_box_post=(2, 5)):\r\n self.network = load_model(model_name + '.hdf5')\r\n self.network.summary() # prints the neural network summary\r\n self.anchor_box_ball = anchor_box_ball\r\n self.anchor_box_post = anchor_box_post","title":""},{"docid":"0db44088eb263bf1f38bc915ec2c8080","score":"0.60656494","text":"def readNNFile(filename):\n f = open(filename, 'r')\n nInput = int(f.readline())\n nHidden = int(f.readline())\n nOutput = int(f.readline())\n nn = NN(nInput, nHidden, nOutput)\n for i in range(nHidden):\n for j in range(nInput):\n nn.w_hid[i, j] = float(f.readline())\n for i in range(nHidden):\n nn.b_hid[i] = float(f.readline())\n for i in range(nOutput):\n for j in range(nHidden):\n nn.w_out[i, j] = float(f.readline())\n for i in range(nOutput):\n nn.b_out[i] = float(f.readline())\n f.close()\n return nn","title":""},{"docid":"8ca78dd1ad7a22c06375f35062ed4537","score":"0.60544026","text":"def load_model():\n Resnet50_model = Sequential()\n Resnet50_model.add(GlobalAveragePooling2D(input_shape=(7, 7, 2048)))\n Resnet50_model.add(Dense(133, activation='softmax'))\n\n Resnet50_model.load_weights('../application_data/weights.best.Resnet50.hdf5')\n global model\n model = Resnet50_model","title":""},{"docid":"e321dd6a6f61f1dd28f2109888fea53f","score":"0.60408264","text":"def load_resnet50(scopes):\n filename = 'resnet50.h5'\n weights_path = get_file(\n filename, __model_url__ + 'resnet/' + filename,\n cache_subdir='models',\n file_hash='9df0843bdadb58ed24d360564c45b119')\n return load_keras_weights(scopes, weights_path)","title":""},{"docid":"5950db59b80351e2c84e867d2ba18eeb","score":"0.60384166","text":"def read_network(fname, full_output=False):\n # read GRN files\n data_columns = [\"tf_target\", \"prob\"]\n if full_output:\n data_columns = [\n \"tf_target\",\n \"prob\",\n \"tf_expression\",\n \"target_expression\",\n \"weighted_binding\",\n \"activity\",\n ]\n # read the GRN file\n rnet = pd.read_csv(\n fname,\n sep=\"\\t\",\n usecols=data_columns,\n dtype=\"float64\",\n converters={\"tf_target\": str},\n index_col=\"tf_target\",\n )\n\n return rnet","title":""},{"docid":"34a8245456d56d22510d33dffcc76a13","score":"0.60269505","text":"def load(self, path):\n checkpoint = torch.load(path, map_location='cpu')\n self.net.load_state_dict(checkpoint['net'])","title":""},{"docid":"0308effb9da6c83aca16b503f486d73f","score":"0.60229796","text":"def load(self, path_to_model):\n chemml_model = pd.read_csv(path_to_model,index_col=0)\n self.model = load_model(chemml_model.loc['path_to_file'][0])\n # optimizer config\n opt = self.model.optimizer.get_config()\n opt_list = [opt['name']]\n del opt['name']\n opt_list.append(opt)\n self.opt = self.parse_opt_config(opt_list)\n \n self.nepochs=int(chemml_model.loc['nepochs'][0])\n self.batch_size=int(chemml_model.loc['batch_size'][0])\n self.loss=chemml_model.loc['loss'][0]\n self.is_regression=eval(chemml_model.loc['is_regression'][0])\n self.nclasses=chemml_model.loc['nclasses'][0]\n \n if str(self.nclasses).lower() == 'nan':\n self.nclasses = None\n else:\n self.nclasses = int(self.nclasses)\n \n self.feature_size = int(chemml_model.loc['feature_size'][0])\n \n # layer config\n self.layers = [(n['class_name'],n['config']) for n in self.model.get_config()['layers']]\n self.nhidden = None\n self.nneurons = None\n self.activations = None\n \n return self","title":""},{"docid":"cf10103822709db63448604d9ba3c97f","score":"0.6021567","text":"def load_model_networks(self, directory, extension=\"_final\"):\n raise NotImplementedError","title":""},{"docid":"cd011126757d029cef4564014f276363","score":"0.6019663","text":"def load_model(path):\n if not path.endswith('.h5'):\n path = '{}.h5'.format(path)\n\n with CustomObjectScope({'relu6': keras.applications.mobilenet.relu6,'DepthwiseConv2D': keras.applications.mobilenet.DepthwiseConv2D}):\n model = keras.models.load_model(path)\n return model","title":""},{"docid":"c1f4485399791997dd44b5f220c356d4","score":"0.60178465","text":"def load_model(filepath, neural_network):\n print (\"Loading model from %s ...\" % filepath)\n if neural_network:\n from keras.models import load_model\n model = load_model(filepath)\n else:\n import pickle\n model = pickle.load(open(filepath, 'rb'))\n print (\"Loading model done.\")\n return model","title":""},{"docid":"c96bddae6ee8c8fb4cf7a27149a9c2d7","score":"0.60125995","text":"def read_file_into_graph(self):\n\n assert_or = False\n assert_and = False\n input_bounds = {}\n input_elements = []\n\n with open(self.filepath, \"r\") as f:\n for line in f:\n if line.startswith(\"#\"):\n continue\n elements = line.split()\n if elements[0] == \"Input\":\n input_bounds[elements[1]] = {\"lb\": None, \"ub\": None}\n self.graph.add_node(elements[1], node_type=\"input\")\n\n if elements[0] == \"ReLU\":\n\n relu_in_name = elements[1] + \"_in\"\n relu_out_name = elements[1]\n bias = float(elements[2])\n variables, coeffs = self.get_vars_and_coefficients(elements, str_only=True)\n\n relu_entry = namedtuple(\"ReLU\", [\"relu_in_name\", \"relu_out_name\", \"bias\", \"variables\", \"coeffs\"])\n self.relu_nodes[elements[1]] = relu_entry(relu_in_name, relu_out_name, bias, variables, coeffs)\n # relu_in node is created and added to self.relu_in_nodes later, other variables also created later\n\n self.graph.add_node(relu_in_name, node_type=\"relu_in\", bias=bias)\n self.graph.add_node(relu_out_name, node_type=\"relu_out\")\n self.graph.add_edge(relu_in_name, relu_out_name)\n for v, w in zip(variables, coeffs):\n self.graph.add_edge(v, relu_in_name, weight=w)\n\n\n if elements[0] == \"MaxPool\":\n\n self.max_pool_nodes[elements[1]] = elements[2:]\n self.graph.add_node(elements[1], node_type=\"max_pool\")\n self.graph.add_edges_from(((v, elements[1]) for v in elements[2:]), weight=1)\n\n\n if elements[0] == \"Linear\":\n variables, coeffs = self.get_vars_and_coefficients(elements, str_only=True)\n bias = float(elements[2])\n self.linear_nodes[elements[1]] = (bias, variables, coeffs)\n\n #self.graph.add_edges_from((v.name, linear.name) for v in variables)\n self.graph.add_node(elements[1], node_type=\"linear\", bias=bias)\n for v, w in zip(variables, coeffs):\n self.graph.add_edge(v, elements[1], weight=w)\n\n\n if elements[0] == \"Assert\":\n input_elements.append(elements)\n\n # explicit bounds for input neurons\n if len(elements) == 5 and elements[-1] in input_bounds:\n if elements[1] == \"<=\":\n new_lb = float(elements[2]) / float(elements[3])\n if input_bounds[elements[-1]][\"lb\"] is None or input_bounds[elements[-1]][\"lb\"] < new_lb:\n input_bounds[elements[-1]][\"lb\"] = new_lb\n\n elif elements[1] == \">=\":\n new_ub = float(elements[2]) / float(elements[3])\n if input_bounds[elements[-1]][\"ub\"] is None or input_bounds[elements[-1]][\"ub\"] > new_ub:\n input_bounds[elements[-1]][\"ub\"] = new_ub\n\n if elements[0] == \"AssertOut\":\n assert elements[1] in [\"<=\", \">=\"] and not assert_or\n assert_and = True\n cons = namedtuple(\"output_cons\", [\"lhs\", \"operator\", \"elements\"])\n self.output_cons.append(cons(float(elements[2]), True if elements[1] == \">=\" else False, elements))\n #print(\"assertout\", elements)\n\n if elements[0] == \"AssertOr\":\n # assertOr properties are basically the same as AND, just the \"imaginary\" operator is turned around\n # we assume that all subsequent ORs in the file form one disjunction\n assert elements[1] in [\"<=\", \">=\"] and not assert_and\n assert_or = True\n cons = namedtuple(\"output_cons\", [\"lhs\", \"operator\", \"elements\"])\n self.output_cons.append(cons(float(elements[2]), False if elements[1] == \">=\" else True, elements))\n\n for var_name, bounds in input_bounds.items():\n self.vars[var_name] = self.model.addVar(name=var_name, lb=bounds[\"lb\"], ub=bounds[\"ub\"])\n self.input_nodes[var_name] = self.vars[var_name]\n\n for assert_input_count, elements in enumerate(input_elements):\n if elements[1] == \"<=\":\n self.model.addCons(float(elements[2]) <= self.quicksum_coeff_var(elements),\n name=\"input_cons_\" + str(assert_input_count))\n elif elements[1] == \">=\":\n self.model.addCons(float(elements[2]) >= self.quicksum_coeff_var(elements),\n name=\"input_cons_\" + str(assert_input_count))\n else:\n raise NotImplementedError(\"This property cannot be verified: \" + elements[1])\n\n self.model.hideOutput()\n for var_name, bounds in input_bounds.items():\n if bounds[\"lb\"] is None:\n self.model.setObjective(self.vars[var_name])\n self.model.optimize()\n if self.model.getStatus() != \"optimal\":\n raise ValueError(\"LP lower bound of input cannot be solved to optimality\")\n else:\n bound = self.model.getDualbound()\n self.model.freeTransform()\n self.model.chgVarLbGlobal(self.vars[var_name], bound - 10 * self.eps)\n if bounds[\"ub\"] is None:\n self.model.setObjective(self.vars[var_name], sense=\"maximize\")\n self.model.optimize()\n if self.model.getStatus() != \"optimal\":\n raise ValueError(\"LP upper bound of input cannot be solved to optimality\")\n else:\n bound = self.model.getDualbound()\n self.model.freeTransform()\n self.model.chgVarUbGlobal(self.vars[var_name], bound + 10 * self.eps)\n\n self.model.setObjective(0.0)\n\n self.model.hideOutput(quiet=False)\n\n return self.model, self.vars","title":""},{"docid":"59dc3681f30d8f9111428804473895bd","score":"0.6010998","text":"def load_model(self):\n \n self.model = Darknet(self.config_path, img_size=self.img_size)\n\n #check if yolov3.weights file exists else download it\n if not os.path.exists(self.weights_path):\n print(\"downloading weights from web\")\n filename=self.weights_path\n url=\"https://pjreddie.com/media/files/yolov3.weights\"\n chunkSize = 1024\n r = requests.get(url, stream=True)\n with open(filename, 'wb') as f:\n pbar = tqdm( unit=\"B\", total=int( r.headers['Content-Length'] ) )\n for chunk in r.iter_content(chunk_size=chunkSize): \n if chunk: # filter out keep-alive new chunks\n pbar.update (len(chunk))\n f.write(chunk)\n\n\n\n self.model.load_weights(self.weights_path)\n self.model.cuda()\n self.model.eval()\n self.classes = utils.load_classes(self.class_path)\n self.Tensor = torch.cuda.FloatTensor","title":""},{"docid":"467f91546c16df05fadba1adefbbc01f","score":"0.60007584","text":"def _load_model(self, model, load_file):\n\n vars = {}\n def _gather(name, obj):\n if isinstance(obj, h5py.Dataset):\n vars[name] = obj[...]\n\n with h5py.File(load_file) as f:\n f.visititems(_gather)\n\n model.assign(vars)","title":""},{"docid":"6611594accf2d6e237abb9ab117e50da","score":"0.5979233","text":"def load(self, filepath):\n var_dict = torch.load(filepath)\n self.rnn_model.load_state_dict(var_dict['rnn_state_dict'])\n self.rnn_init_hidden = nn.Parameter(\n torch.from_numpy(var_dict['rnn_init_hidden']).to(self.device))\n self.transition_bias = float(var_dict['transition_bias'])\n self.transition_bias_denominator = float(\n var_dict['transition_bias_denominator'])\n self.crp_alpha = float(var_dict['crp_alpha'])\n self.sigma2 = nn.Parameter(\n torch.from_numpy(var_dict['sigma2']).to(self.device))\n\n self.logger.print(\n 3, 'Loaded model with transition_bias={}, crp_alpha={}, sigma2={}, '\n 'rnn_init_hidden={}'.format(\n self.transition_bias, self.crp_alpha, var_dict['sigma2'],\n var_dict['rnn_init_hidden']))","title":""},{"docid":"0a5186ae4250bb01f62c3cd51ec1b86b","score":"0.5949441","text":"def load_network(self, which_epoch, train=True):\n save_filename = '%s_net.pth' % which_epoch\n load_path = join(self.save_dir, save_filename)\n net = self.net\n if isinstance(net, torch.nn.DataParallel):\n net = net.module\n print('loading the model from %s' % load_path)\n checkpoint = torch.load(load_path, map_location=self.device)\n if hasattr(checkpoint['model_state_dict'], '_metadata'):\n del checkpoint['model_state_dict']._metadata\n net.load_state_dict(checkpoint['model_state_dict'])\n if train:\n self.optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\n self.scheduler.load_state_dict(checkpoint['scheduler_state_dict'])\n self.opt.epoch_count = checkpoint[\"epoch\"]\n else:\n net.eval()","title":""},{"docid":"9796def368ca05d71d277dea78bd5832","score":"0.59286076","text":"def load_HUXt_run(filepath):\n if os.path.isfile(filepath):\n\n data = h5py.File(filepath, 'r')\n\n cr_num = np.int32(data['cr_num'])\n cr_lon_init = data['cr_lon_init'][()] * u.Unit(data['cr_lon_init'].attrs['unit'])\n simtime = data['simtime'][()] * u.Unit(data['simtime'].attrs['unit'])\n simtime = simtime.to(u.day)\n dt_scale = data['dt_scale'][()]\n v_boundary = data['_v_boundary_init_'][()] * u.Unit(data['_v_boundary_init_'].attrs['unit'])\n r = data['r'][()] * u.Unit(data['r'].attrs['unit'])\n lon = data['lon'][()] * u.Unit(data['lon'].attrs['unit'])\n nlon = lon.size\n map_inwards = np.int32(data['_map_inwards_'])\n if map_inwards == 1:\n map_inwards = True\n else:\n map_inwards = False\n\n if cr_num != 9999:\n if nlon == 1:\n model = HUXt(cr_num=cr_num, cr_lon_init=cr_lon_init, r_min=r.min(), r_max=r.max(),\n lon_out=lon, simtime=simtime, dt_scale=dt_scale, map_inwards=map_inwards)\n elif nlon > 1:\n model = HUXt(cr_num=cr_num, cr_lon_init=cr_lon_init, r_min=r.min(), r_max=r.max(),\n lon_start=lon.min(), lon_stop=lon.max(), simtime=simtime, dt_scale=dt_scale,\n map_inwards=map_inwards)\n else:\n if nlon == 1:\n model = HUXt(v_boundary=v_boundary, cr_lon_init=cr_lon_init, r_min=r.min(), r_max=r.max(),\n lon_out=lon, simtime=simtime, dt_scale=dt_scale, map_inwards=map_inwards)\n elif nlon > 1:\n model = HUXt(v_boundary=v_boundary, cr_lon_init=cr_lon_init, r_min=r.min(), r_max=r.max(),\n lon_start=lon.min(), lon_stop=lon.max(), simtime=simtime, dt_scale=dt_scale,\n map_inwards=map_inwards)\n\n model.v_grid_cme[:, :, :] = data['v_grid_cme'][()] * u.Unit(data['v_boundary'].attrs['unit'])\n model.v_grid_amb[:, :, :] = data['v_grid_amb'][()] * u.Unit(data['v_boundary'].attrs['unit'])\n\n # Create list of the ConeCMEs\n cme_list = []\n all_cmes = data['ConeCMEs']\n for k in all_cmes.keys():\n cme_data = all_cmes[k]\n t_launch = cme_data['t_launch'][()] * u.Unit(cme_data['t_launch'].attrs['unit'])\n lon = cme_data['longitude'][()] * u.Unit(cme_data['longitude'].attrs['unit'])\n lat = cme_data['latitude'][()] * u.Unit(cme_data['latitude'].attrs['unit'])\n width = cme_data['width'][()] * u.Unit(cme_data['width'].attrs['unit'])\n thickness = cme_data['thickness'][()] * u.Unit(cme_data['thickness'].attrs['unit'])\n thickness = thickness.to('solRad')\n v = cme_data['v'][()] * u.Unit(cme_data['v'].attrs['unit'])\n cme = ConeCME(t_launch=t_launch, longitude=lon, latitude=lat, v=v, width=width, thickness=thickness)\n\n # Now sort out coordinates.\n # Use the same dictionary structure as defined in ConeCME._track_2d_\n coords_group = cme_data['coords']\n coords_data = {j: {'lon_pix': np.array([]) * u.pix, 'r_pix': np.array([]) * u.pix,\n 'lon': np.array([]) * model.lon.unit, 'r': np.array([]) * model.r.unit}\n for j in range(len(coords_group))}\n\n for time_key, pos in coords_group.items():\n t = np.int(time_key.split(\"_\")[2])\n coords_data[t]['lon_pix'] = pos['lon_pix'][()] * u.Unit(pos['lon_pix'].attrs['unit'])\n coords_data[t]['r_pix'] = pos['r_pix'][()] * u.Unit(pos['r_pix'].attrs['unit'])\n coords_data[t]['lon'] = pos['lon'][()] * u.Unit(pos['lon'].attrs['unit'])\n coords_data[t]['r'] = pos['r'][()] * u.Unit(pos['r'].attrs['unit'])\n\n cme.coords = coords_data\n cme_list.append(cme)\n\n # Update CMEs in model output\n model.cmes = cme_list\n\n else:\n # File doesnt exist return nothing\n print(\"Warning: {} doesnt exist.\".format(filepath))\n cme_list = []\n model = []\n\n return model, cme_list","title":""},{"docid":"0c8c3bdc0236c1bb9152f24953709ed7","score":"0.5927499","text":"def deeplabv3plus_resnet50(num_classes=21, output_stride=8, pretrained_backbone='imagenet'):\n return load_model('deeplabv3plus', 'resnet50', num_classes, output_stride=output_stride, pretrained_backbone=pretrained_backbone)","title":""},{"docid":"f9214d7a2ccd32be28190440d06178cb","score":"0.5900768","text":"def load(self,fn=None,deep=False,load='full'):\n fh=self._h5(fn=fn,mode='r')\n self._fromh5(fh,deep=deep,load=load)\n if load!='demand':\n self._h5close()","title":""},{"docid":"184d670690f9a64cd1f92957b6e5b40d","score":"0.5899939","text":"def start_network(model_data_path):\n # Default input size\n height = 228\n width = 304\n channels = 3\n batch_size = 1\n\n # Create a placeholder for the input image\n input_node = tf.compat.v1.placeholder(dtype=tf.float32, shape=(1, height, width, channels))\n\n # Construct the network\n net = models.ResNet50UpProj({'data': input_node}, batch_size, 1, False, trainable=False)\n\n sess = tf.compat.v1.Session()\n\n # Load the converted parameters\n print('Loading the model')\n\n net.load(model_data_path, sess)\n\n #using a namedtuple to return the network state.\n Network = collections.namedtuple('Network', ['sess', 'net', 'input_node'])\n network = Network(sess, net, input_node)\n print('returning the model')\n\n return network","title":""},{"docid":"f404a002e23a44b07ce49746917d3678","score":"0.5894621","text":"def deeplabv3_resnet50(num_classes=21, output_stride=8, pretrained_backbone='imagenet'):\n return load_model('deeplabv3', 'resnet50', num_classes, output_stride=output_stride, pretrained_backbone=pretrained_backbone)","title":""},{"docid":"46b3dc3e5ecf606ead22b23c2d12ee9d","score":"0.58853614","text":"def load_dnn(dnnFilename):\n with h5py.File(dnnFilename, 'r') as h5f:\n dnn_layers = list(h5f.keys())\n W = []\n b = []\n print(\"reading in the DNN parameters ...\")\n for l in range(len(dnn_layers)//2):\n W.append(h5f['w'+str(l)][:])\n print(\"layer {}: [{}]\".format(l, W[l].shape))\n b.append(h5f['b'+str(l)][:])\n print(\"done.\")\n return b, W","title":""},{"docid":"bc3da1ea45a92c18a86f3907e17aa7a3","score":"0.5879295","text":"def load(self, brainFile):","title":""},{"docid":"f761f10f59bfa221202a1bd4eb15abdf","score":"0.5877103","text":"def read_neutron_data(pfolder, fname):\n with h5py.File(pfolder + \"\\\\\" + fname, 'r') as file:\n datasetnames = list(file.keys())\n itemnames = list(file[datasetnames[0]].keys())\n datanames = list(file[datasetnames[0]][itemnames[2]].keys())\n # data are on the third layer of these .h5 files\n x_vals = np.array(file[datasetnames[0]][itemnames[2]][datanames[2]])\n y_vals = np.array(file[datasetnames[0]][itemnames[2]][datanames[0]])\n ydev_vals = np.array(file[datasetnames[0]][itemnames[2]][datanames[1]])\n return x_vals, y_vals, ydev_vals","title":""},{"docid":"f7a44d9e471e26b29d2cb0dd6ea2e27f","score":"0.5875665","text":"def load(self):\r\n self.mean=np.load(\"results/models/CNN1D/mean.npy\")\r\n self.aplt=np.load(\"results/models/CNN1D/aplt.npy\")\r\n self.d_threshold=np.load(\"results/models/CNN1D/threshold.npy\")\r\n self.model = load_model('results/models/CNN1D/cnn_.h5')\r\n self.model.summary()","title":""},{"docid":"470c93ba861b860fa0e9118277ba8411","score":"0.5864153","text":"def from_legacy_h5(file_path, filename=None):\n with h5py.File(file_path, 'r') as f:\n if filename is None:\n filename = f.attrs['microstructure_name']\n micro = Microstructure(name=filename, overwrite_hdf5=True)\n if 'symmetry' in f['EnsembleData/CrystalStructure'].attrs:\n sym = f['EnsembleData/CrystalStructure'].attrs['symmetry']\n parameters = f['EnsembleData/CrystalStructure/LatticeParameters'][()]\n micro.set_lattice(Lattice.from_symmetry(Symmetry.from_string(sym),\n parameters))\n if 'data_dir' in f.attrs:\n micro.data_dir = f.attrs['data_dir']\n # load feature data\n if 'R_vectors' in f['FeatureData']:\n print('some grains')\n avg_rods = f['FeatureData/R_vectors'][()]\n print(avg_rods.shape)\n if 'grain_ids' in f['FeatureData']:\n grain_ids = f['FeatureData/grain_ids'][()]\n else:\n grain_ids = range(1, 1 + avg_rods.shape[0])\n if 'centers' in f['FeatureData']:\n centers = f['FeatureData/centers'][()]\n else:\n centers = np.zeros_like(avg_rods)\n # add all grains to the microstructure\n grain = micro.grains.row\n for i in range(avg_rods.shape[0]):\n grain['idnumber'] = grain_ids[i]\n grain['orientation'] = avg_rods[i, :]\n grain['center'] = centers[i]\n grain.append()\n micro.grains.flush()\n # load cell data\n if 'grain_ids' in f['CellData']:\n micro.set_grain_map(f['CellData/grain_ids'][()],\n f['CellData/grain_ids'].attrs['voxel_size'])\n micro.recompute_grain_bounding_boxes()\n micro.recompute_grain_volumes()\n if 'mask' in f['CellData']:\n micro.set_mask(f['CellData/mask'][()],\n f['CellData/mask'].attrs['voxel_size'])\n return micro","title":""},{"docid":"4c1060779239e0bb3a046ba2fc94db09","score":"0.58499646","text":"def __init__(self, weight_file='model/weights_40-0.56.h5'):\n\n\n print(\"Loading model...\")\n self.model = self.get_model()\n self.model.load_weights(weight_file)\n# self.combined_model = Model(inputs=self.model.input, outputs=NoiseAwareLayer(name='noise')(self.model.output))\n# self.combined_model.compile(loss='categorical_crossentropy',\n# optimizer='adam',\n# metrics=['accuracy'])\n# self.combined_model.load_weights('model/combined_weights_31-0.49.h5')\n print(\"Model loaded!\")","title":""},{"docid":"73d7fb95578f796a58679b57e1c2500a","score":"0.5814465","text":"def load_gradient(file_path):\n obj = load_hdf5(filename=file_path, obj_class=Gradient_calculator, ignore_attrs_if_err=[])\n\n\n return obj","title":""},{"docid":"abae4b9c3254d1201f4e9d8e40dc6e83","score":"0.58138245","text":"def loadModel(self, filepath):\n saved = torch.load(filepath, map_location='cpu')\n state_dict = saved['state_dict']\n config = XLNetConfig(num_labels = 2)\n model = XLNetForSequenceClassification(config)\n # loading the trained parameters with model\n model.load_state_dict(state_dict)\n return model","title":""},{"docid":"fc8fdfcd8a564ba20b4bdcbcbe31e284","score":"0.58108306","text":"def reload_net(self):\n import caffe\n self.net = caffe.Net(self.prototxt, 1, weights=self.caffemodel)","title":""},{"docid":"17e72d422b0e4ed3462237a79c0ff135","score":"0.5799166","text":"def test_file_hdf5(self, file_name):\n sess = tf.Session()\n self.load_model(sess, log_dir = config.log_dir)\n mix_stft, feats = self.read_hdf5_file(file_name)\n out_feats = self.process_file(mix_stft, sess)\n self.plot_features(feats, out_feats)","title":""},{"docid":"ca0d68eb63250e72d9c9022a837a8954","score":"0.5793921","text":"def h5_to_ml_ready_numpy(file_path: str) -> np.ndarray:\n h5_file = h5py.File(file_path, \"r\")\n latent_rep = np.asarray(h5_file.get(\"latent_space\"))\n print(\"raw data: \" + tcols.OKBLUE + f\"{file_path}\" + tcols.ENDC + \", \", end=\"\")\n latent_rep_flat = reshaper(latent_rep)\n return latent_rep_flat","title":""},{"docid":"187e6d810484733ce23761dba6f18add","score":"0.5792707","text":"def loadFromFile(self, kpath):\n kpath = os.path.abspath(kpath)\n (dirname, basename) = os.path.split(kpath)\n self.name = basename.rpartition('.')[0]\n self.opath = os.path.join(dirname, self.name+\".onnx\")\n self.kmodel = Kmodels.load_model(kpath, custom_objects={ 'tf': tf, 'relu6': helper.relu6 })","title":""},{"docid":"2e43e708b6b1c0cd698f78b4cb7bd645","score":"0.5788703","text":"def load_model(self, model_file):\n net_params = torch.load(model_file)\n for action_type in self.action_types:\n self.nets[action_type]['net'].load_state_dict(net_params[action_type])","title":""},{"docid":"db97dea8774addf1211cc98ec865b680","score":"0.57791567","text":"def load_model(self, modelpath):\n try:\n self.model = model_from_json(open(modelpath + \".json\").read())\n self.model.load_weights(modelpath + \".h5\")\n except Exception, e:\n self.log(\"Failed to load CNN model %s. (%s)\" % (modelpath,str(e)) )\n return\n\n self.log(\"Loaded CNN model %s.\" % modelpath)\n return","title":""},{"docid":"f96e24d2cdc207c95d2dd3cd76d1d52a","score":"0.5769877","text":"def load_graph(filename):\r\n with tf.gfile.GFile(filename, 'rb') as f:\r\n graph_def = tf.GraphDef()\r\n graph_def.ParseFromString(f.read())\r\n tf.import_graph_def(graph_def, name='')","title":""},{"docid":"f2b96ca746274fb7c1215f04b712c4af","score":"0.5768957","text":"def load_model(self, model_name, network):\n if model_name[-4:] != '.npz':\n log.error(\"Model not found\")\n raise ValueError\n\n with np.load(os.path.join(self.path_to_model_dir, model_name)) as f:\n param_values = [f['arr_%d' % i] for i in range(len(f.files))]\n\n lasagne.layers.set_all_param_values(network, param_values, trainable=True)","title":""},{"docid":"3b3d171071dc7c3e93a6801d5ab8ae5c","score":"0.5761669","text":"def __load_model_from_file__(self, h5file):\n if not \"Models shape\" in h5file.attrs.keys() or \\\n not \"Num models\" in h5file.attrs.keys() or \\\n not \"Cell size\" in h5file.attrs.keys():\n return False\n \n self.CELL_SIZE = h5file.attrs[\"Cell size\"]\n \n self.models = np.empty(shape=h5file.attrs[\"Models shape\"], dtype=object)\n\n with tqdm(desc=\"Loading models\", total=h5file.attrs[\"Num models\"], file=sys.stderr) as pbar:\n def _add_model(name, g):\n if \"v\" in g.attrs.keys() and \"u\" in g.attrs.keys():\n v = g.attrs[\"v\"]\n u = g.attrs[\"u\"]\n self.models[v, u] = self.CREATE_ANOMALY_MODEL_FUNC()\n self.models[v, u].__load_model_from_file__(g)\n pbar.update()\n\n h5file.visititems(_add_model)\n \n if isinstance(self.patches, PatchArray):\n self.patches.calculate_rasterization(self.CELL_SIZE)\n\n return True","title":""},{"docid":"4a6e2b523e79bc9dfc224b245bd125e3","score":"0.57497185","text":"def load_h5(fname):\n f = h5py.File(fname, 'r')\n data = dict()\n for k in f.keys():\n data[k] = f[k][:]\n return data","title":""},{"docid":"cf81b5d3ae9044c2bddec3b4f03509d2","score":"0.57453305","text":"def load_networks(self, epoch):\n for name in self.model_names:\n if isinstance(name, str):\n load_filename = '%s_net_%s.pth' % (epoch, name)\n load_path = os.path.join(self.save_dir, load_filename)\n net = getattr(self, 'net' + name)\n if isinstance(net, torch.nn.DataParallel):\n net = net.module\n None\n state_dict = torch.load(load_path, map_location=self.device)\n if hasattr(state_dict, '_metadata'):\n del state_dict._metadata\n net.load_state_dict(state_dict)","title":""},{"docid":"94f3b7c7f83cdaaec9aea35fd4ea7bac","score":"0.5736626","text":"def load_graph(filename):\n with tf.gfile.FastGFile(filename, 'rb') as f:\n graph_def = tf.GraphDef()\n graph_def.ParseFromString(f.read())\n fix_graph_def(graph_def)\n tf.import_graph_def(graph_def, name='')","title":""},{"docid":"f455f4d7f1073c9f70bf76e3c4d2bf0e","score":"0.5728204","text":"def load_graph(filename):\n with tf.gfile.FastGFile(filename, 'rb') as f:\n graph_def = tf.GraphDef()\n graph_def.ParseFromString(f.read())\n tf.import_graph_def(graph_def, name='')","title":""},{"docid":"f455f4d7f1073c9f70bf76e3c4d2bf0e","score":"0.5728204","text":"def load_graph(filename):\n with tf.gfile.FastGFile(filename, 'rb') as f:\n graph_def = tf.GraphDef()\n graph_def.ParseFromString(f.read())\n tf.import_graph_def(graph_def, name='')","title":""},{"docid":"77f1feedc87b1494cdc26b98c12fa545","score":"0.5725236","text":"def load_darknet_weights(self, weights_path):\n # Open the weights file\n with open(weights_path, \"rb\") as f:\n header = np.fromfile(\n f, dtype=np.int32, count=5\n ) # First five are header values\n self.header_info = header # Needed to write header when saving weights\n self.seen = header[3] # number of images seen during training\n weights = np.fromfile(f, dtype=np.float32) # The rest are weights\n # Establish cutoff for loading backbone weights\n cutoff = None\n if \"darknet53.conv.74\" in weights_path:\n cutoff = 75\n\n ptr = 0\n for i, (module_def, module) in enumerate(\n zip(self.module_defs, self.module_list)\n ):\n if i == cutoff:\n break\n if module_def[\"type\"] == \"convolutional\":\n conv_layer = module[0]\n if module_def[\"batch_normalize\"]:\n # Load BN bias, weights, running mean and running variance\n bn_layer = module[1]\n num_b = bn_layer.bias.numel() # Number of biases\n # Bias\n bn_b = torch.from_numpy(weights[ptr: ptr + num_b]).view_as(\n bn_layer.bias\n )\n bn_layer.bias.data.copy_(bn_b)\n ptr += num_b\n # Weight\n bn_w = torch.from_numpy(weights[ptr: ptr + num_b]).view_as(\n bn_layer.weight\n )\n bn_layer.weight.data.copy_(bn_w)\n ptr += num_b\n # Running Mean\n bn_rm = torch.from_numpy(weights[ptr: ptr + num_b]).view_as(\n bn_layer.running_mean\n )\n bn_layer.running_mean.data.copy_(bn_rm)\n ptr += num_b\n # Running Var\n bn_rv = torch.from_numpy(weights[ptr: ptr + num_b]).view_as(\n bn_layer.running_var\n )\n bn_layer.running_var.data.copy_(bn_rv)\n ptr += num_b\n else:\n # Load conv. bias\n num_b = conv_layer.bias.numel()\n conv_b = torch.from_numpy(weights[ptr: ptr + num_b]).view_as(\n conv_layer.bias\n )\n conv_layer.bias.data.copy_(conv_b)\n ptr += num_b\n # Load conv. weights\n num_w = conv_layer.weight.numel()\n conv_w = torch.from_numpy(weights[ptr: ptr + num_w]).view_as(\n conv_layer.weight\n )\n conv_layer.weight.data.copy_(conv_w)\n ptr += num_w","title":""},{"docid":"462eebb3635eebb63e58969151eeccbc","score":"0.57218695","text":"def load_graph(filename):\n with tf.gfile.GFile(filename, 'rb') as f:\n graph_def = tf.GraphDef()\n graph_def.ParseFromString(f.read())\n tf.import_graph_def(graph_def, name='')","title":""},{"docid":"22aa906e9ef7f13b161df66fd5f3471d","score":"0.5718611","text":"def __init__(self, path_model='./data/models/facenet/facenet_keras.h5'):\n self.path_model = path_model\n self.model = tf.keras.models.load_model(self.path_model, compile=False)","title":""},{"docid":"552a3157f02c58c63171db3a1cf21a83","score":"0.57166165","text":"def LoadH5(file_name):\n obj = {}\n with h5py.File(file_name, 'r') as hf:\n for k in hf.keys():\n obj[k] = np.asarray(hf.get(k))\n\n return obj","title":""},{"docid":"b499d517ad6ab73566d2d54c69f6a64f","score":"0.5703583","text":"def load_weights(self, filename):\r\n f = open(filename)\r\n rawData = [[float(n) for n in line.split()] for line in f.read().splitlines()]\r\n\r\n self.hiddenLayer = np.array(rawData[:self.K])\r\n self.outputLayer = np.array(rawData[self.K])\r\n\r\n self.hiddenBias = np.array(rawData[self.K+1])\r\n self.outputBias = rawData[self.K+2][0]\r\n\r\n f.close()","title":""},{"docid":"b965b7e7a90f68232174fbb44bee41b3","score":"0.57018536","text":"def load_networks(self, epoch):\n for name in self.model_names:\n if isinstance(name, str):\n load_filename = 'epoch_%d_net_%s.pth' % (epoch, name)\n load_path = os.path.join(self.config.checkpoints_dir, load_filename)\n if not os.path.exists(load_path):\n continue\n net = getattr(self, 'net' + name)\n print('loading the models from %s' % load_path)\n state_dict = torch.load(load_path, map_location=str(self.device))\n if hasattr(state_dict, '_metadata'):\n del state_dict._metadata\n\n net.load_state_dict(state_dict)","title":""},{"docid":"c281a275bf1818c138701f3180ce2d38","score":"0.5700677","text":"def scNT_seq_neuron_labeling(\n url=\"https://www.dropbox.com/s/lk9cl63yd28mfuq/neuron_labeling.h5ad?dl=1\",\n filename=\"neuron_labeling.h5ad\",\n):\n adata = get_adata(url, filename)\n\n return adata","title":""},{"docid":"25e9afc9df40ca200835ce0817d1cf9e","score":"0.5690368","text":"def read_network(file, config):\n SPEED_OF_LIGHT = 299792458 # meter per second\n PROPAGATION_FACTOR = 0.77 # https://en.wikipedia.org/wiki/Propagation_delay\n\n # default values\n default_link_delay = 3\n\n if not file.endswith(\".graphml\"):\n raise ValueError(\"{} is not a GraphML file\".format(file))\n graphml_network = nx.read_graphml(file, node_type=int)\n networkx_network = nx.Graph()\n\n # Setting the nodes of the NetworkX Graph\n for n in graphml_network.nodes(data=True):\n node_id = \"pop{}\".format(n[0])\n\n if config['node_parameter_mode'] == 'probabilistic_continuous':\n cap = np.random.normal(config['node_cap_mean'], config['flow_dr_stdev'])\n elif config['node_parameter_mode'] == 'probabilistic_discrete':\n cap = np.random.choice(config['node_cap_values'], p=config['node_cap_weights'])\n cap = n[1].get('NodeCap', cap)\n\n node_type = n[1].get(\"NodeType\", \"Normal\")\n node_name = n[1].get(\"label\", None)\n if cap is None:\n raise ValueError(\"No NodeCap. set for node{} in file {} (as cmd argument or in graphml)\".format(n, file))\n # Adding a Node in the NetworkX Graph\n # Type of node. For now it is either \"Normal\" or \"Ingress\"\n # Init 'remaining_cap' to the node capacity\n networkx_network.add_node(node_id, name=node_name, type=node_type, cap=cap, available_sf={},\n remaining_cap=cap)\n\n # set links\n # calculate link delay based on geo positions of nodes;\n for e in graphml_network.edges(data=True):\n # Check whether LinkDelay value is set, otherwise default to None\n source = \"pop{}\".format(e[0])\n target = \"pop{}\".format(e[1])\n # As edges are undirectional, only LinkFwdCap determines the available data rate\n if config['link_cap_parameter_mode'] == 'probabilistic_continuous':\n link_fwd_cap = np.random.normal(config['link_cap_mean'], config['link_cap_stdev'])\n elif config['link_cap_parameter_mode'] == 'probabilistic_discrete':\n link_fwd_cap = np.random.choice(config['link_cap_values'], p=config['link_cap_weights'])\n link_fwd_cap = e[2].get(\"LinkFwdCap\", link_fwd_cap)\n\n # Setting a default delay of 3 incase no delay specified in GraphML file\n # and we are unable to set it based on Geo location\n if config['link_delay_parameter_mode'] == 'geo_location':\n n1 = graphml_network.nodes(data=True)[e[0]]\n n2 = graphml_network.nodes(data=True)[e[1]]\n n1_lat, n1_long = n1.get(\"Latitude\", None), n1.get(\"Longitude\", None)\n n2_lat, n2_long = n2.get(\"Latitude\", None), n2.get(\"Longitude\", None)\n if n1_lat is None or n1_long is None or n2_lat is None or n2_long is None:\n log.warning(f'Unable to calc based on Geo Location,'\n f' Now using default delay {default_link_delay} for edge: ({source},{target})')\n link_delay = default_link_delay\n else:\n distance = dist((n1_lat, n1_long), (n2_lat, n2_long)).meters # in meters\n # round delay to int using np.around for consistency with emulator\n link_delay = (distance / SPEED_OF_LIGHT * 1000) * PROPAGATION_FACTOR # in milliseconds\n elif config['link_delay_parameter_mode'] == 'probabilistic_continuous':\n link_delay = np.random.normal(config['link_delay_mean'], config['link_delay_stdev'])\n elif config['link_delay_parameter_mode'] == 'probabilistic_discrete':\n link_delay = np.random.choice(config['link_delay_values'], p=config['link_delay_weights'])\n link_delay = e[2].get(\"LinkDelay\", link_delay)\n\n # Adding the undirected edges for each link defined in the network.\n # delay = edge delay , cap = edge capacity\n networkx_network.add_edge(source, target, delay=link_delay, cap=link_fwd_cap, remaining_cap=link_fwd_cap)\n\n # setting the weight property for each edge in the NetworkX Graph\n # weight attribute is used to find the shortest paths\n for edge in networkx_network.edges.values():\n edge['weight'] = weight(edge['cap'], edge['delay'])\n # Setting the all-pairs shortest path in the NetworkX network as a graph attribute\n shortest_paths(networkx_network)\n\n # Filter ingress nodes\n p_ingress = config['node_ingress_probability']\n p_egress = config['node_egress_probability']\n log.info(f'Ingress node probability {p_ingress}.')\n log.info(f'Egress node probability {p_egress}.')\n ing_nodes = []\n eg_nodes = []\n for node in networkx_network.nodes.items():\n if node[1][\"type\"] == \"Ingress\" or np.random.choice([True, False], p=[p_ingress, 1-p_ingress]):\n ing_nodes.append(node[0])\n if node[1][\"type\"] == \"Egress\" or np.random.choice([True, False], p=[p_egress, 1-p_egress]):\n eg_nodes.append(node[0])\n log.info(\"Total of {} ingress nodes available\".format(len(ing_nodes)))\n log.info(\"Total of {} egress nodes available\".format(len(eg_nodes)))\n log.info(nx.info(networkx_network))\n\n return networkx_network, ing_nodes, eg_nodes","title":""},{"docid":"9eba6489c691e6709af3040dda7f2152","score":"0.5689411","text":"def load_from_checkpoint(self, checkpoint_file):\n assert isinstance(checkpoint_file, str) and checkpoint_file.endswith('.h5'), \"Wrong checkpoint file argument\"\n self.model = Models.load_from_cpt(checkpoint_file)","title":""},{"docid":"d96b9ac5c760aaea2be32ce65f43e66d","score":"0.56861275","text":"def load_checkpoint(self, folder, filename_no):\n 'no37.neural.data'\n filename = f\"no{filename_no}.neural.data\"\n filepath = os.path.join(folder, filename)\n if os.path.exists(filepath):\n self.model.load_weights(filepath)\n else:\n print(\"No model in path '{}'\".format(filepath))","title":""},{"docid":"da98671e8670ed64ead085282d110de4","score":"0.56785977","text":"def load_h5_file(file_path):\n # load\n fr = h5py.File(file_path, \"r\")\n a_group_key = list(fr.keys())[0]\n data = list(fr[a_group_key])\n # transform to appropriate numpy array\n data = data[0:]\n data = np.stack(data, axis=0)\n return data","title":""},{"docid":"b1c1b9226f3cad90cf60a03d27c38c64","score":"0.56785023","text":"def load_darknet_weights(self, weights_path):\n\n # Open the weights file\n with open(weights_path, \"rb\") as f:\n header = np.fromfile(f, dtype=np.int32, count=5) # First five are header values\n self.header_info = header # Needed to write header when saving weights\n self.seen = header[3] # number of images seen during training\n weights = np.fromfile(f, dtype=np.float32) # The rest are weights\n\n # Establish cutoff for loading backbone weights\n cutoff = None\n if \"darknet53.conv.74\" in weights_path:\n cutoff = 75\n\n ptr = 0\n for i, (module_def, module) in enumerate(zip(self.module_defs, self.module_list)):\n if i == cutoff:\n break\n if module_def[\"type\"] == \"convolutional\":\n conv_layer = module[0]\n if module_def[\"batch_normalize\"]:\n # Load BN bias, weights, running mean and running variance\n bn_layer = module[1]\n num_b = bn_layer.bias.numel() # Number of biases\n # Bias\n bn_b = torch.from_numpy(weights[ptr : ptr + num_b]).view_as(bn_layer.bias)\n bn_layer.bias.data.copy_(bn_b)\n ptr += num_b\n # Weight\n bn_w = torch.from_numpy(weights[ptr : ptr + num_b]).view_as(bn_layer.weight)\n bn_layer.weight.data.copy_(bn_w)\n ptr += num_b\n # Running Mean\n bn_rm = torch.from_numpy(weights[ptr : ptr + num_b]).view_as(bn_layer.running_mean)\n bn_layer.running_mean.data.copy_(bn_rm)\n ptr += num_b\n # Running Var\n bn_rv = torch.from_numpy(weights[ptr : ptr + num_b]).view_as(bn_layer.running_var)\n bn_layer.running_var.data.copy_(bn_rv)\n ptr += num_b\n else:\n # Load conv. bias\n num_b = conv_layer.bias.numel()\n conv_b = torch.from_numpy(weights[ptr : ptr + num_b]).view_as(conv_layer.bias)\n conv_layer.bias.data.copy_(conv_b)\n ptr += num_b\n # Load conv. weights\n num_w = conv_layer.weight.numel()\n conv_w = torch.from_numpy(weights[ptr : ptr + num_w]).view_as(conv_layer.weight)\n conv_layer.weight.data.copy_(conv_w)\n ptr += num_w","title":""},{"docid":"cade5accc2125511f88d25ee6c9f12ba","score":"0.56767017","text":"def test_file_hdf5(self, file_name):\n\t\tsess = tf.Session()\n\t\tself.load_model(sess, log_dir = config.log_dir)\n\t\tmix_stft, feats = self.read_hdf5_file(file_name)\n\t\tout_feats = self.process_file(mix_stft, sess)\n\t\tself.plot_features(feats, out_feats)","title":""},{"docid":"8a1da216e63d000006943e3abc9b2549","score":"0.5672409","text":"def __init__(self, file_path):\n assert os.path.exists(file_path), \"Config file doesn't exist, filepath: {}\".format(file_path)\n self._file_path = file_path\n super(NeuralNetConfig, self).__init__(self._file_path)\n # object.__init__(self._file_path)\n self.__net_data = self.get_net_data('CNN')\n\n assert os.path.exists(self._train_dataset_dir), f'train_dataset_dir \\'{self._train_dataset_dir}\\' does not exist'\n\n if 'version' in self.__net_data.keys():\n # if this is wrong, then it will fail when initializing neuralnet\n self.__net = self.__net_data['version']\n\n if 'model_dir' in self.__net_data:\n self.model_dir = self.__net_data['model_dir']\n\n if 'checkpoint_dir' in self.__net_data:\n self._checkpoint_dir = self.__net_data['checkpoint_dir']\n\n if 'num_classes' in self.__net_data:\n self.num_classes = int(self.__net_data['num_classes'])\n\n if 'optimizer' in self.__net_data:\n assert self.__net_data['optimizer'].lower() in optimizer,\\\n \"Supplied optimizer, \\'{}\\', doesn\\'t exist, available options are {}\".format(\n self.__net_data['optimizer'], list(optimizer))\n self._optimizer = self.__net_data['optimizer'].lower()\n\n if 'learning_rate' in self.__net_data:\n self.learning_rate = float(self.__net_data['learning_rate'])\n\n if 'momentum' in self.__net_data:\n self.momentum = float(self.__net_data['momentum'])\n\n if 'loss' in self.__net_data:\n assert self.__net_data['loss'].lower() in loss_function,\\\n \"Supplied loss fn, \\'{}\\', doesn\\'t exist, available options are {}\".format(\n self.__net_data['loss'], list(loss_function))\n self._loss_fn = self.__net_data['loss'].lower()\n\n if 'metrics' in self.__net_data:\n if self.__net_data['metrics'].lower() != 'none':\n for m in self.__net_data['metrics'].lower().split(','):\n assert m.strip() in metrics, \"Supplied metric, \\'{}\\', doesn\\'t exist, available options are {}\".format(\n m, list(metrics))\n self._metrics = self.__net_data['metrics'].lower()\n\n if 'evaluate_metrics' in self.__net_data:\n for m in self.__net_data['evaluate_metrics'].lower().split(','):\n assert m in metrics, \"Supplied evaluate_metrics, \\'{}\\', doesn\\'t exist, available options are {}\"\\\n .format(m, list(metrics))\n self._evaluate_metrics = self.__net_data['evaluate_metrics'].lower()\n\n if 'distributed_strategy' in self.__net_data:\n assert self.__net_data['distributed_strategy'].lower() in distributed_strategy, \\\n \"Supplied distributed_strategy, \\'{}\\', doesn\\'t exist, available options are {}\".format(\n self.__net_data['distributed_strategy'], list(distributed_strategy))\n self._distributed_strategy = self.__net_data['distributed_strategy'].lower()\n\n if 'batch_size' in self.__net_data:\n self.batch_size = int(self.__net_data['batch_size'])\n\n if 'log_dir' in self.__net_data:\n self.log_dir = self.__net_data['log_dir']\n\n if 'log_every_n_steps' in self.__net_data:\n self.log_every_n_steps = int(self.__net_data['log_every_n_steps'])\n\n if 'max_steps_per_epoch' in self.__net_data:\n self._max_steps_per_epoch = self.__net_data['max_steps_per_epoch']\n\n if 'num_validation_images' in self.__net_data:\n self.num_validation_images = int(self.__net_data['num_validation_images'])\n\n if 'epochs' in self.__net_data:\n self.__epochs = int(self.__net_data['epochs'])\n\n if 'bench_timings' in self.__net_data:\n assert bool('False') # I have this to remind me of what python does here\n self.bench_timings = self.__net_data['bench_timings'].lower() in ['true', '1', 't', 'y', 'yes', 'yeah', 'yup', 'certainly', 'uh-huh']\n\n if 'bench_file' in self.__net_data:\n self.bench_path = self.__net_data['bench_file']\n\n if 'input_shape' in self.__net_data:\n self.__input_shape = self.__net_data['input_shape']","title":""},{"docid":"3eeeb5c881b62288050a1993bececc3b","score":"0.56712586","text":"def load(path):\n with open(path, 'rb') as f:\n qrnn = pickle.load(f)\n backend = importlib.import_module(qrnn.backend)\n model = backend.load_model(f, qrnn.quantiles)\n qrnn.model = model\n return qrnn","title":""},{"docid":"d0443dcdd5e135df9a98592b64b89a94","score":"0.5662143","text":"def load_from_file(self, net_file):\n self.ann.create_from_file(net_file)","title":""},{"docid":"cb7040d99593af28d4b2ec0cf7564c61","score":"0.5661993","text":"def load_model(self, weight_file):\n self.W = np.fromfile(weight_file, dtype=np.float32)\n print ('model loaded from', weight_file)","title":""},{"docid":"ef01248c9779053af420065729ea3d2b","score":"0.5660996","text":"def load_model(self, filename):\n import numpy as np\n from lasagne.layers import set_all_param_values\n\n if not os.path.isfile(filename):\n print_warning(\"Could not find '.npz' weights file at path: {}\".format(filename))\n return False\n \n try:\n ### Load the generator model's weights\n print_info(\"Attempting to load model...\")\n with np.load(filename) as fp:\n param_values = [fp['arr_%d' % i] for i in range(len(fp.files))]\n set_all_param_values(self.network_out, param_values)\n except Exception as e:\n print_warning(\"Found '.npz' weights file, but it must be corrupted, cannot read or parse it properly: {}.\".format(filename))\n return False\n return True","title":""},{"docid":"5f144205c857950b9c47c73ed9f1c0c3","score":"0.5658707","text":"def load_model(self, filename):\n self.load_state_dict(torch.load(filename))","title":""},{"docid":"699dfc98cee5382e7c34941a98802785","score":"0.56567544","text":"def load_model(cls, path):\n self = cls.__new__(cls)\n with h5py.File(path, mode=\"r\") as f:\n # Default to output_length of 1 for backward compatibility,\n # but allow override.\n # TODO: should figure out how to deprecate this.\n self.metadata = {\"output_length\": 1}\n for k, v in f.attrs.items():\n if k.startswith(\"metadata_\"):\n _, k = k.split(\"_\", 1)\n self.metadata[k] = json.loads(v)\n self.keras_model = tf.keras.models.load_model(\n f, custom_objects=custom_layers\n )\n self.preprocessor = self.create_preprocessor()\n return self","title":""},{"docid":"caa94d28cf8d7979f0735d4063e970e5","score":"0.5650151","text":"def load_model(self, filename):\n \n self.load_state_dict(torch.load(filename))","title":""},{"docid":"2bd69316944d9a6032cd1e45ab48f87b","score":"0.56500286","text":"def load(path):\n if not os.path.exists(path):\n raise FileNotFoundError(f\"Unable to locate file at {path}\")\n extension = path.split(\".\")[-1]\n if not extension == \"pth\":\n raise ValueError(f\"Expected pth extension, got {extension}\")\n\n model = CNN()\n model.load_state_dict(torch.load(path))\n model.to(DEVICE)\n return model","title":""},{"docid":"203a9a8b83af38bf2d4e3de6a17ead56","score":"0.56495863","text":"def simulation_with_previous_h5m_file(self):\n\n os.system('rm *.h5m')\n\n my_model = paramak.NeutronicsModel(\n geometry=self.my_shape,\n source=self.source,\n materials={'center_column_shield_mat': 'WC'},\n )\n\n my_model.create_neutronics_geometry(method='pymoab')\n\n my_model.simulate(method=None)\n\n my_model.results is not None","title":""},{"docid":"411e498be2fc4343d09dedea24717d03","score":"0.5648832","text":"def resnet50(pretrained=False):\n model = ResNet(Bottleneck, [3, 4, 6, 3])\n # model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))\n model.load_state_dict(torch.load('./resnet50-19c8e357.pth'))\n return model","title":""},{"docid":"a904ff9356fa886e4f871d4d41c67b55","score":"0.56369436","text":"def set_nn(self, nn_file):\n n = read_yaml_file(nn_file)['neural_network'][0] # Currently we consider only 1 layer\n self.nn = NeuralNetwork(n['name'], n['nn_type'], n['dimensions'], n['start'], n['end'])","title":""},{"docid":"57b086ea0aa2214441f249b6d332ac9f","score":"0.5635668","text":"def load_model(model_name):\n hyperparams = json.loads(open(model_name+'.json').read())\n parser = RNNGparser(hyperparams['brown_file'],\n vocab_thresh=hyperparams['vocab_thresh'],\\\n stack_memory_size=hyperparams['stack_hidden_size'],\\\n word_embedding_size=hyperparams['word_embedding_size'],\\\n char_embedding_size=hyperparams['char_embedding_size'],\\\n char_memory_size=hyperparams['char_memory_size'])\n\n parser.lexicon = SymbolLexicon.load(model_name+'.lex') \n parser.nonterminals = SymbolLexicon.load(model_name+'.nt') \n parser.charset = SymbolLexicon.load(model_name+'.char')\n parser.code_struct_actions()\n parser.allocate_structure()\n parser.model.populate(model_name+\".weights\")\n return parser","title":""},{"docid":"ceb4539ace71690119e99072db495745","score":"0.56335205","text":"def load(cls, filename) :\n \n f = open(filename)\n pkl = cPickle.load(f)\n f.close()\n\n for l1, l2 in pkl[\"network\"][\"edges\"] :\n network = pkl[\"layers\"][l1] > pkl[\"layers\"][l2]\n\n network.log = pkl[\"network\"][\"log\"]\n network.notes = pkl[\"network\"][\"notes\"]\n\n return network","title":""},{"docid":"7a2c886378ff53151a46b0b184f811b3","score":"0.5630964","text":"def load_weights(self, weight_file: str) -> None:\n requires_grad = False\n\n with h5py.File(weight_file, 'r') as fin:\n for i_layer, lstms in enumerate(\n zip(self.forward_layers, self.backward_layers)\n ):\n for j_direction, lstm in enumerate(lstms):\n # lstm is an instance of LSTMCellWithProjection\n cell_size = lstm.cell_size\n\n dataset = fin['RNN_%s' % j_direction]['RNN']['MultiRNNCell']['Cell%s' % i_layer\n ]['LSTMCell']\n\n # tensorflow packs together both W and U matrices into one matrix,\n # but pytorch maintains individual matrices. In addition, tensorflow\n # packs the gates as input, memory, forget, output but pytorch\n # uses input, forget, memory, output. So we need to modify the weights.\n tf_weights = numpy.transpose(dataset['W_0'][...])\n torch_weights = tf_weights.copy()\n\n # split the W from U matrices\n input_size = lstm.input_size\n input_weights = torch_weights[:, :input_size]\n recurrent_weights = torch_weights[:, input_size:]\n tf_input_weights = tf_weights[:, :input_size]\n tf_recurrent_weights = tf_weights[:, input_size:]\n\n # handle the different gate order convention\n for torch_w, tf_w in [[input_weights, tf_input_weights],\n [recurrent_weights, tf_recurrent_weights]]:\n torch_w[(1 * cell_size):(2 * cell_size), :] = tf_w[(2 * cell_size):(3 * cell_size), :]\n torch_w[(2 * cell_size):(3 * cell_size), :] = tf_w[(1 * cell_size):(2 * cell_size), :]\n\n lstm.input_linearity.weight.data.copy_(torch.FloatTensor(input_weights))\n lstm.state_linearity.weight.data.copy_(torch.FloatTensor(recurrent_weights))\n lstm.input_linearity.weight.requires_grad = requires_grad\n lstm.state_linearity.weight.requires_grad = requires_grad\n\n # the bias weights\n tf_bias = dataset['B'][...]\n # tensorflow adds 1.0 to forget gate bias instead of modifying the\n # parameters...\n tf_bias[(2 * cell_size):(3 * cell_size)] += 1\n torch_bias = tf_bias.copy()\n torch_bias[(1 * cell_size):(2 * cell_size)\n ] = tf_bias[(2 * cell_size):(3 * cell_size)]\n torch_bias[(2 * cell_size):(3 * cell_size)\n ] = tf_bias[(1 * cell_size):(2 * cell_size)]\n lstm.state_linearity.bias.data.copy_(torch.FloatTensor(torch_bias))\n lstm.state_linearity.bias.requires_grad = requires_grad\n\n # the projection weights\n proj_weights = numpy.transpose(dataset['W_P_0'][...])\n lstm.state_projection.weight.data.copy_(torch.FloatTensor(proj_weights))\n lstm.state_projection.weight.requires_grad = requires_grad","title":""},{"docid":"abe2146cf941b9172433096750596bff","score":"0.56304467","text":"def load_checkpoint(self, folder, filename_no):\n 'no37.neural.data'\n with self.lock:\n filename = f\"no{filename_no}.neural.data\"\n filepath = os.path.join(folder, filename)\n if os.path.exists(filepath):\n self.model.load_weights(filepath)\n else:\n print(\"No model in path '{}'\".format(filepath))","title":""},{"docid":"f1427f05cba2505d04aee0482312c171","score":"0.5617891","text":"def load_from_pickle( cls, filename, catmaid_interface = None ):\n f = gzip.GzipFile(filename, 'rb')\n nrn_list = pickle.load(f)\n f.close()\n return NeuronList( nrn_list.neurons,\n CatmaidInterface = catmaid_interface,\n project_name = nrn_list.project_name,\n export_date = nrn_list.export_date\n )","title":""},{"docid":"83d70915515cde877f9f8e49406a3701","score":"0.5614241","text":"def load_file(self, file_path):\n self.f = h5py.File(file_path)\n try:\n self.sampling_rate = int(self.f['raw_data'].attrs['sampling_rate'])\n except:\n print \"Sampling rate could not be loaded from HDF5 file. Certain functions cannot be used without sampling rate.\"\n self.staged_dataset = None\n print \"File \" + `file_path` + \" has been loaded.\"","title":""},{"docid":"ef14266bd2b64cedc20201a1576faa4f","score":"0.5602503","text":"def resnet34(pretrained=False):\n model = ResNet(BasicBlock, [3, 4, 6, 3])\n if pretrained:\n model.load_state_dict(model_zoo.load_url(model_urls['resnet34']))\n return model","title":""},{"docid":"ca8c817bf9783001410cf632332794a0","score":"0.55959725","text":"def inferLeNet5(Directory_infer='/images/user' ,ModelPath='/models/model3/saved/'):\n TypeOfImage=2\n X_test,Y_test,inferFolder=LeNet5.getImagesAndLabels(Directory_infer,TypeOfImage)\n with tf.Session() as sess: \n loader = tf.train.import_meta_graph(os.getcwd()+ModelPath+'.meta')\n loader.restore(sess, tf.train.latest_checkpoint(os.getcwd()+ModelPath))\n graph = tf.get_default_graph()\n X=graph.get_tensor_by_name(\"X:0\")\n Logits=graph.get_tensor_by_name(\"logits:0\")\n cont=0\n DictClasses=GetImages.getDictClasses()\n for i in X_test:\n i=i.reshape(1,32,32,1)\n Proba=sess.run(Logits, feed_dict={X: i})\n plt.figure(str(Y_test[cont])+' '+str(np.argmax(Proba,1)))\n im = Image.open(inferFolder+'/'+Y_test[cont])\n plt.text(-1,-1,'file'+Y_test[cont]+' belongs to class '+DictClasses[int(np.argmax(Proba,1))])\n plt.imshow(im,vmin = 0, vmax = 255)\n# plt.imshow(i[0,:,:,0],cmap='gray', vmin = 0, vmax = 1)\n# plt.show()\n cont=cont+1\n plt.show()\n return","title":""}],"string":"[\n {\n \"docid\": \"a472291e19ca01e1c6c4f112fa1d57ec\",\n \"score\": \"0.7449959\",\n \"text\": \"def load(path: str):\\n with h5py.File(path, 'r') as net_file:\\n net = TensorNetwork(backend=net_file[\\\"backend\\\"][()])\\n nodes = list(net_file[\\\"nodes\\\"].keys())\\n edges = list(net_file[\\\"edges\\\"].keys())\\n\\n for node_name in nodes:\\n node_data = net_file[\\\"nodes/\\\" + node_name]\\n node_type = get_component(node_data['type'][()])\\n node_type._load_node(net, node_data)\\n\\n nodes_dict = {node.name: node for node in net.nodes_set}\\n\\n for edge in edges:\\n edge_data = net_file[\\\"edges/\\\" + edge]\\n Edge._load_edge(edge_data, nodes_dict)\\n return net\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ee11035c32d78a0774d9aafc2156b52\",\n \"score\": \"0.6647814\",\n \"text\": \"def load(filepath, load_weights=True):\\n # TODO: support loading LambdaLayer that includes parametric self defined function with outside variables\\n M = utils.load_hdf5_graph(filepath=filepath, load_weights=load_weights)\\n return M\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d2a45d91cd2380dc051484acca2d50b\",\n \"score\": \"0.6638091\",\n \"text\": \"def load_model(self):\\n self.online_network.total_model.load_weights(\\\"models/{}.h5\\\".format(self.name))\\n self.target_network.total_model.load_weights(\\\"models/{}.h5\\\".format(self.name))\\n # load memory\\n self.memory.samples = []\\n self.memory.read_from_file(\\\"{}{}.mem\\\".format(self.save_big_path, self.name))\\n # load steps and epsilon\\n mylogfile = open(\\\"models/{}.csv\\\".format(self.name), 'r')\\n firstRow = mylogfile.readline()\\n fieldnames = firstRow.strip('\\\\n').split(\\\";\\\")\\n self.steps = int(fieldnames[0])\\n self.epsilon = float(fieldnames[1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29183a0011a8b619fefeac47d13cca0c\",\n \"score\": \"0.6504803\",\n \"text\": \"def load(cls, filename):\\n data = np.load(filename, allow_pickle=True)\\n neurons = [w.shape[0] for w in data[\\\"weights\\\"]]\\n neurons.append(data[\\\"weights\\\"][-1].shape[1])\\n network = cls(neurons)\\n network.weights = data[\\\"weights\\\"]\\n network.biases = data[\\\"biases\\\"]\\n return network\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a27968244c4e77e7422b8fe518551d1\",\n \"score\": \"0.6485048\",\n \"text\": \"def load_example(file_path):\\n data = h5py.File(file_path, 'r')\\n rgb_images = list(data['image'])\\n depth_images = list(data['depth_image'])\\n frame_indices = np.arange(len(rgb_images))\\n gripper_status = list(data['gripper'])\\n action_status = list(data['label'])\\n gripper_action_goal_idx = []\\n\\n # print(\\\"gripper \\\",gripper_status)\\n # print(\\\"frames \\\", frame_indices)\\n\\n # generate new action labels and goal action indices\\n gripper_action_label, gripper_action_goal_idx = generate_gripper_action_label(data, action_status, gripper_status, gripper_action_goal_idx)\\n\\n rgb_images = ConvertImageListToNumpy(np.squeeze(rgb_images), format='list')\\n depth_images = ConvertImageListToNumpy(np.squeeze(depth_images), format='list')\\n numpy_data = {\\n 'rgb_images': rgb_images,\\n 'depth_images': depth_images,\\n 'frame_indices': frame_indices,\\n 'gripper_status': gripper_status,\\n 'action_status': action_status,\\n 'gripper_action_label': gripper_action_label,\\n 'gripper_action_goal_idx': gripper_action_goal_idx\\n }\\n\\n return data, numpy_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b132f70c1944a3fb40d32205e40ecc67\",\n \"score\": \"0.64657694\",\n \"text\": \"def load_model(self, filename):\\n if \\\"/\\\" in filename:\\n basename = \\\"/\\\".join(filename.split(\\\"/\\\")[:-1]) + \\\"/\\\" + filename.split(\\\"/\\\")[-1].split(\\\".\\\")[0]\\n else:\\n basename = filename.split(\\\".\\\")[0]\\n serializers.load_hdf5(filename + '.weights', self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0eb3eb7df6ea26390f936d3b13e8978\",\n \"score\": \"0.64656603\",\n \"text\": \"def reload_net(path):\\n return keras.models.load_model(os.path.normpath(path))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22be45c34475cfec1667433e2a31deac\",\n \"score\": \"0.64272606\",\n \"text\": \"def load_model(self, model_dir: str) -> \\\"OneClassNeuralNetwork\\\":\\n params = np.load(f'{model_dir}/params.npz')\\n w = params['w']\\n V = params['V']\\n nu = params['nu'].tolist()\\n model = load_model(f'{model_dir}/model.h5',\\n custom_objects={'custom_hinge': self.custom_ocnn_loss(nu, w, V)})\\n return model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9fcacf30761b31a91c3aa57c955d8e4\",\n \"score\": \"0.6398747\",\n \"text\": \"def load(path):\\r\\n net = torch.load(path)\\r\\n return net\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc9057976e1fd6334d78a3616bef8eec\",\n \"score\": \"0.63440007\",\n \"text\": \"def load_model(self, filename):\\n summary = torch.load(filename)\\n gen = Generator(self.args)\\n gen.load_state_dict(summary['gnet'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abd314ad5cc9da2e0a547244052e1387\",\n \"score\": \"0.62680495\",\n \"text\": \"def load_h5(fname):\\n with h5py.File(fname, 'r') as h5f:\\n return h5f['data'][:]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cceb570ec15af08c764ea25d1849252a\",\n \"score\": \"0.62357044\",\n \"text\": \"def load(self, file_name):\\n with self.graph.as_default():\\n saver = tf.train.Saver(tf.trainable_variables())\\n saver.restore(self.session, self.io_nn.model_dir_checkpoints + file_name + '.ckpt')\\n print(\\\"Loaded network from file \\\" + file_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e2585576168677c2cfba83b27970410\",\n \"score\": \"0.6230742\",\n \"text\": \"def read_data(self):\\n file = h5py.File(self.fname, 'r')\\n \\n self.x_train = np.float32(file['x_train'][:])\\n self.y_train = np.float32(file['y_train'][:, 0])\\n self.x_test = np.float32(file['x_test'][:])\\n self.y_test = np.float32(file['y_test'][:, 0])\\n \\n file.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd7bc8ac8f3b1d36a9e565230c42ddfa\",\n \"score\": \"0.62071514\",\n \"text\": \"def load(cls, filename):\\n data = np.load(filename)\\n network = cls(data[\\\"neuron_counts\\\"])\\n network.W[...] = data[\\\"W\\\"]\\n network.b[...] = data[\\\"b\\\"]\\n index = 0\\n for cell in network.cells:\\n for p in cell.parameters():\\n p[...] = data[\\\"arr_\\\" + str(index)]\\n index += 1\\n return network\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ceca592c1b2d4eb082fb8efc3fcd179f\",\n \"score\": \"0.62018853\",\n \"text\": \"def load_network(path, separator=\\\";,\\\"):\\n\\n layers = []\\n weights = []\\n biases = []\\n\\n file = open(path, \\\"r\\\")\\n\\n layers_line = file.readline()\\n\\n for size in layers_line.split(separator[0]):\\n layers.append(int(size))\\n\\n for k in range(len(layers) - 1):\\n weight = numpy.zeros((layers[k+1], layers[k]), dtype=float)\\n line = file.readline()\\n nodes = line.split(separator[0])\\n\\n for i in range(len(nodes)):\\n node = nodes[i].split(separator[1])\\n\\n for j in range(len(node)):\\n weight[i, j] = float(node[j])\\n\\n weights.append(weight)\\n\\n for k in range(len(layers) - 1):\\n bias = numpy.zeros((layers[k+1], 1), dtype=float)\\n line = file.readline()\\n nodes = line.split(separator[0])\\n\\n for i in range(len(nodes)):\\n bias[i, 0] = float(nodes[i])\\n\\n biases.append(bias)\\n\\n network = MultiPerceptron(layers)\\n network.set_weights_and_biases(weights, biases)\\n\\n return network\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4cba79333cbabfb4dd6d42374806bd87\",\n \"score\": \"0.6201638\",\n \"text\": \"def model_from_file(cls, model_path, input_shape, n_dense_neurons=1000,\\n dropout_ratio=0.5, pool_size=(2, 2), activation_name='sigmoid',\\n depth=4, n_base_filters=16, n_classes=2,\\n batch_normalization=False, min_receptive_field_size=None):\\n net_name = os.path.splitext(os.path.basename(model_path))[0]\\n net = cls(input_shape, net_name, n_dense_neurons=n_dense_neurons,\\n dropout_ratio=dropout_ratio, pool_size=pool_size,\\n activation_name=activation_name, depth=depth,\\n n_base_filters=n_base_filters, n_classes=n_classes,\\n batch_normalization=batch_normalization,\\n min_receptive_field_size=min_receptive_field_size)\\n net.model = net._get_model()\\n net.model.load_weights(model_path)\\n return net\",\n \"title\": \"\"\n },\n {\n \"docid\": \"237fc295d7127bdbf31d2a606a593214\",\n \"score\": \"0.6157838\",\n \"text\": \"def load_network(filepath):\\n\\n with open(filepath, 'rb') as input_f:\\n network = pickle.load(input_f)\\n\\n return network\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf653656691fd5022fc48c0783ea3b27\",\n \"score\": \"0.61289394\",\n \"text\": \"def load_model(self, filename):\\n summary = torch.load(filename)\\n discriminator = Discriminator(self.args)\\n discriminator.load_state_dict(summary['dnet'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f444d81bef1d15e11564c7d21f871464\",\n \"score\": \"0.6128591\",\n \"text\": \"def load_from_file(model_fname):\\n log_info(\\\"Loading reranker from %s...\\\" % model_fname)\\n with file_stream(model_fname, 'rb', encoding=None) as fh:\\n data = pickle.load(fh)\\n ret = RerankingClassifier(cfg=data['cfg'])\\n ret.load_all_settings(data)\\n\\n # re-build TF graph and restore the TF session\\n tf_session_fname = os.path.abspath(re.sub(r'(.pickle)?(.gz)?$', '.tfsess', model_fname))\\n ret._init_neural_network()\\n ret.saver.restore(ret.session, tf_session_fname)\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58f777ba64a0883012697793bf3e9c37\",\n \"score\": \"0.6106363\",\n \"text\": \"def load(self, file_name):\\n with self.graph.as_default():\\n saver = tf.train.Saver()\\n saver.restore(self.session, io.tf_save_path + file_name + '.ckpt')\\n print \\\"Loaded network from file \\\" + file_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4ccc7a477a89c3a24efa7aa8a05f7bc\",\n \"score\": \"0.6103693\",\n \"text\": \"def load_model(model_weights_path):\\n\\n model = models.resnet50(pretrained=False)\\n for param in model.parameters():\\n param.requires_grad = False\\n in_fea = model.fc.in_features\\n model.fc = nn.Linear(in_fea, 120)\\n model_weights = torch.load(model_weights_path, map_location=torch.device('cpu'))\\n model.load_state_dict(model_weights)\\n return model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cec903bb713cc8fd2fb2a89f08498220\",\n \"score\": \"0.61029655\",\n \"text\": \"def load_network_from_file(self, model_path = \\\"model.bin\\\") -> str:\\n # TODO: change assert to Raise.\\n assert os.path.exists(model_path), \\\"File does not exist. You can download network with download_network()\\\"\\n self.network.load_state_dict(torch.load(model_path, map_location = torch.device(self.device)))\\n self.network.device = self.device\\n return f'Weights for network loaded from {model_path}'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"152d8dff1732ec69c7223ce4caf9f8c8\",\n \"score\": \"0.60978454\",\n \"text\": \"def load_model(self,filename):\\r\\n file_name_model = \\\"best_DNN_models\\\\\\\\\\\" + filename + '.json'\\r\\n file_name_weights = \\\"best_DNN_models\\\\\\\\\\\" + filename + '.h5'\\r\\n print(\\\"Loading model from: \\\" + file_name_model)\\r\\n json_file = open(file_name_model, 'r')\\r\\n loaded_model_json = json_file.read()\\r\\n json_file.close()\\r\\n loaded_model = model_from_json(loaded_model_json)\\r\\n loaded_model.load_weights(file_name_weights)\\r\\n self.model = loaded_model\\r\\n print(\\\"Model loaded successfully!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74290cd0501ece4fbb1377c9f46397d9\",\n \"score\": \"0.60837543\",\n \"text\": \"def load(file):\\n model_file=open(file,'rb') \\n nn= pickle.load(model_file)\\n model_file.close()\\n return nn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c609b96fd6939402b2ae44091b2c130\",\n \"score\": \"0.60827225\",\n \"text\": \"def load_model_from_h5(run_hyperparameters: Dict, model_filepath: str) -> keras.Model:\\n custom_objects = _get_custom_objects(run_hyperparameters[\\\"metrics\\\"])\\n return keras.models.load_model(model_filepath, custom_objects=custom_objects)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f93bd5b97a5442c08a4249157074e87b\",\n \"score\": \"0.6067554\",\n \"text\": \"def __init__(self, model_name, anchor_box_ball=(5, 5), anchor_box_post=(2, 5)):\\r\\n self.network = load_model(model_name + '.hdf5')\\r\\n self.network.summary() # prints the neural network summary\\r\\n self.anchor_box_ball = anchor_box_ball\\r\\n self.anchor_box_post = anchor_box_post\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0db44088eb263bf1f38bc915ec2c8080\",\n \"score\": \"0.60656494\",\n \"text\": \"def readNNFile(filename):\\n f = open(filename, 'r')\\n nInput = int(f.readline())\\n nHidden = int(f.readline())\\n nOutput = int(f.readline())\\n nn = NN(nInput, nHidden, nOutput)\\n for i in range(nHidden):\\n for j in range(nInput):\\n nn.w_hid[i, j] = float(f.readline())\\n for i in range(nHidden):\\n nn.b_hid[i] = float(f.readline())\\n for i in range(nOutput):\\n for j in range(nHidden):\\n nn.w_out[i, j] = float(f.readline())\\n for i in range(nOutput):\\n nn.b_out[i] = float(f.readline())\\n f.close()\\n return nn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ca78dd1ad7a22c06375f35062ed4537\",\n \"score\": \"0.60544026\",\n \"text\": \"def load_model():\\n Resnet50_model = Sequential()\\n Resnet50_model.add(GlobalAveragePooling2D(input_shape=(7, 7, 2048)))\\n Resnet50_model.add(Dense(133, activation='softmax'))\\n\\n Resnet50_model.load_weights('../application_data/weights.best.Resnet50.hdf5')\\n global model\\n model = Resnet50_model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e321dd6a6f61f1dd28f2109888fea53f\",\n \"score\": \"0.60408264\",\n \"text\": \"def load_resnet50(scopes):\\n filename = 'resnet50.h5'\\n weights_path = get_file(\\n filename, __model_url__ + 'resnet/' + filename,\\n cache_subdir='models',\\n file_hash='9df0843bdadb58ed24d360564c45b119')\\n return load_keras_weights(scopes, weights_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5950db59b80351e2c84e867d2ba18eeb\",\n \"score\": \"0.60384166\",\n \"text\": \"def read_network(fname, full_output=False):\\n # read GRN files\\n data_columns = [\\\"tf_target\\\", \\\"prob\\\"]\\n if full_output:\\n data_columns = [\\n \\\"tf_target\\\",\\n \\\"prob\\\",\\n \\\"tf_expression\\\",\\n \\\"target_expression\\\",\\n \\\"weighted_binding\\\",\\n \\\"activity\\\",\\n ]\\n # read the GRN file\\n rnet = pd.read_csv(\\n fname,\\n sep=\\\"\\\\t\\\",\\n usecols=data_columns,\\n dtype=\\\"float64\\\",\\n converters={\\\"tf_target\\\": str},\\n index_col=\\\"tf_target\\\",\\n )\\n\\n return rnet\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34a8245456d56d22510d33dffcc76a13\",\n \"score\": \"0.60269505\",\n \"text\": \"def load(self, path):\\n checkpoint = torch.load(path, map_location='cpu')\\n self.net.load_state_dict(checkpoint['net'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0308effb9da6c83aca16b503f486d73f\",\n \"score\": \"0.60229796\",\n \"text\": \"def load(self, path_to_model):\\n chemml_model = pd.read_csv(path_to_model,index_col=0)\\n self.model = load_model(chemml_model.loc['path_to_file'][0])\\n # optimizer config\\n opt = self.model.optimizer.get_config()\\n opt_list = [opt['name']]\\n del opt['name']\\n opt_list.append(opt)\\n self.opt = self.parse_opt_config(opt_list)\\n \\n self.nepochs=int(chemml_model.loc['nepochs'][0])\\n self.batch_size=int(chemml_model.loc['batch_size'][0])\\n self.loss=chemml_model.loc['loss'][0]\\n self.is_regression=eval(chemml_model.loc['is_regression'][0])\\n self.nclasses=chemml_model.loc['nclasses'][0]\\n \\n if str(self.nclasses).lower() == 'nan':\\n self.nclasses = None\\n else:\\n self.nclasses = int(self.nclasses)\\n \\n self.feature_size = int(chemml_model.loc['feature_size'][0])\\n \\n # layer config\\n self.layers = [(n['class_name'],n['config']) for n in self.model.get_config()['layers']]\\n self.nhidden = None\\n self.nneurons = None\\n self.activations = None\\n \\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf10103822709db63448604d9ba3c97f\",\n \"score\": \"0.6021567\",\n \"text\": \"def load_model_networks(self, directory, extension=\\\"_final\\\"):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd011126757d029cef4564014f276363\",\n \"score\": \"0.6019663\",\n \"text\": \"def load_model(path):\\n if not path.endswith('.h5'):\\n path = '{}.h5'.format(path)\\n\\n with CustomObjectScope({'relu6': keras.applications.mobilenet.relu6,'DepthwiseConv2D': keras.applications.mobilenet.DepthwiseConv2D}):\\n model = keras.models.load_model(path)\\n return model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1f4485399791997dd44b5f220c356d4\",\n \"score\": \"0.60178465\",\n \"text\": \"def load_model(filepath, neural_network):\\n print (\\\"Loading model from %s ...\\\" % filepath)\\n if neural_network:\\n from keras.models import load_model\\n model = load_model(filepath)\\n else:\\n import pickle\\n model = pickle.load(open(filepath, 'rb'))\\n print (\\\"Loading model done.\\\")\\n return model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c96bddae6ee8c8fb4cf7a27149a9c2d7\",\n \"score\": \"0.60125995\",\n \"text\": \"def read_file_into_graph(self):\\n\\n assert_or = False\\n assert_and = False\\n input_bounds = {}\\n input_elements = []\\n\\n with open(self.filepath, \\\"r\\\") as f:\\n for line in f:\\n if line.startswith(\\\"#\\\"):\\n continue\\n elements = line.split()\\n if elements[0] == \\\"Input\\\":\\n input_bounds[elements[1]] = {\\\"lb\\\": None, \\\"ub\\\": None}\\n self.graph.add_node(elements[1], node_type=\\\"input\\\")\\n\\n if elements[0] == \\\"ReLU\\\":\\n\\n relu_in_name = elements[1] + \\\"_in\\\"\\n relu_out_name = elements[1]\\n bias = float(elements[2])\\n variables, coeffs = self.get_vars_and_coefficients(elements, str_only=True)\\n\\n relu_entry = namedtuple(\\\"ReLU\\\", [\\\"relu_in_name\\\", \\\"relu_out_name\\\", \\\"bias\\\", \\\"variables\\\", \\\"coeffs\\\"])\\n self.relu_nodes[elements[1]] = relu_entry(relu_in_name, relu_out_name, bias, variables, coeffs)\\n # relu_in node is created and added to self.relu_in_nodes later, other variables also created later\\n\\n self.graph.add_node(relu_in_name, node_type=\\\"relu_in\\\", bias=bias)\\n self.graph.add_node(relu_out_name, node_type=\\\"relu_out\\\")\\n self.graph.add_edge(relu_in_name, relu_out_name)\\n for v, w in zip(variables, coeffs):\\n self.graph.add_edge(v, relu_in_name, weight=w)\\n\\n\\n if elements[0] == \\\"MaxPool\\\":\\n\\n self.max_pool_nodes[elements[1]] = elements[2:]\\n self.graph.add_node(elements[1], node_type=\\\"max_pool\\\")\\n self.graph.add_edges_from(((v, elements[1]) for v in elements[2:]), weight=1)\\n\\n\\n if elements[0] == \\\"Linear\\\":\\n variables, coeffs = self.get_vars_and_coefficients(elements, str_only=True)\\n bias = float(elements[2])\\n self.linear_nodes[elements[1]] = (bias, variables, coeffs)\\n\\n #self.graph.add_edges_from((v.name, linear.name) for v in variables)\\n self.graph.add_node(elements[1], node_type=\\\"linear\\\", bias=bias)\\n for v, w in zip(variables, coeffs):\\n self.graph.add_edge(v, elements[1], weight=w)\\n\\n\\n if elements[0] == \\\"Assert\\\":\\n input_elements.append(elements)\\n\\n # explicit bounds for input neurons\\n if len(elements) == 5 and elements[-1] in input_bounds:\\n if elements[1] == \\\"<=\\\":\\n new_lb = float(elements[2]) / float(elements[3])\\n if input_bounds[elements[-1]][\\\"lb\\\"] is None or input_bounds[elements[-1]][\\\"lb\\\"] < new_lb:\\n input_bounds[elements[-1]][\\\"lb\\\"] = new_lb\\n\\n elif elements[1] == \\\">=\\\":\\n new_ub = float(elements[2]) / float(elements[3])\\n if input_bounds[elements[-1]][\\\"ub\\\"] is None or input_bounds[elements[-1]][\\\"ub\\\"] > new_ub:\\n input_bounds[elements[-1]][\\\"ub\\\"] = new_ub\\n\\n if elements[0] == \\\"AssertOut\\\":\\n assert elements[1] in [\\\"<=\\\", \\\">=\\\"] and not assert_or\\n assert_and = True\\n cons = namedtuple(\\\"output_cons\\\", [\\\"lhs\\\", \\\"operator\\\", \\\"elements\\\"])\\n self.output_cons.append(cons(float(elements[2]), True if elements[1] == \\\">=\\\" else False, elements))\\n #print(\\\"assertout\\\", elements)\\n\\n if elements[0] == \\\"AssertOr\\\":\\n # assertOr properties are basically the same as AND, just the \\\"imaginary\\\" operator is turned around\\n # we assume that all subsequent ORs in the file form one disjunction\\n assert elements[1] in [\\\"<=\\\", \\\">=\\\"] and not assert_and\\n assert_or = True\\n cons = namedtuple(\\\"output_cons\\\", [\\\"lhs\\\", \\\"operator\\\", \\\"elements\\\"])\\n self.output_cons.append(cons(float(elements[2]), False if elements[1] == \\\">=\\\" else True, elements))\\n\\n for var_name, bounds in input_bounds.items():\\n self.vars[var_name] = self.model.addVar(name=var_name, lb=bounds[\\\"lb\\\"], ub=bounds[\\\"ub\\\"])\\n self.input_nodes[var_name] = self.vars[var_name]\\n\\n for assert_input_count, elements in enumerate(input_elements):\\n if elements[1] == \\\"<=\\\":\\n self.model.addCons(float(elements[2]) <= self.quicksum_coeff_var(elements),\\n name=\\\"input_cons_\\\" + str(assert_input_count))\\n elif elements[1] == \\\">=\\\":\\n self.model.addCons(float(elements[2]) >= self.quicksum_coeff_var(elements),\\n name=\\\"input_cons_\\\" + str(assert_input_count))\\n else:\\n raise NotImplementedError(\\\"This property cannot be verified: \\\" + elements[1])\\n\\n self.model.hideOutput()\\n for var_name, bounds in input_bounds.items():\\n if bounds[\\\"lb\\\"] is None:\\n self.model.setObjective(self.vars[var_name])\\n self.model.optimize()\\n if self.model.getStatus() != \\\"optimal\\\":\\n raise ValueError(\\\"LP lower bound of input cannot be solved to optimality\\\")\\n else:\\n bound = self.model.getDualbound()\\n self.model.freeTransform()\\n self.model.chgVarLbGlobal(self.vars[var_name], bound - 10 * self.eps)\\n if bounds[\\\"ub\\\"] is None:\\n self.model.setObjective(self.vars[var_name], sense=\\\"maximize\\\")\\n self.model.optimize()\\n if self.model.getStatus() != \\\"optimal\\\":\\n raise ValueError(\\\"LP upper bound of input cannot be solved to optimality\\\")\\n else:\\n bound = self.model.getDualbound()\\n self.model.freeTransform()\\n self.model.chgVarUbGlobal(self.vars[var_name], bound + 10 * self.eps)\\n\\n self.model.setObjective(0.0)\\n\\n self.model.hideOutput(quiet=False)\\n\\n return self.model, self.vars\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59dc3681f30d8f9111428804473895bd\",\n \"score\": \"0.6010998\",\n \"text\": \"def load_model(self):\\n \\n self.model = Darknet(self.config_path, img_size=self.img_size)\\n\\n #check if yolov3.weights file exists else download it\\n if not os.path.exists(self.weights_path):\\n print(\\\"downloading weights from web\\\")\\n filename=self.weights_path\\n url=\\\"https://pjreddie.com/media/files/yolov3.weights\\\"\\n chunkSize = 1024\\n r = requests.get(url, stream=True)\\n with open(filename, 'wb') as f:\\n pbar = tqdm( unit=\\\"B\\\", total=int( r.headers['Content-Length'] ) )\\n for chunk in r.iter_content(chunk_size=chunkSize): \\n if chunk: # filter out keep-alive new chunks\\n pbar.update (len(chunk))\\n f.write(chunk)\\n\\n\\n\\n self.model.load_weights(self.weights_path)\\n self.model.cuda()\\n self.model.eval()\\n self.classes = utils.load_classes(self.class_path)\\n self.Tensor = torch.cuda.FloatTensor\",\n \"title\": \"\"\n },\n {\n \"docid\": \"467f91546c16df05fadba1adefbbc01f\",\n \"score\": \"0.60007584\",\n \"text\": \"def _load_model(self, model, load_file):\\n\\n vars = {}\\n def _gather(name, obj):\\n if isinstance(obj, h5py.Dataset):\\n vars[name] = obj[...]\\n\\n with h5py.File(load_file) as f:\\n f.visititems(_gather)\\n\\n model.assign(vars)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6611594accf2d6e237abb9ab117e50da\",\n \"score\": \"0.5979233\",\n \"text\": \"def load(self, filepath):\\n var_dict = torch.load(filepath)\\n self.rnn_model.load_state_dict(var_dict['rnn_state_dict'])\\n self.rnn_init_hidden = nn.Parameter(\\n torch.from_numpy(var_dict['rnn_init_hidden']).to(self.device))\\n self.transition_bias = float(var_dict['transition_bias'])\\n self.transition_bias_denominator = float(\\n var_dict['transition_bias_denominator'])\\n self.crp_alpha = float(var_dict['crp_alpha'])\\n self.sigma2 = nn.Parameter(\\n torch.from_numpy(var_dict['sigma2']).to(self.device))\\n\\n self.logger.print(\\n 3, 'Loaded model with transition_bias={}, crp_alpha={}, sigma2={}, '\\n 'rnn_init_hidden={}'.format(\\n self.transition_bias, self.crp_alpha, var_dict['sigma2'],\\n var_dict['rnn_init_hidden']))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a5186ae4250bb01f62c3cd51ec1b86b\",\n \"score\": \"0.5949441\",\n \"text\": \"def load_network(self, which_epoch, train=True):\\n save_filename = '%s_net.pth' % which_epoch\\n load_path = join(self.save_dir, save_filename)\\n net = self.net\\n if isinstance(net, torch.nn.DataParallel):\\n net = net.module\\n print('loading the model from %s' % load_path)\\n checkpoint = torch.load(load_path, map_location=self.device)\\n if hasattr(checkpoint['model_state_dict'], '_metadata'):\\n del checkpoint['model_state_dict']._metadata\\n net.load_state_dict(checkpoint['model_state_dict'])\\n if train:\\n self.optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\\n self.scheduler.load_state_dict(checkpoint['scheduler_state_dict'])\\n self.opt.epoch_count = checkpoint[\\\"epoch\\\"]\\n else:\\n net.eval()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9796def368ca05d71d277dea78bd5832\",\n \"score\": \"0.59286076\",\n \"text\": \"def load_HUXt_run(filepath):\\n if os.path.isfile(filepath):\\n\\n data = h5py.File(filepath, 'r')\\n\\n cr_num = np.int32(data['cr_num'])\\n cr_lon_init = data['cr_lon_init'][()] * u.Unit(data['cr_lon_init'].attrs['unit'])\\n simtime = data['simtime'][()] * u.Unit(data['simtime'].attrs['unit'])\\n simtime = simtime.to(u.day)\\n dt_scale = data['dt_scale'][()]\\n v_boundary = data['_v_boundary_init_'][()] * u.Unit(data['_v_boundary_init_'].attrs['unit'])\\n r = data['r'][()] * u.Unit(data['r'].attrs['unit'])\\n lon = data['lon'][()] * u.Unit(data['lon'].attrs['unit'])\\n nlon = lon.size\\n map_inwards = np.int32(data['_map_inwards_'])\\n if map_inwards == 1:\\n map_inwards = True\\n else:\\n map_inwards = False\\n\\n if cr_num != 9999:\\n if nlon == 1:\\n model = HUXt(cr_num=cr_num, cr_lon_init=cr_lon_init, r_min=r.min(), r_max=r.max(),\\n lon_out=lon, simtime=simtime, dt_scale=dt_scale, map_inwards=map_inwards)\\n elif nlon > 1:\\n model = HUXt(cr_num=cr_num, cr_lon_init=cr_lon_init, r_min=r.min(), r_max=r.max(),\\n lon_start=lon.min(), lon_stop=lon.max(), simtime=simtime, dt_scale=dt_scale,\\n map_inwards=map_inwards)\\n else:\\n if nlon == 1:\\n model = HUXt(v_boundary=v_boundary, cr_lon_init=cr_lon_init, r_min=r.min(), r_max=r.max(),\\n lon_out=lon, simtime=simtime, dt_scale=dt_scale, map_inwards=map_inwards)\\n elif nlon > 1:\\n model = HUXt(v_boundary=v_boundary, cr_lon_init=cr_lon_init, r_min=r.min(), r_max=r.max(),\\n lon_start=lon.min(), lon_stop=lon.max(), simtime=simtime, dt_scale=dt_scale,\\n map_inwards=map_inwards)\\n\\n model.v_grid_cme[:, :, :] = data['v_grid_cme'][()] * u.Unit(data['v_boundary'].attrs['unit'])\\n model.v_grid_amb[:, :, :] = data['v_grid_amb'][()] * u.Unit(data['v_boundary'].attrs['unit'])\\n\\n # Create list of the ConeCMEs\\n cme_list = []\\n all_cmes = data['ConeCMEs']\\n for k in all_cmes.keys():\\n cme_data = all_cmes[k]\\n t_launch = cme_data['t_launch'][()] * u.Unit(cme_data['t_launch'].attrs['unit'])\\n lon = cme_data['longitude'][()] * u.Unit(cme_data['longitude'].attrs['unit'])\\n lat = cme_data['latitude'][()] * u.Unit(cme_data['latitude'].attrs['unit'])\\n width = cme_data['width'][()] * u.Unit(cme_data['width'].attrs['unit'])\\n thickness = cme_data['thickness'][()] * u.Unit(cme_data['thickness'].attrs['unit'])\\n thickness = thickness.to('solRad')\\n v = cme_data['v'][()] * u.Unit(cme_data['v'].attrs['unit'])\\n cme = ConeCME(t_launch=t_launch, longitude=lon, latitude=lat, v=v, width=width, thickness=thickness)\\n\\n # Now sort out coordinates.\\n # Use the same dictionary structure as defined in ConeCME._track_2d_\\n coords_group = cme_data['coords']\\n coords_data = {j: {'lon_pix': np.array([]) * u.pix, 'r_pix': np.array([]) * u.pix,\\n 'lon': np.array([]) * model.lon.unit, 'r': np.array([]) * model.r.unit}\\n for j in range(len(coords_group))}\\n\\n for time_key, pos in coords_group.items():\\n t = np.int(time_key.split(\\\"_\\\")[2])\\n coords_data[t]['lon_pix'] = pos['lon_pix'][()] * u.Unit(pos['lon_pix'].attrs['unit'])\\n coords_data[t]['r_pix'] = pos['r_pix'][()] * u.Unit(pos['r_pix'].attrs['unit'])\\n coords_data[t]['lon'] = pos['lon'][()] * u.Unit(pos['lon'].attrs['unit'])\\n coords_data[t]['r'] = pos['r'][()] * u.Unit(pos['r'].attrs['unit'])\\n\\n cme.coords = coords_data\\n cme_list.append(cme)\\n\\n # Update CMEs in model output\\n model.cmes = cme_list\\n\\n else:\\n # File doesnt exist return nothing\\n print(\\\"Warning: {} doesnt exist.\\\".format(filepath))\\n cme_list = []\\n model = []\\n\\n return model, cme_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c8c3bdc0236c1bb9152f24953709ed7\",\n \"score\": \"0.5927499\",\n \"text\": \"def deeplabv3plus_resnet50(num_classes=21, output_stride=8, pretrained_backbone='imagenet'):\\n return load_model('deeplabv3plus', 'resnet50', num_classes, output_stride=output_stride, pretrained_backbone=pretrained_backbone)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9214d7a2ccd32be28190440d06178cb\",\n \"score\": \"0.5900768\",\n \"text\": \"def load(self,fn=None,deep=False,load='full'):\\n fh=self._h5(fn=fn,mode='r')\\n self._fromh5(fh,deep=deep,load=load)\\n if load!='demand':\\n self._h5close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"184d670690f9a64cd1f92957b6e5b40d\",\n \"score\": \"0.5899939\",\n \"text\": \"def start_network(model_data_path):\\n # Default input size\\n height = 228\\n width = 304\\n channels = 3\\n batch_size = 1\\n\\n # Create a placeholder for the input image\\n input_node = tf.compat.v1.placeholder(dtype=tf.float32, shape=(1, height, width, channels))\\n\\n # Construct the network\\n net = models.ResNet50UpProj({'data': input_node}, batch_size, 1, False, trainable=False)\\n\\n sess = tf.compat.v1.Session()\\n\\n # Load the converted parameters\\n print('Loading the model')\\n\\n net.load(model_data_path, sess)\\n\\n #using a namedtuple to return the network state.\\n Network = collections.namedtuple('Network', ['sess', 'net', 'input_node'])\\n network = Network(sess, net, input_node)\\n print('returning the model')\\n\\n return network\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f404a002e23a44b07ce49746917d3678\",\n \"score\": \"0.5894621\",\n \"text\": \"def deeplabv3_resnet50(num_classes=21, output_stride=8, pretrained_backbone='imagenet'):\\n return load_model('deeplabv3', 'resnet50', num_classes, output_stride=output_stride, pretrained_backbone=pretrained_backbone)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46b3dc3e5ecf606ead22b23c2d12ee9d\",\n \"score\": \"0.58853614\",\n \"text\": \"def load_dnn(dnnFilename):\\n with h5py.File(dnnFilename, 'r') as h5f:\\n dnn_layers = list(h5f.keys())\\n W = []\\n b = []\\n print(\\\"reading in the DNN parameters ...\\\")\\n for l in range(len(dnn_layers)//2):\\n W.append(h5f['w'+str(l)][:])\\n print(\\\"layer {}: [{}]\\\".format(l, W[l].shape))\\n b.append(h5f['b'+str(l)][:])\\n print(\\\"done.\\\")\\n return b, W\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc3da1ea45a92c18a86f3907e17aa7a3\",\n \"score\": \"0.5879295\",\n \"text\": \"def load(self, brainFile):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f761f10f59bfa221202a1bd4eb15abdf\",\n \"score\": \"0.5877103\",\n \"text\": \"def read_neutron_data(pfolder, fname):\\n with h5py.File(pfolder + \\\"\\\\\\\\\\\" + fname, 'r') as file:\\n datasetnames = list(file.keys())\\n itemnames = list(file[datasetnames[0]].keys())\\n datanames = list(file[datasetnames[0]][itemnames[2]].keys())\\n # data are on the third layer of these .h5 files\\n x_vals = np.array(file[datasetnames[0]][itemnames[2]][datanames[2]])\\n y_vals = np.array(file[datasetnames[0]][itemnames[2]][datanames[0]])\\n ydev_vals = np.array(file[datasetnames[0]][itemnames[2]][datanames[1]])\\n return x_vals, y_vals, ydev_vals\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7a44d9e471e26b29d2cb0dd6ea2e27f\",\n \"score\": \"0.5875665\",\n \"text\": \"def load(self):\\r\\n self.mean=np.load(\\\"results/models/CNN1D/mean.npy\\\")\\r\\n self.aplt=np.load(\\\"results/models/CNN1D/aplt.npy\\\")\\r\\n self.d_threshold=np.load(\\\"results/models/CNN1D/threshold.npy\\\")\\r\\n self.model = load_model('results/models/CNN1D/cnn_.h5')\\r\\n self.model.summary()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"470c93ba861b860fa0e9118277ba8411\",\n \"score\": \"0.5864153\",\n \"text\": \"def from_legacy_h5(file_path, filename=None):\\n with h5py.File(file_path, 'r') as f:\\n if filename is None:\\n filename = f.attrs['microstructure_name']\\n micro = Microstructure(name=filename, overwrite_hdf5=True)\\n if 'symmetry' in f['EnsembleData/CrystalStructure'].attrs:\\n sym = f['EnsembleData/CrystalStructure'].attrs['symmetry']\\n parameters = f['EnsembleData/CrystalStructure/LatticeParameters'][()]\\n micro.set_lattice(Lattice.from_symmetry(Symmetry.from_string(sym),\\n parameters))\\n if 'data_dir' in f.attrs:\\n micro.data_dir = f.attrs['data_dir']\\n # load feature data\\n if 'R_vectors' in f['FeatureData']:\\n print('some grains')\\n avg_rods = f['FeatureData/R_vectors'][()]\\n print(avg_rods.shape)\\n if 'grain_ids' in f['FeatureData']:\\n grain_ids = f['FeatureData/grain_ids'][()]\\n else:\\n grain_ids = range(1, 1 + avg_rods.shape[0])\\n if 'centers' in f['FeatureData']:\\n centers = f['FeatureData/centers'][()]\\n else:\\n centers = np.zeros_like(avg_rods)\\n # add all grains to the microstructure\\n grain = micro.grains.row\\n for i in range(avg_rods.shape[0]):\\n grain['idnumber'] = grain_ids[i]\\n grain['orientation'] = avg_rods[i, :]\\n grain['center'] = centers[i]\\n grain.append()\\n micro.grains.flush()\\n # load cell data\\n if 'grain_ids' in f['CellData']:\\n micro.set_grain_map(f['CellData/grain_ids'][()],\\n f['CellData/grain_ids'].attrs['voxel_size'])\\n micro.recompute_grain_bounding_boxes()\\n micro.recompute_grain_volumes()\\n if 'mask' in f['CellData']:\\n micro.set_mask(f['CellData/mask'][()],\\n f['CellData/mask'].attrs['voxel_size'])\\n return micro\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c1060779239e0bb3a046ba2fc94db09\",\n \"score\": \"0.58499646\",\n \"text\": \"def __init__(self, weight_file='model/weights_40-0.56.h5'):\\n\\n\\n print(\\\"Loading model...\\\")\\n self.model = self.get_model()\\n self.model.load_weights(weight_file)\\n# self.combined_model = Model(inputs=self.model.input, outputs=NoiseAwareLayer(name='noise')(self.model.output))\\n# self.combined_model.compile(loss='categorical_crossentropy',\\n# optimizer='adam',\\n# metrics=['accuracy'])\\n# self.combined_model.load_weights('model/combined_weights_31-0.49.h5')\\n print(\\\"Model loaded!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73d7fb95578f796a58679b57e1c2500a\",\n \"score\": \"0.5814465\",\n \"text\": \"def load_gradient(file_path):\\n obj = load_hdf5(filename=file_path, obj_class=Gradient_calculator, ignore_attrs_if_err=[])\\n\\n\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abae4b9c3254d1201f4e9d8e40dc6e83\",\n \"score\": \"0.58138245\",\n \"text\": \"def loadModel(self, filepath):\\n saved = torch.load(filepath, map_location='cpu')\\n state_dict = saved['state_dict']\\n config = XLNetConfig(num_labels = 2)\\n model = XLNetForSequenceClassification(config)\\n # loading the trained parameters with model\\n model.load_state_dict(state_dict)\\n return model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc8fdfcd8a564ba20b4bdcbcbe31e284\",\n \"score\": \"0.58108306\",\n \"text\": \"def reload_net(self):\\n import caffe\\n self.net = caffe.Net(self.prototxt, 1, weights=self.caffemodel)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17e72d422b0e4ed3462237a79c0ff135\",\n \"score\": \"0.5799166\",\n \"text\": \"def test_file_hdf5(self, file_name):\\n sess = tf.Session()\\n self.load_model(sess, log_dir = config.log_dir)\\n mix_stft, feats = self.read_hdf5_file(file_name)\\n out_feats = self.process_file(mix_stft, sess)\\n self.plot_features(feats, out_feats)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca0d68eb63250e72d9c9022a837a8954\",\n \"score\": \"0.5793921\",\n \"text\": \"def h5_to_ml_ready_numpy(file_path: str) -> np.ndarray:\\n h5_file = h5py.File(file_path, \\\"r\\\")\\n latent_rep = np.asarray(h5_file.get(\\\"latent_space\\\"))\\n print(\\\"raw data: \\\" + tcols.OKBLUE + f\\\"{file_path}\\\" + tcols.ENDC + \\\", \\\", end=\\\"\\\")\\n latent_rep_flat = reshaper(latent_rep)\\n return latent_rep_flat\",\n \"title\": \"\"\n },\n {\n \"docid\": \"187e6d810484733ce23761dba6f18add\",\n \"score\": \"0.5792707\",\n \"text\": \"def loadFromFile(self, kpath):\\n kpath = os.path.abspath(kpath)\\n (dirname, basename) = os.path.split(kpath)\\n self.name = basename.rpartition('.')[0]\\n self.opath = os.path.join(dirname, self.name+\\\".onnx\\\")\\n self.kmodel = Kmodels.load_model(kpath, custom_objects={ 'tf': tf, 'relu6': helper.relu6 })\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e43e708b6b1c0cd698f78b4cb7bd645\",\n \"score\": \"0.5788703\",\n \"text\": \"def load_model(self, model_file):\\n net_params = torch.load(model_file)\\n for action_type in self.action_types:\\n self.nets[action_type]['net'].load_state_dict(net_params[action_type])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db97dea8774addf1211cc98ec865b680\",\n \"score\": \"0.57791567\",\n \"text\": \"def load_model(self, modelpath):\\n try:\\n self.model = model_from_json(open(modelpath + \\\".json\\\").read())\\n self.model.load_weights(modelpath + \\\".h5\\\")\\n except Exception, e:\\n self.log(\\\"Failed to load CNN model %s. (%s)\\\" % (modelpath,str(e)) )\\n return\\n\\n self.log(\\\"Loaded CNN model %s.\\\" % modelpath)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f96e24d2cdc207c95d2dd3cd76d1d52a\",\n \"score\": \"0.5769877\",\n \"text\": \"def load_graph(filename):\\r\\n with tf.gfile.GFile(filename, 'rb') as f:\\r\\n graph_def = tf.GraphDef()\\r\\n graph_def.ParseFromString(f.read())\\r\\n tf.import_graph_def(graph_def, name='')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2b96ca746274fb7c1215f04b712c4af\",\n \"score\": \"0.5768957\",\n \"text\": \"def load_model(self, model_name, network):\\n if model_name[-4:] != '.npz':\\n log.error(\\\"Model not found\\\")\\n raise ValueError\\n\\n with np.load(os.path.join(self.path_to_model_dir, model_name)) as f:\\n param_values = [f['arr_%d' % i] for i in range(len(f.files))]\\n\\n lasagne.layers.set_all_param_values(network, param_values, trainable=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b3d171071dc7c3e93a6801d5ab8ae5c\",\n \"score\": \"0.5761669\",\n \"text\": \"def __load_model_from_file__(self, h5file):\\n if not \\\"Models shape\\\" in h5file.attrs.keys() or \\\\\\n not \\\"Num models\\\" in h5file.attrs.keys() or \\\\\\n not \\\"Cell size\\\" in h5file.attrs.keys():\\n return False\\n \\n self.CELL_SIZE = h5file.attrs[\\\"Cell size\\\"]\\n \\n self.models = np.empty(shape=h5file.attrs[\\\"Models shape\\\"], dtype=object)\\n\\n with tqdm(desc=\\\"Loading models\\\", total=h5file.attrs[\\\"Num models\\\"], file=sys.stderr) as pbar:\\n def _add_model(name, g):\\n if \\\"v\\\" in g.attrs.keys() and \\\"u\\\" in g.attrs.keys():\\n v = g.attrs[\\\"v\\\"]\\n u = g.attrs[\\\"u\\\"]\\n self.models[v, u] = self.CREATE_ANOMALY_MODEL_FUNC()\\n self.models[v, u].__load_model_from_file__(g)\\n pbar.update()\\n\\n h5file.visititems(_add_model)\\n \\n if isinstance(self.patches, PatchArray):\\n self.patches.calculate_rasterization(self.CELL_SIZE)\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a6e2b523e79bc9dfc224b245bd125e3\",\n \"score\": \"0.57497185\",\n \"text\": \"def load_h5(fname):\\n f = h5py.File(fname, 'r')\\n data = dict()\\n for k in f.keys():\\n data[k] = f[k][:]\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf81b5d3ae9044c2bddec3b4f03509d2\",\n \"score\": \"0.57453305\",\n \"text\": \"def load_networks(self, epoch):\\n for name in self.model_names:\\n if isinstance(name, str):\\n load_filename = '%s_net_%s.pth' % (epoch, name)\\n load_path = os.path.join(self.save_dir, load_filename)\\n net = getattr(self, 'net' + name)\\n if isinstance(net, torch.nn.DataParallel):\\n net = net.module\\n None\\n state_dict = torch.load(load_path, map_location=self.device)\\n if hasattr(state_dict, '_metadata'):\\n del state_dict._metadata\\n net.load_state_dict(state_dict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94f3b7c7f83cdaaec9aea35fd4ea7bac\",\n \"score\": \"0.5736626\",\n \"text\": \"def load_graph(filename):\\n with tf.gfile.FastGFile(filename, 'rb') as f:\\n graph_def = tf.GraphDef()\\n graph_def.ParseFromString(f.read())\\n fix_graph_def(graph_def)\\n tf.import_graph_def(graph_def, name='')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f455f4d7f1073c9f70bf76e3c4d2bf0e\",\n \"score\": \"0.5728204\",\n \"text\": \"def load_graph(filename):\\n with tf.gfile.FastGFile(filename, 'rb') as f:\\n graph_def = tf.GraphDef()\\n graph_def.ParseFromString(f.read())\\n tf.import_graph_def(graph_def, name='')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f455f4d7f1073c9f70bf76e3c4d2bf0e\",\n \"score\": \"0.5728204\",\n \"text\": \"def load_graph(filename):\\n with tf.gfile.FastGFile(filename, 'rb') as f:\\n graph_def = tf.GraphDef()\\n graph_def.ParseFromString(f.read())\\n tf.import_graph_def(graph_def, name='')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77f1feedc87b1494cdc26b98c12fa545\",\n \"score\": \"0.5725236\",\n \"text\": \"def load_darknet_weights(self, weights_path):\\n # Open the weights file\\n with open(weights_path, \\\"rb\\\") as f:\\n header = np.fromfile(\\n f, dtype=np.int32, count=5\\n ) # First five are header values\\n self.header_info = header # Needed to write header when saving weights\\n self.seen = header[3] # number of images seen during training\\n weights = np.fromfile(f, dtype=np.float32) # The rest are weights\\n # Establish cutoff for loading backbone weights\\n cutoff = None\\n if \\\"darknet53.conv.74\\\" in weights_path:\\n cutoff = 75\\n\\n ptr = 0\\n for i, (module_def, module) in enumerate(\\n zip(self.module_defs, self.module_list)\\n ):\\n if i == cutoff:\\n break\\n if module_def[\\\"type\\\"] == \\\"convolutional\\\":\\n conv_layer = module[0]\\n if module_def[\\\"batch_normalize\\\"]:\\n # Load BN bias, weights, running mean and running variance\\n bn_layer = module[1]\\n num_b = bn_layer.bias.numel() # Number of biases\\n # Bias\\n bn_b = torch.from_numpy(weights[ptr: ptr + num_b]).view_as(\\n bn_layer.bias\\n )\\n bn_layer.bias.data.copy_(bn_b)\\n ptr += num_b\\n # Weight\\n bn_w = torch.from_numpy(weights[ptr: ptr + num_b]).view_as(\\n bn_layer.weight\\n )\\n bn_layer.weight.data.copy_(bn_w)\\n ptr += num_b\\n # Running Mean\\n bn_rm = torch.from_numpy(weights[ptr: ptr + num_b]).view_as(\\n bn_layer.running_mean\\n )\\n bn_layer.running_mean.data.copy_(bn_rm)\\n ptr += num_b\\n # Running Var\\n bn_rv = torch.from_numpy(weights[ptr: ptr + num_b]).view_as(\\n bn_layer.running_var\\n )\\n bn_layer.running_var.data.copy_(bn_rv)\\n ptr += num_b\\n else:\\n # Load conv. bias\\n num_b = conv_layer.bias.numel()\\n conv_b = torch.from_numpy(weights[ptr: ptr + num_b]).view_as(\\n conv_layer.bias\\n )\\n conv_layer.bias.data.copy_(conv_b)\\n ptr += num_b\\n # Load conv. weights\\n num_w = conv_layer.weight.numel()\\n conv_w = torch.from_numpy(weights[ptr: ptr + num_w]).view_as(\\n conv_layer.weight\\n )\\n conv_layer.weight.data.copy_(conv_w)\\n ptr += num_w\",\n \"title\": \"\"\n },\n {\n \"docid\": \"462eebb3635eebb63e58969151eeccbc\",\n \"score\": \"0.57218695\",\n \"text\": \"def load_graph(filename):\\n with tf.gfile.GFile(filename, 'rb') as f:\\n graph_def = tf.GraphDef()\\n graph_def.ParseFromString(f.read())\\n tf.import_graph_def(graph_def, name='')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22aa906e9ef7f13b161df66fd5f3471d\",\n \"score\": \"0.5718611\",\n \"text\": \"def __init__(self, path_model='./data/models/facenet/facenet_keras.h5'):\\n self.path_model = path_model\\n self.model = tf.keras.models.load_model(self.path_model, compile=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"552a3157f02c58c63171db3a1cf21a83\",\n \"score\": \"0.57166165\",\n \"text\": \"def LoadH5(file_name):\\n obj = {}\\n with h5py.File(file_name, 'r') as hf:\\n for k in hf.keys():\\n obj[k] = np.asarray(hf.get(k))\\n\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b499d517ad6ab73566d2d54c69f6a64f\",\n \"score\": \"0.5703583\",\n \"text\": \"def load_weights(self, filename):\\r\\n f = open(filename)\\r\\n rawData = [[float(n) for n in line.split()] for line in f.read().splitlines()]\\r\\n\\r\\n self.hiddenLayer = np.array(rawData[:self.K])\\r\\n self.outputLayer = np.array(rawData[self.K])\\r\\n\\r\\n self.hiddenBias = np.array(rawData[self.K+1])\\r\\n self.outputBias = rawData[self.K+2][0]\\r\\n\\r\\n f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b965b7e7a90f68232174fbb44bee41b3\",\n \"score\": \"0.57018536\",\n \"text\": \"def load_networks(self, epoch):\\n for name in self.model_names:\\n if isinstance(name, str):\\n load_filename = 'epoch_%d_net_%s.pth' % (epoch, name)\\n load_path = os.path.join(self.config.checkpoints_dir, load_filename)\\n if not os.path.exists(load_path):\\n continue\\n net = getattr(self, 'net' + name)\\n print('loading the models from %s' % load_path)\\n state_dict = torch.load(load_path, map_location=str(self.device))\\n if hasattr(state_dict, '_metadata'):\\n del state_dict._metadata\\n\\n net.load_state_dict(state_dict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c281a275bf1818c138701f3180ce2d38\",\n \"score\": \"0.5700677\",\n \"text\": \"def scNT_seq_neuron_labeling(\\n url=\\\"https://www.dropbox.com/s/lk9cl63yd28mfuq/neuron_labeling.h5ad?dl=1\\\",\\n filename=\\\"neuron_labeling.h5ad\\\",\\n):\\n adata = get_adata(url, filename)\\n\\n return adata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25e9afc9df40ca200835ce0817d1cf9e\",\n \"score\": \"0.5690368\",\n \"text\": \"def read_network(file, config):\\n SPEED_OF_LIGHT = 299792458 # meter per second\\n PROPAGATION_FACTOR = 0.77 # https://en.wikipedia.org/wiki/Propagation_delay\\n\\n # default values\\n default_link_delay = 3\\n\\n if not file.endswith(\\\".graphml\\\"):\\n raise ValueError(\\\"{} is not a GraphML file\\\".format(file))\\n graphml_network = nx.read_graphml(file, node_type=int)\\n networkx_network = nx.Graph()\\n\\n # Setting the nodes of the NetworkX Graph\\n for n in graphml_network.nodes(data=True):\\n node_id = \\\"pop{}\\\".format(n[0])\\n\\n if config['node_parameter_mode'] == 'probabilistic_continuous':\\n cap = np.random.normal(config['node_cap_mean'], config['flow_dr_stdev'])\\n elif config['node_parameter_mode'] == 'probabilistic_discrete':\\n cap = np.random.choice(config['node_cap_values'], p=config['node_cap_weights'])\\n cap = n[1].get('NodeCap', cap)\\n\\n node_type = n[1].get(\\\"NodeType\\\", \\\"Normal\\\")\\n node_name = n[1].get(\\\"label\\\", None)\\n if cap is None:\\n raise ValueError(\\\"No NodeCap. set for node{} in file {} (as cmd argument or in graphml)\\\".format(n, file))\\n # Adding a Node in the NetworkX Graph\\n # Type of node. For now it is either \\\"Normal\\\" or \\\"Ingress\\\"\\n # Init 'remaining_cap' to the node capacity\\n networkx_network.add_node(node_id, name=node_name, type=node_type, cap=cap, available_sf={},\\n remaining_cap=cap)\\n\\n # set links\\n # calculate link delay based on geo positions of nodes;\\n for e in graphml_network.edges(data=True):\\n # Check whether LinkDelay value is set, otherwise default to None\\n source = \\\"pop{}\\\".format(e[0])\\n target = \\\"pop{}\\\".format(e[1])\\n # As edges are undirectional, only LinkFwdCap determines the available data rate\\n if config['link_cap_parameter_mode'] == 'probabilistic_continuous':\\n link_fwd_cap = np.random.normal(config['link_cap_mean'], config['link_cap_stdev'])\\n elif config['link_cap_parameter_mode'] == 'probabilistic_discrete':\\n link_fwd_cap = np.random.choice(config['link_cap_values'], p=config['link_cap_weights'])\\n link_fwd_cap = e[2].get(\\\"LinkFwdCap\\\", link_fwd_cap)\\n\\n # Setting a default delay of 3 incase no delay specified in GraphML file\\n # and we are unable to set it based on Geo location\\n if config['link_delay_parameter_mode'] == 'geo_location':\\n n1 = graphml_network.nodes(data=True)[e[0]]\\n n2 = graphml_network.nodes(data=True)[e[1]]\\n n1_lat, n1_long = n1.get(\\\"Latitude\\\", None), n1.get(\\\"Longitude\\\", None)\\n n2_lat, n2_long = n2.get(\\\"Latitude\\\", None), n2.get(\\\"Longitude\\\", None)\\n if n1_lat is None or n1_long is None or n2_lat is None or n2_long is None:\\n log.warning(f'Unable to calc based on Geo Location,'\\n f' Now using default delay {default_link_delay} for edge: ({source},{target})')\\n link_delay = default_link_delay\\n else:\\n distance = dist((n1_lat, n1_long), (n2_lat, n2_long)).meters # in meters\\n # round delay to int using np.around for consistency with emulator\\n link_delay = (distance / SPEED_OF_LIGHT * 1000) * PROPAGATION_FACTOR # in milliseconds\\n elif config['link_delay_parameter_mode'] == 'probabilistic_continuous':\\n link_delay = np.random.normal(config['link_delay_mean'], config['link_delay_stdev'])\\n elif config['link_delay_parameter_mode'] == 'probabilistic_discrete':\\n link_delay = np.random.choice(config['link_delay_values'], p=config['link_delay_weights'])\\n link_delay = e[2].get(\\\"LinkDelay\\\", link_delay)\\n\\n # Adding the undirected edges for each link defined in the network.\\n # delay = edge delay , cap = edge capacity\\n networkx_network.add_edge(source, target, delay=link_delay, cap=link_fwd_cap, remaining_cap=link_fwd_cap)\\n\\n # setting the weight property for each edge in the NetworkX Graph\\n # weight attribute is used to find the shortest paths\\n for edge in networkx_network.edges.values():\\n edge['weight'] = weight(edge['cap'], edge['delay'])\\n # Setting the all-pairs shortest path in the NetworkX network as a graph attribute\\n shortest_paths(networkx_network)\\n\\n # Filter ingress nodes\\n p_ingress = config['node_ingress_probability']\\n p_egress = config['node_egress_probability']\\n log.info(f'Ingress node probability {p_ingress}.')\\n log.info(f'Egress node probability {p_egress}.')\\n ing_nodes = []\\n eg_nodes = []\\n for node in networkx_network.nodes.items():\\n if node[1][\\\"type\\\"] == \\\"Ingress\\\" or np.random.choice([True, False], p=[p_ingress, 1-p_ingress]):\\n ing_nodes.append(node[0])\\n if node[1][\\\"type\\\"] == \\\"Egress\\\" or np.random.choice([True, False], p=[p_egress, 1-p_egress]):\\n eg_nodes.append(node[0])\\n log.info(\\\"Total of {} ingress nodes available\\\".format(len(ing_nodes)))\\n log.info(\\\"Total of {} egress nodes available\\\".format(len(eg_nodes)))\\n log.info(nx.info(networkx_network))\\n\\n return networkx_network, ing_nodes, eg_nodes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9eba6489c691e6709af3040dda7f2152\",\n \"score\": \"0.5689411\",\n \"text\": \"def load_from_checkpoint(self, checkpoint_file):\\n assert isinstance(checkpoint_file, str) and checkpoint_file.endswith('.h5'), \\\"Wrong checkpoint file argument\\\"\\n self.model = Models.load_from_cpt(checkpoint_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d96b9ac5c760aaea2be32ce65f43e66d\",\n \"score\": \"0.56861275\",\n \"text\": \"def load_checkpoint(self, folder, filename_no):\\n 'no37.neural.data'\\n filename = f\\\"no{filename_no}.neural.data\\\"\\n filepath = os.path.join(folder, filename)\\n if os.path.exists(filepath):\\n self.model.load_weights(filepath)\\n else:\\n print(\\\"No model in path '{}'\\\".format(filepath))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da98671e8670ed64ead085282d110de4\",\n \"score\": \"0.56785977\",\n \"text\": \"def load_h5_file(file_path):\\n # load\\n fr = h5py.File(file_path, \\\"r\\\")\\n a_group_key = list(fr.keys())[0]\\n data = list(fr[a_group_key])\\n # transform to appropriate numpy array\\n data = data[0:]\\n data = np.stack(data, axis=0)\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1c1b9226f3cad90cf60a03d27c38c64\",\n \"score\": \"0.56785023\",\n \"text\": \"def load_darknet_weights(self, weights_path):\\n\\n # Open the weights file\\n with open(weights_path, \\\"rb\\\") as f:\\n header = np.fromfile(f, dtype=np.int32, count=5) # First five are header values\\n self.header_info = header # Needed to write header when saving weights\\n self.seen = header[3] # number of images seen during training\\n weights = np.fromfile(f, dtype=np.float32) # The rest are weights\\n\\n # Establish cutoff for loading backbone weights\\n cutoff = None\\n if \\\"darknet53.conv.74\\\" in weights_path:\\n cutoff = 75\\n\\n ptr = 0\\n for i, (module_def, module) in enumerate(zip(self.module_defs, self.module_list)):\\n if i == cutoff:\\n break\\n if module_def[\\\"type\\\"] == \\\"convolutional\\\":\\n conv_layer = module[0]\\n if module_def[\\\"batch_normalize\\\"]:\\n # Load BN bias, weights, running mean and running variance\\n bn_layer = module[1]\\n num_b = bn_layer.bias.numel() # Number of biases\\n # Bias\\n bn_b = torch.from_numpy(weights[ptr : ptr + num_b]).view_as(bn_layer.bias)\\n bn_layer.bias.data.copy_(bn_b)\\n ptr += num_b\\n # Weight\\n bn_w = torch.from_numpy(weights[ptr : ptr + num_b]).view_as(bn_layer.weight)\\n bn_layer.weight.data.copy_(bn_w)\\n ptr += num_b\\n # Running Mean\\n bn_rm = torch.from_numpy(weights[ptr : ptr + num_b]).view_as(bn_layer.running_mean)\\n bn_layer.running_mean.data.copy_(bn_rm)\\n ptr += num_b\\n # Running Var\\n bn_rv = torch.from_numpy(weights[ptr : ptr + num_b]).view_as(bn_layer.running_var)\\n bn_layer.running_var.data.copy_(bn_rv)\\n ptr += num_b\\n else:\\n # Load conv. bias\\n num_b = conv_layer.bias.numel()\\n conv_b = torch.from_numpy(weights[ptr : ptr + num_b]).view_as(conv_layer.bias)\\n conv_layer.bias.data.copy_(conv_b)\\n ptr += num_b\\n # Load conv. weights\\n num_w = conv_layer.weight.numel()\\n conv_w = torch.from_numpy(weights[ptr : ptr + num_w]).view_as(conv_layer.weight)\\n conv_layer.weight.data.copy_(conv_w)\\n ptr += num_w\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cade5accc2125511f88d25ee6c9f12ba\",\n \"score\": \"0.56767017\",\n \"text\": \"def test_file_hdf5(self, file_name):\\n\\t\\tsess = tf.Session()\\n\\t\\tself.load_model(sess, log_dir = config.log_dir)\\n\\t\\tmix_stft, feats = self.read_hdf5_file(file_name)\\n\\t\\tout_feats = self.process_file(mix_stft, sess)\\n\\t\\tself.plot_features(feats, out_feats)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a1da216e63d000006943e3abc9b2549\",\n \"score\": \"0.5672409\",\n \"text\": \"def __init__(self, file_path):\\n assert os.path.exists(file_path), \\\"Config file doesn't exist, filepath: {}\\\".format(file_path)\\n self._file_path = file_path\\n super(NeuralNetConfig, self).__init__(self._file_path)\\n # object.__init__(self._file_path)\\n self.__net_data = self.get_net_data('CNN')\\n\\n assert os.path.exists(self._train_dataset_dir), f'train_dataset_dir \\\\'{self._train_dataset_dir}\\\\' does not exist'\\n\\n if 'version' in self.__net_data.keys():\\n # if this is wrong, then it will fail when initializing neuralnet\\n self.__net = self.__net_data['version']\\n\\n if 'model_dir' in self.__net_data:\\n self.model_dir = self.__net_data['model_dir']\\n\\n if 'checkpoint_dir' in self.__net_data:\\n self._checkpoint_dir = self.__net_data['checkpoint_dir']\\n\\n if 'num_classes' in self.__net_data:\\n self.num_classes = int(self.__net_data['num_classes'])\\n\\n if 'optimizer' in self.__net_data:\\n assert self.__net_data['optimizer'].lower() in optimizer,\\\\\\n \\\"Supplied optimizer, \\\\'{}\\\\', doesn\\\\'t exist, available options are {}\\\".format(\\n self.__net_data['optimizer'], list(optimizer))\\n self._optimizer = self.__net_data['optimizer'].lower()\\n\\n if 'learning_rate' in self.__net_data:\\n self.learning_rate = float(self.__net_data['learning_rate'])\\n\\n if 'momentum' in self.__net_data:\\n self.momentum = float(self.__net_data['momentum'])\\n\\n if 'loss' in self.__net_data:\\n assert self.__net_data['loss'].lower() in loss_function,\\\\\\n \\\"Supplied loss fn, \\\\'{}\\\\', doesn\\\\'t exist, available options are {}\\\".format(\\n self.__net_data['loss'], list(loss_function))\\n self._loss_fn = self.__net_data['loss'].lower()\\n\\n if 'metrics' in self.__net_data:\\n if self.__net_data['metrics'].lower() != 'none':\\n for m in self.__net_data['metrics'].lower().split(','):\\n assert m.strip() in metrics, \\\"Supplied metric, \\\\'{}\\\\', doesn\\\\'t exist, available options are {}\\\".format(\\n m, list(metrics))\\n self._metrics = self.__net_data['metrics'].lower()\\n\\n if 'evaluate_metrics' in self.__net_data:\\n for m in self.__net_data['evaluate_metrics'].lower().split(','):\\n assert m in metrics, \\\"Supplied evaluate_metrics, \\\\'{}\\\\', doesn\\\\'t exist, available options are {}\\\"\\\\\\n .format(m, list(metrics))\\n self._evaluate_metrics = self.__net_data['evaluate_metrics'].lower()\\n\\n if 'distributed_strategy' in self.__net_data:\\n assert self.__net_data['distributed_strategy'].lower() in distributed_strategy, \\\\\\n \\\"Supplied distributed_strategy, \\\\'{}\\\\', doesn\\\\'t exist, available options are {}\\\".format(\\n self.__net_data['distributed_strategy'], list(distributed_strategy))\\n self._distributed_strategy = self.__net_data['distributed_strategy'].lower()\\n\\n if 'batch_size' in self.__net_data:\\n self.batch_size = int(self.__net_data['batch_size'])\\n\\n if 'log_dir' in self.__net_data:\\n self.log_dir = self.__net_data['log_dir']\\n\\n if 'log_every_n_steps' in self.__net_data:\\n self.log_every_n_steps = int(self.__net_data['log_every_n_steps'])\\n\\n if 'max_steps_per_epoch' in self.__net_data:\\n self._max_steps_per_epoch = self.__net_data['max_steps_per_epoch']\\n\\n if 'num_validation_images' in self.__net_data:\\n self.num_validation_images = int(self.__net_data['num_validation_images'])\\n\\n if 'epochs' in self.__net_data:\\n self.__epochs = int(self.__net_data['epochs'])\\n\\n if 'bench_timings' in self.__net_data:\\n assert bool('False') # I have this to remind me of what python does here\\n self.bench_timings = self.__net_data['bench_timings'].lower() in ['true', '1', 't', 'y', 'yes', 'yeah', 'yup', 'certainly', 'uh-huh']\\n\\n if 'bench_file' in self.__net_data:\\n self.bench_path = self.__net_data['bench_file']\\n\\n if 'input_shape' in self.__net_data:\\n self.__input_shape = self.__net_data['input_shape']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3eeeb5c881b62288050a1993bececc3b\",\n \"score\": \"0.56712586\",\n \"text\": \"def load(path):\\n with open(path, 'rb') as f:\\n qrnn = pickle.load(f)\\n backend = importlib.import_module(qrnn.backend)\\n model = backend.load_model(f, qrnn.quantiles)\\n qrnn.model = model\\n return qrnn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0443dcdd5e135df9a98592b64b89a94\",\n \"score\": \"0.5662143\",\n \"text\": \"def load_from_file(self, net_file):\\n self.ann.create_from_file(net_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb7040d99593af28d4b2ec0cf7564c61\",\n \"score\": \"0.5661993\",\n \"text\": \"def load_model(self, weight_file):\\n self.W = np.fromfile(weight_file, dtype=np.float32)\\n print ('model loaded from', weight_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef01248c9779053af420065729ea3d2b\",\n \"score\": \"0.5660996\",\n \"text\": \"def load_model(self, filename):\\n import numpy as np\\n from lasagne.layers import set_all_param_values\\n\\n if not os.path.isfile(filename):\\n print_warning(\\\"Could not find '.npz' weights file at path: {}\\\".format(filename))\\n return False\\n \\n try:\\n ### Load the generator model's weights\\n print_info(\\\"Attempting to load model...\\\")\\n with np.load(filename) as fp:\\n param_values = [fp['arr_%d' % i] for i in range(len(fp.files))]\\n set_all_param_values(self.network_out, param_values)\\n except Exception as e:\\n print_warning(\\\"Found '.npz' weights file, but it must be corrupted, cannot read or parse it properly: {}.\\\".format(filename))\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f144205c857950b9c47c73ed9f1c0c3\",\n \"score\": \"0.5658707\",\n \"text\": \"def load_model(self, filename):\\n self.load_state_dict(torch.load(filename))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"699dfc98cee5382e7c34941a98802785\",\n \"score\": \"0.56567544\",\n \"text\": \"def load_model(cls, path):\\n self = cls.__new__(cls)\\n with h5py.File(path, mode=\\\"r\\\") as f:\\n # Default to output_length of 1 for backward compatibility,\\n # but allow override.\\n # TODO: should figure out how to deprecate this.\\n self.metadata = {\\\"output_length\\\": 1}\\n for k, v in f.attrs.items():\\n if k.startswith(\\\"metadata_\\\"):\\n _, k = k.split(\\\"_\\\", 1)\\n self.metadata[k] = json.loads(v)\\n self.keras_model = tf.keras.models.load_model(\\n f, custom_objects=custom_layers\\n )\\n self.preprocessor = self.create_preprocessor()\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"caa94d28cf8d7979f0735d4063e970e5\",\n \"score\": \"0.5650151\",\n \"text\": \"def load_model(self, filename):\\n \\n self.load_state_dict(torch.load(filename))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2bd69316944d9a6032cd1e45ab48f87b\",\n \"score\": \"0.56500286\",\n \"text\": \"def load(path):\\n if not os.path.exists(path):\\n raise FileNotFoundError(f\\\"Unable to locate file at {path}\\\")\\n extension = path.split(\\\".\\\")[-1]\\n if not extension == \\\"pth\\\":\\n raise ValueError(f\\\"Expected pth extension, got {extension}\\\")\\n\\n model = CNN()\\n model.load_state_dict(torch.load(path))\\n model.to(DEVICE)\\n return model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"203a9a8b83af38bf2d4e3de6a17ead56\",\n \"score\": \"0.56495863\",\n \"text\": \"def simulation_with_previous_h5m_file(self):\\n\\n os.system('rm *.h5m')\\n\\n my_model = paramak.NeutronicsModel(\\n geometry=self.my_shape,\\n source=self.source,\\n materials={'center_column_shield_mat': 'WC'},\\n )\\n\\n my_model.create_neutronics_geometry(method='pymoab')\\n\\n my_model.simulate(method=None)\\n\\n my_model.results is not None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"411e498be2fc4343d09dedea24717d03\",\n \"score\": \"0.5648832\",\n \"text\": \"def resnet50(pretrained=False):\\n model = ResNet(Bottleneck, [3, 4, 6, 3])\\n # model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))\\n model.load_state_dict(torch.load('./resnet50-19c8e357.pth'))\\n return model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a904ff9356fa886e4f871d4d41c67b55\",\n \"score\": \"0.56369436\",\n \"text\": \"def set_nn(self, nn_file):\\n n = read_yaml_file(nn_file)['neural_network'][0] # Currently we consider only 1 layer\\n self.nn = NeuralNetwork(n['name'], n['nn_type'], n['dimensions'], n['start'], n['end'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57b086ea0aa2214441f249b6d332ac9f\",\n \"score\": \"0.5635668\",\n \"text\": \"def load_model(model_name):\\n hyperparams = json.loads(open(model_name+'.json').read())\\n parser = RNNGparser(hyperparams['brown_file'],\\n vocab_thresh=hyperparams['vocab_thresh'],\\\\\\n stack_memory_size=hyperparams['stack_hidden_size'],\\\\\\n word_embedding_size=hyperparams['word_embedding_size'],\\\\\\n char_embedding_size=hyperparams['char_embedding_size'],\\\\\\n char_memory_size=hyperparams['char_memory_size'])\\n\\n parser.lexicon = SymbolLexicon.load(model_name+'.lex') \\n parser.nonterminals = SymbolLexicon.load(model_name+'.nt') \\n parser.charset = SymbolLexicon.load(model_name+'.char')\\n parser.code_struct_actions()\\n parser.allocate_structure()\\n parser.model.populate(model_name+\\\".weights\\\")\\n return parser\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ceb4539ace71690119e99072db495745\",\n \"score\": \"0.56335205\",\n \"text\": \"def load(cls, filename) :\\n \\n f = open(filename)\\n pkl = cPickle.load(f)\\n f.close()\\n\\n for l1, l2 in pkl[\\\"network\\\"][\\\"edges\\\"] :\\n network = pkl[\\\"layers\\\"][l1] > pkl[\\\"layers\\\"][l2]\\n\\n network.log = pkl[\\\"network\\\"][\\\"log\\\"]\\n network.notes = pkl[\\\"network\\\"][\\\"notes\\\"]\\n\\n return network\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a2c886378ff53151a46b0b184f811b3\",\n \"score\": \"0.5630964\",\n \"text\": \"def load_weights(self, weight_file: str) -> None:\\n requires_grad = False\\n\\n with h5py.File(weight_file, 'r') as fin:\\n for i_layer, lstms in enumerate(\\n zip(self.forward_layers, self.backward_layers)\\n ):\\n for j_direction, lstm in enumerate(lstms):\\n # lstm is an instance of LSTMCellWithProjection\\n cell_size = lstm.cell_size\\n\\n dataset = fin['RNN_%s' % j_direction]['RNN']['MultiRNNCell']['Cell%s' % i_layer\\n ]['LSTMCell']\\n\\n # tensorflow packs together both W and U matrices into one matrix,\\n # but pytorch maintains individual matrices. In addition, tensorflow\\n # packs the gates as input, memory, forget, output but pytorch\\n # uses input, forget, memory, output. So we need to modify the weights.\\n tf_weights = numpy.transpose(dataset['W_0'][...])\\n torch_weights = tf_weights.copy()\\n\\n # split the W from U matrices\\n input_size = lstm.input_size\\n input_weights = torch_weights[:, :input_size]\\n recurrent_weights = torch_weights[:, input_size:]\\n tf_input_weights = tf_weights[:, :input_size]\\n tf_recurrent_weights = tf_weights[:, input_size:]\\n\\n # handle the different gate order convention\\n for torch_w, tf_w in [[input_weights, tf_input_weights],\\n [recurrent_weights, tf_recurrent_weights]]:\\n torch_w[(1 * cell_size):(2 * cell_size), :] = tf_w[(2 * cell_size):(3 * cell_size), :]\\n torch_w[(2 * cell_size):(3 * cell_size), :] = tf_w[(1 * cell_size):(2 * cell_size), :]\\n\\n lstm.input_linearity.weight.data.copy_(torch.FloatTensor(input_weights))\\n lstm.state_linearity.weight.data.copy_(torch.FloatTensor(recurrent_weights))\\n lstm.input_linearity.weight.requires_grad = requires_grad\\n lstm.state_linearity.weight.requires_grad = requires_grad\\n\\n # the bias weights\\n tf_bias = dataset['B'][...]\\n # tensorflow adds 1.0 to forget gate bias instead of modifying the\\n # parameters...\\n tf_bias[(2 * cell_size):(3 * cell_size)] += 1\\n torch_bias = tf_bias.copy()\\n torch_bias[(1 * cell_size):(2 * cell_size)\\n ] = tf_bias[(2 * cell_size):(3 * cell_size)]\\n torch_bias[(2 * cell_size):(3 * cell_size)\\n ] = tf_bias[(1 * cell_size):(2 * cell_size)]\\n lstm.state_linearity.bias.data.copy_(torch.FloatTensor(torch_bias))\\n lstm.state_linearity.bias.requires_grad = requires_grad\\n\\n # the projection weights\\n proj_weights = numpy.transpose(dataset['W_P_0'][...])\\n lstm.state_projection.weight.data.copy_(torch.FloatTensor(proj_weights))\\n lstm.state_projection.weight.requires_grad = requires_grad\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abe2146cf941b9172433096750596bff\",\n \"score\": \"0.56304467\",\n \"text\": \"def load_checkpoint(self, folder, filename_no):\\n 'no37.neural.data'\\n with self.lock:\\n filename = f\\\"no{filename_no}.neural.data\\\"\\n filepath = os.path.join(folder, filename)\\n if os.path.exists(filepath):\\n self.model.load_weights(filepath)\\n else:\\n print(\\\"No model in path '{}'\\\".format(filepath))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1427f05cba2505d04aee0482312c171\",\n \"score\": \"0.5617891\",\n \"text\": \"def load_from_pickle( cls, filename, catmaid_interface = None ):\\n f = gzip.GzipFile(filename, 'rb')\\n nrn_list = pickle.load(f)\\n f.close()\\n return NeuronList( nrn_list.neurons,\\n CatmaidInterface = catmaid_interface,\\n project_name = nrn_list.project_name,\\n export_date = nrn_list.export_date\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83d70915515cde877f9f8e49406a3701\",\n \"score\": \"0.5614241\",\n \"text\": \"def load_file(self, file_path):\\n self.f = h5py.File(file_path)\\n try:\\n self.sampling_rate = int(self.f['raw_data'].attrs['sampling_rate'])\\n except:\\n print \\\"Sampling rate could not be loaded from HDF5 file. Certain functions cannot be used without sampling rate.\\\"\\n self.staged_dataset = None\\n print \\\"File \\\" + `file_path` + \\\" has been loaded.\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef14266bd2b64cedc20201a1576faa4f\",\n \"score\": \"0.5602503\",\n \"text\": \"def resnet34(pretrained=False):\\n model = ResNet(BasicBlock, [3, 4, 6, 3])\\n if pretrained:\\n model.load_state_dict(model_zoo.load_url(model_urls['resnet34']))\\n return model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca8c817bf9783001410cf632332794a0\",\n \"score\": \"0.55959725\",\n \"text\": \"def inferLeNet5(Directory_infer='/images/user' ,ModelPath='/models/model3/saved/'):\\n TypeOfImage=2\\n X_test,Y_test,inferFolder=LeNet5.getImagesAndLabels(Directory_infer,TypeOfImage)\\n with tf.Session() as sess: \\n loader = tf.train.import_meta_graph(os.getcwd()+ModelPath+'.meta')\\n loader.restore(sess, tf.train.latest_checkpoint(os.getcwd()+ModelPath))\\n graph = tf.get_default_graph()\\n X=graph.get_tensor_by_name(\\\"X:0\\\")\\n Logits=graph.get_tensor_by_name(\\\"logits:0\\\")\\n cont=0\\n DictClasses=GetImages.getDictClasses()\\n for i in X_test:\\n i=i.reshape(1,32,32,1)\\n Proba=sess.run(Logits, feed_dict={X: i})\\n plt.figure(str(Y_test[cont])+' '+str(np.argmax(Proba,1)))\\n im = Image.open(inferFolder+'/'+Y_test[cont])\\n plt.text(-1,-1,'file'+Y_test[cont]+' belongs to class '+DictClasses[int(np.argmax(Proba,1))])\\n plt.imshow(im,vmin = 0, vmax = 255)\\n# plt.imshow(i[0,:,:,0],cmap='gray', vmin = 0, vmax = 1)\\n# plt.show()\\n cont=cont+1\\n plt.show()\\n return\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":896,"cells":{"query_id":{"kind":"string","value":"0026b325ddb64c5a21bdaac90ec27543"},"query":{"kind":"string","value":"custom list display field with detail page link"},"positive_passages":{"kind":"list like","value":[{"docid":"b9a3176761efd1812c32b02f6fd6589b","score":"0.6043342","text":"def name_with_detail_link(self, obj):\n detail_url = reverse(\"admin:program_program_detail\", kwargs={\"object_id\": obj.pk})\n return format_html(f\"<a href='{detail_url}'>{obj.name}</a>\")","title":""}],"string":"[\n {\n \"docid\": \"b9a3176761efd1812c32b02f6fd6589b\",\n \"score\": \"0.6043342\",\n \"text\": \"def name_with_detail_link(self, obj):\\n detail_url = reverse(\\\"admin:program_program_detail\\\", kwargs={\\\"object_id\\\": obj.pk})\\n return format_html(f\\\"<a href='{detail_url}'>{obj.name}</a>\\\")\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"f84c2a47205e9e79022c971200627e3c","score":"0.6725328","text":"def UserListShow(self, listType, itemList):","title":""},{"docid":"60551363c68ef31e6434f1d0b4dca0d0","score":"0.6423562","text":"def detail_page(self, obj):\n url = reverse('materials_inventory', args=(obj.id,))\n\n return format_html('<a href=\"{}\">Mostrar detalle</a>', url)","title":""},{"docid":"0b1b7e347c45ade502f1e638b61e6bb3","score":"0.6376594","text":"def detail_page(self, obj):\n url = reverse('products_inventory', args=(obj.id,))\n\n return format_html('<a href=\"{}\">Mostrar detalle</a>', url)","title":""},{"docid":"5c2061456c9de554bbb2e9a2b2393b02","score":"0.63654363","text":"def detail_page(self, obj):\n url = reverse('consumables_inventory', args=(obj.id,))\n\n return format_html('<a href=\"{}\">Mostrar detalle</a>', url)","title":""},{"docid":"c10d0ca59bd6632b5152cce175cd5887","score":"0.6363512","text":"def detail_page(self, obj):\n url = reverse('durable_goods_inventory', args=(obj.id,))\n\n return format_html('<a href=\"{}\">Mostrar detalle</a>', url)","title":""},{"docid":"54307a6a3e21b6157b33355c2354ef95","score":"0.59984475","text":"def linkify(field_name):\n\n # def _linkify(obj):\n # app_label = obj._meta.app_label\n # linked_obj = getattr(obj, field_name)\n # # model_name = linked_obj._meta.model_name\n # view_name = f\"admin:{app_label}_{model_name}_change\"\n # link_url = reverse(view_name, args=[linked_obj.id])\n # return format_html('<a href=\"{}\">{}</a>', link_url, linked_obj)\n\n # _linkify.short_description = field_name # Sets column name\n # return _linkify","title":""},{"docid":"192c42383930e738e939880b54d18d7a","score":"0.5941618","text":"def linkify(field_name):\n\n def _linkify(obj):\n if obj.ordered and obj is not None:\n app_label = obj._meta.app_label\n linked_obj = getattr(obj, field_name)\n model_name = linked_obj._meta.model_name\n view_name = f\"admin:{app_label}_{model_name}_change\"\n link_url = reverse(view_name, args=[linked_obj.pk])\n return format_html('<a href=\"{}\">{}</a>', link_url, linked_obj)\n else:\n return 'Nill'\n\n _linkify.short_description = field_name # Sets column name\n return _linkify","title":""},{"docid":"c4396cf5e7fc36d78fe96e39934e09f8","score":"0.5622063","text":"def get_list_display(self, request):\n email = request.user.email\n if email in [\"gbozee@gmail.com\", \"gbozee@example.com\", \"pyconnigeria@pycon.ng\"]:\n return self.list_display + (\"hijack_field\",)\n return self.list_display","title":""},{"docid":"b0903928d4f98297d85ecb1d0bb1566b","score":"0.56031096","text":"def __str__(self):\n return self.detail","title":""},{"docid":"b0903928d4f98297d85ecb1d0bb1566b","score":"0.56031096","text":"def __str__(self):\n return self.detail","title":""},{"docid":"b0903928d4f98297d85ecb1d0bb1566b","score":"0.56031096","text":"def __str__(self):\n return self.detail","title":""},{"docid":"6e151bf4f3bfb1ebe748d5674fb8dcf5","score":"0.55662256","text":"def DisplayIndex(self):","title":""},{"docid":"6e151bf4f3bfb1ebe748d5674fb8dcf5","score":"0.55662256","text":"def DisplayIndex(self):","title":""},{"docid":"a0065d5c86c3fcdfa69cbd7664d3fc79","score":"0.5555436","text":"def linkify(field_name):\n # docs.djangoproject.com/en/2.1/ref/contrib/admin/#reversing-admin-urls\n def _linkify(obj):\n app_label = obj._meta.app_label\n linked_obj = getattr(obj, field_name)\n model_name = linked_obj._meta.model_name\n view_name = f\"admin:{app_label}_{model_name}_change\"\n link_url = reverse(view_name, args=[str(linked_obj.id)])\n return format_html('<a href=\"{}\">{}</a>', link_url, linked_obj)\n\n _linkify.short_description = field_name\n return _linkify","title":""},{"docid":"62c26a4e5ed1c25c6a546099cd8a0c72","score":"0.5551432","text":"def detail(request, listing_id):\n listing = Listing.objects.get(id=listing_id)\n return render(request, 'detail.html', {'listing': listing})","title":""},{"docid":"73b1951bc0a6103aae3cb080bbd1fab9","score":"0.5526","text":"def fetch_details(self, search_url, listing):\n pass","title":""},{"docid":"486e9b5837d287a8cb8db36f891ef793","score":"0.5523238","text":"def web_get_detail_url(self):\n try:\n return reverse(\n \"%s-detail\" % slugify(self._meta.verbose_name),\n kwargs={\"slug\": slugify(self.db_key)},\n )\n except:\n return \"#\"","title":""},{"docid":"d47b1de5943dfe1d38a3669a920e48be","score":"0.5520827","text":"def extractLink(model, item):","title":""},{"docid":"2f9a05b61b43b2887869802ea1b88ae3","score":"0.5508284","text":"def bmfmodule_detail(self):\n return ('%s:detail' % self._bmfmeta.url_namespace, (), {\"pk\": self.pk})","title":""},{"docid":"2a17b37efcd5a7e7d490d37dcb35c92f","score":"0.54910445","text":"def test_list_display_links_check_skipped_if_get_list_display_overridden(self):\n\n class TestModelAdmin(ModelAdmin):\n list_display_links = [\"name\", \"subtitle\"]\n\n def get_list_display(self, request):\n pass\n\n self.assertIsValid(TestModelAdmin, ValidationTestModel)","title":""},{"docid":"79c09b6920b886d4cd1979ed337fb2d3","score":"0.5461656","text":"def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.fields['page'].to_field_name = self.link_type.id","title":""},{"docid":"cc5be1380e74cf8643a28d50aa3e0bf5","score":"0.54097587","text":"def display_content(self, item):\r\n self.selected_item = \"Relationship\"\r\n self.details_list.clear()\r\n # Look for the selected relation in our list of relationships\r\n found_relation = None\r\n for relation_loop in self.controller_object.relationships_main:\r\n if item.text() == relation_loop.name:\r\n found_relation = relation_loop\r\n\r\n self.relation_selected = found_relation\r\n count = 0\r\n\r\n if found_relation != None:\r\n for observation in found_relation.observation_list:\r\n self.details_list.addItem(observation.show())\r\n\r\n # sets font to gray\r\n if self.relation_selected.observation_list[count].ignore == 1:\r\n self.details_list.item(count).setForeground(QtCore.Qt.gray)\r\n\r\n # sets salient artifact color to red\r\n if self.relation_selected.observation_list[count].artifact == 1 and \\\r\n self.relation_selected.observation_list[count].ignore != 1:\r\n self.details_list.item(count).setForeground(QtCore.Qt.red)\r\n count += 1\r\n\r\n disable_button(self.edit_button)\r\n disable_button(self.ignore_button)","title":""},{"docid":"a406f9187ee253b19314ff64a45efdb2","score":"0.5357906","text":"def get_absolute_url(self):\n return reverse('food:item_detail_view', kwargs={'pk': self.pk})","title":""},{"docid":"b71c6fc7fbfd0b71028049052a6da9ae","score":"0.5354236","text":"def on_showDetailsButton_clicked(self):\n item = self.searchResultList.selectedItems()[0]\n self.__showDetails(item)","title":""},{"docid":"f46dc4497bebeaa2d63dbdd5d427dfa8","score":"0.53189486","text":"def additional_detail(self) -> str:\n return pulumi.get(self, \"additional_detail\")","title":""},{"docid":"786171954e186607b1faf36eb25aff11","score":"0.52787507","text":"def detail_url(self):\n\n return reverse('favorite-detail', kwargs={'pk': self.favorte_thing.pk})","title":""},{"docid":"68c453999953232f074ebb533fbb73bd","score":"0.5275353","text":"def object_list (request, **kwargs):\n return list_detail.object_list (request, **kwargs)","title":""},{"docid":"2b1b07fba06343d78328a92413cf390a","score":"0.52725506","text":"def link_relation(field_name):\n\n def _link_relation(obj):\n linked_obj = getattr(obj, field_name)\n if linked_obj:\n model_name = linked_obj._meta.model_name\n app_label = linked_obj._meta.app_label\n view_name = f\"admin:{app_label}_{model_name}_change\"\n link_url = reverse(view_name, args=[linked_obj.id])\n return format_html('<a href=\"{}\">{}</a>', link_url, linked_obj)\n else:\n return \"-\"\n\n _link_relation.short_description = field_name # Sets column name\n return _link_relation","title":""},{"docid":"0d33770b7c93fa7a2636881950c18b94","score":"0.527234","text":"def detail_list(self, request, templates):\n return self._list_view(self.detail, request, templates)","title":""},{"docid":"29cfb733eea619f0bc5cc860b60ea702","score":"0.5269969","text":"def test_list_display_first_item_in_list_editable(self):\n\n class ProductAdmin(ModelAdmin):\n list_display = [\"name\", \"slug\", \"pub_date\"]\n list_editable = [\"slug\", \"name\"]\n list_display_links = [\"pub_date\"]\n\n self.assertIsValid(ProductAdmin, ValidationTestModel)","title":""},{"docid":"73e42579eed3907e07d2d06721fb9d51","score":"0.5248537","text":"def get_detail_url(id):\n\treturn f'{TODO_URL}{id}'","title":""},{"docid":"b6b92feb40255eda4f8525eb24cd6c2c","score":"0.5245222","text":"def callback():\n query = db.recipe.title.contains(request.vars.keyword)\n recipes = db(query).select(orderby=db.recipe.id)\n links = [A(p.title, _href=URL('show',args=r.id)) for r in recipes]\n return UL(*links)","title":""},{"docid":"6ee73d47e4ad2725eb5df0c27794542d","score":"0.52327496","text":"def list_tag(self, event):\n\n eadd = self.ui.builder.get_object(\"EAdd\")\n val = eadd.get()\n eadd.delete(0, END)\n\n self.ui.builder.get_object(\"ListSelected\").insert(END, val)","title":""},{"docid":"8890a63d8bfdd6c3a0b299c57050e7ea","score":"0.52312136","text":"def snippet_detail(request, pk):\n return handle_crud(snippet_crud, request, pk)","title":""},{"docid":"aa887b286a1ab4c3d18b4a7054e43014","score":"0.5207898","text":"def print_details(self):\n self.__linked_list.print_list()","title":""},{"docid":"e7537c04481458e2d081244a1cfe50bb","score":"0.5201767","text":"def detail_view(request):\n md = markdown.Markdown(safe_mode='replace', html_replacement_text='NO')\n this_id = request.matchdict['this_id']\n entry = DBSession.query(Entry).get(this_id)\n text = md.convert(entry.text)\n return {'entry': entry, 'text': text}","title":""},{"docid":"6234b166d96ab5fff042c4fbbe67ebcc","score":"0.52010375","text":"def test_list_display_link_checked_for_list_tuple_if_get_list_display_overridden(\n self,\n ):\n\n class TestModelAdmin(ModelAdmin):\n list_display_links = \"non-list/tuple\"\n\n def get_list_display(self, request):\n pass\n\n self.assertIsInvalid(\n TestModelAdmin,\n ValidationTestModel,\n \"The value of 'list_display_links' must be a list, a tuple, or None.\",\n \"admin.E110\",\n )","title":""},{"docid":"efe871546d430bf97c9c3452230ecd2b","score":"0.51984733","text":"def field_show(field_id):\n field = fields.find_one({'_id': ObjectId(field_id)})\n field_reviews = reviews.find({'field_id': ObjectId(field_id)})\n return render_template('field_show.html', field=field)","title":""},{"docid":"a26f0e4826f0a6aca048e51271c3f6b5","score":"0.516312","text":"def list_item(self):\n return self._list_item","title":""},{"docid":"c3513901f4e495a77a37e379360f97c8","score":"0.5162738","text":"def show(self, **kwargs):\n if self.molecular:\n return display_molecular(self, **kwargs)\n else:\n return display_atomic(self, **kwargs)","title":""},{"docid":"8280c00090557ff3cd6b4d1dfc584b1a","score":"0.5158203","text":"def listitem_form(**kwargs):\n # Tags must be joined back into a comma-delimited string\n if 'values' in kwargs and kwargs['values'] and 'tags' in kwargs['values']:\n kwargs['values']['tags'] = ','.join(kwargs['values']['tags'])\n\n return gen_doc_as_div(ListItem, **kwargs)","title":""},{"docid":"49fb60a7655fff55668356e01bdc189d","score":"0.51439613","text":"def get_admin_url(self):\r\n return mark_safe(u\"%s/%s/%s/\" % (self.content_type.app_label, self.content_type.model, quote(self.object_id)))","title":""},{"docid":"a2345f469ab049330438bee2b25e1b0c","score":"0.51439095","text":"def convert_fields_to_detail_url(self, data, field_array):\n for field in field_array:\n if isinstance(data[field], list):\n data['{}_urls'.format(field)] = [d.detail_url\n for d in data[field]]\n else:\n data['{}_url'.format(field)] = data[field].detail_url\n\n del data[field]\n\n return data","title":""},{"docid":"cf8dac92c1e5d24779ec0cf836b54da5","score":"0.5140119","text":"def get_details(self): \r\n return(self.name)","title":""},{"docid":"56bae459fb871b48710a393a3a78d9a4","score":"0.5123366","text":"def get_urls(self):\n urls = super().get_urls()\n custom_urls = [\n path('<int:object_id>/detail/', self.admin_site.admin_view(self.detail_view), name=\"program_program_detail\")\n ]\n return custom_urls + urls","title":""},{"docid":"4bcf50c48c25c0c5086de31c2bb1ec27","score":"0.51169133","text":"def get_form_field(self, **kwargs):\r\n defaults = {'form_class': forms.URLField}\r\n defaults.update(kwargs)\r\n return super(LinkProperty, self).get_form_field(**defaults)","title":""},{"docid":"7a72b713c4f6a9731ba34c6a9fc34a63","score":"0.510741","text":"def get_admin_url(self):\r\n return \"%s/%s/%s/\" % (self.content_type.app_label, self.content_type.model, self.object_id)","title":""},{"docid":"30729231b57eaf90ba3a3e53683bc716","score":"0.51051366","text":"def templatePath(self):\n return 'soc/list/lists.html'","title":""},{"docid":"d94dfb8f3760dbac8903a2b7b3f37f30","score":"0.5104054","text":"def get_display_name(self):\n return \"Item %d\" % self.index","title":""},{"docid":"0e9fef8666763a881fdc7e976628a763","score":"0.5102591","text":"def HomeDisplayExtend(self):","title":""},{"docid":"38d9f9cd245f687bde56d3ae1d89cbfd","score":"0.5080766","text":"def todolist_show ( request, item_container ):\n\n def get_section_view(items, sections):\n \"\"\" erzeugt die Section-Ansicht der im Ordner enthaltenen Objekte \"\"\"\n from django.template.loader import get_template\n from django.template import Context\n tSection = get_template('app/todolist/section.html')\n t_link = get_template('app/todolist/linkitem.html')\n content = ''\n unknown = _(u'Unbekannter Zwischentitel')\n section = '--START--'\n links = []\n comment_counts = get_visible_comment_count_by_item_containers(items)\n for i in items :\n if section != i.section :\n if section != unknown :\n if section != '--START--' and links != [] :\n cSection = Context ( { 'section': section, 'links': links } )\n content += tSection.render ( cSection)\n if i.section in sections :\n section = i.section\n else :\n section = unknown\n links = []\n if i.item.url_more != '':\n title_link = show_link(i.item.url_more, i.item.title)\n else:\n title_link = i.item.title\n cSection = Context ({\n 'id' : i.item.id,\n 'title' :i.item.title,\n 'text' : i.item.text,\n 'text_more' : i.item.text_more,\n 'user_name' : i.item.string_1,\n 'email' : i.item.string_2,\n 'date' : i.get_last_modified(),\n 'image_url' : i.item.image_url,\n 'image_url_url': i.item.image_url_url,\n 'image_extern' : i.item.image_extern,\n 'last_modified': i.get_last_modified(),\n 'show_item' : i.get_absolute_url(),\n 'comments' : comment_counts[i.item.id]\n })\n links.append(t_link.render(cSection))\n\n if section != '--START--' and links != []:\n cSection = Context ( { 'section': section, 'links': links } )\n content += tSection.render ( cSection)\n return content\n\n app_name = 'todolist'\n items, sections, d_sections = get_folder_content(item_container)\n vars = get_folderish_vars_show(request, item_container, app_name, get_section_view(items, sections),\n get_user_support(item_container))\n return render_to_response ( 'app/base_folderish.html', vars )","title":""},{"docid":"35500789e35ce04955cd6877a1f1896f","score":"0.50806147","text":"def test_list_display_links_is_none(self):\n\n class ProductAdmin(ModelAdmin):\n list_display = [\"name\", \"slug\", \"pub_date\"]\n list_editable = list_display\n list_display_links = None\n\n self.assertIsValid(ProductAdmin, ValidationTestModel)","title":""},{"docid":"06887960a695f96f572256da98e00cbb","score":"0.5078089","text":"def flatpages_link_list(request):\n from django.contrib.flatpages.models import FlatPage\n link_list = [(page.title, page.url) for page in FlatPage.objects.all()]\n return render_to_link_list(link_list)","title":""},{"docid":"ae679839a1234345ce8b0992b93744fc","score":"0.5074564","text":"def list(request):\n pass","title":""},{"docid":"0f79dafedfebbed68050f21fc88b36fa","score":"0.5071394","text":"def list_name(self):\n if self.addToListCheck.isChecked() and self.listBox.currentText():\n return self.listBox.currentText()\n else:\n return None","title":""},{"docid":"157be649ad27df433b5c6e980b40c483","score":"0.5067877","text":"def GetLink(self):","title":""},{"docid":"fb08848ff9a795405ee814061da0634f","score":"0.5062921","text":"def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n\n for obj in context['object_list']:\n obj.materiaalklasse_str = makl2str[obj.materiaalklasse]\n # obj.leeftijdscategorie_str = lcat2str[obj.leeftijdscategorie]\n\n obj.url_details = reverse('Records:specifiek', kwargs={'discipline': obj.discipline,\n 'nummer': obj.volg_nr})\n # for\n\n context['kruimels'] = (\n (reverse('Records:overzicht'), 'Records'),\n (None, self.kruimel)\n )\n\n menu_dynamics(self.request, context)\n return context","title":""},{"docid":"c664919e9218109658de796861705666","score":"0.50435853","text":"def contentitem_detail(request, template='browse/detail.html'):\n if request.GET and request.is_ajax():\n item = ContentItem.objects.get(pk=request.GET['id'])\n\n context = {\n 'item': item,\n }\n\n return render_to_response(template, context, context_instance=RequestContext(request))","title":""},{"docid":"81c44727fa729632e70ce3263553f8ea","score":"0.5042079","text":"def __str__(self) -> str:\n return self.link","title":""},{"docid":"23b0736abf24d33bd2741a78448d81b0","score":"0.50390184","text":"def __unicode__(self):\n return self.desc","title":""},{"docid":"f68b829bb6983c27b540b7994fd64f2e","score":"0.50320196","text":"def __str__(self):\r\n return \"[\" + str(self.item.id) + \" \" + self.label() + \"]\"","title":""},{"docid":"72d5b1d1e1fcbaebc9da26642e1ea5b3","score":"0.50230783","text":"def PopupList(self):\r\n pass","title":""},{"docid":"cc3d7b373678e798d980ec9be014e157","score":"0.50203204","text":"def get_absolute_url(self):\n return reverse('model-detail-view', args=[str(self.id)])","title":""},{"docid":"cc3d7b373678e798d980ec9be014e157","score":"0.50203204","text":"def get_absolute_url(self):\n return reverse('model-detail-view', args=[str(self.id)])","title":""},{"docid":"de0f7ba07f1a3652d8e6c42bc72f3f4f","score":"0.50079083","text":"def get_absolute_url(self):\n return reverse('inventory:detail_product', self.id)","title":""},{"docid":"e2355f7d4718beb82a09aa24d8c9d653","score":"0.5007547","text":"def edit_link(self, obj):\n return \"<a href='%s'>%s</a>\" % (reverse(\"edit-category\", args=[obj.id]),\n _(\"Edit\"))","title":""},{"docid":"d26ca9a0e5a0187496e2b060bc84dd27","score":"0.5004382","text":"def show_name(self, o):\n from django.utils.safestring import SafeUnicode\n return html.format_html(\n '<a href=\"{url}\" title=\"{comment}\">{name}</a>',\n url=o.get_absolute_url(),\n name=T.truncate(o.name, 40),\n comment=SafeUnicode(o.comment))","title":""},{"docid":"ccd60bac485bb5db95fe97e3675f83ba","score":"0.49974918","text":"def chapter_list(self, manga_link):","title":""},{"docid":"65cf91bbf7e8cd238d509260bee1d83a","score":"0.49908912","text":"def test_PrettyLinkWithAdditionalInfosColumn(self):\n table = self.faceted_z3ctable_view\n column = PrettyLinkWithAdditionalInfosColumn(self.portal, self.portal.REQUEST, table)\n # attrName is set during table.setUpColumns\n column.attrName = 'Title'\n # this column use 'sortable_title' as sort_index\n self.assertEqual(column.sort_index, 'sortable_title')\n # this column only works with DX content types\n tt = api.content.create(\n container=self.eea_folder,\n type='testingtype',\n title='My testing type',\n description='My description',\n bool_field=False)\n brain = self.portal.portal_catalog(UID=tt.UID())[0]\n # no additional informations defined so nothing more than pretty link is returned\n self.assertTrue(column.renderCell(brain).startswith(IPrettyLink(tt).getLink()))\n # define some informations\n tt.afield = u'My field content'\n rendered = column.renderCell(brain)\n self.assertTrue(\n '<span id=\"form-widgets-afield\" class=\"text-widget textline-field\">My field content</span>'\n in rendered)\n # ai_extra_fields, by default id, UID and description\n self.assertTrue(\n '<div class=\"discreet\"><label class=\"horizontal\">Id</label>'\n '<div class=\"type-textarea-widget\">my-testing-type</div></div>'\n in rendered)\n self.assertTrue(\n '<div class=\"discreet\"><label class=\"horizontal\">Uid</label>'\n '<div class=\"type-textarea-widget\">{0}</div></div>'.format(brain.UID)\n in rendered)\n self.assertTrue(\n '<div class=\"discreet\"><label class=\"horizontal\">Description</label>'\n '<div class=\"type-textarea-widget\">My description</div></div>'\n in rendered)","title":""},{"docid":"237df58f6fd43fd67d3b045f340603cf","score":"0.4990774","text":"def __unicode__(self):\n return self.display_name","title":""},{"docid":"a8a124f09750b72dfa823631b2fd14ff","score":"0.4988007","text":"def _get_actions(self, obj, **kwargs):\n # ctx = super().get_context_data(**kwargs)\n t = get_template(\"customadmin/partials/list_basic_actions.html\")\n # ctx.update({\"obj\": obj})\n # print(ctx)\n return t.render({\"o\": obj})","title":""},{"docid":"695492582da8a37cffdc7ace1cbb6138","score":"0.49803072","text":"def test_list_display_first_item_same_as_list_editable_first_item(self):\n\n class ProductAdmin(ModelAdmin):\n list_display = [\"name\", \"slug\", \"pub_date\"]\n list_editable = [\"name\", \"slug\"]\n list_display_links = [\"pub_date\"]\n\n self.assertIsValid(ProductAdmin, ValidationTestModel)","title":""},{"docid":"f8195c3d01337bc5a3a8aae4beb5981f","score":"0.49724123","text":"def get_absolute_url(self):\n return reverse('tipoProducto-detail', args=[str(self.id)])","title":""},{"docid":"ac1566b6bfd9ce61c01bd127a1e38a02","score":"0.49715075","text":"def get_admin_url(self):\n return mark_safe(u\"%s/%s/%s/\" % (self.content_type.app_label, self.content_type.model, quote(self.object_id)))","title":""},{"docid":"0eedb4c5e228c35af78a6c140956193b","score":"0.49593964","text":"def link(self) -> str:\n\t\treturn self.details.get(\"htmlLink\")","title":""},{"docid":"32eb49dffc241f01efcc7d3ce42d0ee9","score":"0.49593285","text":"def _get_detail_options(self, request, obj=None, **kwargs):\n actions = []\n\n url_index = self.get_url_reverse('changelist')\n\n\n if obj:\n url_delete = self.get_url_reverse('delete', obj.pk)\n actions.append(\n Button('delete', 'Delete', url_delete, 'danger', icon='trash')\n )\n\n actions.append(\n Button('cancel', 'Back', url_index, icon='close')\n )\n\n actions += copy.copy(self.get_detail_actions(request=request, obj=obj))\n actions.append(\n Button('_continue', 'Save', 'submit', 'primary', icon='save')\n )\n\n return actions","title":""},{"docid":"763db36ca3adbd16ffa7428fa589ee82","score":"0.49584964","text":"def display_doc(self):\n selected = self.documentation_tab.doc_list.currentItem().text()\n self.documentation_tab.detailed_view.setHtml(self.docs[selected.lower()]['content'])","title":""},{"docid":"3783e76c445d92ed893141dcaa690065","score":"0.49526608","text":"def genHTMLListItem(f, link, linkname):\r\n f.write(\"<li>\")\r\n genHTMLLink(f, link, linkname)\r\n f.write(\"\\n\")","title":""},{"docid":"c38ea33ab7785b4b95e5bc743e75ec54","score":"0.49498272","text":"def get_absolute_url(self):\n return reverse('pelicula-detail', args=[str(self.id)])","title":""},{"docid":"3ae4884eebb5785f330b6b13cb146599","score":"0.49496","text":"def _edit(self, request, entity, context, params):\n\n context['page_name'] = \"%s titled '%s'\" % (context['page_name'],\n entity.title)\n\n return super(View, self)._edit(request, entity, context, params)","title":""},{"docid":"d5e2730c9b5e6d5c20924d14bfcee1f0","score":"0.4948899","text":"def fields_new():\n return render_template('fields_new.html', field={}, title='New field')","title":""},{"docid":"1d6ab390b2be88f304d6b4d1203cd354","score":"0.49440944","text":"def __getattr__(self, name):\n return getattr(self.objectListView, name)","title":""},{"docid":"bb8627944a42dc5ef587a33d8863a6d4","score":"0.49407375","text":"def item_details(request, slug):\n item = get_object_or_404(Item, slug=slug)\n\n context = {'result': item}\n\n return render(request, 'database_view/detail_pages/item_details.html', context)","title":""},{"docid":"02c33c86440af3c1133dce84eccc201a","score":"0.4940715","text":"def viewitems(self, *args):\n pass","title":""},{"docid":"77d39190568be9d58b4bbbf64f07e9bc","score":"0.4931772","text":"def object_link(self, obj):\n if obj.action_flag == DELETION:\n link = escape(obj.object_repr)\n else:\n try:\n ct = obj.content_type\n url = reverse(f'admin:{ct.app_label}_{ct.model}_change',\n args=[obj.object_id])\n link = f'<a href=\"{url}\">{escape(obj.object_repr)}</a>'\n except Exception as e:\n logger.debug(e)\n link = escape(obj.object_repr)\n return mark_safe(link)","title":""},{"docid":"f4c35283773e544e8a67eb12bb54639d","score":"0.49304467","text":"def detail(self, *args, **kwargs):\r\n self.obj = self._load_obj(*args, **kwargs)\r\n context = self._get_detail_context(object=self.obj)\r\n response = self._render(\"detail\", **context)\r\n # # TODO: 19.11.13 wooyek this is silly, tiw ill not work with all models from SQLAlchemy\r\n # http://stackoverflow.com/questions/6745189/how-do-i-get-the-name-of-an-sqlalchemy-objects-primary-key\r\n # headers = {\r\n # 'X-PrimaryKey': self.obj.id, #\r\n # 'X-Key': self.obj.key.urlsafe(),\r\n # }\r\n # response.headers.extend(headers)\r\n return response","title":""},{"docid":"dda5ef6bf49458e31d5409445c88c07e","score":"0.4929825","text":"def __str__(self):\n url = self.resolve()\n if len(self.getData) < 3:\n return '<a href=\"%s\" %s>%s</a>' % (url, self.strAttr(), self.vals)\n\n data = json.dumps(self.getData, cls=AresHtml.SetEncoder).replace('\"$(', '$(').replace('.val()\"', '.val()')\n jsDef = '''\n %s.on(\"click\", function (event){\n var baseUrl = \"%s\";\n if (baseUrl.indexOf(\"?\") !== -1) { var ullUrl = baseUrl + \"&\" + %s ; }\n else { var ullUrl = baseUrl + \"?\" + %s ; }\n window.location.href = ullUrl ;\n }\n ) ;\n ''' % (self.jqId, url, data, data)\n self.get('click', url, data, '')\n #self.aresObj.jsOnLoadFnc.add(jsDef)\n return '<a href=\"#\" %s>%s</a>' % (self.strAttr(), self.vals)","title":""},{"docid":"44be2e0102478e1fd69b15cbddc6b879","score":"0.49279672","text":"def oneforty_list_all(request):","title":""},{"docid":"620bfb098f39c97c5bbe646acbba1056","score":"0.4926012","text":"def _select(self, r, **attr):\n\n if r.component_id or not r.component:\n # No List View\n return {}\n\n output = self._datatable(r, **attr)\n\n if r.representation == \"aadata\":\n return output\n\n resource = self.method.resource\n if resource.get_config(\"insertable\", True) and \"showadd_btn\" not in output:\n # Add a button to activate the add form which gets hidden in the view\n output[\"showadd_btn\"] = crud_button(None,\n tablename = resource.tablename,\n name = \"label_create\",\n icon = \"add\",\n _id = \"show-add-btn\",\n )\n \n return output","title":""},{"docid":"8b18e082cd1e0007a7553f451aba027e","score":"0.4924166","text":"def show_title(self, o):\n n = T.truncate(o.product.name, 40)\n v = o.product.vendor.name\n r = html.format_html('<a href=\"{}\">{}', o.get_absolute_url(), n)\n r += '<br>' if len(n) + len(v) > 37 else ' '\n r += html.format_html('[{}]</a>', v)\n return html.mark_safe(r)","title":""},{"docid":"129230cb42233f8f7ddff579d32a2d7f","score":"0.4920084","text":"def test_list_display_first_item_in_list_editable_no_list_display_links(self):\n\n class ProductAdmin(ModelAdmin):\n list_display = [\"name\", \"slug\", \"pub_date\"]\n list_editable = [\"slug\", \"name\"]\n\n self.assertIsInvalid(\n ProductAdmin,\n ValidationTestModel,\n \"The value of 'list_editable[1]' refers to the first field \"\n \"in 'list_display' ('name'), which cannot be used unless \"\n \"'list_display_links' is set.\",\n id=\"admin.E124\",\n )","title":""},{"docid":"a9c8de5677f1ff5f48c52f6a2b91f1b6","score":"0.49196154","text":"def detail_template(self):\n return \"wheelcms_spokes/image_detail.html\"","title":""},{"docid":"593f75a0ee9a7f5c35538a4fac191a13","score":"0.4919407","text":"def list_name(self):\n return self.listBox.currentText()","title":""},{"docid":"a61ef0cd4fcb5a1254e95f4f2f18a7d6","score":"0.4919314","text":"def url_links(record):\n return apply_template_on_array(\n [url[\"value\"] for url in record.get('urls', [])],\n 'inspirehep_theme/format/record/field_templates/link.tpl')","title":""},{"docid":"c299eb897d4da990064bd5bd0ec7b5c2","score":"0.49177134","text":"def __getitem__(*args):\n return _coin.SoDetailList___getitem__(*args)","title":""},{"docid":"9a027004bb767b6b76d330b5a18a3c28","score":"0.49177012","text":"def __init__(self, \n collection, recordfield, view_context=None, \n field_property=None, field_placement=None, \n field_list=None, field_ids_seen=[],\n field_placement_classes=None\n ):\n self._collection = collection\n # log.debug(\"FieldDescription recordfield: %r\"%(recordfield,))\n field_id = recordfield.get(ANNAL.CURIE.id, \"_missing_id_\")\n field_name = recordfield.get(ANNAL.CURIE.field_name, field_id) # Field name in form\n field_label = recordfield.get(RDFS.CURIE.label, \"\")\n field_help = recordfield.get(RDFS.CURIE.comment, \"\")\n field_property = field_property or recordfield.get(ANNAL.CURIE.property_uri, \"\")\n field_placement = field_placement or recordfield.get(ANNAL.CURIE.field_placement, \"\")\n field_placement_c = field_placement_classes or get_placement_classes(field_placement)\n field_placeholder = recordfield.get(ANNAL.CURIE.placeholder, \"\")\n field_tooltip = recordfield.get(ANNAL.CURIE.tooltip, \"\")\n field_render_type = extract_entity_id(recordfield.get(ANNAL.CURIE.field_render_type, \"\"))\n field_value_mode = extract_entity_id(recordfield.get(ANNAL.CURIE.field_value_mode, \"@@FieldDescription:value_mode@@\"))\n field_ref_type = extract_entity_id(recordfield.get(ANNAL.CURIE.field_ref_type, None))\n field_entity_type = recordfield.get(ANNAL.CURIE.field_entity_type, None)\n field_group_ref = extract_entity_id(recordfield.get(ANNAL.CURIE.group_ref, None))\n self._field_desc = (\n { 'field_id': field_id\n , 'field_name': field_name\n , 'field_instance_name': field_name\n , 'field_render_type': field_render_type\n , 'field_value_mode': field_value_mode\n , 'field_value_type': recordfield.get(ANNAL.CURIE.field_value_type, \"\")\n , 'field_label': field_label\n , 'field_help': field_help\n , 'field_property_uri': field_property\n , 'field_placement': field_placement_c\n , 'field_placeholder': field_placeholder\n , 'field_tooltip': field_tooltip\n , 'field_tooltip_test': field_tooltip or (field_help) or \"\"\n , 'field_default_value': recordfield.get(ANNAL.CURIE.default_value, None)\n , 'field_ref_type': field_ref_type\n , 'field_ref_field': recordfield.get(ANNAL.CURIE.field_ref_field, None)\n , 'field_ref_restriction': recordfield.get(ANNAL.CURIE.field_ref_restriction, \"ALL\")\n , 'field_entity_type': field_entity_type\n , 'field_choices': None\n , 'field_group_ref': field_group_ref\n , 'group_label': None\n , 'group_add_label': None\n , 'group_delete_label': None\n , 'group_field_list': None\n , 'group_field_descs': None\n , 'field_renderer': FieldRenderer(field_render_type, field_value_mode)\n , 'field_value_mapper': get_value_mapper(field_render_type) # Used by fieldvaluemap.py\n })\n self._field_suffix_index = 0 # No dup\n self._field_suffix = \"\"\n # If field references type, pull in copy of type id and link values\n type_ref = self._field_desc['field_ref_type']\n if type_ref:\n restrict_values = self._field_desc['field_ref_restriction']\n entity_finder = EntityFinder(collection, selector=restrict_values)\n entities = entity_finder.get_entities_sorted(\n type_id=type_ref, context=view_context, altscope=\"select\"\n )\n # Note: the options list may be used more than once, so the id generator\n # returned must be materialized as a list\n # Uses collections.OrderedfDict to preserve entity ordering\n self._field_desc['field_choices'] = OrderedDict()\n if field_render_type in [\"Enum_optional\", \"Enum_choice_opt\"]:\n # Add blank choice for optional selections\n self._field_desc['field_choices'][''] = FieldChoice('', label=field_placeholder)\n for e in entities:\n eid = e.get_id()\n val = e.get_type_entity_id()\n if eid != layout.INITIAL_VALUES_ID:\n self._field_desc['field_choices'][val] = FieldChoice(\n val, label=e.get_label(), link=e.get_view_url_path()\n )\n # If field references or contains field list, pull in field details\n if field_list:\n if field_id in field_ids_seen:\n raise Annalist_Error(field_id, \"Recursive field reference in field group\")\n field_ids_seen = field_ids_seen + [field_id]\n group_label = field_label\n add_label = recordfield.get(ANNAL.CURIE.repeat_label_add, None) or \"Add \"+field_id\n remove_label = recordfield.get(ANNAL.CURIE.repeat_label_delete, None) or \"Remove \"+field_id\n group_field_descs = []\n for subfield in field_list:\n f = field_description_from_view_field(collection, subfield, view_context, field_ids_seen)\n group_field_descs.append(f)\n self._field_desc.update(\n { 'group_id': field_id\n , 'group_label': group_label\n , 'group_add_label': add_label\n , 'group_delete_label': remove_label\n , 'group_field_list': field_list # Description from field/group\n , 'group_field_descs': group_field_descs # Resulting field description list\n })\n # log.debug(\"FieldDescription: %s\"%field_id)\n # log.info(\"FieldDescription._field_desc %r\"%(self._field_desc,))\n # log.info(\"FieldDescription.field_placement %r\"%(self._field_desc['field_placement'],))\n return","title":""},{"docid":"0fddfef6258defa7729725569ce0043d","score":"0.4911086","text":"def edit_list(model):\n commands = {\n Model.HP_8753D: 'EDITLIST',\n }\n return commands.get(model)","title":""},{"docid":"b94005567f9701056e0d0b57c15d253e","score":"0.49073035","text":"def GetLinkInfo(self):","title":""},{"docid":"8ae6970dc23cc3c838db421817efd9ab","score":"0.48991945","text":"def labels(self):\n\n if type(self.object_list) == type([]):\n model = self.formset.model\n else:\n model = self.object_list.model\n\n for field in self.visible_fields:\n name = None\n if self.formset:\n f = self.formset.empty_form.fields.get(field, None)\n if f:\n name = f.label\n\n if name is None:\n name = label_for_field(field, model)\n\n if name == model._meta.verbose_name:\n name = self.model_name and self.model_name or \\\n model._meta.verbose_name\n\n stype = None\n cur_sorted = False\n\n sortable = False\n\n if self.order_type:\n sortable = get_sort_field(field, model)\n stype = self.ASC\n\n # change order_type so that next sorting on the same\n # field will give reversed results\n if sortable and field == self.sort_field:\n cur_sorted = True\n if self.order_type == self.ASC:\n stype = self.DESC\n elif self.order_type == self.DESC:\n stype = self.ASC\n else:\n stype = self.ASC\n\n\n yield AdminListLabel(name, field, stype, cur_sorted, bool(sortable))","title":""},{"docid":"2f90a7f8d3ec9f3455f8c1188594fb80","score":"0.48915747","text":"def parse_detail(self, response):\n modal, make = self.model, self.make\n data = {\"hyperlink\":response.url}\n description = response.css('.navbar>.nav-caption>strong::text').get()\n data[\"description\"] = description\n\n for item in make:\n if item in (description):\n data[\"manufacturer\"] =item\n temp_list_item = []\n for item in model:\n for sub_item in item.split(\"\\n\"):\n check = sub_item.split(\":\")[1][2:-2]\n if check in description:\n temp_list_item.append(check)\n if temp_list_item:\n data['model'] = (max(temp_list_item, key=len))\n data[\"yearOfManufacture\"] = description.split(\" \")[0]\n if \"**\" in description:\n data[\"vrt\"] = ((description.split(\"**\"))[1].split(\"**\")[0])\n else: data[\"vrt\"] = \"\"\n for technical_item in response.css('.technical-params>.row'):\n title = technical_item.css('.technical-headers::text').get()\n detail = technical_item.css('.technical-info::text').get()\n if(title != None):\n #Tax Cost\n if(title == \"Tax Cost \"):\n title = \"yearlyTax\"\n detail = self.formatting_text(technical_item.css('.technical-info>a::text').get())[1:]\n detail = self.check_int_float(re.sub(\"[^0-9]\", \"\", detail))\n\n #Seller Section // Features\n elif(\"Seller\" in title):\n title = \"sellerType\"\n if technical_item.css('.technical-info>span::text').get():\n detail = technical_item.css('.technical-info>span::text').get()\n elif technical_item.css('.technical-info>img').get():\n detail = \"Dealer\"\n else:detail = \"\"\n\n #History check section\n if(technical_item.css('.technical-headers>.technical-p-headers::text').get() == \"History Check\"):\n title = technical_item.css('.technical-headers>.technical-p-headers::text').get()\n detail = {}\n for i in [1,3]:\n for item in technical_item.css(\".technical-info>div:nth-child({})>div\".format(i)):\n if(item.css(\"span:nth-child(1)::text\") != None and item.css(\"span:nth-child(2)>a::text\").get() != None):\n detail[item.css(\"span:nth-child(1)::text\").get()] = self.formatting_text(item.css(\"span:nth-child(2)>a::text\").get())\n\n #Engine Size\n if title == \"Engine Size\":\n title = \"engineSize\"\n #fuelType\n if title == \"Fuel Type\":\n title = \"fuelType\"\n #bodyStyle\n if title == \"Body Style\":\n title = \"bodyStyle\"\n #transmissionType\n if title == \"Transmission\":\n title = \"transmissionType\"\n #mileage\n if title == \"Mileage\":\n title = \"mileage\"\n #colour\n if title == \"Colour\":\n title = \"colour\"\n #co2emission\n if title == \"CO2 Emission\":\n title = \"co2emission\"\n #doors\n if title == \"Doors\":\n title = \"doors\"\n #location\n if title == \"Location\":\n title = \"location\"\n\n #Price Section\n if(len(technical_item.css('.finance-purchase__purchase-content')) > 0):\n title = \"price\"\n detail = technical_item.css(\".finance-purchase__payment-content>.finance-purchase__caption>span::text\").get()[1:]\n detail = self.check_int_float(re.sub(\"[^0-9]\", \"\", detail))\n\n if(title != None and detail != None):\n if(isinstance(detail,dict)):\n for item in detail.keys():\n if \"was\" in detail[item]:\n data[\"was\"+item[:5]] = True\n else: data[\"was\"+item[:5]] = False\n break\n if(title[0] != \"\\n\") & (\"MOT Expiry\" not in title) & (\"Insurance\" not in title) & (\"Warranty\" not in title):\n number_list = [\"mileage\",\"engineSize\",\"co2emission\",\"Towing Weight\",\"Payload\",\"doors\"]\n if (title in number_list):\n data[title] = self.check_int_float((re.sub(\"[^0-9]\", \"\", detail)))\n else:data[title] = detail\n if (self.update):\n self.update_csv(data)\n return data","title":""}],"string":"[\n {\n \"docid\": \"f84c2a47205e9e79022c971200627e3c\",\n \"score\": \"0.6725328\",\n \"text\": \"def UserListShow(self, listType, itemList):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60551363c68ef31e6434f1d0b4dca0d0\",\n \"score\": \"0.6423562\",\n \"text\": \"def detail_page(self, obj):\\n url = reverse('materials_inventory', args=(obj.id,))\\n\\n return format_html('<a href=\\\"{}\\\">Mostrar detalle</a>', url)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b1b7e347c45ade502f1e638b61e6bb3\",\n \"score\": \"0.6376594\",\n \"text\": \"def detail_page(self, obj):\\n url = reverse('products_inventory', args=(obj.id,))\\n\\n return format_html('<a href=\\\"{}\\\">Mostrar detalle</a>', url)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c2061456c9de554bbb2e9a2b2393b02\",\n \"score\": \"0.63654363\",\n \"text\": \"def detail_page(self, obj):\\n url = reverse('consumables_inventory', args=(obj.id,))\\n\\n return format_html('<a href=\\\"{}\\\">Mostrar detalle</a>', url)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c10d0ca59bd6632b5152cce175cd5887\",\n \"score\": \"0.6363512\",\n \"text\": \"def detail_page(self, obj):\\n url = reverse('durable_goods_inventory', args=(obj.id,))\\n\\n return format_html('<a href=\\\"{}\\\">Mostrar detalle</a>', url)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54307a6a3e21b6157b33355c2354ef95\",\n \"score\": \"0.59984475\",\n \"text\": \"def linkify(field_name):\\n\\n # def _linkify(obj):\\n # app_label = obj._meta.app_label\\n # linked_obj = getattr(obj, field_name)\\n # # model_name = linked_obj._meta.model_name\\n # view_name = f\\\"admin:{app_label}_{model_name}_change\\\"\\n # link_url = reverse(view_name, args=[linked_obj.id])\\n # return format_html('<a href=\\\"{}\\\">{}</a>', link_url, linked_obj)\\n\\n # _linkify.short_description = field_name # Sets column name\\n # return _linkify\",\n \"title\": \"\"\n },\n {\n \"docid\": \"192c42383930e738e939880b54d18d7a\",\n \"score\": \"0.5941618\",\n \"text\": \"def linkify(field_name):\\n\\n def _linkify(obj):\\n if obj.ordered and obj is not None:\\n app_label = obj._meta.app_label\\n linked_obj = getattr(obj, field_name)\\n model_name = linked_obj._meta.model_name\\n view_name = f\\\"admin:{app_label}_{model_name}_change\\\"\\n link_url = reverse(view_name, args=[linked_obj.pk])\\n return format_html('<a href=\\\"{}\\\">{}</a>', link_url, linked_obj)\\n else:\\n return 'Nill'\\n\\n _linkify.short_description = field_name # Sets column name\\n return _linkify\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4396cf5e7fc36d78fe96e39934e09f8\",\n \"score\": \"0.5622063\",\n \"text\": \"def get_list_display(self, request):\\n email = request.user.email\\n if email in [\\\"gbozee@gmail.com\\\", \\\"gbozee@example.com\\\", \\\"pyconnigeria@pycon.ng\\\"]:\\n return self.list_display + (\\\"hijack_field\\\",)\\n return self.list_display\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0903928d4f98297d85ecb1d0bb1566b\",\n \"score\": \"0.56031096\",\n \"text\": \"def __str__(self):\\n return self.detail\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0903928d4f98297d85ecb1d0bb1566b\",\n \"score\": \"0.56031096\",\n \"text\": \"def __str__(self):\\n return self.detail\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0903928d4f98297d85ecb1d0bb1566b\",\n \"score\": \"0.56031096\",\n \"text\": \"def __str__(self):\\n return self.detail\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e151bf4f3bfb1ebe748d5674fb8dcf5\",\n \"score\": \"0.55662256\",\n \"text\": \"def DisplayIndex(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e151bf4f3bfb1ebe748d5674fb8dcf5\",\n \"score\": \"0.55662256\",\n \"text\": \"def DisplayIndex(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0065d5c86c3fcdfa69cbd7664d3fc79\",\n \"score\": \"0.5555436\",\n \"text\": \"def linkify(field_name):\\n # docs.djangoproject.com/en/2.1/ref/contrib/admin/#reversing-admin-urls\\n def _linkify(obj):\\n app_label = obj._meta.app_label\\n linked_obj = getattr(obj, field_name)\\n model_name = linked_obj._meta.model_name\\n view_name = f\\\"admin:{app_label}_{model_name}_change\\\"\\n link_url = reverse(view_name, args=[str(linked_obj.id)])\\n return format_html('<a href=\\\"{}\\\">{}</a>', link_url, linked_obj)\\n\\n _linkify.short_description = field_name\\n return _linkify\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62c26a4e5ed1c25c6a546099cd8a0c72\",\n \"score\": \"0.5551432\",\n \"text\": \"def detail(request, listing_id):\\n listing = Listing.objects.get(id=listing_id)\\n return render(request, 'detail.html', {'listing': listing})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73b1951bc0a6103aae3cb080bbd1fab9\",\n \"score\": \"0.5526\",\n \"text\": \"def fetch_details(self, search_url, listing):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"486e9b5837d287a8cb8db36f891ef793\",\n \"score\": \"0.5523238\",\n \"text\": \"def web_get_detail_url(self):\\n try:\\n return reverse(\\n \\\"%s-detail\\\" % slugify(self._meta.verbose_name),\\n kwargs={\\\"slug\\\": slugify(self.db_key)},\\n )\\n except:\\n return \\\"#\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d47b1de5943dfe1d38a3669a920e48be\",\n \"score\": \"0.5520827\",\n \"text\": \"def extractLink(model, item):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f9a05b61b43b2887869802ea1b88ae3\",\n \"score\": \"0.5508284\",\n \"text\": \"def bmfmodule_detail(self):\\n return ('%s:detail' % self._bmfmeta.url_namespace, (), {\\\"pk\\\": self.pk})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a17b37efcd5a7e7d490d37dcb35c92f\",\n \"score\": \"0.54910445\",\n \"text\": \"def test_list_display_links_check_skipped_if_get_list_display_overridden(self):\\n\\n class TestModelAdmin(ModelAdmin):\\n list_display_links = [\\\"name\\\", \\\"subtitle\\\"]\\n\\n def get_list_display(self, request):\\n pass\\n\\n self.assertIsValid(TestModelAdmin, ValidationTestModel)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79c09b6920b886d4cd1979ed337fb2d3\",\n \"score\": \"0.5461656\",\n \"text\": \"def __init__(self, *args, **kwargs):\\n super().__init__(*args, **kwargs)\\n self.fields['page'].to_field_name = self.link_type.id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc5be1380e74cf8643a28d50aa3e0bf5\",\n \"score\": \"0.54097587\",\n \"text\": \"def display_content(self, item):\\r\\n self.selected_item = \\\"Relationship\\\"\\r\\n self.details_list.clear()\\r\\n # Look for the selected relation in our list of relationships\\r\\n found_relation = None\\r\\n for relation_loop in self.controller_object.relationships_main:\\r\\n if item.text() == relation_loop.name:\\r\\n found_relation = relation_loop\\r\\n\\r\\n self.relation_selected = found_relation\\r\\n count = 0\\r\\n\\r\\n if found_relation != None:\\r\\n for observation in found_relation.observation_list:\\r\\n self.details_list.addItem(observation.show())\\r\\n\\r\\n # sets font to gray\\r\\n if self.relation_selected.observation_list[count].ignore == 1:\\r\\n self.details_list.item(count).setForeground(QtCore.Qt.gray)\\r\\n\\r\\n # sets salient artifact color to red\\r\\n if self.relation_selected.observation_list[count].artifact == 1 and \\\\\\r\\n self.relation_selected.observation_list[count].ignore != 1:\\r\\n self.details_list.item(count).setForeground(QtCore.Qt.red)\\r\\n count += 1\\r\\n\\r\\n disable_button(self.edit_button)\\r\\n disable_button(self.ignore_button)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a406f9187ee253b19314ff64a45efdb2\",\n \"score\": \"0.5357906\",\n \"text\": \"def get_absolute_url(self):\\n return reverse('food:item_detail_view', kwargs={'pk': self.pk})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b71c6fc7fbfd0b71028049052a6da9ae\",\n \"score\": \"0.5354236\",\n \"text\": \"def on_showDetailsButton_clicked(self):\\n item = self.searchResultList.selectedItems()[0]\\n self.__showDetails(item)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f46dc4497bebeaa2d63dbdd5d427dfa8\",\n \"score\": \"0.53189486\",\n \"text\": \"def additional_detail(self) -> str:\\n return pulumi.get(self, \\\"additional_detail\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"786171954e186607b1faf36eb25aff11\",\n \"score\": \"0.52787507\",\n \"text\": \"def detail_url(self):\\n\\n return reverse('favorite-detail', kwargs={'pk': self.favorte_thing.pk})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68c453999953232f074ebb533fbb73bd\",\n \"score\": \"0.5275353\",\n \"text\": \"def object_list (request, **kwargs):\\n return list_detail.object_list (request, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b1b07fba06343d78328a92413cf390a\",\n \"score\": \"0.52725506\",\n \"text\": \"def link_relation(field_name):\\n\\n def _link_relation(obj):\\n linked_obj = getattr(obj, field_name)\\n if linked_obj:\\n model_name = linked_obj._meta.model_name\\n app_label = linked_obj._meta.app_label\\n view_name = f\\\"admin:{app_label}_{model_name}_change\\\"\\n link_url = reverse(view_name, args=[linked_obj.id])\\n return format_html('<a href=\\\"{}\\\">{}</a>', link_url, linked_obj)\\n else:\\n return \\\"-\\\"\\n\\n _link_relation.short_description = field_name # Sets column name\\n return _link_relation\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d33770b7c93fa7a2636881950c18b94\",\n \"score\": \"0.527234\",\n \"text\": \"def detail_list(self, request, templates):\\n return self._list_view(self.detail, request, templates)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29cfb733eea619f0bc5cc860b60ea702\",\n \"score\": \"0.5269969\",\n \"text\": \"def test_list_display_first_item_in_list_editable(self):\\n\\n class ProductAdmin(ModelAdmin):\\n list_display = [\\\"name\\\", \\\"slug\\\", \\\"pub_date\\\"]\\n list_editable = [\\\"slug\\\", \\\"name\\\"]\\n list_display_links = [\\\"pub_date\\\"]\\n\\n self.assertIsValid(ProductAdmin, ValidationTestModel)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73e42579eed3907e07d2d06721fb9d51\",\n \"score\": \"0.5248537\",\n \"text\": \"def get_detail_url(id):\\n\\treturn f'{TODO_URL}{id}'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6b92feb40255eda4f8525eb24cd6c2c\",\n \"score\": \"0.5245222\",\n \"text\": \"def callback():\\n query = db.recipe.title.contains(request.vars.keyword)\\n recipes = db(query).select(orderby=db.recipe.id)\\n links = [A(p.title, _href=URL('show',args=r.id)) for r in recipes]\\n return UL(*links)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ee73d47e4ad2725eb5df0c27794542d\",\n \"score\": \"0.52327496\",\n \"text\": \"def list_tag(self, event):\\n\\n eadd = self.ui.builder.get_object(\\\"EAdd\\\")\\n val = eadd.get()\\n eadd.delete(0, END)\\n\\n self.ui.builder.get_object(\\\"ListSelected\\\").insert(END, val)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8890a63d8bfdd6c3a0b299c57050e7ea\",\n \"score\": \"0.52312136\",\n \"text\": \"def snippet_detail(request, pk):\\n return handle_crud(snippet_crud, request, pk)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa887b286a1ab4c3d18b4a7054e43014\",\n \"score\": \"0.5207898\",\n \"text\": \"def print_details(self):\\n self.__linked_list.print_list()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7537c04481458e2d081244a1cfe50bb\",\n \"score\": \"0.5201767\",\n \"text\": \"def detail_view(request):\\n md = markdown.Markdown(safe_mode='replace', html_replacement_text='NO')\\n this_id = request.matchdict['this_id']\\n entry = DBSession.query(Entry).get(this_id)\\n text = md.convert(entry.text)\\n return {'entry': entry, 'text': text}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6234b166d96ab5fff042c4fbbe67ebcc\",\n \"score\": \"0.52010375\",\n \"text\": \"def test_list_display_link_checked_for_list_tuple_if_get_list_display_overridden(\\n self,\\n ):\\n\\n class TestModelAdmin(ModelAdmin):\\n list_display_links = \\\"non-list/tuple\\\"\\n\\n def get_list_display(self, request):\\n pass\\n\\n self.assertIsInvalid(\\n TestModelAdmin,\\n ValidationTestModel,\\n \\\"The value of 'list_display_links' must be a list, a tuple, or None.\\\",\\n \\\"admin.E110\\\",\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efe871546d430bf97c9c3452230ecd2b\",\n \"score\": \"0.51984733\",\n \"text\": \"def field_show(field_id):\\n field = fields.find_one({'_id': ObjectId(field_id)})\\n field_reviews = reviews.find({'field_id': ObjectId(field_id)})\\n return render_template('field_show.html', field=field)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a26f0e4826f0a6aca048e51271c3f6b5\",\n \"score\": \"0.516312\",\n \"text\": \"def list_item(self):\\n return self._list_item\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3513901f4e495a77a37e379360f97c8\",\n \"score\": \"0.5162738\",\n \"text\": \"def show(self, **kwargs):\\n if self.molecular:\\n return display_molecular(self, **kwargs)\\n else:\\n return display_atomic(self, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8280c00090557ff3cd6b4d1dfc584b1a\",\n \"score\": \"0.5158203\",\n \"text\": \"def listitem_form(**kwargs):\\n # Tags must be joined back into a comma-delimited string\\n if 'values' in kwargs and kwargs['values'] and 'tags' in kwargs['values']:\\n kwargs['values']['tags'] = ','.join(kwargs['values']['tags'])\\n\\n return gen_doc_as_div(ListItem, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49fb60a7655fff55668356e01bdc189d\",\n \"score\": \"0.51439613\",\n \"text\": \"def get_admin_url(self):\\r\\n return mark_safe(u\\\"%s/%s/%s/\\\" % (self.content_type.app_label, self.content_type.model, quote(self.object_id)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2345f469ab049330438bee2b25e1b0c\",\n \"score\": \"0.51439095\",\n \"text\": \"def convert_fields_to_detail_url(self, data, field_array):\\n for field in field_array:\\n if isinstance(data[field], list):\\n data['{}_urls'.format(field)] = [d.detail_url\\n for d in data[field]]\\n else:\\n data['{}_url'.format(field)] = data[field].detail_url\\n\\n del data[field]\\n\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf8dac92c1e5d24779ec0cf836b54da5\",\n \"score\": \"0.5140119\",\n \"text\": \"def get_details(self): \\r\\n return(self.name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56bae459fb871b48710a393a3a78d9a4\",\n \"score\": \"0.5123366\",\n \"text\": \"def get_urls(self):\\n urls = super().get_urls()\\n custom_urls = [\\n path('<int:object_id>/detail/', self.admin_site.admin_view(self.detail_view), name=\\\"program_program_detail\\\")\\n ]\\n return custom_urls + urls\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4bcf50c48c25c0c5086de31c2bb1ec27\",\n \"score\": \"0.51169133\",\n \"text\": \"def get_form_field(self, **kwargs):\\r\\n defaults = {'form_class': forms.URLField}\\r\\n defaults.update(kwargs)\\r\\n return super(LinkProperty, self).get_form_field(**defaults)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a72b713c4f6a9731ba34c6a9fc34a63\",\n \"score\": \"0.510741\",\n \"text\": \"def get_admin_url(self):\\r\\n return \\\"%s/%s/%s/\\\" % (self.content_type.app_label, self.content_type.model, self.object_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30729231b57eaf90ba3a3e53683bc716\",\n \"score\": \"0.51051366\",\n \"text\": \"def templatePath(self):\\n return 'soc/list/lists.html'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d94dfb8f3760dbac8903a2b7b3f37f30\",\n \"score\": \"0.5104054\",\n \"text\": \"def get_display_name(self):\\n return \\\"Item %d\\\" % self.index\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e9fef8666763a881fdc7e976628a763\",\n \"score\": \"0.5102591\",\n \"text\": \"def HomeDisplayExtend(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38d9f9cd245f687bde56d3ae1d89cbfd\",\n \"score\": \"0.5080766\",\n \"text\": \"def todolist_show ( request, item_container ):\\n\\n def get_section_view(items, sections):\\n \\\"\\\"\\\" erzeugt die Section-Ansicht der im Ordner enthaltenen Objekte \\\"\\\"\\\"\\n from django.template.loader import get_template\\n from django.template import Context\\n tSection = get_template('app/todolist/section.html')\\n t_link = get_template('app/todolist/linkitem.html')\\n content = ''\\n unknown = _(u'Unbekannter Zwischentitel')\\n section = '--START--'\\n links = []\\n comment_counts = get_visible_comment_count_by_item_containers(items)\\n for i in items :\\n if section != i.section :\\n if section != unknown :\\n if section != '--START--' and links != [] :\\n cSection = Context ( { 'section': section, 'links': links } )\\n content += tSection.render ( cSection)\\n if i.section in sections :\\n section = i.section\\n else :\\n section = unknown\\n links = []\\n if i.item.url_more != '':\\n title_link = show_link(i.item.url_more, i.item.title)\\n else:\\n title_link = i.item.title\\n cSection = Context ({\\n 'id' : i.item.id,\\n 'title' :i.item.title,\\n 'text' : i.item.text,\\n 'text_more' : i.item.text_more,\\n 'user_name' : i.item.string_1,\\n 'email' : i.item.string_2,\\n 'date' : i.get_last_modified(),\\n 'image_url' : i.item.image_url,\\n 'image_url_url': i.item.image_url_url,\\n 'image_extern' : i.item.image_extern,\\n 'last_modified': i.get_last_modified(),\\n 'show_item' : i.get_absolute_url(),\\n 'comments' : comment_counts[i.item.id]\\n })\\n links.append(t_link.render(cSection))\\n\\n if section != '--START--' and links != []:\\n cSection = Context ( { 'section': section, 'links': links } )\\n content += tSection.render ( cSection)\\n return content\\n\\n app_name = 'todolist'\\n items, sections, d_sections = get_folder_content(item_container)\\n vars = get_folderish_vars_show(request, item_container, app_name, get_section_view(items, sections),\\n get_user_support(item_container))\\n return render_to_response ( 'app/base_folderish.html', vars )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35500789e35ce04955cd6877a1f1896f\",\n \"score\": \"0.50806147\",\n \"text\": \"def test_list_display_links_is_none(self):\\n\\n class ProductAdmin(ModelAdmin):\\n list_display = [\\\"name\\\", \\\"slug\\\", \\\"pub_date\\\"]\\n list_editable = list_display\\n list_display_links = None\\n\\n self.assertIsValid(ProductAdmin, ValidationTestModel)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06887960a695f96f572256da98e00cbb\",\n \"score\": \"0.5078089\",\n \"text\": \"def flatpages_link_list(request):\\n from django.contrib.flatpages.models import FlatPage\\n link_list = [(page.title, page.url) for page in FlatPage.objects.all()]\\n return render_to_link_list(link_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae679839a1234345ce8b0992b93744fc\",\n \"score\": \"0.5074564\",\n \"text\": \"def list(request):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f79dafedfebbed68050f21fc88b36fa\",\n \"score\": \"0.5071394\",\n \"text\": \"def list_name(self):\\n if self.addToListCheck.isChecked() and self.listBox.currentText():\\n return self.listBox.currentText()\\n else:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"157be649ad27df433b5c6e980b40c483\",\n \"score\": \"0.5067877\",\n \"text\": \"def GetLink(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb08848ff9a795405ee814061da0634f\",\n \"score\": \"0.5062921\",\n \"text\": \"def get_context_data(self, **kwargs):\\n context = super().get_context_data(**kwargs)\\n\\n for obj in context['object_list']:\\n obj.materiaalklasse_str = makl2str[obj.materiaalklasse]\\n # obj.leeftijdscategorie_str = lcat2str[obj.leeftijdscategorie]\\n\\n obj.url_details = reverse('Records:specifiek', kwargs={'discipline': obj.discipline,\\n 'nummer': obj.volg_nr})\\n # for\\n\\n context['kruimels'] = (\\n (reverse('Records:overzicht'), 'Records'),\\n (None, self.kruimel)\\n )\\n\\n menu_dynamics(self.request, context)\\n return context\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c664919e9218109658de796861705666\",\n \"score\": \"0.50435853\",\n \"text\": \"def contentitem_detail(request, template='browse/detail.html'):\\n if request.GET and request.is_ajax():\\n item = ContentItem.objects.get(pk=request.GET['id'])\\n\\n context = {\\n 'item': item,\\n }\\n\\n return render_to_response(template, context, context_instance=RequestContext(request))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81c44727fa729632e70ce3263553f8ea\",\n \"score\": \"0.5042079\",\n \"text\": \"def __str__(self) -> str:\\n return self.link\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23b0736abf24d33bd2741a78448d81b0\",\n \"score\": \"0.50390184\",\n \"text\": \"def __unicode__(self):\\n return self.desc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f68b829bb6983c27b540b7994fd64f2e\",\n \"score\": \"0.50320196\",\n \"text\": \"def __str__(self):\\r\\n return \\\"[\\\" + str(self.item.id) + \\\" \\\" + self.label() + \\\"]\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72d5b1d1e1fcbaebc9da26642e1ea5b3\",\n \"score\": \"0.50230783\",\n \"text\": \"def PopupList(self):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc3d7b373678e798d980ec9be014e157\",\n \"score\": \"0.50203204\",\n \"text\": \"def get_absolute_url(self):\\n return reverse('model-detail-view', args=[str(self.id)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc3d7b373678e798d980ec9be014e157\",\n \"score\": \"0.50203204\",\n \"text\": \"def get_absolute_url(self):\\n return reverse('model-detail-view', args=[str(self.id)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de0f7ba07f1a3652d8e6c42bc72f3f4f\",\n \"score\": \"0.50079083\",\n \"text\": \"def get_absolute_url(self):\\n return reverse('inventory:detail_product', self.id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2355f7d4718beb82a09aa24d8c9d653\",\n \"score\": \"0.5007547\",\n \"text\": \"def edit_link(self, obj):\\n return \\\"<a href='%s'>%s</a>\\\" % (reverse(\\\"edit-category\\\", args=[obj.id]),\\n _(\\\"Edit\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d26ca9a0e5a0187496e2b060bc84dd27\",\n \"score\": \"0.5004382\",\n \"text\": \"def show_name(self, o):\\n from django.utils.safestring import SafeUnicode\\n return html.format_html(\\n '<a href=\\\"{url}\\\" title=\\\"{comment}\\\">{name}</a>',\\n url=o.get_absolute_url(),\\n name=T.truncate(o.name, 40),\\n comment=SafeUnicode(o.comment))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ccd60bac485bb5db95fe97e3675f83ba\",\n \"score\": \"0.49974918\",\n \"text\": \"def chapter_list(self, manga_link):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65cf91bbf7e8cd238d509260bee1d83a\",\n \"score\": \"0.49908912\",\n \"text\": \"def test_PrettyLinkWithAdditionalInfosColumn(self):\\n table = self.faceted_z3ctable_view\\n column = PrettyLinkWithAdditionalInfosColumn(self.portal, self.portal.REQUEST, table)\\n # attrName is set during table.setUpColumns\\n column.attrName = 'Title'\\n # this column use 'sortable_title' as sort_index\\n self.assertEqual(column.sort_index, 'sortable_title')\\n # this column only works with DX content types\\n tt = api.content.create(\\n container=self.eea_folder,\\n type='testingtype',\\n title='My testing type',\\n description='My description',\\n bool_field=False)\\n brain = self.portal.portal_catalog(UID=tt.UID())[0]\\n # no additional informations defined so nothing more than pretty link is returned\\n self.assertTrue(column.renderCell(brain).startswith(IPrettyLink(tt).getLink()))\\n # define some informations\\n tt.afield = u'My field content'\\n rendered = column.renderCell(brain)\\n self.assertTrue(\\n '<span id=\\\"form-widgets-afield\\\" class=\\\"text-widget textline-field\\\">My field content</span>'\\n in rendered)\\n # ai_extra_fields, by default id, UID and description\\n self.assertTrue(\\n '<div class=\\\"discreet\\\"><label class=\\\"horizontal\\\">Id</label>'\\n '<div class=\\\"type-textarea-widget\\\">my-testing-type</div></div>'\\n in rendered)\\n self.assertTrue(\\n '<div class=\\\"discreet\\\"><label class=\\\"horizontal\\\">Uid</label>'\\n '<div class=\\\"type-textarea-widget\\\">{0}</div></div>'.format(brain.UID)\\n in rendered)\\n self.assertTrue(\\n '<div class=\\\"discreet\\\"><label class=\\\"horizontal\\\">Description</label>'\\n '<div class=\\\"type-textarea-widget\\\">My description</div></div>'\\n in rendered)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"237df58f6fd43fd67d3b045f340603cf\",\n \"score\": \"0.4990774\",\n \"text\": \"def __unicode__(self):\\n return self.display_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8a124f09750b72dfa823631b2fd14ff\",\n \"score\": \"0.4988007\",\n \"text\": \"def _get_actions(self, obj, **kwargs):\\n # ctx = super().get_context_data(**kwargs)\\n t = get_template(\\\"customadmin/partials/list_basic_actions.html\\\")\\n # ctx.update({\\\"obj\\\": obj})\\n # print(ctx)\\n return t.render({\\\"o\\\": obj})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"695492582da8a37cffdc7ace1cbb6138\",\n \"score\": \"0.49803072\",\n \"text\": \"def test_list_display_first_item_same_as_list_editable_first_item(self):\\n\\n class ProductAdmin(ModelAdmin):\\n list_display = [\\\"name\\\", \\\"slug\\\", \\\"pub_date\\\"]\\n list_editable = [\\\"name\\\", \\\"slug\\\"]\\n list_display_links = [\\\"pub_date\\\"]\\n\\n self.assertIsValid(ProductAdmin, ValidationTestModel)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8195c3d01337bc5a3a8aae4beb5981f\",\n \"score\": \"0.49724123\",\n \"text\": \"def get_absolute_url(self):\\n return reverse('tipoProducto-detail', args=[str(self.id)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac1566b6bfd9ce61c01bd127a1e38a02\",\n \"score\": \"0.49715075\",\n \"text\": \"def get_admin_url(self):\\n return mark_safe(u\\\"%s/%s/%s/\\\" % (self.content_type.app_label, self.content_type.model, quote(self.object_id)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0eedb4c5e228c35af78a6c140956193b\",\n \"score\": \"0.49593964\",\n \"text\": \"def link(self) -> str:\\n\\t\\treturn self.details.get(\\\"htmlLink\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32eb49dffc241f01efcc7d3ce42d0ee9\",\n \"score\": \"0.49593285\",\n \"text\": \"def _get_detail_options(self, request, obj=None, **kwargs):\\n actions = []\\n\\n url_index = self.get_url_reverse('changelist')\\n\\n\\n if obj:\\n url_delete = self.get_url_reverse('delete', obj.pk)\\n actions.append(\\n Button('delete', 'Delete', url_delete, 'danger', icon='trash')\\n )\\n\\n actions.append(\\n Button('cancel', 'Back', url_index, icon='close')\\n )\\n\\n actions += copy.copy(self.get_detail_actions(request=request, obj=obj))\\n actions.append(\\n Button('_continue', 'Save', 'submit', 'primary', icon='save')\\n )\\n\\n return actions\",\n \"title\": \"\"\n },\n {\n \"docid\": \"763db36ca3adbd16ffa7428fa589ee82\",\n \"score\": \"0.49584964\",\n \"text\": \"def display_doc(self):\\n selected = self.documentation_tab.doc_list.currentItem().text()\\n self.documentation_tab.detailed_view.setHtml(self.docs[selected.lower()]['content'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3783e76c445d92ed893141dcaa690065\",\n \"score\": \"0.49526608\",\n \"text\": \"def genHTMLListItem(f, link, linkname):\\r\\n f.write(\\\"<li>\\\")\\r\\n genHTMLLink(f, link, linkname)\\r\\n f.write(\\\"\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c38ea33ab7785b4b95e5bc743e75ec54\",\n \"score\": \"0.49498272\",\n \"text\": \"def get_absolute_url(self):\\n return reverse('pelicula-detail', args=[str(self.id)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ae4884eebb5785f330b6b13cb146599\",\n \"score\": \"0.49496\",\n \"text\": \"def _edit(self, request, entity, context, params):\\n\\n context['page_name'] = \\\"%s titled '%s'\\\" % (context['page_name'],\\n entity.title)\\n\\n return super(View, self)._edit(request, entity, context, params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5e2730c9b5e6d5c20924d14bfcee1f0\",\n \"score\": \"0.4948899\",\n \"text\": \"def fields_new():\\n return render_template('fields_new.html', field={}, title='New field')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d6ab390b2be88f304d6b4d1203cd354\",\n \"score\": \"0.49440944\",\n \"text\": \"def __getattr__(self, name):\\n return getattr(self.objectListView, name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb8627944a42dc5ef587a33d8863a6d4\",\n \"score\": \"0.49407375\",\n \"text\": \"def item_details(request, slug):\\n item = get_object_or_404(Item, slug=slug)\\n\\n context = {'result': item}\\n\\n return render(request, 'database_view/detail_pages/item_details.html', context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02c33c86440af3c1133dce84eccc201a\",\n \"score\": \"0.4940715\",\n \"text\": \"def viewitems(self, *args):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77d39190568be9d58b4bbbf64f07e9bc\",\n \"score\": \"0.4931772\",\n \"text\": \"def object_link(self, obj):\\n if obj.action_flag == DELETION:\\n link = escape(obj.object_repr)\\n else:\\n try:\\n ct = obj.content_type\\n url = reverse(f'admin:{ct.app_label}_{ct.model}_change',\\n args=[obj.object_id])\\n link = f'<a href=\\\"{url}\\\">{escape(obj.object_repr)}</a>'\\n except Exception as e:\\n logger.debug(e)\\n link = escape(obj.object_repr)\\n return mark_safe(link)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4c35283773e544e8a67eb12bb54639d\",\n \"score\": \"0.49304467\",\n \"text\": \"def detail(self, *args, **kwargs):\\r\\n self.obj = self._load_obj(*args, **kwargs)\\r\\n context = self._get_detail_context(object=self.obj)\\r\\n response = self._render(\\\"detail\\\", **context)\\r\\n # # TODO: 19.11.13 wooyek this is silly, tiw ill not work with all models from SQLAlchemy\\r\\n # http://stackoverflow.com/questions/6745189/how-do-i-get-the-name-of-an-sqlalchemy-objects-primary-key\\r\\n # headers = {\\r\\n # 'X-PrimaryKey': self.obj.id, #\\r\\n # 'X-Key': self.obj.key.urlsafe(),\\r\\n # }\\r\\n # response.headers.extend(headers)\\r\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dda5ef6bf49458e31d5409445c88c07e\",\n \"score\": \"0.4929825\",\n \"text\": \"def __str__(self):\\n url = self.resolve()\\n if len(self.getData) < 3:\\n return '<a href=\\\"%s\\\" %s>%s</a>' % (url, self.strAttr(), self.vals)\\n\\n data = json.dumps(self.getData, cls=AresHtml.SetEncoder).replace('\\\"$(', '$(').replace('.val()\\\"', '.val()')\\n jsDef = '''\\n %s.on(\\\"click\\\", function (event){\\n var baseUrl = \\\"%s\\\";\\n if (baseUrl.indexOf(\\\"?\\\") !== -1) { var ullUrl = baseUrl + \\\"&\\\" + %s ; }\\n else { var ullUrl = baseUrl + \\\"?\\\" + %s ; }\\n window.location.href = ullUrl ;\\n }\\n ) ;\\n ''' % (self.jqId, url, data, data)\\n self.get('click', url, data, '')\\n #self.aresObj.jsOnLoadFnc.add(jsDef)\\n return '<a href=\\\"#\\\" %s>%s</a>' % (self.strAttr(), self.vals)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44be2e0102478e1fd69b15cbddc6b879\",\n \"score\": \"0.49279672\",\n \"text\": \"def oneforty_list_all(request):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"620bfb098f39c97c5bbe646acbba1056\",\n \"score\": \"0.4926012\",\n \"text\": \"def _select(self, r, **attr):\\n\\n if r.component_id or not r.component:\\n # No List View\\n return {}\\n\\n output = self._datatable(r, **attr)\\n\\n if r.representation == \\\"aadata\\\":\\n return output\\n\\n resource = self.method.resource\\n if resource.get_config(\\\"insertable\\\", True) and \\\"showadd_btn\\\" not in output:\\n # Add a button to activate the add form which gets hidden in the view\\n output[\\\"showadd_btn\\\"] = crud_button(None,\\n tablename = resource.tablename,\\n name = \\\"label_create\\\",\\n icon = \\\"add\\\",\\n _id = \\\"show-add-btn\\\",\\n )\\n \\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b18e082cd1e0007a7553f451aba027e\",\n \"score\": \"0.4924166\",\n \"text\": \"def show_title(self, o):\\n n = T.truncate(o.product.name, 40)\\n v = o.product.vendor.name\\n r = html.format_html('<a href=\\\"{}\\\">{}', o.get_absolute_url(), n)\\n r += '<br>' if len(n) + len(v) > 37 else ' '\\n r += html.format_html('[{}]</a>', v)\\n return html.mark_safe(r)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"129230cb42233f8f7ddff579d32a2d7f\",\n \"score\": \"0.4920084\",\n \"text\": \"def test_list_display_first_item_in_list_editable_no_list_display_links(self):\\n\\n class ProductAdmin(ModelAdmin):\\n list_display = [\\\"name\\\", \\\"slug\\\", \\\"pub_date\\\"]\\n list_editable = [\\\"slug\\\", \\\"name\\\"]\\n\\n self.assertIsInvalid(\\n ProductAdmin,\\n ValidationTestModel,\\n \\\"The value of 'list_editable[1]' refers to the first field \\\"\\n \\\"in 'list_display' ('name'), which cannot be used unless \\\"\\n \\\"'list_display_links' is set.\\\",\\n id=\\\"admin.E124\\\",\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9c8de5677f1ff5f48c52f6a2b91f1b6\",\n \"score\": \"0.49196154\",\n \"text\": \"def detail_template(self):\\n return \\\"wheelcms_spokes/image_detail.html\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"593f75a0ee9a7f5c35538a4fac191a13\",\n \"score\": \"0.4919407\",\n \"text\": \"def list_name(self):\\n return self.listBox.currentText()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a61ef0cd4fcb5a1254e95f4f2f18a7d6\",\n \"score\": \"0.4919314\",\n \"text\": \"def url_links(record):\\n return apply_template_on_array(\\n [url[\\\"value\\\"] for url in record.get('urls', [])],\\n 'inspirehep_theme/format/record/field_templates/link.tpl')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c299eb897d4da990064bd5bd0ec7b5c2\",\n \"score\": \"0.49177134\",\n \"text\": \"def __getitem__(*args):\\n return _coin.SoDetailList___getitem__(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a027004bb767b6b76d330b5a18a3c28\",\n \"score\": \"0.49177012\",\n \"text\": \"def __init__(self, \\n collection, recordfield, view_context=None, \\n field_property=None, field_placement=None, \\n field_list=None, field_ids_seen=[],\\n field_placement_classes=None\\n ):\\n self._collection = collection\\n # log.debug(\\\"FieldDescription recordfield: %r\\\"%(recordfield,))\\n field_id = recordfield.get(ANNAL.CURIE.id, \\\"_missing_id_\\\")\\n field_name = recordfield.get(ANNAL.CURIE.field_name, field_id) # Field name in form\\n field_label = recordfield.get(RDFS.CURIE.label, \\\"\\\")\\n field_help = recordfield.get(RDFS.CURIE.comment, \\\"\\\")\\n field_property = field_property or recordfield.get(ANNAL.CURIE.property_uri, \\\"\\\")\\n field_placement = field_placement or recordfield.get(ANNAL.CURIE.field_placement, \\\"\\\")\\n field_placement_c = field_placement_classes or get_placement_classes(field_placement)\\n field_placeholder = recordfield.get(ANNAL.CURIE.placeholder, \\\"\\\")\\n field_tooltip = recordfield.get(ANNAL.CURIE.tooltip, \\\"\\\")\\n field_render_type = extract_entity_id(recordfield.get(ANNAL.CURIE.field_render_type, \\\"\\\"))\\n field_value_mode = extract_entity_id(recordfield.get(ANNAL.CURIE.field_value_mode, \\\"@@FieldDescription:value_mode@@\\\"))\\n field_ref_type = extract_entity_id(recordfield.get(ANNAL.CURIE.field_ref_type, None))\\n field_entity_type = recordfield.get(ANNAL.CURIE.field_entity_type, None)\\n field_group_ref = extract_entity_id(recordfield.get(ANNAL.CURIE.group_ref, None))\\n self._field_desc = (\\n { 'field_id': field_id\\n , 'field_name': field_name\\n , 'field_instance_name': field_name\\n , 'field_render_type': field_render_type\\n , 'field_value_mode': field_value_mode\\n , 'field_value_type': recordfield.get(ANNAL.CURIE.field_value_type, \\\"\\\")\\n , 'field_label': field_label\\n , 'field_help': field_help\\n , 'field_property_uri': field_property\\n , 'field_placement': field_placement_c\\n , 'field_placeholder': field_placeholder\\n , 'field_tooltip': field_tooltip\\n , 'field_tooltip_test': field_tooltip or (field_help) or \\\"\\\"\\n , 'field_default_value': recordfield.get(ANNAL.CURIE.default_value, None)\\n , 'field_ref_type': field_ref_type\\n , 'field_ref_field': recordfield.get(ANNAL.CURIE.field_ref_field, None)\\n , 'field_ref_restriction': recordfield.get(ANNAL.CURIE.field_ref_restriction, \\\"ALL\\\")\\n , 'field_entity_type': field_entity_type\\n , 'field_choices': None\\n , 'field_group_ref': field_group_ref\\n , 'group_label': None\\n , 'group_add_label': None\\n , 'group_delete_label': None\\n , 'group_field_list': None\\n , 'group_field_descs': None\\n , 'field_renderer': FieldRenderer(field_render_type, field_value_mode)\\n , 'field_value_mapper': get_value_mapper(field_render_type) # Used by fieldvaluemap.py\\n })\\n self._field_suffix_index = 0 # No dup\\n self._field_suffix = \\\"\\\"\\n # If field references type, pull in copy of type id and link values\\n type_ref = self._field_desc['field_ref_type']\\n if type_ref:\\n restrict_values = self._field_desc['field_ref_restriction']\\n entity_finder = EntityFinder(collection, selector=restrict_values)\\n entities = entity_finder.get_entities_sorted(\\n type_id=type_ref, context=view_context, altscope=\\\"select\\\"\\n )\\n # Note: the options list may be used more than once, so the id generator\\n # returned must be materialized as a list\\n # Uses collections.OrderedfDict to preserve entity ordering\\n self._field_desc['field_choices'] = OrderedDict()\\n if field_render_type in [\\\"Enum_optional\\\", \\\"Enum_choice_opt\\\"]:\\n # Add blank choice for optional selections\\n self._field_desc['field_choices'][''] = FieldChoice('', label=field_placeholder)\\n for e in entities:\\n eid = e.get_id()\\n val = e.get_type_entity_id()\\n if eid != layout.INITIAL_VALUES_ID:\\n self._field_desc['field_choices'][val] = FieldChoice(\\n val, label=e.get_label(), link=e.get_view_url_path()\\n )\\n # If field references or contains field list, pull in field details\\n if field_list:\\n if field_id in field_ids_seen:\\n raise Annalist_Error(field_id, \\\"Recursive field reference in field group\\\")\\n field_ids_seen = field_ids_seen + [field_id]\\n group_label = field_label\\n add_label = recordfield.get(ANNAL.CURIE.repeat_label_add, None) or \\\"Add \\\"+field_id\\n remove_label = recordfield.get(ANNAL.CURIE.repeat_label_delete, None) or \\\"Remove \\\"+field_id\\n group_field_descs = []\\n for subfield in field_list:\\n f = field_description_from_view_field(collection, subfield, view_context, field_ids_seen)\\n group_field_descs.append(f)\\n self._field_desc.update(\\n { 'group_id': field_id\\n , 'group_label': group_label\\n , 'group_add_label': add_label\\n , 'group_delete_label': remove_label\\n , 'group_field_list': field_list # Description from field/group\\n , 'group_field_descs': group_field_descs # Resulting field description list\\n })\\n # log.debug(\\\"FieldDescription: %s\\\"%field_id)\\n # log.info(\\\"FieldDescription._field_desc %r\\\"%(self._field_desc,))\\n # log.info(\\\"FieldDescription.field_placement %r\\\"%(self._field_desc['field_placement'],))\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fddfef6258defa7729725569ce0043d\",\n \"score\": \"0.4911086\",\n \"text\": \"def edit_list(model):\\n commands = {\\n Model.HP_8753D: 'EDITLIST',\\n }\\n return commands.get(model)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b94005567f9701056e0d0b57c15d253e\",\n \"score\": \"0.49073035\",\n \"text\": \"def GetLinkInfo(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ae6970dc23cc3c838db421817efd9ab\",\n \"score\": \"0.48991945\",\n \"text\": \"def labels(self):\\n\\n if type(self.object_list) == type([]):\\n model = self.formset.model\\n else:\\n model = self.object_list.model\\n\\n for field in self.visible_fields:\\n name = None\\n if self.formset:\\n f = self.formset.empty_form.fields.get(field, None)\\n if f:\\n name = f.label\\n\\n if name is None:\\n name = label_for_field(field, model)\\n\\n if name == model._meta.verbose_name:\\n name = self.model_name and self.model_name or \\\\\\n model._meta.verbose_name\\n\\n stype = None\\n cur_sorted = False\\n\\n sortable = False\\n\\n if self.order_type:\\n sortable = get_sort_field(field, model)\\n stype = self.ASC\\n\\n # change order_type so that next sorting on the same\\n # field will give reversed results\\n if sortable and field == self.sort_field:\\n cur_sorted = True\\n if self.order_type == self.ASC:\\n stype = self.DESC\\n elif self.order_type == self.DESC:\\n stype = self.ASC\\n else:\\n stype = self.ASC\\n\\n\\n yield AdminListLabel(name, field, stype, cur_sorted, bool(sortable))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f90a7f8d3ec9f3455f8c1188594fb80\",\n \"score\": \"0.48915747\",\n \"text\": \"def parse_detail(self, response):\\n modal, make = self.model, self.make\\n data = {\\\"hyperlink\\\":response.url}\\n description = response.css('.navbar>.nav-caption>strong::text').get()\\n data[\\\"description\\\"] = description\\n\\n for item in make:\\n if item in (description):\\n data[\\\"manufacturer\\\"] =item\\n temp_list_item = []\\n for item in model:\\n for sub_item in item.split(\\\"\\\\n\\\"):\\n check = sub_item.split(\\\":\\\")[1][2:-2]\\n if check in description:\\n temp_list_item.append(check)\\n if temp_list_item:\\n data['model'] = (max(temp_list_item, key=len))\\n data[\\\"yearOfManufacture\\\"] = description.split(\\\" \\\")[0]\\n if \\\"**\\\" in description:\\n data[\\\"vrt\\\"] = ((description.split(\\\"**\\\"))[1].split(\\\"**\\\")[0])\\n else: data[\\\"vrt\\\"] = \\\"\\\"\\n for technical_item in response.css('.technical-params>.row'):\\n title = technical_item.css('.technical-headers::text').get()\\n detail = technical_item.css('.technical-info::text').get()\\n if(title != None):\\n #Tax Cost\\n if(title == \\\"Tax Cost \\\"):\\n title = \\\"yearlyTax\\\"\\n detail = self.formatting_text(technical_item.css('.technical-info>a::text').get())[1:]\\n detail = self.check_int_float(re.sub(\\\"[^0-9]\\\", \\\"\\\", detail))\\n\\n #Seller Section // Features\\n elif(\\\"Seller\\\" in title):\\n title = \\\"sellerType\\\"\\n if technical_item.css('.technical-info>span::text').get():\\n detail = technical_item.css('.technical-info>span::text').get()\\n elif technical_item.css('.technical-info>img').get():\\n detail = \\\"Dealer\\\"\\n else:detail = \\\"\\\"\\n\\n #History check section\\n if(technical_item.css('.technical-headers>.technical-p-headers::text').get() == \\\"History Check\\\"):\\n title = technical_item.css('.technical-headers>.technical-p-headers::text').get()\\n detail = {}\\n for i in [1,3]:\\n for item in technical_item.css(\\\".technical-info>div:nth-child({})>div\\\".format(i)):\\n if(item.css(\\\"span:nth-child(1)::text\\\") != None and item.css(\\\"span:nth-child(2)>a::text\\\").get() != None):\\n detail[item.css(\\\"span:nth-child(1)::text\\\").get()] = self.formatting_text(item.css(\\\"span:nth-child(2)>a::text\\\").get())\\n\\n #Engine Size\\n if title == \\\"Engine Size\\\":\\n title = \\\"engineSize\\\"\\n #fuelType\\n if title == \\\"Fuel Type\\\":\\n title = \\\"fuelType\\\"\\n #bodyStyle\\n if title == \\\"Body Style\\\":\\n title = \\\"bodyStyle\\\"\\n #transmissionType\\n if title == \\\"Transmission\\\":\\n title = \\\"transmissionType\\\"\\n #mileage\\n if title == \\\"Mileage\\\":\\n title = \\\"mileage\\\"\\n #colour\\n if title == \\\"Colour\\\":\\n title = \\\"colour\\\"\\n #co2emission\\n if title == \\\"CO2 Emission\\\":\\n title = \\\"co2emission\\\"\\n #doors\\n if title == \\\"Doors\\\":\\n title = \\\"doors\\\"\\n #location\\n if title == \\\"Location\\\":\\n title = \\\"location\\\"\\n\\n #Price Section\\n if(len(technical_item.css('.finance-purchase__purchase-content')) > 0):\\n title = \\\"price\\\"\\n detail = technical_item.css(\\\".finance-purchase__payment-content>.finance-purchase__caption>span::text\\\").get()[1:]\\n detail = self.check_int_float(re.sub(\\\"[^0-9]\\\", \\\"\\\", detail))\\n\\n if(title != None and detail != None):\\n if(isinstance(detail,dict)):\\n for item in detail.keys():\\n if \\\"was\\\" in detail[item]:\\n data[\\\"was\\\"+item[:5]] = True\\n else: data[\\\"was\\\"+item[:5]] = False\\n break\\n if(title[0] != \\\"\\\\n\\\") & (\\\"MOT Expiry\\\" not in title) & (\\\"Insurance\\\" not in title) & (\\\"Warranty\\\" not in title):\\n number_list = [\\\"mileage\\\",\\\"engineSize\\\",\\\"co2emission\\\",\\\"Towing Weight\\\",\\\"Payload\\\",\\\"doors\\\"]\\n if (title in number_list):\\n data[title] = self.check_int_float((re.sub(\\\"[^0-9]\\\", \\\"\\\", detail)))\\n else:data[title] = detail\\n if (self.update):\\n self.update_csv(data)\\n return data\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":897,"cells":{"query_id":{"kind":"string","value":"bbd2618f260fa87d81098f2ff45fac81"},"query":{"kind":"string","value":"Calculate mean value of each individual ensemble. Output is array of values, with the row denoting the ensemble size, and the column the ensemble number"},"positive_passages":{"kind":"list like","value":[{"docid":"b82ed16086ee487a802c2e45ce774e8b","score":"0.85388535","text":"def get_ensemble_mean(self):\n #edit 08/08/2019: new function, virtually the same as get_ensemble_min and get_ensemble_max, but for mean value of each ensemble\n\t\t\n self.ensemble_mean=np.zeros((np.size(self.data),np.shape(self.data)[0]))\t\t\n for i in range(np.size(self.data)):\n if i<2:\n self.ensemble_mean[i,:]=np.nan\n else: \n puff_numbers=self.get_ensembles(ensemble_size=i)\t\n self.ensemble_mean[i,:]=np.matlib.repmat(self.data,np.shape(self.data)[0],1)[0,puff_numbers].mean(axis=1)","title":""}],"string":"[\n {\n \"docid\": \"b82ed16086ee487a802c2e45ce774e8b\",\n \"score\": \"0.85388535\",\n \"text\": \"def get_ensemble_mean(self):\\n #edit 08/08/2019: new function, virtually the same as get_ensemble_min and get_ensemble_max, but for mean value of each ensemble\\n\\t\\t\\n self.ensemble_mean=np.zeros((np.size(self.data),np.shape(self.data)[0]))\\t\\t\\n for i in range(np.size(self.data)):\\n if i<2:\\n self.ensemble_mean[i,:]=np.nan\\n else: \\n puff_numbers=self.get_ensembles(ensemble_size=i)\\t\\n self.ensemble_mean[i,:]=np.matlib.repmat(self.data,np.shape(self.data)[0],1)[0,puff_numbers].mean(axis=1)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"50290dcda63e53e00a4ec5bc68d2e814","score":"0.71021867","text":"def get_ensemble_average(self, outputs):\n predictions = [output for output in outputs]\n predictions = torch.stack(predictions, dim=1)\n average_prediction = predictions.mean(1)\n\n return average_prediction","title":""},{"docid":"bb33ee0aa954a88e7e4180831e1f65fd","score":"0.6930079","text":"def mean(self):\n vec = self.data\n numrows = len(vec) \n numcols = len(vec[0]) \n m_vec = []\n sum = 0.0;\n for i in range(0,numrows):\n for j in range(0,numcols):\n sum += vec[i][j]\n m_vec = sum/(numrows*numcols)\n return m_vec","title":""},{"docid":"acdbce169e103ad3293f6311102cfc75","score":"0.67743635","text":"def mean(self) -> chex.Array:\n return jnp.sum(self.probs_parameter() * self._values, axis=-1)","title":""},{"docid":"7a4e9bda4f5c6d97dfd185649ca92ee1","score":"0.6698942","text":"def mean(self):\n return np.mean(self.facepicsrep, axis=0)","title":""},{"docid":"71c1a0c2ceb04cdcd30bfbe947b40f1d","score":"0.6663481","text":"def mean(self) -> pa.FloatingPointArray:\n return ListChunk.reduce(self, pc.mean, pa.float64())","title":""},{"docid":"d41a60d3c94c4c0724e536686d2fea37","score":"0.66515636","text":"def average(x, nbins):\n n = len(x); \n nm = n/nbins * nbins;\n x_mean = x[:nm];\n x_mean = x_mean.reshape([nm/nbins, nbins]);\n x_mean = np.nanmean(x_mean, axis = 1);\n x_mean = np.hstack([x_mean, [np.nanmean(x[nm:])]]);\n return x_mean;","title":""},{"docid":"f9e18a46a24a31777bec5f0c02910aee","score":"0.66183436","text":"def calculate_mean(num_array):\n mean = 0\n for elem in num_array:\n mean += elem\n\n return mean / len(num_array)","title":""},{"docid":"bb4b1d55949d7ed98bc0d4f9a0d00e45","score":"0.6616439","text":"def mean(X) :\r\n\r\n return np.sum(X, axis = 0) / X.shape[0]","title":""},{"docid":"c4671b0711227660db1afd3802b066bf","score":"0.6594574","text":"def compute_mean(data):\n\n\tm = np.mean(data,axis=0)\n\treturn m","title":""},{"docid":"977fecd74e8156325666a091153fb66a","score":"0.657964","text":"def _ave(self):\n\n return np.mean(self.full_estim.X, axis=0)","title":""},{"docid":"17d129ddc9498494a840092a843293a8","score":"0.6578661","text":"def mean(self):\n return self.apply(lambda x: np.mean(x, axis=0))","title":""},{"docid":"5c955f14959e30100cae106a81828594","score":"0.6569316","text":"def __mean(self):\n for elm, elm_wls in self.data.elements.items():\n count = len(elm_wls)\n total_x_var = 0\n total_1_var = 0\n for wl_label in elm_wls:\n total_x_var += self.results[wl_label][2] \\\n / self.results[wl_label][3]**2\n total_1_var += 1 / self.results[wl_label][3]**2\n self.means[elm] = (total_x_var / total_1_var,\n np.sqrt(count / total_1_var))","title":""},{"docid":"9fe9f7aea58c817d251fbe479e422fd0","score":"0.65499353","text":"def _emprical_mean(self,x_value,mb):\n value = 0\n sum_mean = sum([x_value[k] for k in range(1, mb)])\n return sum_mean/mb","title":""},{"docid":"3e4dc30cdbef0ec0dd08472793d9d505","score":"0.6549698","text":"def mean(container):\n avg = 0\n \n for i in container:\n avg+=i/float(len(container))\n return(avg)","title":""},{"docid":"02f179d041b3b0cec8d1071124ae4fb1","score":"0.6522487","text":"def mean(self):\n return self._calculate_stats(np.mean)","title":""},{"docid":"de1c49452d034d4175259b4af808531c","score":"0.6516505","text":"def manual_mean(arr):\n sum = 0\n for i in xrange(0, arr.shape[0]):\n for j in xrange(0, arr.shape[1]):\n sum += arr[i, j]\n return sum / arr.size","title":""},{"docid":"58bbba4ac73aa076fb4543c90a09f81c","score":"0.65153676","text":"def multi_mean(data):\n return np.mean(data, axis=0)","title":""},{"docid":"fa5722e560b65ebe6cd3067419efd73b","score":"0.6515073","text":"def mean(values):\r\n return sum(values) / len(values)","title":""},{"docid":"729b9055921410cb095e73f0fe9c060e","score":"0.65086097","text":"def mean_accum(data):\n result = np.zeros(data[0].shape, np.float64) # init output array\n for dataslice in data:\n # this for loop isn't such a bad thing cuz the massive add step inside the loop\n # is the limiting factor\n result += dataslice\n result /= len(data)\n return result","title":""},{"docid":"afd094e912a15a288e485a20d67cf39a","score":"0.65070194","text":"def mean(X):\n # mean = np.sum(X, axis=0) / X.shape[0]\n mean = np.mean(X, axis=0)\n return mean","title":""},{"docid":"d46e3473e6c7dfcc3696414eccb96d0d","score":"0.6460672","text":"def _mean_filter(eda):\n mean_arr = np.array([])\n for i in range(MEAN_KERNEL_WIDTH, len(eda) - MEAN_KERNEL_WIDTH):\n mean = np.mean(eda[i - MEAN_KERNEL_WIDTH: i + MEAN_KERNEL_WIDTH + 1])\n mean_arr = np.append(mean_arr, mean)\n return mean_arr","title":""},{"docid":"69fb3ee387953a7b7b4062a3ecc2b0e4","score":"0.645016","text":"def ensemble_average():\n fig, ax = plt.subplots(figsize=(6*0.7, 3.5*0.7))\n\n # Ensure graph is the same if needed\n np.random.seed(1)\n\n npts = 200\n navg = 20000\n maxt = 10\n\n x = np.linspace(0, maxt, npts)\n\n # Ensemble averaging\n y = np.random.random(npts) * 0.5*np.sin(np.pi/5*x) + \\\n 0.5*np.random.random(npts) + 0.75\n ax.plot(x, y, 'k', lw=1, label=r'$u_i(\\vec{x}, t)$')\n\n ymean = 0\n for i in range(navg):\n np.random.seed(i)\n ymean += np.random.random(npts) * 0.5*np.sin(np.pi/5*x) + \\\n 0.5*np.random.random(npts) + 0.75\n ymean = ymean/navg\n ax.plot(x, ymean, '--k', lw=1, label=r'$u_E(\\vec{x}, t)$')\n\n ax.set_ylim([0, 2])\n ax.set_xlim([0, maxt])\n ax.set_xlabel(r'$t$')\n ax.set_ylabel(r'$u$')\n\n fig, ax = convert_to_graph(fig, ax)\n plt.legend()\n plt.savefig('ensemble_averaging.png')\n plt.close(fig)","title":""},{"docid":"72fb105a127ec34efdae84337fff051e","score":"0.6445197","text":"def average(self):\n return np.average(self)","title":""},{"docid":"457e486da5a24897762e720c78ac55d3","score":"0.6443342","text":"def _ave(self):\n\n return np.mean(self.model_estim.X, axis=0)","title":""},{"docid":"457e486da5a24897762e720c78ac55d3","score":"0.6443342","text":"def _ave(self):\n\n return np.mean(self.model_estim.X, axis=0)","title":""},{"docid":"cce6d95e891a91a0aa26a2c2e90d20fc","score":"0.6434196","text":"def intra_mean(self, values):\n # TODO Check that quantity is valid\n if values.ndim > 1:\n return np.array([np.mean(values[x,:], axis=0) for x in self.allocations_])\n else:\n return np.array([np.mean(values[x]) for x in self.allocations_])","title":""},{"docid":"25fddd5d7e634105fc44552ae28e9521","score":"0.6406734","text":"def _genaverage(self, parray, **kwargs):\n output = np.empty((0, 5))\n for array in parray:\n output = np.append(output, np.mean(array, axis=0).reshape((1, 5,)),\n axis=0)\n\n return output","title":""},{"docid":"b454ffb63d60ee8248b1b6ff6401f062","score":"0.6396374","text":"def _ave(self):\n return self.X.mean(axis=0)","title":""},{"docid":"372cd0068dba53320967f135b5cc612e","score":"0.6390503","text":"def array_mean(array):\n \n width = len(array[0])\n return np.sum(array, axis = 1)/width","title":""},{"docid":"0aebc878553a5f6c96142889e305ad27","score":"0.6371311","text":"def mean(self):\n return self.results.mean()","title":""},{"docid":"842175806b3596f834f63e3609a4316c","score":"0.63698643","text":"def X_means(self):\n return np.average(self.arrX_input, axis=0).reshape(1,-1)","title":""},{"docid":"842175806b3596f834f63e3609a4316c","score":"0.63698643","text":"def X_means(self):\n return np.average(self.arrX_input, axis=0).reshape(1,-1)","title":""},{"docid":"dd9b4e3a65610f4b01359c5f84e522cb","score":"0.63667053","text":"def mean(values):\n return sum(values) / float(len(values))","title":""},{"docid":"4e20e27b7aa6d8ead42fc1b3c4fb262a","score":"0.6364998","text":"def mean(self):\n return np.average(self.state_vector, axis=1)","title":""},{"docid":"c0efbe98fd6b8c40ab2f3afd7f34575c","score":"0.6343549","text":"def _calc_mean(self):\n return sum(self.data, 0.0) / len(self.data)","title":""},{"docid":"0fb87aa65e6db861bb9a911ad046dd56","score":"0.6339938","text":"def mean_vector(self):\n return self._mean","title":""},{"docid":"ffcf6bed0b4fd44a42af0fe399ac80f4","score":"0.63343066","text":"def mean(self):\n data = self.data.values()\n sum = 0.\n for item in data:\n sum += float(item)\n return sum/len(data)","title":""},{"docid":"94b1dacc77c588f33ce8f1f4d4bee328","score":"0.63334805","text":"def _calc_mean(self, chunk):\n\n # for sample in chunk:\n # self.n += 1.0\n # self.av = (self.n - 1) / self.n * self.av + sample / self.n\n\n # nx + sum(m) n sum(m)\n # -------------- = x ------------ + ------------\n # n + len(m) n + len(m) n + len(m)\n \n self.n += float(len(chunk))\n self.av = (self.n - len(chunk)) / self.n * self.av + sum(chunk) / self.n\n\n return self.av","title":""},{"docid":"8362935d5690f14dea59d63b2ed5fcc7","score":"0.63212097","text":"def mean_naive(X):\n N, D = X.shape\n mean = np.zeros(D)\n for m in range(D):\n k=0\n for n in range(N):\n k+=X[n,m]\n smean=k/N\n mean[m]=smean\n return mean","title":""},{"docid":"ce774e034bd522f93539b62f0617d811","score":"0.6318303","text":"def Mean(self):\n return self._sum_x / self._n","title":""},{"docid":"1f2a25b70bfbb1d3fa2fcd369700c8b8","score":"0.63081944","text":"def get_ensemble_variance(self):\n #edit 08/08/2019: new function, virtually the same as get_ensemble_min, get_ensemble_max, and get_ensemble_mean but for variance of each ensemble.\n\t\t\n self.ensemble_var=np.zeros((np.size(self.data),np.shape(self.data)[0]))\t\t\n for i in range(np.size(self.data)):\n if i<2:\n self.ensemble_var[i,:]=np.nan\n else: \n puff_numbers=self.get_ensembles(ensemble_size=i)\t\n self.ensemble_var[i,:]=np.matlib.repmat(self.data,np.shape(self.data)[0],1)[0,puff_numbers].var(axis=1)","title":""},{"docid":"cf4662fa5bfbd10d143a51dd779873aa","score":"0.6300281","text":"def mean_(X: ndarray) -> ndarray:\n return mean(X, axis=0).reshape(1, X.shape[1])","title":""},{"docid":"3f2b8ee0c1ce4978c681b73688863a84","score":"0.6296941","text":"def get_mean(data):\n mean = sum(data)/len(data)\n return mean","title":""},{"docid":"f2e4934041268fd50894ba1f0f1f862a","score":"0.6278936","text":"def get_average(data):\n \n n_meas = len(data)\n \n if len(data.shape) != 1:\n n_obs = len(data[0])\n avg = np.zeros((n_obs))\n for i in range(n_meas):\n for j in range(n_obs):\n avg[j] += data[i,j]\n \n else:\n avg = 0.0\n for i in range(n_meas):\n avg += data[i]\n \n avg /= 1.0*n_meas\n return avg","title":""},{"docid":"bfc50aac7e5900bc88c6cd7faaad92cd","score":"0.6268079","text":"def calculate_mean(self): \r\n\r\n return self.n * self.p","title":""},{"docid":"ffba68cbecec76c419a5de7d72945d8f","score":"0.6266025","text":"def find_mean(array):\n mean = np.mean(array,axis=0)\n return mean","title":""},{"docid":"d6acfedf6d551d33dd203d91fd0116fc","score":"0.62597954","text":"def calculate_mean(self):\n self.mean = st.mean(self.data)\n return self.mean","title":""},{"docid":"60d0c8e24ee6350dfdd03df2c809500a","score":"0.6253372","text":"def calc_mean(values):\n values_sum = 0\n for value in values:\n values_sum += value\n return values_sum / len(values)","title":""},{"docid":"4de2021e018a4266b60a8c8b5fc2dfd1","score":"0.6252569","text":"def mean(data): \n return pd.Series(data).mean()","title":""},{"docid":"d4188792dc15c6b377ab2026f69bf0e3","score":"0.62512314","text":"def gmean(self):\n return self.apply(lambda x: gmean(x, axis=0))","title":""},{"docid":"e9685be676ca6c7aba40c5e5043248fe","score":"0.62506455","text":"def approx_mean(self):\n\n for i in range(self.n):\n self.means[self.y[i]:self.y[i] + 1] += self.X[i:i + 1]\n\n for k in range(self.k):\n self.means[k] = np.multiply((1 / self.counts[k]), self.means[k])","title":""},{"docid":"2b94b540dab95bbe0e384d1b3a6403da","score":"0.62366754","text":"def mean(self, x):\n return np.mean(x)","title":""},{"docid":"318bca156854da8891ab573ec3fc4e64","score":"0.62319255","text":"def find_average(feature_list):\n avg_feature_list = [np.mean(np.array(feature_list[i])) for i in range(6)]\n \n return avg_feature_list","title":""},{"docid":"304a8ed2c9ef58ed766505371508f42a","score":"0.6229052","text":"def mean(x):\n return sum(x) / len(x)","title":""},{"docid":"43a8ffd0f20993286fe450cbad661f66","score":"0.6220122","text":"def _getmean(self):\n b = [self.coefs[term] for term in self.terms]\n return np.sum(np.array(b)*self.terms)","title":""},{"docid":"ab4acd971c491cbdb5b056e8408b14be","score":"0.6219637","text":"def mean(self, values):\n return sum(values) / len(values)","title":""},{"docid":"d82668af57077e65156dfac86ab4228f","score":"0.6206405","text":"def mean(datasets):\n return [np.mean(x, axis=0) for x in datasets]","title":""},{"docid":"06493b962bede5a55c76ed176e7cb943","score":"0.6204836","text":"def average(self):\n return self._data[\"sum\"]/self._data[\"nValues\"]","title":""},{"docid":"e0964800cf41c745a5ed8488cc17d42b","score":"0.61913675","text":"def mean(X):\n mean = np.zeros(X.shape[1]) # EDIT THIS\n mean = np.mean(X, axis=0) \n return mean","title":""},{"docid":"f6cda23dadabff4916e0a7fcddaf6795","score":"0.6184613","text":"def get_mean(self, X):\n\n #handles both vectors and scalars\n if len(X) > 0:\n return [0.5] * len(X)\n else:\n return 0.5","title":""},{"docid":"72d9a49c2aad009c939c1e34c37b984f","score":"0.61800694","text":"def mean(self):\n return self.m","title":""},{"docid":"bd8553fcfe97646e18952a7dfe4b2d0c","score":"0.6178019","text":"def get_ensemble_variance(self):\n #edit 08/08/2019: new function, virtually the same as get_ensemble_min, get_ensemble_max, and get_ensemble_mean but for variance of each ensemble.\n\t\t\n self.ensemble_std=np.zeros((np.size(self.data),np.shape(self.data)[0]))\t\t\n for i in range(np.size(self.data)):\n if i<2:\n self.ensemble_std[i,:]=np.nan\n else: \n puff_numbers=self.get_ensembles(ensemble_size=i)\t\n self.ensemble_std[i,:]=np.matlib.repmat(self.data,np.shape(self.data)[0],1)[0,puff_numbers].std(axis=1)","title":""},{"docid":"ac290414ef04d0c5029f0fe629ffb9cf","score":"0.616326","text":"def mean(self):\n return self.value","title":""},{"docid":"2b98ec184148cbfe91e697c1827469ee","score":"0.61389476","text":"def mean(self):\n return np.mean(self.space_array, axis=0)","title":""},{"docid":"1e6ef76d71d681745eb51dfb5ab4560c","score":"0.6127086","text":"def mean(X):\n # given a dataset of size (D, N), the mean should be an array of size (D,1)\n # you can use np.mean, but pay close attention to the\n # shape of the mean vector you are returning.\n D, N = X.shape\n # Edit the code to compute a (D,1) array `mean` for the mean of dataset.\n mean = X.mean(axis=1).reshape(D, 1)\n return mean","title":""},{"docid":"e5d6aa72bc675001a51c11d4742dccaa","score":"0.61259","text":"def mean(self):\n total = 0.0\n for bin in self:\n total += bin.count\n return total / len(self)","title":""},{"docid":"169ef6060fca7202f5294284892771a5","score":"0.61168236","text":"def average(values):\n return sum(values) / len(values)","title":""},{"docid":"169ef6060fca7202f5294284892771a5","score":"0.61168236","text":"def average(values):\n return sum(values) / len(values)","title":""},{"docid":"450b63cec789fe650e01ac87b61f94ff","score":"0.60981137","text":"def getAverageAggregator(self):\n return self._average","title":""},{"docid":"d3309eb7434c32e65cc0e40ab159c2bd","score":"0.6097863","text":"def calculate_mean(self):\n return self.p * self.n","title":""},{"docid":"95634e9921a71924ae80a9aee345a015","score":"0.60886467","text":"def mean(input, dim):\n pass","title":""},{"docid":"3372df6bd10e495cde44dac7fa82f0aa","score":"0.6084342","text":"def reduce_mean(input):","title":""},{"docid":"7ff4e0e37a0f46f7a915997bd6b1959e","score":"0.6083269","text":"def mean(self,*,axis=1):\n try:\n means = np.nanmean(self.data, axis=axis).squeeze()\n if means.size == 1:\n return np.asscalar(means)\n return means\n except IndexError:\n raise IndexError(\"Empty BinnedEventArray; cannot calculate mean.\")","title":""},{"docid":"5e94e5a2246b4d5d628eb1cc8450b7bd","score":"0.6075295","text":"def running_mean(x, N):\n out = np.zeros_like(x, dtype=np.float64)\n dim_len = x.shape[0]\n for i in range(dim_len):\n if N%2 == 0:\n a, b = i - (N-1)//2, i + (N-1)//2 + 2\n else:\n a, b = i - (N-1)//2, i + (N-1)//2 + 1\n\n #cap indices to min and max indices\n a = max(0, a)\n b = min(dim_len, b)\n out[i] = np.mean(x[a:b])\n return out","title":""},{"docid":"e1ec77fef07d85e916e2239912b32661","score":"0.60750586","text":"def mean(self):\n data = self.get_data(None)\n if data is not None:\n return data.mean(squeeze=True)\n\n raise ValueError(\n \"ERROR: Can't get the mean when there is no data array\"\n )","title":""},{"docid":"9afc4876748aaf5319ce13e997a150a6","score":"0.6072623","text":"def grand_mean(x):\r\n return np.mean(x)","title":""},{"docid":"40f6abf5871b408ca94d04ba009a734d","score":"0.6066953","text":"def meanAge(self):\n data = self.data\n ds = data.groupby(['PARTICIPANT_ID']).agg('mean').reset_index()\n ndr_age = ds.AvgAge.tolist()\n return np.array(ndr_age).reshape(-1,1)","title":""},{"docid":"7038629cbfc18db2ec95bf10702225c0","score":"0.6066128","text":"def avg_fn (data):\n\treturn np.mean(data)","title":""},{"docid":"198f98a357ca69b088578613ea2092f4","score":"0.605865","text":"def get_mean(sampling_fun, N, M):\n ans = []\n for m in range(M):\n ans.append(np.mean(sampling_fun(N)))\n return np.asarray(ans)","title":""},{"docid":"afd8538865ea38c80c958ec9766548ba","score":"0.6057457","text":"def mean(self):\n return self.copy(coefficients=numpy.mean(self.coefficients, axis=0))","title":""},{"docid":"366abb996c2a3ef360be8aac9ca1f184","score":"0.6054785","text":"def mean_pooling(img, scale):\n h_, w_ = 0, 0\n save_mean = []\n for h in range(10, 110, scale):\n h_ = h_ + 1\n for w in range(10, 189, scale):\n save_mean.append(img[h - 10: h, w - 10: w].mean())\n w_ = len(save_mean) // h_\n save_mean = np.asarray(save_mean).reshape(h_, w_)\n return save_mean","title":""},{"docid":"473d1aa731f91bdcf3067f4f80126c7f","score":"0.60534716","text":"def mean(inp_list):\n return np.mean(inp_list)","title":""},{"docid":"9533f63a83a4599134664d8460e515ec","score":"0.6048591","text":"def avg(self):\n return sum(range(1,self.num+1))/self.num","title":""},{"docid":"083c4edec51bf0509599a23ba0e65ec4","score":"0.604489","text":"def mean_face(faces):\n mean_face = mean(faces)\n return mean_face","title":""},{"docid":"cd9c634216bdb2c3c94e43ec383c5350","score":"0.6044063","text":"def _mean(listvalue):\n return sum(listvalue) / len(listvalue)","title":""},{"docid":"5d5c3660ffcb7d8ccb59f81e1c0f6757","score":"0.6042098","text":"def mean(self):\n if len(self) == 1: # No need to calculate mean\n return self.state_vector\n return np.average(self.state_vector, axis=1, weights=np.exp(self.log_weight))","title":""},{"docid":"5119f6654fe1245ffd96889f32389c56","score":"0.60396236","text":"def arithmetic_mean(array, axis=0):\n import numpy as np\n \n return np.mean(array, axis=axis)","title":""},{"docid":"baf359db4df5958ea0e021860786ddcf","score":"0.60371935","text":"def _mean(x, mode=\"arithmetic\"):\r\n assert mode in [\"arithmetic\", \"geometric\"]\r\n if mode == \"arithmetic\":\r\n x_mean = x.mean(axis=1)\r\n else:\r\n x_mean = np.exp(np.log(x + 1e-7).mean(axis=1))\r\n x_mean = x_mean / x_mean.sum(axis=1, keepdims=True)\r\n return x_mean","title":""},{"docid":"fcb287891fe196ff64a90390b55c5b31","score":"0.6029203","text":"def get_mean(data):\r\n if not data:\r\n raise Exception(\"Empty data.\")\r\n sum_x = sum(data)\r\n mean = float(sum_x) / len(data)\r\n return mean","title":""},{"docid":"3adf1d3982acadc7cc1389cb85931105","score":"0.6026552","text":"def mean(self) -> Tensor:\n return self.values","title":""},{"docid":"00baf3f8c60b1b0ea0688869f1d2af54","score":"0.60240227","text":"def mean(self):\n return self._mean","title":""},{"docid":"00baf3f8c60b1b0ea0688869f1d2af54","score":"0.60240227","text":"def mean(self):\n return self._mean","title":""},{"docid":"43ffd54d71d8812bf00f0eafb286e3c8","score":"0.60188514","text":"def mean(self):\n return self._impl.stats(self).mean()","title":""},{"docid":"12ee1ffd1900504a7e43ed83bae9ba40","score":"0.6016554","text":"def mean(self):\n return self.concentration / self.concentration.sum(-1, keepdim=True)","title":""},{"docid":"ee2966ef3a8081d3fa00a827acff5e4b","score":"0.6014511","text":"def meanColor(self):\n return np.array([f.meanColor() for f in self])","title":""},{"docid":"fa631caf7d48781222ba181383cb5d36","score":"0.6010814","text":"def get_mean(a, axis=None):\n return np.mean(a, axis=axis)","title":""},{"docid":"f2979dccbb7f3249a966968060d49cc0","score":"0.60059345","text":"def mean(numbers):\n return 1.0 * summation(numbers)/len(numbers)","title":""},{"docid":"74310ea3eeb998929bfc072a04d743f5","score":"0.59986997","text":"def mean(self, values, axis=0):\n values = np.asarray(values)\n if axis: values = np.rollaxis(values, axis)\n count = self.count.reshape(-1,*(1,)*(values.ndim-1))\n return self.unique, self.reduce(values) / count","title":""},{"docid":"32275121eb7f909a17d9db009af0f566","score":"0.5996093","text":"def average(values):\n return sum(values, 0.0) / len(values)","title":""},{"docid":"32275121eb7f909a17d9db009af0f566","score":"0.5996093","text":"def average(values):\n return sum(values, 0.0) / len(values)","title":""}],"string":"[\n {\n \"docid\": \"50290dcda63e53e00a4ec5bc68d2e814\",\n \"score\": \"0.71021867\",\n \"text\": \"def get_ensemble_average(self, outputs):\\n predictions = [output for output in outputs]\\n predictions = torch.stack(predictions, dim=1)\\n average_prediction = predictions.mean(1)\\n\\n return average_prediction\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb33ee0aa954a88e7e4180831e1f65fd\",\n \"score\": \"0.6930079\",\n \"text\": \"def mean(self):\\n vec = self.data\\n numrows = len(vec) \\n numcols = len(vec[0]) \\n m_vec = []\\n sum = 0.0;\\n for i in range(0,numrows):\\n for j in range(0,numcols):\\n sum += vec[i][j]\\n m_vec = sum/(numrows*numcols)\\n return m_vec\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acdbce169e103ad3293f6311102cfc75\",\n \"score\": \"0.67743635\",\n \"text\": \"def mean(self) -> chex.Array:\\n return jnp.sum(self.probs_parameter() * self._values, axis=-1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a4e9bda4f5c6d97dfd185649ca92ee1\",\n \"score\": \"0.6698942\",\n \"text\": \"def mean(self):\\n return np.mean(self.facepicsrep, axis=0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71c1a0c2ceb04cdcd30bfbe947b40f1d\",\n \"score\": \"0.6663481\",\n \"text\": \"def mean(self) -> pa.FloatingPointArray:\\n return ListChunk.reduce(self, pc.mean, pa.float64())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d41a60d3c94c4c0724e536686d2fea37\",\n \"score\": \"0.66515636\",\n \"text\": \"def average(x, nbins):\\n n = len(x); \\n nm = n/nbins * nbins;\\n x_mean = x[:nm];\\n x_mean = x_mean.reshape([nm/nbins, nbins]);\\n x_mean = np.nanmean(x_mean, axis = 1);\\n x_mean = np.hstack([x_mean, [np.nanmean(x[nm:])]]);\\n return x_mean;\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9e18a46a24a31777bec5f0c02910aee\",\n \"score\": \"0.66183436\",\n \"text\": \"def calculate_mean(num_array):\\n mean = 0\\n for elem in num_array:\\n mean += elem\\n\\n return mean / len(num_array)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb4b1d55949d7ed98bc0d4f9a0d00e45\",\n \"score\": \"0.6616439\",\n \"text\": \"def mean(X) :\\r\\n\\r\\n return np.sum(X, axis = 0) / X.shape[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4671b0711227660db1afd3802b066bf\",\n \"score\": \"0.6594574\",\n \"text\": \"def compute_mean(data):\\n\\n\\tm = np.mean(data,axis=0)\\n\\treturn m\",\n \"title\": \"\"\n },\n {\n \"docid\": \"977fecd74e8156325666a091153fb66a\",\n \"score\": \"0.657964\",\n \"text\": \"def _ave(self):\\n\\n return np.mean(self.full_estim.X, axis=0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17d129ddc9498494a840092a843293a8\",\n \"score\": \"0.6578661\",\n \"text\": \"def mean(self):\\n return self.apply(lambda x: np.mean(x, axis=0))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c955f14959e30100cae106a81828594\",\n \"score\": \"0.6569316\",\n \"text\": \"def __mean(self):\\n for elm, elm_wls in self.data.elements.items():\\n count = len(elm_wls)\\n total_x_var = 0\\n total_1_var = 0\\n for wl_label in elm_wls:\\n total_x_var += self.results[wl_label][2] \\\\\\n / self.results[wl_label][3]**2\\n total_1_var += 1 / self.results[wl_label][3]**2\\n self.means[elm] = (total_x_var / total_1_var,\\n np.sqrt(count / total_1_var))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fe9f7aea58c817d251fbe479e422fd0\",\n \"score\": \"0.65499353\",\n \"text\": \"def _emprical_mean(self,x_value,mb):\\n value = 0\\n sum_mean = sum([x_value[k] for k in range(1, mb)])\\n return sum_mean/mb\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e4dc30cdbef0ec0dd08472793d9d505\",\n \"score\": \"0.6549698\",\n \"text\": \"def mean(container):\\n avg = 0\\n \\n for i in container:\\n avg+=i/float(len(container))\\n return(avg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02f179d041b3b0cec8d1071124ae4fb1\",\n \"score\": \"0.6522487\",\n \"text\": \"def mean(self):\\n return self._calculate_stats(np.mean)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de1c49452d034d4175259b4af808531c\",\n \"score\": \"0.6516505\",\n \"text\": \"def manual_mean(arr):\\n sum = 0\\n for i in xrange(0, arr.shape[0]):\\n for j in xrange(0, arr.shape[1]):\\n sum += arr[i, j]\\n return sum / arr.size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58bbba4ac73aa076fb4543c90a09f81c\",\n \"score\": \"0.65153676\",\n \"text\": \"def multi_mean(data):\\n return np.mean(data, axis=0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa5722e560b65ebe6cd3067419efd73b\",\n \"score\": \"0.6515073\",\n \"text\": \"def mean(values):\\r\\n return sum(values) / len(values)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"729b9055921410cb095e73f0fe9c060e\",\n \"score\": \"0.65086097\",\n \"text\": \"def mean_accum(data):\\n result = np.zeros(data[0].shape, np.float64) # init output array\\n for dataslice in data:\\n # this for loop isn't such a bad thing cuz the massive add step inside the loop\\n # is the limiting factor\\n result += dataslice\\n result /= len(data)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"afd094e912a15a288e485a20d67cf39a\",\n \"score\": \"0.65070194\",\n \"text\": \"def mean(X):\\n # mean = np.sum(X, axis=0) / X.shape[0]\\n mean = np.mean(X, axis=0)\\n return mean\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d46e3473e6c7dfcc3696414eccb96d0d\",\n \"score\": \"0.6460672\",\n \"text\": \"def _mean_filter(eda):\\n mean_arr = np.array([])\\n for i in range(MEAN_KERNEL_WIDTH, len(eda) - MEAN_KERNEL_WIDTH):\\n mean = np.mean(eda[i - MEAN_KERNEL_WIDTH: i + MEAN_KERNEL_WIDTH + 1])\\n mean_arr = np.append(mean_arr, mean)\\n return mean_arr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69fb3ee387953a7b7b4062a3ecc2b0e4\",\n \"score\": \"0.645016\",\n \"text\": \"def ensemble_average():\\n fig, ax = plt.subplots(figsize=(6*0.7, 3.5*0.7))\\n\\n # Ensure graph is the same if needed\\n np.random.seed(1)\\n\\n npts = 200\\n navg = 20000\\n maxt = 10\\n\\n x = np.linspace(0, maxt, npts)\\n\\n # Ensemble averaging\\n y = np.random.random(npts) * 0.5*np.sin(np.pi/5*x) + \\\\\\n 0.5*np.random.random(npts) + 0.75\\n ax.plot(x, y, 'k', lw=1, label=r'$u_i(\\\\vec{x}, t)$')\\n\\n ymean = 0\\n for i in range(navg):\\n np.random.seed(i)\\n ymean += np.random.random(npts) * 0.5*np.sin(np.pi/5*x) + \\\\\\n 0.5*np.random.random(npts) + 0.75\\n ymean = ymean/navg\\n ax.plot(x, ymean, '--k', lw=1, label=r'$u_E(\\\\vec{x}, t)$')\\n\\n ax.set_ylim([0, 2])\\n ax.set_xlim([0, maxt])\\n ax.set_xlabel(r'$t$')\\n ax.set_ylabel(r'$u$')\\n\\n fig, ax = convert_to_graph(fig, ax)\\n plt.legend()\\n plt.savefig('ensemble_averaging.png')\\n plt.close(fig)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72fb105a127ec34efdae84337fff051e\",\n \"score\": \"0.6445197\",\n \"text\": \"def average(self):\\n return np.average(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"457e486da5a24897762e720c78ac55d3\",\n \"score\": \"0.6443342\",\n \"text\": \"def _ave(self):\\n\\n return np.mean(self.model_estim.X, axis=0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"457e486da5a24897762e720c78ac55d3\",\n \"score\": \"0.6443342\",\n \"text\": \"def _ave(self):\\n\\n return np.mean(self.model_estim.X, axis=0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cce6d95e891a91a0aa26a2c2e90d20fc\",\n \"score\": \"0.6434196\",\n \"text\": \"def intra_mean(self, values):\\n # TODO Check that quantity is valid\\n if values.ndim > 1:\\n return np.array([np.mean(values[x,:], axis=0) for x in self.allocations_])\\n else:\\n return np.array([np.mean(values[x]) for x in self.allocations_])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25fddd5d7e634105fc44552ae28e9521\",\n \"score\": \"0.6406734\",\n \"text\": \"def _genaverage(self, parray, **kwargs):\\n output = np.empty((0, 5))\\n for array in parray:\\n output = np.append(output, np.mean(array, axis=0).reshape((1, 5,)),\\n axis=0)\\n\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b454ffb63d60ee8248b1b6ff6401f062\",\n \"score\": \"0.6396374\",\n \"text\": \"def _ave(self):\\n return self.X.mean(axis=0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"372cd0068dba53320967f135b5cc612e\",\n \"score\": \"0.6390503\",\n \"text\": \"def array_mean(array):\\n \\n width = len(array[0])\\n return np.sum(array, axis = 1)/width\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0aebc878553a5f6c96142889e305ad27\",\n \"score\": \"0.6371311\",\n \"text\": \"def mean(self):\\n return self.results.mean()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"842175806b3596f834f63e3609a4316c\",\n \"score\": \"0.63698643\",\n \"text\": \"def X_means(self):\\n return np.average(self.arrX_input, axis=0).reshape(1,-1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"842175806b3596f834f63e3609a4316c\",\n \"score\": \"0.63698643\",\n \"text\": \"def X_means(self):\\n return np.average(self.arrX_input, axis=0).reshape(1,-1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd9b4e3a65610f4b01359c5f84e522cb\",\n \"score\": \"0.63667053\",\n \"text\": \"def mean(values):\\n return sum(values) / float(len(values))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e20e27b7aa6d8ead42fc1b3c4fb262a\",\n \"score\": \"0.6364998\",\n \"text\": \"def mean(self):\\n return np.average(self.state_vector, axis=1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0efbe98fd6b8c40ab2f3afd7f34575c\",\n \"score\": \"0.6343549\",\n \"text\": \"def _calc_mean(self):\\n return sum(self.data, 0.0) / len(self.data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fb87aa65e6db861bb9a911ad046dd56\",\n \"score\": \"0.6339938\",\n \"text\": \"def mean_vector(self):\\n return self._mean\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffcf6bed0b4fd44a42af0fe399ac80f4\",\n \"score\": \"0.63343066\",\n \"text\": \"def mean(self):\\n data = self.data.values()\\n sum = 0.\\n for item in data:\\n sum += float(item)\\n return sum/len(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94b1dacc77c588f33ce8f1f4d4bee328\",\n \"score\": \"0.63334805\",\n \"text\": \"def _calc_mean(self, chunk):\\n\\n # for sample in chunk:\\n # self.n += 1.0\\n # self.av = (self.n - 1) / self.n * self.av + sample / self.n\\n\\n # nx + sum(m) n sum(m)\\n # -------------- = x ------------ + ------------\\n # n + len(m) n + len(m) n + len(m)\\n \\n self.n += float(len(chunk))\\n self.av = (self.n - len(chunk)) / self.n * self.av + sum(chunk) / self.n\\n\\n return self.av\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8362935d5690f14dea59d63b2ed5fcc7\",\n \"score\": \"0.63212097\",\n \"text\": \"def mean_naive(X):\\n N, D = X.shape\\n mean = np.zeros(D)\\n for m in range(D):\\n k=0\\n for n in range(N):\\n k+=X[n,m]\\n smean=k/N\\n mean[m]=smean\\n return mean\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce774e034bd522f93539b62f0617d811\",\n \"score\": \"0.6318303\",\n \"text\": \"def Mean(self):\\n return self._sum_x / self._n\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f2a25b70bfbb1d3fa2fcd369700c8b8\",\n \"score\": \"0.63081944\",\n \"text\": \"def get_ensemble_variance(self):\\n #edit 08/08/2019: new function, virtually the same as get_ensemble_min, get_ensemble_max, and get_ensemble_mean but for variance of each ensemble.\\n\\t\\t\\n self.ensemble_var=np.zeros((np.size(self.data),np.shape(self.data)[0]))\\t\\t\\n for i in range(np.size(self.data)):\\n if i<2:\\n self.ensemble_var[i,:]=np.nan\\n else: \\n puff_numbers=self.get_ensembles(ensemble_size=i)\\t\\n self.ensemble_var[i,:]=np.matlib.repmat(self.data,np.shape(self.data)[0],1)[0,puff_numbers].var(axis=1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf4662fa5bfbd10d143a51dd779873aa\",\n \"score\": \"0.6300281\",\n \"text\": \"def mean_(X: ndarray) -> ndarray:\\n return mean(X, axis=0).reshape(1, X.shape[1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f2b8ee0c1ce4978c681b73688863a84\",\n \"score\": \"0.6296941\",\n \"text\": \"def get_mean(data):\\n mean = sum(data)/len(data)\\n return mean\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2e4934041268fd50894ba1f0f1f862a\",\n \"score\": \"0.6278936\",\n \"text\": \"def get_average(data):\\n \\n n_meas = len(data)\\n \\n if len(data.shape) != 1:\\n n_obs = len(data[0])\\n avg = np.zeros((n_obs))\\n for i in range(n_meas):\\n for j in range(n_obs):\\n avg[j] += data[i,j]\\n \\n else:\\n avg = 0.0\\n for i in range(n_meas):\\n avg += data[i]\\n \\n avg /= 1.0*n_meas\\n return avg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfc50aac7e5900bc88c6cd7faaad92cd\",\n \"score\": \"0.6268079\",\n \"text\": \"def calculate_mean(self): \\r\\n\\r\\n return self.n * self.p\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffba68cbecec76c419a5de7d72945d8f\",\n \"score\": \"0.6266025\",\n \"text\": \"def find_mean(array):\\n mean = np.mean(array,axis=0)\\n return mean\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6acfedf6d551d33dd203d91fd0116fc\",\n \"score\": \"0.62597954\",\n \"text\": \"def calculate_mean(self):\\n self.mean = st.mean(self.data)\\n return self.mean\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60d0c8e24ee6350dfdd03df2c809500a\",\n \"score\": \"0.6253372\",\n \"text\": \"def calc_mean(values):\\n values_sum = 0\\n for value in values:\\n values_sum += value\\n return values_sum / len(values)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4de2021e018a4266b60a8c8b5fc2dfd1\",\n \"score\": \"0.6252569\",\n \"text\": \"def mean(data): \\n return pd.Series(data).mean()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4188792dc15c6b377ab2026f69bf0e3\",\n \"score\": \"0.62512314\",\n \"text\": \"def gmean(self):\\n return self.apply(lambda x: gmean(x, axis=0))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9685be676ca6c7aba40c5e5043248fe\",\n \"score\": \"0.62506455\",\n \"text\": \"def approx_mean(self):\\n\\n for i in range(self.n):\\n self.means[self.y[i]:self.y[i] + 1] += self.X[i:i + 1]\\n\\n for k in range(self.k):\\n self.means[k] = np.multiply((1 / self.counts[k]), self.means[k])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b94b540dab95bbe0e384d1b3a6403da\",\n \"score\": \"0.62366754\",\n \"text\": \"def mean(self, x):\\n return np.mean(x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"318bca156854da8891ab573ec3fc4e64\",\n \"score\": \"0.62319255\",\n \"text\": \"def find_average(feature_list):\\n avg_feature_list = [np.mean(np.array(feature_list[i])) for i in range(6)]\\n \\n return avg_feature_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"304a8ed2c9ef58ed766505371508f42a\",\n \"score\": \"0.6229052\",\n \"text\": \"def mean(x):\\n return sum(x) / len(x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43a8ffd0f20993286fe450cbad661f66\",\n \"score\": \"0.6220122\",\n \"text\": \"def _getmean(self):\\n b = [self.coefs[term] for term in self.terms]\\n return np.sum(np.array(b)*self.terms)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab4acd971c491cbdb5b056e8408b14be\",\n \"score\": \"0.6219637\",\n \"text\": \"def mean(self, values):\\n return sum(values) / len(values)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d82668af57077e65156dfac86ab4228f\",\n \"score\": \"0.6206405\",\n \"text\": \"def mean(datasets):\\n return [np.mean(x, axis=0) for x in datasets]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06493b962bede5a55c76ed176e7cb943\",\n \"score\": \"0.6204836\",\n \"text\": \"def average(self):\\n return self._data[\\\"sum\\\"]/self._data[\\\"nValues\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0964800cf41c745a5ed8488cc17d42b\",\n \"score\": \"0.61913675\",\n \"text\": \"def mean(X):\\n mean = np.zeros(X.shape[1]) # EDIT THIS\\n mean = np.mean(X, axis=0) \\n return mean\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6cda23dadabff4916e0a7fcddaf6795\",\n \"score\": \"0.6184613\",\n \"text\": \"def get_mean(self, X):\\n\\n #handles both vectors and scalars\\n if len(X) > 0:\\n return [0.5] * len(X)\\n else:\\n return 0.5\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72d9a49c2aad009c939c1e34c37b984f\",\n \"score\": \"0.61800694\",\n \"text\": \"def mean(self):\\n return self.m\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd8553fcfe97646e18952a7dfe4b2d0c\",\n \"score\": \"0.6178019\",\n \"text\": \"def get_ensemble_variance(self):\\n #edit 08/08/2019: new function, virtually the same as get_ensemble_min, get_ensemble_max, and get_ensemble_mean but for variance of each ensemble.\\n\\t\\t\\n self.ensemble_std=np.zeros((np.size(self.data),np.shape(self.data)[0]))\\t\\t\\n for i in range(np.size(self.data)):\\n if i<2:\\n self.ensemble_std[i,:]=np.nan\\n else: \\n puff_numbers=self.get_ensembles(ensemble_size=i)\\t\\n self.ensemble_std[i,:]=np.matlib.repmat(self.data,np.shape(self.data)[0],1)[0,puff_numbers].std(axis=1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac290414ef04d0c5029f0fe629ffb9cf\",\n \"score\": \"0.616326\",\n \"text\": \"def mean(self):\\n return self.value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b98ec184148cbfe91e697c1827469ee\",\n \"score\": \"0.61389476\",\n \"text\": \"def mean(self):\\n return np.mean(self.space_array, axis=0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e6ef76d71d681745eb51dfb5ab4560c\",\n \"score\": \"0.6127086\",\n \"text\": \"def mean(X):\\n # given a dataset of size (D, N), the mean should be an array of size (D,1)\\n # you can use np.mean, but pay close attention to the\\n # shape of the mean vector you are returning.\\n D, N = X.shape\\n # Edit the code to compute a (D,1) array `mean` for the mean of dataset.\\n mean = X.mean(axis=1).reshape(D, 1)\\n return mean\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5d6aa72bc675001a51c11d4742dccaa\",\n \"score\": \"0.61259\",\n \"text\": \"def mean(self):\\n total = 0.0\\n for bin in self:\\n total += bin.count\\n return total / len(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"169ef6060fca7202f5294284892771a5\",\n \"score\": \"0.61168236\",\n \"text\": \"def average(values):\\n return sum(values) / len(values)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"169ef6060fca7202f5294284892771a5\",\n \"score\": \"0.61168236\",\n \"text\": \"def average(values):\\n return sum(values) / len(values)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"450b63cec789fe650e01ac87b61f94ff\",\n \"score\": \"0.60981137\",\n \"text\": \"def getAverageAggregator(self):\\n return self._average\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3309eb7434c32e65cc0e40ab159c2bd\",\n \"score\": \"0.6097863\",\n \"text\": \"def calculate_mean(self):\\n return self.p * self.n\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95634e9921a71924ae80a9aee345a015\",\n \"score\": \"0.60886467\",\n \"text\": \"def mean(input, dim):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3372df6bd10e495cde44dac7fa82f0aa\",\n \"score\": \"0.6084342\",\n \"text\": \"def reduce_mean(input):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ff4e0e37a0f46f7a915997bd6b1959e\",\n \"score\": \"0.6083269\",\n \"text\": \"def mean(self,*,axis=1):\\n try:\\n means = np.nanmean(self.data, axis=axis).squeeze()\\n if means.size == 1:\\n return np.asscalar(means)\\n return means\\n except IndexError:\\n raise IndexError(\\\"Empty BinnedEventArray; cannot calculate mean.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e94e5a2246b4d5d628eb1cc8450b7bd\",\n \"score\": \"0.6075295\",\n \"text\": \"def running_mean(x, N):\\n out = np.zeros_like(x, dtype=np.float64)\\n dim_len = x.shape[0]\\n for i in range(dim_len):\\n if N%2 == 0:\\n a, b = i - (N-1)//2, i + (N-1)//2 + 2\\n else:\\n a, b = i - (N-1)//2, i + (N-1)//2 + 1\\n\\n #cap indices to min and max indices\\n a = max(0, a)\\n b = min(dim_len, b)\\n out[i] = np.mean(x[a:b])\\n return out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1ec77fef07d85e916e2239912b32661\",\n \"score\": \"0.60750586\",\n \"text\": \"def mean(self):\\n data = self.get_data(None)\\n if data is not None:\\n return data.mean(squeeze=True)\\n\\n raise ValueError(\\n \\\"ERROR: Can't get the mean when there is no data array\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9afc4876748aaf5319ce13e997a150a6\",\n \"score\": \"0.6072623\",\n \"text\": \"def grand_mean(x):\\r\\n return np.mean(x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40f6abf5871b408ca94d04ba009a734d\",\n \"score\": \"0.6066953\",\n \"text\": \"def meanAge(self):\\n data = self.data\\n ds = data.groupby(['PARTICIPANT_ID']).agg('mean').reset_index()\\n ndr_age = ds.AvgAge.tolist()\\n return np.array(ndr_age).reshape(-1,1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7038629cbfc18db2ec95bf10702225c0\",\n \"score\": \"0.6066128\",\n \"text\": \"def avg_fn (data):\\n\\treturn np.mean(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"198f98a357ca69b088578613ea2092f4\",\n \"score\": \"0.605865\",\n \"text\": \"def get_mean(sampling_fun, N, M):\\n ans = []\\n for m in range(M):\\n ans.append(np.mean(sampling_fun(N)))\\n return np.asarray(ans)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"afd8538865ea38c80c958ec9766548ba\",\n \"score\": \"0.6057457\",\n \"text\": \"def mean(self):\\n return self.copy(coefficients=numpy.mean(self.coefficients, axis=0))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"366abb996c2a3ef360be8aac9ca1f184\",\n \"score\": \"0.6054785\",\n \"text\": \"def mean_pooling(img, scale):\\n h_, w_ = 0, 0\\n save_mean = []\\n for h in range(10, 110, scale):\\n h_ = h_ + 1\\n for w in range(10, 189, scale):\\n save_mean.append(img[h - 10: h, w - 10: w].mean())\\n w_ = len(save_mean) // h_\\n save_mean = np.asarray(save_mean).reshape(h_, w_)\\n return save_mean\",\n \"title\": \"\"\n },\n {\n \"docid\": \"473d1aa731f91bdcf3067f4f80126c7f\",\n \"score\": \"0.60534716\",\n \"text\": \"def mean(inp_list):\\n return np.mean(inp_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9533f63a83a4599134664d8460e515ec\",\n \"score\": \"0.6048591\",\n \"text\": \"def avg(self):\\n return sum(range(1,self.num+1))/self.num\",\n \"title\": \"\"\n },\n {\n \"docid\": \"083c4edec51bf0509599a23ba0e65ec4\",\n \"score\": \"0.604489\",\n \"text\": \"def mean_face(faces):\\n mean_face = mean(faces)\\n return mean_face\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd9c634216bdb2c3c94e43ec383c5350\",\n \"score\": \"0.6044063\",\n \"text\": \"def _mean(listvalue):\\n return sum(listvalue) / len(listvalue)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d5c3660ffcb7d8ccb59f81e1c0f6757\",\n \"score\": \"0.6042098\",\n \"text\": \"def mean(self):\\n if len(self) == 1: # No need to calculate mean\\n return self.state_vector\\n return np.average(self.state_vector, axis=1, weights=np.exp(self.log_weight))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5119f6654fe1245ffd96889f32389c56\",\n \"score\": \"0.60396236\",\n \"text\": \"def arithmetic_mean(array, axis=0):\\n import numpy as np\\n \\n return np.mean(array, axis=axis)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"baf359db4df5958ea0e021860786ddcf\",\n \"score\": \"0.60371935\",\n \"text\": \"def _mean(x, mode=\\\"arithmetic\\\"):\\r\\n assert mode in [\\\"arithmetic\\\", \\\"geometric\\\"]\\r\\n if mode == \\\"arithmetic\\\":\\r\\n x_mean = x.mean(axis=1)\\r\\n else:\\r\\n x_mean = np.exp(np.log(x + 1e-7).mean(axis=1))\\r\\n x_mean = x_mean / x_mean.sum(axis=1, keepdims=True)\\r\\n return x_mean\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcb287891fe196ff64a90390b55c5b31\",\n \"score\": \"0.6029203\",\n \"text\": \"def get_mean(data):\\r\\n if not data:\\r\\n raise Exception(\\\"Empty data.\\\")\\r\\n sum_x = sum(data)\\r\\n mean = float(sum_x) / len(data)\\r\\n return mean\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3adf1d3982acadc7cc1389cb85931105\",\n \"score\": \"0.6026552\",\n \"text\": \"def mean(self) -> Tensor:\\n return self.values\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00baf3f8c60b1b0ea0688869f1d2af54\",\n \"score\": \"0.60240227\",\n \"text\": \"def mean(self):\\n return self._mean\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00baf3f8c60b1b0ea0688869f1d2af54\",\n \"score\": \"0.60240227\",\n \"text\": \"def mean(self):\\n return self._mean\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43ffd54d71d8812bf00f0eafb286e3c8\",\n \"score\": \"0.60188514\",\n \"text\": \"def mean(self):\\n return self._impl.stats(self).mean()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12ee1ffd1900504a7e43ed83bae9ba40\",\n \"score\": \"0.6016554\",\n \"text\": \"def mean(self):\\n return self.concentration / self.concentration.sum(-1, keepdim=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee2966ef3a8081d3fa00a827acff5e4b\",\n \"score\": \"0.6014511\",\n \"text\": \"def meanColor(self):\\n return np.array([f.meanColor() for f in self])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa631caf7d48781222ba181383cb5d36\",\n \"score\": \"0.6010814\",\n \"text\": \"def get_mean(a, axis=None):\\n return np.mean(a, axis=axis)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2979dccbb7f3249a966968060d49cc0\",\n \"score\": \"0.60059345\",\n \"text\": \"def mean(numbers):\\n return 1.0 * summation(numbers)/len(numbers)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74310ea3eeb998929bfc072a04d743f5\",\n \"score\": \"0.59986997\",\n \"text\": \"def mean(self, values, axis=0):\\n values = np.asarray(values)\\n if axis: values = np.rollaxis(values, axis)\\n count = self.count.reshape(-1,*(1,)*(values.ndim-1))\\n return self.unique, self.reduce(values) / count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32275121eb7f909a17d9db009af0f566\",\n \"score\": \"0.5996093\",\n \"text\": \"def average(values):\\n return sum(values, 0.0) / len(values)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32275121eb7f909a17d9db009af0f566\",\n \"score\": \"0.5996093\",\n \"text\": \"def average(values):\\n return sum(values, 0.0) / len(values)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":898,"cells":{"query_id":{"kind":"string","value":"b457f85545bdbe626233021296bda052"},"query":{"kind":"string","value":"Inserts current datetime into self.text_field from menu"},"positive_passages":{"kind":"list like","value":[{"docid":"95ccaab5628f955541d65e71bfae1ca1","score":"0.78224796","text":"def do_time_date(self) -> None:\n text = datetime.datetime.now().isoformat()\n self.text_field.buffer.insert_text(text)","title":""}],"string":"[\n {\n \"docid\": \"95ccaab5628f955541d65e71bfae1ca1\",\n \"score\": \"0.78224796\",\n \"text\": \"def do_time_date(self) -> None:\\n text = datetime.datetime.now().isoformat()\\n self.text_field.buffer.insert_text(text)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"504bed008c61e56657817a7d85fd8522","score":"0.68654907","text":"def OnInsertDate(self, event):\n self.AddText(str(time.ctime()))\n event.Skip()","title":""},{"docid":"41bb243f0f260b6a653da25124d49ba4","score":"0.66584486","text":"def changeText(self):\n text = datetime.now().strftime(\"%Y/%m/%d %H:%M:%S\")\n self.setText(text)","title":""},{"docid":"34bbafb9222c3c3dc47ac7a1336383e2","score":"0.6424974","text":"def update_time(self):\n now = time.localtime()\n hour = now[3]\n minute = now[4]\n year = now[0]\n month = now[1]\n day = now[2]\n time_format_str = \"%d:%02d\"\n date_format_str = \"%d-%02d-%02d\"\n if self.am_pm:\n if hour >= 12:\n hour -= 12\n time_format_str = time_format_str+\" PM\"\n else:\n time_format_str = time_format_str+\" AM\"\n if hour == 0:\n hour = 12\n time_str = time_format_str % (hour, minute)\n print(time_str)\n date_str = date_format_str % (year, month, day)\n self.date_text.text = date_str\n self.time_text.text = time_str","title":""},{"docid":"459c4b8da60de833cd8628cbe5eb6a13","score":"0.625469","text":"def menu_time(self, time=None):\r\n if (time == None):\r\n time = float(self.widgets['update_time'].get())\r\n else:\r\n self.widgets['update_time'].setentry(str(time))\r\n self.update_time = time\r\n if (self.after_id != None): self.top.after_cancel(self.after_id)\r\n self.timer()","title":""},{"docid":"614f5b59c36d76d550255642537bd82a","score":"0.6088324","text":"def update_value(self, inst):\n\n self.text = \"%s.%s.%s\" % tuple(self.cal.active_date)\n self.dispatch('on_confirm', self.text)","title":""},{"docid":"5a10b97e8dd6999b11e6e38a03d3bbb5","score":"0.60697716","text":"def show_time_and_date(self, t=None):\n\n current_time = rtc.get_time()\n current_date = rtc.get_date()\n\n lcd.text(lcd.CENTER, 20, \"%02d:%02d:%02d\" % current_time, color=lcd.WHITE)\n\n if current_date[2] != self.date:\n lcd.font(\"data/fonts/arial16.fon\")\n\n lcd.textClear(lcd.CENTER, 55, \"00-00-00\")\n lcd.text(lcd.CENTER, 55, \"%02d-%02d-%02d\" % (current_date[2] + 1, current_date[1] + 1, current_date[0]), color=lcd.WHITE)\n\n lcd.font(\"data/fonts/ariblk28.fon\") # Restore Time font.\n\n self.date = current_date[2]","title":""},{"docid":"98f35949e0ec54edf07bc790584e22fd","score":"0.6013537","text":"def get_date(self, widget):\n pass","title":""},{"docid":"bf1f09fa27bfb2d5fc12959a6c3614b8","score":"0.5967441","text":"def init_date(self):\n now = datetime.datetime.now()\n day = now.day\n month = now.month\n year = now.year\n today = QtCore.QDate(year, month, day)\n self.master_date_value = today\n self.ui.master_date.setDate(today)","title":""},{"docid":"7ba7eb6e064970c9f4f34b7cce4ef631","score":"0.58008516","text":"def change_date(self, widget, date):\n pass","title":""},{"docid":"aee839aad9bd10cee3f9d93a88cbccef","score":"0.5800275","text":"def get_date(self):\n\n self.date = datetime.now().strftime(\"%Y-%m-%d_%H:%M:%S\")","title":""},{"docid":"4c2025be5152d677bbba37afb1428385","score":"0.5787586","text":"def updtTime(self):\n currentTime = QDateTime.currentDateTime().toString('hh:mm:ss')\n self.myTimeDisplay.display(currentTime)","title":""},{"docid":"5dca56a9ec99896aa382fa7f823e55a2","score":"0.57127273","text":"def set_service_level_begin_date(self):\n self.set_value_into_input_field(self.begin_date_inputbox_locator, self.get_date(current_date=True))","title":""},{"docid":"1c76f605a0413729efd27caf8d12b1d0","score":"0.5697221","text":"def set_datetime_field(self, num, loading = False):\n self.editor.paramSizer.Clear(True)\n if num == 1:\n self.editor.paramWidget = DateTimeCtrl( parent = self.editor.parent, width = 450, \\\n update_state = self.whereController.change_made, \\\n condition = self.condition, isLoading = loading)\n else:\n self.editor.paramWidget = DateBetweenValue(self.editor.parent, width = 450, update_state = self.whereController.change_made,\\\n condition = self.condition, typeDetails = self.typeDetails, isLoading = loading)","title":""},{"docid":"4783ab693cac81391d16221a6fed6ae6","score":"0.5694268","text":"def add_time(self):\n calendar_time = str(self.dockwidget.calendar.selectedDate().toPyDate())\n\n if self.active_time == 'time0' and (self.dockwidget.exactDate.isChecked() or not self.time1 or\n calendar_time <= self.time1):\n self.time0 = calendar_time\n self.dockwidget.time0.setText(calendar_time)\n elif self.active_time == 'time1' and (not self.time0 or self.time0 <= calendar_time):\n self.time1 = calendar_time\n self.dockwidget.time1.setText(calendar_time)\n else:\n self.show_message('Start date must not be larger than end date', Message.INFO)","title":""},{"docid":"6993b86355515d35c9b3c461323e07f3","score":"0.5678011","text":"def _update(self):\n time = QTime.currentTime().toString()\n self.display(time)","title":""},{"docid":"080d4700b2294b1e88a413c45e2027b2","score":"0.56624186","text":"def set_now_str(self, is_start_time):\n pass","title":""},{"docid":"b855fe350b346887468d7e6b360c78cb","score":"0.56537503","text":"def clicked(self):\n\n # creates string for time display\n timer = strftime('Day: %d %b %Y\\nTime: %H:%M:%S %p\\n', localtime())\n\n # display time in a pop-up window\n showinfo(message=timer)","title":""},{"docid":"8f75dcaabc86df4a25e0206e089e6f1f","score":"0.5652776","text":"def arrive(self):\n\n def callback(*args):\n \"\"\"Callback to update start_date of the destination\"\"\"\n if args[1] is None:\n return # cancel\n\n try:\n start_date = iso_date(args[1])\n except:\n Alert(title='Arrive Date', text=\"Please input a date in yyyy-mm-dd format\")\n return\n\n self._update(start_date=iso_date_string(start_date))\n\n InputPopup(title='Arrive Date',\n text='When did you arrive {}?'.format(self.item.name),\n value=self.item.start_date,\n initial=iso_date_string(today()),\n on_value=callback)","title":""},{"docid":"e3804309414bdad9022ddaa54ad4cfaa","score":"0.5650339","text":"def set_now(self):\n pass","title":""},{"docid":"8916c9db2835f80084b9d4db9bd6d910","score":"0.5643638","text":"def get_current_date_time(self, choice):\n\n if choice in ('1', '2', '3'):\n\n # Get current date and time.\n now = datetime.now()\n # Get the time stamp.\n timestamp = datetime.timestamp(now)\n\n if choice == '1':\n # Display current date and time.\n date_time = now.strftime(\"%m/%d/%Y, %H:%M:%S\")\n print(\"current date and time =\",date_time)\n elif choice == '2':\n # Dispay the time stamp.\n print(\"time stamp =\", timestamp)\n elif choice == '3':\n # Get current date and time from the time stamp.\n # and display the result.\n dt_object = datetime.fromtimestamp(timestamp)\n print(\"(dt_object - date and time from time stamp =\", dt_object)\n print(\"type(dt_object) =\", type(dt_object))\n else:\n print(\"invalid choice\")","title":""},{"docid":"4005b68441fc23d03b38e680ca033edf","score":"0.55390495","text":"def timestamp_changed(self):\n self.text_settings.timestamps = self.CBTime.isChecked()","title":""},{"docid":"daa525cac4f1f64651f57ad640012b78","score":"0.5507961","text":"def set_current_date(self, date):\r\n self._current_date = date","title":""},{"docid":"3339be49ee8bc8c4aaf44ef2d46b798b","score":"0.5499776","text":"def get_add_new_transaction_date(self):\n return self.get_text_from_element(self.transaction_date_locator, is_a_input_field=True)","title":""},{"docid":"7aaea09cb0911bf113b23bf1afbf2e99","score":"0.54724926","text":"def set_current_date(self, input_date=date.today()):\n if not isinstance(input_date, date):\n raise ValueError(\"input date must be of type datetime.date\")\n td = timedelta(days=0)\n self.current_date = input_date + td","title":""},{"docid":"b44f90f7bfc8313a01cd72a202580762","score":"0.5467083","text":"def current_date():\n dt_string = datetime.now().strftime(\"%A, %B\")\n date_ = engine().ordinal(datetime.now().strftime(\"%d\"))\n year = datetime.now().strftime(\"%Y\")\n if time_travel.has_been_called:\n dt_string = dt_string + date_\n else:\n dt_string = dt_string + date_ + ', ' + year\n speaker.say(f\"It's {dt_string}\")\n if event and event == 'Birthday':\n speaker.say(f\"It's also your {event} sir!\")\n elif event:\n speaker.say(f\"It's also {event} sir!\")\n if report.has_been_called or time_travel.has_been_called:\n speaker.say(f'The current time is, ')","title":""},{"docid":"98405e14b178d0700fc258ee6ec4108f","score":"0.54468","text":"def __init__(self, *args, **kwargs):\n super(InlineReminderForm, self).__init__(*args, **kwargs)\n if self.instance.sent_time is not None:\n self.fields['scheduled_time'].disabled = True\n sent_time = self.instance.sent_time.strftime(DATETIME_FORMAT)\n self.fields['scheduled_time'].help_text = 'Sent %s' % sent_time","title":""},{"docid":"ae40379c47333c21b9fcfed20b36dd52","score":"0.54321253","text":"def update_time(screen):\n _, width = screen.getmaxyx()\n time_str = pscheduler.datetime_as_iso8601(pscheduler.time_now())\n if len(time_str) > width:\n time_str = \"%s%s\" % (time_str[:width-1], \"+\")\n if width > 2:\n screen.addstr(0, 0, time_str, color_pair(\"default\"))\n screen.refresh()","title":""},{"docid":"fc51f3defbba433e9a2534cb94eb0191","score":"0.5424865","text":"def timeDisplay(self):\n\n print(datetime.datetime.now)","title":""},{"docid":"dafc41b2f6c3bcc5d0fe41fdfba44537","score":"0.54218936","text":"def update_dates(self):\n new_time0 = self.parse_date(self.dockwidget.time0.text())\n new_time1 = self.parse_date(self.dockwidget.time1.text())\n\n if new_time0 is None or new_time1 is None:\n self.show_message('Please insert a valid date in format YYYY-MM-DD', Message.INFO)\n elif new_time0 and new_time1 and new_time0 > new_time1 and not self.dockwidget.exactDate.isChecked():\n self.show_message('Start date must not be larger than end date', Message.INFO)\n else:\n self.time0 = new_time0\n self.time1 = new_time1\n Settings.parameters['time'] = self.get_time()\n\n self.dockwidget.time0.setText(self.time0)\n self.dockwidget.time1.setText(self.time1)","title":""},{"docid":"667be556a5c2c6f9cf662d5f225ea5a4","score":"0.5418961","text":"def trigger_setTSNow(self, record, fldname,**kwargs):\n if not getattr(record, '_notUserChange', None):\n record[fldname] = datetime.datetime.today()","title":""},{"docid":"5f00d4644e738282a2bde1e79551fff7","score":"0.5416571","text":"def __init__(self, item_date, item_time, item_name, item_details):\n\n from datetime import datetime\n now = datetime.now()\n\n # item_date = date of today\n self.item_date = now.year + now.month + now.day\n self.item_time = now.hour\n\n print(\"Schedule of \" + item_date )","title":""},{"docid":"5483f432eec3f8582963ab02dbf190f9","score":"0.54099405","text":"def get_current_date(self): \n return strftime(\"%Y-%m-%d\", time.gmtime())","title":""},{"docid":"bebb17ff1eb01d9aff1e585d6e77ff9b","score":"0.54091305","text":"def set_new_date(self):\n min = (time.daylight and time.altzone or time.timezone) // 60\n hr = min // 60\n min = -(min % 60 + hr * 100)\n self.new_date = (time.strftime('%a, %d %b %Y %X', self.release_date + (0, 0, 0))\n + ' %+.4d' % min)","title":""},{"docid":"7c102bb74da0b88aa9ed5f3b68b7e577","score":"0.5403179","text":"def date(self, control_def):\n if control_def.appearance == \"\":\n control_def.appearance = '{\"format\":\"dd-mm-yyyy\",\"viewmode\":\"days\",\"minviewmode\":\"days\", \"minviewmode\":\"years\"}'\n self.controls_js += '\\n dateControlCreate(\"' + control_def.control_id + '\",\"' + self.parent_div_id + '\",\"' + control_def.control_name + '\",\"' + control_def.control_label + '\",' + control_def.appearance + ', \"\" );'","title":""},{"docid":"a8c33bcd1df06b35a4a7da8fcf37a002","score":"0.536979","text":"def set_begin_date_for_add_new_rate(self):\n self.wait_for_ajax_spinner_load()\n begin_date = self.get_current_date()\n self.set_value_into_input_field(self.add_new_rate_begin_date_locator, begin_date)\n return begin_date","title":""},{"docid":"8948df95eb737c5896344ad0c790025e","score":"0.5356948","text":"def selected_date():\n day = parser.parse(wrf_init_date_input.value)\n hour = wrf_init_time_select.value\n hour = timedelta(hours=int(hour[:-1]))\n return day + hour","title":""},{"docid":"495ce7cd72c907ac9b3b4967d61dee53","score":"0.5320208","text":"def get_current_date(self):\r\n return self._current_date","title":""},{"docid":"d1c3a6e8b2c1c3b16472d01f8c0ad073","score":"0.5317576","text":"def set_time(self,time_now):\n self.start_time = time_now\n # changing the time label widget after 10 second.\n self._label_time.config(text = self.start_time)","title":""},{"docid":"a366a0569c90e3254bde33c382fb6be8","score":"0.5313397","text":"def update_label():\n current_time = strftime(\"%H: %M: %S\\n %d-%m-%Y \")\n clock_label.configure(text=current_time)\n clock_label.after(80, update_label)\n clock_label.pack(anchor=\"center\")","title":""},{"docid":"1f3a3f4d4645abc9b48800370ab97ef9","score":"0.52927345","text":"def set_begin_date_for_add_new_rate_plan(self, begin_date):\n begin_date_textbox_element = self.wait().until(EC.presence_of_element_located(self.begin_date_textbox_locator), 'begin date textbox locator not found before specified time out')\n begin_date_textbox_element.clear()\n if begin_date == \"\":\n begin_date = self.get_current_date()\n begin_date_textbox_element.send_keys(begin_date)\n global current_begin_date\n current_begin_date = datetime.datetime.strptime(begin_date, \"%m/%d/%Y\")","title":""},{"docid":"8aa046ffb665291a70d96c3513ef44b6","score":"0.5290201","text":"def set_time_field(self, num, loading = False):\n self.editor.paramSizer.Clear(True)\n if num == 1:\n self.editor.paramWidget = TimeCtrl( parent = self.editor.parent, width = 450, \\\n update_state = self.whereController.change_made, \\\n condition = self.condition, isLoading = loading)\n else:\n self.editor.paramWidget = DateBetweenValue(self.editor.parent, width = 450, update_state = self.whereController.change_made,\\\n condition = self.condition, typeDetails = self.typeDetails, isLoading = loading)","title":""},{"docid":"4f99ee9af6c334aa9f9a74b8aab26cb3","score":"0.52899706","text":"def change_exact_date(self):\n\n if self.dockwidget.exactDate.isChecked():\n self.dockwidget.time1.hide()\n self.dockwidget.timeLabel.hide()\n self.move_calendar('time0')\n self.add_time()\n else:\n if self.time0 and self.time1 and self.time0 > self.time1:\n self.time1 = ''\n Settings.parameters['time'] = self.get_time()\n self.dockwidget.time1.setText(self.time1)\n\n self.dockwidget.time1.show()\n self.dockwidget.timeLabel.show()","title":""},{"docid":"0d254e3bb996c42fc379c51965d42a8e","score":"0.5271335","text":"def setPublicationDate(datetime):","title":""},{"docid":"34ab5eea380c06ca8f05b84f5eb34932","score":"0.5268397","text":"def update_datetime_text(\n node,\n node_tree,\n frame,\n time_text=\"\",\n decode=False,\n):\n if not time_text:\n time = str(bnc_gutils.get_time(node, frame))[:10]\n else:\n time = time_text\n # TODO allow user to define format.\n\n if \"Camera\" in bpy.data.objects.keys() and time:\n Camera = bpy.data.objects.get(\"Camera\")\n size = 0.03\n coords = (-0.35, 0.17, -1)\n children_name = [children.name for children in Camera.children]\n if \"BlenderNC_time\" not in children_name:\n bpy.ops.object.text_add(radius=size)\n text = bpy.context.object\n text.name = \"BlenderNC_time\"\n text.parent = Camera\n text.location = coords\n mat = bnc_pyfunc.ui_material()\n text.data.materials.append(mat)\n else:\n children = Camera.children\n text = [c for c in children if c.name == \"BlenderNC_time\"][-1]\n text.data.body = time\n if text.select_get():\n text.select_set(False)","title":""},{"docid":"61376a29067fe91b0af8a1af90f756c1","score":"0.5268081","text":"def currentDate():\n return(datetime.now())","title":""},{"docid":"345e26d4b83efbdef69fbbc42eac4881","score":"0.5267875","text":"def date():\n year = int(datetime.datetime.now().year)#obtener año\n month = int(datetime.datetime.now().month)#obtener mes\n day = int(datetime.datetime.now().day)#obtener dia\n speech.speak(\"The current date is\")\n speech.speak(day)#decir dia\n speech.speak(month)#decir mes\n speech.speak(year)#decir año","title":""},{"docid":"33b6b2816551d6f892b83cf77453719a","score":"0.52652377","text":"def isoDate():\n time.sleep(0.1) # wait a little in order to be sure that user not pressing a hotkey anymore\n output = datetime.datetime.now().strftime(\"%Y-%m-%d\")\n storedClipboard = ClipboardUtil.pasteFromClipboardXsel()\n sendText(output)\n ClipboardUtil.copyToClipboardXclip(storedClipboard)","title":""},{"docid":"86f82c0afd14b03583b7ada5e24f471c","score":"0.52589375","text":"def set_date(self):\n self.date = datetime.fromisoformat(self.time[:-1])","title":""},{"docid":"2aa451c71859736b9ebf61f260ddcb18","score":"0.5250717","text":"def __init__(self):\n super().__init__()\n self.control = libui.uiNewDateTimePicker()","title":""},{"docid":"e4c671a0b4e6875683906dcfefb52db7","score":"0.5240346","text":"def _current_datetime_string(self):\n datetime_object = datetime.datetime.now()\n datetime_string = datetime_object.strftime(self.DATETIME_FORMAT)\n\n return datetime_string","title":""},{"docid":"01ead656edf74760c9011169e782e1af","score":"0.52229536","text":"def set_date(self, date):\n self.datetime = date","title":""},{"docid":"f35f0e5523bec52fe604aeecdedcbd78","score":"0.52226985","text":"def current_date():\n date = datetime.datetime.now()\n return f'{date:%Y-%m-%d}'","title":""},{"docid":"599e8da9d20062e134ba1bb67b5bd50c","score":"0.52187943","text":"def add_new_computer_introduce_date(self):\n self.driver.find_element_by_id('introduced').send_keys(self.introduced)","title":""},{"docid":"ca05ec6dd72b3e34499a859a45d4efe2","score":"0.52115065","text":"def get_now_datetime(self):\n return datetime.now().strftime(\"%a %I:%M %p\")","title":""},{"docid":"bbc3b1d1ae4a68d47b987de04d1e2fde","score":"0.52006274","text":"def editTime(self, datetime=False):\n if self._editTime and datetime:\n import datetime\n return datetime.datetime.strptime(str(self._editTime), '%Y%m%d%H%M%S')\n\n return self._editTime","title":""},{"docid":"7a15205df637e6ff8ebc944480aa3cfe","score":"0.5197612","text":"def get_now(self):\n pass","title":""},{"docid":"87e829c6e004e25f9fedb75c91970aa6","score":"0.5184889","text":"def registration_datetime_text(course, date):\n\n strftime = course.runtime.service(course, \"i18n\").strftime\n date_time = strftime(date, \"SHORT_DATE\")\n return date_time","title":""},{"docid":"494377c7c79d50b784ef6481f9cd0c4f","score":"0.51836145","text":"def _current_date_string(self):\n datetime_object = datetime.datetime.now()\n datetime_string = datetime_object.strftime(self.DATE_FORMAT)\n\n return datetime_string","title":""},{"docid":"2cc2acdca56dc78448e923b1c9b68284","score":"0.5178246","text":"def set_begin_date_for_add_number_plan(self, begin_date):\n if begin_date == \"\":\n begin_date = datetime.date.today()\n begin_date = \"%d/%d/%d\" % (begin_date.month, begin_date.day, begin_date.year)\n self.set_value_into_input_field(self.begin_date_inputbox_locator, begin_date)","title":""},{"docid":"464381afc7e9e7071dffa0fab0c9e580","score":"0.51722026","text":"def datetime_now():\n return datetime.now()","title":""},{"docid":"f1f814833b1089c3baf412429d98d005","score":"0.5170177","text":"def set_date_field(self, num, loading = False):\n self.editor.paramSizer.Clear(True)\n if num == 1:\n self.editor.paramWidget = DateCtrl( parent = self.editor.parent, width = 450, \\\n update_state = self.whereController.change_made, \\\n condition = self.condition, isLoading = loading)\n else:\n self.editor.paramWidget = DateBetweenValue(self.editor.parent, width = 450, update_state = self.whereController.change_made,\\\n condition = self.condition, typeDetails = self.typeDetails, isLoading = loading)","title":""},{"docid":"f2d90c1f60397f7cdf9517c3db0fc73e","score":"0.51671505","text":"def change_date(self, widget, date):\n self.widget.setDate(date)","title":""},{"docid":"ccb995688a4c03aff03c6725dd412427","score":"0.5151949","text":"def default(self, event, entry, texto_insertado):\n self.informacion_entry = entry.get()\n if (self.informacion_entry == \"Numero de caja\" or self.informacion_entry == \"Nombre de usuario\" or self.informacion_entry == \"DD/M/AAAA\" or self.informacion_entry == \"Hora\"):\n entry.delete(0, tk.END)\n entry.config(fg=\"black\")\n\n elif (self.informacion_entry == \"\"):\n\n entry.insert(0, texto_insertado)\n entry.config(fg=\"grey\")","title":""},{"docid":"bc78c39dda9564dee1ed0690b6e58f1e","score":"0.5150202","text":"def set_yob_text_to_field(self, value):\n\n WebDriverWait(self.driver, 100).until(\n lambda driver: driver.find_element(*MainPageLocators.YOB_DATE_PICK))\n element = self.driver.find_element(*MainPageLocators.YOB_DATE_PICK)\n element.send_keys(value)","title":""},{"docid":"a6a97b7db7ed6e3ca467a2ed3a88ded1","score":"0.5147219","text":"def format_datetime(self, request, obj, fieldname, *args, **kwargs):\n model = make_model(obj)\n return html.div(style='min-width:130px;')(\n getattr(model, fieldname).strftime(\n app.config.get('ADMIN_DATE_FORMAT', '%Y-%m-%d')\n )\n )","title":""},{"docid":"0c8636bacf337f43f28f49838767cde4","score":"0.5139439","text":"def current_datetime():\n timeoffset = datetime.timedelta(hours=app.config['TIMEZONE'])\n return datetime.datetime.now() + timeoffset","title":""},{"docid":"79cd2ce8e200051af0a0f07caff6575f","score":"0.5136384","text":"async def _cmdf_time(self, substr, msg, privilege_level):\n await self._client.send_msg(msg, datetime.datetime.utcnow().strftime(\"My current system time: %c UTC\"))\n return","title":""},{"docid":"5dcfb77f2c2f47a33b80eff881617671","score":"0.5135393","text":"def current_date(request):\n dt = datetime.now() #Return the current local date and time.\n return HttpResponse(dt.strftime('Today is %d, %B %Y')) #Return a string representing the date and time\n # Today is 10, January 2018","title":""},{"docid":"1ea5cff10e5fac23b9ac67688571ec00","score":"0.5135189","text":"def test_get_modelform_datetimefield(self):\n form = get_modelform(model=Song, fields=[\"recording_time\"])\n self.assertIsInstance(\n form().fields[\"recording_time\"].widget, VegaDateTimeWidget\n )\n self.assertHTMLEqual(\n \"\"\"<p><label for=\"id_recording_time\">Recording Time:</label> <input type=\"datetime-local\" name=\"recording_time\" required id=\"id_recording_time\"></p>\"\"\", # noqa pylint: disable=line-too-long\n form().as_p(),\n )","title":""},{"docid":"a5e43f528e248ed5ff08a8af89a85a93","score":"0.5128961","text":"def on_save(self, init):\n super().on_save(init)\n\n self.meta.date = Time.now().isot","title":""},{"docid":"8ab7596f8306100e47b11982ebd2ed65","score":"0.512536","text":"def set_select_date(self, select_date):\n self.set_value_into_input_field(self.select_date_textbox_locator, select_date)","title":""},{"docid":"95176884859ea2abf24e2154703e0cb0","score":"0.511812","text":"def get_now_str(self):\n pass","title":""},{"docid":"7ddc49d3991e4c28701032f0afc76e44","score":"0.51154304","text":"def _today_date(self):\n return time.strftime('%Y-%m-%d')","title":""},{"docid":"732170b624aec930e9eb09b91105af2e","score":"0.5097639","text":"def log_text(self, text):\n text_tr =text\n\n text_tr = \"\".join([datetime.datetime.now().strftime(\"%H:%M:%S: \"), text_tr])\n text_tr = text_tr + '\\n' \n self.activity_log_text.AppendText(text_tr)\n self.is_saved = False","title":""},{"docid":"3ef1e3a73522e136fb56b91a6b832da8","score":"0.5093067","text":"def show_entry_fields():\r\n\r\n set_alarm_timer = e1.get()\r\n my_main_function(set_alarm_timer)","title":""},{"docid":"ed6a4a675572b82f58abb5edef0be283","score":"0.5091672","text":"def _add_date_stamp(self, fig):\n image_size = self.input_data.get_value(InputType.IMAGE_SIZE)\n\n if image_size == 900:\n font_size = 10\n elif image_size == 1200:\n font_size = 12\n else: # 2400\n font_size = 24\n\n datetime_obj = datetime.now()\n date_str = datetime_obj.strftime(\"%H:%M %d/%m/%y\")\n\n fig.text(0.02, 0.02, date_str, fontsize=font_size)","title":""},{"docid":"cf08a082024276146a3ed8b032eabe0d","score":"0.5090117","text":"def get_time(self):\n if self.dockwidget.exactDate.isChecked():\n return '{}/{}/P1D'.format(self.time0, self.time0)\n if self.time0 == '':\n return self.time1\n if self.time1 == '':\n return '{}/{}/P1D'.format(self.time0, datetime.datetime.now().strftime(\"%Y-%m-%d\"))\n return '{}/{}/P1D'.format(self.time0, self.time1)","title":""},{"docid":"173bac03a418c45ab908e5c3afe50d62","score":"0.508486","text":"def set_to_date(self, to_date):\n self.set_value_into_input_field(self.to_date_textbox_locator, to_date)","title":""},{"docid":"7fce48d53bdc35c78d1267d00bdda296","score":"0.5078583","text":"def set_transaction_date(self, date):\n self.set_value_into_input_field(self.transaction_date_locator, date)","title":""},{"docid":"8313f65084297b5a97acb75f0da4a968","score":"0.5075052","text":"def test_date_time(self, inquiry=Interface()):\n result = Interface.ask(inquiry, \"What time is it?\")\n self.assertEqual(result, 'The current time is :' +\n datetime.utcnow().strftime(\"%m-%d-%Y %H:%M\"))","title":""},{"docid":"55fcf36cd3667b12800accd16746c2f9","score":"0.507464","text":"def render_date_time_field_into(into, field_added, date_time, *, optional = True, title = 'Date'):\n if (not optional) or (date_time is not None):\n if field_added:\n into.append('\\n')\n else:\n field_added = True\n \n into.append(title)\n into.append(': ')\n if (date_time is None):\n into.append('*none*')\n else:\n into.append(format(date_time, DATETIME_FORMAT_CODE))\n \n return into, field_added","title":""},{"docid":"601b500f35d2b9fcbf7668ee61a1e167","score":"0.50739926","text":"def save(self, *args, **kwargs):\n self.dt = self.timecalc\n super(ZeitErfassung, self).save(*args, **kwargs) # Call the \"real\" save() method.","title":""},{"docid":"2ad43f26b3db5e9d75c12944ec03e156","score":"0.5063896","text":"def __init__(self, dateformat=None):\n self.current_date = datetime.datetime.now()\n self.date_format = dateformat if dateformat else \"%d.%m.%Y\"","title":""},{"docid":"ef8dc713321a789408e8d1bdab843a89","score":"0.5063799","text":"def bot_time(self, mess, args):\n return str(datetime.datetime.now()) + \" EST/EDT\"","title":""},{"docid":"8a75589c121911836a21d95eedfbcffe","score":"0.50584316","text":"def default(self, event, entry, texto_insertado):\n self.informacion_entry = entry.get()\n if(self.informacion_entry == \"Nombre\" or self.informacion_entry == \"Codigo de producto\" or self.informacion_entry == \"Precio\"\n or self.informacion_entry == \"Cantidad\" or self.informacion_entry == \"Marca\" or self.informacion_entry == \"Proveedor\"\n or self.informacion_entry == \"Fecha de entrega\"):\n entry.delete(0, tk.END)\n entry.config(fg=\"black\")\n\n elif(self.informacion_entry == \"\"):\n\n entry.insert(0,texto_insertado)\n entry.config(fg=\"grey\")","title":""},{"docid":"081cbcd3344dfffc38deb5c820cf2aad","score":"0.5057015","text":"def format_time(self, record):\n record.dbtime = time.strftime(\n \"%Y-%m-%d %H:%M:%S\", time.localtime(record.created)\n )","title":""},{"docid":"cce69a9a361a1e2a38017542a99025f2","score":"0.50563437","text":"def edition_date(self) -> datetime:\n return None","title":""},{"docid":"94c3ea42bc688f8a3e7bd361b2634426","score":"0.5051675","text":"def set_date_or_time(css, date_or_time):\n world.css_fill(css, date_or_time)\n e = world.css_find(css).first\n # hit Enter to apply the changes\n e._element.send_keys(Keys.ENTER)","title":""},{"docid":"a5c52b1e2ec722faeb00a1b093a1638e","score":"0.50502485","text":"def __str__(self):\n return f\"{self._date}\"","title":""},{"docid":"eb9a36602a8b271c982198be65f206c6","score":"0.5037006","text":"def default_date():\n return datetime.now().strftime(\"%y%m%d\")","title":""},{"docid":"7d9ec2a166e9a32ae41a43f737548e14","score":"0.5025333","text":"def onUpdateText(self, text):\n cursor = self.log_textEdit.textCursor()\n cursor.movePosition(QtGui.QTextCursor.End)\n cursor.insertText(text)\n self.log_textEdit.setTextCursor(cursor)\n self.log_textEdit.ensureCursorVisible()","title":""},{"docid":"74818bce52bb4ec41603a8da437dc3e3","score":"0.50235665","text":"def get_date_as_string(self, widget):\n pass","title":""},{"docid":"3215e624a65b5cd631718cc14ba53cdf","score":"0.5021368","text":"def display_simple(self):\n if type(self.date) == datetime.datetime:\n self.date = self.date.strftime('%m/%d/%Y %H:%M')\n self.dis_simp_print(self.date, self.username, self.task_name, \n self.time_spent, self.notes, self.task_number)","title":""},{"docid":"59703b4dee8c6167e1447525730bb42f","score":"0.5016059","text":"def current_time_changed(self, *args):\n if not self.dragging:\n self.set(self.player.current_time.get())","title":""},{"docid":"1310d9e39f98240abb6091e1ca63f4de","score":"0.5011271","text":"def get_date(self, widget):\n date = widget.date()\n return qdate_to_python(date)","title":""},{"docid":"b6a174c2b242ff4f48aa1c341fe468fd","score":"0.5010716","text":"def get_current_date(self):\n td = timedelta(days=0)\n return date.today() + td","title":""},{"docid":"b43d11ac91cc5dcaa4e4c47a44604363","score":"0.49877292","text":"def new_date(*args):\n return dt.now()","title":""},{"docid":"3dc591fe474b71940db312923b86dd64","score":"0.49858266","text":"def pull_current_acc_text(self, d):\n \n widge_text = self.desc_text.get(1.0, tk.END)\n if len(widge_text) <= 1:\n return\n \n else:\n c = self.conn.cursor()\n c.execute(\"\"\"INSERT INTO ? VALUES(?)\"\"\", (d, widge_text,))\n self.conn.commit()\n\n return","title":""},{"docid":"de9172dc9f7c015665e1a918dfe661dc","score":"0.49800152","text":"def update(self):\n self.birthdayFormatted = self.context.start.strftime(\"%d %b %Y\")\n self.birthdayYear = self.context.start.year\n self.dimensions = \"%d × %d cm\" % (self.context.width, self.context.height)\n# self.context.image = self.context.picture # to be able to treat illustration as image in TinyMCE\n# self.context.picture = self.context.image # temporary before I fixed templates\n# self.context.description = \"%s %s %s %s %d\" % (self.dimensions, self.context.genre, self.context.style, self.context.technique, self.context.start.year)\n body = \"\"\n if self.context.body:\n body = self.context.body.output\n self.context.description = \"%s   %s   %d %s\" % (self.context.translate(_(u'%s'% self.context.technique.capitalize())), self.dimensions, self.context.start.year, body)","title":""},{"docid":"9600babe3a110b68f80511fdfcb35079","score":"0.49761078","text":"def edit_due_date(update, context):\n print(context.chat_data)\n\n # Get the user reply (new name)\n new_task_due_date = dateparser.parse(update.message.text)\n\n if new_task_due_date is None or len(update.message.text) < 4:\n # Tell the user it's an invalid date and prompt again.\n text = \"Invalid Date! Reenter a correct date\"\n context.bot.sendMessage(update.message.chat_id, text=text)\n return\n\n # Retrieve the chat data and get database object\n taskid = context.chat_data['taskid']\n task = Task.objects.get(id=taskid)\n \n # Change the deadline and save\n task.deadline = new_task_due_date\n task.save()\n\n # Reply to user that process is completed\n text = \"✅Task saved!\"\n\n # Send the message\n context.bot.sendMessage(update.message.chat_id, text=text)\n\n return ConversationHandler.END","title":""}],"string":"[\n {\n \"docid\": \"504bed008c61e56657817a7d85fd8522\",\n \"score\": \"0.68654907\",\n \"text\": \"def OnInsertDate(self, event):\\n self.AddText(str(time.ctime()))\\n event.Skip()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41bb243f0f260b6a653da25124d49ba4\",\n \"score\": \"0.66584486\",\n \"text\": \"def changeText(self):\\n text = datetime.now().strftime(\\\"%Y/%m/%d %H:%M:%S\\\")\\n self.setText(text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34bbafb9222c3c3dc47ac7a1336383e2\",\n \"score\": \"0.6424974\",\n \"text\": \"def update_time(self):\\n now = time.localtime()\\n hour = now[3]\\n minute = now[4]\\n year = now[0]\\n month = now[1]\\n day = now[2]\\n time_format_str = \\\"%d:%02d\\\"\\n date_format_str = \\\"%d-%02d-%02d\\\"\\n if self.am_pm:\\n if hour >= 12:\\n hour -= 12\\n time_format_str = time_format_str+\\\" PM\\\"\\n else:\\n time_format_str = time_format_str+\\\" AM\\\"\\n if hour == 0:\\n hour = 12\\n time_str = time_format_str % (hour, minute)\\n print(time_str)\\n date_str = date_format_str % (year, month, day)\\n self.date_text.text = date_str\\n self.time_text.text = time_str\",\n \"title\": \"\"\n },\n {\n \"docid\": \"459c4b8da60de833cd8628cbe5eb6a13\",\n \"score\": \"0.625469\",\n \"text\": \"def menu_time(self, time=None):\\r\\n if (time == None):\\r\\n time = float(self.widgets['update_time'].get())\\r\\n else:\\r\\n self.widgets['update_time'].setentry(str(time))\\r\\n self.update_time = time\\r\\n if (self.after_id != None): self.top.after_cancel(self.after_id)\\r\\n self.timer()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"614f5b59c36d76d550255642537bd82a\",\n \"score\": \"0.6088324\",\n \"text\": \"def update_value(self, inst):\\n\\n self.text = \\\"%s.%s.%s\\\" % tuple(self.cal.active_date)\\n self.dispatch('on_confirm', self.text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a10b97e8dd6999b11e6e38a03d3bbb5\",\n \"score\": \"0.60697716\",\n \"text\": \"def show_time_and_date(self, t=None):\\n\\n current_time = rtc.get_time()\\n current_date = rtc.get_date()\\n\\n lcd.text(lcd.CENTER, 20, \\\"%02d:%02d:%02d\\\" % current_time, color=lcd.WHITE)\\n\\n if current_date[2] != self.date:\\n lcd.font(\\\"data/fonts/arial16.fon\\\")\\n\\n lcd.textClear(lcd.CENTER, 55, \\\"00-00-00\\\")\\n lcd.text(lcd.CENTER, 55, \\\"%02d-%02d-%02d\\\" % (current_date[2] + 1, current_date[1] + 1, current_date[0]), color=lcd.WHITE)\\n\\n lcd.font(\\\"data/fonts/ariblk28.fon\\\") # Restore Time font.\\n\\n self.date = current_date[2]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98f35949e0ec54edf07bc790584e22fd\",\n \"score\": \"0.6013537\",\n \"text\": \"def get_date(self, widget):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf1f09fa27bfb2d5fc12959a6c3614b8\",\n \"score\": \"0.5967441\",\n \"text\": \"def init_date(self):\\n now = datetime.datetime.now()\\n day = now.day\\n month = now.month\\n year = now.year\\n today = QtCore.QDate(year, month, day)\\n self.master_date_value = today\\n self.ui.master_date.setDate(today)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ba7eb6e064970c9f4f34b7cce4ef631\",\n \"score\": \"0.58008516\",\n \"text\": \"def change_date(self, widget, date):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aee839aad9bd10cee3f9d93a88cbccef\",\n \"score\": \"0.5800275\",\n \"text\": \"def get_date(self):\\n\\n self.date = datetime.now().strftime(\\\"%Y-%m-%d_%H:%M:%S\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c2025be5152d677bbba37afb1428385\",\n \"score\": \"0.5787586\",\n \"text\": \"def updtTime(self):\\n currentTime = QDateTime.currentDateTime().toString('hh:mm:ss')\\n self.myTimeDisplay.display(currentTime)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5dca56a9ec99896aa382fa7f823e55a2\",\n \"score\": \"0.57127273\",\n \"text\": \"def set_service_level_begin_date(self):\\n self.set_value_into_input_field(self.begin_date_inputbox_locator, self.get_date(current_date=True))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c76f605a0413729efd27caf8d12b1d0\",\n \"score\": \"0.5697221\",\n \"text\": \"def set_datetime_field(self, num, loading = False):\\n self.editor.paramSizer.Clear(True)\\n if num == 1:\\n self.editor.paramWidget = DateTimeCtrl( parent = self.editor.parent, width = 450, \\\\\\n update_state = self.whereController.change_made, \\\\\\n condition = self.condition, isLoading = loading)\\n else:\\n self.editor.paramWidget = DateBetweenValue(self.editor.parent, width = 450, update_state = self.whereController.change_made,\\\\\\n condition = self.condition, typeDetails = self.typeDetails, isLoading = loading)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4783ab693cac81391d16221a6fed6ae6\",\n \"score\": \"0.5694268\",\n \"text\": \"def add_time(self):\\n calendar_time = str(self.dockwidget.calendar.selectedDate().toPyDate())\\n\\n if self.active_time == 'time0' and (self.dockwidget.exactDate.isChecked() or not self.time1 or\\n calendar_time <= self.time1):\\n self.time0 = calendar_time\\n self.dockwidget.time0.setText(calendar_time)\\n elif self.active_time == 'time1' and (not self.time0 or self.time0 <= calendar_time):\\n self.time1 = calendar_time\\n self.dockwidget.time1.setText(calendar_time)\\n else:\\n self.show_message('Start date must not be larger than end date', Message.INFO)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6993b86355515d35c9b3c461323e07f3\",\n \"score\": \"0.5678011\",\n \"text\": \"def _update(self):\\n time = QTime.currentTime().toString()\\n self.display(time)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"080d4700b2294b1e88a413c45e2027b2\",\n \"score\": \"0.56624186\",\n \"text\": \"def set_now_str(self, is_start_time):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b855fe350b346887468d7e6b360c78cb\",\n \"score\": \"0.56537503\",\n \"text\": \"def clicked(self):\\n\\n # creates string for time display\\n timer = strftime('Day: %d %b %Y\\\\nTime: %H:%M:%S %p\\\\n', localtime())\\n\\n # display time in a pop-up window\\n showinfo(message=timer)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f75dcaabc86df4a25e0206e089e6f1f\",\n \"score\": \"0.5652776\",\n \"text\": \"def arrive(self):\\n\\n def callback(*args):\\n \\\"\\\"\\\"Callback to update start_date of the destination\\\"\\\"\\\"\\n if args[1] is None:\\n return # cancel\\n\\n try:\\n start_date = iso_date(args[1])\\n except:\\n Alert(title='Arrive Date', text=\\\"Please input a date in yyyy-mm-dd format\\\")\\n return\\n\\n self._update(start_date=iso_date_string(start_date))\\n\\n InputPopup(title='Arrive Date',\\n text='When did you arrive {}?'.format(self.item.name),\\n value=self.item.start_date,\\n initial=iso_date_string(today()),\\n on_value=callback)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3804309414bdad9022ddaa54ad4cfaa\",\n \"score\": \"0.5650339\",\n \"text\": \"def set_now(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8916c9db2835f80084b9d4db9bd6d910\",\n \"score\": \"0.5643638\",\n \"text\": \"def get_current_date_time(self, choice):\\n\\n if choice in ('1', '2', '3'):\\n\\n # Get current date and time.\\n now = datetime.now()\\n # Get the time stamp.\\n timestamp = datetime.timestamp(now)\\n\\n if choice == '1':\\n # Display current date and time.\\n date_time = now.strftime(\\\"%m/%d/%Y, %H:%M:%S\\\")\\n print(\\\"current date and time =\\\",date_time)\\n elif choice == '2':\\n # Dispay the time stamp.\\n print(\\\"time stamp =\\\", timestamp)\\n elif choice == '3':\\n # Get current date and time from the time stamp.\\n # and display the result.\\n dt_object = datetime.fromtimestamp(timestamp)\\n print(\\\"(dt_object - date and time from time stamp =\\\", dt_object)\\n print(\\\"type(dt_object) =\\\", type(dt_object))\\n else:\\n print(\\\"invalid choice\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4005b68441fc23d03b38e680ca033edf\",\n \"score\": \"0.55390495\",\n \"text\": \"def timestamp_changed(self):\\n self.text_settings.timestamps = self.CBTime.isChecked()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"daa525cac4f1f64651f57ad640012b78\",\n \"score\": \"0.5507961\",\n \"text\": \"def set_current_date(self, date):\\r\\n self._current_date = date\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3339be49ee8bc8c4aaf44ef2d46b798b\",\n \"score\": \"0.5499776\",\n \"text\": \"def get_add_new_transaction_date(self):\\n return self.get_text_from_element(self.transaction_date_locator, is_a_input_field=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7aaea09cb0911bf113b23bf1afbf2e99\",\n \"score\": \"0.54724926\",\n \"text\": \"def set_current_date(self, input_date=date.today()):\\n if not isinstance(input_date, date):\\n raise ValueError(\\\"input date must be of type datetime.date\\\")\\n td = timedelta(days=0)\\n self.current_date = input_date + td\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b44f90f7bfc8313a01cd72a202580762\",\n \"score\": \"0.5467083\",\n \"text\": \"def current_date():\\n dt_string = datetime.now().strftime(\\\"%A, %B\\\")\\n date_ = engine().ordinal(datetime.now().strftime(\\\"%d\\\"))\\n year = datetime.now().strftime(\\\"%Y\\\")\\n if time_travel.has_been_called:\\n dt_string = dt_string + date_\\n else:\\n dt_string = dt_string + date_ + ', ' + year\\n speaker.say(f\\\"It's {dt_string}\\\")\\n if event and event == 'Birthday':\\n speaker.say(f\\\"It's also your {event} sir!\\\")\\n elif event:\\n speaker.say(f\\\"It's also {event} sir!\\\")\\n if report.has_been_called or time_travel.has_been_called:\\n speaker.say(f'The current time is, ')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98405e14b178d0700fc258ee6ec4108f\",\n \"score\": \"0.54468\",\n \"text\": \"def __init__(self, *args, **kwargs):\\n super(InlineReminderForm, self).__init__(*args, **kwargs)\\n if self.instance.sent_time is not None:\\n self.fields['scheduled_time'].disabled = True\\n sent_time = self.instance.sent_time.strftime(DATETIME_FORMAT)\\n self.fields['scheduled_time'].help_text = 'Sent %s' % sent_time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae40379c47333c21b9fcfed20b36dd52\",\n \"score\": \"0.54321253\",\n \"text\": \"def update_time(screen):\\n _, width = screen.getmaxyx()\\n time_str = pscheduler.datetime_as_iso8601(pscheduler.time_now())\\n if len(time_str) > width:\\n time_str = \\\"%s%s\\\" % (time_str[:width-1], \\\"+\\\")\\n if width > 2:\\n screen.addstr(0, 0, time_str, color_pair(\\\"default\\\"))\\n screen.refresh()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc51f3defbba433e9a2534cb94eb0191\",\n \"score\": \"0.5424865\",\n \"text\": \"def timeDisplay(self):\\n\\n print(datetime.datetime.now)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dafc41b2f6c3bcc5d0fe41fdfba44537\",\n \"score\": \"0.54218936\",\n \"text\": \"def update_dates(self):\\n new_time0 = self.parse_date(self.dockwidget.time0.text())\\n new_time1 = self.parse_date(self.dockwidget.time1.text())\\n\\n if new_time0 is None or new_time1 is None:\\n self.show_message('Please insert a valid date in format YYYY-MM-DD', Message.INFO)\\n elif new_time0 and new_time1 and new_time0 > new_time1 and not self.dockwidget.exactDate.isChecked():\\n self.show_message('Start date must not be larger than end date', Message.INFO)\\n else:\\n self.time0 = new_time0\\n self.time1 = new_time1\\n Settings.parameters['time'] = self.get_time()\\n\\n self.dockwidget.time0.setText(self.time0)\\n self.dockwidget.time1.setText(self.time1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"667be556a5c2c6f9cf662d5f225ea5a4\",\n \"score\": \"0.5418961\",\n \"text\": \"def trigger_setTSNow(self, record, fldname,**kwargs):\\n if not getattr(record, '_notUserChange', None):\\n record[fldname] = datetime.datetime.today()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f00d4644e738282a2bde1e79551fff7\",\n \"score\": \"0.5416571\",\n \"text\": \"def __init__(self, item_date, item_time, item_name, item_details):\\n\\n from datetime import datetime\\n now = datetime.now()\\n\\n # item_date = date of today\\n self.item_date = now.year + now.month + now.day\\n self.item_time = now.hour\\n\\n print(\\\"Schedule of \\\" + item_date )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5483f432eec3f8582963ab02dbf190f9\",\n \"score\": \"0.54099405\",\n \"text\": \"def get_current_date(self): \\n return strftime(\\\"%Y-%m-%d\\\", time.gmtime())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bebb17ff1eb01d9aff1e585d6e77ff9b\",\n \"score\": \"0.54091305\",\n \"text\": \"def set_new_date(self):\\n min = (time.daylight and time.altzone or time.timezone) // 60\\n hr = min // 60\\n min = -(min % 60 + hr * 100)\\n self.new_date = (time.strftime('%a, %d %b %Y %X', self.release_date + (0, 0, 0))\\n + ' %+.4d' % min)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c102bb74da0b88aa9ed5f3b68b7e577\",\n \"score\": \"0.5403179\",\n \"text\": \"def date(self, control_def):\\n if control_def.appearance == \\\"\\\":\\n control_def.appearance = '{\\\"format\\\":\\\"dd-mm-yyyy\\\",\\\"viewmode\\\":\\\"days\\\",\\\"minviewmode\\\":\\\"days\\\", \\\"minviewmode\\\":\\\"years\\\"}'\\n self.controls_js += '\\\\n dateControlCreate(\\\"' + control_def.control_id + '\\\",\\\"' + self.parent_div_id + '\\\",\\\"' + control_def.control_name + '\\\",\\\"' + control_def.control_label + '\\\",' + control_def.appearance + ', \\\"\\\" );'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8c33bcd1df06b35a4a7da8fcf37a002\",\n \"score\": \"0.536979\",\n \"text\": \"def set_begin_date_for_add_new_rate(self):\\n self.wait_for_ajax_spinner_load()\\n begin_date = self.get_current_date()\\n self.set_value_into_input_field(self.add_new_rate_begin_date_locator, begin_date)\\n return begin_date\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8948df95eb737c5896344ad0c790025e\",\n \"score\": \"0.5356948\",\n \"text\": \"def selected_date():\\n day = parser.parse(wrf_init_date_input.value)\\n hour = wrf_init_time_select.value\\n hour = timedelta(hours=int(hour[:-1]))\\n return day + hour\",\n \"title\": \"\"\n },\n {\n \"docid\": \"495ce7cd72c907ac9b3b4967d61dee53\",\n \"score\": \"0.5320208\",\n \"text\": \"def get_current_date(self):\\r\\n return self._current_date\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1c3a6e8b2c1c3b16472d01f8c0ad073\",\n \"score\": \"0.5317576\",\n \"text\": \"def set_time(self,time_now):\\n self.start_time = time_now\\n # changing the time label widget after 10 second.\\n self._label_time.config(text = self.start_time)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a366a0569c90e3254bde33c382fb6be8\",\n \"score\": \"0.5313397\",\n \"text\": \"def update_label():\\n current_time = strftime(\\\"%H: %M: %S\\\\n %d-%m-%Y \\\")\\n clock_label.configure(text=current_time)\\n clock_label.after(80, update_label)\\n clock_label.pack(anchor=\\\"center\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f3a3f4d4645abc9b48800370ab97ef9\",\n \"score\": \"0.52927345\",\n \"text\": \"def set_begin_date_for_add_new_rate_plan(self, begin_date):\\n begin_date_textbox_element = self.wait().until(EC.presence_of_element_located(self.begin_date_textbox_locator), 'begin date textbox locator not found before specified time out')\\n begin_date_textbox_element.clear()\\n if begin_date == \\\"\\\":\\n begin_date = self.get_current_date()\\n begin_date_textbox_element.send_keys(begin_date)\\n global current_begin_date\\n current_begin_date = datetime.datetime.strptime(begin_date, \\\"%m/%d/%Y\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8aa046ffb665291a70d96c3513ef44b6\",\n \"score\": \"0.5290201\",\n \"text\": \"def set_time_field(self, num, loading = False):\\n self.editor.paramSizer.Clear(True)\\n if num == 1:\\n self.editor.paramWidget = TimeCtrl( parent = self.editor.parent, width = 450, \\\\\\n update_state = self.whereController.change_made, \\\\\\n condition = self.condition, isLoading = loading)\\n else:\\n self.editor.paramWidget = DateBetweenValue(self.editor.parent, width = 450, update_state = self.whereController.change_made,\\\\\\n condition = self.condition, typeDetails = self.typeDetails, isLoading = loading)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f99ee9af6c334aa9f9a74b8aab26cb3\",\n \"score\": \"0.52899706\",\n \"text\": \"def change_exact_date(self):\\n\\n if self.dockwidget.exactDate.isChecked():\\n self.dockwidget.time1.hide()\\n self.dockwidget.timeLabel.hide()\\n self.move_calendar('time0')\\n self.add_time()\\n else:\\n if self.time0 and self.time1 and self.time0 > self.time1:\\n self.time1 = ''\\n Settings.parameters['time'] = self.get_time()\\n self.dockwidget.time1.setText(self.time1)\\n\\n self.dockwidget.time1.show()\\n self.dockwidget.timeLabel.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d254e3bb996c42fc379c51965d42a8e\",\n \"score\": \"0.5271335\",\n \"text\": \"def setPublicationDate(datetime):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34ab5eea380c06ca8f05b84f5eb34932\",\n \"score\": \"0.5268397\",\n \"text\": \"def update_datetime_text(\\n node,\\n node_tree,\\n frame,\\n time_text=\\\"\\\",\\n decode=False,\\n):\\n if not time_text:\\n time = str(bnc_gutils.get_time(node, frame))[:10]\\n else:\\n time = time_text\\n # TODO allow user to define format.\\n\\n if \\\"Camera\\\" in bpy.data.objects.keys() and time:\\n Camera = bpy.data.objects.get(\\\"Camera\\\")\\n size = 0.03\\n coords = (-0.35, 0.17, -1)\\n children_name = [children.name for children in Camera.children]\\n if \\\"BlenderNC_time\\\" not in children_name:\\n bpy.ops.object.text_add(radius=size)\\n text = bpy.context.object\\n text.name = \\\"BlenderNC_time\\\"\\n text.parent = Camera\\n text.location = coords\\n mat = bnc_pyfunc.ui_material()\\n text.data.materials.append(mat)\\n else:\\n children = Camera.children\\n text = [c for c in children if c.name == \\\"BlenderNC_time\\\"][-1]\\n text.data.body = time\\n if text.select_get():\\n text.select_set(False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61376a29067fe91b0af8a1af90f756c1\",\n \"score\": \"0.5268081\",\n \"text\": \"def currentDate():\\n return(datetime.now())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"345e26d4b83efbdef69fbbc42eac4881\",\n \"score\": \"0.5267875\",\n \"text\": \"def date():\\n year = int(datetime.datetime.now().year)#obtener año\\n month = int(datetime.datetime.now().month)#obtener mes\\n day = int(datetime.datetime.now().day)#obtener dia\\n speech.speak(\\\"The current date is\\\")\\n speech.speak(day)#decir dia\\n speech.speak(month)#decir mes\\n speech.speak(year)#decir año\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33b6b2816551d6f892b83cf77453719a\",\n \"score\": \"0.52652377\",\n \"text\": \"def isoDate():\\n time.sleep(0.1) # wait a little in order to be sure that user not pressing a hotkey anymore\\n output = datetime.datetime.now().strftime(\\\"%Y-%m-%d\\\")\\n storedClipboard = ClipboardUtil.pasteFromClipboardXsel()\\n sendText(output)\\n ClipboardUtil.copyToClipboardXclip(storedClipboard)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86f82c0afd14b03583b7ada5e24f471c\",\n \"score\": \"0.52589375\",\n \"text\": \"def set_date(self):\\n self.date = datetime.fromisoformat(self.time[:-1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2aa451c71859736b9ebf61f260ddcb18\",\n \"score\": \"0.5250717\",\n \"text\": \"def __init__(self):\\n super().__init__()\\n self.control = libui.uiNewDateTimePicker()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4c671a0b4e6875683906dcfefb52db7\",\n \"score\": \"0.5240346\",\n \"text\": \"def _current_datetime_string(self):\\n datetime_object = datetime.datetime.now()\\n datetime_string = datetime_object.strftime(self.DATETIME_FORMAT)\\n\\n return datetime_string\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01ead656edf74760c9011169e782e1af\",\n \"score\": \"0.52229536\",\n \"text\": \"def set_date(self, date):\\n self.datetime = date\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f35f0e5523bec52fe604aeecdedcbd78\",\n \"score\": \"0.52226985\",\n \"text\": \"def current_date():\\n date = datetime.datetime.now()\\n return f'{date:%Y-%m-%d}'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"599e8da9d20062e134ba1bb67b5bd50c\",\n \"score\": \"0.52187943\",\n \"text\": \"def add_new_computer_introduce_date(self):\\n self.driver.find_element_by_id('introduced').send_keys(self.introduced)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca05ec6dd72b3e34499a859a45d4efe2\",\n \"score\": \"0.52115065\",\n \"text\": \"def get_now_datetime(self):\\n return datetime.now().strftime(\\\"%a %I:%M %p\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbc3b1d1ae4a68d47b987de04d1e2fde\",\n \"score\": \"0.52006274\",\n \"text\": \"def editTime(self, datetime=False):\\n if self._editTime and datetime:\\n import datetime\\n return datetime.datetime.strptime(str(self._editTime), '%Y%m%d%H%M%S')\\n\\n return self._editTime\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a15205df637e6ff8ebc944480aa3cfe\",\n \"score\": \"0.5197612\",\n \"text\": \"def get_now(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87e829c6e004e25f9fedb75c91970aa6\",\n \"score\": \"0.5184889\",\n \"text\": \"def registration_datetime_text(course, date):\\n\\n strftime = course.runtime.service(course, \\\"i18n\\\").strftime\\n date_time = strftime(date, \\\"SHORT_DATE\\\")\\n return date_time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"494377c7c79d50b784ef6481f9cd0c4f\",\n \"score\": \"0.51836145\",\n \"text\": \"def _current_date_string(self):\\n datetime_object = datetime.datetime.now()\\n datetime_string = datetime_object.strftime(self.DATE_FORMAT)\\n\\n return datetime_string\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2cc2acdca56dc78448e923b1c9b68284\",\n \"score\": \"0.5178246\",\n \"text\": \"def set_begin_date_for_add_number_plan(self, begin_date):\\n if begin_date == \\\"\\\":\\n begin_date = datetime.date.today()\\n begin_date = \\\"%d/%d/%d\\\" % (begin_date.month, begin_date.day, begin_date.year)\\n self.set_value_into_input_field(self.begin_date_inputbox_locator, begin_date)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"464381afc7e9e7071dffa0fab0c9e580\",\n \"score\": \"0.51722026\",\n \"text\": \"def datetime_now():\\n return datetime.now()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1f814833b1089c3baf412429d98d005\",\n \"score\": \"0.5170177\",\n \"text\": \"def set_date_field(self, num, loading = False):\\n self.editor.paramSizer.Clear(True)\\n if num == 1:\\n self.editor.paramWidget = DateCtrl( parent = self.editor.parent, width = 450, \\\\\\n update_state = self.whereController.change_made, \\\\\\n condition = self.condition, isLoading = loading)\\n else:\\n self.editor.paramWidget = DateBetweenValue(self.editor.parent, width = 450, update_state = self.whereController.change_made,\\\\\\n condition = self.condition, typeDetails = self.typeDetails, isLoading = loading)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2d90c1f60397f7cdf9517c3db0fc73e\",\n \"score\": \"0.51671505\",\n \"text\": \"def change_date(self, widget, date):\\n self.widget.setDate(date)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ccb995688a4c03aff03c6725dd412427\",\n \"score\": \"0.5151949\",\n \"text\": \"def default(self, event, entry, texto_insertado):\\n self.informacion_entry = entry.get()\\n if (self.informacion_entry == \\\"Numero de caja\\\" or self.informacion_entry == \\\"Nombre de usuario\\\" or self.informacion_entry == \\\"DD/M/AAAA\\\" or self.informacion_entry == \\\"Hora\\\"):\\n entry.delete(0, tk.END)\\n entry.config(fg=\\\"black\\\")\\n\\n elif (self.informacion_entry == \\\"\\\"):\\n\\n entry.insert(0, texto_insertado)\\n entry.config(fg=\\\"grey\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc78c39dda9564dee1ed0690b6e58f1e\",\n \"score\": \"0.5150202\",\n \"text\": \"def set_yob_text_to_field(self, value):\\n\\n WebDriverWait(self.driver, 100).until(\\n lambda driver: driver.find_element(*MainPageLocators.YOB_DATE_PICK))\\n element = self.driver.find_element(*MainPageLocators.YOB_DATE_PICK)\\n element.send_keys(value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6a97b7db7ed6e3ca467a2ed3a88ded1\",\n \"score\": \"0.5147219\",\n \"text\": \"def format_datetime(self, request, obj, fieldname, *args, **kwargs):\\n model = make_model(obj)\\n return html.div(style='min-width:130px;')(\\n getattr(model, fieldname).strftime(\\n app.config.get('ADMIN_DATE_FORMAT', '%Y-%m-%d')\\n )\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c8636bacf337f43f28f49838767cde4\",\n \"score\": \"0.5139439\",\n \"text\": \"def current_datetime():\\n timeoffset = datetime.timedelta(hours=app.config['TIMEZONE'])\\n return datetime.datetime.now() + timeoffset\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79cd2ce8e200051af0a0f07caff6575f\",\n \"score\": \"0.5136384\",\n \"text\": \"async def _cmdf_time(self, substr, msg, privilege_level):\\n await self._client.send_msg(msg, datetime.datetime.utcnow().strftime(\\\"My current system time: %c UTC\\\"))\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5dcfb77f2c2f47a33b80eff881617671\",\n \"score\": \"0.5135393\",\n \"text\": \"def current_date(request):\\n dt = datetime.now() #Return the current local date and time.\\n return HttpResponse(dt.strftime('Today is %d, %B %Y')) #Return a string representing the date and time\\n # Today is 10, January 2018\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ea5cff10e5fac23b9ac67688571ec00\",\n \"score\": \"0.5135189\",\n \"text\": \"def test_get_modelform_datetimefield(self):\\n form = get_modelform(model=Song, fields=[\\\"recording_time\\\"])\\n self.assertIsInstance(\\n form().fields[\\\"recording_time\\\"].widget, VegaDateTimeWidget\\n )\\n self.assertHTMLEqual(\\n \\\"\\\"\\\"<p><label for=\\\"id_recording_time\\\">Recording Time:</label> <input type=\\\"datetime-local\\\" name=\\\"recording_time\\\" required id=\\\"id_recording_time\\\"></p>\\\"\\\"\\\", # noqa pylint: disable=line-too-long\\n form().as_p(),\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5e43f528e248ed5ff08a8af89a85a93\",\n \"score\": \"0.5128961\",\n \"text\": \"def on_save(self, init):\\n super().on_save(init)\\n\\n self.meta.date = Time.now().isot\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ab7596f8306100e47b11982ebd2ed65\",\n \"score\": \"0.512536\",\n \"text\": \"def set_select_date(self, select_date):\\n self.set_value_into_input_field(self.select_date_textbox_locator, select_date)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95176884859ea2abf24e2154703e0cb0\",\n \"score\": \"0.511812\",\n \"text\": \"def get_now_str(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ddc49d3991e4c28701032f0afc76e44\",\n \"score\": \"0.51154304\",\n \"text\": \"def _today_date(self):\\n return time.strftime('%Y-%m-%d')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"732170b624aec930e9eb09b91105af2e\",\n \"score\": \"0.5097639\",\n \"text\": \"def log_text(self, text):\\n text_tr =text\\n\\n text_tr = \\\"\\\".join([datetime.datetime.now().strftime(\\\"%H:%M:%S: \\\"), text_tr])\\n text_tr = text_tr + '\\\\n' \\n self.activity_log_text.AppendText(text_tr)\\n self.is_saved = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ef1e3a73522e136fb56b91a6b832da8\",\n \"score\": \"0.5093067\",\n \"text\": \"def show_entry_fields():\\r\\n\\r\\n set_alarm_timer = e1.get()\\r\\n my_main_function(set_alarm_timer)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed6a4a675572b82f58abb5edef0be283\",\n \"score\": \"0.5091672\",\n \"text\": \"def _add_date_stamp(self, fig):\\n image_size = self.input_data.get_value(InputType.IMAGE_SIZE)\\n\\n if image_size == 900:\\n font_size = 10\\n elif image_size == 1200:\\n font_size = 12\\n else: # 2400\\n font_size = 24\\n\\n datetime_obj = datetime.now()\\n date_str = datetime_obj.strftime(\\\"%H:%M %d/%m/%y\\\")\\n\\n fig.text(0.02, 0.02, date_str, fontsize=font_size)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf08a082024276146a3ed8b032eabe0d\",\n \"score\": \"0.5090117\",\n \"text\": \"def get_time(self):\\n if self.dockwidget.exactDate.isChecked():\\n return '{}/{}/P1D'.format(self.time0, self.time0)\\n if self.time0 == '':\\n return self.time1\\n if self.time1 == '':\\n return '{}/{}/P1D'.format(self.time0, datetime.datetime.now().strftime(\\\"%Y-%m-%d\\\"))\\n return '{}/{}/P1D'.format(self.time0, self.time1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"173bac03a418c45ab908e5c3afe50d62\",\n \"score\": \"0.508486\",\n \"text\": \"def set_to_date(self, to_date):\\n self.set_value_into_input_field(self.to_date_textbox_locator, to_date)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fce48d53bdc35c78d1267d00bdda296\",\n \"score\": \"0.5078583\",\n \"text\": \"def set_transaction_date(self, date):\\n self.set_value_into_input_field(self.transaction_date_locator, date)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8313f65084297b5a97acb75f0da4a968\",\n \"score\": \"0.5075052\",\n \"text\": \"def test_date_time(self, inquiry=Interface()):\\n result = Interface.ask(inquiry, \\\"What time is it?\\\")\\n self.assertEqual(result, 'The current time is :' +\\n datetime.utcnow().strftime(\\\"%m-%d-%Y %H:%M\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55fcf36cd3667b12800accd16746c2f9\",\n \"score\": \"0.507464\",\n \"text\": \"def render_date_time_field_into(into, field_added, date_time, *, optional = True, title = 'Date'):\\n if (not optional) or (date_time is not None):\\n if field_added:\\n into.append('\\\\n')\\n else:\\n field_added = True\\n \\n into.append(title)\\n into.append(': ')\\n if (date_time is None):\\n into.append('*none*')\\n else:\\n into.append(format(date_time, DATETIME_FORMAT_CODE))\\n \\n return into, field_added\",\n \"title\": \"\"\n },\n {\n \"docid\": \"601b500f35d2b9fcbf7668ee61a1e167\",\n \"score\": \"0.50739926\",\n \"text\": \"def save(self, *args, **kwargs):\\n self.dt = self.timecalc\\n super(ZeitErfassung, self).save(*args, **kwargs) # Call the \\\"real\\\" save() method.\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ad43f26b3db5e9d75c12944ec03e156\",\n \"score\": \"0.5063896\",\n \"text\": \"def __init__(self, dateformat=None):\\n self.current_date = datetime.datetime.now()\\n self.date_format = dateformat if dateformat else \\\"%d.%m.%Y\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef8dc713321a789408e8d1bdab843a89\",\n \"score\": \"0.5063799\",\n \"text\": \"def bot_time(self, mess, args):\\n return str(datetime.datetime.now()) + \\\" EST/EDT\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a75589c121911836a21d95eedfbcffe\",\n \"score\": \"0.50584316\",\n \"text\": \"def default(self, event, entry, texto_insertado):\\n self.informacion_entry = entry.get()\\n if(self.informacion_entry == \\\"Nombre\\\" or self.informacion_entry == \\\"Codigo de producto\\\" or self.informacion_entry == \\\"Precio\\\"\\n or self.informacion_entry == \\\"Cantidad\\\" or self.informacion_entry == \\\"Marca\\\" or self.informacion_entry == \\\"Proveedor\\\"\\n or self.informacion_entry == \\\"Fecha de entrega\\\"):\\n entry.delete(0, tk.END)\\n entry.config(fg=\\\"black\\\")\\n\\n elif(self.informacion_entry == \\\"\\\"):\\n\\n entry.insert(0,texto_insertado)\\n entry.config(fg=\\\"grey\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"081cbcd3344dfffc38deb5c820cf2aad\",\n \"score\": \"0.5057015\",\n \"text\": \"def format_time(self, record):\\n record.dbtime = time.strftime(\\n \\\"%Y-%m-%d %H:%M:%S\\\", time.localtime(record.created)\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cce69a9a361a1e2a38017542a99025f2\",\n \"score\": \"0.50563437\",\n \"text\": \"def edition_date(self) -> datetime:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94c3ea42bc688f8a3e7bd361b2634426\",\n \"score\": \"0.5051675\",\n \"text\": \"def set_date_or_time(css, date_or_time):\\n world.css_fill(css, date_or_time)\\n e = world.css_find(css).first\\n # hit Enter to apply the changes\\n e._element.send_keys(Keys.ENTER)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5c52b1e2ec722faeb00a1b093a1638e\",\n \"score\": \"0.50502485\",\n \"text\": \"def __str__(self):\\n return f\\\"{self._date}\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb9a36602a8b271c982198be65f206c6\",\n \"score\": \"0.5037006\",\n \"text\": \"def default_date():\\n return datetime.now().strftime(\\\"%y%m%d\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d9ec2a166e9a32ae41a43f737548e14\",\n \"score\": \"0.5025333\",\n \"text\": \"def onUpdateText(self, text):\\n cursor = self.log_textEdit.textCursor()\\n cursor.movePosition(QtGui.QTextCursor.End)\\n cursor.insertText(text)\\n self.log_textEdit.setTextCursor(cursor)\\n self.log_textEdit.ensureCursorVisible()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74818bce52bb4ec41603a8da437dc3e3\",\n \"score\": \"0.50235665\",\n \"text\": \"def get_date_as_string(self, widget):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3215e624a65b5cd631718cc14ba53cdf\",\n \"score\": \"0.5021368\",\n \"text\": \"def display_simple(self):\\n if type(self.date) == datetime.datetime:\\n self.date = self.date.strftime('%m/%d/%Y %H:%M')\\n self.dis_simp_print(self.date, self.username, self.task_name, \\n self.time_spent, self.notes, self.task_number)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59703b4dee8c6167e1447525730bb42f\",\n \"score\": \"0.5016059\",\n \"text\": \"def current_time_changed(self, *args):\\n if not self.dragging:\\n self.set(self.player.current_time.get())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1310d9e39f98240abb6091e1ca63f4de\",\n \"score\": \"0.5011271\",\n \"text\": \"def get_date(self, widget):\\n date = widget.date()\\n return qdate_to_python(date)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6a174c2b242ff4f48aa1c341fe468fd\",\n \"score\": \"0.5010716\",\n \"text\": \"def get_current_date(self):\\n td = timedelta(days=0)\\n return date.today() + td\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b43d11ac91cc5dcaa4e4c47a44604363\",\n \"score\": \"0.49877292\",\n \"text\": \"def new_date(*args):\\n return dt.now()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3dc591fe474b71940db312923b86dd64\",\n \"score\": \"0.49858266\",\n \"text\": \"def pull_current_acc_text(self, d):\\n \\n widge_text = self.desc_text.get(1.0, tk.END)\\n if len(widge_text) <= 1:\\n return\\n \\n else:\\n c = self.conn.cursor()\\n c.execute(\\\"\\\"\\\"INSERT INTO ? VALUES(?)\\\"\\\"\\\", (d, widge_text,))\\n self.conn.commit()\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de9172dc9f7c015665e1a918dfe661dc\",\n \"score\": \"0.49800152\",\n \"text\": \"def update(self):\\n self.birthdayFormatted = self.context.start.strftime(\\\"%d %b %Y\\\")\\n self.birthdayYear = self.context.start.year\\n self.dimensions = \\\"%d × %d cm\\\" % (self.context.width, self.context.height)\\n# self.context.image = self.context.picture # to be able to treat illustration as image in TinyMCE\\n# self.context.picture = self.context.image # temporary before I fixed templates\\n# self.context.description = \\\"%s %s %s %s %d\\\" % (self.dimensions, self.context.genre, self.context.style, self.context.technique, self.context.start.year)\\n body = \\\"\\\"\\n if self.context.body:\\n body = self.context.body.output\\n self.context.description = \\\"%s   %s   %d %s\\\" % (self.context.translate(_(u'%s'% self.context.technique.capitalize())), self.dimensions, self.context.start.year, body)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9600babe3a110b68f80511fdfcb35079\",\n \"score\": \"0.49761078\",\n \"text\": \"def edit_due_date(update, context):\\n print(context.chat_data)\\n\\n # Get the user reply (new name)\\n new_task_due_date = dateparser.parse(update.message.text)\\n\\n if new_task_due_date is None or len(update.message.text) < 4:\\n # Tell the user it's an invalid date and prompt again.\\n text = \\\"Invalid Date! Reenter a correct date\\\"\\n context.bot.sendMessage(update.message.chat_id, text=text)\\n return\\n\\n # Retrieve the chat data and get database object\\n taskid = context.chat_data['taskid']\\n task = Task.objects.get(id=taskid)\\n \\n # Change the deadline and save\\n task.deadline = new_task_due_date\\n task.save()\\n\\n # Reply to user that process is completed\\n text = \\\"✅Task saved!\\\"\\n\\n # Send the message\\n context.bot.sendMessage(update.message.chat_id, text=text)\\n\\n return ConversationHandler.END\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":899,"cells":{"query_id":{"kind":"string","value":"b86cfeadcd0e778c255b943bd13ea05c"},"query":{"kind":"string","value":"This method translates a single line of comma separated values to a dictionary which can be loaded into BigQuery."},"positive_passages":{"kind":"list like","value":[{"docid":"0b4dc123bd3df140da9c0d4e624e267c","score":"0.0","text":"def parse_method(self, string_input):\n # strip out the return characters and quote characters.\n values = re.split(\",\",\n re.sub('\\r\\n', '', re.sub(u'\"', '', string_input)))\n row = dict(zip(('permlink', 'numEmps', 'category', 'city', 'state',\n 'fundedDate', 'raisedAmt', 'raisedCurrency', 'round'),\n values))\n # hash over the `fundedDate` column\n if 'fundedDate' in row:\n row['hash_code'] = hashlib.sha256(row['fundedDate']).hexdigest()\n return row","title":""}],"string":"[\n {\n \"docid\": \"0b4dc123bd3df140da9c0d4e624e267c\",\n \"score\": \"0.0\",\n \"text\": \"def parse_method(self, string_input):\\n # strip out the return characters and quote characters.\\n values = re.split(\\\",\\\",\\n re.sub('\\\\r\\\\n', '', re.sub(u'\\\"', '', string_input)))\\n row = dict(zip(('permlink', 'numEmps', 'category', 'city', 'state',\\n 'fundedDate', 'raisedAmt', 'raisedCurrency', 'round'),\\n values))\\n # hash over the `fundedDate` column\\n if 'fundedDate' in row:\\n row['hash_code'] = hashlib.sha256(row['fundedDate']).hexdigest()\\n return row\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"ac1b5d6fb18bcf081f1eb7a62476847b","score":"0.6674348","text":"def parse_line_to_dict(line, header):\n\tfields = line.strip().split(',')\n\tif len(header) != len(fields):\n\t\traise DataImportFormatError(\"Mismatch in header and data line in incoming data file.\")\n\trecord = {}\n\tfor i in range(len(header)):\n\t\tvalue = fields[i] if header[i] not in INT_TYPES else 0 if len(fields[i]) < 1 else int(float(fields[i])) \n\t\trecord[header[i]] = value\n\treturn record","title":""},{"docid":"b6ad32b853f4494e14e4219300315e02","score":"0.6514467","text":"def _create_dict_instance(self, line):\n new_dict = {}\n for item in line:\n if \"=\" in item:\n # creating list from value and key\n # if \"=\" found\n new_arg = item.split(\"=\", 1)\n key = new_arg[0]\n value = new_arg[1]\n if value[0] == '\"' == value[-1]:\n value = value.replace('\"', \"\").replace(\"_\", \" \")\n else:\n try:\n value = int(value)\n except Exception:\n try:\n value = float(value)\n except Exception:\n continue\n new_dict[key] = value\n return new_dict","title":""},{"docid":"5b1529a873aa25b3ccde288631c2963a","score":"0.64161897","text":"def dictFromString(src, seperator=\",\"):\n lst = src.split(\",\")\n dct = {}\n name = None\n for val in lst:\n if name is None:\n name = val\n else:\n dct[name] = val\n name = None\n return dct","title":""},{"docid":"814c4e966457f6d614e33ff4f42cf5c6","score":"0.59911543","text":"def getData()->dict:\r\n datas = {}\r\n with open(\"../data/data.txt\", \"r\") as f:\r\n data_list = f.readlines()\r\n for i in range(len(data_list)):\r\n str = data_list[i].replace(\"\\n\", \"\")\r\n temp = str.split(',')\r\n datas[i] = temp\r\n return datas","title":""},{"docid":"9e929bf5a9686ec6beca9576b8542bea","score":"0.59718925","text":"def parse_line(self, line):\n try:\n key, value = [x.strip() for x in line.split('=', 1)]\n except ValueError:\n return\n value = value.strip('\"')\n return self.parse_value(key, value)","title":""},{"docid":"f12f28635d949ca98c8a0a8afaf2625f","score":"0.5938792","text":"def str_handler(l):\n l = l.rstrip(\"\\n\").strip(\" ;\")\n cols = l.split(\";\")\n dic = {}\n for col in cols:\n key, value = col.split(\"=\")\n value = parse_escape_char(value)\n if key == \"fields\":\n fields_list = get_fields(value)\n dic[key] = fields_list\n elif key == \"col_cnt\":\n value = int(value)\n dic[key] = value\n elif key == \"ratio\":\n value = float(value)\n dic[key] = value\n elif key == \"args\":\n values = value.split(\",\")\n dic[key] = values\n elif key == \"sep\":\n value = parse_escape_char(value)\n dic[key] = value\n else:\n values = value.split(\",\")\n if len(values) == 1:\n values = values[0]\n dic[key] = values\n \n return dic","title":""},{"docid":"d9495ad20a23f2c41da34b45a96d2304","score":"0.59204066","text":"def parse_file_line(line: str) -> dict:\n\n ret = {}\n\n product_attrs = line.split(',')\n\n # Product PK (id)\n ret['pk'] = int(product_attrs[PK].strip())\n\n # Product description\n ret['description'] = product_attrs[DESC].strip()\n\n # Product last sold date\n ret['last_sold'] = datetime.strptime(product_attrs[LAST_SOLD].strip(), \"%m/%d/%Y\").date()\n\n # Product shelf life in days\n ret['shelf_life'] = int(product_attrs[SHELF_LIFE].strip()[0])\n\n # Product department\n ret['department'] = product_attrs[DEPT].strip().upper()\n\n # Product price\n ret['price'] = Decimal(product_attrs[PRICE].strip()[1:])\n\n # Product unit\n ret['unit'] = product_attrs[UNIT].strip().upper()\n\n # Product units for price\n ret['x_for'] = int(product_attrs[XFOR].strip())\n\n # Product cost\n ret['cost'] = Decimal(product_attrs[COST].strip()[1:])\n\n return ret","title":""},{"docid":"4f93c6bcb8bde217674294c940a8ae71","score":"0.58666193","text":"def _line_to_dict(self, file, field_names):\n for line in file:\n decoded_line = line.decode(settings.IPGEOBASE_FILE_ENCODING)\n delimiter = settings.IPGEOBASE_FILE_FIELDS_DELIMITER\n yield self._extract_data_from_line(decoded_line, field_names, delimiter)","title":""},{"docid":"68f24174137d77795449f94f5c1de03b","score":"0.5853324","text":"def turn_line_into_map(line, column_to_enum, dropdown_map):\n line_map = {}\n\n for i, local_e in enumerate(column_to_enum):\n # Split if enumerator is present.\n n = line[i].count(ENUMERATOR + ' ')\n items = np.empty(n+1, dtype=object)\n items = line[i].split(ENUMERATOR + ' ')\n\n res = ''\n for item in items:\n if res != '':\n res = res + ENUMERATOR + ' '\n\n if local_e in dropdown_map:\n # Remove \"1. \"\n item = re.sub(\"^\\d+\\. \", \"\", item)\n # Turn \"1 Interesting\" into \"1 - Interesting\"\n item = re.sub(\"^(\\d+) ([^-])\", r\"\\1 - \\2\", item)\n # item = re.sub(\"^(\\d+)\\. (\\d) ([^-])\", r\"\\1. \\2 - \\3\", item)\n # if local_e == Fields.Quality:\n # print(\"item\", item)\n\n if local_e in NUMERICAL_HEADINGS:\n item = api.turn_text_into_dropdown(item, enum_text_to_id[local_e])\n\n res = res + str(item)\n # str(api.turn_text_into_dropdown(item, dropdown_map[local_e]))\n else:\n res = res + item\n\n # To be consistent with Affinity.\n if local_e == Fields.OrganizationURL and res == '':\n res = 'None'\n\n line_map[local_e] = res\n\n return line_map","title":""},{"docid":"2a55712dbe13bc40609e1d3657941a60","score":"0.5787207","text":"def import_dict(d: str = None) -> dict:\n if not d:\n cpath = os.path.dirname(os.path.realpath(__file__))\n d = os.path.join(cpath, \"sentiment_dict_en.txt\")\n with open(d, 'r') as rf:\n reader = csv.reader(rf, delimiter=\",\")\n # Header has no commas (manually checked)\n reader = [r for r in reader if len(r) == 2]\n ret = dict(zip([r[0] for r in reader], [r[1] for r in reader]))\n return ret","title":""},{"docid":"95d411fd0ad1522a840b2b7b83071bbb","score":"0.57542294","text":"def convert_csv_to_dict(csv_file):\n # Holds all corresponding lines as a list of lines\n csv_dict = {\"SUPERBLOCK\": [],\n \"GROUP\": [],\n \"BFREE\": [],\n \"IFREE\": [],\n \"INODE\": [],\n \"DIRENT\": [],\n \"INDIRECT\": []}\n\n for line in csv_file:\n line = line.split(\",\")\n csv_dict[line[0]].append(line)\n \n return csv_dict","title":""},{"docid":"af4ee47ddce43d69e3e70d0a2f4ca1c7","score":"0.5753948","text":"def _ParseBluefaceLogs(label, line):\n output = {}\n header = label.split(\",\")\n data = line.split(\",\")\n for k, v in zip(header, data):\n output[k.lower()] = v\n return output","title":""},{"docid":"f7908f309b52921160bf8b65d288f7a4","score":"0.57516104","text":"def parse_csv(lines):\n data =[]\n # pdb.set_trace()\n names = [item.strip(' ,\\n') for item in lines[0].split(',') if item!=\"#\"]\n # endlines = [line[-100:] for line in lines]\n for i,line in enumerate(lines[1:]):\n # if line[-100:] in endlines[:i+1]:\n # continue\n items = line.split(',')\n tmp = {}\n for i in range(len(names)):\n try:\n value = int(items[i].strip())\n except ValueError:\n try:\n value = float(items[i].strip())\n except ValueError:\n value = items[i].strip()\n except IndexError:\n print items \n tmp[names[i]] = value\n data.append(tmp)\n return data","title":""},{"docid":"bc13adad67f692c0ac96075062e5b6d9","score":"0.5728059","text":"def specific2kv(s):\r\n ret = {}\r\n for e in s.strip().split(','):\r\n if e:\r\n k,v = e.split(\":\")\r\n ret[k] = v\r\n\r\n return ret","title":""},{"docid":"a27afc39bc31acb2d7504d6247769737","score":"0.5621865","text":"def mapper(self, _, line):\n tweet=line.replace(\"\\,\",\"\")\n tweet=tweet.split(\",\")\n date=tweet[4]\n hour=tweet[5]\n #Return date/hour tuple as key\n yield (date,hour),1","title":""},{"docid":"a3e29bf1d45a903a59806284207aff56","score":"0.5603128","text":"def parse_line(line):\n return [element.strip() for element in line.strip().split('=')[1].strip()[1:-1].split(',')]","title":""},{"docid":"9d4f843ff4443fd82faefca874f8c8d5","score":"0.5588743","text":"def parse_line(line):\n entries = line.split(\",\")\n entries = Publisher.clean_entries(entries)\n if len(entries) == 2:\n name = entries[0]\n email = entries[1]\n if validate_email(email) and name:\n data = dict(name=name, email=email)\n return True, data\n return False, {}","title":""},{"docid":"b0be551aecc98989d370c732e64f7338","score":"0.55834085","text":"def parse_line(line):\n tmp_res = OrderedDict()\n # split line into array of \"key=value\" metrics\n metrics = line.split()\n for metric in metrics:\n key, value = metric.split('=')\n tmp_res[key] = value\n\n return tmp_res","title":""},{"docid":"3a4f329145a857a20a4f1fb6fb5beb83","score":"0.55626476","text":"def load_line(self, line: str) -> JsonDict: # pylint: disable=no-self-use\n return json.loads(line)","title":""},{"docid":"fe6f239feba69ecb3e79462802fcdb06","score":"0.54828405","text":"def deserialize(self, data):\n values = data.split(',')\n return self.deserializeHelper(values)","title":""},{"docid":"cfffc17d34900dde4f1c82fc5802fd7f","score":"0.5478965","text":"def parse_line(parts):\n\tline_parts = dict()\n\tfor pair in parts:\n\t\tpair = pair.split(\"=\", 1)\n\t\tline_parts[pair[0]] = pair[1]\n\treturn line_parts","title":""},{"docid":"a5513e63f2e4c7b54b0f2f2f74cfbf1d","score":"0.54685783","text":"def parseline(line):\n fields = line.split(',')\n custid = int(fields[0])\n order_amount = float(fields[2])\n return (custid, order_amount)","title":""},{"docid":"ae5148d8088c2fb3afe050c3054d8d00","score":"0.5463482","text":"def processData(contents):\n my_dictionary = {}\n csv_file=csv.reader(contents)\n csv_file.next()\n\n for row in csv_file:\n try:\n row[2] = datetime.datetime.strptime(row[2], \"%d/%m/%Y\").date()\n except ValueError:\n number = int(row[0])\n line = int(row[0])+1\n logger = logging.getLogger(\"assignment 2\")\n logger.error(\"Error processing line#{} for ID #{}.\".format(line, number))\n my_dictionary[int(row[0])] = (row[1], row[2])\n return my_dictionary","title":""},{"docid":"4e447e64755674a22f7a5e994fb6d02e","score":"0.5447638","text":"def parseentry(row, cols):\n entry = {}\n for c, r in zip(cols, row):\n if r:\n if c in helper.namefields:\n a = [k.split(',') for k in r.split(\";\")]\n if a:\n entry[c] = a\n pass\n else:\n entry[c] = r\n\n return entry","title":""},{"docid":"6f5b4d34960271e3331e55a0fa3e91e9","score":"0.54375726","text":"def tokenize(self, line):\n tokens = line.split()\n return {'address': tokens[1], 'taken': int(tokens[2])}","title":""},{"docid":"25095d05e102425d3227c6b031d412c5","score":"0.542845","text":"def parse_line(line):\n return {m.group('label'): m.group('contents')\n for m in ARGUMENT_TAG.finditer(line)}","title":""},{"docid":"400715f865f28c2ff5b1563f43be228b","score":"0.54214805","text":"def read_csv_as_dict(path, delimiter=','):\n\n with open(path, 'r', newline='', encoding='utf-8') as csv_file:\n data = []\n reader = csv.DictReader(csv_file, delimiter=delimiter)\n for line in reader:\n data.append(line) # OrderedDict()\n # data.append(dict(line)) # convert OrderedDict() to dict\n\n return data","title":""},{"docid":"a001914d55a555b62e7b92e2d4891945","score":"0.5420997","text":"def create_ordered_dictionary_from(self, raw):\n li = self.get_list_by_newline_from(raw)\n if len(li) == 0:\n return OrderedDict()\n orDic = OrderedDict()\n # To ensure the key row is only used here, pop it\n for key in li.pop(0).split(','):\n orDic[key.strip()] = []\n for row in li:\n i = 0 # Used for accessing the coloumn of a row.\n for key in orDic:\n orDic[key].append(row.split(',')[i].strip())\n i += 1\n return orDic","title":""},{"docid":"b3a65356e98c611c9754d320ea6b4a6f","score":"0.54040056","text":"def decode_csv(string_like): # type: (str) -> np.array\n stream = StringIO(string_like)\n request_list = list(csv.DictReader(stream))\n if \"inputs\" in request_list[0].keys():\n return {\"inputs\": [entry[\"inputs\"] for entry in request_list]}\n else:\n return {\"inputs\": request_list}","title":""},{"docid":"10a019731ad019378439b0f2ffa96ad2","score":"0.5396028","text":"def _parse_record(self, record, customization=None):\n d = {}\n\n if not record.startswith('@'):\n logger.debug('The record does not start with @. Return empty dict.')\n return {}\n\n # prepare record\n record = '\\n'.join([i.strip() for i in record.split('\\n')])\n if '}\\n' in record:\n record, rubbish = record.replace('\\r\\n', '\\n').replace('\\r', '\\n').rsplit('}\\n', 1)\n\n # if a string record, put it in the replace_dict\n if record.lower().startswith('@string'):\n logger.debug('The record startswith @string')\n key, val = [i.strip().strip('\"').strip('{').strip('}').replace('\\n', ' ') for i in record.split('{', 1)[1].strip('\\n').strip(',').strip('}').split('=')]\n self.replace_dict[key] = val\n logger.debug('Return a dict')\n return d\n\n # for each line in record\n logger.debug('Split the record of its lines and treat them')\n kvs = [i.strip() for i in record.split(',\\n')]\n inkey = \"\"\n inval = \"\"\n for kv in kvs:\n logger.debug('Inspect: %s', kv)\n if kv.startswith('@') and not inkey:\n # it is the start of the record - set the bibtype and citekey (id)\n logger.debug('Line starts with @ and the key is not stored yet.')\n bibtype, id = kv.split('{', 1)\n bibtype = self._add_key(bibtype)\n id = id.strip('}').strip(',')\n elif '=' in kv and not inkey:\n # it is a line with a key value pair on it\n logger.debug('Line contains a key-pair value and the key is not stored yet.')\n key, val = [i.strip() for i in kv.split('=', 1)]\n key = self._add_key(key)\n # if it looks like the value spans lines, store details for next loop\n if (val.count('{') != val.count('}')) or (val.startswith('\"') and not val.replace('}', '').endswith('\"')):\n logger.debug('The line is not ending the record.')\n inkey = key\n inval = val\n else:\n logger.debug('The line is the end of the record.')\n d[key] = self._add_val(val)\n elif inkey:\n logger.debug('Continues the previous line to complete the key pair value...')\n # if this line continues the value from a previous line, append\n inval += ', ' + kv\n # if it looks like this line finishes the value, store it and clear for next loop\n if (inval.startswith('{') and inval.endswith('}')) or (inval.startswith('\"') and inval.endswith('\"')):\n logger.debug('This line represents the end of the current key-pair value')\n d[inkey] = self._add_val(inval)\n inkey = \"\"\n inval = \"\"\n else:\n logger.debug('This line does NOT represent the end of the current key-pair value')\n\n logger.debug('All lines have been treated')\n if not d:\n logger.debug('The dict is empty, return it.')\n return d\n\n # put author names into persons list\n if 'author_data' in d:\n self.persons = [i for i in d['author_data'].split('\\n')]\n del d['author_data']\n\n d['type'] = bibtype\n d['id'] = id\n if not self.has_metadata and 'type' in d:\n if d['type'] == 'personal bibliography' or d['type'] == 'comment':\n self.has_metadata = True\n\n if customization is None:\n logger.debug('No customization to apply, return dict')\n return d\n else:\n # apply any customizations to the record object then return it\n logger.debug('Apply customizations and return dict')\n return customization(d)","title":""},{"docid":"4816d7ca57b6fae0d1e7b21f7eeb8503","score":"0.5379898","text":"def csv_to_dict(filename,ret={},key=\"Specific Implementation\"):\n csvtd = csv_translation_dict\n with open(filename,\"r\") as f:\n header = None\n for row in csv.reader(f):\n if header is None:\n header = row\n else:\n cur = {}\n for k,v in zip(header,row):\n if k in csvtd and csvtd[k][0] not in cur:\n cur[csvtd[k][0]] = parse_num(v)*csvtd[k][1]\n elif k == key: ret[v] = cur\n elif k == \"Submission\": cur[k] = v\n return ret","title":""},{"docid":"59773abdb4fadcc8568503769c63b4f5","score":"0.53607273","text":"def _read_csv(csv_ffn, header_line):\n df = pandas.read_csv(csv_ffn, header=header_line)\n as_dict = df.to_dict(orient='list')\n return as_dict","title":""},{"docid":"2a98cb30f0bcd9188daeb0e519b1376a","score":"0.53544474","text":"def _extract_fields(self, line):\n acc = {}\n field_tokens = line.split(\"\\t\")\n for field_token in field_tokens:\n kv_tokens = field_token.split('::')\n if len(kv_tokens) == 2:\n (key, value) = kv_tokens\n acc[key] = value\n\n return acc","title":""},{"docid":"2fefa2a84cd375622ca95eefbfd99db9","score":"0.5346579","text":"def parse_method(self, string_input):\n # Strip out carriage return, newline and quote characters.\n values = re.split(\",\", re.sub('\\r\\n', '', re.sub('\"', '',\n string_input)))\n row = dict(\n zip(('name', 'surname', 'age'),\n values))\n return row","title":""},{"docid":"06afedfa95a9cafd87bc373a15bfe2fb","score":"0.5330888","text":"def parse_csv_line(line):\n\n if line == '':\n # return np.empty(0)\n return []\n\n values = List()\n value = ''\n quotes = False\n\n for character in line:\n if character == '\\n':\n break\n elif character == '\"':\n quotes = not quotes\n elif not quotes and character == ',':\n values.append(value.strip())\n value = ''\n else:\n value += character\n\n values.append(value)\n return values.to_array()","title":""},{"docid":"c483df4761e85d8d4635469a946990c6","score":"0.5324691","text":"def getParams(self, line):\n \n line = line.strip()\n line = line.strip('{}')\n \n str1, str2 = line.split(',')\n \n str1, str_D = str1.split(':')\n str_D = str_D.strip(' ')\n str_D = str_D.strip('\\\"')\n D = int(str_D)\n\n str2, str_T = str2.split(':')\n str_T = str_T.strip(' ')\n str_T = str_T.strip('\\\"')\n T = int(str_T)\n\n return D,T","title":""},{"docid":"4a46ed15f24eb524a57368da6204ea0a","score":"0.5286351","text":"def _input_line(line):\n l = line.split()\n r = {\n \"year\": int(l[0]),\n \"month\": int(l[1]),\n \"day\": int(l[2]),\n \"hour\": int(l[3]),\n \"minute\": int(l[4]),\n \"second\": float(l[5]),\n \"mag\": float(l[9]),\n \"lat\": float(l[6]),\n \"lon\": float(l[7]),\n \"dep\": float(l[8]),\n #\"evid\": int(l[10]),\n }\n r['timestamp'] = _ts(datetime.datetime(r['year'], r['month'], r['day'],\n r['hour'], r['minute'], 0)) + r['second']\n return r","title":""},{"docid":"d73a2999781178f95c28a620f7b3e86b","score":"0.5284623","text":"def csv_to_dict(self, source_path, type_='stream_gauges'):\n print 'parsing to dict'\n delimiter = ','\n working_dict = {}\n result = {}\n with open(source_path, 'r') as data_file:\n data = csv.reader(data_file, delimiter=delimiter)\n headers = next(data)[1:]\n for row in data:\n temp_dict = {}\n id = row[0]\n values = []\n if type_ == 'stream_gauges':\n name = '{} {}'.format(row[0], row[3])\n for x in row[1:]:\n # print row\n try:\n values.append(str(x))\n # print str(x)\n except ValueError:\n try:\n values.append(int(x))\n # print int(x)\n except ValueError:\n try:\n values.append(float(x))\n # print float(x)\n except ValueError:\n print(\"Skipping value '{}' that cannot be converted \" +\n \"to a number - see following row: {}\"\n .format(x, delimiter.join(row)))\n values.append(0)\n elif type_ == 'floods':\n name = 'Rank {}'.format(row[0])\n print 'name: {}'.format(name)\n for i, x in enumerate(row[1:]):\n if i == 0:\n values.append(datetime.strptime(x, '%m/%d/%Y'))\n else:\n values.append(int(x))\n\n for i in range(len(values)):\n if values[i]:\n temp_dict[headers[i]] = values[i]\n\n result[name] = temp_dict\n\n if type_ == 'stream_gauges':\n temp_dict['Dataframe'] = None\n temp_dict['Rating'] = None\n temp_dict['ID'] = id\n elif type_ == 'floods':\n pass\n working_dict.update({name: temp_dict})\n return working_dict","title":""},{"docid":"94a293fa04f134e4213de59aab272122","score":"0.5274642","text":"def __parse_line(self, line: str):\n exploded_line: List[str] = line.split(self.__column_delimiter)\n\n country_raw = exploded_line[self.__column_country]\n region_raw = exploded_line[self.__column_region]\n city_raw = exploded_line[self.__column_city]\n population_raw = int(exploded_line[self.__column_population]) if exploded_line[self.__column_population] else 0\n long_raw = float(exploded_line[self.__column_longitude]) if exploded_line[self.__column_longitude] else None\n lat_raw = float(exploded_line[self.__column_latitude]) if exploded_line[self.__column_latitude] else None\n\n if population_raw < self.minimum_population:\n return self\n\n if long_raw is None or lat_raw is None:\n return self\n\n country: Country\n if country_raw in self.__database.countries:\n country = self.__database.countries[country_raw]\n else:\n country = Country(country_raw)\n self.__database.countries[country_raw] = country\n\n region: Region\n if f\"{country_raw}-{region_raw}\" in self.__database.countries:\n region = self.__database[f\"{country_raw}-{region_raw}\"]\n else:\n region = Region(region_raw, country)\n country.add_region(region)\n self.__database.regions[f\"{country_raw}-{region_raw}\"] = region\n\n city: City\n if f\"{country_raw}-{region_raw}-{city_raw}\" in self.__database.cities:\n city = self.__database.cities[f\"{country_raw}-{region_raw}-{city_raw}\"]\n else:\n city = City(city_raw, region, country)\n city.set_population(population_raw)\n city.set_coordinates(longitude=long_raw, latitude=lat_raw)\n country.add_city(city)\n region.add_city(city)\n self.__database.cities[f\"{country_raw}-{region_raw}-{city_raw}\"] = city\n\n return self","title":""},{"docid":"db3b644f938ea728d805d29ee87d2167","score":"0.5270934","text":"def parse_csv_table(readable, separator = \",\"):\n data = defaultdict(dict)\n \n # First line is the columns\n columns = filter(None, readable.readline().strip().split(separator))\n\n for line in readable:\n row, rest = line.strip().split(separator, 1)\n for value, col in zip(rest.split(separator), columns):\n data[row][col] = float(value)\n\n return data","title":""},{"docid":"ae82105ad16744eac636ce7e436c89a6","score":"0.52584505","text":"def _convert_dict(self, value):\n dict_value = value.strip(\"{\")\n result = {}\n while True:\n (key, found, rest) = dict_value.partition(\"=\")\n if not found:\n break\n\n key = key.strip('\"')\n\n item = None\n new_dict_value = \"\"\n\n if rest[0] == '\"':\n start = 1\n while True:\n pos = rest.find('\"', start)\n if pos == len(rest):\n # Error, we did not find the end of the string\n raise Exception(\"Wrong format %s\" % value)\n\n if rest[pos - 1] != \"\\\\\":\n # found a non escaped \"\n item = rest[1:pos]\n new_dict_value = rest[pos + 1 :].strip(\", \")\n break\n\n start = pos + 1\n else:\n (item, comma, new_dict_value) = rest.partition(\", \")\n if not comma:\n (item, curly, new_dict_value) = rest.partition(\"}\")\n if not curly:\n raise Exception(\"Wrong format %s\" % value)\n\n result[key] = self._convert_single_value(item)\n dict_value = new_dict_value\n\n return result","title":""},{"docid":"0064f7ad2b08f33759feecb8b2a1e802","score":"0.5256032","text":"def loc_split(self,raw_loc):\n return {raw_loc.split(\",\")[0].strip('\"'):raw_loc.split(\",\")[1].strip('\"')}","title":""},{"docid":"a9b6af951d6666f5cc5abf676d67d6e7","score":"0.5246892","text":"def turn_csv_into_map(local_csv_fields, dropdown_map):\n fields = []\n for line in local_csv_fields:\n fields.append(turn_line_into_map(line, \n CSV_COLUMN_TO_ENUM, dropdown_map))\n\n return fields","title":""},{"docid":"f79df0dbc12be829f7bd989d2c473089","score":"0.52441436","text":"def parse_bel_resource(lines: Iterable[str]) -> Dict:\n lines = list(lines)\n\n value_line = 1 + max(\n index\n for index, line in enumerate(lines)\n if '[Values]' == line.strip()\n )\n\n metadata_config = ConfigParser(strict=False)\n metadata_config.optionxform = lambda option: option\n metadata_config.read_file(lines[:value_line])\n\n delimiter = metadata_config['Processing']['DelimiterString']\n\n value_dict = dict(\n _get_bel_resource_kvp(line, delimiter)\n for line in lines[value_line:]\n )\n\n res = {\n key: dict(values)\n for key, values in metadata_config.items()\n }\n res['Values'] = value_dict\n\n return res","title":""},{"docid":"45db0418eb0244995c742504c23f623c","score":"0.5238875","text":"def _parse_values(data: str):\n res = {}\n for l in filter(lambda s: s != '', data.splitlines()):\n try:\n k, v = l.strip().split('=')\n except ValueError:\n raise serial.SerialException(\"Couldn't parse data {!r}\".format(l))\n else:\n res[k] = v\n return res","title":""},{"docid":"ffce026b36ac238ff2530e525b5602d2","score":"0.523299","text":"def parse_to_dict(self,fsf_lines):\n fsf_dict=dict()\n fsf_line_key=list()\n for line in fsf_lines:\n line=line.strip()\n if len(line) > 0 and line[0] != \"#\":\n set_line=re.search(\"set [^ ]* [^s]*\",line.strip())\n if set_line:\n split_line=line.split(\" \")\n if len(split_line) > 3:\n #combine and strip mri values that have spaces in them\n temp=list()\n for i in range (2,len(split_line)):\n temp.append(split_line[i])\n cleaned=' '.join(temp)\n else:\n cleaned=split_line[2]#.strip('\\\"')\n fsf_dict[split_line[1]]=cleaned\n fsf_line_key.append(split_line[1])\n return fsf_dict,fsf_line_key","title":""},{"docid":"7ba3574ed02c09c112515c11c493df96","score":"0.52149475","text":"def create_dictionary(log_data):\n dict_cvar = {}\n split_log_data = log_data.split('\\n')\n for line in split_log_data:\n if 'cvar' in line:\n parse_string = line[19:].rstrip(')').split(',')\n dict_cvar[parse_string[0]] = parse_string[1]\n return dict_cvar","title":""},{"docid":"895edae8d241d967919182115796c937","score":"0.5214545","text":"def morph2dict(line):\n d = {}\n _ = line.strip().split('\\t')\n try:\n _meta = _[1].split(',')\n d[\"surface\"] = _[0]\n d[\"pos\"] = _meta[0]\n d[\"pos1\"] = _meta[1]\n d[\"base\"] = _meta[6]\n return d\n except IndexError:\n return None","title":""},{"docid":"871c65da3772d56c8cba8f1fe67c7c65","score":"0.52137345","text":"def map1(row):\n rr = row.split(\",\")\n return [(rr[1],(rr[0],float(rr[2])))]","title":""},{"docid":"dcef199456de8a31c7822bb61ff1120c","score":"0.52110595","text":"def _atq_line(self, line):\n split = str(line).split()\n return dict(id = int(split[0]), \n when = datetime.datetime.strptime(\n (' ').join(split[1:6]),\n AT_OUTPUT_DATETIME_FORMAT),\n queue = split[6],\n who = split[7])","title":""},{"docid":"57e9fc69de0df307b2bd7a2cfed62880","score":"0.51856875","text":"def dict_creator(file):\n large_lst = []\n final_dict = {}\n f = open(file, 'r')\n for i in f.readlines():\n stats = i.split(',')\n large_lst.append(stats)\n for i in large_lst:\n i[-1] = i[-1][:-1]\n final_dict[i[0]] = i[1:]\n return final_dict","title":""},{"docid":"9d7bf9311ad1c644390075de41c125f3","score":"0.5176835","text":"def _parse_attributes(line: str) -> dict:\n attr_def = map(\n lambda s: re.sub(r\"[\\\"'](.*)[\\\"']\", r\"\\1\", s),\n line.split()\n )\n\n return {\n attr: val\n for attr, val in map(lambda s: s.split('='), attr_def)\n }","title":""},{"docid":"88ce50a240a4242aafe227d0d97da78a","score":"0.5175366","text":"def parse(data):\n\t\tresult = {}\n\t\tdata_array = data.strip().split('; ')\n\t\tfor datum in data_array:\n\t\t\tkey, value = datum.split(': ')\n\t\t\tkey = key.lower()\n\t\t\tif key == 'washer_states':\n\t\t\t\tresult[key] = [int(x) for x in value.split(', ')]\n\t\t\telse:\n\t\t\t\tresult[key] = value\n\t\treturn result","title":""},{"docid":"ff79eade237d29024cf1d5af73f55b78","score":"0.5172581","text":"def proccessed(self, column):\n r = {}\n for c in column.split(\",\"):\n # print(\"Next column: %s\" % c)\n moss, idss = self.decode(c.strip())\n ids = set(moss.values_list(\"id\", flat=True))\n for i_set in idss:\n ids = ids.intersection(i_set)\n r[c.strip()] = ids.copy()\n return r","title":""},{"docid":"eca7e4b38dc94546e3c84b2c7715f6e5","score":"0.5163745","text":"def process_line(line: str):\n line = line.rstrip(\"\\n\") # del \\n\n line = line.split(\"\\t\")[1] # split input line and get key value\n # get size, mean var\n chunk_size, mean, var = tuple(map(float, line.split(\", \")))\n return chunk_size, mean, var","title":""},{"docid":"6e1cf0a3d55567746de961f36844cc6d","score":"0.515424","text":"def conver_csv_into_dict(filename):\n with open(filename) as File:\n\n if \"images\" in filename:\n csv_dict_reader = list(DictReader(File,fieldnames = [\"ID\", \"IMAGE_NAME\", \"Latitude\", \"Longitude\", \"Date\"]))\n elif \"sites\" in filename:\n csv_dict_reader = list(DictReader(File,fieldnames = [\"ID\", \"SITE_NAME\", \"Latitude\", \"Longitude\", \"Date\"]))\n return csv_dict_reader","title":""},{"docid":"b4022cc8e91188a0ed179ca216a5b3ee","score":"0.51479685","text":"def parse(lines):\n data = {}\n for line in lines:\n line = line.strip()\n name, job = line.split(\": \")\n try:\n name1, operation, name2 = job.split(\" \")\n job = (operation, name1, name2)\n except ValueError:\n operation = \"yell\"\n val = int(job)\n job = (operation, val)\n data[name] = job\n return data","title":""},{"docid":"bc705cf3d505b32d641ea30e91b2c17e","score":"0.5146445","text":"def normalize_line(line):\n inside_str = False\n str = ''\n for i in line:\n if i == '\"':\n inside_str = not inside_str\n if not inside_str and i == ',':\n str += '#'\n else:\n str += i \n str = str.replace('\\'', '`')\n return str","title":""},{"docid":"b435bf71ffd7c6a9ddbf772a851eb641","score":"0.5137678","text":"def load_csv_as_dict(Path, start_row=1, skip_rows=None, conv_numstrings=True, \\\n empty_entries=True):\n import csv\n csv_read = csv.DictReader(open(Path))\n i = 1 \n # Shift skip_rows back 1 to correspond with correct csv_read entry indices\n if skip_rows is not None:\n skip_rows = [skip_rows[k] - 1 for k in range(len(skip_rows))]\n \n\n Dict = {} # Empty dictionary to return\n for row in csv_read:\n if start_row == 1: # Build dictionary without skipping rows\n if skip_rows is not None:\n if i in skip_rows: # Skip rows specified in input list\n i += 1 \n continue\n \n for key, value in row.items():\n Dict.setdefault(key, []).append(value)\n \n i += 1 \n continue\n \n if i < start_row - 1: # Skip row\n i += 1 \n continue\n \n if i == start_row - 1: # Redefine row keys\n keys = []\n for column, value in row.iteritems():\n keys.append(value)\n i += 1 \n continue \n \n if i > start_row - 1: # Build new dictionary from column entries\n if skip_rows is not None:\n if i in skip_rows: # Skip rows specified in input list\n i += 1 \n continue\n \n for key, (column, value) in zip(keys, row.iteritems()):\n Dict.setdefault(key, []).append(value)\n \n i += 1 \n \n # Convert number strings to floats\n if conv_numstrings: dict_conv_numstrings(Dict)\n \n # Remove empty entires\n if empty_entries:\n for key, value in Dict.items(): # Filter out empty '' values\n if not value.__class__ == float:\n Dict[key] = list(filter(lambda a: a != '', value))\n \n return Dict","title":""},{"docid":"d30d503ff18794cd2f00144ca1c79e59","score":"0.5134373","text":"def parse_line(self, line):\n def parse_arg(a):\n assert a is not None\n if a == 'nil' or a == 'null':\n return None\n elif a == '{}':\n return {}\n elif a == '[]':\n return []\n elif a.isdigit():\n return int(a)\n elif a.startswith('$'):\n return resolve_var(a[1:], self.vars)\n elif a and a[0] == \"[\" and a[-1] == \"]\":\n return [parse_arg(x) for x in a[1:-1].split(',')]\n elif a and a[0] == \"{\" and a[-1] == \"}\":\n return list_or_dict([parse_arg(x) for x in a[1:-1].split(',')])\n elif '=' in a:\n k, v = a.split('=', 1)\n return {k.strip(): v.strip()}\n elif '://' in a: # special case for protocols\n return a\n elif ':' in a:\n k, v = a.split(':', 1)\n if ',' in v:\n if v[0] == '{' and v[-1] == '}': # unnecesary, but may help readability\n v = v[1:-1]\n vv = list_or_dict([parse_arg(x.strip())\n for x in v.split(',')])\n return {k.strip(): vv}\n\n return {k.strip(): parse_arg(v.strip())}\n # literal\n return a\n\n def list_or_dict(ld):\n if not ld:\n return []\n if all([type(x) == dict for x in ld]):\n r = {}\n for d in ld:\n r.update(d)\n return r\n return ld\n\n def resolve_var(expr, vars):\n if not type(vars) in (dict, list):\n return '%s.%s' % (str(vars), expr)\n if '.' in expr:\n (pre, post) = expr.split('.', 1)\n if pre.isdigit():\n pre = int(pre)\n return resolve_var(post, vars[pre])\n if expr.isdigit():\n expr = int(expr)\n return vars[expr]\n\n def parse_params(line, sep):\n if not sep in line:\n return []\n line = line[line.index(sep):].strip()\n if line == '{}':\n return {}\n if line == '[]':\n return []\n if line.startswith('{'):\n return json.loads(line)\n\n params = shlex.split(line)\n return list_or_dict([parse_arg(x) for x in params])\n\n\n if line.startswith('#'):\n return None\n cmd = shlex.split(line)\n if not cmd:\n return None\n if len(cmd) >= 3 and cmd[1] == '=':\n params = parse_params(line, '=')\n return {\n 'method': parse_arg(cmd[2]),\n 'params': params,\n 'assign_to': cmd[0]\n }\n\n return {\n 'method': parse_arg(cmd[0]),\n 'params': parse_params(line, ' ')\n }","title":""},{"docid":"d24d92de04ba25793c06d0cf2a52e871","score":"0.5130751","text":"def get_from_csv2(self, line):\n print(len(line))\n (self.name, self.webpage, self.institution,\n self.country, self.address, self.lat,\n self.long, self.exp_theor, self.fields,\n self.people, self.atom, self.comment) = line\n self.lat = float(self.lat)\n self.long = float(self.long)","title":""},{"docid":"82fed3c9634c05eb18ea258ade17de2e","score":"0.5123542","text":"def csv2dict(csvname, delimiter=','):\n lines = open(csvname, 'r').readlines()\n header = lines[0].rstrip().split(delimiter)\n lines = lines[1:]\n nlines = len(lines)\n\n dict_list = [{} for _ in range(nlines)]\n for i, line in enumerate(lines):\n line2 = convert_symbol(line.rstrip(), delimiter, '|')\n items = line2.split(delimiter)\n items2 = [s.replace('|', delimiter) for s in items]\n dict_list[i] = {k:items2[j] for j,k in enumerate(header)}\n\n return dict_list","title":""},{"docid":"7af55d2906d7249f7da0771801c0f22b","score":"0.51226807","text":"def parse_param_file(param_file):\n with gfile.GFile(param_file, 'r') as fh:\n lines = fh.readlines()\n d = {}\n for l in lines:\n l = l.rstrip('\\n')\n splits = l.split(':')\n key = splits[0]\n val_ = splits[1].strip()\n if not val_:\n val = ''\n else:\n try:\n val = ast.literal_eval(val_)\n except (ValueError, SyntaxError):\n val = str(val_)\n d[key] = val\n return d","title":""},{"docid":"42a175bad53d70487f25534bb8691c0b","score":"0.5103795","text":"def _get_values(line):\n return line[line.find(\"` VALUES \") + 9 :]","title":""},{"docid":"ad1ebac9e2cefed0374c631858d4b1c4","score":"0.5103102","text":"def process_record(raw_record):\n\n record = {}\n \n raw_record = raw_record.replace(os.linesep, ' ')\n field_record = raw_record.split(' ')\n for fld in field_record:\n fld = fld.split(':')\n try:\n record[fld[0]] = fld[1]\n except IndexError:\n continue\n return record","title":""},{"docid":"0818f8ec5f65d0b8f22cf151b6f763f4","score":"0.5093003","text":"def populate_csv_dict(self):\n with open(self.source_csv_path) as f:\n reader = csv.reader(f)\n first = True\n self.csv_dict = {}\n header_index_map = {}\n for row in reader:\n for i in range(len(row)): # be explicit about reading the indices of the row\n if first:\n self.csv_dict[row[i]] = {}\n header_index_map[i] = row[i]\n else:\n key = header_index_map[i]\n try:\n self.csv_dict[key][int(row[0])] = self._tokenise(row[i])\n except ValueError as ve:\n print 'One of the id-s doesn\\'t seem to be an integer:', row[0]\n print\n raise\n if first: \n first = False","title":""},{"docid":"a5400da42673cdfd4d7b921d4199522e","score":"0.5092853","text":"def state_numeric_codes(inFile):\n with open(inFile, \"r\") as csvfile: \n state_code_dict = {}\n descr = csvfile.readline()\n\n while descr != \"\":\n split_descr = descr.split(\",\")\n\n if len(split_descr) >= 3:\n state_name = split_descr[3]\n state_code = split_descr[1]\n\n new_name = remove_quotes(state_name)\n code = remove_quotes(state_code)\n new_code = remove_lead0s(code)\n\n state = state_name.split()\n if len(state) == 1:\n state_code_dict[new_name] = int(new_code)\n descr = csvfile.readline()\n \n return state_code_dict","title":""},{"docid":"6c0b19d5bacb6e1dc6bddffe700e6020","score":"0.5089802","text":"def parse_line(line_str):\n field_list = line_str.strip().split(', ')\n name_str = field_list[1] + ' ' + field_list[0]\n score_list = []\n for element in field_list[2:]:\n score_list.append(int(element))\n return name_str, score_list # a tuple is returned","title":""},{"docid":"7d087e208d20bc99ae9254cb8335b309","score":"0.50845295","text":"def _parse_str_to_dict(self, args):\n arg_dict = dict()\n args = args.split()\n nargs = len(args)\n arg_dict['analysis_id'] = args[0]\n for i in range(1, nargs, 2):\n arg_key = args[i]\n arg_arg = args[i + 1]\n if arg_key in self._valid_settings:\n arg_dict[arg_key] = arg_arg\n else:\n warnings.warn(\n \"ignoring invalid argument key \" + arg_key + \" for analysis \" + self.analysis_key)\n return arg_dict","title":""},{"docid":"8150670710202a6a5078d03388ac63d0","score":"0.5080663","text":"def _extract_and_parse_csv(input_csv, key_header_label, values_header_label):\n freeform_labels = {}\n\n with open(input_csv, 'rb') as csvfile:\n reader = csv.reader(csvfile)\n\n # Parse the header row.\n header = reader.next()\n key_idx = (None if key_header_label is None\n else header.index(key_header_label))\n val_idx = header.index(values_header_label)\n\n # Parse the data rows.\n row_idx = 2\n for row in reader:\n text = row[val_idx].decode('utf-8').lower()\n if len(text) == 0:\n continue\n\n # Only one type of delimiter is expected in the free-form text field.\n if text.find(';') != -1 and text.find('\\n') != -1:\n raise ValueError('Free-form text field cannot contain both '';'' and '\n 'newline delimiters.')\n\n # Parse if necessary.\n if text.find(';') != -1:\n text = text.split(';')\n elif text.find('\\n') != -1:\n text = text.splitlines()\n else:\n text = [text]\n\n freeform_labels[row_idx if key_idx is None else row[key_idx]] = text\n row_idx += 1\n\n return freeform_labels","title":""},{"docid":"1d425be8f579ee0712c97909f31aba3a","score":"0.50765157","text":"def parse_csv(self):\n self.data = {}\n csv_lines = self.csv.split(\"\\n\")\n\n for csv_line in csv_lines:\n if csv_line.startswith('#'):\n continue\n if csv_line.startswith('pxname'):\n continue\n\n csv_fields = csv_line.split(',')\n\n if len(csv_fields) < len(self.status_fields):\n continue\n\n service_field = self.status_fields['pxname']\n server_field = self.status_fields['svname']\n\n for status_field_name in self.status_fields:\n status_field = self.status_fields[status_field_name]\n status_field_number = status_field['number']\n\n service_field_value = csv_fields[service_field['number']]\n server_field_value = csv_fields[server_field['number']]\n\n if service_field_value not in self.data:\n self.data[service_field_value] = {}\n\n if server_field_value not in self.data[service_field_value]:\n self.data[service_field_value][server_field_value] = {}\n\n csv_field_value = csv_fields[status_field_number]\n\n server = self.data[service_field_value][server_field_value]\n server[status_field_name] = csv_field_value","title":""},{"docid":"4814a84016878fda5533ffbc40ee2426","score":"0.5052376","text":"def coordinates(line):\n contents = line.split(\",\")\n lng, lat = map(float, contents[3:5])\n return lng, lat","title":""},{"docid":"b8e0b131f9c1b30cadb5ed703c0dc185","score":"0.5050806","text":"def import_from_file(input_file):\n list_of_dicts = []\n floats = ('Longitude', 'Latitude')\n tmp_dict = {}\n try:\n csv_file = open(input_file, 'rU')\n except IOError:\n print \"Error:\", sys.exc_info()[0]\n sys.exit()\n else:\n reader = csv.reader(csv_file)\n keys = reader.next()\n for values in reader:\n tuples = zip(keys, values)\n for key, value in tuples:\n if key in floats:\n try:\n tmp_dict[key] = float(value)\n except ValueError:\n print \"Error:\", sys.exc_info()[0]\n tmp_dict[key] = value\n else:\n tmp_dict[key] = value\n list_of_dicts.append(tmp_dict)\n csv_file.close()\n return list_of_dicts","title":""},{"docid":"a6b145aa1dea2f5edc2d5d446d777c4b","score":"0.50342846","text":"def _get_args(line: str) -> Dict[str, str]:\n line = f' {line}'\n args_dict: Dict[str, Any] = {}\n args = line.split(\" --\")[1:]\n\n for arg in args:\n arg_tuple = arg.split(\" \")\n args_dict[arg_tuple[0]] = \" \".join(arg_tuple[1:])\n\n return args_dict","title":""},{"docid":"78fe8d4d74b4afbab6de7af652d31902","score":"0.5033904","text":"def deserialize(self, data):\n lst = data.split(',')\n return self.deserializeHelper(lst)","title":""},{"docid":"5fbdad267118fd1e2458c0ebe88cc23b","score":"0.5032945","text":"def deserialize(self, data):\n data = data[1:-1]\n if len(data)==0: return None\n input = re.split(',',data)\n return self.build(input,0,0)","title":""},{"docid":"049be26d77c1c767b5c6bbb78a415e72","score":"0.5029139","text":"def load_data(path: str) -> Dict[int, str]:\n data = {}\n with open(path, 'r') as file:\n lines = file.readlines()\n for line in lines:\n row_data = literal_eval(line)\n data[row_data[QUESTION_CONTENT_KEY]] = row_data[QUESTION_ID_KEY]\n\n return data","title":""},{"docid":"8f8be612c88c867c8cc5f1adef803cc1","score":"0.5020023","text":"def get_tag_dict():\n tag_source_file = open(\"../tags_config.txt\", 'r')\n tag_dict = {}\n for line in tag_source_file.readlines():\n if line[0] == \"#\" or len(line) < 5:\n continue\n target_line = line.replace(\"\\n\", \"\")\n target_line = target_line.replace(\" \", \"\")\n try:\n tags, category = target_line.split(\"->\")\n except:\n print(\"The config file may be formatted incorrectly, the following line could not be parsed: \\n{}\\n \"\n \"please make sure each line is in the format a, b, c ... -> d\".format(line))\n exit(0)\n for tag in tags.split(\",\"):\n tag_dict[tag] = category\n tag_source_file.close()\n return tag_dict","title":""},{"docid":"72d0e2d63d6dddd6669804fcd818b34f","score":"0.50171804","text":"def parse_csv(rows_string_tensor):\n\n # Takes a rank-1 tensor and converts it into rank-2 tensor\n # Example if the data is ['csv,line,1', 'csv,line,2', ..] to\n # [['csv,line,1'], ['csv,line,2']] which after parsing will result in a\n # tuple of tensors: [['csv'], ['csv']], [['line'], ['line']], [[1], [2]]\n row_columns = tf.expand_dims(rows_string_tensor, -1)\n columns = tf.decode_csv(row_columns, record_defaults=CSV_COLUMN_DEFAULTS)\n features = dict(zip(CSV_COLUMNS, columns))\n\n # Remove unused columns\n for col in UNUSED_COLUMNS:\n features.pop(col)\n return features","title":""},{"docid":"228d117c5211df533001478cb385dd9a","score":"0.5015171","text":"def __extract_key_value(self, line):\n values = line.split('=')\n return values[0].strip(),values[1].strip()","title":""},{"docid":"70f896f99cd939bd5bbf2377e7223b16","score":"0.5013919","text":"def get_csv_as_dict_list():\n dict_list = []\n with open('../../data/EHRdataSample.csv') as csv_file:\n csv_reader = csv.DictReader(csv_file, delimiter=',')\n for line in csv_reader:\n dict_list.append(line)\n return dict_list","title":""},{"docid":"663242c3c86bf8c1ff04c61f398410d6","score":"0.5012478","text":"def tsv_to_dict(self):\n tsv_rows = []\n for line in self.tsv_string.split(\"\\n\"):\n #header lines\n if line.startswith(\"##\"):\n self.header.append(line)\n #column header line.\n elif line.startswith(\"#\"):\n self.column_header = line[1:].split(\"\\t\")\n elif line:\n tsv_rows.append(line)\n for x in range(len(tsv_rows)):\n #seperates each row element seperated by tabs into a dictionary\n inner_dict = dict(zip(self.column_header, tsv_rows[x].split(\"\\t\")))\n #if not unique key is provided the outer dictionary is simply saved by a number.\n if self.key_name:\n self.tsv_dict[inner_dict[self.key_name]] = inner_dict\n else:\n self.tsv_dict[str(x)] = inner_dict","title":""},{"docid":"4f915c8697e81b68656ee9646a62f6a7","score":"0.50063443","text":"def parse_line(self, line: str):\n pass","title":""},{"docid":"097ad3977eeacac389fabcaf03e856eb","score":"0.5004433","text":"def parse_content(self, content):\n _lines = get_active_lines(content, comment_char=\"#\")\n self.data = {}\n current_key = \"\"\n for line in _lines:\n if \"=\" in line:\n k, v = line.split('=')\n current_key = k.strip()\n self.data[current_key] = v.strip(\"\\\"' \")\n else:\n self.data[current_key] = self.data[current_key] + line\n # if the value like \"[ , ,]\", convert to list\n if self.data[current_key].endswith(\"]\"):\n self.data[current_key] = json.loads(self.data[current_key])","title":""},{"docid":"c8161b8825e009fe9b9914cc35e14bfc","score":"0.49966922","text":"def _parse_group_vars_line(self, line):\n tokens = shlex.split(line.strip())\n key_values = self._parse_vars(tokens)\n return key_values","title":""},{"docid":"d7dd1d952013d90cc57e78491b609a98","score":"0.49955785","text":"def _convert_variant_file_to_dict(varfile):\n patient_variant_map = {}\n # line_num = 0 note this is builtin to the reader as reader.line_num\n reader = csv.reader(varfile, delimiter=\"\\t\")\n col = [\n 'patient', 'family', 'chromosome', 'build', 'position', 'reference_allele',\n 'variant_allele', 'parent_of_origin', 'allele_type', 'mutation_type',\n 'gene_symbol', 'transcript', 'reference_aa', 'variant_aa, aa_change',\n 'segregates_with', 'locus', 'exon', 'inheritance_model', 'zygosity',\n 'dbsnp_id', 'frequency', 'num_of_alleles'\n ]\n # row = next(reader) # there is no header\n # if len(row) != len(col):\n # LOG.error('Got:\\n\\t%s\\nExpected:\\n\\t%s\\n', row, col)\n # raise TypeError('header does not match expected format')\n for row in reader:\n\n patient = row[col.index('patient')]\n # family,\n chromosome = row[col.index('chromosome')]\n build = row[col.index('build')]\n position = row[col.index('position')]\n reference_allele = row[col.index('reference_allele')]\n variant_allele = row[col.index('variant_allele')]\n # parent_of_origin,\n # allele_type,\n mutation_type = row[col.index('mutation_type')]\n gene_symbol = row[col.index('gene_symbol')]\n # transcript,\n # reference_aa,\n # variant_aa,\n # aa_change,\n # segregates_with,\n # locus,\n # exon,\n # inheritance_model,\n # zygosity,\n dbsnp_id = row[col.index('dbsnp_id')]\n # frequency,\n # num_of_alleles\n\n if patient not in patient_variant_map:\n patient_variant_map[patient] = {}\n\n formatted_chr = re.sub(r'^CHR', 'chr', chromosome, flags=re.I)\n\n if re.fullmatch(r'[XY]|[0-9]{1,2}', chromosome, flags=re.I):\n formatted_chr = \"chr{0}\".format(chromosome.upper())\n\n formatted_build = re.sub(r'^HG', 'hg', build, flags=re.I)\n ref_base = reference_allele.upper()\n var_base = variant_allele.upper()\n rs_id = ''\n\n # Catch misformatted data\n if re.search(r'LEFT FLANK|NM_|EXON', ref_base):\n ref_base = ''\n\n if re.search(r'LEFT FLANK|NM_|EXON', var_base):\n var_base = ''\n\n if dbsnp_id != '':\n match = re.fullmatch(r'^(rs\\d+).*', dbsnp_id)\n if match:\n rs_id = match.group(1)\n\n # Format variant object\n variant_info = [formatted_chr, formatted_build, position,\n ref_base, var_base]\n\n if '' in variant_info:\n filt_list = [info for info in variant_info if info != '']\n variant_id = str(reader.line_num) + '-' + '-'.join(filt_list)\n else:\n variant_id = '-'.join(variant_info)\n\n if variant_id in patient_variant_map[patient]:\n patient_variant_map[patient][variant_id]['genes_of_interest'].append(\n gene_symbol)\n else:\n patient_variant_map[patient][variant_id] = {\n 'build': formatted_build,\n 'position': position,\n 'chromosome': formatted_chr,\n 'reference_allele': ref_base,\n 'variant_allele': var_base,\n 'type': mutation_type,\n 'rs_id': ''\n }\n if rs_id:\n patient_variant_map[patient][variant_id]['rs_id'] = rs_id\n\n patient_variant_map[patient][variant_id]['genes_of_interest']\\\n = [gene_symbol]\n\n return patient_variant_map","title":""},{"docid":"bd07986733e7eb236a09d82bef825d01","score":"0.49793538","text":"def consume_line(self, line):\n data = RE_VALUE_KEY.split(line.strip(), 1)\n if len(data) == 1:\n return float(data[0]), None\n else:\n return float(data[0]), data[1].strip()","title":""},{"docid":"6c7d00eb920000871cb4ddd7750935b1","score":"0.49764368","text":"def parse_dict(text):\n\n substitutions = {\"{}\": {}, \"[]\": [], \"True\": True, \"False\": False}\n\n def parse_value(some_val):\n some_val = substitutions.get(some_val, some_val)\n\n try:\n int_val = int(some_val)\n if str(int_val) == some_val:\n return int_val\n except ValueError:\n pass\n\n try:\n float_val = float(some_val)\n if str(float_val) == some_val:\n return float_val\n except ValueError:\n pass\n\n return some_val\n\n def chomp_key_list(out_dict, keys, value):\n \"\"\"\n turn keys like ['a', 'b', 'c', 'd'] and a value into\n out_dict['a']['b']['c']['d'] = value\n \"\"\"\n dict_ptr = out_dict\n last_ptr = out_dict\n for i, key in enumerate(keys):\n last_ptr = dict_ptr\n if not key in dict_ptr:\n dict_ptr[key] = {}\n else:\n if type(dict_ptr[key]) is not dict:\n raise BadRequest(\"Building a dict in %s field, but it exists as %s already\" %\n (key, type(dict_ptr[key])))\n dict_ptr = dict_ptr[key]\n last_ptr[keys[-1]] = value\n\n out = {}\n if text is None:\n return out\n\n pairs = text.split(',') # pairs separated by commas\n for pair in pairs:\n if pair.count(':') == 0:\n continue\n fields = pair.split(':', 1) # pair separated by first colon\n key = fields[0].strip()\n value = fields[1].strip()\n\n keyparts = key.split(\".\")\n chomp_key_list(out, keyparts, parse_value(value))\n\n return out","title":""},{"docid":"c1d432ee9552e21862e03201c19d5015","score":"0.4974465","text":"def dict_from_path(self, path):\n return dict(re.split(\"\\s+\", line) for line in self.get_param_lines(path))","title":""},{"docid":"1beffeb4fefe1bac76abd78830d48ced","score":"0.49729833","text":"def clean_line(self, line, joint_names):\n def try_float(x):\n try:\n return float(x)\n except ValueError:\n return None\n #convert the line of strings to a float or None\n line = [try_float(x) for x in line.rstrip().split(',')]\n #zip the values with the joint names\n combined = zip(joint_names[1:], line[1:])\n #take out any tuples that have a none value\n cleaned = [x for x in combined if x[1] is not None]\n #convert it to a dictionary with only valid commands\n command = dict(cleaned)\n return (command, line,)","title":""},{"docid":"0f87c32bbc889f291d782f825389cd91","score":"0.49717426","text":"def load_expression(fn=None, sep=\"\\t\"):\n with gzip.open(fn, \"rt\") as f:\n reader = csv.DictReader(f, delimiter=sep)\n return {row[\"Gene\"]: float(row[\"Expression\"]) for row in reader}","title":""},{"docid":"9f09ba7c58cf44448e81a9ff7ad46d68","score":"0.49692282","text":"def parse_line(line, custom_columns):\n results = {}\n if line.startswith('\\t'):\n applog = line.strip() # remove leading tab and trailing newline.\n results['applog'] = applog\n # Some applogs have severity; some are continuations and do not.\n if (len(applog) > 2 and applog[1] == ':'\n and applog[0] >= '0' and applog[0] <= '9'):\n results['applog_severity'] = applog[0]\n for column, regexp in custom_columns.iteritems():\n match = re.search(regexp, applog)\n if match:\n # TODO: Consider allowing named groups.\n results[column] = match.group(1)\n return results\n\n matches = LINE_RE_COMPILED.match(line)\n if not matches:\n raise LineParsingFailure('Fail. %s', line)\n\n results['remotehost'] = matches.group(1)\n results['user'] = matches.group(2)\n results['request_time_str'] = matches.group(3)\n results['request_line'] = matches.group(4)\n # Submatch = matches.group(5)\n results['status'] = matches.group(6)\n results['bytes'] = matches.group(7)\n results['referer'] = matches.group(8)\n # Submatch = matches.group(9)\n results['useragent'] = matches.group(10)\n\n more_values = line[len(matches.group(0)):].strip().split(' ')\n if more_values:\n results['host'] = more_values.pop(0)\n for pair in more_values:\n key, value = pair.split('=')\n results[key] = value\n if more_values and 'loading_request' not in results:\n # The user requested --include_all, so if this were a loading request\n # it would be in the line. But it's not in the line, so it's not a loading\n # request. Otherwise, we want it to be NULL for unknown.\n results['loading_request'] = '0'\n\n return results","title":""},{"docid":"d9bfebb31a2b327226359a4029d55445","score":"0.49632904","text":"def process_csv(csv_file):\n csv_f = open(csv_file)\n csv_extract = csv.DictReader(csv_f, delimiter=\"\\t\") # dialect='excel'\n csv_list = [] \n for row in csv_extract:\n u_row = {}\n for k,v in row.iteritems():\n if type(v) == str:\n u_row[k] = v.decode(\"utf-8\")\n else:\n u_row[k] = v\n csv_list.append(u_row)\n return csv_list","title":""},{"docid":"6ddb58c3b67085a2ba881a16dbe43155","score":"0.496286","text":"def convert_tsv_line(cls, mapping, tsv_header):\n custom_field_infos = tsv_header.custom_field_infos\n table = {\n \"integer\": int,\n \"number\": float,\n \"boolean\": lambda x: BOOL_VALUES.get(x, False),\n }\n for key, value in mapping.items():\n if value is not None and key in custom_field_infos:\n mapping[key] = table.get(custom_field_infos[key].field_type, lambda x: x)(value)\n return mapping","title":""},{"docid":"643e91813ee39ce764ce3cb90cb8ffe8","score":"0.49558353","text":"def parseCSVRow(self, row):\n if row[0] == 'id':\n self.dataHeaders = row\n else:\n self.genDataInput(row)\n self.setDataObjectToDict()","title":""},{"docid":"7b78dc127644dc995f4502ef722af8d6","score":"0.49547195","text":"def format_line(line):\n result = {'type': 'gauge', 'severity': 'warning'}\n parts = line.split('|')\n for part in parts:\n if '=' in part:\n index = part.index('=')\n result[part[:index]] = part[index+1:]\n return result","title":""},{"docid":"a024d46690571ceb9b3a9b57fb849243","score":"0.49533784","text":"def _parse(\n self,\n record: Mapping[str,\n tf.Tensor]) -> Tuple[Dict[str, tf.Tensor], tf.Tensor]:\n x, y = super()._parse(record)\n feature_name_mapping = {\n 'input_ids': 'input_word_ids',\n 'input_mask': 'input_mask',\n 'segment_ids': 'input_type_ids'\n }\n converted_x = {\n feature_name_mapping.get(name, name): tensor\n for name, tensor in x.items()}\n return converted_x, y","title":""},{"docid":"a7f6154f791bf6b3374fed927b07828a","score":"0.4949671","text":"def read_csv():\n yelp_ids_hours_file = open('insta9')\n yelp_ids_hours = csv.reader(yelp_ids_hours_file)\n\n yelp_ids = {}\n\n for row in yelp_ids_hours:\n infos = row[0].split('|')\n yelp_ids[infos[0]] = { \\\n 'Monday': infos[1], \\\n 'Tuesday': infos[2], \\\n 'Wednesday': infos[3], \\\n 'Thursday': infos[4], \\\n 'Friday': infos[5], \\\n 'Saturday': infos[6], \\\n 'Sunday': infos[7] }\n\n # print infos[0], yelp_ids[infos[0]]['Monday']\n\n return yelp_ids","title":""},{"docid":"9a252e6b39b088627844d5c27ede1cb3","score":"0.49438286","text":"def parse_csv_line_to_resource(line):\n if not line:\n return None\n # Capping splits prevents commas in URL from being caught.\n (\n url_string,\n etag_string,\n size_string,\n storage_class_string,\n atime_string,\n mtime_string,\n uid_string,\n gid_string,\n mode_base_eight_string,\n crc32c_string,\n md5_string,\n ) = line.rstrip().rsplit(',', _CSV_COLUMNS_COUNT)\n\n url_object = storage_url.storage_url_from_string(url_string)\n if isinstance(url_object, storage_url.FileUrl):\n return resource_reference.FileObjectResource(url_object)\n cloud_object = resource_reference.ObjectResource(\n url_object,\n etag=etag_string if etag_string else None,\n size=int(size_string) if size_string else None,\n storage_class=storage_class_string if storage_class_string else None,\n crc32c_hash=crc32c_string if crc32c_string else None,\n md5_hash=md5_string if md5_string else None,\n custom_fields={},\n )\n posix_util.update_custom_metadata_dict_with_posix_attributes(\n cloud_object.custom_fields,\n posix_util.PosixAttributes(\n atime=int(atime_string) if atime_string else None,\n mtime=int(mtime_string) if mtime_string else None,\n uid=int(uid_string) if uid_string else None,\n gid=int(gid_string) if gid_string else None,\n mode=posix_util.PosixMode.from_base_eight_str(mode_base_eight_string)\n if mode_base_eight_string\n else None,\n ),\n )\n return cloud_object","title":""},{"docid":"ab948b704445615643142c0986918f03","score":"0.49387118","text":"def line_to_dict(self, line, protocol):\n d = {}\n connection_states = {\n '01':'ESTABLISHED',\n '02':'SYN_SENT',\n '03':'SYN_RECV',\n '04':'FIN_WAIT1',\n '05':'FIN_WAIT2',\n '06':'TIME_WAIT',\n '07':'CLOSE',\n '08':'CLOSE_WAIT',\n '09':'LAST_ACK',\n '0A':'LISTEN',\n '0B':'CLOSING' }\n line_array = self._remove_empty(line.split(' '))\n d['protocol'] = protocol\n d['local_ip'], d['local_port'] = self._convert_ip_port(line_array[1])\n d['remote_ip'], d['remote_port'] = self._convert_ip_port(line_array[2])\n if 'tcp' in protocol:\n d['state'] = connection_states[line_array[3]]\n else:\n d['state'] = ''\n d['pid'] = self.pid_of_inode(line_array[9])\n d['program'] = self.name_of_pid(d['pid'])\n return d","title":""},{"docid":"2d9ae3e4e2762e0382d327ee5a073a76","score":"0.49384794","text":"def decode(line):\n try:\n return json.loads(line)\n except:\n fake_record = {}\n fake_record['fip'] = 0\n fake_record['user_id'] = 0\n fake_record['text'] = ''\n fake_record['filtered_text'] = ''\n fake_record['url_count'] = 0\n fake_record['emoji_count'] = 0\n fake_record['education_level_1'] = 0\n fake_record['education_level_2'] = 0\n fake_record['education_level_3'] = 0\n fake_record['education_level_4'] = 0\n return fake_record","title":""}],"string":"[\n {\n \"docid\": \"ac1b5d6fb18bcf081f1eb7a62476847b\",\n \"score\": \"0.6674348\",\n \"text\": \"def parse_line_to_dict(line, header):\\n\\tfields = line.strip().split(',')\\n\\tif len(header) != len(fields):\\n\\t\\traise DataImportFormatError(\\\"Mismatch in header and data line in incoming data file.\\\")\\n\\trecord = {}\\n\\tfor i in range(len(header)):\\n\\t\\tvalue = fields[i] if header[i] not in INT_TYPES else 0 if len(fields[i]) < 1 else int(float(fields[i])) \\n\\t\\trecord[header[i]] = value\\n\\treturn record\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6ad32b853f4494e14e4219300315e02\",\n \"score\": \"0.6514467\",\n \"text\": \"def _create_dict_instance(self, line):\\n new_dict = {}\\n for item in line:\\n if \\\"=\\\" in item:\\n # creating list from value and key\\n # if \\\"=\\\" found\\n new_arg = item.split(\\\"=\\\", 1)\\n key = new_arg[0]\\n value = new_arg[1]\\n if value[0] == '\\\"' == value[-1]:\\n value = value.replace('\\\"', \\\"\\\").replace(\\\"_\\\", \\\" \\\")\\n else:\\n try:\\n value = int(value)\\n except Exception:\\n try:\\n value = float(value)\\n except Exception:\\n continue\\n new_dict[key] = value\\n return new_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b1529a873aa25b3ccde288631c2963a\",\n \"score\": \"0.64161897\",\n \"text\": \"def dictFromString(src, seperator=\\\",\\\"):\\n lst = src.split(\\\",\\\")\\n dct = {}\\n name = None\\n for val in lst:\\n if name is None:\\n name = val\\n else:\\n dct[name] = val\\n name = None\\n return dct\",\n \"title\": \"\"\n },\n {\n \"docid\": \"814c4e966457f6d614e33ff4f42cf5c6\",\n \"score\": \"0.59911543\",\n \"text\": \"def getData()->dict:\\r\\n datas = {}\\r\\n with open(\\\"../data/data.txt\\\", \\\"r\\\") as f:\\r\\n data_list = f.readlines()\\r\\n for i in range(len(data_list)):\\r\\n str = data_list[i].replace(\\\"\\\\n\\\", \\\"\\\")\\r\\n temp = str.split(',')\\r\\n datas[i] = temp\\r\\n return datas\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e929bf5a9686ec6beca9576b8542bea\",\n \"score\": \"0.59718925\",\n \"text\": \"def parse_line(self, line):\\n try:\\n key, value = [x.strip() for x in line.split('=', 1)]\\n except ValueError:\\n return\\n value = value.strip('\\\"')\\n return self.parse_value(key, value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f12f28635d949ca98c8a0a8afaf2625f\",\n \"score\": \"0.5938792\",\n \"text\": \"def str_handler(l):\\n l = l.rstrip(\\\"\\\\n\\\").strip(\\\" ;\\\")\\n cols = l.split(\\\";\\\")\\n dic = {}\\n for col in cols:\\n key, value = col.split(\\\"=\\\")\\n value = parse_escape_char(value)\\n if key == \\\"fields\\\":\\n fields_list = get_fields(value)\\n dic[key] = fields_list\\n elif key == \\\"col_cnt\\\":\\n value = int(value)\\n dic[key] = value\\n elif key == \\\"ratio\\\":\\n value = float(value)\\n dic[key] = value\\n elif key == \\\"args\\\":\\n values = value.split(\\\",\\\")\\n dic[key] = values\\n elif key == \\\"sep\\\":\\n value = parse_escape_char(value)\\n dic[key] = value\\n else:\\n values = value.split(\\\",\\\")\\n if len(values) == 1:\\n values = values[0]\\n dic[key] = values\\n \\n return dic\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9495ad20a23f2c41da34b45a96d2304\",\n \"score\": \"0.59204066\",\n \"text\": \"def parse_file_line(line: str) -> dict:\\n\\n ret = {}\\n\\n product_attrs = line.split(',')\\n\\n # Product PK (id)\\n ret['pk'] = int(product_attrs[PK].strip())\\n\\n # Product description\\n ret['description'] = product_attrs[DESC].strip()\\n\\n # Product last sold date\\n ret['last_sold'] = datetime.strptime(product_attrs[LAST_SOLD].strip(), \\\"%m/%d/%Y\\\").date()\\n\\n # Product shelf life in days\\n ret['shelf_life'] = int(product_attrs[SHELF_LIFE].strip()[0])\\n\\n # Product department\\n ret['department'] = product_attrs[DEPT].strip().upper()\\n\\n # Product price\\n ret['price'] = Decimal(product_attrs[PRICE].strip()[1:])\\n\\n # Product unit\\n ret['unit'] = product_attrs[UNIT].strip().upper()\\n\\n # Product units for price\\n ret['x_for'] = int(product_attrs[XFOR].strip())\\n\\n # Product cost\\n ret['cost'] = Decimal(product_attrs[COST].strip()[1:])\\n\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f93c6bcb8bde217674294c940a8ae71\",\n \"score\": \"0.58666193\",\n \"text\": \"def _line_to_dict(self, file, field_names):\\n for line in file:\\n decoded_line = line.decode(settings.IPGEOBASE_FILE_ENCODING)\\n delimiter = settings.IPGEOBASE_FILE_FIELDS_DELIMITER\\n yield self._extract_data_from_line(decoded_line, field_names, delimiter)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68f24174137d77795449f94f5c1de03b\",\n \"score\": \"0.5853324\",\n \"text\": \"def turn_line_into_map(line, column_to_enum, dropdown_map):\\n line_map = {}\\n\\n for i, local_e in enumerate(column_to_enum):\\n # Split if enumerator is present.\\n n = line[i].count(ENUMERATOR + ' ')\\n items = np.empty(n+1, dtype=object)\\n items = line[i].split(ENUMERATOR + ' ')\\n\\n res = ''\\n for item in items:\\n if res != '':\\n res = res + ENUMERATOR + ' '\\n\\n if local_e in dropdown_map:\\n # Remove \\\"1. \\\"\\n item = re.sub(\\\"^\\\\d+\\\\. \\\", \\\"\\\", item)\\n # Turn \\\"1 Interesting\\\" into \\\"1 - Interesting\\\"\\n item = re.sub(\\\"^(\\\\d+) ([^-])\\\", r\\\"\\\\1 - \\\\2\\\", item)\\n # item = re.sub(\\\"^(\\\\d+)\\\\. (\\\\d) ([^-])\\\", r\\\"\\\\1. \\\\2 - \\\\3\\\", item)\\n # if local_e == Fields.Quality:\\n # print(\\\"item\\\", item)\\n\\n if local_e in NUMERICAL_HEADINGS:\\n item = api.turn_text_into_dropdown(item, enum_text_to_id[local_e])\\n\\n res = res + str(item)\\n # str(api.turn_text_into_dropdown(item, dropdown_map[local_e]))\\n else:\\n res = res + item\\n\\n # To be consistent with Affinity.\\n if local_e == Fields.OrganizationURL and res == '':\\n res = 'None'\\n\\n line_map[local_e] = res\\n\\n return line_map\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a55712dbe13bc40609e1d3657941a60\",\n \"score\": \"0.5787207\",\n \"text\": \"def import_dict(d: str = None) -> dict:\\n if not d:\\n cpath = os.path.dirname(os.path.realpath(__file__))\\n d = os.path.join(cpath, \\\"sentiment_dict_en.txt\\\")\\n with open(d, 'r') as rf:\\n reader = csv.reader(rf, delimiter=\\\",\\\")\\n # Header has no commas (manually checked)\\n reader = [r for r in reader if len(r) == 2]\\n ret = dict(zip([r[0] for r in reader], [r[1] for r in reader]))\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95d411fd0ad1522a840b2b7b83071bbb\",\n \"score\": \"0.57542294\",\n \"text\": \"def convert_csv_to_dict(csv_file):\\n # Holds all corresponding lines as a list of lines\\n csv_dict = {\\\"SUPERBLOCK\\\": [],\\n \\\"GROUP\\\": [],\\n \\\"BFREE\\\": [],\\n \\\"IFREE\\\": [],\\n \\\"INODE\\\": [],\\n \\\"DIRENT\\\": [],\\n \\\"INDIRECT\\\": []}\\n\\n for line in csv_file:\\n line = line.split(\\\",\\\")\\n csv_dict[line[0]].append(line)\\n \\n return csv_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af4ee47ddce43d69e3e70d0a2f4ca1c7\",\n \"score\": \"0.5753948\",\n \"text\": \"def _ParseBluefaceLogs(label, line):\\n output = {}\\n header = label.split(\\\",\\\")\\n data = line.split(\\\",\\\")\\n for k, v in zip(header, data):\\n output[k.lower()] = v\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7908f309b52921160bf8b65d288f7a4\",\n \"score\": \"0.57516104\",\n \"text\": \"def parse_csv(lines):\\n data =[]\\n # pdb.set_trace()\\n names = [item.strip(' ,\\\\n') for item in lines[0].split(',') if item!=\\\"#\\\"]\\n # endlines = [line[-100:] for line in lines]\\n for i,line in enumerate(lines[1:]):\\n # if line[-100:] in endlines[:i+1]:\\n # continue\\n items = line.split(',')\\n tmp = {}\\n for i in range(len(names)):\\n try:\\n value = int(items[i].strip())\\n except ValueError:\\n try:\\n value = float(items[i].strip())\\n except ValueError:\\n value = items[i].strip()\\n except IndexError:\\n print items \\n tmp[names[i]] = value\\n data.append(tmp)\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc13adad67f692c0ac96075062e5b6d9\",\n \"score\": \"0.5728059\",\n \"text\": \"def specific2kv(s):\\r\\n ret = {}\\r\\n for e in s.strip().split(','):\\r\\n if e:\\r\\n k,v = e.split(\\\":\\\")\\r\\n ret[k] = v\\r\\n\\r\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a27afc39bc31acb2d7504d6247769737\",\n \"score\": \"0.5621865\",\n \"text\": \"def mapper(self, _, line):\\n tweet=line.replace(\\\"\\\\,\\\",\\\"\\\")\\n tweet=tweet.split(\\\",\\\")\\n date=tweet[4]\\n hour=tweet[5]\\n #Return date/hour tuple as key\\n yield (date,hour),1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3e29bf1d45a903a59806284207aff56\",\n \"score\": \"0.5603128\",\n \"text\": \"def parse_line(line):\\n return [element.strip() for element in line.strip().split('=')[1].strip()[1:-1].split(',')]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d4f843ff4443fd82faefca874f8c8d5\",\n \"score\": \"0.5588743\",\n \"text\": \"def parse_line(line):\\n entries = line.split(\\\",\\\")\\n entries = Publisher.clean_entries(entries)\\n if len(entries) == 2:\\n name = entries[0]\\n email = entries[1]\\n if validate_email(email) and name:\\n data = dict(name=name, email=email)\\n return True, data\\n return False, {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0be551aecc98989d370c732e64f7338\",\n \"score\": \"0.55834085\",\n \"text\": \"def parse_line(line):\\n tmp_res = OrderedDict()\\n # split line into array of \\\"key=value\\\" metrics\\n metrics = line.split()\\n for metric in metrics:\\n key, value = metric.split('=')\\n tmp_res[key] = value\\n\\n return tmp_res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a4f329145a857a20a4f1fb6fb5beb83\",\n \"score\": \"0.55626476\",\n \"text\": \"def load_line(self, line: str) -> JsonDict: # pylint: disable=no-self-use\\n return json.loads(line)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe6f239feba69ecb3e79462802fcdb06\",\n \"score\": \"0.54828405\",\n \"text\": \"def deserialize(self, data):\\n values = data.split(',')\\n return self.deserializeHelper(values)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cfffc17d34900dde4f1c82fc5802fd7f\",\n \"score\": \"0.5478965\",\n \"text\": \"def parse_line(parts):\\n\\tline_parts = dict()\\n\\tfor pair in parts:\\n\\t\\tpair = pair.split(\\\"=\\\", 1)\\n\\t\\tline_parts[pair[0]] = pair[1]\\n\\treturn line_parts\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5513e63f2e4c7b54b0f2f2f74cfbf1d\",\n \"score\": \"0.54685783\",\n \"text\": \"def parseline(line):\\n fields = line.split(',')\\n custid = int(fields[0])\\n order_amount = float(fields[2])\\n return (custid, order_amount)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae5148d8088c2fb3afe050c3054d8d00\",\n \"score\": \"0.5463482\",\n \"text\": \"def processData(contents):\\n my_dictionary = {}\\n csv_file=csv.reader(contents)\\n csv_file.next()\\n\\n for row in csv_file:\\n try:\\n row[2] = datetime.datetime.strptime(row[2], \\\"%d/%m/%Y\\\").date()\\n except ValueError:\\n number = int(row[0])\\n line = int(row[0])+1\\n logger = logging.getLogger(\\\"assignment 2\\\")\\n logger.error(\\\"Error processing line#{} for ID #{}.\\\".format(line, number))\\n my_dictionary[int(row[0])] = (row[1], row[2])\\n return my_dictionary\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e447e64755674a22f7a5e994fb6d02e\",\n \"score\": \"0.5447638\",\n \"text\": \"def parseentry(row, cols):\\n entry = {}\\n for c, r in zip(cols, row):\\n if r:\\n if c in helper.namefields:\\n a = [k.split(',') for k in r.split(\\\";\\\")]\\n if a:\\n entry[c] = a\\n pass\\n else:\\n entry[c] = r\\n\\n return entry\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f5b4d34960271e3331e55a0fa3e91e9\",\n \"score\": \"0.54375726\",\n \"text\": \"def tokenize(self, line):\\n tokens = line.split()\\n return {'address': tokens[1], 'taken': int(tokens[2])}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25095d05e102425d3227c6b031d412c5\",\n \"score\": \"0.542845\",\n \"text\": \"def parse_line(line):\\n return {m.group('label'): m.group('contents')\\n for m in ARGUMENT_TAG.finditer(line)}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"400715f865f28c2ff5b1563f43be228b\",\n \"score\": \"0.54214805\",\n \"text\": \"def read_csv_as_dict(path, delimiter=','):\\n\\n with open(path, 'r', newline='', encoding='utf-8') as csv_file:\\n data = []\\n reader = csv.DictReader(csv_file, delimiter=delimiter)\\n for line in reader:\\n data.append(line) # OrderedDict()\\n # data.append(dict(line)) # convert OrderedDict() to dict\\n\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a001914d55a555b62e7b92e2d4891945\",\n \"score\": \"0.5420997\",\n \"text\": \"def create_ordered_dictionary_from(self, raw):\\n li = self.get_list_by_newline_from(raw)\\n if len(li) == 0:\\n return OrderedDict()\\n orDic = OrderedDict()\\n # To ensure the key row is only used here, pop it\\n for key in li.pop(0).split(','):\\n orDic[key.strip()] = []\\n for row in li:\\n i = 0 # Used for accessing the coloumn of a row.\\n for key in orDic:\\n orDic[key].append(row.split(',')[i].strip())\\n i += 1\\n return orDic\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3a65356e98c611c9754d320ea6b4a6f\",\n \"score\": \"0.54040056\",\n \"text\": \"def decode_csv(string_like): # type: (str) -> np.array\\n stream = StringIO(string_like)\\n request_list = list(csv.DictReader(stream))\\n if \\\"inputs\\\" in request_list[0].keys():\\n return {\\\"inputs\\\": [entry[\\\"inputs\\\"] for entry in request_list]}\\n else:\\n return {\\\"inputs\\\": request_list}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10a019731ad019378439b0f2ffa96ad2\",\n \"score\": \"0.5396028\",\n \"text\": \"def _parse_record(self, record, customization=None):\\n d = {}\\n\\n if not record.startswith('@'):\\n logger.debug('The record does not start with @. Return empty dict.')\\n return {}\\n\\n # prepare record\\n record = '\\\\n'.join([i.strip() for i in record.split('\\\\n')])\\n if '}\\\\n' in record:\\n record, rubbish = record.replace('\\\\r\\\\n', '\\\\n').replace('\\\\r', '\\\\n').rsplit('}\\\\n', 1)\\n\\n # if a string record, put it in the replace_dict\\n if record.lower().startswith('@string'):\\n logger.debug('The record startswith @string')\\n key, val = [i.strip().strip('\\\"').strip('{').strip('}').replace('\\\\n', ' ') for i in record.split('{', 1)[1].strip('\\\\n').strip(',').strip('}').split('=')]\\n self.replace_dict[key] = val\\n logger.debug('Return a dict')\\n return d\\n\\n # for each line in record\\n logger.debug('Split the record of its lines and treat them')\\n kvs = [i.strip() for i in record.split(',\\\\n')]\\n inkey = \\\"\\\"\\n inval = \\\"\\\"\\n for kv in kvs:\\n logger.debug('Inspect: %s', kv)\\n if kv.startswith('@') and not inkey:\\n # it is the start of the record - set the bibtype and citekey (id)\\n logger.debug('Line starts with @ and the key is not stored yet.')\\n bibtype, id = kv.split('{', 1)\\n bibtype = self._add_key(bibtype)\\n id = id.strip('}').strip(',')\\n elif '=' in kv and not inkey:\\n # it is a line with a key value pair on it\\n logger.debug('Line contains a key-pair value and the key is not stored yet.')\\n key, val = [i.strip() for i in kv.split('=', 1)]\\n key = self._add_key(key)\\n # if it looks like the value spans lines, store details for next loop\\n if (val.count('{') != val.count('}')) or (val.startswith('\\\"') and not val.replace('}', '').endswith('\\\"')):\\n logger.debug('The line is not ending the record.')\\n inkey = key\\n inval = val\\n else:\\n logger.debug('The line is the end of the record.')\\n d[key] = self._add_val(val)\\n elif inkey:\\n logger.debug('Continues the previous line to complete the key pair value...')\\n # if this line continues the value from a previous line, append\\n inval += ', ' + kv\\n # if it looks like this line finishes the value, store it and clear for next loop\\n if (inval.startswith('{') and inval.endswith('}')) or (inval.startswith('\\\"') and inval.endswith('\\\"')):\\n logger.debug('This line represents the end of the current key-pair value')\\n d[inkey] = self._add_val(inval)\\n inkey = \\\"\\\"\\n inval = \\\"\\\"\\n else:\\n logger.debug('This line does NOT represent the end of the current key-pair value')\\n\\n logger.debug('All lines have been treated')\\n if not d:\\n logger.debug('The dict is empty, return it.')\\n return d\\n\\n # put author names into persons list\\n if 'author_data' in d:\\n self.persons = [i for i in d['author_data'].split('\\\\n')]\\n del d['author_data']\\n\\n d['type'] = bibtype\\n d['id'] = id\\n if not self.has_metadata and 'type' in d:\\n if d['type'] == 'personal bibliography' or d['type'] == 'comment':\\n self.has_metadata = True\\n\\n if customization is None:\\n logger.debug('No customization to apply, return dict')\\n return d\\n else:\\n # apply any customizations to the record object then return it\\n logger.debug('Apply customizations and return dict')\\n return customization(d)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4816d7ca57b6fae0d1e7b21f7eeb8503\",\n \"score\": \"0.5379898\",\n \"text\": \"def csv_to_dict(filename,ret={},key=\\\"Specific Implementation\\\"):\\n csvtd = csv_translation_dict\\n with open(filename,\\\"r\\\") as f:\\n header = None\\n for row in csv.reader(f):\\n if header is None:\\n header = row\\n else:\\n cur = {}\\n for k,v in zip(header,row):\\n if k in csvtd and csvtd[k][0] not in cur:\\n cur[csvtd[k][0]] = parse_num(v)*csvtd[k][1]\\n elif k == key: ret[v] = cur\\n elif k == \\\"Submission\\\": cur[k] = v\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59773abdb4fadcc8568503769c63b4f5\",\n \"score\": \"0.53607273\",\n \"text\": \"def _read_csv(csv_ffn, header_line):\\n df = pandas.read_csv(csv_ffn, header=header_line)\\n as_dict = df.to_dict(orient='list')\\n return as_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a98cb30f0bcd9188daeb0e519b1376a\",\n \"score\": \"0.53544474\",\n \"text\": \"def _extract_fields(self, line):\\n acc = {}\\n field_tokens = line.split(\\\"\\\\t\\\")\\n for field_token in field_tokens:\\n kv_tokens = field_token.split('::')\\n if len(kv_tokens) == 2:\\n (key, value) = kv_tokens\\n acc[key] = value\\n\\n return acc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fefa2a84cd375622ca95eefbfd99db9\",\n \"score\": \"0.5346579\",\n \"text\": \"def parse_method(self, string_input):\\n # Strip out carriage return, newline and quote characters.\\n values = re.split(\\\",\\\", re.sub('\\\\r\\\\n', '', re.sub('\\\"', '',\\n string_input)))\\n row = dict(\\n zip(('name', 'surname', 'age'),\\n values))\\n return row\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06afedfa95a9cafd87bc373a15bfe2fb\",\n \"score\": \"0.5330888\",\n \"text\": \"def parse_csv_line(line):\\n\\n if line == '':\\n # return np.empty(0)\\n return []\\n\\n values = List()\\n value = ''\\n quotes = False\\n\\n for character in line:\\n if character == '\\\\n':\\n break\\n elif character == '\\\"':\\n quotes = not quotes\\n elif not quotes and character == ',':\\n values.append(value.strip())\\n value = ''\\n else:\\n value += character\\n\\n values.append(value)\\n return values.to_array()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c483df4761e85d8d4635469a946990c6\",\n \"score\": \"0.5324691\",\n \"text\": \"def getParams(self, line):\\n \\n line = line.strip()\\n line = line.strip('{}')\\n \\n str1, str2 = line.split(',')\\n \\n str1, str_D = str1.split(':')\\n str_D = str_D.strip(' ')\\n str_D = str_D.strip('\\\\\\\"')\\n D = int(str_D)\\n\\n str2, str_T = str2.split(':')\\n str_T = str_T.strip(' ')\\n str_T = str_T.strip('\\\\\\\"')\\n T = int(str_T)\\n\\n return D,T\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a46ed15f24eb524a57368da6204ea0a\",\n \"score\": \"0.5286351\",\n \"text\": \"def _input_line(line):\\n l = line.split()\\n r = {\\n \\\"year\\\": int(l[0]),\\n \\\"month\\\": int(l[1]),\\n \\\"day\\\": int(l[2]),\\n \\\"hour\\\": int(l[3]),\\n \\\"minute\\\": int(l[4]),\\n \\\"second\\\": float(l[5]),\\n \\\"mag\\\": float(l[9]),\\n \\\"lat\\\": float(l[6]),\\n \\\"lon\\\": float(l[7]),\\n \\\"dep\\\": float(l[8]),\\n #\\\"evid\\\": int(l[10]),\\n }\\n r['timestamp'] = _ts(datetime.datetime(r['year'], r['month'], r['day'],\\n r['hour'], r['minute'], 0)) + r['second']\\n return r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d73a2999781178f95c28a620f7b3e86b\",\n \"score\": \"0.5284623\",\n \"text\": \"def csv_to_dict(self, source_path, type_='stream_gauges'):\\n print 'parsing to dict'\\n delimiter = ','\\n working_dict = {}\\n result = {}\\n with open(source_path, 'r') as data_file:\\n data = csv.reader(data_file, delimiter=delimiter)\\n headers = next(data)[1:]\\n for row in data:\\n temp_dict = {}\\n id = row[0]\\n values = []\\n if type_ == 'stream_gauges':\\n name = '{} {}'.format(row[0], row[3])\\n for x in row[1:]:\\n # print row\\n try:\\n values.append(str(x))\\n # print str(x)\\n except ValueError:\\n try:\\n values.append(int(x))\\n # print int(x)\\n except ValueError:\\n try:\\n values.append(float(x))\\n # print float(x)\\n except ValueError:\\n print(\\\"Skipping value '{}' that cannot be converted \\\" +\\n \\\"to a number - see following row: {}\\\"\\n .format(x, delimiter.join(row)))\\n values.append(0)\\n elif type_ == 'floods':\\n name = 'Rank {}'.format(row[0])\\n print 'name: {}'.format(name)\\n for i, x in enumerate(row[1:]):\\n if i == 0:\\n values.append(datetime.strptime(x, '%m/%d/%Y'))\\n else:\\n values.append(int(x))\\n\\n for i in range(len(values)):\\n if values[i]:\\n temp_dict[headers[i]] = values[i]\\n\\n result[name] = temp_dict\\n\\n if type_ == 'stream_gauges':\\n temp_dict['Dataframe'] = None\\n temp_dict['Rating'] = None\\n temp_dict['ID'] = id\\n elif type_ == 'floods':\\n pass\\n working_dict.update({name: temp_dict})\\n return working_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94a293fa04f134e4213de59aab272122\",\n \"score\": \"0.5274642\",\n \"text\": \"def __parse_line(self, line: str):\\n exploded_line: List[str] = line.split(self.__column_delimiter)\\n\\n country_raw = exploded_line[self.__column_country]\\n region_raw = exploded_line[self.__column_region]\\n city_raw = exploded_line[self.__column_city]\\n population_raw = int(exploded_line[self.__column_population]) if exploded_line[self.__column_population] else 0\\n long_raw = float(exploded_line[self.__column_longitude]) if exploded_line[self.__column_longitude] else None\\n lat_raw = float(exploded_line[self.__column_latitude]) if exploded_line[self.__column_latitude] else None\\n\\n if population_raw < self.minimum_population:\\n return self\\n\\n if long_raw is None or lat_raw is None:\\n return self\\n\\n country: Country\\n if country_raw in self.__database.countries:\\n country = self.__database.countries[country_raw]\\n else:\\n country = Country(country_raw)\\n self.__database.countries[country_raw] = country\\n\\n region: Region\\n if f\\\"{country_raw}-{region_raw}\\\" in self.__database.countries:\\n region = self.__database[f\\\"{country_raw}-{region_raw}\\\"]\\n else:\\n region = Region(region_raw, country)\\n country.add_region(region)\\n self.__database.regions[f\\\"{country_raw}-{region_raw}\\\"] = region\\n\\n city: City\\n if f\\\"{country_raw}-{region_raw}-{city_raw}\\\" in self.__database.cities:\\n city = self.__database.cities[f\\\"{country_raw}-{region_raw}-{city_raw}\\\"]\\n else:\\n city = City(city_raw, region, country)\\n city.set_population(population_raw)\\n city.set_coordinates(longitude=long_raw, latitude=lat_raw)\\n country.add_city(city)\\n region.add_city(city)\\n self.__database.cities[f\\\"{country_raw}-{region_raw}-{city_raw}\\\"] = city\\n\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db3b644f938ea728d805d29ee87d2167\",\n \"score\": \"0.5270934\",\n \"text\": \"def parse_csv_table(readable, separator = \\\",\\\"):\\n data = defaultdict(dict)\\n \\n # First line is the columns\\n columns = filter(None, readable.readline().strip().split(separator))\\n\\n for line in readable:\\n row, rest = line.strip().split(separator, 1)\\n for value, col in zip(rest.split(separator), columns):\\n data[row][col] = float(value)\\n\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae82105ad16744eac636ce7e436c89a6\",\n \"score\": \"0.52584505\",\n \"text\": \"def _convert_dict(self, value):\\n dict_value = value.strip(\\\"{\\\")\\n result = {}\\n while True:\\n (key, found, rest) = dict_value.partition(\\\"=\\\")\\n if not found:\\n break\\n\\n key = key.strip('\\\"')\\n\\n item = None\\n new_dict_value = \\\"\\\"\\n\\n if rest[0] == '\\\"':\\n start = 1\\n while True:\\n pos = rest.find('\\\"', start)\\n if pos == len(rest):\\n # Error, we did not find the end of the string\\n raise Exception(\\\"Wrong format %s\\\" % value)\\n\\n if rest[pos - 1] != \\\"\\\\\\\\\\\":\\n # found a non escaped \\\"\\n item = rest[1:pos]\\n new_dict_value = rest[pos + 1 :].strip(\\\", \\\")\\n break\\n\\n start = pos + 1\\n else:\\n (item, comma, new_dict_value) = rest.partition(\\\", \\\")\\n if not comma:\\n (item, curly, new_dict_value) = rest.partition(\\\"}\\\")\\n if not curly:\\n raise Exception(\\\"Wrong format %s\\\" % value)\\n\\n result[key] = self._convert_single_value(item)\\n dict_value = new_dict_value\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0064f7ad2b08f33759feecb8b2a1e802\",\n \"score\": \"0.5256032\",\n \"text\": \"def loc_split(self,raw_loc):\\n return {raw_loc.split(\\\",\\\")[0].strip('\\\"'):raw_loc.split(\\\",\\\")[1].strip('\\\"')}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9b6af951d6666f5cc5abf676d67d6e7\",\n \"score\": \"0.5246892\",\n \"text\": \"def turn_csv_into_map(local_csv_fields, dropdown_map):\\n fields = []\\n for line in local_csv_fields:\\n fields.append(turn_line_into_map(line, \\n CSV_COLUMN_TO_ENUM, dropdown_map))\\n\\n return fields\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f79df0dbc12be829f7bd989d2c473089\",\n \"score\": \"0.52441436\",\n \"text\": \"def parse_bel_resource(lines: Iterable[str]) -> Dict:\\n lines = list(lines)\\n\\n value_line = 1 + max(\\n index\\n for index, line in enumerate(lines)\\n if '[Values]' == line.strip()\\n )\\n\\n metadata_config = ConfigParser(strict=False)\\n metadata_config.optionxform = lambda option: option\\n metadata_config.read_file(lines[:value_line])\\n\\n delimiter = metadata_config['Processing']['DelimiterString']\\n\\n value_dict = dict(\\n _get_bel_resource_kvp(line, delimiter)\\n for line in lines[value_line:]\\n )\\n\\n res = {\\n key: dict(values)\\n for key, values in metadata_config.items()\\n }\\n res['Values'] = value_dict\\n\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45db0418eb0244995c742504c23f623c\",\n \"score\": \"0.5238875\",\n \"text\": \"def _parse_values(data: str):\\n res = {}\\n for l in filter(lambda s: s != '', data.splitlines()):\\n try:\\n k, v = l.strip().split('=')\\n except ValueError:\\n raise serial.SerialException(\\\"Couldn't parse data {!r}\\\".format(l))\\n else:\\n res[k] = v\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffce026b36ac238ff2530e525b5602d2\",\n \"score\": \"0.523299\",\n \"text\": \"def parse_to_dict(self,fsf_lines):\\n fsf_dict=dict()\\n fsf_line_key=list()\\n for line in fsf_lines:\\n line=line.strip()\\n if len(line) > 0 and line[0] != \\\"#\\\":\\n set_line=re.search(\\\"set [^ ]* [^s]*\\\",line.strip())\\n if set_line:\\n split_line=line.split(\\\" \\\")\\n if len(split_line) > 3:\\n #combine and strip mri values that have spaces in them\\n temp=list()\\n for i in range (2,len(split_line)):\\n temp.append(split_line[i])\\n cleaned=' '.join(temp)\\n else:\\n cleaned=split_line[2]#.strip('\\\\\\\"')\\n fsf_dict[split_line[1]]=cleaned\\n fsf_line_key.append(split_line[1])\\n return fsf_dict,fsf_line_key\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ba3574ed02c09c112515c11c493df96\",\n \"score\": \"0.52149475\",\n \"text\": \"def create_dictionary(log_data):\\n dict_cvar = {}\\n split_log_data = log_data.split('\\\\n')\\n for line in split_log_data:\\n if 'cvar' in line:\\n parse_string = line[19:].rstrip(')').split(',')\\n dict_cvar[parse_string[0]] = parse_string[1]\\n return dict_cvar\",\n \"title\": \"\"\n },\n {\n \"docid\": \"895edae8d241d967919182115796c937\",\n \"score\": \"0.5214545\",\n \"text\": \"def morph2dict(line):\\n d = {}\\n _ = line.strip().split('\\\\t')\\n try:\\n _meta = _[1].split(',')\\n d[\\\"surface\\\"] = _[0]\\n d[\\\"pos\\\"] = _meta[0]\\n d[\\\"pos1\\\"] = _meta[1]\\n d[\\\"base\\\"] = _meta[6]\\n return d\\n except IndexError:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"871c65da3772d56c8cba8f1fe67c7c65\",\n \"score\": \"0.52137345\",\n \"text\": \"def map1(row):\\n rr = row.split(\\\",\\\")\\n return [(rr[1],(rr[0],float(rr[2])))]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dcef199456de8a31c7822bb61ff1120c\",\n \"score\": \"0.52110595\",\n \"text\": \"def _atq_line(self, line):\\n split = str(line).split()\\n return dict(id = int(split[0]), \\n when = datetime.datetime.strptime(\\n (' ').join(split[1:6]),\\n AT_OUTPUT_DATETIME_FORMAT),\\n queue = split[6],\\n who = split[7])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57e9fc69de0df307b2bd7a2cfed62880\",\n \"score\": \"0.51856875\",\n \"text\": \"def dict_creator(file):\\n large_lst = []\\n final_dict = {}\\n f = open(file, 'r')\\n for i in f.readlines():\\n stats = i.split(',')\\n large_lst.append(stats)\\n for i in large_lst:\\n i[-1] = i[-1][:-1]\\n final_dict[i[0]] = i[1:]\\n return final_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d7bf9311ad1c644390075de41c125f3\",\n \"score\": \"0.5176835\",\n \"text\": \"def _parse_attributes(line: str) -> dict:\\n attr_def = map(\\n lambda s: re.sub(r\\\"[\\\\\\\"'](.*)[\\\\\\\"']\\\", r\\\"\\\\1\\\", s),\\n line.split()\\n )\\n\\n return {\\n attr: val\\n for attr, val in map(lambda s: s.split('='), attr_def)\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88ce50a240a4242aafe227d0d97da78a\",\n \"score\": \"0.5175366\",\n \"text\": \"def parse(data):\\n\\t\\tresult = {}\\n\\t\\tdata_array = data.strip().split('; ')\\n\\t\\tfor datum in data_array:\\n\\t\\t\\tkey, value = datum.split(': ')\\n\\t\\t\\tkey = key.lower()\\n\\t\\t\\tif key == 'washer_states':\\n\\t\\t\\t\\tresult[key] = [int(x) for x in value.split(', ')]\\n\\t\\t\\telse:\\n\\t\\t\\t\\tresult[key] = value\\n\\t\\treturn result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff79eade237d29024cf1d5af73f55b78\",\n \"score\": \"0.5172581\",\n \"text\": \"def proccessed(self, column):\\n r = {}\\n for c in column.split(\\\",\\\"):\\n # print(\\\"Next column: %s\\\" % c)\\n moss, idss = self.decode(c.strip())\\n ids = set(moss.values_list(\\\"id\\\", flat=True))\\n for i_set in idss:\\n ids = ids.intersection(i_set)\\n r[c.strip()] = ids.copy()\\n return r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eca7e4b38dc94546e3c84b2c7715f6e5\",\n \"score\": \"0.5163745\",\n \"text\": \"def process_line(line: str):\\n line = line.rstrip(\\\"\\\\n\\\") # del \\\\n\\n line = line.split(\\\"\\\\t\\\")[1] # split input line and get key value\\n # get size, mean var\\n chunk_size, mean, var = tuple(map(float, line.split(\\\", \\\")))\\n return chunk_size, mean, var\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e1cf0a3d55567746de961f36844cc6d\",\n \"score\": \"0.515424\",\n \"text\": \"def conver_csv_into_dict(filename):\\n with open(filename) as File:\\n\\n if \\\"images\\\" in filename:\\n csv_dict_reader = list(DictReader(File,fieldnames = [\\\"ID\\\", \\\"IMAGE_NAME\\\", \\\"Latitude\\\", \\\"Longitude\\\", \\\"Date\\\"]))\\n elif \\\"sites\\\" in filename:\\n csv_dict_reader = list(DictReader(File,fieldnames = [\\\"ID\\\", \\\"SITE_NAME\\\", \\\"Latitude\\\", \\\"Longitude\\\", \\\"Date\\\"]))\\n return csv_dict_reader\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4022cc8e91188a0ed179ca216a5b3ee\",\n \"score\": \"0.51479685\",\n \"text\": \"def parse(lines):\\n data = {}\\n for line in lines:\\n line = line.strip()\\n name, job = line.split(\\\": \\\")\\n try:\\n name1, operation, name2 = job.split(\\\" \\\")\\n job = (operation, name1, name2)\\n except ValueError:\\n operation = \\\"yell\\\"\\n val = int(job)\\n job = (operation, val)\\n data[name] = job\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc705cf3d505b32d641ea30e91b2c17e\",\n \"score\": \"0.5146445\",\n \"text\": \"def normalize_line(line):\\n inside_str = False\\n str = ''\\n for i in line:\\n if i == '\\\"':\\n inside_str = not inside_str\\n if not inside_str and i == ',':\\n str += '#'\\n else:\\n str += i \\n str = str.replace('\\\\'', '`')\\n return str\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b435bf71ffd7c6a9ddbf772a851eb641\",\n \"score\": \"0.5137678\",\n \"text\": \"def load_csv_as_dict(Path, start_row=1, skip_rows=None, conv_numstrings=True, \\\\\\n empty_entries=True):\\n import csv\\n csv_read = csv.DictReader(open(Path))\\n i = 1 \\n # Shift skip_rows back 1 to correspond with correct csv_read entry indices\\n if skip_rows is not None:\\n skip_rows = [skip_rows[k] - 1 for k in range(len(skip_rows))]\\n \\n\\n Dict = {} # Empty dictionary to return\\n for row in csv_read:\\n if start_row == 1: # Build dictionary without skipping rows\\n if skip_rows is not None:\\n if i in skip_rows: # Skip rows specified in input list\\n i += 1 \\n continue\\n \\n for key, value in row.items():\\n Dict.setdefault(key, []).append(value)\\n \\n i += 1 \\n continue\\n \\n if i < start_row - 1: # Skip row\\n i += 1 \\n continue\\n \\n if i == start_row - 1: # Redefine row keys\\n keys = []\\n for column, value in row.iteritems():\\n keys.append(value)\\n i += 1 \\n continue \\n \\n if i > start_row - 1: # Build new dictionary from column entries\\n if skip_rows is not None:\\n if i in skip_rows: # Skip rows specified in input list\\n i += 1 \\n continue\\n \\n for key, (column, value) in zip(keys, row.iteritems()):\\n Dict.setdefault(key, []).append(value)\\n \\n i += 1 \\n \\n # Convert number strings to floats\\n if conv_numstrings: dict_conv_numstrings(Dict)\\n \\n # Remove empty entires\\n if empty_entries:\\n for key, value in Dict.items(): # Filter out empty '' values\\n if not value.__class__ == float:\\n Dict[key] = list(filter(lambda a: a != '', value))\\n \\n return Dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d30d503ff18794cd2f00144ca1c79e59\",\n \"score\": \"0.5134373\",\n \"text\": \"def parse_line(self, line):\\n def parse_arg(a):\\n assert a is not None\\n if a == 'nil' or a == 'null':\\n return None\\n elif a == '{}':\\n return {}\\n elif a == '[]':\\n return []\\n elif a.isdigit():\\n return int(a)\\n elif a.startswith('$'):\\n return resolve_var(a[1:], self.vars)\\n elif a and a[0] == \\\"[\\\" and a[-1] == \\\"]\\\":\\n return [parse_arg(x) for x in a[1:-1].split(',')]\\n elif a and a[0] == \\\"{\\\" and a[-1] == \\\"}\\\":\\n return list_or_dict([parse_arg(x) for x in a[1:-1].split(',')])\\n elif '=' in a:\\n k, v = a.split('=', 1)\\n return {k.strip(): v.strip()}\\n elif '://' in a: # special case for protocols\\n return a\\n elif ':' in a:\\n k, v = a.split(':', 1)\\n if ',' in v:\\n if v[0] == '{' and v[-1] == '}': # unnecesary, but may help readability\\n v = v[1:-1]\\n vv = list_or_dict([parse_arg(x.strip())\\n for x in v.split(',')])\\n return {k.strip(): vv}\\n\\n return {k.strip(): parse_arg(v.strip())}\\n # literal\\n return a\\n\\n def list_or_dict(ld):\\n if not ld:\\n return []\\n if all([type(x) == dict for x in ld]):\\n r = {}\\n for d in ld:\\n r.update(d)\\n return r\\n return ld\\n\\n def resolve_var(expr, vars):\\n if not type(vars) in (dict, list):\\n return '%s.%s' % (str(vars), expr)\\n if '.' in expr:\\n (pre, post) = expr.split('.', 1)\\n if pre.isdigit():\\n pre = int(pre)\\n return resolve_var(post, vars[pre])\\n if expr.isdigit():\\n expr = int(expr)\\n return vars[expr]\\n\\n def parse_params(line, sep):\\n if not sep in line:\\n return []\\n line = line[line.index(sep):].strip()\\n if line == '{}':\\n return {}\\n if line == '[]':\\n return []\\n if line.startswith('{'):\\n return json.loads(line)\\n\\n params = shlex.split(line)\\n return list_or_dict([parse_arg(x) for x in params])\\n\\n\\n if line.startswith('#'):\\n return None\\n cmd = shlex.split(line)\\n if not cmd:\\n return None\\n if len(cmd) >= 3 and cmd[1] == '=':\\n params = parse_params(line, '=')\\n return {\\n 'method': parse_arg(cmd[2]),\\n 'params': params,\\n 'assign_to': cmd[0]\\n }\\n\\n return {\\n 'method': parse_arg(cmd[0]),\\n 'params': parse_params(line, ' ')\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d24d92de04ba25793c06d0cf2a52e871\",\n \"score\": \"0.5130751\",\n \"text\": \"def get_from_csv2(self, line):\\n print(len(line))\\n (self.name, self.webpage, self.institution,\\n self.country, self.address, self.lat,\\n self.long, self.exp_theor, self.fields,\\n self.people, self.atom, self.comment) = line\\n self.lat = float(self.lat)\\n self.long = float(self.long)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82fed3c9634c05eb18ea258ade17de2e\",\n \"score\": \"0.5123542\",\n \"text\": \"def csv2dict(csvname, delimiter=','):\\n lines = open(csvname, 'r').readlines()\\n header = lines[0].rstrip().split(delimiter)\\n lines = lines[1:]\\n nlines = len(lines)\\n\\n dict_list = [{} for _ in range(nlines)]\\n for i, line in enumerate(lines):\\n line2 = convert_symbol(line.rstrip(), delimiter, '|')\\n items = line2.split(delimiter)\\n items2 = [s.replace('|', delimiter) for s in items]\\n dict_list[i] = {k:items2[j] for j,k in enumerate(header)}\\n\\n return dict_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7af55d2906d7249f7da0771801c0f22b\",\n \"score\": \"0.51226807\",\n \"text\": \"def parse_param_file(param_file):\\n with gfile.GFile(param_file, 'r') as fh:\\n lines = fh.readlines()\\n d = {}\\n for l in lines:\\n l = l.rstrip('\\\\n')\\n splits = l.split(':')\\n key = splits[0]\\n val_ = splits[1].strip()\\n if not val_:\\n val = ''\\n else:\\n try:\\n val = ast.literal_eval(val_)\\n except (ValueError, SyntaxError):\\n val = str(val_)\\n d[key] = val\\n return d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42a175bad53d70487f25534bb8691c0b\",\n \"score\": \"0.5103795\",\n \"text\": \"def _get_values(line):\\n return line[line.find(\\\"` VALUES \\\") + 9 :]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad1ebac9e2cefed0374c631858d4b1c4\",\n \"score\": \"0.5103102\",\n \"text\": \"def process_record(raw_record):\\n\\n record = {}\\n \\n raw_record = raw_record.replace(os.linesep, ' ')\\n field_record = raw_record.split(' ')\\n for fld in field_record:\\n fld = fld.split(':')\\n try:\\n record[fld[0]] = fld[1]\\n except IndexError:\\n continue\\n return record\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0818f8ec5f65d0b8f22cf151b6f763f4\",\n \"score\": \"0.5093003\",\n \"text\": \"def populate_csv_dict(self):\\n with open(self.source_csv_path) as f:\\n reader = csv.reader(f)\\n first = True\\n self.csv_dict = {}\\n header_index_map = {}\\n for row in reader:\\n for i in range(len(row)): # be explicit about reading the indices of the row\\n if first:\\n self.csv_dict[row[i]] = {}\\n header_index_map[i] = row[i]\\n else:\\n key = header_index_map[i]\\n try:\\n self.csv_dict[key][int(row[0])] = self._tokenise(row[i])\\n except ValueError as ve:\\n print 'One of the id-s doesn\\\\'t seem to be an integer:', row[0]\\n print\\n raise\\n if first: \\n first = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5400da42673cdfd4d7b921d4199522e\",\n \"score\": \"0.5092853\",\n \"text\": \"def state_numeric_codes(inFile):\\n with open(inFile, \\\"r\\\") as csvfile: \\n state_code_dict = {}\\n descr = csvfile.readline()\\n\\n while descr != \\\"\\\":\\n split_descr = descr.split(\\\",\\\")\\n\\n if len(split_descr) >= 3:\\n state_name = split_descr[3]\\n state_code = split_descr[1]\\n\\n new_name = remove_quotes(state_name)\\n code = remove_quotes(state_code)\\n new_code = remove_lead0s(code)\\n\\n state = state_name.split()\\n if len(state) == 1:\\n state_code_dict[new_name] = int(new_code)\\n descr = csvfile.readline()\\n \\n return state_code_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c0b19d5bacb6e1dc6bddffe700e6020\",\n \"score\": \"0.5089802\",\n \"text\": \"def parse_line(line_str):\\n field_list = line_str.strip().split(', ')\\n name_str = field_list[1] + ' ' + field_list[0]\\n score_list = []\\n for element in field_list[2:]:\\n score_list.append(int(element))\\n return name_str, score_list # a tuple is returned\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d087e208d20bc99ae9254cb8335b309\",\n \"score\": \"0.50845295\",\n \"text\": \"def _parse_str_to_dict(self, args):\\n arg_dict = dict()\\n args = args.split()\\n nargs = len(args)\\n arg_dict['analysis_id'] = args[0]\\n for i in range(1, nargs, 2):\\n arg_key = args[i]\\n arg_arg = args[i + 1]\\n if arg_key in self._valid_settings:\\n arg_dict[arg_key] = arg_arg\\n else:\\n warnings.warn(\\n \\\"ignoring invalid argument key \\\" + arg_key + \\\" for analysis \\\" + self.analysis_key)\\n return arg_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8150670710202a6a5078d03388ac63d0\",\n \"score\": \"0.5080663\",\n \"text\": \"def _extract_and_parse_csv(input_csv, key_header_label, values_header_label):\\n freeform_labels = {}\\n\\n with open(input_csv, 'rb') as csvfile:\\n reader = csv.reader(csvfile)\\n\\n # Parse the header row.\\n header = reader.next()\\n key_idx = (None if key_header_label is None\\n else header.index(key_header_label))\\n val_idx = header.index(values_header_label)\\n\\n # Parse the data rows.\\n row_idx = 2\\n for row in reader:\\n text = row[val_idx].decode('utf-8').lower()\\n if len(text) == 0:\\n continue\\n\\n # Only one type of delimiter is expected in the free-form text field.\\n if text.find(';') != -1 and text.find('\\\\n') != -1:\\n raise ValueError('Free-form text field cannot contain both '';'' and '\\n 'newline delimiters.')\\n\\n # Parse if necessary.\\n if text.find(';') != -1:\\n text = text.split(';')\\n elif text.find('\\\\n') != -1:\\n text = text.splitlines()\\n else:\\n text = [text]\\n\\n freeform_labels[row_idx if key_idx is None else row[key_idx]] = text\\n row_idx += 1\\n\\n return freeform_labels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d425be8f579ee0712c97909f31aba3a\",\n \"score\": \"0.50765157\",\n \"text\": \"def parse_csv(self):\\n self.data = {}\\n csv_lines = self.csv.split(\\\"\\\\n\\\")\\n\\n for csv_line in csv_lines:\\n if csv_line.startswith('#'):\\n continue\\n if csv_line.startswith('pxname'):\\n continue\\n\\n csv_fields = csv_line.split(',')\\n\\n if len(csv_fields) < len(self.status_fields):\\n continue\\n\\n service_field = self.status_fields['pxname']\\n server_field = self.status_fields['svname']\\n\\n for status_field_name in self.status_fields:\\n status_field = self.status_fields[status_field_name]\\n status_field_number = status_field['number']\\n\\n service_field_value = csv_fields[service_field['number']]\\n server_field_value = csv_fields[server_field['number']]\\n\\n if service_field_value not in self.data:\\n self.data[service_field_value] = {}\\n\\n if server_field_value not in self.data[service_field_value]:\\n self.data[service_field_value][server_field_value] = {}\\n\\n csv_field_value = csv_fields[status_field_number]\\n\\n server = self.data[service_field_value][server_field_value]\\n server[status_field_name] = csv_field_value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4814a84016878fda5533ffbc40ee2426\",\n \"score\": \"0.5052376\",\n \"text\": \"def coordinates(line):\\n contents = line.split(\\\",\\\")\\n lng, lat = map(float, contents[3:5])\\n return lng, lat\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8e0b131f9c1b30cadb5ed703c0dc185\",\n \"score\": \"0.5050806\",\n \"text\": \"def import_from_file(input_file):\\n list_of_dicts = []\\n floats = ('Longitude', 'Latitude')\\n tmp_dict = {}\\n try:\\n csv_file = open(input_file, 'rU')\\n except IOError:\\n print \\\"Error:\\\", sys.exc_info()[0]\\n sys.exit()\\n else:\\n reader = csv.reader(csv_file)\\n keys = reader.next()\\n for values in reader:\\n tuples = zip(keys, values)\\n for key, value in tuples:\\n if key in floats:\\n try:\\n tmp_dict[key] = float(value)\\n except ValueError:\\n print \\\"Error:\\\", sys.exc_info()[0]\\n tmp_dict[key] = value\\n else:\\n tmp_dict[key] = value\\n list_of_dicts.append(tmp_dict)\\n csv_file.close()\\n return list_of_dicts\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6b145aa1dea2f5edc2d5d446d777c4b\",\n \"score\": \"0.50342846\",\n \"text\": \"def _get_args(line: str) -> Dict[str, str]:\\n line = f' {line}'\\n args_dict: Dict[str, Any] = {}\\n args = line.split(\\\" --\\\")[1:]\\n\\n for arg in args:\\n arg_tuple = arg.split(\\\" \\\")\\n args_dict[arg_tuple[0]] = \\\" \\\".join(arg_tuple[1:])\\n\\n return args_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78fe8d4d74b4afbab6de7af652d31902\",\n \"score\": \"0.5033904\",\n \"text\": \"def deserialize(self, data):\\n lst = data.split(',')\\n return self.deserializeHelper(lst)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5fbdad267118fd1e2458c0ebe88cc23b\",\n \"score\": \"0.5032945\",\n \"text\": \"def deserialize(self, data):\\n data = data[1:-1]\\n if len(data)==0: return None\\n input = re.split(',',data)\\n return self.build(input,0,0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"049be26d77c1c767b5c6bbb78a415e72\",\n \"score\": \"0.5029139\",\n \"text\": \"def load_data(path: str) -> Dict[int, str]:\\n data = {}\\n with open(path, 'r') as file:\\n lines = file.readlines()\\n for line in lines:\\n row_data = literal_eval(line)\\n data[row_data[QUESTION_CONTENT_KEY]] = row_data[QUESTION_ID_KEY]\\n\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f8be612c88c867c8cc5f1adef803cc1\",\n \"score\": \"0.5020023\",\n \"text\": \"def get_tag_dict():\\n tag_source_file = open(\\\"../tags_config.txt\\\", 'r')\\n tag_dict = {}\\n for line in tag_source_file.readlines():\\n if line[0] == \\\"#\\\" or len(line) < 5:\\n continue\\n target_line = line.replace(\\\"\\\\n\\\", \\\"\\\")\\n target_line = target_line.replace(\\\" \\\", \\\"\\\")\\n try:\\n tags, category = target_line.split(\\\"->\\\")\\n except:\\n print(\\\"The config file may be formatted incorrectly, the following line could not be parsed: \\\\n{}\\\\n \\\"\\n \\\"please make sure each line is in the format a, b, c ... -> d\\\".format(line))\\n exit(0)\\n for tag in tags.split(\\\",\\\"):\\n tag_dict[tag] = category\\n tag_source_file.close()\\n return tag_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72d0e2d63d6dddd6669804fcd818b34f\",\n \"score\": \"0.50171804\",\n \"text\": \"def parse_csv(rows_string_tensor):\\n\\n # Takes a rank-1 tensor and converts it into rank-2 tensor\\n # Example if the data is ['csv,line,1', 'csv,line,2', ..] to\\n # [['csv,line,1'], ['csv,line,2']] which after parsing will result in a\\n # tuple of tensors: [['csv'], ['csv']], [['line'], ['line']], [[1], [2]]\\n row_columns = tf.expand_dims(rows_string_tensor, -1)\\n columns = tf.decode_csv(row_columns, record_defaults=CSV_COLUMN_DEFAULTS)\\n features = dict(zip(CSV_COLUMNS, columns))\\n\\n # Remove unused columns\\n for col in UNUSED_COLUMNS:\\n features.pop(col)\\n return features\",\n \"title\": \"\"\n },\n {\n \"docid\": \"228d117c5211df533001478cb385dd9a\",\n \"score\": \"0.5015171\",\n \"text\": \"def __extract_key_value(self, line):\\n values = line.split('=')\\n return values[0].strip(),values[1].strip()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70f896f99cd939bd5bbf2377e7223b16\",\n \"score\": \"0.5013919\",\n \"text\": \"def get_csv_as_dict_list():\\n dict_list = []\\n with open('../../data/EHRdataSample.csv') as csv_file:\\n csv_reader = csv.DictReader(csv_file, delimiter=',')\\n for line in csv_reader:\\n dict_list.append(line)\\n return dict_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"663242c3c86bf8c1ff04c61f398410d6\",\n \"score\": \"0.5012478\",\n \"text\": \"def tsv_to_dict(self):\\n tsv_rows = []\\n for line in self.tsv_string.split(\\\"\\\\n\\\"):\\n #header lines\\n if line.startswith(\\\"##\\\"):\\n self.header.append(line)\\n #column header line.\\n elif line.startswith(\\\"#\\\"):\\n self.column_header = line[1:].split(\\\"\\\\t\\\")\\n elif line:\\n tsv_rows.append(line)\\n for x in range(len(tsv_rows)):\\n #seperates each row element seperated by tabs into a dictionary\\n inner_dict = dict(zip(self.column_header, tsv_rows[x].split(\\\"\\\\t\\\")))\\n #if not unique key is provided the outer dictionary is simply saved by a number.\\n if self.key_name:\\n self.tsv_dict[inner_dict[self.key_name]] = inner_dict\\n else:\\n self.tsv_dict[str(x)] = inner_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f915c8697e81b68656ee9646a62f6a7\",\n \"score\": \"0.50063443\",\n \"text\": \"def parse_line(self, line: str):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"097ad3977eeacac389fabcaf03e856eb\",\n \"score\": \"0.5004433\",\n \"text\": \"def parse_content(self, content):\\n _lines = get_active_lines(content, comment_char=\\\"#\\\")\\n self.data = {}\\n current_key = \\\"\\\"\\n for line in _lines:\\n if \\\"=\\\" in line:\\n k, v = line.split('=')\\n current_key = k.strip()\\n self.data[current_key] = v.strip(\\\"\\\\\\\"' \\\")\\n else:\\n self.data[current_key] = self.data[current_key] + line\\n # if the value like \\\"[ , ,]\\\", convert to list\\n if self.data[current_key].endswith(\\\"]\\\"):\\n self.data[current_key] = json.loads(self.data[current_key])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8161b8825e009fe9b9914cc35e14bfc\",\n \"score\": \"0.49966922\",\n \"text\": \"def _parse_group_vars_line(self, line):\\n tokens = shlex.split(line.strip())\\n key_values = self._parse_vars(tokens)\\n return key_values\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7dd1d952013d90cc57e78491b609a98\",\n \"score\": \"0.49955785\",\n \"text\": \"def _convert_variant_file_to_dict(varfile):\\n patient_variant_map = {}\\n # line_num = 0 note this is builtin to the reader as reader.line_num\\n reader = csv.reader(varfile, delimiter=\\\"\\\\t\\\")\\n col = [\\n 'patient', 'family', 'chromosome', 'build', 'position', 'reference_allele',\\n 'variant_allele', 'parent_of_origin', 'allele_type', 'mutation_type',\\n 'gene_symbol', 'transcript', 'reference_aa', 'variant_aa, aa_change',\\n 'segregates_with', 'locus', 'exon', 'inheritance_model', 'zygosity',\\n 'dbsnp_id', 'frequency', 'num_of_alleles'\\n ]\\n # row = next(reader) # there is no header\\n # if len(row) != len(col):\\n # LOG.error('Got:\\\\n\\\\t%s\\\\nExpected:\\\\n\\\\t%s\\\\n', row, col)\\n # raise TypeError('header does not match expected format')\\n for row in reader:\\n\\n patient = row[col.index('patient')]\\n # family,\\n chromosome = row[col.index('chromosome')]\\n build = row[col.index('build')]\\n position = row[col.index('position')]\\n reference_allele = row[col.index('reference_allele')]\\n variant_allele = row[col.index('variant_allele')]\\n # parent_of_origin,\\n # allele_type,\\n mutation_type = row[col.index('mutation_type')]\\n gene_symbol = row[col.index('gene_symbol')]\\n # transcript,\\n # reference_aa,\\n # variant_aa,\\n # aa_change,\\n # segregates_with,\\n # locus,\\n # exon,\\n # inheritance_model,\\n # zygosity,\\n dbsnp_id = row[col.index('dbsnp_id')]\\n # frequency,\\n # num_of_alleles\\n\\n if patient not in patient_variant_map:\\n patient_variant_map[patient] = {}\\n\\n formatted_chr = re.sub(r'^CHR', 'chr', chromosome, flags=re.I)\\n\\n if re.fullmatch(r'[XY]|[0-9]{1,2}', chromosome, flags=re.I):\\n formatted_chr = \\\"chr{0}\\\".format(chromosome.upper())\\n\\n formatted_build = re.sub(r'^HG', 'hg', build, flags=re.I)\\n ref_base = reference_allele.upper()\\n var_base = variant_allele.upper()\\n rs_id = ''\\n\\n # Catch misformatted data\\n if re.search(r'LEFT FLANK|NM_|EXON', ref_base):\\n ref_base = ''\\n\\n if re.search(r'LEFT FLANK|NM_|EXON', var_base):\\n var_base = ''\\n\\n if dbsnp_id != '':\\n match = re.fullmatch(r'^(rs\\\\d+).*', dbsnp_id)\\n if match:\\n rs_id = match.group(1)\\n\\n # Format variant object\\n variant_info = [formatted_chr, formatted_build, position,\\n ref_base, var_base]\\n\\n if '' in variant_info:\\n filt_list = [info for info in variant_info if info != '']\\n variant_id = str(reader.line_num) + '-' + '-'.join(filt_list)\\n else:\\n variant_id = '-'.join(variant_info)\\n\\n if variant_id in patient_variant_map[patient]:\\n patient_variant_map[patient][variant_id]['genes_of_interest'].append(\\n gene_symbol)\\n else:\\n patient_variant_map[patient][variant_id] = {\\n 'build': formatted_build,\\n 'position': position,\\n 'chromosome': formatted_chr,\\n 'reference_allele': ref_base,\\n 'variant_allele': var_base,\\n 'type': mutation_type,\\n 'rs_id': ''\\n }\\n if rs_id:\\n patient_variant_map[patient][variant_id]['rs_id'] = rs_id\\n\\n patient_variant_map[patient][variant_id]['genes_of_interest']\\\\\\n = [gene_symbol]\\n\\n return patient_variant_map\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd07986733e7eb236a09d82bef825d01\",\n \"score\": \"0.49793538\",\n \"text\": \"def consume_line(self, line):\\n data = RE_VALUE_KEY.split(line.strip(), 1)\\n if len(data) == 1:\\n return float(data[0]), None\\n else:\\n return float(data[0]), data[1].strip()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c7d00eb920000871cb4ddd7750935b1\",\n \"score\": \"0.49764368\",\n \"text\": \"def parse_dict(text):\\n\\n substitutions = {\\\"{}\\\": {}, \\\"[]\\\": [], \\\"True\\\": True, \\\"False\\\": False}\\n\\n def parse_value(some_val):\\n some_val = substitutions.get(some_val, some_val)\\n\\n try:\\n int_val = int(some_val)\\n if str(int_val) == some_val:\\n return int_val\\n except ValueError:\\n pass\\n\\n try:\\n float_val = float(some_val)\\n if str(float_val) == some_val:\\n return float_val\\n except ValueError:\\n pass\\n\\n return some_val\\n\\n def chomp_key_list(out_dict, keys, value):\\n \\\"\\\"\\\"\\n turn keys like ['a', 'b', 'c', 'd'] and a value into\\n out_dict['a']['b']['c']['d'] = value\\n \\\"\\\"\\\"\\n dict_ptr = out_dict\\n last_ptr = out_dict\\n for i, key in enumerate(keys):\\n last_ptr = dict_ptr\\n if not key in dict_ptr:\\n dict_ptr[key] = {}\\n else:\\n if type(dict_ptr[key]) is not dict:\\n raise BadRequest(\\\"Building a dict in %s field, but it exists as %s already\\\" %\\n (key, type(dict_ptr[key])))\\n dict_ptr = dict_ptr[key]\\n last_ptr[keys[-1]] = value\\n\\n out = {}\\n if text is None:\\n return out\\n\\n pairs = text.split(',') # pairs separated by commas\\n for pair in pairs:\\n if pair.count(':') == 0:\\n continue\\n fields = pair.split(':', 1) # pair separated by first colon\\n key = fields[0].strip()\\n value = fields[1].strip()\\n\\n keyparts = key.split(\\\".\\\")\\n chomp_key_list(out, keyparts, parse_value(value))\\n\\n return out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1d432ee9552e21862e03201c19d5015\",\n \"score\": \"0.4974465\",\n \"text\": \"def dict_from_path(self, path):\\n return dict(re.split(\\\"\\\\s+\\\", line) for line in self.get_param_lines(path))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1beffeb4fefe1bac76abd78830d48ced\",\n \"score\": \"0.49729833\",\n \"text\": \"def clean_line(self, line, joint_names):\\n def try_float(x):\\n try:\\n return float(x)\\n except ValueError:\\n return None\\n #convert the line of strings to a float or None\\n line = [try_float(x) for x in line.rstrip().split(',')]\\n #zip the values with the joint names\\n combined = zip(joint_names[1:], line[1:])\\n #take out any tuples that have a none value\\n cleaned = [x for x in combined if x[1] is not None]\\n #convert it to a dictionary with only valid commands\\n command = dict(cleaned)\\n return (command, line,)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f87c32bbc889f291d782f825389cd91\",\n \"score\": \"0.49717426\",\n \"text\": \"def load_expression(fn=None, sep=\\\"\\\\t\\\"):\\n with gzip.open(fn, \\\"rt\\\") as f:\\n reader = csv.DictReader(f, delimiter=sep)\\n return {row[\\\"Gene\\\"]: float(row[\\\"Expression\\\"]) for row in reader}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f09ba7c58cf44448e81a9ff7ad46d68\",\n \"score\": \"0.49692282\",\n \"text\": \"def parse_line(line, custom_columns):\\n results = {}\\n if line.startswith('\\\\t'):\\n applog = line.strip() # remove leading tab and trailing newline.\\n results['applog'] = applog\\n # Some applogs have severity; some are continuations and do not.\\n if (len(applog) > 2 and applog[1] == ':'\\n and applog[0] >= '0' and applog[0] <= '9'):\\n results['applog_severity'] = applog[0]\\n for column, regexp in custom_columns.iteritems():\\n match = re.search(regexp, applog)\\n if match:\\n # TODO: Consider allowing named groups.\\n results[column] = match.group(1)\\n return results\\n\\n matches = LINE_RE_COMPILED.match(line)\\n if not matches:\\n raise LineParsingFailure('Fail. %s', line)\\n\\n results['remotehost'] = matches.group(1)\\n results['user'] = matches.group(2)\\n results['request_time_str'] = matches.group(3)\\n results['request_line'] = matches.group(4)\\n # Submatch = matches.group(5)\\n results['status'] = matches.group(6)\\n results['bytes'] = matches.group(7)\\n results['referer'] = matches.group(8)\\n # Submatch = matches.group(9)\\n results['useragent'] = matches.group(10)\\n\\n more_values = line[len(matches.group(0)):].strip().split(' ')\\n if more_values:\\n results['host'] = more_values.pop(0)\\n for pair in more_values:\\n key, value = pair.split('=')\\n results[key] = value\\n if more_values and 'loading_request' not in results:\\n # The user requested --include_all, so if this were a loading request\\n # it would be in the line. But it's not in the line, so it's not a loading\\n # request. Otherwise, we want it to be NULL for unknown.\\n results['loading_request'] = '0'\\n\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9bfebb31a2b327226359a4029d55445\",\n \"score\": \"0.49632904\",\n \"text\": \"def process_csv(csv_file):\\n csv_f = open(csv_file)\\n csv_extract = csv.DictReader(csv_f, delimiter=\\\"\\\\t\\\") # dialect='excel'\\n csv_list = [] \\n for row in csv_extract:\\n u_row = {}\\n for k,v in row.iteritems():\\n if type(v) == str:\\n u_row[k] = v.decode(\\\"utf-8\\\")\\n else:\\n u_row[k] = v\\n csv_list.append(u_row)\\n return csv_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ddb58c3b67085a2ba881a16dbe43155\",\n \"score\": \"0.496286\",\n \"text\": \"def convert_tsv_line(cls, mapping, tsv_header):\\n custom_field_infos = tsv_header.custom_field_infos\\n table = {\\n \\\"integer\\\": int,\\n \\\"number\\\": float,\\n \\\"boolean\\\": lambda x: BOOL_VALUES.get(x, False),\\n }\\n for key, value in mapping.items():\\n if value is not None and key in custom_field_infos:\\n mapping[key] = table.get(custom_field_infos[key].field_type, lambda x: x)(value)\\n return mapping\",\n \"title\": \"\"\n },\n {\n \"docid\": \"643e91813ee39ce764ce3cb90cb8ffe8\",\n \"score\": \"0.49558353\",\n \"text\": \"def parseCSVRow(self, row):\\n if row[0] == 'id':\\n self.dataHeaders = row\\n else:\\n self.genDataInput(row)\\n self.setDataObjectToDict()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b78dc127644dc995f4502ef722af8d6\",\n \"score\": \"0.49547195\",\n \"text\": \"def format_line(line):\\n result = {'type': 'gauge', 'severity': 'warning'}\\n parts = line.split('|')\\n for part in parts:\\n if '=' in part:\\n index = part.index('=')\\n result[part[:index]] = part[index+1:]\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a024d46690571ceb9b3a9b57fb849243\",\n \"score\": \"0.49533784\",\n \"text\": \"def _parse(\\n self,\\n record: Mapping[str,\\n tf.Tensor]) -> Tuple[Dict[str, tf.Tensor], tf.Tensor]:\\n x, y = super()._parse(record)\\n feature_name_mapping = {\\n 'input_ids': 'input_word_ids',\\n 'input_mask': 'input_mask',\\n 'segment_ids': 'input_type_ids'\\n }\\n converted_x = {\\n feature_name_mapping.get(name, name): tensor\\n for name, tensor in x.items()}\\n return converted_x, y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7f6154f791bf6b3374fed927b07828a\",\n \"score\": \"0.4949671\",\n \"text\": \"def read_csv():\\n yelp_ids_hours_file = open('insta9')\\n yelp_ids_hours = csv.reader(yelp_ids_hours_file)\\n\\n yelp_ids = {}\\n\\n for row in yelp_ids_hours:\\n infos = row[0].split('|')\\n yelp_ids[infos[0]] = { \\\\\\n 'Monday': infos[1], \\\\\\n 'Tuesday': infos[2], \\\\\\n 'Wednesday': infos[3], \\\\\\n 'Thursday': infos[4], \\\\\\n 'Friday': infos[5], \\\\\\n 'Saturday': infos[6], \\\\\\n 'Sunday': infos[7] }\\n\\n # print infos[0], yelp_ids[infos[0]]['Monday']\\n\\n return yelp_ids\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a252e6b39b088627844d5c27ede1cb3\",\n \"score\": \"0.49438286\",\n \"text\": \"def parse_csv_line_to_resource(line):\\n if not line:\\n return None\\n # Capping splits prevents commas in URL from being caught.\\n (\\n url_string,\\n etag_string,\\n size_string,\\n storage_class_string,\\n atime_string,\\n mtime_string,\\n uid_string,\\n gid_string,\\n mode_base_eight_string,\\n crc32c_string,\\n md5_string,\\n ) = line.rstrip().rsplit(',', _CSV_COLUMNS_COUNT)\\n\\n url_object = storage_url.storage_url_from_string(url_string)\\n if isinstance(url_object, storage_url.FileUrl):\\n return resource_reference.FileObjectResource(url_object)\\n cloud_object = resource_reference.ObjectResource(\\n url_object,\\n etag=etag_string if etag_string else None,\\n size=int(size_string) if size_string else None,\\n storage_class=storage_class_string if storage_class_string else None,\\n crc32c_hash=crc32c_string if crc32c_string else None,\\n md5_hash=md5_string if md5_string else None,\\n custom_fields={},\\n )\\n posix_util.update_custom_metadata_dict_with_posix_attributes(\\n cloud_object.custom_fields,\\n posix_util.PosixAttributes(\\n atime=int(atime_string) if atime_string else None,\\n mtime=int(mtime_string) if mtime_string else None,\\n uid=int(uid_string) if uid_string else None,\\n gid=int(gid_string) if gid_string else None,\\n mode=posix_util.PosixMode.from_base_eight_str(mode_base_eight_string)\\n if mode_base_eight_string\\n else None,\\n ),\\n )\\n return cloud_object\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab948b704445615643142c0986918f03\",\n \"score\": \"0.49387118\",\n \"text\": \"def line_to_dict(self, line, protocol):\\n d = {}\\n connection_states = {\\n '01':'ESTABLISHED',\\n '02':'SYN_SENT',\\n '03':'SYN_RECV',\\n '04':'FIN_WAIT1',\\n '05':'FIN_WAIT2',\\n '06':'TIME_WAIT',\\n '07':'CLOSE',\\n '08':'CLOSE_WAIT',\\n '09':'LAST_ACK',\\n '0A':'LISTEN',\\n '0B':'CLOSING' }\\n line_array = self._remove_empty(line.split(' '))\\n d['protocol'] = protocol\\n d['local_ip'], d['local_port'] = self._convert_ip_port(line_array[1])\\n d['remote_ip'], d['remote_port'] = self._convert_ip_port(line_array[2])\\n if 'tcp' in protocol:\\n d['state'] = connection_states[line_array[3]]\\n else:\\n d['state'] = ''\\n d['pid'] = self.pid_of_inode(line_array[9])\\n d['program'] = self.name_of_pid(d['pid'])\\n return d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d9ae3e4e2762e0382d327ee5a073a76\",\n \"score\": \"0.49384794\",\n \"text\": \"def decode(line):\\n try:\\n return json.loads(line)\\n except:\\n fake_record = {}\\n fake_record['fip'] = 0\\n fake_record['user_id'] = 0\\n fake_record['text'] = ''\\n fake_record['filtered_text'] = ''\\n fake_record['url_count'] = 0\\n fake_record['emoji_count'] = 0\\n fake_record['education_level_1'] = 0\\n fake_record['education_level_2'] = 0\\n fake_record['education_level_3'] = 0\\n fake_record['education_level_4'] = 0\\n return fake_record\",\n \"title\": \"\"\n }\n]","isConversational":false}}}],"truncated":false,"partial":false},"paginationData":{"pageIndex":8,"numItemsPerPage":100,"numTotalItems":994482,"offset":800,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sInJlcG9JZHMiOlsiNjgzOGE5MTA0YTNhNzFhOTE3OTU0Y2YwIl0sImlhdCI6MTc5MTQzODMzNSwianRpIjoiMDMxNWEwODctODJkZi00OTZkLWE3OWYtMzkwYmJjZmRlYjc3Iiwic3ViIjoiL2RhdGFzZXRzL250aGFrdXIvY29ybnN0YWNrLXB5dGhvbi12MS10ZXZhdHJvbi0xTSIsImV4cCI6MTc5MTQ0MTkzNSwiaXNzIjoiaHR0cHM6Ly9odWdnaW5nZmFjZS5jbyJ9.kKe1yU2BfEj07hVkrkZE8oQ52T0Oven8YGWxHWVWO2VqNhzxcA1numU0X6k_0C3FBIzA-qXjH0ltIkNicSqvAg","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":994482,"numBytesParquet":23972089656}]},"discussionsStats":{"closed":0,"open":1,"total":1},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query_id stringlengths 32 32 | query stringlengths 9 4.01k | positive_passages listlengths 1 1 | negative_passages listlengths 88 101 |
|---|---|---|---|
00586226ffc77d02ebb50ba9e226c65f | Wrap the target function and start a thread to run it | [
{
"docid": "a71cffd8c668547f2fa0fa27a88b87e5",
"score": "0.62145805",
"text": "def thread_start(target, args):\n run_thread = threading.Thread(target=target,\n args=args)\n run_thread.setDaemon(True)\n run_thread.start()\n return run_thread",
"title": ""
... | [
{
"docid": "6e924478f945629b09fa4ba2dcac36b5",
"score": "0.75560284",
"text": "def run_thread(targetfunction, argsforfunction=[]):\n threads[targetfunction.__name__] = Thread(\n target=targetfunction, args=argsforfunction\n )\n threads[targetfunction.__name__].start()",
"title": ""
... |
1d03a6c432b1dfb23de19f78958d8c0c | GetInverseOrder(itkPermuteAxesImageFilterIUC3 self) > itkFixedArrayUI3 | [
{
"docid": "7426202c5fb06698cee92a700e33aceb",
"score": "0.8726155",
"text": "def GetInverseOrder(self) -> \"itkFixedArrayUI3 const &\":\n return _itkPermuteAxesImageFilterPython.itkPermuteAxesImageFilterIUC3_GetInverseOrder(self)",
"title": ""
}
] | [
{
"docid": "f869bf6280669dbb704d90b2fc2419df",
"score": "0.86088556",
"text": "def GetInverseOrder(self) -> \"itkFixedArrayUI3 const &\":\n return _itkPermuteAxesImageFilterPython.itkPermuteAxesImageFilterIF3_GetInverseOrder(self)",
"title": ""
},
{
"docid": "f69d662c93f72881fce6f0fbc... |
c3bebcd033b560947ad52049832a17d9 | Get domain numberings for a domain if it is present in target | [
{
"docid": "2dc32ead8e14e19b59bd0851c38169d1",
"score": "0.7273047",
"text": "def get_domain_number(target, domain, database):\n query = \"SELECT num FROM component WHERE target='{}' AND domain = {} ORDER BY num\".format(target, domain)\n return [num for (num,) in database.execute(query)]",
"t... | [
{
"docid": "0be2d57dcdfadd4f8ef78a3cb7b24073",
"score": "0.5897098",
"text": "def find_domain():\r\n task_list=[]\r\n for x in range(0, len(input_dict[1])):\r\n task_list.append(input_dict[1][x].split(\",\")[1].split())\r\n for task_num in range(0,len(task_list)):\r\n domain[task_... |
2f8cd6721dc274ac9d37686b45269349 | get the match number of two sequences | [
{
"docid": "fe06a3652b1ead85337a756c0cf74983",
"score": "0.80829686",
"text": "def GetSeqMatch(seq1, seq2):\r\n matchCnt = 0\r\n for idx, curChar1 in enumerate(seq1):\r\n curChar2 = seq2[idx]\r\n if curChar1 == curChar2: matchCnt += 1\r\n return matchCnt",
"title": ""
}
] | [
{
"docid": "24ebcce5635738a22d8a171f3d367e30",
"score": "0.68315715",
"text": "def num_matches(list1, list2):\n list1.sort()\n list2.sort()\n if list1 == [] or list2 == []:\n return 0\n if list1[0] == list2[0]:\n return 1 + num_matches(list1[1:], list2[1:])\n if list1[0] < l... |
5796bc034f99d5527a461c1ea52e0712 | Divide two functions >>> f1 = ... >>> f2 = ... >>> ff = f1 / f2 | [
{
"docid": "4ed6aca71ab02122c6ea963f6b92d76a",
"score": "0.6818164",
"text": "def __div__ ( self , other ) :\n if self is other : return self.__constant ( 1 )\n elif isinstance ( other , constant_types ) and isequal ( other , 1 ) : return... | [
{
"docid": "dadd470302d0200c7346cf74ed6fd6a2",
"score": "0.79255766",
"text": "def division(first, second):\n return first / second",
"title": ""
},
{
"docid": "cea96b88efb4ec0b45112fa00abc625a",
"score": "0.7900606",
"text": "def div(a,b):\n return a/b",
"title": ""
},
... |
4ab5c8df5747eb88cda571a3127f936a | Here we are displaying index page. | [
{
"docid": "669f62e23491941533b3b0a5faad4be9",
"score": "0.0",
"text": "def payment(request):\n user = User.objects.get(username=request.user)\n settings = Billing.objects.filter(user=user).first()\n\n # What you want the button to do.\n paypal_dict = {\n \"business\": settings.paypal... | [
{
"docid": "6ed18a3b9c8828349f7b90206b8317e0",
"score": "0.85786694",
"text": "def index():\n # Render the main page-index\n return render_template(\"index.html\")",
"title": ""
},
{
"docid": "d331dfd34f01bb05ab7a084f36c479b7",
"score": "0.83920103",
"text": "def index():\n ... |
b4955fe99573a0588e1f42e9a7e5df02 | Ensure marking a test is backing up old results if asked to. | [
{
"docid": "4bc7f7bfe7076f8dca7e042f5371926a",
"score": "0.6014928",
"text": "def test_fixture_is_backing_up_old_results_to_default_path_if_no_path_provided(testdir):\n results_path = testdir.tmpdir.join(\"results.json\")\n results_path.ensure(file=True) # 'touch' the file\n\n # Run a dummy te... | [
{
"docid": "2e1730d8980b0b51499f0f539e52bcfb",
"score": "0.7026157",
"text": "def test_fixture_is_backing_up_old_results(testdir):\n results_path = testdir.tmpdir.join(\"results.json\")\n old_results_path = testdir.tmpdir.join(\"results.old.json\")\n\n # Run a dummy test that performs queries\n... |
acda800e0f29b50094bc5190cd90f6cc | private method of searching target node in the input subtree (backtracking version) | [
{
"docid": "ce043f14d2a3c226d3dcfba097277b2b",
"score": "0.0",
"text": "def __treeSearch(self, root: BinaryTreeNode, key: int) -> BinaryTreeNode:\n if root == None or root.val == key: return root\n if root.val < key: return self.__treeSearch(root.right, key)\n else: return self.__tr... | [
{
"docid": "09e7622b90f87cdc4caafc95b3d23ebb",
"score": "0.6933266",
"text": "def breadth_first_search(self, source_node, target_node):\n return False",
"title": ""
},
{
"docid": "99a99728f1faac3dd8cf8e1e6093c62f",
"score": "0.68992054",
"text": "def recursive_search(node, ind... |
9c56f06c5aedb2c724ba0b2b16b1ec9b | Create a file containing blow count vs depth. | [
{
"docid": "a69ffe7e037a5f03d7d6e710e693f126",
"score": "0.59456706",
"text": "def create_bcount(\n did,\n bcounts,\n depths\n):\n bcounts_ = [bcounts, depths]\n\n with open(\n 'bcounts/{}.p'.format(did),\n 'wb'\n ) as out_file:\n pickle.dump(\n bcounts_... | [
{
"docid": "a591fb0a1c1f83821fcb324131b569db",
"score": "0.6140474",
"text": "def write_png_depth(filename: str, depth: int) -> None:\n data = struct.pack('!i', depth)\n with open(filename, 'r+b') as f:\n # seek to the beginning of the IEND chunk\n f.seek(-LEN_IEND, 2)\n # ove... |
0520fe6183a36493bd169bfdd893568e | Perform a Bayesian linear fit for a heteroscedastic set of points with uncertainties along both axes. See the accompanying article and D'Agostini2005 for further details of the method. The method allows not only to attach uncertainties to the data points, but also to the functional relation itself. Basically, this mean... | [
{
"docid": "298708af0c0b338d80b4ce04e9f63123",
"score": "0.7031217",
"text": "def bayesian_linear_fit(x, y, Vx, Vy, c=True, prior=None):\n\n sigmaV_guess = 0\n m_guess = 1\n plot = False\n\n if c:\n # If not passing through the origin\n phi = posterior(x, y, Vx, Vy, c=True, pri... | [
{
"docid": "e2fa5000de7ab8b17be83cb851c11008",
"score": "0.59692013",
"text": "def fit_line(\n x, y, xhat=None, fitprobs=None, fitlogs=None, dist=None, through_origin=False\n):\n\n fitprobs = validate.fit_arguments(fitprobs, \"fitprobs\")\n fitlogs = validate.fit_arguments(fitlogs, \"fitlogs\")... |
9bc9e8fd60f198e6e2715832dcc25ddd | Reads a CODON MSA file and translates it to an integer array coded for Amino acids. | [
{
"docid": "1ac40774e3fb5be53773cc01e88e2d93",
"score": "0.7213606",
"text": "def get_aa_msa_from_codon_msa(filename):\n msa = get_codon_msa_as_int_array(filename)\n return CODON_AA_MAP[msa.long()]",
"title": ""
}
] | [
{
"docid": "7d60cdf54d31288c30a114ffd553da6f",
"score": "0.7629135",
"text": "def get_codon_msa_as_int_array(filename, as_torch=True):\n seq_iter = get_msa_from_aln_iter(filename)\n ret = np.array([codon_seq_to_int_list(seq) for seq in seq_iter], \n dtype=np.uint8) \n if ... |
60ce49f563c0f7593b8c42628f149bc4 | Takes data from a dictionary with a particular structure, and stores it in several Problem instances. | [
{
"docid": "d9f4542ee50cec3b083462f12883bad2",
"score": "0.59248495",
"text": "def problems_from_dict(\n data: Mapping[str, Any], yaml_filename: str\n) -> Mapping[str, Problem]:\n problems, _ = _problems_and_ids_from_dict(data, yaml_filename)\n return problems",
"title": ""
}
] | [
{
"docid": "7612f7c7c51a296b0cfd6ad809783f41",
"score": "0.6503",
"text": "def _problems_and_ids_from_dict(\n data: Mapping[str, Any], yaml_filename: str\n) -> Tuple[Mapping[str, Problem], Dict[Any, Any]]:\n\n # Mapping to remember which dictionaries were already converted to objects\n # Keys a... |
468b816a7ffedcccb4557d5212ec44ff | plugins > print the number of plugins currently loaded. | [
{
"docid": "f1c3235b866d5f55ca388bb8ebf47c15",
"score": "0.712377",
"text": "def show_plugins(bot, nick, chan, arg):\n plugins = len(bot.cmdhandler.loaded_plugins)\n out = bot.hicolor(\"Module Manager\" + box[\"vert\"])\n out += bot.style.color(\" %d plugins loaded\" % (plugins), color=\"silve... | [
{
"docid": "dcacb8f68b550a3690bca2b827313e38",
"score": "0.79757935",
"text": "def num_available_plugins():\n return len(conf['plugins'])",
"title": ""
},
{
"docid": "7cbc4fe4ae87850590bc366de633aac8",
"score": "0.7857803",
"text": "def __get_plugins_count(self):\n # First ... |
19a4fb3923a20f6b31e95b0130b064d2 | Gets an SQL insert statement suitable for finding the name. The SQL requires a parameter of name { getVariableName()}. the SQL, not null String | [
{
"docid": "e09e1ef33e7ddc15d8d5a38872a17353",
"score": "0.7751664",
"text": "def sql_insert(self):\n return 'INSERT INTO ' + self.get_table_name() + ' (id, name) ' + \\\n 'VALUES (:dim_id, :' + self.get_variable_name() + ')'",
"title": ""
}
] | [
{
"docid": "797df8230cf2fca03ac17ee5362befc1",
"score": "0.7569555",
"text": "def get_insert_statement(self) -> str:\r\n return (f\"INSERT INTO {self.table_name} VALUES({self.id}, \"\r\n f\"$${self.name}$$) ON CONFLICT (id) DO NOTHING\")",
"title": ""
},
{
"docid": "797... |
0ec645bd7a0883f088fd2421ec337771 | Enables TLS on a domain using a certificate managed by Fastly. DNS records need to be modified on the domain being secured, in order to respond to the ACME domain ownership challenge. | [
{
"docid": "8ee7609bb5435519e1fcd4dac819054c",
"score": "0.0",
"text": "def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n certificate_authority: Optional[pulumi.Input[str]] = None,\n common_name... | [
{
"docid": "cd5cd5edb2b9bb7e31879024608cb884",
"score": "0.59300214",
"text": "def test_tls(self):\n self.setupTrafficDirectorGrpc()\n self.setupSecurityPolicies(server_tls=True,\n server_mtls=False,\n client_tls=True,\n ... |
1a4b322d5b57367bcf7b5797e458b438 | Convert arbitrary document to a certain output format. | [
{
"docid": "c2f43e81af0deb2eb6f73c379e1c1b2e",
"score": "0.0",
"text": "def parse_document(self, state):\n # preprocess document\n d = self.parse_block(state)\n d += self.close_auto_break(state)\n return d",
"title": ""
}
] | [
{
"docid": "901e4f5b03e58a84e897711711bdfa76",
"score": "0.665919",
"text": "def convert(record: cfg.OpenAPI, outformat: cfg.Format = None) -> NoReturn:\n content = record.oas\n # Output\n out(content, outformat \\\n or cfg.Format.JSON if record.oastype == cfg.Format.YAML \\\n els... |
617a52f77ef670d3ce933cddbcc6dee0 | Generate a proof that the ith leaf is in the tree. | [
{
"docid": "47ae9b279773cebefee87327b4deffc7",
"score": "0.6056217",
"text": "def proof_of_inclusion(self, node_number):\n proof = str(self.root.data)\n node = self.leaves[node_number]\n while node.data != self.root.data:\n proof += \" \" + node.get_prefix_of_brother() + ... | [
{
"docid": "ce657d99ef16ca127dd9ea0934ed7499",
"score": "0.61243963",
"text": "def test_proofs(self):\n\n for i in range(0, self.num_iters):\n elems = [random.randint(0, 100000000) for i in range(0, 100)]\n elems.sort()\n mht = MHT.new(elems)\n\n for el... |
6b168d7bc2fb1b6ba301b47a626ba2d1 | itkImageFunctionIRGBAUC3RGBAUCD_cast(itkLightObject obj) > itkImageFunctionIRGBAUC3RGBAUCD | [
{
"docid": "27974120579307c54033c0a2f2e79c83",
"score": "0.9448457",
"text": "def itkImageFunctionIRGBAUC3RGBAUCD_cast(obj: 'itkLightObject') -> \"itkImageFunctionIRGBAUC3RGBAUCD *\":\n return _itkImageFunctionBasePython.itkImageFunctionIRGBAUC3RGBAUCD_cast(obj)",
"title": ""
}
] | [
{
"docid": "452631e89920cd61ceb0e3baa7f6b392",
"score": "0.93457943",
"text": "def itkImageFunctionIRGBAUC2RGBAUCD_cast(obj: 'itkLightObject') -> \"itkImageFunctionIRGBAUC2RGBAUCD *\":\n return _itkImageFunctionBasePython.itkImageFunctionIRGBAUC2RGBAUCD_cast(obj)",
"title": ""
},
{
"docid... |
a315eac5e01b7996f85b0ee3e9037907 | this functions return True is every slot is full with 'X' or 'O' | [
{
"docid": "d57d5981fa5aba15ebedafea2aed08c3",
"score": "0.6083814",
"text": "def is_full(self):\r\n for i in range(self.width):\r\n if self.can_add_to(i):\r\n return False\r\n return True",
"title": ""
}
] | [
{
"docid": "174b00346f9041dc36393673ea361c7d",
"score": "0.6845584",
"text": "def check_full():\n if table[1] in ['x', 'o'] and table[2] in ['x', 'o'] and table[3] in ['x', 'o'] and table[4] in ['x', 'o'] \\\n and table[5] in ['x', 'o'] and table[6] in ['x', 'o'] and table[7] in ['x', 'o']... |
efecd4f2db07fcb8a9c5c70ca798be42 | Get the map's content at the given x,y coordinates. | [
{
"docid": "66c2928aa06147e1c947647c9b300532",
"score": "0.58302176",
"text": "def get_content(self, x: float, y: float) -> protocol.NavNodeContentType:\n return self._root_node.get_content(x, y)",
"title": ""
}
] | [
{
"docid": "bad62267e3c84fb20f4d89fa0d887810",
"score": "0.6279652",
"text": "def get(self, x, y, z):\n cursor = self._conn.execute(\n \"\"\"\n SELECT tile_data FROM tiles\n WHERE zoom_level = :z AND\n tile_column = :x AND\n tile_... |
5c729b495a476373c33f734718cc8c46 | find optimal parameters on crossvalidation for xgboost by Hyperopt | [
{
"docid": "9239610441cd07314801ad5c69e648da",
"score": "0.71515936",
"text": "def find_params_xgb(train_x, train_y, task, type, eval_metric, n_classes, folds, total_evals = 50, sparse = False, stopping_rounds = -1, missing = np.nan, verbose = False):\r\n\r\n np.random.seed(777)\r\n\r\n def score(... | [
{
"docid": "3fb6f0fbafc0533ccb54bbf5551f4622",
"score": "0.74352896",
"text": "def xgb_hyperopt():\n\n xgb_best = xgb.XGBClassifier(n_estimators=300, **best_parameters, tree_method='gpu_hist')\n #xgb_best = xgboost.XGBClassifier(n_estimators=300, param_grid = best_parameters, tree_method='gpu_hist... |
1b1435d1683487ecf3365967c1afada2 | Define test class to handle assertEqual with `pandas.DataFrame`. | [
{
"docid": "fea0b94710088a4ab085ced5d9880434",
"score": "0.70121086",
"text": "def add_data_frame_equality_func(test):\n def frame_equal(lhs, rhs, msg=None):\n \"\"\"Adapter for pandas.testing.assert_frame_equal.\"\"\"\n if msg:\n try:\n pdt.assert_frame_equal(... | [
{
"docid": "7b1984d44b85fdf72e8e1b13af6da2c9",
"score": "0.7090182",
"text": "def test_dataframe(self):\n self.assertTrue(test_create_dataframe(DATA_FRAME, LIST_COL_NAME))",
"title": ""
},
{
"docid": "7470b99ef3d62c2b4e8d0eaa6ab5121f",
"score": "0.69681454",
"text": "def test_... |
e1c767c8f6243e23f22514657e32bb27 | Sends rows to a BigQuery table. Iterates until all rows are sent. | [
{
"docid": "5d1b54000dae9618b2358fb1d06f5ad1",
"score": "0.79960674",
"text": "def send_to_bq(table_name, rows):\n\n if rows:\n logging.info('Sending %d rows', len(rows))\n _send_to_bq_raw('swarming', table_name, rows)",
"title": ""
}
] | [
{
"docid": "afe5eff31f8c2d1e4f035edc6ff89430",
"score": "0.7848489",
"text": "def _send_to_bq_raw(dataset, table_name, rows):\n # BigQuery API doc:\n # https://cloud.google.com/bigquery/docs/reference/rest/v2/tabledata/insertAll\n url = (\n 'https://www.googleapis.com/bigquery/v2/projects/%s/dat... |
08b1a4d5ce1065b6a12128ff7982476e | Calculates the hyper_period of the network | [
{
"docid": "5f1a2b49e80ae1417065d8a0744fd9a3",
"score": "0.6522371",
"text": "def calculate_hyper_period(periods):\n return lcm_multiple(*periods)",
"title": ""
}
] | [
{
"docid": "51df2fd3ebacba7e4edc7b7815f617d9",
"score": "0.6120404",
"text": "def __get_hyperperiod(self):\r\n for task in self.taskset:\r\n pass",
"title": ""
},
{
"docid": "1b2923e8789a715024dae5ad1d39142d",
"score": "0.58434445",
"text": "def period( self, treq):... |
b81cda7c44c9a95a958a4ec21e97f111 | Logic for handling sub dictionaries in table_output (hide, reparse) | [
{
"docid": "8777b65c057a3b1fc26fb145ec0e0bc0",
"score": "0.5488281",
"text": "def check_sub_dict(parsed_dict, key, value):\n # check for specific well-known keys we want to format and show differently.\n if key == 'address':\n # street city state post_code country\n pretty_address = ... | [
{
"docid": "166e72bbf193ac96653fef6836052aed",
"score": "0.5735426",
"text": "def printptable(typec, t=\"adv\", at=typedict, ad=typedefdict):\n typec = typec[0].upper() + typec[1:].lower() ## This line capitalizes the type correctly for future use with the dictionaries.\n if t == \"adv\":\n ... |
fbf142af4779a30ee9ab42abf9b5eef3 | Returns the value of the objective after solve. | [
{
"docid": "33896780b6f57a9b89dd03d9bccfe9c4",
"score": "0.84447485",
"text": "def objective_value(self):\n self.__check_has_feasible_solution()\n return self.__solve_helper.objective_value()",
"title": ""
}
] | [
{
"docid": "ac988b24dde3b60a3417a5ab21dcaaf5",
"score": "0.79332906",
"text": "def objective_value(self: \"Model\") -> Optional[numbers.Real]:\n return self.solver.get_objective_value()",
"title": ""
},
{
"docid": "fd35a53ae58889ad4bf5d206e4ec6411",
"score": "0.7464509",
"text... |
177d8bb99822d9f182c26406bbdbf2c4 | Puts a list of the referenced UID into the loadable for use in the node if this is loaded. | [
{
"docid": "b13acc039b95bfdb1c55439a4009eb24",
"score": "0.6025457",
"text": "def addReferences(self, loadable):\n dcm = pydicom.read_file(loadable.files[0])\n loadable.referencedInstanceUIDs = []\n self._addReferencedSeries(loadable, dcm)\n self._addReferencedImages(loadable, dcm)\n load... | [
{
"docid": "d96bf21a885a86eebd587b28717baf1d",
"score": "0.5601305",
"text": "def put_list(self):\n self._check(pn_data_put_list(self._data))",
"title": ""
},
{
"docid": "8f6609cd5d5501b1d2c674e337a5f338",
"score": "0.54111516",
"text": "def setInternalList(self, lst):\n\n se... |
22c51d61993faeb008bf5a86d6f28863 | Initialize new Tileset The heavy lifting is done by | [
{
"docid": "23f5432f6952651e35d889a1b63d1c6f",
"score": "0.0",
"text": "def __init__(self, path, output='.', param_file=None, skip_file=None,\n label=None, **kwargs):\n self.basepath = os.path.dirname(__file__)\n try:\n os.makedirs(output)\n except OSError... | [
{
"docid": "a88e6185afc685196ac28a3ff0eae075",
"score": "0.796752",
"text": "def __init__(self):\n self.tileset = None\n self.dim = 0\n self.size = None # dim of tileset in pixels\n self.tsize = None # dim of tileset in tiles\n self.tiles = [] # the tiles",
"title"... |
41b4b6d54777344fd608b93357ba30d7 | Test listing directory with client method. | [
{
"docid": "d7b854ac41c3b166c486145556ed7936",
"score": "0.6901601",
"text": "def test_client_list_dir_index():\n # Fetch list\n result = openedgar.clients.edgar.list_path(\"/Archives/edgar/daily-index/1994/\")\n\n # Compare lists\n expected = ['/Archives/edgar/daily-index/1994//QTR3/',\n ... | [
{
"docid": "5f0f5133fb45190a7e0195fba7f7e1d2",
"score": "0.7233427",
"text": "def listdir(self, path):",
"title": ""
},
{
"docid": "226f798688c7595c07b73d3ef7349b0f",
"score": "0.7106514",
"text": "def list_directory(self, uri: str) -> DirectoryListing:",
"title": ""
},
{
... |
72c48f399241f9a7b82f9f34ccb5ce32 | Function for reading in our full dataset, creating estimates for each citytime pair, and writing these estimates to a new dataset in addition to our old dataset. | [
{
"docid": "79c372426cf4ef285186dc49f07be7da",
"score": "0.0",
"text": "def make_age_data():\n\n # Read in input dataframe\n dataset = pd.read_excel(os.path.join(\"..\", \"data\", \"dataset.xlsx\"))\n age_percents = []\n\n # Read in dataframe for age\n age_data = pd.read_excel(os.path.joi... | [
{
"docid": "45a16e3c5f171aa393bb3e0b4a7b0e73",
"score": "0.679058",
"text": "def load_data(city, month, day):\n \n #Washington data have 8 columns not 10 like others. Gender and Birth Year left.\n \n c_path = 'chicago.csv'\n ny_path = 'new_york_city.csv'\n w_path = 'washington.csv'\n ... |
dc6c2135517ccd8025af10f5c0c7967e | Return the reference ID for the current command section. | [
{
"docid": "60f7faf63c367a3e1cdf197b3fb8a03f",
"score": "0.7987671",
"text": "def get_current_command_section_ref_id(env, for_subcommand=False):\n if for_subcommand:\n key = 'rbt-subcommand'\n else:\n key = 'rbt-command'\n\n return env.temp_data['%s:doc-prefix' % key]",
"title... | [
{
"docid": "b94e34f76c06201a2311a9fa0ae4c714",
"score": "0.7150189",
"text": "def get_current_command_usage_ref_id(env, for_subcommand=False):\n return ('%s-usage'\n % get_current_command_section_ref_id(\n env,\n for_subcommand=for_subcommand))",
"title": ... |
e3f2ebcfdf18e31720df7eb11cb19c61 | Return a custom 404 error. | [
{
"docid": "1af86d53d490245475ae70338cc85d7b",
"score": "0.72552013",
"text": "def page_not_found(e):\n return 'Sorry, Nothing at this URL.', 404",
"title": ""
}
] | [
{
"docid": "6105f813d803e8846ed5b1e4e8e3cdd2",
"score": "0.8263737",
"text": "def not_found(error):\n return \"404\"",
"title": ""
},
{
"docid": "1013b6ae3377c6fd3298615cea6c209e",
"score": "0.79933435",
"text": "def notFound404Error():\n return Response (HTTP_STATUS_NOT_FO... |
af97e0480981b7f93e21e2b4b8b745de | Copy the configuration directory from keycloak into the temporary storage directory. If an error is raised because the copied directory already exists at the destination, abort the copy and ignore the error. | [
{
"docid": "d6f701fa44752d860340bd53e4c4c5bd",
"score": "0.64012563",
"text": "def copy_base_files(keycloak_path, temp_dir):\n config_src = keycloak_path / \"standalone/configuration\"\n\n if not os.path.exists(config_src):\n raise FileNotFoundError(f\"The configuration file '{config_src}' ... | [
{
"docid": "a96c21963a51716355afa845ded38cad",
"score": "0.63141984",
"text": "def _copy_setup_storage(self, tmp):\n for d in [ 'setup-storage', 'setup-storage/conf.d' ]:\n for src in glob.glob('%s/*' % d):\n if os.path.isfile(src):\n tmp_src = self._c... |
5eb4f9e0ed93aa4bbff6383b5a544e3a | Return whether proper Docker version is installed. | [
{
"docid": "ae878a289e1b547295f2bc227ba43dcb",
"score": "0.8707321",
"text": "def supported_docker_installed():\n\n try:\n clean_version = remove_leading_zeros_from_version(commands.version())\n return Version(clean_version) >= Version(SUPPORTED_DOCKER_V)\n # OSError = Not installed\... | [
{
"docid": "70b9e6dcc648fff5b07e32daadad2677",
"score": "0.77932274",
"text": "def docker_is_present():\n return which('docker') is not None",
"title": ""
},
{
"docid": "78f6a116b9517d3022997fb477775efb",
"score": "0.7396688",
"text": "def _check_docker_version():\n if os.envir... |
e76147aff1fffc6c19c1dc908a51f3de | Return the queryset backing annotations. Executing this queryset is costly because there is no way to optimize the query execution. Since this is a related_set queryset, that was further filtered, each item in the queryset causes a db hit. | [
{
"docid": "52440f6bcf1797455bab587bc901e505",
"score": "0.6528129",
"text": "def GetAnnotationsQS(self):\n return self._costly_annotations_qs",
"title": ""
}
] | [
{
"docid": "187637764ad2b8c1f55ef062d620afcb",
"score": "0.6831465",
"text": "def get_queryset(self):\r\n queryset: QuerySet = super().get_queryset().prefetch_related('film_work_genre', 'genres', 'film_work_person',\r\n 'persons'... |
7106374d64bb075e2635bcc0187ac5f2 | This function needs to be hooked into the 100ms hook | [
{
"docid": "216068cebb425908243433a81679ee0c",
"score": "0.5795586",
"text": "def clock_timer_100ms():\n global lastTime\n cTime = get_tmr_count(TMR5) >> 8\n if cTime < lastTime:\n clock_tick()\n lastTime = cTime",
"title": ""
}
] | [
{
"docid": "d2757f26752208b1a80b70734a7e3c96",
"score": "0.6760372",
"text": "def work(self):\r\n time.sleep(1)",
"title": ""
},
{
"docid": "843d0bf6411351159fc198d19ed80c12",
"score": "0.6745067",
"text": "def slow_down(self):\n pass",
"title": ""
},
{
"doc... |
15443b0a0d8c8f241d58c896a0964e66 | Edit the inherit replication flag for volume. | [
{
"docid": "c4835037b882671342707bd1e738352f",
"score": "0.80905753",
"text": "def edit_inherit_replication_flag(self, pool, project, volume, set=True):\n svc = ('/api/storage/v1/pools/%(pool)s/projects/%(project)s'\n '/filesystems/%(volume)s/replication'\n % {'pool': ... | [
{
"docid": "46fd0cf5360d7f2c5330c34ba629af79",
"score": "0.55705565",
"text": "def extend_volume(self, volume, size):\n return self.set_volume(volume, size=size, truncate=False)",
"title": ""
},
{
"docid": "ecad12e7ca1d65657ff9c51410beccdc",
"score": "0.5562828",
"text": "def ... |
fcaca5e573437f485f9b4e4a15161943 | r""" Import graphs from ``file`` into the database. This function is used to import new censuses of graphs and is not meant to be used by users of DiscreteZOO. To properly import the graphs, all graphs of the same order must be together in the file, and no graph of this order must be present in the database. | [
{
"docid": "23e07053e4212c9cf56777e7c8f4894f",
"score": "0.7322005",
"text": "def import_graphs(file, cl=ZooGraph, db=None, format=\"sparse6\",\n index=\"index\", verbose=False):\n info = ZooInfo(cl)\n if db is None:\n db = info.getdb()\n info.initdb(db=db, commit=False)... | [
{
"docid": "7139a8d85797a92497b30657ec0db74d",
"score": "0.72724146",
"text": "def load_graph(file):\n g = nx.DiGraph()\n mode = \"N\"\n for l in file:\n l = l.strip()\n if mode == \"N\":\n if l == \"// Nodes\":\n mode = \"LN\"\n elif mode == \"LN\":\n if l == \"// Edges\":\... |
3fa9d87096dc3fad08e3952a0081d446 | Called after a property has changed. | [
{
"docid": "719cdb4e44572b9c254ca4771693d64f",
"score": "0.0",
"text": "def _on_time_formatter_property_changed(self, evt=None):\n \n self._is_dirty = True",
"title": ""
}
] | [
{
"docid": "d91682be3dcb80886000459abf2b8b65",
"score": "0.71458226",
"text": "def onPropertyStoreChanged(self):\n self.haschanged = True\n self.update()",
"title": ""
},
{
"docid": "06f1063962948be0a25c6606187672d7",
"score": "0.6950608",
"text": "def property_changed(... |
dfcac79a457a0f57ceddfe715a787505 | basic stability test of a Numba CPUDispatcher function (i.e., function compiled via / ) | [
{
"docid": "a3918b70ff5560c9c9829ff65beb3349",
"score": "0.0",
"text": "def stability_test(func, func_kw, ref_path, ignore_fails=False, define_as_ref=False):\n func_name = func.py_func.__name__\n logging.info(\"stability testing `%s`\", func_name)\n ref_path = expand(ref_path)\n\n test = exe... | [
{
"docid": "b7dd18680ed15bae6aa27388de7f3f2a",
"score": "0.6352842",
"text": "def test_basic(self):\n a = 1\n\n @njit\n def foo(x):\n return x + 1\n\n foo(a)\n int_int_fc = types.FunctionType(types.int64(types.int64,))\n\n @njit(types.int64(int_int_fc... |
afa6717e6fb11a1ff402ef058e0f7c37 | Create binary array to pick out which idxs are accepted. | [
{
"docid": "7b15995a47c10cea86fcd8a454f84d0c",
"score": "0.0",
"text": "def _get_accept_masks(accept_prob: tf.Tensor):\n accept_mask = tf.cast(\n accept_prob > tf.random.uniform(tf.shape(accept_prob)),\n dtype=TF_FLOAT,\n )\n reject_mask = 1. - accept_mask\n\n ... | [
{
"docid": "2239fb1e619ab3b14d59c8d1c33c740e",
"score": "0.585864",
"text": "def bit_ids(self):\r\n return self.bit_IFT[:, 0]",
"title": ""
},
{
"docid": "46d00f8d3b3120319105711da9cbabac",
"score": "0.57368064",
"text": "def encode_bin(data: list, cat: set) -> np.ndarray:\n ... |
819ec84c2c0ec3aff03438ab3c1dd5fc | Delete Vserver's VLAN configuration from ports | [
{
"docid": "c8810e698d34c56b1cabdec3c2ee9b0d",
"score": "0.7316536",
"text": "def _delete_vserver_vlans(self, network_interfaces_on_vlans):\n for interface in network_interfaces_on_vlans:\n try:\n home_port = interface['home-port']\n port, vlan = home_port... | [
{
"docid": "8dc58697abd63fd1230d85da89499814",
"score": "0.66775584",
"text": "def _delete_vsx_interface_vlan_v1(vlan_id, **kwargs):\n ports_list = port.get_all_ports(**kwargs)\n vlan_name = \"vlan\" + str(vlan_id)\n\n if \"/rest/v1/system/ports/%s\" % vlan_name not in ports_list:\n logg... |
27d6330cd2ac3e15327c1b9ecb2d6a37 | return query for insert/update/delete our attributes preferrably this method should return an IBSQuery instance this is important when query can be large this method maybe overidded to customize the behaviour | [
{
"docid": "e79cc87d22407219176bb96d2e160572",
"score": "0.0",
"text": "def getQuery(self,ibs_query,src,action,**args):\n\tself.checkInput(src,action,args)\n\tif self.query_funcs.has_key(src+\"_\"+action):\n\t return self.__callQueryFunc(ibs_query,src,action,args)\n\telse:\n\t return \"\"",
"t... | [
{
"docid": "fdbd1a3ebbdc263a93c58c51a21926da",
"score": "0.6932648",
"text": "def create_query(self):\n return db.models.sql.Query(self.model, connection)",
"title": ""
},
{
"docid": "08defddebdbbeebcc24c766343365faf",
"score": "0.675028",
"text": "def query(self):\n qu... |
e86259982dceb897147a0f82302c3f73 | Called when a ticket is created. | [
{
"docid": "8c67b05089d8ecae10759092b41e8665",
"score": "0.81197613",
"text": "def ticket_created(self, ticket):\r\n self.watch_complete(ticket)",
"title": ""
}
] | [
{
"docid": "91f7bc790a62ecdcd4fcbc04a83db5af",
"score": "0.7272276",
"text": "def create_ticket(self, ticket):\n\n if ticket.user:\n person = self.require_person(ticket.user.email, user=ticket.user)\n elif ticket.email:\n person = self.require_person(ticket.email)\n ... |
a4e6545ac2bd52a24944ef49a2ee388a | Test case for patch_hyperflex_software_version_policy | [
{
"docid": "ab51cc834c4d97dcc025b138e2442456",
"score": "0.9544049",
"text": "def test_patch_hyperflex_software_version_policy(self):\n pass",
"title": ""
}
] | [
{
"docid": "4d5e955de88f71dff747e6600e7521ce",
"score": "0.91287255",
"text": "def test_update_hyperflex_software_version_policy(self):\n pass",
"title": ""
},
{
"docid": "4c50caac1f2c8f81955c4ae6602bfcf9",
"score": "0.8505383",
"text": "def test_create_hyperflex_software_vers... |
f5b658fb54dc3fd91de64cbe1c75e84c | Handle the "change password" task both form display and validation. | [
{
"docid": "667729a74ce5f7559dc6c6426bbaf866",
"score": "0.0",
"text": "def edit_my_shop(self, request, extra_context=None):\n defaults = {\n 'extra_context': {**self.each_context(request), **(extra_context or {})},\n }\n request.current_app = self.name\n return My... | [
{
"docid": "a59271f0af92a9a76696c145a4dd8436",
"score": "0.77990556",
"text": "def _change_password(request):\n context = {\n 'change_password_form': PasswordChangeForm(request.user, request.POST),\n 'subscribe_form': SubscribeForm(request.user)\n }\n if context['change_password_f... |
2f37efc678815635b5de026c34c64cf8 | Create test fixture for source meta | [
{
"docid": "7e10778b7fef0a7a07fbe08678b5d731",
"score": "0.0",
"text": "def source_meta():\n return [SourceName.HGNC.value, SourceName.ENSEMBL.value, SourceName.NCBI.value]",
"title": ""
}
] | [
{
"docid": "1092eee1fd63f4e443c3f1744e987661",
"score": "0.6867291",
"text": "def pytest_generate_tests(metafunc):\n if \"lf_data\" in metafunc.fixturenames:\n with open(DATA_DIR / \"reference_data.csv\") as f:\n reader = csv.DictReader(f)\n records = list(reader)\n ... |
886e76f5557ff2af61fd624b497e0ebd | Plot the completeness against the metric binned by bins. injgroups contains n arrays of t/F of ways you want to group against any other metric. labels should give the text to go into a legend for the plot | [
{
"docid": "a304c3cec3aa3a275ef2186546d4e3fc",
"score": "0.7307003",
"text": "def plot1DCompletenessGroups(inj,metric,bins,injgroup,s=[0.0,1.0],xlabel='metric',labels=['a','b','c','d']):\n bins=np.array(bins)\n injpcs=passes(inj,s=s)\n #injfps=~injpcs\n \n for i in np.arange(0,len(injgrou... | [
{
"docid": "4bcacfe659a2f825cb821ddf845ab2a7",
"score": "0.64017665",
"text": "def plot1DReliabilityGroups(ops,inv,metric,bins,opsgroup,invgroup,s=[0.0,1.0],xlabel='metric',labels=['a','b','c','d']):\n bins=np.array(bins)\n opspcs=passes(ops,s=s)\n opsfps=~opspcs\n invpcs=passes(inv,s=s)\n ... |
55d49f29e95ea569ff907a293f75ed60 | Generates summary statistics for the extracted unmapped regions | [
{
"docid": "491efe2883d60911dd457945db5f1089",
"score": "0.58510685",
"text": "def unmapsum(unmappeddict, idunmap):\n \n # Create GC content, length and amino acid residues list to store values for each unmapped region\n gc_unmap = list()\n len_unmap = list()\n amino = pd.DataFrame(column... | [
{
"docid": "0b7767a929343f878d5391ab16351e29",
"score": "0.67602116",
"text": "def compute_statistics(self, region_dict):\n min_area = 15\n stats = {}\n for k,pixels in region_dict.items():\n area = len(pixels)\n if area < min_area:\n continue\n ... |
5985dba22fcc927115102f2c73371d31 | Test equal initialization class methods | [
{
"docid": "ad2b04faa22a7acf60ea7cd712f88359",
"score": "0.0",
"text": "def test_payload_and_api_init_are_equal(self, mock):\n # Camera ID Mocks\n subdomain = self.c_mock_esession['activeBrandSubdomain']\n setup_ee_camera_mock(\n mock, subdomain, 'device_camera.json')\n\n... | [
{
"docid": "0714b55a928f10390dd112e4a71d64e7",
"score": "0.76406264",
"text": "def test_init(self):\n pass",
"title": ""
},
{
"docid": "e1d913a5d348c714c776c3a28899f32b",
"score": "0.7467957",
"text": "def test_bad_init(self):",
"title": ""
},
{
"docid": "eaa2604b3... |
62b727fc7e53feddb778032e637fb7ac | place a phase subplot on a figure, given figure handle and axis postion | [
{
"docid": "c0ed67a6293866971bdb811fe6115b47",
"score": "0.6554235",
"text": "def phase_sub_plot(self, ax, ttl_str=\"\", axRect=None, pred=None):\n\n phi = self.tf.phi\n # rotate phases so all are positive:\n negative_phi_indices = np.where(phi < 0)[0]\n phi[negative_phi_indi... | [
{
"docid": "58cf15113cf22c51d3d0f51fc0925655",
"score": "0.7136438",
"text": "def phase_plot(self, pred=None):\n axRect = [0.1446, 0.2150, 0.7604, 0.7100]\n # plt.figure(22, figsize = (8.5, 11), dpi=300)\n fig, ax = plt.subplots()\n if pred is not None:\n self.phas... |
edebb6923f67de52c31b16487d8a82c9 | Sets the id of this BackupDestinationDetails. The `OCID`__ of the backup destination. | [
{
"docid": "505f131f3c7d30d913060d9471641e05",
"score": "0.0",
"text": "def id(self, id):\n self._id = id",
"title": ""
}
] | [
{
"docid": "02ade94f2aa174c9825ecc16daee95c3",
"score": "0.68016785",
"text": "def set_id(self, id):\n self._id = id",
"title": ""
},
{
"docid": "ee1f68ce7dc94a233e968c37748ab47b",
"score": "0.6691179",
"text": "def set_id(self, id):\n self.id = id",
"title": ""
}... |
509bc6bda7806d989647518f1dc299e5 | Move a tile of the right color to the image. | [
{
"docid": "241d5a5fe336a77a4759122d4c88cc13",
"score": "0.6386266",
"text": "def make_move_color(result, tile, scale, yx, bgr):\n patt = tile.copy()\n patt += bgr\n patt[ma.masked_where(patt > 255, patt).mask] = 255.\n\n y, x = yx[0]*scale, yx[1]*scale\n result[y:y+scale, x:x+scale] = pa... | [
{
"docid": "e4076fcb157d174c58557f0ab492b0e2",
"score": "0.73865634",
"text": "def move_tiles_right(self):\r\n\r\n # inits\r\n\r\n _at = self.matrix.get_object_at\r\n\r\n _acted = False\r\n\r\n # loop on rows\r\n\r\n for _row in range(self.rows):\r\n\r\n # p... |
1d066022498a74c3b6eadd3778b8f510 | A func implementation of | [
{
"docid": "f436231d28e816d9acda70fe43627bf9",
"score": "0.0",
"text": "def new_func(*args, **kwargs):\n new_func.__name__ = 'func_' + func.__name__\n source_code = inspect.getsource(new_func)\n\n new_func.__doc__ = new_func.__doc__.replace('<name>', new_func.__name__)\n new_... | [
{
"docid": "78e33815d207e4aa34d4e52320435483",
"score": "0.80307794",
"text": "def func():",
"title": ""
},
{
"docid": "e4d7e7c9fc12a76c3bb65941c64fde7f",
"score": "0.77042663",
"text": "def fn():",
"title": ""
},
{
"docid": "bd8d0afbc87dd05c9fb56bf3f08e2dcf",
"score"... |
ce483546bd51b522d94fd650cefc14df | Return the batch indicator/group and file data to be sent to the ThreatConnect API. | [
{
"docid": "0390df010960df7d2deeb30283b87181",
"score": "0.5622479",
"text": "def data(self) -> dict:\n data = {'file': {}, 'group': [], 'indicator': []}\n tracker = {'count': 0, 'bytes': 0}\n\n # process group from memory, returning if max values have been reached\n if self.... | [
{
"docid": "ced6a377b7eb95e3bb703e69b7b02b11",
"score": "0.60656387",
"text": "def get_batch(self):\n raise NotImplementedError",
"title": ""
},
{
"docid": "21a6509f723e3d726cb18ae306a5f136",
"score": "0.58504844",
"text": "def _process_indicators_batch(self, owner):\n ... |
b9094e1a5259b445aa8ed3a37cca49db | If selected, the controller sends a Role Request message after the connection is established; to change its role according to the Role Request option selected. | [
{
"docid": "0fcce8ed0edd5cff1d5750eadf85482d",
"score": "0.6079378",
"text": "def SendRoleRequest(self):\r\n\t\treturn self._get_attribute('sendRoleRequest')",
"title": ""
}
] | [
{
"docid": "2ddc31c9901295f34a525afe424dc013",
"score": "0.6534953",
"text": "def change_role():",
"title": ""
},
{
"docid": "496b8d6a6fdd9ab0593bd8fbbd972a75",
"score": "0.6400035",
"text": "def userRequestedRole(self):\n return self.user_role == ROLE",
"title": ""
},
{
... |
07b2fb0da537e30f7339871c8dec4c79 | r""" Default train function takes a torch.utils.data.Dataset and train the model on the dataset | [
{
"docid": "29a7a7381c73f8b1eda040c948d86d6c",
"score": "0.0",
"text": "def test_only_train_fn(model, train_data, loss_fn=_default_loss_fn):\n optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n train_loss, correct, total = 0, 0, 0\n for i in range(10):\n model.train()\n ... | [
{
"docid": "8d17761d82e3f4144fbbd8ca43b5cb03",
"score": "0.81942797",
"text": "def train(self, dataset):\n raise NotImplementedError()",
"title": ""
},
{
"docid": "8d17761d82e3f4144fbbd8ca43b5cb03",
"score": "0.81942797",
"text": "def train(self, dataset):\n raise NotIm... |
4dbf8edcdf7c02def36a3203902b1dc2 | Add object to scene. When you add a new object to scene after that object will be drawn to scene in every `draw` function call. | [
{
"docid": "40fa2826ed55ed8f0ea314fa88b4a1e1",
"score": "0.6973952",
"text": "def add(self, obj: GameObject) -> None:\n self.objects[obj.get_id()] = obj",
"title": ""
}
] | [
{
"docid": "1924230e8fc78f76f86e7c8506bfc203",
"score": "0.7627554",
"text": "def add_object(self, obj):\n found = self.lookup_object(obj)\n obj.scene = self\n if found:\n self.objects[found] = obj\n else:\n self.objects.append(obj)",
"title": ""
}... |
a321327085845f89b15a4fd1c9bff20f | Respond to bulletalien collisions. | [
{
"docid": "f6d3eff3d1b5e970d3681a443bc145f3",
"score": "0.7386176",
"text": "def check_bullet_alien_collisions(ai_settings,screen, stats, sb, ship, aliens, super_aliens, bullets):\n\n # Check if any bullet collision with any alien\n bullet_alien_collision(ai_settings,screen, stats, sb, ship, alie... | [
{
"docid": "2da448b58ac9f807dd2ed67e78fb61c3",
"score": "0.79089516",
"text": "def handle_alien_bullets(self):\n for bullet in self.alien_bullet_sprite_list:\n if bullet.rect.colliderect(self.player.rect):\n # ammo hit the player\n bullet.kill() # remove ... |
7e4a43f45c30c775c92adf178b8662af | Construct a Shannontype partition of the information contained in `dist`. | [
{
"docid": "fee24e7a88089622770130c49c7c61af",
"score": "0.6277066",
"text": "def __init__(self, dist):\n self.dist = dist\n self._partition()",
"title": ""
}
] | [
{
"docid": "868849b8dc673a2701134d0f8961766d",
"score": "0.5895211",
"text": "def write_st_dist(self, dist):",
"title": ""
},
{
"docid": "ec4a4790f9292e43a14d8bbd4fe30ac2",
"score": "0.58232087",
"text": "def init_dist(dist, ndim):\n if isinstance(dist, str):\n return ndim*... |
ca841fb507bc6522b9eec458a6c55011 | Normalizing Filter for scaling data in incomming array so the max value is equal to 1 | [
{
"docid": "ed9c720cbe03e2c99b02ea9557002bb4",
"score": "0.6344063",
"text": "def normalized(x):\n maxi = 0\n X = np.zeros(len(x))\n for i in range(0, len(x)):\n if (x[i] > maxi):\n maxi = float(x[i])\n for i in range(0,len(x)):\n X[i] = x[i]/maxi\n return X",
... | [
{
"docid": "6e1d1cee28ebf937cf6fbee5e8fe60df",
"score": "0.6841447",
"text": "def normalize(data):\r\n array_min = data.min()\r\n array_max = data.max()\r\n\r\n return ((data - array_min) / (array_max - array_min))\r\n # return new_array\r",
"title": ""
... |
b60ca2ec9e7d03edaa107d4c760a27f7 | ID that can be used to find errors in the log files. | [
{
"docid": "c7aef613a851f171aa2b4e8b4ee17ba2",
"score": "0.5365587",
"text": "def error_code(self) -> str:\n return pulumi.get(self, \"error_code\")",
"title": ""
}
] | [
{
"docid": "d5c865f049bd4181090bf5764b5dba62",
"score": "0.6419121",
"text": "def get_id(self):\n import re\n last_line = self.list_of_logs[-1]\n if re.search(\"error\", last_line, re.IGNORECASE):\n return None\n elif re.search(\"successfully built\", last_line, re... |
b97fc381e1474d4b8691b703445d2fe9 | Enable/Disable best practices validation for all items in the model. | [
{
"docid": "e9b7d93b3391eae201f07c6faec0ae1c",
"score": "0.6128238",
"text": "def enable_best_practices(self, enabled=True):\n for item in self._data:\n item.validate_best_practices = enabled\n\n self.reset_results()",
"title": ""
}
] | [
{
"docid": "e438db992897457c6a9e2dc4169a1ad7",
"score": "0.67385113",
"text": "def validate_all(self):\n \n pass",
"title": ""
},
{
"docid": "7850a42c15eedad44f2c8a953044cf06",
"score": "0.65433544",
"text": "def set_validated(self):\n self.__validationerrors=[]\n ... |
32ab93180ad5e9e197cf9d5f682e4033 | set_test(self) Set the default test for the cipher. | [
{
"docid": "b1b9d037a48909c5d03ec5c61560f832",
"score": "0.80053985",
"text": "def set_test(self):\r\n\t\tself.test_plain = 'Help me I am under attack.'\r\n\t\tself.test_cipher = 'HENTEIDTLAEAPMRCMUAK'\r\n\t\tself.test_kwargs = {'n': 4}",
"title": ""
}
] | [
{
"docid": "5d2b0cc46955f4a7aa07f3a74f53bcc4",
"score": "0.8267654",
"text": "def set_test(self):\r\n\t\tself.test_cipher = 'LXFOPVEFRNHR'\r\n\t\tself.test_plain = 'ATTACKATDAWN'\r\n\t\tself.test_kwargs = {'key': 'LEMON'}",
"title": ""
},
{
"docid": "abc7ad75e8c28691219b37f21f63f512",
"s... |
ba9232b89afd208790466ffcc6ae83e3 | Test get model factory on CPU. | [
{
"docid": "c63ab4ef24642f0cd5bad78718a743ce",
"score": "0.7142461",
"text": "def test_get_model_cpu():\n arch = {'conv1_filters': 20, 'conv2_filters': 50, 'output_classes': 10}\n model = get_model('lenet5', F.nll_loss, arch, torch.device('cpu'), 0)\n\n assert isinstance(model, QLeNet5)\n as... | [
{
"docid": "33d4e4f053607709d6aba0227c9d267f",
"score": "0.69362444",
"text": "def test_cpu_model():\n\n trainer_options = dict(\n progress_bar=False,\n experiment=get_exp(),\n max_nb_epochs=1,\n train_percent_check=0.4,\n val_percent_check=0.4\n )\n\n model, ... |
71cad424af1cfed320da85bfeb9e1129 | Can either use index (idx) or the direct path (img_path) to return image | [
{
"docid": "6a31ecf2731166221430055dac13630f",
"score": "0.7684441",
"text": "def get_image(self, idx, img_path=None):\n if idx is not None:\n img_path = self.image_paths[idx]\n else:\n assert(img_path is not None)\n image = io.imread(img_path)\n if self... | [
{
"docid": "1857528a91e680dc985e3882fa6cac7c",
"score": "0.78116846",
"text": "def get_image(self,idx):\n return get_image_prepared(self.cfg, self.imdb.roidb[idx])",
"title": ""
},
{
"docid": "0e3a0717ee1ff6c6a49001e595bdf651",
"score": "0.7789988",
"text": "def image_path... |
afcebe25fd3accfad4d0eea5b1a8e1b7 | Delete property file(s) for the specified set of resources | [
{
"docid": "9614b83b901b0dd7a9cfddd121b33f78",
"score": "0.5541111",
"text": "def delete(\n ctx,\n resource_type=None,\n select=None,\n models=None,\n exclude=None,\n selector=None,\n project_dir=None,\n profiles_dir=None,\n profile=None,\n target=None,\n vars=None,\n ... | [
{
"docid": "07091de612c92dcf179b0b7a0ce53403",
"score": "0.7127628",
"text": "def _delete_all_property_files(ctx, transformed_ls_results):\n resource_paths = [\n Path(ctx.config['project_path'], resource_location)\n for resource_location in transformed_ls_results\n ]\n property_pa... |
cb367b9c25cd0cb0be785c2e03d99dda | 1. Scrape Wikipedia for list of Illinois counties and store in list 2. Loop through county list including county name in each request submission | [
{
"docid": "e31debce864abfd1529b4c1bc7b14036",
"score": "0.6705514",
"text": "def get_response(self):\n\n # Scrape Illinois counties in order to pass county name to URL\n url_counties = \"https://en.wikipedia.org/wiki/List_of_counties_in_Illinois\"\n response_url_counties = requests... | [
{
"docid": "8ee020d5481124082ff88a983611dbf9",
"score": "0.6404991",
"text": "def _input_county_to_search_on_list_page(self):\n self.driver.get(secrets.SITE_URL + '/list/geography')\n time.sleep(2)\n previous_counties_to_remove = self.driver.find_elements_by_class_name('listed')\n ... |
a66c5d07085932ee8d1d62e0ee04be2d | New() > itkBayesianClassifierImageFilterVIUC2ULDD Create a new object of the class itkBayesianClassifierImageFilterVIUC2ULDD and set the input and the parameters if some named or nonnamed arguments are passed to that method. New() tries to assign all the non named parameters to the input of the new objects the first no... | [
{
"docid": "7ca29369721e24c3f4d9c79cebb5103c",
"score": "0.7953373",
"text": "def New(*args, **kargs):\n obj = itkBayesianClassifierImageFilterVIUC2ULDD.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj",
"title": ""
}
] | [
{
"docid": "a4fda03e1794568db25e2955babdf8f2",
"score": "0.7995358",
"text": "def New(*args, **kargs):\n obj = itkBayesianClassifierImageFilterVIUC2USDD.__New_orig__()\n import itkTemplate\n itkTemplate.New(obj, *args, **kargs)\n return obj",
"title": ""
},
{
"doc... |
1ae17b54df4db987d1f0422e0571a527 | Update existing records to set live_attendance with timestamps keys. | [
{
"docid": "bee43b2fcf1520aea12722da671c22df",
"score": "0.7211444",
"text": "def migrate_live_attendance(apps, schema_editor):\n try:\n livesessions = apps.get_model(\"core\", \"LiveSession\")\n except LookupError:\n return\n livesessions_to_migrate = livesessions.objects.filter(... | [
{
"docid": "14d72cecd8716d4bdc3c82e4f041fb1e",
"score": "0.5741195",
"text": "def update_timestamp(self, pit_entry: PendingInterestTableEntry):",
"title": ""
},
{
"docid": "df41953ba4f601f9b8fe31a11bf1886a",
"score": "0.5737282",
"text": "def _update_something_ts(data, key, value):\n... |
2557a78fa169d34f7413297f037539f7 | Convert a naive datetime that uses the machine's timezone to a UTC one. | [
{
"docid": "08004ba29b1d185fd4128e6bebd710b7",
"score": "0.7784218",
"text": "def to_utc(dt):\n # Don't modify it if it already has a timezone -- even if it's not UTC!\n # Yes, this is kinda limited, but should be enough for working with Taskwarrior.\n if dt.tzinfo is not None:\n return ... | [
{
"docid": "f9217e57807189c69cdcecfdc048ff30",
"score": "0.80217886",
"text": "def to_naive_utc(d):\n assert isinstance(d, datetime)\n if not is_naive_datetime(d):\n d = d.astimezone(pytz.UTC).replace(tzinfo=None)\n return d",
"title": ""
},
{
"docid": "e4409cefe9bdb32ff193e3... |
2d932ff2982e42f300390ffbc62bb91c | Get the nth variable saved internally in the tuple (used for inhomogeneous variables) | [
{
"docid": "38f8b634d70dc51636de80aa80500e02",
"score": "0.60192716",
"text": "def get_var(self, variable_idx):\n return self._vars[variable_idx]",
"title": ""
}
] | [
{
"docid": "42e60083787ccb0582288c5fbc1a95cf",
"score": "0.68985456",
"text": "def get_variable_by_index(self, i):\n if not isinstance(i, int):\n raise TypeError('i must be int: %s' % i)\n var = None\n if i < self.get_num_variables():\n name, var = self._vars.items()[i]\n return va... |
4742e9667543d7672b73995437beb541 | Returns source minus comments and docstrings. | [
{
"docid": "8857504e72a705b5c341849824fc17b4",
"score": "0.6218315",
"text": "def remove_comments(source, docstrings=False):\n io_obj = io.StringIO(source)\n out = \"\"\n prev_toktype = tokenize.INDENT\n last_lineno = -1\n last_col = 0\n for tok in tokenize.generate_tokens(io_obj.readl... | [
{
"docid": "d3b9a8f99b5f4315f21e1380ddb89caa",
"score": "0.7017617",
"text": "def source(self):\r\n # return source only for that part of code\r\n return py.code.Source(self.raw)",
"title": ""
},
{
"docid": "c41a2ee9aac8a62938784c9bb5443b37",
"score": "0.7007371",
"text... |
ad67e6976ca0a0dbf36c4e643c745b65 | Return the curve with rounded corners Replaces two points with their midpoint while retaining the start and end points | [
{
"docid": "30b1f13669be15452c8148cddd08e497",
"score": "0.6989627",
"text": "def round_corners(self, curve):\n round_weight = 3\n rounded_curve = [curve[0]] # retain the first point\n current_point = curve[0]\n for next_point in curve[1:]:\n mid_point = (\n ... | [
{
"docid": "71e372926e3cc59de3c212a9607ba596",
"score": "0.6365892",
"text": "def roundJoin(p1, p2, x, y, dist):\n\n (x0, y0) = p1.atend()\n (x1, y1) = p2.atbegin()\n\n (dx0, dy0) = (pyx.unit.topt(x - x0), pyx.unit.topt(y - y0))\n (dx1, dy1) = (pyx.unit.topt(x - x1), pyx.unit.topt(y - y1))\n... |
ecb0ee01b6ccb11e75677aa7d9ee8dfb | Plot the strain data in the frequency domain | [
{
"docid": "d9bd7a000ba278376e63a5fe428de478",
"score": "0.6397094",
"text": "def _frequency_domain_plot(self, *args, **kwargs):\n from pesummary.gw.plots.detchar import frequency_domain_strain_data\n\n return frequency_domain_strain_data(*args, **kwargs)[self.IFO]",
"title": ""
}
] | [
{
"docid": "9a1712a9bdcd3cfbf3521c02ffd920db",
"score": "0.6559505",
"text": "def plot(self):\n x_range = np.arange(0, 1.0, 0.01)\n y_vals = [self.get_freq(x) for x in x_range]\n plt.plot(x_range, y_vals)\n plt.show()",
"title": ""
},
{
"docid": "32df4677233cf5ac0... |
09c4e58955a8b4e19f869fca7078f2d8 | subscribe the web client to this socket | [
{
"docid": "8806147f6d946aa383d7fb9f5fb8964f",
"score": "0.6003841",
"text": "def sub(self, uri, prefix=''):\n\n ident = self.ident(uri, prefix)\n idents = self.recievers.iterkeys()\n self.log('subscribe:', 'requested', ident)\n\n # we don't need to actually create more filte... | [
{
"docid": "109c83d9994e8d99aa6478bff90b40e6",
"score": "0.70212555",
"text": "def subscribe(self):\n try:\n self.client.subscribe()\n except Exception as e:\n logger.error(\"Unknown error: {}\".format(e))\n raise Exception",
"title": ""
},
{
"d... |
9afc8cba396106e0c0e2d2cf5abd0df5 | Search a word's phonetic in 'WordReference'. | [
{
"docid": "a39f189fce2e8d571f78c75ac7966b03",
"score": "0.66305864",
"text": "def getPhonetic(word, language, variant=\"UK\") :\n\n\tlangFrom = \"\"\n\tlangTo = \"\"\n\n\tif language == \"English\":\n\t\tlangFrom = \"en\"\n\t\tlangTo = \"fr\"\n\n\ttry:\n\t\tpage = requests.get(f\"https://www.wordrefere... | [
{
"docid": "f621e037c12702cc9d483850528970ea",
"score": "0.6215948",
"text": "def check_word_as_pounct(self,word):\t\t\n\t\tdetailed_result=[]\n\t\t# ToDo : fix it to isdigit, by moatz saad\n\t\tif word.isnumeric():\n\t\t\tdetailed_result.append(wordCase.wordCase({\n\t\t\t'word':word,\n\t\t\t'affix': ('... |
a62790cae6789b1d12d43b936f35c3a6 | Decoder block for albedo | [
{
"docid": "e2767a3a5b8c9cf3aa792d1df864d5aa",
"score": "0.5774206",
"text": "def _albedo_decoder(albedo_mlp_output, encoder_op_each_layer):\n\t\tfilters = [ 256, 256, 256, 128, 64]\n\t\tdeconvolved = albedo_mlp_output\n\n\t\tfor i in range(5):\n\t\t\tdeconvolved_input = tf.concat(\n\t\t\t\t[deconvolved... | [
{
"docid": "626baf0b964a809a74e5496021d993f6",
"score": "0.6972518",
"text": "def _decode(self):\n pass",
"title": ""
},
{
"docid": "38895bd4f86a4a78899953d8615e9326",
"score": "0.68361384",
"text": "def decoder(self, decoder: decoders.Decoder):\n pass",
"title": ""... |
814a86ccb163c5aa3b30a811286a1675 | Get the currently selected job | [
{
"docid": "9ccb19c631ea05b86427f4c455904752",
"score": "0.83990645",
"text": "def get_job(self) -> Job:\n return self.jobs_list[self.sel_idx]",
"title": ""
}
] | [
{
"docid": "af8b12dff6f2530fbbc6ac201ccc6a00",
"score": "0.76543224",
"text": "def job(self):\n return self.batch[self.job_id]",
"title": ""
},
{
"docid": "c77c5c99ee3ce12ea54d8fa4e31cad56",
"score": "0.74811244",
"text": "def current_job(self):\n assert(ExecutorThread.... |
02855788f1ab0f7e4223d629dd005483 | Recursive function that checks for nonindexable columns. | [
{
"docid": "8a7947e76ac9931b926d1f3a3c9fbd55",
"score": "0.5383323",
"text": "def check_indexable(token_list):\n for tok in token_list.tokens:\n if tok.ttype == tokens.Name:\n col = self.schema.get_column(self.table.value, tok.value)\n if col and not col.indexable:\n ... | [
{
"docid": "e3fa63a2b75dd4515481dc104bb244c8",
"score": "0.6160125",
"text": "def _check(self, df):\n cols_to_check = []\n try:\n cols_to_check.append(getattr(self, 'field'))\n except AttributeError:\n pass\n try:\n cols_to_check += getattr(se... |
7d4440ed8fc28e2206639809e8615ddc | Tests if this user can change the hierarchy. A return of true does not guarantee successful authorization. A return of false indicates that it is known performing any update will result in a ``PermissionDenied``. This is intended as a hint to an application that may opt not to offer these operations to an unauthorized ... | [
{
"docid": "96a1e125c7c2c93baf4e3b63c5b3e402",
"score": "0.66537386",
"text": "def can_modify_family_hierarchy(self):\n return # boolean",
"title": ""
}
] | [
{
"docid": "f086a73bf3494ee3a3cea9a84eaad19a",
"score": "0.7123733",
"text": "def can_modify_objective_hierarchy(self):\n return # boolean",
"title": ""
},
{
"docid": "4ae42bba99802e77eb731d9315fbcaf2",
"score": "0.71223724",
"text": "def change(self, user, energy, *args):\n ... |
1659d94502ed19e7b08785e4a0c75390 | Add a service variable of type ``ty`` to this model | [
{
"docid": "88bde6386277d2c2911bb53b42cd1230",
"score": "0.6668062",
"text": "def service_define(self, service, ty):\n\n if service in self._data:\n raise NameError('Service variable <{}> already defined in _data'.format(service))\n if service in self._algebs:\n raise... | [
{
"docid": "9defd39fc9f8510cf3e2c3a479654431",
"score": "0.6076422",
"text": "def add(stype: ServiceType, service, name: str = None, *args, **kwargs):\n name = service.__name__ if name is None else name\n dynamic_provider = stype.value(service, *args, **kwargs)\n containers.DynamicC... |
16d45c9acf46f40f244ccc434e3d5dfb | Adds liquid fuel or atomic fuel tanks to design | [
{
"docid": "6723dea64db5b09e54a14db3774b557c",
"score": "0.69049513",
"text": "def add_conventional_tanks(self, lf):\n if self.mainengine.name == \"LFB Twin-Boar\":\n lf = max(lf, 36000)\n self.notes.append(\"6400 units of liquid fuel are already included in the engine\")\n ... | [
{
"docid": "c4376dbfe4ecc36c450fad9f25cf7233",
"score": "0.63634956",
"text": "def add_special_tanks(self, xf, tank):\n tankcount = ceil(xf / tank.m_full)\n if tank.size == parts.RadialSize.RadiallyMounted:\n tankcount = max(tankcount, 2)\n self.requiredscience.add(tank.l... |
53115a545456a4f0c2aade0d3a8c94ed | loads the specified image. | [
{
"docid": "49beec7092879a0b08ea6d7fed006276",
"score": "0.0",
"text": "def __load__(self, item):\n \n # the name for parent of parent directory where the image is located and the name of the image are same.\n # an example directory breakup is shown below -\n # - data-science... | [
{
"docid": "8738269cbd579759308a2a386d6ce210",
"score": "0.77812254",
"text": "def load_image(self, image_id):\n info = self.image_info[image_id]\n image = info['img']\n return image",
"title": ""
},
{
"docid": "a978904aa9088dba70ec448b95ab2849",
"score": "0.7713859"... |
06fa4b50e745e149999779d1fefdcc29 | Get this user's given name. | [
{
"docid": "a9bedb947418ec71ca1ce03e332b80e8",
"score": "0.7923014",
"text": "def get_given_name(self):\r\n return self.__given_name",
"title": ""
}
] | [
{
"docid": "d3e35db1a2327055baa7cf8197681877",
"score": "0.85153365",
"text": "def get_name(self):\n return self._user.name",
"title": ""
},
{
"docid": "cd962d543d325b6cb86da8222ad995fe",
"score": "0.84088993",
"text": "def get_user_name(self):\n user = User.by_id(self.... |
7b22a96ecec5b39ecad79489fa76e28b | Given a dictionary and a set of valid keys, return a dictionary which only contains keys found in `valid`. | [
{
"docid": "08a3ed9cbce96c06a4e9829a12d1274d",
"score": "0.65921986",
"text": "def _strip_keys(obj, valid):\n return {key: obj[key] for key in obj if key in valid}",
"title": ""
}
] | [
{
"docid": "e7640a96ac8c550bd84f13f830e1a7f1",
"score": "0.6878941",
"text": "def subset_dict(dictionary, keys):\n\n result = dictionary.copy()\n for original_key in dictionary:\n if original_key not in keys:\n del result[original_key]\n\n return result",
"title": ""
},
... |
c35652b26ac9b90ab52b8a7de68039e5 | Returns true if there is any boolean True in A | [
{
"docid": "488f7cc7ab70b194a5fd819ee1942cf5",
"score": "0.7278255",
"text": "def any_(A):\n return A.any()",
"title": ""
}
] | [
{
"docid": "2b72524cb32c8aea61e7fb5e34fc692e",
"score": "0.7511237",
"text": "def __nonzero__(self):\n return any([bool(i) for i in self])",
"title": ""
},
{
"docid": "fb85a89afbc2a2508032f356aedf04dd",
"score": "0.6771524",
"text": "def all(self):\n if not all(dt == 'b... |
3471ed748751f1988b094a1dd75394f2 | Allows to get Piano C chord with predetermined octave, harmony and duration | [
{
"docid": "19446f336a41d59941282262ac238341",
"score": "0.844064",
"text": "def create_c_chord(self, octave, duration, harmony):\n\n result = None\n\n if str(harmony) == \"major\":\n c_note = AudioSegment.from_file(self.path_to_directory + \"/samples/piano/\"+str(octave)+\"/c.w... | [
{
"docid": "07e77d4f3665179daa18aa85ae1ad833",
"score": "0.84081155",
"text": "def create_c_chord(self, octave, duration, harmony):\n\n result = None\n\n if str(harmony) == \"major\":\n c_note = AudioSegment.from_file(self.path_to_directory + \"/samples/guitar/\"+str(octave)+\"/... |
ccdbd001147d5b9402f617c20d9e2566 | MC approximation of individual Generalized GaussNewton/Fisher diagonal. | [
{
"docid": "e09aba5e7a974d75ea6a635b3bb67e70",
"score": "0.0",
"text": "def diag_ggn_mc_batch(self, mc_samples: int) -> List[Tensor]:\n return",
"title": ""
}
] | [
{
"docid": "83fc45443edde46c289583e7f26e160b",
"score": "0.6200017",
"text": "def _mmd_g_(self):\r\n # calculate pairwise distance\r\n dist_gg, dist_gd, dist_dd = get_squared_dist(\r\n self.score_gen, self.score_data, z_score=False, do_summary=self.do_summary)\r\n\r\n # m... |
7f5f715f96636816652301188d4a7d08 | Return the linear transformation of `y` by `x` or `x` by `y` when one or both of `x` and `y` is a LinearTransform instance | [
{
"docid": "8e445e1df5116820f63b2719a02595de",
"score": "0.5793339",
"text": "def dot_shape(x, y):\n if isinstance(x, LinearTransform):\n return dot_shape_from_shape(x, tuple(y.shape))\n elif isinstance(y, LinearTransform):\n return dot_shape_from_shape(tuple(x.shape), y)\n else:\... | [
{
"docid": "345bd03e2d81a8f108ff75220e9a3080",
"score": "0.66478336",
"text": "def transform(self, x, y):\n \n return numpy.array((x, y, 1)).dot(self._matrix.T)[0:2]",
"title": ""
},
{
"docid": "fa8bde99f2c683e4d0c5f040c3610a54",
"score": "0.61888194",
"text": "def _tra... |
78b9154d02d7e2e82a6670697ecad797 | Called when app is resized. | [
{
"docid": "54a5013ed2c7440077ce98fbcf991d1d",
"score": "0.65565294",
"text": "def on_resize_parent(self,event):\n #print(\"parent event size=\"+str(event.width)+\" X \"+str(event.height))\n self.canvas_width = event.width\n self.canvas_height = event.height\n self.canvas.con... | [
{
"docid": "cf95f21cd7739a672db6e5c661a5ca90",
"score": "0.8178788",
"text": "def onResize(self, window, width, height):\n pass",
"title": ""
},
{
"docid": "e1743c098b05d52132a2b8d8e82d29e0",
"score": "0.80237764",
"text": "def _resize(self, event):\n pass",
"title"... |
a926b398d55b0d419a47a274dabe7003 | Return distbelief conditional_gradient values. Return values been generated from the distbelief conditional_gradient unittest, running with a learning rate of 0.1 and a lambda_ of 0.1. These values record how a parameter vector of size 10, initialized with 0.0, gets updated with 10 consecutive conditional_gradient step... | [
{
"docid": "28f73ae6b769b9399f6857f05c9b9ce7",
"score": "0.0",
"text": "def _dbParamsCG01(self):\n db_grad = [[]] * 10\n db_out = [[]] * 10\n # pylint: disable=line-too-long\n db_grad[0] = [\n 0.00096264342, 0.17914793, 0.93945462, 0.41396621, 0.53037018,\n ... | [
{
"docid": "36c653aeb3b687e085e9534cdbd33142",
"score": "0.608587",
"text": "def value_gradient():\n # sess.run(calculated) to calculate value of state\n state = tf.placeholder(\"float\",[None,4])\n w1 = tf.get_variable(\"w1\",[4,10])\n b1 = tf.get_variable(\"b1\",[10])\n h1 = tf.nn.relu(... |
32f78e2a0364d8bb29944dbe2da16fc5 | Tracker for next neuron id in the given genome | [
{
"docid": "dedcb910b62f8b3963721ac1eaea7b52",
"score": "0.8468444",
"text": "def next_nid(genome):\n nid = genome['last_neuron'] + 1\n genome['last_neuron'] = nid\n return nid",
"title": ""
}
] | [
{
"docid": "fcf8bdb7177722960dff82a8755c738f",
"score": "0.59789985",
"text": "def next_id(self):\n nid = self._next_id\n self._next_id = self._next_id + 1\n return nid",
"title": ""
},
{
"docid": "a3d6cfa9e8896e9291b73b372ea92d75",
"score": "0.59274477",
"text":... |
a32121a9940e96988968f7fa32f121dc | Load training data from file in ``.npz`` format. | [
{
"docid": "f8d684abd15e1b6a8abcd32d14e978da",
"score": "0.0",
"text": "def load_training_data(file, validation_split=0, axes=None, n_images=None, verbose=False):\n\n f = np.load(file)\n X, Y = f['X'], f['Y']\n if axes is None:\n axes = f['axes']\n axes = axes_check_and_normalize(axes... | [
{
"docid": "ace84a1c87b92c64a54709a4a12c05a0",
"score": "0.72628057",
"text": "def load_npy(self, filename):\n self.set_data(np.load(filename))",
"title": ""
},
{
"docid": "7b2e39750ff5265c68e2283630227104",
"score": "0.70330673",
"text": "def load(self, filename: str) -> None... |
55581e6316965b25df6fc0a49bab86fe | Check pickling of an object across another process. python is the path to the python interpreter (defaults to sys.executable) Set verbose=True to print the unpickled object in the other process. | [
{
"docid": "03fa3ae7b2e67f43a813be7bd9c17298",
"score": "0.63948804",
"text": "def check(obj, *args, **kwds):\n # == undocumented ==\n # python -- the string path or executable name of the selected python\n # verbose -- if True, be verbose about printing warning messages\n # all other args and k... | [
{
"docid": "be5a0d70305d6329b2a939b438987978",
"score": "0.57015604",
"text": "def is_picklable(obj):\n try:\n pickle.dumps(obj)\n except Exception:\n return False\n return True",
"title": ""
},
{
"docid": "f261160b7dd7376478f85f0b1a0037bc",
"score": "0.554075",
... |
cfba5384395dd909785d5d1aa4c50363 | Load recurrent neural network from h5.file | [
{
"docid": "091e5e3e7c9e117ec65308a08f85f7cb",
"score": "0.60439736",
"text": "def load_RNN(self, model=None):\n\n if model != None:\n return models.load_model(model)",
"title": ""
}
] | [
{
"docid": "a472291e19ca01e1c6c4f112fa1d57ec",
"score": "0.7449959",
"text": "def load(path: str):\n with h5py.File(path, 'r') as net_file:\n net = TensorNetwork(backend=net_file[\"backend\"][()])\n nodes = list(net_file[\"nodes\"].keys())\n edges = list(net_file[\"edges\"].keys())\n\n for ... |
0026b325ddb64c5a21bdaac90ec27543 | custom list display field with detail page link | [
{
"docid": "b9a3176761efd1812c32b02f6fd6589b",
"score": "0.6043342",
"text": "def name_with_detail_link(self, obj):\n detail_url = reverse(\"admin:program_program_detail\", kwargs={\"object_id\": obj.pk})\n return format_html(f\"<a href='{detail_url}'>{obj.name}</a>\")",
"title": ""
... | [
{
"docid": "f84c2a47205e9e79022c971200627e3c",
"score": "0.6725328",
"text": "def UserListShow(self, listType, itemList):",
"title": ""
},
{
"docid": "60551363c68ef31e6434f1d0b4dca0d0",
"score": "0.6423562",
"text": "def detail_page(self, obj):\n url = reverse('materials_inven... |
bbd2618f260fa87d81098f2ff45fac81 | Calculate mean value of each individual ensemble. Output is array of values, with the row denoting the ensemble size, and the column the ensemble number | [
{
"docid": "b82ed16086ee487a802c2e45ce774e8b",
"score": "0.85388535",
"text": "def get_ensemble_mean(self):\n #edit 08/08/2019: new function, virtually the same as get_ensemble_min and get_ensemble_max, but for mean value of each ensemble\n\t\t\n self.ensemble_mean=np.zeros((np.size(self.d... | [
{
"docid": "50290dcda63e53e00a4ec5bc68d2e814",
"score": "0.71021867",
"text": "def get_ensemble_average(self, outputs):\n predictions = [output for output in outputs]\n predictions = torch.stack(predictions, dim=1)\n average_prediction = predictions.mean(1)\n\n return ave... |
b457f85545bdbe626233021296bda052 | Inserts current datetime into self.text_field from menu | [
{
"docid": "95ccaab5628f955541d65e71bfae1ca1",
"score": "0.78224796",
"text": "def do_time_date(self) -> None:\n text = datetime.datetime.now().isoformat()\n self.text_field.buffer.insert_text(text)",
"title": ""
}
] | [
{
"docid": "504bed008c61e56657817a7d85fd8522",
"score": "0.68654907",
"text": "def OnInsertDate(self, event):\n self.AddText(str(time.ctime()))\n event.Skip()",
"title": ""
},
{
"docid": "41bb243f0f260b6a653da25124d49ba4",
"score": "0.66584486",
"text": "def changeText(... |
b86cfeadcd0e778c255b943bd13ea05c | This method translates a single line of comma separated values to a dictionary which can be loaded into BigQuery. | [
{
"docid": "0b4dc123bd3df140da9c0d4e624e267c",
"score": "0.0",
"text": "def parse_method(self, string_input):\n # strip out the return characters and quote characters.\n values = re.split(\",\",\n re.sub('\\r\\n', '', re.sub(u'\"', '', string_input)))\n row ... | [
{
"docid": "ac1b5d6fb18bcf081f1eb7a62476847b",
"score": "0.6674348",
"text": "def parse_line_to_dict(line, header):\n\tfields = line.strip().split(',')\n\tif len(header) != len(fields):\n\t\traise DataImportFormatError(\"Mismatch in header and data line in incoming data file.\")\n\trecord = {}\n\tfor i ... |