= self.formant_step_count - 4:\n raise ValueError('step index {0} is out of bounds [4, {1})'.format(\n step_index, self.formant_step_count - 4))\n\n # Checking if we already computed required formant point value.\n\n if self.formant_list[step_index] != None:\n return self.formant_list[step_index]\n\n # No, we haven't, so we are going to compute it.\n\n window_list = get_gaussian_window(self.formant_window_size)\n sample_from = (step_index - 4) * self.formant_step_size\n\n sample_list = [self.formant_sample_list[sample_from + i] * window_list[i]\n for i in range(self.formant_window_size)]\n\n # Computing Linear Prediction coefficients via Burg method, number of coefficients is twice the\n # number of formants we want to detect (hence 2 * 5 = 10).\n #\n # These coefficients a[0], a[1], ... a[10] are coefficients of a polynomial 1 - a[1] * x -\n # a[1] * x^2 - ... - a[10] * x^10 of degree 10 defined on [-1, 1], which is a so-called\n # characteristic polynomial, see https://en.wikipedia.org/wiki/Autoregressive_model. We then find\n # the roots of this polynomial.\n\n a0, coefficient_list = burg(sample_list, 10)\n\n polynomial = numpy.polynomial.Polynomial([1.0] + [-c for c in coefficient_list])\n root_list = polynomial.roots()\n\n derivative = polynomial.deriv()\n\n better_root_list = []\n better_root_index = 0\n\n # Finding better root approximations via Newton-Raphson iteration (see https://en.wikipedia.org/\n # wiki/Newton's_method.\n\n while better_root_index < len(root_list):\n\n previous = root_list[better_root_index]\n previous_delta = abs(polynomial(previous))\n\n current = previous - polynomial(previous) / derivative(previous)\n current_delta = abs(polynomial(current))\n\n while current_delta < previous_delta:\n\n previous = current\n previous_delta = current_delta\n\n current = previous - polynomial(previous) / derivative(previous)\n current_delta = abs(polynomial(current))\n\n # If it is a complex root, the next one is just its complex conjugate.\n\n better_root_list.append(previous)\n better_root_index += 1\n\n if abs(previous.imag) > 0:\n\n better_root_list.append(previous.conjugate())\n better_root_index += 1\n\n continue\n\n # Moving all roots into the unit circle. If a root is outside, we replace it with reciprocal of its\n # conjugate, reflecting it about the real line and projecting it inside the unit circle. Then we\n # find formants by looking at roots above the real line.\n\n nyquist_frequency = self.formant_sound.frame_rate * 0.5\n formant_list = []\n\n for root in better_root_list:\n\n if abs(root) > 1.0:\n root = 1.0 / root.conjugate()\n\n if root.imag >= 0:\n frequency = abs(math.atan2(root.imag, root.real)) * nyquist_frequency / math.pi\n\n if frequency >= 50 and frequency <= nyquist_frequency - 50:\n formant_list.append(frequency)\n\n # Memoizing and returning first two formants.\n\n formant_list.sort()\n self.formant_list[step_index] = formant_list[:2]\n\n return formant_list[:2]","def get_corr_weights(dfr, filen, use_pt=False, acc_only=False, min_acc=None):\n cmapfile = r.TFile.Open(filen)\n cmap = get_correction_map(cmapfile, use_pt, acc_only, min_acc)\n\n # Check how far down the costh goes in the correction map\n is_abs_costh = np.min(cmap.var_binnings[0]) == 0\n\n # Determine the appropriate eval function for the correction map\n if use_pt:\n if is_abs_costh:\n logging.info('Using corrections in abs(costh), phi and Jpsi pT')\n eval_f = eval_abs_costh_phi_fold_pt_HX\n else:\n logging.info('Using corrections in costh, phi and Jpsi pT')\n eval_f = eval_costh_phi_fold_pt_HX\n else:\n if is_abs_costh:\n logging.info('Using corrections in abs(costh) and phi')\n eval_f = eval_abs_costh_phi_fold_HX\n else:\n logging.info('Using corrections in costh and phi')\n eval_f = eval_costh_phi_fold_HX\n\n return eval_corrmap(cmap, eval_f)(dfr)","def get_highly_correlated_feature_names(self):","def plot_feature_correlation(self, N=20, save=None):\n makedir(self.plotdir)\n if save is None:\n save = '/feature_correlation.png'.format(self.plotdir)\n \n # compile feature frequencies\n feats = []\n fls = glob('{:s}/*.fts'.format(self.modeldir))\n for i,fl in enumerate(fls):\n with open(fl) as fp:\n lns = fp.readlines()\n feats += [' '.join(ln.rstrip().split()[1:]) for ln in lns] \n labels = list(set(feats) - set(['']))\n freqs = [feats.count(label) for label in labels]\n\n labels = [label for _,label in sorted(zip(freqs,labels))][::-1]\n freqs = sorted(freqs)[::-1]\n fts = copy(labels)\n \n N = np.min([N, len(freqs)])\n labels = ['\\n'.join(wrap(' '.join(l.split('_')), 30)) for l in labels ][:N]\n \n f = plt.figure(figsize=(8, 8))\n\n fM,ys = self._extract_features(self.ti_forecast, self.tf_forecast)\n filt_df = np.log10(fM[fts[:N]]).replace([np.inf, -np.inf], np.nan).dropna()\n low, high = [0.005, 0.995]\n quant_df = filt_df.quantile([low, high])\n filt_df.index = range(filt_df.shape[0])\n filt_df = filt_df.apply(lambda x: x[(x>quant_df.loc[low,x.name]) & (x < quant_df.loc[high,x.name])], axis=0)\n fM[fts[:N]]\n\n inds = []\n for te in self.data.tes:\n inds.append(np.where((ys['label']>0)&(abs((te-ys.index).total_seconds()/(3600*24))<5.)))\n cols = ['b','g','r','m','c']\n truths = []\n for i, ind, col in zip(range(-2,3), inds, cols):\n truths.append(np.log10(fM[fts[:N]].iloc[ind]).replace([np.inf, -np.inf], np.nan).dropna())\n #te = self.data.tes[i+2]\n \n fig = corner(filt_df.dropna(), labels=labels[:N])\n axes = np.array(fig.axes).reshape((N, N))\n for yi in range(N):\n for xi in range(yi):\n ax = axes[yi,xi]\n for col,truth in zip(cols,truths):\n ax.scatter(truth[fts[xi]].values, truth[fts[yi]].values, np.arange(1,5)*6, col)\n \n plt.savefig(save,dpi=300)\n plt.close(fig)","def read_conformers (mol, pathname, method='RDKit') :\n dcdfilename = os.path.join(pathname,'%s.dcd'%(method))\n dcd = DCDTrajectoryFile(dcdfilename)\n nsteps = dcd.get_length()\n \n # Read in the corresponding energies\n enfilename = os.path.join(pathname,'energies%s.dat'%(method))\n infile = open(enfilename)\n lines = infile.readlines()\n infile.close()\n energies = []\n for line in lines :\n words = line.split() \n if len(words) == 0 : continue\n energies.append(float(words[-1]))\n \n # Filter the conformers with Marks filter\n conformers = UniqueConformersMark()\n conformers.prepare_state(mol)\n for i,energy in enumerate(energies) :\n crd,cell = dcd.read_next()\n # Filtering is not necessary, since the conformers were already filtered\n duplicate = conformers.add_conformer(crd,energy,check_for_duplicates=False)\n\n return conformers","def correLation(df):\n for i in range(0, 3):\n ## randomly pick 20 holders\n l1 = random.sample(range(0, len(df.columns)), 20)\n temp_df = df.iloc[:, l1]\n plt.imshow(temp_df.corr(), cmap=plt.cm.Blues, interpolation='nearest')\n plt.colorbar()\n tick_marks = [i for i in range(len(temp_df.columns))]\n plt.xticks(tick_marks, temp_df.columns, rotation=90.)\n plt.yticks(tick_marks, temp_df.columns)\n plt.show()","def get_reference_no_from_narration(narration,validatorlist=[validate_western_code,validate_ria,validate_placid, validate_xpress,validate_moneygram, validate_prabhu_ref, validate_merchantrade_ref, validate_necmoney_ref, validate_necitaly_ref, validate_cbl_ref, swift_re]):\n ref_list = []\n for e in narration.split():\n for fn in validatorlist:\n try:\n fn(e)\n if e not in ref_list:\n ref_list.append(e)\n except ValidationError:\n pass\n return ref_list","def registration_with_ransac_from_correspondences(\n src_corr_points,\n tgt_corr_points,\n correspondences=None,\n distance_threshold=0.05,\n ransac_n=3,\n num_iterations=10000,\n):\n src_pcd = make_open3d_point_cloud(src_corr_points)\n tgt_pcd = make_open3d_point_cloud(tgt_corr_points)\n\n if correspondences is None:\n indices = np.arange(src_corr_points.shape[0])\n correspondences = np.stack([indices, indices], axis=1)\n correspondences = o3d.utility.Vector2iVector(correspondences)\n\n result = o3d.pipelines.registration.registration_ransac_based_on_correspondence(\n src_pcd,\n tgt_pcd,\n correspondences,\n distance_threshold,\n estimation_method=o3d.pipelines.registration.TransformationEstimationPointToPoint(False),\n ransac_n=ransac_n,\n criteria=o3d.pipelines.registration.RANSACConvergenceCriteria(num_iterations, num_iterations),\n )\n\n return result.transformation","def calculate_overf_correlation(amp, index, f0, dt, n_lags):\n \n # Cast inputs as floats as I do a bunch of division.\n dt = float(dt)\n f0 = float(f0)\n index = float(index)\n # Number of points used in calculation needs to be at least 10 times bigger\n # than final number of point returned. This is so we are not affected by\n # the periodicity of the correlation function.\n buff_factor = 64\n n = buff_factor * n_lags\n n_return = n_lags\n # Generate the power spectrum.\n # Need to add a low frequency cut off, since there is an IR divergence.\n # Choose to cut off at 1/2df (so we get a bit of slope mode).\n power = overf_power_spectrum(amp, index, f0, dt, n,\n cut_off=1./n_lags/dt/2.0)\n # FFT it to the correlation function.\n corr = fft.ifft(power)\n # Complex part should be zero.\n corr = corr.real\n # In previous versions of this function, we shifted the output function.\n # however this screws up positive definiteness of the correlation matrix\n # and is unnecessary if you have the IR cut off.\n #corr -= corr[2 * n_return]\n # Trim to return size.\n corr = corr[:n_return]\n # To normalize, need to multiply by twice the bandwidth.\n corr *= 1.0/dt\n return corr","def read_conformers (mol, pathname, method='RDKit') :\n dcdfilename = os.path.join(pathname,'%s.dcd'%(method))\n dcd = DCDTrajectoryFile(dcdfilename)\n nsteps = dcd.get_length()\n \n # Read in the corresponding energies\n enfilename = os.path.join(pathname,'energies%s.dat'%(method))\n infile = open(enfilename)\n lines = infile.readlines()\n infile.close()\n energies = []\n for line in lines :\n words = line.split() \n if len(words) == 0 : continue\n energies.append(float(words[-1]))\n \n # Filter the conformers with Marks filter\n conformers = UniqueConformersMark()\n conformers.prepare_state(mol)\n for i,energy in enumerate(energies) :\n crd,cell = dcd.read_next()\n # Filtering is not necessary, since the conformers were already filtered\n duplicate = conformers.add_conformer(crd,energy)\n\n return conformers","def getCorrelograms(self, dtype=float64):\n fname = '%s::%s'%(self.__class__.__name__, self.getCorrelograms.__name__)\n if (not self.lhaveImage):\n print(\"%s: DSM image not yet computed\"%fname)\n return None\n # Get number of correlograms\n nxcs = c_int(1)\n ierr = c_int(1)\n self.lib.xcloc_getNumberOfCorrelograms(nxcs, ierr)\n if (ierr.value != 0):\n print(\"%s: Failed to get number of correlograms\"%fname)\n return None\n nxcs = nxcs.value\n # Get correlogram length\n nptsInXCs = c_int(1)\n self.lib.xcloc_getCorrelogramLength(nptsInXCs, ierr)\n if (ierr.value != 0):\n print(\"%s: Failed to get correlogram length\"%fname)\n return None\n nptsInXCs = nptsInXCs.value\n # Get the correlograms\n if (dtype == xctypes.XCLOC_SINGLE_PRECISION):\n xcs = ascontiguousarray(zeros(nxcs*nptsInXCs, dtype=c_float))\n xcsPtr = xcs.ctypes.data_as(POINTER(c_float))\n self.lib.xcloc_getCorrelograms64f(nptsInXCs, nxcs, xcsPtr,\n byref(ierr))\n if (ierr.value != 0):\n print(\"%s: Error getting float correlograms\"%fname)\n return None\n elif (dtype == xctypes.XCLOC_DOUBLE_PRECISION):\n xcs = ascontiguousarray(zeros(nxcs*nptsInXCs, dtype=c_double))\n xcsPtr = xcs.ctypes.data_as(POINTER(c_double))\n self.lib.xcloc_getCorrelograms64f(nptsInXCs, nxcs, xcsPtr,\n byref(ierr))\n if (ierr.value != 0):\n print(\"%s: Error getting double correlograms\"%fname)\n return None\n else:\n print(\"%s: Unknown precision %d\"%(fname, precision))\n return None\n xcs = xcs.reshape([nxcs, nptsInXCs], order='C')\n return xcs","def fast_corr(df, col_name):\n\n if not isinstance(df, pd.DataFrame):\n raise TypeError(\"The type of the input data must be dataframe.\")\n\n if not isinstance(col_name, list):\n raise TypeError(\"The col_name must be list.\")\n\n if all(isinstance(item, str) for item in col_name) is False and all(\n isinstance(item, int) for item in col_name) is False:\n raise ValueError(\n \"The col_name must be a list of strings or a list of integers.\")\n\n if len(col_name) < 2:\n raise ValueError(\n \"At least two columns must be selected for correlation analysis.\")\n\n if all(isinstance(item, str) for item in col_name) is True and all(\n elem in df.columns.to_list() for elem in col_name) is False:\n raise ValueError(\"The column names were not found.\")\n\n if all(isinstance(item, int) for item in col_name) is True and max(\n col_name) > (df.shape[1] - 1):\n raise ValueError(\"The column indexes were out of range.\")\n\n if all(isinstance(item, str) for item in col_name):\n data = df.loc[:, col_name]\n else:\n data = df.iloc[:, col_name]\n\n data2 = data._get_numeric_data()\n rm_n = data.shape[1] - data2.shape[1]\n print(\"Removed\", rm_n, \"non-numberical columns from your selected columns\")\n\n sns.set(style=\"white\")\n corr = data2.corr()\n mask = np.triu(np.ones_like(corr, dtype=np.bool))\n f, ax = plt.subplots(figsize=(9, 11))\n ax.set_title('Correlation Matrix', size=20)\n ax.tick_params(axis='x', labelsize=15)\n ax.tick_params(axis='y', labelsize=15)\n\n cmap = sns.diverging_palette(220, 20, as_cmap=True)\n p = sns.heatmap(corr, mask=mask, cmap=cmap, vmin=-1, vmax=1, center=0,\n square=True, linewidths=.5, cbar_kws={\"shrink\": .5})\n p.set_yticklabels(p.get_yticklabels(), rotation=360)\n return p","def calculate_correlation(data):\n pass","def load_data(config):\n cor_file = os.path.join(config.data_dir, \"{}_cor.txt\".format(config.type.strip()))\n err_file = os.path.join(config.data_dir, \"{}_err.txt\".format(config.type.strip()))\n corrects = []\n errors = []\n positions = []\n with open(cor_file, 'r') as fc, open(err_file, 'r') as fe:\n clines = fc.readlines()\n elines = fe.readlines()\n cnt = 0\n for cline, eline in zip(clines, elines):\n cnt += 1\n try:\n correct, pos1 = cline.strip().split('\\t')\n except:\n correct = cline.strip()\n pos1 = '-1'\n try:\n error, pos2 = eline.strip().split('\\t')\n except:\n error = eline.strip()\n pos2 = '-1'\n\n assert pos1 == pos2\n\n corrects.append(correct)\n errors.append(error)\n positions.append(int(pos2))\n return corrects, errors, positions","def findLinkedTerms(self):\n for key in self.summaryDict.keys(): # v' in the formula\n if self.getCoverFromModalityInDictionnary(self.summaryDict, key) == 0:\n correlation = 0\n else:\n dep = self.getCoverFromModalityInDictionnary(self.summaryFilteredDict,key) / self.getCoverFromModalityInDictionnary(self.summaryDict, key) #cover(v',R')/cover(v'R)\n if dep <= 1:\n correlation = 0\n else:\n correlation = 1 - (1 / dep)\n self.correlationDict[key] = correlation","def analyse_donnees(self, mere, foetus, pere, log):\n concordance_mf = 0\n concordance_pf = None\n if len(pere) != 0:\n concordance_pf = 0\n log = log + \"Père détecté.................................\\n\"\n log = log + \"\\n\\nVérification concordance des ADNs entre père et foetus..............................\\n\"\n for Alleles in range(len(foetus)):\n for Allele_Foe in range(3):\n if foetus[Alleles].allele[Allele_Foe] in pere[Alleles].allele:\n if foetus[Alleles].allele[Allele_Foe] != 0.0:\n pere[Alleles].concordance_pere_foetus = \"OUI\"\n concordance_pf = concordance_pf + 1\n log = log + \"Concordance pour marqueur \" + str(\n foetus[Alleles].marqueur) + \" OK..................\\n\"\n break\n else:\n pere[Alleles].concordance_pere_foetus = \"NON\"\n log = log + \"Concordance pour marqueur \" + foetus[\n Alleles].marqueur + \" PAS OK..............\\n\"\n break\n log = log + \"\\n\\nVérification concordance des ADNs entre mère et foetus..............................\\n\"\n for Alleles in range(len(foetus)):\n for Allele_Foe in range(3):\n if foetus[Alleles].allele[Allele_Foe] in mere[Alleles].allele:\n if foetus[Alleles].allele[Allele_Foe] != 0.0:\n foetus[Alleles].concordance_mere_foetus = \"OUI\"\n concordance_mf = concordance_mf + 1\n log = log + \"Concordance pour marqueur \" + str(\n foetus[Alleles].marqueur) + \" OK..................\\n\"\n break\n else:\n foetus[Alleles].concordance_mere_foetus = \"NON\"\n log = log + \"Concordance pour marqueur \" + foetus[Alleles].marqueur + \" PAS OK..............\\n\"\n break\n log = log + \"Vérification concordance des ADns terminée..................................\\n\\n\\n\"\n if concordance_mf != len(foetus):\n resultats, conclusion = self.resultat(concordance_mf, concordance_pf, foetus, mere, pere)\n log = log + \"Concordance des ADNs PAS OK....................\\n\"\n log = log + \"Erreur dans l'échantillon...................\\n\"\n log = log + \"Revérifier s'il vous plaît.............\\n\"\n return resultats, conclusion, log\n else:\n log = log + \"Traitement des 15 autres marqueurs..............................\\n\"\n for nbre_lignes in range(1, len(mere)):\n log = log + \"Traitement du marqueur \" + str(foetus[nbre_lignes].marqueur) + \"..........\\n\"\n pic = foetus[nbre_lignes].foetus_pics()\n log = log + \"Calcul du nombre d'allèles pour le foetus......................\\n\"\n log = log + \"Nombre d'allèles pour le foetus : \" + str(pic) + \".........\\n\"\n log = log + \"Vérification de l'homozygotie de la mère......................\\n\"\n mere[nbre_lignes].homozygotie()\n log = log + \"Mère homozygote : \" + str(mere[nbre_lignes].homozygote) + \"...............\\n\"\n log = log + \"Vérification mère et foetus mêmes allèles......................\\n\"\n foetus[nbre_lignes].allele_semblable(mere[nbre_lignes])\n log = log + \"Code de retour vérification allèles semblables: \" + str(\n foetus[nbre_lignes].informatif) + \"...............\\n\"\n log = log + \"Initialisation du taux de contamination pour calcul à venir...............\\n\"\n foetus[nbre_lignes].taux = 0.0\n log = log + \"Taux initialisé.................................\\n\"\n log = log + \"Si code informatif de retour allèles semblables différent de 2, vérification écho.............\\n\"\n log = log + \"Si écho, affection code informatif 3...............\\n\"\n if foetus[nbre_lignes].informatif != 2:\n log = log + \"Vérification si écho......................\\n\"\n mere[nbre_lignes].echo(foetus[nbre_lignes])\n log = log + \"Code retour vérification écho : \" + str(\n foetus[nbre_lignes].informatif) + \"...............\\n\"\n log = log + \"Début chaîne de traitement...........................\\n\"\n if pic == 3:\n log = log + \"Trois allèles détectés......................\\n\"\n foetus[nbre_lignes].contamination_heterozygote(mere[nbre_lignes])\n log = log + \"Marqueur informatif, affectation du code contamination 1..............\\n\"\n foetus[nbre_lignes].informatif = 1\n log = log + \"Calcul taux de contamination du marqueur..........\\n\"\n foetus[nbre_lignes].contamination = 2\n log = log + \"Calcul terminé....................\\n\"\n elif mere[nbre_lignes].homozygote:\n log = log + \"Mère homozygote.......................\\n\"\n log = log + \"Marqueur non informatif, affectation du code informatif 0............\\n\"\n foetus[nbre_lignes].informatif = 0\n elif pic == 2:\n log = log + \"Deux allèles détectés..............\\n\"\n if foetus[nbre_lignes].informatif == 2:\n log = log + \"Si mêmes allèles, vérification homozygote contaminé...............\\n\"\n foetus[nbre_lignes].verif_homozygote_contamine(self)\n if foetus[nbre_lignes].contamination == 1:\n log = log + \"Homozygote contaminé identifié.....................\\n\"\n log = log + \"Calcul du taux de contamination....................\\n\"\n foetus[nbre_lignes].homozygote_contamine(self)\n log = log + \"Calcul du taux de contamination effectué...........\\n\"\n else:\n if foetus[nbre_lignes].informatif != 3:\n log = log + \"Code calcul écho différent de 3..................\\n\"\n log = log + \"Marqueur informatif, affectation du code informatif 1.............\\n\"\n foetus[nbre_lignes].informatif = 1\n log = log + \"Marqueur non contaminé, affectation du code contamination 0................\\n\"\n foetus[nbre_lignes].contamination = 0\n else:\n log = log + \"Un seul allèle détecté............\\n\"\n if foetus[nbre_lignes].informatif != 3:\n log = log + \"Code informatif différent de 3...........\\n\"\n log = log + \"Marqueur informatif, affectation du code informatif 1.............\\n\"\n foetus[nbre_lignes].informatif = 1\n log = log + \"Marqueur non contaminé, affectation du code contamination 0................\\n\"\n foetus[nbre_lignes].contamination = 0\n log = log + \"\\n\\n\"\n log = log + \"Calcul échantillon contaminé ou non......\\n\"\n log = log + \"Marqueur contaminé si >\" + str(self.seuil_taux_conta) + \".......\\n\"\n log = log + \"Echantillon contaminé si plus de \" + str(\n self.seuil_nbre_marqueurs) + \"marqueurs contaminés...\\n\"\n self.conclusion_echantillon(foetus)\n log = log + \"Calcul échantillon terminé.....\\n\"\n log = log + \"Fin de traitement...........\\n\"\n resultats, conclusion = self.resultat(concordance_mf, concordance_pf, foetus, mere, pere)\n return resultats, conclusion, log","def _get_next_correlation_index(self, index: int) -> int:\n pass","def get_correlated_geometric_brownian_motions(params: 'ModelParameters',\n correlation_matrix: 'np.array',\n n: int) -> 'np.array':\n decomposition = sp.linalg.cholesky(correlation_matrix, lower=False)\n uncorrelated_paths = []\n sqrt_delta_sigma = np.sqrt(params.all_delta) * params.all_sigma\n # Construct uncorrelated paths to convert into correlated paths\n for i in range(params.all_time):\n uncorrelated_random_numbers = []\n for j in range(n):\n uncorrelated_random_numbers.append(random.normalvariate(0, sqrt_delta_sigma))\n uncorrelated_paths.append(np.array(uncorrelated_random_numbers))\n uncorrelated_matrix = np.asmatrix(uncorrelated_paths)\n correlated_matrix = uncorrelated_matrix * decomposition\n assert isinstance(correlated_matrix, np.matrix)\n # The rest of this method just extracts paths from the matrix\n extracted_paths = []\n for i in range(1, n + 1):\n extracted_paths.append([])\n for j in range(0, len(correlated_matrix) * n - n, n):\n for i in range(n):\n extracted_paths[i].append(correlated_matrix.item(j + i))\n return extracted_paths","def output_new_correlation_values():\n correlation_builder = _get_correlation_values()\n bv_data = correlation_builder.correlation_store.query(\n criteria={'property_x': 'bulk_modulus',\n 'property_y': 'vickers_hardness'}\n )\n \n print(\"Bulk Modulus/Vickers Hardness\")\n \n for item in bv_data:\n print(\"'{}': {},\".format(item['correlation_func'],\n item['correlation']))\n\n vb_data = correlation_builder.correlation_store.query(\n criteria={'property_y': 'bulk_modulus',\n 'property_x': 'vickers_hardness'}\n )\n\n print(\"Vickers Hardness/Bulk Modulus\")\n\n for item in vb_data:\n print(\"'{}': {},\".format(item['correlation_func'],\n item['correlation']))\n \n print('linlsq correlation values')\n\n bg_ad = correlation_builder.correlation_store.query_one(\n criteria={'property_x': 'band_gap_pbe',\n 'property_y': 'atomic_density',\n 'correlation_func': 'linlsq'}\n )\n\n bm_vh = correlation_builder.correlation_store.query_one(\n criteria={'property_x': 'bulk_modulus',\n 'property_y': 'vickers_hardness',\n 'correlation_func': 'linlsq'}\n )\n \n print(\"[{}, {}]\".format(bg_ad['correlation'], \n bm_vh['correlation']))","def localization_full(Gint, focal_genes, \n num_reps = 200,\n method = 'LCC', \n print_counter = False,\n label = 'focal genes',\n line_height = 0.1,\n legend_loc = 'upper left'):\n \n numedges_list, numedges_rand, LCC_list, LCC_rand = localization(Gint, focal_genes, num_reps, \n sample_frac = 1, \n method = method, \n plot = False,\n print_counter = print_counter)\n if method == 'numedges':\n analysis_list = numedges_list\n analysis_rand = numedges_rand\n title = 'number of edges'\n else:\n analysis_list = LCC_list\n analysis_rand = LCC_rand\n title = 'largest connected component'\n\n # plot distributions for non-sampled case\n fig, ax = plt.subplots(figsize = (12, 7))\n sns.set_style('white') \n plt.vlines(np.mean(analysis_list), ymin = 0, ymax = line_height, color = 'r', lw = 2, label = label)\n sns.kdeplot(analysis_rand, ax = ax, color = 'k', lw = 2, alpha = 0.5, shade = True, label = 'random')\n plt.legend(loc = legend_loc, fontsize = 12)\n plt.ylabel('frequency', fontsize = 16)\n plt.xlabel(title, fontsize = 16)\n\n # print the z-score and fdr\n analysis_z = (np.mean(analysis_list) - np.mean(analysis_rand))/float(np.std(analysis_rand))\n\n print(1 - ndtr(analysis_z))\n\n plt.title('permutation p = ' + str(1 - ndtr(analysis_z)))\n \n return numedges_list, numedges_rand, LCC_list, LCC_rand","def make_all_corr_matrices(isotracers, formula_dict, na_dict, autodetect, corr_limit):\n corr_mats = {}\n for isotracer in isotracers:\n trac_atom = get_isotope_element(isotracer)\n try:\n indist_list = corr_limit[str(trac_atom)].keys()\n except KeyError:\n indist_list = []\n corr_mats[isotracer] = make_correction_matrix(isotracer, formula_dict, na_dict, indist_list, autodetect, corr_limit[trac_atom])\n return corr_mats","def addCorrelator(corrType) :\n try :\n s.addCorrelator(corrType)\n except Exception, ex:\n print(ex.errorMsg)\n e = \"Unable to add the %s correlator. If it is currently\\n\" % CORR_STRING[corrType]\n e += \"owned by another subarray, you will have to issue\\n\"\n e += \"removeCorrelator(%s) in that subarray first.\" % CORR_STRING[corrType]\n print e","def process_conformers_file(conformers_path: str) -> Tuple[List[Dict[str, tuple]], List[float]]:\n if not os.path.isfile(conformers_path):\n raise InputError('Conformers file {0} could not be found'.format(conformers_path))\n with open(conformers_path, 'r') as f:\n lines = f.readlines()\n xyzs, energies = list(), list()\n line_index = 0\n while line_index < len(lines):\n if 'conformer' in lines[line_index] and ':' in lines[line_index] and lines[line_index].strip()[-2].isdigit():\n xyz, energy = '', None\n line_index += 1\n while len(lines) and line_index < len(lines) and lines[line_index].strip() \\\n and 'SMILES' not in lines[line_index] \\\n and 'energy' not in lines[line_index].lower() \\\n and 'guess method' not in lines[line_index].lower():\n xyz += lines[line_index]\n line_index += 1\n while len(lines) and line_index < len(lines) and 'conformer' not in lines[line_index]:\n if 'relative energy:' in lines[line_index].lower():\n energy = float(lines[line_index].split()[2])\n line_index += 1\n xyzs.append(str_to_xyz(xyz))\n energies.append(energy)\n else:\n line_index += 1\n return xyzs, energies","def read_forces_on_pointcharges(self):\n from ase.units import Hartree, Bohr\n infile = open(os.path.join(self.directory, 'detailed.out'), 'r')\n lines = infile.readlines()\n infile.close()\n\n external_forces = []\n for n, line in enumerate(lines):\n if ('Forces on external charges' in line):\n chargestart = n + 1\n break\n else:\n raise RuntimeError(\n 'Problem in reading forces on MM external-charges')\n lines1 = lines[chargestart:(chargestart + len(self.mmcharges))]\n for line in lines1:\n external_forces.append(\n [float(i) for i in line.split()])\n return np.array(external_forces) * Hartree / Bohr","def calculate_correlations(input_data, index_col, cat_features, exclu_elements): \r\n try:\r\n # encode the categorical features\r\n encoded_data = pd.get_dummies(input_data,columns=cat_features,drop_first=True)\r\n\r\n pd_transposed_data = encoded_data.set_index('Style_display_code').T\r\n\r\n # get the number of items\r\n items_list = [str(a) for a in pd_transposed_data.columns]\r\n\r\n print(\"Number of items to correlate :{}_Timestamp:{}\".format(str(len(items_list)), \r\n format(str(datetime.now()))))\r\n \r\n\r\n #compute correlations and save the pickle file\r\n# matrix = pd_transposed_data.corr().values\r\n# pickle.dump(matrix, open(staging_dir+ '/corr_matrix_output_py3.p', 'wb'))\r\n \r\n # read from the saved pickle file - ONLY FOR CONSECUTIVE RUNS, TO SAVE TIME\r\n matrix = pickle.load(open(staging_dir+ '/corr_matrix_output_py3.p', \"rb\" ) )\r\n\r\n print(\"Corr Matrix size:{}_Timestamp:{}\".format(str(matrix.size),\r\n format(str(datetime.now()))))\r\n\r\n except Exception as e:\r\n print(\" Error !!\", e)\r\n \r\n # return the top correlated items\r\n return top_correlateditems(items_list,matrix, index_col, exclu_elements)","def read_CORNERS(self, fp):\r\n corners = []\r\n count = 0\r\n nb = self.size[0] * self.size[1] * self.size[2] * 24\r\n\r\n for line in fp:\r\n item = line.split()\r\n if len(item) > 0:\r\n if \"CORNERS\" in item[0]:\r\n break\r\n for line in fp:\r\n item = line.split()\r\n if len(item) > 0:\r\n for c in item:\r\n if \"*\" in c:\r\n item = c.split(\"*\")\r\n for i in range(int(item[0])):\r\n corners.append(item[1])\r\n count += 1\r\n else:\r\n corners.append(c)\r\n count += 1\r\n # all attributes have been read\r\n if count == nb:\r\n break\r\n sp = int(len(corners) / 3)\r\n X = list(map(float, corners[:sp]))\r\n Y = list(map(float, corners[sp:2*sp]))\r\n Z = list(map(float, corners[2*sp:]))\r\n return X, Y, Z","def read_forces(filename):\n f=open(filename,\"r\")\n castep_forces = f.readlines()\n f.close() \n nruter = []\n for index, line in enumerate(castep_forces):\n if 'Total number of ions in cell' in line:\n n_atoms = int(line.split()[7])\n if 'Cartesian components (eV/A)' in line:\n starting_line = index + 4\n for i in range(n_atoms):\n f = starting_line + i\n nruter.append([float(castep_forces[f].split()[m]) for m in range(3,6)]) \n nruter=np.array(nruter,dtype=np.double)\n return nruter","def get_best_fit_information(engine, form_factor_id):\n Nstates = collections.namedtuple(\n 'NStates', ['n', 'no', 'm', 'mo'], defaults=(1, 0, 0, 0)\n )\n\n def _float_or_none(astr):\n if astr is None:\n return None\n if (astr.lower() == 'nan') or (astr.lower() == 'none'):\n return None\n return float(astr)\n\n query = f\"\"\"\n select\n campaign.form_factor_id,\n ens.ens_id,\n ens.ns,\n form_factor.momentum,\n form_factor.spin_taste_current,\n result_id,\n n_decay_ll as n,\n n_oscillating_ll as \"no\",\n n_decay_hl as m,\n n_oscillating_hl as mo,\n tmin_ll as tmin_src,\n tmax_ll as tmax_src,\n tmin_hl as tmin_snk,\n tmax_hl as tmax_snk,\n binsize,\n shrinkage,\n fold as do_fold,\n sign,\n pedestal,\n params\n from campaign_form_factor as campaign\n join form_factor using(form_factor_id)\n join ensemble as ens using(ens_id)\n join sign_form_factor using(ens_id, spin_taste_current)\n join result_form_factor as result\n on (result.form_factor_id = campaign.form_factor_id) and\n (result.id = campaign.result_id)\n join analysis_form_factor as analysis\n on (analysis.analysis_id = result.analysis_id)\n join reduction_form_factor as reduction\n on (reduction.reduction_id = result.reduction_id)\n where\n campaign.form_factor_id in ({form_factor_id});\"\"\"\n\n best_fit = pd.read_sql_query(query, engine)\n best_fit['params'] = best_fit['params'].apply(parse_string_dict)\n best_fit['pedestal'] = best_fit['pedestal'].apply(_float_or_none)\n best_fit['nstates'] = best_fit[['n', 'no', 'm', 'mo']].apply(\n lambda args: Nstates(*args), axis=1)\n best_fit['params'] = best_fit[['params', 'nstates']].apply(\n lambda pair: reshape_params(*pair), axis=1)\n best_fit, = best_fit.to_dict('records') # Unpack single entry\n return best_fit","def cmd_makecldf(self, args):\n wl = lingpy.Wordlist(self.raw_dir.joinpath(\"GEM-CNL.csv\").as_posix())\n concepts = args.writer.add_concepts(\n id_factory=lambda x: x.id.split(\"-\")[-1] + \"_\" + slug(x.english), lookup_factory=\"Name\"\n )\n for concept in self.conceptlists[0].concepts.values():\n for cis in concept.attributes[\"lexibank_gloss\"]:\n if cis not in concepts:\n concepts[cis] = concepts[concept.english]\n\n languages = args.writer.add_languages(lookup_factory=\"STEDT_Name\")\n args.writer.add_sources()\n\n for idx, language, concept, value, pos in wl.iter_rows(\n \"doculect\", \"concept\", \"reflex\", \"gfn\"\n ):\n # Fix for 251479\n if concept == \"top (i.e. highest point\":\n concept = \"top (i.e. highest point)\"\n\n if concept not in concepts:\n args.log.warning(concept)\n else:\n args.writer.add_forms_from_value(\n Language_ID=languages[language],\n Parameter_ID=concepts[concept],\n Value=value,\n Source=[\"Marrison1967\"],\n )","def get_exp_value_from_spin_spin_corr(norm_correlators,norm_correlators_u):\r\n\r\n\r\n exp_vals = range(len(norm_correlators))\r\n exp_vals_u = range(len(norm_correlators))\r\n\r\n for i in range(len(norm_correlators)):\r\n exp_vals[i] = 1-2*(norm_correlators[i][1]+norm_correlators[i][2])\r\n exp_vals_u[i] = 2*np.sqrt((norm_correlators_u[i][1])**2+(norm_correlators_u[i][2])**2)\r\n\r\n return exp_vals,exp_vals_u","async def fetch_referrals(self):\n data = await self.http.get_referral_statistics()","def _corr_ax1(input_image):\n dim = input_image.shape[1]\n m_ones = np.ones(dim)\n norm_mask = np.correlate(m_ones, m_ones, mode=\"full\")\n # not sure that the /2 is the correct correction\n est_by_row = [np.argmax(np.correlate(v, v[::-1], mode=\"full\") / norm_mask) / 2 for v in input_image]\n return np.histogram(est_by_row, bins=np.arange(0, dim + 1))","def read(self, file_name, *, file_type=None):\n self.logger.info('Reading KG correlations from %s',file_name)\n with make_reader(file_name, file_type, self.logger) as reader:\n self._read(reader)","def _analyze_formcoords(self):\n # ONLY FOR EXTERNAL GEARS SO FAR !!!\n\n # transform formcoords to NumPy-array\n point = None\n half_tooth = self.formcoords\n\n # convert to polar coordinates\n half_tooth_polar = np.zeros([np.size(half_tooth, 0) - 1, 2])\n for index in range(0, np.size(half_tooth, 0) - 1):\n [r, phi] = CartesianCoordinatesToPolarCoordinates(half_tooth[index + 1, 0], half_tooth[index + 1, 1])\n half_tooth_polar[index, 0] = r\n half_tooth_polar[index, 1] = phi\n d_f = 2 * min(half_tooth_polar[:, 0]) # minimum radius --> root circle\n d_a = 2 * max(half_tooth_polar[:, 0]) # maximum radius --> tip circle\n tau = 2 * (max(half_tooth_polar[:, 1]) - min(half_tooth_polar[:, 1])) # pitch angle [radians]\n z = int(round(2 * pi / tau)) # number of teeth from pitch angle\n\n # for finding form diameters, it is checked if the points are part of the flank involute\n # the limiting points of the flank involute define the form diameters\n if 'alpha_n' in self.data and 'alpha_t' in self.data and 'x_E' in self.data:\n tol = self._tol_default * self.data.get('m_n') # tolerance for comparisons\n point_on_flank = False\n first_limit_diameter = None\n second_limit_diameter = None\n for point in range(0, np.size(half_tooth_polar, 0)):\n [x, y] = self._tooth_thickness(2 * half_tooth_polar[point, 0])\n if abs(x + 2 * half_tooth[point + 1, 0]) < tol and abs(y - 2 * half_tooth[point + 1, 1]) < tol:\n if not point_on_flank:\n first_limit_diameter = 2 * half_tooth_polar[point, 0]\n point_on_flank = True\n else:\n if point_on_flank:\n second_limit_diameter = 2 * half_tooth_polar[point, 0]\n point_on_flank = False\n if second_limit_diameter is None:\n second_limit_diameter = 2 * half_tooth_polar[point, 0]\n if first_limit_diameter == second_limit_diameter:\n raise ValueError('tooth form coordinate analysis failed')\n if first_limit_diameter > second_limit_diameter:\n d_fa = first_limit_diameter\n d_ff = second_limit_diameter\n else:\n d_fa = second_limit_diameter\n d_ff = first_limit_diameter\n if 'd_ff' in self.data: # use user-parameter if supplied\n d_ff = self.data.get('d_ff')\n if 'd_fa' in self.data:\n d_ff = self.data.get('d_fa')\n return {'d_f': d_f, 'd_a': d_a, 'd_ff': d_ff, 'd_fa': d_fa, 'z': z}\n\n else:\n return {'d_f': d_f, 'd_a': d_a, 'z': z}","def compute_couplings(corr_mat=None):\n couplings = np.linalg.inv(corr_mat)\n couplings = -1.0 * couplings\n return couplings","def parseBancorStatments(self, fname, mode=2, idx=[[15,76,137,198,259], [45,106,167,228,262]]):\n print fname\n res = open(fname, 'r').read().split('\\n')\n if mode == 4:\n for i in res:\n print i\n idx = p.DataFrame(idx)\n p0 = p.DataFrame()\n if mode == 1:\n print idx.transpose().get_values()\n print n.diff(idx)\n print idx[0]\n for [i, j] in enumerate(res):\n print \"{0} {1}\".format(i,j)\n for i in idx:\n p0 = p0.combine_first(p.DataFrame(res).ix[idx[i][0]:idx[i][1],:])\n ms = []\n for i in list(p0.get_values()):\n if mode == 4:\n print i[0]\n try:\n #m = re.match(re.compile(r'([\\d\\/]+)[\\s]+([\\w\\s\\.\\%\\d]+?)[\\s]+([\\d\\,^%]+)(.*)'), i[0]).groups()\n #m = re.match(re.compile(r'([\\d\\/]+)[\\s]+(.+?)[\\s]+([\\.\\d\\,^\\%]+)(.*)'), i[0]).groups()\n #m = re.match(re.compile(r'([\\d\\/]+)[\\s]+(.+)[\\s]+([\\.\\d\\,^\\%]+)[\\s]+([\\d\\,]+)'), i[0]).groups()\n m = re.match(re.compile(r'([\\d\\/]+)[\\s]+(.+)[\\s]([\\.\\d\\,^\\%]+)[\\s]([\\d\\,]+)'), i[0]).groups()\n if mode == 3:\n #print i[0]\n print m\n ms.append(m)\n except AttributeError, e:\n if mode == 1:\n print i[0]\n #print e\n ''\n ms = p.DataFrame(ms, columns=['Fecha', 'Concepto/Empresa', 'Debito/Credito', 'Saldo'])\n for i in range(len(ms.ix[:,2])):\n ms.ix[i,0] = self.cleanBancorDate(ms.ix[i,0], self.getBancorYear(fname))\n ms.ix[i,2] = self.cleanBancorNumber(ms.ix[i,2])\n ms.ix[i,3] = self.cleanBancorNumber(ms.ix[i,3])\n #print ms\n pres = p.DataFrame(res).ix[:,:]\n ms = ms.set_index('Fecha')\n #ms.ix[:,'Saldo'].plot(); show();\n #print ms\n return ms\n print '==========================================================================================='\n print '==========================================================================================='","def find_facades_ransac(pcd, num_facades, dist_thresh):\r\n groups = []\r\n remaining_pcd = pcd\r\n for i in range(num_facades):\r\n print(\"iteration:\", i + 1)\r\n \r\n equation, inliers = remaining_pcd.segment_plane(distance_threshold=dist_thresh, ransac_n=3, num_iterations=1000)\r\n [a, b, c, d] = equation\r\n print(f\"Plane equation facade {i + 1:.2f}: {a:.2f}x + {b:.2f}y + {c:.2f}z + {d:.2f} = 0\")\r\n groups.append([equation, inliers])\r\n remaining_pcd = remaining_pcd.select_by_index(inliers, invert=True)\r\n\r\n return groups","def load_all_corr():\n cache_path = SAVE_ROOT / 'corr' / 'all' / 'cache.pkl'\n if cache_path.is_file():\n return pickle.load(cache_path.open('rb'))\n\n root = SAVE_ROOT / 'corr'\n files = root.rglob('*.json')\n\n def path_to_items(path: Path):\n key = path.parts[-4:-1]\n return key, load_corr_matrix(*key)\n\n # Create the data afresh.\n all_corr = dict(map(path_to_items, files))\n make_parent_dirs(cache_path)\n pickle.dump(all_corr, cache_path.open('wb'))\n return all_corr","def analysis(field: str, key_column: str, relative_count: bool) -> None:\n key_count_dict = get_cached_count_dicts(field, key_column)\n words = get_all_words(key_count_dict)\n results = []\n for word in tqdm(words):\n if key_column == 'publication':\n corr, p_value = get_publication_correlation(key_count_dict, word, relative_count)\n elif key_column == 'year':\n corr, p_value = get_year_correlation(key_count_dict, word, relative_count)\n else:\n raise ValueError(f'Wrong key_column: {key_column}')\n if not math.isnan(corr) and not math.isnan(p_value):\n results.append((word, corr, p_value))\n results.sort(key=lambda e: e[1])\n pprint(results[:10])\n pprint(results[-10:])","def _calc_corrections(self): \n searchIter= self._niter-1\n while searchIter > 0:\n trySavefilename= self._createSavefilename(searchIter)\n if os.path.exists(trySavefilename):\n trySavefile= open(trySavefilename,'rb')\n corrections= sc.array(pickle.load(trySavefile))\n trySavefile.close()\n break\n else:\n searchIter-= 1\n if searchIter == 0:\n corrections= sc.ones((self._npoints,2))\n for ii in range(searchIter,self._niter):\n if ii == 0:\n currentDF= self._dftype(surfaceSigma=self._surfaceSigmaProfile,\n beta=self._beta)\n else:\n currentDF= self._dftype(surfaceSigma=self._surfaceSigmaProfile,\n beta=self._beta,\n corrections=corrections,\n npoints=self._npoints,\n rmax=self._rmax,\n savedir=self._savedir,\n interp_k=self._interp_k)\n newcorrections= sc.zeros((self._npoints,2))\n for jj in range(self._npoints):\n thisSurface= currentDF.surfacemass(self._rs[jj],\n use_physical=False)\n newcorrections[jj,0]= currentDF.targetSurfacemass(self._rs[jj],use_physical=False)/thisSurface\n newcorrections[jj,1]= currentDF.targetSigma2(self._rs[jj],use_physical=False)*thisSurface\\\n /currentDF.sigma2surfacemass(self._rs[jj],\n use_physical=False)\n #print(jj, newcorrections[jj,:])\n corrections*= newcorrections\n #Save\n picklethis= []\n for arr in list(corrections):\n picklethis.append([float(a) for a in arr])\n save_pickles(self._savefilename,picklethis) #We pickle a list for platform-independence)\n return corrections","def get_correlation(df):\n frame_correlation = df.corr()\n return frame_correlation","def get_all_relaxed_candidates_after_generation(self, gen):\n q = 'relaxed=1,extinct=0,generation<={0}'\n entries = self.c.select(q.format(gen))\n\n trajs = []\n for v in entries:\n t = self.get_atoms(id=v.id)\n t.info['confid'] = v.gaid\n t.info['relax_id'] = v.id\n trajs.append(t)\n trajs.sort(key=lambda x: get_raw_score(x),\n reverse=True)\n return trajs","def reference(facet_index, filename, cation, end_radius, start_radius=4.0,\n nradii=10, verbose=False):\n if verbose:\n print(\"Loading structure file \" + filename + \"...\")\n\n try:\n # Load the structure file into a Cage object\n anion = OccupiedCage.from_file(filename)\n except ValueError:\n # If the conventional formats fail, try using the from_poscar method.\n anion = Cage.from_poscar(filename)\n\n if verbose:\n print(\"Setting up surface facets...\")\n\n anion.center()\n anion.find_surface_facets(ignore=IGNORE)\n\n if verbose:\n print(\"Found \" + str(len(anion.facets)) + \" facets.\")\n\n reference_dir = 'reference_fa' + str(facet_index)\n try:\n os.mkdir(reference_dir)\n except FileExistsError:\n pass\n\n if verbose:\n print(\"Setting up reference calculations...\")\n\n reference_facet = anion.facets[facet_index]\n\n # Write out a facet json file\n reference_facet.to(fmt='json', filename=os.path.join(reference_dir,\n 'facet.json'))\n\n for radius in np.linspace(start_radius, end_radius, nradii):\n\n if verbose:\n print(\"Adding cation to reference facet at distance = \"\n + str(radius) + \" Angstroms.\")\n\n # Set up the cation site\n mol = anion.copy()\n mol.append(pmg.Specie(cation, 1),\n unit_vector(reference_facet.center) * radius)\n\n mol.set_charge_and_spin(charge=anion.charge + 1)\n\n if verbose:\n print(\"Setting up the task...\")\n\n # Set up the task for the calculation\n tasks = [nwchem.NwTask(mol.charge, None, BASIS,\n theory='dft',\n operation=\"energy\",\n theory_directives=THEORY_SETUP,\n alternate_directives=ALT_SETUP)]\n\n if verbose:\n print(\"Setting up the input file...\")\n\n # Set up input\n nw_input = nwchem.NwInput(mol, tasks, geometry_options=GEO_SETUP)\n\n calculation_dir = os.path.join(reference_dir, \"radius=\"\n + '{0:.2f}'.format(radius))\n\n try:\n os.mkdir(os.path.join(calculation_dir))\n except FileExistsError:\n pass\n\n nw_input.write_file(os.path.join(calculation_dir, 'input'))\n\n # Write a xyz file of the molecule with the reference cation\n mol.to(fmt='xyz', filename=os.path.join(calculation_dir,\n 'geometry.xyz'))","def get_all_translations(rna_sequence, genetic_code):\n #Convert all rna_sequence to upper case:\n rna_sequence=rna_sequence.upper()\n #get the lengh of RNA seq.\n total_rna_bases=len(rna_sequence)\n #Create an empty list to store all possible AA seq.\n polypeptide_list = []\n #Looping through all the RNA bases, selecting all 3 possible reading frames to scan for tranlation.\n for i in range(total_rna_bases):\n i_end= i +3\n next_three=rna_sequence[i:i_end]\n #Condition to check if the condon is start codon\n if next_three=='AUG':\n #If condition satisfies, translate all rna seq from start to stop codon using first function,\n #translate_sequence\n polypeptide=translate_sequence(rna_sequence[i:], genetic_code)\n polypeptide_list.append(polypeptide)\n #Return all 3 possible reading frames as a list in polypeptide_list\n return polypeptide_list","def correlations(self, data,label, **kwds):\n corrmat = data.corr(**kwds)\n\n fig, ax1 = plt.subplots(ncols=1, figsize=(6,5))\n\n opts = {'cmap': plt.get_cmap(\"RdBu\"),\n 'vmin': -1, 'vmax': +1}\n heatmap1 = ax1.pcolor(corrmat, **opts)\n plt.colorbar( heatmap1, ax=ax1 )\n\n ax1.set_title( \"Correlations\" )\n\n labels = corrmat.columns.values\n for ax in (ax1,):\n # shift location of ticks to center of the bins\n ax.set_xticks(np.arange(len(labels))+0.5, minor=False)\n ax.set_yticks(np.arange(len(labels))+0.5, minor=False)\n ax.set_xticklabels(labels, minor=False, ha='right', rotation=70)\n ax.set_yticklabels(labels, minor=False)\n plt.tight_layout()\n plt.savefig('plots/correlation_' + self.version + '_' + label + '.pdf')","def _transcode_crf(binary: Path, params: str, crf: float) -> Dict[str, float]:\n with tqdm(FILES, leave=False, desc=f'Try CRF={crf}') as paths:\n scores = [_transcode_crf_one(binary, params, crf, p) for p in paths]\n return {k: hmean([score[k] for score in scores]) for k in COLUMNS}","def get_correlation(self, f, peaks):\n c = 0\n for s in peaks:\n c += e ** -((2 * (f - s[0]) / self.spacing) ** 2)\n \n return c","def resultat(self, concordance_mf, concordance_pf, liste_F, liste_M, liste_P):\n resultat = {\"Marqueur\": [], \"Conclusion\": [], \"Concordance Mere/Foetus\": [], \"Détails M/F\": [],\n \"Concordance Pere/Foetus\": [], \"Détails P/F\": []}\n marqueurs_conta = 0\n marqueurs_non_conta = 0\n somme_conta = 0\n if liste_F[0].allele[1] == 0.0:\n self.set_sexe(\"F\")\n else:\n self.set_sexe(\"M\")\n if concordance_mf != 16 and concordance_pf != 16 and concordance_pf != None:\n self.set_concordance_mere_foet(\"NON\")\n self.set_concordance_pere_foet(\"NON\")\n del resultat[\"Conclusion\"]\n for nbres in range(1, len(liste_F)):\n resultat[\"Marqueur\"].append(str(liste_F[nbres].marqueur))\n resultat[\"Concordance Mere/Foetus\"].append(liste_F[nbres].concordance_mere_foetus)\n resultat[\"Concordance Pere/Foetus\"].append(liste_P[nbres].concordance_pere_foetus)\n if liste_F[nbres].concordance_mere_foetus == \"NON\" and liste_P[nbres].concordance_pere_foetus == \"NON\":\n resultat[\"Détails M/F\"].append(\n \"M : \" + str(liste_M[nbres].normalisation(liste_M[nbres].allele)) + \" F: \" + str(\n liste_F[nbres].normalisation(liste_F[nbres].allele)))\n resultat[\"Détails P/F\"].append(\n \"P : \" + str(liste_P[nbres].normalisation(liste_P[nbres].allele)) + \" F : \" + str(\n liste_F[nbres].normalisation(liste_F[nbres].allele)))\n elif liste_F[nbres].concordance_mere_foetus == \"NON\":\n resultat[\"Détails M/F\"].append(\n \"M: \" + str(liste_M[nbres].normalisation(liste_M[nbres].allele)) + \" F : \" + str(\n liste_F[nbres].normalisation(liste_F[nbres].allele)))\n resultat[\"Détails P/F\"].append(\"\")\n elif liste_P[nbres].concordance_pere_foetus == \"NON\":\n resultat[\"Détails P/F\"].append(\n \"P: \" + str(liste_P[nbres].normalisation(liste_P[nbres].allele)) + \" F: \" + str(\n liste_F[nbres].normalisation(liste_F[nbres].allele)))\n resultat[\"Détails M/F\"].append(\"\")\n else:\n resultat[\"Détails M/F\"].append(\"\")\n resultat[\"Détails P/F\"].append(\"\")\n conclusion = pd.DataFrame({\"1\": [\"Non calculé\", \"Non calculé\", \"Non calculé\", self.get_date()]},\n index=[\"Nombre de marqueurs informatifs non contaminés\",\n \"Nombre de marqueurs informatifs contaminés\",\n \"Moyenne du pourcentage de contamination\", \"Date\"])\n resultats = pd.DataFrame(resultat, columns=[\"Marqueur\", \"Concordance Mere/Foetus\", \"Détails M/F\",\n \"Concordance Pere/Foetus\", \"Détails P/F\"])\n return resultats, conclusion\n elif concordance_mf != len(liste_F) and concordance_pf == len(liste_F) or concordance_mf != len(\n liste_F) and concordance_pf == None:\n self.set_concordance_mere_foet(\"NON\")\n self.set_concordance_pere_foet(\"OUI\")\n if concordance_pf == None:\n self.set_concordance_pere_foet(\"ABS\")\n del resultat[\"Conclusion\"]\n del resultat[\"Concordance Pere/Foetus\"]\n del resultat[\"Détails P/F\"]\n for nbres in range(1, len(liste_F)):\n resultat[\"Marqueur\"].append(str(liste_F[nbres].marqueur))\n resultat[\"Concordance Mere/Foetus\"].append(liste_F[nbres].concordance_mere_foetus)\n if liste_F[nbres].concordance_mere_foetus == \"NON\":\n resultat[\"Détails M/F\"].append(\n \"M: \" + str(liste_M[nbres].normalisation(liste_M[nbres].allele)) + \" F: \" + str(\n liste_F[nbres].normalisation(liste_F[nbres].allele)))\n else:\n resultat[\"Détails M/F\"].append(\"\")\n conclusion = pd.DataFrame({\"1\": [\"Non calculé\", \"Non calculé\", \"Non calculé\", self.get_date()]},\n index=[\"Nombre de marqueurs informatifs non contaminés\",\n \"Nombre de marqueurs informatifs contaminés\",\n \"Moyenne du pourcentage de contamination\", \"Date\"])\n resultats = pd.DataFrame(resultat, columns=[\"Marqueur\", \"Concordance Mere/Foetus\", \"Détails M/F\"])\n return resultats, conclusion\n elif concordance_mf == len(liste_F) and concordance_pf == len(liste_F) or concordance_mf == len(\n liste_F) and concordance_pf == None:\n self.set_concordance_mere_foet(\"OUI\")\n self.set_concordance_pere_foet(\"OUI\")\n if concordance_pf == None:\n self.set_concordance_pere_foet(\"ABS\")\n del resultat[\"Concordance Mere/Foetus\"]\n del resultat[\"Concordance Pere/Foetus\"]\n del resultat[\"Détails P/F\"]\n for nbres in range(1, len(liste_F)):\n resultat[\"Marqueur\"].append(str(liste_F[nbres].marqueur))\n if liste_F[nbres].informatif == 0:\n resultat[\"Conclusion\"].append(\"Non informatif\")\n resultat[\"Détails M/F\"].append(\"Mère homozygote\")\n elif liste_F[nbres].informatif == 1:\n if liste_F[nbres].contamination == 0:\n marqueurs_non_conta += 1\n resultat[\"Conclusion\"].append(\"Non contaminé\")\n resultat[\"Détails M/F\"].append(\"\")\n elif liste_F[nbres].contamination == 1:\n marqueurs_conta += 1\n somme_conta = somme_conta + liste_F[nbres].taux\n resultat[\"Conclusion\"].append(\"Contaminé\")\n resultat[\"Détails M/F\"].append(\"Taux contamination : \" + str(liste_F[nbres].taux) + \"%\")\n else:\n marqueurs_conta += 1\n somme_conta = somme_conta + liste_F[nbres].taux\n resultat[\"Conclusion\"].append(\"Contaminé\")\n resultat[\"Détails M/F\"].append(\"Taux contamination : \" + str(liste_F[nbres].taux) + \"%\")\n elif liste_F[nbres].informatif == 2:\n resultat[\"Conclusion\"].append(\"Non informatif\")\n resultat[\"Détails M/F\"].append(\"Allèles semblables\")\n else:\n resultat[\"Conclusion\"].append(\"Non informatif\")\n resultat[\"Détails M/F\"].append(\"Echo\")\n resultats = pd.DataFrame(resultat, columns=[\"Marqueur\", \"Conclusion\", \"Détails M/F\"])\n try:\n moyenne_conta = somme_conta / marqueurs_conta\n except ZeroDivisionError:\n moyenne_conta = 0\n conclusion = pd.DataFrame(\n {\"1\": [int(marqueurs_non_conta), int(marqueurs_conta), round(moyenne_conta, 2), self.get_date()]},\n index=[\"Nombre de marqueurs informatifs non contaminés\", \"Nombre de marqueurs informatifs contaminés\",\n \"Moyenne du pourcentage de contamination\", \"Date\"])\n return resultats, conclusion\n elif concordance_mf == len(liste_F) and concordance_pf != len(liste_F):\n self.set_concordance_mere_foet(\"OUI\")\n self.set_concordance_pere_foet(\"NON\")\n del resultat[\"Concordance Mere/Foetus\"]\n for nbres in range(1, len(liste_F)):\n resultat[\"Concordance Pere/Foetus\"].append(liste_P[nbres].concordance_pere_foetus)\n if liste_P[nbres].concordance_pere_foetus == \"NON\":\n resultat[\"Détails P/F\"].append(\n \"P: \" + str(liste_P[nbres].normalisation(liste_P[nbres].allele)) + \" F: \" + str(liste_P[nbres].normalisation(liste_P[nbres].allele)))\n else:\n resultat[\"Détails P/F\"].append(\"\")\n for nbres in range(1, len(liste_F)):\n resultat[\"Marqueur\"].append(str(liste_F[nbres].marqueur))\n if liste_F[nbres].informatif == 0:\n resultat[\"Conclusion\"].append(\"Non informatif\")\n resultat[\"Détails M/F\"].append(\"Mère homozygote\")\n elif liste_F[nbres].informatif == 1:\n if liste_F[nbres].contamination == 0:\n marqueurs_non_conta += 1\n resultat[\"Conclusion\"].append(\"Non contaminé\")\n resultat[\"Détails M/F\"].append(\"\")\n elif liste_F[nbres].contamination == 1:\n marqueurs_conta += 1\n somme_conta = somme_conta + liste_F[nbres].taux\n resultat[\"Conclusion\"].append(\"Contaminé\")\n resultat[\"Détails M/F\"].append(\"Taux contamination : \" + str(liste_F[nbres].taux) + \"%\")\n else:\n marqueurs_conta += 1\n somme_conta = somme_conta + liste_F[nbres].taux\n resultat[\"Conclusion\"].append(\"Contaminé\")\n resultat[\"Détails M/F\"].append(\"Taux contamination : \" + str(liste_F[nbres].taux) + \"%\")\n elif liste_F[nbres].informatif == 2:\n resultat[\"Conclusion\"].append(\"Non informatif\")\n resultat[\"Détails M/F\"].append(\"Allèles semblables\")\n else:\n resultat[\"Conclusion\"].append(\"Non informatif\")\n resultat[\"Détails M/F\"].append(\"Echo\")\n resultats = pd.DataFrame(resultat,\n columns=[\"Marqueur\", \"Conclusion\", \"Détails M/F\", \"Concordance Pere/Foetus\",\n \"Détails P/F\"])\n try:\n moyenne_conta = somme_conta / marqueurs_conta\n except ZeroDivisionError:\n moyenne_conta = 0\n conclusion = pd.DataFrame(\n {\"1\": [int(marqueurs_non_conta), int(marqueurs_conta), round(moyenne_conta, 2), self.get_date()]},\n index=[\"Nombre de marqueurs informatifs non contaminés\", \"Nombre de marqueurs informatifs contaminés\",\n \"Moyenne du pourcentage de contamination\", \"Date\"])\n return resultats, conclusion","def get_forms(console: Console, sess: requests.Session, form_id: str = \"General_Record_2020v2.0\"):\n raw_resp = get_url(url=f\"https://forms.agterra.com/api/{form_id}/GetAll/0\", sess=sess)\n\n if raw_resp.status_code != 200:\n console.log(f\"[red] Something went wrong, we got status [white]{raw_resp.status_code}\")\n json_data = raw_resp.json()\n console.log(f\"Message Data: {json_data}\")\n\n json_data = raw_resp.json()\n\n return json_data","def full_colexification(forms):\n # We assume the forms to be sorted by clics_form already:\n expected, seen = len(forms), 0\n for _, _forms in itertools.groupby(forms, lambda f: f.clics_form):\n cids = set()\n fs = []\n for f in _forms:\n seen += 1\n if f.concepticon_id not in cids:\n fs.append(f)\n cids.add(f.concepticon_id)\n yield fs\n if expected != seen: # pragma: no cover\n raise ValueError('forms not properly ordered')","def estimate_F_ransac(corr, num_iter, inlier_thresh):\n _, inlier_mask = ransac(corr, estimate_F, sym_epipolar_dist, 8, num_iter, inlier_thresh)\n # inlier_mask = np.ones(9)\n # inlier_mask[0] = 0\n F = estimate_F(corr[inlier_mask.astype(np.bool)])\n return F","def _set_correlations(self) -> None:\n pass","def add_restypes(df, compl_data, recept_info, recept_info_order):\n AAs = {'C': 'CYS', 'D': 'ASP', 'S': 'SER', 'Q': 'GLN', 'K': 'LYS',\n 'I': 'ILE', 'P': 'PRO', 'T': 'THR', 'F': 'PHE', 'N': 'ASN', \n 'G': 'GLY', 'H': 'HIS', 'L': 'LEU', 'R': 'ARG', 'W': 'TRP', \n 'A': 'ALA', 'V': 'VAL', 'E': 'GLU', 'Y': 'TYR', 'M': 'MET'}\n GPCRclass_numbers = {'A':1, 'B':2, 'C':3, 'F':4}\n class_index = recept_info_order['gpcr_class']\n\n def get_restype_and_realpos(dynid_col, pos_col):\n \"\"\"\n Return residue type of position and the position identifier for this position in this protein\n It's a bit complex, for I need to distinguish which GPCR class nomenclature uses this protein\n \"\"\"\n restype_list = []\n prot_pos = []\n for dynid,pos in zip(dynid_col, pos_col):\n if pos == 'Ligand':\n restype_list.append('')\n prot_pos.append('Ligand')\n else: \n GPCRclass_number = GPCRclass_numbers[recept_info[dynid][class_index]] \n class_pos_array = pos.split('\\n')\n class_pos = class_pos_array[0]+class_pos_array[GPCRclass_number]\n\n if class_pos in compl_data[dynid]['gpcr_pdb']: \n restype = compl_data[dynid]['gpcr_pdb'][class_pos][-1]\n restype_list.append(restype)\n prot_pos.append(class_pos)\n else:\n #print(\"Position %s not found in %s\" %(class_pos, dynid)) #Too much output\n restype_list.append(\"(N/A)\")\n prot_pos.append(class_pos)\n\n return(restype_list,prot_pos)\n\n #Split Position column\n new = df['Position'].str.split(\"\\n\\n\", n = 1, expand = True)\n df['Position 1'] = new[0]\n df['Position 2'] = new[1]\n\n #Add residue type and protein position column\n (restype_1, protein_Position_1) = get_restype_and_realpos(df['Id'], df['Position 1'].values)\n df['restype_1'] = restype_1\n df['protein_Position 1'] = protein_Position_1\n (restype_2, protein_Position_2) = get_restype_and_realpos(df['Id'], df['Position 2'].values)\n df['restype_2'] = restype_2\n df['protein_Position 2'] = protein_Position_2\n \n df['restypes'] = df['restype_1'] +\" \"+ df['restype_2'] \n df['protein_Position'] = df['protein_Position 1'] +\" \"+ df['protein_Position 2']\n df['restypePosition'] = df['restype_1'] + df['protein_Position 1'] + \" \" + df['restype_2'] + df['protein_Position 2']\n \n #Drop Position, proteinPosition and restype columns once they are not needed\n df.drop(columns = ['Position 1','Position 2','restype_1','restype_2','protein_Position 1','protein_Position 2'], inplace = True)\n \n return df","def read_corr(file):\n corrTable = open(file, 'r').read()\n corrTable = corrTable.split('\\n')\n for i in range(4):\n corrTable[i] = corrTable[i].split(':')\n corrTable[i][1] = corrTable[i][1].split(',')\n corr_dict = {}\n for i in range(4):\n corr_dict[corrTable[i][0]] = corrTable[i][1]\n return corr_dict","def get_kpi_only_france(name, column, mean, transformDF=False):\n indicateurResult = get_empty_kpi()\n config = get_config(name)\n log.debug('Processing - '+name)\n\n indicateurResult['nom'] = config['nom']\n indicateurResult['unite'] = config['unite']\n indicateurResult['unite_short'] = config['unite_short']\n indicateurResult['trendType'] = config['trendType']\n indicateurResult['color'] = get_color_indicateur(config)\n\n df = pd.read_csv(\n 'files_new/'+config['res_id'],\n sep=None,\n engine='python',\n dtype={'reg': str, 'dep': str}\n )\n \n\n df = enrich_dataframe(df, name)\n df = df[['date', column]]\n for country in tqdm(countries, desc=\"Processing National\"):\n res = get_kpi_by_type(\n df.groupby(['date'], as_index=False).sum(),\n 'nat',\n 'fra',\n config['trendType'],\n column,\n mean\n )\n indicateurResult['france'].append(res)\n\n save_result(indicateurResult, name)","def comp_ind_finder(self, n, minfreq, maxfreq, lat1, lat0, abs = False, norm = True,\\\n pole = \"north\"):\n \n assert self.shifted, \"Data has not been index-shifted\"\n\n inds = self.mlat_finder(lat1, lat0, pole)[1]\n self.inds = inds\n NeA = self.NeA[inds]\n NeB = self.NeB[inds]\n NeC = self.NeC[inds]\n secondsA = self.secondsA[inds]\n secondsB = self.secondsB[inds]\n secondsC = self.secondsC[inds]\n\n timesA, fftA = self.fft_time_holes_integral(signal = NeA,\\\n seconds = secondsA, n = n, fs = self.fs, minfreq\\\n = minfreq, maxfreq = maxfreq)\n\n timesB, fftB = self.fft_time_holes_integral(signal = NeB,\\\n seconds = secondsB, n = n, fs = self.fs, minfreq\\\n = minfreq, maxfreq = maxfreq)\n\n timesC, fftC = self.fft_time_holes_integral(signal = NeC,\\\n seconds = secondsC, n = n, fs = self.fs, minfreq\\\n = minfreq, maxfreq = maxfreq)\n\n\n BAdiff = self.relative_diff(fftB, fftA, norm = norm, abs = abs)\n BCdiff = self.relative_diff(fftB, fftC, norm = norm, abs = abs)\n ACdiff = self.relative_diff(fftA, fftC, norm = norm, abs = abs)\n \n return(BAdiff, BCdiff, ACdiff)","def linearize_spatial_corr(Mcorr):\n assert type(Mcorr) is numpy.ndarray, 'bad type for matrix argument'\n assert Mcorr.shape == (199,199), 'invalid shape for autocorrelation matrix'\n \n # Scan the correlogram and compute the radius from the midpoint\n n = numpy.zeros(101, 'h')\n c = numpy.zeros(101, 'd')\n mid_x, mid_y = 99.5, 99.5\n for i in xrange(199):\n for j in xrange(199):\n d = numpy.sqrt((mid_y - i)**2 + (mid_x - j)**2)\n if d > 100:\n d = 100\n n[int(d)] += 1\n c[int(d)] += Mcorr[i, j]\n c /= n # get the sample means\n \n # Create the return array: reflect 0->Max correlations to -Max->0 \n Lcorr = numpy.zeros((2,201), 'd')\n Lcorr[0] = numpy.arange(-100, 101)\n Lcorr[1] = numpy.r_[c[::-1], c[1:]]\n \n return Lcorr","def refere(eeg, channels, mode='contralateral'):\n\tbipolar_map = {'Fp1':'Fp2', 'Fp2':'Fp2', 'F3':'F4', 'F4':'F4', 'C3':'C4', 'C4':'C4', 'T3':'T4', 'T4':'T4', 'P3':'P4', 'P4':'P4', 'O1':'O2', 'O2':'O2'}\n\tif mode not in ['monopolar', 'contralateral', 'bipolar', 'linked', 'average']:\n\t\tprint 'WARNING - refere(): parameter \"mode\" can only be \"monopolar\", \"contralateral\", \"bipolar\" or \"linked\". Using \"contralateral\"!'\n\t\tmode = 'contralateral'\n\tif mode == 'linked':\t\t\n\t\treference = (eeg[:,channels.index('A1')] + eeg[:,channels.index('A2')])/2.\n\tif mode == 'average':\n\t\treference = np.zeros(len(eeg), dtype=np.float32)\n\t\tchcounter = 0\n\t\tfor channel in range(len(channels)):\n\t\t\tif (channels[channel] in EEG_CHANNELS):\n\t\t\t\treference += eeg[:, channel]\n\t\t\t\tchcounter += 1\n\t\treference /= chcounter\n\tfor channel in range(len(channels)):\n\t\tif (channels[channel] in EEG_CHANNELS):\n\t\t\t# mindenkit referalunk kiveve magukat a referencia csatornakat\n\t\t\tif mode == 'contralateral':\n\t\t\t\tif (channels[channel] in ['Fp2', 'F4', 'C4', 'T4', 'P4', 'O2']):\n\t\t\t\t\tref_channel = channels.index('A1')\n\t\t\t\telif (channels[channel] in ['Fp1', 'F3', 'C3', 'T3', 'P3', 'O1']):\n\t\t\t\t\tref_channel = channels.index('A2')\n\t\t\t\telse:\n\t\t\t\t\tprint \"Error: what kind of channel is this: \", channels[channel], \" cannot reference!!!!\"\n\t\t\t\treference = eeg[:, ref_channel]\n\t\t\t\tprint \"channel \", channels[channel], \" referenced to \", channels[ref_channel]\n\t\t\tif mode == 'bipolar':\n\t\t\t\tref_channel = channels.index(bipolar_map[channels[channel]])\n\t\t\t\treference = eeg[:, ref_channel]\n\t\t\t\tprint \"channel \", channels[channel], \" referenced to \", channels[ref_channel]\n\t\t\teeg[:, channel] -= reference","def highlight_correlated_feature_twoclass(\n file_samples=100,\n match_types_in=match_types, figsize=(12, 14),\n process_data=None, pca_vec_to_plot=5, debug=1):\n corr_accum = None\n if process_data is not None:\n data_type = '(frqeuency)'\n else:\n data_type = '(time-domain)'\n for match_type in match_types_in:\n for aidx, is_alcoholic in enumerate([True, False]):\n corr_accum = cov_accum = None\n if debug:\n print(f'getting example data for match_type[{match_type}]'\n f' and is_alcoholic[{is_alcoholic}]')\n file_list = sample_file_list(\n limitby=dict(\n match=match_type,\n alcoholic=is_alcoholic),\n limit_mult_files=file_samples,\n balance_types=[('subject', 10)], df_type='wide',\n seed=42, debug=max(0, debug - 1))\n\n for file in tqdm.tqdm(file_list):\n df, info = my_read_eeg_generic(\n file, orig_tt_indic=('test' in str.lower(file)))\n\n corr = df.corr()\n cov = df.cov().values\n if corr_accum is None:\n sen_names = corr.columns.levels[\n corr.columns.names.index('sensor')]\n nsen = len(sen_names)\n if process_data:\n x, Z, xl, yl = process_data([], df.values, '',\n '', fs=SAMP_FREQ)\n df = pd.DataFrame(Z)\n corr = df.corr()\n cov = df.cov().values\n if corr_accum is None:\n corr_accum = corr.values\n cov_accum = cov/nsen\n else:\n corr_accum += corr.values\n cov_accum += cov/nsen\n\n corr_accum /= len(file_list)\n if aidx == 0:\n corr_alcoholic = corr_accum.copy()\n cov_alcoholic = cov_accum.copy()\n else:\n corr_nonalch = corr_accum.copy()\n cov_nonalch = cov_accum.copy()\n\n if debug > 1:\n pyplot.figure(figsize=figsize)\n pyplot.pcolor(np.flipud(corr_accum))\n pyplot.xticks(np.arange(nsen), sen_names)\n pyplot.yticks(np.arange(nsen), reversed(sen_names))\n pyplot.title(f'corr - across sensors {data_type} - '\n f'is_alcoholic[{is_alcoholic}] - match[{match_type}]')\n pyplot.colorbar()\n pyplot.show()\n\n Ua, svs_a, Va = np.linalg.svd(cov_alcoholic, full_matrices=False,\n compute_uv=True)\n Una, svs_na, Vna = np.linalg.svd(cov_nonalch, full_matrices=False,\n compute_uv=True)\n print('SVec size', Una.shape)\n\n pyplot.figure(figsize=(figsize[0], 6))\n leg = []\n lg, = pyplot.plot(svs_a, label='alcoholic')\n leg.append(lg)\n lg, = pyplot.plot(svs_na, label='not alcoholic')\n leg.append(lg)\n pyplot.legend(handles=leg)\n pyplot.title(f'PCA decomposition: SVs - across sensors {data_type} - '\n f'- match[{match_type}]')\n pyplot.show()\n\n pyplot.figure(figsize=(figsize[0]+4, 8))\n leg = []\n is_slice = isinstance(pca_vec_to_plot, tuple)\n if is_slice:\n pca_vec_to_plot_count = pca_vec_to_plot[1] - pca_vec_to_plot[0]\n else:\n pca_vec_to_plot_count = pca_vec_to_plot\n pca_vec_to_plot = (0, pca_vec_to_plot)\n pca_vec_to_plot_count = min(pca_vec_to_plot_count, nsen)\n for i, idx in enumerate(range(*pca_vec_to_plot)):\n lg, = pyplot.plot(-i + Ua[:, idx], '-',\n linewidth=2,\n label=f'feat[{idx}]: alcoholic')\n leg.append(lg)\n for i, idx in enumerate(range(*pca_vec_to_plot)):\n lg, = pyplot.plot(-i + Una[:, idx], '--',\n linewidth=2,\n label=f'feat[{idx}]: not alcoholic')\n leg.append(lg)\n\n pyplot.xticks(np.arange(nsen), sen_names, fontsize=7)\n pyplot.xlim((0, nsen + 10))\n pyplot.yticks(-np.arange(pca_vec_to_plot_count), [\n f'PCA_dim[{i}]' for i in np.arange(pca_vec_to_plot_count)])\n pyplot.legend(handles=leg)\n # pyplot.xticks(np.arange(nsen), sen_names)\n # pyplot.yticks(np.arange(nsen), reversed(sen_names))\n ind_str = (f\"{pca_vec_to_plot[0]} to {pca_vec_to_plot[1]-1}\" if is_slice\n else f\"- first {pca_vec_to_plot_count}\")\n pyplot.title(f'PCA decomposition: singular vectors {ind_str} '\n f'- across sensors {data_type} - '\n f'match[{match_type}]')\n\n pyplot.figure(figsize=figsize)\n pyplot.pcolor(np.flipud(corr_alcoholic - corr_nonalch))\n pyplot.xticks(np.arange(nsen), sen_names)\n pyplot.yticks(np.arange(nsen), reversed(sen_names))\n pyplot.title(f'corr - across sensors {data_type} - '\n f'(alcoholic-nonalcoholic) - match[{match_type}]')\n pyplot.colorbar()\n pyplot.show()\n if is_slice:\n return Ua[:, pca_vec_to_plot[0]:pca_vec_to_plot[1]], Una[:,\n pca_vec_to_plot[0]:pca_vec_to_plot[1]]\n else:\n return Ua[:, :pca_vec_to_plot_count], Una[:, :pca_vec_to_plot_count]","def read_IGRF13coeffs(file):\r\n\r\n # Standard Library dependencies\r\n import os\r\n\r\n # 3rd party dependencies\r\n import numpy as np\r\n import pandas as pd\r\n\r\n file = os.path.join(os.path.dirname(__file__), file)\r\n\r\n df = pd.read_csv(file, header=3, sep=\"\\s+\")\r\n Years = [x for x in df.columns if x[-2:] == \".0\"]\r\n v = []\r\n for x in df.groupby(\"g/h\"):\r\n v.append(x)\r\n g = v[0][1]\r\n h = v[1][1]\r\n dic_dic_g = {}\r\n dic_dic_h = {}\r\n dic_dic_SV_g = {}\r\n dic_dic_SV_h = {}\r\n dic_N = {}\r\n for Year in Years:\r\n key_Year = str(int(float(Year)))\r\n dic_dic_g[key_Year] = {(x[0], x[1]): x[2] for x in zip(g[\"m\"], g[\"n\"], g[Year])}\r\n dic_dic_h[key_Year] = {(x[0], x[1]): x[2] for x in zip(h[\"m\"], h[\"n\"], h[Year])}\r\n dic_dic_SV_g[key_Year] = {(x[0], x[1]): 0 for x in zip(g[\"m\"], g[\"n\"])}\r\n dic_dic_SV_h[key_Year] = {(x[0], x[1]): 0 for x in zip(g[\"m\"], g[\"n\"])}\r\n index = set([x[0] for x in dic_dic_h[key_Year].keys()])\r\n N = max(index)\r\n dic_N[key_Year] = N # must be 13\r\n for n in range(1, N + 1):\r\n dic_dic_h[key_Year][(0, n)] = 0\r\n dic_dic_SV_h[key_Year][(0, n)] = 0\r\n dic_dic_SV_h[\"2020\"] = {\r\n (x[0], x[1]): x[2] for x in zip(h[\"m\"], h[\"n\"], h[\"2020-25\"])\r\n }\r\n dic_dic_SV_g[\"2020\"] = {\r\n (x[0], x[1]): x[2] for x in zip(g[\"m\"], g[\"n\"], g[\"2020-25\"])\r\n }\r\n Years = np.array([float(x) for x in Years])\r\n return dic_dic_h, dic_dic_g, dic_dic_SV_h, dic_dic_SV_g, dic_N, Years","def cramers_v1(confusion_matrix):\n chi2 = ss.chi2_contingency(confusion_matrix)[0]\n n = confusion_matrix.sum()\n phi2 = chi2 / n\n r, k = confusion_matrix.shape\n phi2corr = max(0, phi2 - ((k-1)*(r-1))/(n-1))\n rcorr = r - ((r-1)**2)/(n-1)\n kcorr = k - ((k-1)**2)/(n-1)\n return np.sqrt(phi2corr / min((kcorr-1), (rcorr-1)))","def parse_pion(m1, m2, config):\n f = open(in_name(m1, m2, config), 'r')\n \n # Skip to the mixed pion correlator section.\n # This assumes it is the first PION entry.\n x = f.readline()\n while x:\n if re.match('correlator:\\s+PION', x):\n break\n x = f.readline()\n\n # Throw away header.\n print x\n for i in range(5):\n print f.readline().strip()\n\n result = []\n for i in range(64):\n t, r, im = f.readline().strip().split('\\t')\n result.append(complex(float(r), float(im))) \n \n f.close()\n\n return np.array(result)","def representatives_from_nrlist(res, mapped_to=None):\n \n # Read the NR file\n nr_code = min([i for i in [1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 20.0] if i >= res])\n fpath = f\"{path_to_3D_data}/latest_nr_list_{nr_code}A.csv\"\n repres = []\n df = pd.read_csv(os.path.abspath(fpath))\n\n # define a function to transform a code into a filename\n def query_mapping_to(structure, model, chain, family):\n if family is None:\n return structure + '_' + model + '_' + chain + \".cif\"\n\n # if we need a mapping start and end, query database\n with sqlite3.connect(runDir + \"/results/RNANet.db\") as conn:\n conn.execute('pragma journal_mode=wal')\n r = sql_ask_database(conn, f\"SELECT pdb_start, pdb_end FROM chain WHERE rfam_acc='{family}' AND structure_id='{structure}' AND chain_name='{chain}' AND issue=0;\")\n\n if not len(r):\n # there is no chain named like this and mapped to this family\n return None\n\n if len(r) > 1:\n warn(f\"Several entries found for structure {structure}-{chain} ({family}) : {len(r)} entries\")\n return structure + '_' + model + '_' + chain + '_' + str(r[0][0]) + '-' + str(r[0][1]) + \".cif\"\n\n # build the list \n for i in range(df.shape[0]):\n up_name = df[\"representative\"][i]\n if '+' in up_name:\n up_name = up_name.split('+')\n for i in range(len(up_name)):\n chain = up_name[i].split('|')\n reference = query_mapping_to(chain[0].lower(), chain[1], chain[2], mapped_to)\n if reference is not None:\n repres.append(reference)\n else :\n up_name = up_name.split('|')\n reference = query_mapping_to(up_name[0].lower(), up_name[1], up_name[2], mapped_to)\n if reference is not None:\n repres.append(reference)\n\n return repres","def get_lig_ref_df():\n filepath = os.path.abspath(os.path.join(__file__, \"..\", \"data\", \"ligtype_angle_reference.csv\"))\n ref_df = pd.read_csv(filepath)\n return ref_df","def compute_correspondances(source_p, source_reconstructed_p, target_p, target_reconstructed_p):\n # inputs are all filepaths\n with torch.no_grad():\n source = trimesh.load(source_p, process=False)\n source_reconstructed = trimesh.load(source_reconstructed_p, process=False)\n target = trimesh.load(target_p, process=False)\n target_reconstructed = trimesh.load(target_reconstructed_p, process=False)\n\n # project on source_reconstructed\n neigh.fit(source_reconstructed.vertices)\n idx_knn = neigh.kneighbors(source.vertices, return_distance=False)\n\n #correspondances throught template\n closest_points = target_reconstructed.vertices[idx_knn]\n closest_points = np.mean(closest_points, 1, keepdims=False)\n\n\n # project on target\n if global_variables.opt.project_on_target:\n print(\"projection on target...\")\n neigh.fit(target.vertices)\n idx_knn = neigh.kneighbors(closest_points, return_distance=False)\n closest_points = target.vertices[idx_knn]\n closest_points = np.mean(closest_points, 1, keepdims=False)\n\n # save output\n mesh = trimesh.Trimesh(vertices=closest_points, faces=source.faces, process=False)\n trimesh.io.export.export_mesh(mesh, \"results/correspondences.ply\")\n np.savetxt(\"results/correspondences.txt\", closest_points, fmt='%1.10f')\n return","def correlation(letter_list):\n counts = Counter(letter_list)\n text_freq = [counts[ch] / len(letter_list) for ch in ascii_lowercase]\n english_freq = [ENGLISH_DICT[ch] for ch in ascii_lowercase]\n return pearsonr(text_freq, english_freq)[0]","def calc_qcorr(self) -> Dict[int, float]:\n return self._calc_qcorr","def _read_born_charges(self, line):\n line = self.file_descriptor.readline()\n self.born_charges = []\n for i in range(self.nions):\n b = []\n line = self.file_descriptor.readline()\n b.append([float(f) for f in line.split()[2:5]])\n line = self.file_descriptor.readline()\n b.append([float(f) for f in line.split()[0:3]])\n line = self.file_descriptor.readline()\n b.append([float(f) for f in line.split()[0:3]])\n B = np.array(b)\n C = B.T\n self.born_charges.append(C.tolist())\n return","def plot_feature_correlations(self):\n\n fig = plt.figure(figsize=(18,18), tight_layout=True)\n fig.suptitle('Feature correlations', fontsize=24)\n\n sns.heatmap(self.train_data.astype(float).corr(method='kendall'), linewidths=0.1, vmin=-1.0,\n vmax=1.0, square=True, linecolor='white', annot=True, \n cmap=\"PiYG\")\n plt.savefig(r'data_analysis\\correlations_kendall_' + self.file_name + '.png', \n facecolor=fig.get_facecolor())","def read_fusion_domains(fus_path, wt_path, fusion_annot, min_domain_len=25, min_domain_frac=0.5):\n # read in domains\n domains = pd.read_csv(fus_path, sep='\\t')\n wt_domains = pd.read_csv(wt_path, sep='\\t')\n\n # filter out transmembrane helix\n is_tm = domains[\"Domain_ID\"].str.startswith('TMhelix')\n domains = domains[~is_tm].copy()\n\n # filter out short domains\n domain_len = domains['Protein_end'] - domains['Protein_start']\n domains = domains[domain_len>=min_domain_len].copy()\n\n # merge in info about where the fusion happened\n domains = pd.merge(domains, fusion_annot[['ID', 'CodonPos1', 'CodonPos2']].drop_duplicates('ID'), on='ID', how='left')\n domains['origin of domain'] = \"3'_gene domain\"\n domains.loc[domains['Protein_end']<=domains['CodonPos1'], 'origin of domain'] = \"5'_gene domain\"\n\n # separate out protein ID\n domains['ID_five'] = domains['ID'].str.split('-', expand=True)[0]\n domains['ID_three'] = domains['ID'].str.split('-', expand=True)[1].str.split(':', expand=True)[0]\n\n # check 5' domains\n domains_five = domains[domains[\"origin of domain\"]==\"5'_gene domain\"].copy()\n is_good_domain = []\n for ix, row in domains_five.iterrows():\n domain_end = row['Protein_end']\n fus_codon_pos = row['CodonPos1']\n if fus_codon_pos==domain_end:\n pid = row['ID_five']\n is_pid = wt_domains['PROT_ID']==pid\n is_loc = (fus_codon_pos>=wt_domains['Protein_start']) & (fus_codon_pos<=wt_domains['Protein_end'])\n tmp_df = wt_domains.loc[is_pid & is_loc,:]\n if len(tmp_df)==0:\n is_good_domain.append(True)\n continue\n else:\n tmp = tmp_df.iloc[0]\n frac_domain = (fus_codon_pos - tmp['Protein_start']) / (tmp['Protein_end'] - tmp['Protein_start'])\n if frac_domain>=min_domain_frac:\n is_good_domain.append(True)\n else:\n is_good_domain.append(False)\n else:\n is_good_domain.append(True)\n domains_five = domains_five.loc[is_good_domain,:]\n # check 3' domains\n domains_three = domains[domains[\"origin of domain\"]==\"3'_gene domain\"].copy()\n is_good_domain = []\n for ix, row in domains_three.iterrows():\n domain_start = row['Protein_start']\n fus_codon_pos = row['CodonPos2']\n if fus_codon_pos==domain_start:\n pid = row['ID_three']\n is_pid = wt_domains['PROT_ID']==pid\n is_loc = (fus_codon_pos>=wt_domains['Protein_start']) & (fus_codon_pos<=wt_domains['Protein_end'])\n tmp_df = wt_domains.loc[is_pid & is_loc,:]\n if len(tmp_df)==0:\n is_good_domain.append(True)\n continue\n else:\n tmp = tmp_df.iloc[0]\n frac_domain = (tmp['Protein_end']-fus_codon_pos) / (tmp['Protein_end'] - tmp['Protein_start'])\n if frac_domain>=min_domain_frac:\n is_good_domain.append(True)\n else:\n is_good_domain.append(False)\n else:\n is_good_domain.append(True)\n domains_three = domains_three.loc[is_good_domain,:]\n # append results\n domains = pd.concat([domains_five, domains_three])\n\n # aggregate all domains for one fusion into a single line\n domains = domains[~domains['Domain_name'].isnull()]\n domains_agg = domains.groupby(['ID', 'origin of domain'])['Domain_name'].agg(lambda x: ','.join(x))\n domains_agg = domains_agg.reset_index()\n domains_agg = domains_agg.pivot(index='ID', columns='origin of domain', values='Domain_name')\n domains_agg = domains_agg.reset_index()\n\n return domains_agg"],"string":"[\n \"def find_corr (mdp,num):\\n ctr=0 # line counter\\n mdp.corr_file.seek(0)\\n lfsp=mdp.corr_file.read().split(\\\"\\\\n\\\")\\n for i in range(0,len(lfsp)-1):\\n lin=lfsp[i].strip() # strip preceeding and trailing spaces?\\n if lin.startswith(\\\"0\\\"): # check whether it is the right form\\n if test_line_type(lin,0):\\n ctr += 1\\n if ctr == num: # only reaches this point if it's the needed correlator\\n for j in range(i+1,i+mdp.corr_len): # test for the correct format, number of lines\\n if not test_line_type(lfsp[j].strip(),j-i):\\n print \\\"Invalid correlator\\\"\\n return -1\\n ##endif ! test_line_type\\n ##endfor j in range\\n #print \\\"valid correlator\\\"\\n return i\\n ##endif ctr==num\\n ##endif test_line_type\\n ##endif startswith(0)\\n ##endfor i in range\\n return -2\",\n \"def __load_correspondences(self):\\n self.path_to_correspondences = QFileDialog.getOpenFileName(self,\\n 'Open file',\\n os.getcwd(),\\n \\\"Text files (*.txt)\\\"\\n )[0]\\n try:\\n with open(self.path_to_correspondences, 'r') as corr_file:\\n for line in corr_file.readlines():\\n point_a, point_b = map(int, line.strip().split(\\\" \\\"))\\n except FileNotFoundError:\\n QtWidgets.QMessageBox.warning(self,\\n \\\"Error\\\",\\n f\\\"Correspondences file was not found at {self.path_to_correspondences}\\\"\\n )\\n except ValueError:\\n QtWidgets.QMessageBox.warning(self,\\n \\\"Error\\\",\\n f\\\"Correspondences file at {self.path_to_correspondences} has wrong format\\\"\\n )\",\n \"def read_corr(no_of_specs,base_name=\\\"spec\\\"):\\n count_r=[]\\n corr_fact=[]\\n for i in range(no_of_specs):\\n path=\\\"./\\\"+base_name+str(i)+\\\"/\\\"+base_name+str(i)+\\\"pc_fit.fit\\\"\\n # path=\\\"./spec0/spec0pc_fit.fit\\\"\\n \\n temp_count_r=linecache.getline(path,9).strip().replace('File count rate: ','')\\n count_r.append(temp_count_r)\\n temp_corr_fact=linecache.getline(path,10).strip().replace('Corr factor:\\\\t','')\\n corr_fact.append(temp_corr_fact)\\n \\n count_r=np.array(count_r,dtype=np.float32)\\n corr_fact=np.array(corr_fact,dtype=np.float32)\\n return [count_r,corr_fact]\",\n \"def fetch_basenames(engine, form_factor):\\n for key in ['current', 'm_mother', 'm_daughter', 'm_spectator', 'momentum']:\\n if key not in form_factor:\\n raise KeyError(f\\\"Required key '{key}' is missing.\\\")\\n\\n def abspath(dirname):\\n return os.path.join(pathlib.Path(__file__).parent.absolute(), dirname)\\n\\n # 2pt correlators like 'P5-P5_RW_RW_d_d_m0.002426_m0.002426_p000'\\n mother = \\\"%_RW_RW_d_d_m{m_mother}_m{m_spectator}_p000%fine\\\"\\n daughter = \\\"%_RW_RW_d_d_m{m_daughter}_m{m_spectator}_{momentum}%fine\\\"\\n if form_factor['m_daughter'] < form_factor['m_spectator']:\\n daughter = \\\"%_RW_RW_d_d_m{m_spectator}_m{m_daughter}_{momentum}%fine\\\"\\n\\n # 3pt correlators like 'P5-P5_RW_RW_d_d_m0.002426_m0.002426_p000',\\n corr3 = \\\"%_{current}_T%_m{m_mother}_RW_RW_x_d_m{m_spectator}_m{m_daughter}_{momentum}%fine\\\"\\n\\n params = {\\n 'mother': mother.format(**form_factor),\\n 'daughter': daughter.format(**form_factor),\\n 'corr3': corr3.format(**form_factor)}\\n queries = aiosql.from_path(abspath(\\\"sql/\\\"), \\\"sqlite3\\\")\\n with db.connection_scope(engine) as conn:\\n corrs = queries.postgres.get_correlator_names(conn, **params)\\n \\n return np.squeeze(np.array(corrs))\",\n \"def extract_data_fast(mdp,pos):\\n mdp.corr_file.seek(pos)\\n mdp.corr_file.readline() # correlator_key line\\n mdp.corr_file.readline() # --- line\\n cdat = []\\n ## -- choose input type\\n for i in range(mdp.corr_len):\\n line = mdp.corr_file.readline() # read data\\n if mdp.input_type == \\\"real\\\":\\n cdat.append(float(line.split('\\\\t')[1]))\\n elif mdp.input_type == \\\"imag\\\":\\n cdat.append(float(line.split('\\\\t')[2]))\\n elif mdp.input_type == \\\"mod\\\":\\n temp1=float(line.split('\\\\t')[1])\\n temp2=float(line.split('\\\\t')[2])\\n cdat.append(math.sqrt(temp1*temp1+temp2*temp2))\\n if df.do_baryon:\\n bpar = [ (np.power(-1,t) if t>=mdp.corr_len/2 else 1) for t in range(mdp.corr_len) ]\\n cdat = [x*y for x,y in zip(cdat,bpar)]\\n return cdat\",\n \"def read_formations():\\n\\n with resource_stream('component_contribution',\\n FullTrainingData.FORMATION_ENERGY_FNAME) as fp:\\n formation_df = pd.read_csv(gzip.GzipFile(fileobj=fp))\\n\\n cids_that_dont_decompose = set(\\n formation_df.loc[formation_df['decompose'] == 0, 'cid'])\\n\\n for col in [\\\"dG'0\\\", \\\"T\\\", \\\"I\\\", \\\"pH\\\", \\\"pMg\\\"]:\\n formation_df[col] = formation_df[col].apply(float)\\n\\n formation_df = formation_df[~pd.isnull(formation_df[\\\"dG'0\\\"])]\\n formation_df['reaction'] = formation_df['cid'].apply(\\n lambda c: Reaction({c: 1}))\\n\\n formation_df['balance'] = False\\n formation_df['description'] = formation_df['name'] + ' formation'\\n formation_df.rename(columns={'compound_ref': 'reference'},\\n inplace=True)\\n formation_df.drop(['name', 'cid', 'remark', 'decompose'],\\n axis=1, inplace=True)\\n\\n logger.debug('Successfully added %d formation energies' %\\n formation_df.shape[0])\\n return formation_df, cids_that_dont_decompose\",\n \"def get_form_factor_data(form_factor_id, engines, apply_alias=True, sanitize=True):\\n query = (\\n \\\"SELECT ens_id, RTRIM(name, '-_fine') as BASENAME, corr_type \\\"\\n \\\"FROM junction_form_factor AS junction \\\"\\n \\\"JOIN correlator_n_point AS corr ON (corr.corr_id = junction.corr_id) \\\"\\n \\\"WHERE (form_factor_id = {form_factor_id}) AND (name LIKE '%%fine');\\\"\\n )\\n query = query.format(form_factor_id=form_factor_id)\\n dataframe = pd.read_sql_query(query, engines['postgres'])\\n ens_id = dataframe['ens_id'].unique().item()\\n basenames = dataframe['basename'].values\\n\\n # Grab a list of necessary correlators, in particular identifying the\\n # source and sink 2pt functions. This line gives a map from the full\\n # basename to a name like 'source' or 'sink'.\\n aliases = alias.get_aliases(basenames)\\n # Apply any further renaming, e.g., 'sink' --> 'heavy-light'\\n name_map = alias.apply_naming_convention(aliases)\\n data = {}\\n for basename in aliases:\\n key = name_map[basename] if apply_alias else basename\\n try:\\n data[key] = hdf5_cache.get_correlator(engines[ens_id], basename)\\n except ValueError as err:\\n LOGGER.warning(\\\"WARNING: Unable to load %s\\\", key)\\n if sanitize:\\n data, nan_rows = sanitize_data(data)\\n if nan_rows:\\n LOGGER.warning(\\\"WARNING: NaNs found while sanitizing: %s\\\", nan_rows)\\n return data\",\n \"def get_corrmat(self, f):\\n return self._get_corrmat(f)\",\n \"def check_relevant_forms(L):\\n L2 = list()\\n for F in L.values():\\n # needed_ev=(\\n S = F.shim_corr\\n # print \\\"S=\\\",S\\n ok_ev = 0\\n for g in S:\\n if g.atkin_lehner_eigenvalue() == -1:\\n ok_ev = ok_ev + 1\\n if ok_ev > 0:\\n print(\\\"Number of ok forms on \\\", F.space.WR.N, \\\" :\\\", ok_ev)\\n F.list_coefficents('D', fd=True, neg=False, nmin=0, nmax=1000, latex=False, nd=50, prime=True)\\n L2.append(F)\\n return L2\",\n \"def harvest_corr_fact(self):\\n # Search in the file #\\n lines = [line for line in self.paths.sas if \\\"if _2='\\\" in str(line)]\\n # Do each line #\\n query = \\\"if _2='([A-Z][A-Z])' then CF=([0-9].[0-9]+);\\\"\\n extract = lambda line: re.findall(query, str(line))\\n result = list(map(extract, lines))\\n result = [found[0] for found in result if found]\\n # Make a data frame #\\n df = pandas.DataFrame(result, columns=['forest_type', 'corr_fact'])\\n # Write back into a CSV #\\n df.to_csv(str(self.paths.corr_fact), index=False)\",\n \"def extract_data(mdp,lnum):\\n if lnum > -1:\\n mdp.corr_file.seek(0)\\n lfsp = mdp.corr_file.read().split('\\\\n') # read data\\n cdat = []\\n ## -- choose input type\\n for i in range(lnum,lnum+mdp.corr_len):\\n if mdp.input_type == \\\"real\\\":\\n cdat.append(float(lfsp[i].split('\\\\t')[1]))\\n elif mdp.input_type == \\\"imag\\\":\\n cdat.append(float(lfsp[i].split('\\\\t')[2]))\\n elif mdp.input_type == \\\"mod\\\":\\n temp1=float(lfsp[i].split('\\\\t')[1])\\n temp2=float(lfsp[i].split('\\\\t')[2])\\n cdat.append(math.sqrt(temp1*temp1+temp2*temp2))\\n if df.do_baryon:\\n bpar = [ (np.power(-1,t) if t>=mdp.corr_len/2 else 1) for t in range(mdp.corr_len) ]\\n cdat = [x*y for x,y in zip(cdat,bpar)]\\n return cdat\\n return []\",\n \"def compute_correlations(struc_df, option, gamma, alpha):\\n n_states = len(np.unique(struc_df.objnum))\\n nodes = network.temp_node_info()\\n adjacency = network.adjacency_mat(nodes)\\n L = compute_limit_matrix(0.5, adjacency, n_states)\\n L_vector = L.flatten()\\n M = learn_sr(struc_df, gamma, alpha)\\n M = M[2, 6]\\n M_vector = M.flatten()\\n\\n if option == \\\"norm\\\":\\n print(\\\"Norm of L - M: \\\")\\n print(la.norm(L_vector - M_vector, np.inf))\\n\\n if option == \\\"correlation\\\":\\n print(\\\"Correlation of L, M: \\\")\\n print(np.dot(L_vector, M_vector) /\\n (la.norm(L_vector) * la.norm(M_vector)))\",\n \"def correlationZernike():\\n Tfile='/home/jghao/research/decamFocus/psf_withseeing/finerGrid_coeff_matrix/zernike_coeff_finerGrid_training.cp'\\n b=p.load(open(Tfile))\\n x = b[:,0]\\n y = b[:,1]\\n z = b[:,2]\\n theta = b[:,3]\\n phi = b[:,4]\\n fwhm = b[:,5]\\n e1 = b[:,6]\\n e2 = b[:,7]\\n thetax = theta*np.cos(np.deg2rad(phi))\\n thetay = theta*np.sin(np.deg2rad(phi))\\n bb = b[:,7:68] #choose only those corresponding to M202\\n #idx = np.concatenate((np.arange(9,28),np.arange(29,48),np.arange(49,68)))\\n #bb = b[:,idx]\\n evalue,evector,pca = getPCA(bb)\\n coeff = np.corrcoef(bb.T)\\n ok = coeff >= 0.65\\n pl.matshow(coeff*ok)\\n ind = np.arange(0,60)\\n pl.xticks(ind,('Piston','Tip','Tilt','Astignism','Defocus','Astignism','Trefoil','Coma','Coma','Trefoil','Ashtray','Astigm.5th','Spherical','Astigm.5th','Ashtray','16','17','18','19','20','Piston','Tip','Tilt','Astignism','Defocus','Astignism','Trefoil','Coma','Coma','Trefoil','Ashtray','Astigm.5th','Spherical','Astigm.5th','Ashtray','16','17','18','19','20','Piston','Tip','Tilt','Astignism','Defocus','Astignism','Trefoil','Coma','Coma','Trefoil','Ashtray','Astigm.5th','Spherical','Astigm.5th','Ashtray','16','17','18','19','20'),rotation=90,color='black')\\n pl.yticks(ind,('Piston','Tip','Tilt','Astignism','Defocus','Astignism','Trefoil','Coma','Coma','Trefoil','Ashtray','Astigm.5th','Spherical','Astigm.5th','Ashtray','16','17','18','19','20','Piston','Tip','Tilt','Astignism','Defocus','Astignism','Trefoil','Coma','Coma','Trefoil','Ashtray','Astigm.5th','Spherical','Astigm.5th','Ashtray','16','17','18','19','20','Piston','Tip','Tilt','Astignism','Defocus','Astignism','Trefoil','Coma','Coma','Trefoil','Ashtray','Astigm.5th','Spherical','Astigm.5th','Ashtray','16','17','18','19','20'))\\n pl.grid(color='yellow')\",\n \"def get_recommended_formations(opponent_formation, league, limit):\\n\\n cursor = mc.get_db_cursor(mc.DB_NAME)\\n select_query = \\\"\\\"\\\"SELECT formation_1 FROM recom_formation\\n WHERE formation_2 = %s\\n AND league = %s\\n ORDER BY formation_points DESC limit %s\\\"\\\"\\\"\\n insert_value = (opponent_formation, league, limit)\\n cursor.execute(select_query, insert_value)\\n formations = cursor.fetchall()\\n\\n # initializes a dictionary for storing the recom formations\\n result_dict = {\\n \\\"recoms\\\": []\\n }\\n # save all the formations in the dictionary\\n for formation in formations:\\n result_dict[\\\"recoms\\\"].append(formation[0])\\n return result_dict\",\n \"def fetchChannelMapping(verbose=False):\\n\\n # Check (from pbworks):\\n #High freq. 305 473 360 170.312 \\n #Medium freq. 154 424 209 140.820 \\n #Low freq. 079 400 134 126.172 \\n\\n calibrator_chans=[55, 57, 63, 66, 68, 69, 71, 72, 73, 76, 77, 79, 83, 84, 85, 86, 90, 92, 99, 100, 101, 103, 104, 107, 108, 110, 117, 119, 121, 122, 123, 126, 130, 131, 134, 136, 137, 140, 141, 145, 147, 150, 151, 156, 157, 161, 162, 168, 170, 175, 176, 178, 185, 188, 196, 197, 201, 202, 209, 213, 217, 221, 224, 230, 235, 238, 242, 246, 254, 255, 258, 259, 260, 264, 268, 269, 271, 274, 278, 279, 280, 283, 286, 291, 295, 297, 301, 306, 307, 312, 313, 314, 317, 319, 322, 323, 325, 328, 329, 332, 341, 343, 344, 346, 347, 352, 356, 360, 361, 364, 369, 371, 382, 383, 390, 398, 401, 404, 411, 412, 414, 419]\\n\\n lofar2beams={}; cal2field={}; field2cal={}; cal2lofar={}; field2lofar={}\\n\\n zero_chan=55\\n num_field_chans=366\\n num_total_chans=488\\n for nf in range(zero_chan,zero_chan+num_total_chans):\\n lofar_chan=nf\\n field_chan=lofar_chan-zero_chan\\n field2lofar[field_chan]=lofar_chan\\n if lofar_chan in calibrator_chans:\\n cal_chan=num_field_chans+calibrator_chans.index(lofar_chan)\\n if verbose:\\n print lofar_chan, field_chan, ' C ', cal_chan\\n lofar2beams[lofar_chan]=(field_chan,cal_chan)\\n cal2field[cal_chan]=field_chan\\n field2cal[field_chan]=cal_chan\\n cal2lofar[cal_chan]=lofar_chan\\n else:\\n if verbose:\\n print lofar_chan, lofar_chan-zero_chan, ' - ', ' - '\\n lofar2beams[lofar_chan]=(field_chan,None)\\n field2cal[field_chan]=None\\n\\n return lofar2beams,cal2field,field2cal,cal2lofar,field2lofar\",\n \"def extract(self, butler, data, **kwargs):\\n self.safe_update(**kwargs)\\n\\n if butler is not None:\\n self.log.warn(\\\"Ignoring butler.\\\")\\n\\n datakey = 'correl'\\n\\n data_dict = dict(s_correl_mean=[],\\n s_correl_median=[],\\n s_correl_std=[],\\n s_correl_min=[],\\n s_correl_max=[],\\n p_correl_mean=[],\\n p_correl_median=[],\\n p_correl_std=[],\\n p_correl_min=[],\\n p_correl_max=[],\\n slot=[],\\n amp=[])\\n\\n\\n self.log_info_raft_msg(self.config, \\\"\\\")\\n\\n for islot, slot in enumerate(ALL_SLOTS):\\n\\n self.log_progress(\\\" %s\\\" % slot)\\n basename = data[slot]\\n datapath = basename.replace('biasoscorr_stats.fits', 'biasoscorr.fits')\\n\\n try:\\n dtables = TableDict(datapath, [datakey])\\n table = dtables[datakey]\\n except FileNotFoundError:\\n self.log.error(\\\"Could not open %s\\\\n\\\" % datapath)\\n table = None\\n\\n for amp in range(16):\\n if table is not None:\\n tablevals_s = table['s_correl_a%02i' % amp]\\n tablevals_p = table['p_correl_a%02i' % amp]\\n mask_s = np.isfinite(tablevals_s)\\n mask_p = np.isfinite(tablevals_p)\\n if mask_s.sum() > 0:\\n tablevals_s = tablevals_s[mask_s]\\n else:\\n tablevals_s = [0., 0.]\\n if mask_p.sum() > 0:\\n tablevals_p = tablevals_p[mask_p]\\n else:\\n tablevals_p = [0., 0.]\\n else:\\n tablevals_s = [0., 0.]\\n tablevals_p = [0., 0.]\\n data_dict['s_correl_mean'].append(np.mean(tablevals_s))\\n data_dict['s_correl_median'].append(np.median(tablevals_s))\\n data_dict['s_correl_std'].append(np.std(tablevals_s))\\n data_dict['s_correl_min'].append(np.min(tablevals_s))\\n data_dict['s_correl_max'].append(np.max(tablevals_s))\\n data_dict['p_correl_mean'].append(np.mean(tablevals_p))\\n data_dict['p_correl_median'].append(np.median(tablevals_p))\\n data_dict['p_correl_std'].append(np.std(tablevals_p))\\n data_dict['p_correl_min'].append(np.min(tablevals_p))\\n data_dict['p_correl_max'].append(np.max(tablevals_p))\\n data_dict['slot'].append(islot)\\n data_dict['amp'].append(amp)\\n\\n self.log_progress(\\\"Done!\\\")\\n\\n outtables = TableDict()\\n outtables.make_datatable(\\\"biasoscorr_stats\\\", data_dict)\\n return outtables\",\n \"def corr_list(self):\\n c = self.df.corr().abs()\\n s = c.unstack()\\n so = s.sort_values(ascending=False)\\n i = int(len(so) ** (1/2))\\n charts = so[i:]\\n charts = charts[::2]\\n if len(charts) > 3:\\n charts = charts[:3]\\n return charts.index, charts.values\",\n \"def get_corr_genes(ensemble, query):\\n\\tif \\\";\\\" in query:\\n\\t\\treturn []\\n\\n\\ttry:\\n\\t\\tcorr_genes = db.get_engine(current_app, 'methylation_data').execute(\\\"SELECT * FROM {}_correlated_genes WHERE gene1 LIKE %s\\\".format(ensemble), (query+'%%',)).fetchall()\\n\\texcept exc.ProgrammingError as e:\\n\\t\\tnow = datetime.datetime.now()\\n\\t\\tprint(\\\"[{}] ERROR in app(get_corr_genes): {}\\\".format(str(now), e))\\n\\t\\tsys.stdout.flush()\\n\\t\\treturn []\\n\\n\\tcorr_genes = [ {\\\"rank\\\": i+1, \\\"gene_name\\\": get_gene_by_id(row.gene2)[0]['gene_name'], \\\"correlation\\\": row.correlation, \\\"gene_id\\\": row.gene2} for i, row in enumerate(corr_genes)]\\n\\treturn corr_genes\",\n \"def list_legislation(self, place_code):\\n results = []\\n params = {\\n 'filter[field_frbr_uri][value]': f'/akn/{place_code}/',\\n 'filter[field_frbr_uri][operator]': 'STARTS_WITH',\\n 'fields[node--legislation]': 'field_frbr_uri',\\n }\\n url = self.url + '/jsonapi/node/legislation'\\n while url:\\n resp = self.session.get(url, params=params, timeout=self.timeout)\\n self.check_for_error(resp)\\n info = resp.json()\\n results.extend(info['data'])\\n # pagination\\n url = info['links'].get('next', {}).get('href')\\n if url and url.startswith('http://'):\\n url = 'https://' + url[7:]\\n # clear these, they're baked into the url now\\n params = {}\\n\\n return results\",\n \"def run_r_corr(df, corr_type='spearman', p_correction='BH'):\\r\\n num_cols = len(df.columns.values)\\r\\n r_dataframe = pandas2ri.py2ri(df)\\r\\n r_as = r['as.matrix']\\r\\n rcorr = r['rcorr'] \\r\\n r_p_adjust = r['p.adjust']\\r\\n result = rcorr(r_as(r_dataframe), type=corr_type)\\r\\n rho = result[0]\\r\\n n = result[1]\\r\\n p = result[2]\\r\\n \\r\\n if p_correction is not None:\\r\\n p = r_p_adjust(p, p_correction)\\r\\n r_corrs = pandas2ri.ri2py(rho)\\r\\n r_p_vals = pandas2ri.ri2py(p)\\r\\n r_counts = pandas2ri.ri2py(n)\\r\\n r_p_vals = np.reshape(r_p_vals, (num_cols,num_cols))\\r\\n return r_corrs, r_counts, r_p_vals\",\n \"def read_corr_jack(lens_run, rand_run, bin_scheme):\\n\\n fname=get_corr_jack_file(lens_run, rand_run, bin_scheme)\\n print(\\\"reading:\\\",fname)\\n return fitsio.read(fname)\",\n \"def read_corr_binned(lens_run, rand_run, bin_scheme):\\n\\n fname=get_corr_binned_file(lens_run, rand_run, bin_scheme)\\n print(\\\"reading:\\\",fname)\\n return fitsio.read(fname)\",\n \"def get_head_correct_info(raw_filename, epoch_filename, N=-1):\\n trans = get_ctf_trans(raw_filename)\\n fiducials = get_ref_head_pos(epoch_filename, trans, N=N)\\n raw = mne.io.ctf.read_raw_ctf(raw_filename)\\n info = replace_fiducials(raw.info, fiducials)\\n return trans, fiducials, info\",\n \"def read_data(engine, correlator_ids):\\n def _handle_ids(correlator_ids):\\n list_of_ids = [str(elt) for elt in correlator_ids]\\n return '({0})'.format(','.join(list_of_ids))\\n query = (\\n \\\"SELECT data.*, correlators.name FROM data \\\"\\n \\\"JOIN correlators ON correlators.id = data.correlator_id \\\"\\n f\\\"WHERE correlator_id IN {_handle_ids(correlator_ids)};\\\")\\n return pd.read_sql_query(query, engine)\",\n \"def line_ratio_map(quant1='L_[NII]122', quant2='L_[NII]205', ContourFunct='ne_mw', res=0.5, plane='xy', units='Jy', **kwargs):\\n\\n p = copy.copy(params)\\n for key,val in kwargs.items():\\n setattr(p,key,val)\\n GR = glo.global_results()\\n location = aux.moment0_map_location(res=res,plane=plane,gal_index=p.gal_index)\\n\\n try:\\n momentmap = np.load(location, allow_pickle=True)\\n print('Found stored momentmap data')\\n except:\\n print('Did not find stored momentmap data - creating')\\n aux.convert_cell_data_to_regular_grid(res=res, plane=plane, gal_index=p.gal_index)\\n momentmap = np.load(location, allow_pickle=True)\\n\\n indexes = momentmap[-1]\\n index1, index2 = int(indexes[1]), int(indexes[2])\\n \\n momentmap = momentmap[:-1] \\n \\n dictionary = p.moment0_dict\\n \\n num1=dictionary[quant1]\\n num2=dictionary[quant2]\\n num =dictionary[ContourFunct]\\n x = momentmap[:,1]\\n y = momentmap[:,2]\\n lumus = np.array(momentmap[:,3])\\n \\n line1=[]\\n line2=[]\\n Contour_Function=[]\\n m=[]\\n for row in lumus:\\n \\n line1.append(row[num1])\\n line2.append(row[num2])\\n \\n \\n if ContourFunct == 'ne_mw':\\n if row[dictionary['m']] == 0:\\n \\n Contour_Function.append(0)\\n else:\\n Contour_Function.append(row[num]/row[dictionary['m']])\\n else:\\n Contour_Function.append(row[num]) \\n \\n line1 = np.array(line1)\\n line2 = np.array(line2)\\n Contour_Function = np.array(Contour_Function)\\n \\n ratio = np.divide(line1, line2, out=np.zeros_like(line1), where=line2!=0)\\n \\n ratio = ratio.reshape(index1, index2)\\n x = x.reshape(index1, index2)\\n y = y.reshape(index1, index2)\\n line1 = line1.reshape(index1, index2)\\n line2 = line2.reshape(index1, index2)\\n #pdb.set_trace()\\n Contour_Function=Contour_Function.reshape(index1,index2)\\n \\n ratio[ratio==0] = np.min(ratio[ratio>0])\\n Contour_Function[Contour_Function==0] = 1e-30\\n\\n\\n if p.add:\\n fig,ax = plt.gcf(),p.ax #plot already available \\n else:\\n fig, ax = plt.subplots(figsize=(10,8))\\n plt.subplots_adjust(left=0.1,bottom=0.2,right=0.8)\\n \\n if p.log: cs = ax.pcolormesh(x, y, np.log10(ratio), cmap=plt.cm.viridis, vmin=np.log10(ratio).max()-1.5, shading='auto')\\n if not p.log: cs = ax.pcolormesh(x, y, ratio, cmap=plt.cm.viridis, vmin=ratio.max()/100, shading='auto')\\n\\n if not p.add:\\n\\n ax.set_title('Line Ratio map of ' + quant1.replace('L_','') + \\\"/\\\" + quant2.replace('L_',''))\\n ax.set_xlabel('x [kpc]')\\n ax.set_ylabel('y [kpc]')\\n levels = np.arange(np.min(np.log10(Contour_Function[Contour_Function > 1e-30])).round(), np.max(np.log10(Contour_Function)).round(), 1)\\n cr=ax.contour(x,y,np.log10(Contour_Function),cmap=plt.cm.plasma, levels=levels)\\n\\n if p.add:labels:''\\n cbaxes=fig.add_axes([.15, 0.09, 0.6, 0.027])\\n cbar=fig.colorbar(cr,cax=cbaxes,orientation='horizontal', label= 'log '+ getlabel(ContourFunct))\\n cbaxes2 = fig.add_axes([0.82, 0.24, 0.027, 0.6])\\n if p.log: fig.colorbar(cs, cax=cbaxes2, label= 'log ' + quant1.replace('L_','') + \\\" / \\\" + quant2.replace('L_','') )\\n if not p.log: fig.colorbar(cs, cax=cbaxes2, label= quant1.replace('L_','') + \\\" / \\\" + quant2.replace('L_','') )\\n if p.R_max:\\n ax.set_xlim([-p.R_max,p.R_max])\\n ax.set_ylim([-p.R_max,p.R_max])\\n if p.savefig:\\n if not os.path.isdir(p.d_plot + 'lineratios/'): os.mkdir(p.d_plot + 'lineratios/') \\n plt.savefig(p.d_plot+'lineratios/map_%s%s_%i_%s_%s' % (p.sim_name,p.sim_run,p.gal_index,quant1.replace('L_',''),quant2.replace('L_',''))+ '_' + plane + '_res' + str(res) +'.png', facecolor='w', dpi=500)\",\n \"def fix_pyscf_l1(fock, frame, orbs):\\n idx = []\\n iorb = 0;\\n atoms = list(frame.symbols)\\n for atype in atoms:\\n cur=()\\n for ia, a in enumerate(orbs[atype]):\\n n,l,m = a\\n if (n,l) != cur:\\n if l == 1:\\n idx += [iorb+1, iorb+2, iorb]\\n else:\\n idx += range(iorb, iorb+2*l+1)\\n iorb += 2*l+1\\n cur = (n,l)\\n return fock[idx][:,idx]\",\n \"def get_corr_ids(engine, basename):\\n query = (\\n \\\"SELECT id AS correlator_id \\\"\\n \\\"FROM correlators \\\"\\n f\\\"WHERE name LIKE '{basename}%%';\\\")\\n corr_ids = pd.read_sql_query(query, engine)['correlator_id']\\n return corr_ids\",\n \"def get_contacts_plots(itype, ligandonly): \\n\\n # Creating set_itypes and loading data\\n if itype == \\\"all\\\":\\n set_itypes = set((\\\"sb\\\", \\\"pc\\\", \\\"ps\\\", \\\"ts\\\", \\\"vdw\\\", \\\"hp\\\", \\\"hb\\\", \\\"hbbb\\\", \\\"hbsb\\\", \\\"hbss\\\", \\\"wb\\\", \\\"wb2\\\", \\\"hbls\\\", \\\"hblb\\\", \\\"all\\\"))\\n df_raw = None\\n for itype_df in set_itypes:\\n df_raw_itype = pd.read_csv(str(basepath + \\\"contact_tables/compare_\\\" + itype_df + \\\".tsv\\\"), sep=\\\"\\\\s+\\\")\\n df_raw = pd.concat([df_raw, df_raw_itype])\\n else: \\n set_itypes = { itype }\\n df_raw = pd.read_csv(str(basepath + \\\"contact_tables/compare_\\\" + itype + \\\".tsv\\\"), sep=\\\"\\\\s+\\\")\\n\\n print(\\\"Computing contmaps inputs for %s-%s\\\" % (itype, ligandonly))\\n\\n #Loading files\\n compl_data = json_dict(str(basepath + \\\"compl_info.json\\\"))\\n flare_template = json_dict(basepath + \\\"template.json\\\")\\n\\n # Adapting to Mariona's format\\n df_original = adapt_to_marionas(df_raw)\\n\\n # If is working with total frequency and all interaction partners, create a new flareplot template file\\n if (itype=='all') and (ligandonly=='prt_lg'):\\n flareplot_template(df_original, basepath)\\n \\n # Filtering out non-ligand interactions if option ligandonly is True\\n if ligandonly == \\\"lg\\\":\\n ligandfilter = df_original['Position'].str.contains('Ligand')\\n df_original = df_original[ligandfilter]\\n elif ligandonly == \\\"prt\\\":\\n ligandfilter = ~df_original['Position'].str.contains('Ligand')\\n df_original = df_original[ligandfilter]\\n\\n df_original = filter_same_helix(df_original)\\n\\n #Removing low-frequency contacts\\n df_original = filter_lowfreq(df_original, itype)\\n\\n #Add \\\\n between GPCR nomenclatures, to show it multiline in the heatmap axis \\n df_original = set_new_axis(df_original)\\n\\n # Excluding non-standard (and by standard I'm saying \\\"made by us\\\", in the simulation rounds) simulations\\n (df_standard) = split_by_standard(df_original, compl_data)\\n \\n #Repeat everything for standartd and non-standard dataframes (our simulations and the simulations from everone in GPCRmd)\\n for (stnd,df) in ((\\\"cmpl\\\", df_original), (\\\"stnd\\\", df_standard)):\\n \\n #If doesn't exists yet, create base input folder\\n options_path = \\\"%scontmaps_inputs/%s/%s/%s/\\\" %(basepath, itype, stnd, ligandonly)\\n os.makedirs(options_path, exist_ok=True)\\n\\n # If there are no interactions with this ligandonly-itype combination\\n if df.empty:\\n print(\\\"No interactions avalible for this molecular partners and interaction type: %s and %s\\\" % (ligandonly, itype) )\\n return\\n\\n # Setting columns 'Position', 'leter+Position1' and 'leter+Position2' in df for jsons files \\n df_columned = new_columns(df, itype)\\n df_columned.to_pickle(options_path+\\\"dataframe_customflareplot.pkl\\\")\\n\\n #Dropping away Position columns, once they are not needed\\n df_drop = df.drop(['Position1','Position2'], 1)\\n \\n # Stack matrix (one row for each interaction pair and dynamic. Colnames are position, dynid and itypes)\\n df_ts = stack_matrix(df_drop, set_itypes)\\n \\n #Dropping away non selected-type interaction rows.\\n df_drop = df_drop[df_drop['itype'] == itype]\\n df_drop.drop('itype',axis=1, inplace=True)\\n\\n # Set position as row index of the dataframe\\n df_drop = df_drop.set_index('Position') \\n\\n # Labels for dendogram\\n dendlabels_dyns = list(df_drop.columns)\\n \\n # Making one-simulation flareplots. Only done in cmpl to avoid repeating same Simulations\\n if stnd == \\\"cmpl\\\":\\n sim_jsons_path = '%scontmaps_inputs/%s/simulation_jsons/%s/' % (basepath, itype, ligandonly)\\n dyn_flareplots(df_columned, sim_jsons_path, dendlabels_dyns, itype, flare_template)\\n\\n #Computing frequency matrix\\n dend_matrix = frequencies(df_drop)\\n (recept_info,recept_info_order,df_ts,dyn_gpcr_pdb,index_dict)=improve_receptor_names(df_ts,compl_data)\\n \\n # Apending column with PDB ids\\n pdb_id = recept_info_order['pdb_id']\\n df_ts['pdb_id'] = df_ts['Id'].apply(lambda x: recept_info[x][pdb_id])\\n \\n #Storing dataframe with results in a CSV file, downloadable from web\\n create_csvfile(options_path, recept_info,df_drop)\\n\\n # Add residue types to dataframe\\n df_ts = add_restypes(df_ts, compl_data, recept_info, recept_info_order)\\n\\n #Preparing dendrogram folders and parameters\\n dendfolder = options_path + \\\"dendrograms/\\\" \\n os.makedirs(dendfolder, exist_ok = True)\\n dend_height = int( int(df.shape[1]) * 18.5)\\n dend_width = 450\\n\\n # Computing several dendrograms and corresponding json files\\n #for cluster in [2]:# DEBUG\\n for cluster in list(range(2,21)):\\n print(' computing dendrogram with '+str(cluster)+' clusters')\\n dendfile = (\\\"%s%iclusters_dendrogram.html\\\" % (dendfolder, cluster))\\n (dyn_dend_order, clustdict) = dendrogram_clustering(dend_matrix, dendlabels_dyns, dend_height, dend_width, dendfile, cluster, recept_info, recept_info_order)\\n # Write dynamicID-cluster dictionary on a json\\n clustdir = \\\"%sflarejsons/%sclusters/\\\" % (options_path, cluster)\\n os.makedirs(clustdir, exist_ok= True)\\n with open(clustdir + \\\"clustdict.json\\\", 'w') as clusdictfile:\\n dump(clustdict, clusdictfile, ensure_ascii=False, indent = 4)\\n\\n #Jsons for the flareplots of this combinations of clusters\\n flareplot_json(df_columned, clustdict, clustdir, flare_template)\\n \\n #Store Simulation names and dyn on file\\n create_dyntoname_file(dyn_dend_order, recept_info, recept_info_order, options_path)\\n \\n for rev in [\\\"norev\\\",\\\"rev\\\"]:\\n #for rev in [\\\"norev\\\"]:# DEBUG\\n # If rev option is setted to rev, duplicate all lines with the reversed-position version \\n #(4x32-2x54 duplicates to 2x54-4x32)\\n if rev == \\\"rev\\\":\\n df_ts_rev = reverse_positions(df_ts)\\n else:\\n df_ts_rev = df_ts\\n \\n df_ts_rev = sort_simulations(df_ts_rev, dyn_dend_order)\\n\\n #Taking some variables for dataframe slicing\\n max_columns = 45\\n pairs_number = df_drop.shape[0]\\n inter_number = df_ts_rev.shape[0]\\n inter_per_pair = (inter_number/pairs_number)/2 if rev == \\\"rev\\\" else inter_number/pairs_number \\n number_heatmaps = ceil((inter_number/inter_per_pair)/max_columns)\\n \\n #Create heatmap folder if not yet exists\\n heatmap_path_jupyter = settings.MEDIA_ROOT + \\\"Precomputed/get_contacts_files/contmaps_inputs/%s/%s/%s/heatmaps/%s/\\\" % (itype,stnd,ligandonly,rev)\\n heatmap_path = \\\"%sheatmaps/%s/\\\" % (options_path,rev)\\n os.makedirs(heatmap_path, exist_ok=True)\\n\\n #Saving dataframe for future uses in customized heatmaps\\n df_ts_rev.to_pickle(heatmap_path+\\\"dataframe_for_customized.pkl\\\")\\n \\n #Make heatmaps each 50 interacting pairs\\n div_list = []\\n heatmap_filename_list = []\\n number_heatmap_list = []\\n prev_slicepoint = 0\\n for i in range(1,number_heatmaps+1):\\n number_heatmap_list.append(str(i))\\n\\n #Slice dataframe. Also definig heigth and width of the heatmap\\n slicepoint = int(i*inter_per_pair*max_columns)\\n if i == number_heatmaps:\\n df_slided = df_ts_rev[prev_slicepoint:]\\n else:\\n df_slided = df_ts_rev[prev_slicepoint:slicepoint]\\n w = int(df_slided.shape[0]/inter_per_pair*20+40)\\n prev_slicepoint = slicepoint\\n h=dend_height\\n\\n # Define bokeh figure and hovertool\\n \\n hover = create_hovertool(itype, itypes_order, hb_itypes, typelist)\\n mysource,p = define_figure(w, h, df_slided, hover, itype)\\n\\n # Creating javascript for side-window\\n mysource = select_tool_callback(recept_info, recept_info_order, dyn_gpcr_pdb, itype, typelist, mysource)\\n\\n # Extract bokeh plot components and store them in lists\\n script, div = components(p)\\n div_list.append(div.lstrip())\\n heatmap_filename = \\\"%s%iheatmap.html\\\" % (heatmap_path_jupyter,i)\\n heatmap_filename_list.append(heatmap_filename)\\n\\n # Write heatmap on file\\n heatmap_filename = \\\"%s%iheatmap.html\\\" % (heatmap_path,i)\\n with open(heatmap_filename, 'w') as heatmap:\\n heatmap.write(script)\\n\\n # Write lists as python variables in a python file\\n variables_file = \\\"%svariables.py\\\" % (heatmap_path)\\n with open(variables_file, 'w') as varfile:\\n varfile.write(\\\"div_list = [\\\\'%s\\\\']\\\\n\\\" % \\\"\\\\',\\\\'\\\".join(div_list))\\n varfile.write(\\\"heatmap_filename_list = [\\\\'%s\\\\']\\\\n\\\" % \\\"\\\\',\\\\'\\\".join(heatmap_filename_list))\\n varfile.write(\\\"number_heatmaps_list = [\\\\'%s\\\\']\\\\n\\\" % \\\"\\\\',\\\\'\\\".join(number_heatmap_list))\",\n \"def find_legislation(self, frbr_uri_prefix, fields=('field_frbr_uri',)):\\n params = {\\n 'filter[field_frbr_uri][value]': frbr_uri_prefix,\\n 'filter[field_frbr_uri][operator]': 'STARTS_WITH'\\n }\\n\\n if fields:\\n params['fields[node--legislation]'] = ','.join(fields)\\n\\n resp = self.session.get(self.url + '/jsonapi/node/legislation', params=params, timeout=self.timeout)\\n self.check_for_error(resp)\\n info = resp.json()\\n if info['data']:\\n return info['data'][0]\",\n \"def getCorrelationForMetaData(self, name, tax_level):\\n grouped = self.abundance_df.groupAllSamples()\\n grouped.set_index(tax_level, drop=False, inplace=True) \\n samplenames = self.abundance_df.getSamplesList()\\n grouped_series = grouped.loc[name,samplenames]\\n meta_samples_df = pd.DataFrame(columns=[], index=[])\\n for sample in grouped_series.index:\\n for sample_meta in self.meta_df.index:\\n if sample_meta.upper() in sample.upper():\\n meta_samples_df[sample] = list(self.meta_df.loc[sample_meta,:])\\n meta_samples_df.index = self.meta_df.columns\\n\\n corr_list = []\\n for item in meta_samples_df.index:\\n corr_coef = meta_samples_df.loc[item,:].corr(grouped_series, method='spearman') \\n corr_list.append('{0:.3}'.format(meta_samples_df.loc[item,:].corr(grouped_series, method='spearman')) + '\\\\t' + '{0:.3}'.format(meta_samples_df.loc[item,meta_samples_df.columns[1]:].corr(grouped_series[:-1], method='spearman')))\\n text = 'spearman (rank) correlation' + ':\\\\n'\\n corr_series = pd.Series(corr_list, index=meta_samples_df.index)\\n return text, corr_series\",\n \"def get_formants(self, step_index):\\n\\n if step_index < 4 or step_index >= self.formant_step_count - 4:\\n raise ValueError('step index {0} is out of bounds [4, {1})'.format(\\n step_index, self.formant_step_count - 4))\\n\\n # Checking if we already computed required formant point value.\\n\\n if self.formant_list[step_index] != None:\\n return self.formant_list[step_index]\\n\\n # No, we haven't, so we are going to compute it.\\n\\n window_list = get_gaussian_window(self.formant_window_size)\\n sample_from = (step_index - 4) * self.formant_step_size\\n\\n sample_list = [self.formant_sample_list[sample_from + i] * window_list[i]\\n for i in range(self.formant_window_size)]\\n\\n # Computing Linear Prediction coefficients via Burg method, number of coefficients is twice the\\n # number of formants we want to detect (hence 2 * 5 = 10).\\n #\\n # These coefficients a[0], a[1], ... a[10] are coefficients of a polynomial 1 - a[1] * x -\\n # a[1] * x^2 - ... - a[10] * x^10 of degree 10 defined on [-1, 1], which is a so-called\\n # characteristic polynomial, see https://en.wikipedia.org/wiki/Autoregressive_model. We then find\\n # the roots of this polynomial.\\n\\n a0, coefficient_list = burg(sample_list, 10)\\n\\n polynomial = numpy.polynomial.Polynomial([1.0] + [-c for c in coefficient_list])\\n root_list = polynomial.roots()\\n\\n derivative = polynomial.deriv()\\n\\n better_root_list = []\\n better_root_index = 0\\n\\n # Finding better root approximations via Newton-Raphson iteration (see https://en.wikipedia.org/\\n # wiki/Newton's_method.\\n\\n while better_root_index < len(root_list):\\n\\n previous = root_list[better_root_index]\\n previous_delta = abs(polynomial(previous))\\n\\n current = previous - polynomial(previous) / derivative(previous)\\n current_delta = abs(polynomial(current))\\n\\n while current_delta < previous_delta:\\n\\n previous = current\\n previous_delta = current_delta\\n\\n current = previous - polynomial(previous) / derivative(previous)\\n current_delta = abs(polynomial(current))\\n\\n # If it is a complex root, the next one is just its complex conjugate.\\n\\n better_root_list.append(previous)\\n better_root_index += 1\\n\\n if abs(previous.imag) > 0:\\n\\n better_root_list.append(previous.conjugate())\\n better_root_index += 1\\n\\n continue\\n\\n # Moving all roots into the unit circle. If a root is outside, we replace it with reciprocal of its\\n # conjugate, reflecting it about the real line and projecting it inside the unit circle. Then we\\n # find formants by looking at roots above the real line.\\n\\n nyquist_frequency = self.formant_sound.frame_rate * 0.5\\n formant_list = []\\n\\n for root in better_root_list:\\n\\n if abs(root) > 1.0:\\n root = 1.0 / root.conjugate()\\n\\n if root.imag >= 0:\\n frequency = abs(math.atan2(root.imag, root.real)) * nyquist_frequency / math.pi\\n\\n if frequency >= 50 and frequency <= nyquist_frequency - 50:\\n formant_list.append(frequency)\\n\\n # Memoizing and returning first two formants.\\n\\n formant_list.sort()\\n self.formant_list[step_index] = formant_list[:2]\\n\\n return formant_list[:2]\",\n \"def get_corr_weights(dfr, filen, use_pt=False, acc_only=False, min_acc=None):\\n cmapfile = r.TFile.Open(filen)\\n cmap = get_correction_map(cmapfile, use_pt, acc_only, min_acc)\\n\\n # Check how far down the costh goes in the correction map\\n is_abs_costh = np.min(cmap.var_binnings[0]) == 0\\n\\n # Determine the appropriate eval function for the correction map\\n if use_pt:\\n if is_abs_costh:\\n logging.info('Using corrections in abs(costh), phi and Jpsi pT')\\n eval_f = eval_abs_costh_phi_fold_pt_HX\\n else:\\n logging.info('Using corrections in costh, phi and Jpsi pT')\\n eval_f = eval_costh_phi_fold_pt_HX\\n else:\\n if is_abs_costh:\\n logging.info('Using corrections in abs(costh) and phi')\\n eval_f = eval_abs_costh_phi_fold_HX\\n else:\\n logging.info('Using corrections in costh and phi')\\n eval_f = eval_costh_phi_fold_HX\\n\\n return eval_corrmap(cmap, eval_f)(dfr)\",\n \"def get_highly_correlated_feature_names(self):\",\n \"def plot_feature_correlation(self, N=20, save=None):\\n makedir(self.plotdir)\\n if save is None:\\n save = '/feature_correlation.png'.format(self.plotdir)\\n \\n # compile feature frequencies\\n feats = []\\n fls = glob('{:s}/*.fts'.format(self.modeldir))\\n for i,fl in enumerate(fls):\\n with open(fl) as fp:\\n lns = fp.readlines()\\n feats += [' '.join(ln.rstrip().split()[1:]) for ln in lns] \\n labels = list(set(feats) - set(['']))\\n freqs = [feats.count(label) for label in labels]\\n\\n labels = [label for _,label in sorted(zip(freqs,labels))][::-1]\\n freqs = sorted(freqs)[::-1]\\n fts = copy(labels)\\n \\n N = np.min([N, len(freqs)])\\n labels = ['\\\\n'.join(wrap(' '.join(l.split('_')), 30)) for l in labels ][:N]\\n \\n f = plt.figure(figsize=(8, 8))\\n\\n fM,ys = self._extract_features(self.ti_forecast, self.tf_forecast)\\n filt_df = np.log10(fM[fts[:N]]).replace([np.inf, -np.inf], np.nan).dropna()\\n low, high = [0.005, 0.995]\\n quant_df = filt_df.quantile([low, high])\\n filt_df.index = range(filt_df.shape[0])\\n filt_df = filt_df.apply(lambda x: x[(x>quant_df.loc[low,x.name]) & (x < quant_df.loc[high,x.name])], axis=0)\\n fM[fts[:N]]\\n\\n inds = []\\n for te in self.data.tes:\\n inds.append(np.where((ys['label']>0)&(abs((te-ys.index).total_seconds()/(3600*24))<5.)))\\n cols = ['b','g','r','m','c']\\n truths = []\\n for i, ind, col in zip(range(-2,3), inds, cols):\\n truths.append(np.log10(fM[fts[:N]].iloc[ind]).replace([np.inf, -np.inf], np.nan).dropna())\\n #te = self.data.tes[i+2]\\n \\n fig = corner(filt_df.dropna(), labels=labels[:N])\\n axes = np.array(fig.axes).reshape((N, N))\\n for yi in range(N):\\n for xi in range(yi):\\n ax = axes[yi,xi]\\n for col,truth in zip(cols,truths):\\n ax.scatter(truth[fts[xi]].values, truth[fts[yi]].values, np.arange(1,5)*6, col)\\n \\n plt.savefig(save,dpi=300)\\n plt.close(fig)\",\n \"def read_conformers (mol, pathname, method='RDKit') :\\n dcdfilename = os.path.join(pathname,'%s.dcd'%(method))\\n dcd = DCDTrajectoryFile(dcdfilename)\\n nsteps = dcd.get_length()\\n \\n # Read in the corresponding energies\\n enfilename = os.path.join(pathname,'energies%s.dat'%(method))\\n infile = open(enfilename)\\n lines = infile.readlines()\\n infile.close()\\n energies = []\\n for line in lines :\\n words = line.split() \\n if len(words) == 0 : continue\\n energies.append(float(words[-1]))\\n \\n # Filter the conformers with Marks filter\\n conformers = UniqueConformersMark()\\n conformers.prepare_state(mol)\\n for i,energy in enumerate(energies) :\\n crd,cell = dcd.read_next()\\n # Filtering is not necessary, since the conformers were already filtered\\n duplicate = conformers.add_conformer(crd,energy,check_for_duplicates=False)\\n\\n return conformers\",\n \"def correLation(df):\\n for i in range(0, 3):\\n ## randomly pick 20 holders\\n l1 = random.sample(range(0, len(df.columns)), 20)\\n temp_df = df.iloc[:, l1]\\n plt.imshow(temp_df.corr(), cmap=plt.cm.Blues, interpolation='nearest')\\n plt.colorbar()\\n tick_marks = [i for i in range(len(temp_df.columns))]\\n plt.xticks(tick_marks, temp_df.columns, rotation=90.)\\n plt.yticks(tick_marks, temp_df.columns)\\n plt.show()\",\n \"def get_reference_no_from_narration(narration,validatorlist=[validate_western_code,validate_ria,validate_placid, validate_xpress,validate_moneygram, validate_prabhu_ref, validate_merchantrade_ref, validate_necmoney_ref, validate_necitaly_ref, validate_cbl_ref, swift_re]):\\n ref_list = []\\n for e in narration.split():\\n for fn in validatorlist:\\n try:\\n fn(e)\\n if e not in ref_list:\\n ref_list.append(e)\\n except ValidationError:\\n pass\\n return ref_list\",\n \"def registration_with_ransac_from_correspondences(\\n src_corr_points,\\n tgt_corr_points,\\n correspondences=None,\\n distance_threshold=0.05,\\n ransac_n=3,\\n num_iterations=10000,\\n):\\n src_pcd = make_open3d_point_cloud(src_corr_points)\\n tgt_pcd = make_open3d_point_cloud(tgt_corr_points)\\n\\n if correspondences is None:\\n indices = np.arange(src_corr_points.shape[0])\\n correspondences = np.stack([indices, indices], axis=1)\\n correspondences = o3d.utility.Vector2iVector(correspondences)\\n\\n result = o3d.pipelines.registration.registration_ransac_based_on_correspondence(\\n src_pcd,\\n tgt_pcd,\\n correspondences,\\n distance_threshold,\\n estimation_method=o3d.pipelines.registration.TransformationEstimationPointToPoint(False),\\n ransac_n=ransac_n,\\n criteria=o3d.pipelines.registration.RANSACConvergenceCriteria(num_iterations, num_iterations),\\n )\\n\\n return result.transformation\",\n \"def calculate_overf_correlation(amp, index, f0, dt, n_lags):\\n \\n # Cast inputs as floats as I do a bunch of division.\\n dt = float(dt)\\n f0 = float(f0)\\n index = float(index)\\n # Number of points used in calculation needs to be at least 10 times bigger\\n # than final number of point returned. This is so we are not affected by\\n # the periodicity of the correlation function.\\n buff_factor = 64\\n n = buff_factor * n_lags\\n n_return = n_lags\\n # Generate the power spectrum.\\n # Need to add a low frequency cut off, since there is an IR divergence.\\n # Choose to cut off at 1/2df (so we get a bit of slope mode).\\n power = overf_power_spectrum(amp, index, f0, dt, n,\\n cut_off=1./n_lags/dt/2.0)\\n # FFT it to the correlation function.\\n corr = fft.ifft(power)\\n # Complex part should be zero.\\n corr = corr.real\\n # In previous versions of this function, we shifted the output function.\\n # however this screws up positive definiteness of the correlation matrix\\n # and is unnecessary if you have the IR cut off.\\n #corr -= corr[2 * n_return]\\n # Trim to return size.\\n corr = corr[:n_return]\\n # To normalize, need to multiply by twice the bandwidth.\\n corr *= 1.0/dt\\n return corr\",\n \"def read_conformers (mol, pathname, method='RDKit') :\\n dcdfilename = os.path.join(pathname,'%s.dcd'%(method))\\n dcd = DCDTrajectoryFile(dcdfilename)\\n nsteps = dcd.get_length()\\n \\n # Read in the corresponding energies\\n enfilename = os.path.join(pathname,'energies%s.dat'%(method))\\n infile = open(enfilename)\\n lines = infile.readlines()\\n infile.close()\\n energies = []\\n for line in lines :\\n words = line.split() \\n if len(words) == 0 : continue\\n energies.append(float(words[-1]))\\n \\n # Filter the conformers with Marks filter\\n conformers = UniqueConformersMark()\\n conformers.prepare_state(mol)\\n for i,energy in enumerate(energies) :\\n crd,cell = dcd.read_next()\\n # Filtering is not necessary, since the conformers were already filtered\\n duplicate = conformers.add_conformer(crd,energy)\\n\\n return conformers\",\n \"def getCorrelograms(self, dtype=float64):\\n fname = '%s::%s'%(self.__class__.__name__, self.getCorrelograms.__name__)\\n if (not self.lhaveImage):\\n print(\\\"%s: DSM image not yet computed\\\"%fname)\\n return None\\n # Get number of correlograms\\n nxcs = c_int(1)\\n ierr = c_int(1)\\n self.lib.xcloc_getNumberOfCorrelograms(nxcs, ierr)\\n if (ierr.value != 0):\\n print(\\\"%s: Failed to get number of correlograms\\\"%fname)\\n return None\\n nxcs = nxcs.value\\n # Get correlogram length\\n nptsInXCs = c_int(1)\\n self.lib.xcloc_getCorrelogramLength(nptsInXCs, ierr)\\n if (ierr.value != 0):\\n print(\\\"%s: Failed to get correlogram length\\\"%fname)\\n return None\\n nptsInXCs = nptsInXCs.value\\n # Get the correlograms\\n if (dtype == xctypes.XCLOC_SINGLE_PRECISION):\\n xcs = ascontiguousarray(zeros(nxcs*nptsInXCs, dtype=c_float))\\n xcsPtr = xcs.ctypes.data_as(POINTER(c_float))\\n self.lib.xcloc_getCorrelograms64f(nptsInXCs, nxcs, xcsPtr,\\n byref(ierr))\\n if (ierr.value != 0):\\n print(\\\"%s: Error getting float correlograms\\\"%fname)\\n return None\\n elif (dtype == xctypes.XCLOC_DOUBLE_PRECISION):\\n xcs = ascontiguousarray(zeros(nxcs*nptsInXCs, dtype=c_double))\\n xcsPtr = xcs.ctypes.data_as(POINTER(c_double))\\n self.lib.xcloc_getCorrelograms64f(nptsInXCs, nxcs, xcsPtr,\\n byref(ierr))\\n if (ierr.value != 0):\\n print(\\\"%s: Error getting double correlograms\\\"%fname)\\n return None\\n else:\\n print(\\\"%s: Unknown precision %d\\\"%(fname, precision))\\n return None\\n xcs = xcs.reshape([nxcs, nptsInXCs], order='C')\\n return xcs\",\n \"def fast_corr(df, col_name):\\n\\n if not isinstance(df, pd.DataFrame):\\n raise TypeError(\\\"The type of the input data must be dataframe.\\\")\\n\\n if not isinstance(col_name, list):\\n raise TypeError(\\\"The col_name must be list.\\\")\\n\\n if all(isinstance(item, str) for item in col_name) is False and all(\\n isinstance(item, int) for item in col_name) is False:\\n raise ValueError(\\n \\\"The col_name must be a list of strings or a list of integers.\\\")\\n\\n if len(col_name) < 2:\\n raise ValueError(\\n \\\"At least two columns must be selected for correlation analysis.\\\")\\n\\n if all(isinstance(item, str) for item in col_name) is True and all(\\n elem in df.columns.to_list() for elem in col_name) is False:\\n raise ValueError(\\\"The column names were not found.\\\")\\n\\n if all(isinstance(item, int) for item in col_name) is True and max(\\n col_name) > (df.shape[1] - 1):\\n raise ValueError(\\\"The column indexes were out of range.\\\")\\n\\n if all(isinstance(item, str) for item in col_name):\\n data = df.loc[:, col_name]\\n else:\\n data = df.iloc[:, col_name]\\n\\n data2 = data._get_numeric_data()\\n rm_n = data.shape[1] - data2.shape[1]\\n print(\\\"Removed\\\", rm_n, \\\"non-numberical columns from your selected columns\\\")\\n\\n sns.set(style=\\\"white\\\")\\n corr = data2.corr()\\n mask = np.triu(np.ones_like(corr, dtype=np.bool))\\n f, ax = plt.subplots(figsize=(9, 11))\\n ax.set_title('Correlation Matrix', size=20)\\n ax.tick_params(axis='x', labelsize=15)\\n ax.tick_params(axis='y', labelsize=15)\\n\\n cmap = sns.diverging_palette(220, 20, as_cmap=True)\\n p = sns.heatmap(corr, mask=mask, cmap=cmap, vmin=-1, vmax=1, center=0,\\n square=True, linewidths=.5, cbar_kws={\\\"shrink\\\": .5})\\n p.set_yticklabels(p.get_yticklabels(), rotation=360)\\n return p\",\n \"def calculate_correlation(data):\\n pass\",\n \"def load_data(config):\\n cor_file = os.path.join(config.data_dir, \\\"{}_cor.txt\\\".format(config.type.strip()))\\n err_file = os.path.join(config.data_dir, \\\"{}_err.txt\\\".format(config.type.strip()))\\n corrects = []\\n errors = []\\n positions = []\\n with open(cor_file, 'r') as fc, open(err_file, 'r') as fe:\\n clines = fc.readlines()\\n elines = fe.readlines()\\n cnt = 0\\n for cline, eline in zip(clines, elines):\\n cnt += 1\\n try:\\n correct, pos1 = cline.strip().split('\\\\t')\\n except:\\n correct = cline.strip()\\n pos1 = '-1'\\n try:\\n error, pos2 = eline.strip().split('\\\\t')\\n except:\\n error = eline.strip()\\n pos2 = '-1'\\n\\n assert pos1 == pos2\\n\\n corrects.append(correct)\\n errors.append(error)\\n positions.append(int(pos2))\\n return corrects, errors, positions\",\n \"def findLinkedTerms(self):\\n for key in self.summaryDict.keys(): # v' in the formula\\n if self.getCoverFromModalityInDictionnary(self.summaryDict, key) == 0:\\n correlation = 0\\n else:\\n dep = self.getCoverFromModalityInDictionnary(self.summaryFilteredDict,key) / self.getCoverFromModalityInDictionnary(self.summaryDict, key) #cover(v',R')/cover(v'R)\\n if dep <= 1:\\n correlation = 0\\n else:\\n correlation = 1 - (1 / dep)\\n self.correlationDict[key] = correlation\",\n \"def analyse_donnees(self, mere, foetus, pere, log):\\n concordance_mf = 0\\n concordance_pf = None\\n if len(pere) != 0:\\n concordance_pf = 0\\n log = log + \\\"Père détecté.................................\\\\n\\\"\\n log = log + \\\"\\\\n\\\\nVérification concordance des ADNs entre père et foetus..............................\\\\n\\\"\\n for Alleles in range(len(foetus)):\\n for Allele_Foe in range(3):\\n if foetus[Alleles].allele[Allele_Foe] in pere[Alleles].allele:\\n if foetus[Alleles].allele[Allele_Foe] != 0.0:\\n pere[Alleles].concordance_pere_foetus = \\\"OUI\\\"\\n concordance_pf = concordance_pf + 1\\n log = log + \\\"Concordance pour marqueur \\\" + str(\\n foetus[Alleles].marqueur) + \\\" OK..................\\\\n\\\"\\n break\\n else:\\n pere[Alleles].concordance_pere_foetus = \\\"NON\\\"\\n log = log + \\\"Concordance pour marqueur \\\" + foetus[\\n Alleles].marqueur + \\\" PAS OK..............\\\\n\\\"\\n break\\n log = log + \\\"\\\\n\\\\nVérification concordance des ADNs entre mère et foetus..............................\\\\n\\\"\\n for Alleles in range(len(foetus)):\\n for Allele_Foe in range(3):\\n if foetus[Alleles].allele[Allele_Foe] in mere[Alleles].allele:\\n if foetus[Alleles].allele[Allele_Foe] != 0.0:\\n foetus[Alleles].concordance_mere_foetus = \\\"OUI\\\"\\n concordance_mf = concordance_mf + 1\\n log = log + \\\"Concordance pour marqueur \\\" + str(\\n foetus[Alleles].marqueur) + \\\" OK..................\\\\n\\\"\\n break\\n else:\\n foetus[Alleles].concordance_mere_foetus = \\\"NON\\\"\\n log = log + \\\"Concordance pour marqueur \\\" + foetus[Alleles].marqueur + \\\" PAS OK..............\\\\n\\\"\\n break\\n log = log + \\\"Vérification concordance des ADns terminée..................................\\\\n\\\\n\\\\n\\\"\\n if concordance_mf != len(foetus):\\n resultats, conclusion = self.resultat(concordance_mf, concordance_pf, foetus, mere, pere)\\n log = log + \\\"Concordance des ADNs PAS OK....................\\\\n\\\"\\n log = log + \\\"Erreur dans l'échantillon...................\\\\n\\\"\\n log = log + \\\"Revérifier s'il vous plaît.............\\\\n\\\"\\n return resultats, conclusion, log\\n else:\\n log = log + \\\"Traitement des 15 autres marqueurs..............................\\\\n\\\"\\n for nbre_lignes in range(1, len(mere)):\\n log = log + \\\"Traitement du marqueur \\\" + str(foetus[nbre_lignes].marqueur) + \\\"..........\\\\n\\\"\\n pic = foetus[nbre_lignes].foetus_pics()\\n log = log + \\\"Calcul du nombre d'allèles pour le foetus......................\\\\n\\\"\\n log = log + \\\"Nombre d'allèles pour le foetus : \\\" + str(pic) + \\\".........\\\\n\\\"\\n log = log + \\\"Vérification de l'homozygotie de la mère......................\\\\n\\\"\\n mere[nbre_lignes].homozygotie()\\n log = log + \\\"Mère homozygote : \\\" + str(mere[nbre_lignes].homozygote) + \\\"...............\\\\n\\\"\\n log = log + \\\"Vérification mère et foetus mêmes allèles......................\\\\n\\\"\\n foetus[nbre_lignes].allele_semblable(mere[nbre_lignes])\\n log = log + \\\"Code de retour vérification allèles semblables: \\\" + str(\\n foetus[nbre_lignes].informatif) + \\\"...............\\\\n\\\"\\n log = log + \\\"Initialisation du taux de contamination pour calcul à venir...............\\\\n\\\"\\n foetus[nbre_lignes].taux = 0.0\\n log = log + \\\"Taux initialisé.................................\\\\n\\\"\\n log = log + \\\"Si code informatif de retour allèles semblables différent de 2, vérification écho.............\\\\n\\\"\\n log = log + \\\"Si écho, affection code informatif 3...............\\\\n\\\"\\n if foetus[nbre_lignes].informatif != 2:\\n log = log + \\\"Vérification si écho......................\\\\n\\\"\\n mere[nbre_lignes].echo(foetus[nbre_lignes])\\n log = log + \\\"Code retour vérification écho : \\\" + str(\\n foetus[nbre_lignes].informatif) + \\\"...............\\\\n\\\"\\n log = log + \\\"Début chaîne de traitement...........................\\\\n\\\"\\n if pic == 3:\\n log = log + \\\"Trois allèles détectés......................\\\\n\\\"\\n foetus[nbre_lignes].contamination_heterozygote(mere[nbre_lignes])\\n log = log + \\\"Marqueur informatif, affectation du code contamination 1..............\\\\n\\\"\\n foetus[nbre_lignes].informatif = 1\\n log = log + \\\"Calcul taux de contamination du marqueur..........\\\\n\\\"\\n foetus[nbre_lignes].contamination = 2\\n log = log + \\\"Calcul terminé....................\\\\n\\\"\\n elif mere[nbre_lignes].homozygote:\\n log = log + \\\"Mère homozygote.......................\\\\n\\\"\\n log = log + \\\"Marqueur non informatif, affectation du code informatif 0............\\\\n\\\"\\n foetus[nbre_lignes].informatif = 0\\n elif pic == 2:\\n log = log + \\\"Deux allèles détectés..............\\\\n\\\"\\n if foetus[nbre_lignes].informatif == 2:\\n log = log + \\\"Si mêmes allèles, vérification homozygote contaminé...............\\\\n\\\"\\n foetus[nbre_lignes].verif_homozygote_contamine(self)\\n if foetus[nbre_lignes].contamination == 1:\\n log = log + \\\"Homozygote contaminé identifié.....................\\\\n\\\"\\n log = log + \\\"Calcul du taux de contamination....................\\\\n\\\"\\n foetus[nbre_lignes].homozygote_contamine(self)\\n log = log + \\\"Calcul du taux de contamination effectué...........\\\\n\\\"\\n else:\\n if foetus[nbre_lignes].informatif != 3:\\n log = log + \\\"Code calcul écho différent de 3..................\\\\n\\\"\\n log = log + \\\"Marqueur informatif, affectation du code informatif 1.............\\\\n\\\"\\n foetus[nbre_lignes].informatif = 1\\n log = log + \\\"Marqueur non contaminé, affectation du code contamination 0................\\\\n\\\"\\n foetus[nbre_lignes].contamination = 0\\n else:\\n log = log + \\\"Un seul allèle détecté............\\\\n\\\"\\n if foetus[nbre_lignes].informatif != 3:\\n log = log + \\\"Code informatif différent de 3...........\\\\n\\\"\\n log = log + \\\"Marqueur informatif, affectation du code informatif 1.............\\\\n\\\"\\n foetus[nbre_lignes].informatif = 1\\n log = log + \\\"Marqueur non contaminé, affectation du code contamination 0................\\\\n\\\"\\n foetus[nbre_lignes].contamination = 0\\n log = log + \\\"\\\\n\\\\n\\\"\\n log = log + \\\"Calcul échantillon contaminé ou non......\\\\n\\\"\\n log = log + \\\"Marqueur contaminé si >\\\" + str(self.seuil_taux_conta) + \\\".......\\\\n\\\"\\n log = log + \\\"Echantillon contaminé si plus de \\\" + str(\\n self.seuil_nbre_marqueurs) + \\\"marqueurs contaminés...\\\\n\\\"\\n self.conclusion_echantillon(foetus)\\n log = log + \\\"Calcul échantillon terminé.....\\\\n\\\"\\n log = log + \\\"Fin de traitement...........\\\\n\\\"\\n resultats, conclusion = self.resultat(concordance_mf, concordance_pf, foetus, mere, pere)\\n return resultats, conclusion, log\",\n \"def _get_next_correlation_index(self, index: int) -> int:\\n pass\",\n \"def get_correlated_geometric_brownian_motions(params: 'ModelParameters',\\n correlation_matrix: 'np.array',\\n n: int) -> 'np.array':\\n decomposition = sp.linalg.cholesky(correlation_matrix, lower=False)\\n uncorrelated_paths = []\\n sqrt_delta_sigma = np.sqrt(params.all_delta) * params.all_sigma\\n # Construct uncorrelated paths to convert into correlated paths\\n for i in range(params.all_time):\\n uncorrelated_random_numbers = []\\n for j in range(n):\\n uncorrelated_random_numbers.append(random.normalvariate(0, sqrt_delta_sigma))\\n uncorrelated_paths.append(np.array(uncorrelated_random_numbers))\\n uncorrelated_matrix = np.asmatrix(uncorrelated_paths)\\n correlated_matrix = uncorrelated_matrix * decomposition\\n assert isinstance(correlated_matrix, np.matrix)\\n # The rest of this method just extracts paths from the matrix\\n extracted_paths = []\\n for i in range(1, n + 1):\\n extracted_paths.append([])\\n for j in range(0, len(correlated_matrix) * n - n, n):\\n for i in range(n):\\n extracted_paths[i].append(correlated_matrix.item(j + i))\\n return extracted_paths\",\n \"def output_new_correlation_values():\\n correlation_builder = _get_correlation_values()\\n bv_data = correlation_builder.correlation_store.query(\\n criteria={'property_x': 'bulk_modulus',\\n 'property_y': 'vickers_hardness'}\\n )\\n \\n print(\\\"Bulk Modulus/Vickers Hardness\\\")\\n \\n for item in bv_data:\\n print(\\\"'{}': {},\\\".format(item['correlation_func'],\\n item['correlation']))\\n\\n vb_data = correlation_builder.correlation_store.query(\\n criteria={'property_y': 'bulk_modulus',\\n 'property_x': 'vickers_hardness'}\\n )\\n\\n print(\\\"Vickers Hardness/Bulk Modulus\\\")\\n\\n for item in vb_data:\\n print(\\\"'{}': {},\\\".format(item['correlation_func'],\\n item['correlation']))\\n \\n print('linlsq correlation values')\\n\\n bg_ad = correlation_builder.correlation_store.query_one(\\n criteria={'property_x': 'band_gap_pbe',\\n 'property_y': 'atomic_density',\\n 'correlation_func': 'linlsq'}\\n )\\n\\n bm_vh = correlation_builder.correlation_store.query_one(\\n criteria={'property_x': 'bulk_modulus',\\n 'property_y': 'vickers_hardness',\\n 'correlation_func': 'linlsq'}\\n )\\n \\n print(\\\"[{}, {}]\\\".format(bg_ad['correlation'], \\n bm_vh['correlation']))\",\n \"def localization_full(Gint, focal_genes, \\n num_reps = 200,\\n method = 'LCC', \\n print_counter = False,\\n label = 'focal genes',\\n line_height = 0.1,\\n legend_loc = 'upper left'):\\n \\n numedges_list, numedges_rand, LCC_list, LCC_rand = localization(Gint, focal_genes, num_reps, \\n sample_frac = 1, \\n method = method, \\n plot = False,\\n print_counter = print_counter)\\n if method == 'numedges':\\n analysis_list = numedges_list\\n analysis_rand = numedges_rand\\n title = 'number of edges'\\n else:\\n analysis_list = LCC_list\\n analysis_rand = LCC_rand\\n title = 'largest connected component'\\n\\n # plot distributions for non-sampled case\\n fig, ax = plt.subplots(figsize = (12, 7))\\n sns.set_style('white') \\n plt.vlines(np.mean(analysis_list), ymin = 0, ymax = line_height, color = 'r', lw = 2, label = label)\\n sns.kdeplot(analysis_rand, ax = ax, color = 'k', lw = 2, alpha = 0.5, shade = True, label = 'random')\\n plt.legend(loc = legend_loc, fontsize = 12)\\n plt.ylabel('frequency', fontsize = 16)\\n plt.xlabel(title, fontsize = 16)\\n\\n # print the z-score and fdr\\n analysis_z = (np.mean(analysis_list) - np.mean(analysis_rand))/float(np.std(analysis_rand))\\n\\n print(1 - ndtr(analysis_z))\\n\\n plt.title('permutation p = ' + str(1 - ndtr(analysis_z)))\\n \\n return numedges_list, numedges_rand, LCC_list, LCC_rand\",\n \"def make_all_corr_matrices(isotracers, formula_dict, na_dict, autodetect, corr_limit):\\n corr_mats = {}\\n for isotracer in isotracers:\\n trac_atom = get_isotope_element(isotracer)\\n try:\\n indist_list = corr_limit[str(trac_atom)].keys()\\n except KeyError:\\n indist_list = []\\n corr_mats[isotracer] = make_correction_matrix(isotracer, formula_dict, na_dict, indist_list, autodetect, corr_limit[trac_atom])\\n return corr_mats\",\n \"def addCorrelator(corrType) :\\n try :\\n s.addCorrelator(corrType)\\n except Exception, ex:\\n print(ex.errorMsg)\\n e = \\\"Unable to add the %s correlator. If it is currently\\\\n\\\" % CORR_STRING[corrType]\\n e += \\\"owned by another subarray, you will have to issue\\\\n\\\"\\n e += \\\"removeCorrelator(%s) in that subarray first.\\\" % CORR_STRING[corrType]\\n print e\",\n \"def process_conformers_file(conformers_path: str) -> Tuple[List[Dict[str, tuple]], List[float]]:\\n if not os.path.isfile(conformers_path):\\n raise InputError('Conformers file {0} could not be found'.format(conformers_path))\\n with open(conformers_path, 'r') as f:\\n lines = f.readlines()\\n xyzs, energies = list(), list()\\n line_index = 0\\n while line_index < len(lines):\\n if 'conformer' in lines[line_index] and ':' in lines[line_index] and lines[line_index].strip()[-2].isdigit():\\n xyz, energy = '', None\\n line_index += 1\\n while len(lines) and line_index < len(lines) and lines[line_index].strip() \\\\\\n and 'SMILES' not in lines[line_index] \\\\\\n and 'energy' not in lines[line_index].lower() \\\\\\n and 'guess method' not in lines[line_index].lower():\\n xyz += lines[line_index]\\n line_index += 1\\n while len(lines) and line_index < len(lines) and 'conformer' not in lines[line_index]:\\n if 'relative energy:' in lines[line_index].lower():\\n energy = float(lines[line_index].split()[2])\\n line_index += 1\\n xyzs.append(str_to_xyz(xyz))\\n energies.append(energy)\\n else:\\n line_index += 1\\n return xyzs, energies\",\n \"def read_forces_on_pointcharges(self):\\n from ase.units import Hartree, Bohr\\n infile = open(os.path.join(self.directory, 'detailed.out'), 'r')\\n lines = infile.readlines()\\n infile.close()\\n\\n external_forces = []\\n for n, line in enumerate(lines):\\n if ('Forces on external charges' in line):\\n chargestart = n + 1\\n break\\n else:\\n raise RuntimeError(\\n 'Problem in reading forces on MM external-charges')\\n lines1 = lines[chargestart:(chargestart + len(self.mmcharges))]\\n for line in lines1:\\n external_forces.append(\\n [float(i) for i in line.split()])\\n return np.array(external_forces) * Hartree / Bohr\",\n \"def calculate_correlations(input_data, index_col, cat_features, exclu_elements): \\r\\n try:\\r\\n # encode the categorical features\\r\\n encoded_data = pd.get_dummies(input_data,columns=cat_features,drop_first=True)\\r\\n\\r\\n pd_transposed_data = encoded_data.set_index('Style_display_code').T\\r\\n\\r\\n # get the number of items\\r\\n items_list = [str(a) for a in pd_transposed_data.columns]\\r\\n\\r\\n print(\\\"Number of items to correlate :{}_Timestamp:{}\\\".format(str(len(items_list)), \\r\\n format(str(datetime.now()))))\\r\\n \\r\\n\\r\\n #compute correlations and save the pickle file\\r\\n# matrix = pd_transposed_data.corr().values\\r\\n# pickle.dump(matrix, open(staging_dir+ '/corr_matrix_output_py3.p', 'wb'))\\r\\n \\r\\n # read from the saved pickle file - ONLY FOR CONSECUTIVE RUNS, TO SAVE TIME\\r\\n matrix = pickle.load(open(staging_dir+ '/corr_matrix_output_py3.p', \\\"rb\\\" ) )\\r\\n\\r\\n print(\\\"Corr Matrix size:{}_Timestamp:{}\\\".format(str(matrix.size),\\r\\n format(str(datetime.now()))))\\r\\n\\r\\n except Exception as e:\\r\\n print(\\\" Error !!\\\", e)\\r\\n \\r\\n # return the top correlated items\\r\\n return top_correlateditems(items_list,matrix, index_col, exclu_elements)\",\n \"def read_CORNERS(self, fp):\\r\\n corners = []\\r\\n count = 0\\r\\n nb = self.size[0] * self.size[1] * self.size[2] * 24\\r\\n\\r\\n for line in fp:\\r\\n item = line.split()\\r\\n if len(item) > 0:\\r\\n if \\\"CORNERS\\\" in item[0]:\\r\\n break\\r\\n for line in fp:\\r\\n item = line.split()\\r\\n if len(item) > 0:\\r\\n for c in item:\\r\\n if \\\"*\\\" in c:\\r\\n item = c.split(\\\"*\\\")\\r\\n for i in range(int(item[0])):\\r\\n corners.append(item[1])\\r\\n count += 1\\r\\n else:\\r\\n corners.append(c)\\r\\n count += 1\\r\\n # all attributes have been read\\r\\n if count == nb:\\r\\n break\\r\\n sp = int(len(corners) / 3)\\r\\n X = list(map(float, corners[:sp]))\\r\\n Y = list(map(float, corners[sp:2*sp]))\\r\\n Z = list(map(float, corners[2*sp:]))\\r\\n return X, Y, Z\",\n \"def read_forces(filename):\\n f=open(filename,\\\"r\\\")\\n castep_forces = f.readlines()\\n f.close() \\n nruter = []\\n for index, line in enumerate(castep_forces):\\n if 'Total number of ions in cell' in line:\\n n_atoms = int(line.split()[7])\\n if 'Cartesian components (eV/A)' in line:\\n starting_line = index + 4\\n for i in range(n_atoms):\\n f = starting_line + i\\n nruter.append([float(castep_forces[f].split()[m]) for m in range(3,6)]) \\n nruter=np.array(nruter,dtype=np.double)\\n return nruter\",\n \"def get_best_fit_information(engine, form_factor_id):\\n Nstates = collections.namedtuple(\\n 'NStates', ['n', 'no', 'm', 'mo'], defaults=(1, 0, 0, 0)\\n )\\n\\n def _float_or_none(astr):\\n if astr is None:\\n return None\\n if (astr.lower() == 'nan') or (astr.lower() == 'none'):\\n return None\\n return float(astr)\\n\\n query = f\\\"\\\"\\\"\\n select\\n campaign.form_factor_id,\\n ens.ens_id,\\n ens.ns,\\n form_factor.momentum,\\n form_factor.spin_taste_current,\\n result_id,\\n n_decay_ll as n,\\n n_oscillating_ll as \\\"no\\\",\\n n_decay_hl as m,\\n n_oscillating_hl as mo,\\n tmin_ll as tmin_src,\\n tmax_ll as tmax_src,\\n tmin_hl as tmin_snk,\\n tmax_hl as tmax_snk,\\n binsize,\\n shrinkage,\\n fold as do_fold,\\n sign,\\n pedestal,\\n params\\n from campaign_form_factor as campaign\\n join form_factor using(form_factor_id)\\n join ensemble as ens using(ens_id)\\n join sign_form_factor using(ens_id, spin_taste_current)\\n join result_form_factor as result\\n on (result.form_factor_id = campaign.form_factor_id) and\\n (result.id = campaign.result_id)\\n join analysis_form_factor as analysis\\n on (analysis.analysis_id = result.analysis_id)\\n join reduction_form_factor as reduction\\n on (reduction.reduction_id = result.reduction_id)\\n where\\n campaign.form_factor_id in ({form_factor_id});\\\"\\\"\\\"\\n\\n best_fit = pd.read_sql_query(query, engine)\\n best_fit['params'] = best_fit['params'].apply(parse_string_dict)\\n best_fit['pedestal'] = best_fit['pedestal'].apply(_float_or_none)\\n best_fit['nstates'] = best_fit[['n', 'no', 'm', 'mo']].apply(\\n lambda args: Nstates(*args), axis=1)\\n best_fit['params'] = best_fit[['params', 'nstates']].apply(\\n lambda pair: reshape_params(*pair), axis=1)\\n best_fit, = best_fit.to_dict('records') # Unpack single entry\\n return best_fit\",\n \"def cmd_makecldf(self, args):\\n wl = lingpy.Wordlist(self.raw_dir.joinpath(\\\"GEM-CNL.csv\\\").as_posix())\\n concepts = args.writer.add_concepts(\\n id_factory=lambda x: x.id.split(\\\"-\\\")[-1] + \\\"_\\\" + slug(x.english), lookup_factory=\\\"Name\\\"\\n )\\n for concept in self.conceptlists[0].concepts.values():\\n for cis in concept.attributes[\\\"lexibank_gloss\\\"]:\\n if cis not in concepts:\\n concepts[cis] = concepts[concept.english]\\n\\n languages = args.writer.add_languages(lookup_factory=\\\"STEDT_Name\\\")\\n args.writer.add_sources()\\n\\n for idx, language, concept, value, pos in wl.iter_rows(\\n \\\"doculect\\\", \\\"concept\\\", \\\"reflex\\\", \\\"gfn\\\"\\n ):\\n # Fix for 251479\\n if concept == \\\"top (i.e. highest point\\\":\\n concept = \\\"top (i.e. highest point)\\\"\\n\\n if concept not in concepts:\\n args.log.warning(concept)\\n else:\\n args.writer.add_forms_from_value(\\n Language_ID=languages[language],\\n Parameter_ID=concepts[concept],\\n Value=value,\\n Source=[\\\"Marrison1967\\\"],\\n )\",\n \"def get_exp_value_from_spin_spin_corr(norm_correlators,norm_correlators_u):\\r\\n\\r\\n\\r\\n exp_vals = range(len(norm_correlators))\\r\\n exp_vals_u = range(len(norm_correlators))\\r\\n\\r\\n for i in range(len(norm_correlators)):\\r\\n exp_vals[i] = 1-2*(norm_correlators[i][1]+norm_correlators[i][2])\\r\\n exp_vals_u[i] = 2*np.sqrt((norm_correlators_u[i][1])**2+(norm_correlators_u[i][2])**2)\\r\\n\\r\\n return exp_vals,exp_vals_u\",\n \"async def fetch_referrals(self):\\n data = await self.http.get_referral_statistics()\",\n \"def _corr_ax1(input_image):\\n dim = input_image.shape[1]\\n m_ones = np.ones(dim)\\n norm_mask = np.correlate(m_ones, m_ones, mode=\\\"full\\\")\\n # not sure that the /2 is the correct correction\\n est_by_row = [np.argmax(np.correlate(v, v[::-1], mode=\\\"full\\\") / norm_mask) / 2 for v in input_image]\\n return np.histogram(est_by_row, bins=np.arange(0, dim + 1))\",\n \"def read(self, file_name, *, file_type=None):\\n self.logger.info('Reading KG correlations from %s',file_name)\\n with make_reader(file_name, file_type, self.logger) as reader:\\n self._read(reader)\",\n \"def _analyze_formcoords(self):\\n # ONLY FOR EXTERNAL GEARS SO FAR !!!\\n\\n # transform formcoords to NumPy-array\\n point = None\\n half_tooth = self.formcoords\\n\\n # convert to polar coordinates\\n half_tooth_polar = np.zeros([np.size(half_tooth, 0) - 1, 2])\\n for index in range(0, np.size(half_tooth, 0) - 1):\\n [r, phi] = CartesianCoordinatesToPolarCoordinates(half_tooth[index + 1, 0], half_tooth[index + 1, 1])\\n half_tooth_polar[index, 0] = r\\n half_tooth_polar[index, 1] = phi\\n d_f = 2 * min(half_tooth_polar[:, 0]) # minimum radius --> root circle\\n d_a = 2 * max(half_tooth_polar[:, 0]) # maximum radius --> tip circle\\n tau = 2 * (max(half_tooth_polar[:, 1]) - min(half_tooth_polar[:, 1])) # pitch angle [radians]\\n z = int(round(2 * pi / tau)) # number of teeth from pitch angle\\n\\n # for finding form diameters, it is checked if the points are part of the flank involute\\n # the limiting points of the flank involute define the form diameters\\n if 'alpha_n' in self.data and 'alpha_t' in self.data and 'x_E' in self.data:\\n tol = self._tol_default * self.data.get('m_n') # tolerance for comparisons\\n point_on_flank = False\\n first_limit_diameter = None\\n second_limit_diameter = None\\n for point in range(0, np.size(half_tooth_polar, 0)):\\n [x, y] = self._tooth_thickness(2 * half_tooth_polar[point, 0])\\n if abs(x + 2 * half_tooth[point + 1, 0]) < tol and abs(y - 2 * half_tooth[point + 1, 1]) < tol:\\n if not point_on_flank:\\n first_limit_diameter = 2 * half_tooth_polar[point, 0]\\n point_on_flank = True\\n else:\\n if point_on_flank:\\n second_limit_diameter = 2 * half_tooth_polar[point, 0]\\n point_on_flank = False\\n if second_limit_diameter is None:\\n second_limit_diameter = 2 * half_tooth_polar[point, 0]\\n if first_limit_diameter == second_limit_diameter:\\n raise ValueError('tooth form coordinate analysis failed')\\n if first_limit_diameter > second_limit_diameter:\\n d_fa = first_limit_diameter\\n d_ff = second_limit_diameter\\n else:\\n d_fa = second_limit_diameter\\n d_ff = first_limit_diameter\\n if 'd_ff' in self.data: # use user-parameter if supplied\\n d_ff = self.data.get('d_ff')\\n if 'd_fa' in self.data:\\n d_ff = self.data.get('d_fa')\\n return {'d_f': d_f, 'd_a': d_a, 'd_ff': d_ff, 'd_fa': d_fa, 'z': z}\\n\\n else:\\n return {'d_f': d_f, 'd_a': d_a, 'z': z}\",\n \"def compute_couplings(corr_mat=None):\\n couplings = np.linalg.inv(corr_mat)\\n couplings = -1.0 * couplings\\n return couplings\",\n \"def parseBancorStatments(self, fname, mode=2, idx=[[15,76,137,198,259], [45,106,167,228,262]]):\\n print fname\\n res = open(fname, 'r').read().split('\\\\n')\\n if mode == 4:\\n for i in res:\\n print i\\n idx = p.DataFrame(idx)\\n p0 = p.DataFrame()\\n if mode == 1:\\n print idx.transpose().get_values()\\n print n.diff(idx)\\n print idx[0]\\n for [i, j] in enumerate(res):\\n print \\\"{0} {1}\\\".format(i,j)\\n for i in idx:\\n p0 = p0.combine_first(p.DataFrame(res).ix[idx[i][0]:idx[i][1],:])\\n ms = []\\n for i in list(p0.get_values()):\\n if mode == 4:\\n print i[0]\\n try:\\n #m = re.match(re.compile(r'([\\\\d\\\\/]+)[\\\\s]+([\\\\w\\\\s\\\\.\\\\%\\\\d]+?)[\\\\s]+([\\\\d\\\\,^%]+)(.*)'), i[0]).groups()\\n #m = re.match(re.compile(r'([\\\\d\\\\/]+)[\\\\s]+(.+?)[\\\\s]+([\\\\.\\\\d\\\\,^\\\\%]+)(.*)'), i[0]).groups()\\n #m = re.match(re.compile(r'([\\\\d\\\\/]+)[\\\\s]+(.+)[\\\\s]+([\\\\.\\\\d\\\\,^\\\\%]+)[\\\\s]+([\\\\d\\\\,]+)'), i[0]).groups()\\n m = re.match(re.compile(r'([\\\\d\\\\/]+)[\\\\s]+(.+)[\\\\s]([\\\\.\\\\d\\\\,^\\\\%]+)[\\\\s]([\\\\d\\\\,]+)'), i[0]).groups()\\n if mode == 3:\\n #print i[0]\\n print m\\n ms.append(m)\\n except AttributeError, e:\\n if mode == 1:\\n print i[0]\\n #print e\\n ''\\n ms = p.DataFrame(ms, columns=['Fecha', 'Concepto/Empresa', 'Debito/Credito', 'Saldo'])\\n for i in range(len(ms.ix[:,2])):\\n ms.ix[i,0] = self.cleanBancorDate(ms.ix[i,0], self.getBancorYear(fname))\\n ms.ix[i,2] = self.cleanBancorNumber(ms.ix[i,2])\\n ms.ix[i,3] = self.cleanBancorNumber(ms.ix[i,3])\\n #print ms\\n pres = p.DataFrame(res).ix[:,:]\\n ms = ms.set_index('Fecha')\\n #ms.ix[:,'Saldo'].plot(); show();\\n #print ms\\n return ms\\n print '==========================================================================================='\\n print '==========================================================================================='\",\n \"def find_facades_ransac(pcd, num_facades, dist_thresh):\\r\\n groups = []\\r\\n remaining_pcd = pcd\\r\\n for i in range(num_facades):\\r\\n print(\\\"iteration:\\\", i + 1)\\r\\n \\r\\n equation, inliers = remaining_pcd.segment_plane(distance_threshold=dist_thresh, ransac_n=3, num_iterations=1000)\\r\\n [a, b, c, d] = equation\\r\\n print(f\\\"Plane equation facade {i + 1:.2f}: {a:.2f}x + {b:.2f}y + {c:.2f}z + {d:.2f} = 0\\\")\\r\\n groups.append([equation, inliers])\\r\\n remaining_pcd = remaining_pcd.select_by_index(inliers, invert=True)\\r\\n\\r\\n return groups\",\n \"def load_all_corr():\\n cache_path = SAVE_ROOT / 'corr' / 'all' / 'cache.pkl'\\n if cache_path.is_file():\\n return pickle.load(cache_path.open('rb'))\\n\\n root = SAVE_ROOT / 'corr'\\n files = root.rglob('*.json')\\n\\n def path_to_items(path: Path):\\n key = path.parts[-4:-1]\\n return key, load_corr_matrix(*key)\\n\\n # Create the data afresh.\\n all_corr = dict(map(path_to_items, files))\\n make_parent_dirs(cache_path)\\n pickle.dump(all_corr, cache_path.open('wb'))\\n return all_corr\",\n \"def analysis(field: str, key_column: str, relative_count: bool) -> None:\\n key_count_dict = get_cached_count_dicts(field, key_column)\\n words = get_all_words(key_count_dict)\\n results = []\\n for word in tqdm(words):\\n if key_column == 'publication':\\n corr, p_value = get_publication_correlation(key_count_dict, word, relative_count)\\n elif key_column == 'year':\\n corr, p_value = get_year_correlation(key_count_dict, word, relative_count)\\n else:\\n raise ValueError(f'Wrong key_column: {key_column}')\\n if not math.isnan(corr) and not math.isnan(p_value):\\n results.append((word, corr, p_value))\\n results.sort(key=lambda e: e[1])\\n pprint(results[:10])\\n pprint(results[-10:])\",\n \"def _calc_corrections(self): \\n searchIter= self._niter-1\\n while searchIter > 0:\\n trySavefilename= self._createSavefilename(searchIter)\\n if os.path.exists(trySavefilename):\\n trySavefile= open(trySavefilename,'rb')\\n corrections= sc.array(pickle.load(trySavefile))\\n trySavefile.close()\\n break\\n else:\\n searchIter-= 1\\n if searchIter == 0:\\n corrections= sc.ones((self._npoints,2))\\n for ii in range(searchIter,self._niter):\\n if ii == 0:\\n currentDF= self._dftype(surfaceSigma=self._surfaceSigmaProfile,\\n beta=self._beta)\\n else:\\n currentDF= self._dftype(surfaceSigma=self._surfaceSigmaProfile,\\n beta=self._beta,\\n corrections=corrections,\\n npoints=self._npoints,\\n rmax=self._rmax,\\n savedir=self._savedir,\\n interp_k=self._interp_k)\\n newcorrections= sc.zeros((self._npoints,2))\\n for jj in range(self._npoints):\\n thisSurface= currentDF.surfacemass(self._rs[jj],\\n use_physical=False)\\n newcorrections[jj,0]= currentDF.targetSurfacemass(self._rs[jj],use_physical=False)/thisSurface\\n newcorrections[jj,1]= currentDF.targetSigma2(self._rs[jj],use_physical=False)*thisSurface\\\\\\n /currentDF.sigma2surfacemass(self._rs[jj],\\n use_physical=False)\\n #print(jj, newcorrections[jj,:])\\n corrections*= newcorrections\\n #Save\\n picklethis= []\\n for arr in list(corrections):\\n picklethis.append([float(a) for a in arr])\\n save_pickles(self._savefilename,picklethis) #We pickle a list for platform-independence)\\n return corrections\",\n \"def get_correlation(df):\\n frame_correlation = df.corr()\\n return frame_correlation\",\n \"def get_all_relaxed_candidates_after_generation(self, gen):\\n q = 'relaxed=1,extinct=0,generation<={0}'\\n entries = self.c.select(q.format(gen))\\n\\n trajs = []\\n for v in entries:\\n t = self.get_atoms(id=v.id)\\n t.info['confid'] = v.gaid\\n t.info['relax_id'] = v.id\\n trajs.append(t)\\n trajs.sort(key=lambda x: get_raw_score(x),\\n reverse=True)\\n return trajs\",\n \"def reference(facet_index, filename, cation, end_radius, start_radius=4.0,\\n nradii=10, verbose=False):\\n if verbose:\\n print(\\\"Loading structure file \\\" + filename + \\\"...\\\")\\n\\n try:\\n # Load the structure file into a Cage object\\n anion = OccupiedCage.from_file(filename)\\n except ValueError:\\n # If the conventional formats fail, try using the from_poscar method.\\n anion = Cage.from_poscar(filename)\\n\\n if verbose:\\n print(\\\"Setting up surface facets...\\\")\\n\\n anion.center()\\n anion.find_surface_facets(ignore=IGNORE)\\n\\n if verbose:\\n print(\\\"Found \\\" + str(len(anion.facets)) + \\\" facets.\\\")\\n\\n reference_dir = 'reference_fa' + str(facet_index)\\n try:\\n os.mkdir(reference_dir)\\n except FileExistsError:\\n pass\\n\\n if verbose:\\n print(\\\"Setting up reference calculations...\\\")\\n\\n reference_facet = anion.facets[facet_index]\\n\\n # Write out a facet json file\\n reference_facet.to(fmt='json', filename=os.path.join(reference_dir,\\n 'facet.json'))\\n\\n for radius in np.linspace(start_radius, end_radius, nradii):\\n\\n if verbose:\\n print(\\\"Adding cation to reference facet at distance = \\\"\\n + str(radius) + \\\" Angstroms.\\\")\\n\\n # Set up the cation site\\n mol = anion.copy()\\n mol.append(pmg.Specie(cation, 1),\\n unit_vector(reference_facet.center) * radius)\\n\\n mol.set_charge_and_spin(charge=anion.charge + 1)\\n\\n if verbose:\\n print(\\\"Setting up the task...\\\")\\n\\n # Set up the task for the calculation\\n tasks = [nwchem.NwTask(mol.charge, None, BASIS,\\n theory='dft',\\n operation=\\\"energy\\\",\\n theory_directives=THEORY_SETUP,\\n alternate_directives=ALT_SETUP)]\\n\\n if verbose:\\n print(\\\"Setting up the input file...\\\")\\n\\n # Set up input\\n nw_input = nwchem.NwInput(mol, tasks, geometry_options=GEO_SETUP)\\n\\n calculation_dir = os.path.join(reference_dir, \\\"radius=\\\"\\n + '{0:.2f}'.format(radius))\\n\\n try:\\n os.mkdir(os.path.join(calculation_dir))\\n except FileExistsError:\\n pass\\n\\n nw_input.write_file(os.path.join(calculation_dir, 'input'))\\n\\n # Write a xyz file of the molecule with the reference cation\\n mol.to(fmt='xyz', filename=os.path.join(calculation_dir,\\n 'geometry.xyz'))\",\n \"def get_all_translations(rna_sequence, genetic_code):\\n #Convert all rna_sequence to upper case:\\n rna_sequence=rna_sequence.upper()\\n #get the lengh of RNA seq.\\n total_rna_bases=len(rna_sequence)\\n #Create an empty list to store all possible AA seq.\\n polypeptide_list = []\\n #Looping through all the RNA bases, selecting all 3 possible reading frames to scan for tranlation.\\n for i in range(total_rna_bases):\\n i_end= i +3\\n next_three=rna_sequence[i:i_end]\\n #Condition to check if the condon is start codon\\n if next_three=='AUG':\\n #If condition satisfies, translate all rna seq from start to stop codon using first function,\\n #translate_sequence\\n polypeptide=translate_sequence(rna_sequence[i:], genetic_code)\\n polypeptide_list.append(polypeptide)\\n #Return all 3 possible reading frames as a list in polypeptide_list\\n return polypeptide_list\",\n \"def correlations(self, data,label, **kwds):\\n corrmat = data.corr(**kwds)\\n\\n fig, ax1 = plt.subplots(ncols=1, figsize=(6,5))\\n\\n opts = {'cmap': plt.get_cmap(\\\"RdBu\\\"),\\n 'vmin': -1, 'vmax': +1}\\n heatmap1 = ax1.pcolor(corrmat, **opts)\\n plt.colorbar( heatmap1, ax=ax1 )\\n\\n ax1.set_title( \\\"Correlations\\\" )\\n\\n labels = corrmat.columns.values\\n for ax in (ax1,):\\n # shift location of ticks to center of the bins\\n ax.set_xticks(np.arange(len(labels))+0.5, minor=False)\\n ax.set_yticks(np.arange(len(labels))+0.5, minor=False)\\n ax.set_xticklabels(labels, minor=False, ha='right', rotation=70)\\n ax.set_yticklabels(labels, minor=False)\\n plt.tight_layout()\\n plt.savefig('plots/correlation_' + self.version + '_' + label + '.pdf')\",\n \"def _transcode_crf(binary: Path, params: str, crf: float) -> Dict[str, float]:\\n with tqdm(FILES, leave=False, desc=f'Try CRF={crf}') as paths:\\n scores = [_transcode_crf_one(binary, params, crf, p) for p in paths]\\n return {k: hmean([score[k] for score in scores]) for k in COLUMNS}\",\n \"def get_correlation(self, f, peaks):\\n c = 0\\n for s in peaks:\\n c += e ** -((2 * (f - s[0]) / self.spacing) ** 2)\\n \\n return c\",\n \"def resultat(self, concordance_mf, concordance_pf, liste_F, liste_M, liste_P):\\n resultat = {\\\"Marqueur\\\": [], \\\"Conclusion\\\": [], \\\"Concordance Mere/Foetus\\\": [], \\\"Détails M/F\\\": [],\\n \\\"Concordance Pere/Foetus\\\": [], \\\"Détails P/F\\\": []}\\n marqueurs_conta = 0\\n marqueurs_non_conta = 0\\n somme_conta = 0\\n if liste_F[0].allele[1] == 0.0:\\n self.set_sexe(\\\"F\\\")\\n else:\\n self.set_sexe(\\\"M\\\")\\n if concordance_mf != 16 and concordance_pf != 16 and concordance_pf != None:\\n self.set_concordance_mere_foet(\\\"NON\\\")\\n self.set_concordance_pere_foet(\\\"NON\\\")\\n del resultat[\\\"Conclusion\\\"]\\n for nbres in range(1, len(liste_F)):\\n resultat[\\\"Marqueur\\\"].append(str(liste_F[nbres].marqueur))\\n resultat[\\\"Concordance Mere/Foetus\\\"].append(liste_F[nbres].concordance_mere_foetus)\\n resultat[\\\"Concordance Pere/Foetus\\\"].append(liste_P[nbres].concordance_pere_foetus)\\n if liste_F[nbres].concordance_mere_foetus == \\\"NON\\\" and liste_P[nbres].concordance_pere_foetus == \\\"NON\\\":\\n resultat[\\\"Détails M/F\\\"].append(\\n \\\"M : \\\" + str(liste_M[nbres].normalisation(liste_M[nbres].allele)) + \\\" F: \\\" + str(\\n liste_F[nbres].normalisation(liste_F[nbres].allele)))\\n resultat[\\\"Détails P/F\\\"].append(\\n \\\"P : \\\" + str(liste_P[nbres].normalisation(liste_P[nbres].allele)) + \\\" F : \\\" + str(\\n liste_F[nbres].normalisation(liste_F[nbres].allele)))\\n elif liste_F[nbres].concordance_mere_foetus == \\\"NON\\\":\\n resultat[\\\"Détails M/F\\\"].append(\\n \\\"M: \\\" + str(liste_M[nbres].normalisation(liste_M[nbres].allele)) + \\\" F : \\\" + str(\\n liste_F[nbres].normalisation(liste_F[nbres].allele)))\\n resultat[\\\"Détails P/F\\\"].append(\\\"\\\")\\n elif liste_P[nbres].concordance_pere_foetus == \\\"NON\\\":\\n resultat[\\\"Détails P/F\\\"].append(\\n \\\"P: \\\" + str(liste_P[nbres].normalisation(liste_P[nbres].allele)) + \\\" F: \\\" + str(\\n liste_F[nbres].normalisation(liste_F[nbres].allele)))\\n resultat[\\\"Détails M/F\\\"].append(\\\"\\\")\\n else:\\n resultat[\\\"Détails M/F\\\"].append(\\\"\\\")\\n resultat[\\\"Détails P/F\\\"].append(\\\"\\\")\\n conclusion = pd.DataFrame({\\\"1\\\": [\\\"Non calculé\\\", \\\"Non calculé\\\", \\\"Non calculé\\\", self.get_date()]},\\n index=[\\\"Nombre de marqueurs informatifs non contaminés\\\",\\n \\\"Nombre de marqueurs informatifs contaminés\\\",\\n \\\"Moyenne du pourcentage de contamination\\\", \\\"Date\\\"])\\n resultats = pd.DataFrame(resultat, columns=[\\\"Marqueur\\\", \\\"Concordance Mere/Foetus\\\", \\\"Détails M/F\\\",\\n \\\"Concordance Pere/Foetus\\\", \\\"Détails P/F\\\"])\\n return resultats, conclusion\\n elif concordance_mf != len(liste_F) and concordance_pf == len(liste_F) or concordance_mf != len(\\n liste_F) and concordance_pf == None:\\n self.set_concordance_mere_foet(\\\"NON\\\")\\n self.set_concordance_pere_foet(\\\"OUI\\\")\\n if concordance_pf == None:\\n self.set_concordance_pere_foet(\\\"ABS\\\")\\n del resultat[\\\"Conclusion\\\"]\\n del resultat[\\\"Concordance Pere/Foetus\\\"]\\n del resultat[\\\"Détails P/F\\\"]\\n for nbres in range(1, len(liste_F)):\\n resultat[\\\"Marqueur\\\"].append(str(liste_F[nbres].marqueur))\\n resultat[\\\"Concordance Mere/Foetus\\\"].append(liste_F[nbres].concordance_mere_foetus)\\n if liste_F[nbres].concordance_mere_foetus == \\\"NON\\\":\\n resultat[\\\"Détails M/F\\\"].append(\\n \\\"M: \\\" + str(liste_M[nbres].normalisation(liste_M[nbres].allele)) + \\\" F: \\\" + str(\\n liste_F[nbres].normalisation(liste_F[nbres].allele)))\\n else:\\n resultat[\\\"Détails M/F\\\"].append(\\\"\\\")\\n conclusion = pd.DataFrame({\\\"1\\\": [\\\"Non calculé\\\", \\\"Non calculé\\\", \\\"Non calculé\\\", self.get_date()]},\\n index=[\\\"Nombre de marqueurs informatifs non contaminés\\\",\\n \\\"Nombre de marqueurs informatifs contaminés\\\",\\n \\\"Moyenne du pourcentage de contamination\\\", \\\"Date\\\"])\\n resultats = pd.DataFrame(resultat, columns=[\\\"Marqueur\\\", \\\"Concordance Mere/Foetus\\\", \\\"Détails M/F\\\"])\\n return resultats, conclusion\\n elif concordance_mf == len(liste_F) and concordance_pf == len(liste_F) or concordance_mf == len(\\n liste_F) and concordance_pf == None:\\n self.set_concordance_mere_foet(\\\"OUI\\\")\\n self.set_concordance_pere_foet(\\\"OUI\\\")\\n if concordance_pf == None:\\n self.set_concordance_pere_foet(\\\"ABS\\\")\\n del resultat[\\\"Concordance Mere/Foetus\\\"]\\n del resultat[\\\"Concordance Pere/Foetus\\\"]\\n del resultat[\\\"Détails P/F\\\"]\\n for nbres in range(1, len(liste_F)):\\n resultat[\\\"Marqueur\\\"].append(str(liste_F[nbres].marqueur))\\n if liste_F[nbres].informatif == 0:\\n resultat[\\\"Conclusion\\\"].append(\\\"Non informatif\\\")\\n resultat[\\\"Détails M/F\\\"].append(\\\"Mère homozygote\\\")\\n elif liste_F[nbres].informatif == 1:\\n if liste_F[nbres].contamination == 0:\\n marqueurs_non_conta += 1\\n resultat[\\\"Conclusion\\\"].append(\\\"Non contaminé\\\")\\n resultat[\\\"Détails M/F\\\"].append(\\\"\\\")\\n elif liste_F[nbres].contamination == 1:\\n marqueurs_conta += 1\\n somme_conta = somme_conta + liste_F[nbres].taux\\n resultat[\\\"Conclusion\\\"].append(\\\"Contaminé\\\")\\n resultat[\\\"Détails M/F\\\"].append(\\\"Taux contamination : \\\" + str(liste_F[nbres].taux) + \\\"%\\\")\\n else:\\n marqueurs_conta += 1\\n somme_conta = somme_conta + liste_F[nbres].taux\\n resultat[\\\"Conclusion\\\"].append(\\\"Contaminé\\\")\\n resultat[\\\"Détails M/F\\\"].append(\\\"Taux contamination : \\\" + str(liste_F[nbres].taux) + \\\"%\\\")\\n elif liste_F[nbres].informatif == 2:\\n resultat[\\\"Conclusion\\\"].append(\\\"Non informatif\\\")\\n resultat[\\\"Détails M/F\\\"].append(\\\"Allèles semblables\\\")\\n else:\\n resultat[\\\"Conclusion\\\"].append(\\\"Non informatif\\\")\\n resultat[\\\"Détails M/F\\\"].append(\\\"Echo\\\")\\n resultats = pd.DataFrame(resultat, columns=[\\\"Marqueur\\\", \\\"Conclusion\\\", \\\"Détails M/F\\\"])\\n try:\\n moyenne_conta = somme_conta / marqueurs_conta\\n except ZeroDivisionError:\\n moyenne_conta = 0\\n conclusion = pd.DataFrame(\\n {\\\"1\\\": [int(marqueurs_non_conta), int(marqueurs_conta), round(moyenne_conta, 2), self.get_date()]},\\n index=[\\\"Nombre de marqueurs informatifs non contaminés\\\", \\\"Nombre de marqueurs informatifs contaminés\\\",\\n \\\"Moyenne du pourcentage de contamination\\\", \\\"Date\\\"])\\n return resultats, conclusion\\n elif concordance_mf == len(liste_F) and concordance_pf != len(liste_F):\\n self.set_concordance_mere_foet(\\\"OUI\\\")\\n self.set_concordance_pere_foet(\\\"NON\\\")\\n del resultat[\\\"Concordance Mere/Foetus\\\"]\\n for nbres in range(1, len(liste_F)):\\n resultat[\\\"Concordance Pere/Foetus\\\"].append(liste_P[nbres].concordance_pere_foetus)\\n if liste_P[nbres].concordance_pere_foetus == \\\"NON\\\":\\n resultat[\\\"Détails P/F\\\"].append(\\n \\\"P: \\\" + str(liste_P[nbres].normalisation(liste_P[nbres].allele)) + \\\" F: \\\" + str(liste_P[nbres].normalisation(liste_P[nbres].allele)))\\n else:\\n resultat[\\\"Détails P/F\\\"].append(\\\"\\\")\\n for nbres in range(1, len(liste_F)):\\n resultat[\\\"Marqueur\\\"].append(str(liste_F[nbres].marqueur))\\n if liste_F[nbres].informatif == 0:\\n resultat[\\\"Conclusion\\\"].append(\\\"Non informatif\\\")\\n resultat[\\\"Détails M/F\\\"].append(\\\"Mère homozygote\\\")\\n elif liste_F[nbres].informatif == 1:\\n if liste_F[nbres].contamination == 0:\\n marqueurs_non_conta += 1\\n resultat[\\\"Conclusion\\\"].append(\\\"Non contaminé\\\")\\n resultat[\\\"Détails M/F\\\"].append(\\\"\\\")\\n elif liste_F[nbres].contamination == 1:\\n marqueurs_conta += 1\\n somme_conta = somme_conta + liste_F[nbres].taux\\n resultat[\\\"Conclusion\\\"].append(\\\"Contaminé\\\")\\n resultat[\\\"Détails M/F\\\"].append(\\\"Taux contamination : \\\" + str(liste_F[nbres].taux) + \\\"%\\\")\\n else:\\n marqueurs_conta += 1\\n somme_conta = somme_conta + liste_F[nbres].taux\\n resultat[\\\"Conclusion\\\"].append(\\\"Contaminé\\\")\\n resultat[\\\"Détails M/F\\\"].append(\\\"Taux contamination : \\\" + str(liste_F[nbres].taux) + \\\"%\\\")\\n elif liste_F[nbres].informatif == 2:\\n resultat[\\\"Conclusion\\\"].append(\\\"Non informatif\\\")\\n resultat[\\\"Détails M/F\\\"].append(\\\"Allèles semblables\\\")\\n else:\\n resultat[\\\"Conclusion\\\"].append(\\\"Non informatif\\\")\\n resultat[\\\"Détails M/F\\\"].append(\\\"Echo\\\")\\n resultats = pd.DataFrame(resultat,\\n columns=[\\\"Marqueur\\\", \\\"Conclusion\\\", \\\"Détails M/F\\\", \\\"Concordance Pere/Foetus\\\",\\n \\\"Détails P/F\\\"])\\n try:\\n moyenne_conta = somme_conta / marqueurs_conta\\n except ZeroDivisionError:\\n moyenne_conta = 0\\n conclusion = pd.DataFrame(\\n {\\\"1\\\": [int(marqueurs_non_conta), int(marqueurs_conta), round(moyenne_conta, 2), self.get_date()]},\\n index=[\\\"Nombre de marqueurs informatifs non contaminés\\\", \\\"Nombre de marqueurs informatifs contaminés\\\",\\n \\\"Moyenne du pourcentage de contamination\\\", \\\"Date\\\"])\\n return resultats, conclusion\",\n \"def get_forms(console: Console, sess: requests.Session, form_id: str = \\\"General_Record_2020v2.0\\\"):\\n raw_resp = get_url(url=f\\\"https://forms.agterra.com/api/{form_id}/GetAll/0\\\", sess=sess)\\n\\n if raw_resp.status_code != 200:\\n console.log(f\\\"[red] Something went wrong, we got status [white]{raw_resp.status_code}\\\")\\n json_data = raw_resp.json()\\n console.log(f\\\"Message Data: {json_data}\\\")\\n\\n json_data = raw_resp.json()\\n\\n return json_data\",\n \"def full_colexification(forms):\\n # We assume the forms to be sorted by clics_form already:\\n expected, seen = len(forms), 0\\n for _, _forms in itertools.groupby(forms, lambda f: f.clics_form):\\n cids = set()\\n fs = []\\n for f in _forms:\\n seen += 1\\n if f.concepticon_id not in cids:\\n fs.append(f)\\n cids.add(f.concepticon_id)\\n yield fs\\n if expected != seen: # pragma: no cover\\n raise ValueError('forms not properly ordered')\",\n \"def estimate_F_ransac(corr, num_iter, inlier_thresh):\\n _, inlier_mask = ransac(corr, estimate_F, sym_epipolar_dist, 8, num_iter, inlier_thresh)\\n # inlier_mask = np.ones(9)\\n # inlier_mask[0] = 0\\n F = estimate_F(corr[inlier_mask.astype(np.bool)])\\n return F\",\n \"def _set_correlations(self) -> None:\\n pass\",\n \"def add_restypes(df, compl_data, recept_info, recept_info_order):\\n AAs = {'C': 'CYS', 'D': 'ASP', 'S': 'SER', 'Q': 'GLN', 'K': 'LYS',\\n 'I': 'ILE', 'P': 'PRO', 'T': 'THR', 'F': 'PHE', 'N': 'ASN', \\n 'G': 'GLY', 'H': 'HIS', 'L': 'LEU', 'R': 'ARG', 'W': 'TRP', \\n 'A': 'ALA', 'V': 'VAL', 'E': 'GLU', 'Y': 'TYR', 'M': 'MET'}\\n GPCRclass_numbers = {'A':1, 'B':2, 'C':3, 'F':4}\\n class_index = recept_info_order['gpcr_class']\\n\\n def get_restype_and_realpos(dynid_col, pos_col):\\n \\\"\\\"\\\"\\n Return residue type of position and the position identifier for this position in this protein\\n It's a bit complex, for I need to distinguish which GPCR class nomenclature uses this protein\\n \\\"\\\"\\\"\\n restype_list = []\\n prot_pos = []\\n for dynid,pos in zip(dynid_col, pos_col):\\n if pos == 'Ligand':\\n restype_list.append('')\\n prot_pos.append('Ligand')\\n else: \\n GPCRclass_number = GPCRclass_numbers[recept_info[dynid][class_index]] \\n class_pos_array = pos.split('\\\\n')\\n class_pos = class_pos_array[0]+class_pos_array[GPCRclass_number]\\n\\n if class_pos in compl_data[dynid]['gpcr_pdb']: \\n restype = compl_data[dynid]['gpcr_pdb'][class_pos][-1]\\n restype_list.append(restype)\\n prot_pos.append(class_pos)\\n else:\\n #print(\\\"Position %s not found in %s\\\" %(class_pos, dynid)) #Too much output\\n restype_list.append(\\\"(N/A)\\\")\\n prot_pos.append(class_pos)\\n\\n return(restype_list,prot_pos)\\n\\n #Split Position column\\n new = df['Position'].str.split(\\\"\\\\n\\\\n\\\", n = 1, expand = True)\\n df['Position 1'] = new[0]\\n df['Position 2'] = new[1]\\n\\n #Add residue type and protein position column\\n (restype_1, protein_Position_1) = get_restype_and_realpos(df['Id'], df['Position 1'].values)\\n df['restype_1'] = restype_1\\n df['protein_Position 1'] = protein_Position_1\\n (restype_2, protein_Position_2) = get_restype_and_realpos(df['Id'], df['Position 2'].values)\\n df['restype_2'] = restype_2\\n df['protein_Position 2'] = protein_Position_2\\n \\n df['restypes'] = df['restype_1'] +\\\" \\\"+ df['restype_2'] \\n df['protein_Position'] = df['protein_Position 1'] +\\\" \\\"+ df['protein_Position 2']\\n df['restypePosition'] = df['restype_1'] + df['protein_Position 1'] + \\\" \\\" + df['restype_2'] + df['protein_Position 2']\\n \\n #Drop Position, proteinPosition and restype columns once they are not needed\\n df.drop(columns = ['Position 1','Position 2','restype_1','restype_2','protein_Position 1','protein_Position 2'], inplace = True)\\n \\n return df\",\n \"def read_corr(file):\\n corrTable = open(file, 'r').read()\\n corrTable = corrTable.split('\\\\n')\\n for i in range(4):\\n corrTable[i] = corrTable[i].split(':')\\n corrTable[i][1] = corrTable[i][1].split(',')\\n corr_dict = {}\\n for i in range(4):\\n corr_dict[corrTable[i][0]] = corrTable[i][1]\\n return corr_dict\",\n \"def get_kpi_only_france(name, column, mean, transformDF=False):\\n indicateurResult = get_empty_kpi()\\n config = get_config(name)\\n log.debug('Processing - '+name)\\n\\n indicateurResult['nom'] = config['nom']\\n indicateurResult['unite'] = config['unite']\\n indicateurResult['unite_short'] = config['unite_short']\\n indicateurResult['trendType'] = config['trendType']\\n indicateurResult['color'] = get_color_indicateur(config)\\n\\n df = pd.read_csv(\\n 'files_new/'+config['res_id'],\\n sep=None,\\n engine='python',\\n dtype={'reg': str, 'dep': str}\\n )\\n \\n\\n df = enrich_dataframe(df, name)\\n df = df[['date', column]]\\n for country in tqdm(countries, desc=\\\"Processing National\\\"):\\n res = get_kpi_by_type(\\n df.groupby(['date'], as_index=False).sum(),\\n 'nat',\\n 'fra',\\n config['trendType'],\\n column,\\n mean\\n )\\n indicateurResult['france'].append(res)\\n\\n save_result(indicateurResult, name)\",\n \"def comp_ind_finder(self, n, minfreq, maxfreq, lat1, lat0, abs = False, norm = True,\\\\\\n pole = \\\"north\\\"):\\n \\n assert self.shifted, \\\"Data has not been index-shifted\\\"\\n\\n inds = self.mlat_finder(lat1, lat0, pole)[1]\\n self.inds = inds\\n NeA = self.NeA[inds]\\n NeB = self.NeB[inds]\\n NeC = self.NeC[inds]\\n secondsA = self.secondsA[inds]\\n secondsB = self.secondsB[inds]\\n secondsC = self.secondsC[inds]\\n\\n timesA, fftA = self.fft_time_holes_integral(signal = NeA,\\\\\\n seconds = secondsA, n = n, fs = self.fs, minfreq\\\\\\n = minfreq, maxfreq = maxfreq)\\n\\n timesB, fftB = self.fft_time_holes_integral(signal = NeB,\\\\\\n seconds = secondsB, n = n, fs = self.fs, minfreq\\\\\\n = minfreq, maxfreq = maxfreq)\\n\\n timesC, fftC = self.fft_time_holes_integral(signal = NeC,\\\\\\n seconds = secondsC, n = n, fs = self.fs, minfreq\\\\\\n = minfreq, maxfreq = maxfreq)\\n\\n\\n BAdiff = self.relative_diff(fftB, fftA, norm = norm, abs = abs)\\n BCdiff = self.relative_diff(fftB, fftC, norm = norm, abs = abs)\\n ACdiff = self.relative_diff(fftA, fftC, norm = norm, abs = abs)\\n \\n return(BAdiff, BCdiff, ACdiff)\",\n \"def linearize_spatial_corr(Mcorr):\\n assert type(Mcorr) is numpy.ndarray, 'bad type for matrix argument'\\n assert Mcorr.shape == (199,199), 'invalid shape for autocorrelation matrix'\\n \\n # Scan the correlogram and compute the radius from the midpoint\\n n = numpy.zeros(101, 'h')\\n c = numpy.zeros(101, 'd')\\n mid_x, mid_y = 99.5, 99.5\\n for i in xrange(199):\\n for j in xrange(199):\\n d = numpy.sqrt((mid_y - i)**2 + (mid_x - j)**2)\\n if d > 100:\\n d = 100\\n n[int(d)] += 1\\n c[int(d)] += Mcorr[i, j]\\n c /= n # get the sample means\\n \\n # Create the return array: reflect 0->Max correlations to -Max->0 \\n Lcorr = numpy.zeros((2,201), 'd')\\n Lcorr[0] = numpy.arange(-100, 101)\\n Lcorr[1] = numpy.r_[c[::-1], c[1:]]\\n \\n return Lcorr\",\n \"def refere(eeg, channels, mode='contralateral'):\\n\\tbipolar_map = {'Fp1':'Fp2', 'Fp2':'Fp2', 'F3':'F4', 'F4':'F4', 'C3':'C4', 'C4':'C4', 'T3':'T4', 'T4':'T4', 'P3':'P4', 'P4':'P4', 'O1':'O2', 'O2':'O2'}\\n\\tif mode not in ['monopolar', 'contralateral', 'bipolar', 'linked', 'average']:\\n\\t\\tprint 'WARNING - refere(): parameter \\\"mode\\\" can only be \\\"monopolar\\\", \\\"contralateral\\\", \\\"bipolar\\\" or \\\"linked\\\". Using \\\"contralateral\\\"!'\\n\\t\\tmode = 'contralateral'\\n\\tif mode == 'linked':\\t\\t\\n\\t\\treference = (eeg[:,channels.index('A1')] + eeg[:,channels.index('A2')])/2.\\n\\tif mode == 'average':\\n\\t\\treference = np.zeros(len(eeg), dtype=np.float32)\\n\\t\\tchcounter = 0\\n\\t\\tfor channel in range(len(channels)):\\n\\t\\t\\tif (channels[channel] in EEG_CHANNELS):\\n\\t\\t\\t\\treference += eeg[:, channel]\\n\\t\\t\\t\\tchcounter += 1\\n\\t\\treference /= chcounter\\n\\tfor channel in range(len(channels)):\\n\\t\\tif (channels[channel] in EEG_CHANNELS):\\n\\t\\t\\t# mindenkit referalunk kiveve magukat a referencia csatornakat\\n\\t\\t\\tif mode == 'contralateral':\\n\\t\\t\\t\\tif (channels[channel] in ['Fp2', 'F4', 'C4', 'T4', 'P4', 'O2']):\\n\\t\\t\\t\\t\\tref_channel = channels.index('A1')\\n\\t\\t\\t\\telif (channels[channel] in ['Fp1', 'F3', 'C3', 'T3', 'P3', 'O1']):\\n\\t\\t\\t\\t\\tref_channel = channels.index('A2')\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tprint \\\"Error: what kind of channel is this: \\\", channels[channel], \\\" cannot reference!!!!\\\"\\n\\t\\t\\t\\treference = eeg[:, ref_channel]\\n\\t\\t\\t\\tprint \\\"channel \\\", channels[channel], \\\" referenced to \\\", channels[ref_channel]\\n\\t\\t\\tif mode == 'bipolar':\\n\\t\\t\\t\\tref_channel = channels.index(bipolar_map[channels[channel]])\\n\\t\\t\\t\\treference = eeg[:, ref_channel]\\n\\t\\t\\t\\tprint \\\"channel \\\", channels[channel], \\\" referenced to \\\", channels[ref_channel]\\n\\t\\t\\teeg[:, channel] -= reference\",\n \"def highlight_correlated_feature_twoclass(\\n file_samples=100,\\n match_types_in=match_types, figsize=(12, 14),\\n process_data=None, pca_vec_to_plot=5, debug=1):\\n corr_accum = None\\n if process_data is not None:\\n data_type = '(frqeuency)'\\n else:\\n data_type = '(time-domain)'\\n for match_type in match_types_in:\\n for aidx, is_alcoholic in enumerate([True, False]):\\n corr_accum = cov_accum = None\\n if debug:\\n print(f'getting example data for match_type[{match_type}]'\\n f' and is_alcoholic[{is_alcoholic}]')\\n file_list = sample_file_list(\\n limitby=dict(\\n match=match_type,\\n alcoholic=is_alcoholic),\\n limit_mult_files=file_samples,\\n balance_types=[('subject', 10)], df_type='wide',\\n seed=42, debug=max(0, debug - 1))\\n\\n for file in tqdm.tqdm(file_list):\\n df, info = my_read_eeg_generic(\\n file, orig_tt_indic=('test' in str.lower(file)))\\n\\n corr = df.corr()\\n cov = df.cov().values\\n if corr_accum is None:\\n sen_names = corr.columns.levels[\\n corr.columns.names.index('sensor')]\\n nsen = len(sen_names)\\n if process_data:\\n x, Z, xl, yl = process_data([], df.values, '',\\n '', fs=SAMP_FREQ)\\n df = pd.DataFrame(Z)\\n corr = df.corr()\\n cov = df.cov().values\\n if corr_accum is None:\\n corr_accum = corr.values\\n cov_accum = cov/nsen\\n else:\\n corr_accum += corr.values\\n cov_accum += cov/nsen\\n\\n corr_accum /= len(file_list)\\n if aidx == 0:\\n corr_alcoholic = corr_accum.copy()\\n cov_alcoholic = cov_accum.copy()\\n else:\\n corr_nonalch = corr_accum.copy()\\n cov_nonalch = cov_accum.copy()\\n\\n if debug > 1:\\n pyplot.figure(figsize=figsize)\\n pyplot.pcolor(np.flipud(corr_accum))\\n pyplot.xticks(np.arange(nsen), sen_names)\\n pyplot.yticks(np.arange(nsen), reversed(sen_names))\\n pyplot.title(f'corr - across sensors {data_type} - '\\n f'is_alcoholic[{is_alcoholic}] - match[{match_type}]')\\n pyplot.colorbar()\\n pyplot.show()\\n\\n Ua, svs_a, Va = np.linalg.svd(cov_alcoholic, full_matrices=False,\\n compute_uv=True)\\n Una, svs_na, Vna = np.linalg.svd(cov_nonalch, full_matrices=False,\\n compute_uv=True)\\n print('SVec size', Una.shape)\\n\\n pyplot.figure(figsize=(figsize[0], 6))\\n leg = []\\n lg, = pyplot.plot(svs_a, label='alcoholic')\\n leg.append(lg)\\n lg, = pyplot.plot(svs_na, label='not alcoholic')\\n leg.append(lg)\\n pyplot.legend(handles=leg)\\n pyplot.title(f'PCA decomposition: SVs - across sensors {data_type} - '\\n f'- match[{match_type}]')\\n pyplot.show()\\n\\n pyplot.figure(figsize=(figsize[0]+4, 8))\\n leg = []\\n is_slice = isinstance(pca_vec_to_plot, tuple)\\n if is_slice:\\n pca_vec_to_plot_count = pca_vec_to_plot[1] - pca_vec_to_plot[0]\\n else:\\n pca_vec_to_plot_count = pca_vec_to_plot\\n pca_vec_to_plot = (0, pca_vec_to_plot)\\n pca_vec_to_plot_count = min(pca_vec_to_plot_count, nsen)\\n for i, idx in enumerate(range(*pca_vec_to_plot)):\\n lg, = pyplot.plot(-i + Ua[:, idx], '-',\\n linewidth=2,\\n label=f'feat[{idx}]: alcoholic')\\n leg.append(lg)\\n for i, idx in enumerate(range(*pca_vec_to_plot)):\\n lg, = pyplot.plot(-i + Una[:, idx], '--',\\n linewidth=2,\\n label=f'feat[{idx}]: not alcoholic')\\n leg.append(lg)\\n\\n pyplot.xticks(np.arange(nsen), sen_names, fontsize=7)\\n pyplot.xlim((0, nsen + 10))\\n pyplot.yticks(-np.arange(pca_vec_to_plot_count), [\\n f'PCA_dim[{i}]' for i in np.arange(pca_vec_to_plot_count)])\\n pyplot.legend(handles=leg)\\n # pyplot.xticks(np.arange(nsen), sen_names)\\n # pyplot.yticks(np.arange(nsen), reversed(sen_names))\\n ind_str = (f\\\"{pca_vec_to_plot[0]} to {pca_vec_to_plot[1]-1}\\\" if is_slice\\n else f\\\"- first {pca_vec_to_plot_count}\\\")\\n pyplot.title(f'PCA decomposition: singular vectors {ind_str} '\\n f'- across sensors {data_type} - '\\n f'match[{match_type}]')\\n\\n pyplot.figure(figsize=figsize)\\n pyplot.pcolor(np.flipud(corr_alcoholic - corr_nonalch))\\n pyplot.xticks(np.arange(nsen), sen_names)\\n pyplot.yticks(np.arange(nsen), reversed(sen_names))\\n pyplot.title(f'corr - across sensors {data_type} - '\\n f'(alcoholic-nonalcoholic) - match[{match_type}]')\\n pyplot.colorbar()\\n pyplot.show()\\n if is_slice:\\n return Ua[:, pca_vec_to_plot[0]:pca_vec_to_plot[1]], Una[:,\\n pca_vec_to_plot[0]:pca_vec_to_plot[1]]\\n else:\\n return Ua[:, :pca_vec_to_plot_count], Una[:, :pca_vec_to_plot_count]\",\n \"def read_IGRF13coeffs(file):\\r\\n\\r\\n # Standard Library dependencies\\r\\n import os\\r\\n\\r\\n # 3rd party dependencies\\r\\n import numpy as np\\r\\n import pandas as pd\\r\\n\\r\\n file = os.path.join(os.path.dirname(__file__), file)\\r\\n\\r\\n df = pd.read_csv(file, header=3, sep=\\\"\\\\s+\\\")\\r\\n Years = [x for x in df.columns if x[-2:] == \\\".0\\\"]\\r\\n v = []\\r\\n for x in df.groupby(\\\"g/h\\\"):\\r\\n v.append(x)\\r\\n g = v[0][1]\\r\\n h = v[1][1]\\r\\n dic_dic_g = {}\\r\\n dic_dic_h = {}\\r\\n dic_dic_SV_g = {}\\r\\n dic_dic_SV_h = {}\\r\\n dic_N = {}\\r\\n for Year in Years:\\r\\n key_Year = str(int(float(Year)))\\r\\n dic_dic_g[key_Year] = {(x[0], x[1]): x[2] for x in zip(g[\\\"m\\\"], g[\\\"n\\\"], g[Year])}\\r\\n dic_dic_h[key_Year] = {(x[0], x[1]): x[2] for x in zip(h[\\\"m\\\"], h[\\\"n\\\"], h[Year])}\\r\\n dic_dic_SV_g[key_Year] = {(x[0], x[1]): 0 for x in zip(g[\\\"m\\\"], g[\\\"n\\\"])}\\r\\n dic_dic_SV_h[key_Year] = {(x[0], x[1]): 0 for x in zip(g[\\\"m\\\"], g[\\\"n\\\"])}\\r\\n index = set([x[0] for x in dic_dic_h[key_Year].keys()])\\r\\n N = max(index)\\r\\n dic_N[key_Year] = N # must be 13\\r\\n for n in range(1, N + 1):\\r\\n dic_dic_h[key_Year][(0, n)] = 0\\r\\n dic_dic_SV_h[key_Year][(0, n)] = 0\\r\\n dic_dic_SV_h[\\\"2020\\\"] = {\\r\\n (x[0], x[1]): x[2] for x in zip(h[\\\"m\\\"], h[\\\"n\\\"], h[\\\"2020-25\\\"])\\r\\n }\\r\\n dic_dic_SV_g[\\\"2020\\\"] = {\\r\\n (x[0], x[1]): x[2] for x in zip(g[\\\"m\\\"], g[\\\"n\\\"], g[\\\"2020-25\\\"])\\r\\n }\\r\\n Years = np.array([float(x) for x in Years])\\r\\n return dic_dic_h, dic_dic_g, dic_dic_SV_h, dic_dic_SV_g, dic_N, Years\",\n \"def cramers_v1(confusion_matrix):\\n chi2 = ss.chi2_contingency(confusion_matrix)[0]\\n n = confusion_matrix.sum()\\n phi2 = chi2 / n\\n r, k = confusion_matrix.shape\\n phi2corr = max(0, phi2 - ((k-1)*(r-1))/(n-1))\\n rcorr = r - ((r-1)**2)/(n-1)\\n kcorr = k - ((k-1)**2)/(n-1)\\n return np.sqrt(phi2corr / min((kcorr-1), (rcorr-1)))\",\n \"def parse_pion(m1, m2, config):\\n f = open(in_name(m1, m2, config), 'r')\\n \\n # Skip to the mixed pion correlator section.\\n # This assumes it is the first PION entry.\\n x = f.readline()\\n while x:\\n if re.match('correlator:\\\\s+PION', x):\\n break\\n x = f.readline()\\n\\n # Throw away header.\\n print x\\n for i in range(5):\\n print f.readline().strip()\\n\\n result = []\\n for i in range(64):\\n t, r, im = f.readline().strip().split('\\\\t')\\n result.append(complex(float(r), float(im))) \\n \\n f.close()\\n\\n return np.array(result)\",\n \"def representatives_from_nrlist(res, mapped_to=None):\\n \\n # Read the NR file\\n nr_code = min([i for i in [1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 20.0] if i >= res])\\n fpath = f\\\"{path_to_3D_data}/latest_nr_list_{nr_code}A.csv\\\"\\n repres = []\\n df = pd.read_csv(os.path.abspath(fpath))\\n\\n # define a function to transform a code into a filename\\n def query_mapping_to(structure, model, chain, family):\\n if family is None:\\n return structure + '_' + model + '_' + chain + \\\".cif\\\"\\n\\n # if we need a mapping start and end, query database\\n with sqlite3.connect(runDir + \\\"/results/RNANet.db\\\") as conn:\\n conn.execute('pragma journal_mode=wal')\\n r = sql_ask_database(conn, f\\\"SELECT pdb_start, pdb_end FROM chain WHERE rfam_acc='{family}' AND structure_id='{structure}' AND chain_name='{chain}' AND issue=0;\\\")\\n\\n if not len(r):\\n # there is no chain named like this and mapped to this family\\n return None\\n\\n if len(r) > 1:\\n warn(f\\\"Several entries found for structure {structure}-{chain} ({family}) : {len(r)} entries\\\")\\n return structure + '_' + model + '_' + chain + '_' + str(r[0][0]) + '-' + str(r[0][1]) + \\\".cif\\\"\\n\\n # build the list \\n for i in range(df.shape[0]):\\n up_name = df[\\\"representative\\\"][i]\\n if '+' in up_name:\\n up_name = up_name.split('+')\\n for i in range(len(up_name)):\\n chain = up_name[i].split('|')\\n reference = query_mapping_to(chain[0].lower(), chain[1], chain[2], mapped_to)\\n if reference is not None:\\n repres.append(reference)\\n else :\\n up_name = up_name.split('|')\\n reference = query_mapping_to(up_name[0].lower(), up_name[1], up_name[2], mapped_to)\\n if reference is not None:\\n repres.append(reference)\\n\\n return repres\",\n \"def get_lig_ref_df():\\n filepath = os.path.abspath(os.path.join(__file__, \\\"..\\\", \\\"data\\\", \\\"ligtype_angle_reference.csv\\\"))\\n ref_df = pd.read_csv(filepath)\\n return ref_df\",\n \"def compute_correspondances(source_p, source_reconstructed_p, target_p, target_reconstructed_p):\\n # inputs are all filepaths\\n with torch.no_grad():\\n source = trimesh.load(source_p, process=False)\\n source_reconstructed = trimesh.load(source_reconstructed_p, process=False)\\n target = trimesh.load(target_p, process=False)\\n target_reconstructed = trimesh.load(target_reconstructed_p, process=False)\\n\\n # project on source_reconstructed\\n neigh.fit(source_reconstructed.vertices)\\n idx_knn = neigh.kneighbors(source.vertices, return_distance=False)\\n\\n #correspondances throught template\\n closest_points = target_reconstructed.vertices[idx_knn]\\n closest_points = np.mean(closest_points, 1, keepdims=False)\\n\\n\\n # project on target\\n if global_variables.opt.project_on_target:\\n print(\\\"projection on target...\\\")\\n neigh.fit(target.vertices)\\n idx_knn = neigh.kneighbors(closest_points, return_distance=False)\\n closest_points = target.vertices[idx_knn]\\n closest_points = np.mean(closest_points, 1, keepdims=False)\\n\\n # save output\\n mesh = trimesh.Trimesh(vertices=closest_points, faces=source.faces, process=False)\\n trimesh.io.export.export_mesh(mesh, \\\"results/correspondences.ply\\\")\\n np.savetxt(\\\"results/correspondences.txt\\\", closest_points, fmt='%1.10f')\\n return\",\n \"def correlation(letter_list):\\n counts = Counter(letter_list)\\n text_freq = [counts[ch] / len(letter_list) for ch in ascii_lowercase]\\n english_freq = [ENGLISH_DICT[ch] for ch in ascii_lowercase]\\n return pearsonr(text_freq, english_freq)[0]\",\n \"def calc_qcorr(self) -> Dict[int, float]:\\n return self._calc_qcorr\",\n \"def _read_born_charges(self, line):\\n line = self.file_descriptor.readline()\\n self.born_charges = []\\n for i in range(self.nions):\\n b = []\\n line = self.file_descriptor.readline()\\n b.append([float(f) for f in line.split()[2:5]])\\n line = self.file_descriptor.readline()\\n b.append([float(f) for f in line.split()[0:3]])\\n line = self.file_descriptor.readline()\\n b.append([float(f) for f in line.split()[0:3]])\\n B = np.array(b)\\n C = B.T\\n self.born_charges.append(C.tolist())\\n return\",\n \"def plot_feature_correlations(self):\\n\\n fig = plt.figure(figsize=(18,18), tight_layout=True)\\n fig.suptitle('Feature correlations', fontsize=24)\\n\\n sns.heatmap(self.train_data.astype(float).corr(method='kendall'), linewidths=0.1, vmin=-1.0,\\n vmax=1.0, square=True, linecolor='white', annot=True, \\n cmap=\\\"PiYG\\\")\\n plt.savefig(r'data_analysis\\\\correlations_kendall_' + self.file_name + '.png', \\n facecolor=fig.get_facecolor())\",\n \"def read_fusion_domains(fus_path, wt_path, fusion_annot, min_domain_len=25, min_domain_frac=0.5):\\n # read in domains\\n domains = pd.read_csv(fus_path, sep='\\\\t')\\n wt_domains = pd.read_csv(wt_path, sep='\\\\t')\\n\\n # filter out transmembrane helix\\n is_tm = domains[\\\"Domain_ID\\\"].str.startswith('TMhelix')\\n domains = domains[~is_tm].copy()\\n\\n # filter out short domains\\n domain_len = domains['Protein_end'] - domains['Protein_start']\\n domains = domains[domain_len>=min_domain_len].copy()\\n\\n # merge in info about where the fusion happened\\n domains = pd.merge(domains, fusion_annot[['ID', 'CodonPos1', 'CodonPos2']].drop_duplicates('ID'), on='ID', how='left')\\n domains['origin of domain'] = \\\"3'_gene domain\\\"\\n domains.loc[domains['Protein_end']<=domains['CodonPos1'], 'origin of domain'] = \\\"5'_gene domain\\\"\\n\\n # separate out protein ID\\n domains['ID_five'] = domains['ID'].str.split('-', expand=True)[0]\\n domains['ID_three'] = domains['ID'].str.split('-', expand=True)[1].str.split(':', expand=True)[0]\\n\\n # check 5' domains\\n domains_five = domains[domains[\\\"origin of domain\\\"]==\\\"5'_gene domain\\\"].copy()\\n is_good_domain = []\\n for ix, row in domains_five.iterrows():\\n domain_end = row['Protein_end']\\n fus_codon_pos = row['CodonPos1']\\n if fus_codon_pos==domain_end:\\n pid = row['ID_five']\\n is_pid = wt_domains['PROT_ID']==pid\\n is_loc = (fus_codon_pos>=wt_domains['Protein_start']) & (fus_codon_pos<=wt_domains['Protein_end'])\\n tmp_df = wt_domains.loc[is_pid & is_loc,:]\\n if len(tmp_df)==0:\\n is_good_domain.append(True)\\n continue\\n else:\\n tmp = tmp_df.iloc[0]\\n frac_domain = (fus_codon_pos - tmp['Protein_start']) / (tmp['Protein_end'] - tmp['Protein_start'])\\n if frac_domain>=min_domain_frac:\\n is_good_domain.append(True)\\n else:\\n is_good_domain.append(False)\\n else:\\n is_good_domain.append(True)\\n domains_five = domains_five.loc[is_good_domain,:]\\n # check 3' domains\\n domains_three = domains[domains[\\\"origin of domain\\\"]==\\\"3'_gene domain\\\"].copy()\\n is_good_domain = []\\n for ix, row in domains_three.iterrows():\\n domain_start = row['Protein_start']\\n fus_codon_pos = row['CodonPos2']\\n if fus_codon_pos==domain_start:\\n pid = row['ID_three']\\n is_pid = wt_domains['PROT_ID']==pid\\n is_loc = (fus_codon_pos>=wt_domains['Protein_start']) & (fus_codon_pos<=wt_domains['Protein_end'])\\n tmp_df = wt_domains.loc[is_pid & is_loc,:]\\n if len(tmp_df)==0:\\n is_good_domain.append(True)\\n continue\\n else:\\n tmp = tmp_df.iloc[0]\\n frac_domain = (tmp['Protein_end']-fus_codon_pos) / (tmp['Protein_end'] - tmp['Protein_start'])\\n if frac_domain>=min_domain_frac:\\n is_good_domain.append(True)\\n else:\\n is_good_domain.append(False)\\n else:\\n is_good_domain.append(True)\\n domains_three = domains_three.loc[is_good_domain,:]\\n # append results\\n domains = pd.concat([domains_five, domains_three])\\n\\n # aggregate all domains for one fusion into a single line\\n domains = domains[~domains['Domain_name'].isnull()]\\n domains_agg = domains.groupby(['ID', 'origin of domain'])['Domain_name'].agg(lambda x: ','.join(x))\\n domains_agg = domains_agg.reset_index()\\n domains_agg = domains_agg.pivot(index='ID', columns='origin of domain', values='Domain_name')\\n domains_agg = domains_agg.reset_index()\\n\\n return domains_agg\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.56094223","0.5485572","0.52754664","0.5258015","0.5176021","0.5160623","0.4980546","0.49767244","0.49409404","0.48738986","0.48393074","0.47858018","0.4778757","0.47687766","0.47029915","0.46986908","0.4646122","0.4598063","0.45766872","0.4559931","0.45571792","0.45520255","0.45090413","0.44916174","0.44849125","0.4460599","0.4449885","0.44470355","0.44341484","0.4434119","0.44216552","0.4399914","0.43906274","0.43865165","0.4384438","0.43753535","0.43725452","0.4371289","0.4363091","0.43581632","0.43566138","0.4353903","0.43522856","0.43493387","0.43461245","0.43355778","0.43293136","0.43267483","0.43199334","0.43176585","0.431586","0.43142048","0.43099996","0.42930844","0.42690304","0.4265883","0.42597035","0.4257214","0.42567128","0.42558986","0.42518383","0.42503628","0.42307243","0.42153654","0.4211404","0.42108485","0.42101455","0.4209984","0.42016825","0.41931677","0.4192781","0.41780183","0.41777593","0.41747695","0.4173196","0.4169227","0.415453","0.4153692","0.41482988","0.4146879","0.41459316","0.41428873","0.41396397","0.41375214","0.41357276","0.4133358","0.41285503","0.41215408","0.41150412","0.41135573","0.41104898","0.41040954","0.40978193","0.4091823","0.40914974","0.409124","0.40893176","0.40880287","0.40862906","0.4086033"],"string":"[\n \"0.56094223\",\n \"0.5485572\",\n \"0.52754664\",\n \"0.5258015\",\n \"0.5176021\",\n \"0.5160623\",\n \"0.4980546\",\n \"0.49767244\",\n \"0.49409404\",\n \"0.48738986\",\n \"0.48393074\",\n \"0.47858018\",\n \"0.4778757\",\n \"0.47687766\",\n \"0.47029915\",\n \"0.46986908\",\n \"0.4646122\",\n \"0.4598063\",\n \"0.45766872\",\n \"0.4559931\",\n \"0.45571792\",\n \"0.45520255\",\n \"0.45090413\",\n \"0.44916174\",\n \"0.44849125\",\n \"0.4460599\",\n \"0.4449885\",\n \"0.44470355\",\n \"0.44341484\",\n \"0.4434119\",\n \"0.44216552\",\n \"0.4399914\",\n \"0.43906274\",\n \"0.43865165\",\n \"0.4384438\",\n \"0.43753535\",\n \"0.43725452\",\n \"0.4371289\",\n \"0.4363091\",\n \"0.43581632\",\n \"0.43566138\",\n \"0.4353903\",\n \"0.43522856\",\n \"0.43493387\",\n \"0.43461245\",\n \"0.43355778\",\n \"0.43293136\",\n \"0.43267483\",\n \"0.43199334\",\n \"0.43176585\",\n \"0.431586\",\n \"0.43142048\",\n \"0.43099996\",\n \"0.42930844\",\n \"0.42690304\",\n \"0.4265883\",\n \"0.42597035\",\n \"0.4257214\",\n \"0.42567128\",\n \"0.42558986\",\n \"0.42518383\",\n \"0.42503628\",\n \"0.42307243\",\n \"0.42153654\",\n \"0.4211404\",\n \"0.42108485\",\n \"0.42101455\",\n \"0.4209984\",\n \"0.42016825\",\n \"0.41931677\",\n \"0.4192781\",\n \"0.41780183\",\n \"0.41777593\",\n \"0.41747695\",\n \"0.4173196\",\n \"0.4169227\",\n \"0.415453\",\n \"0.4153692\",\n \"0.41482988\",\n \"0.4146879\",\n \"0.41459316\",\n \"0.41428873\",\n \"0.41396397\",\n \"0.41375214\",\n \"0.41357276\",\n \"0.4133358\",\n \"0.41285503\",\n \"0.41215408\",\n \"0.41150412\",\n \"0.41135573\",\n \"0.41104898\",\n \"0.41040954\",\n \"0.40978193\",\n \"0.4091823\",\n \"0.40914974\",\n \"0.409124\",\n \"0.40893176\",\n \"0.40880287\",\n \"0.40862906\",\n \"0.4086033\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.51926357"},"document_rank":{"kind":"string","value":"4"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":262,"numItemsPerPage":100,"numTotalItems":95772,"offset":26200,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sInJlcG9JZHMiOlsiNjc1Mzc2ODJkMmE2Mjg0NzVhMWJmZmNkIl0sImlhdCI6MTc5MTIwNDA2OCwianRpIjoiYWNmMzAxN2UtNzc0YS00NjA0LWJhMzAtYjViZGVhODcwODM2Iiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3OTEyMDc2NjgsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.Xwgjexclbl0Zc-SYvwbeeJ3T1beSqQ_7L7E5hTj0h9xrBYn62Sr_z7XgcUl3qFyzr70CJ1gMTGxIhRaghLmYDA","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":95772,"numBytesParquet":1928375891}]},"discussionsStats":{"closed":1,"open":1,"total":2},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query stringlengths 9 3.4k | document stringlengths 9 87.4k | metadata dict | negatives listlengths 4 101 | negative_scores listlengths 4 101 | document_score stringlengths 3 10 | document_rank stringclasses 102
values |
|---|---|---|---|---|---|---|
Sets the gateway_serial of this UpdateVehicleRequest. | def gateway_serial(self, gateway_serial):
self._gateway_serial = gateway_serial | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setGateway(self, gateway):\n # type: (str)->None\n\n self._validator.validate_one(\n 'gateway', VALID_OPTS['gateway'], gateway)\n self._ifAttributes['gateway'] = gateway",
"def gateway_id(self, gateway_id):\n\n self._gateway_id = gateway_id",
"def set_serial(self, ser... | [
"0.63021106",
"0.5979875",
"0.56400514",
"0.5633011",
"0.54569376",
"0.526742",
"0.52273846",
"0.51936847",
"0.5030974",
"0.49815518",
"0.49481562",
"0.4906431",
"0.4822673",
"0.47393987",
"0.47254622",
"0.47191525",
"0.4696486",
"0.46910635",
"0.46563208",
"0.46563208",
"0.4... | 0.8186771 | 0 |
Sets the harsh_acceleration_setting_type of this UpdateVehicleRequest. | def harsh_acceleration_setting_type(self, harsh_acceleration_setting_type):
allowed_values = ["passengerCar", "lightTruck", "heavyDuty", "off", "automatic"] # noqa: E501
if self.local_vars_configuration.client_side_validation and harsh_acceleration_setting_type not in allowed_values: # noqa: E501
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_acceleration(self, acceleration):\n self.device.set_acceleration(acceleration)\n return \"OK\"",
"def set_adjustment_type(self, adjustment_type):\n self.single_selection_from_kendo_dropdown(self.adjustment_type_dropdown_locator, adjustment_type)\n self.wait_for_ajax_spinner_lo... | [
"0.5853659",
"0.50912744",
"0.49172306",
"0.48173192",
"0.47014672",
"0.4634827",
"0.46120846",
"0.4590229",
"0.4576279",
"0.45042148",
"0.4476712",
"0.43912166",
"0.43879282",
"0.4383811",
"0.4366613",
"0.4360031",
"0.4352631",
"0.43397018",
"0.43366387",
"0.43263713",
"0.43... | 0.8702775 | 0 |
Sets the license_plate of this UpdateVehicleRequest. | def license_plate(self, license_plate):
if (self.local_vars_configuration.client_side_validation and
license_plate is not None and len(license_plate) > 12):
raise ValueError("Invalid value for `license_plate`, length must be less than or equal to `12`") # noqa: E501
self._l... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def license(self, license):\n\n self._license = license",
"def save(self, *args, **kwargs):\n if self.license_plate:\n self.license_plate = self.license_plate.replace('-','').replace(' ','')\n super(VehicleRegistration,self).save(*args, **kwargs)",
"def license_number(self, lice... | [
"0.6482215",
"0.60094345",
"0.5971417",
"0.57393056",
"0.5617041",
"0.55561376",
"0.53961915",
"0.5359062",
"0.5322539",
"0.5291167",
"0.52001405",
"0.5070711",
"0.5057579",
"0.49738538",
"0.49601606",
"0.49601606",
"0.49461326",
"0.4945529",
"0.49165577",
"0.4910613",
"0.487... | 0.7658375 | 0 |
Sets the notes of this UpdateVehicleRequest. | def notes(self, notes):
if (self.local_vars_configuration.client_side_validation and
notes is not None and len(notes) > 255):
raise ValueError("Invalid value for `notes`, length must be less than or equal to `255`") # noqa: E501
self._notes = notes | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def notes(self, notes):\n\n self._notes = notes",
"def notes(self, notes):\n\n self._notes = notes",
"def notes(self, notes):\n\n self._notes = notes",
"def notes(self, notes):\n\n self._notes = notes",
"def notes(self, notes):\n\n self._notes = notes",
"def notes(self,... | [
"0.68845856",
"0.68845856",
"0.68845856",
"0.68845856",
"0.68845856",
"0.6636343",
"0.65447444",
"0.6384112",
"0.59179527",
"0.58897984",
"0.5507248",
"0.5370536",
"0.51564264",
"0.51564264",
"0.5099891",
"0.5076104",
"0.50644374",
"0.505969",
"0.5039264",
"0.4936329",
"0.493... | 0.64006484 | 7 |
Sets the odometer_meters of this UpdateVehicleRequest. | def odometer_meters(self, odometer_meters):
self._odometer_meters = odometer_meters | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def obd_odometer_meters(self, obd_odometer_meters):\n\n self._obd_odometer_meters = obd_odometer_meters",
"def gps_odometer_meters(self, gps_odometer_meters):\n\n self._gps_odometer_meters = gps_odometer_meters",
"def drive_distance_meters(self, drive_distance_meters):\n\n self._drive_dist... | [
"0.78520304",
"0.6952166",
"0.6625439",
"0.5700278",
"0.5330142",
"0.51499796",
"0.49836195",
"0.49621317",
"0.49096128",
"0.4897436",
"0.4849823",
"0.4838554",
"0.4810013",
"0.47394198",
"0.4714336",
"0.4705788",
"0.4705542",
"0.46347386",
"0.46201065",
"0.45941296",
"0.4515... | 0.8017434 | 0 |
Sets the static_assigned_driver_id of this UpdateVehicleRequest. | def static_assigned_driver_id(self, static_assigned_driver_id):
self._static_assigned_driver_id = static_assigned_driver_id | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def driver_id(self, driver_id):\n\n self._driver_id = driver_id",
"def driver_id(self, driver_id: int):\n if driver_id is None:\n raise ValueError(\"Invalid value for `driver_id`, must not be `None`\") # noqa: E501\n\n self._driver_id = driver_id",
"def static_finding(self, sta... | [
"0.58979875",
"0.52223",
"0.50875014",
"0.45650956",
"0.45579106",
"0.45233056",
"0.45233056",
"0.44840404",
"0.44335097",
"0.43853715",
"0.4371814",
"0.43593413",
"0.43487522",
"0.42879018",
"0.42818657",
"0.42814112",
"0.42524332",
"0.4240414",
"0.4240414",
"0.4225884",
"0.... | 0.8492683 | 0 |
Sets the tag_ids of this UpdateVehicleRequest. | def tag_ids(self, tag_ids):
self._tag_ids = tag_ids | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_tags(self, tags):\r\n current_tags = set(self.tag_names())\r\n updated_tags = set(tags)\r\n removed_tags = current_tags.difference(updated_tags)\r\n new_tags = updated_tags.difference(current_tags)\r\n \r\n for tag in new_tags:\r\n self.add_tag(tag)\r\n ... | [
"0.6124682",
"0.61125207",
"0.60852545",
"0.6005471",
"0.6005471",
"0.6005471",
"0.6005471",
"0.6005471",
"0.6005471",
"0.6005471",
"0.6005471",
"0.6005471",
"0.6005471",
"0.5952815",
"0.59259295",
"0.59259295",
"0.59259295",
"0.5924576",
"0.5871416",
"0.5602764",
"0.55914205... | 0.75711256 | 0 |
Sets the vin of this UpdateVehicleRequest. | def vin(self, vin):
if (self.local_vars_configuration.client_side_validation and
vin is not None and len(vin) > 17):
raise ValueError("Invalid value for `vin`, length must be less than or equal to `17`") # noqa: E501
if (self.local_vars_configuration.client_side_validation a... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_vin(self, value):\n return self.sendCMD(\"ATSET VIN={}\".format(value))",
"def vm_volume_num_in(self, vm_volume_num_in):\n\n self._vm_volume_num_in = vm_volume_num_in",
"def vehicle(self, vehicle):\n\n self._vehicle = vehicle",
"def vehicle(self, vehicle):\n\n self._vehicl... | [
"0.6181508",
"0.54808533",
"0.5342333",
"0.5342333",
"0.5278952",
"0.51032424",
"0.50570464",
"0.4902143",
"0.48931664",
"0.48747736",
"0.48244667",
"0.4768621",
"0.47286236",
"0.46440622",
"0.4581164",
"0.4552281",
"0.44910336",
"0.44724986",
"0.44671312",
"0.44606727",
"0.4... | 0.7512381 | 0 |
Returns the model properties as a dict | def to_dict(self):
result = {}
for attr, _ in six.iteritems(self.openapi_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map(
lambda x: x.to_dict() if hasattr(x, "to_dict") else x,
value
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def to_dict(self):\n return self.properties",
"def to_dict(self):\n return self.properties",
"def get_properties(self):\n return self.properties",
"def asdict(self):\n return self._prop_dict",
"def json(self):\n rv = {\n prop: getattr(self, prop)\n f... | [
"0.7751993",
"0.7751993",
"0.73391134",
"0.7334895",
"0.7297356",
"0.727818",
"0.7159078",
"0.71578115",
"0.71494967",
"0.71494967",
"0.71283495",
"0.71275014",
"0.7122587",
"0.71079814",
"0.7060394",
"0.7043251",
"0.7034103",
"0.70233124",
"0.69635814",
"0.69586295",
"0.6900... | 0.0 | -1 |
Returns the string representation of the model | def to_str(self):
return pprint.pformat(self.to_dict()) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __str__(self):\n return super().__str__() + self.model.__str__()",
"def __str__(self) -> str:\n # noinspection PyUnresolvedReferences\n opts = self._meta\n if self.name_field:\n result = str(opts.get_field(self.name_field).value_from_object(self))\n else:\n ... | [
"0.8585678",
"0.7814723",
"0.77902746",
"0.7750817",
"0.7750817",
"0.7713574",
"0.7699132",
"0.7670784",
"0.76510423",
"0.7600937",
"0.7582941",
"0.7570682",
"0.75406617",
"0.75233835",
"0.75168735",
"0.75013274",
"0.74877244",
"0.74877244",
"0.74700385",
"0.7451798",
"0.7446... | 0.0 | -1 |
For `print` and `pprint` | def __repr__(self):
return self.to_str() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def pprint(*args, **kwargs):\n if PRINTING:\n print(*args, **kwargs)",
"def print_out():\n pass",
"def custom_print(*objects):\n print(*objects, sep=OFS, end=ORS)",
"def _print(self, *args):\n return _ida_hexrays.vd_printer_t__print(self, *args)",
"def _printable(self):\n ... | [
"0.7558307",
"0.73389274",
"0.6988327",
"0.69856334",
"0.6945482",
"0.6925032",
"0.6899363",
"0.6898346",
"0.6816579",
"0.68078375",
"0.6752314",
"0.67512006",
"0.6746616",
"0.6699348",
"0.6691914",
"0.6676349",
"0.66583097",
"0.6610652",
"0.66092956",
"0.66036814",
"0.656279... | 0.0 | -1 |
Returns true if both objects are equal | def __eq__(self, other):
if not isinstance(other, UpdateVehicleRequest):
return False
return self.to_dict() == other.to_dict() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __eq__(self, other):\n return are_equal(self, other)",
"def __eq__(self, other):\n return are_equal(self, other)",
"def __eq__(self,other):\n try: return self.object==other.object and isinstance(self,type(other))\n except: return False",
"def __eq__(self, other):\n if i... | [
"0.8088132",
"0.8088132",
"0.8054589",
"0.7982687",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
... | 0.0 | -1 |
Returns true if both objects are not equal | def __ne__(self, other):
if not isinstance(other, UpdateVehicleRequest):
return True
return self.to_dict() != other.to_dict() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __ne__(self, other: object) -> bool:\n if self.__eq__(other):\n return False\n return True",
"def __ne__(self, other: object) -> bool:\n return not self.__eq__(other)",
"def __ne__(self, other) -> bool:\n return not self.__eq__(other)",
"def __eq__(self, other):\n ... | [
"0.845611",
"0.8391477",
"0.8144138",
"0.81410587",
"0.8132492",
"0.8093973",
"0.80920255",
"0.80920255",
"0.80920255",
"0.8085325",
"0.8085325",
"0.8076365",
"0.8076365",
"0.8065748",
"0.8042487",
"0.8042487",
"0.8042487",
"0.8042487",
"0.8042487",
"0.8042487",
"0.8042487",
... | 0.0 | -1 |
Return command display name | def display_name(self):
answer = self._call('display_name')
return answer.display_name | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def command_name(self):\n return None",
"def get_description(self):\n return self['command_name']",
"def get_command(self) -> str:\n return 'title'",
"def get_commandname(self):\n for line in self.helplines:\n if \"Usage:\" in line and self.parser_type is 'optparse':\n ... | [
"0.7897006",
"0.77417815",
"0.7687815",
"0.7538277",
"0.7479918",
"0.7479918",
"0.7479918",
"0.7479918",
"0.7479918",
"0.7479918",
"0.7479918",
"0.7479918",
"0.7479918",
"0.7479918",
"0.7331462",
"0.7311507",
"0.72114635",
"0.72114635",
"0.72114635",
"0.72114635",
"0.72114635... | 0.715899 | 23 |
Set command display name | def set_display_name(self, display_name):
self._call('set_display_name', display_name=display_name) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setDisplayName( self, name ):\n self._displayName = name\n self._titleFont = None",
"def display_name(self, display_name):\n self._display_name = display_name",
"def display_name(self, display_name):\n self._display_name = display_name",
"def display_name(self, display_name):\... | [
"0.69433814",
"0.6910722",
"0.6910722",
"0.6910722",
"0.6910722",
"0.6910722",
"0.6910722",
"0.66846985",
"0.6682046",
"0.6644311",
"0.66321164",
"0.66321164",
"0.66321164",
"0.66321164",
"0.66321164",
"0.66321164",
"0.65823704",
"0.6447359",
"0.6352545",
"0.63462025",
"0.633... | 0.6806753 | 7 |
Return True if command return type is string | def is_string(self):
answer = self._call('is_string')
return answer.yes | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _is_string(arg):\n return isinstance(arg, types.StringTypes)",
"def is_string(value):\n return isinstance(value, (str, bytes))",
"def is_string_action(func: CLIActionType) -> bool:\n return check_function_type(func, [HammerDriver, Callable[[str], None]], Optional[str]) is None",
"def _is... | [
"0.72101504",
"0.67675126",
"0.67398417",
"0.66946656",
"0.6686076",
"0.667609",
"0.6644599",
"0.66220903",
"0.65879357",
"0.65586376",
"0.65518516",
"0.6525141",
"0.6505778",
"0.6503069",
"0.6468777",
"0.64370143",
"0.6400437",
"0.6391969",
"0.63687015",
"0.6319832",
"0.6310... | 0.7352662 | 0 |
Return True if command return type is long | def is_long(self):
answer = self._call('is_long')
return answer.yes | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def convertToLong(boolean: bool) -> int:\n ...",
"def is_of_type(cmd):\r\n raise NotImplementedError()",
"def hasNextLong(self) -> bool:\n raise NotImplementedError",
"def command_type(self):\n if self.cmd_type is not None:\n return self.cmd_type # should be true only ... | [
"0.5989811",
"0.57864743",
"0.5738203",
"0.5530481",
"0.55091196",
"0.5501726",
"0.5487508",
"0.54735243",
"0.5433546",
"0.5368896",
"0.534729",
"0.5277097",
"0.52574474",
"0.5248837",
"0.51971203",
"0.5174398",
"0.5158678",
"0.515556",
"0.51363266",
"0.5130246",
"0.5119427",... | 0.74627966 | 0 |
Return True if command return type is double | def is_double(self):
answer = self._call('is_double')
return answer.yes | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_double(self, size=None):\n return False",
"def _type_check_double(self, data):\n if type(data) not in self._VALID_TYPES:\n return False\n return True",
"def double(self):\n if self.__valeur1 == self.__valeur2:\n return True\n else:\n return... | [
"0.68060154",
"0.6694105",
"0.6278531",
"0.6156558",
"0.6122468",
"0.60317355",
"0.59308475",
"0.59101456",
"0.58420163",
"0.5835771",
"0.56978625",
"0.5695271",
"0.56179863",
"0.55873126",
"0.5555344",
"0.5545467",
"0.54628015",
"0.54531217",
"0.5441905",
"0.5435553",
"0.540... | 0.7801225 | 0 |
Return True if command return type is datetime | def is_datetime(self):
answer = self._call('is_datetime')
return answer.yes | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_datetime(self) -> bool:\n return False",
"def has_datetime_type(obj: _std_typing.Any) -> bool:\n return obj.dtype == sc.DType.datetime64",
"def is_datetime(s: Union[str, int, float]):\n if is_number(s):\n return False\n\n try:\n parse_datetime(s)\n return True\n e... | [
"0.7951076",
"0.6984303",
"0.6577533",
"0.6427492",
"0.6338919",
"0.62653446",
"0.62532926",
"0.6224089",
"0.6207788",
"0.6151861",
"0.5996967",
"0.59684855",
"0.59437066",
"0.58614635",
"0.584531",
"0.5838218",
"0.5837798",
"0.5829952",
"0.5794449",
"0.57770663",
"0.57770663... | 0.7957955 | 0 |
Return True if command return type is bool | def is_bool(self):
answer = self._call('is_bool')
return answer.yes | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_bool(self):\n return False",
"def is_bool (self, phrase):\r\n \r\n return isinstance(phrase,bool)",
"def __bool__(self) -> bool:\n return self.return_code == 0",
"def __bool__(self) -> bool:\n return self.return_code == 0",
"def __bool__(self) -> bool:\n ... | [
"0.7300168",
"0.6982025",
"0.69479877",
"0.69479877",
"0.69479877",
"0.6931669",
"0.6860637",
"0.684384",
"0.6820059",
"0.6804663",
"0.6776833",
"0.6743398",
"0.67413884",
"0.67388",
"0.6701831",
"0.66715646",
"0.66706675",
"0.6627555",
"0.6617246",
"0.6615943",
"0.65937847",... | 0.71876997 | 1 |
Return True if command return type is map | def is_map(self):
answer = self._call('is_map')
return answer.yes | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _msg_is_command(self, msg):\n return isinstance(msg, dict)",
"def is_mapping(self) -> bool:\n return isinstance(self.yaml_node, yaml.MappingNode)",
"def is_map(self, alias):\n maps = {\"Ensembl2Reactome_All_Levels\": False,\n \"ReactomePathways\": True,\n ... | [
"0.6289078",
"0.6281217",
"0.6242795",
"0.61640793",
"0.6034122",
"0.5948415",
"0.58508986",
"0.58115065",
"0.5724521",
"0.56458396",
"0.5560745",
"0.55398226",
"0.55355984",
"0.5446891",
"0.54456025",
"0.5439933",
"0.5427514",
"0.54028404",
"0.53899914",
"0.53831494",
"0.537... | 0.69451547 | 0 |
Return True if command return type is list | def is_list(self):
answer = self._call('is_list')
return answer.yes | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_list(self) -> bool:\n return False",
"def is_multi_commands(args: list) -> bool:\n for arg in args:\n if not isinstance(arg, list):\n return False\n # all elements must be lists\n return True",
"def _is_list(arg):\n return isinstance(arg, collecti... | [
"0.74882597",
"0.7068153",
"0.69201654",
"0.68849444",
"0.68486714",
"0.6833843",
"0.6674785",
"0.66685194",
"0.6642766",
"0.66335183",
"0.66288596",
"0.6529805",
"0.65188724",
"0.6512905",
"0.6460788",
"0.64399505",
"0.6422735",
"0.6361648",
"0.6349589",
"0.63341206",
"0.630... | 0.7490665 | 0 |
Set command return value | def set_result(self, value):
value_rpc = utils.get_rpc_value(type(value), value)
self._call('set_result', value=value_rpc) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def execute_success(self, *args, **kwargs):\n return 0, self.shell_output, None",
"def execute_return(cmd):\n args = cmd.split()\n proc = Popen(args,stdout=PIPE,stderr=PIPE)\n out,err = proc.communicate()\n return out,err",
"def execute(self, rc):\n pass",
"def _set_returncode(self,... | [
"0.64630413",
"0.6332911",
"0.62538105",
"0.6227985",
"0.61576617",
"0.6150565",
"0.61106575",
"0.6073182",
"0.59837717",
"0.59783006",
"0.5948261",
"0.5886567",
"0.58848333",
"0.58677804",
"0.58380526",
"0.5837768",
"0.5819132",
"0.5764613",
"0.5763976",
"0.5747505",
"0.5742... | 0.63785946 | 1 |
Set exception in command. Information about exception will be called for adapter's side. | def set_exception(self, reason):
self._call('set_exception', exception=reason) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_exception(self, exception):\n self._is_done = True\n if self._exception:\n self._log(logging.DEBUG, \"'{}.{}' has overwritten exception. From '{}.{}' to '{}.{}'.\".format(\n self.__class__.__module__,\n self.__class__.__name__,\n self._e... | [
"0.65251386",
"0.65130377",
"0.640264",
"0.6400604",
"0.6305449",
"0.6302403",
"0.62972367",
"0.6162168",
"0.6148929",
"0.6125187",
"0.60864145",
"0.60796374",
"0.6046624",
"0.6018471",
"0.59831905",
"0.5967945",
"0.59093523",
"0.5906773",
"0.5851401",
"0.58388805",
"0.581141... | 0.67082244 | 0 |
Return list of command arguments | def argument_list(self):
answer = self._call('argument_list')
return answer.names | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def args(self):\n return self.cmd_args",
"def command_args(self):\n return self._command_args",
"def get_cli_arguments(self):\n pass",
"def get_list_cmd_args(self):\r\n return self.get_args(OSPL.list)",
"def argv(self) -> List[str]:\n if self.command:\n rtn = [... | [
"0.7973005",
"0.79610753",
"0.790767",
"0.7871847",
"0.7840145",
"0.78197145",
"0.7674125",
"0.7649114",
"0.7543827",
"0.74511504",
"0.7441568",
"0.74175024",
"0.7386672",
"0.7376045",
"0.7367075",
"0.7319357",
"0.7280918",
"0.7225539",
"0.71992487",
"0.7197082",
"0.71324563"... | 0.7323704 | 15 |
Return command argument with value by argument name | def argument(self, name_argument):
answer = self._call('argument', argument=name_argument)
return answer.name, answer.value | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_arg(self, name):\n return getattr(self.args, f\"{self.key}_{self.alias}_{name}\")",
"def get_argument(self, name):\n val = self.arguments.get(name)\n if val:\n return val[0]\n return None",
"def _get_arg_name(self, arg, variable_name):",
"def get_commandlinearg(... | [
"0.7868346",
"0.7503411",
"0.7101116",
"0.70896965",
"0.69831675",
"0.6580327",
"0.6580327",
"0.6569697",
"0.65403503",
"0.64810604",
"0.6404887",
"0.6322416",
"0.6288537",
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"0.62477183",
"0.62242115",
"0.61972386",
"0.6181981",
"0.6164855",
"0.6125793",
"0.612284... | 0.6707078 | 5 |
Add command argument / overwrite argument value | def update_or_create_argument(self, name_arg, value):
cur_choices = self.choices[name_arg] if name_arg in self.choices else None
if cur_choices is None:
value_rpc = utils.get_rpc_value(type(value), value)
self._call('update_or_create_argument', argument=name_arg, value=value_rpc)... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_cli_arg(self, name, value):\n if value:\n self._cli[name] = value",
"def add_argument(self, *args, **kwargs):\n self.parser.add_argument(*args, **kwargs)",
"def add_argument(self, *args, **kwargs):\n self.parser.add_argument(*args, **kwargs)",
"def add_argument(self, *... | [
"0.76907533",
"0.74852",
"0.74852",
"0.74852",
"0.73830616",
"0.7321316",
"0.72542834",
"0.7191633",
"0.7164311",
"0.69988096",
"0.694312",
"0.69101125",
"0.67392266",
"0.6697106",
"0.66787034",
"0.6673152",
"0.6657486",
"0.66390574",
"0.657421",
"0.65677625",
"0.65416545",
... | 0.6180719 | 51 |
Return ID of the command's owner | def owner(self):
answer = self._call('owner')
return answer.owner | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def owner_id(self) -> str:\n return pulumi.get(self, \"owner_id\")",
"def owner_id(self) -> str:\n return self.__owner_id",
"def owner_id(self) -> int:\n return self.proto.owner",
"def owner_id(self):\n return self._owner_id",
"def owner_id(self) -> Optional[str]:\n retur... | [
"0.7799616",
"0.7753633",
"0.76906127",
"0.76573384",
"0.7629729",
"0.7515706",
"0.7453457",
"0.73210293",
"0.73210293",
"0.73126256",
"0.7185304",
"0.7171606",
"0.71175456",
"0.7111413",
"0.705751",
"0.70522135",
"0.703091",
"0.69719386",
"0.692131",
"0.68922853",
"0.6857497... | 0.7285534 | 10 |
Call function when command executed | def call_function(self):
try:
arg_list = self.argument_list()
function_dict = {}
info = []
for name_arg in arg_list:
type_arg = self.arguments_type[name_arg]
function_dict[name_arg] = utils.value_from_rpc(self.argument(name_arg)[1])... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def command():\n pass",
"def oncmnd(self, func):\n self._oncmnd = func",
"def _command(self, *cmd, handler=None):",
"def cmd(self):",
"def ConsoleRun(self, command, sender):\n pass",
"def on_execute(self):\n pass",
"def do_command(self, args):\n pass",
"def on_comma... | [
"0.7412077",
"0.7403746",
"0.7057318",
"0.6878284",
"0.6829968",
"0.6819889",
"0.6819451",
"0.67954874",
"0.6765732",
"0.6708852",
"0.6705811",
"0.67045647",
"0.6689721",
"0.66895926",
"0.66880935",
"0.6661755",
"0.6661755",
"0.6661755",
"0.6661755",
"0.66312814",
"0.6618891"... | 0.629185 | 43 |
The pure, immutable value of this datatype, as a Python value, which is unique for each datatype. | def value(self):
return self._value | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def Value(self) -> _n_0_t_14:",
"def value_type(self) -> global___Type:",
"def value(self) -> any:\r\n\r\n return self.__value",
"def value(self):\n return self.value()._value",
"def new_value(self):\n return data_value_factory(self.type)",
"def value(self):\n return self._val... | [
"0.7019517",
"0.6918032",
"0.6752796",
"0.67268085",
"0.67126024",
"0.6682515",
"0.6682515",
"0.6663475",
"0.6654946",
"0.6654946",
"0.6651974",
"0.6647169",
"0.6625683",
"0.6625683",
"0.6623158",
"0.66080546",
"0.65716547",
"0.65649635",
"0.6532604",
"0.6529765",
"0.65256613... | 0.6407975 | 54 |
The opaque context for this type, if it was previously fetched. | def context(self):
if self._context:
return self._context[:] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def context(self) -> _C_out:\n return self._context",
"def context(self):\n return self._context",
"def context(self):\n return self._context",
"def context(self):\n return self._context",
"def context(self):\n return self._context",
"def context(self):\n return ... | [
"0.6449475",
"0.6230645",
"0.6230645",
"0.6230645",
"0.6230645",
"0.6230645",
"0.6230645",
"0.6230645",
"0.61244285",
"0.61199605",
"0.60543615",
"0.6044594",
"0.5966144",
"0.5949891",
"0.5942338",
"0.5937547",
"0.59329396",
"0.5925352",
"0.5906197",
"0.59033287",
"0.58872193... | 0.6135836 | 8 |
Whether this datatype has staged local modifications. | def modified(self):
raise NotImplementedError | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def has_local_state(self) -> bool:\n return True",
"def is_modified(self):\n return len(self.modified_fields) > 0",
"def is_local(self) -> bool:\n if not self.source:\n return False\n\n if self.source.master_name.startswith(MODULE_NAME):\n return True\n\n ... | [
"0.7210056",
"0.7106617",
"0.70620275",
"0.69745135",
"0.69522744",
"0.69439024",
"0.6663222",
"0.66631716",
"0.66554046",
"0.6641709",
"0.66392136",
"0.6631838",
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"0.6613594",
"0.66110885",
"0.6608613",
"0.6608613",
"0.65780777",
"0.65697205",
"0.6561284",
"0.655... | 0.0 | -1 |
Reloads the datatype from Riak. | def reload(self, **params):
if not self.bucket:
raise ValueError('bucket property not assigned')
if not self.key:
raise ValueError('key property not assigned')
dtype, value, context = self.bucket._client._fetch_datatype(
self.bucket, self.key, **params)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def reload_data(self):\n self._avro_payload.reload_data()",
"def reload(self):\n self.restore()",
"def reloadData(self):\n self.dto.readFromData()\n print(\"Record reloaded.\")",
"def reload(self):",
"def reload(self):",
"def reload(self):\n\n pass",
"def reload_data(self... | [
"0.69468844",
"0.6571157",
"0.652524",
"0.6443367",
"0.6443367",
"0.62622434",
"0.6048451",
"0.6042948",
"0.6036892",
"0.60049",
"0.5873903",
"0.5867182",
"0.5855046",
"0.58414084",
"0.5838228",
"0.58149874",
"0.57650065",
"0.5740696",
"0.57311875",
"0.5674174",
"0.56657064",... | 0.6577614 | 1 |
Sends locally staged mutations to Riak. | def update(self, **params):
if not self.modified:
raise ValueError("No operation to perform")
params.setdefault('return_body', True)
self.bucket._client.update_datatype(self, **params)
self.clear()
return self | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def send(self) -> None:\n await self._mutations.send()\n await self._counters.send()",
"def _send(self, batch):\n return self.agent.emitBatch(batch)",
"def send(self, payload):\n self.emitter.input(payload)",
"async def send(self):",
"def sendTrame(self,ident,newState):\n ... | [
"0.6106689",
"0.543575",
"0.50269085",
"0.5017469",
"0.49475446",
"0.49401817",
"0.4917079",
"0.48982352",
"0.48926118",
"0.48706728",
"0.48510456",
"0.48082152",
"0.48071888",
"0.47755742",
"0.47697464",
"0.47678038",
"0.47575727",
"0.475492",
"0.4753376",
"0.47499636",
"0.4... | 0.0 | -1 |
Removes all locally staged mutations. | def clear(self):
self._post_init() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def purge():\n all_hashes = read_all()\n used_hashes = read_used()\n\n for kind, hashes in used_hashes.items():\n to_remove = all_hashes[kind].difference(hashes)\n if kind == 'evs':\n delete_from_directory_by_hashes(EV_DIRECTORY, to_remove)\n elif kind == 'cache':\n ... | [
"0.612864",
"0.6037659",
"0.6037659",
"0.59406775",
"0.59103316",
"0.5905609",
"0.5856978",
"0.5830242",
"0.58279985",
"0.5785966",
"0.5730709",
"0.57132196",
"0.5688773",
"0.5679879",
"0.5665355",
"0.56624067",
"0.5651841",
"0.5639386",
"0.5639386",
"0.5627537",
"0.5626215",... | 0.0 | -1 |
Extracts the mutation operation from this datatype, if any. Each type must implement this method, returning the appropriate operation, or `None` if there is no queued mutation. | def to_op(self):
raise NotImplementedError | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_update_op():\n update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)\n if update_ops is not None:\n return tf.group(*update_ops)\n return None",
"def resolve(self) -> Mutation:\n return Mutation(self.is_delete, self.col, self.value, self.wal)",
"def op(self) -> ... | [
"0.6340196",
"0.6219872",
"0.6157111",
"0.6128742",
"0.6128742",
"0.6057564",
"0.60432756",
"0.6023882",
"0.5999299",
"0.5998789",
"0.59433234",
"0.5942077",
"0.58093363",
"0.5725967",
"0.56265205",
"0.5621546",
"0.55118114",
"0.54859066",
"0.5438187",
"0.5438187",
"0.5438187... | 0.5308875 | 27 |
Checks that initial values of the type are appropriate. Each type must implement this method. | def _check_type(self, new_value):
raise NotImplementedError | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _check_vals(self):\n\n try:\n self.is_set = True\n self.pack()\n except Exception as err:\n # Set default values again\n raise ValueError(\"Invalid arguments. Could not packed since: {}\".format(err))\n self.__init__()",
"def validate(self)... | [
"0.72296953",
"0.68276614",
"0.6670829",
"0.6625079",
"0.65226156",
"0.6468805",
"0.6381933",
"0.6350073",
"0.6303018",
"0.62150484",
"0.62101245",
"0.61515135",
"0.6143909",
"0.607562",
"0.60541034",
"0.605045",
"0.60136133",
"0.5985306",
"0.59744245",
"0.5973214",
"0.594714... | 0.6040458 | 16 |
Coerces the input value into the internal representation for the type. Datatypes may override this method. | def _coerce_value(self, new_value):
return new_value | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cast(self, value):\n\n return value",
"def convert(self, value):\r\n return value",
"def convert(self, value):\n return value",
"def cast(self, value):\n if value is None:\n return None\n return self.type(value)",
"def convert_type(self, value, schema_type,... | [
"0.7340099",
"0.7334982",
"0.71864617",
"0.70573217",
"0.6916039",
"0.6905666",
"0.6802524",
"0.67618734",
"0.6731091",
"0.6684977",
"0.6664414",
"0.6664414",
"0.661957",
"0.661957",
"0.65944105",
"0.65559506",
"0.6547864",
"0.6535139",
"0.652875",
"0.65159434",
"0.64829975",... | 0.6767681 | 7 |
Returns what the initial value of an empty datatype should be. | def _default_value(self):
raise NotImplementedError | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def mempty(self):\n return identity",
"def initial_value(self):\n return self._initial_value",
"def noneType(value):\r\n return ''",
"def empty_value(self, context):\n if self._default is NoValueSet:\n return None\n return self._default",
"def get_default_value... | [
"0.6657491",
"0.654826",
"0.6507052",
"0.6405415",
"0.64019907",
"0.6348621",
"0.6144814",
"0.61387604",
"0.60783243",
"0.6064034",
"0.60356885",
"0.5976399",
"0.5905335",
"0.58722705",
"0.5867641",
"0.5866738",
"0.58534896",
"0.58521265",
"0.582249",
"0.5811923",
"0.579623",... | 0.58259237 | 18 |
Raises an exception if the context is not present | def _require_context(self):
if not self._context:
raise ContextRequired() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_get_context_invalid():\n\n with pytest.raises(ContextAttributeError):\n application_services.get_context('not_present_context')",
"def handle_context_missing(self):",
"def test_stored_context_err(self):\n self.stack = stack.Stack(self.ctx, 'creds_stack', self.tmpl)\n ex = self.... | [
"0.76646805",
"0.7559883",
"0.6614295",
"0.6527365",
"0.64800733",
"0.64293313",
"0.6406461",
"0.6245151",
"0.6196592",
"0.61848545",
"0.6142678",
"0.60623604",
"0.60348666",
"0.6032899",
"0.60134035",
"0.60021675",
"0.5999621",
"0.59995925",
"0.5892164",
"0.5860368",
"0.5839... | 0.800951 | 0 |
Scrubs sys.path and sys.modules to a raw state. | def _scrub_import_environment(sys_modules_whitelist: typing.List[str], logger: typing.Callable):
pex_root = pathlib.Path(Variables().PEX_ROOT)
# A generator that emits sys.path elements
def scrubbed_sys_path():
"""Yields a scrubbed version of sys.path."""
for p in sys.path[:]:
if not isinstance(p, ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def patch_sys(cls):\n def patch_dict(old_value, new_value):\n old_value.clear()\n old_value.update(new_value)\n\n def patch_all(path, path_importer_cache, modules):\n sys.path[:] = path\n patch_dict(sys.path_importer_cache, path_importer_cache)\n patch_dict(sys.modules, modules)\n\n ... | [
"0.6124312",
"0.6062598",
"0.59543145",
"0.5901678",
"0.56766284",
"0.56067514",
"0.5542571",
"0.54748863",
"0.5422463",
"0.5348224",
"0.53475314",
"0.53056484",
"0.52948594",
"0.51196456",
"0.51149005",
"0.51118445",
"0.50935435",
"0.5014209",
"0.50100213",
"0.5005435",
"0.4... | 0.64339405 | 0 |
Yields a scrubbed version of sys.path. | def scrubbed_sys_path():
for p in sys.path[:]:
if not isinstance(p, str):
yield p
# Scrub any/all pex locations from sys.path.
pp = pathlib.Path(p)
if pex_root not in pp.parents:
yield p | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def scrub_from_sys_modules():\n for k, m in sys.modules.items():\n if k in sys_modules_whitelist:\n continue\n\n if hasattr(m, '__file__') and m.__file__ is not None:\n mp = pathlib.Path(m.__file__)\n if pex_root in mp.parents:\n yield k",
"def add_sys_paths(paths):\n ... | [
"0.6704874",
"0.64456856",
"0.63815147",
"0.61018676",
"0.6091217",
"0.6007068",
"0.596048",
"0.5937054",
"0.5828322",
"0.5781934",
"0.5675717",
"0.567165",
"0.56604296",
"0.5641513",
"0.56188345",
"0.5617912",
"0.55805993",
"0.5579308",
"0.5575357",
"0.5502024",
"0.54931635"... | 0.80444944 | 0 |
Yields keys of sys.modules as candidates for scrubbing/removal. | def scrub_from_sys_modules():
for k, m in sys.modules.items():
if k in sys_modules_whitelist:
continue
if hasattr(m, '__file__') and m.__file__ is not None:
mp = pathlib.Path(m.__file__)
if pex_root in mp.parents:
yield k | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def all_registered_modules():\n yield from iterchain(modules.values() for modules in Registry.monomers.values())",
"def modules(self):\n for desc in self._mappings.values():\n if hasattr(desc, 'module'):\n yield desc.module\n else:\n continue",
"def... | [
"0.6842569",
"0.6502843",
"0.6499838",
"0.647568",
"0.6403681",
"0.6330229",
"0.62831795",
"0.62433136",
"0.61260855",
"0.6123235",
"0.60469973",
"0.6040864",
"0.603489",
"0.59373957",
"0.59103936",
"0.58853215",
"0.5881549",
"0.5876056",
"0.5848127",
"0.5820939",
"0.5803232"... | 0.80843186 | 0 |
Extracts exactly 1 binary from a dir and returns a Path. | def _extract_resulting_binary(self, build_dir: pathlib.PosixPath, extension: str) -> pathlib.PosixPath:
assert build_dir.is_dir(), f'build_dir {build_dir} was not a dir!'
# N.B. It's important we use pathlib.Path.rglob (recursive) here, since pants v2 prefixes dist dirs
# with their address namespace.
b... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def dir_bin():\n return abspath('bin')",
"def _search_path_to_file(self, directory, binary_name):\n for root, dirs, files in os.walk(directory):\n if binary_name in files:\n return os.path.join(root, binary_name)\n raise micp_kernel.NoExecutableError",
"def _extract_a... | [
"0.57858336",
"0.5713639",
"0.5676852",
"0.5615714",
"0.5572907",
"0.541237",
"0.53744745",
"0.5344345",
"0.5302235",
"0.5284864",
"0.52758974",
"0.52424115",
"0.51901644",
"0.5142074",
"0.51340926",
"0.5104739",
"0.50982463",
"0.50942296",
"0.50817",
"0.50708187",
"0.5032798... | 0.65847987 | 0 |
Creates an Accordion widget and yields under care of its output capturer. | def _accordion_widget(self, title, height='300px', collapsed=True):
# Generate unique class for multiple invocations
unique_class = self._append_random_id('nb-console-output')
auto_scroll_script = '''
const config = { childList: true, subtree: true };
const callback = function(mutationsList, observe... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate_accordion(summary, main):\n return \"\"\"\n <details>\n <summary>{}</summary>\n <main>{}</main>\n </details>\n \"\"\".format(summary, main)",
"def render_accordion(request, course, chapter, section, field_data_cache):\r\n # grab the table ... | [
"0.5763516",
"0.5742236",
"0.53615534",
"0.50737333",
"0.50410753",
"0.5024527",
"0.49162087",
"0.4911946",
"0.48652512",
"0.47383875",
"0.45927936",
"0.44744775",
"0.44410545",
"0.44230998",
"0.43919158",
"0.4383531",
"0.43767828",
"0.43757036",
"0.4363259",
"0.4358932",
"0.... | 0.72215044 | 0 |
Runs a pexproducing command with streaming output and returns the pex location. | def _stream_binary_build_with_output(
self,
cmd: str,
title: str,
work_dir: pathlib.PosixPath,
extension: str,
spin_refresh_rate: float = .3
) -> pathlib.PosixPath:
async def spin_driver(
set_glyph: typing.Callable,
is_complete: asyncio.Event,
seq: str = SPINNER_SEQ
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def paexec_out_stream(buffer_size=4096):\n b_data = pkgutil.get_data('pypsexec', 'paexec.exe')\n byte_count = len(b_data)\n for i in range(0, byte_count, buffer_size):\n yield b_data[i:i + buffer_size], i",
"def _run_pex(self, requirements: str) -> pathlib.PosixPath:\n with temporary_dir(clean... | [
"0.5712724",
"0.56551373",
"0.56470096",
"0.5454696",
"0.5122776",
"0.507601",
"0.49487308",
"0.49423507",
"0.49052173",
"0.4890243",
"0.48846006",
"0.48796877",
"0.48617193",
"0.48416793",
"0.4835402",
"0.48257113",
"0.48232624",
"0.4817348",
"0.48100555",
"0.47776985",
"0.4... | 0.0 | -1 |
Runs pex with widget UI display. | def _run_pex(self, requirements: str) -> pathlib.PosixPath:
with temporary_dir(cleanup=False) as tmp_dir:
tmp_path = pathlib.PosixPath(tmp_dir)
output_pex = tmp_path.joinpath('requirements.pex')
title = f'[Resolve] {requirements}'
safe_requirements = ' '.join(shlex.quote(r) for r in shlex.sp... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __call__(self):\n self.show()",
"def main():\n PanelDemo().mainloop()",
"def XPShowWidget(inWidget):\n pass",
"def main():\n LayoutsWithPanels().mainloop()",
"def start_ui(self):\n\t\tself.start_animation()\n\t\tself.app.exec()",
"def run():\n gui = GUI()\n gui.mainloop()",
... | [
"0.6615083",
"0.6588207",
"0.6397241",
"0.6359986",
"0.6233552",
"0.61983424",
"0.6130469",
"0.6127166",
"0.6080013",
"0.6046031",
"0.60392225",
"0.6005095",
"0.59748983",
"0.59184897",
"0.5896124",
"0.5874832",
"0.58666897",
"0.58025247",
"0.57882017",
"0.5737901",
"0.573191... | 0.0 | -1 |
Runs pants with widget UI display. | def _run_pants(
self,
pants_repo: pathlib.PosixPath,
pants_target: str,
extension: str
) -> pathlib.PosixPath:
# Version check for pants v1 vs v2 flags/behavior.
is_pants_v1 = pants_repo.joinpath('pants.ini').exists()
if is_pants_v1:
goal_name = 'binary'
tmp_root = None
el... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main():\r\n return render_template(\"UI.html\")",
"def main():\n LayoutsWithPanels().mainloop()",
"def run(self):\n self.ui['main_window'].widgets['main'].show_all()\n gtk.main()",
"def __call__(self):\n self.show()",
"def show():\n from siding.addons import ui\n ui... | [
"0.647995",
"0.6331577",
"0.6194317",
"0.6100214",
"0.6095271",
"0.60570043",
"0.6056826",
"0.60278785",
"0.602337",
"0.5976977",
"0.59542185",
"0.59354967",
"0.5921027",
"0.5767655",
"0.57602316",
"0.5725509",
"0.57194537",
"0.57082164",
"0.5705206",
"0.5700503",
"0.5695546"... | 0.0 | -1 |
Bootstraps a pex with widget UI display. | def _bootstrap_pex(self, pex_path: pathlib.PosixPath):
title = f'[Bootstrap] {pex_path.name}'
with self._accordion_widget(title) as (expand, collapse, set_output_glyph):
try:
with environment_as(PEX_VERBOSE='2'):
# Scrub the environment.
_scrub_import_environment(self._ORIGINAT... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def bootstrap():\n Bootstrap()",
"def bootstrap(self):\n None",
"def create_widgets(self):",
"def _do_bootstrap(self, configs=None):\n pass",
"def create_widgets( self ):",
"def widgets(self):\r\n self.setWindowTitle(\"PyCrypt\")\r\n self.setMinimumSize(QSize(500, 5... | [
"0.63333535",
"0.5799136",
"0.5798117",
"0.57466966",
"0.57229584",
"0.5599561",
"0.5393727",
"0.5355387",
"0.5324116",
"0.5302776",
"0.52597296",
"0.5256661",
"0.5252348",
"0.52225083",
"0.522084",
"0.521721",
"0.51877075",
"0.5168642",
"0.5132846",
"0.51302344",
"0.51216316... | 0.61450976 | 1 |
Validates a given or stored path is a valid pants repo. | def _validate_pants_repo(self, pants_repo: pathlib.PosixPath) -> bool:
return (
pants_repo and
pants_repo.is_dir() and
pants_repo.joinpath('pants').is_file()
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ValidateRepoPath(context, parameter, value):\n if value.startswith('/TEST/'):\n # Hackish command to allow for unit testing\n return value\n\n for name in ['BUILD.gn', '.gn', os.path.join('scripts', 'bootstrap.sh')]:\n expected_file = os.path.join(value, name)\n if not os.path... | [
"0.6693658",
"0.64390516",
"0.6414603",
"0.6258947",
"0.62280005",
"0.6122691",
"0.6122691",
"0.61224514",
"0.60444325",
"0.5846379",
"0.5826569",
"0.58253604",
"0.5780301",
"0.57039803",
"0.57003903",
"0.5675232",
"0.5675232",
"0.5665029",
"0.5649705",
"0.56372535",
"0.56351... | 0.77448916 | 0 |
Do not return anything, modify root inplace instead. | def recoverTree(self, root: TreeNode) -> None:
self.tmp, self.left, self.right = None, None, None
self.helper(root)
self.left.val, self.right.val = self.right.val, self.left.val | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def uproot(self):\n self.__root__ = self\n return self",
"def root_replace(self,node):\r\n self.feature_index = node.feature_index\r\n self.threshold = node.threshold\r\n self.label = node.label\r\n self.left = node.left\r\n self.right = node.right\r\n self.substit... | [
"0.7168473",
"0.7090963",
"0.7052572",
"0.6962144",
"0.6789953",
"0.6550694",
"0.65128064",
"0.65128064",
"0.65128064",
"0.65128064",
"0.6433237",
"0.6413718",
"0.63980967",
"0.63980967",
"0.63697755",
"0.636143",
"0.63303053",
"0.63263005",
"0.6307079",
"0.62776786",
"0.6276... | 0.66157037 | 5 |
Starts the game loop to control the sequence of play. | def start_game(self):
self._puzzle.get_puzzle()
self._do_outputs()
while self._keep_playing:
print("")
print("+-----+-----+-----")
print("")
self._get_inputs()
self._do_updates()
self._do_outputs()
print("+-----... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __loop(self):\n if self.loops is \"inf\":\n self.play()\n else:\n if self.loops > self.current_loop:\n self.current_loop += 1\n self.play()\n else:\n self.stop()",
"def start_game(self) -> None:\n self.init_gam... | [
"0.7627953",
"0.7565402",
"0.7463202",
"0.7400862",
"0.7386307",
"0.73736656",
"0.7361174",
"0.72038347",
"0.71543676",
"0.70834076",
"0.70724666",
"0.70218325",
"0.699285",
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"0.69162875",
"0.6906955",
"0.6866626",
"0.6851633",
"0.68471336",
"0.68463635",
"0.6830... | 0.71472657 | 9 |
Gets the inputs at the beginning of each round of play. In this case, that means getting a guess of a letter from a user. | def _get_inputs(self):
getting_letter = True
while getting_letter:
try:
guess = self._console.read("Guess a letter [a-z]:")
if guess.lower() >= "a" and guess.lower() <= "z":
self._puzzle.get_guess(guess)
getting_letter =... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_guess():\n print('Choose a letter:')\n return input()",
"def get_input(self, guess):\r\n print\r\n print \"The player guessed = \", guess\r\n result = self.process_player_input(guess)\r\n print result\r\n if ((self.remaining_guesses == 0) or ( result == self.corre... | [
"0.7509543",
"0.7491407",
"0.74310565",
"0.7291458",
"0.69766635",
"0.68765074",
"0.6809997",
"0.6743882",
"0.6592322",
"0.6544128",
"0.65432173",
"0.6507807",
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"0.6294809",
"0.62744343",
"0.6259689",
"0.62528425",
"0.6220888",
"0.6186611",
"0.6178402"... | 0.7264798 | 4 |
Updates the important game information for each round of play. In this case, that means the puzzle is revealed and the jumper is cut if necessary. | def _do_updates(self):
is_right = self._puzzle.is_guess_right()
if is_right:
self._puzzle.reveal_puzzle()
else:
self._jumper.cut_line() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def game_updated(self):\n # <<-- Creer-Merge: game-updated -->> - Code you add between this comment and the end comment will be preserved between Creer re-runs.\n game = self.game\n\n for x in range(game.board_width):\n for y in range(game.board_height):\n self.checke... | [
"0.6821897",
"0.65869373",
"0.6301358",
"0.6244406",
"0.6205162",
"0.61089224",
"0.60870314",
"0.6009552",
"0.59923613",
"0.59637344",
"0.5959768",
"0.59509295",
"0.5935243",
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"0.59208655",
"0.5909505",
"0.59034586",
"0.5883476",
"0.5867642",
"0.58632934",
"0.586... | 0.69500154 | 0 |
Outputs the important game information for each round of play. In this case, that means the hider provides a hint. | def _do_outputs(self):
self._puzzle.display_revealed_puzzle()
hint = self._puzzle.get_hint()
self._console.write(hint)
print("")
self._jumper.draw_jumper()
print("")
# These ifs end the game
if self._puzzle.is_solved():
self._keep_playing = Fa... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def info():\n print(\"Made using the OOP RPG game creator (c) Claire.\\n\")",
"def player_tie(self):\r\n\r\n self.summary = (\" \"* 78) + \"TIE. TRY AGAIN\"\r\n print(\"Match ends in a draw.\\n\")",
"def print_statistics(self):\n print 'Ran %s iterations in %0.3f seconds\\n' % (\n ... | [
"0.66959804",
"0.662896",
"0.661329",
"0.65989345",
"0.6507802",
"0.64726734",
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"0.6197924",
"0.618897",
"0.6177045",
"0.617191",
"0.6145641",
"0.61397576",
"0... | 0.66408557 | 1 |
Locate optimizer from hparams, take a step | def optimize_step(self, loss, glbl_step):
Opt = locate("tensorflow.train." + hparams.optimizer)
if Opt is None:
raise ValueError("Invalid optimizer: " + hparams.optimizer)
optimizer = Opt(hparams.l_rate)
grads_vars = optimizer.compute_gradients(loss)
capped_grads = [(None if grad is None else ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_optimizer(self):\n raise NotImplementedError",
"def optimization_step(self):\n \n if \"CSS\" in self.algorithm:\n \n input_dict = {self.x: self.train_inputs[self.minibatch_set,:]}\n \n var_list = [self.x_tilda, self.minibatch_set]... | [
"0.6217628",
"0.61910707",
"0.6007577",
"0.5974602",
"0.5930321",
"0.5929612",
"0.58880615",
"0.5886796",
"0.58155364",
"0.5755176",
"0.57254344",
"0.5718326",
"0.5693191",
"0.567812",
"0.56665564",
"0.56651974",
"0.56435186",
"0.5641517",
"0.5597855",
"0.5471214",
"0.5469395... | 0.51227474 | 79 |
If trainable, returns variable, otherwise the original embedding | def embedding_setup(self, embedding, emb_trainable):
if emb_trainable == True:
emb_variable = tf.get_variable(
name="embedding_matrix", shape=embedding.shape,
initializer = tf.constant_initializer(embedding))
return emb_variable
else:
return embedding | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def embedding_trainable_variables(self) -> Sequence[tf.Variable]:\n return self._embedding_layer.trainable_variables",
"def forward(self, input_variable):\r\n return self.embedding(input_variable)",
"def embedding_layer(self):\n with tf.name_scope(\"Embedding_Layer\"):\n V_size = le... | [
"0.7058622",
"0.702349",
"0.6737134",
"0.66756374",
"0.665749",
"0.6568265",
"0.65501094",
"0.6522559",
"0.6482883",
"0.64559466",
"0.64411855",
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"0.62392634",
"0.62351906",
"0.62319773",
"0.6220624",
"0.6140934",
"0.6062287... | 0.7045865 | 1 |
Swap ints for dense embeddings, on cpu. word_ids correspond the proper row index of the embedding_tensor | def embedded(self, word_ids, embedding_tensor, scope="embedding"):
with tf.variable_scope(scope):
with tf.device("/cpu:0"):
inputs = tf.nn.embedding_lookup(embedding_tensor, word_ids)
return inputs | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def init_embeddings(self, weight, words):\n # wrap in tensor\n if isinstance(weight, list):\n weight = torch.Tensor(weight).float()\n if isinstance(weight, np.ndarray):\n weight = torch.from_numpy(weight).float()\n # check embedding size\n if weight.size(1) ... | [
"0.6119837",
"0.59848696",
"0.5960104",
"0.5842461",
"0.5810946",
"0.5780748",
"0.5766295",
"0.57428074",
"0.57310194",
"0.56487286",
"0.5628087",
"0.56140584",
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"0.5505317",
"0.5502685",
"0.54835695",
"0.5437871",
"0.5430117",
"0.5428947",
"0.541484... | 0.563496 | 10 |
Dynamic encoder for one direction | def encoder_one_way(self, cell, x, seq_len, init_state=None):
# Output is the outputs at all time steps, state is the last state
with tf.variable_scope("dynamic_rnn"):
outputs, state = tf.nn.dynamic_rnn(\
cell, x, sequence_length=seq_len, initial_state=init_state,
dtype... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def encoder(self, inputs):\n pass",
"def _define_encoder(self):\n raise NotImplementedError",
"def build_encoder(shift):\n ### TODO.",
"def _define_encoder(self):\n self.encoder = nn.Sequential(View((-1, 64 * 64 * 3)),\n nn.Linear(64 * 64 * 3, 5120, bias=False)... | [
"0.71723115",
"0.7158449",
"0.68865305",
"0.67140174",
"0.6679422",
"0.65452635",
"0.65233946",
"0.6394681",
"0.6371668",
"0.63569164",
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"0.63349515",
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"0.6112942",
"0.60763985",
"0.60718226",
"0.60718226",
"0.6067417",
"0.6055014",
"0.60... | 0.5588849 | 64 |
Dynamic encoder for two directions | def encoder_bi(self, cell_fw, cell_bw, x, seq_len, init_state_fw=None,
init_state_bw=None):
# Output is the outputs at all time steps, state is the last state
with tf.variable_scope("bidirectional_dynamic_rnn"):
outputs, state = tf.nn.bidirectional_dynamic_rnn(\
cell_fw... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def build_encoder(shift):\n ### TODO.",
"def encoder(self, inputs):\n pass",
"def _define_encoder(self):\n raise NotImplementedError",
"def build_encoder_bi(tparams, options):\n\t# word embedding (source)\n\tembedding = tensor.tensor3('embedding', dtype='float32')\n\tembeddingr = embedding[::-1]... | [
"0.6631168",
"0.65565383",
"0.6201778",
"0.62013143",
"0.59247094",
"0.58589214",
"0.5825668",
"0.5816675",
"0.5806047",
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"0.5655551",
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"0.5614309",
"0.55975485",
"0.55925363",
"0.5589105",
"0.5589105",
"0.5589105",
"0.5578... | 0.5178888 | 59 |
Concatenate input and classes. Do not use for classification | def emb_add_class(self, enc_embedded, classes):
num_classes = tf.shape(classes)[1]
# final_emb_dim = tf.to_int32(tf.shape(enc_embedded)[2] + num_classes)
time_steps = tf.shape(enc_embedded)[1]
classes = tf.tile(classes, [1, time_steps]) # copy along axis=1 only
classes = tf.reshape(classes, [-1, ti... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def concat_model():\n x = tf.keras.Input(shape=[10, 10, 3, ])\n x1 = tf.keras.layers.Conv2D(5, (2, 2))(x)\n x2 = tf.keras.layers.Conv2D(6, (2, 2))(x)\n x3 = tf.keras.layers.Conv2D(7, (2, 2))(x)\n z = tf.keras.layers.concatenate([x2, x1, x3], axis=-1)\n z1 = tf.keras.layers.Conv2D(10, (2, 2))(z)\n... | [
"0.6314391",
"0.6092329",
"0.60063756",
"0.5983007",
"0.59544957",
"0.58480245",
"0.58458734",
"0.58191264",
"0.5818284",
"0.5805948",
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"0.56583315",
"0.5657247",
"0.56488144",
"0.56450874",
"0.56161106",
"0.5582381",
"0.5574... | 0.5815715 | 9 |
Concatenate hidden state with class labels | def add_classes_to_state(self, state_tuple, classes):
# h is shape [batch_size, num_units]
classes = tf.cast(classes, self.floatX)
h_new = tf.concat([state_tuple.h, classes], 1) # concat along 1st axis
new_state_tuple = tf.contrib.rnn.LSTMStateTuple(state_tuple.c, h_new)
return new_state_tuple | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _hide_labels(self):\n pass",
"def __str__(self):\n return ''.join(str(e) + ' ' for e in self.state)",
"def prepare_labels(labels, class_mask):\n mask = [1 if elt else -1 for elt in class_mask]\n mask = np.array(mask)\n return labels.dot(mask)",
"def is_hidden(self):\n return se... | [
"0.5912269",
"0.589453",
"0.56474185",
"0.5618521",
"0.54821837",
"0.54815704",
"0.5479171",
"0.5475139",
"0.5450685",
"0.53647417",
"0.53514713",
"0.5336808",
"0.5324306",
"0.531324",
"0.5307073",
"0.5299472",
"0.5298668",
"0.5289933",
"0.5288108",
"0.5247261",
"0.52441347",... | 0.5141507 | 30 |
Output projection function To be used for single timestep in RNN decoder | def output_logits(self, decoded_outputs, num_units, vocab_size, scope):
with tf.variable_scope(scope):
w = tf.get_variable("weights", [num_units, vocab_size],
dtype=self.floatX, initializer=glorot())
b = tf.get_variable("biases", [vocab_size],
dtype=self.floatX, initializer=tf.consta... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def output_projection(self):\n return self.projection(what='output')",
"def projection(self, rnn_outputs):\n \n with tf.variable_scope('Projection'):\n # U = tf.get_variable('Matrix', [self.config.mRNN._hidden_size*2, self.config.data_sets._len_vocab])\n U = tf.get_vari... | [
"0.6591502",
"0.6589933",
"0.6281211",
"0.6193335",
"0.61072457",
"0.5835417",
"0.5775195",
"0.57721055",
"0.5758339",
"0.5727146",
"0.5715693",
"0.5715132",
"0.56472784",
"0.5622119",
"0.5531096",
"0.54391927",
"0.54026216",
"0.5401652",
"0.53889",
"0.5381454",
"0.53649795",... | 0.0 | -1 |
Logits for the sequence | def sequence_class_logits(self, decoded_outputs, pool_size, max_seq_len, num_classes):
with tf.variable_scope("pooling"):
features = tf.expand_dims(self.decoded_outputs.rnn_output, axis=-1)
pooled = tf.nn.max_pool(
value=features, # [batch, height, width, channels]
ksize=[1, 1, pool_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __convert_to_log(self):\n for i in range(self.nStates):\n if self.pi[i]>0:\n self.pi[i]=log(self.pi[i])\n else:\n self.pi[i]=float('-inf')\n for j in range(self.nStates):\n if self.t[i][j]>0:\n self.t[i][j]=... | [
"0.70746166",
"0.6858346",
"0.6793603",
"0.67382306",
"0.6614052",
"0.6479097",
"0.6446664",
"0.6408251",
"0.63404495",
"0.6335923",
"0.6316904",
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"0.63032484",
"0.63011235",
"0.6253789",
"0.6236438",
"0.622002",
"0.6219435",
"0.620701",
"0.62040913"... | 0.0 | -1 |
Class loss. If binary, two outputs | def classification_loss(self, classes_true, classes_logits):
entropy_fn = tf.nn.sparse_softmax_cross_entropy_with_logits
classes_max = tf.argmax(classes_true, axis=1)
class_loss = entropy_fn(
labels=classes_max,
logits=classes_logits)
return class_loss | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def rpn_cls_loss(*args):\n y_true, y_pred = args if len(args) == 2 else args[0]\n indices = tf.where(tf.not_equal(y_true, -1))\n target = tf.gather_nd(y_true, indices)\n output = tf.gather_nd(y_pred, indices)\n lf = tf.losses.BinaryCrossentropy()\n return lf(target, output)",
"def class_balance... | [
"0.6800373",
"0.6765104",
"0.6725985",
"0.6667496",
"0.6605275",
"0.65954643",
"0.65091467",
"0.6487406",
"0.6465686",
"0.6384776",
"0.6372204",
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"0.63248986",
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"0.63045424",
"0.6264657",
"0.6255827",
"0.62363243",
"0.62358147",
"0.6233365"... | 0.6565122 | 6 |
Returns class label (int) for prediction and gold | def predict(self, pred_logits, classes):
y_pred = tf.nn.softmax(pred_logits)
y_pred = tf.argmax(y_pred, axis=1)
y_true = tf.argmax(classes, axis=1)
return y_pred, y_true | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def decode_prediction(self, prediction):\n index = np.argmax(prediction)\n\n inv_map = {v: k for k, v in self.class_index.items()}\n label = inv_map[index]\n return label, np.amax(prediction)",
"def get_classLabel(self, dataset, class_label): \n\t\tnode = self.root\n\t\tbroken=0\n... | [
"0.71386623",
"0.7114175",
"0.6994599",
"0.69635314",
"0.6898009",
"0.6869533",
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"0.68268377",
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"0.67069185",
"0.670076",
"0.66936386",
"0.6687495",
"0.66666144",
"0.6652521",
"0.6644... | 0.0 | -1 |
Loss on sequence, given logits and onehot targets Default loss below is softmax cross ent on logits | def sequence_loss(self, logits, targets, seq_len):
# creates mask [batch_size, seq_len]
mask = tf.sequence_mask(seq_len, dtype=tf.float32)
# We need to delete zeroed elements in targets, beyond max sequence
max_seq = tf.reduce_max(seq_len)
max_seq = tf.to_int32(max_seq)
# Slice time dimension t... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setup_loss(logits, labels):\n predictions = tf.nn.softmax(logits)\n cost = tf.losses.softmax_cross_entropy(onehot_labels=labels,\n logits=logits,\n )\n return predictions, cost",
"def loss(self, labels, logits, mask=... | [
"0.7389702",
"0.72658104",
"0.70894176",
"0.7064109",
"0.70640814",
"0.70170397",
"0.69140756",
"0.69122744",
"0.6881433",
"0.68809944",
"0.68779224",
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"0.68332094",
"0.683017",
"0.6797566",
"0.6795389",
"0.6769141",
"0.67469865",
"0.6745877",
"0.674... | 0.6511102 | 54 |
Output projection over all timesteps | def sequence_output_logits(self, decoded_outputs, num_units, vocab_size):
# We need to get the sequence length for *this* batch, this will not be
# equal for each batch since the decoder is dynamic. Meaning length is
# equal to the longest sequence in the batch, not the max over data
max_seq_len = tf.sh... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _project(self):\n ghosts_w = self.input_field.topology.ghosts()\n self.input_field.data[0], self.input_field.data[1], \\\n self.input_field.data[2] = \\\n fftw2py.projection_om_3d(self.input_field.data[0],\n self.input_field.data[1],... | [
"0.57561207",
"0.5720776",
"0.5714521",
"0.56881714",
"0.5681513",
"0.56547904",
"0.5637628",
"0.5561602",
"0.5546136",
"0.5512237",
"0.5484176",
"0.54627526",
"0.54291105",
"0.5403807",
"0.53816235",
"0.53636557",
"0.53605634",
"0.5359459",
"0.5359459",
"0.5342748",
"0.53420... | 0.0 | -1 |
This function runs as a thread. It is responsible for listening to the neighboring nodes | def runNodesListener(self):
socketNodes = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
socketNodes.bind((self.ip_address, 5003))
while True:
socketNodes.listen(5)
try :
conn, addr1 = socketNodes.accept()
data = conn.recv(self... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def receive_broadcast_thread():\r\n while True:\r\n data, (ip, port) = broadcaster.recvfrom(4096)\r\n print_blue(f\"RECV: {data} FROM: {ip}:{port}\")\r\n data = data.decode(\"utf-8\").split()\r\n node_id = data[0]\r\n node_port = data[2]\r\n\r\n # to put a condition for... | [
"0.659177",
"0.6297411",
"0.62773734",
"0.62352943",
"0.6219053",
"0.61664134",
"0.615619",
"0.6095733",
"0.60638076",
"0.60447717",
"0.60328406",
"0.59789217",
"0.59214437",
"0.58944476",
"0.58753955",
"0.5852265",
"0.58251584",
"0.58106416",
"0.5809751",
"0.5801766",
"0.580... | 0.60037047 | 11 |
Function sending infomrmations to other nodes | def runNodesMessage(self):
while True:
for neighbour in self.nextIP:
socketNodes = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
while True:
try:
socketNodes.connect((neighbour, 5003))
socketNodes... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def invoke(self, msg, req):\n node = Node.create()\n node.acquire_lock()\n\n if msg.name == 'forward':\n try:\n with node.graph.as_default():\n if node.num_devices == 5:\n output, name = Model_5.forward(req['input'], req['next... | [
"0.5952947",
"0.5846011",
"0.57124156",
"0.5598955",
"0.5477898",
"0.54727274",
"0.5461603",
"0.54390836",
"0.54278105",
"0.5402687",
"0.53933555",
"0.53694874",
"0.5343483",
"0.5285376",
"0.5242901",
"0.52267",
"0.52222073",
"0.519734",
"0.5159041",
"0.5159041",
"0.5159041",... | 0.48762402 | 79 |
Looks if the received block is in the waiting list. If yes we check if the address is already recorded. If no it is added to the waiting list and broadcasted. | def arrivingBlock(self,data, addr, receivedBlock):
if self.blockchain.waiting_blocks == []:
self.confirmed.clear()
self.neighboursOk.clear()
self.confirmed.append(addr)
self.blockchain.putting_block(receivedBlock)
self.message = self.setMessa... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_call_waiting(self) -> bool:",
"def BlockheightCheck(self):\n if self.CurrentBlockheight == BC.Default().Height:\n if len(self.Peers) > 0:\n logger.debug(\"Blockheight is not advancing ...\")\n next_hash = BC.Default().GetHeaderHash(self.CurrentBlockheight + ... | [
"0.5706097",
"0.5541932",
"0.5535595",
"0.55307394",
"0.5490348",
"0.5457724",
"0.5444099",
"0.54275596",
"0.542084",
"0.54196537",
"0.5404514",
"0.53694296",
"0.53550977",
"0.5339095",
"0.5308351",
"0.5284784",
"0.5277014",
"0.52734613",
"0.526288",
"0.52274597",
"0.5192736"... | 0.614878 | 0 |
Constructor of the node | def __init__(self):
config=ConfigParser()
config.read('../config/host.ini')
self.ip_address=config.get('node','ip_address')
self.username=config.get('node','username')
self.server_address=config.get('registration','ip_address')
self.password=config.get('registrat... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self):\n self.root = Node('')",
"def __init__(self):\n self.start = Node('-1')",
"def __init__(self):\n self.root = Node(\"\")",
"def __init__(self):\n self.root = Node(\"\")",
"def __init__(self, node_text=\"\", node_type=0, node_parent=None):\n self.node_te... | [
"0.782463",
"0.7795853",
"0.7745167",
"0.7745167",
"0.7734531",
"0.7705406",
"0.7700045",
"0.769647",
"0.76576585",
"0.7647589",
"0.76450074",
"0.762918",
"0.7583373",
"0.7583373",
"0.7583373",
"0.7575243",
"0.7563161",
"0.75337684",
"0.7517065",
"0.7516046",
"0.74705166",
... | 0.0 | -1 |
Test updating a cadence_frequency for an existing cadence | def test_submit_calibration_valid(self):
new_form_data = {
'site': 'tlv',
'cadence_frequency': 2, # new cadence_frequency
'target_id': self.target.id
}
response = self.client.post(reverse('nres_calibrations:nres_submission'),
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_update_occurrence(self):\n pass",
"def update_frequencies():\n pass",
"def UpdateFrequency(self, newfreq):\n\n if self.strategy:\n setattr(self.strategy, managers.UTICK, newfreq)",
"def test_check_freq_crashed(self):\n self.assertEqual(check_freq(self.jobset2), 'fc... | [
"0.6354806",
"0.6300095",
"0.6059406",
"0.60465586",
"0.5993218",
"0.57028705",
"0.56660575",
"0.56660575",
"0.5645263",
"0.5643915",
"0.55231625",
"0.55107856",
"0.55028033",
"0.55010307",
"0.55003303",
"0.5495829",
"0.54729825",
"0.5471072",
"0.5441614",
"0.5434374",
"0.543... | 0.62852836 | 2 |
Test that a new DynamicCadence is created by form submission | def test_create_cadence_for_new_site(self):
new_form_data = {
'site': 'cpt',
'cadence_frequency': 10,
'target_id': self.target.id
}
original_dc_count = DynamicCadence.objects.all().count()
response = self.client.post(reverse('nres_calibrations:nres_sub... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_submit_calibration_valid(self):\n new_form_data = {\n 'site': 'tlv',\n 'cadence_frequency': 2, # new cadence_frequency\n 'target_id': self.target.id\n }\n response = self.client.post(reverse('nres_calibrations:nres_submission'),\n ... | [
"0.70193005",
"0.6708431",
"0.66458005",
"0.6469265",
"0.64464295",
"0.6391832",
"0.6252196",
"0.62489915",
"0.6191535",
"0.6154543",
"0.6122482",
"0.6101032",
"0.60874194",
"0.6073931",
"0.604443",
"0.6040367",
"0.6038633",
"0.6028261",
"0.6002224",
"0.59992266",
"0.5994009"... | 0.7397608 | 0 |
Test that the nres_home target list contains the NRES calibration targets | def test_nres_targets_list(self):
response = self.client.get(reverse('nres_calibrations:nres_home'))
self.assertContains(response, self.target.id) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_which_targets():\n num_multi_targets = 0\n for which_targets_day in which_targets:\n # All inputs have a label\n assert np.all(which_targets_day.sum(axis=1) > 0)\n # No inputs have more than 3 targets\n assert np.all(which_targets_day.sum(axis=1) < 4)\n\n num_multi... | [
"0.58674073",
"0.5667292",
"0.5577143",
"0.5518126",
"0.54473",
"0.54211724",
"0.5411076",
"0.5409779",
"0.54086864",
"0.53584373",
"0.53370976",
"0.531879",
"0.52858996",
"0.52696484",
"0.52417976",
"0.52324855",
"0.5231467",
"0.51975954",
"0.51834714",
"0.5156341",
"0.51380... | 0.7668798 | 0 |
Test that the NRES Cadence list contains the ObservationGroup name of the DynamicCadence | def test_nres_cadence_list(self):
response = self.client.get(reverse('nres_calibrations:nres_home'))
self.assertContains(response, self.observation_group_name) # should appear in History column | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_check_ca_groups(self, admin_dashboard):\n ca_tab = admin_dashboard.select_custom_attributes()\n expected_ca_groups_set = set(\n [objects.get_normal_form(item) for item in objects.ALL_CA_OBJS])\n actual_ca_groups_set = set(\n [item.text for item in ca_tab.get_items_list()])\n asse... | [
"0.51378435",
"0.502584",
"0.50022244",
"0.49259534",
"0.48699087",
"0.48539323",
"0.48403198",
"0.4828131",
"0.48271948",
"0.48038292",
"0.47950238",
"0.47871473",
"0.4785799",
"0.4756255",
"0.47553796",
"0.47491336",
"0.4746582",
"0.4743038",
"0.47408164",
"0.47399324",
"0.... | 0.6860291 | 0 |
Gets user input such as the localhost and the similarity value for the comparision. Reads all the ringsugars in the given database and and creates a data frame with aglycons, their coconut_id and taxonomy. The biological names are delete and if there are two different taxonomies for an aglycon, the taxonomy is called '... | def complete_databank(port="localhost:27017",coconut_database="COCONUT2020-10",sweetcoconut_database="sweetcoconut"):
client = MongoClient(port)
db_complete = client[coconut_database]
collection = db_complete.uniqueNaturalProduct
db_complete_only_ring_sugars = pd.DataFrame(list(collection.find({"contain... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sweetcoconut_databank(df_tax_id_fromCompleteDatabank, taxonomy_Double,sweetcoconut_database,port):\n client2 = MongoClient(port)\n db_s = client2[sweetcoconut_database]\n collection2 = db_s.sweetNaturalProduct\n sweetnp = pd.DataFrame(list(collection2.find({\"contains_sugar\": True})))\n sweetnp... | [
"0.58430374",
"0.5564186",
"0.54623526",
"0.5419678",
"0.5353768",
"0.5343155",
"0.5265147",
"0.5263287",
"0.5258324",
"0.52520347",
"0.5207315",
"0.51895225",
"0.51715577",
"0.51707095",
"0.5153093",
"0.51300794",
"0.50969017",
"0.5094018",
"0.5078065",
"0.50657225",
"0.5031... | 0.652284 | 0 |
Gets the created data frame with the three columns aglycon, coconut id and taxonomy Merges sweetcocunt data frame with incoming data frame via their coconut id. Replaces nan with "no" if there isn't a known taxonomy in the row for the aglycon. Summarize all aglycons with the same structure into one row. Writes a .pkl f... | def sweetcoconut_databank(df_tax_id_fromCompleteDatabank, taxonomy_Double,sweetcoconut_database,port):
client2 = MongoClient(port)
db_s = client2[sweetcoconut_database]
collection2 = db_s.sweetNaturalProduct
sweetnp = pd.DataFrame(list(collection2.find({"contains_sugar": True})))
sweetnp_with_tax = ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def reduce_and_save():\n ### Get the signature information\n sig_info = pd.read_csv(join(FILE_PATH, \"GSE92742_Broad_LINCS_sig_info.txt\"), sep=\"\\t\")\n ### Columns are:\n ### Index([u'sig_id', u'pert_id', u'pert_iname', u'pert_type', u'cell_id',\n ### u'pert_dose', u'pert_dose_unit', u'per... | [
"0.615126",
"0.5918436",
"0.5613667",
"0.55950755",
"0.5493555",
"0.5440564",
"0.53833544",
"0.53545773",
"0.53458494",
"0.53149766",
"0.5311221",
"0.5273746",
"0.526471",
"0.5234631",
"0.5224225",
"0.52036613",
"0.5177393",
"0.5147963",
"0.5131519",
"0.5126816",
"0.51184547"... | 0.6074063 | 1 |
Gets a data frame with all the same aglycon structures in one row. Counts all taxonomies and create a barplot. 'Double' is also a taxonomy. Saves the bar plot with the numbers of different taxonomies as .png. | def bar_plot(df_NP):
cnt = Counter()
for tax_list in df_NP.taxonomy:
for tax in list(tax_list):
if tax != 'no':
cnt[tax] += 1
plt.bar(cnt.keys(),cnt.values())
plt.xlabel('taxonomic provenance')
plt.ylabel('number of molecules')
plt.title('number of aglycons wi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def eda_plot():\n\n df1 = pd.read_csv('eda_malware.csv')\n df2 = pd.read_csv('eda_random.csv')\n df3 = pd.read_csv('eda_popular.csv')\n\n df = pd.concat([df1, df2, df3], ignore_index=True)\n df['label'].replace([0,1],['Benign','Malware'],inplace=True)\n\n colors = ['#EAB6AB','#D9E6F3','#CBAACB','... | [
"0.670801",
"0.6379678",
"0.62803435",
"0.62481606",
"0.621397",
"0.6171354",
"0.614709",
"0.612319",
"0.6084341",
"0.604334",
"0.6034777",
"0.5924511",
"0.5897462",
"0.5889659",
"0.5855359",
"0.58464646",
"0.58460945",
"0.57800704",
"0.57795185",
"0.57522184",
"0.572955",
... | 0.7676816 | 0 |
Gets a data frame with all the same aglycon structures in one row. Counts all taxonomies and creates a venn diagram with the four taxonomies plants, bacteria, animals, fungi. Reads the original taxonmies of the 'Double' entries. Saves a venndiagram of the different taxonmies as .png. | def venn_diagram(df_NP, taxonomy_Double):
taxonomy_Single = [list(tax) for tax in df_NP.taxonomy if 'double' not in tax]
taxonomy_All = taxonomy_Single + taxonomy_Double
plants = set()
bacteria = set()
animals = set()
fungi = set()
for tax_list in taxonomy_All:
if "plants" in tax_lis... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def eda_plot():\n\n df1 = pd.read_csv('eda_malware.csv')\n df2 = pd.read_csv('eda_random.csv')\n df3 = pd.read_csv('eda_popular.csv')\n\n df = pd.concat([df1, df2, df3], ignore_index=True)\n df['label'].replace([0,1],['Benign','Malware'],inplace=True)\n\n colors = ['#EAB6AB','#D9E6F3','#CBAACB','... | [
"0.61547685",
"0.5806181",
"0.5550293",
"0.5549266",
"0.5513179",
"0.54985404",
"0.5479837",
"0.54608345",
"0.54200816",
"0.54113746",
"0.5385822",
"0.5380591",
"0.53727204",
"0.53407085",
"0.5332196",
"0.5309217",
"0.5293119",
"0.5277534",
"0.526739",
"0.52268946",
"0.520937... | 0.7490416 | 0 |
Gets a data frame with all the same aglycon structures in one row. Deletes all rows with more than one entry in the taxonomy row. Passes a data frame with only one entry (superkingdom or 'no') in the taxonomy row. | def aglycon_single_tax(df_NP):
# **seperate aglycons with at least two different entries in taxonomy**
index_Unique_Tax = [ind for ind, tax_list in enumerate(df_NP.taxonomy) if len(tax_list) == 1]
df_Without_Double = df_NP.iloc[index_Unique_Tax[:]]
#df_Without_Double
# **check for 'double' or 'tripl... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def clean(df):",
"def gb_cleaner(df):\n df['tag'] = df.tags.apply(retagger)\n \n c_list = df.text.tolist()\n\n clean_corpus = []\n for docs in c_list:\n clean_corpus.append(data_cleaner(docs))\n \n df['clean'] = clean_corpus\n\n df = df.drop(['text', 'tags', 'stars'], axis= 1)\n ... | [
"0.5444545",
"0.5277261",
"0.52311677",
"0.5200804",
"0.51652235",
"0.51347816",
"0.51267016",
"0.51248044",
"0.50985575",
"0.5084206",
"0.50411373",
"0.5032984",
"0.5006409",
"0.49748224",
"0.49653932",
"0.48951414",
"0.48935908",
"0.48864013",
"0.4885388",
"0.48809195",
"0.... | 0.5743044 | 0 |
Get a list of all available capabilities. | def get(self):
try:
response = requests.get(CONF.api.github_api_capabilities_url)
LOG.debug("Response Status: %s / Used Requests Cache: %s" %
(response.status_code,
getattr(response, 'from_cache', False)))
if response.status_code =... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def capabilities(self):\n return []",
"def capabilities(self):\n pass",
"def capabilities(self) -> Optional[Sequence[str]]:\n return pulumi.get(self, \"capabilities\")",
"def list_caps():\n global _CAPABILITIES_MAP\n\n try:\n return tuple(sorted(_CAPABILITIES_MAP.keys()))\n\... | [
"0.8241211",
"0.75479233",
"0.7545116",
"0.73705965",
"0.73676735",
"0.73322165",
"0.7275096",
"0.72728455",
"0.71944684",
"0.7132316",
"0.712093",
"0.70567423",
"0.7039903",
"0.6985127",
"0.695252",
"0.69255227",
"0.6894365",
"0.6881602",
"0.6828689",
"0.67913085",
"0.677973... | 0.53967273 | 93 |
Handler for getting contents of specific capability file. | def get_one(self, file_name):
github_url = ''.join((CONF.api.github_raw_base_url.rstrip('/'),
'/', file_name, ".json"))
try:
response = requests.get(github_url)
LOG.debug("Response Status: %s / Used Requests Cache: %s" %
(respon... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get(self):\n try:\n response = requests.get(CONF.api.github_api_capabilities_url)\n LOG.debug(\"Response Status: %s / Used Requests Cache: %s\" %\n (response.status_code,\n getattr(response, 'from_cache', False)))\n if response.... | [
"0.588543",
"0.588543",
"0.57889295",
"0.5763682",
"0.5716423",
"0.5572932",
"0.55056",
"0.5486656",
"0.5476102",
"0.54738456",
"0.5465829",
"0.5389476",
"0.5356889",
"0.5339783",
"0.53378046",
"0.52965707",
"0.52622104",
"0.52595115",
"0.5175876",
"0.51711446",
"0.5162143",
... | 0.0 | -1 |
Draw a histogram given the graph. | def draw_histogram(graph: Graph) -> Optional[Graph]:
if not graph:
return None
try:
# generate and open a new figure
figure, ax = plt.subplots()
# When graph.x or y is str, the histogram is ill-defined.
ax.barh(graph.y, graph.x, color=graph.color)
ax.set_title(graph.title)
if graph.xlabe... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def draw_histogram(xx, hist_ax, alpha=1.0, colorV=None, facecolor='#80D080', edgecolor=None, nbins=75,\n fontsize=8, linewidth=1, xlabel=None, ylabel=None, label=None):\n plt.sca(hist_ax)\n if colorV is None:\n n, bins, patches = hist_ax.hist(xx, nbins, histtype='stepfilled', alpha=a... | [
"0.6689416",
"0.65681064",
"0.6548414",
"0.6526117",
"0.64784855",
"0.6347681",
"0.62804496",
"0.6251401",
"0.62138206",
"0.62123525",
"0.6209556",
"0.62087065",
"0.6201147",
"0.6197796",
"0.61838293",
"0.61743563",
"0.6154292",
"0.61535",
"0.61413497",
"0.61310756",
"0.61112... | 0.74443585 | 0 |
Converts a Matplotlib figure to a base64 string encoding. | def figure_to_base64str(fig: matplotlib.figure.Figure) -> str:
buf = io.BytesIO()
fig.savefig(buf, bbox_inches='tight', format='png')
return base64.b64encode(buf.getbuffer().tobytes()).decode('ascii') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def plot2uri(figure):\n image = io.BytesIO()\n figure.savefig(image, format=\"png\")\n image.seek((0))\n string = base64.b64encode(image.read())\n uri = urllib.parse.quote(string)\n\n return uri",
"def base64(self):\n image = self.png.getvalue()\n return base64.encodestring(image)... | [
"0.7147997",
"0.68165886",
"0.6809981",
"0.6809981",
"0.6721276",
"0.6467986",
"0.6416611",
"0.64131576",
"0.6268338",
"0.62255985",
"0.6189878",
"0.6181082",
"0.5922518",
"0.5921818",
"0.5912652",
"0.5896791",
"0.5858558",
"0.5847519",
"0.5846032",
"0.58170253",
"0.5803382",... | 0.88419247 | 0 |
Stringifies a slice key. | def stringify_slice_key(slice_key: SliceKeyType) -> Tuple[str, str]:
key_count = len(slice_key)
if not key_count:
return ('Overall', 'Overall')
keys = []
values = []
separator = ', '
for (feature, value) in slice_key:
keys.append(feature)
values.append(value)
# To use u'{}' instead of '{}' ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _key_to_str(self, key: Any) -> Any:\n if isinstance(key, str):\n return key\n if isinstance(key, int):\n return list(self._data_vars.keys())[key]\n if isinstance(key, slice):\n s = key.indices(len(self))\n return self._key_to_str(list(range(*s)))... | [
"0.7414618",
"0.7386717",
"0.680454",
"0.6604581",
"0.62448543",
"0.6166507",
"0.5966563",
"0.58570135",
"0.5843735",
"0.58304656",
"0.5780478",
"0.57421565",
"0.57346904",
"0.5700808",
"0.56465274",
"0.5616167",
"0.5615734",
"0.5599783",
"0.55980146",
"0.55510175",
"0.554406... | 0.73211503 | 2 |
Get the report data and do the computation for each and every product in the stock and divided it into the relevant time period | def compute_ageing(self):
self.env['ageing.result'].search([]).unlink()
# Get current company id
current_compnay_id = self.env.user.company_id.id
# Get products that related to the company
products = self.env['product.product'].search([('company_id', '=', current_compnay_id)])
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def prepare_data_with_warehouse(self,from_date,to_date,warehouses,all_products):\n data_dict = {}\n stock_quant_obj=self.env['stock.quant']\n for warehouse in warehouses:\n all_locations = self.get_all_locations(warehouse)\n if not all_locations:\n continu... | [
"0.686364",
"0.6636723",
"0.65827537",
"0.65092677",
"0.6497481",
"0.64481914",
"0.6316854",
"0.6294505",
"0.6278176",
"0.61984044",
"0.6180829",
"0.6168524",
"0.6148737",
"0.61187345",
"0.6070621",
"0.6064129",
"0.605158",
"0.6049545",
"0.60449344",
"0.6044796",
"0.60137326"... | 0.0 | -1 |
Test 'image lookup t days' and check for correct display and enum value printing. | def test(self):
self.build()
exe = self.getBuildArtifact("a.out")
self.runCmd("file " + exe, CURRENT_EXECUTABLE_SET)
lldbutil.run_to_source_breakpoint(
self, '// Breakpoint for bitfield', lldb.SBFileSpec("main.c"))
self.expect("fr var a", DATA_TYPES_DISPLAYED_CORREC... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_Image():\n assert Image(cur, \"Simple_Linear\").detect_image() == True\n assert Image(cur, \"Logistic_Linear\").detect_image() == False\n assert Image(cur, \"Simple_Linear\").date == \"2021-04-20\"\n assert Image(cur, \"Breslow-Day_Test\").source == \"Course BIOSTAT703 slide\"",
"def test_ch... | [
"0.5828093",
"0.5741669",
"0.5531521",
"0.5446059",
"0.54444784",
"0.54125243",
"0.5405248",
"0.5379844",
"0.5291747",
"0.5263877",
"0.5116462",
"0.51097274",
"0.51079166",
"0.5055522",
"0.50463736",
"0.5033744",
"0.50264347",
"0.5025332",
"0.50177395",
"0.49971616",
"0.49969... | 0.0 | -1 |
Simulatesa walker in a 1D potential. | def simulate_1Dsystem(inps, mdps, method, potfunc, bcs, filetitle,
makeplot, plot_freq, make_movie, ebound):
steps = inps[0]
dt = inps[1]
x0 = inps[2]
T = inps[3]
m = inps[4]
xmin = inps[5]
xmax = inps[6]
xinc = inps[7]
kb = inps[13]
winit = mdps[0]
del... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def walk(self):\n self.speed = self.speed + (0.2 * self.legs)",
"def simple_walker(data_simple_tracking):\n def dist_fun(tracks, detections_test):\n \"\"\"Function to calculate distance between track and detection.\"\"\"\n np_track = np.array([(track.meta[DETKEY][-1].x, track.meta[DETKEY]... | [
"0.6046353",
"0.5641975",
"0.5520832",
"0.55019075",
"0.54966307",
"0.53194624",
"0.530925",
"0.529421",
"0.52837753",
"0.5268337",
"0.5266113",
"0.5240376",
"0.5227442",
"0.517567",
"0.5159675",
"0.5157245",
"0.5153656",
"0.51477164",
"0.5130458",
"0.51271415",
"0.5116963",
... | 0.5240417 | 11 |
Receive and returns an array that recreates the FES. | def recreate_1DFES(FES, icount, coord, xinc, xmin, xmax, E):
index = int(round((round(coord, int(abs(math.log10(xinc)))) +
(0-xmin))/xinc))
if coord > xmin and coord < xmax:
FES[index] = ((FES[index] * (icount[index]) + E) /
(icount[index] + 1))
icount... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _recv(self) -> List[np.ndarray]:",
"def recv_array(self, flags=0, copy=True, track=False):\n md = self.recv_json(flags=flags)\n msg = self.recv(flags=flags, copy=copy, track=track)\n A = numpy.frombuffer(msg, dtype=md['dtype'])\n return A.reshape(md['shape'])",
"def recv_array(s... | [
"0.62769",
"0.574053",
"0.574053",
"0.5360778",
"0.531635",
"0.52670115",
"0.52535814",
"0.52504945",
"0.5225411",
"0.52028364",
"0.51303285",
"0.51203173",
"0.50971043",
"0.50925493",
"0.50638926",
"0.50428754",
"0.5041201",
"0.5023313",
"0.49668166",
"0.4948956",
"0.4937544... | 0.0 | -1 |
Get person's ID from mbank.tcredits (turnes DB) | def get_person_id(contract_num, phone):
exfin_connection = MySQLdb.connect(
host="10.10.100.27", # host of MySQL database
user="root", # user's username
passwd="Orraveza(99)", # your password
db="mbank", # nam... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_person_id_and_tel(contract_num):\n exfin_connection = MySQLdb.connect(\n host=\"10.10.100.27\", # host of MySQL database\n user=\"root\", # user's username\n passwd=\"Orraveza(99)\", # your password\n db=\"mbank\", ... | [
"0.6560628",
"0.6496635",
"0.6305405",
"0.62735915",
"0.6225594",
"0.6144446",
"0.60823274",
"0.5909578",
"0.5867553",
"0.5829623",
"0.5829623",
"0.5829623",
"0.5829623",
"0.5757211",
"0.57547575",
"0.57257414",
"0.5696543",
"0.56823105",
"0.56737465",
"0.56495553",
"0.564790... | 0.68747634 | 0 |
Get person's ID from mbank.tcredits (turnes DB) | def get_person_id_and_tel(contract_num):
exfin_connection = MySQLdb.connect(
host="10.10.100.27", # host of MySQL database
user="root", # user's username
passwd="Orraveza(99)", # your password
db="mbank", # na... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_person_id(contract_num, phone):\n exfin_connection = MySQLdb.connect(\n host=\"10.10.100.27\", # host of MySQL database\n user=\"root\", # user's username\n passwd=\"Orraveza(99)\", # your password\n db=\"mbank\", ... | [
"0.68761975",
"0.6497191",
"0.63046885",
"0.6275422",
"0.62251246",
"0.61442626",
"0.6080249",
"0.59082514",
"0.58671093",
"0.58295715",
"0.58295715",
"0.58295715",
"0.58295715",
"0.5758327",
"0.5754676",
"0.5724665",
"0.56968856",
"0.56817",
"0.5672638",
"0.5647657",
"0.5647... | 0.65625286 | 1 |
Test for validity based on requirements | def check_for_validity_puzzle_1(limits: tuple, rep_char: str, password: str):
reps = password.count(rep_char)
lower, upper = limits
if lower <= reps <= upper:
return True
else:
return False | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check_validity(self):",
"def _check_validity(self):\n pass",
"def validate():",
"def _is_valid(self):\n self._is_allows_valid()\n self._is_denies_valid()",
"def check_validity(self) -> None: # pylint: disable=no-self-use # pragma: nocover\n return None",
"def validate(se... | [
"0.82199275",
"0.79411346",
"0.743704",
"0.742443",
"0.7148247",
"0.7051586",
"0.7041749",
"0.70136297",
"0.7004299",
"0.6997353",
"0.6975964",
"0.69480985",
"0.6946532",
"0.6919911",
"0.69067806",
"0.6880522",
"0.68750596",
"0.68696904",
"0.6835599",
"0.6833236",
"0.68319005... | 0.0 | -1 |
count number of valid passwords in a list | def number_of_valid_pass_puzzle_1(input_list: list):
num_of_valid = 0
for item in input_list:
data = split_data(item)
if check_for_validity_puzzle_1(*data):
num_of_valid += 1
return num_of_valid | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def count_valid_passwords(passwords, validator):\n count = len(list(filter(validator, passwords)))\n print(f\"Found {count} valid passwords\")\n return count",
"def find_valid_passwords(values: List[str]) -> int:\n search_reg = re.compile(\n r\"\\b(?P<first>[0-9]+)-(?P<second>[0-9]+)\\s(?P<let... | [
"0.8193186",
"0.77443373",
"0.68906647",
"0.6878371",
"0.6711917",
"0.66513747",
"0.65652156",
"0.6556296",
"0.6551484",
"0.64362067",
"0.6362422",
"0.6224782",
"0.62063307",
"0.6202379",
"0.61953527",
"0.6195113",
"0.609859",
"0.6089273",
"0.60875905",
"0.60206604",
"0.59888... | 0.67687905 | 4 |
Test for validity based on requirements | def check_for_validity_puzzle_2(pos: tuple, char: str, password: str):
valid_pos, invalid_pos = pos
# using xor
if (password[valid_pos-1] == char) ^ (password[invalid_pos-1] == char):
return True
else:
return False | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check_validity(self):",
"def _check_validity(self):\n pass",
"def validate():",
"def _is_valid(self):\n self._is_allows_valid()\n self._is_denies_valid()",
"def check_validity(self) -> None: # pylint: disable=no-self-use # pragma: nocover\n return None",
"def validate(se... | [
"0.8219967",
"0.79417014",
"0.74356925",
"0.74248713",
"0.7148919",
"0.7052276",
"0.70417863",
"0.70133173",
"0.7005105",
"0.69962347",
"0.69762856",
"0.69475913",
"0.6946524",
"0.6920208",
"0.69087297",
"0.6881058",
"0.6874956",
"0.6867467",
"0.68364275",
"0.68317467",
"0.68... | 0.0 | -1 |
count number of valid passwords in a list | def number_of_valid_pass_puzzle_2(input_list: list):
num_of_valid = 0
for item in input_list:
data = split_data(item)
if check_for_validity_puzzle_2(*data):
num_of_valid += 1
return num_of_valid | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def count_valid_passwords(passwords, validator):\n count = len(list(filter(validator, passwords)))\n print(f\"Found {count} valid passwords\")\n return count",
"def find_valid_passwords(values: List[str]) -> int:\n search_reg = re.compile(\n r\"\\b(?P<first>[0-9]+)-(?P<second>[0-9]+)\\s(?P<let... | [
"0.8193358",
"0.7743638",
"0.6891725",
"0.6769792",
"0.67102826",
"0.6652063",
"0.6565883",
"0.6556342",
"0.655014",
"0.6436412",
"0.6360499",
"0.62254614",
"0.6205677",
"0.62009424",
"0.61975265",
"0.61939204",
"0.6100244",
"0.6088316",
"0.6086228",
"0.6019474",
"0.5987997",... | 0.687942 | 3 |
Adapting numpy.int64 type to SQLconform int type using psycopg extension, see [1]_ for more info. | def adapt_numpy_int64(numpy_int64):
return AsIs(numpy_int64) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def castData(data, type='int64'):\n data = data.astype(type)\n return data",
"def cast_to_integer(array, attributes):\n atts = array.att_names\n\n for nm, typ, null in array.sdbtype.full_rep:\n if nm not in attributes:\n continue\n if 'int' in typ:\n continue\n ... | [
"0.6342146",
"0.62293154",
"0.6208453",
"0.5914053",
"0.5862272",
"0.5841508",
"0.5763204",
"0.5722632",
"0.57082486",
"0.5705555",
"0.5675615",
"0.5662397",
"0.565685",
"0.5637229",
"0.5554309",
"0.54482377",
"0.5430291",
"0.53955346",
"0.5389029",
"0.53852504",
"0.53627807"... | 0.7526429 | 0 |
Reshapes arrays of amplitudes which may have been flattened. | def reshape_params(params, nstates):
params['Vnn'] = params['Vnn'].reshape(nstates.n, nstates.m)
params['Vno'] = params['Vno'].reshape(nstates.n, nstates.mo)
params['Von'] = params['Von'].reshape(nstates.no, nstates.m)
params['Voo'] = params['Voo'].reshape(nstates.no, nstates.mo)
for key in ['fluctu... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def flatten_stimulus(stimulus):\n n, h, w = stimulus.shape\n return stimulus.reshape((n, h * w))",
"def flatten_layers(data):\n return data.reshape((data.shape[0], data.shape[1], -1))",
"def flatten_numpy(ndarray):\n return np.reshape(ndarray, (-1,), 'F')",
"def flatten_image(inputs):\n ... | [
"0.66989696",
"0.63066256",
"0.62929034",
"0.62222356",
"0.6163883",
"0.60748667",
"0.60659355",
"0.60219",
"0.5942159",
"0.59354687",
"0.59175",
"0.5854167",
"0.5835814",
"0.58319926",
"0.5804624",
"0.57921964",
"0.5790089",
"0.5758622",
"0.57350016",
"0.5733529",
"0.5720575... | 0.0 | -1 |
Gets a dict of bestfit information from the database regarding the specified form factor. Also gets some "meta" information like the associated momentum, current, and lattice size. | def get_best_fit_information(engine, form_factor_id):
Nstates = collections.namedtuple(
'NStates', ['n', 'no', 'm', 'mo'], defaults=(1, 0, 0, 0)
)
def _float_or_none(astr):
if astr is None:
return None
if (astr.lower() == 'nan') or (astr.lower() == 'none'):
r... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_stats(trj_datasets, ff_form):\n\n stats_data = {}\n\n params = ff_form['hyperparams']\n stats_func = ff_func[ff_form['potential']]\n\n for key, trj in trj_datasets.items():\n \n stats_dict = {'energy':[]}\n \n for ii, (xyz, box) in enumerate(zip(trj['xyz'], trj['box'])):... | [
"0.5544596",
"0.54875684",
"0.5375364",
"0.5314797",
"0.5159251",
"0.51468754",
"0.51262325",
"0.5124719",
"0.50992376",
"0.5055376",
"0.5029532",
"0.5029249",
"0.49983555",
"0.4981368",
"0.4980224",
"0.49532855",
"0.49397266",
"0.4906085",
"0.48953032",
"0.48906365",
"0.4883... | 0.75372094 | 0 |
Reads all the required correlators for analyzing the specified form factor from the table "glance_correlator_n_point". The data from this table lacks any information about correlations and usually only employs partial statistics (often having restricted to 'fine' solves only). However, such data serves a useful cache o... | def get_glance_data(form_factor_id, engine, apply_alias=True):
query = f"""
select
form_factor.form_factor_id,
rtrim(name, '-_fine') as basename,
glance_correlator_n_point.data
from form_factor
join junction_form_factor using(form_factor_id)
join correlator_n_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def find_corr (mdp,num):\n ctr=0 # line counter\n mdp.corr_file.seek(0)\n lfsp=mdp.corr_file.read().split(\"\\n\")\n for i in range(0,len(lfsp)-1):\n lin=lfsp[i].strip() # strip preceeding and trailing spaces?\n if lin.startswith(\"0\"): # check whether it is the right form\n if test_line_type(lin,0):\n ... | [
"0.56094223",
"0.5485572",
"0.52754664",
"0.5258015",
"0.5176021",
"0.5160623",
"0.4980546",
"0.49767244",
"0.49409404",
"0.48738986",
"0.48393074",
"0.47858018",
"0.4778757",
"0.47687766",
"0.47029915",
"0.46986908",
"0.4646122",
"0.4598063",
"0.45766872",
"0.4559931",
"0.45... | 0.51926357 | 4 |
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