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2a6dd12fe1983a1b5f53e4fe5bda3cbdfd058734 | sdw95927/InstanceSegmentation-CycleGAN | segmentation_functions.py | [
"MIT"
] | Python | make_8UC3 | <not_specific> | def make_8UC3(mat, normalized=True):
"""Convert the matrix to the equivalent matrix of the unsigned 8 bit integer datatype with 3 channels."""
mat_uint8 = make_8UC(mat, normalized)
mat_uint8 = np.stack((mat_uint8,)*3, axis = -1)
return mat_uint8 | Convert the matrix to the equivalent matrix of the unsigned 8 bit integer datatype with 3 channels. | Convert the matrix to the equivalent matrix of the unsigned 8 bit integer datatype with 3 channels. | [
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mat_uint8 = make_8UC(mat, normalized)
mat_uint8 = np.stack((mat_uint8,)*3, axis = -1)
return mat_uint8 | [
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2a6dd12fe1983a1b5f53e4fe5bda3cbdfd058734 | sdw95927/InstanceSegmentation-CycleGAN | segmentation_functions.py | [
"MIT"
] | Python | watershed | <not_specific> | def watershed(mask, img, plot_image=False, kernel_size=2):
"""Do watershed segmentation for input mask and image.
Args:
mask: A 0/255 matrix with 255 indicating objects.
img: An 8UC3 matrix for watershed segmentation.
plot_image: Set True if want to real-time display results. Defaul... | Do watershed segmentation for input mask and image.
Args:
mask: A 0/255 matrix with 255 indicating objects.
img: An 8UC3 matrix for watershed segmentation.
plot_image: Set True if want to real-time display results. Default is False.
kernel_size: Kernal size for inner marker eros... | Do watershed segmentation for input mask and image. | [
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] | def watershed(mask, img, plot_image=False, kernel_size=2):
img_copy = img.copy()
mask_copy = np.array(mask.copy(), dtype=np.uint8)
mask_closed = closing(np.array(mask_copy, dtype=np.uint8))
mask_closed = closing(np.array(mask_closed, dtype=np.uint8))
kernel = np.ones((kernel_size, kernel_size), np.u... | [
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2a6dd12fe1983a1b5f53e4fe5bda3cbdfd058734 | sdw95927/InstanceSegmentation-CycleGAN | segmentation_functions.py | [
"MIT"
] | Python | generate_mask | <not_specific> | def generate_mask(channel, original_img=None, overlap_color=(0, 1, 0),
plot_process=False, plot_result=False, title="",
fill_hole=False, thresh=None,
use_watershed=True, watershed_kernel_size=2,
save_img=False, save_path=None):
"""Generate mas... | Generate mask for a gray-value image.
Args:
channel: Channel returned by function 'channel_deconvolution'. A gray-value image is also accepted.
original_img: A image used for plotting overlapped segmentation result, optional.
overlap_color: A 3-value tuple setting the color used to mark... | Generate mask for a gray-value image. | [
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plot_process=False, plot_result=False, title="",
fill_hole=False, thresh=None,
use_watershed=True, watershed_kernel_size=2,
save_img=False, save_path=None):
if not check_ch... | [
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2a6dd12fe1983a1b5f53e4fe5bda3cbdfd058734 | sdw95927/InstanceSegmentation-CycleGAN | segmentation_functions.py | [
"MIT"
] | Python | calculateRatio | <not_specific> | def calculateRatio(levelDims):
""" Calculates the ratio between the highest resolution image and lowest resolution image.
Returns the ratio as a tuple (Xratio, Yratio).
"""
highestReso = np.asarray(levelDims[0])
lowestReso = np.asarray(levelDims[-1])
Xratio, Yratio = highestReso/lowestReso
... | Calculates the ratio between the highest resolution image and lowest resolution image.
Returns the ratio as a tuple (Xratio, Yratio).
| Calculates the ratio between the highest resolution image and lowest resolution image.
Returns the ratio as a tuple (Xratio, Yratio). | [
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highestReso = np.asarray(levelDims[0])
lowestReso = np.asarray(levelDims[-1])
Xratio, Yratio = highestReso/lowestReso
return (Xratio, Yratio) | [
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2a6dd12fe1983a1b5f53e4fe5bda3cbdfd058734 | sdw95927/InstanceSegmentation-CycleGAN | segmentation_functions.py | [
"MIT"
] | Python | createMask | <not_specific> | def createMask(levelDims, vertices, pattern):
"""
Input: levelDims (nested list): dimensions of each layer of the slide.
vertices (dict object as describe above)
Output: (tuple) mask
numpy nd array of 0/1, where 1 indicates inside the region
and 0 is outside the region
... |
Input: levelDims (nested list): dimensions of each layer of the slide.
vertices (dict object as describe above)
Output: (tuple) mask
numpy nd array of 0/1, where 1 indicates inside the region
and 0 is outside the region
| levelDims (nested list): dimensions of each layer of the slide.
vertices (dict object as describe above)
Output: (tuple) mask
numpy nd array of 0/1, where 1 indicates inside the region
and 0 is outside the region | [
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nRows, nCols = levelDims[-1]
mask = np.zeros((nRows, nCols), dtype=np.uint8)
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2a6dd12fe1983a1b5f53e4fe5bda3cbdfd058734 | sdw95927/InstanceSegmentation-CycleGAN | segmentation_functions.py | [
"MIT"
] | Python | RGB_to_index | <not_specific> | def RGB_to_index(RGB_img, RGB_markers=None, RGB_labels=None):
"""Change RGB to 2D index matrix; each RGB color corresponds to one index.
Args:
RGB_markers: start from background (marked as 0);
Example format:
[[255, 255, 255],
[160, 255, 0]]
RGB_... | Change RGB to 2D index matrix; each RGB color corresponds to one index.
Args:
RGB_markers: start from background (marked as 0);
Example format:
[[255, 255, 255],
[160, 255, 0]]
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if np.shape(RGB_img)[2] is not 3:
print("Not an RGB image")
return RGB_img
else:
if RGB_markers == None:
RGB_markers = [[255, 255, 255]]
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2a6dd12fe1983a1b5f53e4fe5bda3cbdfd058734 | sdw95927/InstanceSegmentation-CycleGAN | segmentation_functions.py | [
"MIT"
] | Python | index_to_RGB | <not_specific> | def index_to_RGB(mask_index, RGB_markers=None):
"""Change index to 2D image; each index corresponds to one color"""
mask_index_copy = mask_index.copy()
mask_index_copy = np.squeeze(mask_index_copy) # In case the mask shape is not [height, width]
if RGB_markers == None:
print("RGB_markers not pr... | Change index to 2D image; each index corresponds to one color | Change index to 2D image; each index corresponds to one color | [
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mask_index_copy = mask_index.copy()
mask_index_copy = np.squeeze(mask_index_copy)
if RGB_markers == None:
print("RGB_markers not provided!")
RGB_markers = [[255, 255, 255]]
RGB_img = np.zeros((np.shape(mask_index_copy)[0], np.shape(mask_i... | [
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2a6dd12fe1983a1b5f53e4fe5bda3cbdfd058734 | sdw95927/InstanceSegmentation-CycleGAN | segmentation_functions.py | [
"MIT"
] | Python | shift_HSV | <not_specific> | def shift_HSV(img, amount=(0.9, 0.9, 0.9)):
"""Function to tune Hue, Saturation, and Value for image img"""
img = Image.fromarray(img, 'RGB')
hsv = img.convert('HSV')
hsv = np.array(hsv)
hsv[..., 0] = np.clip((hsv[..., 0] * amount[0]), a_max=255, a_min=0)
hsv[..., 1] = np.clip((hsv[..., 1] * amo... | Function to tune Hue, Saturation, and Value for image img | Function to tune Hue, Saturation, and Value for image img | [
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img = Image.fromarray(img, 'RGB')
hsv = img.convert('HSV')
hsv = np.array(hsv)
hsv[..., 0] = np.clip((hsv[..., 0] * amount[0]), a_max=255, a_min=0)
hsv[..., 1] = np.clip((hsv[..., 1] * amount[1]), a_max=255, a_min=0)
hsv[..., 2] = np.clip((hsv[..., 2] ... | [
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b0357be31e9570f5b347f312902a2e76b4b0beb4 | Mateaus/PersonalWebsite | personalwebsite/aboutme/admin.py | [
"MIT"
] | Python | has_add_permission | <not_specific> | def has_add_permission(self, request):
"""
Allows the admin to only add a single entry for Introduction.
"""
return Introduction.objects.all().count() == 0 |
Allows the admin to only add a single entry for Introduction.
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return Introduction.objects.all().count() == 0 | [
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084bf5acdfe31ec1d0c4aa400f2983aa1aef667e | spacegal-spiff/AST341 | Spring2020/PythonPhotometry/Sarah_DiffPhotometry.py | [
"MIT"
] | Python | construct_astrometry | <not_specific> | def construct_astrometry(hdr_wcs):
'''
construct_astrometry
make the pixel to RA/Dec conversion (and back) from the header of an astrometry.net return
inputs
------------------------------
hdr_wcs : header with astrometry information, typically from astrometry.net
returns
... |
construct_astrometry
make the pixel to RA/Dec conversion (and back) from the header of an astrometry.net return
inputs
------------------------------
hdr_wcs : header with astrometry information, typically from astrometry.net
returns
------------------------------
w ... | construct_astrometry
make the pixel to RA/Dec conversion (and back) from the header of an astrometry.net return
inputs
hdr_wcs : header with astrometry information, typically from astrometry.net
returns
w : the WCS instance | [
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w = WCS(naxis=2)
w.wcs.ctype = ["RA---TAN", "DEC--TAN"]
w.wcs.cd = np.array([[hdr_wcs['CD1_1'],hdr_wcs['CD1_2']],[hdr_wcs['CD2_1'],hdr_wcs['CD2_2']]])
w.wcs.crpix = [hdr_wcs['CRPIX1'], hdr_wcs['CRPIX2']]
w.wcs.crval = [hdr_wcs['CRVAL1'],hdr_wcs['CRVAL2']]
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make the pixel to RA/Dec conversion (and back) from the header of an astrometry.net return | [
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084bf5acdfe31ec1d0c4aa400f2983aa1aef667e | spacegal-spiff/AST341 | Spring2020/PythonPhotometry/Sarah_DiffPhotometry.py | [
"MIT"
] | Python | StarFind | <not_specific> | def StarFind(imname, FWHM, nsigma, aprad, skywidth, border_scale = 2):
'''
StarFind*
find all stars in a .fits image
inputs
----------
imname: name of .fits image to open.
FWHM: fwhm of stars in field
nsigma: number of sigma above background above which to select sources. (~... |
StarFind*
find all stars in a .fits image
inputs
----------
imname: name of .fits image to open.
FWHM: fwhm of stars in field
nsigma: number of sigma above background above which to select sources. (~3 to 4 is a good estimate)
outputs
--------
xpos: x positions... | StarFind
find all stars in a .fits image
inputs
name of .fits image to open.
FWHM: fwhm of stars in field
nsigma: number of sigma above background above which to select sources. (~3 to 4 is a good estimate)
outputs
x positions of sources
ypos: y positions of sources
nstars: number of stars found in image | [
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im,hdr=fits.getdata(imname, header=True)
im = np.array(im).astype('float')
bkg_sigma = mad_std(im)
print('begin: DAOStarFinder')
daofind = DAOStarFinder(fwhm=FWHM, threshold=nsigma*bkg_sigma, exclude_border=True)
sources = da... | [
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084bf5acdfe31ec1d0c4aa400f2983aa1aef667e | spacegal-spiff/AST341 | Spring2020/PythonPhotometry/Sarah_DiffPhotometry.py | [
"MIT"
] | Python | makeApertures | <not_specific> | def makeApertures(xpos, ypos, aprad,skybuff, skywidth):
'''
makeApertures
makes a master list of apertures and the annuli
inputs
---------
xpos: list - x positions of stars in image
ypos: list - y positions of stars in image
aprad: float - aperture radius
skybuff: float - ... |
makeApertures
makes a master list of apertures and the annuli
inputs
---------
xpos: list - x positions of stars in image
ypos: list - y positions of stars in image
aprad: float - aperture radius
skybuff: float - sky annulus inner radius
skywidth: float - sky annulus oute... | makeApertures
makes a master list of apertures and the annuli
inputs
x positions of stars in image
ypos: list - y positions of stars in image
aprad: float - aperture radius
skybuff: float - sky annulus inner radius
skywidth: float - sky annulus outer radius
outputs
| [
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apertures = CircularAperture((xpos, ypos), r=aprad)
annulus_apertures = CircularAnnulus((xpos, ypos), r_in=aprad+skybuff, r_out=aprad+skybuff+skywidth)
apers = [apertures, annulus_apertures]
return apertures, annulus_apertures | [
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084bf5acdfe31ec1d0c4aa400f2983aa1aef667e | spacegal-spiff/AST341 | Spring2020/PythonPhotometry/Sarah_DiffPhotometry.py | [
"MIT"
] | Python | detrend | <not_specific> | def detrend(photometry, ephotometry, nstars):
'''
detrend
detrend the background for each night so we don't have to worry about changes in background noise levels
inputs
-------
photometry: list - list of flux values from aperture photometry
ephotometry: list - list of flux errors ... |
detrend
detrend the background for each night so we don't have to worry about changes in background noise levels
inputs
-------
photometry: list - list of flux values from aperture photometry
ephotometry: list - list of flux errors from aperture photometry
nstars: float - number o... | detrend
detrend the background for each night so we don't have to worry about changes in background noise levels
inputs
list of flux values from aperture photometry
ephotometry: list - list of flux errors from aperture photometry
nstars: float - number of stars in the field
outputs
final aperture photometry of sour... | [
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photometry = np.array(photometry)
photometry[photometry<=0] = np.nan
cPhotometry = np.zeros_like(photometry)
finalPhot = np.zeros_like(photometry)
checkstars = np.linspace(0,nstars,10).astype(int)
for star in np.arange(nstars):
if star in che... | [
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detrend the background for each night so we don't have to worry about changes in background noise levels | [
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084bf5acdfe31ec1d0c4aa400f2983aa1aef667e | spacegal-spiff/AST341 | Spring2020/PythonPhotometry/Sarah_DiffPhotometry.py | [
"MIT"
] | Python | CaliforniaCoast | <not_specific> | def CaliforniaCoast(Photometry,cPhotometry,comp_num=9,flux_bins=6):
"""
Find the least-variable stars as a function of star brightness*
(it's called California Coast because the plot looks like California and we are looking for edge values: the coast)
inputs
--------------
Photometry ... |
Find the least-variable stars as a function of star brightness*
(it's called California Coast because the plot looks like California and we are looking for edge values: the coast)
inputs
--------------
Photometry : input Photometry catalog
cPhotometry : input detrended Photometry ca... | Find the least-variable stars as a function of star brightness
(it's called California Coast because the plot looks like California and we are looking for edge values: the coast)
inputs
Photometry : input Photometry catalog
cPhotometry : input detrended Photometry catalog
flux_bins : (default=10) maximum number of... | [
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tmpX = np.nanmedian(Photometry,axis=0)
tmpY = np.nanstd(cPhotometry, axis=0)
xvals = tmpX[(np.isfinite(tmpX) & np.isfinite(tmpY))]
yvals = tmpY[(np.isfinite(tmpX) & np.isfinite(tmpY))]
kept_vals = np.where((np.isfinite(tmpX) & np.is... | [
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084bf5acdfe31ec1d0c4aa400f2983aa1aef667e | spacegal-spiff/AST341 | Spring2020/PythonPhotometry/Sarah_DiffPhotometry.py | [
"MIT"
] | Python | findComparisonStars | <not_specific> | def findComparisonStars(Photometry, cPhotometry, accuracy_threshold = 0.2, plot=True,comp_num=6): #0.025
'''
findComparisonStars*
finds stars that are similar over the various nights to use as comparison stars
inputs
--------
Photometry: list - photometric values taken from detrend() f... |
findComparisonStars*
finds stars that are similar over the various nights to use as comparison stars
inputs
--------
Photometry: list - photometric values taken from detrend() function.
cPhotometry: list - detrended photometric values from detrend() function
accuracy_threshold: f... | findComparisonStars
finds stars that are similar over the various nights to use as comparison stars
inputs
photometric values taken from detrend() function.
cPhotometry: list - detrended photometric values from detrend() function
accuracy_threshold: float - level of accuracy in fluxes between various nights
plot: boo... | [
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BinStars,LeastVariable = CaliforniaCoast(Photometry,cPhotometry,comp_num=comp_num)
star_err = np.nanstd(cPhotometry, axis=0)
if plot:
xvals = np.log10(np.nanmedian(Photometry,axis=0))
yvals = n... | [
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084bf5acdfe31ec1d0c4aa400f2983aa1aef667e | spacegal-spiff/AST341 | Spring2020/PythonPhotometry/Sarah_DiffPhotometry.py | [
"MIT"
] | Python | runDifferentialPhotometry | <not_specific> | def runDifferentialPhotometry(Photometry, ePhotometry, nstars, most_accurate):
'''
runDifferentialPhotometry
as the name says!
inputs
----------
Photometry: list - list of photometric values from detrend() function
ePhotometry: list - list of photometric error values
nstars: f... |
runDifferentialPhotometry
as the name says!
inputs
----------
Photometry: list - list of photometric values from detrend() function
ePhotometry: list - list of photometric error values
nstars: float - number of stars
most_accurate: list - list of indices of non variable compa... | runDifferentialPhotometry
as the name says!
inputs
list of photometric values from detrend() function
ePhotometry: list - list of photometric error values
nstars: float - number of stars
most_accurate: list - list of indices of non variable comparison stars
outputs
differential photometry list
edPhotometry: list - ... | [
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nimages = len(Photometry)
imgindex = np.arange(0,nimages,1)
dPhotometry = np.zeros([nimages,len(Photometry[0])])
edPhotometry = np.zeros([nimages,len(Photometry[0])])
eedPhotometry = np.zeros([nimages,len(Photometry[0])])... | [
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6db80bdc7fab60251a429cc8bc2ed60a193ee0dd | spacegal-spiff/AST341 | shift_methods.py | [
"MIT"
] | Python | shift_image | <not_specific> | def shift_image(image,xshift,yshift):
'''
shift_image
-------------
wrapper for scipy's implementation that shifts images according to values from cross_image
inputs
------------
image : (matrix of floats) image to be shifted
xshift : (float) x-shift in pixels
... |
shift_image
-------------
wrapper for scipy's implementation that shifts images according to values from cross_image
inputs
------------
image : (matrix of floats) image to be shifted
xshift : (float) x-shift in pixels
yshift : (float) y-shift in pixels
... | shift_image
wrapper for scipy's implementation that shifts images according to values from cross_image
inputs
image : (matrix of floats) image to be shifted
xshift : (float) x-shift in pixels
yshift : (float) y-shift in pixels
outputs
shifted image : shifted, interpolated image.
same s... | [
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return scipy.ndimage.interpolation.shift(image,(xshift,yshift)) | [
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e44bc84e93d525cf63376c10c4e4483cde8ea533 | spacegal-spiff/AST341 | HDI_io.py | [
"MIT"
] | Python | find_files | <not_specific> | def find_files(directory,img_type,filternum=-1):
'''
#
# definition to go through HDI headers and get the images desired of a particular type
# and filter
#
#
# limitations:
# - not set up to sort by specific targets
# - will not enforce one of the filter wheel slots be... |
#
# definition to go through HDI headers and get the images desired of a particular type
# and filter
#
#
# limitations:
# - not set up to sort by specific targets
# - will not enforce one of the filter wheel slots being empty
#
| definition to go through HDI headers and get the images desired of a particular type
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files = [infile for infile in glob.glob(directory+'c*t*fits') if not os.path.isdir(infile)]
out_files = []
for f in files:
phdr = fits.getheader(f,0)
if (img_type == phdr['OBSTYPE']) \
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c9909e5ab2c84492a8104708c640293d90909f4d | spacegal-spiff/AST341 | DifferentialPhotometry_functions_student.py | [
"MIT"
] | Python | StarFind | <not_specific> | def StarFind(imname, FWHM, nsigma):
'''
StarFind
find all stars in a .fits image
inputs
----------
imname: name of .fits image to open.
FWHM: fwhm of stars in field
nsigma: number of sigma above background above which to select sources. (~3 to 4 is a good estimate)
... |
StarFind
find all stars in a .fits image
inputs
----------
imname: name of .fits image to open.
FWHM: fwhm of stars in field
nsigma: number of sigma above background above which to select sources. (~3 to 4 is a good estimate)
outputs
--------
xpos: x positions ... | StarFind
find all stars in a .fits image
inputs
name of .fits image to open.
FWHM: fwhm of stars in field
nsigma: number of sigma above background above which to select sources. (~3 to 4 is a good estimate)
outputs
x positions of sources
ypos: y positions of sources
nstars: number of stars found in image | [
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im,hdr=fits.getdata(imname, header=True)
im = np.array(im).astype('float')
bkg_sigma = mad_std(im)
print('begin: DAOStarFinder')
daofind = DAOStarFinder(fwhm=FWHM, threshold=nsigma*bkg_sigma, exclude_border=True)
sources = daofind(im)
xpos = sources['xcent... | [
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c9909e5ab2c84492a8104708c640293d90909f4d | spacegal-spiff/AST341 | DifferentialPhotometry_functions_student.py | [
"MIT"
] | Python | doPhotometry | <not_specific> | def doPhotometry(imglist, xpos, ypos, aprad, skybuff, skywidth,timekey='MJD-OBS',verbose=1):
'''
doPhotomoetry*
determine the flux for each star from aperture photometry
inputs
-------
imglist: list - list of .fits images
xpos, ypos: lists - lists of x and y positions of stars
... |
doPhotomoetry*
determine the flux for each star from aperture photometry
inputs
-------
imglist: list - list of .fits images
xpos, ypos: lists - lists of x and y positions of stars
aprad, skybuff, skywidth: floats - aperture, sky annuli inner, sky annuli outer radii
outp... | doPhotomoetry
determine the flux for each star from aperture photometry
inputs
list of .fits images
xpos, ypos: lists - lists of x and y positions of stars
aprad, skybuff, skywidth: floats - aperture, sky annuli inner, sky annuli outer radii
outputs
time stamps of each observation from the .fits header
Photometry: ... | [
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nimages = len(imglist)
nstars = len(xpos)
print('Found {} images'.format(nimages))
Times = np.zeros(nimages)
Photometry = np.zeros((nimages,nstars))
print('making apertures')
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c9909e5ab2c84492a8104708c640293d90909f4d | spacegal-spiff/AST341 | DifferentialPhotometry_functions_student.py | [
"MIT"
] | Python | doPhotometryError | <not_specific> | def doPhotometryError(imglist,xpos, ypos,aprad, skybuff, skywidth, flux0, GAIN=1.3, manual = False, **kwargs):
'''
doPhotometryError
determine error in photometry from background noise
two options:
- use sigma clipping and use whole background
- manually input background box positions as k... |
doPhotometryError
determine error in photometry from background noise
two options:
- use sigma clipping and use whole background
- manually input background box positions as kwargs
inputs
--------
imglist: list - list of .fits images
xpos, ypos: lists - lists of x and y p... | doPhotometryError
determine error in photometry from background noise
two options:
use sigma clipping and use whole background
manually input background box positions as kwargs
inputs
| [
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] | def doPhotometryError(imglist,xpos, ypos,aprad, skybuff, skywidth, flux0, GAIN=1.3, manual = False, **kwargs):
nimages = len(imglist)
nstars = len(xpos)
print('Found {} images'.format(nimages))
apertures, annulus_apertures = makeApertures(xpos, ypos, aprad, skybuff, skywidth)
area_of_ap = apertureAr... | [
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c9909e5ab2c84492a8104708c640293d90909f4d | spacegal-spiff/AST341 | DifferentialPhotometry_functions_student.py | [
"MIT"
] | Python | detrend | <not_specific> | def detrend(idx, Photometry_initial, ePhotometry, nstars, sn_thresh):
'''
detrend the background for each night to find least variable comparison stars
inputs
-------
idx : list - list of indices of target stars
Photometry_initial : list - list of flux values from apert... |
detrend the background for each night to find least variable comparison stars
inputs
-------
idx : list - list of indices of target stars
Photometry_initial : list - list of flux values from aperture photometry
ePhotometry : list - list of flux errors from ap... | detrend the background for each night to find least variable comparison stars
inputs
outputs
Photometry_fin_mask : list - final aperture photometry of sources with bad sources masked out
cPhotometry_mask : list - detrended aperture photometry with bad sources masked out | [
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sn = Photometry_initial / ePhotometry
Photometry_mask1 = ma.masked_where(Photometry_initial <= 0, Photometry_initial)
Photometry_mask2 = ma.masked_where(sn < sn_thresh, Photometry_mask1)
m = np.zeros_like(Photometry_mask2)
m[:,idx... | [
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c9909e5ab2c84492a8104708c640293d90909f4d | spacegal-spiff/AST341 | DifferentialPhotometry_functions_student.py | [
"MIT"
] | Python | findComparisonStars | <not_specific> | def findComparisonStars(Photometry, cPhotometry, plot=True, comp_num=6): #0.025
'''
findComparisonStars*
finds stars that are similar over the various nights to use as comparison stars
inputs
--------
Photometry: list - photometric values taken from detrend() function.
cPhotometry... |
findComparisonStars*
finds stars that are similar over the various nights to use as comparison stars
inputs
--------
Photometry: list - photometric values taken from detrend() function.
cPhotometry: list - detrended photometric values from detrend() function
plot: boolean - True/... | findComparisonStars
finds stars that are similar over the various nights to use as comparison stars
inputs
photometric values taken from detrend() function.
cPhotometry: list - detrended photometric values from detrend() function
plot: boolean - True/False plot various stars and highlight comparison stars
comp_num: f... | [
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BinStars,LeastVariable = CaliforniaCoast(Photometry,cPhotometry,comp_num=comp_num)
star_err = ma.std(cPhotometry, axis=0)
if plot:
xvals = np.log10(ma.median(Photometry,axis=0))
yvals = np.log10(ma.std(cPhotometry, axi... | [
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c9909e5ab2c84492a8104708c640293d90909f4d | spacegal-spiff/AST341 | DifferentialPhotometry_functions_student.py | [
"MIT"
] | Python | runDifferentialPhotometry | <not_specific> | def runDifferentialPhotometry(photometry, ephotometry, nstars, most_accurate):
'''
runDifferentialPhotometry
as the name says!
inputs
----------
Photometry: list - list of photometric values from detrend() function
ePhotometry: list - list of photometric error values
nstars: fl... |
runDifferentialPhotometry
as the name says!
inputs
----------
Photometry: list - list of photometric values from detrend() function
ePhotometry: list - list of photometric error values
nstars: float - number of stars
most_accurate: list - list of indices of non variable compa... | runDifferentialPhotometry
as the name says!
inputs
list of photometric values from detrend() function
ePhotometry: list - list of photometric error values
nstars: float - number of stars
most_accurate: list - list of indices of non variable comparison stars
outputs
differential photometry list
edPhotometry: list - ... | [
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Photometry = ma.masked_where(photometry <= 0, photometry)
ePhotometry = ma.masked_where(photometry <= 0, ephotometry)
nimages = len(Photometry)
imgindex = np.arange(0,nimages,1)
dPhotometry = ma.ones([nimages, len(Photome... | [
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c9909e5ab2c84492a8104708c640293d90909f4d | spacegal-spiff/AST341 | DifferentialPhotometry_functions_student.py | [
"MIT"
] | Python | target_list | <not_specific> | def target_list(memberlist, ra_all, dec_all):
''' finds the index, ra, dec of target star(s) from another catalogue (e.g. DAOstarfinder) either from inputted tuple or region file. '''
#checks to see if memberlist is a tuple or region file
if isinstance(memberlist, tuple):
ra_mem = [memberlist[0]]
... | finds the index, ra, dec of target star(s) from another catalogue (e.g. DAOstarfinder) either from inputted tuple or region file. | finds the index, ra, dec of target star(s) from another catalogue either from inputted tuple or region file. | [
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if isinstance(memberlist, tuple):
ra_mem = [memberlist[0]]
dec_mem = [memberlist[1]]
elif isinstance(memberlist, str):
try:
regions = read_ds9(memberlist)
ra_mem = [i.center.ra.deg for i in regions]
dec_mem... | [
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b44245825537bcd75b75c7b8de6d26f5a9cc3807 | xyh888/vnpy | vnpy/trader/krutility.py | [
"MIT"
] | Python | update_bar | null | def update_bar(self, bar):
"""
Update new bar data into array manager.
"""
self.count += 1
if not self.inited and self.count >= self.size:
self.inited = True
self.datetime_array[:-1] = self.datetime_array[1:]
self.open_array[:-1] = self.open_array[1:]... |
Update new bar data into array manager.
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self.count += 1
if not self.inited and self.count >= self.size:
self.inited = True
self.datetime_array[:-1] = self.datetime_array[1:]
self.open_array[:-1] = self.open_array[1:]
self.high_array[:-1] = self.high_array[1:]
self.low_arra... | [
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46df2c7fc6d4aad38c6a860c843f7fe12f0d1d9c | akaminetzkyp/pyJoules | tests/unit/handler/test_CSVHandler.py | [
"MIT"
] | Python | non_writable_folder | <not_specific> | def non_writable_folder(fs):
"""
A filesystem with a /dir directory that is not writable
"""
fs.create_dir('/dir', perm_bits=000)
return fs |
A filesystem with a /dir directory that is not writable
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fs.create_dir('/dir', perm_bits=000)
return fs | [
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635c85cd19ff7ef20d08d71f639bfb5eaee4afd6 | akaminetzkyp/pyJoules | pyJoules/handler/print_handler.py | [
"MIT"
] | Python | process | null | def process(self, trace: EnergyTrace):
"""
Print the given sample on the standard output
"""
for sample in trace:
begin_string = f'begin timestamp : {sample.timestamp}; tag : {sample.tag}; duration : {sample.duration}'
energy_strings = [f'; {domain} : {value}' for... |
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begin_string = f'begin timestamp : {sample.timestamp}; tag : {sample.tag}; duration : {sample.duration}'
energy_strings = [f'; {domain} : {value}' for domain, value in sample.energy.items()]
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5a51ea46c82fb431dca54dacb2ad5bc0c361e0b9 | akaminetzkyp/pyJoules | tests/utils/rapl_fs.py | [
"MIT"
] | Python | empty_fs | <not_specific> | def empty_fs(fs):
"""
filesystem describing a machine with one CPU but no RAPL API
"""
return RaplFS(fs) |
filesystem describing a machine with one CPU but no RAPL API
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return RaplFS(fs) | [
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5a51ea46c82fb431dca54dacb2ad5bc0c361e0b9 | akaminetzkyp/pyJoules | tests/utils/rapl_fs.py | [
"MIT"
] | Python | fs_pkg_one_socket | <not_specific> | def fs_pkg_one_socket(fs):
"""
filesystem describing a machine with one CPU and RAPL API for package
"""
rapl_fs = RaplFS(fs)
rapl_fs.add_domain(SOCKET_0_DIR_NAME, 'package-0', 'package_0')
rapl_fs.reset_values()
return rapl_fs |
filesystem describing a machine with one CPU and RAPL API for package
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rapl_fs = RaplFS(fs)
rapl_fs.add_domain(SOCKET_0_DIR_NAME, 'package-0', 'package_0')
rapl_fs.reset_values()
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5a51ea46c82fb431dca54dacb2ad5bc0c361e0b9 | akaminetzkyp/pyJoules | tests/utils/rapl_fs.py | [
"MIT"
] | Python | fs_pkg_dram_one_socket | <not_specific> | def fs_pkg_dram_one_socket(fs):
"""
filesystem describing a machine with one CPU and RAPL API for package and dram
"""
rapl_fs = RaplFS(fs)
rapl_fs.add_domain(SOCKET_0_DIR_NAME, 'package-0', 'package_0')
rapl_fs.add_domain(DRAM_0_DIR_NAME, 'dram', 'dram_0')
rapl_fs.reset_values()
return ... |
filesystem describing a machine with one CPU and RAPL API for package and dram
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rapl_fs = RaplFS(fs)
rapl_fs.add_domain(SOCKET_0_DIR_NAME, 'package-0', 'package_0')
rapl_fs.add_domain(DRAM_0_DIR_NAME, 'dram', 'dram_0')
rapl_fs.reset_values()
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5a51ea46c82fb431dca54dacb2ad5bc0c361e0b9 | akaminetzkyp/pyJoules | tests/utils/rapl_fs.py | [
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] | Python | fs_pkg_psys_one_socket | <not_specific> | def fs_pkg_psys_one_socket(fs):
"""
filesystem describing a machine with one CPU and RAPL API for package and psys
"""
rapl_fs = RaplFS(fs)
rapl_fs.add_domain(SOCKET_0_DIR_NAME, 'package-0', 'package_0')
rapl_fs.add_domain('/sys/class/powercap/intel-rapl/intel-rapl:1', 'psys', 'psys')
rapl_f... |
filesystem describing a machine with one CPU and RAPL API for package and psys
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rapl_fs = RaplFS(fs)
rapl_fs.add_domain(SOCKET_0_DIR_NAME, 'package-0', 'package_0')
rapl_fs.add_domain('/sys/class/powercap/intel-rapl/intel-rapl:1', 'psys', 'psys')
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5a51ea46c82fb431dca54dacb2ad5bc0c361e0b9 | akaminetzkyp/pyJoules | tests/utils/rapl_fs.py | [
"MIT"
] | Python | fs_pkg_dram_core_one_socket | <not_specific> | def fs_pkg_dram_core_one_socket(fs):
"""
filesystem describing a machine with one CPU and RAPL API for package dram and core
"""
rapl_fs = RaplFS(fs)
rapl_fs.add_domain(SOCKET_0_DIR_NAME, 'package-0', 'package_0')
rapl_fs.add_domain(DRAM_0_DIR_NAME, 'dram', 'dram_0')
rapl_fs.add_domain(CORE_... |
filesystem describing a machine with one CPU and RAPL API for package dram and core
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rapl_fs = RaplFS(fs)
rapl_fs.add_domain(SOCKET_0_DIR_NAME, 'package-0', 'package_0')
rapl_fs.add_domain(DRAM_0_DIR_NAME, 'dram', 'dram_0')
rapl_fs.add_domain(CORE_0_DIR_NAME, 'core', 'core_0')
rapl_fs.reset_values()
return rapl_fs | [
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5a51ea46c82fb431dca54dacb2ad5bc0c361e0b9 | akaminetzkyp/pyJoules | tests/utils/rapl_fs.py | [
"MIT"
] | Python | fs_pkg_dram_uncore_one_socket | <not_specific> | def fs_pkg_dram_uncore_one_socket(fs):
"""
filesystem describing a machine with one CPU and RAPL API for package dram and core
"""
rapl_fs = RaplFS(fs)
rapl_fs.add_domain(SOCKET_0_DIR_NAME, 'package-0', 'package_0')
rapl_fs.add_domain(DRAM_0_DIR_NAME, 'dram', 'dram_0')
rapl_fs.add_domain(COR... |
filesystem describing a machine with one CPU and RAPL API for package dram and core
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rapl_fs = RaplFS(fs)
rapl_fs.add_domain(SOCKET_0_DIR_NAME, 'package-0', 'package_0')
rapl_fs.add_domain(DRAM_0_DIR_NAME, 'dram', 'dram_0')
rapl_fs.add_domain(CORE_0_DIR_NAME, 'uncore', 'uncore_0')
rapl_fs.reset_values()
return rapl_fs | [
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5a51ea46c82fb431dca54dacb2ad5bc0c361e0b9 | akaminetzkyp/pyJoules | tests/utils/rapl_fs.py | [
"MIT"
] | Python | fs_pkg_two_socket | <not_specific> | def fs_pkg_two_socket(fs):
"""
filesystem describing a machine with two CPU and RAPL API for package
"""
rapl_fs = RaplFS(fs)
rapl_fs.add_domain(SOCKET_0_DIR_NAME, 'package-0', 'package_0')
rapl_fs.add_domain(SOCKET_1_DIR_NAME, 'package-1', 'package_1')
rapl_fs.reset_values()
return rapl... |
filesystem describing a machine with two CPU and RAPL API for package
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rapl_fs = RaplFS(fs)
rapl_fs.add_domain(SOCKET_0_DIR_NAME, 'package-0', 'package_0')
rapl_fs.add_domain(SOCKET_1_DIR_NAME, 'package-1', 'package_1')
rapl_fs.reset_values()
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5a51ea46c82fb431dca54dacb2ad5bc0c361e0b9 | akaminetzkyp/pyJoules | tests/utils/rapl_fs.py | [
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] | Python | fs_pkg_dram_two_socket | <not_specific> | def fs_pkg_dram_two_socket(fs):
"""
filesystem describing a machine with two CPU and RAPL API for package and dram
"""
rapl_fs = RaplFS(fs)
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rapl_fs.add_domain(SOCKET_1_DIR_NAME, 'package-1', 'package_1')
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filesystem describing a machine with two CPU and RAPL API for package and dram
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rapl_fs = RaplFS(fs)
rapl_fs.add_domain(SOCKET_0_DIR_NAME, 'package-0', 'package_0')
rapl_fs.add_domain(SOCKET_1_DIR_NAME, 'package-1', 'package_1')
rapl_fs.add_domain(DRAM_0_DIR_NAME, 'dram', 'dram_0')
rapl_fs.add_domain(DRAM_1_DIR_NAME, 'dram', 'dram_1')
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5a51ea46c82fb431dca54dacb2ad5bc0c361e0b9 | akaminetzkyp/pyJoules | tests/utils/rapl_fs.py | [
"MIT"
] | Python | fs_pkg_psys_two_socket | <not_specific> | def fs_pkg_psys_two_socket(fs):
"""
filesystem describing a machine with two CPU and RAPL API for package
"""
rapl_fs = RaplFS(fs)
rapl_fs.add_domain(SOCKET_0_DIR_NAME, 'package-0', 'package_0')
rapl_fs.add_domain(SOCKET_1_DIR_NAME, 'package-1', 'package_1')
rapl_fs.add_domain('/sys/class/p... |
filesystem describing a machine with two CPU and RAPL API for package
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rapl_fs = RaplFS(fs)
rapl_fs.add_domain(SOCKET_0_DIR_NAME, 'package-0', 'package_0')
rapl_fs.add_domain(SOCKET_1_DIR_NAME, 'package-1', 'package_1')
rapl_fs.add_domain('/sys/class/powercap/intel-rapl/intel-rapl:2/name', 'psys', 'psys')
rapl_fs.reset_values()
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b3fb4b7c3e9a3afb361c7d6cb91602df03f42cda | akaminetzkyp/pyJoules | pyJoules/device/rapl_device.py | [
"MIT"
] | Python | available_domains | List[RaplDomain] | def available_domains() -> List[RaplDomain]:
"""
return a the list of the available energy domains
"""
if not RaplDevice._rapl_api_available():
raise NoSuchDeviceError()
return (RaplDevice.available_package_domains() + RaplDevice.available_dram_domains() +
... |
return a the list of the available energy domains
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if not RaplDevice._rapl_api_available():
raise NoSuchDeviceError()
return (RaplDevice.available_package_domains() + RaplDevice.available_dram_domains() +
RaplDevice.available_core_domains() + RaplDevice.available_uncore_domains()) | [
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b3fb4b7c3e9a3afb361c7d6cb91602df03f42cda | akaminetzkyp/pyJoules | pyJoules/device/rapl_device.py | [
"MIT"
] | Python | available_package_domains | List[RaplPackageDomain] | def available_package_domains() -> List[RaplPackageDomain]:
"""
return a the list of the available energy Package domains
"""
package_domains = []
for socket_id in RaplDevice._get_socket_id_list():
domain_name_file_str = RAPL_API_DIR + '/intel-rapl:' + str(socket_id)... |
return a the list of the available energy Package domains
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package_domains = []
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domain_name_file_str = RAPL_API_DIR + '/intel-rapl:' + str(socket_id) + '/name'
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b3fb4b7c3e9a3afb361c7d6cb91602df03f42cda | akaminetzkyp/pyJoules | pyJoules/device/rapl_device.py | [
"MIT"
] | Python | available_dram_domains | List[RaplDramDomain] | def available_dram_domains() -> List[RaplDramDomain]:
"""
return a the list of the available energy Dram domains
"""
dram_domains = []
for socket_id in RaplDevice._get_socket_id_list():
if RaplDevice._domain_exist_on_socket(socket_id, 'dram'):
dram_dom... |
return a the list of the available energy Dram domains
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dram_domains = []
for socket_id in RaplDevice._get_socket_id_list():
if RaplDevice._domain_exist_on_socket(socket_id, 'dram'):
dram_domains.append(RaplDramDomain(socket_id))
return dram_domains | [
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b3fb4b7c3e9a3afb361c7d6cb91602df03f42cda | akaminetzkyp/pyJoules | pyJoules/device/rapl_device.py | [
"MIT"
] | Python | available_core_domains | List[RaplCoreDomain] | def available_core_domains() -> List[RaplCoreDomain]:
"""
return a the list of the available energy Core domains
"""
core_domains = []
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return a the list of the available energy Core domains
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core_domains = []
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b3fb4b7c3e9a3afb361c7d6cb91602df03f42cda | akaminetzkyp/pyJoules | pyJoules/device/rapl_device.py | [
"MIT"
] | Python | available_uncore_domains | List[RaplUncoreDomain] | def available_uncore_domains() -> List[RaplUncoreDomain]:
"""
return a the list of the available energy Uncore domains
"""
uncore_domains = []
for socket_id in RaplDevice._get_socket_id_list():
if RaplDevice._domain_exist_on_socket(socket_id, 'uncore'):
... |
return a the list of the available energy Uncore domains
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uncore_domains = []
for socket_id in RaplDevice._get_socket_id_list():
if RaplDevice._domain_exist_on_socket(socket_id, 'uncore'):
uncore_domains.append(RaplUncoreDomain(socket_id))
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aae0299836669c6c1fd2c8eb462b60bbdbf51e94 | akaminetzkyp/pyJoules | pyJoules/energy_trace.py | [
"MIT"
] | Python | append | null | def append(self, sample: EnergySample):
"""
append a new sample to the trace
"""
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aae0299836669c6c1fd2c8eb462b60bbdbf51e94 | akaminetzkyp/pyJoules | pyJoules/energy_trace.py | [
"MIT"
] | Python | remove_idle | null | def remove_idle(self, idle: List[Dict[str, float]]):
"""
substract idle energy values from the current trace
:param idle: list of idle consumption values to substract to current trace
idle consumption values must be grouped in a dictionary with their domain as key
:... |
substract idle energy values from the current trace
:param idle: list of idle consumption values to substract to current trace
idle consumption values must be grouped in a dictionary with their domain as key
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if len(idle) != len(self._samples):
raise ValueError('idle list havn\'t the same length than the trace')
for idle_energy, sample in zip(idle, self._samples):
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aae0299836669c6c1fd2c8eb462b60bbdbf51e94 | akaminetzkyp/pyJoules | pyJoules/energy_trace.py | [
"MIT"
] | Python | clean_data | null | def clean_data(self, guards: List[Callable[[EnergySample], bool]] = []):
"""
Remove sample with negative energy values from the trace
Guards can be added to specify rules to remove sample
:param guards: list of function that is used as rules to remove samples. A guard is a function that... |
Remove sample with negative energy values from the trace
Guards can be added to specify rules to remove sample
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extended_guards = [lambda s: self._sample_havnt_negative_values(s)] + guards
valid_samples = []
for sample in self._samples:
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cfeb0ba5e7679d3f9161f59a06b9405eb5dc5be4 | akaminetzkyp/pyJoules | pyJoules/handler/pandas_handler.py | [
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] | Python | trace_to_dataframe | pandas.DataFrame | def trace_to_dataframe(trace: Iterable[EnergySample]) -> pandas.DataFrame:
"""
convert an energy trace into a pandas DataFrame
"""
if len(trace) == 0:
return pandas.DataFrame()
return pandas.DataFrame(columns=_gen_column_names(trace), data=_gen_data(trace)) |
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e39dc002a54952793beb00d31290a5208fb6fcd3 | akaminetzkyp/pyJoules | pyJoules/handler/csv_handler.py | [
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"""
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99c8400936d5688eccb642d3e669351fc6ad4a9f | akaminetzkyp/pyJoules | pyJoules/handler/mongo_handler.py | [
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"""
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'tag': sample.tag,
'duration': sample.duration,
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99c8400936d5688eccb642d3e669351fc6ad4a9f | akaminetzkyp/pyJoules | pyJoules/handler/mongo_handler.py | [
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] | Python | trace_to_dict | Dict | def trace_to_dict(trace: Iterable[EnergySample], trace_name: str) -> Dict:
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99c8400936d5688eccb642d3e669351fc6ad4a9f | akaminetzkyp/pyJoules | pyJoules/handler/mongo_handler.py | [
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b78199a3751444bbf67991e837a720cf6e8f1af5 | akaminetzkyp/pyJoules | pyJoules/device/device_factory.py | [
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domains = DeviceFactory._gen_all_available_domains()
grouped_domains = {}
for device_type, domain in map(lambda d: (d.get_device_type(), d), domains):
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cf2433688004e16e7864eef105ef03666438fc6f | akaminetzkyp/pyJoules | pyJoules/device/device.py | [
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Returns names of the domain that could be monitored on the Device
:return: a list of domain names
:raise NoSuchDeviceError: if the device is not available on this machine
"""
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cf2433688004e16e7864eef105ef03666438fc6f | akaminetzkyp/pyJoules | pyJoules/device/device.py | [
"MIT"
] | Python | configure | null | def configure(self, domains: List[Domain] = None):
"""
configure the device to return only the energy consumption of the given energy domain when calling the
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356f876c35e9fd6e563d5cf849614cf815c67c11 | matplotlib/ipympl | ipympl/backend_nbagg.py | [
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925d5bc9939d0b7d41aa831f7a60638c311ef0f8 | federicoruggeri/argscichat_project | collection_tool/utility/json_utils.py | [
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925d5bc9939d0b7d41aa831f7a60638c311ef0f8 | federicoruggeri/argscichat_project | collection_tool/utility/json_utils.py | [
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112d1fdf49d0bd77d757b81f89c41788f0448613 | federicoruggeri/argscichat_project | argscichat_allennlp/scripts/fact_selection_baselines.py | [
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] | Python | punctuation_filtering | <not_specific> | def punctuation_filtering(line):
"""
Filters given sentences by removing punctuation
"""
table = str.maketrans('', '', string.punctuation)
trans = [w.translate(table) for w in line.split()]
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ff9d43e56ff692097b30ad596beb13078ada7524 | federicoruggeri/argscichat_project | argscichat_allennlp/utility/json_utils.py | [
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ff9d43e56ff692097b30ad596beb13078ada7524 | federicoruggeri/argscichat_project | argscichat_allennlp/utility/json_utils.py | [
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"""
Read list of objects from a JSON lines file.
"""
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for line in f:
data.append(simplejson.loads(line.rstrip('\n|\r')))
print('Loaded {} records from {}'.format(len(data), input_path))
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0906f11fa1a7a01afe084ce68ea5be45b133291c | federicoruggeri/argscichat_project | argscichat_allennlp/argscichat_baselines/dataset_reader.py | [
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Filters given sentences by removing punctuation
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line = line.lower().strip()
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0906f11fa1a7a01afe084ce68ea5be45b133291c | federicoruggeri/argscichat_project | argscichat_allennlp/argscichat_baselines/dataset_reader.py | [
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"""
Takes a list of evidence snippets, and the list of all the paragraphs from the
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Takes a list of evidence snippets, and the list of all the paragraphs from the
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4d30a400bee58afdccac4ff4ab0ff28ae05327bd | tww-software/word-list-aggregator | wordlistagg.py | [
"Unlicense"
] | Python | open_wordlist | <not_specific> | def open_wordlist(path):
"""
open a wordlist
Note:
a wordlist should be a text document with one word per line
Args:
path(str): full path to the wordlist
Returns:
passwordlist(list): list of all the words (as strings) in the wordlist
"""
with open(path, 'r', errors... |
open a wordlist
Note:
a wordlist should be a text document with one word per line
Args:
path(str): full path to the wordlist
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passwordlist(list): list of all the words (as strings) in the wordlist
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4d30a400bee58afdccac4ff4ab0ff28ae05327bd | tww-software/word-list-aggregator | wordlistagg.py | [
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] | Python | create_wordlist | null | def create_wordlist(wordlist, outpath):
"""
create our own wordlist
write each word on a new line in a text file
Args:
wordlist(list): list of strings (words) to write to file
outpath(str): full path to write wordlist to
"""
with open(outpath, 'w') as wordout:
for line i... |
create our own wordlist
write each word on a new line in a text file
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4d30a400bee58afdccac4ff4ab0ff28ae05327bd | tww-software/word-list-aggregator | wordlistagg.py | [
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"""
parse arguments off the command line
Returns:
args(argparse.Namespace): the CLI arguments
"""
parser = argparse.ArgumentParser(
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parser.add_argument('inputdir', help='directory contai... |
parse arguments off the command line
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args(argparse.Namespace): the CLI arguments
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parser = argparse.ArgumentParser(
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parser.add_argument('inputdir', help='directory containing wordlists')
parser.add_argument('outputfile', help='file to output passwords')
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a9dbd06f056c0b4d7cf25d138fa459db5e483c8d | zurek11/django-project-template | {{cookiecutter.directory_name}}/apps/api/decorators.py | [
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"""
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:param view_func:
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"""
def wrapped_view(*args, **kwargs):
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wrapped_view.require_apikey = True
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a80f436b09a934fff8646ae24d26056e4cee2e08 | marksmall/upload-file | quickstart.py | [
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"""Shows basic usage of the Drive v3 API.
Prints the names and ids of the first 10 files the user has access to.
"""
"""Generate service to interact with Google Drive."""
store = file.Storage('token.json')
creds = store.get()
if not creds or creds.invalid:
flow = client.f... | Shows basic usage of the Drive v3 API.
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creds = tools.run_flow(flow, store)
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a083f93c230228f629d4153049e136f7d73d62cf | jinbeizame007/planet_pybullet | planet/networks/conv_ha.py | [
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"""Extract deterministic features from an observation."""
obs = obs['image']
hidden = tf.layers.dense(obs, 1024, tf.nn.relu)#keras.layers.Dense(500, activation='relu')(obs)
hidden = tf.layers.dense(hidden, 1024, tf.nn.relu)#keras.layers.Dense(500, activation='relu')(hidden)
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a083f93c230228f629d4153049e136f7d73d62cf | jinbeizame007/planet_pybullet | planet/networks/conv_ha.py | [
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#hidden = keras.layers.Dense(500, activation='relu')(hidden)
#hidden = keras.layers.Dense(26)(hidden)
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hidden = tf.layers.dense(hidden, 500, tf.nn.relu)
hidden = tf.layers.dense(hidden, 26, None)
mean = hidden
mean = tf.reshape(mean, tools.shape(state)[:-1] + data_shape)
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4438a0dadd8ec5c09e6dc666dc434c22f3bba5ea | jinbeizame007/planet_pybullet | planet/networks/basic.py | [
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hidden = state
if cut_gradient:
hidden = tf.stop_gradient(hidden)
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2f49c0dd780bcf4c5e4aca2d76db5c351ad2abb4 | mtreinish/qiskit-nature | qiskit_nature/properties/second_quantization/electronic/electronic_structure_driver_result.py | [
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"""Returns the list of `FermioncOp`s given by the properties contained in this one."""
ops: List[FermionicOp] = []
# TODO: make aux_ops a Dict? Then we don't need to hard-code the order of these properties.
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7b014b17dda45df1973b882dc801b2878653af01 | mtreinish/qiskit-nature | qiskit_nature/properties/second_quantization/electronic/integrals/electronic_integrals.py | [
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17577122a94682dd94d556e3187a7a9ba4e394a7 | mtreinish/qiskit-nature | qiskit_nature/properties/grouped_property.py | [
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Args:
prop: the property to be added.
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if prop is not None:
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17577122a94682dd94d556e3187a7a9ba4e394a7 | mtreinish/qiskit-nature | qiskit_nature/properties/grouped_property.py | [
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17577122a94682dd94d556e3187a7a9ba4e394a7 | mtreinish/qiskit-nature | qiskit_nature/properties/grouped_property.py | [
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af1d5d8b65e5043e7b12538b5c52987d7d631b70 | mtreinish/qiskit-nature | qiskit_nature/properties/second_quantization/electronic/integrals/two_body_electronic_integrals.py | [
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af1d5d8b65e5043e7b12538b5c52987d7d631b70 | mtreinish/qiskit-nature | qiskit_nature/properties/second_quantization/electronic/integrals/two_body_electronic_integrals.py | [
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if self._basis == transform.final_basis:
return self
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af1d5d8b65e5043e7b12538b5c52987d7d631b70 | mtreinish/qiskit-nature | qiskit_nature/properties/second_quantization/electronic/integrals/two_body_electronic_integrals.py | [
"Apache-2.0"
] | Python | to_spin | np.ndarray | def to_spin(self) -> np.ndarray:
"""Transforms the integrals into the special ``ElectronicBasis.SO`` basis.
Returns:
A single matrix containing the ``n-body`` integrals in the spin orbital basis.
"""
if self._basis == ElectronicBasis.SO:
return self._matrices # ... | Transforms the integrals into the special ``ElectronicBasis.SO`` basis.
Returns:
A single matrix containing the ``n-body`` integrals in the spin orbital basis.
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af1d5d8b65e5043e7b12538b5c52987d7d631b70 | mtreinish/qiskit-nature | qiskit_nature/properties/second_quantization/electronic/integrals/two_body_electronic_integrals.py | [
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] | Python | compose | OneBodyElectronicIntegrals | def compose(
self, other: ElectronicIntegrals, einsum_subscript: Optional[str] = None
) -> OneBodyElectronicIntegrals:
"""Composes these TwoBodyElectronicIntegrals with an instance of OneBodyElectronicIntegrals.
This method requires an `einsum_subscript` subscript and produces a new instanc... | Composes these TwoBodyElectronicIntegrals with an instance of OneBodyElectronicIntegrals.
This method requires an `einsum_subscript` subscript and produces a new instance of
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Args:
other: an instance of OneBodyElectronicIntegrals.
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5ddd01b52263df46236e72ffc0073a818a2dda1f | mtreinish/qiskit-nature | qiskit_nature/properties/second_quantization/electronic/electronic_energy.py | [
"Apache-2.0"
] | Python | orbital_energies | np.ndarray | def orbital_energies(self) -> np.ndarray:
"""Returns the orbital energies.
If no spin-distinction is made, this is a 1-D array, otherwise it is a 2-D array.
"""
return self._orbital_energies | Returns the orbital energies.
If no spin-distinction is made, this is a 1-D array, otherwise it is a 2-D array.
| Returns the orbital energies.
If no spin-distinction is made, this is a 1-D array, otherwise it is a 2-D array. | [
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5ddd01b52263df46236e72ffc0073a818a2dda1f | mtreinish/qiskit-nature | qiskit_nature/properties/second_quantization/electronic/electronic_energy.py | [
"Apache-2.0"
] | Python | from_legacy_driver_result | "ElectronicEnergy" | def from_legacy_driver_result(cls, result: LegacyDriverResult) -> "ElectronicEnergy":
"""Construct an ElectronicEnergy instance from a QMolecule.
Args:
result: the driver result from which to extract the raw data. For this property, a
QMolecule is required!
Returns:... | Construct an ElectronicEnergy instance from a QMolecule.
Args:
result: the driver result from which to extract the raw data. For this property, a
QMolecule is required!
Returns:
An instance of this property.
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cls._validate_input_type(result, LegacyElectronicStructureDriverResult)
qmol = cast(QMolecule, result)
energy_shift = qmol.energy_shift.copy()
integrals: List[ElectronicIntegrals] = []
if qmol.h... | [
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5ddd01b52263df46236e72ffc0073a818a2dda1f | mtreinish/qiskit-nature | qiskit_nature/properties/second_quantization/electronic/electronic_energy.py | [
"Apache-2.0"
] | Python | integral_operator | OneBodyElectronicIntegrals | def integral_operator(self, density: OneBodyElectronicIntegrals) -> OneBodyElectronicIntegrals:
"""Constructs the Fock operator resulting from this `ElectronicEnergy`.
Args:
density: the electronic density at which to compute the operator.
Returns:
OneBodyElectronicInte... | Constructs the Fock operator resulting from this `ElectronicEnergy`.
Args:
density: the electronic density at which to compute the operator.
Returns:
OneBodyElectronicIntegrals: the operator stored as ElectronicIntegrals.
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if ElectronicBasis.AO not in self._electronic_integrals.keys():
raise NotImplementedError(
"Construction of the Fock operator outside of the AO basis is not yet implemented."
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5ddd01b52263df46236e72ffc0073a818a2dda1f | mtreinish/qiskit-nature | qiskit_nature/properties/second_quantization/electronic/electronic_energy.py | [
"Apache-2.0"
] | Python | interpret | None | def interpret(self, result: EigenstateResult) -> None:
"""Interprets an :class:~qiskit_nature.result.EigenstateResult in this property's context.
Args:
result: the result to add meaning to.
"""
result.hartree_fock_energy = self._reference_energy
result.nuclear_repuls... | Interprets an :class:~qiskit_nature.result.EigenstateResult in this property's context.
Args:
result: the result to add meaning to.
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result.hartree_fock_energy = self._reference_energy
result.nuclear_repulsion_energy = self._nuclear_repulsion_energy
result.extracted_transformer_energies = self._shift.copy() | [
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996bedc43ec07fe0b2b6859cea58e34e5f669b3b | mtreinish/qiskit-nature | qiskit_nature/properties/second_quantization/electronic/particle_number.py | [
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"""Construct a ParticleNumber instance from a QMolecule.
Args:
result: the driver result from which to extract the raw data. For this property, a
QMolecule is required!
Returns:
... | Construct a ParticleNumber instance from a QMolecule.
Args:
result: the driver result from which to extract the raw data. For this property, a
QMolecule is required!
Returns:
An instance of this property.
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cls._validate_input_type(result, LegacyElectronicStructureDriverResult)
qmol = cast(QMolecule, result)
return cls(
qmol.num_molecular_orbitals * 2,
(qmol.num_alpha, qmol.num_beta),
... | [
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996bedc43ec07fe0b2b6859cea58e34e5f669b3b | mtreinish/qiskit-nature | qiskit_nature/properties/second_quantization/electronic/particle_number.py | [
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"""Returns a list containing the particle number operator."""
op = FermionicOp(
[(f"N_{o}", 1.0) for o in range(self._num_spin_orbitals)],
register_length=self._num_spin_orbitals,
)
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op = FermionicOp(
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996bedc43ec07fe0b2b6859cea58e34e5f669b3b | mtreinish/qiskit-nature | qiskit_nature/properties/second_quantization/electronic/particle_number.py | [
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result: the result to add meaning to.
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result.num_particles = []
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10e74c15653497ba4d2b8370de280581d0cf314a | mtreinish/qiskit-nature | qiskit_nature/properties/second_quantization/electronic/angular_momentum.py | [
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"""Construct an AngularMomentum instance from a QMolecule.
Args:
result: the driver result from which to extract the raw data. For this property, a
QMolecule is required!
Returns:
... | Construct an AngularMomentum instance from a QMolecule.
Args:
result: the driver result from which to extract the raw data. For this property, a
QMolecule is required!
Returns:
An instance of this property.
Raises:
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cls._validate_input_type(result, LegacyElectronicStructureDriverResult)
qmol = cast(QMolecule, result)
return cls(
qmol.num_molecular_orbitals * 2,
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10e74c15653497ba4d2b8370de280581d0cf314a | mtreinish/qiskit-nature | qiskit_nature/properties/second_quantization/electronic/angular_momentum.py | [
"Apache-2.0"
] | Python | interpret | None | def interpret(self, result: EigenstateResult) -> None:
"""Interprets an :class:~qiskit_nature.result.EigenstateResult in this property's context.
Args:
result: the result to add meaning to.
"""
result.total_angular_momentum = []
if not isinstance(result.aux_operator... | Interprets an :class:~qiskit_nature.result.EigenstateResult in this property's context.
Args:
result: the result to add meaning to.
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result.total_angular_momentum = []
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aux_operator_eigenvalues = [result.aux_operator_eigenvalues]
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ef7dbe98a8041758fda8604af5ab597cf703f4fb | faisalnawazmir/Copulas | copulas/univariate/truncnorm.py | [
"MIT"
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"""Prepare necessary params and call super().fit."""
min_ = X.min() - EPSILON
max_ = X.max() + EPSILON
self.mean = X.mean()
self.std = X.std()
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ef7dbe98a8041758fda8604af5ab597cf703f4fb | faisalnawazmir/Copulas | copulas/univariate/truncnorm.py | [
"MIT"
] | Python | _fit_params | <not_specific> | def _fit_params(self):
"""Return attributes from self.model to serialize.
Returns:
dict: Parameters to recreate self.model in its current fit status.
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return {
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5c0e6461c97c2980b16a92bc6332ccb39e57f198 | faisalnawazmir/Copulas | copulas/univariate/gaussian.py | [
"MIT"
] | Python | cumulative_distribution | <not_specific> | def cumulative_distribution(self, X):
"""Cumulative distribution function for gaussian distribution.
Arguments:
X: `np.ndarray` of shape (n, 1).
Returns:
np.ndarray: Cumulative density for X.
"""
self.check_fit()
return norm.cdf(X, loc=self.mean,... | Cumulative distribution function for gaussian distribution.
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np.ndarray: Cumulative density for X.
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5c0e6461c97c2980b16a92bc6332ccb39e57f198 | faisalnawazmir/Copulas | copulas/univariate/gaussian.py | [
"MIT"
] | Python | percent_point | <not_specific> | def percent_point(self, U):
"""Given a cumulated distribution value, returns a value in original space.
Arguments:
U: `np.ndarray` of shape (n, 1) and values in [0,1]
Returns:
`np.ndarray`: Estimated values in original space.
"""
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`np.ndarray`: Estimated values in original space.
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self.check_fit()
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5c0e6461c97c2980b16a92bc6332ccb39e57f198 | faisalnawazmir/Copulas | copulas/univariate/gaussian.py | [
"MIT"
] | Python | sample | <not_specific> | def sample(self, num_samples=1):
"""Returns new data point based on model.
Arguments:
n_samples: `int`
Returns:
np.ndarray: Generated samples
"""
self.check_fit()
return np.random.normal(self.mean, self.std, num_samples) | Returns new data point based on model.
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n_samples: `int`
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np.ndarray: Generated samples
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5c0e6461c97c2980b16a92bc6332ccb39e57f198 | faisalnawazmir/Copulas | copulas/univariate/gaussian.py | [
"MIT"
] | Python | from_dict | <not_specific> | def from_dict(cls, copula_dict):
"""Set attributes with provided values."""
instance = cls()
instance.fitted = copula_dict['fitted']
instance.constant_value = copula_dict['constant_value']
if instance.fitted and instance.constant_value is None:
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instance.fitted = copula_dict['fitted']
instance.constant_value = copula_dict['constant_value']
if instance.fitted and instance.constant_value is None:
instance.mean = copula_dict['mean']
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c7d3c831adaf9a6a4071066ba3dce2ae8097df0c | faisalnawazmir/Copulas | copulas/multivariate/tree.py | [
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"""Check if two edges satisfy vine constraint.
Args:
:param edge1: edge object representing edge1
:param edge2: edge object representing edge2
:type edge1: Edge object
:type edge2: Edge object
Returns:
... | Check if two edges satisfy vine constraint.
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full_node = set([edge1.L, edge1.R, edge2.L, edge2.R])
full_node.update(edge1.D)
full_node.update(edge2.D)
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c7d3c831adaf9a6a4071066ba3dce2ae8097df0c | faisalnawazmir/Copulas | copulas/multivariate/tree.py | [
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] | Python | prepare_next_tree | null | def prepare_next_tree(self):
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c7d3c831adaf9a6a4071066ba3dce2ae8097df0c | faisalnawazmir/Copulas | copulas/multivariate/tree.py | [
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] | Python | from_dict | <not_specific> | def from_dict(cls, tree_dict, previous=None):
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instance = cls(tree_dict['tree_type'])
fitted = tree_dict['fitted']
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c7d3c831adaf9a6a4071066ba3dce2ae8097df0c | faisalnawazmir/Copulas | copulas/multivariate/tree.py | [
"MIT"
] | Python | load | <not_specific> | def load(cls, tree_path):
"""Create a new instance from a file."""
with open(tree_path) as f:
tree_dict = json.load(f)
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c7d3c831adaf9a6a4071066ba3dce2ae8097df0c | faisalnawazmir/Copulas | copulas/multivariate/tree.py | [
"MIT"
] | Python | _build_first_tree | null | def _build_first_tree(self):
"""Build the first tree with n-1 variable."""
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neg_tau = -1.0 * abs(self.tau_matrix)
X = {0}
while len(X) != self.n_nodes:
adj_set = set()
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for x in X:
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c7d3c831adaf9a6a4071066ba3dce2ae8097df0c | faisalnawazmir/Copulas | copulas/multivariate/tree.py | [
"MIT"
] | Python | _build_kth_tree | null | def _build_kth_tree(self):
"""Build tree for level k."""
neg_tau = -1.0 * abs(self.tau_matrix)
edges = self.previous_tree.edges
visited = set([0])
unvisited = set(range(self.n_nodes))
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0a9fca65f2e8773643ce81cde182085ec4d28c90 | faisalnawazmir/Copulas | copulas/univariate/kde.py | [
"MIT"
] | Python | fit | null | def fit(self, X):
"""Fit Kernel density estimation to an list of values.
Args:
X: 1-d `np.ndarray` or `pd.Series` or `list` datapoints to be estimated from.
This function will fit a gaussian_kde model to a list of datapoints
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... | Fit Kernel density estimation to an list of values.
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X: 1-d `np.ndarray` or `pd.Series` or `list` datapoints to be estimated from.
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self.constant_value = self._get_constant_value(X)
if self.constant_value is None:
self.model = scipy.stats.gaussian_kde(X)
else:
self._replace_constant_methods()
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