ytzi/the-stack-dedup-python-scored · Datasets at Hugging Face

a71396c8eccbd499f64ee47c8235e9246d3bc275

32,867

py

Python

saber/xbrain/xbrain.py

elenimath/saber

71acab9798cf3aee1c4d64b09453e5234f8fdf1e

[ "Apache-2.0" ]

12

2018-05-14T17:43:18.000Z

2021-11-16T04:03:33.000Z

saber/xbrain/xbrain.py

elenimath/saber

71acab9798cf3aee1c4d64b09453e5234f8fdf1e

[ "Apache-2.0" ]

34

2019-05-06T19:13:36.000Z

2021-05-06T19:12:35.000Z

saber/xbrain/xbrain.py

elenimath/saber

71acab9798cf3aee1c4d64b09453e5234f8fdf1e

[ "Apache-2.0" ]

3

2019-10-08T17:42:17.000Z

2021-07-28T05:52:02.000Z

# Copyright 2019 The Johns Hopkins University Applied Physics Laboratory # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. #!/usr/bin/env python from __future__ import (absolute_import, division, print_function, unicode_literals) def classify_pixel(input_data, classifier, threads=8, ram=4000): """ Runs a pre-trained ilastik classifier on a volume of data Adapted from Stuart Berg's example here: https://github.com/ilastik/ilastik/blob/master/examples/example_python_client.py Arguments: input_data: data to be classified - 3D numpy array classifier: ilastik trained/classified file threads: number of thread to use for classifying input data ram: RAM to use in MB Returns: pixel_out: The raw trained classifier """ import numpy as np import six import pdb from collections import OrderedDict import vigra import os import ilastik_main from ilastik.applets.dataSelection import DatasetInfo from ilastik.workflows.pixelClassification import PixelClassificationWorkflow # Before we start ilastik, prepare these environment variable settings. os.environ["LAZYFLOW_THREADS"] = str(threads) os.environ["LAZYFLOW_TOTAL_RAM_MB"] = str(ram) # Set the command-line arguments directly into argparse.Namespace object # Provide your project file, and don't forget to specify headless. args = ilastik_main.parser.parse_args([]) args.headless = True args.project = classifier # Instantiate the 'shell', (an instance of ilastik.shell.HeadlessShell) # This also loads the project file into shell.projectManager shell = ilastik_main.main(args) assert isinstance(shell.workflow, PixelClassificationWorkflow) # Obtain the training operator opPixelClassification = shell.workflow.pcApplet.topLevelOperator # Sanity checks assert len(opPixelClassification.InputImages) > 0 assert opPixelClassification.Classifier.ready() # For this example, we'll use random input data to "batch process" print("input_data.shape", input_data.shape) # In this example, we're using 2D data (extra dimension for channel). # Tagging the data ensures that ilastik interprets the axes correctly. input_data = vigra.taggedView(input_data, 'xyz') # In case you're curious about which label class is which, # let's read the label names from the project file. label_names = opPixelClassification.LabelNames.value label_colors = opPixelClassification.LabelColors.value probability_colors = opPixelClassification.PmapColors.value print("label_names, label_colors, probability_colors", label_names, label_colors, probability_colors) # Construct an OrderedDict of role-names -> DatasetInfos # (See PixelClassificationWorkflow.ROLE_NAMES) role_data_dict = OrderedDict([("Raw Data", [DatasetInfo(preloaded_array=input_data)])]) # Run the export via the BatchProcessingApplet # Note: If you don't provide export_to_array, then the results will # be exported to disk according to project's DataExport settings. # In that case, run_export() returns None. predictions = shell.workflow.batchProcessingApplet.\ run_export(role_data_dict, export_to_array=True) predictions = np.squeeze(predictions) print("predictions.dtype, predictions.shape", predictions.dtype, predictions.shape) print("DONE.") return predictions #Unsupervised gmm clasification def gmm_classify_pixel(volume,numsamp,numcomp,erodesz): import numpy as np import sklearn.mixture import scipy.ndimage.morphology IM = volume whichsamp = np.random.randint(IM.shape[0]*IM.shape[1],size=numsamp) trainind = IM.ravel()[whichsamp].astype(float) im_gmm = sklearn.mixture.GaussianMixture(n_components=numcomp, covariance_type='diag') im_gmm.fit(trainind.reshape(-1, 1)) #gm = fitgmdist(traind,numcomp, 'CovType','diagonal','Options',options); whichCell = np.argmin(im_gmm.means_) #[~,whichCell] = min(gm.mu); Probx = im_gmm.predict_proba(IM.ravel().astype(float).reshape(-1, 1)) #Probx = posterior(gm,IM(:)); CellMap = np.reshape(Probx[:,whichCell],IM.shape) if erodesz > 0: CellMapErode = scipy.ndimage.morphology.grey_erosion(CellMap,footprint=strel2D(erodesz)) #print(strel2D(args.erodesz)) #print(CellMap[0:5,0:5]) #print(CellMapErode[0:5,0:5]) else: CellMapErode = CellMap return CellMapErode #Unsupervised gmm clasification (3D for xbrain data) #cell_class=1, then cells are darker than background. Cell_class=0, cells are lighter than background def gmm_classify_pixel3D(volume,numsamp,numcomp,vessel_thres,min_size,cell_class=1): import numpy as np import sklearn.mixture import scipy.ndimage.morphology import skimage.measure IM = volume whichsamp = np.random.randint(IM.size,size=numsamp) trainind = IM.ravel()[whichsamp].astype(float) im_gmm = sklearn.mixture.GaussianMixture(n_components=numcomp, covariance_type='diag') im_gmm.fit(trainind.reshape(-1, 1)) #gm = fitgmdist(traind,numcomp, 'CovType','diagonal','Options',options); if cell_class==1: whichCell = np.argmin(im_gmm.means_) else: whichCell = np.argmax(im_gmm.means_) #[~,whichCell] = min(gm.mu); Probx = im_gmm.predict_proba(IM.ravel().astype(float).reshape(-1, 1)) #Probx = posterior(gm,IM(:)); CellMap = np.reshape(Probx[:,whichCell],IM.shape) #Now remove vessels that have been detected CellMapT = np.multiply(CellMap,(CellMap>vessel_thres).astype(int)) #foot = strel(1) foot = [[[True]]] CellMapErode = scipy.ndimage.morphology.grey_erosion((CellMapT*255).astype(int),footprint=foot) cc_labels,num = skimage.measure.label((CellMapErode>0).astype(int),connectivity=1,background=0,return_num=True) numslices = [] for i_label in range(1,num+1): inds = np.where(cc_labels==i_label) z_inds = inds[2] numslices.append(len(np.unique(z_inds))) # np.unique(tempinds))) #unique z inds for i_label in range(1,num+1): if numslices[i_label-1] < np.round(min_size * CellMapErode.shape[2]): CellMapErode[np.where(cc_labels==i_label)] = 0 #eliminate vessels Vmap = scipy.ndimage.morphology.grey_dilation(CellMapErode,footprint=strel(1)) #note that tjs strel3d function is not same as this strel VmapT = (Vmap==0).astype(int) ProbMap = np.multiply(CellMap,VmapT) return ProbMap #print(strel2D(args.erodesz)) #print(CellMap[0:5,0:5]) #print(CellMapErode[0:5,0:5]) def segment_vessels(vessel_probability, probability_threshold, dilation_size, minimum_size): """ This function produces a binary image with segmented vessels from a probability map (from ilastik or another classifier). Copyright (c) 2016, UChicago Argonne, LLC. Parameters ---------- vessel_probability : ndarray Nr x Nc x Nz matrix which contains the probability of each voxel being a vessel. probability_threshold : float threshold between (0,1) to apply to probability map (only consider voxels for which vessel_probability(r,c,z) > probability_threshold). dilation_size : int Sphere Structural Element diameter size. minimum_size : int components smaller than this are removed from image. Returns ------- ndarry Binary Image """ import numpy as np import scipy.io as sio from scipy import ndimage as ndi from skimage import morphology smallsize = 100 # components smaller than this size are removed. WHY Fixed Size?? unfiltered_im = (vessel_probability >= probability_threshold) im_removed_small_objects = morphology.remove_small_objects(unfiltered_im, min_size = smallsize, in_place = True) dilated_im = ndi.binary_dilation(im_removed_small_objects, morphology.ball((dilation_size-1)/2)) image_out = morphology.remove_small_objects(dilated_im, min_size = minimum_size, in_place = True) return(image_out) def detect_cells2D(cell_probability, probability_threshold, stopping_criterion, initial_template_size, dilation_size, max_no_cells): """ This is the top level function to infer the position (and eventually size) of all cells in a 2D volume of image data. We assume that we already have computed a "probability map" which encodes the probability that each voxel corresponds to a cell body. Copyright (c) 2016, UChicago Argonne, LLC. Parameters ---------- cell_probability : ndarray Nr x Nc x Nz matrix which contains the probability of each voxel being a cell body. probability_threshold : float threshold between (0,1) to apply to probability map (only consider voxels for which cell_probability(r,c,z) > probability_threshold) stopping_criterion : float stopping criterion is a value between (0,1) (minimum normalized correlation between template and probability map) (Example = 0.47) initial_template_size : int initial size of spherical template (to use in sweep) dilation_size : int size to increase mask around each detected cell (zero out sphere of radius with initial_template_size+dilation_size around each centroid) max_no_cells : int maximum number of cells (alternative stopping criterion) Returns ------- ndarray centroids = D x 4 matrix, where D = number of detected cells. The (x,y,z) coordinate of each cell are in columns 1-3. The fourth column contains the correlation (ptest) between the template and probability map and thus represents our "confidence" in the estimate. The algorithm terminates when ptest<=stopping_criterion. ndarray new_map = Nr x Nc x Nz matrix containing labeled detected cells (1,...,D) """ # following imports to be updated when directory structure are finalized #import create_synth_dict #from compute3dvec import compute3dvec from scipy import signal import numpy as np import pdb import logging # threshold probability map. newtest = (cell_probability * (cell_probability > probability_threshold)).astype('float32') #initial_template_size is an int now but could a vector later on - convert it to an array initial_template_size = np.atleast_1d(initial_template_size) # create dictionary of spherical templates box_radius = np.ceil(np.max(initial_template_size)/2) + 1 dict = create_synth_dict2D(initial_template_size, box_radius) dilate_dict = create_synth_dict2D(initial_template_size + dilation_size, box_radius) box_length = int(round(np.shape(dict)[0] ** (1/2))) new_map = np.zeros((np.shape(cell_probability)), dtype='uint8') newid = 1 centroids = np.empty((0, 3)) # run greedy search step for at most max_no_cells steps (# cells <= max_no_cells) for ktot in range(max_no_cells): val = np.zeros((np.shape(dict)[1], 1), dtype='float32') id = np.zeros((np.shape(dict)[1], 1), dtype='uint32') # loop to convolve the probability cube with each template in dict for j in range(np.shape(dict)[1]): convout = signal.fftconvolve(newtest, np.reshape(dict[:,j], (box_length, box_length)), mode='same') # get the max value of the flattened convout array and its index val[j],id[j] = np.real(np.amax(convout)), np.argmax(convout) # find position in image with max correlation which_atom = np.argmax(val) which_loc = id[which_atom] # Save dict into a cube array with its center given by which_loc and place it into a 3-D array. x2 = compute2dvec(dict[:, which_atom], which_loc, box_length, np.shape(newtest)) xid = np.nonzero(x2) # Save dilate_dict into a cube array with its center given by which_loc and place it into a 3-D array. x3 = compute2dvec(dilate_dict[:, which_atom], which_loc, box_length, np.shape(newtest)) newtest = newtest * (x3 == 0) ptest = val/np.sum(dict, axis=0) if ptest < stopping_criterion: print("Cell Detection is done") return(centroids, new_map) # Label detected cell new_map[xid] = newid newid = newid + 1 #Convert flat index to indices rr, cc = np.unravel_index(which_loc, np.shape(newtest)) new_centroid = cc, rr #Check - why cc is first? Flip indices # insert a row into centroids centroids = np.vstack((centroids, np.append(new_centroid, ptest))) # for later: convert to logging and print with much less frequency if(ktot % 10 == 0): print('Iteration remaining = ', (max_no_cells - ktot - 1), 'Correlation = ', ptest ) print("Cell Detection is done") return(centroids, new_map) def detect_cells(cell_probability, probability_threshold, stopping_criterion, initial_template_size, dilation_size, max_no_cells): """ This is the top level function to infer the position (and eventually size) of all cells in a 3D volume of image data. We assume that we already have computed a "probability map" which encodes the probability that each voxel corresponds to a cell body. Copyright (c) 2016, UChicago Argonne, LLC. Parameters ---------- cell_probability : ndarray Nr x Nc x Nz matrix which contains the probability of each voxel being a cell body. probability_threshold : float threshold between (0,1) to apply to probability map (only consider voxels for which cell_probability(r,c,z) > probability_threshold) stopping_criterion : float stopping criterion is a value between (0,1) (minimum normalized correlation between template and probability map) (Example = 0.47) initial_template_size : int initial size of spherical template (to use in sweep) dilation_size : int size to increase mask around each detected cell (zero out sphere of radius with initial_template_size+dilation_size around each centroid) max_no_cells : int maximum number of cells (alternative stopping criterion) Returns ------- ndarray centroids = D x 4 matrix, where D = number of detected cells. The (x,y,z) coordinate of each cell are in columns 1-3. The fourth column contains the correlation (ptest) between the template and probability map and thus represents our "confidence" in the estimate. The algorithm terminates when ptest<=stopping_criterion. ndarray new_map = Nr x Nc x Nz matrix containing labeled detected cells (1,...,D) """ # following imports to be updated when directory structure are finalized #import create_synth_dict #from compute3dvec import compute3dvec from scipy import signal import numpy as np import pdb import logging if len(cell_probability.shape) == 4: print('Assuming Z, Chan, Y, X input') cell_probability = np.transpose(cell_probability[:,0,:,:], (2,1,0)) # threshold probability map. newtest = (cell_probability * (cell_probability > probability_threshold)).astype('float32') #initial_template_size is an int now but could a vector later on - convert it to an array initial_template_size = np.atleast_1d(initial_template_size) # create dictionary of spherical templates box_radius = np.ceil(np.max(initial_template_size)/2) + 1 dict = create_synth_dict(initial_template_size, box_radius) dilate_dict = create_synth_dict(initial_template_size + dilation_size, box_radius) box_length = int(round(np.shape(dict)[0] ** (1/3))) new_map = np.zeros((np.shape(cell_probability)), dtype='uint8') newid = 1 centroids = np.empty((0, 4)) # run greedy search step for at most max_no_cells steps (# cells <= max_no_cells) for ktot in range(max_no_cells): val = np.zeros((np.shape(dict)[1], 1), dtype='float32') id = np.zeros((np.shape(dict)[1], 1), dtype='uint32') # loop to convolve the probability cube with each template in dict for j in range(np.shape(dict)[1]): convout = signal.fftconvolve(newtest, np.reshape(dict[:,j], (box_length, box_length, box_length)), mode='same') # get the max value of the flattened convout array and its index val[j],id[j] = np.real(np.amax(convout)), np.argmax(convout) # find position in image with max correlation which_atom = np.argmax(val) which_loc = id[which_atom] # Save dict into a cube array with its center given by which_loc and place it into a 3-D array. x2 = compute3dvec(dict[:, which_atom], which_loc, box_length, np.shape(newtest)) xid = np.nonzero(x2) # Save dilate_dict into a cube array with its center given by which_loc and place it into a 3-D array. x3 = compute3dvec(dilate_dict[:, which_atom], which_loc, box_length, np.shape(newtest)) newtest = newtest * (x3 == 0) ptest = val/np.sum(dict, axis=0) if ptest < stopping_criterion: print("Cell Detection is done") return(centroids, new_map) # Label detected cell new_map[xid] = newid newid = newid + 1 #Convert flat index to indices rr, cc, zz = np.unravel_index(which_loc, np.shape(newtest)) new_centroid = rr, cc, zz #Check - why cc is first? # insert a row into centroids centroids = np.vstack((centroids, np.append(new_centroid, ptest))) # for later: convert to logging and print with much less frequency if(ktot % 10 == 0): print('Iteration remaining = ', (max_no_cells - ktot - 1), 'Correlation = ', ptest ) print("Cell Detection is done, centroids: {} map: {}".format(centroids.shape, new_map.shape)) return(centroids, new_map) def create_synth_dict(radii, box_radius): """ This function creates a collection of spherical templates of different sizes. Copyright (c) 2016, UChicago Argonne, LLC. Parameters ---------- radii : int radii coubld be 1xN vector but currently is an integer box_radius : float Returns ------- ndarray dictionary of template vectors, of size (box_length ** 3 x length(radii)), where box_length = box_radius*2 +1 and radii is an input to the function which contains a vector of different sphere sizes. """ import numpy as np from numpy import linalg as LA from scipy import ndimage as ndi from skimage.morphology import ball box_length = int(box_radius * 2 + 1) #used for array dimension dict = np.zeros((box_length**3, np.size(radii)), dtype='float32') cvox = int((box_length-1)/2 + 1) for i in range(len(radii)): template = np.zeros((box_length, box_length, box_length)) template[cvox, cvox, cvox] = 1 dict[:, i] = np.reshape(ndi.binary_dilation(template, ball((radii[i] - 1)/2)), (box_length**3)) dict[:, i] = dict[:, i]/(LA.norm(dict[:, i])) return(dict) def create_synth_dict2D(radii, box_radius): """ This function creates a collection of spherical templates of different sizes. Copyright (c) 2016, UChicago Argonne, LLC. Parameters ---------- radii : int radii coubld be 1xN vector but currently is an integer box_radius : float Returns ------- ndarray dictionary of template vectors, of size (box_length ** 3 x length(radii)), where box_length = box_radius*2 +1 and radii is an input to the function which contains a vector of different sphere sizes. """ import numpy as np from numpy import linalg as LA from scipy import ndimage as ndi from skimage.morphology import ball box_length = int(box_radius * 2 + 1) #used for array dimension dict = np.zeros((box_length**2, np.size(radii)), dtype='float32') cvox = int((box_length-1)/2 + 1) for i in range(len(radii)): template = np.zeros((box_length, box_length, box_length)) template[cvox, cvox, cvox] = 1 tmp = ndi.binary_dilation(template, ball((radii[i] - 1)/2)) dict[:, i] = np.reshape(tmp[:,:,cvox], (box_length**2)) if(LA.norm(dict[:, i])>0): dict[:, i] = dict[:, i]/(LA.norm(dict[:, i])) return(dict) def placeatom(vector, box_length, which_loc, stacksz): """ Copies the data from vector into a cube with the width of "box_length" and places the cube into a 3-D array with the shape/size defined by the "stacksz" parameter. The center of cube is given by the "which_loc" parameter. Copyright (c) 2016, UChicago Argonne, LLC. Parameters ---------- vector : ndarray Nx1 array box_length : int Lenght which_loc : int location to place atom in the flattened array stacksz : ndarry shape of the array (3D) Returns ------- ndarray """ import numpy as np output_array = np.zeros((stacksz), dtype='float32') #Convert flat index to indices r, c, z = np.unravel_index(which_loc, (stacksz)) output_array[r, c, z] = 1 # Increase every dimension by box_length at the top and at the bottom and fill them with zeroes. output_array = np.lib.pad(output_array, ((box_length, box_length), (box_length, box_length), (box_length, box_length)), 'constant', constant_values=(0, 0)) # get the indices of the center of cube into increased dimensions output_array. r, c, z = np.nonzero(output_array) #save the output of round() function to avoid multiple calls to it. half_length = np.int(round(box_length/2)) # TODO: casting to int to avoid problems downstream with indexing c = np.int(c) r = np.int(r) z = np.int(z) #Save the data from the cube into output_array. output_array[(r - half_length +1) : (r + box_length - half_length +1), \ (c - half_length +1) : (c + box_length - half_length +1), \ (z - half_length +1) : (z + box_length - half_length +1)] = \ np.reshape(vector, (box_length, box_length, box_length)) return(output_array) def placeatom2D(vector, box_length, which_loc, stacksz): """ Copies the data from vector into a cube with the width of "box_length" and places the into a 2-D array with the shape/size defined by the "stacksz" parameter. The center of tbhe data is given by the "which_loc" parameter. Copyright (c) 2016, UChicago Argonne, LLC. Parameters ---------- vector : ndarray Nx1 array box_length : int Lenght which_loc : int location to place atom in the flattened array stacksz : ndarry shape of the array (3D) Returns ------- ndarray """ import numpy as np output_array = np.zeros((stacksz), dtype='float32') #Convert flat index to indices r, c = np.unravel_index(which_loc, (stacksz)) output_array[r, c] = 1 # Increase every dimension by box_length at the top and at the bottom and fill them with zeroes. output_array = np.lib.pad(output_array, ((box_length, box_length), (box_length, box_length)), 'constant', constant_values=(0, 0)) # get the indices of the center of cube into increased dimensions output_array. r, c = np.nonzero(output_array) #save the output of round() function to avoid multiple calls to it. half_length = np.int(round(box_length/2)) # TODO: casting to int to avoid problems downstream with indexing c = np.int(c) r = np.int(r) #Save the data from the cube into output_array. output_array[(r - half_length +1) : (r + box_length - half_length +1), \ (c - half_length +1) : (c + box_length - half_length +1)] = \ np.reshape(vector, (box_length, box_length)) return(output_array) def compute3dvec(vector, which_loc, box_length, stacksz): """ Resizes the array dimension returned by placeatom() to the shape/size given by "stacksz" parameter. Copyright (c) 2016, UChicago Argonne, LLC. Parameters ---------- vector : ndarray Nx1 array box_length : int Lenght which_loc : int location to place atom stacksz : ndarry shape of the array (3D) Returns ------- ndarray """ import numpy as np output_array = placeatom(vector, box_length, which_loc, stacksz) #delete the top "box_length" arrays for all dimensions. x, y, z = np.shape(output_array) output_array = output_array[box_length:x, box_length:y, box_length:z] #delete the bottom "box_length" arrays for all dimensions. x, y, z = np.shape(output_array) output_array = output_array[0 : (x - box_length), 0 : (y - box_length), 0 : (z - box_length)] return output_array def compute2dvec(vector, which_loc, box_length, stacksz): """ Resizes the array dimension returned by placeatom() to the shape/size given by "stacksz" parameter. Copyright (c) 2016, UChicago Argonne, LLC. Parameters ---------- vector : ndarray Nx1 array box_length : int Lenght which_loc : int location to place atom stacksz : ndarry shape of the array (3D) Returns ------- ndarray """ import numpy as np output_array = placeatom2D(vector, box_length, which_loc, stacksz) #delete the top "box_length" arrays for all dimensions. x, y = np.shape(output_array) output_array = output_array[box_length:x, box_length:y] #delete the bottom "box_length" arrays for all dimensions. x, y = np.shape(output_array) output_array = output_array[0 : (x - box_length), 0 : (y - box_length)] return output_array def strel2D(sesize): import numpy as np #sw = ((sesize-1)/2) #ses2 = int(math.ceil(sesize/2)) [y,x] = np.meshgrid(list(range(-sesize,sesize+1)), list(range(-sesize,sesize+1))) se = ((((x/sesize)**2.0 + (y/sesize)**2.0) **(1/2.0))<=1) return(se) def strel(sesize): import numpy as np #sw = ((sesize-1)/2) #ses2 = int(math.ceil(sesize/2)) [y,x,z] = np.meshgrid(list(range(-sesize,sesize+1)), list(range(-sesize,sesize+1)), list(range(-sesize,sesize+1))) se = ((((x/sesize)**2.0 + (y/sesize)**2.0 + (z/sesize)**2.0) **(1/2.0))<=1) return(se) #Call this function for centroid-level f1 score on 2d (nii) data def cell_metrics2D(cells,im_train,initial_template_size): import numpy as np from skimage.measure import label, regionprops im_truth_labeled = label(im_train) regions = regionprops(im_truth_labeled) C0_prev = np.empty((0, 2)) for props in regions: y0, x0 = props.centroid C0_prev = np.concatenate((C0_prev, [[x0,y0]]), axis=0) #CC = measure.label(im_train) #CC = measure.label(im_train, background=0) #regions = regionprops(CC) #C0_prev = np.zeros((1,2)) #for props in regions: # y0, x0 = props.centroid # C0_prev = np.concatenate((C0_prev, [x0,y0]), axis=0) C1 = pad2D(cells[:,:2], initial_template_size, im_train.shape[0], im_train.shape[1]) #C1 = pad(C1_prev, args.initial_template_size, im_train.shape[0], im_train.shape[1]) C0 = pad2D(C0_prev, initial_template_size, im_train.shape[0], im_train.shape[1]) C0 = np.transpose(C0) C1 = np.transpose(C1) thresh = initial_template_size f1 = centroid_f1(C0,C1,thresh) return f1 def pad2D(C0_prev, sphere_sz, improb0_sz, improb1_sz): import numpy as np C0 = np.empty((0,2)) for i in range(0,C0_prev.shape[0]): curr_row = C0_prev[i,:] if curr_row[0] > sphere_sz and curr_row[0] < improb0_sz - sphere_sz and curr_row[1] > sphere_sz and curr_row[1] < improb1_sz - sphere_sz : C0 = np.concatenate((C0,[curr_row]),axis=0) return C0 #Call this function for centroid-level f1 score on 2d (nii) data def f1_centroid3D(cells,im_train,initial_template_size): import numpy as np from skimage.measure import label, regionprops cells = cells[:,0:3] #Chop off last column, which is correlation score im_truth_labeled = label(im_train) regions = regionprops(im_truth_labeled) C0_prev = np.empty((0, 3)) for props in regions: x0, y0, z0 = props.centroid #poss pull should put in x,y,z C0_prev = np.concatenate((C0_prev, [[x0,y0,z0]]), axis=0) #CC = measure.label(im_train) #CC = measure.label(im_train, background=0) #regions = regionprops(CC) #C0_prev = np.zeros((1,2)) #for props in regions: # y0, x0 = props.centroid # C0_prev = np.concatenate((C0_prev, [x0,y0]), axis=0) C1 = pad3D(cells, initial_template_size, im_train.shape[0], im_train.shape[1], im_train.shape[2]) #C1 = pad(C1_prev, args.initial_template_size, im_train.shape[0], im_train.shape[1]) C0 = pad3D(C0_prev, initial_template_size, im_train.shape[0], im_train.shape[1], im_train.shape[2]) C0 = np.transpose(C0) C1 = np.transpose(C1) thresh = initial_template_size f1 = centroid_f1(C0,C1,thresh) return f1 def pad3D(C0_prev, sphere_sz, improb0_sz, improb1_sz, improb2_sz): import numpy as np C0 = np.empty((0,3)) for i in range(0,C0_prev.shape[0]): curr_row = C0_prev[i,:] if curr_row[0] > sphere_sz and curr_row[0] < improb0_sz - sphere_sz and curr_row[1] > sphere_sz and curr_row[1] < improb1_sz - sphere_sz and curr_row[2] > sphere_sz and curr_row[2] < improb2_sz - sphere_sz : C0 = np.concatenate((C0,[curr_row]),axis=0) return C0 #Dense, 3D f1 score of cell detection def dense_f1_3D(cell_map,cell_gt_map): import numpy as np import math # processing params bin_cell_map = cell_map bin_cell_map[bin_cell_map>0]=1 bin_cell_map[bin_cell_map<=0]=0 bin_gt_map = cell_gt_map bin_gt_map[bin_gt_map>0]=1 bin_gt_map[bin_gt_map<=0]=0 beta = 2 #cells cell_true_detect = np.sum(np.logical_and(bin_cell_map,bin_gt_map).astype(int).ravel()) cell_detections = np.sum(bin_cell_map.ravel()) cell_true_positives = np.sum(bin_gt_map.ravel()) if(cell_detections>0): cell_p = cell_true_detect/cell_detections else: cell_p = 0 if(cell_true_positives>0): cell_r = cell_true_detect/cell_true_positives else: cell_r = 0 if(cell_p + cell_r >0): f_cell = (1+math.pow(beta,2)) * (cell_p*cell_r)/(math.pow(beta,2)*cell_p + cell_r) else: f_cell = 0 return f_cell def centroid_f1(C0,C1,thres): import scipy import numpy as np C0 = np.transpose(C0) C1 = np.transpose(C1) Y = scipy.spatial.distance.cdist(C0, C1, 'euclidean') try: vals = np.sort(np.amin(Y,axis=1)) valinds = np.argsort(np.min(Y,axis=1)) except ValueError: print("No Detected Objects") return 0 L = len(vals[np.where(vals<=thres)]) C0 = np.transpose(C0) C0 = C0[:,valinds] Y2 = scipy.spatial.distance.cdist(C1, np.transpose(C0), 'euclidean') matches = np.zeros((2,L)) dvec = np.zeros((L,1)) for i in range(0,L): idcol = i valtmp = np.amin(Y2[:,i]) idrow = np.argmin(Y2[:,i]) #idrow = np.argmin(Y2[i,:]) if valtmp<=thres: matches[:,i] = [idrow,idcol] dvec[i] = valtmp Y2[idrow,:]=thres+100 Y2[:,idcol]=thres+100 idd = np.where(dvec>thres) matches[:,idd]=[] matches = np.asarray(matches) numcorrect = sum([sum(x)!=0 for x in zip(*matches)]) numgt = C0.shape[1] numrecov = C1.shape[0] b=1 #f1 score TP = numcorrect/numrecov FP = 1 - TP FN = (numgt - numcorrect)/numgt p = TP /(TP + FP) r = TP /(TP + FN) f1 = (1 + b**2)*p*r/(((b**2)*p)+r) return f1

37.562286

220

0.66334

0

14,643

0.445523

a71437b5469d3a544e7b8017e8d77b89874193c2

2,088

py

Python

migrations/versions/b846613b404e_.py

python-02/flask-spa-CoopApp

8ecd9e22847401c6ee18b76a80c68c8ba5d77401

[ "MIT" ]

6

2021-04-16T06:37:04.000Z

2021-11-11T23:37:04.000Z

migrations/versions/b846613b404e_.py

python-02/flask-spa-CoopApp

8ecd9e22847401c6ee18b76a80c68c8ba5d77401

[ "MIT" ]

null

migrations/versions/b846613b404e_.py

python-02/flask-spa-CoopApp

8ecd9e22847401c6ee18b76a80c68c8ba5d77401

[ "MIT" ]

2

2021-06-01T15:35:17.000Z

2022-03-05T03:50:57.000Z

"""empty message Revision ID: b846613b404e Revises: fc25bf71d841 Create Date: 2020-01-06 21:43:28.958558 """ from alembic import op import sqlalchemy as sa from sqlalchemy.dialects import postgresql # revision identifiers, used by Alembic. revision = 'b846613b404e' down_revision = 'fc25bf71d841' branch_labels = None depends_on = None def upgrade(): # ### commands auto generated by Alembic - please adjust! ### op.create_table('loan', sa.Column('uuid', postgresql.UUID(as_uuid=True), nullable=False), sa.Column('code', sa.String(length=20), nullable=True), sa.Column('borrower_code', sa.String(length=20), nullable=True), sa.Column('type_loan', sa.String(length=20), nullable=True), sa.Column('date_loan', sa.DateTime(), nullable=True), sa.Column('date_start', sa.Date(), nullable=True), sa.Column('date_end', sa.Date(), nullable=True), sa.Column('term', sa.Integer(), nullable=True), sa.Column('type_schedule', sa.String(length=20), nullable=True), sa.Column('is_settled', sa.Boolean(), nullable=True), sa.Column('amount', sa.Float(), nullable=True), sa.Column('interest_rate', sa.Float(), nullable=True), sa.Column('interest_amount', sa.Float(), nullable=True), sa.Column('remarks', sa.String(length=50), nullable=True), sa.PrimaryKeyConstraint('uuid'), sa.UniqueConstraint('code') ) op.create_table('loan_detail', sa.Column('uuid', postgresql.UUID(as_uuid=True), nullable=False), sa.Column('loan_code', sa.String(length=20), nullable=True), sa.Column('type_line', sa.String(length=20), nullable=True), sa.Column('amount_to_pay', sa.Float(), nullable=True), sa.Column('amount_payed', sa.Float(), nullable=True), sa.Column('date_to_pay', sa.Date(), nullable=True), sa.Column('date_payed', sa.Date(), nullable=True), sa.PrimaryKeyConstraint('uuid') ) # ### end Alembic commands ### def downgrade(): # ### commands auto generated by Alembic - please adjust! ### op.drop_table('loan_detail') op.drop_table('loan') # ### end Alembic commands ###

36.631579

69

0.684866

0

651

0.311782

a7143837d4f1b09881e05cb620fce36372532de7

2,010

py

Python

alipay/aop/api/domain/AlipayEcoCityserviceIndustryEnergySendModel.py

antopen/alipay-sdk-python-all

8e51c54409b9452f8d46c7bb10eea7c8f7e8d30c

[ "Apache-2.0" ]

null

alipay/aop/api/domain/AlipayEcoCityserviceIndustryEnergySendModel.py

antopen/alipay-sdk-python-all

8e51c54409b9452f8d46c7bb10eea7c8f7e8d30c

[ "Apache-2.0" ]

null

alipay/aop/api/domain/AlipayEcoCityserviceIndustryEnergySendModel.py

antopen/alipay-sdk-python-all

8e51c54409b9452f8d46c7bb10eea7c8f7e8d30c

[ "Apache-2.0" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- import json from alipay.aop.api.constant.ParamConstants import * from alipay.aop.api.domain.EnergyExtRequest import EnergyExtRequest class AlipayEcoCityserviceIndustryEnergySendModel(object): def __init__(self): self._ext_info = None self._outer_no = None self._scene = None @property def ext_info(self): return self._ext_info @ext_info.setter def ext_info(self, value): if isinstance(value, EnergyExtRequest): self._ext_info = value else: self._ext_info = EnergyExtRequest.from_alipay_dict(value) @property def outer_no(self): return self._outer_no @outer_no.setter def outer_no(self, value): self._outer_no = value @property def scene(self): return self._scene @scene.setter def scene(self, value): self._scene = value def to_alipay_dict(self): params = dict() if self.ext_info: if hasattr(self.ext_info, 'to_alipay_dict'): params['ext_info'] = self.ext_info.to_alipay_dict() else: params['ext_info'] = self.ext_info if self.outer_no: if hasattr(self.outer_no, 'to_alipay_dict'): params['outer_no'] = self.outer_no.to_alipay_dict() else: params['outer_no'] = self.outer_no if self.scene: if hasattr(self.scene, 'to_alipay_dict'): params['scene'] = self.scene.to_alipay_dict() else: params['scene'] = self.scene return params @staticmethod def from_alipay_dict(d): if not d: return None o = AlipayEcoCityserviceIndustryEnergySendModel() if 'ext_info' in d: o.ext_info = d['ext_info'] if 'outer_no' in d: o.outer_no = d['outer_no'] if 'scene' in d: o.scene = d['scene'] return o

26.8

69

0.584577

1,825

0.90796

0

895

0.445274

0

200

0.099502

a715a55b0649d434e3e3db7475617b277a5112ae

1,657

py

Python

project_receipt/receipt/urls.py

Guilouf/django-receipt

fb42de12311cd1a20cc28c74a732d818f28ef551

[ "Apache-2.0" ]

null

project_receipt/receipt/urls.py

Guilouf/django-receipt

fb42de12311cd1a20cc28c74a732d818f28ef551

[ "Apache-2.0" ]

8

2021-02-01T12:47:02.000Z

2021-12-13T09:34:38.000Z

project_receipt/receipt/urls.py

Guilouf/django-receipt

fb42de12311cd1a20cc28c74a732d818f28ef551

[ "Apache-2.0" ]

null

from django.urls import path from receipt import views urlpatterns = [ path('', views.ReceiptList.as_view(), name='home'), path('receipt/', views.ReceiptList.as_view(), name='receipt_list'), path('receipt/create', views.ReceiptCreate.as_view(), name='receipt_create'), path('receipt/<int:pk>/edit', views.ReceiptUpdate.as_view(), name='receipt_update'), path('establishment/', views.EstablishmentList.as_view(), name='establishment_list'), path('establishment/create', views.EstablishmentCreate.as_view(), name='establishment_create'), path('establishment/<int:pk>/edit', views.EstablishmentUpdate.as_view(), name='establishment_update'), path('establishment/<int:pk>', views.EstablishmentDetail.as_view(), name='establishment_detail'), path('establishment/<int:pk>/add_receipt', views.ReceiptFromEstablishmentCreate.as_view(), name='establishment_add_receipt'), path('company/', views.CompanyList.as_view(), name='company_list'), path('company/create', views.CompanyCreate.as_view(), name='company_create'), path('company/<int:pk>/edit', views.CompanyUpdate.as_view(), name='company_update'), path('company/<int:pk>', views.CompanyDetail.as_view(), name='company_detail'), path('company/<int:pk>/add_establishment', views.EstablishmentFromCompanyCreate.as_view(), name='company_add_establishment'), path('tag/', views.TagList.as_view(), name='tag_list'), path('tag/create', views.TagCreate.as_view(), name='tag_create'), path('tag/<int:pk>/edit', views.TagUpdate.as_view(), name='tag_update'), path('tag/<int:pk>', views.TagDetail.as_view(), name='tag_detail'), ]

61.37037

106

0.719976

0

632

0.381412

a71e3a4361a99f178927d847326e3096eeaee755

4,216

py

Python

utils/common/_common.py

Pzqqt/Django_Transportation_Management_System

f4f0905d8e007920ae190252eeaefbc6ee67ed85

[ "MIT" ]

null

utils/common/_common.py

Pzqqt/Django_Transportation_Management_System

f4f0905d8e007920ae190252eeaefbc6ee67ed85

[ "MIT" ]

null

utils/common/_common.py

Pzqqt/Django_Transportation_Management_System

f4f0905d8e007920ae190252eeaefbc6ee67ed85

[ "MIT" ]

null

from functools import partial from itertools import chain from collections import UserList import logging import traceback from django import forms from django.db.models import Model from django.core.validators import validate_comma_separated_integer_list from django.core.serializers.json import DjangoJSONEncoder from django.db.models.fields.related import ForeignKey from django.http import JsonResponse from django.utils import timezone class UnescapedDjangoJSONEncoder(DjangoJSONEncoder): """ 自定义的JSON编码器, 强制ensure_ascii为False, 避免中文字符被编码为乱码 """ def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) # 强制ensure_ascii为False self.ensure_ascii = False UnescapedJsonResponse = partial(JsonResponse, encoder=UnescapedDjangoJSONEncoder) class SortableModelChoiceField(forms.ModelChoiceField): """ 为ModelChoiceField的choices进行排序是件很麻烦的事尽管我们可以对queryset属性使用`order_by`进行排序但是还需要考虑对数据库的优化(尽可能避免explain中出现`using filesort`) 因此, 我们在ModelChoiceIterator中添加一个额外可选的属性, 以允许在遍历choices时对其进行排序这是在应用层的排序, 意在减少数据库的压力 """ class _ModelChoiceIterator(forms.models.ModelChoiceIterator): class _FakeQuerySet(UserList): _prefetch_related_lookups = () def iterator(self): yield from self def __iter__(self): sort_key = self.field.sort_key if sort_key is not None: # sorted之后(立即执行数据库查询), _prefetch_related_lookups就没有意义了 self.queryset = self._FakeQuerySet(sorted(self.queryset, key=sort_key)) return super().__iter__() iterator = _ModelChoiceIterator def __init__(self, queryset, **kwargs): super().__init__(queryset, **kwargs) self.sort_key = kwargs.get("sort_key", None) def multi_lines_log(logger: logging.Logger, string: str, level=logging.INFO): """ 记录多行日志 """ for line in string.splitlines(): logger.log(level, line) def traceback_log(logger: logging.Logger, level=logging.ERROR): """ 记录异常栈 """ multi_lines_log(logger=logger, string=traceback.format_exc(), level=level) def traceback_and_detail_log(request, logger: logging.Logger, level=logging.ERROR): """ 记录异常栈和其他一些详细信息 """ logger.log(level, "=" * 100) logger.log(level, "Exception:") logger.log(level, "Time: %s" % timezone.make_naive(timezone.now()).strftime("%Y-%m-%d %H:%M:%S")) logger.log(level, "Url: %s" % request.path) logger.log(level, "Method: %s" % request.method) logger.log(level, "Cookies: %s" % request.COOKIES) logger.log(level, "Session: %s" % dict(request.session.items())) if request.method == "POST": logger.log(level, "Post data: %s" % request.POST.dict()) logger.log(level, "") traceback_log(logger=logger, level=level) logger.log(level, "=" * 100) def validate_comma_separated_integer_list_and_split(string: str, auto_strip=True) -> list: """ 判断字符串是否是一个以逗号分隔的数字列表如果是, 则自动进行分割并返回列表; 如果不是, 则抛出ValidationError异常 :param string: 要解析的字符串 :param auto_strip: 为True时则提前对string进行strip(默认) :return: list """ if auto_strip: string = string.strip() validate_comma_separated_integer_list(string) return [int(x) for x in string.split(',')] def model_to_dict_(instance: Model) -> dict: """ Django有一个内置的django.forms.models.model_to_dict方法(以下简称原model_to_dict方法) 可以方便地把模型转为字典, 但是有一个坑, 被标记为不可编辑(editable为False)的字段不会包含在输出的字典中原model_to_dict方法仅在初始化ModelForm时被使用, 为了安全起见, 这样做无可厚非但是我们想要的"模型转为字典"的方法应该包含模型的所有字段所以我们参考原model_to_dict方法编写了新的model_to_dict_方法比起原model_to_dict方法缺少了fields和exclude参数, 因为我们暂时不需要 """ opts = instance._meta data = {} for f in chain(opts.concrete_fields, opts.private_fields, opts.many_to_many): # 对于一对一和多对一外键, 返回外键模型对象 (多对多外键会在else子句中合适地处理) # 注: 由于ForeignKey的attname属性值为"字段名_id", 所以调用value_from_object方法的话, 返回的是外键对象的id if isinstance(f, ForeignKey): data[f.name] = getattr(instance, f.name, None) else: data[f.name] = f.value_from_object(instance) return data def del_session_item(request, *items): """ 从request会话中删除键值 """ for item in items: request.session.pop(item, None)

35.728814

101

0.708491

1,540

0.302079

51

0.010004

0

2,086

0.40918

a71f0fb6127bf9b694c0e036c4b163b042f9e29b

127

py

Python

landingpage/urls.py

aurphillus/Django-Library-Completed

f46e45f85c888e7694323e22f6e966c291a4a0be

[ "MIT" ]

null

landingpage/urls.py

aurphillus/Django-Library-Completed

f46e45f85c888e7694323e22f6e966c291a4a0be

[ "MIT" ]

null

landingpage/urls.py

aurphillus/Django-Library-Completed

f46e45f85c888e7694323e22f6e966c291a4a0be

[ "MIT" ]

null

from django.urls import path from landingpage.views import * urlpatterns = [ path('',landingpage,name="landingpage"), ]

14.111111

44

0.716535

0

15

0.11811

a71fe8e9c812b790a9f8e10c54db7ff385e01808

31,509

py

Python

cloud-v2.0/verify/verify.py

13242084001/api

71f57b485d685caae94a84b625d64be832cf8910

[ "Apache-2.0" ]

null

cloud-v2.0/verify/verify.py

13242084001/api

71f57b485d685caae94a84b625d64be832cf8910

[ "Apache-2.0" ]

1

2021-03-25T23:58:32.000Z

cloud-v2.0/verify/verify.py

13242084001/api

71f57b485d685caae94a84b625d64be832cf8910

[ "Apache-2.0" ]

null

from common import sshClient import time import eventlet from .gol import * import requests from common.uploadMirror import login from common.sqlquery import Query #import pytest import json def check_login_response_headers(response): result = False if "cloud0" in response.headers.get("Set-Cookie"): result = True assert result == True def logout_ok(response): pass def check_stop_py_machine(response): #print(json.dumps(response.json())) #print(response.json().get("code"), "yyyyyyyyyyyyyyy") assert response.json().get("code") == 0 def check_add_role(response): body_json = response.json() assert body_json.get("code") == 1 assert body_json.get("error") == None def check_remove_role(response): body = response.json() assert body.get("code") == 1 assert body.get("error") == None #校验添加区域 def check_add_zone(response): body = response.json() resourceIds = body.get("resourceIds") #print(body) assert body.get("code") == 1 #assert isinstance(resourceIds,list) def check_query_zone(response): body = response.json() assert body.get("code") == 1 def check_query_cluster(response): body = response.json() print("####################################################") assert body.get("code") == 1 assert isinstance(body.get("rows"), list) #json 校验，暂未使用 def check_cluster_add(response): body = response.json() print(body) def check_physicalmachine_query_ok(response): body = response.json() print(body) assert body.get("code") == 1 #assert body.get("rows") def check_physical_update_ok(response): body = response.json() print(body) assert body.get("code") == 1 assert isinstance(body.get("resourceIds"), list) def check_stop_start_pysicalmachine_ok(response): body = response.json() assert body.get("code") == 1 assert isinstance(body.get("resourceIds"), list) # 校验查询主存储 def check_mainStorage_query_ok(response): body = response.json() assert body.get("code") == 1 assert isinstance(body.get("rows"), list) # 校验修改主存储 def check_mainStorage_update_ok(response): body = response.json() assert body.get("code") == 1 assert isinstance(body.get("resourceIds"), list) # 校验主存储添加集群查询集群列表 def check_query_clusterUnload_list_ok(response): body = response.json() assert body.get("code") == 1 assert isinstance(body.get("rows"), list) # 校验主存储添加集群 def check_mainStorage_addCluster_ok(response, clusterId, uri): assert response.json().get('code') == 1 #print(response.json()) result = Query()('SELECT * FROM `cl_host_inf` WHERE CLUSTERID="{0}" AND STATE=1 AND DELETED=0'.format(clusterId)) #print(result) #print(555555555555555555555555) username = "root" password = "user@dev" ip = "172.16.130.254" cmd = 'kubectl get vmp|grep "{0}"|wc -l'.format(uri) flag = False eventlet.monkey_patch() with eventlet.Timeout(180, False): while 1: time.sleep(0.5) ret = sshClient.tunction(ip=ip, username=username, password=password, cmd=cmd) if int(ret) == len(result): # print(1111) flag = True break assert flag def check_delete_mainStorage_ok(response, ids): id_list = ids.split(",") #result = Query()( # 'SELECT COUNT(*) FROM `cl_host_inf` WHERE CLUSTERID="{0}" AND STATE=1 AND DELETED=0'.format(clusterid)) username = "root" password = "user@dev" ip = "172.16.130.254" for id in id_list: cmd = 'kubectl get vmp|grep "{0}"|wc -l'.format(id) flag = False eventlet.monkey_patch() with eventlet.Timeout(180, False): while 1: time.sleep(0.5) ret = sshClient.tunction(ip=ip, username=username, password=password, cmd=cmd) try: if not int(ret): # print(1111) flag = True break except Exception as e: print(e) flag = True break assert flag def check_add_mirrorServer_ok(response): print(response.json()) username = "root" password = "user@dev" ip = "172.16.130.254" cmd = 'kubectl get vmp|grep vmdi|wc -l' flag = False eventlet.monkey_patch() with eventlet.Timeout(180, False): while 1: time.sleep(0.5) ret = sshClient.tunction(ip=ip, username=username, password=password, cmd=cmd) if int(ret) > 1: # print(1111) flag = True break assert flag #校验添加云主机成功 def check_cloudHost_add_ok(response): body = response.json() print(body) assert body.get("code") == 1 id = body.get("id") id_len = len(id.split(",")) id = id.replace(",", "|") username = "root" password = "user@dev" ip = "172.16.130.254" cmd = 'kubectl get vm|grep -E "{0}"|wc -l'.format(id) flag = False eventlet.monkey_patch() with eventlet.Timeout(180, False): while 1: time.sleep(0.5) ret = sshClient.tunction(ip=ip, username=username, password=password, cmd=cmd) if int(ret) == id_len: #print(1111) flag = True break assert flag #校验查询running状态的云主机 def check_query_vm_status_ok(response, state): #print("zheshi jjjjjj ", state) verify_rows = get_value("rows") num = 0 for row in verify_rows: if row.get("state") == state: num += 1 local_rows = response.json().get("rows") for row in local_rows: assert row.get("state") == state continue assert len(local_rows) == num def check_query_vm_ok(response, keyword, searchtype): searchtype_dict = {0: "name", 2: "hostip"} verify_rows = get_value("rows") #print(verify_rows,"f"*30) num = 0 for row in verify_rows: if keyword in row.get(searchtype_dict.get(searchtype)): num += 1 local_rows = response.json().get("rows") for row in local_rows: assert keyword in row.get(searchtype_dict.get(searchtype)) continue assert len(local_rows) == num def search_vmip_list(keyword): des_url = "http://172.16.130.254:38080/networkCard/query.do" vm_list = get_value("rows") #print(vm_list, "8"*10) vmid_list = [i.get("vmid") for i in vm_list] result = 0 cookie = login() for vmid in vmid_list: params = { "order": "asc", "offset": 0, "limit": 20, "vmid": vmid } res = requests.get(des_url, params=params, headers={"Content-Type": "application/x-www-form-urlencoded; charset=UTF-8", "Cookie": cookie}) #print(res.json()) rows = res.json().get("rows") for row in rows: if keyword in row.get("ip"): result += 1 return result def check_query_vm_ip_ok(response, keyword): cmp_num = search_vmip_list(keyword=keyword) rows = response.json().get("rows") #print(cmp_num, "hhhhhhh") #print(len(rows)) assert len(rows) == cmp_num def check_reboot_vm_ok(response): assert response.json().get("code") == 1 def check_pause_forceStop_stop_ok(response, state, hostip): vmid = response.json().get("id") username = "root" password = "user@dev" ip = hostip cmd = 'virsh list --all|grep {0}|grep -E "{1}"|wc -l'.format(state, vmid) flag = False eventlet.monkey_patch() with eventlet.Timeout(180, False): while 1: time.sleep(0.5) ret = sshClient.tunction(ip=ip, username=username, password=password, cmd=cmd) if int(ret): # print(1111) flag = True break assert flag des_url = "http://172.16.130.254:38080/networkCard/query.do" params = { "order": "asc", "offset": 0, "limit": 20, "searchtype": 0, "keyword": None, "state": None, } cookie = login() res = requests.get(des_url, params=params, headers={"Content-Type": "application/x-www-form-urlencoded; charset=UTF-8", "Cookie": cookie}) # print(res.json()) rows = res.json().get("rows") if state == "shut": st = "stopped" elif state == "paused": st = state else: st = "running" for row in rows: if row.get("vmid") == vmid: assert row.get("state") == st def check_all_vm_stop(response, ids): username = "root" password = "user@dev" ip = "172.16.130.254" for i in ids.split(","): cmd = 'kubectl get vm|grep {0}|grep -i shut|wc -l'.format(i) flag = False eventlet.monkey_patch() with eventlet.Timeout(180, False): while 1: time.sleep(0.5) ret = sshClient.tunction(ip=ip, username=username, password=password, cmd=cmd) if not (int(ret) - 3): # print(1111) flag = True break assert flag def check_cloudDisk_add_ok(response, template=0): id = response.json().get("id") username = "root" password = "user@dev" ip = "172.16.130.254" if template: cmd = 'find /var/lib/libvirt/cstor/ -name {0}|wc -l'.format(id) else: cmd = 'kubectl get vmd|grep {0}|wc -l'.format(id) flag = False eventlet.monkey_patch() with eventlet.Timeout(180, False): while 1: time.sleep(0.5) ret = sshClient.tunction(ip=ip, username=username, password=password, cmd=cmd) if int(ret): # print(1111) flag = True break assert flag def check_cloudDiskLoad_or_unload_ok(response, vmid, volumeid, typee=1): username = "root" password = "user@dev" ip = "172.16.130.254" cmd = 'kubectl get vm {0} -o yaml|grep {1}|wc -l'.format(vmid, volumeid) flag = False eventlet.monkey_patch() with eventlet.Timeout(180, False): while 1: time.sleep(0.5) ret = sshClient.tunction(ip=ip, username=username, password=password, cmd=cmd) if typee: if int(ret): # print(1111) flag = True break else: if not int(ret): # print(1111) flag = True break assert flag def check_cloudDisk_queryImageserver_ok(response): rows = response.json().get("rows") for row in rows: assert row.get("state") == 1 def check_cloudDisk_snapshot_add_ok(response): id = response.json().get('id') username = "root" password = "user@dev" ip = "172.16.130.254" cmd = 'kubectl get vmd|grep {0}|wc -l'.format(id) flag = False eventlet.monkey_patch() with eventlet.Timeout(180, False): while 1: time.sleep(0.5) ret = sshClient.tunction(ip=ip, username=username, password=password, cmd=cmd) if int(ret): flag = True break assert flag def check_cloudDisk_setQos_ok(response, vmid, rx, tx): assert response.json().get("id") username = "root" password = "user@dev" ip = "172.16.130.254" for i in [rx, tx]: cmd = "kubectl get vm {0} -i yaml|grep 'text: {1}'|wc -l".format(vmid, i*1024*1024) flag = False eventlet.monkey_patch() with eventlet.Timeout(180, False): while 1: time.sleep(0.5) ret = sshClient.tunction(ip=ip, username=username, password=password, cmd=cmd) if int(ret): flag = True break assert flag def check_cloudDisk_cancleQos_ok(response, vmid): assert response.json().get("id") username = "root" password = "user@dev" ip = "172.16.130.254" cmd = "kubectl get vm {0} -i yaml|grep -E 'write|read'|wc -l".format(vmid) flag = False eventlet.monkey_patch() with eventlet.Timeout(180, False): while 1: time.sleep(0.5) ret = sshClient.tunction(ip=ip, username=username, password=password, cmd=cmd) if int(ret): flag = True break assert flag def check_cloudDisk_expandVol_ok(response, installpath, size, hostip): assert response.json().get("id") username = "root" password = "user@dev" ip = hostip cmd = "qume-img info %s|grep virtual|awk '{print $3}'" % (installpath,) flag = False eventlet.monkey_patch() with eventlet.Timeout(180, False): while 1: time.sleep(0.5) ret = sshClient.tunction(ip=ip, username=username, password=password, cmd=cmd) if str(ret) == size: flag = True break assert flag #这个函数本来是用来验证存储迁移查询可选择的物理机列表的，但是开发傻逼，传参没传clusterid，导致这里无法验证 def verify_query_cluster_all_phymachine_ok(response): pass def check_cloudDisk_migrate_ok(response, installpath, pmip, msurl, msname): cloudDiskId = response.json().get("resourceIds")[0] username = "root" password = "user@dev" ip = pmip cmd = "kubectl get vmd|grep %s|awk '{print $3}'" % (cloudDiskId,) flag = False eventlet.monkey_patch() with eventlet.Timeout(180, False): while 1: time.sleep(0.5) ret = sshClient.tunction(ip=ip, username=username, password=password, cmd=cmd) if msurl in str(ret) and (msurl not in installpath): flag = True break assert flag des_url = "http://172.16.130.254:38080/cloudDisk/query.do" params = { "order": "asc", "offset": 0, "limit": 20, "searchtype": 0, "keyword": None, "state": None, } cookie = login() res = requests.get(des_url, params=params, headers={"Content-Type": "application/x-www-form-urlencoded; charset=UTF-8", "Cookie": cookie}) # print(res.json()) rows = res.json().get("rows") for row in rows: if row.get("volumeid") == cloudDiskId: assert row.get("msname") == msname break def check_query_cloudHost_loadable_or_unloadable_disk_ok(response, vmid, load=1): if load: sql_result = Query()("SELECT * FROM `cl_volume_inf` where STATE = 0 and VMID is null;") else: sql_result = Query()('SELECT * FROM `cl_volume_inf` where VMID="{0}" and TYPE=2;'.format(vmid,)) sql_volid_list = [x.get("VOLUMEID") for x in sql_result] json_volid_list = [x.get("volumeid") for x in response.json().get("rows")] assert len(sql_volid_list) == len(json_volid_list) for volid in sql_volid_list: assert volid in json_volid_list def check_cloudHost_setHa_ok(response, vmid, hostip, cancle=0): username = "root" password = "user@dev" ip = hostip cmd = 'kubectl get vm {0} -o yaml|grep -w ha|wc -l'.format(vmid) flag = False eventlet.monkey_patch() with eventlet.Timeout(180, False): while 1: time.sleep(0.5) ret = sshClient.tunction(ip=ip, username=username, password=password, cmd=cmd) if not cancle: if int(ret): flag = True break else: if not int(ret): flag = True break assert flag def check_cloudHost_makeSnapshot_ok(response, vmid, hostip): id = response.json().get("id") assert id username = "root" password = "user@dev" ip = hostip cmd = 'kubectl get vmd|grep {0}|wc -l'.format(vmid) flag = False eventlet.monkey_patch() with eventlet.Timeout(180, False): while 1: time.sleep(0.5) ret = sshClient.tunction(ip=ip, username=username, password=password, cmd=cmd) if int(ret): flag = True break assert flag def check_makeVMimage_ok(response, hostip): id = response.json().get("id") assert id username = "root" password = "user@dev" ip = hostip cmd = 'find / -name {0}|wc -l'.format(id) flag = False eventlet.monkey_patch() with eventlet.Timeout(180, False): while 1: time.sleep(0.5) ret = sshClient.tunction(ip=ip, username=username, password=password, cmd=cmd) if int(ret): flag = True break assert flag def check_modify_cpu_num_ok(response, cpunum_new, hostip): id = response.json().get("id") assert id username = "root" password = "user@dev" ip = hostip cmd = "virsh vcpucount %s|grep current|awk '{print $3}'|tail -1" % (id,) flag = False eventlet.monkey_patch() with eventlet.Timeout(180, False): while 1: time.sleep(0.5) ret = sshClient.tunction(ip=ip, username=username, password=password, cmd=cmd) if int(ret) == cpunum_new: flag = True break assert flag def check_modify_mem_ok(response, memorysize, hostip): #print(11111111111111111111111111111111111111111) #print(response.json()) id = response.json().get("id") #print("this is id....", id) assert id username = "root" password = "user@dev" ip = hostip cmd = "virsh dominfo %s|grep Use|awk '{print $3}'" % (id,) flag = False eventlet.monkey_patch() with eventlet.Timeout(180, False): while 1: time.sleep(0.5) ret = sshClient.tunction(ip=ip, username=username, password=password, cmd=cmd) if int(int(ret)/(1024*1024)) == memorysize: flag = True break assert flag def check_query_cmrom_iso(response, vmid): mirrorid_list = Query()('SELECT MIRRORID FROM `cl_mirror_inf` WHERE status=1 and MFORMAT="iso" AND ' 'DOMAINID=(SELECT DOMAINID FROM `cl_vm_inf` WHERE VMID="{0}") ' 'AND MIRRORID NOT IN (SELECT ISOID FROM `cl_vmcdrom_inf` WHERE' ' VMID="{1}")'.format(vmid, vmid)) rows = response.json().get("rows") assert len(mirrorid_list) == len(rows) for row in rows: assert row.get("mirrorid") in mirrorid_list def check_addCdrom_ok(vmid, mirrorid, hostip): username = "root" password = "user@dev" ip = hostip cmd = "kubectl get vm {0} -o yaml|grep {1}.iso|wc -l".format(vmid, mirrorid) flag = False eventlet.monkey_patch() with eventlet.Timeout(180, False): while 1: time.sleep(0.5) ret = sshClient.tunction(ip=ip, username=username, password=password, cmd=cmd) print("this is flag...", flag) if int(ret): flag = True break assert flag def check_changeBootSequence_ok(response, vmid, bootSeq, hostip): assert response.json().get("id") username = "root" password = "user@dev" ip = hostip cmd = "kubectl get vm {0} -o yaml|grep order|cut -d: -f 2".format(vmid, ) flag = False eventlet.monkey_patch() with eventlet.Timeout(180, False): while 1: time.sleep(0.5) ret = sshClient.tunction(ip=ip, username=username, password=password, cmd=cmd) ret = ret.decode("utf-8").replace("\n", "").replace(" ", "") if bootSeq == 1: if ret == "12": flag = True break elif bootSeq == 2: if ret == "21": flag = True break assert flag def check_changeSystem_querySystem_ok(response, vmid): mirrorid_list = Query()('SELECT MIRRORID FROM `cl_mirror_inf` WHERE status=1 and MFORMAT!="iso" AND ' 'DOMAINID=(SELECT DOMAINID FROM `cl_vm_inf` WHERE VMID="{0}") ' 'AND MIRRORID NOT IN (SELECT ISOID FROM `cl_vmcdrom_inf` WHERE' ' VMID="{1}")'.format(vmid, vmid)) rows = response.json().get("rows") assert len(mirrorid_list) == len(rows) for row in rows: assert row.get("mirrorid") in mirrorid_list def check_changeOs_ok(response, template_url, rootvolumeid, hostip): username = "root" password = "user@dev" ip = hostip cmd = "diff %s `kubectl get vmd %s|tail -1|awk '{print $3}'`|wc -l" % (template_url, rootvolumeid) flag = False eventlet.monkey_patch() with eventlet.Timeout(30, False): while 1: time.sleep(0.5) ret = sshClient.tunction(ip=ip, username=username, password=password, cmd=cmd) print("this is flag...", flag) if not int(ret): flag = True break assert flag def check_delete_mirror_all_ok(response): print(response.json()) def check_delete_mirrorServer_ok(response, mirrorServerId): print(response.json()) username = "root" password = "user@dev" ip = "172.16.130.254" cmd = "kubectl get vmp|grep {0}|wc -l".format(mirrorServerId) flag = False eventlet.monkey_patch() with eventlet.Timeout(30, False): while 1: time.sleep(0.5) ret = sshClient.tunction(ip=ip, username=username, password=password, cmd=cmd) print("this is flag...", flag) if not int(ret): flag = True break assert flag def check_delete_all_resource_ok(response, flag="vm"): username = "root" password = "user@dev" ip = "172.16.130.254" ids = response.json().get("id") ids_list = ids.split(",") for id in ids_list: if flag == "vm": cmd = "kubectl get vm|grep {0}|wc -l".format(id) else: cmd = "kubectl get vmp|grep {0}|wc -l".format(id) flag = False eventlet.monkey_patch() with eventlet.Timeout(30, False): while 1: time.sleep(0.5) ret = sshClient.tunction(ip=ip, username=username, password=password, cmd=cmd) #print("this is flag...", flag) if not int(ret): flag = True break assert flag def check_delete_net(response, l2vmn_num=2): username = "root" password = "user@dev" ip = "172.16.130.254" cmd = "kubectl get vmn|grep l2network|wc -l" flag = False eventlet.monkey_patch() with eventlet.Timeout(30, False): while 1: time.sleep(0.5) try: ret = sshClient.tunction(ip=ip, username=username, password=password, cmd=cmd) ret = int(ret) except Exception: flag = True break if int(l2vmn_num) - ret == 2: flag = True break assert flag #l2vmn check def check_creat_net_ok(response, l2vmn_num=0): username = "root" password = "user@dev" ip = "172.16.130.254" cmd = "kubectl get vmn|grep l2network|wc -l" flag = False eventlet.monkey_patch() with eventlet.Timeout(30, False): while 1: time.sleep(0.5) ret = sshClient.tunction(ip=ip, username=username, password=password, cmd=cmd) if int(ret) - int(l2vmn_num) == 2: flag = True break assert flag def check_creat_l3_net_ok(response): id = response.json().get("id") assert id username = "root" password = "user@dev" ip = "172.16.130.254" cmd = "kubectl get vmn|grep {0}|wc -l".format(id) flag = False eventlet.monkey_patch() with eventlet.Timeout(30, False): while 1: time.sleep(0.5) ret = sshClient.tunction(ip=ip, username=username, password=password, cmd=cmd) if int(ret): flag = True break assert flag def check_creat_vxlanPool_ok(response, pool_name): #print(response) try: code = response.json().get("code") if "1" in pool_name: assert -1 == code else: assert 1 == code except Exception as e: print(e) assert True def check_add_vxlan_vni_ok(response, flag): print(response.json()) if 3 == flag: assert response.json().get("code") == 1 if 2 == flag: assert response.json().get("code") == -1 if 1 == flag: assert response.json().get("code") == -1 def check_delete_vni_range_ok(response, vni_list, vnistart, endvni): for vni in vni_list.split(","): if vni in range(int(vnistart), int(endvni) + 1): assert -1 == response.json().get("code") assert 1 == response.json().get("code") def check_delete_vxlan_net_ok(response, vni, vxlan_clusterid_list): assert response.json().get("code") == 1 #print(vxlan_clusterid_list) #print(7777777777777777777777777) try: vxlan_clusterid_list = json.loads(vxlan_clusterid_list) except Exception: vxlan_clusterid_list = tuple(vxlan_clusterid_list.split(",")) #print(vxlan_clusterid_list) #print(66666666666666) if len(vxlan_clusterid_list) > 1: sql_cmd = 'SELECT HOSTIP FROM `cl_host_inf` WHERE STATE=1 AND DELETED=0 AND `STATUS`="Ready" and CLUSTERID IN {0};'.format(str(vxlan_clusterid_list)) else: sql_cmd = 'SELECT HOSTIP FROM `cl_host_inf` WHERE STATE=1 AND DELETED=0 AND `STATUS`="Ready" and CLUSTERID="{0}";'.format(vxlan_clusterid_list[0]) #print(sql_cmd) #print(555555555555555555555) result = Query()(sql_cmd) ip_list = [] for re in result: ip_list.append(re.get("HOSTIP")) username = "root" password = "user@dev" for ip in ip_list: cmd = "ovs-vsctl list-br|grep vx{0}|wc -l".format(vni) flag = False eventlet.monkey_patch() with eventlet.Timeout(30, False): while 1: time.sleep(0.1) ret = sshClient.tunction(ip=ip, username=username, password=password, cmd=cmd) if not int(ret): flag = True break assert flag def check_modify_l3network_mtu(response, mtu): id = response.json().get("id") cmd = "ovn-nbctl dhcp-options-get-options `ovn-nbctl show %s|grep dhcp|awk -F\"-\" '{print $3\"-\"$4\"-\"$5\"-\"$6\"-\"$7}'`|grep mtu|cut -d\"=\" -f2" % (id,) username = "root" password = "user@dev" ip = "172.16.130.254" flag = False eventlet.monkey_patch() with eventlet.Timeout(30, False): while 1: time.sleep(0.5) ret = sshClient.tunction(ip=ip, username=username, password=password, cmd=cmd) if int(ret) == int(mtu): flag = True break assert flag def check_l3network_add_dns(response, mtu, rows, nid, dns_addr): cmd = "ovn-nbctl dhcp-options-get-options `ovn-nbctl show %s|grep dhcp|awk -F\"-\" '{print $3\"-\"$4\"-\"$5\"-\"$6\"-\"$7}'`|grep -E 'mtu|dns'|sed ':a;N;s/\n/\t/;ba;'" % (nid,) dns_list = [row.get("dns") for row in rows] re_mtu = 0 re_dns_list = [] username = "root" password = "user@dev" ip = "172.16.130.254" flag = False eventlet.monkey_patch() with eventlet.Timeout(30, False): while 1: time.sleep(0.2) ret = sshClient.tunction(ip=ip, username=username, password=password, cmd=cmd) tp_str = ret.split() for i in tp_str: if "mtu" in i: re_mtu = int(i.split("=")[1]) elif "dns" in i: if "," in i: re_dns_list = i[12:-1].split(",") else: re_dns_list.append(i.split("=")[1]) assert int(mtu) == re_mtu assert dns_addr in re_dns_list flag_2 = True for dns in dns_list: if dns not in re_dns_list: flag_2 = False break if flag_2: flag = True break assert flag def check_vpc_network_add_ok(response): id = response.json().get("id") assert id cmd = "kubectl get vmn|grep {0}|wc -l".format(id,) username = "root" password = "user@dev" ip = "172.16.130.254" flag = False eventlet.monkey_patch() with eventlet.Timeout(40, False): while 1: time.sleep(0.5) ret = sshClient.tunction(ip=ip, username=username, password=password, cmd=cmd) if 1 == int(ret): flag = True break assert flag def check_vpc_router_stop_or_start(response): id = response.json().get("id") cmd = "kubectl get vm|grep {0}|grep -i shut|wc -l".format(id, ) username = "root" password = "user@dev" ip = "172.16.130.254" flag = False eventlet.monkey_patch() with eventlet.Timeout(100, False): while 1: time.sleep(0.5) ret = sshClient.tunction(ip=ip, username=username, password=password, cmd=cmd) if 1 == int(ret): flag = True break assert flag def check_setConsolePasswd_ok(response, hostip, passwd=None): id = response.json().get("id") if passwd: cmd = 'cat /tmp/%s.xml |grep passwd|awk -F"passwd=" \'{print $2}\'|cut -d"\"" -f2' % (id,) else: cmd = 'cat /tmp/%s.xml |grep passwd|wc -l' % (id,) username = "root" password = "user@dev" ip = hostip flag = False eventlet.monkey_patch() with eventlet.Timeout(30, False): while 1: time.sleep(0.5) ret = sshClient.tunction(ip=ip, username=username, password=password, cmd=cmd) if passwd: if ret == str(passwd): flag = True break else: if not int(ret): flag = True break assert flag def check_modifyCpuNum_ok(response, hostip, cpunum): id = response.json().get("id") cmd = "virsh vcpucount %s|grep current|grep live|awk '{print $3}'" % (id,) username = "root" password = "user@dev" ip = hostip flag = False eventlet.monkey_patch() with eventlet.Timeout(30, False): while 1: time.sleep(0.5) ret = sshClient.tunction(ip=ip, username=username, password=password, cmd=cmd) if int(ret) == int(cpunum): flag = True break assert flag def check_modifyVpcMem_ok(response, memory, hostip): id = response.json().get("id") cmd = "virsh dominfo %s|grep 'Used mem'|awk '{print $3}'" % (id,) username = "root" password = "user@dev" ip = hostip flag = False eventlet.monkey_patch() with eventlet.Timeout(30, False): while 1: time.sleep(0.5) ret = sshClient.tunction(ip=ip, username=username, password=password, cmd=cmd) if int(ret)/(1024*1024) == int(memory): flag = True break assert flag

31.8917

180

0.564061

0

6,105

0.192314

a72993531283fe9cd45b23f3481f393933bdc390

15,777

py

Python

main.py

chilipolygon/Amazon-Requests-Module

20fcfa9b9764e097bc107aa9dc5b0db772ce3ad9

[ "Apache-2.0" ]

3

2022-01-18T20:54:08.000Z

2022-02-05T23:27:13.000Z

main.py

chilipolygon/Amazon-Requests-Module

20fcfa9b9764e097bc107aa9dc5b0db772ce3ad9

[ "Apache-2.0" ]

null

main.py

chilipolygon/Amazon-Requests-Module

20fcfa9b9764e097bc107aa9dc5b0db772ce3ad9

[ "Apache-2.0" ]

null

# --------------------- from bs4 import BeautifulSoup as bs import requests import urllib3 import urllib from urllib.parse import unquote import re import os import sys import json import time from colorama import Fore, init from pprint import pprint from datetime import datetime import uuid import threading # ---------------------- from dhooks import Webhook from dhooks import Webhook, Embed # --------------------- init() init(autoreset=True) urllib3.disable_warnings() os.system('cls' if os.name == 'nt' else 'clear') # --------------------- # MUST HAVE PRIME # MUST HAVE ONE CLICK # MUST SELECT "Keep me signed in" # MUST USE AGED ACCOUNT # ==================================== # MUST HAVE THESE FOR BEST SUCCESS class main: def __init__(self, sku, code, account) -> None: self.account = account f = open(f'./appdata/cookies.json') self.cookies = json.load(f) self.sku = sku self.code = code print(Fore.WHITE + f"Session: {self.account} || " + Fore.YELLOW + '[+] Making Session') self.session = requests.Session() print(Fore.WHITE + f"Session: {self.account} || " + Fore.YELLOW + '[+] Fetching Cookies') for cookie in self.cookies: self.session.cookies.set( self.cookies[cookie]['name'], self.cookies[cookie]['value']) self.productPage() def productPage(self): print(Fore.WHITE + f"Session: {self.account} || " + Fore.YELLOW + '[+] Getting Product Page') self.asin_page = self.session.get( 'https://smile.amazon.com/dp/' + str(self.sku), headers={ "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/93.0.4577.82 Safari/537.36"} ) print(Fore.WHITE + f"Session: {self.account} || " + Fore.YELLOW + '[*] Getting Product Price:', end=" ") soup = bs(self.asin_page.text, "lxml") self.og_price = soup.find( 'span', {'class': 'a-offscreen'}).getText().strip() print(f'{self.og_price}') print(Fore.WHITE + f"Session: {self.account} || " + Fore.YELLOW + '[*] Getting Session ID:', end=" ") self.session_id = self.asin_page.text.split( 'id="session-id" name="session-id" value="')[1].split('"')[0] print(f'{self.session_id}') try: print(Fore.WHITE + f"Session: {self.account} || " + Fore.YELLOW + '[*] Getting Offer Id:', end=" ") self.offerListingId = re.search( "&offerListingId=(.*?)\&", self.asin_page.text).group(1) print(f'{self.offerListingId}') self.promoPage() # if we find an OID, it means the the listing have an UNREDEEMED coupon except Exception as e: # This error will occur when the coupon is redeemed OR there is no coupon print(Fore.RED + '[-] Coupon Clipped') self.addToCart() pass def promoPage(self): headers = { "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/93.0.4577.82 Safari/537.36", 'sec-fetch-dest': 'document', 'sec-fetch-mode': 'navigate', 'sec-fetch-site': 'none', 'sec-fetch-user': '?1', 'upgrade-insecure-requests': '1', 'accept-encoding': 'gzip, deflate, br', } self.productPage = self.session.get( f'https://smile.amazon.com/gp/aod/ajax/ref=auto_load_aod?asin={self.sku}', headers=headers) print(Fore.WHITE + f"Session: {self.account} || " + Fore.YELLOW + '[+] Getting Promo Object') self.promoObj = { 'promoId': re.search("&promotionId=(.*?)\&", self.productPage.text).group(1), 'merchantID': re.search(";seller=(.*?)\&", self.productPage.text).group(1), 'sku': re.search("&sku=(.*?)\&", self.productPage.text).group(1), 'anti-csrftoken-a2z': re.search("&anti-csrftoken-a2z=(.*?)\'", self.productPage.text).group(1) } for i in self.promoObj: print(Fore.WHITE + f"Session: {self.account} || " + Fore.YELLOW + f'[*] {i.title()}: ' + Fore.WHITE + f'{self.promoObj[i]}') self.clipCoupon() # --------------------- def clipCoupon(self): headers = { 'anti-csrftoken-a2z': unquote(self.promoObj['anti-csrftoken-a2z']), 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/93.0.4577.82 Safari/537.36', 'Sec-Fetch-Site': 'same-origin', 'Sec-Fetch-Mode': 'cors', 'Sec-Fetch-Dest': 'empty', 'x-requested-with': 'XMLHttpRequest', 'referer': f'https://www.amazon.com/dp/{self.sku}' } params = { 'promotionId': self.promoObj['promoId'], 'asin': self.sku, 'offerListingId': self.offerListingId, 'sku': self.promoObj['sku'], 'anti-csrftoken-a2z': unquote(self.promoObj['anti-csrftoken-a2z']), 'source': 'dp_cxcw' } print(Fore.WHITE + f"Session: {self.account} || " + Fore.YELLOW + '[+] Clipping Coupon') promoUrl = f'https://www.amazon.com/promotion/redeem/?{urllib.parse.urlencode(params)}' while True: clipCoupon = self.session.get(promoUrl, headers=headers) if 'SUCCESS' in clipCoupon.text: print( Fore.WHITE + f"Session: {self.account} || " + Fore.GREEN + '[+] Coupon Clipped') break self.addToCart() def addToCart(self): headers = { 'Connection': 'keep-alive', 'sec-ch-ua': '" Not;A Brand";v="99", "Google Chrome";v="91", "Chromium";v="91"', 'x-amz-checkout-entry-referer-url': 'https://smile.amazon.com/dp/' + self.sku, 'x-amz-turbo-checkout-dp-url': 'https://smile.amazon.com/dp/' + self.sku, 'sec-ch-ua-mobile': '?0', 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/93.0.4577.82 Safari/537.36', 'x-amz-support-custom-signin': '1', 'x-amz-checkout-csrf-token': self.session_id, 'Origin': 'https://smile.amazon.com', 'Sec-Fetch-Site': 'same-origin', 'Sec-Fetch-Mode': 'cors', 'Sec-Fetch-Dest': 'empty', 'Referer': 'https://smile.amazon.com/dp/' + self.sku } payload = { 'addressID': 'nmqgnomolpkq', 'isAsync': '1', 'quantity.1': '1', } print(Fore.WHITE + f"Session: {self.account} || " + Fore.YELLOW + '[+] Adding To Cart') while True: try: self.session_atc = self.session.post( f'https://smile.amazon.com/checkout/turbo-initiate?ref_=dp_start-bbf_1_glance_buyNow_2-1&referrer=detail&pipelineType=turbo&clientId=retailwebsite&weblab=RCX_CHECKOUT_TURBO_DESKTOP_PRIME_87783&temporaryAddToCart=1&asin.1={self.sku}', data=payload, headers=headers ) break except self.session_atc.status_code != 200: print(Fore.WHITE + f"Session: {self.account} || " + Fore.RED + '[-] Error Adding To Cart', end=" ") time.sleep(1) print( Fore.WHITE + f"Session: {self.account} || " + Fore.RED + '[-] Retrying', end=" ") print( Fore.WHITE + f"Session: {self.account} || " + Fore.GREEN + '[+] Added to Cart') checkout_url_tuple = re.search( '\/(.*)shipmentId=(.*)\d', self.session_atc.text).group(0) self.checkout_url_str = ''.join(checkout_url_tuple) print(Fore.WHITE + f"Session: {self.account} || " + Fore.YELLOW + '[*] Getting PID:', end=" ") self.pid = re.search( "pid=(.*?)\&", str(self.checkout_url_str)).group(1) print(f'{self.pid}') print(Fore.WHITE + f"Session: {self.account} || " + Fore.YELLOW + '[*] Getting Anti CSRF:', end=" ") self.AntiCSRF = re.search( "anti-csrftoken-a2z'.value='(.*?)\'", str(self.session_atc.text)).group(1) print(f'{self.AntiCSRF}') # use this to checkout print(Fore.WHITE + f"Session: {self.account} || " + Fore.YELLOW + '[*] Getting SID:', end=" ") self.sid = re.search( "'CacheDetection.RequestID': \"(.*?)\",", self.session_atc.text).group(1) print(f'{self.sid}') if not self.code: # check if there is no code print( Fore.WHITE + f"Session: {self.account} || " + Fore.RED + '[-] No Code Found') self.checkSummary() else: self.claimCode() def claimCode(self): if '' in self.code: return else: headers = { 'Connection': 'keep-alive', 'sec-ch-ua': '" Not;A Brand";v="99", "Google Chrome";v="91", "Chromium";v="91"', 'x-amz-checkout-entry-referer-url': 'https://smile.amazon.com/dp/' + self.sku, 'anti-csrftoken-a2z': self.AntiCSRF, 'sec-ch-ua-mobile': '?0', 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/93.0.4577.82 Safari/537.36', 'x-amz-checkout-csrf-token': self.session_id, 'Origin': 'https://smile.amazon.com', 'Sec-Fetch-Site': 'same-origin', 'Sec-Fetch-Mode': 'cors', 'Sec-Fetch-Dest': 'empty', 'Referer': 'https://smile.amazon.com/checkout/pay?pid=' + self.pid + '&pipelineType=turbo&clientId=retailwebsite&temporaryAddToCart=1&hostPage=detail&weblab=RCX_CHECKOUT_TURBO_DESKTOP_PRIME_87783' } payload = { 'claimcode': self.code, 'isClientTimeBased': '1' } print( Fore.WHITE + f"Session: {self.account} || " + Fore.YELLOW + '[*] Applying Code') claimurl = f'https://smile.amazon.com/checkout/pay/add-gc-promo?ref_=chk_pay_addGcPromo&referrer=pay&temporaryAddToCart=1&hostPage=detail&weblab=RCX_CHECKOUT_TURBO_DESKTOP_PRIME_87783&_srcRID={self.sid}&clientId=retailwebsite&pipelineType=turbo&pid={self.pid}' claim = self.session.post( claimurl, headers=headers, data=payload, allow_redirects=True) with open("./html/claimCode.html", "w", encoding='utf-8') as f: f.write(claim.text) self.checkSummary() def checkSummary(self): headers = { 'sec-fetch-dest': 'document', 'sec-fetch-mode': 'navigate', 'sec-fetch-site': 'none', 'sec-fetch-user': '?1', 'upgrade-insecure-requests': '1', 'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/93.0.4577.82 Safari/537.36' } print(Fore.WHITE + f"Session: {self.account} || " + Fore.GREEN + '[+] Reviewing Summary') summaryUrl = f'https://www.amazon.com/checkout/ordersummary?ref_=chk_spc_select__summary&referrer=spc&pid={self.pid}&pipelineType=turbo&clientId=retailwebsite&temporaryAddToCart=1&hostPage=detail&weblab=RCX_CHECKOUT_TURBO_DESKTOP_PRIME_87783' summary = self.session.get(summaryUrl, headers=headers) soup = bs(summary.text, "lxml") self.finalPrice = soup.find( 'td', {'class': 'a-color-price a-text-right a-align-bottom a-text-bold a-nowrap'}).getText().strip() print(Fore.WHITE + f"Session: {self.account} || " + Fore.YELLOW + '[+] Order Total: ' + Fore.WHITE + f'{self.finalPrice}') self.checkout() def checkout(self): print(Fore.WHITE + f"Session: {self.account} || " + Fore.GREEN + '[+] Checking Out') headers = { 'accept-encoding': 'gzip, deflate, br', 'accept-language': 'en-US,en;q=0.9', 'sec-ch-ua-mobile': '?0', 'sec-fetch-dest': 'document', 'sec-fetch-mode': 'cors', 'sec-fetch-site': 'same-origin', 'sec-fetch-user': '?1', 'upgrade-insecure-requests': '1', 'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/93.0.4577.82 Safari/537.36', 'anti-csrftoken-a2z': self.AntiCSRF } payload = { 'x-amz-checkout-csrf-token': self.session_id, 'ref_': 'chk_summary_placeOrder', 'referrer': 'summary', 'pid': self.pid, 'pipelineType': 'turbo', 'clientId': 'retailwebsite', 'temporaryAddToCart': 1, 'hostPage': 'detail', 'weblab': 'RCX_CHECKOUT_TURBO_DESKTOP_PRIME_87783', 'isClientTimeBased': 1 } params = { 'ref_': 'chk_summary_placeOrder', '_srcRID': self.sid, 'clientId': 'retailwebsite', 'pipelineType': 'turbo', 'pid': self.pid } print(Fore.WHITE + f"Session: {self.account} || " + Fore.YELLOW + '[*] Status: ', end=' ') checkoutUrl = f'https://www.amazon.com/checkout/spc/place-order?{urllib.parse.urlencode(params)}' checkout = self.session.post( checkoutUrl, data=payload, headers=headers) if checkout.status_code == 200: print(Fore.GREEN + 'Success') self.sendWebhook(self.sku, self.finalPrice) else: print(f'something went wrong {checkout.text}') def sendWebhook(self, sku, finalPrice): soup = bs(self.asin_page.text, "lxml") title = soup.find('span', {'id': 'productTitle'}).getText().strip() a = soup.find('div', {'id': 'imgTagWrapperId'}) if a.img: img = a.img['src'] price = soup.find('span', {'class': 'a-offscreen'}).getText().strip() product_url = f'https://www.amazon.com/dp/{sku}?tag=Chili' f = open('./appdata/config.json') data = json.load(f) url = data['webhook'] hook = Webhook(url) embed = Embed( color=0x8AFF8A, timestamp='now' ) embed.set_title( title='🎉Successful Checkout') embed.set_thumbnail(img) embed.add_field( name='Item', value=f'[{title}]({product_url})', inline=False) embed.add_field(name='Original Price', value=f'{price}', inline=False) embed.add_field(name='Check Out Price', value=f'{finalPrice}', inline=False) embed.add_field( name='Account', value=f'||{self.account.replace(".json", "")}||', inline=False) embed.set_footer( text='Made by #chili9999') print(Fore.GREEN + '[+] Sending Webhook') hook.send(embed=embed) def callback(account: str): sku = input('Put in a product asin:') promo = input('Put in a product promo code, if none, press Enter:') threads = [] threads.append(threading.Thread( target=main, args=[sku, promo, account])) for thread in threads: thread.start() time.sleep(.1) for thread in threads: thread.join() if __name__ == "__main__": f = open(f'./appdata/config.json') account = json.load(f)['account'] callback(account) # asin, promo code, email # if you don't have a promocode, leave it as ''

41.518421

272

0.548647

14,473

0.917174

0

7,060

0.447402

a72b62dfb661d28b942c1bbe2cd44f6d11909efd

10,504

py

Python

tests/test_word_distance.py

hasibaasma/alfpy

c8c0c1300108015746320cede2207ac57e630d3e

[ "MIT" ]

19

2017-02-20T17:42:02.000Z

2021-12-16T19:07:17.000Z

tests/test_word_distance.py

eggleader/alfpy

e0782e9551458ef17ab29df8af13fc0f8925e894

[ "MIT" ]

3

2018-03-12T23:54:27.000Z

2020-12-09T21:53:19.000Z

tests/test_word_distance.py

eggleader/alfpy

e0782e9551458ef17ab29df8af13fc0f8925e894

[ "MIT" ]

6

2016-12-06T09:12:04.000Z

2021-09-24T14:40:47.000Z

import unittest from alfpy import word_pattern from alfpy import word_vector from alfpy import word_distance from alfpy.utils import distmatrix from . import utils class DistanceTest(unittest.TestCase, utils.ModulesCommonTest): def __init__(self, *args, **kwargs): super(DistanceTest, self).__init__(*args, **kwargs) utils.ModulesCommonTest.set_test_data() self.pattern = word_pattern.create(self.dna_records.seq_list, 2) self.counts = word_vector.Counts(self.dna_records.length_list, self.pattern) self.freqs = word_vector.Freqs(self.dna_records.length_list, self.pattern) def test_angle_cos_diss_freqs(self): # The result of this method is identical to that from decaf+py. dist = word_distance.Distance(self.freqs, 'angle_cos_diss') matrix = distmatrix.create(self.dna_records.id_list, dist) data = [' 3', 'seq1 0.0000000 0.2797355 0.1500672', 'seq2 0.2797355 0.0000000 0.1261027', 'seq3 0.1500672 0.1261027 0.0000000'] self.assertEqual(matrix.format(), "\n".join(data)) def test_angle_cos_evol_freqs(self): # The result of this method is identical to that from decaf+py. dist = word_distance.Distance(self.freqs, 'angle_cos_evol') matrix = distmatrix.create(self.dna_records.id_list, dist) data = [' 3', 'seq1 0.0000000 0.3281368 0.1625980', 'seq2 0.3281368 0.0000000 0.1347925', 'seq3 0.1625980 0.1347925 0.0000000'] self.assertEqual(matrix.format(), "\n".join(data)) def test_diff_abs_add_freqs(self): # The result of this method is identical to that from decaf+py. dist = word_distance.Distance(self.freqs, 'diff_abs_add') matrix = distmatrix.create(self.dna_records.id_list, dist) data = [' 3', 'seq1 0.0000000 0.0810458 0.0507937', 'seq2 0.0810458 0.0000000 0.0526611', 'seq3 0.0507937 0.0526611 0.0000000'] self.assertEqual(matrix.format(), "\n".join(data)) def test_diff_abs_mult1_freqs(self): # The result of this method is identical to that from decaf+py. dist = word_distance.Distance(self.freqs, 'diff_abs_mult1') matrix = distmatrix.create(self.dna_records.id_list, dist) data = [' 3', 'seq1 0.0000000 0.0621975 0.0501075', 'seq2 0.0621975 0.0000000 0.0955847', 'seq3 0.0501075 0.0955847 0.0000000'] self.assertEqual(matrix.format(), "\n".join(data)) def test_diff_abs_mult2_freqs(self): # The result of this method is identical to that from decaf+py. dist = word_distance.Distance(self.freqs, 'diff_abs_mult2') matrix = distmatrix.create(self.dna_records.id_list, dist) data = [' 3', 'seq1 0.0000000 0.0621975 0.0404611', 'seq2 0.0621975 0.0000000 0.0531478', 'seq3 0.0404611 0.0531478 0.0000000'] self.assertEqual(matrix.format(), "\n".join(data)) def test_euclid_seqlen1_freqs(self): # The result of this method is identical to that from decaf+py. dist = word_distance.Distance(self.freqs, 'euclid_seqlen1') matrix = distmatrix.create(self.dna_records.id_list, dist) data = [' 3', 'seq1 0.0000000 0.0065879 0.0032065', 'seq2 0.0065879 0.0000000 0.0041065', 'seq3 0.0032065 0.0041065 0.0000000'] self.assertEqual(matrix.format(), "\n".join(data)) def test_euclid_seqlen2_freqs(self): # The result of this method is identical to that from decaf+py. dist = word_distance.Distance(self.freqs, 'euclid_seqlen2') matrix = distmatrix.create(self.dna_records.id_list, dist) data = [' 3', 'seq1 0.0000000 0.0072101 0.0038263', 'seq2 0.0072101 0.0000000 0.0039866', 'seq3 0.0038263 0.0039866 0.0000000'] self.assertEqual(matrix.format(), "\n".join(data)) def test_manhattan_freqs(self): dist = word_distance.Distance(self.freqs, 'manhattan') matrix = distmatrix.create(self.dna_records.id_list, dist) data = [ " 3", "seq1 0.0000000 1.2156863 0.7619048", "seq2 1.2156863 0.0000000 0.7899160", "seq3 0.7619048 0.7899160 0.0000000" ] self.assertEqual(matrix.format(), "\n".join(data)) def test_chebyshev_freqs(self): dist = word_distance.Distance(self.freqs, 'chebyshev') matrix = distmatrix.create(self.dna_records.id_list, dist) data = [ " 3", "seq1 0.0000000 0.1936275 0.1250000", "seq2 0.1936275 0.0000000 0.1428571", "seq3 0.1250000 0.1428571 0.0000000" ] self.assertEqual(matrix.format(), "\n".join(data)) def test_braycurtis_freqs(self): dist = word_distance.Distance(self.freqs, 'braycurtis') matrix = distmatrix.create(self.dna_records.id_list, dist) data = [ " 3", "seq1 0.0000000 0.6078431 0.3809524", "seq2 0.6078431 0.0000000 0.3949580", "seq3 0.3809524 0.3949580 0.0000000" ] self.assertEqual(matrix.format(), "\n".join(data)) def test_diff_abs_mult_freqs(self): dist = word_distance.Distance(self.freqs, 'diff_abs_mult') matrix = distmatrix.create(self.dna_records.id_list, dist) data = [ " 3", "seq1 0.0000000 0.0621975 0.0404611", "seq2 0.0621975 0.0000000 0.0531478", "seq3 0.0404611 0.0531478 0.0000000" ] self.assertEqual(matrix.format(), "\n".join(data)) def test_kld_freqs(self): dist = word_distance.Distance(self.freqs, 'kld') matrix = distmatrix.create(self.dna_records.id_list, dist) data = [ " 3", "seq1 0.0000000 0.0932800 0.0435210", "seq2 0.0932800 0.0000000 0.0447391", "seq3 0.0435210 0.0447391 0.0000000" ] self.assertEqual(matrix.format(), "\n".join(data)) def test_lcc_freqs(self): dist = word_distance.Distance(self.freqs, 'lcc') matrix = distmatrix.create(self.dna_records.id_list, dist) data = [ " 3", "seq1 0.0000000 0.6205496 0.4017554", "seq2 0.6205496 0.0000000 0.2550506", "seq3 0.4017554 0.2550506 0.0000000" ] self.assertEqual(matrix.format(), "\n".join(data)) def test_canberra_freqs(self): dist = word_distance.Distance(self.freqs, 'canberra') matrix = distmatrix.create(self.dna_records.id_list, dist) data = [ " 3", "seq1 0.0000000 10.3372258 7.1836838", "seq2 10.3372258 0.0000000 6.6280959", "seq3 7.1836838 6.6280959 0.0000000" ] self.assertEqual(matrix.format(), "\n".join(data)) def test_minkowski_freqs(self): dist = word_distance.Distance(self.freqs, 'minkowski') matrix = distmatrix.create(self.dna_records.id_list, dist) data = [ " 3", "seq1 0.0000000 0.3763512 0.2532387", "seq2 0.3763512 0.0000000 0.2603008", "seq3 0.2532387 0.2603008 0.0000000" ] self.assertEqual(matrix.format(), "\n".join(data)) def test_minkowski_throws_exception(self): dist = word_distance.Distance(self.freqs, 'minkowski') with self.assertRaises(Exception) as context: dist.pwdist_minkowski(0, 1, 0.2) self.assertIn('p must be at least 1', str(context.exception)) def test_jsd_freqs(self): dist = word_distance.Distance(self.freqs, 'jsd') matrix = distmatrix.create(self.dna_records.id_list, dist) data = [ " 3", "seq1 0.0000000 0.4608882 0.2550278", "seq2 0.4608882 0.0000000 0.2457790", "seq3 0.2550278 0.2457790 0.0000000" ] self.assertEqual(matrix.format(), "\n".join(data)) def test_euclid_squared_freqs(self): # The result of this method is identical to that from decaf+py. dist = word_distance.Distance(self.freqs, 'euclid_squared') matrix = distmatrix.create(self.dna_records.id_list, dist) data = [' 3', 'seq1 0.0000000 0.1416402 0.0641298', 'seq2 0.1416402 0.0000000 0.0677565', 'seq3 0.0641298 0.0677565 0.0000000'] self.assertEqual(matrix.format(), "\n".join(data)) def test_euclid_norm_counts(self): # The result of this method is identical to that from decaf+py. dist = word_distance.Distance(self.counts, 'euclid_norm') matrix = distmatrix.create(self.dna_records.id_list, dist) data = [' 3', 'seq1 0.0000000 7.5498344 5.4772256', 'seq2 7.5498344 0.0000000 4.3588989', 'seq3 5.4772256 4.3588989 0.0000000'] self.assertEqual(matrix.format(), "\n".join(data)) def test_euclid_norm_freqs(self): # The result of this method is identical to that from decaf+py. dist = word_distance.Distance(self.freqs, 'euclid_norm') matrix = distmatrix.create(self.dna_records.id_list, dist) data = [' 3', 'seq1 0.0000000 0.3763512 0.2532387', 'seq2 0.3763512 0.0000000 0.2603008', 'seq3 0.2532387 0.2603008 0.0000000'] self.assertEqual(matrix.format(), "\n".join(data)) def test_google_freqs(self): dist = word_distance.Distance(self.freqs, 'google') matrix = distmatrix.create(self.dna_records.id_list, dist) data = [' 3', 'seq1 0.0000000 0.6078431 0.3809524', 'seq2 0.6078431 0.0000000 0.3949580', 'seq3 0.3809524 0.3949580 0.0000000'] self.assertEqual(matrix.format(), "\n".join(data)) if __name__ == '__main__': unittest.main()

43.949791

77

0.58035

10,286

0.979246

0

3,640

0.346535

a72d7496d5e3f428cdf8342b764e52a9a68ac6a0

3,092

py

Python

cdparser/Features.py

opengulf/nyc-directories-support-scripts

e22582b8f4cb3c365e9aac1d860d9c36831277a5

[ "MIT" ]

1

2021-09-07T20:41:00.000Z

cdparser/Features.py

opengulf/nyc-directories-support-scripts

e22582b8f4cb3c365e9aac1d860d9c36831277a5

[ "MIT" ]

null

cdparser/Features.py

opengulf/nyc-directories-support-scripts

e22582b8f4cb3c365e9aac1d860d9c36831277a5

[ "MIT" ]

2

2021-09-07T20:49:14.000Z

2021-11-05T02:03:47.000Z

from functools import partial class Features: @staticmethod def __emit_word_features(rel_pos, word): features = {} for f in Features.__word_feature_functions().items(): features.update({str(rel_pos) + ":" + f[0]: f[1](word)}) return features @staticmethod def get_word_features(sentence,i): features = {} for x in range(i - 2, i + 3): if 0 <= x < len(sentence): features.update(Features.__emit_word_features(-(i - x), sentence[x][0])) if i == 0: features.update({'BOS' : True}) if i == len(sentence) - 1: features.update({'EOS': True}) return features @staticmethod def __word_feature_functions(): return { "word.junior": Features.__is_junior_token, "word.widow.token": Features.__is_widow_token, "word.contains.digit": Features.__contains_digit, "word.is.delimiter": Features.__is_delimiter, "word.is.start.token": Features.__is_start, "word.is.end.token": Features.__is_end, "word.is.lower": str.islower, "word.is.title": str.istitle, "word.is.upper": str.isupper, "word.substr[-2:]" : partial(Features.__substr, 2), "word.substr[-1:]": partial(Features.__substr, 1) } @staticmethod def get_sentence_features(sentence): return [Features.get_word_features(sentence, i) for i in range(len(sentence))] @staticmethod def get_sentence_labels(sentence): return [label for token, label in sentence] @staticmethod def get_sentence_tokens(sentence): return [token for token, label in sentence] @staticmethod def __contains_digit(input): for c in input: if c.isdigit(): return True return False @staticmethod def __substr(amount, word): return word[amount:] @staticmethod def __is_start(input): if input == "START": return True return False @staticmethod def __is_end(input): if input == "END": return True return False @staticmethod def __is_delimiter(input): for c in input: if c == '.' or c == ',': return True return False @staticmethod def __is_known_position_adj(input): if len(input) == 1: if input == 'h' or input == 'r': return True return False @staticmethod def __is_junior_token(input): dc = input.lower() if dc == "jr": return True return False @staticmethod def __segment_of_sentence(sent, i, div): sent_length = len(sent) pos = i + 1 for j in range(1,div + 1): if pos <= j*(sent_length / float(div)): return j @staticmethod def __is_widow_token(input): dc = input.lower() if dc == "wid" or dc == "widow": return True return False

28.366972

88

0.559185

3,061

0.989974

0

2,956

0.956016

0

245

0.079237

a73018c4b01cc941e04ea8bb39a52a6d8c243fb6

10,631

py

Python

IRIS_data_download/IRIS_download_support/obspy/core/tests/test_util_attribdict.py

earthinversion/Fnet_IRIS_data_automated_download

09a6e0c992662feac95744935e038d1c68539fa1

[ "MIT" ]

2

2020-03-05T01:03:01.000Z

2020-12-17T05:04:07.000Z

IRIS_data_download/IRIS_download_support/obspy/core/tests/test_util_attribdict.py

earthinversion/Fnet_IRIS_data_automated_download

09a6e0c992662feac95744935e038d1c68539fa1

[ "MIT" ]

4

2021-03-31T19:25:55.000Z

2021-12-13T20:32:46.000Z

IRIS_data_download/IRIS_download_support/obspy/core/tests/test_util_attribdict.py

earthinversion/Fnet_IRIS_data_automated_download

09a6e0c992662feac95744935e038d1c68539fa1

[ "MIT" ]

2

2020-09-08T19:33:40.000Z

2021-04-05T09:47:50.000Z

# -*- coding: utf-8 -*- from __future__ import (absolute_import, division, print_function, unicode_literals) from future.builtins import * # NOQA @UnusedWildImport import unittest from obspy.core import AttribDict class AttribDictTestCase(unittest.TestCase): """ Test suite for obspy.core.util.attribdict """ def test_pop(self): """ Tests pop method of AttribDict class. """ ad = AttribDict() ad.test = 1 ad['test2'] = 'test' # removing via pop temp = ad.pop('test') self.assertEqual(temp, 1) self.assertFalse('test' in ad) self.assertIn('test2', ad) self.assertFalse('test' in ad.__dict__) self.assertIn('test2', ad.__dict__) self.assertFalse(hasattr(ad, 'test')) self.assertTrue(hasattr(ad, 'test2')) # using pop() for not existing element raises a KeyError self.assertRaises(KeyError, ad.pop, 'test') def test_popitem(self): """ Tests pop method of AttribDict class. """ ad = AttribDict() ad['test2'] = 'test' # removing via popitem temp = ad.popitem() self.assertEqual(temp, ('test2', 'test')) self.assertFalse('test2' in ad) self.assertFalse('test2' in ad.__dict__) self.assertFalse(hasattr(ad, 'test2')) # popitem for empty AttribDict raises a KeyError self.assertRaises(KeyError, ad.popitem) def test_delete(self): """ Tests delete method of AttribDict class. """ ad = AttribDict() ad.test = 1 ad['test2'] = 'test' # deleting test using dictionary del ad['test'] self.assertFalse('test' in ad) self.assertIn('test2', ad) self.assertFalse('test' in ad.__dict__) self.assertIn('test2', ad.__dict__) self.assertFalse(hasattr(ad, 'test')) self.assertTrue(hasattr(ad, 'test2')) # deleting test2 using attribute del ad.test2 self.assertFalse('test2' in ad) self.assertFalse('test2' in ad.__dict__) self.assertFalse(hasattr(ad, 'test2')) def test_init(self): """ Tests initialization of AttribDict class. """ ad = AttribDict({'test': 'NEW'}) self.assertEqual(ad['test'], 'NEW') self.assertEqual(ad.test, 'NEW') self.assertEqual(ad.get('test'), 'NEW') self.assertEqual(ad.__getattr__('test'), 'NEW') self.assertEqual(ad.__getitem__('test'), 'NEW') self.assertEqual(ad.__dict__['test'], 'NEW') self.assertEqual(ad.__dict__.get('test'), 'NEW') self.assertIn('test', ad) self.assertIn('test', ad.__dict__) def test_setitem(self): """ Tests __setitem__ method of AttribDict class. """ # 1 ad = AttribDict() ad['test'] = 'NEW' self.assertEqual(ad['test'], 'NEW') self.assertEqual(ad.test, 'NEW') self.assertEqual(ad.get('test'), 'NEW') self.assertEqual(ad.__getattr__('test'), 'NEW') self.assertEqual(ad.__getitem__('test'), 'NEW') self.assertEqual(ad.__dict__['test'], 'NEW') self.assertEqual(ad.__dict__.get('test'), 'NEW') self.assertIn('test', ad) self.assertIn('test', ad.__dict__) # 2 ad = AttribDict() ad.__setitem__('test', 'NEW') self.assertEqual(ad['test'], 'NEW') self.assertEqual(ad.test, 'NEW') self.assertEqual(ad.get('test'), 'NEW') self.assertEqual(ad.__getattr__('test'), 'NEW') self.assertEqual(ad.__getitem__('test'), 'NEW') self.assertEqual(ad.__dict__['test'], 'NEW') self.assertEqual(ad.__dict__.get('test'), 'NEW') self.assertIn('test', ad) self.assertIn('test', ad.__dict__) def test_setattr(self): """ Tests __setattr__ method of AttribDict class. """ # 1 ad = AttribDict() ad.test = 'NEW' self.assertEqual(ad['test'], 'NEW') self.assertEqual(ad.test, 'NEW') self.assertEqual(ad.get('test'), 'NEW') self.assertEqual(ad.__getattr__('test'), 'NEW') self.assertEqual(ad.__getitem__('test'), 'NEW') self.assertEqual(ad.__dict__['test'], 'NEW') self.assertEqual(ad.__dict__.get('test'), 'NEW') self.assertIn('test', ad) self.assertIn('test', ad.__dict__) # 2 ad = AttribDict() ad.__setattr__('test', 'NEW') self.assertEqual(ad['test'], 'NEW') self.assertEqual(ad.test, 'NEW') self.assertEqual(ad.get('test'), 'NEW') self.assertEqual(ad.__getattr__('test'), 'NEW') self.assertEqual(ad.__getitem__('test'), 'NEW') self.assertEqual(ad.__dict__['test'], 'NEW') self.assertEqual(ad.__dict__.get('test'), 'NEW') self.assertIn('test', ad) self.assertIn('test', ad.__dict__) def test_setdefault(self): """ Tests setdefault method of AttribDict class. """ ad = AttribDict() # 1 default = ad.setdefault('test', 'NEW') self.assertEqual(default, 'NEW') self.assertEqual(ad['test'], 'NEW') self.assertEqual(ad.test, 'NEW') self.assertEqual(ad.get('test'), 'NEW') self.assertEqual(ad.__getattr__('test'), 'NEW') self.assertEqual(ad.__getitem__('test'), 'NEW') self.assertEqual(ad.__dict__['test'], 'NEW') self.assertEqual(ad.__dict__.get('test'), 'NEW') self.assertIn('test', ad) self.assertIn('test', ad.__dict__) # 2 - existing key should not be overwritten default = ad.setdefault('test', 'SOMETHINGDIFFERENT') self.assertEqual(default, 'NEW') self.assertEqual(ad['test'], 'NEW') self.assertEqual(ad.test, 'NEW') self.assertEqual(ad.get('test'), 'NEW') self.assertEqual(ad.__getattr__('test'), 'NEW') self.assertEqual(ad.__getitem__('test'), 'NEW') self.assertEqual(ad.__dict__['test'], 'NEW') self.assertEqual(ad.__dict__.get('test'), 'NEW') self.assertIn('test', ad) self.assertIn('test', ad.__dict__) # 3 - default value isNone ad = AttribDict() default = ad.setdefault('test') self.assertEqual(default, None) self.assertEqual(ad['test'], None) self.assertEqual(ad.test, None) self.assertEqual(ad.get('test'), None) self.assertEqual(ad.__getattr__('test'), None) self.assertEqual(ad.__getitem__('test'), None) self.assertEqual(ad.__dict__['test'], None) self.assertEqual(ad.__dict__.get('test'), None) self.assertIn('test', ad) self.assertIn('test', ad.__dict__) def test_clear(self): """ Tests clear method of AttribDict class. """ ad = AttribDict() ad.test = 1 ad['test2'] = 'test' # removing via pop ad.clear() self.assertFalse('test' in ad) self.assertFalse('test2' in ad) self.assertFalse('test' in ad.__dict__) self.assertFalse('test2' in ad.__dict__) self.assertFalse(hasattr(ad, 'test')) self.assertFalse(hasattr(ad, 'test2')) # class attributes should be still present self.assertTrue(hasattr(ad, 'readonly')) self.assertTrue(hasattr(ad, 'defaults')) def test_init_argument(self): """ Tests initialization of AttribDict with various arguments. """ # one dict works as expected ad = AttribDict({'test': 1}) self.assertEqual(ad.test, 1) # multiple dicts results into TypeError self.assertRaises(TypeError, AttribDict, {}, {}) self.assertRaises(TypeError, AttribDict, {}, {}, blah=1) # non-dicts results into TypeError self.assertRaises(TypeError, AttribDict, 1) self.assertRaises(TypeError, AttribDict, object()) def test_defaults(self): """ Tests default of __getitem__/__getattr__ methods of AttribDict class. """ # 1 ad = AttribDict() ad['test'] = 'NEW' self.assertEqual(ad.__getitem__('test'), 'NEW') self.assertEqual(ad.__getitem__('xxx', 'blub'), 'blub') self.assertEqual(ad.__getitem__('test', 'blub'), 'NEW') self.assertEqual(ad.__getattr__('test'), 'NEW') self.assertEqual(ad.__getattr__('xxx', 'blub'), 'blub') self.assertEqual(ad.__getattr__('test', 'blub'), 'NEW') # should raise KeyError without default item self.assertRaises(KeyError, ad.__getitem__, 'xxx') self.assertRaises(AttributeError, ad.__getattr__, 'xxx') # 2 ad2 = AttribDict(defaults={'test2': 'NEW'}) self.assertEqual(ad2.__getitem__('test2'), 'NEW') self.assertRaises(KeyError, ad2.__getitem__, 'xxx') def test_set_readonly(self): """ Tests of setting readonly attributes. """ class MyAttribDict(AttribDict): readonly = ['test'] defaults = {'test': 1} ad = MyAttribDict() self.assertEqual(ad.test, 1) self.assertRaises(AttributeError, ad.__setitem__, 'test', 1) def test_deepcopy(self): """ Tests __deepcopy__ method of AttribDict. """ class MyAttribDict(AttribDict): defaults = {'test': 1} ad = MyAttribDict() ad.muh = 2 ad2 = ad.__deepcopy__() self.assertEqual(ad2.test, 1) self.assertEqual(ad2.muh, 2) def test_compare_with_dict(self): """ Checks if AttribDict is still comparable to a dict object. """ adict = {'test': 1} ad = AttribDict(adict) self.assertEqual(ad, adict) self.assertEqual(adict, ad) def test_pretty_str(self): """ Test _pretty_str method of AttribDict. """ # 1 ad = AttribDict({'test1': 1, 'test2': 2}) out = ' test1: 1\n test2: 2' self.assertEqual(ad._pretty_str(), out) # 2 ad = AttribDict({'test1': 1, 'test2': 2}) out = ' test2: 2\n test1: 1' self.assertEqual(ad._pretty_str(priorized_keys=['test2']), out) # 3 ad = AttribDict({'test1': 1, 'test2': 2}) out = ' test1: 1\n test2: 2' self.assertEqual(ad._pretty_str(min_label_length=6), out) def suite(): return unittest.makeSuite(AttribDictTestCase, 'test') if __name__ == '__main__': unittest.main(defaultTest='suite')

35.555184

77

0.577462

10,246

0.963785

0

2,928

0.275421

a730e555a53175f843e80e26bb1889169e4678c3

458

py

Python

data/datasetFactory.py

dcsgfl/acceleratefl

9c928ff06dd4dd02eb27cb71d7d539ba4527ec58

[ "MIT" ]

null

data/datasetFactory.py

dcsgfl/acceleratefl

9c928ff06dd4dd02eb27cb71d7d539ba4527ec58

[ "MIT" ]

null

data/datasetFactory.py

dcsgfl/acceleratefl

9c928ff06dd4dd02eb27cb71d7d539ba4527ec58

[ "MIT" ]

null

from cifar10 import CIFAR10 from mnist import MNIST class DatasetFactory: factories = {} def addFactory(id, dftory): DatasetFactory.factories.put[id] = dftory addFactory = staticmethod(addFactory) def getDataset(id): if id not in DatasetFactory.factories: DatasetFactory.factories[id] = eval(id + '.Factory()') return DatasetFactory.factories[id].get() getDataset = staticmethod(getDataset)

28.625

66

0.676856

405

0.884279

0

12

0.026201

a73131170f5bdfaf1161caf237d671d9dbf5663d

253

py

Python

jsonresume/__init__.py

kelvintaywl/jsonresume-validator

73ac162cb30ca70699c942def629188f7dfd4d3c

[ "MIT" ]

42

2016-06-03T18:17:24.000Z

2021-12-09T04:13:14.000Z

jsonresume/__init__.py

kelvintaywl/jsonresume-validator

73ac162cb30ca70699c942def629188f7dfd4d3c

[ "MIT" ]

3

2016-04-27T12:32:41.000Z

2020-09-29T16:43:35.000Z

jsonresume/__init__.py

kelvintaywl/jsonresume-validator

73ac162cb30ca70699c942def629188f7dfd4d3c

[ "MIT" ]

9

2016-05-08T15:31:53.000Z

2021-04-28T09:17:47.000Z

# -*- coding: utf-8 -*- """ JSON Resume Validator ~~~~~~ JSON Resume Validator helps validate python dictionaries to ensure they are valid representation of a JSON Resume. """ from jsonresume.resume import Resume __all__ = ['Resume']

19.461538

63

0.675889

0

199

0.786561

a731c3353defbbffeebffba89c597908966a9fbc

936

py

Python

Catchphrase.py

YaruKatsaros/Catchphrase

5d674cc251be226e233fd427f9533a56f1a24284

[ "MIT" ]

null

Catchphrase.py

YaruKatsaros/Catchphrase

5d674cc251be226e233fd427f9533a56f1a24284

[ "MIT" ]

null

Catchphrase.py

YaruKatsaros/Catchphrase

5d674cc251be226e233fd427f9533a56f1a24284

[ "MIT" ]

null

import glob import os import sys import re savedlines = [] def startreading(): if os.path.isdir(sys.argv[1]): os.chdir(sys.argv[1]) target = sys.argv[2] # TODO: Multiple lines. for file in glob.glob("*.srt"): read(sys.argv[1], file, target) savelines() print("Finished!") else: print("Not a valid path!") def savelines(): try: outfile = open('result.txt', 'w') outfile.writelines(savedlines) outfile.close() except Exception as e: print("Something went wrong when saving the file: " + str(e)) # TODO: Custom exception. def read(path, file, target): openfile = open(file, 'r') lines = openfile.readlines() for line in lines: if re.search(target, line, re.IGNORECASE): ln = line.strip() savedlines.append(ln + "\n") print(ln) openfile.close() startreading()

21.767442

96

0.573718

0

152

0.162393

a733182bb7d063e48b371c3b9b8871a0afe48521

19,712

py

Python

dashboard/api/config.py

x3niasweden/fomalhaut-panel

8b4b3d81e2c91bef8f24ccbaf9cf898a47ac38a6

[ "MIT" ]

14

2017-08-01T08:28:00.000Z

2020-08-29T06:55:16.000Z

dashboard/api/config.py

x3niasweden/fomalhaut-panel

8b4b3d81e2c91bef8f24ccbaf9cf898a47ac38a6

[ "MIT" ]

1

2021-03-29T06:16:34.000Z

dashboard/api/config.py

x3niasweden/fomalhaut-panel

8b4b3d81e2c91bef8f24ccbaf9cf898a47ac38a6

[ "MIT" ]

12

2017-07-18T02:59:03.000Z

2021-03-23T04:04:58.000Z

# !/usr/bin/env python # -*- coding: utf-8 -*- # created by restran on 2016/1/2 from __future__ import unicode_literals, absolute_import import traceback from django.views.decorators.http import require_http_methods from django.views.decorators.csrf import csrf_protect from django.db import transaction from cerberus import Validator import redis from fomalhaut import settings from ..forms import * from common.utils import http_response_json, json_dumps, json_loads from accounts.decorators import login_required from common.utils import error_404 logger = logging.getLogger(__name__) @login_required @require_http_methods(["GET"]) def get_model_data(request, model_name): logger.debug('run api_get_model_data') return_data = {'success': False, 'msg': ''} get_default_form = request.GET.get('get_default_form', False) if model_name == 'client': model = Client model_form = ClientForm data = model.get_all_in_json() elif model_name == 'endpoint': model = Endpoint model_form = EndpointForm data = model.get_all_in_json() elif model_name == 'client_endpoint': model = ClientEndpoint model_form = None client_id = request.GET.get('client_id') data = model.get_all_in_json(client_id) else: model = None model_form = None data = [] if model is None: raise error_404(request) # 获取一个缺省值用来添加和编辑数据 if get_default_form: t = model_form.get_default_form_json() return_data['default_form'] = t return_data['data'] = data return_data['success'] = True return http_response_json(return_data) def do_create_or_update_model_data(request, model_name, is_update, post_data, form): return_data = {'success': False, 'msg': ''} if model_name == 'client_endpoint': client_id = post_data.get('client_id', []) endpoints = post_data.get('endpoints', []) client = Client.get_client(client_id) if client is None: return_data['msg'] = '提交的数据有误, client_id 不存在' return return_data ClientEndpoint.objects.filter(client_id=client_id).delete() endpoint_list = [] for t in endpoints: ce = ClientEndpoint(client=client, endpoint_id=t['id'], enable=t['enable']) endpoint_list.append(ce) # bulk_create 不会返回 id ClientEndpoint.objects.bulk_create(endpoint_list) return_data['success'] = True return_data['data'] = ClientEndpoint.get_all_in_json(client_id) return return_data else: form_is_valid = form.is_valid() return_validation = {} acl_rules = post_data.get('acl_rules', []) if model_name == 'endpoint': acl_rules_validation = {'data': [], 'has_error': False, 'errors': ''} for t in acl_rules: tf = ACLRuleForm(t) if not tf.is_valid(): acl_rules_validation['has_error'] = True acl_rules_validation['errors'] = '访问控制列表数据为空或不正确' break return_validation['acl_rules'] = acl_rules_validation form_is_valid = form_is_valid and not acl_rules_validation['has_error'] elif model_name == 'client_endpoint': pass if form_is_valid: # logger.debug(form.cleaned_data) logger.debug('form is valid') entry = form.save(commit=False) if model_name == 'endpoint': entry.save() acl_rules = [ACLRule(endpoint_id=entry.id, re_uri=t['re_uri'], is_permit=t['is_permit']) for t in acl_rules] # 删除旧的 ACLRule.objects.filter(endpoint_id=entry.id).delete() # 创建 ACLRule ACLRule.objects.bulk_create(acl_rules) entry.acl_rules = acl_rules else: entry.save() return_data['success'] = True return_data['data'] = entry.to_json_dict() logger.debug(return_data['data']) else: return_data['msg'] = '提交的数据有误' logger.debug('form is not valid') logger.debug(form.get_form_json()) return_validation.update(form.get_form_json()) return_data['data'] = return_validation return return_data @login_required @csrf_protect @require_http_methods(["POST"]) def create_model_data(request, model_name): """ 创建或更新数据 :param request: :param model_name: :return: """ logger.debug('run api_create_model_data') post_data = json_loads(request.body) logger.debug(post_data) if model_name == 'client': form = ClientForm(post_data['data']) elif model_name == 'endpoint': form = EndpointForm(post_data['data']) elif model_name == 'client_endpoint': form = None else: form = None return_data = do_create_or_update_model_data( request, model_name, False, post_data, form) return http_response_json(return_data) @login_required @csrf_protect @require_http_methods(["POST"]) def update_model_data(request, model_name, entry_id): """ 创建或更新数据 :param request: :param model_name: :param entry_id: :return: """ logger.debug('run api_update_model_data') return_data = {'success': False, 'msg': ''} if model_name == 'client': model = Client model_form = ClientForm elif model_name == 'endpoint': model = Endpoint model_form = EndpointForm elif model_name == 'client_endpoint': model = None model_form = None else: model = None model_form = None post_data = json_loads(request.body) logger.debug(post_data) if model_name != 'client_endpoint': try: entry = model.objects.get(id=entry_id) except models.Model.DoesNotExist: return_data['msg'] = '数据不存在' return http_response_json(return_data) if model_name == 'client': form = model_form(post_data['data'], instance=entry) elif model_name == 'endpoint': form = model_form(post_data['data'], instance=entry) else: form = None else: form = None return_data = do_create_or_update_model_data( request, model_name, True, post_data, form) return http_response_json(return_data) @login_required @csrf_protect @require_http_methods(["POST"]) def delete_model_data(request, model_name, entry_id=None): """ 删除数据 :param request: :param model_name: :param entry_id: :return: """ logger.debug('run api_delete_model_data') return_data = {'success': False, 'msg': ''} if model_name == 'client': model = Client elif model_name == 'endpoint': model = Endpoint elif model_name == 'client_endpoint': model = ClientEndpoint else: model = None if model and entry_id is not None: try: entry = model.objects.get(id=entry_id) entry.delete() return_data['success'] = True except models.Model.DoesNotExist: return_data['msg'] = u'数据不存在' return http_response_json(return_data) @login_required @csrf_protect @require_http_methods(["POST"]) def update_enable_state_model_data(request, model_name, entry_id=None): """ 点击启用按钮，更新启用状态 :param request: :param model_name: :param entry_id: :return: """ logger.debug('run api_update_enable_state_model_data') return_data = {'success': False, 'msg': ''} if model_name == 'client': model = Client elif model_name == 'endpoint': model = Endpoint elif model_name == 'client_endpoint': model = ClientEndpoint else: model = None post_data = json_loads(request.body) if model and entry_id: try: model.objects.filter(id=entry_id).update(enable=post_data['enable']) return_data['success'] = True except Exception as e: logger.error(e.message) return_data['msg'] = u'更新启用状态失败' return http_response_json(return_data) def do_import_config(upload_file): """ 从json文件导入配置 :param upload_file: :return: """ file_contents = upload_file.read() try: json_data = json_loads(file_contents) except Exception as e: logger.error(e.message) return False, u'上传的文件不是JSON或者格式有误', [] json_data_schema = { 'clients': { 'type': 'list', 'required': True, 'schema': { 'type': 'dict', 'schema': { 'id': { 'type': 'integer', 'required': True, }, 'name': { 'type': 'string', 'required': True, }, 'app_id': { 'type': 'string', 'required': True, }, 'secret_key': { 'type': 'string', 'required': True, }, 'enable': { 'type': 'boolean', 'required': True, }, 'memo': { 'type': 'string', 'required': True, } } } }, 'client_endpoints': { 'type': 'list', 'required': True, 'schema': { 'type': 'dict', 'schema': { 'id': { 'type': 'integer', 'required': True, }, 'client_id': { 'type': 'integer', 'required': True, }, 'endpoint_id': { 'type': 'integer', 'required': True, }, 'enable': { 'type': 'boolean', 'required': True, } } } }, 'endpoints': { 'type': 'list', 'required': True, 'schema': { 'type': 'dict', 'schema': { 'id': { 'type': 'integer', 'required': True, }, 'unique_name': { 'type': 'string', 'required': True, }, 'name': { 'type': 'string', 'required': True, }, 'version': { 'type': 'string', 'required': True, }, 'url': { 'type': 'string', 'required': True, }, 'memo': { 'type': 'string', 'required': True, }, 'async_http_connect_timeout': { 'type': 'integer', 'required': True, }, 'async_http_request_timeout': { 'type': 'integer', 'required': True, }, 'enable_acl': { 'type': 'boolean', 'required': True, }, 'acl_rules': { 'type': 'list', 'required': True, 'schema': { 'type': 'dict', 'schema': { 'is_permit': { 'type': 'boolean', 'required': True, }, 're_uri': { 'type': 'string', 'required': True, } } } } } } } } validator = Validator(json_data_schema, allow_unknown=True) if not validator.validate(json_data): errors = [] for (k, v) in validator.errors.items(): errors.append('%s: %s' % (k, v)) return False, '上传的 JSON 配置文件格式有误，请先导出 JSON 配置文件再修改', errors else: success, msg, errors = False, '', [] try: # 出现异常的时候，会自动回滚 with transaction.atomic(): # 清除旧的数据，不包含 Client 和 Endpoint ClientEndpoint.objects.all().delete() ACLRule.objects.all().delete() old_client_list = Client.objects.all() old_client_dict = {} for t in old_client_list: old_client_dict[t.app_id] = t old_endpoint_list = Endpoint.objects.all() old_endpoint_dict = {} for t in old_endpoint_list: old_endpoint_dict[t.unique_name] = t new_client_dict = {} for t in json_data['clients']: # del t['id'] old_client = old_client_dict.get(t['app_id']) # 如果已存在相同的，则更新 if old_client is not None: form = ClientForm(t, instance=old_client) del old_client_dict[t['app_id']] else: form = ClientForm(t) if not form.is_valid(): errors = [] form_errors = form.get_form_json() for (k, v) in form_errors.items(): if v['has_error']: errors.append('%s: %s' % (k, v['errors'])) msg, errors = '上传的 JSON 配置文件格式有误，请先导出 JSON 配置文件再修改', errors raise Exception('error') client = form.save() new_client_dict[t['id']] = client new_endpoint_dict = {} for t in json_data['endpoints']: # del t['id'] old_endpoint = old_endpoint_dict.get(t['unique_name']) # 如果已存在相同的，则更新 if old_endpoint is not None: form = EndpointForm(t, instance=old_endpoint) del old_endpoint_dict[t['unique_name']] else: form = EndpointForm(t) if not form.is_valid(): errors = [] form_errors = form.get_form_json() for (k, v) in form_errors.items(): if v['has_error']: errors.append('%s: %s' % (k, v['errors'])) msg, errors = '上传的 JSON 配置文件格式有误，请先导出 JSON 配置文件再修改', errors raise Exception('error') endpoint = form.save(commit=False) endpoint.save() new_endpoint_dict[t['id']] = endpoint acl_rules = t['acl_rules'] for y in acl_rules: # del t['id'] tf = ACLRuleForm(y) if not tf.is_valid(): msg, errors = '上传的 JSON 配置文件格式有误，请先导出 JSON 配置文件再修改', \ ['访问控制列表数据为空或不正确'] raise Exception('error') acl_rules = [ACLRule(endpoint_id=endpoint.id, re_uri=t['re_uri'], is_permit=t['is_permit']) for t in acl_rules] # 创建 ACLRule ACLRule.objects.bulk_create(acl_rules) # 根据新的 id 匹配正确的 client_endpoint client_endpoint_list = [] for t in json_data['client_endpoints']: client = new_client_dict.get(t['client_id']) endpoint = new_endpoint_dict.get(t['endpoint_id']) enable = t['enable'] ce = ClientEndpoint(client=client, endpoint=endpoint, enable=enable) client_endpoint_list.append(ce) ClientEndpoint.objects.bulk_create(client_endpoint_list) # 删除导入的配置中，不存在的 Client Client.objects.filter(id__in=[t.id for t in old_client_dict.values()]).delete() # 删除导入的配置中，不存在的 Endpoint Endpoint.objects.filter(id__in=[t.id for t in old_endpoint_dict.values()]).delete() success, msg = True, u'导入配置成功' except Exception as e: logger.error(e.message) return success, msg, errors @login_required @csrf_protect @require_http_methods(["POST"]) def import_config(request): """ 上传文件，导入配置 """ if request.FILES: success, msg, errors = False, '', [] for _file in request.FILES: # 关闭了分块上传，上传上来的就是完整的 # 只允许选择一份文件，处理完就break success, msg, errors = do_import_config(request.FILES[_file]) break return http_response_json({'success': success, 'msg': msg, 'errors': errors}) else: raise error_404(request) @login_required @csrf_protect @require_http_methods(["POST"]) def transfer_to_redis(request): """ 将配置数据同步到Redis中 """ success, msg = False, '' try: config_data = get_config_redis_json() logger.debug(config_data) r = redis.StrictRedis(host=settings.REDIS_HOST, port=settings.REDIS_PORT, db=settings.REDIS_DB, password=settings.REDIS_PASSWORD) # 默认transaction=True pipe = r.pipeline(transaction=True) # 按模式匹配批量删除 pattern_delete_lua = """ local keys = redis.call('keys', ARGV[1]) for i = 1, table.getn(keys) do redis.call('del', keys[i]) end """ pattern_delete = r.register_script(pattern_delete_lua) pattern_delete(keys=[''], args=['%s:*' % settings.CLIENT_CONFIG_REDIS_PREFIX], client=pipe) for t in config_data: logger.debug(t) # # client = {} # for k, v in t.iteritems(): # if k != 'endpoints': # client[k] = v pipe.set('%s:%s' % (settings.CLIENT_CONFIG_REDIS_PREFIX, t['app_id']), json_dumps(t)) # for s in t['endpoints']: # pipe.set('%s:%s:%s:%s' % (settings.PROXY_CONFIG_REDIS_PREFIX, t['access_key'], s['name'], s['version']), # json_dumps(s)) # pipe.delete('config:*') # the EXECUTE call sends all buffered commands to the server, returning # a list of responses, one for each command. pipe.execute() success = True except Exception as e: msg = '同步配置数据到 Redis 出现异常' logger.error(e.message) logger.error(traceback.format_exc()) return http_response_json({'success': success, 'msg': msg})

33.241147

122

0.491985

0

7,487

0.365184

0

4,712

0.229831

a733c76add330a704c87d51a39a3121429990715

2,209

py

Python

WX_BG.py

boristown/WX_BG

c715d1f3ffeef60187be0289f26549204d6b963f

[ "MIT" ]

1

2019-08-17T23:21:28.000Z

WX_BG.py

boristown/WX_BG

c715d1f3ffeef60187be0289f26549204d6b963f

[ "MIT" ]

null

WX_BG.py

boristown/WX_BG

c715d1f3ffeef60187be0289f26549204d6b963f

[ "MIT" ]

null

# -*- coding: utf-8 -*- # filename: WX_BG.py import prices import glob import prediction import os import time import random #预测数据文件 prices_file_pattern = "Output\\prices\\*.csv" #预测数据文件 predict_file_pattern = "Output\\predict\\*.csv" #预测数据文件 prices_file_second_pattern = "Output\\prices_second\\*.csv" #预测数据文件 predict_file_second_pattern = "Output\\predict_second\\*.csv" modeStr = {0: "v1", 1:"v2"} predict_batch_size = 10000 while True: ''' randint = random.randint(0, 9) if randint == 0: modeType = 0 else: modeType = 1 ''' modeType = 1 print( "mode = " + modeStr[modeType] ) #删除旧的价格数据 prices_files = glob.glob(prices_file_pattern) for prices_file in prices_files: os.remove(prices_file) prices_files_second = glob.glob(prices_file_second_pattern) for prices_file_second in prices_files_second: os.remove(prices_file_second) #删除旧的预测数据 predict_files = glob.glob(predict_file_pattern) for predict_file in predict_files: os.remove(predict_file) predict_files_second = glob.glob(predict_file_second_pattern) for predict_file_second in predict_files_second: os.remove(predict_file_second) time.sleep(10) print("正在读取价格……") #读取价格并生成输入数据 if modeType == 0: symbol_id_list = prices.read_prices() else: symbol_id_list = prices.read_pricehistory(predict_batch_size) try: if len(symbol_id_list) == 0: continue except: continue print("正在执行预测……") # 预测并读取结果 while True: time.sleep(1) predict_files = glob.glob(predict_file_pattern) predict_files_second = glob.glob(predict_file_second_pattern) if len(predict_files) == 0 or len(predict_files_second) == 0: continue print("检测到预测文件：", predict_files[0]) print("检测到预测文件2：", predict_files_second[0]) time.sleep(2) if modeType == 0: prediction.get_prediction(symbol_id_list, predict_files[0]) else: prediction.get_predictionhistory(symbol_id_list, predict_files[0], predict_files_second[0]) break print("预测执行完毕！") time.sleep(20)

26.939024

103

0.663649

0

599

0.249272

a734a04a2790536248f0af4b3c7aedde27c72873

929

py

Python

hyppo/d_variate/tests/test_dhsic.py

zdbzdb123123/hyppo

c22dcfb7bdf25c9945e6d4ddd7c6bfe5fcdd0cde

[ "MIT" ]

116

2020-02-28T10:29:22.000Z

2022-03-22T12:19:39.000Z

hyppo/d_variate/tests/test_dhsic.py

zdbzdb123123/hyppo

c22dcfb7bdf25c9945e6d4ddd7c6bfe5fcdd0cde

[ "MIT" ]

253

2020-02-17T16:18:56.000Z

2022-03-30T16:55:02.000Z

hyppo/d_variate/tests/test_dhsic.py

zdbzdb123123/hyppo

c22dcfb7bdf25c9945e6d4ddd7c6bfe5fcdd0cde

[ "MIT" ]

27

2020-03-02T21:07:41.000Z

2022-03-08T08:33:23.000Z

import numpy as np import pytest from numpy.testing import assert_almost_equal from ...tools import linear, power from .. import dHsic # type: ignore class TestdHsicStat: @pytest.mark.parametrize("n, obs_stat", [(100, 0.04561), (200, 0.03911)]) @pytest.mark.parametrize("obs_pvalue", [1 / 1000]) def test_linear_oned(self, n, obs_stat, obs_pvalue): np.random.seed(123456789) x, y = linear(n, 1) stat, pvalue = dHsic(gamma=0.5).test(x, y) assert_almost_equal(stat, obs_stat, decimal=2) assert_almost_equal(pvalue, obs_pvalue, decimal=2) class TestdHsicTypeIError: def test_oned(self): np.random.seed(123456789) est_power = power( "dhsic", sim_type="multi", sim="multimodal_independence", n=100, p=1, alpha=0.05, ) assert_almost_equal(est_power, 0.05, decimal=2)

27.323529

77

0.620022

771

0.829925

0

413

0.444564

0

78

0.083961

a7351f98fb299d1d929cbe7b4a8c9742f60b725d

2,844

py

Python

Pages/showHistory.py

ajaydeepsingh/ATLZoo

ab5ba27dc8602da39ce8bb47c4a050ff09d79b82

[ "MIT" ]

null

Pages/showHistory.py

ajaydeepsingh/ATLZoo

ab5ba27dc8602da39ce8bb47c4a050ff09d79b82

[ "MIT" ]

null

Pages/showHistory.py

ajaydeepsingh/ATLZoo

ab5ba27dc8602da39ce8bb47c4a050ff09d79b82

[ "MIT" ]

null

from tkinter import * from PIL import ImageTk, Image import pymysql from tkinter import messagebox from tkinter import ttk from datetime import datetime, timedelta import decimal class ATLzooShowHistory: def __init__(self): self.createShowHistoryWindow() self.buildShowHistoryWindow(self.showHistoryWindow) self.showHistoryWindow.mainloop() sys.exit() def createShowHistoryWindow(self): self.showHistoryWindow=Toplevel() self.showHistoryWindow.title("Zoo Atlanta") self.showHistoryWindow.geometry("800x600") def buildShowHistoryWindow(self, showHistoryWindow): titleLabel= Label(showHistoryWindow,text = "Show History", font = "Verdana 16 bold ") titleLabel.grid(row=1,column=2,sticky=W+E) # Labels showLabel = Label(showHistoryWindow,text = "Name") showLabel.grid(row=2,column=0,pady=10) self.showNameString = StringVar() showNameEntry = Entry(showHistoryWindow, textvariable=self.showNameString, width=20) showNameEntry.grid(row=2,column=1,pady=10) exhibitLabel = Label(showHistoryWindow,text = "Exhibit") exhibitLabel.grid(row=2,column=2,pady=10) exhibitDefault = StringVar() exhibitDefault.set("options") exhibitMenu = OptionMenu(showHistoryWindow, exhibitDefault, "this","will","have","options","later") exhibitMenu.grid(row=2, column=3,pady=10) dateLabel = Label(showHistoryWindow,text = "Date") dateLabel.grid(row=3, column=0,pady=10) #showDateEntry = CalendarDialog.main() showDateEntry= Entry(showHistoryWindow) showDateEntry.grid(row=3, column=1,pady=10) # Button findShowsButton = Button(showHistoryWindow, text="Search", command=self.showHistoryWindowFindShowsButtonClicked) findShowsButton.grid(row=3,column=2,pady=10) selectShowTree = ttk.Treeview(showHistoryWindow, columns=("Name", "Exhibit", "Date")) selectShowTree.heading('#0', text = "Name") selectShowTree.heading('#1', text = "Exhibit") selectShowTree.heading('#2', text = "Date") selectShowTree.column('#0', width = 200, anchor = "center") selectShowTree.column('#1', width = 200, anchor = "center") selectShowTree.column('#2', width = 200, anchor = "center") selectShowTree.place(x=20, y=130,width=600) backButton = Button(showHistoryWindow, text="Back", command=self.showHistoryWindowBackButtonClicked) backButton.place(x=310,y=370) def showHistoryWindowFindShowsButtonClicked(self): self.showHistoryWindow.destroy() self.createShowsDetailWindow() def showHistoryWindowBackButtonClicked(self): self.showHistoryWindow.withdraw() import visitorFunctionality a = ATLzooShowHistory()

37.92

120

0.688819

2,637

0.927215

0

276

0.097046

a738885fc845ac09ce24d938e1de039911e09569

6,061

py

Python

python/federatedml/protobuf/generated/sample_weight_model_param_pb2.py

rubenlozanoaht3m/DataDogm

cd605e8072cca31e8418830c3300657ae2fa5b16

[ "Apache-2.0" ]

715

2019-01-24T10:52:03.000Z

2019-10-31T12:19:22.000Z

python/federatedml/protobuf/generated/sample_weight_model_param_pb2.py

rubenlozanoaht3m/DataDogm

cd605e8072cca31e8418830c3300657ae2fa5b16

[ "Apache-2.0" ]

270

2019-02-11T02:57:36.000Z

2019-08-29T11:22:33.000Z

python/federatedml/protobuf/generated/sample_weight_model_param_pb2.py

rubenlozanoaht3m/DataDogm

cd605e8072cca31e8418830c3300657ae2fa5b16

[ "Apache-2.0" ]

200

2019-01-26T14:21:35.000Z

2019-11-01T01:14:36.000Z

# -*- coding: utf-8 -*- # Generated by the protocol buffer compiler. DO NOT EDIT! # source: sample-weight-model-param.proto import sys _b = sys.version_info[0] < 3 and (lambda x: x) or (lambda x: x.encode('latin1')) from google.protobuf import descriptor as _descriptor from google.protobuf import message as _message from google.protobuf import reflection as _reflection from google.protobuf import symbol_database as _symbol_database # @@protoc_insertion_point(imports) _sym_db = _symbol_database.Default() DESCRIPTOR = _descriptor.FileDescriptor(name='sample-weight-model-param.proto', package='com.webank.ai.fate.core.mlmodel.buffer', syntax='proto3', serialized_options=_b('B\033SampleWeightModelParamProto'), serialized_pb=_b( '\n\x1fsample-weight-model-param.proto\x12&com.webank.ai.fate.core.mlmodel.buffer\"\xd8\x01\n\x16SampleWeightModelParam\x12\x0e\n\x06header\x18\x01 \x03(\t\x12\x13\n\x0bweight_mode\x18\x02 \x01(\t\x12\x65\n\x0c\x63lass_weight\x18\x03 \x03(\x0b\x32O.com.webank.ai.fate.core.mlmodel.buffer.SampleWeightModelParam.ClassWeightEntry\x1a\x32\n\x10\x43lassWeightEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12\r\n\x05value\x18\x02 \x01(\x01:\x02\x38\x01\x42\x1d\x42\x1bSampleWeightModelParamProtob\x06proto3')) _SAMPLEWEIGHTMODELPARAM_CLASSWEIGHTENTRY = _descriptor.Descriptor( name='ClassWeightEntry', full_name='com.webank.ai.fate.core.mlmodel.buffer.SampleWeightModelParam.ClassWeightEntry', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='key', full_name='com.webank.ai.fate.core.mlmodel.buffer.SampleWeightModelParam.ClassWeightEntry.key', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='value', full_name='com.webank.ai.fate.core.mlmodel.buffer.SampleWeightModelParam.ClassWeightEntry.value', index=1, number=2, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[], nested_types=[], enum_types=[], serialized_options=_b('8\001'), is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[], serialized_start=242, serialized_end=292, ) _SAMPLEWEIGHTMODELPARAM = _descriptor.Descriptor( name='SampleWeightModelParam', full_name='com.webank.ai.fate.core.mlmodel.buffer.SampleWeightModelParam', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='header', full_name='com.webank.ai.fate.core.mlmodel.buffer.SampleWeightModelParam.header', index=0, number=1, type=9, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='weight_mode', full_name='com.webank.ai.fate.core.mlmodel.buffer.SampleWeightModelParam.weight_mode', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='class_weight', full_name='com.webank.ai.fate.core.mlmodel.buffer.SampleWeightModelParam.class_weight', index=2, number=3, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[_SAMPLEWEIGHTMODELPARAM_CLASSWEIGHTENTRY, ], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=76, serialized_end=292, ) _SAMPLEWEIGHTMODELPARAM_CLASSWEIGHTENTRY.containing_type = _SAMPLEWEIGHTMODELPARAM _SAMPLEWEIGHTMODELPARAM.fields_by_name['class_weight'].message_type = _SAMPLEWEIGHTMODELPARAM_CLASSWEIGHTENTRY DESCRIPTOR.message_types_by_name['SampleWeightModelParam'] = _SAMPLEWEIGHTMODELPARAM _sym_db.RegisterFileDescriptor(DESCRIPTOR) SampleWeightModelParam = _reflection.GeneratedProtocolMessageType('SampleWeightModelParam', (_message.Message,), { 'ClassWeightEntry': _reflection.GeneratedProtocolMessageType('ClassWeightEntry', (_message.Message,), { 'DESCRIPTOR': _SAMPLEWEIGHTMODELPARAM_CLASSWEIGHTENTRY, '__module__': 'sample_weight_model_param_pb2' # @@protoc_insertion_point(class_scope:com.webank.ai.fate.core.mlmodel.buffer.SampleWeightModelParam.ClassWeightEntry) }), 'DESCRIPTOR': _SAMPLEWEIGHTMODELPARAM, '__module__': 'sample_weight_model_param_pb2' # @@protoc_insertion_point(class_scope:com.webank.ai.fate.core.mlmodel.buffer.SampleWeightModelParam) }) _sym_db.RegisterMessage(SampleWeightModelParam) _sym_db.RegisterMessage(SampleWeightModelParam.ClassWeightEntry) DESCRIPTOR._options = None _SAMPLEWEIGHTMODELPARAM_CLASSWEIGHTENTRY._options = None # @@protoc_insertion_point(module_scope)

42.985816

502

0.707144

0

1,907

0.314635

a739bd10614848db1a73028a77c6c885008e1463

63,679

py

Python

postprocessing/pyplotgen/config/Case_definitions.py

larson-group/clubb_release

b4d671e3e238dbe00752c0dead6a0d4f9897350a

[ "Intel", "Unlicense", "NetCDF" ]

null

postprocessing/pyplotgen/config/Case_definitions.py

larson-group/clubb_release

b4d671e3e238dbe00752c0dead6a0d4f9897350a

[ "Intel", "Unlicense", "NetCDF" ]

null

postprocessing/pyplotgen/config/Case_definitions.py

larson-group/clubb_release

b4d671e3e238dbe00752c0dead6a0d4f9897350a

[ "Intel", "Unlicense", "NetCDF" ]

1

2022-01-28T22:22:04.000Z

""" :author: Nicolas Strike :date: Early 2019 This file is mostly a definition of Cases. Each case is defined in the following format using python dictionaries (values surrounded with < > must have the < > removed to be valid). .. code-block:: python :linenos: CASENAME = {'name': 'casename', 'description': "", 'start_time': <numeric value>, 'end_time': <numeric value>, 'height_min_value': <numeric value>, 'height_max_value': <numeric value>, 'blacklisted_vars': ['list', 'of', 'variable', 'names', 'to', 'exclude', 'from', 'plotting'], 'sam_benchmark_file': <path to sam file>", 'clubb_file': {'zm': <path to file>, 'zt': <path to file>, 'sfc': <path to file>}, 'coamps_benchmark_file': {'sm': <path to file>, 'sw': <path to file>}, 'clubb_r408_benchmark_file': {'zm': <path to file>, 'zt': <path to file>, 'sfc': <path to file>}, 'clubb_hoc_benchmark_file': {'zm': <path to file>', 'zt': <path to file>', 'sfc': <path to file>}, 'e3sm_file': <path to file>, 'cam_file': <path to file>, 'sam_file': <path to file>, 'wrf_file': {'zm': <path to file>, 'zt': <path to file>, 'sfc': <path to file>}, 'var_groups': [VariableGroupBase, <other variable groups to plot>]} **Important note**: When creating a new case, add it to the CASES_TO_PLOT list at the bottom of the file. Additionally, please add it in alphabetical order. **Case Definition values explained**: *name*: must be the same as the filename without the extention. E.g. to use lba_zt.nc and lba_zm.nc the case's name must be 'lba'. Extensions are determined by the last instance of _ *start_time*: An integer value representing which timestep to begin the time-averaging interval. Valid options are from 1 -> list minute value. Give in terms of clubb minutes. *end_time*: An integer value representing which timestep to end the time-averaging interval. Valid options are from 1 -> list minute value. Give in terms of clubb minutes. Also used to determine where to stop timeseries plots *height_min_value*: The elevation to begin height plots at *height_max_value*: The elevation to end height plots at *blacklisted_vars*: List of variables to avoid plotting for this case. Names must use the clubb-name version *<model name>_file*: The path(s) to nc files for the given model. (please use the <model name>_OUTPUT_ROOT variables as the beginning of the path). *var_groups*: These are the groups of variables to be plotted for the given case. var_groups is defined as a list of python class names, where the classes use the naming scheme VariableGroup____.py and define a variable group. An example would be: 'var_groups': [VariableGroupBase, VariableGroupWs]. The variables inside a VariableGroup can be found in the file with the same name, i.e. config/VariableGroupBase.py. An example would be thlm in VariableGroupBase. """ import os from config.VariableGroupBase import VariableGroupBase from config.VariableGroupCorrelations import VariableGroupCorrelations from config.VariableGroupIceMP import VariableGroupIceMP from config.VariableGroupKKMP import VariableGroupKKMP from config.VariableGroupLiquidMP import VariableGroupLiquidMP from config.VariableGroupSamProfiles import VariableGroupSamProfiles from config.VariableGroupScalars import VariableGroupScalars from config.VariableGroupWs import VariableGroupWs from config.VariableGroupTaus import VariableGroupTaus from config.VariableGroupNondimMoments import VariableGroupNondimMoments from config.VariableGroupNormalizedVariations import VariableGroupNormalizedVariations # --------------------------- BENCHMARK_OUTPUT_ROOT = "/home/pub/les_and_clubb_benchmark_runs/" if not os.path.isdir(BENCHMARK_OUTPUT_ROOT) and \ not os.path.islink(BENCHMARK_OUTPUT_ROOT): print("Benchmark output was not found in " + BENCHMARK_OUTPUT_ROOT + ".\n\tChecking local location: " + os.path.dirname(os.path.realpath(__file__)) + "/../les_and_clubb_benchmark_runs/") BENCHMARK_OUTPUT_ROOT = os.path.dirname(os.path.realpath(__file__)) + "/../les_and_clubb_benchmark_runs/" SAM_BENCHMARK_OUTPUT_ROOT = BENCHMARK_OUTPUT_ROOT + "sam_benchmark_runs" COAMPS_BENCHMARK_OUTPUT_ROOT = BENCHMARK_OUTPUT_ROOT + "les_runs" WRF_LASSO_BENCHMARK_OUTPUT_ROOT = BENCHMARK_OUTPUT_ROOT + "wrf_lasso_runs" ARCHIVED_CLUBB_OUTPUT_ROOT = BENCHMARK_OUTPUT_ROOT + "archived_clubb_runs" R408_OUTPUT_ROOT = BENCHMARK_OUTPUT_ROOT + "" HOC_OUTPUT_ROOT = BENCHMARK_OUTPUT_ROOT + "HOC_20051217" # This folder is passed in as a command line parameter # It is not capitalized because it is not intended to # be final, i.e. is changed depending on the cmd line arg e3sm_output_root = "" sam_output_root = "" wrf_output_root = "" cam_output_root = "" clubb_output_root = "" # --------------------------- # These are all the names that represent the height variable within different models HEIGHT_VAR_NAMES = ['z', 'Z3', 'altitude', 'lev', 'CSP_Zm', 'CSP_Z8Wm'] # CSP_* added for WRF-LASSO cases TIME_VAR_NAMES = ['time', 'XTIME'] """ To plot only a subset of cases, reguardless of what output exists in the clubb folder, uncomment the last line of this file and fill that array with the cases you'd like to plot. This overwrites the CASES_TO_PLOT variable such that pyplotgen will only know about cases in that list and ignore all others. The name must match the python variable name below (all caps). For example, to plot only bomex and fire: CASES_TO_PLOT = [BOMEX, FIRE] """ ARM = {'name': 'arm', 'description': "Output may differ from plotgen in some models (e.g. WRF) due to a difference in the time " "averaging interval.", 'start_time': 481, 'end_time': 540, 'height_min_value': 0, 'height_max_value': 3500, 'blacklisted_vars': ['radht'], 'sam_benchmark_file': {'sam_benchmark': SAM_BENCHMARK_OUTPUT_ROOT + "/JULY_2017/ARM_96x96x110/GCSSARM_96x96x110_67m_40m_1s.nc"}, 'clubb_file': {'zm': clubb_output_root + '/arm_zm.nc', 'zt': clubb_output_root + '/arm_zt.nc', 'sfc': clubb_output_root + '/arm_sfc.nc'}, 'coamps_benchmark_file': {'sm': COAMPS_BENCHMARK_OUTPUT_ROOT + "/arm_coamps_sm.nc", 'sw': COAMPS_BENCHMARK_OUTPUT_ROOT + "/arm_coamps_sw.nc"}, 'wrf_benchmark_file': None, 'clubb_r408_benchmark_file': {'zm': R408_OUTPUT_ROOT + '/Chris_Golaz_best_ever/arm_zm.nc', 'zt': R408_OUTPUT_ROOT + '/Chris_Golaz_best_ever/arm_zt.nc', 'sfc': R408_OUTPUT_ROOT + '/Chris_Golaz_best_ever/arm_sfc.nc'}, 'clubb_hoc_benchmark_file': {'zm': HOC_OUTPUT_ROOT + '/arm_zm.nc', 'zt': HOC_OUTPUT_ROOT + '/arm_zt.nc', 'sfc': HOC_OUTPUT_ROOT + '/arm_sfc.nc'}, 'e3sm_file': { 'e3sm': e3sm_output_root + "/arm.nc"}, 'cam_file': None, 'sam_file': {'sam': sam_output_root + "/GCSSARM_96x96x110_67m_40m_1s.nc"}, 'wrf_file': {'zm': wrf_output_root + "/arm_zm_wrf.nc", 'zt': wrf_output_root + "/arm_zt_wrf.nc", 'sfc': wrf_output_root + "/arm_sfc_wrf.nc" }, 'var_groups': [VariableGroupBase, VariableGroupWs]} ARM_97 = {'name': 'arm_97', 'description': "", 'start_time': 4321, 'end_time': 5580, 'height_min_value': 0, 'height_max_value': 18000, 'blacklisted_vars': ['rtp3', 'Skrt_zt', 'Skthl_zt', 'thlp3', 'rtpthvp', 'thlpthvp'], 'sam_benchmark_file': {'sam_benchmark': SAM_BENCHMARK_OUTPUT_ROOT + "/ARM97_r1315_128x128x128_1km_Morrison/ARM9707.nc"}, 'clubb_file': {'zm': clubb_output_root + '/arm_97_zm.nc', 'zt': clubb_output_root + '/arm_97_zt.nc', 'sfc': clubb_output_root + '/arm_97_sfc.nc', 'subcolumns': clubb_output_root + '/arm_97_nl_lh_sample_points_2D.nc'}, 'coamps_benchmark_file': None, 'wrf_benchmark_file': None, 'clubb_r408_benchmark_file': None, 'clubb_hoc_benchmark_file': None, 'e3sm_file': None, 'cam_file': None, 'sam_file': {'sam': sam_output_root + "/ARM9707_SAM_CLUBB.nc"}, 'wrf_file': None, 'var_groups': [VariableGroupBase, VariableGroupWs, VariableGroupLiquidMP, VariableGroupIceMP]} ASTEX_A209 = {'name': 'astex_a209', 'description': "", 'start_time': 2340, 'end_time': 2400, 'height_min_value': 0, 'height_max_value': 6000, 'blacklisted_vars': [], 'sam_benchmark_file': None, 'clubb_file': {'zm': clubb_output_root + '/astex_a209_zm.nc', 'zt': clubb_output_root + '/astex_a209_zt.nc', 'sfc': clubb_output_root + '/astex_a209_sfc.nc'}, 'coamps_benchmark_file': None, 'wrf_benchmark_file': None, 'clubb_r408_benchmark_file': None, 'clubb_hoc_benchmark_file': None, 'e3sm_file': None, 'cam_file': None, 'sam_file': None, 'wrf_file': None, 'var_groups': [VariableGroupBase, VariableGroupWs, VariableGroupLiquidMP, VariableGroupCorrelations, VariableGroupKKMP]} ATEX = {'name': 'atex', 'description': "", 'start_time': 421, 'end_time': 480, 'height_min_value': 0, 'height_max_value': 2500, 'blacklisted_vars': [], 'sam_benchmark_file': None, 'clubb_file': {'zm': clubb_output_root + '/atex_zm.nc', 'zt': clubb_output_root + '/atex_zt.nc', 'sfc': clubb_output_root + '/atex_sfc.nc'}, 'coamps_benchmark_file': {'sm': COAMPS_BENCHMARK_OUTPUT_ROOT + "/atex_coamps_sm.nc", 'sw': COAMPS_BENCHMARK_OUTPUT_ROOT + "/atex_coamps_sw.nc"}, 'wrf_benchmark_file': None, 'clubb_r408_benchmark_file': {'zm': R408_OUTPUT_ROOT + '/Chris_Golaz_best_ever/atex_zm.nc', 'zt': R408_OUTPUT_ROOT + '/Chris_Golaz_best_ever/atex_zt.nc', 'sfc': R408_OUTPUT_ROOT + '/Chris_Golaz_best_ever/atex_sfc.nc'}, 'clubb_hoc_benchmark_file': {'zm': HOC_OUTPUT_ROOT + '/atex_zm.nc', 'zt': HOC_OUTPUT_ROOT + '/atex_zt.nc', 'sfc': HOC_OUTPUT_ROOT + '/atex_sfc.nc'}, 'e3sm_file': None, 'cam_file': {'cam': cam_output_root + "/atex_cam.nc"}, 'sam_file': None, 'wrf_file': {'zm': wrf_output_root + "/atex_zm_wrf.nc", 'zt': wrf_output_root + "/atex_zt_wrf.nc", 'sfc': wrf_output_root + "/atex_sfc_wrf.nc" }, 'var_groups': [VariableGroupBase, VariableGroupWs, VariableGroupLiquidMP, VariableGroupIceMP]} BOMEX = {'name': 'bomex', 'description': "", 'start_time': 181, 'end_time': 360, 'height_min_value': 0, 'height_max_value': 2500, 'blacklisted_vars': [], 'sam_benchmark_file': {'sam_benchmark': SAM_BENCHMARK_OUTPUT_ROOT + "/JULY_2017/BOMEX_64x64x75/BOMEX_64x64x75_100m_40m_1s.nc"}, 'clubb_file': {'zm': clubb_output_root + '/bomex_zm.nc', 'zt': clubb_output_root + '/bomex_zt.nc', 'sfc': clubb_output_root + '/bomex_sfc.nc'}, 'coamps_benchmark_file': {'sm': COAMPS_BENCHMARK_OUTPUT_ROOT + "/bomex_coamps_sm.nc", 'sw': COAMPS_BENCHMARK_OUTPUT_ROOT + "/bomex_coamps_sw.nc"}, 'wrf_benchmark_file': None, 'clubb_r408_benchmark_file': {'zm': R408_OUTPUT_ROOT + '/Chris_Golaz_best_ever/bomex_zm.nc', 'zt': R408_OUTPUT_ROOT + '/Chris_Golaz_best_ever/bomex_zt.nc', 'sfc': R408_OUTPUT_ROOT + '/Chris_Golaz_best_ever/bomex_sfc.nc'}, 'clubb_hoc_benchmark_file': {'zm': HOC_OUTPUT_ROOT + '/bomex_zm.nc', 'zt': HOC_OUTPUT_ROOT + '/bomex_zt.nc', 'sfc': HOC_OUTPUT_ROOT + '/bomex_sfc.nc'}, 'e3sm_file': { 'e3sm': e3sm_output_root + '/bomex.nc'}, 'cam_file': None, 'sam_file': {'sam': sam_output_root + "/BOMEX_SAM_CLUBB.nc"}, 'wrf_file': {'zm': wrf_output_root + '/bomex_zm_wrf.nc', 'zt': wrf_output_root + '/bomex_zt_wrf.nc', 'sfc': wrf_output_root + '/bomex_sfc_wrf.nc'}, 'var_groups': [VariableGroupBase, VariableGroupWs]} CGILS_S6 = {'name': 'cgils_s6', 'description': "", 'start_time': 12960, 'end_time': 14400, 'height_min_value': 0, 'height_max_value': 5950, 'blacklisted_vars': ['Ngm', 'rgm', 'Skrt_zt', 'Skthl_zt', 'thlp3', 'rtpthvp', 'thlpthvp', 'wprrp', 'wpNrp'], 'sam_benchmark_file': {'sam_benchmark': SAM_BENCHMARK_OUTPUT_ROOT + "/SAM6.6/CLOUD_FEEDBACK_s6/ctl_s6_96x96x128_100m_DRZ_N100_tqndg.nc"}, 'clubb_file': {'zm': clubb_output_root + '/cgils_s6_zm.nc', 'zt': clubb_output_root + '/cgils_s6_zt.nc', 'sfc': clubb_output_root + '/cgils_s6_sfc.nc'}, 'coamps_benchmark_file': None, 'wrf_benchmark_file': None, 'clubb_r408_benchmark_file': None, 'clubb_hoc_benchmark_file': None, 'e3sm_file': None, 'cam_file': None, 'sam_file': None, 'wrf_file': None, 'var_groups': [VariableGroupBase, VariableGroupLiquidMP, VariableGroupIceMP]} CGILS_S11 = {'name': 'cgils_s11', 'description': "", 'start_time': 12960, 'end_time': 14400, 'height_min_value': 0, 'height_max_value': 5950, 'blacklisted_vars': ['Ngm', 'rgm', 'Skthl_zt', 'Skrt_zt', 'rtpthvp', 'thlpthvp', 'wprrp', 'wpNrp'], 'sam_benchmark_file': {'sam_benchmark': SAM_BENCHMARK_OUTPUT_ROOT + "/SAM6.6/CLOUD_FEEDBACK_s11/ctl_s11_96x96x320_50m_DRZ_N100_ref.nc"}, 'clubb_file': {'zm': clubb_output_root + '/cgils_s11_zm.nc', 'zt': clubb_output_root + '/cgils_s11_zt.nc', 'sfc': clubb_output_root + '/cgils_s11_sfc.nc'}, 'coamps_benchmark_file': None, 'wrf_benchmark_file': None, 'clubb_r408_benchmark_file': None, 'clubb_hoc_benchmark_file': None, 'e3sm_file': None, 'cam_file': None, 'sam_file': None, 'wrf_file': None, 'var_groups': [VariableGroupBase, VariableGroupLiquidMP, VariableGroupIceMP]} CGILS_S12 = {'name': 'cgils_s12', 'description': "", 'start_time': 12960, 'end_time': 14400, 'height_min_value': 0, 'height_max_value': 5950, 'blacklisted_vars': ['Ngm', 'rgm', 'Skrt_zt', 'Skthl_zt', 'rtpthvp', 'thlpthvp', 'wprrp', 'wpNrp'], 'sam_benchmark_file': {'sam_benchmark': SAM_BENCHMARK_OUTPUT_ROOT + "/SAM6.6/CLOUD_FEEDBACK_s12/ctl_s12_96x96x192_25m_DRZ_N100_fixnudge.nc"}, 'clubb_file': {'zm': clubb_output_root + '/cgils_s12_zm.nc', 'zt': clubb_output_root + '/cgils_s12_zt.nc', 'sfc': clubb_output_root + '/cgils_s12_sfc.nc'}, 'coamps_benchmark_file': None, 'wrf_benchmark_file': None, 'clubb_r408_benchmark_file': None, 'clubb_hoc_benchmark_file': None, 'e3sm_file': None, 'cam_file': None, 'sam_file': None, 'wrf_file': None, 'var_groups': [VariableGroupBase, VariableGroupLiquidMP, VariableGroupIceMP]} CLEX9_NOV02 = {'name': 'clex9_nov02', 'description': "", 'start_time': 181, 'end_time': 240, 'height_min_value': 4000, 'height_max_value': 6072, 'blacklisted_vars': ['Ngm'], 'sam_benchmark_file': None, 'clubb_file': {'zm': clubb_output_root + '/clex9_nov02_zm.nc', 'zt': clubb_output_root + '/clex9_nov02_zt.nc', 'sfc': clubb_output_root + '/clex9_nov02_sfc.nc'}, 'coamps_benchmark_file': {'sm': COAMPS_BENCHMARK_OUTPUT_ROOT + "/clex9_nov02_coamps_sm.nc", 'sw': COAMPS_BENCHMARK_OUTPUT_ROOT + "/clex9_nov02_coamps_sw.nc"}, 'wrf_benchmark_file': None, 'clubb_r408_benchmark_file': None, 'clubb_hoc_benchmark_file': None, 'e3sm_file': None, 'cam_file': None, 'sam_file': None, 'wrf_file': None, 'var_groups': [VariableGroupBase, VariableGroupLiquidMP, VariableGroupIceMP]} CLEX9_OCT14 = {'name': 'clex9_oct14', 'description': "", 'start_time': 181, 'end_time': 240, 'height_min_value': 2230, 'height_max_value': 6688, 'blacklisted_vars': ['Ngm'], 'sam_benchmark_file': None, 'clubb_file': {'zm': clubb_output_root + '/clex9_oct14_zm.nc', 'zt': clubb_output_root + '/clex9_oct14_zt.nc', 'sfc': clubb_output_root + '/clex9_oct14_sfc.nc'}, 'coamps_benchmark_file': {'sm': COAMPS_BENCHMARK_OUTPUT_ROOT + "/clex9_oct14_coamps_sm.nc", 'sw': COAMPS_BENCHMARK_OUTPUT_ROOT + "/clex9_oct14_coamps_sw.nc"}, 'wrf_benchmark_file': None, 'clubb_r408_benchmark_file': None, 'clubb_hoc_benchmark_file': None, 'e3sm_file': None, 'cam_file': None, 'sam_file': None, 'wrf_file': None, 'var_groups': [VariableGroupBase, VariableGroupLiquidMP, VariableGroupIceMP]} DYCOMS2_RF01 = {'name': 'dycoms2_rf01', 'description': "", 'start_time': 181, 'end_time': 240, 'height_min_value': 0, 'height_max_value': 1200, 'blacklisted_vars': [], 'sam_benchmark_file': {'sam_benchmark': SAM_BENCHMARK_OUTPUT_ROOT + "/JULY_2017/DYCOMS_RF01_96x96x320/DYCOMS_RF01_96x96x320.nc"}, 'clubb_file': {'zm': clubb_output_root + '/dycoms2_rf01_zm.nc', 'zt': clubb_output_root + '/dycoms2_rf01_zt.nc', 'sfc': clubb_output_root + '/dycoms2_rf01_sfc.nc'}, 'coamps_benchmark_file': None, 'wrf_benchmark_file': None, 'clubb_r408_benchmark_file': {'zm': R408_OUTPUT_ROOT + '/Chris_Golaz_best_ever/dycoms2_rf01_zm.nc', 'zt': R408_OUTPUT_ROOT + '/Chris_Golaz_best_ever/dycoms2_rf01_zt.nc', 'sfc': R408_OUTPUT_ROOT + '/Chris_Golaz_best_ever/dycoms2_rf01_sfc.nc'}, 'clubb_hoc_benchmark_file': {'zm': HOC_OUTPUT_ROOT + '/dycoms2_rf01_zm.nc', 'zt': HOC_OUTPUT_ROOT + '/dycoms2_rf01_zt.nc', 'sfc': HOC_OUTPUT_ROOT + '/dycoms2_rf01_sfc.nc'}, 'e3sm_file': { 'e3sm': e3sm_output_root + "/dycoms2_rf01.nc"}, 'cam_file': None, 'sam_file': None, 'wrf_file': None, 'var_groups': [VariableGroupBase, VariableGroupWs]} DYCOMS2_RF01_FIXED_SST = {'name': 'dycoms2_rf01_fixed_sst', 'description': "Copied from plotgen: Ran with a 5 min timestep and a 48-level grid", 'start_time': 2520, 'end_time': 2700, 'height_min_value': 0, 'height_max_value': 1200, 'blacklisted_vars': ['rtp3', 'Skrt_zt', 'Skthl_zt', 'rtpthvp', 'thlpthvp'], 'sam_benchmark_file': {'sam_benchmark': SAM_BENCHMARK_OUTPUT_ROOT + "/SAM6.6/DYCOMS_RF01_fixed_sst/DYCOMS_RF01_96x96x320_LES_fixed_sst.nc"}, 'clubb_file': {'zm': clubb_output_root + '/dycoms2_rf01_fixed_sst_zm.nc', 'zt': clubb_output_root + '/dycoms2_rf01_fixed_sst_zt.nc', 'sfc': clubb_output_root + '/dycoms2_rf01_fixed_sst_sfc.nc'}, 'coamps_benchmark_file': None, 'wrf_benchmark_file': None, 'clubb_r408_benchmark_file': None, 'clubb_hoc_benchmark_file': None, 'e3sm_file': None, 'cam_file': None, 'sam_file': None, 'wrf_file': None, 'var_groups': [VariableGroupBase]} DYCOMS2_RF02_DO = {'name': 'dycoms2_rf02_do', 'description': "", 'start_time': 301, 'end_time': 360, 'height_min_value': 0, 'height_max_value': 1200, 'blacklisted_vars': [], 'sam_benchmark_file': {'sam_benchmark': SAM_BENCHMARK_OUTPUT_ROOT + "/JULY_2017/DYCOMS_RF02_128x128x96_dr_nosed/DYCOMS_RF02_128x128x96_dr_nosed.nc"}, 'clubb_file': {'zm': clubb_output_root + '/dycoms2_rf02_do_zm.nc', 'zt': clubb_output_root + '/dycoms2_rf02_do_zt.nc', 'sfc': clubb_output_root + '/dycoms2_rf02_do_sfc.nc'}, 'coamps_benchmark_file': None, 'wrf_benchmark_file': None, 'clubb_r408_benchmark_file': {'zm': R408_OUTPUT_ROOT + '/Chris_Golaz_best_ever/dycoms2_rf02_do_zm.nc', 'zt': R408_OUTPUT_ROOT + '/Chris_Golaz_best_ever/dycoms2_rf02_do_zt.nc', 'sfc': R408_OUTPUT_ROOT + '/Chris_Golaz_best_ever/dycoms2_rf02_do_sfc.nc'}, 'clubb_hoc_benchmark_file': {'zm': HOC_OUTPUT_ROOT + '/dycoms2_rf02_do_zm.nc', 'zt': HOC_OUTPUT_ROOT + '/dycoms2_rf02_do_zt.nc', 'sfc': HOC_OUTPUT_ROOT + '/dycoms2_rf02_do_sfc.nc'}, 'e3sm_file': None, 'cam_file': None, 'sam_file': {'sam': sam_output_root + "/DYCOMS_RF02_SAM_CLUBB.nc"}, 'wrf_file': None, 'var_groups': [VariableGroupBase, VariableGroupWs, VariableGroupLiquidMP, VariableGroupCorrelations, VariableGroupKKMP]} DYCOMS2_RF02_DS = {'name': 'dycoms2_rf02_ds', 'description': "", 'start_time': 301, 'end_time': 360, 'height_min_value': 0, 'height_max_value': 1200, 'blacklisted_vars': [], 'sam_benchmark_file': {'sam_benchmark': SAM_BENCHMARK_OUTPUT_ROOT + "/JULY_2017/DYCOMS_RF02_128x128x96_dr_sed/DYCOMS_RF02_128x128x96_dr_sed.nc"}, 'clubb_file': {'zm': clubb_output_root + '/dycoms2_rf02_ds_zm.nc', 'zt': clubb_output_root + '/dycoms2_rf02_ds_zt.nc', 'sfc': clubb_output_root + '/dycoms2_rf02_ds_sfc.nc'}, 'coamps_benchmark_file': None, 'wrf_benchmark_file': None, 'clubb_r408_benchmark_file': None, 'clubb_hoc_benchmark_file': {'zm': HOC_OUTPUT_ROOT + '/dycoms2_rf02_ds_zm.nc', 'zt': HOC_OUTPUT_ROOT + '/dycoms2_rf02_ds_zt.nc', 'sfc': HOC_OUTPUT_ROOT + '/dycoms2_rf02_ds_sfc.nc'}, 'e3sm_file': {'e3sm': e3sm_output_root + "/dycoms2_rf02_ds.nc"}, 'cam_file': None, 'sam_file': None, 'wrf_file': None, 'var_groups': [VariableGroupBase, VariableGroupWs, VariableGroupLiquidMP, VariableGroupCorrelations, VariableGroupKKMP]} DYCOMS2_RF02_ND = {'name': 'dycoms2_rf02_nd', 'description': "Copied from plotgen: ** Generated by doing a restart run after 7200 seconds. Note: " "t = 0 corresponds to start time of the restart run, not the original run. ** ", 'start_time': 301, 'end_time': 360, 'height_min_value': 0, 'height_max_value': 1200, 'blacklisted_vars': ['wprrp', 'wpNrp', 'corr_w_rr_1', 'corr_w_Nr_1'], 'sam_benchmark_file': {'sam_benchmark': SAM_BENCHMARK_OUTPUT_ROOT + "/JULY_2017/DYCOMS_RF02_128x128x96_nodr_nosed/DYCOMS_RF02_128x128x96_nodr_nosed.nc"}, 'clubb_file': {'zm': clubb_output_root + '/dycoms2_rf02_nd_zm.nc', 'zt': clubb_output_root + '/dycoms2_rf02_nd_zt.nc', 'sfc': clubb_output_root + '/dycoms2_rf02_nd_sfc.nc'}, 'coamps_benchmark_file': None, 'wrf_benchmark_file': None, 'clubb_r408_benchmark_file': {'zm': R408_OUTPUT_ROOT + '/Chris_Golaz_best_ever/dycoms2_rf02_nd_zm.nc', 'zt': R408_OUTPUT_ROOT + '/Chris_Golaz_best_ever/dycoms2_rf02_nd_zt.nc', 'sfc': R408_OUTPUT_ROOT + '/Chris_Golaz_best_ever/dycoms2_rf02_nd_sfc.nc'}, 'clubb_hoc_benchmark_file': {'zm': HOC_OUTPUT_ROOT + '/dycoms2_rf02_nd_zm.nc', 'zt': HOC_OUTPUT_ROOT + '/dycoms2_rf02_nd_zt.nc', 'sfc': HOC_OUTPUT_ROOT + '/dycoms2_rf02_nd_sfc.nc'}, 'e3sm_file': None, 'cam_file': None, 'sam_file': None, 'wrf_file': None, 'var_groups': [VariableGroupBase, VariableGroupWs, VariableGroupLiquidMP, VariableGroupKKMP]} DYCOMS2_RF02_DS_RESTART = {'name': 'dycoms2_rf02_ds_restart', 'description': "Copied from plotgen: ** Uniform, coarse verticle grid spacing of 40 m. **", 'start_time': 181, 'end_time': 240, 'height_min_value': 0, 'height_max_value': 1200, 'blacklisted_vars': [], 'sam_benchmark_file': {'sam_benchmark': SAM_BENCHMARK_OUTPUT_ROOT + "/JULY_2017/DYCOMS_RF02_128x128x96_dr_sed/DYCOMS_RF02_128x128x96_dr_sed.nc"}, 'clubb_file': {'zm': clubb_output_root + '/dycoms2_rf02_ds_restart_zm.nc', 'zt': clubb_output_root + '/dycoms2_rf02_ds_restart_zt.nc', 'sfc': clubb_output_root + '/dycoms2_rf02_ds_restart_sfc.nc'}, 'coamps_benchmark_file': None, 'wrf_benchmark_file': None, 'clubb_r408_benchmark_file': {'zm': R408_OUTPUT_ROOT + '/Chris_Golaz_best_ever/dycoms2_rf02_ds_zm.nc', 'zt': R408_OUTPUT_ROOT + '/Chris_Golaz_best_ever/dycoms2_rf02_ds_zt.nc', 'sfc': R408_OUTPUT_ROOT + '/Chris_Golaz_best_ever/dycoms2_rf02_ds_sfc.nc'}, 'clubb_hoc_benchmark_file': {'zm': HOC_OUTPUT_ROOT + '/dycoms2_rf02_ds_zm.nc', 'zt': HOC_OUTPUT_ROOT + '/dycoms2_rf02_ds_zt.nc', 'sfc': HOC_OUTPUT_ROOT + '/dycoms2_rf02_ds_sfc.nc'}, 'e3sm_file': None, 'cam_file': None, 'sam_file': None, 'wrf_file': None, 'var_groups': [VariableGroupBase, VariableGroupWs, VariableGroupLiquidMP, VariableGroupCorrelations, VariableGroupKKMP]} DYCOMS2_RF02_SO = {'name': 'dycoms2_rf02_so', 'description': "Copied from plotgen: " + "** WRF-type stretched (unevenly spaced) grid (grid_type = 3) ** ", 'start_time': 301, 'end_time': 360, 'height_min_value': 0, 'height_max_value': 1200, 'blacklisted_vars': ['wprrp', 'wpNrp'], 'sam_benchmark_file': {'sam_benchmark': SAM_BENCHMARK_OUTPUT_ROOT + "/JULY_2017/DYCOMS_RF02_128x128x96_nodr_sed/DYCOMS_RF02_128x128x96_nodr_sed.nc"}, 'clubb_file': {'zm': clubb_output_root + '/dycoms2_rf02_so_zm.nc', 'zt': clubb_output_root + '/dycoms2_rf02_so_zt.nc', 'sfc': clubb_output_root + '/dycoms2_rf02_so_sfc.nc'}, 'coamps_benchmark_file': None, 'wrf_benchmark_file': None, 'clubb_r408_benchmark_file': {'zm': R408_OUTPUT_ROOT + '/Chris_Golaz_best_ever/dycoms2_rf02_so_zm.nc', 'zt': R408_OUTPUT_ROOT + '/Chris_Golaz_best_ever/dycoms2_rf02_so_zt.nc', 'sfc': R408_OUTPUT_ROOT + '/Chris_Golaz_best_ever/dycoms2_rf02_so_sfc.nc'}, 'clubb_hoc_benchmark_file': {'zm': HOC_OUTPUT_ROOT + '/dycoms2_rf02_so_zm.nc', 'zt': HOC_OUTPUT_ROOT + '/dycoms2_rf02_so_zt.nc', 'sfc': HOC_OUTPUT_ROOT + '/dycoms2_rf02_so_sfc.nc'}, 'e3sm_file': None, 'cam_file': None, 'sam_file': {'sam': sam_output_root + "/DYCOMS_RF02_SAM_CLUBB.nc"}, 'wrf_file': None, 'var_groups': [VariableGroupBase, VariableGroupWs, VariableGroupLiquidMP, VariableGroupKKMP]} FIRE = {'name': 'fire', 'description': "", 'start_time': 61, 'end_time': 120, 'height_min_value': 0, 'height_max_value': 1000, 'blacklisted_vars': [], 'sam_benchmark_file': None, 'clubb_file': {'zm': clubb_output_root + '/fire_zm.nc', 'zt': clubb_output_root + '/fire_zt.nc', 'sfc': clubb_output_root + '/fire_sfc.nc'}, 'coamps_benchmark_file': {'sm': COAMPS_BENCHMARK_OUTPUT_ROOT + "/fire_coamps_sm.nc", 'sw': COAMPS_BENCHMARK_OUTPUT_ROOT + "/fire_coamps_sw.nc"}, 'wrf_benchmark_file': None, 'clubb_r408_benchmark_file': {'zm': R408_OUTPUT_ROOT + '/Chris_Golaz_best_ever/fire_zm.nc', 'zt': R408_OUTPUT_ROOT + '/Chris_Golaz_best_ever/fire_zt.nc', 'sfc': R408_OUTPUT_ROOT + '/Chris_Golaz_best_ever/fire_sfc.nc'}, 'clubb_hoc_benchmark_file': {'zm': HOC_OUTPUT_ROOT + "/fire_zm.nc", 'zt': HOC_OUTPUT_ROOT + '/fire_zt.nc', 'sfc': HOC_OUTPUT_ROOT + '/fire_sfc.nc'}, 'e3sm_file': None, 'cam_file': None, 'sam_file': None, 'wrf_file': {'zm': wrf_output_root + "/fire_zm_wrf.nc", 'zt': wrf_output_root + "/fire_zt_wrf.nc", 'sfc': wrf_output_root + "/fire_sfc_wrf.nc" }, 'var_groups': [VariableGroupBase, VariableGroupWs]} # No budgets GABLS2 = {'name': 'gabls2', 'description': "", 'start_time': 2101, 'end_time': 2160, 'height_min_value': 0, 'height_max_value': 2500, 'blacklisted_vars': ['tau_zm', 'radht', 'Skw_zt', 'Skrt_zt', 'Skthl_zt', 'corr_w_chi_1', 'corr_chi_eta_1', 'rcp2', 'thlpthvp', 'rtpthvp'], 'sam_benchmark_file': None, 'clubb_file': {'zm': clubb_output_root + '/gabls2_zm.nc', 'zt': clubb_output_root + '/gabls2_zt.nc', 'sfc': clubb_output_root + '/gabls2_sfc.nc'}, 'coamps_benchmark_file': {'sm': COAMPS_BENCHMARK_OUTPUT_ROOT + "/gabls2_coamps_sm.nc", 'sw': COAMPS_BENCHMARK_OUTPUT_ROOT + "/gabls2_coamps_sw.nc", 'sfc': COAMPS_BENCHMARK_OUTPUT_ROOT + "/gabls2_coamps_sfc.nc"}, 'wrf_benchmark_file': None, 'clubb_r408_benchmark_file': None, 'clubb_hoc_benchmark_file': None, 'e3sm_file': None, 'cam_file': None, 'sam_file': None, 'wrf_file': None, 'var_groups': [VariableGroupBase]} GABLS2_NIGHTLY = {'name': 'gabls2_nightly', 'description': "", 'start_time': 2101, 'end_time': 2160, 'height_min_value': 0, 'height_max_value': 2500, 'blacklisted_vars': [], 'sam_benchmark_file': None, 'clubb_file': {'zm': clubb_output_root + '/gabls2_zm.nc', 'zt': clubb_output_root + '/gabls2_zt.nc', 'sfc': clubb_output_root + '/gabls2_sfc.nc'}, 'coamps_benchmark_file': None, 'wrf_benchmark_file': None, 'clubb_r408_benchmark_file': None, 'clubb_hoc_benchmark_file': None, 'e3sm_file': None, 'cam_file': None, 'sam_file': None, 'wrf_file': None, 'var_groups': [VariableGroupBase, VariableGroupScalars]} GABLS3 = {'name': 'gabls3', 'description': "", 'start_time': 1081, 'end_time': 1200, 'height_min_value': 0, 'height_max_value': 4970, 'blacklisted_vars': [], 'sam_benchmark_file': None, 'clubb_file': {'zm': clubb_output_root + '/gabls3_zm.nc', 'zt': clubb_output_root + '/gabls3_zt.nc', 'sfc': clubb_output_root + '/gabls3_sfc.nc'}, 'coamps_benchmark_file': None, 'wrf_benchmark_file': None, 'clubb_r408_benchmark_file': None, 'clubb_hoc_benchmark_file': None, 'e3sm_file': None, 'cam_file': None, 'sam_file': None, 'wrf_file': None, 'var_groups': [VariableGroupBase]} GABLS3_NIGHT = {'name': 'gabls3_night', 'description': "Copied from plotgen: Uses a 5-min timestep with 48 levels", 'start_time': 421, 'end_time': 480, 'height_min_value': 0, 'height_max_value': 800, 'blacklisted_vars': [], 'sam_benchmark_file': {'sam_benchmark': SAM_BENCHMARK_OUTPUT_ROOT + "/SAM6.6/GABLS3_NIGHT/gabls3_night.nc"}, 'clubb_file': {'zm': clubb_output_root + '/gabls3_night_zm.nc', 'zt': clubb_output_root + '/gabls3_night_zt.nc', 'sfc': clubb_output_root + '/gabls3_night_sfc.nc'}, 'coamps_benchmark_file': None, 'wrf_benchmark_file': None, 'clubb_r408_benchmark_file': None, 'clubb_hoc_benchmark_file': None, 'e3sm_file': None, 'cam_file': None, 'sam_file': None, 'wrf_file': None, 'var_groups': [VariableGroupBase]} GATE_SHEAR_RLSF = {'name': 'gate_shear_rlsf', 'description': "", 'start_time': 540, 'end_time': 720, 'height_min_value': 0, 'height_max_value': 24000, 'blacklisted_vars': [], 'sam_benchmark_file': {'sam_benchmark': SAM_BENCHMARK_OUTPUT_ROOT + "/SAM6.6/GATE_shear_rlsf/GATE_shear_rlsf_64x64x128_1km_5s.nc"}, 'clubb_file': None, 'coamps_benchmark_file': None, 'wrf_benchmark_file': None, 'clubb_r408_benchmark_file': None, 'clubb_hoc_benchmark_file': None, 'e3sm_file': None, 'cam_file': None, 'sam_file': {'sam': sam_output_root + "/GATE_SAM_CLUBB.nc"}, 'wrf_file': None, 'var_groups': [VariableGroupBase]} # Use to plot IOP forced SAM runs IOP = {'name': 'iop', 'description': "", 'start_time': 181, 'end_time': 1440, 'height_min_value': 0, 'height_max_value': 27750, 'blacklisted_vars': [], 'clubb_datasets': None, 'sam_benchmark_file': None, 'clubb_file': None, 'coamps_benchmark_file': None, 'wrf_benchmark_file': None, 'clubb_r408_benchmark_file': None, 'clubb_hoc_benchmark_file': None, 'e3sm_file': None, 'cam_file': None, 'var_groups': [VariableGroupBase, VariableGroupSamProfiles]} JUN25_ALTOCU = {'name': 'jun25_altocu', 'description': "", 'start_time': 181, 'end_time': 240, 'height_min_value': 4825, 'height_max_value': 7290, 'blacklisted_vars': ['Ngm', 'wprrp', 'wpNrp'], 'sam_benchmark_file': None, 'clubb_file': {'zm': clubb_output_root + '/jun25_altocu_zm.nc', 'zt': clubb_output_root + '/jun25_altocu_zt.nc', 'sfc': clubb_output_root + '/jun25_altocu_sfc.nc'}, 'coamps_benchmark_file': {'sm': COAMPS_BENCHMARK_OUTPUT_ROOT + "/jun25_altocu_qc3_coamps_sm.nc", 'sw': COAMPS_BENCHMARK_OUTPUT_ROOT + "/jun25_altocu_qc3_coamps_sw.nc"}, 'wrf_benchmark_file': None, 'clubb_r408_benchmark_file': None, 'clubb_hoc_benchmark_file': None, 'e3sm_file': None, 'cam_file': None, 'sam_file': None, 'wrf_file': None, 'var_groups': [VariableGroupBase, VariableGroupLiquidMP, VariableGroupIceMP]} LBA = {'name': 'lba', 'description': "Note that sam-plotgen plots up to a height of 16000 not 12000.\n" "Copied from plotgen: SAM-LES uses Morrison microphysics " + "and CLUBB standalone uses COAMPS microphysics", 'start_time': 300, 'end_time': 360, 'height_min_value': 0, 'height_max_value': 14000, 'blacklisted_vars': ['wprrp', 'wpNrp', 'Ngm'], 'sam_benchmark_file': {'sam_benchmark': SAM_BENCHMARK_OUTPUT_ROOT + "/JULY_2017/LBA_128kmx128kmx128_1km_Morrison/LBA_128kmx128kmx128_1km_Morrison.nc"}, 'clubb_file': {'zm': clubb_output_root + '/lba_zm.nc', 'zt': clubb_output_root + '/lba_zt.nc', 'sfc': clubb_output_root + '/lba_sfc.nc', 'subcolumns': clubb_output_root + '/lba_nl_lh_sample_points_2D.nc'}, 'coamps_benchmark_file': None, 'wrf_benchmark_file': None, 'clubb_r408_benchmark_file': None, 'clubb_hoc_benchmark_file': None, 'e3sm_file': None, 'cam_file': None, 'sam_file': {'sam': sam_output_root + "/LBA_SAM_CLUBB.nc"}, 'wrf_file': None, 'var_groups': [VariableGroupBase, VariableGroupLiquidMP, VariableGroupIceMP, VariableGroupWs]} MC3E = {'name': 'mc3e', 'description': "", 'start_time': 60, 'end_time': 64800, 'height_min_value': 0, 'height_max_value': 18000, 'blacklisted_vars': ['rtp3', 'Skrt_zt', 'Skthl_zt', 'rtpthvp', 'thlpthvp', 'wprrp', 'wpNrp'], 'sam_benchmark_file': {'sam_benchmark': SAM_BENCHMARK_OUTPUT_ROOT + "/MC3E_r1359_128x128x128_1km_Morrison/MC3E.nc"}, 'clubb_file': {'zm': clubb_output_root + '/mc3e_zm.nc', 'zt': clubb_output_root + '/mc3e_zt.nc', 'sfc': clubb_output_root + '/mc3e_sfc.nc', 'subcolumns': clubb_output_root + '/mc3e_nl_lh_sample_points_2D.nc'}, 'coamps_benchmark_file': None, 'wrf_benchmark_file': None, 'clubb_r408_benchmark_file': None, 'clubb_hoc_benchmark_file': None, 'e3sm_file': None, 'cam_file': None, 'sam_file': None, 'wrf_file': None, 'var_groups': [VariableGroupBase, VariableGroupLiquidMP, VariableGroupIceMP]} MPACE_A = {'name': 'mpace_a', 'description': "Copied from plotgen: SAM-LES and CLUBB standalone use Morrison microphysics", 'start_time': 4141, 'end_time': 4320, 'height_min_value': 0, 'height_max_value': 10000, 'blacklisted_vars': ['Skrt_zt', 'Skthl_zt', 'rtpthvp', 'thlpthvp', 'Ngm', 'wpNrp'], 'sam_benchmark_file': {'sam_benchmark': SAM_BENCHMARK_OUTPUT_ROOT + "/SAM6.6/MPACE_A/MPACE_A_128x128x69_morr_CEM.nc"}, 'clubb_file': {'zm': clubb_output_root + '/mpace_a_zm.nc', 'zt': clubb_output_root + '/mpace_a_zt.nc', 'sfc': clubb_output_root + '/mpace_a_sfc.nc'}, 'coamps_benchmark_file': None, 'wrf_benchmark_file': None, 'clubb_r408_benchmark_file': None, 'clubb_hoc_benchmark_file': None, 'e3sm_file': None, 'cam_file': None, 'sam_file': None, 'wrf_file': None, 'var_groups': [VariableGroupBase, VariableGroupLiquidMP, VariableGroupIceMP]} MPACE_B = {'name': 'mpace_b', 'description': "Copied from plotgen: **The nightly simulation uses COAMPS microphysics**", 'start_time': 541, 'end_time': 720, 'height_min_value': 0, 'height_max_value': 2750, 'blacklisted_vars': ['Ngm', 'wpNrp'], 'sam_benchmark_file': None, 'clubb_file': {'zm': clubb_output_root + '/mpace_b_zm.nc', 'zt': clubb_output_root + '/mpace_b_zt.nc', 'sfc': clubb_output_root + '/mpace_b_sfc.nc'}, 'coamps_benchmark_file': {'sm': COAMPS_BENCHMARK_OUTPUT_ROOT + "/mpace_b_coamps_sm.nc", 'sw': COAMPS_BENCHMARK_OUTPUT_ROOT + "/mpace_b_coamps_sw.nc", 'sfc': COAMPS_BENCHMARK_OUTPUT_ROOT + "/mpace_b_coamps_sfc.nc"}, 'wrf_benchmark_file': None, 'clubb_r408_benchmark_file': None, 'clubb_hoc_benchmark_file': None, 'e3sm_file': None, 'cam_file': None, 'sam_file': None, 'wrf_file': None, 'var_groups': [VariableGroupBase, VariableGroupLiquidMP, VariableGroupIceMP]} MPACE_B_SILHS = {'name': 'mpace_b_silhs', 'description': "", 'start_time': 541, 'end_time': 720, 'height_min_value': 0, 'height_max_value': 2750, 'blacklisted_vars': ['Ngm', 'wpNrp'], 'sam_benchmark_file': None, 'clubb_file': {'zm': clubb_output_root + '/mpace_b_silhs_zm.nc', 'zt': clubb_output_root + '/mpace_b_silhs_zt.nc', 'sfc': clubb_output_root + '/mpace_b_silhs_sfc.nc', 'subcolumns': clubb_output_root + '/mpace_b_silhs_nl_lh_sample_points_2D.nc'}, 'coamps_benchmark_file': {'sm': COAMPS_BENCHMARK_OUTPUT_ROOT + "/mpace_b_coamps_sm.nc", 'sw': COAMPS_BENCHMARK_OUTPUT_ROOT + "/mpace_b_coamps_sw.nc"}, 'wrf_benchmark_file': None, 'clubb_r408_benchmark_file': None, 'clubb_hoc_benchmark_file': None, 'e3sm_file': None, 'cam_file': None, 'sam_file': None, 'wrf_file': None, 'var_groups': [VariableGroupBase, VariableGroupLiquidMP, VariableGroupIceMP]} NOV11_ALTOCU = {'name': 'nov11_altocu', 'description': "", 'start_time': 91, 'end_time': 150, 'height_min_value': 4160, 'height_max_value': 6150, 'blacklisted_vars': ['Ngm'], 'sam_benchmark_file': None, 'clubb_file': {'zm': clubb_output_root + '/nov11_altocu_zm.nc', 'zt': clubb_output_root + '/nov11_altocu_zt.nc', 'sfc': clubb_output_root + '/nov11_altocu_sfc.nc'}, 'coamps_benchmark_file': {'sm': COAMPS_BENCHMARK_OUTPUT_ROOT + "/nov11_altocu_coamps_sm.nc", 'sw': COAMPS_BENCHMARK_OUTPUT_ROOT + "/nov11_altocu_coamps_sw.nc"}, 'wrf_benchmark_file': None, 'clubb_r408_benchmark_file': {'zm': R408_OUTPUT_ROOT + '/Chris_Golaz_best_ever/nov11_altocu_zm.nc', 'zt': R408_OUTPUT_ROOT + '/Chris_Golaz_best_ever/nov11_altocu_zt.nc', 'sfc': R408_OUTPUT_ROOT + '/Chris_Golaz_best_ever/nov11_altocu_sfc.nc'}, 'clubb_hoc_benchmark_file': {'zm': HOC_OUTPUT_ROOT + '/nov11_altocu_zm.nc', 'zt': HOC_OUTPUT_ROOT + '/nov11_altocu_zt.nc', 'sfc': HOC_OUTPUT_ROOT + '/nov11_altocu_sfc.nc'}, 'e3sm_file': None, 'cam_file': None, 'sam_file': None, 'wrf_file': None, 'var_groups': [VariableGroupBase, VariableGroupLiquidMP, VariableGroupIceMP]} RICO = {'name': 'rico', 'description': "Cam output may differ from plotgen due to a difference in time averaging.", 'start_time': 4201, 'end_time': 4320, 'height_min_value': 0, 'height_max_value': 5000, 'blacklisted_vars': [], 'sam_benchmark_file': {'sam_benchmark': SAM_BENCHMARK_OUTPUT_ROOT + "/JULY_2017/RICO_256x256x100_drizzle/RICO_256x256x100_drizzle.nc"}, 'clubb_file': {'zm': clubb_output_root + '/rico_zm.nc', 'zt': clubb_output_root + '/rico_zt.nc', 'sfc': clubb_output_root + '/rico_sfc.nc'}, 'coamps_benchmark_file': {'sm': COAMPS_BENCHMARK_OUTPUT_ROOT + "/rico_coamps_sm.nc", 'sw': COAMPS_BENCHMARK_OUTPUT_ROOT + "/rico_coamps_sw.nc"}, 'wrf_benchmark_file': None, 'clubb_r408_benchmark_file': None, 'clubb_hoc_benchmark_file': None, 'e3sm_file': {'e3sm': e3sm_output_root + "/rico.nc"}, 'cam_file': {'cam': cam_output_root + "/rico_cam.nc"}, 'sam_file': {'sam': sam_output_root + "/RICO_256x256x100_drizzle.nc"}, 'wrf_file': None, 'var_groups': [VariableGroupBase, VariableGroupLiquidMP, VariableGroupWs, VariableGroupCorrelations, VariableGroupKKMP]} RICO_SILHS = {'name': 'rico_silhs', 'description': "Copied from plotgen: CLUBB and SAM use Khairoutdinov-Kogan microphysics", 'start_time': 4201, 'end_time': 4320, 'height_min_value': 0, 'height_max_value': 4500, 'blacklisted_vars': ['wpNrp'], 'sam_benchmark_file': {'sam_benchmark': SAM_BENCHMARK_OUTPUT_ROOT + "/JULY_2017/RICO_256x256x100_drizzle/RICO_256x256x100_drizzle.nc"}, 'clubb_file': {'zm': clubb_output_root + '/rico_silhs_zm.nc', 'zt': clubb_output_root + '/rico_silhs_zt.nc', 'sfc': clubb_output_root + '/rico_silhs_sfc.nc', 'subcolumns': clubb_output_root + '/rico_silhs_nl_lh_sample_points_2D.nc'}, 'coamps_benchmark_file': {'sm': COAMPS_BENCHMARK_OUTPUT_ROOT + "/rico_coamps_sm.nc", 'sw': COAMPS_BENCHMARK_OUTPUT_ROOT + "/rico_coamps_sw.nc"}, 'wrf_benchmark_file': None, 'clubb_r408_benchmark_file': None, 'clubb_hoc_benchmark_file': None, 'e3sm_file': None, 'cam_file': None, 'sam_file': None, 'wrf_file': None, 'var_groups': [VariableGroupBase, VariableGroupLiquidMP, VariableGroupWs, VariableGroupCorrelations, VariableGroupKKMP]} NEUTRAL = {'name': 'neutral', 'description': "", 'start_time': 181, 'end_time': 360, 'height_min_value': 0, 'height_max_value': 1500, 'blacklisted_vars': [], 'sam_benchmark_file': {'sam_benchmark': SAM_BENCHMARK_OUTPUT_ROOT + "/NEUTRAL/NEUTRAL_96x96x96_32m_10m_LES.nc"}, 'clubb_file': {'zm': clubb_output_root + '/neutral_zm.nc', 'zt': clubb_output_root + '/neutral_zt.nc', 'sfc': clubb_output_root + '/neutral_sfc.nc'}, 'coamps_benchmark_file': None, 'wrf_benchmark_file': None, 'clubb_r408_benchmark_file': None, 'clubb_hoc_benchmark_file': None, 'e3sm_file': None, 'cam_file': None, 'sam_file': None, 'wrf_file': None, 'var_groups': [VariableGroupBase, VariableGroupWs]} TWP_ICE = {'name': 'twp_ice', 'description': "Copied from plotgen: Both vertical and horizontal fluxes applied to THLM and RTM for LES. " "LES nudged U, V, RTM and THLM toward observed values. Forcings for LES derived from 10mb " "forcing data.", 'start_time': 60, 'end_time': 9900, 'height_min_value': 0, 'height_max_value': 19000, 'blacklisted_vars': ['rtp3', 'Skrt_zt', 'Skthl_zt', 'rtpthvp', 'thlpthvp', 'wprrp', 'wpNrp'], 'sam_benchmark_file': {'sam_benchmark': SAM_BENCHMARK_OUTPUT_ROOT + "/TWP_ICE_r1315_128x128x128_1km_Morrison/TWP_ICE.nc"}, 'clubb_file': {'zm': clubb_output_root + '/twp_ice_zm.nc', 'zt': clubb_output_root + '/twp_ice_zt.nc', 'sfc': clubb_output_root + '/twp_ice_sfc.nc', 'subcolumns': clubb_output_root + '/twp_ice_nl_lh_sample_points_2D.nc'}, 'coamps_benchmark_file': None, 'wrf_benchmark_file': None, 'clubb_r408_benchmark_file': None, 'clubb_hoc_benchmark_file': None, 'e3sm_file': None, 'cam_file': None, 'sam_file': None, 'wrf_file': None, 'var_groups': [VariableGroupBase, VariableGroupWs, VariableGroupLiquidMP, VariableGroupIceMP]} WANGARA = {'name': 'wangara', 'description': "Note that COAMPS benchmark data is actually RAMS data by default.", 'start_time': 181, 'end_time': 240, 'height_min_value': 0, 'height_max_value': 1900, 'blacklisted_vars': ['Ngm'], 'sam_benchmark_file': {'sam_benchmark': SAM_BENCHMARK_OUTPUT_ROOT + "/WANGARA/WANGARA_64x64x80_100m_40m_LES.nc"}, 'clubb_file': {'zm': clubb_output_root + '/wangara_zm.nc', 'zt': clubb_output_root + '/wangara_zt.nc', 'sfc': clubb_output_root + '/wangara_sfc.nc'}, 'coamps_benchmark_file': {'sw': COAMPS_BENCHMARK_OUTPUT_ROOT + "/wangara_rams.nc", 'sm': COAMPS_BENCHMARK_OUTPUT_ROOT + "/wangara_rams.nc"}, 'wrf_benchmark_file': None, 'clubb_r408_benchmark_file': {'zm': R408_OUTPUT_ROOT + '/Chris_Golaz_best_ever/wangara_zm.nc', 'zt': R408_OUTPUT_ROOT + '/Chris_Golaz_best_ever/wangara_zt.nc', 'sfc': R408_OUTPUT_ROOT + '/Chris_Golaz_best_ever/wangara_sfc.nc'}, 'clubb_hoc_benchmark_file': {'zm': HOC_OUTPUT_ROOT + '/wangara_zm.nc', 'zt': HOC_OUTPUT_ROOT + '/wangara_zt.nc', 'sfc': HOC_OUTPUT_ROOT + '/wangara_sfc.nc'}, 'e3sm_file': None, 'cam_file': None, 'sam_file': None, 'wrf_file': {'zm': wrf_output_root + "/wangara_zm_wrf.nc", 'zt': wrf_output_root + "/wangara_zt_wrf.nc", 'sfc': wrf_output_root + "/wangara_sfc_wrf.nc" }, 'var_groups': [VariableGroupBase, VariableGroupWs]} LASSO_20170627 = {'name': 'lasso_20170627', 'description': "Comparing WRF-CLUBB output to WRF-LASSO output.", 'start_time': 301, 'end_time': 600, 'height_min_value': 0, 'height_max_value': 4000, 'blacklisted_vars': [], 'e3sm_file': None, 'cam_file': None, 'sam_file': None, 'wrf_benchmark_file': {'lasso_benchmark': WRF_LASSO_BENCHMARK_OUTPUT_ROOT + "/2017-06-27/wrf_lasso_stats_2017-06-27.nc"}, 'sam_benchmark_file': None, 'coamps_benchmark_file': None, 'clubb_r408_benchmark_file': None, 'clubb_hoc_benchmark_file': None, 'clubb_file': None, 'wrf_file': {'zm': clubb_output_root + '/lasso_2017-06-27_zm_wrf.nc', 'zt': clubb_output_root + '/lasso_2017-06-27_zt_wrf.nc', 'sfc': clubb_output_root + '/lasso_2017-06-27_sfc_wrf.nc', 'subcolumns': clubb_output_root + '/lasso_2017-06-27_nl_lh_sample_points_2D.nc'}, 'var_groups': [VariableGroupBase, VariableGroupWs]} LASSO_20170717 = {'name': 'lasso_20170717', 'description': "Comparing WRF-CLUBB output to WRF-LASSO output.", 'start_time': 301, 'end_time': 600, 'height_min_value': 0, 'height_max_value': 4000, 'blacklisted_vars': [], 'e3sm_file': None, 'cam_file': None, 'sam_file': None, 'wrf_benchmark_file': {'lasso_benchmark': WRF_LASSO_BENCHMARK_OUTPUT_ROOT + "/2017-07-17/wrf_lasso_stats_2017-07-17.nc"}, 'sam_benchmark_file': None, 'coamps_benchmark_file': None, 'clubb_r408_benchmark_file': None, 'clubb_hoc_benchmark_file': None, 'clubb_file': None, 'wrf_file': {'zm': clubb_output_root + '/lasso_2017-07-17_zm_wrf.nc', 'zt': clubb_output_root + '/lasso_2017-07-17_zt_wrf.nc', 'sfc': clubb_output_root + '/lasso_2017-07-17_sfc_wrf.nc', 'subcolumns': clubb_output_root + '/lasso_2017-07-17_nl_lh_sample_points_2D.nc'}, 'var_groups': [VariableGroupBase, VariableGroupWs]} LASSO_20170728 = {'name': 'lasso_20170728', 'description': "Comparing WRF-CLUBB output to WRF-LASSO output.", 'start_time': 301, 'end_time': 600, 'height_min_value': 0, 'height_max_value': 4000, 'blacklisted_vars': [], 'e3sm_file': None, 'cam_file': None, 'sam_file': None, 'wrf_benchmark_file': {'lasso_benchmark': WRF_LASSO_BENCHMARK_OUTPUT_ROOT + "/2017-07-28/wrf_lasso_stats_2017-07-28.nc"}, 'sam_benchmark_file': None, 'coamps_benchmark_file': None, 'clubb_r408_benchmark_file': None, 'clubb_hoc_benchmark_file': None, 'clubb_file': None, 'wrf_file': {'zm': clubb_output_root + '/lasso_2017-07-28_zm_wrf.nc', 'zt': clubb_output_root + '/lasso_2017-07-28_zt_wrf.nc', 'sfc': clubb_output_root + '/lasso_2017-07-28_sfc_wrf.nc', 'subcolumns': clubb_output_root + '/lasso_2017-07-28_nl_lh_sample_points_2D.nc'}, 'var_groups': [VariableGroupBase, VariableGroupWs]} LASSO_20170923 = {'name': 'lasso_20170923', 'description': "Comparing WRF-CLUBB output to WRF-LASSO output.", 'start_time': 301, 'end_time': 600, 'height_min_value': 0, 'height_max_value': 4000, 'blacklisted_vars': [], 'e3sm_file': None, 'cam_file': None, 'sam_file': None, 'wrf_benchmark_file': {'lasso_benchmark': WRF_LASSO_BENCHMARK_OUTPUT_ROOT + "/2017-09-23/wrf_lasso_stats_2017-09-23.nc"}, 'sam_benchmark_file': None, 'coamps_benchmark_file': None, 'clubb_r408_benchmark_file': None, 'clubb_hoc_benchmark_file': None, 'clubb_file': None, 'wrf_file': {'zm': clubb_output_root + '/lasso_2017-09-23_zm_wrf.nc', 'zt': clubb_output_root + '/lasso_2017-09-23_zt_wrf.nc', 'sfc': clubb_output_root + '/lasso_2017-09-23_sfc_wrf.nc', 'subcolumns': clubb_output_root + '/lasso_2017-09-23_nl_lh_sample_points_2D.nc'}, 'var_groups': [VariableGroupBase, VariableGroupWs]} LASSO_20180911 = {'name': 'lasso_20180911', 'description': "Comparing WRF-CLUBB output to WRF-LASSO output.", 'start_time': 301, 'end_time': 600, 'height_min_value': 0, 'height_max_value': 4000, 'blacklisted_vars': [], 'e3sm_file': None, 'cam_file': None, 'sam_file': None, 'wrf_benchmark_file': {'lasso_benchmark': WRF_LASSO_BENCHMARK_OUTPUT_ROOT + "/2018-09-11/wrf_lasso_stats_2018-09-11.nc"}, 'sam_benchmark_file': None, 'coamps_benchmark_file': None, 'clubb_r408_benchmark_file': None, 'clubb_hoc_benchmark_file': None, 'clubb_file': None, 'wrf_file': {'zm': clubb_output_root + '/lasso_2018-09-11_zm_wrf.nc', 'zt': clubb_output_root + '/lasso_2018-09-11_zt_wrf.nc', 'sfc': clubb_output_root + '/lasso_2018-09-11_sfc_wrf.nc', 'subcolumns': clubb_output_root + '/lasso_2018-09-11_nl_lh_sample_points_2D.nc'}, 'var_groups': [VariableGroupBase, VariableGroupWs]} LASSO_20180917 = {'name': 'lasso_20180917', 'description': "Comparing WRF-CLUBB output to WRF-LASSO output.", 'start_time': 301, 'end_time': 600, 'height_min_value': 0, 'height_max_value': 4000, 'blacklisted_vars': [], 'e3sm_file': None, 'cam_file': None, 'sam_file': None, 'wrf_benchmark_file': {'lasso_benchmark': WRF_LASSO_BENCHMARK_OUTPUT_ROOT + "/2018-09-17/wrf_lasso_stats_2018-09-17.nc"}, 'sam_benchmark_file': None, 'coamps_benchmark_file': None, 'clubb_r408_benchmark_file': None, 'clubb_hoc_benchmark_file': None, 'clubb_file': None, 'wrf_file': {'zm': clubb_output_root + '/lasso_2018-09-17_zm_wrf.nc', 'zt': clubb_output_root + '/lasso_2018-09-17_zt_wrf.nc', 'sfc': clubb_output_root + '/lasso_2018-09-17_sfc_wrf.nc', 'subcolumns': clubb_output_root + '/lasso_2018-09-17_nl_lh_sample_points_2D.nc'}, 'var_groups': [VariableGroupBase, VariableGroupWs]} LASSO_20180918 = {'name': 'lasso_20180918', 'description': "Comparing WRF-CLUBB output to WRF-LASSO output.", 'start_time': 301, 'end_time': 600, 'height_min_value': 0, 'height_max_value': 4000, 'blacklisted_vars': [], 'e3sm_file': None, 'cam_file': None, 'sam_file': None, 'wrf_benchmark_file': {'lasso_benchmark': WRF_LASSO_BENCHMARK_OUTPUT_ROOT + "/2018-09-18/wrf_lasso_stats_2018-09-18.nc"}, 'sam_benchmark_file': None, 'coamps_benchmark_file': None, 'clubb_r408_benchmark_file': None, 'clubb_hoc_benchmark_file': None, 'clubb_file': None, 'wrf_file': {'zm': clubb_output_root + '/lasso_2018-09-18_zm_wrf.nc', 'zt': clubb_output_root + '/lasso_2018-09-18_zt_wrf.nc', 'sfc': clubb_output_root + '/lasso_2018-09-18_sfc_wrf.nc', 'subcolumns': clubb_output_root + '/lasso_2018-09-18_nl_lh_sample_points_2D.nc'}, 'var_groups': [VariableGroupBase, VariableGroupWs]} LASSO_20181002 = {'name': 'lasso_20181002', 'description': "Comparing WRF-CLUBB output to WRF-LASSO output.", 'start_time': 301, 'end_time': 600, 'height_min_value': 0, 'height_max_value': 4000, 'blacklisted_vars': [], 'e3sm_file': None, 'cam_file': None, 'sam_file': None, 'wrf_benchmark_file': {'lasso_benchmark': WRF_LASSO_BENCHMARK_OUTPUT_ROOT + "/2018-10-02/wrf_lasso_stats_2018-10-02.nc"}, 'sam_benchmark_file': None, 'coamps_benchmark_file': None, 'clubb_r408_benchmark_file': None, 'clubb_hoc_benchmark_file': None, 'clubb_file': None, 'wrf_file': {'zm': clubb_output_root + '/lasso_2018-10-02_zm_wrf.nc', 'zt': clubb_output_root + '/lasso_2018-10-02_zt_wrf.nc', 'sfc': clubb_output_root + '/lasso_2018-10-02_sfc_wrf.nc', 'subcolumns': clubb_output_root + '/lasso_2018-10-02_nl_lh_sample_points_2D.nc'}, 'var_groups': [VariableGroupBase, VariableGroupWs]} # DO NOT EDIT THIS LIST UNLESS YOU ARE ADDING A NEW CASE. NEVER REMOVE CASES FROM THIS LIST. # You may define a subset of cases at the end of this file. ALL_CASES = [ARM, ARM_97, ASTEX_A209, ATEX, BOMEX, CGILS_S6, CGILS_S11, CGILS_S12, CLEX9_NOV02, CLEX9_OCT14, DYCOMS2_RF01, DYCOMS2_RF01_FIXED_SST, DYCOMS2_RF02_DO, DYCOMS2_RF02_DS, DYCOMS2_RF02_DS_RESTART, DYCOMS2_RF02_ND, DYCOMS2_RF02_SO, FIRE, GABLS2, GABLS2_NIGHTLY, GABLS3, GABLS3_NIGHT, GATE_SHEAR_RLSF, # IOP, JUN25_ALTOCU, LBA, MC3E, MPACE_A, MPACE_B, MPACE_B_SILHS, NEUTRAL, NOV11_ALTOCU, RICO, RICO_SILHS, TWP_ICE, WANGARA, LASSO_20170627, LASSO_20170717, LASSO_20170728, LASSO_20170923, LASSO_20180911, LASSO_20180917, LASSO_20180918, LASSO_20181002 ] CASES_TO_PLOT = ALL_CASES # If uncommented, this line will override the real CASES_TO_PLOT given above, forcing pyplotgen to only plot some cases. # CASES_TO_PLOT = [ARM] # CASES_TO_PLOT = CASES_TO_PLOT[:3]

55.181109

135

0.56254

0

29,881

0.469244

a739e22b895dd7f5b68d4cbbe585f6f9e1e16131

305

py

Python

docker_sdk_api/domain/services/contracts/abstract_dataset_validator_service.py

BMW-InnovationLab/BMW-Semantic-Segmentation-Training-GUI

902f35a7e367e635898f687b16a830db892fbaa5

[ "Apache-2.0" ]

20

2021-07-13T13:08:57.000Z

2022-03-29T09:38:00.000Z

docker_sdk_api/domain/services/contracts/abstract_dataset_validator_service.py

BMW-InnovationLab/BMW-Semantic-Segmentation-Training-GUI

902f35a7e367e635898f687b16a830db892fbaa5

[ "Apache-2.0" ]

null

docker_sdk_api/domain/services/contracts/abstract_dataset_validator_service.py

BMW-InnovationLab/BMW-Semantic-Segmentation-Training-GUI

902f35a7e367e635898f687b16a830db892fbaa5

[ "Apache-2.0" ]

2

2021-07-12T08:42:53.000Z

2022-03-04T18:41:25.000Z

from abc import ABC, ABCMeta, abstractmethod from domain.models.datase_information import DatasetInformation class AbstractDatasetValidatorService(ABC): __metaclass__ = ABCMeta @abstractmethod def validate_dataset(self, dataset_info: DatasetInformation) -> None: raise NotImplementedError

27.727273

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192

0.629508

0

115

0.377049

0

a739f43b0588186a90f5d8f8245209820d58a6a6

1,683

py

Python

setup.py

eltonn/toki

22efd9ce84414380904e3a5ac84e84de9bdb5bce

[ "Apache-2.0" ]

1

2020-11-30T16:52:50.000Z

setup.py

eltonn/toki

22efd9ce84414380904e3a5ac84e84de9bdb5bce

[ "Apache-2.0" ]

7

2020-05-29T23:22:21.000Z

2020-11-30T20:49:37.000Z

setup.py

eltonn/toki

22efd9ce84414380904e3a5ac84e84de9bdb5bce

[ "Apache-2.0" ]

1

2020-04-29T21:59:25.000Z

"""The setup script.""" from setuptools import find_packages, setup with open('README.md') as readme_file: readme = readme_file.read() with open('docs/release-notes.md') as history_file: history = history_file.read() requirements = [] dev_requirements = [ # lint and tools 'black', 'flake8', 'isort', 'mypy', 'pre-commit', 'seed-isort-config', # publishing 're-ver', 'twine', # docs 'jupyter-book', 'Sphinx>=2.0,<3', # tests 'responses', # devops 'docker-compose', ] extra_requires = {'dev': requirements + dev_requirements} setup( author="Ivan Ogasawara", author_email='ivan.ogasawara@gmail.com', classifiers=[ 'Development Status :: 2 - Pre-Alpha', 'Intended Audience :: Developers', 'License :: OSI Approved :: Apache Software License', 'Natural Language :: English', "Programming Language :: Python :: 2", 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.4', 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6', 'Programming Language :: Python :: 3.7', ], description="Toki: Database Expression API", install_requires=requirements, license="Apache Software License 2.0", long_description=readme + '\n\n' + history, include_package_data=True, keywords='toki', name='toki', packages=find_packages(include=['toki']), test_suite='tests', extras_require=extra_requires, url='https://github.com/toki-project/toki', version='0.0.1', zip_safe=False, )

26.296875

61

0.616756

0

840

0.499109

a73aed88b329c068d8782d3c38cdfcf8ff4be7a3

3,109

py

Python

dq0/sdk/estimators/data_handler/csv.py

gradientzero/dq0-sdk

90856dd5ac56216971ffe33004447fd037a21660

[ "0BSD" ]

2

2020-09-16T09:28:00.000Z

2021-03-18T21:26:29.000Z

dq0/sdk/estimators/data_handler/csv.py

gradientzero/dq0-sdk

90856dd5ac56216971ffe33004447fd037a21660

[ "0BSD" ]

22

2020-04-15T10:19:33.000Z

2022-03-12T00:20:57.000Z

dq0/sdk/estimators/data_handler/csv.py

gradientzero/dq0-sdk

90856dd5ac56216971ffe33004447fd037a21660

[ "0BSD" ]

null

# -*- coding: utf-8 -*- """Base data handler. Copyright 2021, Gradient Zero All rights reserved """ import logging import dq0.sdk from dq0.sdk.estimators.data_handler.base import BasicDataHandler import pandas as pd from sklearn.model_selection import train_test_split logger = logging.getLogger(__name__) class CSVDataHandler(BasicDataHandler): """Basic CSV Data Handler for all estimators""" def __init__(self, pipeline_steps=None, pipeline_config_path=None, transformers_root_dir='.', log_key_string='.'): super().__init__(pipeline_steps=pipeline_steps, pipeline_config_path=pipeline_config_path, transformers_root_dir=transformers_root_dir, log_key_string=log_key_string) self.log_key_string = log_key_string def setup_data(self, data_source, train_size=0.66, **kwargs): """ Setup data from CSV file. Using the CSV data source. """ # Check if the data source is of expected type if not isinstance(data_source, dq0.sdk.data.text.csv.CSV): raise ValueError("data_source attached to estimator and handled by the CSV data handler is not of Type: dq0.sdk.data.text.csv.CSV but: {}".format(type(data_source))) # noqa if not hasattr(data_source, 'feature_cols') and not hasattr(data_source, 'target_cols'): raise ValueError("CSV data source has not attribute feature_cols or target_cols. Please set this values on init or in the metadata") self.data = super().setup_data(data_source=data_source, **kwargs) # Check type of data, must be pandas.DataFrame if not isinstance(self.data, pd.DataFrame): raise ValueError("Data loaded is not of type pandas.DataFrame, but: {}".format(type(self.data))) # run pipeline if self.pipeline is not None: self.data = self.pipeline.fit_transform(self.data) X = self._get_X(self.data, data_source.feature_cols) y = self._get_y(self.data, data_source.target_cols) X_train, X_test, y_train, y_test = self._train_test_split(X, y, train_size=train_size) return X_train, X_test, y_train, y_test def get_input_dim(self, X): if not len(X.shape) == 2: raise ValueError("Feature Vector X is not 2-dim. The CSVDataHandler can only handle 2-dim DFs") return X.shape[-1] def get_output_dim(self, y): return len(y.unique()) def _get_X(self, data, feature_cols): """Get X features vectors assuming data is a Pandas DataFrame""" return data[feature_cols] def _get_y(self, data, target_cols): """Get y target vector assuming data is a Pandas DataFrame""" if len(target_cols) == 1: return data[target_cols[-1]] else: raise ValueError("CSVDataHandler currently only supports one target_col (Check Metadata!); len(target_cols): {}".format(len(target_cols))) def _train_test_split(self, X, y, train_size=0.66): X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=train_size) return X_train, X_test, y_train, y_test

42.013514

184

0.690254

2,794

0.898681

0

945

0.303956

59519f91376cf89bef0bf6bdc3580d8bfb999e90

808

py

Python

python/163_missing_ranges.py

liaison/LeetCode

8b10a1f6bbeb3ebfda99248994f7c325140ee2fd

[ "MIT" ]

17

2016-03-01T22:40:53.000Z

2021-04-19T02:15:03.000Z

python/163_missing_ranges.py

liaison/LeetCode

8b10a1f6bbeb3ebfda99248994f7c325140ee2fd

[ "MIT" ]

null

python/163_missing_ranges.py

liaison/LeetCode

8b10a1f6bbeb3ebfda99248994f7c325140ee2fd

[ "MIT" ]

3

2019-03-07T03:48:43.000Z

2020-04-05T01:11:36.000Z

class Solution: def findMissingRanges(self, nums: List[int], lower: int, upper: int) -> List[str]: range_iter = lower num_iter = 0 ranges = [] while range_iter < upper and num_iter < len(nums): if range_iter < nums[num_iter]: if nums[num_iter] - 1 == range_iter: ranges.append(str(range_iter)) else: ranges.append("{}->{}".format(range_iter, nums[num_iter]-1)) range_iter = nums[num_iter] + 1 num_iter += 1 if num_iter >= len(nums) and range_iter == upper: ranges.append("{}".format(range_iter)) elif range_iter < upper: ranges.append("{}->{}".format(range_iter, upper)) return ranges

36.727273

86

0.516089

808

1

0

20

0.024752

595209a149b488a190b55a28e227e0653341e30a

407

py

Python

core/utils/template_updater.py

blockomat2100/vulnman

835ff3aae1168d8e2fa5556279bc86efd2e46472

[ "MIT" ]

3

2021-12-22T07:02:24.000Z

2022-01-27T20:19:11.000Z

core/utils/template_updater.py

vulnman/vulnman

d48ee022bc0e4368060a990a527b1c7a5e437504

[ "MIT" ]

44

2021-12-14T07:24:29.000Z

2022-03-23T07:01:16.000Z

core/utils/template_updater.py

blockomat2100/vulnman

835ff3aae1168d8e2fa5556279bc86efd2e46472

[ "MIT" ]

1

2022-01-21T16:29:56.000Z

import os from django.conf import settings from git import Repo def update_vulnerability_templates(): template_dir = os.path.join( settings.BASE_DIR, "resources/vuln_templates") if os.path.isdir(template_dir): repo = Repo(template_dir) origin = repo.remotes.origin origin.pull() else: Repo.clone_from(settings.VULNERABILITY_TEMPLATE_REPO, template_dir)

27.133333

75

0.712531

0

26

0.063882

5952b761ae49fba6ea7b48e61c02b4ec86ac2f3e

209

py

Python

Isomorphic Strings.py

HalShaw/Leetcode

27c52aac5a8ecc5b5f02e54096a001920661b4bb

[ "MIT" ]

1

2016-12-22T04:09:25.000Z

Isomorphic Strings.py

HalShaw/Leetcode

27c52aac5a8ecc5b5f02e54096a001920661b4bb

[ "MIT" ]

null

Isomorphic Strings.py

HalShaw/Leetcode

27c52aac5a8ecc5b5f02e54096a001920661b4bb

[ "MIT" ]

null

class Solution(object): def isIsomorphic(self, s, t): """ :type s: str :type t: str :rtype: bool """ return map(s.index,s)==map(t.index,t)#相同格式都可以用

20.9

54

0.473684

215

0.955556

0

103

0.457778

5952c5d9520173eb54626c3cf8e791dbdc5d7f03

656

py

Python

pages/basket_page.py

Espad/stepik_autotests_final_tasks

2d9e3408766cc00387a8ddd656006556cce567b4

[ "MIT" ]

null

pages/basket_page.py

Espad/stepik_autotests_final_tasks

2d9e3408766cc00387a8ddd656006556cce567b4

[ "MIT" ]

null

pages/basket_page.py

Espad/stepik_autotests_final_tasks

2d9e3408766cc00387a8ddd656006556cce567b4

[ "MIT" ]

null

from .base_page import BasePage from .locators import BasketPageLocators class BasketPage(BasePage): def should_be_empty_basket_message(self): assert self.is_element_present(*BasketPageLocators.BASKET_EMPTY_MESSAGE), \ "Empty basket message element not found on page" assert self.browser.find_element(*BasketPageLocators.BASKET_EMPTY_MESSAGE).text == "Your basket is empty. Continue shopping", \ "Invalid Basket empty message" def should_be_empty_basket(self): assert self.is_not_element_present(*BasketPageLocators.BASKET_ITEM_EXIST_SELECTOR), \ "Busket is not empty, but should be"

41

135

0.745427

580

0.884146

0

155

0.23628

5955db7626231d3711353993b2796474b288c67c

169

py

Python

tests/collaboration/factories.py

cad106uk/market-access-api

a357c33bbec93408b193e598a5628634126e9e99

[ "MIT" ]

null

tests/collaboration/factories.py

cad106uk/market-access-api

a357c33bbec93408b193e598a5628634126e9e99

[ "MIT" ]

51

2018-05-31T12:16:31.000Z

2022-03-08T09:36:48.000Z

tests/collaboration/factories.py

cad106uk/market-access-api

a357c33bbec93408b193e598a5628634126e9e99

[ "MIT" ]

2

2019-12-24T09:47:42.000Z

2021-02-09T09:36:51.000Z

import factory from api.collaboration.models import TeamMember class TeamMemberFactory(factory.django.DjangoModelFactory): class Meta: model = TeamMember

18.777778

59

0.781065

102

0.60355

0

595945cb1c25f789695dd2fae8ba200ee3b77c80

1,454

py

Python

trypython/extlib/aiohttp/aiohttp01.py

devlights/try-python-extlib

9bfb649d3f5b249b67991a30865201be794e29a9

[ "MIT" ]

null

trypython/extlib/aiohttp/aiohttp01.py

devlights/try-python-extlib

9bfb649d3f5b249b67991a30865201be794e29a9

[ "MIT" ]

null

trypython/extlib/aiohttp/aiohttp01.py

devlights/try-python-extlib

9bfb649d3f5b249b67991a30865201be794e29a9

[ "MIT" ]

null

""" aiohttp モジュールのサンプルです基本的な使い方について REFERENCES:: http://bit.ly/2O2lmeU http://bit.ly/2O08oy3 """ import asyncio from asyncio import Future from typing import List, Dict import aiohttp from trypython.common.commoncls import SampleBase async def fetch_async(index: int, url: str) -> Dict: async with aiohttp.ClientSession() as session: async with session.get(url) as response: html = await response.read() return { 'index': index, 'resp': response, 'length': len(html), 'url': url } def build_futures() -> List[Future]: urls = [ 'https://www.google.co.jp/', 'https://stackoverflow.com/', 'https://www.yahoo.co.jp/', 'https://devlights.hatenablog.com/', 'https://docs.python.org/3.7/index.html', 'https://docs.python.org/ja/3/' ] futures = [asyncio.ensure_future(fetch_async(i, url)) for i, url in enumerate(urls, start=1)] return futures class Sample(SampleBase): def exec(self): # 結果を元の順序で取得したい場合は asyncio.gather を使う future = asyncio.wait(build_futures(), return_when=asyncio.ALL_COMPLETED) done, pending = asyncio.get_event_loop().run_until_complete(future) for r in done: tr = r.result() print(f'{tr["index"]} {tr["url"]} {tr["length"]} bytes') def go(): obj = Sample() obj.exec()

25.068966

97

0.592847

408

0.26528

0

358

0.23277

495

0.321847

595abb6fdb13a008e2f80cf057085a05a97b14a8

1,860

py

Python

models.py

camerongray1515/HackDee-2015

6459c5bd3ad895e0a216ff61342eb73877dc9ee5

[ "MIT" ]

null

models.py

camerongray1515/HackDee-2015

6459c5bd3ad895e0a216ff61342eb73877dc9ee5

[ "MIT" ]

1

2015-04-04T20:55:52.000Z

2015-12-17T23:35:08.000Z

models.py

camerongray1515/HackDee-2015

6459c5bd3ad895e0a216ff61342eb73877dc9ee5

[ "MIT" ]

null

from sqlalchemy import Column, String, Boolean, ForeignKey, Integer from sqlalchemy.orm import relationship from database import Base from string import ascii_letters from random import choice class Playlist(Base): __tablename__ = "playlists" id = Column(String, primary_key=True) name = Column(String) def __init__(self, name): generate = True while generate: random_string = "".join(choice(ascii_letters) for i in range(5)) p = Playlist.query.get(random_string) # Only set value and exit loop if the id is not already in use if p == None: generate = False self.id = random_string self.name = name @staticmethod def get_videos(playlist_id): videos = Video.query.filter(Video.playlist_id==playlist_id).order_by("rank desc") playlist = [] for video in videos: playlist_entry = { "playlist_id": playlist_id, "slug": video.slug, "thumbnail_url": video.thumbnail_url, "title": video.title, "rank": video.rank } playlist.append(playlist_entry) return playlist def __repr__(): return "<Playlist ID:{0}, Name:{1}>".format(self.id, self.name) class Video(Base): __tablename__ = "video" id = Column(Integer, primary_key=True) playlist_id = Column(String, ForeignKey(Playlist.id)) playlist = relationship("Playlist") slug = Column(String) thumbnail_url = Column(String) title = Column(String) rank = Column(Integer) def __init__(self, playlist_id, slug, thumbnail_url, title): self.playlist_id = playlist_id self.slug = slug self.thumbnail_url = thumbnail_url self.title = title self.rank = 0

29.0625

89

0.614516

1,661

0.893011

0

510

0.274194

0

179

0.096237

595b940d98d4c9ba62ad1e7789fd5ad05f9b32ef

3,270

py

Python

Python3/726.py

rakhi2001/ecom7

73790d44605fbd51e8f7e804b9808e364fcfc680

[ "MIT" ]

854

2018-11-09T08:06:16.000Z

2022-03-31T06:05:53.000Z

Python3/726.py

rakhi2001/ecom7

73790d44605fbd51e8f7e804b9808e364fcfc680

[ "MIT" ]

29

2019-06-02T05:02:25.000Z

2021-11-15T04:09:37.000Z

Python3/726.py

rakhi2001/ecom7

73790d44605fbd51e8f7e804b9808e364fcfc680

[ "MIT" ]

347

2018-12-23T01:57:37.000Z

2022-03-12T14:51:21.000Z

__________________________________________________________________________________________________ sample 24 ms submission class Solution: def countOfAtoms(self, formula: str) -> str: stack, atom, dic, count, coeff, c = [], '', collections.defaultdict(int), 0, 1, 0 for i in formula[::-1]: if i.isdigit(): count += int(i) * (10 ** c) c += 1 elif i == ')': stack.append(count) coeff *= count count = c = 0 elif i == '(': coeff //= stack.pop() count = c = 0 elif i.isupper(): atom += i dic[atom[::-1]] += (count or 1) * coeff atom = '' count = c = 0 else: atom += i check = [] for atom in dic: check.append((atom, dic[atom])) check.sort(key=lambda x:x[0]) ans = '' for atom, count in check: ans += atom if count > 1: ans += str(count) return ans __________________________________________________________________________________________________ sample 13188 kb submission class Solution: def multiply(self,multiplier): product=1 for c in multiplier: product = product * c return product def sort_answer(self,dic): output=[] for key,val in sorted(dic.items()): output.append(key) if val==1: continue output.append(str(val)) return ''.join(output) def countOfAtoms(self, formula: str) -> str: from collections import defaultdict multAr = [] parentheses=0 d=defaultdict(int) element=None elems=[] num=None i=len(formula)-1 while i>=0: if formula[i].isdigit(): if i==len(formula)-1 or num==None: num=formula[i] else: num= formula[i]+num if formula[i].islower(): element = formula[i-1:i+1] if num==None: d[element]+= self.multiply(multAr) else: d[element]+=int(num) * self.multiply(multAr) num=None i-=2 elems.append(element) continue elif formula[i].isupper(): element=formula[i] if num==None: d[element]+= self.multiply(multAr) else: d[element]+=int(num) * self.multiply(multAr) num=None elems.append(element) elif formula[i]==')': multAr.append(int(num)) num=None elif formula[i]=='(': multAr.pop() i-=1 return self.sort_answer(d) __________________________________________________________________________________________________

30

98

0.45107

2,920

0.892966

0

20

0.006116

595ecf0b3419dbc932591ff7beb5487e3db35f47

932

py

Python

script/rmLinebyIndFile.py

ASLeonard/danbing-tk

15540124ff408777d0665ace73698b0c2847d1cc

[ "BSD-3-Clause" ]

17

2020-08-16T14:28:11.000Z

2022-03-23T23:30:47.000Z

script/rmLinebyIndFile.py

ASLeonard/danbing-tk

15540124ff408777d0665ace73698b0c2847d1cc

[ "BSD-3-Clause" ]

7

2021-01-25T15:26:18.000Z

2022-03-31T14:30:46.000Z

script/rmLinebyIndFile.py

ASLeonard/danbing-tk

15540124ff408777d0665ace73698b0c2847d1cc

[ "BSD-3-Clause" ]

2

2020-11-01T20:41:38.000Z

2021-05-29T03:22:24.000Z

#!/usr/bin/env python3 import sys import numpy as np if len(sys.argv) == 1 or sys.argv[1] == "-h" or sys.argv[1] == "--help": print( """ Remove line indices (0-based) specified in 'index.txt' usage: program [-k] index.txt inFile -k Keep line indices in 'index.txt' instead of removing them. """) sys.exit() rm = True idxf = "" infile = "" for i, v in enumerate(sys.argv): if i == 0: continue elif v == "-k": rm = False elif not idxf: idxf = v elif not infile: infile = v else: assert False, f"too many arguments {v}" if not idxf: assert False, "index.txt not specified" if not infile: assert False, "inFile not specified" ids = set(np.loadtxt(idxf, dtype=int, ndmin=1).tolist()) with open(infile) as f: ind = 0 for line in f: if (ind not in ids) == rm: print(line, end='') ind += 1

22.731707

78

0.55794

0

306

0.328326

595f827df47c5f2bdd1ecfb6bc095d61ca198a03

538

py

Python

dynaban/tests/postion.py

laukik-hase/imitation_of_human_arm_on_robotic_manipulator

995beb1ab41597ca6cbecd0baecdef1ef13450f9

[ "MIT" ]

3

2021-11-13T16:54:31.000Z

2021-11-13T20:50:18.000Z

dynaban/tests/postion.py

laukik-hase/human_arm_imitation

995beb1ab41597ca6cbecd0baecdef1ef13450f9

[ "MIT" ]

null

dynaban/tests/postion.py

laukik-hase/human_arm_imitation

995beb1ab41597ca6cbecd0baecdef1ef13450f9

[ "MIT" ]

null

#!/usr/bin/env python import arm_control_utils DURATION = 30000 TRAJ_POLY1 = [1000, 100, 100] TORQUE_POLY1 = [1000, 100, 100] MODE = 3 arm_control_utils.initialize_motors() arm_control_utils.enable_state_torque() arm_control_utils.set_debug(1, 0) print("Ready to move") arm_control_utils.set_position_trajectory(1, DURATION, TRAJ_POLY1, TORQUE_POLY1) arm_control_utils.set_mode(1, MODE) arm_control_utils.disable_state_torque() arm_control_utils.stop_motors()

28.315789

80

0.702602

0

36

0.066914

595fa12df823f48a76595c65b488cfd3266708e8

5,758

py

Python

google-datacatalog-connectors-commons/tests/google/datacatalog_connectors/commons/prepare/base_entry_factory_test.py

mesmacosta/datacatalog-connectors

74a4b6272cb00f2831b669d1a41133913f3df3fa

[ "Apache-2.0" ]

53

2020-04-27T21:50:47.000Z

2022-02-18T22:08:49.000Z

google-datacatalog-connectors-commons/tests/google/datacatalog_connectors/commons/prepare/base_entry_factory_test.py

mesmacosta/datacatalog-connectors

74a4b6272cb00f2831b669d1a41133913f3df3fa

[ "Apache-2.0" ]

20

2020-05-26T13:51:45.000Z

2022-01-25T00:06:19.000Z

google-datacatalog-connectors-commons/tests/google/datacatalog_connectors/commons/prepare/base_entry_factory_test.py

mesmacosta/datacatalog-connectors

74a4b6272cb00f2831b669d1a41133913f3df3fa

[ "Apache-2.0" ]

12

2020-04-30T22:14:02.000Z

2021-10-09T03:44:39.000Z

#!/usr/bin/python # coding=utf-8 # # Copyright 2020 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest import mock from google.datacatalog_connectors.commons import prepare class BaseEntryFactoryTestCase(unittest.TestCase): __COMMONS_PACKAGE = 'google.datacatalog_connectors.commons' __PREPARE_PACKAGE = '{}.prepare'.format(__COMMONS_PACKAGE) def test_format_id_should_normalize_non_compliant_id(self): formatted_id = prepare.BaseEntryFactory._format_id(u'ã123 - b456 ') self.assertEqual('a123_b456', formatted_id) def test_format_id_with_hashing_should_normalize_non_compliant_id(self): long_str = 'organization_warehouse7192ecb2__personsc3a8d512_' \ 'business_area_and_segment_of_marketing' expected_str = 'organization_warehouse7192ecb2_personsc3a8d512_' \ 'business_7074c286' formatted_id = prepare.BaseEntryFactory._format_id_with_hashing( long_str, hash_length=8) self.assertEqual(expected_str, formatted_id) def test_format_id_with_provided_pattern_should_normalize_non_compliant_id( # noqa: E501 self): long_str = 'organization__warehouse7192ecb2__personsc3a8d512_' \ 'business_area_and_segment_of_marketing' expected_str = 'organization__warehouse7192ecb2_' \ '_personsc3a8d512_businesa4f7e655' formatted_id = prepare.BaseEntryFactory._format_id_with_hashing( long_str, regex_pattern=r'[^a-zA-Z0-9_]+') self.assertEqual(expected_str, formatted_id) def test_format_display_name_should_normalize_non_compliant_name(self): formatted_name = prepare.BaseEntryFactory._format_display_name( u'ã123 :?: b456 ') self.assertEqual('a123 _ b456', formatted_name) @mock.patch( '{}.DataCatalogStringsHelper.truncate_string'.format(__PREPARE_PACKAGE) ) def test_format_linked_resource_should_not_normalize_compliant_string( self, mock_truncate_string): # Return same value received. mock_truncate_string.side_effect = (lambda *args: args[0]) formatted_linked_resource = prepare.BaseEntryFactory.\ _format_linked_resource( 'hdfs://namenode:8020/user/hive/warehouse/table_company' '_names_from_department_that_keeps_records_with_' 'historical_data_from_every_single_member') self.assertEqual( 'hdfs://namenode:8020/user/hive/warehouse/' 'table_company_names_from_department_that_' 'keeps_records_with_historical_data_' 'from_every_single_member', formatted_linked_resource) @mock.patch( '{}.DataCatalogStringsHelper.truncate_string'.format(__PREPARE_PACKAGE) ) def test_format_linked_resource_should_normalize_non_compliant_string( self, mock_truncate_string): # Return same value received. mock_truncate_string.side_effect = (lambda *args: args[0]) formatted_linked_resource = prepare.BaseEntryFactory. \ _format_linked_resource( 'hdfs://[namenode]:8020/user/{hive}/[warehouse]/table_company' '_names_from_?department?_that_;keeps;_records_with_' 'historical_data_from_every_single_member') self.assertEqual( 'hdfs://_namenode_:8020/user/' '_hive_/_warehouse_/table_company_names_from' '__department__that__keeps__records_with_' 'historical_data_from_every_single_member', formatted_linked_resource) @mock.patch( '{}.DataCatalogStringsHelper.truncate_string'.format(__PREPARE_PACKAGE) ) def test_format_linked_resource_should_not_normalize_non_compliant_string( self, mock_truncate_string): # Return same value received. mock_truncate_string.side_effect = (lambda *args: args[0]) formatted_linked_resource = prepare.BaseEntryFactory. \ _format_linked_resource( 'hdfs://[namenode]:8020/user/{hive}/[warehouse]/table_company' '_names_from_?department?_that_;keeps;_records_with_' 'historical_data_from_every_single_member', False) self.assertEqual( 'hdfs://[namenode]:8020/user/{hive}/[warehouse]/table_company' '_names_from_?department?_that_;keeps;_records_with_' 'historical_data_from_every_single_member', formatted_linked_resource) @mock.patch( '{}.DataCatalogStringsHelper.truncate_string'.format(__PREPARE_PACKAGE) ) def test_format_linked_resource_should_truncate_non_compliant_string( self, mock_truncate_string): expected_value = 'truncated_str...' mock_truncate_string.return_value = expected_value formatted_linked_resource = prepare.BaseEntryFactory. \ _format_linked_resource( 'hdfs://[namenode]:8020/user/{hive}/[warehouse]/table_company' '_names_from_?department?_that_;keeps;_records_with_' 'historical_data_from_every_single_member') self.assertEqual(expected_value, formatted_linked_resource)

42.029197

93

0.70719

5,058

0.878125

0

3,383

0.587326

0

2,419

0.419965

5960088035b5df4aefdc1abf2b6dd9894a0c53be

5,978

py

Python

estimators.py

RakitinDen/pytorch-recursive-gumbel-max-trick

44f9854020e727946a074a6e53b20dd593f96cc1

[ "Apache-2.0" ]

20

2021-12-03T13:20:17.000Z

2022-03-20T18:58:06.000Z

estimators.py

RakitinDen/pytorch-recursive-gumbel-max-trick

44f9854020e727946a074a6e53b20dd593f96cc1

[ "Apache-2.0" ]

null

estimators.py

RakitinDen/pytorch-recursive-gumbel-max-trick

44f9854020e727946a074a6e53b20dd593f96cc1

[ "Apache-2.0" ]

null

# Estimators are partially based on the "estimators.py" from the following repositories: # https://github.com/agadetsky/pytorch-pl-variance-reduction # https://github.com/sdrobert/pydrobert-pytorch import torch def uniform_to_exp(logits, uniform=None, enable_grad=False): ''' Converts a tensor of independent uniform samples into a tensor of independent exponential samples Tensor 'logits' contains log-means of the exponential distributions Parameters of the exponentials can be represented as lambda = exp(-logit), since expected value is equal to 1/lambda ''' if uniform is not None: assert uniform.size() == logits.size() else: uniform = torch.distributions.utils.clamp_probs(torch.rand_like(logits)) exp = torch.exp(logits + torch.log(-torch.log(uniform))) if enable_grad: exp.requires_grad_(True) return exp def reattach_exp_to_new_logits(logits, exp): ''' Creates a new tensor of exponential variables that depends on logits in the same way as if it was obtained by transforming uniform samples via 'uniform_to_exp' Used in 'relax' to obtain gradient for the detached version of the logits ''' exp = torch.exp(torch.log(exp.detach()) + logits - logits.detach()) return exp def E_reinforce(loss_value, logits, exp, plus_samples=1, mask_unused_values=None, **kwargs): ''' Returns the REINFORCE [williams1992] gradient estimate with respect to the exponential score grad = loss(X) * (d / d logits) log p(E ; logits) If plus_samples > 1, the estimate is E-REINFORCE+ / E-REINFORCE with LOO baseline [kool2019buy, richter2020vargrad] ''' batch_size = logits.shape[0] // plus_samples loss_value = loss_value.detach() exp = exp.detach() log_prob = -logits - torch.exp(torch.log(exp) - logits) if mask_unused_values is not None: log_prob = mask_unused_values(log_prob, **kwargs) dims_except_batch = tuple(-i for i in range(1, logits.ndimension())) log_prob = log_prob.sum(dim=dims_except_batch) score = torch.autograd.grad([log_prob], [logits], grad_outputs=torch.ones_like(log_prob))[0] if plus_samples > 1: score_shape = (batch_size, plus_samples) + logits.shape[1:] score = score.view(score_shape) loss_value = loss_value.view(batch_size, plus_samples) loss_value = loss_value - loss_value.mean(dim=-1)[:, None] for i in range(logits.ndimension() - 1): loss_value = loss_value.unsqueeze(-1) grad = (loss_value * score).sum(dim=1) / (plus_samples - 1) else: for i in range(logits.ndimension() - 1): loss_value = loss_value.unsqueeze(-1) grad = loss_value * score return grad def T_reinforce(loss_value, struct_var, logits, f_log_prob, plus_samples=1, **kwargs): ''' Returns the REINFORCE [williams1992] gradient estimate with respect to the score function of the execution trace grad = loss(X) * (d / d logits) log p(T ; logits) If plus_samples > 1, the estimate is T-REINFORCE+ / T-REINFORCE with LOO baseline [kool2019buy, richter2020vargrad] ''' batch_size = logits.shape[0] // plus_samples loss_value = loss_value.detach() struct_var = struct_var.detach() log_prob = f_log_prob(struct_var, logits, **kwargs) score = torch.autograd.grad([log_prob], [logits], grad_outputs=torch.ones_like(log_prob))[0] if plus_samples > 1: score_shape = (batch_size, plus_samples) + logits.shape[1:] score = score.view(score_shape) loss_value = loss_value.view(batch_size, plus_samples) loss_value = loss_value - loss_value.mean(dim=-1)[:, None] for i in range(logits.ndimension() - 1): loss_value = loss_value.unsqueeze(-1) grad = (loss_value * score).sum(dim=1) / (plus_samples - 1) else: for i in range(logits.ndimension() - 1): loss_value = loss_value.unsqueeze(-1) grad = loss_value * score return grad def relax(loss_value, struct_var, logits, exp, critic, f_log_prob, f_cond, uniform=None, **kwargs): ''' Returns the RELAX [grathwohl2017backpropagation] gradient estimate grad = (loss(X(T)) - c(e_2)) * (d / d logits) log p(T ; logits) - (d / d logits) c(e_2) + (d / d logits) c(e_1) e_1 ~ p(E ; logits) - exponential sample T = T(e_1) - execution trace of the algorithm X = X(T) - structured variable, obtained as the output of the algorithm e_2 ~ p(E | T ; logits) - conditional exponential sample c(.) - critic (typically, a neural network) e_1 and e_2 are sampled using the reparameterization trick (d / d logits) c(e_1) and (d / d logits) c(e_2) are the reparameterization gradients In code, exp := e_1, cond_exp := e_2 ''' loss_value = loss_value.detach() struct_var = struct_var.detach() logits = logits.detach().requires_grad_(True) exp = reattach_exp_to_new_logits(logits, exp) cond_exp = f_cond(struct_var, logits, uniform, **kwargs) baseline_exp = critic(exp) baseline_cond = critic(cond_exp).squeeze() diff = loss_value - baseline_cond log_prob = f_log_prob(struct_var, logits, **kwargs) score, = torch.autograd.grad( [log_prob], [logits], grad_outputs = torch.ones_like(log_prob) ) d_baseline_exp, = torch.autograd.grad( [baseline_exp], [logits], create_graph=True, retain_graph=True, grad_outputs=torch.ones_like(baseline_exp) ) d_baseline_cond, = torch.autograd.grad( [baseline_cond], [logits], create_graph=True, retain_graph=True, grad_outputs=torch.ones_like(baseline_cond) ) for i in range(logits.ndimension() - 1): diff = diff.unsqueeze(-1) grad = diff * score + d_baseline_exp - d_baseline_cond assert grad.size() == logits.size() return grad

36.674847

119

0.666109

0

2,072

0.346604

596098c174bcd92a072f4a63dcf655eaaf7c83e8

1,332

py

Python

squareroot.py

martinaobrien/pands-problem-sets

5928f9ed2a743f46a9615f41192fd6dfb810b73c

[ "CNRI-Python" ]

null

squareroot.py

martinaobrien/pands-problem-sets

5928f9ed2a743f46a9615f41192fd6dfb810b73c

[ "CNRI-Python" ]

null

squareroot.py

martinaobrien/pands-problem-sets

5928f9ed2a743f46a9615f41192fd6dfb810b73c

[ "CNRI-Python" ]

null

#Martina O'Brien 10/3/2019 #Problem Set 7 - squareroots #Programming Code to determining the squareroots of positive floating point numbers ## Reference for try and expect https://www.w3schools.com/python/python_try_except.asp while True: # this loop will run to allow the user to input a value again if they do not enter a positive integer try: num = input("Please enter a positive number: ") # Here the user will enter positive number. number = float(num) # using a float(num) to allow numbers with decimal points except ValueError: print('Sorry this is not a number. Can you please try again and enter a positive number.') # If the value is entered is correct then the value will move to the next statement. continue #continue to the next interation of the loop if number <= 0: print('Please enter a number greater than zero') # to ensure that the user inputs a positive number break # break from the while loop to the next variable number_sqrt = (number ** 0.5) # Using ** 0.5 gives the squareroot of the num inputted # Using %0.1f returns the answers to one decimal point print("The square root of %0.1f is approx %0.1f" %(number, number_sqrt)) # print the result of the variable to one decimal place.

45.931034

114

0.693694

0

1,030

0.773273

596187b54ca231442ef296c49a1a09d46c903d01

2,843

py

Python

tests/org_group_tests.py

JonLMyers/MetroTransitAPI

d8f467570368cd563d69564b680cfdd47ad6b622

[ "MIT" ]

null

tests/org_group_tests.py

JonLMyers/MetroTransitAPI

d8f467570368cd563d69564b680cfdd47ad6b622

[ "MIT" ]

null

tests/org_group_tests.py

JonLMyers/MetroTransitAPI

d8f467570368cd563d69564b680cfdd47ad6b622

[ "MIT" ]

null

import requests import json token = '' email_token = '' print("######## Pass ########") target = 'http://127.0.0.1:5000/login' headers = {'Content-type': 'application/json', 'Accept': 'text/plain'} data = {'username': 'jon@aaxus.com', 'password': 'password125'} r = requests.post(target, data=json.dumps(data), headers=headers) print(r.status_code, r.reason) print(r.text) data = json.loads(r.text) token = data['access_token'] print(token) print("######## Pass ########") target = 'http://127.0.0.1:5000/group/manage' headers = {'Content-type': 'application/json', 'Accept': 'text/plain', 'authorization': 'Bearer ' + token} data = { 'name': 'Dev Ops', 'description': 'Devops', 'org_name': 'Aaxus' } r = requests.post(target, data=json.dumps(data), headers=headers) print(r.status_code, r.reason) print(r.text) print("######## Pass ########") target = 'http://127.0.0.1:5000/group/manage' headers = {'Content-type': 'application/json', 'Accept': 'text/plain', 'authorization': 'Bearer ' + token} data = { 'org_name': 'Aaxus', 'id': 'Dev Ops', 'description': 'Developer Operations Organization', 'member_username': ['spiro@aaxus.com', 'anthony@aaxus.com', 'ben@aaxus.com'], 'admin_username': ['spiro@aaxus.com', 'anthony@aaxus.com'] } r = requests.put(target, data=json.dumps(data), headers=headers) print(r.status_code, r.reason) print(r.text) print("######## Pass ########") target = 'http://127.0.0.1:5000/group/manage' headers = {'Content-type': 'application/json', 'Accept': 'text/plain', 'authorization': 'Bearer ' + token} data = { 'org_name': 'Aaxus', 'id': 'Dev Ops', 'remove_admin': ['spiro@aaxus.com'], 'remove_member': ['ben@aaxus.com'] } r = requests.put(target, data=json.dumps(data), headers=headers) print(r.status_code, r.reason) print(r.text) print("######## Pass ########") target = 'http://127.0.0.1:5000/group/manage' headers = {'Content-type': 'application/json', 'Accept': 'text/plain', 'authorization': 'Bearer ' + token} data = { 'name': 'Executives', 'description': 'Devops', 'org_name': 'Aaxus' } r = requests.post(target, data=json.dumps(data), headers=headers) print(r.status_code, r.reason) print(r.text) print("######## Pass ########") target = 'http://127.0.0.1:5000/group/view' headers = {'Content-type': 'application/json', 'Accept': 'text/plain', 'authorization': 'Bearer ' + token} data = { 'org_name': 'Aaxus', 'id': 'Dev Ops', } r = requests.post(target, data=json.dumps(data), headers=headers) print(r.status_code, r.reason) print(r.text) print("######## Pass ########") target = 'http://127.0.0.1:5000/group/view?org_name=Aaxus' headers = {'Content-type': 'application/json', 'Accept': 'text/plain', 'authorization': 'Bearer ' + token} r = requests.get(target, headers=headers) print(r.status_code, r.reason) print(r.text)

33.05814

106

0.638762

0

1,426

0.501583

5961e885fedcd68b3653416c363d4e461726bdc8

5,578

py

Python

pywbemtools/pywbemlistener/_context_obj.py

pywbem/pywbemtools

6b7c3f124324fd3ab7cffb82bc98c8f9555317e4

[ "Apache-2.0" ]

8

2017-04-01T13:55:00.000Z

2022-03-15T18:28:47.000Z

pywbemtools/pywbemlistener/_context_obj.py

pywbem/pywbemtools

6b7c3f124324fd3ab7cffb82bc98c8f9555317e4

[ "Apache-2.0" ]

918

2017-03-03T14:29:03.000Z

2022-03-29T15:32:16.000Z

pywbemtools/pywbemlistener/_context_obj.py

pywbem/pywbemtools

6b7c3f124324fd3ab7cffb82bc98c8f9555317e4

[ "Apache-2.0" ]

2

2020-01-17T15:56:46.000Z

2020-02-12T18:49:30.000Z

# (C) Copyright 2021 Inova Development Inc. # All Rights Reserved # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Click context object for the pybemlistener command. """ from __future__ import absolute_import, print_function, unicode_literals import os import click_spinner class ContextObj(object): # pylint: disable=useless-object-inheritance, too-many-instance-attributes """ Click context object for the pybemlistener command. This object is attached to the Click context, and is used as follows: - Contains all general options for use by command functions. - Serves as the central object for executing command functions. - Has support for starting and stopping the Click spinner. """ spinner_envvar = 'PYWBEMLISTENER_SPINNER' def __init__(self, output_format, logdir, verbose, pdb, warn): """ Parameters: output_format (:term:`string` or `None`): Value of --output-format general option, or `None` if not specified. logdir (:term:`string` or `None`): Value of --logdir general option, or `None` if not specified. verbose (int): Verbosity. See VERBOSE_* constants for a definition. pdb (:class:`py:bool`): Indicates whether the --pdb general option was specified. warn (:class:`py:bool`): Indicates whether the --warn general option was specified. """ self._output_format = output_format self._logdir = logdir self._verbose = verbose self._pdb = pdb self._warn = warn self._spinner_enabled = None # Deferred init in getter self._spinner_obj = click_spinner.Spinner() def __repr__(self): return 'ContextObj(at {:08x}, output_format={s.output_format}, ' \ 'logdir={s.logdir}, verbose={s.verbose}, pdb={s.pdb}, ' \ 'warn={s.warn}, spinner_enabled={s.spinner_enabled}' \ .format(id(self), s=self) @property def output_format(self): """ :term:`string`: String defining the output format requested. This may be `None` meaning that the default format should be used or may be one of the values in the TABLE_FORMATS variable. """ return self._output_format @property def logdir(self): """ :term:`string`: Path name of log directory for the 'run' command, or `None` for no logging. """ return self._logdir @property def verbose(self): """ int: Verbosity. See VERBOSE_* constants for a definition. """ return self._verbose @property def pdb(self): """ bool: Indicates whether to break in the debugger. """ return self._pdb @property def warn(self): """ bool: Indicates whether to enable Python warnings. """ return self._warn @property def spinner_enabled(self): """ :class:`py:bool`: Indicates and controls whether the spinner is enabled. If the spinner is enabled, subcommands will display a spinning wheel while waiting for completion. This attribute can be modified. The initial state of the spinner is enabled, but it can be disabled by setting the {0} environment variable to 'false', '0', or the empty value. """.format(self.spinner_envvar) # Deferred initialization if self._spinner_enabled is None: value = os.environ.get(self.spinner_envvar, None) if value is None: # Default if not set self._spinner_enabled = True elif value == '0' or value == '' or value.lower() == 'false': self._spinner_enabled = False else: self._spinner_enabled = True return self._spinner_enabled @spinner_enabled.setter def spinner_enabled(self, enabled): """Setter method; for a description see the getter method.""" self._spinner_enabled = enabled def spinner_start(self): """ Start the spinner, if the spinner is enabled. """ if self.spinner_enabled: self._spinner_obj.start() def spinner_stop(self): """ Stop the spinner, if the spinner is enabled. """ if self.spinner_enabled: self._spinner_obj.stop() def execute_cmd(self, cmd): """ Call the command function for a command, after enabling the spinner (except when in debug mode) and after entering debug mode if desired. """ if not self.pdb: self.spinner_start() try: if self.pdb: import pdb # pylint: disable=import-outside-toplevel pdb.set_trace() # pylint: disable=forgotten-debug-statement cmd() # The command function for the pywbemlistener command finally: if not self.pdb: self.spinner_stop()

31.693182

80

0.620115

4,797

0.859986

0

2,068

0.370742

0

3,434

0.615633

5962222919ba8cf295722ccc3d990ff5fdab4dcc

1,704

py

Python

ota_xml_api/util/xml_base.py

mihira/opentravel-xml-api

24d1ea4d24cf2575de474becaa665f6fc0d1971d

[ "MIT" ]

3

2016-01-14T01:12:06.000Z

2021-04-16T04:00:47.000Z

ota_xml_api/util/xml_base.py

mihira/opentravel-xml-api

24d1ea4d24cf2575de474becaa665f6fc0d1971d

[ "MIT" ]

null

ota_xml_api/util/xml_base.py

mihira/opentravel-xml-api

24d1ea4d24cf2575de474becaa665f6fc0d1971d

[ "MIT" ]

2

2017-09-04T13:02:09.000Z

2018-06-09T11:10:03.000Z

#!/usr/bin/env python """ This module contains the base xml Node and Period classes """ from xml.dom.minidom import getDOMImplementation from date import Period from constants import START, END class XmlNode(object): """ the name of the class will define the name of the node by default. classes inheriting this class will have their name set. """ _impl = getDOMImplementation() def __init__(self, name=None, **attributes): if not name: name = self.__class__.__name__ self._doc = XmlNode._impl.createDocument(None, name, None) self.element = self._doc.documentElement for key, value in attributes.items(): self.set_attribute(key, value) self.parent = None def set_attribute(self, key, value): self.element.setAttribute(key, str(value)) def set_parent(self, parent_node): self.parent = parent_node def add_child(self, child_node): child_node.set_parent(self) self.element.appendChild(child_node.element) return child_node def add_text(self, data): text = self._doc.createTextNode(data) self.element.appendChild(text) return text def __repr__(self): return self.element.toxml() class PeriodNode(XmlNode): def __init__(self, *args, **kwargs): XmlNode.__init__(self, *args, **kwargs) self._period = None self.set_period(Period()) def get_period(self): return self._period def set_period(self, period): self.set_attribute(START, period.start) self.set_attribute(END, period.end) self._period = period period = property(get_period, set_period)

27.483871

70

0.661385

1,502

0.881455

0

228

0.133803

59629f7a0c5633f940aafc1f0319ef57490ea9f2

9,441

py

Python

phl_courts_scraper/court_summary/schema.py

PhiladelphiaController/phl-courts-scraper

0c3c915a7fa355538c43a138fa7b104b8bf6ef1e

[ "MIT" ]

null

phl_courts_scraper/court_summary/schema.py

PhiladelphiaController/phl-courts-scraper

0c3c915a7fa355538c43a138fa7b104b8bf6ef1e

[ "MIT" ]

4

2020-12-09T18:25:53.000Z

2021-03-19T22:30:18.000Z

phl_courts_scraper/court_summary/schema.py

PhiladelphiaController/phl-courts-scraper

0c3c915a7fa355538c43a138fa7b104b8bf6ef1e

[ "MIT" ]

null

"""Define the schema for the court summary report.""" import datetime from dataclasses import dataclass, field, fields from typing import Any, Iterator, List, Optional, Union import desert import marshmallow import pandas as pd from ..utils import DataclassSchema __all__ = ["CourtSummary", "Docket", "Charge", "Sentence"] class TimeField(marshmallow.fields.DateTime): """Custom time field to handle string to datetime conversion.""" def _serialize(self, value, attr, obj, **kwargs): """Return string representation of datetime objects.""" if not value: return "" if isinstance(value, datetime.datetime): return value.strftime("%m/%d/%Y") return super()._serialize(value, attr, obj, **kwargs) def _deserialize(self, value, attr, data, **kwargs): """Convert strings to datetime objects.""" if value == "": return None if isinstance(value, datetime.datetime): return value return super()._deserialize(value, attr, data, **kwargs) @dataclass class Sentence(DataclassSchema): """ A Sentence object. Parameters ---------- sentence_type : the sentence type program_period : optional the program period sentence_length : optional the length of the sentence sentence_dt : the date of the sentence """ sentence_type: str sentence_dt: str = desert.field( TimeField(format="%m/%d/%Y", allow_none=True) ) program_period: str = "" sentence_length: str = "" def __repr__(self): cls = self.__class__.__name__ if not pd.isna(self.sentence_dt): dt = self.sentence_dt.strftime("%m/%d/%y") dt = f"'{dt}'" else: dt = "NaT" s = f"sentence_dt={dt}, sentence_type='{self.sentence_type}'" return f"{cls}({s})" @dataclass class Charge(DataclassSchema): """ A Charge object. Parameters ---------- seq_no : the charge sequence number statute : the statute description : optional description of the statute grade : optional the grade, e.g., felony, misdemeanor, etc. disposition : optional the disposition for the charge, if present sentences : optional list of any sentences associated with the charge """ seq_no: str statute: str description: str = "" grade: str = "" disposition: str = "" sentences: List[Sentence] = field(default_factory=list) @property def meta(self): """A dict of the meta info associated with the charge""" exclude = ["sentences"] return { f.name: getattr(self, f.name) for f in fields(self) if f.name not in exclude } def __iter__(self) -> Iterator[Sentence]: """Iterate through the sentences.""" return iter(self.sentences) def __len__(self): """Return the length of the sentences.""" return len(self.sentences) def __getitem__(self, index): """Index the sentences.""" return self.sentences.__getitem__(index) def __repr__(self): cls = self.__class__.__name__ cols = ["seq_no", "statute", "description"] s = ", ".join([f"{col}='{getattr(self, col)}'" for col in cols]) s += f", num_sentences={len(self.sentences)}" return f"{cls}({s})" @dataclass class Docket(DataclassSchema): """ A Docket object. Parameters ---------- docket_number : the docket number proc_status : the status of the docket proceedings dc_no : the DC incident number otn : the offense tracking number arrest_dt : the arrest date county : the PA county where case is being conducted status : the docket status as determined by the section on the court summary, e.g., "Active", "Closed", etc. extra : list of any additional header info for the docket psi_num : optional pre-sentence investigation number prob_num : optional the probation number disp_date : optional date of disposition disp_judge : optional the disposition judge def_atty : optional the name of the defense attorney trial_dt : optional the date of the trial legacy_no : optional the legacy number for the docket last_action : optional the last action in the case last_action_date : optional the date of the last action last_action_room : optional the room where last action occurred next_action : optional the next action to occur next_action_date : optional the date of the next action next_action_room : optional the room where next action will occur charges : optional a list of charges associated with this case """ docket_number: str proc_status: str dc_no: str otn: str county: str status: str extra: List[Any] arrest_dt: str = desert.field( TimeField(format="%m/%d/%Y", allow_none=True) ) psi_num: str = "" prob_num: str = "" disp_judge: str = "" def_atty: str = "" legacy_no: str = "" last_action: str = "" last_action_room: str = "" next_action: str = "" next_action_room: str = "" next_action_date: Optional[str] = desert.field( TimeField(format="%m/%d/%Y", allow_none=True), default="" ) last_action_date: Optional[str] = desert.field( TimeField(format="%m/%d/%Y", allow_none=True), default="" ) trial_dt: Optional[str] = desert.field( TimeField(format="%m/%d/%Y", allow_none=True), default="" ) disp_date: Optional[str] = desert.field( TimeField(format="%m/%d/%Y", allow_none=True), default="" ) charges: List[Charge] = field(default_factory=list) def to_pandas(self) -> pd.DataFrame: """ Return a dataframe representation of the data, where each row represents a separate charge. """ # Each row is a Charge out = pd.DataFrame([c.to_dict() for c in self]) # Convert sentences dicts to Sentence objects out["sentences"] = out["sentences"].apply( lambda l: [Sentence(**v) for v in l] ) return out @property def meta(self): """A dict of the meta info associated with the docket""" exclude = ["charges"] return { f.name: getattr(self, f.name) for f in fields(self) if f.name not in exclude } def __getitem__(self, index): """Index the charges.""" return self.charges.__getitem__(index) def __iter__(self) -> Iterator[Charge]: """Iterate through the charges.""" return iter(self.charges) def __len__(self): """The number of charges.""" return len(self.charges) def __repr__(self): cls = self.__class__.__name__ if not pd.isna(self.arrest_dt): dt = self.arrest_dt.strftime("%m/%d/%y") dt = f"'{dt}'" else: dt = "NaT" s = [ f"{self.docket_number}", str(self.status), f"arrest_dt={dt}", f"num_charges={len(self)}", ] return f"{cls}({', '.join(s)})" @dataclass class CourtSummary(DataclassSchema): """A Court Summary object. Parameters ---------- name : The name of the defendant. date_of_birth : The defendant's date of birth. eyes : The defendant's eye color. sex : The defendant's sex. hair : The defendant's hair color. race : The defendant's race. location : Defendant location aliases : List of aliases for the defendant dockets : List of Docket objects on the court summary """ name: str date_of_birth: str eyes: str sex: str hair: str race: str location: str aliases: List[str] dockets: List[Docket] def to_pandas(self) -> pd.DataFrame: """ Return a dataframe representation of the data, where each row represents a separate docket. """ # Each row is a Docket out = pd.DataFrame([c.to_dict() for c in self]) # Convert charge dicts to Charge objects out["charges"] = out["charges"].apply( lambda l: [Charge(**v) for v in l] ) # Each row is a Docket return out @property def meta(self): """A dict of the meta info associated with the court summary.""" exclude = ["dockets"] return { f.name: getattr(self, f.name) for f in fields(self) if f.name not in exclude } def __iter__(self) -> Iterator[Docket]: """Yield the object's dockets.""" return iter(self.dockets) def __len__(self) -> int: """Return the number of dockets.""" return len(self.dockets) def __getitem__(self, index): """Index the dockets.""" return self.dockets.__getitem__(index) def __repr__(self) -> str: """Shorten the default dataclass representation.""" cls = self.__class__.__name__ return f"{cls}(name='{self.name}', num_dockets={len(self)})"

26.594366

72

0.586696

9,055

0.959115

0

8,366

0.886135

0

4,413

0.467429

5962e0c96855173baf9ead74168b62eef51ee37e

216

py

Python

Day_43/json_dump_python.py

kiranrraj/100Days_Of_Coding

ab75d83be9be87fb7bc83a3f3b72a4638dab22a1

[ "MIT" ]

null

Day_43/json_dump_python.py

kiranrraj/100Days_Of_Coding

ab75d83be9be87fb7bc83a3f3b72a4638dab22a1

[ "MIT" ]

null

Day_43/json_dump_python.py

kiranrraj/100Days_Of_Coding

ab75d83be9be87fb7bc83a3f3b72a4638dab22a1

[ "MIT" ]

null

# Title : Json Module Module # Author : Kiran Raj R. # Date : 26/11/2020 python_json = {"name":"kiran", "email":"kiran@gmail.com", "isHappy": "Yes"} import json string_j = json.dumps(python_json) print(string_j)

24

75

0.680556

0

124

0.574074

5963d226f34e95078375678dfe6099b78982408c

573

py

Python

userbot/modules/trd.py

LUCKYRAJPUTOP/VibeXUserbot

257c86ff1775592688815435d8c5ce91e1dd299e

[ "Naumen", "Condor-1.1", "MS-PL" ]

null

userbot/modules/trd.py

LUCKYRAJPUTOP/VibeXUserbot

257c86ff1775592688815435d8c5ce91e1dd299e

[ "Naumen", "Condor-1.1", "MS-PL" ]

null

userbot/modules/trd.py

LUCKYRAJPUTOP/VibeXUserbot

257c86ff1775592688815435d8c5ce91e1dd299e

[ "Naumen", "Condor-1.1", "MS-PL" ]

null

import asyncio from asyncio import sleep from random import choice from userbot.events import register T_R_D = [ "@PrajjuS", "@Vin02vin", "@Iamsaisharan", "@venomsamurai", ] @register(outgoing=True, pattern="^.trd$") async def truthrdare(trd): """Truth or Dare""" await trd.edit("`Choosing Name...`") await sleep(1.5) await trd.edit("`..............`") await sleep(1.5) msg = await trd.edit("`Name is.....`") await sleep(3) await trd.delete() await msg.reply("**∆ Truth or Dare ∆**\n\n__Name:__ " + choice(T_R_D))

22.92

74

0.602094

0

379

0.656846

336

0.582322

173

0.299827

596512b76ad497342148f69daf0ea980f36bbf49

2,384

py

Python

collectors/nct/collector.py

almeidaah/collectors

f03096855b8d702969d22af0b20a4d6a0d820bd0

[ "MIT" ]

17

2016-06-28T21:20:21.000Z

2022-03-02T16:31:25.000Z

collectors/nct/collector.py

almeidaah/collectors

f03096855b8d702969d22af0b20a4d6a0d820bd0

[ "MIT" ]

41

2016-04-04T10:36:45.000Z

2017-04-24T10:04:57.000Z

collectors/nct/collector.py

kenferrara/collectors

e6c1f45df3a1ffd5d60dada1816484812eb51417

[ "MIT" ]

25

2016-05-18T09:27:42.000Z

2021-03-21T14:44:31.000Z

# -*- coding: utf-8 -*- from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals import zipfile import logging import requests import tempfile import contextlib from .parser import parse_record from .. import base logger = logging.getLogger(__name__) # Module API def collect(conf, conn, nct_xml_dump_url): ''' Downloads and parse data from NCT's XML dump. Considering you want the data from 2017-01-01 until 2017-02-01, the XML dump can be downloaded from: https://clinicaltrials.gov/search?resultsxml=True&rcv_s=01/01/2017&rcv_e=01/02/2017 ''' base.helpers.start(conf, 'nct', {'url': nct_xml_dump_url}) with tempfile.TemporaryFile() as fp: _download_to_file(nct_xml_dump_url, fp) file_count = 0 for identifier, record_fp in _iter_nct_dump_files(fp): base.config.SENTRY.extra_context({ 'url': nct_xml_dump_url, 'identifier': identifier, }) rec = parse_record(record_fp) query = {'nct_id': rec['nct_id']} if rec.table in conn['warehouse'].tables: existing = conn['warehouse'][rec.table].find_one(**query) if existing: rec['nct_id'] = existing['nct_id'] rec.write(conf, conn) file_count += 1 logger.info('Collected %s NCT records', file_count) base.helpers.stop(conf, 'nct', { 'url': nct_xml_dump_url, 'collected': file_count, }) def _download_to_file(url, fp): CHUNK_SIZE = 1024 * 1024 # 1 MB bytes_to_mb = lambda value: value / 1048576.0 with contextlib.closing(requests.get(url, stream=True)) as response: completed_bytes = 0 chunk_count = 0 for block in response.iter_content(CHUNK_SIZE): fp.write(block) completed_bytes += len(block) chunk_count += 1 if chunk_count % 1000 == 0: logger.debug('Downloaded %.2f MB', bytes_to_mb(completed_bytes)) fp.seek(0) def _iter_nct_dump_files(fp): with zipfile.ZipFile(fp) as archive: for filename in archive.namelist(): identifier = filename.split('.')[0] with archive.open(filename, 'rU') as rec_file: yield identifier, rec_file

32.657534

87

0.633389

0

264

0.110738

0

451

0.189178

5968638622036a0684e095d3de7062e4e3ce8115

292

py

Python

bigcode-fetcher/tests/fixtures/__init__.py

sourcery-ai-bot/bigcode-tools

87aaa609998017d0312b7f4f102d41cc2942fa9d

[ "MIT" ]

6

2017-10-15T08:21:27.000Z

2018-05-17T12:57:41.000Z

bigcode-fetcher/tests/fixtures/__init__.py

bdqnghi/bigcode-tools

94ce416fbb40b9b25d49bf88284bf7ccb6132bd3

[ "MIT" ]

2

2017-12-17T19:02:06.000Z

2018-03-01T04:00:26.000Z

bigcode-fetcher/tests/fixtures/__init__.py

bdqnghi/bigcode-tools

94ce416fbb40b9b25d49bf88284bf7ccb6132bd3

[ "MIT" ]

2

2017-10-18T08:17:54.000Z

2018-06-28T09:57:36.000Z

from os import path import json from bigcode_fetcher.project import Project FIXTURES_DIR = path.dirname(__file__) PROJECTS_PATH = path.join(FIXTURES_DIR, "projects.json") with open(PROJECTS_PATH, "r") as f: JSON_PROJECTS = json.load(f) PROJECTS = [Project(p) for p in JSON_PROJECTS]

20.857143

56

0.763699

0

18

0.061644

59692f082625d38c4980a6276af160523062869b

1,465

py

Python

examples/timeflies/timeflies_qt.py

yutiansut/RxPY

c3bbba77f9ebd7706c949141725e220096deabd4

[ "ECL-2.0", "Apache-2.0" ]

1

2018-11-16T09:07:13.000Z

examples/timeflies/timeflies_qt.py

yutiansut/RxPY

c3bbba77f9ebd7706c949141725e220096deabd4

[ "ECL-2.0", "Apache-2.0" ]

null

examples/timeflies/timeflies_qt.py

yutiansut/RxPY

c3bbba77f9ebd7706c949141725e220096deabd4

[ "ECL-2.0", "Apache-2.0" ]

1

2021-11-04T11:13:49.000Z

from rx.subjects import Subject from rx.concurrency import QtScheduler import sys try: from PyQt4 import QtCore from PyQt4.QtGui import QWidget, QLabel from PyQt4.QtGui import QApplication except ImportError: try: from PyQt5 import QtCore from PyQt5.QtWidgets import QApplication, QWidget, QLabel except ImportError: from PySide import QtCore from PySide.QtGui import QWidget, QLabel from PySide.QtGui import QApplication class Window(QWidget): def __init__(self): super(QWidget, self).__init__() self.setWindowTitle("Rx for Python rocks") self.resize(600, 600) self.setMouseTracking(True) # This Subject is used to transmit mouse moves to labels self.mousemove = Subject() def mouseMoveEvent(self, event): self.mousemove.on_next((event.x(), event.y())) def main(): app = QApplication(sys.argv) scheduler = QtScheduler(QtCore) window = Window() window.show() text = 'TIME FLIES LIKE AN ARROW' labels = [QLabel(char, window) for char in text] def handle_label(i, label): def on_next(pos): x, y = pos label.move(x + i*12 + 15, y) label.show() window.mousemove.delay(i*100, scheduler=scheduler).subscribe(on_next) for i, label in enumerate(labels): handle_label(i, label) sys.exit(app.exec_()) if __name__ == '__main__': main()

24.416667

77

0.647099

398

0.271672

0

113

0.077133

5969ba0b61715dcc3c0755544d810b16a9ba7f4b

6,116

py

Python

src/contexts/context_local_structure.py

aindrila-ghosh/SmartReduce

b2b28055bc0b269155270c1f8206445e405e8d9b

[ "MIT" ]

null

src/contexts/context_local_structure.py

aindrila-ghosh/SmartReduce

b2b28055bc0b269155270c1f8206445e405e8d9b

[ "MIT" ]

null

src/contexts/context_local_structure.py

aindrila-ghosh/SmartReduce

b2b28055bc0b269155270c1f8206445e405e8d9b

[ "MIT" ]

null

import numpy as np import matplotlib.pyplot as plt from sklearn.manifold import Isomap from scipy.spatial.distance import pdist from sklearn.neighbors import KNeighborsClassifier from sklearn.model_selection import cross_val_score, LeaveOneOut RANDOM_STATE = 42 def calculate_pairwise_distances(df_for_Box_Plot_features, points, distance='euclidean'): """ Computes Pairwise euclidean distances Parameters ---------- df_for_Box_Plot_features : list original features points : nD array embedding distance: String distance, default value is "euclidean" Returns ---------- distance_original : nD array euclidean distances in the original dataset distance_embeddings : nD array euclidean distances in the embedding """ distance_original = pdist(df_for_Box_Plot_features, metric=distance) distance_embeddings = pdist(points, metric=distance) return distance_original, distance_embeddings def calculate_geodesic_distance(df_for_Box_Plot_features, points): """ Computes Pairwise geodesic distances Parameters ---------- df_for_Box_Plot_features : list original features points : nD array embedding Returns ---------- geo_distance_original : nD array geodesic distances in the original dataset geo_distance_embeddings : nD array geodesic distances in the embedding """ embedding = Isomap(n_components=2) embedding.fit(df_for_Box_Plot_features) unsquareform = lambda a: a[np.nonzero(np.triu(a, 1))] ## define a lambda to unsquare the distance matrix geo_distance_original = unsquareform(embedding.dist_matrix_) ## get a condensed matrix of pairwise geodesic distance among points embedding1 = Isomap(n_components=2) embedding1.fit(points) embedding1.dist_matrix_[embedding1.dist_matrix_ == 0] = -9999 ## turn all 0 distances to -9999 geo_distance_embeddings = unsquareform(embedding1.dist_matrix_) ## get a condensed matrix of pairwise geodesic distance among points geo_distance_embeddings[geo_distance_embeddings == -9999] = 0 ## turn all -9999 distances back to 0 return geo_distance_original, geo_distance_embeddings def generate_histograms(distance_original, distance_embeddings, no_of_bins): """ Generates histograms Parameters ---------- distance_original : nD array original distances distance_embeddings : nD array embedding distances no_of_bins : integer number of bins in the histogram Returns ---------- bin_edges_original : list bin edges """ countsOriginal, bin_edges_original = np.histogram(distance_original, bins = no_of_bins) #print("Original Distance Binned Element Counts: ", countsOriginal) countsEmbedding, bin_edges_embedding = np.histogram(distance_embeddings, bins = no_of_bins) #print("Embedding Distance Binned Element Counts: ", countsEmbedding) plt.figure() plt.hist(distance_original, bins = no_of_bins) plt.show() plt.title("Pairwise distances in original data") plt.hist(distance_embeddings, bins = no_of_bins) plt.show() plt.title("Pairwise distances in embeddings") return bin_edges_original def calculate_box_plot_details(distance_original, distance_embeddings, bin_edges_original): """ Computes the details of the Box-plots """ inds_original = np.digitize(distance_original, bins=bin_edges_original) ##print("number of bins = ", np.unique(inds_original)) for i in range(1,52): globals()["array" + str(i)] = [] for j in range(0,len(inds_original)): globals()["array" + str(inds_original[j])].append(distance_embeddings[j]) data_to_plot = [array1, array2, array3, array4, array5, array6, array7, array8, array9, array10, array11, array12, array13, array14, array15, array16, array17, array18, array19, array20, array21, array22, array23, array24, array25, array26, array27, array28, array29, array30, array31, array32, array33, array34, array35, array36, array37, array38, array39, array40, array41, array42, array43, array44, array45, array46, array47, array48, array49, array50, array51] return data_to_plot def generate_box_plots(data_to_plot): """ Generates Box-plots """ fig = plt.figure(1, figsize=(14, 10)) # Create an axes instance ax = fig.add_subplot(111) # Create the boxplot bp = ax.boxplot(data_to_plot) # Save the figure fig.savefig('fig1.png', bbox_inches='tight') ## add patch_artist=True option to ax.boxplot() ## to get fill color bp = ax.boxplot(data_to_plot, patch_artist=True) ## change outline color, fill color and linewidth of the boxes for box in bp['boxes']: # change outline color box.set( color='#7570b3', linewidth=2) # change fill color box.set( facecolor = '#1b9e77' ) ## change color and linewidth of the whiskers for whisker in bp['whiskers']: whisker.set(color='#7570b3', linewidth=2) ## change color and linewidth of the caps for cap in bp['caps']: cap.set(color='#7570b3', linewidth=2) ## change color and linewidth of the medians for median in bp['medians']: median.set(color='#b2df8a', linewidth=2) ## change the style of fliers and their fill for flier in bp['fliers']: flier.set(marker='o', color='#e7298a', alpha=0.5) def gen_error_1_NN(embedding, labels): """ Computes 1-NN generalization error Parameters ---------- embedding : nD array embedding labels : list original labels Returns ---------- gen_error : float generalization error """ model = KNeighborsClassifier(n_neighbors=1) loo = LeaveOneOut() loo.get_n_splits(embedding) scores = cross_val_score(model , X = embedding , y = labels, cv = loo) gen_error = (1 - np.mean(scores)) return gen_error

28.985782

137

0.680347

0

2,557

0.418084

596ab002529af664473cf2cc0c9a6d46e4922281

849

py

Python

ADAMTR.py

akashsuper2000/codechef-archive

e0e4a7daf66812ab7aa3fe42132c3d067a72457b

[ "bzip2-1.0.6" ]

null

ADAMTR.py

akashsuper2000/codechef-archive

e0e4a7daf66812ab7aa3fe42132c3d067a72457b

[ "bzip2-1.0.6" ]

null

ADAMTR.py

akashsuper2000/codechef-archive

e0e4a7daf66812ab7aa3fe42132c3d067a72457b

[ "bzip2-1.0.6" ]

null

def swap(p,j,k,n): a = p[j] b = [] for m in range(n): b.append(p[m][k]) for m in range(n): p[m][k] = a[m] p[j] = b for i in range(int(input())): n = int(input()) p,q = [],[] for j in range(n): p.append([int(k) for k in input().split()]) for j in range(n): q.append([int(k) for k in input().split()]) f = 0 for j in range(n): for k in range(n): if(p[j][k]!=q[j][k] and p[j][k]==q[k][j]): swap(p,j,k,n) elif(p[j][k]==q[j][k]): continue else: f = 1 for j in range(n): for k in range(n): if(p[j][k]!=q[j][k]): f = 1 break if(f==1): break if(f==1): print('No') else: print('Yes')

22.342105

54

0.366313

0

9

0.010601

596bbf6cce06d70f6a325d7a5bf75a3e2280c89c

1,110

py

Python

hparams.py

TanUkkii007/vqvae

6ac433490fd827174e5b925780d32bea14bfb097

[ "MIT" ]

2

2019-03-30T16:49:11.000Z

2019-12-18T22:50:56.000Z

hparams.py

TanUkkii007/vqvae

6ac433490fd827174e5b925780d32bea14bfb097

[ "MIT" ]

null

hparams.py

TanUkkii007/vqvae

6ac433490fd827174e5b925780d32bea14bfb097

[ "MIT" ]

1

2020-01-06T12:37:00.000Z

import tensorflow as tf default_params = tf.contrib.training.HParams( # Encoder encoder_num_hiddens=128, encoder_num_residual_hiddens=32, encoder_num_residual_layers=2, # Decoder decoder_num_hiddens=128, decoder_num_residual_hiddens=32, decoder_num_residual_layers=2, embedding_dim=64, num_embeddings=512, commitment_cost=0.25, # VectorQuantizer vector_quantizer="VectorQuantizer", sampling_count=10, # Training batch_size=32, learning_rate=3e-4, save_summary_steps=100, save_checkpoints_steps=500, keep_checkpoint_max=200, keep_checkpoint_every_n_hours=1, log_step_count_steps=1, shuffle_buffer_size=4, # Validation num_evaluation_steps=32, eval_start_delay_secs=3600, # 1h: disable time based evaluation eval_throttle_secs=86400, # 24h: disable time based evaluation # Misc logfile="log.txt", ) def hparams_debug_string(hparams): values = hparams.values() hp = [' %s: %s' % (name, values[name]) for name in sorted(values)] return 'Hyperparameters:\n' + '\n'.join(hp)

23.617021

71

0.711712

0

194

0.174775

596db7d21a1d0b9384a4b3ba2a66f7f8e7dbfeba

1,080

py

Python

coroutines.py

PraveenMathew92/python-chatroom-asyncio

8b3048f17b76e649aff6bcbb7d084362cab32b58

[ "MIT" ]

null

coroutines.py

PraveenMathew92/python-chatroom-asyncio

8b3048f17b76e649aff6bcbb7d084362cab32b58

[ "MIT" ]

null

coroutines.py

PraveenMathew92/python-chatroom-asyncio

8b3048f17b76e649aff6bcbb7d084362cab32b58

[ "MIT" ]

null

""" File to demonstrate the coroutines api in python """ import asyncio async def coroutine(caller): print(f'entering ${caller}') await asyncio.sleep(1) print(f'exited {caller}') """ asyncio.run takes a coroutine and A RuntimeWarning is generated if the coroutine is not awaited Eg: coroutine('without_run') """ asyncio.run(coroutine('coroutine_call')) """ create_task creates a task which runs a coroutine in the event loop """ async def task_runner(): task = asyncio.create_task(coroutine('task_call')) await task asyncio.run(task_runner()) print(""" \t\t\tRunning with gather task """) async def gather_runner(): """ asyncio.gather takes in a bunch of coroutines and runs them concurrently """ await asyncio.gather( (coroutine('gather')), (task_runner())) asyncio.run(gather_runner()) """ OUTPUT: entering $coroutine_call exited coroutine_call entering $task_call exited task_call Running with gather task entering $gather entering $task_call exited gather exited task_call """

16.363636

76

0.694444

0

413

0.382407

685

0.634259

5970d34126fb063a7fca4ff450fce1eed6c84c32

494

py

Python

projects/tornado_projects/tord/tord/urls.py

bigfoolliu/liu_aistuff

aa661d37c05c257ee293285dd0868fb7e8227628

[ "MIT" ]

1

2019-11-25T07:23:42.000Z

projects/tornado_projects/tord/tord/urls.py

bigfoolliu/liu_aistuff

aa661d37c05c257ee293285dd0868fb7e8227628

[ "MIT" ]

13

2020-01-07T16:09:47.000Z

2022-03-02T12:51:44.000Z

projects/tornado_projects/tord/tord/urls.py

bigfoolliu/liu_aistuff

aa661d37c05c257ee293285dd0868fb7e8227628

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding:utf-8 -*- # author: bigfoolliu from tord.handlers import (block_test, gocron, index, media, upload) url_patterns = [ (r"/", index.IndexHandler), (r"/books", upload.BooksHandler), (r"/images", media.ImageHandler), (r"/videos", media.VideoHandler), # (r"/async/test", async_test.Handler), (r"/block/test", block_test.BlockHandler), # (r"/async/(?P<url>/.*)", async_demo.Handler), # FIXME: (r"/test", gocron.TestHandler), ]

26

68

0.629555

0

215

0.435223

597101821b26dde66f369e5d6c9ba4029fcb1428

140

py

Python

util/emojis.py

Lithimlin/TeaWaiter

fef8d6ef19b8bd10fcd48a2bb320f6cda3ac7156

[ "MIT" ]

null

util/emojis.py

Lithimlin/TeaWaiter

fef8d6ef19b8bd10fcd48a2bb320f6cda3ac7156

[ "MIT" ]

null

util/emojis.py

Lithimlin/TeaWaiter

fef8d6ef19b8bd10fcd48a2bb320f6cda3ac7156

[ "MIT" ]

null

statusEmojis = {'yes':'✅', 'no':'❌'} numEmojis = {1:'1️⃣', 2:'2️⃣', 3:'3️⃣', 4:'4️⃣', 5:'5️⃣', 6:'6️⃣', 7:'7️⃣', 8:'8️⃣', 9:'9️⃣', 0:'0️⃣'}

46.666667

102

0.328571

0

109

0.592391

59728e393c4e17abe11271bfcc3dd74f28baee1f

28

py

Python

platehunter/platehunter/module/__init__.py

ZombieIce/A-Stock-Plate-Crawling

e0478c720513876562ebe2a48b9f3131dad63e47

[ "MIT" ]

20

2018-10-09T18:53:01.000Z

2022-02-20T13:26:43.000Z

platehunter/platehunter/module/__init__.py

ZombieIce/A-Stock-Plate-Crawling

e0478c720513876562ebe2a48b9f3131dad63e47

[ "MIT" ]

36

2018-09-20T19:27:54.000Z

2022-01-23T14:41:39.000Z

insta_hashtag_crawler/__init__.py

point1304/insta-hashtag-crawler

ee056f91d14e19404335fcc49360942acc2e15e8

[ "MIT" ]

6

2021-09-25T14:03:57.000Z

2022-03-19T14:44:04.000Z

from .crawler import Crawler

28

0.857143

0

5972ea55ea758af92089d41c09629539cc06ea40

12,048

py

Python

test/test_subprocess.py

python-useful-helpers/exec-helpers

3e0adfa7dded72ac1c9c93bd88db070f4c9050b6

[ "Apache-2.0" ]

12

2018-03-23T23:37:40.000Z

2021-07-16T16:07:28.000Z

test/test_subprocess.py

penguinolog/exec-helpers

0784a4772f6e9937540b266fdbb1f5a060fd4b76

[ "Apache-2.0" ]

111

2018-03-26T14:10:52.000Z

2021-07-12T07:12:45.000Z

test/test_subprocess.py

penguinolog/exec-helpers

0784a4772f6e9937540b266fdbb1f5a060fd4b76

[ "Apache-2.0" ]

6

2018-03-26T13:37:21.000Z

2018-09-07T03:35:09.000Z

# Copyright 2018 - 2020 Alexey Stepanov aka penguinolog. # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. # Standard Library import logging import random import subprocess import typing from unittest import mock # External Dependencies import pytest # Package Implementation import exec_helpers from exec_helpers import _subprocess_helpers from exec_helpers import proc_enums from exec_helpers.subprocess import SubprocessExecuteAsyncResult pytestmark = pytest.mark.skip("Rewrite whole execute tests.") # All test coroutines will be treated as marked. command = "ls ~\nline 2\nline 3\nline с кирилицей" command_log = f"Executing command:\n{command.rstrip()!r}\n" print_stdin = 'read line; echo "$line"' default_timeout = 60 * 60 # 1 hour class FakeFileStream: """Mock-like object for stream emulation.""" def __init__(self, *args): self.__src = list(args) self.closed = False def __iter__(self): """Normally we iter over source.""" for _ in range(len(self.__src)): yield self.__src.pop(0) def fileno(self): return hash(tuple(self.__src)) def close(self): """We enforce close.""" self.closed = True def read_stream(stream: FakeFileStream): return tuple([line for line in stream]) configs = { "positive_simple": dict( ec=0, stdout=(b" \n", b"2\n", b"3\n", b" \n"), stderr=(), stdin=None, open_stdout=True, open_stderr=True ), "with_stderr": dict( ec=0, stdout=(b" \n", b"2\n", b"3\n", b" \n"), stderr=(b" \n", b"0\n", b"1\n", b" \n"), stdin=None, open_stdout=True, open_stderr=True, ), "negative": dict( ec=1, stdout=(b" \n", b"2\n", b"3\n", b" \n"), stderr=(b" \n", b"0\n", b"1\n", b" \n"), stdin=None, open_stdout=True, open_stderr=True, ), "with_stdin_str": dict( ec=0, stdout=(b" \n", b"2\n", b"3\n", b" \n"), stderr=(), stdin="stdin", open_stdout=True, open_stderr=True ), "with_stdin_bytes": dict( ec=0, stdout=(b" \n", b"2\n", b"3\n", b" \n"), stderr=(), stdin=b"stdin", open_stdout=True, open_stderr=True ), "with_stdin_bytearray": dict( ec=0, stdout=(b" \n", b"2\n", b"3\n", b" \n"), stderr=(), stdin=bytearray(b"stdin"), open_stdout=True, open_stderr=True, ), "no_stderr": dict( ec=0, stdout=(b" \n", b"2\n", b"3\n", b" \n"), stderr=(), stdin=None, open_stdout=True, open_stderr=False ), "no_stdout": dict(ec=0, stdout=(), stderr=(), stdin=None, open_stdout=False, open_stderr=False), } def pytest_generate_tests(metafunc): """Tests parametrization.""" if "run_parameters" in metafunc.fixturenames: metafunc.parametrize( "run_parameters", [ "positive_simple", "with_stderr", "negative", "with_stdin_str", "with_stdin_bytes", "with_stdin_bytearray", "no_stderr", "no_stdout", ], indirect=True, ) @pytest.fixture def run_parameters(request): """Tests configuration apply.""" return configs[request.param] @pytest.fixture def exec_result(run_parameters): return exec_helpers.ExecResult( cmd=command, stdin=run_parameters["stdin"], stdout=tuple([line for line in run_parameters["stdout"]]) if run_parameters["stdout"] else None, stderr=tuple([line for line in run_parameters["stderr"]]) if run_parameters["stderr"] else None, exit_code=run_parameters["ec"], ) @pytest.fixture def execute(mocker, exec_result): return mocker.patch("exec_helpers.subprocess.Subprocess.execute", name="execute", return_value=exec_result) @pytest.fixture def popen(mocker, run_parameters): mocker.patch("psutil.Process") def create_mock( ec: typing.Union[exec_helpers.ExitCodes, int] = exec_helpers.ExitCodes.EX_OK, stdout: typing.Optional[typing.Tuple] = None, stderr: typing.Optional[typing.Tuple] = None, **kwargs, ): """Parametrized code.""" proc = mock.Mock() proc.configure_mock(pid=random.randint(1025, 65536)) if stdout is None: proc.configure_mock(stdout=None) else: proc.attach_mock(FakeFileStream(*stdout), "stdout") if stderr is None: proc.configure_mock(stderr=None) else: proc.attach_mock(FakeFileStream(*stderr), "stderr") proc.attach_mock(mock.Mock(return_value=int(ec)), "wait") proc.configure_mock(returncode=int(ec)) run_shell = mocker.patch("subprocess.Popen", name="popen", return_value=proc) return run_shell return create_mock(**run_parameters) def test_001_execute_async(popen, subprocess_logger, run_parameters) -> None: """Test low level API.""" runner = exec_helpers.Subprocess() res = runner._execute_async( command, stdin=run_parameters["stdin"], open_stdout=run_parameters["open_stdout"], open_stderr=run_parameters["open_stderr"], ) assert isinstance(res, SubprocessExecuteAsyncResult) assert res.interface.wait() == run_parameters["ec"] assert res.interface.returncode == run_parameters["ec"] stdout = run_parameters["stdout"] stderr = run_parameters["stderr"] if stdout is not None: assert read_stream(res.stdout) == stdout else: assert res.stdout is stdout if stderr is not None: assert read_stream(res.stderr) == stderr else: assert res.stderr is stderr if run_parameters["stdin"] is None: stdin = None elif isinstance(run_parameters["stdin"], bytes): stdin = run_parameters["stdin"] elif isinstance(run_parameters["stdin"], str): stdin = run_parameters["stdin"].encode(encoding="utf-8") else: stdin = bytes(run_parameters["stdin"]) if stdin: assert res.stdin is None popen.assert_called_once_with( args=[command], stdout=subprocess.PIPE if run_parameters["open_stdout"] else subprocess.DEVNULL, stderr=subprocess.PIPE if run_parameters["open_stderr"] else subprocess.DEVNULL, stdin=subprocess.PIPE, shell=True, cwd=run_parameters.get("cwd", None), env=run_parameters.get("env", None), universal_newlines=False, **_subprocess_helpers.subprocess_kw, ) if stdin is not None: res.interface.stdin.write.assert_called_once_with(stdin) res.interface.stdin.close.assert_called_once() def test_002_execute(popen, subprocess_logger, exec_result, run_parameters) -> None: """Test API without checkers.""" runner = exec_helpers.Subprocess() res = runner.execute( command, stdin=run_parameters["stdin"], open_stdout=run_parameters["open_stdout"], open_stderr=run_parameters["open_stderr"], ) assert isinstance(res, exec_helpers.ExecResult) assert res == exec_result popen().wait.assert_called_once_with(timeout=default_timeout) assert subprocess_logger.mock_calls[0] == mock.call.log(level=logging.DEBUG, msg=command_log) def test_003_context_manager(mocker, popen, subprocess_logger, exec_result, run_parameters) -> None: """Test context manager for threads synchronization.""" lock_mock = mocker.patch("threading.RLock") with exec_helpers.Subprocess() as runner: res = runner.execute(command, stdin=run_parameters["stdin"]) lock_mock.acquire_assert_called_once() lock_mock.release_assert_called_once() assert isinstance(res, exec_helpers.ExecResult) assert res == exec_result def test_004_check_call(execute, exec_result, subprocess_logger) -> None: """Test exit code validator.""" runner = exec_helpers.Subprocess() if exec_result.exit_code == exec_helpers.ExitCodes.EX_OK: assert runner.check_call(command, stdin=exec_result.stdin) == exec_result else: with pytest.raises(exec_helpers.CalledProcessError) as e: runner.check_call(command, stdin=exec_result.stdin) exc: exec_helpers.CalledProcessError = e.value assert exc.cmd == exec_result.cmd assert exc.returncode == exec_result.exit_code assert exc.stdout == exec_result.stdout_str assert exc.stderr == exec_result.stderr_str assert exc.result == exec_result assert exc.expected == (proc_enums.EXPECTED,) assert subprocess_logger.mock_calls[-1] == mock.call.error( msg=f"Command {exc.result.cmd!r} returned exit code {exc.result.exit_code!s} " f"while expected {exc.expected!r}" ) def test_005_check_call_no_raise(execute, exec_result, subprocess_logger) -> None: """Test exit code validator in permissive mode.""" runner = exec_helpers.Subprocess() res = runner.check_call(command, stdin=exec_result.stdin, raise_on_err=False) assert res == exec_result if exec_result.exit_code != exec_helpers.ExitCodes.EX_OK: expected = (proc_enums.EXPECTED,) assert subprocess_logger.mock_calls[-1] == mock.call.error( msg=f"Command {res.cmd!r} returned exit code {res.exit_code!s} while expected {expected!r}" ) def test_006_check_call_expect(execute, exec_result, subprocess_logger) -> None: """Test exit code validator with custom return codes.""" runner = exec_helpers.Subprocess() assert runner.check_call(command, stdin=exec_result.stdin, expected=[exec_result.exit_code]) == exec_result def test_007_check_stderr(execute, exec_result, subprocess_logger) -> None: """Test STDERR content validator.""" runner = exec_helpers.Subprocess() if not exec_result.stderr: assert runner.check_stderr(command, stdin=exec_result.stdin, expected=[exec_result.exit_code]) == exec_result else: with pytest.raises(exec_helpers.CalledProcessError) as e: runner.check_stderr(command, stdin=exec_result.stdin, expected=[exec_result.exit_code]) exc: exec_helpers.CalledProcessError = e.value assert exc.result == exec_result assert exc.cmd == exec_result.cmd assert exc.returncode == exec_result.exit_code assert exc.stdout == exec_result.stdout_str assert exc.stderr == exec_result.stderr_str assert exc.result == exec_result assert subprocess_logger.mock_calls[-1] == mock.call.error( msg=f"Command {exc.result.cmd!r} output contains STDERR while not expected\n" f"\texit code: {exc.result.exit_code!s}" ) def test_008_check_stderr_no_raise(execute, exec_result, subprocess_logger) -> None: """Test STDERR content validator in permissive mode.""" runner = exec_helpers.Subprocess() assert ( runner.check_stderr(command, stdin=exec_result.stdin, expected=[exec_result.exit_code], raise_on_err=False) == exec_result ) def test_009_call(popen, subprocess_logger, exec_result, run_parameters) -> None: """Test callable.""" runner = exec_helpers.Subprocess() res = runner( command, stdin=run_parameters["stdin"], open_stdout=run_parameters["open_stdout"], open_stderr=run_parameters["open_stderr"], ) assert isinstance(res, exec_helpers.ExecResult) assert res == exec_result popen().wait.assert_called_once_with(timeout=default_timeout)

34.820809

117

0.664011

451

0.037403

140

0.011611

1,693

0.140405

0

2,644

0.219274

59733ab215ceaed85b6503b5568828c87eda4e73

1,943

py

Python

Code/v1.0/message.py

arik-le/Chips-Bits

fa343ea79f13ce3172292871cebd1144b2c3c1c5

[ "MIT" ]

4

2017-11-06T15:12:07.000Z

2020-12-20T13:44:05.000Z

Code/v1.0/message.py

arik-le/Chips-Bits

fa343ea79f13ce3172292871cebd1144b2c3c1c5

[ "MIT" ]

36

2017-11-03T12:07:40.000Z

2018-06-22T11:59:59.000Z

Code/v1.0/message.py

arik-le/Chips-Bits

fa343ea79f13ce3172292871cebd1144b2c3c1c5

[ "MIT" ]

null

import pickle import os from constant_variable import * # class Message class Message: def __init__(self,device, id, type, body): # constructor # message will consist: type of message,content - body,device to send self.id = id self.type = type self.body = body self.device = device # add message to queue to send in proper order def add_to_queue(self): file = open(MESSAGE_QUEUE_FILE,"a") message_pickle=pickle.dumps(self) file.write(message_pickle+BUFFER) # update master def update_master(self,master): if self.device != master: self.device = master def __str__(self): return "From:\t"+self.device.name+"\nMessage:\t"+self.body # get message from queue def get(): new_file=open(MESSAGE_QUEUE_FILE,"r") message_list= new_file.read().split(BUFFER) return pickle.loads(message_list[0]) # take from file and cast it to object def file_to_objects(): if not exist(): return [] objects = [] file = open(MESSAGE_QUEUE_FILE, "r") message_list = file.read().split(BUFFER) for message in message_list: # print pickle.loads(message) try: objects.append(pickle.loads(message)) except: print message return objects # remove the message from queue def remove_from_queue(): if exist(): file = open(MESSAGE_QUEUE_FILE, "r") message_list = file.read().split(BUFFER) file = open(MESSAGE_QUEUE_FILE, 'w') file.writelines(message_list[1:]) # check if there is a message in the queue def exist(): return os.stat(MESSAGE_QUEUE_FILE).st_size != 0 def update_queue(master):#update the master in file messages = file_to_objects() for message in messages: message.update_master(master) open(MESSAGE_QUEUE_FILE,'w').write("") for message in messages: message.add_to_queue()

26.616438

77

0.65054

676

0.347916

0

391

0.201235

597345ee49817e67d67ebede702d14893a6e8c4d

4,732

py

Python

Lib/featureMan/familyFeatures.py

typoman/featureman

f115ea8d3faae042845cfca9502d91da88405c68

[ "MIT" ]

13

2019-07-21T14:00:49.000Z

2019-07-29T21:43:03.000Z

Lib/featureMan/familyFeatures.py

typoman/featureman

f115ea8d3faae042845cfca9502d91da88405c68

[ "MIT" ]

1

2019-07-28T12:06:23.000Z

Lib/featureMan/familyFeatures.py

typoman/featureman

f115ea8d3faae042845cfca9502d91da88405c68

[ "MIT" ]

null

from featureMan.otSingleSubFeatures import * from featureMan.otNumberFeatures import * from featureMan.otLanguages import * from featureMan.otLocalized import * from featureMan.otLigatureFeatures import * from featureMan.otMark import mark from featureMan.otSyntax import fontDic, GDEF from featureMan.otKern import kern from featureMan.otCursive import cursive def l2str(l): return '\n'.join(l) def generateFeatures(f, marksToSkip=None, include=None, base="", path=""): from time import time start = time() if marksToSkip == None: marksToSkip = set("a c d e i k l n o r s t u y z A C D E G I J K L N O R S T U Y Z dotlessi acute breve caron cedilla circumflex dieresis dotaccent grave hungarumlaut macron ogonek ring tilde acute.case breve.case caron.case circumflex.case dieresis.case dotaccent.case grave.case hungarumlaut.case macron.case ring.case tilde.case caronslovak commaturnedtop commaaccent".split(" ")) fDic = fontDic(f, marksToSkip) aaltSet = set() interpretTime = time() print("Elapsed time for interpreting the ufo data: %s" %(interpretTime - start)) marksSet = set() basesSet = set() ligaturesSet = set() componentsSet = set() classes = {} allFeatures = [ ccmpFeature, smcpFeature, caseFeature, arabicFeatures, lnumFeature, onumFeature, pnumFeature, tnumFeature, zeroFeature, localized, ss01Feature, ss02Feature, ss03Feature, ss04Feature, ss05Feature, ss06Feature, ss07Feature, ss08Feature, ss09Feature, ss10Feature, ss11Feature, ss12Feature, ss13Feature, ss14Feature, ss15Feature, ss16Feature, ss17Feature, ss18Feature, ss19Feature, ss20Feature, rligFeature, ligaFeature, dligFeature, cursive, kern, mark ] middleSyntax = [] for feaClass in allFeatures: fea = feaClass(fDic, classes) feaSyntax = fea.syntax() if feaSyntax: middleSyntax.append((fea.tag, feaSyntax)) classes.update(fea.classes) aaltSet.update(fea.aalt) marksSet.update(fea.mark) basesSet.update(fea.base) componentsSet.update(fea.component) ligaturesSet.update(fea.ligature) gdef = GDEF(basesSet, ligaturesSet, marksSet, componentsSet, fDic.glyphs) finalAalt = aaltFeature(aaltSet) langs = languages(fDic) allFeaturesSyntax = [] allFeaturesSyntax.append(('logs' , l2str(fDic.log))) allFeaturesSyntax.append(('lang' , langs.syntax())) allFeaturesSyntax.append(('aalt' , finalAalt.syntax())) allFeaturesSyntax.extend(middleSyntax) allFeaturesSyntax.append(('gdef', gdef.syntax())) finaFea = base if include is not None: if type(include) is str: include = set(include.split(",")) elif type(include) is list: include = set(include) finaFea += l2str([f[1] for f in allFeaturesSyntax if f[0] in include]) else: finaFea += l2str([f[1] for f in allFeaturesSyntax]) featTime = time() print("Elapsed time for generating the features: %s" %(featTime - interpretTime)) fontName = '' fontPath = '' if f.path: fontName = f.path.split("/")[-1].split('.')[0] fontPath = '/'.join(f.path.split("/")[:-1]) if path: fontPath = path feaPath = '%s_features.fea' %(fontPath+'/'+fontName) relativePath = '%s_features.fea' %fontName with open(feaPath, 'w') as File: File.write(finaFea) f.features.text = 'include(%s);' %relativePath f.features.changed() print("Elapsed time for saving the features: %s" %(time() - featTime)) print("Elapsed time for the whole process: %s" %(time() - start)) if __name__ == '__main__': import argparse from fontParts.fontshell.font import RFont parser = argparse.ArgumentParser() parser.add_argument("-u", "--ufo", help="Path to the ufo file.", type=str) parser.add_argument("-b", "--base", help="Base features to include in the begining. It can be used to add some manual features at top of the feature file.", type=str, default="") parser.add_argument("-o", "--only", help="Only unclude the comma seperated feature tags written here. For example: mark,gdef", type=str) parser.add_argument("-p", "--path", help="Path to save the feature file at, default path is next to the UFO.", type=str) args = parser.parse_args() if args.ufo is not None: f = RFont(args.ufo) generateFeatures(f, marksToSkip=None, base=args.base, include=args.only, path=args.path) else: print('You need a UFO for the familyFeatures module to work. Use the following command for help:\npython3 "/path/to/repo/Lib/featureMan/familyFeatures.py" -h')

40.793103

391

0.674134

0

1,137

0.240279

5975a408ae1c989c338845f71aa3900205bb24fd

15,265

py

Python

FFSP/FFSP_MatNet/FFSPModel.py

MinahPark/MatNet

63342de76f6a982bdfb5c1e8d5930d64ec3efa61

[ "MIT" ]

18

2021-11-22T09:37:52.000Z

2022-03-31T03:48:00.000Z

FFSP/FFSP_MatNet/FFSPModel.py

MinahPark/MatNet

63342de76f6a982bdfb5c1e8d5930d64ec3efa61

[ "MIT" ]

1

2021-12-04T05:14:26.000Z

2021-12-14T03:04:55.000Z

FFSP/FFSP_MatNet/FFSPModel.py

MinahPark/MatNet

63342de76f6a982bdfb5c1e8d5930d64ec3efa61

[ "MIT" ]

5

2021-12-15T01:56:02.000Z

2022-03-07T13:13:05.000Z

""" The MIT License Copyright (c) 2021 MatNet Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. """ import torch import torch.nn as nn import torch.nn.functional as F from FFSPModel_SUB import AddAndInstanceNormalization, FeedForward, MixedScore_MultiHeadAttention class FFSPModel(nn.Module): def __init__(self, **model_params): super().__init__() self.model_params = model_params stage_cnt = self.model_params['stage_cnt'] self.stage_models = nn.ModuleList([OneStageModel(stage_idx, **model_params) for stage_idx in range(stage_cnt)]) def pre_forward(self, reset_state): stage_cnt = self.model_params['stage_cnt'] for stage_idx in range(stage_cnt): problems = reset_state.problems_list[stage_idx] model = self.stage_models[stage_idx] model.pre_forward(problems) def soft_reset(self): # Nothing to reset pass def forward(self, state): batch_size = state.BATCH_IDX.size(0) pomo_size = state.BATCH_IDX.size(1) stage_cnt = self.model_params['stage_cnt'] action_stack = torch.empty(size=(batch_size, pomo_size, stage_cnt), dtype=torch.long) prob_stack = torch.empty(size=(batch_size, pomo_size, stage_cnt)) for stage_idx in range(stage_cnt): model = self.stage_models[stage_idx] action, prob = model(state) action_stack[:, :, stage_idx] = action prob_stack[:, :, stage_idx] = prob gathering_index = state.stage_idx[:, :, None] # shape: (batch, pomo, 1) action = action_stack.gather(dim=2, index=gathering_index).squeeze(dim=2) prob = prob_stack.gather(dim=2, index=gathering_index).squeeze(dim=2) # shape: (batch, pomo) return action, prob class OneStageModel(nn.Module): def __init__(self, stage_idx, **model_params): super().__init__() self.model_params = model_params machine_cnt_list = self.model_params['machine_cnt_list'] machine_cnt = machine_cnt_list[stage_idx] embedding_dim = self.model_params['embedding_dim'] self.encoder = FFSP_Encoder(**model_params) self.decoder = FFSP_Decoder(**model_params) self.encoded_col = None # shape: (batch, machine_cnt, embedding) self.encoded_row = None # shape: (batch, job_cnt, embedding) def pre_forward(self, problems): # problems.shape: (batch, job_cnt, machine_cnt) batch_size = problems.size(0) job_cnt = problems.size(1) machine_cnt = problems.size(2) embedding_dim = self.model_params['embedding_dim'] row_emb = torch.zeros(size=(batch_size, job_cnt, embedding_dim)) # shape: (batch, job_cnt, embedding) col_emb = torch.zeros(size=(batch_size, machine_cnt, embedding_dim)) # shape: (batch, machine_cnt, embedding) seed_cnt = self.model_params['one_hot_seed_cnt'] rand = torch.rand(batch_size, seed_cnt) batch_rand_perm = rand.argsort(dim=1) rand_idx = batch_rand_perm[:, :machine_cnt] b_idx = torch.arange(batch_size)[:, None].expand(batch_size, machine_cnt) m_idx = torch.arange(machine_cnt)[None, :].expand(batch_size, machine_cnt) col_emb[b_idx, m_idx, rand_idx] = 1 # shape: (batch, machine_cnt, embedding) self.encoded_row, self.encoded_col = self.encoder(row_emb, col_emb, problems) # encoded_row.shape: (batch, job_cnt, embedding) # encoded_col.shape: (batch, machine_cnt, embedding) self.decoder.set_kv(self.encoded_row) def forward(self, state): batch_size = state.BATCH_IDX.size(0) pomo_size = state.BATCH_IDX.size(1) encoded_current_machine = self._get_encoding(self.encoded_col, state.stage_machine_idx) # shape: (batch, pomo, embedding) all_job_probs = self.decoder(encoded_current_machine, ninf_mask=state.job_ninf_mask) # shape: (batch, pomo, job) if self.training or self.model_params['eval_type'] == 'softmax': while True: # to fix pytorch.multinomial bug on selecting 0 probability elements job_selected = all_job_probs.reshape(batch_size * pomo_size, -1).multinomial(1) \ .squeeze(dim=1).reshape(batch_size, pomo_size) # shape: (batch, pomo) job_prob = all_job_probs[state.BATCH_IDX, state.POMO_IDX, job_selected] \ .reshape(batch_size, pomo_size) # shape: (batch, pomo) job_prob[state.finished] = 1 # do not backprob finished episodes if (job_prob != 0).all(): break else: job_selected = all_job_probs.argmax(dim=2) # shape: (batch, pomo) job_prob = torch.zeros(size=(batch_size, pomo_size)) # any number is okay return job_selected, job_prob def _get_encoding(self, encoded_nodes, node_index_to_pick): # encoded_nodes.shape: (batch, problem, embedding) # node_index_to_pick.shape: (batch, pomo) batch_size = node_index_to_pick.size(0) pomo_size = node_index_to_pick.size(1) embedding_dim = self.model_params['embedding_dim'] gathering_index = node_index_to_pick[:, :, None].expand(batch_size, pomo_size, embedding_dim) # shape: (batch, pomo, embedding) picked_nodes = encoded_nodes.gather(dim=1, index=gathering_index) # shape: (batch, pomo, embedding) return picked_nodes ######################################## # ENCODER ######################################## class FFSP_Encoder(nn.Module): def __init__(self, **model_params): super().__init__() encoder_layer_num = model_params['encoder_layer_num'] self.layers = nn.ModuleList([EncoderLayer(**model_params) for _ in range(encoder_layer_num)]) def forward(self, row_emb, col_emb, cost_mat): # col_emb.shape: (batch, col_cnt, embedding) # row_emb.shape: (batch, row_cnt, embedding) # cost_mat.shape: (batch, row_cnt, col_cnt) for layer in self.layers: row_emb, col_emb = layer(row_emb, col_emb, cost_mat) return row_emb, col_emb class EncoderLayer(nn.Module): def __init__(self, **model_params): super().__init__() self.row_encoding_block = EncodingBlock(**model_params) self.col_encoding_block = EncodingBlock(**model_params) def forward(self, row_emb, col_emb, cost_mat): # row_emb.shape: (batch, row_cnt, embedding) # col_emb.shape: (batch, col_cnt, embedding) # cost_mat.shape: (batch, row_cnt, col_cnt) row_emb_out = self.row_encoding_block(row_emb, col_emb, cost_mat) col_emb_out = self.col_encoding_block(col_emb, row_emb, cost_mat.transpose(1, 2)) return row_emb_out, col_emb_out class EncodingBlock(nn.Module): def __init__(self, **model_params): super().__init__() self.model_params = model_params embedding_dim = self.model_params['embedding_dim'] head_num = self.model_params['head_num'] qkv_dim = self.model_params['qkv_dim'] self.Wq = nn.Linear(embedding_dim, head_num * qkv_dim, bias=False) self.Wk = nn.Linear(embedding_dim, head_num * qkv_dim, bias=False) self.Wv = nn.Linear(embedding_dim, head_num * qkv_dim, bias=False) self.mixed_score_MHA = MixedScore_MultiHeadAttention(**model_params) self.multi_head_combine = nn.Linear(head_num * qkv_dim, embedding_dim) self.add_n_normalization_1 = AddAndInstanceNormalization(**model_params) self.feed_forward = FeedForward(**model_params) self.add_n_normalization_2 = AddAndInstanceNormalization(**model_params) def forward(self, row_emb, col_emb, cost_mat): # NOTE: row and col can be exchanged, if cost_mat.transpose(1,2) is used # input1.shape: (batch, row_cnt, embedding) # input2.shape: (batch, col_cnt, embedding) # cost_mat.shape: (batch, row_cnt, col_cnt) head_num = self.model_params['head_num'] q = reshape_by_heads(self.Wq(row_emb), head_num=head_num) # q shape: (batch, head_num, row_cnt, qkv_dim) k = reshape_by_heads(self.Wk(col_emb), head_num=head_num) v = reshape_by_heads(self.Wv(col_emb), head_num=head_num) # kv shape: (batch, head_num, col_cnt, qkv_dim) out_concat = self.mixed_score_MHA(q, k, v, cost_mat) # shape: (batch, row_cnt, head_num*qkv_dim) multi_head_out = self.multi_head_combine(out_concat) # shape: (batch, row_cnt, embedding) out1 = self.add_n_normalization_1(row_emb, multi_head_out) out2 = self.feed_forward(out1) out3 = self.add_n_normalization_2(out1, out2) return out3 # shape: (batch, row_cnt, embedding) ######################################## # Decoder ######################################## class FFSP_Decoder(nn.Module): def __init__(self, **model_params): super().__init__() self.model_params = model_params embedding_dim = self.model_params['embedding_dim'] head_num = self.model_params['head_num'] qkv_dim = self.model_params['qkv_dim'] self.encoded_NO_JOB = nn.Parameter(torch.rand(1, 1, embedding_dim)) self.Wq_1 = nn.Linear(embedding_dim, head_num * qkv_dim, bias=False) self.Wq_2 = nn.Linear(embedding_dim, head_num * qkv_dim, bias=False) self.Wq_3 = nn.Linear(embedding_dim, head_num * qkv_dim, bias=False) self.Wk = nn.Linear(embedding_dim, head_num * qkv_dim, bias=False) self.Wv = nn.Linear(embedding_dim, head_num * qkv_dim, bias=False) self.multi_head_combine = nn.Linear(head_num * qkv_dim, embedding_dim) self.k = None # saved key, for multi-head attention self.v = None # saved value, for multi-head_attention self.single_head_key = None # saved key, for single-head attention def set_kv(self, encoded_jobs): # encoded_jobs.shape: (batch, job, embedding) batch_size = encoded_jobs.size(0) embedding_dim = self.model_params['embedding_dim'] head_num = self.model_params['head_num'] encoded_no_job = self.encoded_NO_JOB.expand(size=(batch_size, 1, embedding_dim)) encoded_jobs_plus_1 = torch.cat((encoded_jobs, encoded_no_job), dim=1) # shape: (batch, job_cnt+1, embedding) self.k = reshape_by_heads(self.Wk(encoded_jobs_plus_1), head_num=head_num) self.v = reshape_by_heads(self.Wv(encoded_jobs_plus_1), head_num=head_num) # shape: (batch, head_num, job+1, qkv_dim) self.single_head_key = encoded_jobs_plus_1.transpose(1, 2) # shape: (batch, embedding, job+1) def forward(self, encoded_machine, ninf_mask): # encoded_machine.shape: (batch, pomo, embedding) # ninf_mask.shape: (batch, pomo, job_cnt+1) head_num = self.model_params['head_num'] # Multi-Head Attention ####################################################### q = reshape_by_heads(self.Wq_3(encoded_machine), head_num=head_num) # shape: (batch, head_num, pomo, qkv_dim) out_concat = self._multi_head_attention_for_decoder(q, self.k, self.v, rank3_ninf_mask=ninf_mask) # shape: (batch, pomo, head_num*qkv_dim) mh_atten_out = self.multi_head_combine(out_concat) # shape: (batch, pomo, embedding) # Single-Head Attention, for probability calculation ####################################################### score = torch.matmul(mh_atten_out, self.single_head_key) # shape: (batch, pomo, job_cnt+1) sqrt_embedding_dim = self.model_params['sqrt_embedding_dim'] logit_clipping = self.model_params['logit_clipping'] score_scaled = score / sqrt_embedding_dim # shape: (batch, pomo, job_cnt+1) score_clipped = logit_clipping * torch.tanh(score_scaled) score_masked = score_clipped + ninf_mask probs = F.softmax(score_masked, dim=2) # shape: (batch, pomo, job_cnt+1) return probs def _multi_head_attention_for_decoder(self, q, k, v, rank2_ninf_mask=None, rank3_ninf_mask=None): # q shape: (batch, head_num, n, qkv_dim) : n can be either 1 or PROBLEM_SIZE # k,v shape: (batch, head_num, job_cnt+1, qkv_dim) # rank2_ninf_mask.shape: (batch, job_cnt+1) # rank3_ninf_mask.shape: (batch, n, job_cnt+1) batch_size = q.size(0) n = q.size(2) job_cnt_plus_1 = k.size(2) head_num = self.model_params['head_num'] qkv_dim = self.model_params['qkv_dim'] sqrt_qkv_dim = self.model_params['sqrt_qkv_dim'] score = torch.matmul(q, k.transpose(2, 3)) # shape: (batch, head_num, n, job_cnt+1) score_scaled = score / sqrt_qkv_dim if rank2_ninf_mask is not None: score_scaled = score_scaled + rank2_ninf_mask[:, None, None, :].expand(batch_size, head_num, n, job_cnt_plus_1) if rank3_ninf_mask is not None: score_scaled = score_scaled + rank3_ninf_mask[:, None, :, :].expand(batch_size, head_num, n, job_cnt_plus_1) weights = nn.Softmax(dim=3)(score_scaled) # shape: (batch, head_num, n, job_cnt+1) out = torch.matmul(weights, v) # shape: (batch, head_num, n, qkv_dim) out_transposed = out.transpose(1, 2) # shape: (batch, n, head_num, qkv_dim) out_concat = out_transposed.reshape(batch_size, n, head_num * qkv_dim) # shape: (batch, n, head_num*qkv_dim) return out_concat ######################################## # NN SUB FUNCTIONS ######################################## def reshape_by_heads(qkv, head_num): # q.shape: (batch, n, head_num*key_dim) : n can be either 1 or PROBLEM_SIZE batch_s = qkv.size(0) n = qkv.size(1) q_reshaped = qkv.reshape(batch_s, n, head_num, -1) # shape: (batch, n, head_num, key_dim) q_transposed = q_reshaped.transpose(1, 2) # shape: (batch, head_num, n, key_dim) return q_transposed

39.141026

123

0.648411

13,333

0.873436

0

4,495

0.294464

5975bf51cf6b40314443cbac07c50fa49c107d36

1,697

py

Python

compose.py

lvyufeng/mindspore_poems

2f46afa290a8065cd1c774c26a96be76da30873e

[ "MIT" ]

null

compose.py

lvyufeng/mindspore_poems

2f46afa290a8065cd1c774c26a96be76da30873e

[ "MIT" ]

null

compose.py

lvyufeng/mindspore_poems

2f46afa290a8065cd1c774c26a96be76da30873e

[ "MIT" ]

null

import os import numpy as np import mindspore from mindspore import Tensor from mindspore import load_checkpoint, load_param_into_net from src.model import RNNModel, RNNModelInfer from src.utils import process_poems start_token = 'B' end_token = 'E' model_dir = './ckpt/' corpus_file = './data/poems.txt' def to_word(predict, vocabs): t = np.cumsum(predict) s = np.sum(predict) sample = int(np.searchsorted(t, np.random.rand(1) * s)) if sample > len(vocabs): sample = len(vocabs) - 1 return vocabs[sample] def gen_poem(begin_word): print('## loading corpus from %s' % model_dir) poems_vector, word_int_map, vocabularies = process_poems(corpus_file) print(len(vocabularies)) rnn_model = RNNModel(len(vocabularies), rnn_size=128, model='lstm') param_dict = load_checkpoint( os.path.join(model_dir, f'poems.6.ckpt')) param_not_load = load_param_into_net(rnn_model, param_dict) print(param_not_load) rnn_model = RNNModelInfer(rnn_model) x = np.array([list(map(word_int_map.get, start_token))]) predict = rnn_model(Tensor(x, mindspore.int32)) word = begin_word or to_word(predict.asnumpy(), vocabularies) poem_ = '' i = 0 while word != end_token: poem_ += word i += 1 if i > 24: break x = np.array([[word_int_map[word]]]) predict = rnn_model(Tensor(x, mindspore.int32)) word = to_word(predict.asnumpy(), vocabularies) return poem_ if __name__ == '__main__': begin_char = input('## （输入 quit 退出）请输入第一个字 please input the first character: ') if begin_char == 'quit': exit() poem = gen_poem(begin_char) print(poem)

30.303571

83

0.669417

0

184

0.10679

5976b5eadcdfa649651a6db9b9bd714639c5b347

1,523

py

Python

pychemia/core/from_file.py

petavazohi/PyChemia

e779389418771c25c830aed360773c63bb069372

[ "MIT" ]

67

2015-01-31T07:44:55.000Z

2022-03-21T21:43:34.000Z

pychemia/core/from_file.py

petavazohi/PyChemia

e779389418771c25c830aed360773c63bb069372

[ "MIT" ]

13

2016-06-03T19:07:51.000Z

2022-03-31T04:20:40.000Z

pychemia/core/from_file.py

petavazohi/PyChemia

e779389418771c25c830aed360773c63bb069372

[ "MIT" ]

37

2015-01-22T15:37:23.000Z

2022-03-21T15:38:10.000Z

import os import sys from pychemia import HAS_PYMATGEN, pcm_log from .structure import Structure from pychemia.code.vasp import read_poscar from pychemia.code.abinit import AbinitInput def structure_from_file(structure_file): """ Attempts to reconstruct a PyChemia Structure from the contents of any given file. Valid entries :param structure_file: The path to a file where the structure can be reconstructed :type structure_file: str :return: PyChemia Structure if succeed, None otherwise """ st = None basename = os.path.basename(structure_file) if not os.path.isfile(structure_file): raise ValueError("ERROR: Could not open file '%s'" % structure_file) if basename[-4:].lower() == 'json': st = Structure.load_json(structure_file) elif basename[-3:].lower() == 'cif' and HAS_PYMATGEN: import pychemia.external.pymatgen st = pychemia.external.pymatgen.cif2structure(structure_file)[0] elif 'poscar' in basename.lower(): st = read_poscar(structure_file) elif 'contcar' in basename.lower(): st = read_poscar(structure_file) elif 'abinit' in basename.lower(): av = AbinitInput(structure_file) st = av.get_structure() else: try: st = read_poscar(structure_file) except ValueError: raise ValueError('Ćould not convert file as POSCAR') if st is None: pcm_log.debug("ERROR: Could not extract structure from file '%s'" % structure_file) return st

37.146341

99

0.692055

0

443

0.290682

59792e136f9480b5e034aa6d01981255bd1bfdd7

992

py

Python

snptools/vc_matrix.py

pvanheus/variant_exploration_with_tralynca

4ffadc29c19d68909beed2254646e36513311847

[ "MIT" ]

null

snptools/vc_matrix.py

pvanheus/variant_exploration_with_tralynca

4ffadc29c19d68909beed2254646e36513311847

[ "MIT" ]

null

snptools/vc_matrix.py

pvanheus/variant_exploration_with_tralynca

4ffadc29c19d68909beed2254646e36513311847

[ "MIT" ]

null

from os import listdir import os.path import pandas as pd from .count_variants_per_gene import process_vcf from .genetree import make_gene_tree def make_variant_count_matrix(input_directory, output_filename): gene_tree = make_gene_tree() locus_names = sorted([ interval.data['locus'] for interval in gene_tree ]) matrix = [] futures = [] for filename in sorted(listdir(input_directory)): if filename.endswith('.vcf.gz') or filename.endswith('.vcf'): path = os.path.join(input_directory, filename) counts = process_vcf(path, gene_tree) row = [ counts.get(locus, 0) for locus in locus_names ] matrix.append(row) sample_names = [ filename.split('.')[0] for filename in sorted(listdir(input_directory)) if filename.endswith('.vcf.gz') or filename.endswith('.vcf') ] data = pd.DataFrame(matrix, index=sample_names, columns=locus_names) data.to_csv(output_filename)

41.333333

93

0.676411

0

40

0.040323

5979cf5bed5000445a52e27786a6829f4458f888

481

py

Python

oarepo_records_draft/merge.py

oarepo/invenio-records-draft

6d77309996c58fde7731e5f182e9cd5400f81f14

[ "MIT" ]

1

2020-06-03T14:44:49.000Z

oarepo_records_draft/merge.py

oarepo/invenio-records-draft

6d77309996c58fde7731e5f182e9cd5400f81f14

[ "MIT" ]

7

2020-06-02T14:45:48.000Z

2021-11-16T08:38:47.000Z

oarepo_records_draft/merge.py

oarepo/invenio-records-draft

6d77309996c58fde7731e5f182e9cd5400f81f14

[ "MIT" ]

1

2019-08-15T07:59:48.000Z

from deepmerge import Merger def list_merge(config, path, base, nxt): for k in range(0, min(len(base), len(nxt))): if isinstance(base[k], (dict, list, tuple)): draft_merger.merge(base[k], nxt[k]) else: base[k] = nxt[k] for k in range(len(base), len(nxt)): base.append(nxt[k]) return base draft_merger = Merger( [ (list, [list_merge]), (dict, ["merge"]) ], ["override"], ["override"] )

20.913043

52

0.534304

0

27

0.056133

597bfa5b6f7cdb21349ef3d1cce73227ae2c86fc

4,951

py

Python

source/01_make_coordinates/make_coordinates.py

toshi-k/kaggle-airbus-ship-detection-challenge

872a160057592022488b1772b6c7a8982677d1dc

[ "Apache-2.0" ]

90

2018-11-17T21:37:41.000Z

2021-11-24T11:55:34.000Z

source/01_make_coordinates/make_coordinates.py

jackweiwang/kaggle-airbus-ship-detection-challenge

872a160057592022488b1772b6c7a8982677d1dc

[ "Apache-2.0" ]

3

2018-11-27T14:23:15.000Z

2020-03-09T09:23:25.000Z

source/01_make_coordinates/make_coordinates.py

jackweiwang/kaggle-airbus-ship-detection-challenge

872a160057592022488b1772b6c7a8982677d1dc

[ "Apache-2.0" ]

14

2018-11-17T21:37:44.000Z

2020-11-30T02:22:28.000Z

import os import numpy as np import pandas as pd from tqdm import tqdm from PIL import Image from lib.img2_coord_ica import img2_coord_iter, coord2_img from lib.log import Logger # ref: https://www.kaggle.com/paulorzp/run-length-encode-and-decode def rle_decode(mask_rle, shape=(768, 768)): """ Args: mask_rle: run-length as string formated (start length) shape: (height,width) of array to return Returns: numpy array, 1 - mask, 0 - background """ s = mask_rle.split() starts, lengths = [np.asarray(x, dtype=int) for x in (s[0:][::2], s[1:][::2])] starts -= 1 ends = starts + lengths img = np.zeros(shape[0]*shape[1], dtype=np.uint8) for lo, hi in zip(starts, ends): img[lo:hi] = 255 return img.reshape(shape).T def main_test(): i = 5304 # 11, 15, 16, 5398 image_id = segmentations.iloc[i, 0] truth_img = rle_decode(segmentations.iloc[i, 1]) print(np.max(truth_img)) coord = img2_coord_iter(truth_img / 255.0, threshold=0.05) reconst_img = coord2_img(*coord) sse = np.sum((reconst_img - truth_img) ** 2) print('sum of squared error: {}'.format(sse)) os.makedirs('_result_sample', exist_ok=True) Image.fromarray(reconst_img).save(os.path.join('_result_sample', image_id[:-4] + '_reconstruct.png'), format='PNG') Image.fromarray(truth_img).save(os.path.join('_result_sample', image_id[:-4] + '_truth.png'), format='PNG') def main(): logger = Logger('coord_ica') list_mean_x = list() list_mean_y = list() list_height = list() list_aspect_ratio = list() list_rotate = list() num_error = 0 num_zero_ship = 0 os.makedirs('_error_imgs', exist_ok=True) sse_array = np.array([]) for i, image_id in tqdm(enumerate(segmentations.ImageId), total=len(segmentations)): encoded = segmentations.iloc[i, 1] if encoded == '': list_mean_x.append(np.nan) list_mean_y.append(np.nan) list_height.append(np.nan) list_aspect_ratio.append(np.nan) list_rotate.append(np.nan) num_zero_ship += 1 continue truth_img = rle_decode(encoded) reconst_img = np.zeros(truth_img.shape) # initialize threshold_iter = 0.95 threshold_last = 0.6 truth_img_norm = truth_img / 255.0 try: mean_x, mean_y, height, aspect_ratio, rotate, img_size = img2_coord_iter(truth_img_norm, threshold_iter) reconst_img = coord2_img(mean_x, mean_y, height, aspect_ratio, rotate, img_size) reconst_img_norm = reconst_img / 255.0 sse = np.sum((reconst_img_norm - truth_img_norm) ** 2) sse_array = np.append(sse_array, sse) area_intersect = np.sum(truth_img_norm * reconst_img_norm) area_union = np.sum(truth_img_norm) + np.sum(reconst_img_norm) - area_intersect matching_degree = area_intersect / area_union if matching_degree < threshold_last: logger.info('[{}] sse: {} matching_degree: {}'.format(image_id, sse, matching_degree)) raise RuntimeError list_mean_x.append(mean_x) list_mean_y.append(mean_y) list_height.append(height) list_aspect_ratio.append(aspect_ratio) list_rotate.append(rotate) except (RuntimeError, ValueError): num_error += 1 list_mean_x.append(np.nan) list_mean_y.append(np.nan) list_height.append(np.nan) list_aspect_ratio.append(np.nan) list_rotate.append(np.nan) if matching_degree < threshold_last: try: Image.fromarray(reconst_img).save( os.path.join('_error_imgs', image_id[:-4] + '_deg{:.3f}_re.png'.format(matching_degree))) Image.fromarray(truth_img).save( os.path.join('_error_imgs', image_id[:-4] + '_deg{:.3f}_truth.png'.format(matching_degree))) except: pass logger.info('mean of reconstruct error: {:.3f}'.format(np.mean(sse_array))) logger.info('num zero ship: {0:d} / {1:d}'.format(num_zero_ship, len(segmentations))) logger.info('num_error: {0:d} / {1:d}'.format(num_error, len(segmentations))) result = pd.DataFrame() result['ImageID'] = segmentations.ImageId result['x'] = list_mean_y result['y'] = list_mean_x result['height'] = list_height result['width'] = [height / ratio for height, ratio in zip(list_height, list_aspect_ratio)] result['rotate'] = list_rotate result.to_csv('../../input/coordinates.csv', index=False, float_format='%.4f') if __name__ == '__main__': segmentations = pd.read_csv('../../dataset/train_ship_segmentations_v2.csv') print(segmentations.head()) segmentations = segmentations.fillna('') # main_test() main()

30.006061

119

0.626338

0

769

0.155322

597ddcf7272429172b7edee0cb03c0de356cd799

127

py

Python

tests/test_main.py

skypaw/rconcrete

30bc7e5ada2afa975caabcd38461707e094d695b

[ "MIT" ]

null

tests/test_main.py

skypaw/rconcrete

30bc7e5ada2afa975caabcd38461707e094d695b

[ "MIT" ]

2

2022-02-05T18:49:44.000Z

2022-02-06T01:11:07.000Z

tests/test_main.py

skypaw/rconcrete

30bc7e5ada2afa975caabcd38461707e094d695b

[ "MIT" ]

null

from src.main import sample_function def test_addition(): test = sample_function(4) print('test') assert 8 == test

21.166667

36

0.692913

0

6

0.047244

597e7da85300fb6bd6d365c07bb2ba1dbac55565

1,598

py

Python

scripts/combine_errors.py

nbren12/nn_atmos_param

cb138f0b211fd5743e56ad659aec38c082d2b3ac

[ "MIT" ]

4

2018-09-16T20:55:57.000Z

2020-12-06T11:27:50.000Z

scripts/combine_errors.py

nbren12/nn_atmos_param

cb138f0b211fd5743e56ad659aec38c082d2b3ac

[ "MIT" ]

5

2018-04-07T07:40:39.000Z

2018-06-20T06:56:08.000Z

scripts/combine_errors.py

nbren12/nn_atmos_param

cb138f0b211fd5743e56ad659aec38c082d2b3ac

[ "MIT" ]

null

import numpy as np import re import json import xarray as xr import pandas as pd def read_train_loss(epoch, fname, variables=['test_loss', 'train_loss']): """Read the loss.json file for the current epochs test and train loss""" df = pd.read_json(fname) epoch_means = df.groupby('epoch').mean() # need to look for epoch-1 because this data is accumulated over the whole first epoch if epoch > 0: return epoch_means.loc[epoch-1][variables].to_dict() else: return {'test_loss': np.nan, 'train_loss': np.nan} errors = [] dims = [] pattern = re.compile("data/output/model.(.*?)/(.*?)/(.*?)/error.nc") for f in snakemake.input: m = pattern.search(f) if m: model, seed, epoch = m.groups() ds = xr.open_dataset(f) arg_file = f"data/output/model.{model}/{seed}/arguments.json" args = json.load(open(arg_file)) # nhidden is a list, so need to just take the first element # since all the neural networks I fit are single layer args['nhidden'] = args['nhidden'][0] args.pop('seed', None) ds = ds.assign(**args) loss_file = f"data/output/model.{model}/{seed}/loss.json" train_error = read_train_loss(int(epoch), loss_file) ds = ds.assign(**train_error) # append to lists dims.append((model, seed, int(epoch))) errors.append(ds) names = ['model', 'seed', 'epoch'] dim = pd.MultiIndex.from_tuples(dims, names=names) dim.name = 'tmp' ds = xr.concat(errors, dim=dim).unstack('tmp') ds.to_netcdf(snakemake.output[0])

30.150943

90

0.627034

0

536

0.335419

59801917a885910b96ef72a02bd5c83398abe7ef

705

py

Python

tests/acceptance/selene_collection_should_test.py

KalinkinaMaria/selene

859e1102c85740b52af8d0f08dd6b6490b4bd2ff

[ "MIT" ]

null

tests/acceptance/selene_collection_should_test.py

KalinkinaMaria/selene

859e1102c85740b52af8d0f08dd6b6490b4bd2ff

[ "MIT" ]

1

2021-06-02T04:21:17.000Z

tests/acceptance/selene_collection_should_test.py

vkarpenko/selene

4776357430c940be38f38be9981006dd156f9730

[ "MIT" ]

null

import pytest from selenium.common.exceptions import TimeoutException from selene.browser import * from selene.support.conditions import have from selene.support.jquery_style_selectors import ss from tests.acceptance.helpers.helper import get_test_driver from tests.acceptance.helpers.todomvc import given_active def setup_module(m): set_driver(get_test_driver()) def teardown_module(m): driver().quit() def test_assure_passes(): given_active("a", "b") ss("#todo-list>li").should(have.exact_texts("a", "b")) def test_assure_fails(): given_active("a", "b") with pytest.raises(TimeoutException): ss("#todo-list>li").should(have.exact_texts("a.", "b."), timeout=0.1)

25.178571

77

0.741844

0

56

0.079433

5980640bb02c2631ecc30d2c519d9ed76e0a3bab

2,422

py

Python

genomics_data_index/test/unit/variant/service/test_SQLQueryInBatcher.py

apetkau/genomics-data-index

d0cc119fd57b8cbd701affb1c84450cf7832fa01

[ "Apache-2.0" ]

12

2021-05-03T20:56:05.000Z

2022-01-04T14:52:19.000Z

genomics_data_index/test/unit/variant/service/test_SQLQueryInBatcher.py

apetkau/thesis-index

6c96e9ed75d8e661437effe62a939727a0b473fc

[ "Apache-2.0" ]

30

2021-04-26T23:03:40.000Z

2022-02-25T18:41:14.000Z

genomics_data_index/test/unit/variant/service/test_SQLQueryInBatcher.py

apetkau/genomics-data-index

d0cc119fd57b8cbd701affb1c84450cf7832fa01

[ "Apache-2.0" ]

null

from genomics_data_index.storage.service import SQLQueryInBatcherDict, SQLQueryInBatcherList def test_sql_query_in_batcher_dict(): in_data = ['A', 'B', 'C', 'D', 'E'] # Test batch size 1 batcher = SQLQueryInBatcherDict(in_data=in_data, batch_size=1) results = batcher.process(lambda in_batch: {x: True for x in in_batch}) assert isinstance(results, dict) assert 5 == len(results) assert {'A', 'B', 'C', 'D', 'E'} == set(results.keys()) # Test batch size 2 batcher = SQLQueryInBatcherDict(in_data=in_data, batch_size=2) results = batcher.process(lambda in_batch: {x: True for x in in_batch}) assert isinstance(results, dict) assert 5 == len(results) assert {'A', 'B', 'C', 'D', 'E'} == set(results.keys()) # Test batch size 5 batcher = SQLQueryInBatcherDict(in_data=in_data, batch_size=5) results = batcher.process(lambda in_batch: {x: True for x in in_batch}) assert isinstance(results, dict) assert 5 == len(results) assert {'A', 'B', 'C', 'D', 'E'} == set(results.keys()) # Test batch size 6 batcher = SQLQueryInBatcherDict(in_data=in_data, batch_size=6) results = batcher.process(lambda in_batch: {x: True for x in in_batch}) assert isinstance(results, dict) assert 5 == len(results) assert {'A', 'B', 'C', 'D', 'E'} == set(results.keys()) def test_sql_query_in_batcher_list(): in_data = ['A', 'B', 'C', 'D', 'E'] # Test batch size 1 batcher = SQLQueryInBatcherList(in_data=in_data, batch_size=1) results = batcher.process(lambda in_batch: [x for x in in_batch]) assert isinstance(results, list) assert 5 == len(results) assert in_data == results # Test batch size 2 batcher = SQLQueryInBatcherList(in_data=in_data, batch_size=2) results = batcher.process(lambda in_batch: in_batch) assert isinstance(results, list) assert 5 == len(results) assert in_data == results # Test batch size 5 batcher = SQLQueryInBatcherList(in_data=in_data, batch_size=5) results = batcher.process(lambda in_batch: in_batch) assert isinstance(results, list) assert 5 == len(results) assert in_data == results # Test batch size 6 batcher = SQLQueryInBatcherList(in_data=in_data, batch_size=6) results = batcher.process(lambda in_batch: in_batch) assert isinstance(results, list) assert 5 == len(results) assert in_data == results

36.69697

92

0.676301

0

242

0.099917

59807967b291bcc22ce0c7c760cacd407b042fe9

609

py

Python

tipico_server/utils/constants.py

lbusoni/tipico_server

390e1ad8ca6a56ac14cebc1f9c50c9cb5803e287

[ "MIT" ]

null

tipico_server/utils/constants.py

lbusoni/tipico_server

390e1ad8ca6a56ac14cebc1f9c50c9cb5803e287

[ "MIT" ]

null

tipico_server/utils/constants.py

lbusoni/tipico_server

390e1ad8ca6a56ac14cebc1f9c50c9cb5803e287

[ "MIT" ]

null

class Constants: METER_2_NANOMETER= 1e9 APP_NAME= "inaf.arcetri.ao.tipico_server" APP_AUTHOR= "INAF Arcetri Adaptive Optics" THIS_PACKAGE= 'tipico_server' PROCESS_MONITOR_CONFIG_SECTION= 'processMonitor' SERVER_1_CONFIG_SECTION= 'serverOfAnInstrument' SERVER_2_CONFIG_SECTION= 'serverOfAnotherInstrument' # TODO: must be the same of console_scripts in setup.py START_PROCESS_NAME= 'tipico_start' STOP_PROCESS_NAME= 'tipico_stop' KILL_ALL_PROCESS_NAME= 'tipico_kill_all' SERVER_1_PROCESS_NAME= 'tipico_server_1' SERVER_2_PROCESS_NAME= 'tipico_server_2'

30.45

59

0.775041

605

0.993432

0

274

0.449918

5980a13b88db20b5e773819c926a4981f53bb21e

1,611

py

Python

mu.py

cool2645/shadowsocksrr

0a594857f4c3125ab14d27d7fd8143291b7c9fee

[ "Apache-2.0" ]

2

2018-05-14T10:41:38.000Z

2020-05-22T12:40:57.000Z

mu.py

cool2645/shadowsocksrr

0a594857f4c3125ab14d27d7fd8143291b7c9fee

[ "Apache-2.0" ]

null

mu.py

cool2645/shadowsocksrr

0a594857f4c3125ab14d27d7fd8143291b7c9fee

[ "Apache-2.0" ]

1

2018-09-22T16:15:14.000Z

import db_transfer import config import logging from musdk.client import Client class MuApiTransfer(db_transfer.TransferBase): client = None users = [] def __init__(self): super(MuApiTransfer, self).__init__() self.pull_ok = False self.port_uid_table = {} self.init_mu_client() def init_mu_client(self): mu_url = config.mu_uri mu_token = config.token node_id = config.node_id mu_client = Client(mu_url, node_id, mu_token) self.client = mu_client def pull_db_all_user(self): print("pull all users...") return self.pull_db_users() def pull_db_users(self): users = self.client.get_users_res() if users is None: return self.users for user in users: self.port_uid_table[user['port']] = user['id'] self.users = users return users def update_all_user(self, dt_transfer): print('call update all user') print(dt_transfer) update_transfer = {} logs = [] for id in dt_transfer.keys(): transfer = dt_transfer[id] if transfer[0] + transfer[1] < 1024: continue update_transfer[id] = transfer uid = self.port_uid_table[id] log = self.client.gen_traffic_log(uid, transfer[0], transfer[1]) logs.append(log) print("logs ", logs) ok = self.client.update_traffic(logs) if ok is False: logging.error("update traffic failed...") return {} return update_transfer

28.767857

76

0.590937

1,528

0.948479

0

84

0.052142

598126ffcc8da7b8ff9a91f8f601f2ef5306a660

2,001

py

Python

tests/test_json.py

NyntoFive/data_extractor

965e12570d6b7549aa2f8b3bd1951e06b010c444

[ "MIT" ]

null

tests/test_json.py

NyntoFive/data_extractor

965e12570d6b7549aa2f8b3bd1951e06b010c444

[ "MIT" ]

null

tests/test_json.py

NyntoFive/data_extractor

965e12570d6b7549aa2f8b3bd1951e06b010c444

[ "MIT" ]

null

# Standard Library import json # Third Party Library import pytest from jsonpath_rw.lexer import JsonPathLexerError # First Party Library from data_extractor.exceptions import ExprError, ExtractError from data_extractor.json import JSONExtractor @pytest.fixture(scope="module") def text(): return """ { "foo": [ { "baz": 1 }, { "baz": 2 } ] } """ @pytest.fixture(scope="module") def element(text): return json.loads(text) @pytest.mark.parametrize( "expr,expect", [ ("foo[*].baz", [1, 2]), ("foo.baz", []), ("foo[0].baz", [1]), ("foo[1].baz", [2]), ("foo[2].baz", []), ], ids=repr, ) def test_extract(element, expr, expect): assert expect == JSONExtractor(expr).extract(element) @pytest.mark.parametrize( "expr,expect", [ ("foo[*].baz", 1), ("foo.baz", "default"), ("foo[0].baz", 1), ("foo[1].baz", 2), ("foo[2].baz", "default"), ], ids=repr, ) def test_extract_first(element, expr, expect): assert expect == JSONExtractor(expr).extract_first(element, default="default") @pytest.mark.parametrize("expr", ["foo.baz", "foo[2].baz"], ids=repr) def test_extract_first_without_default(element, expr): extractor = JSONExtractor(expr) with pytest.raises(ExtractError) as catch: extractor.extract_first(element) exc = catch.value assert len(exc.extractors) == 1 assert exc.extractors[0] is extractor assert exc.element is element @pytest.mark.parametrize("expr", ["foo..", "a[]", ""], ids=repr) def test_invalid_css_selector_expr(element, expr): extractor = JSONExtractor(expr) with pytest.raises(ExprError) as catch: extractor.extract(element) exc = catch.value assert exc.extractor is extractor assert isinstance(exc.exc, (JsonPathLexerError, Exception))

23.267442

82

0.590705

0

1,734

0.866567

0

487

0.243378

59814b4554d683700762543937d73f8de4e2078a

938

py

Python

demo/predictions/visualize.py

qixuxiang/maskrcnn_tianchi_stage2

52023b64268dc91f0b5b9f085203ab00a542458a

[ "MIT" ]

null

demo/predictions/visualize.py

qixuxiang/maskrcnn_tianchi_stage2

52023b64268dc91f0b5b9f085203ab00a542458a

[ "MIT" ]

null

demo/predictions/visualize.py

qixuxiang/maskrcnn_tianchi_stage2

52023b64268dc91f0b5b9f085203ab00a542458a

[ "MIT" ]

null

import numpy as np from PIL import Image import os npy_file1 = './prediction/1110_1.npy' npy_file2 = './prediction/1110_2.npy' npy_file3 = './prediction/1110_3.npy' npy_file4 = './prediction/1110_4.npy' npy_file5 = './prediction/1110_5.npy' arr1 = np.load(npy_file1) arr2 = np.load(npy_file2) arr3 = np.load(npy_file3) arr4 = np.load(npy_file4) arr5 = np.load(npy_file5) print(sum(sum(arr1))) print(sum(sum(arr2))) print(sum(sum(arr3))) print(sum(sum(arr4))) print(sum(sum(arr5))) arr1 = 50*arr1 arr2 = 50*arr2 arr3 = 50*arr3 arr4 = 50*arr4 arr5 = 50*arr5 img1 = Image.fromarray(arr1).convert("L") img2 = Image.fromarray(arr2).convert("L") img3 = Image.fromarray(arr3).convert("L") img4 = Image.fromarray(arr4).convert("L") img5 = Image.fromarray(arr5).convert("L") img1.save("./test_pic/test1.png") img2.save("./test_pic/test2.png") img3.save("./test_pic/test3.png") img4.save("./test_pic/test4.png") img5.save("./test_pic/test5.png")

26.055556

41

0.715352

0

250

0.266525

59821d30d6e5bb63ead4e418643ab63f3b0a5f6b

1,125

py

Python

examples/gbdt_classifier_example.py

tushushu/Imilu

121c79574d3e6ca35b569dd58661175e5c3668e2

[ "Apache-2.0" ]

407

2018-08-22T05:58:33.000Z

2022-03-31T11:44:48.000Z

examples/gbdt_classifier_example.py

tushushu/Imilu

121c79574d3e6ca35b569dd58661175e5c3668e2

[ "Apache-2.0" ]

9

2018-11-07T07:44:02.000Z

2021-12-10T11:59:47.000Z

examples/gbdt_classifier_example.py

tushushu/Imilu

121c79574d3e6ca35b569dd58661175e5c3668e2

[ "Apache-2.0" ]

286

2018-08-22T08:00:19.000Z

2022-03-30T00:59:20.000Z

# -*- coding: utf-8 -*- """ @Author: tushushu @Date: 2018-08-21 14:33:11 @Last Modified by: tushushu @Last Modified time: 2019-05-22 15:41:11 """ import os os.chdir(os.path.split(os.path.realpath(__file__))[0]) import sys sys.path.append(os.path.abspath("..")) from imylu.ensemble.gbdt_classifier import GradientBoostingClassifier from imylu.utils.load_data import load_breast_cancer from imylu.utils.model_selection import train_test_split, model_evaluation from imylu.utils.utils import run_time @run_time def main(): """Tesing the performance of GBDT classifier""" print("Tesing the performance of GBDT classifier...") # Load data data, label = load_breast_cancer() # Split data randomly, train set rate 70% data_train, data_test, label_train, label_test = train_test_split(data, label, random_state=20) # Train model clf = GradientBoostingClassifier() clf.fit(data_train, label_train, n_estimators=2, learning_rate=0.8, max_depth=3, min_samples_split=2) # Model evaluation model_evaluation(clf, data_test, label_test) if __name__ == "__main__": main()

28.846154

99

0.731556

0

580

0.515556

0

336

0.298667

5985441293e6489af243c2cd16aa10e62e49c056

16,658

py

Python

gamestonk_terminal/cryptocurrency/due_diligence/pycoingecko_view.py

clairvoyant/GamestonkTerminal

7b40cfe61b32782e36f5de8a08d075532a08c294

[ "MIT" ]

null

gamestonk_terminal/cryptocurrency/due_diligence/pycoingecko_view.py

clairvoyant/GamestonkTerminal

7b40cfe61b32782e36f5de8a08d075532a08c294

[ "MIT" ]

null

gamestonk_terminal/cryptocurrency/due_diligence/pycoingecko_view.py

clairvoyant/GamestonkTerminal

7b40cfe61b32782e36f5de8a08d075532a08c294

[ "MIT" ]

null

"""CoinGecko view""" __docformat__ = "numpy" import argparse from typing import List, Tuple import pandas as pd from pandas.plotting import register_matplotlib_converters import matplotlib.pyplot as plt from tabulate import tabulate import mplfinance as mpf from gamestonk_terminal.helper_funcs import ( parse_known_args_and_warn, plot_autoscale, ) from gamestonk_terminal.feature_flags import USE_ION as ion import gamestonk_terminal.cryptocurrency.due_diligence.pycoingecko_model as gecko from gamestonk_terminal.cryptocurrency.dataframe_helpers import wrap_text_in_df register_matplotlib_converters() # pylint: disable=inconsistent-return-statements # pylint: disable=R0904, C0302 def load(other_args: List[str]): """Load selected Cryptocurrency. You can pass either symbol of id of the coin Parameters ---------- other_args : List[str] argparse arguments """ parser = argparse.ArgumentParser( add_help=False, formatter_class=argparse.ArgumentDefaultsHelpFormatter, prog="load", description="""Load cryptocurrency, from CoinGecko. You will have access to a lot of statistics on that coin like price data, coin development stats, social media and many others. Loading coin also will open access to technical analysis menu.""", ) parser.add_argument( "-c", "--coin", required="-h" not in other_args, type=str, dest="coin", help="Coin to load data for (symbol or coin id). You can use either symbol of the coin or coinId" "You can find all coins using command `coins` or visit https://www.coingecko.com/en. " "To use load a coin use command load -c [symbol or coinId]", ) try: if other_args: if "-" not in other_args[0]: other_args.insert(0, "-c") ns_parser = parse_known_args_and_warn(parser, other_args) if not ns_parser: return coin = gecko.Coin(ns_parser.coin) print("") return coin except KeyError: print(f"Could not find coin with the id: {ns_parser.coin}", "\n") return None except SystemExit: print("") return None except Exception as e: print(e, "\n") return None def chart(coin: gecko.Coin, other_args: List[str]): """Plots chart for loaded cryptocurrency Parameters ---------- coin : gecko_coin.Coin Cryptocurrency other_args : List[str] argparse arguments """ parser = argparse.ArgumentParser( add_help=False, formatter_class=argparse.ArgumentDefaultsHelpFormatter, prog="chart", description=""" Display chart for loaded coin. You can specify currency vs which you want to show chart and also number of days to get data for. By default currency: usd and days: 30. E.g. if you loaded in previous step Bitcoin and you want to see it's price vs ethereum in last 90 days range use `chart --vs eth --days 90` """, ) parser.add_argument( "--vs", default="usd", dest="vs", help="Currency to display vs coin" ) parser.add_argument( "-d", "--days", default=30, dest="days", help="Number of days to get data for" ) try: ns_parser = parse_known_args_and_warn(parser, other_args) if not ns_parser: return df = coin.get_coin_market_chart(ns_parser.vs, ns_parser.days) df = df["price"].resample("1D").ohlc().ffill() df.columns = [ "Open", "High", "Low", "Close", ] title = ( f"\n{coin.coin_symbol}/{ns_parser.vs} from {df.index[0].strftime('%Y/%m/%d')} " f"to {df.index[-1].strftime('%Y/%m/%d')}", ) mpf.plot( df, type="candle", volume=False, title=str(title[0]) if isinstance(title, tuple) else title, xrotation=20, style="binance", figratio=(10, 7), figscale=1.10, figsize=(plot_autoscale()), update_width_config=dict( candle_linewidth=1.0, candle_width=0.8, volume_linewidth=1.0 ), ) if ion: plt.ion() plt.show() print("") except SystemExit: print("") except Exception as e: print(e, "\n") def load_ta_data(coin: gecko.Coin, other_args: List[str]) -> Tuple[pd.DataFrame, str]: """Load data for Technical Analysis Parameters ---------- coin : gecko_coin.Coin Cryptocurrency other_args : List[str] argparse arguments Returns ---------- Tuple[pd.DataFrame, str] dataframe with prices quoted currency """ parser = argparse.ArgumentParser( add_help=False, formatter_class=argparse.ArgumentDefaultsHelpFormatter, prog="ta", description=""" Loads data for technical analysis. You can specify currency vs which you want to show chart and also number of days to get data for. By default currency: usd and days: 30. E.g. if you loaded in previous step Bitcoin and you want to see it's price vs ethereum in last 90 days range use `ta --vs eth --days 90` """, ) parser.add_argument( "--vs", default="usd", dest="vs", help="Currency to display vs coin" ) parser.add_argument( "-d", "--days", default=30, dest="days", help="Number of days to get data for" ) try: ns_parser = parse_known_args_and_warn(parser, other_args) if not ns_parser: return pd.DataFrame(), "" df = coin.get_coin_market_chart(ns_parser.vs, ns_parser.days) df = df["price"].resample("1D").ohlc().ffill() df.columns = [ "Open", "High", "Low", "Close", ] df.index.name = "date" return df, ns_parser.vs except SystemExit: print("") return pd.DataFrame(), "" except Exception as e: print(e, "\n") return pd.DataFrame(), "" def info(coin: gecko.Coin, other_args: List[str]): """Shows basic information about loaded coin Parameters ---------- coin : gecko_coin.Coin Cryptocurrency other_args : List[str] argparse arguments """ parser = argparse.ArgumentParser( add_help=False, formatter_class=argparse.ArgumentDefaultsHelpFormatter, prog="info", description=""" Shows basic information about loaded coin like: Name, Symbol, Description, Market Cap, Public Interest, Supply, and Price related metrics """, ) try: ns_parser = parse_known_args_and_warn(parser, other_args) if not ns_parser: return df = wrap_text_in_df(coin.base_info, w=80) print( tabulate( df, headers=df.columns, floatfmt=".2f", showindex=False, tablefmt="fancy_grid", ), "\n", ) except SystemExit: print("") except Exception as e: print(e, "\n") def web(coin: gecko.Coin, other_args: List[str]): """Shows found websites corresponding to loaded coin Parameters ---------- coin : gecko_coin.Coin Cryptocurrency other_args : List[str] argparse arguments """ parser = argparse.ArgumentParser( add_help=False, formatter_class=argparse.ArgumentDefaultsHelpFormatter, prog="web", description="""Websites found for given Coin. You can find there urls to homepage, forum, announcement site and others.""", ) try: ns_parser = parse_known_args_and_warn(parser, other_args) if not ns_parser: return df = coin.websites print( tabulate( df, headers=df.columns, floatfmt=".2f", showindex=False, tablefmt="fancy_grid", ), "\n", ) except SystemExit: print("") except Exception as e: print(e, "\n") def social(coin: gecko.Coin, other_args: List[str]): """Shows social media corresponding to loaded coin Parameters ---------- coin : gecko_coin.Coin Cryptocurrency other_args : List[str] argparse arguments """ parser = argparse.ArgumentParser( add_help=False, formatter_class=argparse.ArgumentDefaultsHelpFormatter, prog="social", description="""Shows social media corresponding to loaded coin. You can find there name of telegram channel, urls to twitter, reddit, bitcointalk, facebook and discord.""", ) try: ns_parser = parse_known_args_and_warn(parser, other_args) if not ns_parser: return df = coin.social_media print( tabulate( df, headers=df.columns, floatfmt=".2f", showindex=False, tablefmt="fancy_grid", ), "\n", ) except SystemExit: print("") except Exception as e: print(e, "\n") def dev(coin: gecko.Coin, other_args: List[str]): """Shows developers data for loaded coin Parameters ---------- coin : gecko_coin.Coin Cryptocurrency other_args : List[str] argparse arguments """ parser = argparse.ArgumentParser( add_help=False, formatter_class=argparse.ArgumentDefaultsHelpFormatter, prog="dev", description="""Developers data for loaded coin. If the development data is available you can see how the code development of given coin is going on. There are some statistics that shows number of stars, forks, subscribers, pull requests, commits, merges, contributors on github.""", ) try: ns_parser = parse_known_args_and_warn(parser, other_args) if not ns_parser: return df = coin.developers_data print( tabulate( df, headers=df.columns, floatfmt=".2f", showindex=False, tablefmt="fancy_grid", ), "\n", ) except SystemExit: print("") except Exception as e: print(e, "\n") def ath(coin: gecko.Coin, other_args: List[str]): """Shows all time high data for loaded coin Parameters ---------- coin : gecko_coin.Coin Cryptocurrency other_args : List[str] argparse arguments """ parser = argparse.ArgumentParser( add_help=False, formatter_class=argparse.ArgumentDefaultsHelpFormatter, prog="ath", description="""All time high data for loaded coin""", ) parser.add_argument( "--vs", dest="vs", help="currency", default="usd", choices=["usd", "btc"] ) try: ns_parser = parse_known_args_and_warn(parser, other_args) if not ns_parser: return df = coin.all_time_high(currency=ns_parser.vs) print( tabulate( df, headers=df.columns, floatfmt=".2f", showindex=False, tablefmt="fancy_grid", ), "\n", ) except SystemExit: print("") except Exception as e: print(e, "\n") def atl(coin: gecko.Coin, other_args: List[str]): """Shows all time low data for loaded coin Parameters ---------- coin : gecko_coin.Coin Cryptocurrency other_args : List[str] argparse arguments """ parser = argparse.ArgumentParser( add_help=False, formatter_class=argparse.ArgumentDefaultsHelpFormatter, prog="atl", description="""All time low data for loaded coin""", ) parser.add_argument( "--vs", dest="vs", help="currency", default="usd", choices=["usd", "btc"] ) try: ns_parser = parse_known_args_and_warn(parser, other_args) if not ns_parser: return df = coin.all_time_low(currency=ns_parser.vs) print( tabulate( df, headers=df.columns, floatfmt=".2f", showindex=False, tablefmt="fancy_grid", ), "\n", ) except SystemExit: print("") except Exception as e: print(e, "\n") def score(coin: gecko.Coin, other_args: List[str]): """Shows different kind of scores for loaded coin Parameters ---------- coin : gecko_coin.Coin Cryptocurrency other_args : List[str] argparse arguments """ parser = argparse.ArgumentParser( add_help=False, formatter_class=argparse.ArgumentDefaultsHelpFormatter, prog="score", description=""" In this view you can find different kind of scores for loaded coin. Those scores represents different rankings, sentiment metrics, some user stats and others. You will see CoinGecko scores, Developer Scores, Community Scores, Sentiment, Reddit scores and many others. """, ) try: ns_parser = parse_known_args_and_warn(parser, other_args) if not ns_parser: return df = coin.scores print( tabulate( df, headers=df.columns, floatfmt=".2f", showindex=False, tablefmt="fancy_grid", ), "\n", ) except SystemExit: print("") except Exception as e: print(e, "\n") def bc(coin: gecko.Coin, other_args: List[str]): """Shows urls to blockchain explorers Parameters ---------- coin : gecko_coin.Coin Cryptocurrency other_args : List[str] argparse arguments """ parser = argparse.ArgumentParser( add_help=False, formatter_class=argparse.ArgumentDefaultsHelpFormatter, prog="bc", description=""" Blockchain explorers URLs for loaded coin. Those are sites like etherescan.io or polkascan.io in which you can see all blockchain data e.g. all txs, all tokens, all contracts... """, ) try: ns_parser = parse_known_args_and_warn(parser, other_args) if not ns_parser: return df = coin.blockchain_explorers print( tabulate( df, headers=df.columns, floatfmt=".2f", showindex=False, tablefmt="fancy_grid", ), "\n", ) except SystemExit: print("") except Exception as e: print(e, "\n") def market(coin: gecko.Coin, other_args: List[str]): """Shows market data for loaded coin Parameters ---------- coin : gecko_coin.Coin Cryptocurrency other_args : List[str] argparse arguments """ parser = argparse.ArgumentParser( add_help=False, formatter_class=argparse.ArgumentDefaultsHelpFormatter, prog="market", description=""" Market data for loaded coin. There you find metrics like: Market Cap, Supply, Circulating Supply, Price, Volume and many others. """, ) try: ns_parser = parse_known_args_and_warn(parser, other_args) if not ns_parser: return df = coin.market_data print( tabulate( df, headers=df.columns, floatfmt=".2f", showindex=False, tablefmt="fancy_grid", ), "\n", ) except SystemExit: print("") except Exception as e: print(e, "\n")

27.308197

117

0.551327

0

6,541

0.392664

5985716e3511f569993e2ea970c450df3042b443

701

py

Python

source/loaders/tploaders.py

rodsom22/gcn_refinement

b1b76811b145a2fa7e595cc6d131d75c0553d5a3

[ "MIT" ]

24

2020-05-04T20:24:35.000Z

2022-03-21T07:57:02.000Z

source/loaders/tploaders.py

rodsom22/gcn_refinement

b1b76811b145a2fa7e595cc6d131d75c0553d5a3

[ "MIT" ]

3

2020-09-02T15:54:10.000Z

2021-05-27T03:09:31.000Z

source/loaders/tploaders.py

rodsom22/gcn_refinement

b1b76811b145a2fa7e595cc6d131d75c0553d5a3

[ "MIT" ]

6

2020-08-03T21:01:37.000Z

2021-02-04T02:24:46.000Z

""" Data loaders based on tensorpack """ import numpy as np from utilities import nparrays as arrtools def get_pancreas_generator(sample_name, volumes_path, references_path): sample_vol_name = volumes_path + sample_name[0] reference_vol_name = references_path + sample_name[1] volume = np.load(sample_vol_name) reference = np.load(reference_vol_name) reference[reference != 0] = 1 y, x, z = volume.shape for i in range(z): vol_slice = volume[:, :, i] reference_slice = reference[:, :, i] vol_slice = arrtools.extend2_before(vol_slice) reference_slice = arrtools.extend2_before(reference_slice) yield[vol_slice, reference_slice]

29.208333

71

0.706134

0

594

0.847361

0

40

0.057061

5986324fbdcbaeae05e084715dcadf5d8b4991a3

1,199

py

Python

app/stages/management/commands/import_stages_from_csv.py

guilloulouis/stage_medecine

7ec9067402e510d812a375bbfe46f2ab545587f9

[ "MIT" ]

null

app/stages/management/commands/import_stages_from_csv.py

guilloulouis/stage_medecine

7ec9067402e510d812a375bbfe46f2ab545587f9

[ "MIT" ]

null

app/stages/management/commands/import_stages_from_csv.py

guilloulouis/stage_medecine

7ec9067402e510d812a375bbfe46f2ab545587f9

[ "MIT" ]

1

2021-04-30T16:38:19.000Z

# from django.core.management import BaseCommand # import pandas as pd # # from stages.models import Category, Stage # # # class Command(BaseCommand): # help = 'Import a list of stage in the database' # # def add_arguments(self, parser): # super(Command, self).add_arguments(parser) # parser.add_argument( # '--csv', dest='csv', default=None, # help='Specify the csv file to parse', # ) # # def handle(self, *args, **options): # csv = options.get('csv') # csv_reader = pd.read_csv(csv) # stages_to_create = [] # for index, item in csv_reader.iterrows(): # stage_raw = item['Stage'] # split = stage_raw.split('(') # stage_name = split[0].strip() # if len(split) > 1: # category_name = split[1].replace(')', '').strip() # category_object, created = Category.objects.get_or_create(name=category_name) # else: # category_object = None # stages_to_create.append(Stage(name=stage_name, place_max=item['places'], category=category_object)) # Stage.objects.bulk_create(stages_to_create)

37.46875

113

0.584654

0

1,168

0.974145

5986b5465c4c37fe33e19dc8df090df96c8f030d

3,137

py

Python

deep_learning/dl.py

remix-yh/moneycount

e8f35549ef96b8ebe6ca56417f0833f519179173

[ "MIT" ]

null

deep_learning/dl.py

remix-yh/moneycount

e8f35549ef96b8ebe6ca56417f0833f519179173

[ "MIT" ]

7

2020-09-26T00:46:23.000Z

2022-02-10T01:08:15.000Z

deep_learning/dl.py

remix-yh/moneycount

e8f35549ef96b8ebe6ca56417f0833f519179173

[ "MIT" ]

null

import os import io import matplotlib as mpl mpl.use('Agg') import matplotlib.pyplot as plt from matplotlib.backends.backend_agg import FigureCanvasAgg from matplotlib.figure import Figure from keras.applications.imagenet_utils import preprocess_input from keras.backend.tensorflow_backend import set_session from keras.preprocessing import image import numpy as np from scipy.misc import imread import tensorflow as tf from ssd_v2 import SSD300v2 from ssd_utils import BBoxUtility voc_classes = ['10', '100', '5', 'Boat', 'Bottle', 'Bus', 'Car', 'Cat', 'Chair', 'Cow', 'Diningtable', 'Dog', 'Horse','Motorbike', 'Person', 'Pottedplant', 'Sheep', 'Sofa', 'Train', 'Tvmonitor'] NUM_CLASSES = len(voc_classes) + 1 def initialize(weight_file_path): np.set_printoptions(suppress=True) config = tf.ConfigProto() config.gpu_options.per_process_gpu_memory_fraction = 0.45 set_session(tf.Session(config=config)) input_shape = (300, 300, 3) model = SSD300v2(input_shape, num_classes=NUM_CLASSES) model.load_weights(weight_file_path, by_name=True) return model def predict(model, img): inputs = [] plt.cla() img = image.img_to_array(img) img = np.asarray(img) inputs.append(img.copy()) inputs = np.asarray(inputs) inputs = preprocess_input(inputs) preds = model.predict(inputs, batch_size=1, verbose=1) bbox_util = BBoxUtility(NUM_CLASSES) results = bbox_util.detection_out(preds) # Parse the outputs. det_label = results[0][:, 0] det_conf = results[0][:, 1] det_xmin = results[0][:, 2] det_ymin = results[0][:, 3] det_xmax = results[0][:, 4] det_ymax = results[0][:, 5] top_indices = [i for i, conf in enumerate(det_conf) if conf >= 0.6] #0.6 top_conf = det_conf[top_indices] top_label_indices = det_label[top_indices].tolist() top_xmin = det_xmin[top_indices] top_ymin = det_ymin[top_indices] top_xmax = det_xmax[top_indices] top_ymax = det_ymax[top_indices] colors = plt.cm.hsv(np.linspace(0, 1, 21)).tolist() plt.imshow(img / 255.) currentAxis = plt.gca() money_total = 0 money_num_list = [10, 100, 5] for i in range(top_conf.shape[0]): xmin = int(round(top_xmin[i] * img.shape[1])) ymin = int(round(top_ymin[i] * img.shape[0])) xmax = int(round(top_xmax[i] * img.shape[1])) ymax = int(round(top_ymax[i] * img.shape[0])) score = top_conf[i] label = int(top_label_indices[i]) label_name = voc_classes[label - 1] display_txt = '{:0.2f}, {}'.format(score, label_name) coords = (xmin, ymin), xmax-xmin+1, ymax-ymin+1 color = colors[label] currentAxis.add_patch(plt.Rectangle(*coords, fill=False, edgecolor=color, linewidth=2)) currentAxis.text(xmin, ymin, display_txt, bbox={'facecolor':color, 'alpha':0.5}) money_total = money_total + money_num_list[label - 1] plt.title(f'Total:{money_total} yen') canvas = FigureCanvasAgg(currentAxis.figure) buf = io.BytesIO() plt.savefig(buf) buf.seek(0) return buf

31.37

95

0.667198

0

227

0.072362

598893a66d83b9b4e168cfcddf559998286b638d

469

py

Python

statping/exceptions.py

danielpalstra/pystatping

eb6325229d45cd452528007b440ca545bacc3e04

[ "Apache-2.0" ]

null

statping/exceptions.py

danielpalstra/pystatping

eb6325229d45cd452528007b440ca545bacc3e04

[ "Apache-2.0" ]

null

statping/exceptions.py

danielpalstra/pystatping

eb6325229d45cd452528007b440ca545bacc3e04

[ "Apache-2.0" ]

null

class BaseExceptions(Exception): pass class AuthException(BaseException): """Raised when an api method requires authentication""" pass class DeleteException(BaseException): """Raised when the delete of an object fails""" pass class UpsertException(BaseException): """Raised when the combined insert or update fails""" pass class NotFoundException(BaseException): """Raised when objects cannot be found by the API""" pass

17.37037

59

0.714286

456

0.972281

0

207

0.441365

598974722569cb3c84cf300f7c787f22839c151a

2,255

py

Python

authors/tests/test_article_filters.py

andela/ah-backend-odin

0e9ef1a10c8a3f6736999a5111736f7bd7236689

[ "BSD-3-Clause" ]

null

authors/tests/test_article_filters.py

andela/ah-backend-odin

0e9ef1a10c8a3f6736999a5111736f7bd7236689

[ "BSD-3-Clause" ]

43

2018-10-25T10:14:52.000Z

2022-03-11T23:33:46.000Z

authors/tests/test_article_filters.py

andela/ah-backend-odin

0e9ef1a10c8a3f6736999a5111736f7bd7236689

[ "BSD-3-Clause" ]

4

2018-10-29T07:04:58.000Z

2020-04-02T14:15:10.000Z

from . import BaseAPITestCase class TestArticleFilters(BaseAPITestCase): def setUp(self): super().setUp() self.authenticate() def test_it_filters_articles_by_article_title(self): self.create_article() self.create_article(title="Some article with another title") response = self.client.get( "/api/articles/?title=Some article with another title" ) self.assertEqual(len(response.data['results']), 1) def test_it_filters_articles_by_article_tag(self): self.create_article() self.create_article(tagList=['learning', 'django']) self.create_article(tagList=['learning', 'vuejs', "aws", "jest"]) response = self.client.get("/api/articles/?tag=learning") self.assertEqual(len(response.data['results']), 2) def test_it_filters_articles_by_article_description(self): description = "Testing django apps" self.create_article(description=description) response = self.client.get( f"/api/articles/?description={description}" ) self.assertEqual(len(response.data['results']), 1) def test_it_filters_articles_by_author_username(self): self.create_articles_with_diferent_authors() response = self.client.get("/api/articles/?author=krm") self.assertEqual(len(response.data['results']), 1) def test_it_filters_articles_by_author_email(self): self.create_articles_with_diferent_authors() response = self.client.get("/api/articles/?author=krm@example.com") self.assertEqual(len(response.data['results']), 1) def create_articles_with_diferent_authors(self): self.create_article() self.authenticate( {"username": "krm", "email": "krm@example.com"} ) self.create_article() def create_article(self, **kwargs): article = { "title": "How to train your dragon", "description": "Ever wonder how?", "body": "You have to believe", "tagList": ["reactjs", "angularjs", "dragons"], "published": True } data = {**article} data.update(kwargs) self.client.post("/api/articles/", {"article": data})

36.370968

75

0.640355

2,222

0.985366

0

541

0.239911

598d5551f035952fc6ef820f0bbd414d1bb129f0

720

py

Python

myexporter/tcpexporter.py

abh15/flower

7e1ab9393e0494f23df65bfa4f858cc35fea290e

[ "Apache-2.0" ]

null

myexporter/tcpexporter.py

abh15/flower

7e1ab9393e0494f23df65bfa4f858cc35fea290e

[ "Apache-2.0" ]

null

myexporter/tcpexporter.py

abh15/flower

7e1ab9393e0494f23df65bfa4f858cc35fea290e

[ "Apache-2.0" ]

null

#!/usr/bin/python3 import subprocess import time from prometheus_client import start_http_server, Gauge def getstat(): s=subprocess.getoutput('ss -i -at \'( dport = :x11 or sport = :x11 )\' | awk \'FNR == 3 { print $4}\'') if s == "": return(0.0,"") else: rtt=s.lstrip("rtt:") r=rtt.split("/", 1)[0] l=subprocess.getoutput('ss -i -at \'( dport = :x11 or sport = :x11 )\' | awk \'FNR == 2 { print $5}\'') label=l.split(":", 1)[0] return(float(r),label) start_http_server(9200) latencygauge = Gauge('tcprtt', 'provides rtt to fed server using ss',['cohort']) while True: stat, lbl= getstat() latencygauge.labels(cohort=lbl).set(stat) time.sleep(2)

32.727273

112

0.590278

0

245

0.340278

598f144f73e5a69e09521df868c498cc54751d48

516

py

Python

tests/features/steps/roman.py

TestowanieAutomatyczneUG/laboratorium_14-maciejSzcz

b92186c574d3f21acd9f3e913e1a8ddcb5ec81fd

[ "MIT" ]

null

tests/features/steps/roman.py

TestowanieAutomatyczneUG/laboratorium_14-maciejSzcz

b92186c574d3f21acd9f3e913e1a8ddcb5ec81fd

[ "MIT" ]

null

tests/features/steps/roman.py

TestowanieAutomatyczneUG/laboratorium_14-maciejSzcz

b92186c574d3f21acd9f3e913e1a8ddcb5ec81fd

[ "MIT" ]

null

from behave import * use_step_matcher("re") @given("user inputs (?P<number>.+) and (?P<guess>.+)") def step_impl(context, number, guess): context.number = int(number) context.user_guess = guess @when("we run the converter") def step_impl(context): try: context.res = context.roman.check_guess(context.number, context.user_guess) except TypeError as e: context.e = e @then("the result should be (?P<value>.+)") def step_impl(context, value): assert str(context.res) == value

24.571429

83

0.672481

0

463

0.897287

0

108

0.209302

599099e8cbd4ce7be2457cb90f171f8cb872d8d1

1,266

py

Python

main.py

AbirLOUARD/AspiRobot

0ea78bfd7c20f1371c01a0e912f5e92bed6648b7

[ "MIT" ]

1

2022-03-31T18:37:11.000Z

main.py

AbirLOUARD/AspiRobot

0ea78bfd7c20f1371c01a0e912f5e92bed6648b7

[ "MIT" ]

null

main.py

AbirLOUARD/AspiRobot

0ea78bfd7c20f1371c01a0e912f5e92bed6648b7

[ "MIT" ]

null

import functions import Aspirobot import time import os import Manoir import Capteur import Etat import threading import Case from threading import Thread manor_size = 5 gameIsRunning = True clearConsole = lambda: os.system('cls' if os.name in ('nt', 'dos') else 'clear') manoir = Manoir.Manoir(manor_size, manor_size) caseRobot = Case.Case(1, 1) agent = Aspirobot.Aspirobot(manoir, caseRobot) manoir.draw() """ while (gameIsRunning): clearConsole() if (functions.shouldThereBeANewDirtySpace(dirtys_number)): functions.generateDirt(manor_dirty) dirtys_number += 1 if (functions.shouldThereBeANewLostJewel(jewels_number)): functions.generateJewel(manor_jewel) jewels_number += 1 functions.drawManor(manor_dirty, manor_jewel) time.sleep(pause_length) """ for init in range(10): manoir.initialisation() init += 1 def runAgent(): while True: agent.run(3) def runManoir(): while True: #clearConsole() manoir.ModifierPositionRobot(agent.getCase()) manoir.run() if __name__ == "__main__": t1 = Thread(target = runAgent) t2 = Thread(target = runManoir) t1.setDaemon(True) t2.setDaemon(True) t1.start() t2.start() while True: pass

21.827586

80

0.691153

0

442

0.349131

599104a205da723279b528df24bd43e2dcb5bdbb

1,169

py

Python

docs/src/newsgroups_data.py

vishalbelsare/RLScore

713f0a402f7a09e41a609f2ddcaf849b2021a0a7

[ "MIT" ]

61

2015-03-06T08:48:01.000Z

2021-04-26T16:13:07.000Z

docs/src/newsgroups_data.py

andrecamara/RLScore

713f0a402f7a09e41a609f2ddcaf849b2021a0a7

[ "MIT" ]

5

2016-09-08T15:47:00.000Z

2019-02-25T17:44:55.000Z

docs/src/newsgroups_data.py

vishalbelsare/RLScore

713f0a402f7a09e41a609f2ddcaf849b2021a0a7

[ "MIT" ]

31

2015-01-28T15:05:33.000Z

2021-04-16T19:39:48.000Z

import numpy as np from scipy import sparse as sp from rlscore.utilities import multiclass def load_newsgroups(): T = np.loadtxt("train.data") #map indices from 1...n to 0...n-1 rows = T[:,0] -1 cols = T[:,1] -1 vals = T[:,2] X_train = sp.coo_matrix((vals, (rows, cols))) X_train = X_train.tocsc() T = np.loadtxt("test.data") #map indices from 1...n to 0...n-1 rows = T[:,0] -1 cols = T[:,1] -1 vals = T[:,2] X_test = sp.coo_matrix((vals, (rows, cols))) X_test = X_test.tocsc() #X_test has additional features not present in X_train X_test = X_test[:,:X_train.shape[1]] Y_train = np.loadtxt("train.label", dtype=int) Y_train = multiclass.to_one_vs_all(Y_train, False) Y_test = np.loadtxt("test.label", dtype=int) Y_test = multiclass.to_one_vs_all(Y_test, False) return X_train, Y_train, X_test, Y_test def print_stats(): X_train, Y_train, X_test, Y_test = load_newsgroups() print("Train X dimensions %d %d" %X_train.shape) print("Test X dimensions %d %d" %X_test.shape) print("Number of labels %d" %Y_train.shape[1]) if __name__=="__main__": print_stats()

30.763158

58

0.638152

0

252

0.215569

59945bb43aee8c097a1605b49beb38bfd751d29b

25

py

Python

1795.py

heltonricardo/URI

160cca22d94aa667177c9ebf2a1c9864c5e55b41

[ "MIT" ]

6

2021-04-13T00:33:43.000Z

2022-02-10T10:23:59.000Z

1795.py

heltonricardo/URI

160cca22d94aa667177c9ebf2a1c9864c5e55b41

[ "MIT" ]

null

1795.py

heltonricardo/URI

160cca22d94aa667177c9ebf2a1c9864c5e55b41

[ "MIT" ]

3

2021-03-23T18:42:24.000Z

2022-02-10T10:24:07.000Z

print(3 ** int(input()))

12.5

24

0.56

0

59962bcd6324fb181e2aeed2776a6d4ee13fa678

1,245

py

Python

5hours/14_dictionaries.py

matiasmasca/python

7631583820d51e3132bdb793fed28cc83f4877a2

[ "MIT" ]

null

5hours/14_dictionaries.py

matiasmasca/python

7631583820d51e3132bdb793fed28cc83f4877a2

[ "MIT" ]

null

5hours/14_dictionaries.py

matiasmasca/python

7631583820d51e3132bdb793fed28cc83f4877a2

[ "MIT" ]

null

# como los hash de ruby, guarda "clave" "valor" # al igual que un diccionario, esta la Palabra, que es la clave y la definción que seria el valor. # las claves tienen que ser unicas nombre_de_diccionario = {} #curly brackets. monthConversions = { "Jan": "January", "Feb": "February", "Mar": "March", "Apr": "April", "May": "May", "Jun": "June", "Jul": "July", "Ago": "August", "Sep": "September", "Oct": "October", "Nov": "November", "Dic": "December", } # acceder a los valores del diccionario # hay varias formas # poner la clave entre brackets print(monthConversions["Mar"]) # Get, permite definir que valor devuelve si no hay esa clave print(monthConversions.get("Nov")) print(monthConversions.get("Mat")) print(monthConversions.get("Mat", "No es una clave valida")) # Pueden ser claves pueden ser numericas, y los valores de diferentes tipos monthConversions = { 1: ("January", "Enero", "Janeiro"), # un tupla 2: ["February", "Febrero", "Fevereiro"], #una lista 3: "March", 4: "April", 5: "May", 6: "June", 7: "July", 8: "August", 9: "September", 10: "October", 11: "November", 12: "December", } print(monthConversions[1]) print(monthConversions[1][1]) print(monthConversions[2][2])

23.055556

98

0.654618

0

777

0.623596

599682564ad210bc55f3314403d4b2babc14038c

578

py

Python

tests/unit/test_runner.py

mariocj89/dothub

bcfdcc5a076e48a73c4e0827c56431522e4cc4ba

[ "MIT" ]

12

2017-05-30T12:46:41.000Z

2019-08-18T18:55:43.000Z

tests/unit/test_runner.py

mariocj89/dothub

bcfdcc5a076e48a73c4e0827c56431522e4cc4ba

[ "MIT" ]

30

2017-07-10T19:28:35.000Z

2021-11-22T11:09:25.000Z

tests/unit/test_runner.py

Mariocj89/dothub

bcfdcc5a076e48a73c4e0827c56431522e4cc4ba

[ "MIT" ]

1

2017-08-02T21:04:43.000Z

from click.testing import CliRunner from dothub.cli import dothub base_args = ["--user=xxx", "--token=yyy"] def test_dothub_help(): runner = CliRunner() result = runner.invoke(dothub, ['--help'], obj={}) assert result.exit_code == 0 def test_dothub_pull_help(): runner = CliRunner() result = runner.invoke(dothub, base_args + ['pull', "--help"], obj={}) assert result.exit_code == 0 def test_dothub_push_help(): runner = CliRunner() result = runner.invoke(dothub, base_args + ['push', "--help"], obj={}) assert result.exit_code == 0

23.12

74

0.652249

0

61

0.105536

5997a4ecb7f8086a5d0b295c0471521ff04b54f7

6,985

py

Python

graph/__init__.py

worldwise001/stylometry

b5a4cc98fb8dfb6d1600d41bb15c96aeaf4ecb72

[ "MIT" ]

14

2015-02-24T16:14:07.000Z

2022-02-19T21:49:55.000Z

graph/__init__.py

worldwise001/stylometry

b5a4cc98fb8dfb6d1600d41bb15c96aeaf4ecb72

[ "MIT" ]

1

2015-02-25T09:45:13.000Z

graph/__init__.py

worldwise001/stylometry

b5a4cc98fb8dfb6d1600d41bb15c96aeaf4ecb72

[ "MIT" ]

4

2015-11-20T10:47:11.000Z

2021-03-30T13:14:20.000Z

import matplotlib matplotlib.use('Agg') import statsmodels.api as sm import statsmodels.formula.api as smf import numpy as np from scipy.stats import linregress import matplotlib.pyplot as plt from sklearn.metrics import roc_curve, auc def hist_prebin(filename, values, width=1, x_title='', y_title='', title=None): if title is None: title = filename left = [ v[0] for v in values ] height = [ v[1] for v in values ] plt.figure(figsize=(24,18), dpi=600) plt.bar(left=left, height=height, width=width) plt.xlabel(x_title) plt.ylabel(y_title) plt.title(title) plt.savefig('%s.png' % filename, format='png') plt.savefig('%s.eps' % filename, format='eps') def hist(filename, values, x_title='', y_title='', title=None): if title is None: title = filename plt.figure(figsize=(24,18), dpi=600) plt.hist(values, bins=20) plt.xlabel(x_title) plt.ylabel(y_title) plt.title(title) plt.savefig('%s.png' % filename, format='png') plt.savefig('%s.eps' % filename, format='eps') def generate(filename, rows, columns, x_title='', y_title='', title=None): rows_num = range(1, len(rows)+1) if title is None: title = filename plt.figure(figsize=(24,18), dpi=600) plt.scatter(rows_num, columns) locs, labels = plt.xticks(rows_num, rows) plt.setp(labels, rotation=90) plt.plot(rows_num, columns) plt.xlabel(x_title) plt.ylabel(y_title) plt.title(title) plt.savefig('%s.png' % filename, format='png') plt.savefig('%s.eps' % filename, format='eps') def scatter(filename, x, y, line=True, xr=None, yr=None, x_title='', y_title='', title=None): if title is None: title = filename plt.figure(figsize=(24,18), dpi=600) plt.scatter(x, y) if xr is not None: plt.xlim(xr) if yr is not None: plt.ylim(yr) if line: est = sm.OLS(y, sm.add_constant(x)).fit() x_prime = np.linspace(min(x), max(x), 100)[:, np.newaxis] x_prime = sm.add_constant(x_prime) y_hat = est.predict(x_prime) line_plot1 = plt.plot(x_prime[:, 1], y_hat, 'r', alpha=0.9, label='r^2 = %s' % est.rsquared) #res = linregress(x,y) #line_plot2 = plt.plot([min(x), max(x)], [res[0]*min(x)+res[1], res[0]*max(x)+res[1]], # 'g', alpha=0.9, label='r^2 = %s' % res[2]) plt.legend(['r^2 = %s' % est.rsquared]) plt.xlabel(x_title) plt.ylabel(y_title) plt.title(title) plt.savefig('%s.png' % filename, format='png') plt.savefig('%s.eps' % filename, format='eps') plt.close() def roc(filename, y_truth, y_predicted, title=None): fpr, tpr, _ = roc_curve(y_truth, y_predicted, 1) roc_auc = auc(fpr, tpr) if title is None: title = filename plt.figure(figsize=(24,18), dpi=600) plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc) plt.plot([0,1], [0,1], 'k--') plt.xlim([0.0, 1.0]) plt.ylim([0.0, 1.05]) plt.xlabel('False Positive Rate') plt.ylabel('True Positive Rate') plt.title('ROC: %s' % title) plt.legend(loc="lower right") plt.savefig('%s.png' % filename, format='png') plt.savefig('%s.eps' % filename, format='eps') def rocs(filename, y_truths, y_predicteds, labels, title=None): if title is None: title = filename plt.figure(figsize=(24,18), dpi=600) for i in range(0, len(y_truths)): fpr, tpr, _ = roc_curve(y_truths[i], y_predicteds[i], 1) roc_auc = auc(fpr, tpr) plt.plot(fpr, tpr, label='%s (area = %0.2f)' % (labels[i], roc_auc)) plt.plot([0,1], [0,1], 'k--') plt.xlim([0.0, 1.0]) plt.ylim([0.0, 1.05]) plt.xlabel('False Positive Rate') plt.ylabel('True Positive Rate') plt.title('ROC: %s' % title) plt.legend(loc="lower right") plt.savefig('%s.png' % filename, format='png') plt.savefig('%s.eps' % filename, format='eps') def setboxcol(bp, i, col): plt.setp(bp['boxes'][i], color=col) plt.setp(bp['caps'][i*2], color=col) plt.setp(bp['caps'][i*2+1], color=col) plt.setp(bp['whiskers'][i*2], color=col) plt.setp(bp['whiskers'][i*2+1], color=col) plt.setp(bp['fliers'][i*2], color=col) plt.setp(bp['fliers'][i*2+1], color=col) plt.setp(bp['medians'][i], color=col) def boxplot_single(filename, data, xr=None, yr=None, x_title='', y_title='', title=None): if title is None: title = filename author_labels = [] author_data = [] for author in data: author_labels.append(author) author_data.append(data[author]) for start in range(0, len(data), 50): end = start+50 if end > len(data): end = len(data) width = end-start fig = plt.figure(figsize=(width,12), dpi=600) ax = plt.axes() bp = plt.boxplot(author_data[start:end], positions=range(1, width+1), widths = 0.8) plt.xlim(0, width+1) ax.set_xticklabels(author_labels[start:end], rotation=70) ax.set_xticks(range(1, width+1)) if xr is not None: plt.xlim(xr) if yr is not None: plt.ylim(yr) plt.xlabel(x_title) plt.ylabel(y_title) plt.title(title) plt.tight_layout() plt.savefig('%s_%d.png' % (filename,start), format='png') plt.savefig('%s_%d.eps' % (filename,start), format='eps') plt.close() def boxplot(filename, data, groups, x_title='', y_title='', title=None): if title is None: title = filename plt.figure(figsize=(1.5*len(data)+3,12), dpi=600) ax = plt.axes() colors=['blue', 'red', 'green']*10 i = 1 k = 0 interval = len(groups) print(groups) author_labels = [] author_label_pos = [] for author in data: author_labels.append(author) author_data = [] if interval == 0: interval = len(data[author]) cols = [] for src_reddit in data[author]: author_data.append(data[author][src_reddit]) print(groups.index(src_reddit)) cols.append(colors[groups.index(src_reddit)]) pos = [ i+j for j in range(0, interval) ] bp = plt.boxplot(author_data, positions=pos, widths = 0.8) for m in range(0, interval): setboxcol(bp, m, cols[m]) author_label_pos.append(i + (interval/2.0)) i += interval + 1 k += 1 plt.xlim(0, i) ax.set_xticklabels(author_labels, rotation=70) ax.set_xticks(author_label_pos) plt.xlabel(x_title) plt.ylabel(y_title) plt.title(title) plt.tight_layout() hB, = plt.plot([1,1],'b-') hR, = plt.plot([1,1],'r-') hG, = plt.plot([1,1],'g-') plt.legend((hB, hR, hG),(groups[0], groups[1], groups[2])) hB.set_visible(False) hR.set_visible(False) hG.set_visible(False) plt.savefig('%s.png' % filename, format='png') plt.savefig('%s.eps' % filename, format='eps') plt.close()

30.502183

100

0.591553

0

714

0.102219

59987eb32850dcd0908c67453364b8a38745fe6e

68

py

Python

tests/unit/test_thicket/test_finders.py

GabrielC101/filer

d506ed804d10891cea33c3884896b6f0dfa08b88

[ "MIT" ]

null

tests/unit/test_thicket/test_finders.py

GabrielC101/filer

d506ed804d10891cea33c3884896b6f0dfa08b88

[ "MIT" ]

1

2017-12-19T19:38:22.000Z

tests/unit/test_thicket/test_finders.py

GabrielC101/filer

d506ed804d10891cea33c3884896b6f0dfa08b88

[ "MIT" ]

null

from thicket import finders def test_import(): assert finders

11.333333

27

0.75

0

59995210d6ac282b5113ee3252c96de5a50256f9

2,251

py

Python

test/test_component.py

gadalang/gada

2dd4f4dfd5b7390c06307040cad23203a015f7a4

[ "MIT" ]

null

test/test_component.py

gadalang/gada

2dd4f4dfd5b7390c06307040cad23203a015f7a4

[ "MIT" ]

null

test/test_component.py

gadalang/gada

2dd4f4dfd5b7390c06307040cad23203a015f7a4

[ "MIT" ]

1

2021-06-15T13:52:33.000Z

__all__ = ["ComponentTestCase"] import os import sys import yaml import unittest from gada import component from test.utils import TestCaseBase class ComponentTestCase(TestCaseBase): def test_load(self): """Test loading the testnodes package that is in PYTHONPATH.""" # Load component configuration config = self.write_config_and_load(TestCaseBase.CONFIG_NODES) self.assertEqual(config["runner"], "generic", "incorrect configuration") # Get node configuration node_config = component.get_node_config(config, "hello") self.assertEqual( node_config["runner"], "generic", "incorrect node configuration" ) self.assertEqual(node_config["bin"], "python", "incorrect node configuration") self.assertEqual( node_config["argv"], r"${comp_dir}/__init__.py ${argv}", "incorrect node configuration", ) def test_load_not_found(self): """Test loading a package that is not in the PYTHONPATH.""" with self.assertRaises(Exception): comp = component.load("invalid") def test_load_config(self): """Test loading config.yml file from testnodes package.""" config = self.write_config_and_load(TestCaseBase.CONFIG_NO_NODES) self.assertEqual( config, TestCaseBase.CONFIG_NO_NODES, "incorrect loaded configuration" ) def test_load_config_empty(self): """Test loading an existing but empty config.yml file.""" with open(TestCaseBase.CONFIG_YML, "w+") as f: f.write("") config = self.load_config() self.assertIsNotNone(config, "invalid configuration") def test_load_config_not_found(self): """Test loading a non existing config.yml file.""" self.remove_config() with self.assertRaises(Exception): component.load_config(sys) def test_get_node_config_not_found(self): """Test loading a config.yml file with unknown node.""" config = self.write_config_and_load(TestCaseBase.CONFIG_NODES) with self.assertRaises(Exception): component.get_node_config(config, "invalid") if __name__ == "__main__": unittest.main()

32.157143

86

0.662372

2,055

0.912928

0

713

0.316748

599a3aac676f1bdb004c22bf7034b685260f3101

17,820

py

Python

color pattern with threading.py

HashtagInnovator/Alpha-Star

f69a35b1924320dfec9610d6b61acae8d9de4afa

[ "Apache-2.0" ]

null

color pattern with threading.py

HashtagInnovator/Alpha-Star

f69a35b1924320dfec9610d6b61acae8d9de4afa

[ "Apache-2.0" ]

null

color pattern with threading.py

HashtagInnovator/Alpha-Star

f69a35b1924320dfec9610d6b61acae8d9de4afa

[ "Apache-2.0" ]

null

import time import random from multiprocessing import pool from playsound import playsound from threading import Thread i = -1 l = 0 count = 0 class loops: def loop(self): print(" ", end="") def A(self): global i global l global i for j in range(i, 5): for k in range(4, i, -1): print(" ", end="") print("*", end="") if i != 0: l = 1 for q in range(0, l): if (i == 3): print(" *" * 3, end="") else: print(" " * (i + (i - 1)), end="*") for k in range(4, i, -1): print(" ", end="") x.loop() return def B(self): global i for j in range(i, 6): print("*", end="") if (i == 0 or i == 2 or i == 4): print(" *" * 3, end=" ") else: print(" " * 6, end="*") x.loop() return def C(self): global i for i in range(i, 5): if (i == 0 or i == 4): print(" " * 2, end=" *" * 3) elif (i == 1 or i == 3): print(" " * 1, end="*") print(" " * 5, end=" ") else: print("*", end=" " *7) x.loop() return def D(self): global i for i in range(i, 5): print("*", end=" ") if (i == 0 or i == 4): print("* " * 2, end=" " * 1) elif (i == 1 or i == 3): print(" " * 4, end="*") else: print(" " * 3, end=" *") x.loop() return def E(self): global i for i in range(i, 5): if (i == 0 or i == 2 or i == 4): print("* " * 3, end="*") else: print("* ", end=" " * 5) x.loop() return def F(self): global i for i in range(i, 5): if (i == 0): print("* " * 3, end="*") elif (i == 2): print("* " * 3, end=" ") else: print("* ", end=" " * 5) x.loop() return def G(self): global i for i in range(i, 5): if (i == 0): print(" " * 2, end=" *" * 3) print(" ", end="") elif (i == 4): print(" " * 2, end=" * " * 2) print(" ", end="") elif (i == 1): print(" " * 1, end="*") print(" " * 7, end="") elif (i == 3): print(" " * 1, end="*") print(" " * 5, end=" *") else: print("*", end=" " * 2) print(" *" * 3, end="") x.loop() return def H(self): global i for i in range(i, 5): if (i == 2): print("* " * 3, end="*") else: print("*", end=" " * 5) print("*", end="") x.loop() return def I(self): global i for i in range(i, 5): if (i == 0 or i == 4): print("* " * 3, end="*") else: print(" " * 3, end="*") print(" " * 3, end="") x.loop() return def J(self): global i for i in range(i, 5): if (i == 0): print("* " * 3, end="*") elif (i == 3 or i == 2): print("* ", end=" *") print(" " * 3, end="") elif (i == 4): print(" ", end="*") print(" " * 2, end="") else: print(" " * 3, end="*") print(" " * 3, end="") x.loop() return def K(self): global i for i in range(i, 5): if i == 0 or i == 4: print("*", end=" " * 3) print("*", end="") elif i == 1 or i == 3: print("*", end=" " * 2) print("* ", end=" ") else: print("* ", end=" *") print(" ", end=" ") x.loop() return def L(self): global i for i in range(i,5): if(i==4): print("* "*3,end="*") else: print("* ",end=" "*5) x.loop() return def M(self): global i for i in range(i,5): print("* ",end="") if(i==1): print("* ",end=" * ") elif(i==2): print(" "*2,end="* ") else: print(" "*3,end="") print("*",end="") x.loop() return def N(self): global i for i in range(i,5): print("*",end="") if(i==0 ): print(" "*3,end="") else: print(" "*i,end="*") print(" "*(5-i),end="") print("*",end="") x.loop() return def O(self): global i for i in range(i,5): if(i==0 or i==4): print(" "*4,end="*") print(" "*3,end=" ") elif(i==2): print("*",end=" "*7) print("*",end="") else: print(" ",end="*") print(" ",end="* ") x.loop() return def P(self): global i for i in range(i,5): print("*",end="") if(i==0 or i==2): print(" *"*3,end=" ") elif(i==1): print(" "*6,end="*") else: print(" "*7,end="") x.loop() return def Q(self): global i for i in range(i,5): if(i==0): print(" "*4,end="*") print(" "*3,end=" ") elif(i==4): print(" "*4,end="*") print(" "*3,end="*") elif(i==2): print("*",end=" "*7) print("*",end="") elif(i==3): print(" ",end="*") print(" "*3,end="* * ") else: print(" ",end="*") print(" ",end="* ") x.loop() return def R(self): global i for i in range(i,5): print("*",end="") if(i==0 or i==2): print(" *"*3,end=" ") elif(i==1): print(" "*6,end="*") else: print(" "*i,end=" *") print(" ",end=" "*(4-i)) x.loop() return def S(self): global i for i in range(i, 5): if (i == 0): print(" " * 2, end="* " * 3) print("", end="") elif (i == 4): print(" ", end="* " * 3) print("", end="") elif (i == 1): print("*", end=" " * 7) elif (i == 2): print(" ", end="*") print(" " * 4, end="") else: print("*", end=" " * 6) print("*", end="") x.loop() return def T(self): global i for i in range(i, 5): if (i == 0): print("* " * 3, end="*") else: print(" " * 2, end=" *") print(" " * 2, end=" ") x.loop() return def U(self): global i for i in range(i, 5): if (i == 4): print(" " * 2, end="* " * 2) print(" " * 2, end="") elif (i == 3): print(" ", end="*") print(" " * 4, end="*") print(" ", end="") else: print("* ", end=" " * 5) print("*", end="") x.loop() return def V(self): global i for i in range(i, 5): if (i == 0): print("*", end=" " * 7) print("*", end="") elif (i == 1): print(" *", end=" " * 5) print("*", end=" ") elif (i == 2): print(" *", end=" " * 3) print("*", end=" ") elif (i == 3): print(" *", end=" ") print("*", end=" ") else: print(" " * 4, end="*") print(" " * 4, end="") x.loop() return def W(self): global i for i in range(i, 5): if (i == 0): print("*", end=" " * 11) print("*", end="") elif i == 1: print(" *", end=" " * 9) print("", end="* ") elif (i == 2): print(" * ", end=" *") print(" ", end=" ") elif (i == 3): print(" " * 3, end="*") print(" * * ", end=" " * 2) else: print(" " * 3, end=" *") print(" *", end=" " * 4) x.loop() return def X(self): global i for i in range(i, 5): if (i == 0 or i == 4): print("*", end=" " * 5) print("*", end="") elif (i == 1 or i == 3): print(" *", end=" " * 3) print("* ", end="") else: print(" " * 3, end="*") print(" " * 3, end="") x.loop() return def Y(self): global i for i in range(i, 5): if (i == 0): print("*", end=" " * 5) print("*", end="") elif (i == 1): print(" *", end=" " * 3) print("* ", end="") else: print(" " * 3, end="*") print(" " * 3, end="") x.loop() return def Z(self): global i for i in range(i, 5): if (i == 0 or i == 4): print("* " * 3, end="*") elif (i == 1): print(" " * 5, end="*") print(" ", end="") elif (i == 2): print(" " * 3, end="*") print(" " * 2, end=" ") else: print(" " * 1, end="*") print(" " * 3, end=" ") x.loop() return print() def play(): soun = input("ENTER SOUND") time.sleep(1.8) print("\n"*30) # CHANGE DIRECTORY HERE ................................................................ playsound("C:\\Users\\chetan\\Desktop\\language\\playsound\\" + soun + ".mp3") # CHANGE DIRECTORY HERE................................................................. time.sleep(1.1) x = loops() # DRIVER CODE n = input("ENTER YOUR TEXT") print("type any song name from here ...") lis=["birth",'rider','standard','teri mitti me','chitrakaar'] print(lis) #WE CAN ADD birthday and rider SONG HERE thread=Thread(target=play) thread.start() time.sleep(7) k = len(n) aa,bb,cc,dd,ee,ff,gg,hh,ii,jj,kk,ll,mm,nn,oo,pp,qq,rr,ss,tt,uu,vv,ww,xx,yy,zz=0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 s=0.5 list=[30,31,32,33,34,35,36,37] color=0 for o in range(5): i = i + 1 for f in range(k): if (n[f] == "A" or n[f] == "a"): if(aa==0): aa=random.choice(list) aa=aa+1 print("\033[1;{}m".format(aa),end="") time.sleep(s) x.A() elif (n[f] == "B" or n[f] == "b"): if(bb==0): bb=random.choice(list) bb=bb+1 print("\033[1;{}m".format(bb),end="") time.sleep(s) x.B() elif (n[f] == "C" or n[f] == "c"): if(cc==0): cc=random.choice(list) cc=cc+1 print("\033[1;{}m".format(cc),end="") time.sleep(s) x.C() elif (n[f] == "D" or n[f] == "d"): if(dd==0): dd=random.choice(list) dd=dd+1 print("\033[1;{}m".format(dd),end="") time.sleep(s) x.D() elif (n[f] == "E" or n[f] == "e"): if(ee==0): ee=random.choice(list) ee=ee+1 print("\033[1;{}m".format(ee),end="") time.sleep(s) x.E() elif (n[f] == "F" or n[f] == "f"): if(ff==0): ff=random.choice(list) ff=ff+1 print("\033[1;{}m".format(ff),end="") time.sleep(s) x.F() elif (n[f] == "G" or n[f] == "g"): if(gg==0): gg=random.choice(list) gg=gg+1 print("\033[1;{}m".format(gg),end="") time.sleep(s) x.G() elif (n[f] == "H" or n[f] == "h"): if(hh==0): hh=random.choice(list) hh=hh+1 print("\033[1;{}m".format(hh),end="") time.sleep(s) x.H() elif (n[f] == "I" or n[f] == "i"): if(ii==0): ii=random.choice(list) ii=ii+1 print("\033[1;{}m".format(ii),end="") time.sleep(s) x.I() elif (n[f] == "J" or n[f] == "j"): if(jj==0): jj=random.choice(list) jj=jj+1 print("\033[1;{}m".format(jj),end="") time.sleep(s) x.J() elif (n[f] == "K" or n[f] == "k"): if(kk==0): kk=random.choice(list) kk=kk+1 print("\033[1;{}m".format(kk),end="") time.sleep(s) x.K() elif (n[f] == "L" or n[f] == "l"): if(ll==0): ll=random.choice(list) ll=ll+1 print("\033[1;{}m".format(ll),end="") time.sleep(s) x.L() elif (n[f] == "m" or n[f] == "M"): if(mm==0): mm=random.choice(list) mm=mm+1 print("\033[1;{}m".format(mm),end="") time.sleep(s) x.M() elif (n[f] == "N" or n[f] == "n"): if(nn==0): nn=random.choice(list) nn=nn+1 print("\033[1;{}m".format(nn),end="") time.sleep(s) x.N() elif (n[f] == "O" or n[f] == "o"): if(oo==0): oo=random.choice(list) oo=oo+1 print("\033[1;{}m".format(oo),end="") time.sleep(s) x.O() elif (n[f] == "P" or n[f] == "p"): if(pp==0): pp=random.choice(list) pp=pp+1 print("\033[1;{}m".format(pp),end="") time.sleep(s) x.P() elif (n[f] == "q" or n[f] == "Q"): if(qq==0): qq=random.choice(list) qq=qq+1 print("\033[1;{}m".format(qq),end="") time.sleep(s) x.Q() elif (n[f] == "R" or n[f] == "r"): if(rr==0): rr=random.choice(list) rr=rr+1 print("\033[1;{}m".format(rr),end="") time.sleep(s) x.R() elif (n[f] == "S" or n[f] == "s"): if(ss==0): ss=random.choice(list) ss=ss+1 print("\033[1;{}m".format(ss),end="") time.sleep(s) x.S() elif (n[f] == "T" or n[f] == "t"): if(tt==0): tt=random.choice(list) tt=tt+1 print("\033[1;{}m".format(tt),end="") time.sleep(s) x.T() elif (n[f] == "U" or n[f] == "u"): if(uu==0): uu=random.choice(list) uu=uu+1 print("\033[1;{}m".format(uu),end="") time.sleep(s) x.U() elif (n[f] == "V" or n[f] == "v"): if(vv==0): vv=random.choice(list) vv=vv+1 print("\033[1;{}m".format(vv),end="") time.sleep(s) x.V() elif (n[f] == "W" or n[f] == "w"): if(ww==0): ww=random.choice(list) ww=ww+1 print("\033[1;{}m".format(ww),end="") time.sleep(s) x.W() elif (n[f] == "X" or n[f] == "x"): if(xx==0): xx=random.choice(list) xx=xx+1 print("\033[1;{}m".format(xx),end="") time.sleep(s) x.X() elif (n[f] == "Y" or n[f] == "y"): if(yy==0): yy=random.choice(list) yy=yy+1 print("\033[1;{}m".format(yy),end="") time.sleep(s) x.Y() elif (n[f] == "Z" or n[f] == "z"): if(zz==0): zz=random.choice(list) zz=zz+1 print("\033[1;{}m".format(zz),end="") time.sleep(s) x.Z() elif(n[f]==" "): x.loop() x.loop() print() time.sleep(6) print("\n"*8) print('THANK YOU ', end='', flush=True) for x in range(8): for frame in r'-\|/-\|/': print('\b', frame, sep='', end='', flush=True) time.sleep(0.2) print('\b ') thread.join()

26.322009

129

0.306285

10,632

0.596633

0

1,910

0.107183

599abd70ab2405fa33e84f2920872f4103dff83c

273

py

Python

tests/conftest.py

eddyvdaker/FlaskSimpleStarter

4992492ac1788d80e5914188f994b3e0ed1e75f4

[ "MIT" ]

null

tests/conftest.py

eddyvdaker/FlaskSimpleStarter

4992492ac1788d80e5914188f994b3e0ed1e75f4

[ "MIT" ]

null

tests/conftest.py

eddyvdaker/FlaskSimpleStarter

4992492ac1788d80e5914188f994b3e0ed1e75f4

[ "MIT" ]

null

import pytest from src.app import create_app @pytest.fixture def app(): app = create_app() app.config['TESTING'] = True ctx = app.app_context() ctx.push() yield app ctx.pop() @pytest.fixture def client(app): return app.test_client()

12.409091

32

0.6337

0

144

0.527473

221

0.809524

0

9

0.032967

599c63fc42e3f63659183c30e8778ab397e4a872

2,533

py

Python

amd64-linux/lib/pmon.py

qiyancos/Simics-3.0.31

9bd52d5abad023ee87a37306382a338abf7885f1

[ "BSD-4-Clause", "FSFAP" ]

1

2020-06-15T10:41:18.000Z

amd64-linux/lib/pmon.py

qiyancos/Simics-3.0.31

9bd52d5abad023ee87a37306382a338abf7885f1

[ "BSD-4-Clause", "FSFAP" ]

null

amd64-linux/lib/pmon.py

qiyancos/Simics-3.0.31

9bd52d5abad023ee87a37306382a338abf7885f1

[ "BSD-4-Clause", "FSFAP" ]

3

2020-08-10T10:25:02.000Z

2021-09-12T01:12:09.000Z

# This file implements the PMON firmware's LEON2 boot setup. It does not # implement the serial port boot loading, only the initial setup. # The PMON firmware for the LEON2 comes with a number of preprocessor defines # that the user typically changes to match the hardware configuration. # The PMON emulation function takes all these parameters as function arguments, # with the exception of the clock frequency, that is picked from the cpu. import conf from sim_core import * def _pmon_start(cpu, stack_init): cpu.wim = 2 cpu.psr = 0x10e0 cpu.gprs[14] = stack_init # %sp = STACK_INIT cpu.gprs[1] = SIM_read_phys_memory(cpu, 0x80000014, 4) cpu.psr = cpu.psr | 7 cpu.gprs[14] = stack_init - 0x40 def _pmon_init(cpu, memcfg1, memcfg2, timer_scaler_val, uart_scaler_val): SIM_write_phys_memory(cpu, 0x80000014, 0x1000f, 4) # cache_ctrl SIM_write_phys_memory(cpu, 0x800000a4, 0xaa00, 4) # io_port_dir SIM_write_phys_memory(cpu, 0x80000090, 0, 4) # irq_mask SIM_write_phys_memory(cpu, 0x80000094, 0, 4) # irq_pending SIM_write_phys_memory(cpu, 0x80000098, 0, 4) # irq_force SIM_write_phys_memory(cpu, 0x80000000, memcfg1, 4) # memcfg1 SIM_write_phys_memory(cpu, 0x80000004, memcfg2, 4) # memcfg2 SIM_write_phys_memory(cpu, 0x80000060, timer_scaler_val, 4) # prescaler_counter SIM_write_phys_memory(cpu, 0x80000064, timer_scaler_val, 4) # prescaler_reload SIM_write_phys_memory(cpu, 0x80000044, 0xffffffff, 4) # t1_reload (-1) SIM_write_phys_memory(cpu, 0x80000048, 7, 4) # t1_control SIM_write_phys_memory(cpu, 0x8000007c, uart_scaler_val, 4) # uart1_scaler SIM_write_phys_memory(cpu, 0x8000008c, uart_scaler_val, 4) # uart2_scaler SIM_write_phys_memory(cpu, 0x80000074, 0, 4) # uart1_status SIM_write_phys_memory(cpu, 0x80000084, 0, 4) # uart2_status SIM_write_phys_memory(cpu, 0x80000078, 3, 4) # uart1_control SIM_write_phys_memory(cpu, 0x80000088, 3, 4) # uart2_control def pmon_setup(cpu, timer_scale, baud_rate, bank_size, ram_banks, mcfg1, mcfg2): cpu_freq = int(cpu.freq_mhz * 1000000.0) stack_init = 0x40000000 + ((bank_size * ram_banks) - 16) timer_scaler_val = (cpu_freq/timer_scale -1) uart_scaler_val = ((((cpu_freq*10) / (8 * baud_rate))-5)/10) _pmon_start(cpu, stack_init) _pmon_init(cpu, mcfg1, mcfg2, timer_scaler_val, uart_scaler_val)

51.693878

83

0.70075

0

682

0.269246

599d3203f355bf0108b50dc6b8026b093b4736fc

395

py

Python

scripts/test_web3.py

AeneasHe/eth-brownie-enhance

e53995924ffb93239b9fab6c1c1a07e9166dd1c6

[ "MIT" ]

1

2021-10-04T23:34:14.000Z

scripts/test_web3.py

AeneasHe/eth-brownie-enhance

e53995924ffb93239b9fab6c1c1a07e9166dd1c6

[ "MIT" ]

null

scripts/test_web3.py

AeneasHe/eth-brownie-enhance

e53995924ffb93239b9fab6c1c1a07e9166dd1c6

[ "MIT" ]

null

import wpath from web3 import Web3 from web3 import Web3, HTTPProvider, IPCProvider, WebsocketProvider def get_web3_by_http_rpc(): address = "http://47.243.92.131:8545" print("===>address:", address) p = HTTPProvider(address) web3 = Web3(p) return web3 w3 = get_web3_by_http_rpc() eth = w3.eth r = eth.getBalance("0x3d32aA995FdD334c671C2d276345DE6fe2F46D88") print(r)

18.809524

67

0.721519

0

85

0.21519

599f0418376070df049179da7c8e1b8f17a142f2

834

py

Python

models/sklearn_model.py

Ailln/stock-prediction

9de77de5047446ffceeed83cb610c7edd2cb1ad3

[ "MIT" ]

11

2020-07-11T06:14:29.000Z

2021-12-02T08:48:53.000Z

models/sklearn_model.py

HaveTwoBrush/stock-prediction

9de77de5047446ffceeed83cb610c7edd2cb1ad3

[ "MIT" ]

null

models/sklearn_model.py

HaveTwoBrush/stock-prediction

9de77de5047446ffceeed83cb610c7edd2cb1ad3

[ "MIT" ]

8

2020-04-15T14:29:47.000Z

2021-12-19T09:26:53.000Z

from sklearn import svm from sklearn import ensemble from sklearn import linear_model class Model(object): def __init__(self): self.model_dict = { "SGDRegressor": linear_model.SGDRegressor(max_iter=1000), "HuberRegressor": linear_model.HuberRegressor(), "LinearRegression": linear_model.LinearRegression(), "LinearSVR": svm.LinearSVR(), "BaggingRegressor": ensemble.BaggingRegressor(), "AdaBoostRegressor": ensemble.AdaBoostRegressor(), "ExtraTreesRegressor": ensemble.ExtraTreesRegressor(), "RandomForestRegressor": ensemble.RandomForestRegressor(), "GradientBoostingRegressor": ensemble.GradientBoostingRegressor() } def sklearn_model(self, model_name): return self.model_dict[model_name]

37.909091

77

0.681055

745

0.893285

0

167

0.20024

59a09df4f04358386749f3598f84da0352793936

189

py

Python

venv/Lib/site-packages/shiboken2/_config.py

gabistoian/Hide-Text-in-image

88b5ef0bd2bcb0e222cfbc7abf6ac2b869f72ec5

[ "X11" ]

null

venv/Lib/site-packages/shiboken2/_config.py

gabistoian/Hide-Text-in-image

88b5ef0bd2bcb0e222cfbc7abf6ac2b869f72ec5

[ "X11" ]

null

venv/Lib/site-packages/shiboken2/_config.py

gabistoian/Hide-Text-in-image

88b5ef0bd2bcb0e222cfbc7abf6ac2b869f72ec5

[ "X11" ]

null

shiboken_library_soversion = str(5.15) version = "5.15.2.1" version_info = (5, 15, 2.1, "", "") __build_date__ = '2022-01-07T13:13:47+00:00' __setup_py_package_version__ = '5.15.2.1'

15.75

44

0.671958

0

51

0.269841

59a0a3b7aa59f29b5ba0e35ea23ff02112e179f9

1,023

py

Python

00Python/day05/basic02.py

HaoZhang95/PythonAndMachineLearning

b897224b8a0e6a5734f408df8c24846a98c553bf

[ "MIT" ]

937

2019-05-08T08:46:25.000Z

2022-03-31T12:56:07.000Z

00Python/day05/basic02.py

Sakura-gh/Python24

b97e18867264a0647d5645c7d757a0040e755577

[ "MIT" ]

47

2019-09-17T10:06:02.000Z

2022-03-11T23:46:52.000Z

00Python/day05/basic02.py

Sakura-gh/Python24

b97e18867264a0647d5645c7d757a0040e755577

[ "MIT" ]

354

2019-05-10T02:15:26.000Z

2022-03-30T05:52:57.000Z

""" list元素的排序 sort() 默认无参数是从小到大 reversed(list) 整个列表直接反过来，返回值是一个新的list """ import random a_list = [] for i in range(10): a_list.append(random.randint(0, 200)) print(a_list) a_list.sort() print(a_list) a_list.sort(reverse=True) # 降序，从大到小 print(a_list) new_list = reversed(a_list) # [12,10,7,9] -> [9,7,10,12] print(new_list) """ 一个学校，三个办公室，八位老师进行随机分配办公室 """ school = [[], [], []] teacher_list = list("ABCDEFGH") for name in teacher_list: index = random.randint(0,2) school[index].append(name) print(school) """ 字符串表示："", '', """""" list表示：[]，可修改元组的表示：(), 元组的元素不能进行修改，元组中如果只有一个元素的话，后面加上逗号表明是一个tuple，否则就是元素真实类型 """ a_tuple = (1, 3.14, "Hello", True) empty_tuple = () empty_tuple2 = tuple() # 特例 b_tuple = (1) # type = int c_tuple = (1,) # type = tuple """ 访问元组tuple 查询的话和list一样使用count， index """ print(a_tuple[2]) # a_tuple[1] = "哈哈" 元组的元素不能重新赋值和修改，因为tuple是不可变的 print(a_tuple.count(1)) # 元组中1对象出现的次数是2，因为Ture在计算机眼中就是1 print(a_tuple.index(3.14))

18.267857

60

0.641251

0

846

0.600426

59a69dfbb3f7dfb97929bbbc436b9c105fe9fa48

1,643

py

Python

ThreeBotPackages/unlock_service/scripts/restore.py

threefoldfoundation/tft-stellar

b36460e8dba547923778273b53fe4f0e06996db0

[ "Apache-2.0" ]

7

2020-02-05T16:10:46.000Z

2021-04-28T10:39:20.000Z

ThreeBotPackages/unlock_service/scripts/restore.py

threefoldfoundation/tft-stellar

b36460e8dba547923778273b53fe4f0e06996db0

[ "Apache-2.0" ]

379

2020-01-13T10:22:21.000Z

2022-03-23T08:59:57.000Z

ThreeBotPackages/unlock_service/scripts/restore.py

threefoldfoundation/tft-stellar

b36460e8dba547923778273b53fe4f0e06996db0

[ "Apache-2.0" ]

3

2020-01-24T09:56:44.000Z

2020-08-03T21:02:38.000Z

#!/usr/bin/env python # pylint: disable=no-value-for-parameter import click import os import sys import requests import json UNLOCK_SERVICE_DEFAULT_HOSTS = {"test": "https://testnet.threefold.io", "public": "https://tokenservices.threefold.io"} @click.command() @click.option("--source", default="export_data", help="Sourcefile to import data from") @click.option("--network", type=click.Choice(["test", "public"], case_sensitive=False), default="public") @click.option("--unlock_service_host", default=None, help="Destination to restore to (overrides the network parameter)") def import_unlockhash_transaction_data(source, network, unlock_service_host): if not unlock_service_host: unlock_service_host = UNLOCK_SERVICE_DEFAULT_HOSTS[network] print(f"Restoring data to {unlock_service_host} from {source}\n") restored=[] with open(source,mode="r") as f: for line in f.readlines(): if line.strip() == "": continue unlockhash_transaction_data = json.loads(line) unlockhash = unlockhash_transaction_data.get("unlockhash") transaction_xdr = unlockhash_transaction_data.get("transaction_xdr") if unlockhash in restored: continue r = requests.post( f"{unlock_service_host}/threefoldfoundation/unlock_service/create_unlockhash_transaction", json={"unlockhash": unlockhash, "transaction_xdr": transaction_xdr}, ) r.raise_for_status() restored.append(unlockhash) if __name__ == "__main__": import_unlockhash_transaction_data()

37.340909

120

0.684114

0

1,322

0.804626

0

533

0.324407

59a7951eb259bc0943a926370fa409960f8cba7c

4,984

py

Python

pgdiff/diff/PgDiffConstraints.py

Onapsis/pgdiff

ee9f618bc339cbfaf7967103e95f9650273550f8

[ "MIT" ]

2

2020-05-11T16:42:48.000Z

2020-08-27T04:11:49.000Z

diff/PgDiffConstraints.py

Gesha3809/PgDiffPy

00466429d0385eb999c32addcbe6e2746782cb5d

[ "MIT" ]

1

2018-04-11T18:19:33.000Z

2018-04-13T15:18:40.000Z

diff/PgDiffConstraints.py

Gesha3809/PgDiffPy

00466429d0385eb999c32addcbe6e2746782cb5d

[ "MIT" ]

1

2018-04-11T15:09:22.000Z

from PgDiffUtils import PgDiffUtils class PgDiffConstraints(object): @staticmethod def createConstraints(writer, oldSchema, newSchema, primaryKey, searchPathHelper): for newTableName, newTable in newSchema.tables.items(): oldTable = None if (oldSchema is not None): oldTable = oldSchema.tables.get(newTableName) # Add new constraints for constraint in PgDiffConstraints.getNewConstraints(oldTable, newTable, primaryKey): searchPathHelper.outputSearchPath(writer) writer.writeln(constraint.getCreationSQL()) @staticmethod def dropConstraints(writer, oldSchema, newSchema, primaryKey, searchPathHelper): for newTableName in newSchema.tables: oldTable = None if oldSchema is not None: oldTable = oldSchema.tables.get(newTableName) newTable = newSchema.tables[newTableName] # Drop constraints that no more exist or are modified for constraint in PgDiffConstraints.getDropConstraints(oldTable, newTable, primaryKey): searchPathHelper.outputSearchPath(writer) writer.writeln(constraint.getDropSQL()) @staticmethod def alterComments(writer, oldSchema, newSchema, searchPathHelper): if oldSchema is None: return for oldTableName, oldTable in oldSchema.tables.items(): newTable = newSchema.tables.get(oldTableName) if newTable is None: continue for oldConstraintName, oldConstraint in oldTable.constraints.items(): newConstraint = newTable.constraints.get(oldConstraintName) if newConstraint is None: continue # sbSQL = [] if (oldConstraint.comment is None and newConstraint.comment is not None or oldConstraint.comment is not None and newConstraint.comment is not None and oldConstraint.comment != newConstraint.comment): searchPathHelper.outputSearchPath(writer) writer.write("COMMENT ON ") if newConstraint.isPrimaryKeyConstraint(): writer.write("INDEX ") writer.write(PgDiffUtils.getQuotedName(newConstraint.name)) else: writer.write("CONSTRAINT ") writer.write(PgDiffUtils.getQuotedName(newConstraint.name)) writer.write(" ON ") writer.write(PgDiffUtils.getQuotedName(newConstraint.tableName)) writer.write(" IS ") writer.write(newConstraint.comment) writer.writeln(';') elif (oldConstraint.comment is not None and newConstraint.comment is None): searchPathHelper.outputSearchPath(writer) writer.write("COMMENT ON ") if newConstraint.isPrimaryKeyConstraint(): writer.write("INDEX "); writer.write(PgDiffUtils.getQuotedName(newConstraint.name)) else: writer.write("CONSTRAINT "); writer.write(PgDiffUtils.getQuotedName(newConstraint.name)) writer.write(" ON "); writer.write(PgDiffUtils.getQuotedName(newConstraint.tableName)) writer.writeln(" IS NULL;") @staticmethod def getNewConstraints(oldTable, newTable, primaryKey): result = [] if newTable is not None: if oldTable is None: for constraintName, constraint in newTable.constraints.items(): if constraint.isPrimaryKeyConstraint() == primaryKey: result.append(constraint) else: for constraintName, constraint in newTable.constraints.items(): if (constraint.isPrimaryKeyConstraint() == primaryKey and (constraintName not in oldTable.constraints or oldTable.constraints[constraintName] != constraint)): result.append(constraint) return result @staticmethod def getDropConstraints(oldTable, newTable, primaryKey): result = list() if newTable is not None and oldTable is not None: for constraintName in oldTable.constraints: oldConstraint = oldTable.constraints[constraintName] newConstraint = newTable.constraints.get(constraintName) if (oldConstraint.isPrimaryKeyConstraint() == primaryKey and (newConstraint is None or newConstraint != oldConstraint)): result.append(oldConstraint) return result

41.190083

99

0.58427

4,947

0.992576

0

4,885

0.980136

0

186

0.037319

59a8688939bcf65bd9fa72756ce61831127d2530

7,715

py

Python

experiments/old_code/result_scripts.py

hytsang/cs-ranking

241626a6a100a27b96990b4f199087a6dc50dcc0

[ "Apache-2.0" ]

null

experiments/old_code/result_scripts.py

hytsang/cs-ranking

241626a6a100a27b96990b4f199087a6dc50dcc0

[ "Apache-2.0" ]

null

experiments/old_code/result_scripts.py

hytsang/cs-ranking

241626a6a100a27b96990b4f199087a6dc50dcc0

[ "Apache-2.0" ]

1

2018-10-30T08:57:14.000Z

import inspect import logging import os from itertools import product import numpy as np import pandas as pd from skopt import load, dump from csrank.constants import OBJECT_RANKING from csrank.util import files_with_same_name, create_dir_recursively, rename_file_if_exist from experiments.util import dataset_options_dict, rankers_dict, lp_metric_dict DIR_NAME = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) def log_best_params(file): opt = load(file) if "ps" in opt.acq_func: best_i = np.argmin(np.array(opt.yi)[:, 0]) best_loss = opt.yi[best_i] best_params = opt.Xi[best_i] logger.info( "Best parameters so far with a loss for file {} of {:.4f} time of {:.4f}:\n {}".format( os.path.basename(file), best_loss[0], best_loss[1], best_params)) else: best_i = np.argmin(opt.yi) best_loss = opt.yi[best_i] best_params = opt.Xi[best_i] logger.info( "Best parameters so far with a loss for file {} of {:.4f}:\n {}".format(os.path.basename(file), best_loss, best_params)) return best_loss def remove_redundant_optimizer_models(model_path, files_list): logger.info('Results Files {} for Path {}'.format(files_list, os.path.basename(model_path))) minimum_error = 50000 if len(files_list) >= 2: for file in files_list: try: opt = load(file) best_loss = log_best_params(file) if best_loss < minimum_error: minimum_error = best_loss if (file != model_path): logger.info('Writing from the file {} to {}'.format(os.path.basename(file), os.path.basename(model_path))) os.remove(model_path) dump(opt, model_path) except KeyError: logger.error('Cannot open the file {}'.format(file)) except ValueError: logger.error('Cannot open the file {}'.format(file)) elif len(files_list) == 1: file = files_list[0] try: best_loss = log_best_params(file) except KeyError: logger.error('Cannot open the file {}'.format(file)) except ValueError: logger.error('Cannot open the file {}'.format(file)) if len(files_list) != 0: files_list.remove(model_path) for file in files_list: logger.error('Removing the File {}'.format(file)) os.remove(file) def remove_redundant_log_files(logs_path, logs_files_list, ranker_name, dataset): logger.info('Log Files {} for Path {}'.format(logs_files_list, os.path.basename(logs_path))) minimum_error = 50000 if len(logs_files_list) >= 2: for file in logs_files_list: lines = np.array([line.rstrip('\n') for line in open(file)]) out = 'zero_one_rank_loss' matching = [s for s in lines if out in s] try: logger.info("For File {} the error is {}".format(file, matching)) err = float(matching[0].split(out + ' : ')[-1]) logger.info("For File {} the zero one rank errro is {}".format(file, err)) if err <= minimum_error: minimum_error = err if (file != logs_path): logger.info('Renaming from the file {} to {}'.format(os.path.basename(file), os.path.basename(logs_path))) os.remove(logs_path) os.system('mv {} {}'.format(file, logs_path)) except IndexError: logger.error('error {} in ranker {} is not evaluated for dataset {}'.format(out, ranker_name, dataset)) except ValueError: logger.error('error {} in ranker {} is not evaluated for dataset {}'.format(out, ranker_name, dataset)) def remove_redundant_results(): for dataset, ranker_name in product(dataset_options.keys(), ranker_options.keys()): model_path = os.path.join(DIR_NAME, 'optimizer_results_single_fold', '{}_{}'.format(dataset, ranker_name)) files_list = files_with_same_name(model_path) remove_redundant_optimizer_models(model_path, files_list) logs_path = os.path.join(DIR_NAME, 'logs_single_fold', '{}_{}.log'.format(dataset, ranker_name)) logs_files_list = files_with_same_name(logs_path) remove_redundant_log_files(logs_path, logs_files_list, ranker_name, dataset) def generate_concise_results_for_dataset(dataset='medoid', directory='logs_single_fold', result_directory='results'): ranker_names = list(ranker_options.keys()) ranker_names.sort() metric_names.sort() data = [] data.append(['**************', dataset.upper(), '**************', ""]) for ranker_name in ranker_names: try: log_path = os.path.join(DIR_NAME, directory, '{}_{}.log'.format(dataset, ranker_name)) lines = np.array([line.rstrip('\n') for line in open(log_path)]) except FileNotFoundError: logger.error('File {} is not found'.format(log_path)) data.append(['NE' for i in range(len(metric_names))]) continue one_row = [] for out in metric_names: try: matching = [s for s in lines if out in s][0] if out in matching: one_row.append(matching.split(out + ' : ')[-1]) except IndexError: logger.error('error {} in ranker {} is not evaluated for dataset {}'.format(out, ranker_name, dataset)) one_row.append('NE') data.append(one_row) columns = [name.upper() for name in metric_names] indexes = [name.upper() for name in ranker_names] indexes.insert(0, 'DATASET') dataFrame = pd.DataFrame(data, index=indexes, columns=columns) file_path = os.path.join(DIR_NAME, result_directory, '{}.csv'.format(dataset)) create_dir_recursively(file_path, True) dataFrame.to_csv(file_path) return dataFrame def create_concise_results(result_directory='results', directory='logs_single_fold'): df_list = [] datasets = list(dataset_options.keys()) datasets.sort() for dataset in datasets: dataFrame = generate_concise_results_for_dataset(dataset=dataset, directory=directory, result_directory=result_directory) df_list.append(dataFrame) full_df = pd.concat(df_list) fout = os.path.join(DIR_NAME, result_directory, 'complete_results.csv') full_df.to_csv(fout) def configure_logging(): log_path = os.path.join(DIR_NAME, 'results', 'compiling_result.log') create_dir_recursively(log_path, True) log_path = rename_file_if_exist(log_path) global logger logging.basicConfig(filename=log_path, level=logging.DEBUG, format='%(asctime)s %(levelname)-8s %(message)s', datefmt='%Y-%m-%d %H:%M:%S') logger = logging.getLogger(name='Compiling Results') if __name__ == '__main__': configure_logging() dataset_options = dataset_options_dict[OBJECT_RANKING] ranker_options = rankers_dict[OBJECT_RANKING] metric_names = list(lp_metric_dict[OBJECT_RANKING].keys()) remove_redundant_results() create_concise_results() # create_concise_results(result_directory='logs_new_experiments', directory='logs_new_experiments')

43.835227

119

0.608425

0

1,137

0.147375

59a98cedbef2ddabf9e787d32a317a09b1db8b5e

13,108

py

Python

notochord/features/BagOfWords.py

jroose/notochord

da9a6ff5d0fabbf0694d0bee1b81a240b66fa006

[ "MIT" ]

null

notochord/features/BagOfWords.py

jroose/notochord

da9a6ff5d0fabbf0694d0bee1b81a240b66fa006

[ "MIT" ]

null

notochord/features/BagOfWords.py

jroose/notochord

da9a6ff5d0fabbf0694d0bee1b81a240b66fa006

[ "MIT" ]

null

from .. import schema, App, QueryCache, batcher, grouper, insert_ignore, export, lookup, persist, lookup_or_persist, ABCArgumentGroup, WorkOrderArgs, filter_widgets, temptable_scope, FeatureCache from ..ObjectStore import ABCObjectStore from sqlalchemy import Column, Integer, String, Float, ForeignKey, UnicodeText, Unicode, LargeBinary, Boolean, Index import collections import csv import os import re import sqlalchemy import sys import tempfile import time import stat from sklearn.feature_extraction.text import CountVectorizer re_word = re.compile(r'[a-zA-Z]+') __all__ = [] class BagOfWordsArgs(ABCArgumentGroup): def __call__(self, group): group.add_argument("--output-feature-set", type=unicode, action="store", metavar="NAME", default=None, help="Name of output feature set (required)") group.add_argument("--input-feature-set", type=unicode, action="store", metavar="NAME", default=None, help="Name of input feature set (required)") group.add_argument("--input-feature", type=unicode, action="store", metavar="NAME", default=None, help="Name of input feature") group.add_argument("--chunk-size", type=int, action="store", metavar="INT", default=None, help="Number or widgets per chunk") @export class BagOfWords(App): @staticmethod def build_parser_groups(): return [BagOfWordsArgs(), WorkOrderArgs()] + App.build_parser_groups() def __init__(self, datadir, input_feature_set=None, output_feature_set=None, input_feature=None, min_idwidget=None, max_idwidget=None, datasources=None, chunk_size=None, **kwargs): super(BagOfWords, self).__init__(datadir, **kwargs) self.config['output_feature_set'] = output_feature_set or self.config['output_feature_set'] self.config['input_feature_set'] = input_feature_set or self.config['input_feature_set'] self.config['input_feature'] = input_feature or self.config.get('input_feature') self.config['datasources'] = datasources or self.config.get('datasources') self.config["chunk_size"] = chunk_size or self.config.get('chunk_size', 1024) self.config['min_idwidget'] = (min_idwidget, None)[min_idwidget is None] self.config['max_idwidget'] = (max_idwidget, None)[max_idwidget is None] def main(self): import MySQLdb from warnings import filterwarnings filterwarnings('ignore', category = MySQLdb.Warning) import sqlalchemy from sqlalchemy import Column, literal, tuple_, insert from ..schema import widget as t_w from ..schema import widget_feature as t_wf from ..schema import feature as t_f from ..schema import feature_set as t_fs from ..schema import datasource as t_ds from ..schema import object_store as t_os with self.session_scope() as session: self.log.info("Preparing") fs_in = lookup(session, t_fs, name=self.config['input_feature_set']) if fs_in is None: raise KeyError("Invalid feature set: '{}'".format(self.config['input_feature_set'])) fs_out = lookup_or_persist(session, t_fs, name=self.config['output_feature_set']) if fs_out is None: raise KeyError("Invalid feature set: '{}'".format(self.config['output_feature_set'])) os_in = lookup(session, t_os, idobject_store=fs_in.idobject_store) if fs_in.idobject_store is None or os_in is None: raise ValueError("Feature set '{}' has no associated object store".format(self.config['input_feature_set'])) else: object_store = ABCObjectStore.open(session, os_in.name) f_in = lookup(session, t_f, name=self.config['input_feature'], idfeature_set=fs_in.idfeature_set) if f_in is None: if self.config['input_feature'] is not None: raise KeyError("Invalid feature: '{}' for feature_set '{}'".format(self.config['input_feature'], self.config['input_feature_set'])) else: raise KeyError("Invalid feature_set '{}' has no default feature".format(self.config['input_feature_set'])) q_w = session.query(t_w.idwidget) q_w = filter_widgets( q_w, min_idwidget = self.config['min_idwidget'], max_idwidget = self.config['max_idwidget'], datasources = self.config['datasources'] ) q_wf = session.query(t_wf.idwidget, t_wf.idfeature) \ .join(t_w, t_w.idwidget == t_wf.idwidget) \ .join(t_f, t_f.idfeature == t_wf.idfeature) \ .filter(t_f.idfeature_set == fs_out.idfeature_set) if self.config['min_idwidget'] is not None: q_wf = q_wf.filter(t_w.idwidget >= self.config['min_idwidget']) if self.config['max_idwidget'] is not None: q_wf = q_wf.filter(t_w.idwidget < self.config['max_idwidget']) if self.config['datasources'] is not None and len(self.config['datasources']) > 0: q_wf = q_wf.join(t_ds, t_ds.iddatasource == t_w.iddatasource) q_wf = q_wf.filter(t_ds.name.in_(self.config['datasources'])) self.log.info("Deleting old features") #q_del = q_wf.delete() #q_del = t_wf.__table__.delete() \ # .where(tuple_(t_wf.idwidget, t_wf.idfeature).in_(q_wf)) #self.log.debug("Delete widget query: {}".format(q_del.compile(bind=session.bind))) #session.execute(q_del) q_w = session.query(t_w.idwidget, t_w.uuid) q_w = filter_widgets( q_w, min_idwidget = self.config['min_idwidget'], max_idwidget = self.config['max_idwidget'], datasources = self.config['datasources'] ) q_w = q_w.join(t_wf, t_wf.idwidget == t_w.idwidget) \ .filter(t_wf.idfeature == f_in.idfeature) class tmp_upload(schema.TableBase): idtmp_upload = Column(t_f.idfeature.type, nullable=False, primary_key=True) idwidget = Column(t_wf.idwidget.type, nullable=False) idfeature = Column(t_wf.idfeature.type, nullable=False) value = Column(t_wf.value.type, nullable=False) __table_args__ = ({'prefixes':["TEMPORARY"]},) __tablename__ = "tmp_upload" class tmp_wf(schema.TableBase): idwidget = Column(Integer, ForeignKey('widget.idwidget', onupdate='RESTRICT', ondelete='CASCADE'), primary_key=True, nullable=False) idfeature = Column(Integer, ForeignKey('feature.idfeature', onupdate='RESTRICT', ondelete='CASCADE'), primary_key=True, nullable=False) value = Column(Float, nullable=True) __table_args__ = ({'prefixes':["TEMPORARY"]},) __tablename__ = "tmp_widget_feature" self.log.info("Beginning Execution") self.log.debug("Widget query: {}".format(q_w.statement.compile(bind=session.bind))) FC = FeatureCache(1024*1024, log=self.log) count_time = 0.0 feature_time = 0.0 widget_time = 0.0 upload_time = 0.0 primary_key_time = 0.0 num_widgets = 0 num_widget_features = 0 start_time = time.time() if session.bind.dialect.name.lower() == 'mysql': session.execute("SET @@foreign_key_checks=0;") session.execute("ALTER TABLE widget_feature DISABLE KEYS;") insert_fout, insert_file = tempfile.mkstemp() os.close(insert_fout) os.chmod(insert_file, stat.S_IREAD | stat.S_IWRITE | stat.S_IROTH) begin_time = time.time() for it, result_chunk in enumerate(grouper(q_w, self.config['chunk_size'])): start_time = time.time() self.log.info("Executing chunk {}".format(it)) upload_chunk = [] N = 0 words = [] widgets = [] values = [] for row in result_chunk: if row is not None: idwidget, uuid = row content = object_store.get(uuid, feature=f_in.name) if content is None: continue cnt = collections.Counter(x.group(0).lower() for x in re_word.finditer(content)) words.extend(cnt.iterkeys()) values.extend(cnt.itervalues()) widgets.extend(idwidget for _ in xrange(len(cnt))) N += len(cnt) end_time = time.time() count_time += (end_time - start_time) start_time = time.time() self.log.info("Getting feature id's") word_idents = FC(session, fs_out.idfeature_set, (w for w in words)) self.log.info("Copying into upload_chunk") upload_chunk = [dict(idwidget=widgets[it], idfeature=word_idents[it], value=values[it]) for it in xrange(N)] num_widget_features += len(upload_chunk) end_time = time.time() feature_time += (end_time - start_time) start_time = time.time() dialect = session.bind.dialect.name with temptable_scope(session, tmp_upload), temptable_scope(session, tmp_wf): self.log.info("Uploading widget_feature chunk of size: {}".format(len(upload_chunk))) session.bulk_insert_mappings(tmp_upload, upload_chunk) end_time = time.time() upload_time += (end_time - start_time) start_time = time.time() self.log.info("Constructing primary key") insert_stmt = insert_ignore(tmp_wf, dialect).from_select( [tmp_wf.idwidget, tmp_wf.idfeature, tmp_wf.value], session.query(tmp_upload.idwidget, tmp_upload.idfeature, tmp_upload.value) \ .select_from(tmp_upload) \ ) session.execute(insert_stmt) end_time = time.time() primary_key_time += (end_time - start_time) start_time = time.time() if session.bind.dialect.name.lower() == 'mysql': with open(insert_file, 'w') as fout: csvout = csv.writer(fout, delimiter=',', escapechar='\\') for row in session.query(tmp_wf.idwidget, tmp_wf.idfeature, tmp_wf.value): csvout.writerow(tuple(row)) del csvout self.log.info("Temp file size: {}".format(os.path.getsize(insert_file))) insert_stmt = sqlalchemy.text(r""" LOAD DATA CONCURRENT LOCAL INFILE '{insert_file}' IGNORE INTO TABLE widget_feature FIELDS TERMINATED BY ',' OPTIONALLY ENCLOSED BY '"' ESCAPED BY '\\' LINES TERMINATED BY '\n' (idwidget, idfeature, value) """.format(insert_file=insert_file)) else: insert_stmt = insert_ignore(t_wf, dialect).from_select( [t_wf.idwidget, t_wf.idfeature, t_wf.value], session.query(tmp_wf.idwidget, tmp_wf.idfeature, tmp_wf.value) ) start_time = time.time() self.log.info("Transferring into place") session.execute(insert_stmt) end_time = time.time() widget_time += (end_time - start_time) num_widgets += len(result_chunk) self.log.info("Average Times: {} {} {} {} {} {}".format(count_time / num_widgets, feature_time / num_widgets, upload_time / num_widgets, primary_key_time / num_widgets, widget_time / num_widgets, num_widget_features / num_widgets)) self.log.info("Average Rate: {}".format(num_widgets / (time.time() - begin_time))) self.log.info("Max Rate: {}".format(num_widgets / widget_time)) if session.bind.dialect.name.lower() == 'mysql': session.execute("ALTER TABLE widget_feature ENABLE KEYS;") session.execute("SET @@foreign_key_checks=1;") os.remove(insert_file) tmp_upload.metadata.remove(tmp_upload.__table__) tmp_upload.metadata.remove(tmp_wf.__table__) if __name__ == "__main__": A = BagOfWords.from_args(sys.argv[1:]) A.run()

51.403922

251

0.580333

12,430

0.948276

0

11,786

0.899146

0

2,369

0.180729

59ac1cf688342acfde23c07e10ca2e33caf1f078

450

py

Python

trains/ATIO.py

Columbine21/TFR-Net

1da01577542e7f477fdf7323ec0696aebc632357

[ "MIT" ]

7

2021-11-19T01:32:01.000Z

2021-12-16T11:42:44.000Z

trains/ATIO.py

Columbine21/TFR-Net

1da01577542e7f477fdf7323ec0696aebc632357

[ "MIT" ]

2

2021-11-25T08:28:08.000Z

2021-12-29T08:42:55.000Z

trains/ATIO.py

Columbine21/TFR-Net

1da01577542e7f477fdf7323ec0696aebc632357

[ "MIT" ]

1

2021-12-02T09:42:51.000Z

""" AIO -- All Trains in One """ from trains.baselines import * from trains.missingTask import * __all__ = ['ATIO'] class ATIO(): def __init__(self): self.TRAIN_MAP = { # single-task 'tfn': TFN, 'mult': MULT, 'misa': MISA, # missing-task 'tfr_net': TFR_NET, } def getTrain(self, args): return self.TRAIN_MAP[args.modelName.lower()](args)

19.565217

59

0.52

330

0.733333

0

91

0.202222

59ac4ecc150b88338555999e74b36af7366e76c2

271

py

Python

method/boardInfo.py

gary920209/LightDance-RPi

41d3ef536f3874fd5dbe092f5c9be42f7204427d

[ "MIT" ]

2

2020-11-14T17:13:55.000Z

2020-11-14T17:42:39.000Z

method/boardInfo.py

gary920209/LightDance-RPi

41d3ef536f3874fd5dbe092f5c9be42f7204427d

[ "MIT" ]

null

method/boardInfo.py

gary920209/LightDance-RPi

41d3ef536f3874fd5dbe092f5c9be42f7204427d

[ "MIT" ]

null

import os from .baseMethod import BaseMethod # BoardInfo class BoardInfo(BaseMethod): def method(self, payload): info = [ "boardInfo", {"name": os.name, "type": "dancer", "OK": True, "msg": "Success"}, ] return info

19.357143

78

0.553506

210

0.774908

0

60

0.221402

59ad06dd6ba9abadeea6a1f889a37f3edb2cafd7

4,928

py

Python

split_data.py

Anchorboy/PR_FinalProject

e744723c9c9dd55e6995ae5929eb45f90c70819b

[ "MIT" ]

null

split_data.py

Anchorboy/PR_FinalProject

e744723c9c9dd55e6995ae5929eb45f90c70819b

[ "MIT" ]

null

split_data.py

Anchorboy/PR_FinalProject

e744723c9c9dd55e6995ae5929eb45f90c70819b

[ "MIT" ]

null

import os import cv2 import random import shutil import numpy as np def split_img(input_path): split_ratio = 0.8 for dir_name in xrange(10): dir_name += 1 dir_name = str(dir_name) dir_path = os.path.join(input_path, dir_name) img_in_class = os.listdir(dir_path) rand_train_img = set(random.sample(img_in_class, int(len(img_in_class) * split_ratio))) rand_test_img = set(img_in_class) - rand_train_img for img_name in rand_train_img: img_path = os.path.join(dir_path, img_name) if not os.path.exists("train/"+dir_name): os.mkdir("train/"+dir_name) shutil.copyfile(img_path, "train/"+dir_name+"/"+img_name) for img_name in rand_test_img: img_path = os.path.join(dir_path, img_name) if not os.path.exists("test/"+dir_name): os.mkdir("test/"+dir_name) shutil.copyfile(img_path, "test/"+dir_name+"/"+img_name) def split_data(samples): split_rate = 0.6 train_all = [] test_all = [] for class_id, img_in_class in enumerate(samples): rand_ind = [ i for i in xrange(len(img_in_class)) ] rand_train_ind = set(random.sample(rand_ind, int(len(img_in_class) * split_rate))) rand_test_ind = set(rand_ind) - rand_train_ind # train_in_class = [] # test_in_class = [] for ind in rand_train_ind: img_vec = img_in_class[ind] img_vec = img_vec.reshape(img_vec.shape[0] * img_vec.shape[1] * img_vec.shape[2],) train_all.append((class_id, img_vec)) for ind in rand_test_ind: img_vec = img_in_class[ind] img_vec = img_vec.reshape(img_vec.shape[0] * img_vec.shape[1] * img_vec.shape[2],) test_all.append((class_id, img_vec)) # train_all.append(train_in_class) # test_all.append(test_in_class) return train_all, test_all def read_img(input_path): img_size = (200, 200) sample_all = [] for dir_name in xrange(10): dir_name += 1 dir_name = str(dir_name) dir_path = os.path.join(input_path, dir_name) img_in_class = [] for img_name in os.listdir(dir_path): img_path = os.path.join(dir_path, img_name) img_vec = cv2.imread(img_path, flags=1) # print img_vec.shape # res = cv2.resize(img_vec, (int(img_vec.shape[0]*0.5), int(img_vec.shape[1]*0.5)), interpolation=cv2.INTER_CUBIC) res = cv2.resize(img_vec, img_size, interpolation=cv2.INTER_CUBIC) nor_res = np.zeros_like(res) nor_res = cv2.normalize(src=res, dst=nor_res, alpha=0, beta=1, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_32F) img_in_class.append(nor_res) sample_all.append(img_in_class) train_all, test_all = split_data(sample_all) return train_all, test_all def read_data(): img_size = (200, 200) train_all = [] test_all = [] current_base = os.path.abspath('.') train_path = os.path.join(current_base, "train") test_path = os.path.join(current_base, "test") # read train for dir_name in os.listdir(train_path): dir_path = os.path.join(train_path, dir_name) img_in_class = [] for img_name in os.listdir(dir_path): img_path = os.path.join(dir_path, img_name) img_vec = cv2.imread(img_path, flags=1) # print img_vec.shape # res = cv2.resize(img_vec, (int(img_vec.shape[0]*0.5), int(img_vec.shape[1]*0.5)), interpolation=cv2.INTER_CUBIC) res = cv2.resize(img_vec, img_size, interpolation=cv2.INTER_CUBIC) nor_res = np.zeros_like(res) nor_res = cv2.normalize(src=res, dst=nor_res, alpha=0, beta=1, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_32F) img_in_class.append(nor_res) train_all.append(img_in_class) # read test for dir_name in os.listdir(test_path): dir_path = os.path.join(test_path, dir_name) img_in_class = [] for img_name in os.listdir(dir_path): img_path = os.path.join(dir_path, img_name) img_vec = cv2.imread(img_path, flags=1) # print img_vec.shape # res = cv2.resize(img_vec, (int(img_vec.shape[0]*0.5), int(img_vec.shape[1]*0.5)), interpolation=cv2.INTER_CUBIC) res = cv2.resize(img_vec, img_size, interpolation=cv2.INTER_CUBIC) nor_res = np.zeros_like(res) nor_res = cv2.normalize(src=res, dst=nor_res, alpha=0, beta=1, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_32F) img_in_class.append(nor_res) test_all.append(img_in_class) return train_all, test_all if __name__ == "__main__": current_base = os.path.abspath('.') input_base = os.path.join(current_base, 'data') split_img(input_base) # train_all, test_all = read_data() # print train_all

36.503704

126

0.631494

0

673

0.136567