xszheng2020/the_stack_dedup_python_hits_4 · Datasets at Hugging Face

b3edb25141ff3bac7e8859e484447cd10c09ff20

85,475

py

Python

models/networks.py

tkuri/CGIntrinsics

e84b73aa3784112b389b955258966f827b1f03d9

[ "MIT" ]

null

models/networks.py

tkuri/CGIntrinsics

e84b73aa3784112b389b955258966f827b1f03d9

[ "MIT" ]

null

models/networks.py

tkuri/CGIntrinsics

e84b73aa3784112b389b955258966f827b1f03d9

[ "MIT" ]

1

2021-06-19T16:50:19.000Z

import torch import torch.nn as nn import torch.sparse from torch.autograd import Variable import numpy as np import sys from torch.autograd import Function import math import h5py import json # from . import resnet1 import matplotlib.pyplot as plt from skimage.transform import resize ############################################################################### # Functions ############################################################################### def weights_init(m): classname = m.__class__.__name__ if classname.find('Conv') != -1: m.weight.data.normal_(0.0, 0.02) elif classname.find('BatchNorm2d') != -1: m.weight.data.normal_(1.0, 0.02) m.bias.data.fill_(0) def get_norm_layer(norm_type): if norm_type == 'batch': norm_layer = nn.BatchNorm2d elif norm_type == 'instance': norm_layer = nn.InstanceNorm2d else: print('normalization layer [%s] is not found' % norm) return norm_layer def define_G(input_nc, output_nc, ngf, which_model_netG, norm='batch', use_dropout=False, gpu_ids=[]): netG = None use_gpu = len(gpu_ids) > 0 norm_layer = get_norm_layer(norm_type=norm) if use_gpu: assert(torch.cuda.is_available()) if which_model_netG == 'resnet_9blocks': netG = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, n_blocks=9, gpu_ids=gpu_ids) elif which_model_netG == 'resnet_6blocks': netG = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, n_blocks=6, gpu_ids=gpu_ids) elif which_model_netG == 'unet_128': netG = UnetGenerator(input_nc, output_nc, 7, ngf, norm_layer=norm_layer, use_dropout=use_dropout, gpu_ids=gpu_ids) elif which_model_netG == 'unet_256': # netG = SingleUnetGenerator_S(input_nc, output_nc, 7, ngf, norm_layer=norm_layer, use_dropout=use_dropout, gpu_ids=gpu_ids) # netG = SingleUnetGenerator_R(input_nc, output_nc, 7, ngf, norm_layer=nn.BatchNorm2d, use_dropout=use_dropout, gpu_ids=gpu_ids, ) output_nc = 3 # netG2 = SingleUnetGenerator_R2(input_nc, output_nc, 7, ngf, norm_layer=nn.BatchNorm2d, use_dropout=use_dropout, gpu_ids=gpu_ids) # netG = UnetGenerator(input_nc, output_nc, 8, ngf, norm_layer=norm_layer, use_dropout=use_dropout, gpu_ids=gpu_ids) netG = MultiUnetGenerator(input_nc, output_nc, 7, ngf, norm_layer=norm_layer, use_dropout=use_dropout, gpu_ids=gpu_ids) # output_nc_R = 3 # netR = SingleUnetGenerator_R(input_nc, output_nc_R, 7, ngf, norm_layer=norm_layer, use_dropout=use_dropout, gpu_ids=gpu_ids) # output_nc_L = 3 # netL = SingleUnetGenerator_R(input_nc, output_nc_L, 7, ngf, norm_layer=norm_layer, use_dropout=use_dropout, gpu_ids=gpu_ids) else: print('Generator model name [%s] is not recognized' % which_model_netG) if len(gpu_ids) > 0: netG.cuda(gpu_ids[0]) # netR.cuda(gpu_ids[0]) # netL.cuda(gpu_ids[0]) netG.apply(weights_init) # netR.apply(weights_init) # netL.apply(weights_init) return netG def define_D(input_nc, ndf, which_model_netD, n_layers_D=3, norm='batch', use_sigmoid=False, gpu_ids=[]): netD = None use_gpu = len(gpu_ids) > 0 norm_layer = get_norm_layer(norm_type=norm) if use_gpu: assert(torch.cuda.is_available()) if which_model_netD == 'basic': netD = NLayerDiscriminator(input_nc, ndf, n_layers=3, norm_layer=norm_layer, use_sigmoid=use_sigmoid, gpu_ids=gpu_ids) elif which_model_netD == 'n_layers': netD = NLayerDiscriminator(input_nc, ndf, n_layers_D, norm_layer=norm_layer, use_sigmoid=use_sigmoid, gpu_ids=gpu_ids) else: print('Discriminator model name [%s] is not recognized' % which_model_netD) if use_gpu: netD.cuda(device_id=gpu_ids[0]) netD.apply(weights_init) return netD def print_network(net): num_params = 0 for param in net.parameters(): num_params += param.numel() print(net) print('Total number of parameters: %d' % num_params) ############################################################################## # Classes ############################################################################## class Sparse(Function): # Sparse matrix for S def forward(self, input, S): self.save_for_backward(S) output = torch.mm(S, input) # output = output.cuda() return output # This function has only a single output, so it gets only one gradient def backward(self, grad_output): S, = self.saved_tensors grad_weight = None grad_input = torch.mm(S.t(), grad_output) # grad_input = grad_input.cuda() return grad_input, grad_weight class JointLoss(nn.Module): def __init__(self): super(JointLoss, self).__init__() self.w_ss_local = 2.0 self.w_SAW = 1.0 self.w_rs_local = 1.0 self.w_reconstr = 2.0 self.w_reconstr_real = 2.0 self.w_rs_dense = 2.0 self.w_ls = 2.0 self.w_ss_dense = 4.0 self.w_sp = 0.25 self.w_IIW = 4.0 self.w_feature = 0.75 self.w_grad = 0.25 self.local_s_w = np.array([[0.5, 0.5, 0.5, 0.5, 0.5], \ [0.5, 1 , 1 , 1, 0.5],\ [0.5, 1, 1, 1, 0.5],\ [0.5, 1, 1, 1, 0.5],\ [0.5, 0.5, 0.5, 0.5, 0.5]]) x = np.arange(-1, 2) y = np.arange(-1, 2) self.X, self.Y = np.meshgrid(x, y) # self.h_offset = [0,0,0,1,1,2,2,2,1] # self.w_offset = [0,1,2,0,2,0,1,2,1] self.total_loss = None self.running_stage = 0 def BilateralRefSmoothnessLoss(self, pred_R, targets, att, num_features): # pred_R = pred_R.cpu() total_loss = Variable(torch.cuda.FloatTensor(1)) total_loss[0] = 0 N = pred_R.size(2) * pred_R.size(3) Z = (pred_R.size(1) * N ) # grad_input = torch.FloatTensor(pred_R.size()) # grad_input = grad_input.zero_() for i in range(pred_R.size(0)): # for each image B_mat = targets[att+'B_list'][i] # still list of blur sparse matrices S_mat = Variable(targets[att + 'S'][i].cuda(), requires_grad = False) # Splat and Slicing matrix n_vec = Variable(targets[att + 'N'][i].cuda(), requires_grad = False) # bi-stochatistic vector, which is diagonal matrix p = pred_R[i,:,:,:].view(pred_R.size(1),-1).t() # NX3 # p'p # p_norm = torch.mm(p.t(), p) # p_norm_sum = torch.trace(p_norm) p_norm_sum = torch.sum(torch.mul(p,p)) # S * N * p Snp = torch.mul(n_vec.repeat(1,pred_R.size(1)), p) sp_mm = Sparse() Snp = sp_mm(Snp, S_mat) Snp_1 = Snp.clone() Snp_2 = Snp.clone() # # blur for f in range(num_features+1): B_var1 = Variable(B_mat[f].cuda(), requires_grad = False) sp_mm1 = Sparse() Snp_1 = sp_mm1(Snp_1, B_var1) B_var2 = Variable(B_mat[num_features-f].cuda(), requires_grad = False) sp_mm2 = Sparse() Snp_2 = sp_mm2(Snp_2, B_var2) Snp_12 = Snp_1 + Snp_2 pAp = torch.sum(torch.mul(Snp, Snp_12)) total_loss = total_loss + ((p_norm_sum - pAp)/Z) total_loss = total_loss/pred_R.size(0) # average over all images return total_loss def SUNCGReconstLoss(self, R, S, mask, targets): rgb_img = Variable(targets['rgb_img'].cuda(), requires_grad = False) S = S.repeat(1,3,1,1) chromaticity = Variable(targets['chromaticity'].cuda(), requires_grad = False) R = torch.mul(chromaticity, R.repeat(1,3,1,1)) return torch.mean( torch.pow(torch.mul(mask, rgb_img - torch.mul(R, S)), 2) ) def IIWReconstLoss(self, R, S, targets): S = S.repeat(1,3,1,1) rgb_img = Variable(targets['rgb_img'].cuda(), requires_grad = False) # 1 channel chromaticity = Variable(targets['chromaticity'].cuda(), requires_grad = False) p_R = torch.mul(chromaticity, R.repeat(1,3,1,1)) # return torch.mean( torch.mul(L, torch.pow( torch.log(rgb_img) - torch.log(p_R) - torch.log(S), 2))) return torch.mean( torch.pow( rgb_img - torch.mul(p_R, S), 2)) def Ranking_Loss(self, prediction_R, judgements, is_flip): #ranking loss for each prediction feature tau = 0.25 #abs(I1 - I2)) ) #1.2 * (1 + math.fabs(math.log(I1) - math.log(I2) ) ) points = judgements['intrinsic_points'] comparisons = judgements['intrinsic_comparisons'] id_to_points = {p['id']: p for p in points} rows = prediction_R.size(1) cols = prediction_R.size(2) num_valid_comparisons = 0 num_valid_comparisons_ineq =0 num_valid_comparisons_eq = 0 total_loss_eq = Variable(torch.cuda.FloatTensor(1)) total_loss_eq[0] = 0 total_loss_ineq = Variable(torch.cuda.FloatTensor(1)) total_loss_ineq[0] = 0 for c in comparisons: # "darker" is "J_i" in our paper darker = c['darker'] if darker not in ('1', '2', 'E'): continue # "darker_score" is "w_i" in our paper # remove unconfident point weight = c['darker_score'] if weight < 0.5 or weight is None: continue point1 = id_to_points[c['point1']] point2 = id_to_points[c['point2']] if not point1['opaque'] or not point2['opaque']: continue # if is_flip: # l1 = prediction_R[:, int(point1['y'] * rows), cols - 1 - int( point1['x'] * cols)] # l2 = prediction_R[:, int(point2['y'] * rows), cols - 1 - int( point2['x'] * cols)] # else: l1 = prediction_R[:, int(point1['y'] * rows), int(point1['x'] * cols)] l2 = prediction_R[:, int(point2['y'] * rows), int(point2['x'] * cols)] l1_m = l1 #torch.mean(l1) l2_m = l2 #torch.mean(l2) # print(int(point1['y'] * rows), int(point1['x'] * cols), int(point2['y'] * rows), int(point2['x'] * cols), darker) # print(point1['y'], point1['x'], point2['y'], point2['x'], c['point1'], c['point2']) # print("===============================================================") # l2 > l1, l2 is brighter # if darker == '1' and ((l1_m.data[0] / l2_m.data[0]) > 1.0/tau): # # loss =0 # loss = weight * torch.mean((tau - (l2_m / l1_m))) # num_valid_comparisons += 1 # # l1 > l2, l1 is brighter # elif darker == '2' and ((l2_m.data[0] / l1_m.data[0]) > 1.0/tau): # # loss =0 # loss = weight * torch.mean((tau - (l1_m / l2_m))) # num_valid_comparisons += 1 # # is equal # elif darker == 'E': # loss = weight * torch.mean(torch.abs(l2 - l1)) # num_valid_comparisons += 1 # else: # loss = 0.0 # l2 is brighter if darker == '1' and ((l1_m.data[0] - l2_m.data[0]) > - tau): # print("dark 1", l1_m.data[0] - l2_m.data[0]) total_loss_ineq += weight * torch.mean( torch.pow( tau - (l2_m - l1_m), 2) ) num_valid_comparisons_ineq += 1. # print("darker 1 loss", l2_m.data[0], l1_m.data[0], loss.data[0]) # l1 > l2, l1 is brighter elif darker == '2' and ((l2_m.data[0] - l1_m.data[0]) > - tau): # print("dark 2", l2_m.data[0] - l1_m.data[0]) total_loss_ineq += weight * torch.mean( torch.pow( tau - (l1_m - l2_m),2) ) num_valid_comparisons_ineq += 1. # print("darker 2 loss", l2_m.data[0], l1_m.data[0], loss.data[0]) elif darker == 'E': total_loss_eq += weight * torch.mean( torch.pow(l2 - l1,2) ) num_valid_comparisons_eq += 1. else: loss = 0.0 total_loss = total_loss_ineq + total_loss_eq num_valid_comparisons = num_valid_comparisons_eq + num_valid_comparisons_ineq # print("average eq loss", total_loss_eq.data[0]/(num_valid_comparisons_eq + 1e-6)) # print("average ineq loss", total_loss_ineq.data[0]/(num_valid_comparisons_ineq + 1e-6)) return total_loss/(num_valid_comparisons + 1e-6) def BatchRankingLoss(self, prediction_R, judgements_eq, judgements_ineq, random_filp): eq_loss, ineq_loss = 0, 0 num_valid_eq = 0 num_valid_ineq = 0 tau = 0.425 rows = prediction_R.size(1) cols = prediction_R.size(2) num_channel = prediction_R.size(0) # evaluate equality annotations densely if judgements_eq.size(1) > 2: judgements_eq = judgements_eq.cuda() R_vec = prediction_R.view(num_channel, -1) # R_vec = torch.exp(R_vec) # I_vec = I.view(1, -1) y_1 = torch.floor(judgements_eq[:,0] * rows).long() y_2 = torch.floor(judgements_eq[:,2] * rows).long() if random_filp: x_1 = cols - 1 - torch.floor(judgements_eq[:,1] * cols).long() x_2 = cols - 1 - torch.floor(judgements_eq[:,3] * cols).long() else: x_1 = torch.floor(judgements_eq[:,1] * cols).long() x_2 = torch.floor(judgements_eq[:,3] * cols).long() # compute linear index for point 1 # y_1 = torch.floor(judgements_eq[:,0] * rows).long() # x_1 = torch.floor(judgements_eq[:,1] * cols).long() point_1_idx_linaer = y_1 * cols + x_1 # compute linear index for point 2 # y_2 = torch.floor(judgements_eq[:,2] * rows).long() # x_2 = torch.floor(judgements_eq[:,3] * cols).long() point_2_idx_linear = y_2 * cols + x_2 # extract all pairs of comparisions points_1_vec = torch.index_select(R_vec, 1, Variable(point_1_idx_linaer, requires_grad = False)) points_2_vec = torch.index_select(R_vec, 1, Variable(point_2_idx_linear, requires_grad = False)) # I1_vec = torch.index_select(I_vec, 1, point_1_idx_linaer) # I2_vec = torch.index_select(I_vec, 1, point_2_idx_linear) weight = Variable(judgements_eq[:,4], requires_grad = False) # weight = confidence#* torch.exp(4.0 * torch.abs(I1_vec - I2_vec) ) # compute loss # eq_loss = torch.sum(torch.mul(weight, torch.mean(torch.abs(points_1_vec - points_2_vec),0) )) eq_loss = torch.sum(torch.mul(weight, torch.mean(torch.pow(points_1_vec - points_2_vec,2),0) )) num_valid_eq += judgements_eq.size(0) # compute inequality annotations if judgements_ineq.size(1) > 2: judgements_ineq = judgements_ineq.cuda() R_intensity = torch.mean(prediction_R, 0) # R_intensity = torch.log(R_intensity) R_vec_mean = R_intensity.view(1, -1) y_1 = torch.floor(judgements_ineq[:,0] * rows).long() y_2 = torch.floor(judgements_ineq[:,2] * rows).long() # x_1 = torch.floor(judgements_ineq[:,1] * cols).long() # x_2 = torch.floor(judgements_ineq[:,3] * cols).long() if random_filp: x_1 = cols - 1 - torch.floor(judgements_ineq[:,1] * cols).long() x_2 = cols - 1 - torch.floor(judgements_ineq[:,3] * cols).long() else: x_1 = torch.floor(judgements_ineq[:,1] * cols).long() x_2 = torch.floor(judgements_ineq[:,3] * cols).long() # y_1 = torch.floor(judgements_ineq[:,0] * rows).long() # x_1 = torch.floor(judgements_ineq[:,1] * cols).long() point_1_idx_linaer = y_1 * cols + x_1 # y_2 = torch.floor(judgements_ineq[:,2] * rows).long() # x_2 = torch.floor(judgements_ineq[:,3] * cols).long() point_2_idx_linear = y_2 * cols + x_2 # extract all pairs of comparisions points_1_vec = torch.index_select(R_vec_mean, 1, Variable(point_1_idx_linaer, requires_grad = False)).squeeze(0) points_2_vec = torch.index_select(R_vec_mean, 1, Variable(point_2_idx_linear, requires_grad = False)).squeeze(0) weight = Variable(judgements_ineq[:,4], requires_grad = False) # point 2 should be always darker than (<) point 1 # compute loss relu_layer = nn.ReLU(True) # ineq_loss = torch.sum(torch.mul(weight, relu_layer(points_2_vec - points_1_vec + tau) ) ) ineq_loss = torch.sum(torch.mul(weight, torch.pow( relu_layer(points_2_vec - points_1_vec + tau),2) ) ) # ineq_loss = torch.sum(torch.mul(weight, torch.pow(relu_layer(tau - points_1_vec/points_2_vec),2))) num_included = torch.sum( torch.ge(points_2_vec.data - points_1_vec.data, -tau).float().cuda() ) # num_included = torch.sum(torch.ge(points_2_vec.data/points_1_vec.data, 1./tau).float().cuda()) num_valid_ineq += num_included # avoid divide by zero return eq_loss/(num_valid_eq + 1e-8) + ineq_loss/(num_valid_ineq + 1e-8) def ShadingPenaltyLoss(self, S): return torch.mean(torch.pow(S - 0.5,2) ) # return torch.sum( torch.mul(sky_mask, torch.abs(S - np.log(0.5))/num_val_pixels )) def AngleLoss(self, prediction_n, targets): mask = Variable(targets['mask'].cuda(), requires_grad = False) normal = Variable(targets['normal'].cuda(), requires_grad = False) num_valid = torch.sum(mask[:,0,:,:]) # compute dot product angle_loss = - torch.sum( torch.mul(mask, torch.mul(prediction_n, normal)), 1) return 1 + torch.sum(angle_loss)/num_valid def GradientLoss(self, prediction_n, mask, gt_n): N = torch.sum(mask) # horizontal angle difference h_mask = torch.mul(mask[:,:,:,0:-2], mask[:,:,:,2:]) h_gradient = prediction_n[:,:,:,0:-2] - prediction_n[:,:,:,2:] h_gradient_gt = gt_n[:,:,:,0:-2] - gt_n[:,:,:,2:] h_gradient_loss = torch.mul(h_mask, torch.abs(h_gradient - h_gradient_gt)) # Vertical angle difference v_mask = torch.mul(mask[:,:,0:-2,:], mask[:,:,2:,:]) v_gradient = prediction_n[:,:,0:-2,:] - prediction_n[:,:,2:,:] v_gradient_gt = gt_n[:,:,0:-2,:] - gt_n[:,:,2:,:] v_gradient_loss = torch.mul(v_mask, torch.abs(v_gradient - v_gradient_gt)) gradient_loss = torch.sum(h_gradient_loss) + torch.sum(v_gradient_loss) gradient_loss = gradient_loss/(N*2.0) return gradient_loss def SmoothLoss(self, prediction_n, mask): N = torch.sum(mask[:,0,:,:]) # horizontal angle difference h_mask = torch.mul(mask[:,:,:,0:-2], mask[:,:,:,2:]) h_gradient = torch.sum( torch.mul(h_mask, torch.mul(prediction_n[:,:,:,0:-2], prediction_n[:,:,:,2:])), 1) h_gradient_loss = 1 - torch.sum(h_gradient)/N # Vertical angle difference v_mask = torch.mul(mask[:,:,0:-2,:], mask[:,:,2:,:]) v_gradient = torch.sum( torch.mul(v_mask, torch.mul(prediction_n[:,:,0:-2,:], prediction_n[:,:,2:,:])), 1) v_gradient_loss = 1 - torch.sum(v_gradient)/N gradient_loss = h_gradient_loss + v_gradient_loss return gradient_loss def UncertaintyLoss(self, prediction_n, uncertainty, targets): uncertainty = torch.squeeze(uncertainty, 1) mask = Variable(targets['mask'].cuda(), requires_grad = False) normal = Variable(targets['normal'].cuda(), requires_grad = False) num_valid = torch.sum(mask[:,0,:,:]) angle_diff = ( torch.sum( torch.mul(prediction_n, normal), 1) + 1.0) * 0.5 uncertainty_loss = torch.sum( torch.mul(mask[:,0,:,:], torch.pow(uncertainty - angle_diff, 2) ) ) return uncertainty_loss/num_valid def MaskLocalSmoothenessLoss(self, R, M, targets): h = R.size(2) w = R.size(3) num_c = R.size(1) half_window_size = 1 total_loss = Variable(torch.cuda.FloatTensor(1)) total_loss[0] = 0 mask_center = M[:,:,half_window_size + self.Y[half_window_size,half_window_size]:h-half_window_size + self.Y[half_window_size,half_window_size], \ half_window_size + self.X[half_window_size,half_window_size]:w-half_window_size + self.X[half_window_size,half_window_size]] R_center = R[:,:,half_window_size + self.Y[half_window_size,half_window_size]:h-half_window_size + self.Y[half_window_size,half_window_size], \ half_window_size + self.X[half_window_size,half_window_size]:w-half_window_size + self.X[half_window_size,half_window_size] ] c_idx = 0 for k in range(0,half_window_size*2+1): for l in range(0,half_window_size*2+1): # albedo_weights = Variable(targets["r_w_s"+str(scale_idx)][:,c_idx,:,:].unsqueeze(1).repeat(1,num_c,1,1).float().cuda(), requires_grad = False) R_N = R[:,:,half_window_size + self.Y[k,l]:h- half_window_size + self.Y[k,l], half_window_size + self.X[k,l]: w-half_window_size + self.X[k,l] ] mask_N = M[:,:,half_window_size + self.Y[k,l]:h- half_window_size + self.Y[k,l], half_window_size + self.X[k,l]: w-half_window_size + self.X[k,l] ] composed_M = torch.mul(mask_N, mask_center) # albedo_weights = torch.mul(albedo_weights, composed_M) r_diff = torch.mul( composed_M, torch.pow(R_center - R_N,2) ) total_loss = total_loss + torch.mean(r_diff) c_idx = c_idx + 1 return total_loss/(8.0 * num_c) def LocalAlebdoSmoothenessLoss(self, R, targets, scale_idx): h = R.size(2) w = R.size(3) num_c = R.size(1) half_window_size = 1 total_loss = Variable(torch.cuda.FloatTensor(1)) total_loss[0] = 0 R_center = R[:,:,half_window_size + self.Y[half_window_size,half_window_size]:h-half_window_size + self.Y[half_window_size,half_window_size], \ half_window_size + self.X[half_window_size,half_window_size]:w-half_window_size + self.X[half_window_size,half_window_size] ] c_idx = 0 for k in range(0,half_window_size*2+1): for l in range(0,half_window_size*2+1): albedo_weights = targets["r_w_s"+str(scale_idx)][:,c_idx,:,:].unsqueeze(1).repeat(1,num_c,1,1).float().cuda() R_N = R[:,:,half_window_size + self.Y[k,l]:h- half_window_size + self.Y[k,l], half_window_size + self.X[k,l]: w-half_window_size + self.X[k,l] ] # mask_N = M[:,:,half_window_size + self.Y[k,l]:h- half_window_size + self.Y[k,l], half_window_size + self.X[k,l]: w-half_window_size + self.X[k,l] ] # composed_M = torch.mul(mask_N, mask_center) # albedo_weights = torch.mul(albedo_weights, composed_M) r_diff = torch.mul( Variable(albedo_weights, requires_grad = False), torch.abs(R_center - R_N) ) total_loss = total_loss + torch.mean(r_diff) c_idx = c_idx + 1 return total_loss/(8.0 * num_c) def Data_Loss(self, log_prediction, mask, log_gt): N = torch.sum(mask) log_diff = log_prediction - log_gt log_diff = torch.mul(log_diff, mask) s1 = torch.sum( torch.pow(log_diff,2) )/N s2 = torch.pow(torch.sum(log_diff),2)/(N*N) data_loss = s1 - s2 return data_loss def L2GradientMatchingLoss(self, log_prediction, mask, log_gt): N = torch.sum(mask) log_diff = log_prediction - log_gt log_diff = torch.mul(log_diff, mask) v_gradient = torch.pow(log_diff[:,:,0:-2,:] - log_diff[:,:,2:,:],2) v_mask = torch.mul(mask[:,:,0:-2,:], mask[:,:,2:,:]) v_gradient = torch.mul(v_gradient, v_mask) h_gradient = torch.pow(log_diff[:,:,:,0:-2] - log_diff[:,:,:,2:],2) h_mask = torch.mul(mask[:,:,:,0:-2], mask[:,:,:,2:]) h_gradient = torch.mul(h_gradient, h_mask) gradient_loss = (torch.sum(h_gradient) + torch.sum(v_gradient)) gradient_loss = gradient_loss/N return gradient_loss def L1GradientMatchingLoss(self, log_prediction, mask, log_gt): N = torch.sum( mask ) log_diff = log_prediction - log_gt log_diff = torch.mul(log_diff, mask) v_gradient = torch.abs(log_diff[:,:,0:-2,:] - log_diff[:,:,2:,:]) v_mask = torch.mul(mask[:,:,0:-2,:], mask[:,:,2:,:]) v_gradient = torch.mul(v_gradient, v_mask) h_gradient = torch.abs(log_diff[:,:,:,0:-2] - log_diff[:,:,:,2:]) h_mask = torch.mul(mask[:,:,:,0:-2], mask[:,:,:,2:]) h_gradient = torch.mul(h_gradient, h_mask) gradient_loss = (torch.sum(h_gradient) + torch.sum(v_gradient))/2.0 gradient_loss = gradient_loss/N return gradient_loss def L1Loss(self, prediction_n, mask, gt): num_valid = torch.sum( mask ) diff = torch.mul(mask, torch.abs(prediction_n - gt)) return torch.sum(diff)/num_valid def L2Loss(self, prediction_n, mask, gt): num_valid = torch.sum( mask ) diff = torch.mul(mask, torch.pow(prediction_n - gt,2)) return torch.sum(diff)/num_valid def HuberLoss(self, prediction, mask, gt): tau = 1.0 num_valid = torch.sum(mask) diff_L1 = torch.abs(prediction - gt) diff_L2 = torch.pow(prediction - gt ,2) mask_L2 = torch.le(diff_L1, tau).float().cuda() mask_L1 = 1.0 - mask_L2 L2_loss = 0.5 * torch.sum(torch.mul(mask, torch.mul(mask_L2, diff_L2))) L1_loss = torch.sum(torch.mul(mask, torch.mul(mask_L1, diff_L1))) - 0.5 final_loss = (L2_loss + L1_loss)/num_valid return final_loss # def DirectFramework(self, input_images, prediction_R, prediction_S, targets, epoch): # # downsample all the images # prediction_R_1 = prediction_R[:,:,::2,::2] # prediction_R_2 = prediction_R_1[:,:,::2,::2] # prediction_R_3 = prediction_R_2[:,:,::2,::2] # mask_0 = Variable(targets['mask'].cuda(), requires_grad = False) # mask_0 = mask_0[:,0,:,:].unsqueeze(1) # mask_1 = mask_0[:,:,::2,::2] # mask_2 = mask_1[:,:,::2,::2] # mask_3 = mask_2[:,:,::2,::2] # R_gt_0 = Variable(targets['gt_R'].cuda(), requires_grad = False) # R_gt_1 = R_gt_0[:,:,::2,::2] # R_gt_2 = R_gt_1[:,:,::2,::2] # R_gt_3 = R_gt_2[:,:,::2,::2] # S_gt_0 = Variable(targets['gt_S'].cuda(), requires_grad = False) # S_gt_1 = S_gt_0[:,:,::2,::2] # S_gt_2 = S_gt_1[:,:,::2,::2] # S_gt_3 = S_gt_2[:,:,::2,::2] # # gt_normal = Variable(targets['normal'].cuda(), requires_grad = False) # prediction_S_1 = prediction_S[:,:,::2,::2] # prediction_S_2 = prediction_S_1[:,:,::2,::2] # prediction_S_3 = prediction_S_2[:,:,::2,::2] # # R L2 loss # w_data = 1.0 # w_grad = 0.5 # R_loss = w_data * self.L2Loss(prediction_R, mask_0, R_gt_0) # R_loss += w_grad * self.L1GradientMatchingLoss(prediction_R , mask_0, R_gt_0) # R_loss += w_grad * self.L1GradientMatchingLoss(prediction_R_1, mask_1, R_gt_1) # R_loss += w_grad * self.L1GradientMatchingLoss(prediction_R_2, mask_2, R_gt_2) # R_loss += w_grad * self.L1GradientMatchingLoss(prediction_R_3, mask_3, R_gt_3) # S_mask_0 = mask_0[:,0,:,:].unsqueeze(1) # S_mask_1 = mask_1[:,0,:,:].unsqueeze(1) # S_mask_2 = mask_2[:,0,:,:].unsqueeze(1) # S_mask_3 = mask_3[:,0,:,:].unsqueeze(1) # # S Huber Loss # S_loss = w_data * self.HuberLoss(prediction_S, S_mask_0, S_gt_0) # S_loss += w_grad * self.L1GradientMatchingLoss(prediction_S , S_mask_0, S_gt_0) # S_loss += w_grad * self.L1GradientMatchingLoss(prediction_S_1, S_mask_1, S_gt_1) # S_loss += w_grad * self.L1GradientMatchingLoss(prediction_S_2, S_mask_2, S_gt_2) # S_loss += w_grad * self.L1GradientMatchingLoss(prediction_S_3, S_mask_3, S_gt_3) # Reconstr_loss = 2.0 * self.SUNCGReconstLoss(input_images, prediction_R, prediction_S, mask_0, targets) # # Ls_loss = 8.0 * self.BilateralRefSmoothnessLoss(prediction_L, targets, 'S', 2) # print("R_loss", R_loss.data[0]) # print("S_loss", S_loss.data[0]) # # print("Reconstr_loss", Reconstr_loss.data[0]) # # print("Lighting Loss", Ls_loss.data[0]) # total_loss = R_loss + S_loss + Reconstr_loss # return total_loss # def ScaleInvarianceFramework(self, input_images, prediction_R, prediction_S, targets, epoch): # prediction_R_1 = prediction_R[:,:,::2,::2] # prediction_R_2 = prediction_R_1[:,:,::2,::2] # prediction_R_3 = prediction_R_2[:,:,::2,::2] # # downsample all the images # mask_0 = Variable(targets['mask'].cuda(), requires_grad = False) # mask_0 = mask_0[:,0,:,:].unsqueeze(1) # mask_1 = mask_0[:,:,::2,::2] # mask_2 = mask_1[:,:,::2,::2] # mask_3 = mask_2[:,:,::2,::2] # R_gt_0 = torch.log(Variable(targets['gt_R'].cuda(), requires_grad = False)) # R_gt_1 = R_gt_0[:,:,::2,::2] # R_gt_2 = R_gt_1[:,:,::2,::2] # R_gt_3 = R_gt_2[:,:,::2,::2] # S_gt_0 = torch.log(Variable(targets['gt_S'].cuda(), requires_grad = False)) # S_gt_1 = S_gt_0[:,:,::2,::2] # S_gt_2 = S_gt_1[:,:,::2,::2] # S_gt_3 = S_gt_2[:,:,::2,::2] # # end of downsample # w_data = 1.0 # w_grad = 0.5 # R_loss = w_data * self.Data_Loss(prediction_R, mask_0, R_gt_0) # R_loss += w_grad * self.L1GradientMatchingLoss(prediction_R , mask_0, R_gt_0) # R_loss += w_grad * self.L1GradientMatchingLoss(prediction_R_1, mask_1, R_gt_1) # R_loss += w_grad * self.L1GradientMatchingLoss(prediction_R_2, mask_2, R_gt_2) # R_loss += w_grad * self.L1GradientMatchingLoss(prediction_R_3, mask_3, R_gt_3) # S_mask_0 = mask_0[:,0,:,:].unsqueeze(1) # S_mask_1 = mask_1[:,0,:,:].unsqueeze(1) # S_mask_2 = mask_2[:,0,:,:].unsqueeze(1) # S_mask_3 = mask_3[:,0,:,:].unsqueeze(1) # prediction_S_1 = prediction_S[:,:,::2,::2] # prediction_S_2 = prediction_S_1[:,:,::2,::2] # prediction_S_3 = prediction_S_2[:,:,::2,::2] # S_loss = w_data * self.Data_Loss(prediction_S, S_mask_0, S_gt_0) # S_loss += w_grad * self.L1GradientMatchingLoss(prediction_S , S_mask_0, S_gt_0) # S_loss += w_grad * self.L1GradientMatchingLoss(prediction_S_1, S_mask_1, S_gt_1) # S_loss += w_grad * self.L1GradientMatchingLoss(prediction_S_2, S_mask_2, S_gt_2) # S_loss += w_grad * self.L1GradientMatchingLoss(prediction_S_3, S_mask_3, S_gt_3) # # S_loss += 2.0 * self. (prediction_S, targets, 'S', 2) # Reconstr_loss = self.SUNCGReconstLoss(input_images, torch.exp(prediction_R), torch.exp(prediction_S), mask_0, targets) # # # lighting smoothness loss # # Ls_loss = 32.0 * self.LocalLightingSmoothenessLoss(prediction_L, targets) # # Ls_loss = 8.0 * self.BilateralRefSmoothnessLoss(prediction_L, targets, 'S', 2) # print("Reconstr_loss", Reconstr_loss.data[0]) # print("R_loss", R_loss.data[0]) # print("S_loss", S_loss.data[0]) # # print("Lighting Loss", Ls_loss.data[0]) # total_loss = R_loss + S_loss + Reconstr_loss #+ Ls_loss # return total_loss # def NormalShadingSmoothnessLoss(self, S, targets, scale_idx): # h = S.size(2) # w = S.size(3) # num_c = S.size(1) # half_window_size = 1 # total_loss = Variable(torch.cuda.FloatTensor(1)) # total_loss[0] = 0 # # mask_center = M[:,:,half_window_size + self.Y[half_window_size,half_window_size]:h-half_window_size + self.Y[half_window_size,half_window_size], \ # # half_window_size + self.X[half_window_size,half_window_size]:w-half_window_size + self.X[half_window_size,half_window_size]] # S_center = S[:,:,half_window_size + self.Y[half_window_size,half_window_size]:h-half_window_size + self.Y[half_window_size,half_window_size], \ # half_window_size + self.X[half_window_size,half_window_size]:w-half_window_size + self.X[half_window_size,half_window_size] ] # c_idx = 0 # for k in range(0,half_window_size*2+1): # for l in range(0,half_window_size*2+1): # normal_weights = targets["s_w_"+str(scale_idx)][:,c_idx,:,:].unsqueeze(1).repeat(1,num_c,1,1).float().cuda() # S_N = S[:,:,half_window_size + self.Y[k,l]:h- half_window_size + self.Y[k,l], half_window_size + self.X[k,l]: w-half_window_size + self.X[k,l] ] # # mask_N = M[:,:,half_window_size + self.Y[k,l]:h- half_window_size + self.Y[k,l], half_window_size + self.X[k,l]: w-half_window_size + self.X[k,l] ] # # composed_M = torch.mul(mask_N, mask_center) # # normal_weights = torch.mul(normal_weights, composed_M) # r_diff = torch.mul( Variable(normal_weights, requires_grad = False), torch.pow(S_center - S_N, 2) ) # total_loss = total_loss + torch.mean(r_diff) # c_idx = c_idx + 1 # return total_loss/(8.0 * num_c) def CCLoss(self, prediction_S, saw_mask, gts, num_cc): diff = prediction_S - gts total_loss = Variable(torch.cuda.FloatTensor(1)) total_loss[0] = 0 num_regions = 0 # for each prediction for i in range(prediction_S.size(0)): log_diff = diff[i,:,:,:] mask = saw_mask[i,:,:,:].int() for k in range(1, num_cc[i]+1): new_mask = (mask == k).float().cuda() masked_log_diff = torch.mul(new_mask, log_diff) N = torch.sum(new_mask) s1 = torch.sum( torch.pow(masked_log_diff,2) )/N s2 = torch.pow(torch.sum(masked_log_diff),2)/(N*N) total_loss += (s1 - s2) num_regions +=1 return total_loss/(num_regions + 1e-6) def SAWLoss(self, prediction_S, targets): # Shading smoothness ignore mask region lambda_1, lambda_2 = 0.1, 1. # saw_mask_0 = Variable(targets['saw_mask_0'].cuda(), requires_grad = False) # prediction_S_1 = prediction_S[:,:,::2,::2] # prediction_S_2 = prediction_S_1[:,:,::2,::2] # prediction_S_3 = prediction_S_2[:,:,::2,::2] # mask_0 = saw_mask_0 # mask_1 = mask_0[:,:,::2,::2] # mask_2 = mask_1[:,:,::2,::2] # mask_3 = mask_2[:,:,::2,::2] # saw_loss_0 = self.w_ss_local * self.MaskLocalSmoothenessLoss(prediction_S, mask_0, targets) # saw_loss_0 += self.w_ss_local * 0.5 * self.MaskLocalSmoothenessLoss(prediction_S_1, mask_1, targets) # saw_loss_0 += self.w_ss_local * 0.333 * self.MaskLocalSmoothenessLoss(prediction_S_2, mask_2, targets) # saw_loss_0 += self.w_ss_local * 0.25 * self.MaskLocalSmoothenessLoss(prediction_S_3, mask_3, targets) # shadow boundary saw_mask_1 = Variable(targets['saw_mask_1'].cuda(), requires_grad = False) linear_I = torch.mean( Variable(targets['rgb_img'].cuda(), requires_grad = False),1) linear_I = linear_I.unsqueeze(1) linear_I[linear_I < 1e-4] = 1e-4 # linear_I = linear_I.data[0,0,:,:].cpu().numpy() # srgb_img = np.transpose(linear_I, (1 , 2 ,0)) # mask_1 = saw_mask_1.data[0,0,:,:].cpu().numpy() # R_np = np.transpose(R_np, (1 , 2 ,0 # print(targets['num_mask_1'][0]) # plt.figure() # plt.imshow(mask_1, cmap='gray') # plt.show() # display i # plt.figure() # plt.imshow(linear_I, cmap='gray') # plt.show() # display i # sys.exit() saw_loss_1 = lambda_1 * self.CCLoss(prediction_S, saw_mask_1, torch.log(linear_I), targets['num_mask_1']) # smooth region saw_mask_2 = Variable(targets['saw_mask_2'].cuda(), requires_grad = False) saw_loss_2 = lambda_2 * self.CCLoss(prediction_S, saw_mask_2, 0, targets['num_mask_2']) # print("saw_loss_1 ", saw_loss_1.data[0]) # print("saw_loss_2 ", saw_loss_2.data[0]) return saw_loss_2 + saw_loss_1 def DirectFramework(self, prediction, gt, mask): w_data = 1.0 w_grad = 0.5 final_loss = w_data * self.L2Loss(prediction, mask, gt) # level 0 prediction_1 = prediction[:,:,::2,::2] prediction_2 = prediction_1[:,:,::2,::2] prediction_3 = prediction_2[:,:,::2,::2] mask_1 = mask[:,:,::2,::2] mask_2 = mask_1[:,:,::2,::2] mask_3 = mask_2[:,:,::2,::2] gt_1 = gt[:,:,::2,::2] gt_2 = gt_1[:,:,::2,::2] gt_3 = gt_2[:,:,::2,::2] final_loss += w_grad * self.L1GradientMatchingLoss(prediction , mask, gt) final_loss += w_grad * self.L1GradientMatchingLoss(prediction_1, mask_1, gt_1) final_loss += w_grad * self.L1GradientMatchingLoss(prediction_2, mask_2, gt_2) final_loss += w_grad * self.L1GradientMatchingLoss(prediction_3, mask_3, gt_3) return final_loss # all parameter in log space, presumption def ScaleInvarianceFramework(self, prediction, gt, mask, w_grad): assert(prediction.size(1) == gt.size(1)) assert(prediction.size(1) == mask.size(1)) w_data = 1.0 final_loss = w_data * self.Data_Loss(prediction, mask, gt) final_loss += w_grad * self.L1GradientMatchingLoss(prediction , mask, gt) # level 0 prediction_1 = prediction[:,:,::2,::2] prediction_2 = prediction_1[:,:,::2,::2] prediction_3 = prediction_2[:,:,::2,::2] mask_1 = mask[:,:,::2,::2] mask_2 = mask_1[:,:,::2,::2] mask_3 = mask_2[:,:,::2,::2] gt_1 = gt[:,:,::2,::2] gt_2 = gt_1[:,:,::2,::2] gt_3 = gt_2[:,:,::2,::2] final_loss += w_grad * self.L1GradientMatchingLoss(prediction_1, mask_1, gt_1) final_loss += w_grad * self.L1GradientMatchingLoss(prediction_2, mask_2, gt_2) final_loss += w_grad * self.L1GradientMatchingLoss(prediction_3, mask_3, gt_3) return final_loss def LinearScaleInvarianceFramework(self, prediction, gt, mask, w_grad): assert(prediction.size(1) == gt.size(1)) assert(prediction.size(1) == mask.size(1)) w_data = 1.0 # w_grad = 0.5 gt_vec = gt[mask > 0.1] pred_vec = prediction[mask > 0.1] gt_vec = gt_vec.unsqueeze(1).float().cpu() pred_vec = pred_vec.unsqueeze(1).float().cpu() scale, _ = torch.gels(gt_vec.data, pred_vec.data) # scale, _ = torch.lstsq(gt_vec.data, pred_vec.data) scale = scale[0,0] # print("scale" , scale) # sys.exit() prediction_scaled = prediction * scale final_loss = w_data * self.L2Loss(prediction_scaled, mask, gt) prediction_1 = prediction_scaled[:,:,::2,::2] prediction_2 = prediction_1[:,:,::2,::2] prediction_3 = prediction_2[:,:,::2,::2] mask_1 = mask[:,:,::2,::2] mask_2 = mask_1[:,:,::2,::2] mask_3 = mask_2[:,:,::2,::2] gt_1 = gt[:,:,::2,::2] gt_2 = gt_1[:,:,::2,::2] gt_3 = gt_2[:,:,::2,::2] final_loss += w_grad * self.L1GradientMatchingLoss(prediction_scaled , mask, gt) final_loss += w_grad * self.L1GradientMatchingLoss(prediction_1, mask_1, gt_1) final_loss += w_grad * self.L1GradientMatchingLoss(prediction_2, mask_2, gt_2) final_loss += w_grad * self.L1GradientMatchingLoss(prediction_3, mask_3, gt_3) return final_loss def WeightedLinearScaleInvarianceFramework(self, prediction, gt, mask, w_grad): w_data = 1.0 assert(prediction.size(1) == gt.size(1)) assert(prediction.size(1) == mask.size(1)) if torch.sum(mask.data) < 10: return 0 # w_grad = 0.5 gt_vec = gt[mask > 0.1] pred_vec = prediction[mask > 0.1] gt_vec = gt_vec.unsqueeze(1).float().cpu() pred_vec = pred_vec.unsqueeze(1).float().cpu() scale, _ = torch.gels(gt_vec.data, pred_vec.data) # scale, _ = torch.lstsq(gt_vec.data, pred_vec.data) scale = scale[0,0] prediction_scaled = prediction * scale ones_matrix = Variable(torch.zeros(gt.size(0), gt.size(1), gt.size(2), gt.size(3)) + 1, requires_grad = False) weight = torch.min(1/gt, ones_matrix.float().cuda()) weight_mask = torch.mul(weight, mask) final_loss = w_data * self.L2Loss(prediction_scaled, weight_mask, gt) prediction_1 = prediction_scaled[:,:,::2,::2] prediction_2 = prediction_1[:,:,::2,::2] prediction_3 = prediction_2[:,:,::2,::2] mask_1 = weight_mask[:,:,::2,::2] mask_2 = mask_1[:,:,::2,::2] mask_3 = mask_2[:,:,::2,::2] gt_1 = gt[:,:,::2,::2] gt_2 = gt_1[:,:,::2,::2] gt_3 = gt_2[:,:,::2,::2] final_loss += w_grad * self.L1GradientMatchingLoss(prediction_scaled , weight_mask, gt) final_loss += w_grad * self.L1GradientMatchingLoss(prediction_1, mask_1, gt_1) final_loss += w_grad * self.L1GradientMatchingLoss(prediction_2, mask_2, gt_2) final_loss += w_grad * self.L1GradientMatchingLoss(prediction_3, mask_3, gt_3) return final_loss def SUNCGBatchRankingLoss(self, prediction_R, judgements_eq, judgements_ineq): eq_loss, ineq_loss = 0, 0 num_valid_eq = 0 num_valid_ineq = 0 tau = 0.4 rows = prediction_R.size(1) cols = prediction_R.size(2) num_channel = prediction_R.size(0) # evaluate equality annotations densely if judgements_eq.size(1) > 2: judgements_eq = judgements_eq.cuda() R_vec = prediction_R.view(num_channel, -1) # R_vec = torch.exp(R_vec) y_1 = judgements_eq[:,0].long() y_2 = judgements_eq[:,2].long() # if random_filp: # x_1 = cols - 1 - judgements_eq[:,1].long() # x_2 = cols - 1 - judgements_eq[:,3].long() # else: x_1 = judgements_eq[:,1].long() x_2 = judgements_eq[:,3].long() # compute linear index for point 1 # y_1 = torch.floor(judgements_eq[:,0] * rows).long() # x_1 = torch.floor(judgements_eq[:,1] * cols).long() point_1_idx_linear = y_1 * cols + x_1 # compute linear index for point 2 # y_2 = torch.floor(judgements_eq[:,2] * rows).long() # x_2 = torch.floor(judgements_eq[:,3] * cols).long() point_2_idx_linear = y_2 * cols + x_2 # extract all pairs of comparisions points_1_vec = torch.index_select(R_vec, 1, Variable(point_1_idx_linear, requires_grad = False)) points_2_vec = torch.index_select(R_vec, 1, Variable(point_2_idx_linear, requires_grad = False)) # I1_vec = torch.index_select(I_vec, 1, point_1_idx_linaer) # I2_vec = torch.index_select(I_vec, 1, point_2_idx_linear) # weight = Variable(judgements_eq[:,4], requires_grad = False) # weight = confidence#* torch.exp(4.0 * torch.abs(I1_vec - I2_vec) ) # compute Loss # eq_loss = torch.sum(torch.mul(weight, torch.mean(torch.abs(points_1_vec - points_2_vec),0) )) eq_loss = torch.sum( torch.mean( torch.pow(points_1_vec - points_2_vec,2) ,0) ) num_valid_eq += judgements_eq.size(0) # # compute inequality annotations if judgements_ineq.size(1) > 2: judgements_ineq = judgements_ineq.cuda() R_intensity = torch.mean(prediction_R, 0) # R_intensity = torch.log(R_intensity) R_vec_mean = R_intensity.view(1, -1) y_1 = judgements_ineq[:,0].long() y_2 = judgements_ineq[:,2].long() # x_1 = torch.floor(judgements_ineq[:,1] * cols).long() # x_2 = torch.floor(judgements_ineq[:,3] * cols).long() x_1 = judgements_ineq[:,1].long() x_2 = judgements_ineq[:,3].long() # y_1 = torch.floor(judgements_ineq[:,0] * rows).long() # x_1 = torch.floor(judgements_ineq[:,1] * cols).long() point_1_idx_linear = y_1 * cols + x_1 # y_2 = torch.floor(judgements_ineq[:,2] * rows).long() # x_2 = torch.floor(judgements_ineq[:,3] * cols).long() point_2_idx_linear = y_2 * cols + x_2 # extract all pairs of comparisions points_1_vec = torch.index_select(R_vec_mean, 1, Variable(point_1_idx_linear, requires_grad = False)).squeeze(0) points_2_vec = torch.index_select(R_vec_mean, 1, Variable(point_2_idx_linear, requires_grad = False)).squeeze(0) # point 2 should be always darker than (<) point 1 # compute loss relu_layer = nn.ReLU(True) # ineq_loss = torch.sum(torch.mul(weight, relu_layer(points_2_vec - points_1_vec + tau) ) ) ineq_loss = torch.sum(torch.pow( relu_layer(points_2_vec - points_1_vec + tau),2) ) # ineq_loss = torch.sum(torch.mul(weight, torch.pow(relu_layer(tau - points_1_vec/points_2_vec),2))) num_included = torch.sum( torch.ge(points_2_vec.data - points_1_vec.data, -tau).float().cuda() ) # num_included = torch.sum(torch.ge(points_2_vec.data/points_1_vec.data, 1./tau).float().cuda()) num_valid_ineq += num_included # avoid divide by zero return (eq_loss)/(num_valid_eq + 1e-8) + ineq_loss/(num_valid_ineq + 1e-8) def __call__(self, input_images, prediction_R, prediction_S, targets, data_set_name, epoch): lambda_CG = 0.5 if data_set_name == "IIW": print("IIW Loss") num_images = prediction_R.size(0) # Albedo smoothness term # rs_loss = self.w_rs_dense * self.BilateralRefSmoothnessLoss(prediction_R, targets, 'R', 5) # multi-scale smoothness term prediction_R_1 = prediction_R[:,:,::2,::2] prediction_R_2 = prediction_R_1[:,:,::2,::2] prediction_R_3 = prediction_R_2[:,:,::2,::2] rs_loss = self.w_rs_local * self.LocalAlebdoSmoothenessLoss(prediction_R, targets,0) rs_loss = rs_loss + 0.5 * self.w_rs_local * self.LocalAlebdoSmoothenessLoss(prediction_R_1, targets,1) rs_loss = rs_loss + 0.3333 * self.w_rs_local * self.LocalAlebdoSmoothenessLoss(prediction_R_2, targets,2) rs_loss = rs_loss + 0.25 * self.w_rs_local * self.LocalAlebdoSmoothenessLoss(prediction_R_3, targets,3) # # Lighting smoothness Loss ss_loss = self.w_ss_dense * self.BilateralRefSmoothnessLoss(prediction_S, targets, 'S', 2) # # Reconstruction Loss reconstr_loss = self.w_reconstr_real * self.IIWReconstLoss(torch.exp(prediction_R), \ torch.exp(prediction_S), targets) # IIW Loss total_iiw_loss = Variable(torch.cuda.FloatTensor(1)) total_iiw_loss[0] = 0 for i in range(0, num_images): # judgements = json.load(open(targets["judgements_path"][i])) # total_iiw_loss += self.w_IIW * self.Ranking_Loss(prediction_R[i,:,:,:], judgements, random_filp) judgements_eq = targets["eq_mat"][i] judgements_ineq = targets["ineq_mat"][i] random_filp = targets["random_filp"][i] total_iiw_loss += self.w_IIW * self.BatchRankingLoss(prediction_R[i,:,:,:], judgements_eq, judgements_ineq, random_filp) total_iiw_loss = (total_iiw_loss)/num_images # print("reconstr_loss ", reconstr_loss.data[0]) # print("rs_loss ", rs_loss.data[0]) # print("ss_loss ", ss_loss.data[0]) # print("total_iiw_loss ", total_iiw_loss.data[0]) total_loss = total_iiw_loss + reconstr_loss + rs_loss + ss_loss elif data_set_name == "Render": print("Render LOSS") mask = Variable(targets['mask'].cuda(), requires_grad = False) mask_R = mask[:,0,:,:].unsqueeze(1).repeat(1,prediction_R.size(1),1,1) mask_S = mask[:,0,:,:].unsqueeze(1).repeat(1,prediction_S.size(1),1,1) mask_img = mask[:,0,:,:].unsqueeze(1).repeat(1,input_images.size(1),1,1) gt_R = Variable(targets['gt_R'].cuda(), requires_grad = False) gt_S = Variable(targets['gt_S'].cuda(), requires_grad = False) R_loss = lambda_CG * self.LinearScaleInvarianceFramework(torch.exp(prediction_R), gt_R, mask_R, 0.5) # using ScaleInvarianceFramework might achieve better performance if we train on both IIW and SAW, # but LinearScaleInvarianceFramework could produce better perforamnce if trained on CGIntrinsics only S_loss = lambda_CG * self.LinearScaleInvarianceFramework(torch.exp(prediction_S), gt_S, mask_S, 0.5) # S_loss = lambda_CG * self.ScaleInvarianceFramework(prediction_S, torch.log(gt_S), mask_S, 0.5) reconstr_loss = lambda_CG * self.w_reconstr * self.SUNCGReconstLoss(torch.exp(prediction_R), torch.exp(prediction_S), mask_img, targets) # print("R_loss ", R_loss.data[0]) # print("S_loss ", S_loss.data[0]) # print("reconstr_loss ", reconstr_loss.data[0]) total_loss = R_loss + S_loss + reconstr_loss elif data_set_name == "CGIntrinsics": # ============================================================================================== This is scale invariance loss =============== print("CGIntrinsics LOSS") mask = Variable(targets['mask'].cuda(), requires_grad = False) mask_R = mask[:,0,:,:].unsqueeze(1).repeat(1,prediction_R.size(1),1,1) mask_S = mask[:,0,:,:].unsqueeze(1).repeat(1,prediction_S.size(1),1,1) mask_img = mask[:,0,:,:].unsqueeze(1).repeat(1,input_images.size(1),1,1) gt_R = Variable(targets['gt_R'].cuda(), requires_grad = False) gt_S = Variable(targets['gt_S'].cuda(), requires_grad = False) R_loss = lambda_CG *self.LinearScaleInvarianceFramework(torch.exp(prediction_R), gt_R, mask_R, 0.5) # using ScaleInvarianceFramework might achieve better performance if we train on both IIW and SAW, # but LinearScaleInvarianceFramework could produce better perforamnce if trained on CGIntrinsics only S_loss = lambda_CG * self.LinearScaleInvarianceFramework(torch.exp(prediction_S), gt_S, mask_S, 0.5) # S_loss = lambda_CG * self.ScaleInvarianceFramework(prediction_S, torch.log(gt_S), mask_S, 0.5) reconstr_loss = lambda_CG * self.w_reconstr * self.SUNCGReconstLoss(torch.exp(prediction_R), torch.exp(prediction_S), mask_img, targets) # Why put this? Because some ground truth shadings are nosiy Ss_loss = lambda_CG * self.w_ss_dense * self.BilateralRefSmoothnessLoss(prediction_S, targets, 'S', 2) total_iiw_loss = 0 for i in range(0, prediction_R.size(0)): judgements_eq = targets["eq_mat"][i] judgements_ineq = targets["ineq_mat"][i] random_filp = targets["random_filp"][i] total_iiw_loss += lambda_CG * self.SUNCGBatchRankingLoss(prediction_R[i,:,:,:], judgements_eq, judgements_ineq) total_iiw_loss = total_iiw_loss/prediction_R.size(0) # print("R_loss ", R_loss.data[0]) # print("S_loss ", S_loss.data[0]) # print("reconstr_loss ", reconstr_loss.data[0]) # print("Ss_loss ", Ss_loss.data[0]) # print("SUNCGBatchRankingLoss ", total_iiw_loss.data[0]) total_loss = R_loss + S_loss + reconstr_loss + Ss_loss + total_iiw_loss elif data_set_name == "SAW": print("SAW Loss") prediction_R_1 = prediction_R[:,:,::2,::2] prediction_R_2 = prediction_R_1[:,:,::2,::2] prediction_R_3 = prediction_R_2[:,:,::2,::2] rs_loss = self.w_rs_local * self.LocalAlebdoSmoothenessLoss(prediction_R, targets,0) rs_loss = rs_loss + 0.5 * self.w_rs_local * self.LocalAlebdoSmoothenessLoss(prediction_R_1, targets,1) rs_loss = rs_loss + 0.3333 * self.w_rs_local * self.LocalAlebdoSmoothenessLoss(prediction_R_2, targets,2) rs_loss = rs_loss + 0.25 * self.w_rs_local * self.LocalAlebdoSmoothenessLoss(prediction_R_3, targets,3) reconstr_loss = self.w_reconstr_real * self.IIWReconstLoss(torch.exp(prediction_R), \ torch.exp(prediction_S), targets) ss_loss = self.w_ss_dense * self.BilateralRefSmoothnessLoss(prediction_S, targets, 'S', 2) SAW_loss = self.w_SAW * self.SAWLoss(prediction_S, targets) # print("rs_loss ", rs_loss.data[0]) # print("SAW_loss ", SAW_loss.data[0]) # print("reconstr_loss ", reconstr_loss.data[0]) # print("ss_loss ", ss_loss.data[0]) total_loss = rs_loss + SAW_loss + reconstr_loss + ss_loss else: print("NORMAL Loss") sys.exit() self.total_loss = total_loss # return total_loss.data[0] return total_loss.data def compute_whdr(self, reflectance, judgements, delta=0.1): points = judgements['intrinsic_points'] comparisons = judgements['intrinsic_comparisons'] id_to_points = {p['id']: p for p in points} rows, cols = reflectance.shape[0:2] error_sum = 0.0 error_equal_sum = 0.0 error_inequal_sum = 0.0 weight_sum = 0.0 weight_equal_sum = 0.0 weight_inequal_sum = 0.0 for c in comparisons: # "darker" is "J_i" in our paper darker = c['darker'] if darker not in ('1', '2', 'E'): continue # "darker_score" is "w_i" in our paper weight = c['darker_score'] if weight <= 0.0 or weight is None: continue point1 = id_to_points[c['point1']] point2 = id_to_points[c['point2']] if not point1['opaque'] or not point2['opaque']: continue # convert to grayscale and threshold l1 = max(1e-10, np.mean(reflectance[ int(point1['y'] * rows), int(point1['x'] * cols), ...])) l2 = max(1e-10, np.mean(reflectance[ int(point2['y'] * rows), int(point2['x'] * cols), ...])) # # convert algorithm value to the same units as human judgements if l2 / l1 > 1.0 + delta: alg_darker = '1' elif l1 / l2 > 1.0 + delta: alg_darker = '2' else: alg_darker = 'E' if darker == 'E': if darker != alg_darker: error_equal_sum += weight weight_equal_sum += weight else: if darker != alg_darker: error_inequal_sum += weight weight_inequal_sum += weight if darker != alg_darker: error_sum += weight weight_sum += weight if weight_sum: return (error_sum / weight_sum), error_equal_sum/( weight_equal_sum + 1e-10), error_inequal_sum/(weight_inequal_sum + 1e-10) else: return None def evaluate_WHDR(self, prediction_R, targets): # num_images = prediction_S.size(0) # must be even number total_whdr = float(0) total_whdr_eq = float(0) total_whdr_ineq = float(0) count = float(0) for i in range(0, prediction_R.size(0)): prediction_R_np = prediction_R.data[i,:,:,:].cpu().numpy() prediction_R_np = np.transpose(np.exp(prediction_R_np * 0.4545), (1,2,0)) # o_h = targets['oringinal_shape'][0].numpy() # o_w = targets['oringinal_shape'][1].numpy() # prediction_R_srgb_np = prediction_R_srgb.data[i,:,:,:].cpu().numpy() # prediction_R_srgb_np = np.transpose(prediction_R_srgb_np, (1,2,0)) o_h = targets['oringinal_shape'][0].numpy() o_w = targets['oringinal_shape'][1].numpy() # resize to original resolution prediction_R_np = resize(prediction_R_np, (o_h[i],o_w[i]), order=1, preserve_range=True) # print(targets["judgements_path"][i]) # load Json judgement judgements = json.load(open(targets["judgements_path"][i])) whdr, whdr_eq, whdr_ineq = self.compute_whdr(prediction_R_np, judgements, 0.1) total_whdr += whdr total_whdr_eq += whdr_eq total_whdr_ineq += whdr_ineq count += 1. return total_whdr, total_whdr_eq, total_whdr_ineq, count def evaluate_RC_loss(self, prediction_n, targets): normal_norm = torch.sqrt( torch.sum(torch.pow(prediction_n , 2) , 1) ) normal_norm = normal_norm.unsqueeze(1).repeat(1,3,1,1) prediction_n = torch.div(prediction_n , normal_norm) # mask_0 = Variable(targets['mask'].cuda(), requires_grad = False) # n_gt_0 = Variable(targets['normal'].cuda(), requires_grad = False) total_loss = self.AngleLoss(prediction_n, targets) # return total_loss.data[0] return total_loss.data def evaluate_L0_loss(self, prediction_R, targets): # num_images = prediction_S.size(0) # must be even number total_whdr = float(0) count = float(0) for i in range(0, 1): prediction_R_np = prediction_R # prediction_R_np = prediction_R.data[i,:,:,:].cpu().numpy() # prediction_R_np = np.transpose(prediction_R_np, (1,2,0)) # load Json judgement judgements = json.load(open(targets["judgements_path"][i])) whdr = self.compute_whdr(prediction_R_np, judgements, 0.1) total_whdr += whdr count += 1 return total_whdr, count def get_loss_var(self): return self.total_loss # Defines the Unet generator. # |num_downs|: number of downsamplings in UNet. For example, # if |num_downs| == 7, image of size 128x128 will become of size 1x1 # at the bottleneck class UnetGenerator(nn.Module): def __init__(self, input_nc, output_nc, num_downs, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False, gpu_ids=[]): super(UnetGenerator, self).__init__() self.gpu_ids = gpu_ids # currently support only input_nc == output_nc # assert(input_nc == output_nc) # construct unet structure unet_block = UnetSkipConnectionBlock(ngf * 8, ngf * 8, innermost=True) for i in range(num_downs - 5): unet_block = UnetSkipConnectionBlock(ngf * 8, ngf * 8, unet_block, norm_layer=norm_layer, use_dropout=use_dropout) unet_block = UnetSkipConnectionBlock(ngf * 4, ngf * 8, unet_block, norm_layer=norm_layer) unet_block = UnetSkipConnectionBlock(ngf * 2, ngf * 4, unet_block, norm_layer=norm_layer) unet_block = UnetSkipConnectionBlock(ngf, ngf * 2, unet_block, norm_layer=norm_layer) unet_block = UnetSkipConnectionBlock(output_nc, ngf, unet_block, outermost=True, norm_layer=norm_layer) self.model = unet_block def forward(self, input): if self.gpu_ids and isinstance(input.data, torch.cuda.FloatTensor): return nn.parallel.data_parallel(self.model, input, self.gpu_ids) else: return self.model(input) # Defines the submodule with skip connection. # X -------------------identity---------------------- X # |-- downsampling -- |submodule| -- upsampling --| class UnetSkipConnectionBlock(nn.Module): def __init__(self, outer_nc, inner_nc, submodule=None, outermost=False, innermost=False, norm_layer=nn.BatchNorm2d, use_dropout=False): super(UnetSkipConnectionBlock, self).__init__() self.outermost = outermost downconv = nn.Conv2d(outer_nc, inner_nc, kernel_size=4, stride=2, padding=1) downrelu = nn.LeakyReLU(0.2, False) downnorm = norm_layer(inner_nc, affine=True) uprelu = nn.ReLU(False) upnorm = norm_layer(outer_nc, affine=True) if outermost: n_output_dim = 3 uprelu1 = nn.ReLU(False) uprelu2 = nn.ReLU(False) upconv_1 = nn.ConvTranspose2d(inner_nc * 2, inner_nc, kernel_size=4, stride=2, padding=1) upconv_2 = nn.ConvTranspose2d(inner_nc * 2, inner_nc, kernel_size=4, stride=2, padding=1) conv_1 = nn.Conv2d(inner_nc, inner_nc, kernel_size=3, stride=1, padding=1) conv_2 = nn.Conv2d(inner_nc, inner_nc, kernel_size=3, stride=1, padding=1) # conv_1_o = nn.Conv2d(inner_nc, 1, kernel_size=3, # stride=1, padding=1) conv_2_o = nn.Conv2d(inner_nc, n_output_dim, kernel_size=3, stride=1, padding=1) upnorm_1 = norm_layer(inner_nc, affine=True) upnorm_2 = norm_layer(inner_nc, affine=True) # uprelu2_o = nn.ReLU(False) down = [downconv] up_1 = [uprelu1, upconv_1, upnorm_1, nn.ReLU(False), conv_1, nn.ReLU(False), conv_1_o] up_2 = [uprelu2, upconv_2, upnorm_2, nn.ReLU(False), conv_2, nn.ReLU(False), conv_2_o] self.downconv_model = nn.Sequential(*down) self.upconv_model_1 = nn.Sequential(*up_1) self.upconv_model_2 = nn.Sequential(*up_2) self.submodule = submodule elif innermost: upconv = nn.ConvTranspose2d(inner_nc, outer_nc, kernel_size=4, stride=2, padding=1) down = [downrelu, downconv] up = [uprelu, upconv, upnorm] model = down + up self.model = nn.Sequential(*model) else: upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc, kernel_size=4, stride=2, padding=1) down = [downrelu, downconv, downnorm] up = [uprelu, upconv, upnorm] if use_dropout: model = down + [submodule] + up + [nn.Dropout(0.5)] else: model = down + [submodule] + up self.model = nn.Sequential(*model) # self.model = nn.Sequential(*model) def forward(self, x): if self.outermost: # return self.model(x) down_x = self.downconv_model(x) y = self.submodule.forward(down_x) y_1 = self.upconv_model_1(y) y_2 = self.upconv_model_2(y) return y_1, y_2 else: return torch.cat([self.model(x), x], 1) class SingleUnetGenerator_S(nn.Module): def __init__(self, input_nc, output_nc, num_downs, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False, gpu_ids=[]): super(SingleUnetGenerator_S, self).__init__() self.gpu_ids = gpu_ids # currently support only input_nc == output_nc # assert(input_nc == output_nc) # construct unet structure unet_block = SingleUnetSkipConnectionBlock_S(ngf * 8, ngf * 8, innermost=True) for i in range(num_downs - 5): unet_block = SingleUnetSkipConnectionBlock_S(ngf * 8, ngf * 8, unet_block, norm_layer=norm_layer, use_dropout=use_dropout) unet_block = SingleUnetSkipConnectionBlock_S(ngf * 4, ngf * 8, unet_block, norm_layer=norm_layer) unet_block = SingleUnetSkipConnectionBlock_S(ngf * 2, ngf * 4, unet_block, norm_layer=norm_layer) unet_block = SingleUnetSkipConnectionBlock_S(ngf, ngf * 2, unet_block, norm_layer=norm_layer) unet_block = SingleUnetSkipConnectionBlock_S(output_nc, ngf, unet_block, outermost=True, norm_layer=norm_layer) self.model = unet_block def forward(self, input): if self.gpu_ids and isinstance(input.data, torch.cuda.FloatTensor): return nn.parallel.data_parallel(self.model, input, self.gpu_ids) else: return self.model(input) class SingleUnetSkipConnectionBlock_S(nn.Module): def __init__(self, outer_nc, inner_nc, submodule=None, outermost=False, innermost=False, norm_layer=nn.BatchNorm2d, use_dropout=False): super(SingleUnetSkipConnectionBlock_S, self).__init__() self.outermost = outermost self.innermost = innermost downconv = nn.Conv2d(outer_nc, inner_nc, kernel_size=4, stride=2, padding=1) downrelu = nn.LeakyReLU(0.2, False) downnorm = norm_layer(inner_nc, affine=True) uprelu = nn.ReLU(False) upnorm = norm_layer(outer_nc, affine=True) if outermost: upconv = nn.ConvTranspose2d(inner_nc * 2, 1, kernel_size=4, stride=2, padding=1) down = [downconv] up = [uprelu, upconv] model = down + [submodule] self.model = nn.Sequential(*model) self.up_model = nn.Sequential(*up) elif innermost: upconv = nn.ConvTranspose2d(inner_nc, outer_nc, kernel_size=4, stride=2, padding=1) down = [downrelu, downconv] up = [uprelu, upconv, upnorm] # model = down + up int_conv = [nn.AdaptiveAvgPool2d((2,2)), nn.Conv2d(inner_nc, inner_nc/2, kernel_size=3, stride=2, padding=1), nn.ReLU(False)] fc = [nn.Linear(256, 3)] self.int_conv = nn.Sequential(* int_conv) self.fc = nn.Sequential(* fc) self.down_model = nn.Sequential(*down) self.up_model = nn.Sequential(*up) else: upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc, kernel_size=4, stride=2, padding=1) down = [downrelu, downconv, downnorm] up = [uprelu, upconv, upnorm] if use_dropout: model = down + [submodule] #+ up + [nn.Dropout(0.5)] else: model = down + [submodule] # + up if use_dropout: upconv_model = up + [nn.Dropout(0.5)] else: upconv_model = up self.model = nn.Sequential(*model) self.up_model = nn.Sequential(*upconv_model) def forward(self, x): if self.outermost: y_1, color_s = self.model(x) y_1 = self.up_model(y_1) return y_1, color_s elif self.innermost: y_1 = self.down_model(x) color_s = self.int_conv(y_1) color_s = color_s.view(color_s.size(0), -1) color_s = self.fc(color_s) y_1 = self.up_model(y_1) y_1 = torch.cat([y_1, x], 1) return y_1, color_s else: y_1, color_s = self.model(x) y_1 = self.up_model(y_1) return torch.cat([y_1, x], 1), color_s class SingleUnetGenerator_R(nn.Module): def __init__(self, input_nc, output_nc, num_downs, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False, gpu_ids=[]): super(SingleUnetGenerator_R, self).__init__() self.gpu_ids = gpu_ids # currently support only input_nc == output_nc # assert(input_nc == output_nc) # construct unet structure unet_block = SingleUnetSkipConnectionBlock_R(ngf * 8, ngf * 8, innermost=True) for i in range(num_downs - 5): unet_block = SingleUnetSkipConnectionBlock_R(ngf * 8, ngf * 8, unet_block, norm_layer=norm_layer, use_dropout=use_dropout) unet_block = SingleUnetSkipConnectionBlock_R(ngf * 4, ngf * 8, unet_block, norm_layer=norm_layer) unet_block = SingleUnetSkipConnectionBlock_R(ngf * 2, ngf * 4, unet_block, norm_layer=norm_layer) unet_block = SingleUnetSkipConnectionBlock_R(ngf, ngf * 2, unet_block, norm_layer=norm_layer) unet_block = SingleUnetSkipConnectionBlock_R(output_nc, ngf, unet_block, outermost=True, norm_layer=norm_layer) self.model = unet_block def forward(self, input): if self.gpu_ids and isinstance(input.data, torch.cuda.FloatTensor): return nn.parallel.data_parallel(self.model, input, self.gpu_ids) else: return self.model(input) class SingleUnetSkipConnectionBlock_R(nn.Module): def __init__(self, outer_nc, inner_nc, submodule=None, outermost=False, innermost=False, norm_layer=nn.BatchNorm2d, use_dropout=False): super(SingleUnetSkipConnectionBlock_R, self).__init__() self.outermost = outermost if outermost: downconv = nn.Conv2d(3, inner_nc, kernel_size=4, stride=2, padding=1) else: downconv = nn.Conv2d(outer_nc, inner_nc, kernel_size=4, stride=2, padding=1) downrelu = nn.LeakyReLU(0.2, True) downnorm = norm_layer(inner_nc, affine=True) uprelu = nn.ReLU(True) upnorm = norm_layer(outer_nc, affine=True) num_output = outer_nc if outermost: # upconv = nn.ConvTranspose2d(inner_nc * 2, num_output, # kernel_size=4, stride=2, # padding=1) upconv = [uprelu, nn.ConvTranspose2d(inner_nc * 2, inner_nc, kernel_size=4, stride=2, padding=1), nn.ReLU(False), nn.Conv2d(inner_nc, num_output, kernel_size=1)] down = [downconv] up = upconv model = down + [submodule] + up self.model = nn.Sequential(*model) elif innermost: upconv = nn.ConvTranspose2d(inner_nc, outer_nc, kernel_size=4, stride=2, padding=1) down = [downrelu, downconv] up = [uprelu, upconv, upnorm] model = down + up self.model = nn.Sequential(*model) else: upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc, kernel_size=4, stride=2, padding=1) down = [downrelu, downconv, downnorm] up = [uprelu, upconv, upnorm] if use_dropout: model = down + [submodule] + up + [nn.Dropout(0.5)] else: model = down + [submodule] + up self.model = nn.Sequential(*model) # self.model = nn.Sequential(*model) def forward(self, x): if self.outermost: return self.model(x) else: return torch.cat([self.model(x), x], 1) class SingleUnetGenerator_L(nn.Module): def __init__(self, input_nc, output_nc, num_downs, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False, gpu_ids=[]): super(SingleUnetGenerator_L, self).__init__() self.gpu_ids = gpu_ids # currently support only input_nc == output_nc # assert(input_nc == output_nc) # construct unet structure unet_block = SingleUnetSkipConnectionBlock_L(ngf * 8, ngf * 8, innermost=True) for i in range(num_downs - 5): unet_block = SingleUnetSkipConnectionBlock_L(ngf * 8, ngf * 8, unet_block, norm_layer=norm_layer, use_dropout=use_dropout) unet_block = SingleUnetSkipConnectionBlock_L(ngf * 4, ngf * 8, unet_block, gird = True, norm_layer=norm_layer) unet_block = SingleUnetSkipConnectionBlock_L(ngf * 2, ngf * 4, unet_block, norm_layer=norm_layer) unet_block = SingleUnetSkipConnectionBlock_L(ngf, ngf * 2, unet_block, norm_layer=norm_layer) unet_block = SingleUnetSkipConnectionBlock_L(output_nc, ngf, unet_block, outermost=True, norm_layer=norm_layer) self.model = unet_block def forward(self, input): if self.gpu_ids and isinstance(input.data, torch.cuda.FloatTensor): return nn.parallel.data_parallel(self.model, input, self.gpu_ids) else: return self.model(input) class SingleUnetSkipConnectionBlock_L(nn.Module): def __init__(self, outer_nc, inner_nc, submodule=None, outermost=False, gird =False, innermost=False, norm_layer=nn.BatchNorm2d, use_dropout=False): super(SingleUnetSkipConnectionBlock_L, self).__init__() self.outermost = outermost self.gird = grid if outermost: downconv = nn.Conv2d(3, inner_nc, kernel_size=4, stride=2, padding=1) else: downconv = nn.Conv2d(outer_nc, inner_nc, kernel_size=4, stride=2, padding=1) downrelu = nn.LeakyReLU(0.2, True) downnorm = norm_layer(inner_nc, affine=True) uprelu = nn.ReLU(True) upnorm = norm_layer(outer_nc, affine=True) num_output = outer_nc if outermost: # upconv = nn.ConvTranspose2d(inner_nc * 2, num_output, # kernel_size=4, stride=2, # padding=1) upconv = [uprelu, nn.ConvTranspose2d(inner_nc * 2, inner_nc, kernel_size=4, stride=2, padding=1), nn.ReLU(False), nn.Conv2d(inner_nc, 1, kernel_size=1), nn.Sigmoid()] down = [downconv] up = upconv model = down + [submodule] + up self.model = nn.Sequential(*model) elif innermost: upconv = nn.ConvTranspose2d(inner_nc, outer_nc, kernel_size=4, stride=2, padding=1) down = [downrelu, downconv, downnorm] up = [uprelu, upconv, upnorm] model = down + up self.model = nn.Sequential(*model) else: upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc, kernel_size=4, stride=2, padding=1) down = [downrelu, downconv, downnorm] up = [uprelu, upconv, upnorm] if use_dropout: model = down + [submodule] + up + [nn.Dropout(0.5)] else: model = down + [submodule] + up if self.gird: grid_layer = [nn.ConvTranspose2d(inner_nc * 2, inner_nc, kernel_size=4, stride=2, padding=1), norm_layer(inner_nc, affine=True), nn.ReLU(False), nn.Conv2d(inner_nc, inner_nc/4, kernel_size=3, padding=1), nn.ReLU(False), nn.Conv2d(inner_nc/4, num_output, kernel_size=1)] self.grid_layer = nn.Sequential(*grid_layer) self.model = nn.Sequential(*model) # self.model = nn.Sequential(*model) def forward(self, x): if self.outermost: y = self.model(x) return y, self.grid_y else: y = self.model(x) if self.grid: upsample_layer = nn.Upsample(scale_factor= 8, mode='bilinear') self.grid_y = upsample_layer(self.grid_layer(y)) return torch.cat([y, x], 1) class MultiUnetGenerator(nn.Module): def __init__(self, input_nc, output_nc, num_downs, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False, gpu_ids=[]): super(MultiUnetGenerator, self).__init__() self.gpu_ids = gpu_ids # currently support only input_nc == output_nc # assert(input_nc == output_nc) # construct unet structure unet_block = MultiUnetSkipConnectionBlock(ngf * 8, ngf * 8, innermost=True) for i in range(num_downs - 5): unet_block = MultiUnetSkipConnectionBlock(ngf * 8, ngf * 8, unet_block, norm_layer=norm_layer, use_dropout=use_dropout) unet_block = MultiUnetSkipConnectionBlock(ngf * 4, ngf * 8, unet_block, norm_layer=norm_layer) unet_block = MultiUnetSkipConnectionBlock(ngf * 2, ngf * 4, unet_block, norm_layer=norm_layer) unet_block = MultiUnetSkipConnectionBlock(ngf, ngf * 2, unet_block, norm_layer=norm_layer) unet_block = MultiUnetSkipConnectionBlock(output_nc, ngf, unet_block, outermost=True, norm_layer=norm_layer) self.model = unet_block def forward(self, input): if self.gpu_ids and isinstance(input.data, torch.cuda.FloatTensor): return nn.parallel.data_parallel(self.model, input, self.gpu_ids) else: return self.model(input) # self.model(input) # Defines the submodule with skip connection. # X -------------------identity---------------------- X # |-- downsampling -- |submodule| -- upsampling --| class MultiUnetSkipConnectionBlock(nn.Module): def __init__(self, outer_nc, inner_nc, submodule=None, outermost=False, innermost=False, norm_layer=nn.BatchNorm2d, use_dropout=False): super(MultiUnetSkipConnectionBlock, self).__init__() self.outermost = outermost self.innermost = innermost # print("we are in mutilUnet") downconv = nn.Conv2d(outer_nc, inner_nc, kernel_size=4, stride=2, padding=1) downrelu = nn.LeakyReLU(0.2, False) downnorm = norm_layer(inner_nc, affine=True) uprelu = nn.ReLU(False) upnorm = norm_layer(outer_nc, affine=True) if outermost: n_output_dim = 3 # upconv = nn.ConvTranspose2d(inner_nc * 2, n_output_dim, # kernel_size=4, stride=2, # padding=1) # downconv = nn.Conv2d(outer_nc, inner_nc, kernel_size=4, # stride=2, padding=1) # conv1 = nn.Conv2d(inner_nc, 1, kernel_size=5, # stride=1, padding=2) # conv2 = nn.Conv2d(inner_nc, 3, kernel_size=5, # stride=1, padding=2) down = [downconv] # upconv_model_1 = [nn.ReLU(False), nn.ConvTranspose2d(inner_nc * 2, 1, # kernel_size=4, stride=2, padding=1)] # upconv_model_2 = [nn.ReLU(False), nn.ConvTranspose2d(inner_nc * 2, 1, # kernel_size=4, stride=2, padding=1)] # upconv_model_u = [nn.ReLU(False), nn.ConvTranspose2d(inner_nc * 2, inner_nc, # kernel_size=4, stride=2, padding=1), nn.ReLU(False), # nn.Conv2d(inner_nc, 1, kernel_size=1) , nn.Sigmoid()] # self.upconv_model_u = nn.Sequential(*upconv_model_u) upconv_model_1 = [nn.ReLU(False), nn.ConvTranspose2d(inner_nc * 2, inner_nc, kernel_size=4, stride=2, padding=1), norm_layer(inner_nc, affine=True), nn.ReLU(False), nn.Conv2d(inner_nc, 1, kernel_size= 1, bias=True)] upconv_model_2 = [nn.ReLU(False), nn.ConvTranspose2d(inner_nc * 2, inner_nc, kernel_size=4, stride=2, padding=1) , norm_layer(inner_nc, affine=True), nn.ReLU(False), nn.Conv2d(inner_nc, 1, kernel_size= 1, bias=True)] # model = down + [submodule] + up # upconv_model_1 = up_1 # upconv_model_2 = up_2 elif innermost: # upconv = nn.ConvTranspose2d(inner_nc, outer_nc, # kernel_size=4, stride=2, # padding=1) down = [downrelu, downconv] upconv_model_1 = [nn.ReLU(False), nn.ConvTranspose2d(inner_nc, outer_nc, kernel_size=4, stride=2, padding=1), norm_layer(outer_nc, affine=True)] upconv_model_2 = [nn.ReLU(False), nn.ConvTranspose2d(inner_nc, outer_nc, kernel_size=4, stride=2, padding=1), norm_layer(outer_nc, affine=True)] # for rgb shading # int_conv = [nn.ReLU(False), nn.Conv2d(inner_nc, inner_nc/2, kernel_size=3, # stride=1, padding=1)] # int_conv = [nn.AdaptiveAvgPool2d((2,2)) , nn.ReLU(False), nn.Conv2d(inner_nc, inner_nc/2, kernel_size=3, stride=2, padding=1), nn.ReLU(False)] # int_conv = [nn.AdaptiveAvgPool2d((2,2)) , nn.ReLU(False), nn.Conv2d(inner_nc, inner_nc/2, kernel_size=3, stride=2, padding=1), nn.ReLU(False) \ # nn.Conv2d(inner_nc/2, inner_nc/4, kernel_size=3, stride=1, padding=1), nn.ReLU(False)] # fc = [nn.Linear(256, 3)] # self.int_conv = nn.Sequential(* int_conv) # self.fc = nn.Sequential(* fc) else: # upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc, # kernel_size=4, stride=2, # padding=1) down = [downrelu, downconv, downnorm] up_1 = [nn.ReLU(False), nn.ConvTranspose2d(inner_nc * 2, outer_nc, kernel_size=4, stride=2, padding=1), norm_layer(outer_nc, affine=True)] up_2 = [nn.ReLU(False), nn.ConvTranspose2d(inner_nc * 2, outer_nc, kernel_size=4, stride=2, padding=1), norm_layer(outer_nc, affine=True)] if use_dropout: upconv_model_1 = up_1 + [nn.Dropout(0.5)] upconv_model_2 = up_2 + [nn.Dropout(0.5)] # model = down + [submodule] + up + [nn.Dropout(0.5)] else: upconv_model_1 = up_1 upconv_model_2 = up_2 # model = down + [submodule] self.downconv_model = nn.Sequential(*down) self.submodule = submodule self.upconv_model_1 = nn.Sequential(*upconv_model_1) self.upconv_model_2 = nn.Sequential(*upconv_model_2) def forward(self, x): if self.outermost: down_x = self.downconv_model(x) y_1, y_2 = self.submodule.forward(down_x) # y_u = self.upconv_model_u(y_1) y_1 = self.upconv_model_1(y_1) y_2 = self.upconv_model_2(y_2) return y_1, y_2 # return self.model(x) elif self.innermost: down_output = self.downconv_model(x) y_1 = self.upconv_model_1(down_output) y_2 = self.upconv_model_2(down_output) y_1 = torch.cat([y_1, x], 1) y_2 = torch.cat([y_2, x], 1) return y_1, y_2 else: down_x = self.downconv_model(x) y_1, y_2 = self.submodule.forward(down_x) y_1 = self.upconv_model_1(y_1) y_2 = self.upconv_model_2(y_2) y_1 = torch.cat([y_1, x], 1) y_2 = torch.cat([y_2, x], 1) return y_1, y_2

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b60403bd375522e254e1a281973d2f31ef65e87d

1,239

py

Python

model/operationoutcome.py

beda-software/fhir-py-experements

363cfb894fa6f971b9be19340cae1b0a3a4377d8

[ "MIT" ]

null

model/operationoutcome.py

beda-software/fhir-py-experements

363cfb894fa6f971b9be19340cae1b0a3a4377d8

[ "MIT" ]

null

model/operationoutcome.py

beda-software/fhir-py-experements

363cfb894fa6f971b9be19340cae1b0a3a4377d8

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- # # Generated from FHIR 4.0.1-9346c8cc45 (http://hl7.org/fhir/StructureDefinition/OperationOutcome) on 2020-02-03. # 2020, SMART Health IT. import sys from dataclasses import dataclass, field from typing import ClassVar, Optional, List from .backboneelement import BackboneElement from .codeableconcept import CodeableConcept from .domainresource import DomainResource @dataclass class OperationOutcomeIssue(BackboneElement): """ A single issue associated with the action. An error, warning, or information message that results from a system action. """ resource_type: ClassVar[str] = "OperationOutcomeIssue" severity: str = None code: str = None details: Optional[CodeableConcept] = None diagnostics: Optional[str] = None location: Optional[List[str]] = None expression: Optional[List[str]] = None @dataclass class OperationOutcome(DomainResource): """ Information about the success/failure of an action. A collection of error, warning, or information messages that result from a system action. """ resource_type: ClassVar[str] = "OperationOutcome" issue: List[OperationOutcomeIssue] = field(default_factory=list)

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b611e06647cf25f662795557ff10a0901313ad2e

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py

Python

mrcnn/prep_notebook.py

kbardool/Mask_RCNN_2

dc0c5ef3615cff8ffea162c347aec7a7ab88188b

[ "MIT" ]

7

2018-08-07T13:56:32.000Z

2021-04-06T11:07:20.000Z

mrcnn/prep_notebook.py

kbardool/Contextual_Inference

dc0c5ef3615cff8ffea162c347aec7a7ab88188b

[ "MIT" ]

null

mrcnn/prep_notebook.py

kbardool/Contextual_Inference

dc0c5ef3615cff8ffea162c347aec7a7ab88188b

[ "MIT" ]

1

2019-02-01T06:49:58.000Z

''' prep_dev_notebook: pred_newshapes_dev: Runs against new_shapes ''' import os import sys import random import math import re import gc import time import numpy as np import cv2 import matplotlib import matplotlib.pyplot as plt import tensorflow as tf import keras import keras.backend as KB import mrcnn.model_mod as modellib import mrcnn.visualize as visualize from mrcnn.config import Config from mrcnn.dataset import Dataset from mrcnn.utils import stack_tensors, stack_tensors_3d, log from mrcnn.datagen import data_generator, load_image_gt import platform syst = platform.system() if syst == 'Windows': # Root directory of the project print(' windows ' , syst) # WINDOWS MACHINE ------------------------------------------------------------------ ROOT_DIR = "E:\\" MODEL_PATH = os.path.join(ROOT_DIR, "models") DATASET_PATH = os.path.join(ROOT_DIR, 'MLDatasets') #### MODEL_DIR = os.path.join(MODEL_PATH, "mrcnn_logs") COCO_MODEL_PATH = os.path.join(MODEL_PATH, "mask_rcnn_coco.h5") DEFAULT_LOGS_DIR = os.path.join(MODEL_PATH, "mrcnn_coco_logs") COCO_DATASET_PATH = os.path.join(DATASET_PATH,"coco2014") RESNET_MODEL_PATH = os.path.join(MODEL_PATH, "resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5") elif syst == 'Linux': print(' Linx ' , syst) # LINUX MACHINE ------------------------------------------------------------------ ROOT_DIR = os.getcwd() MODEL_PATH = os.path.expanduser('~/models') DATASET_PATH = os.path.expanduser('~/MLDatasets') #### MODEL_DIR = os.path.join(MODEL_PATH, "mrcnn_development_logs") COCO_MODEL_PATH = os.path.join(MODEL_PATH, "mask_rcnn_coco.h5") COCO_DATASET_PATH = os.path.join(DATASET_PATH,"coco2014") DEFAULT_LOGS_DIR = os.path.join(MODEL_PATH, "mrcnn_coco_logs") RESNET_MODEL_PATH = os.path.join(MODEL_PATH, "resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5") else : raise Error('unreconized system ' ) print("Tensorflow Version: {} Keras Version : {} ".format(tf.__version__,keras.__version__)) import pprint pp = pprint.PrettyPrinter(indent=2, width=100) np.set_printoptions(linewidth=100,precision=4,threshold=1000, suppress = True) ##------------------------------------------------------------------------------------ ## Old Shapes TRAINING ##------------------------------------------------------------------------------------ def prep_oldshapes_train(init_with = None, FCN_layers = False, batch_sz = 5, epoch_steps = 4, folder_name= "mrcnn_oldshape_training_logs"): import mrcnn.shapes as shapes MODEL_DIR = os.path.join(MODEL_PATH, folder_name) # Build configuration object ----------------------------------------------- config = shapes.ShapesConfig() config.BATCH_SIZE = batch_sz # Batch size is 2 (# GPUs * images/GPU). config.IMAGES_PER_GPU = batch_sz # Must match BATCH_SIZE config.STEPS_PER_EPOCH = epoch_steps config.FCN_INPUT_SHAPE = config.IMAGE_SHAPE[0:2] # Build shape dataset ----------------------------------------------- dataset_train = shapes.ShapesDataset() dataset_train.load_shapes(3000, config.IMAGE_SHAPE[0], config.IMAGE_SHAPE[1]) dataset_train.prepare() # Validation dataset dataset_val = shapes.ShapesDataset() dataset_val.load_shapes(500, config.IMAGE_SHAPE[0], config.IMAGE_SHAPE[1]) dataset_val.prepare() try : del model print('delete model is successful') gc.collect() except: pass KB.clear_session() model = modellib.MaskRCNN(mode="training", config=config, model_dir=MODEL_DIR, FCN_layers = FCN_layers) print(' COCO Model Path : ', COCO_MODEL_PATH) print(' Checkpoint folder Path: ', MODEL_DIR) print(' Model Parent Path : ', MODEL_PATH) print(' Resent Model Path : ', RESNET_MODEL_PATH) load_model(model, init_with = init_with) train_generator = data_generator(dataset_train, model.config, shuffle=True, batch_size=model.config.BATCH_SIZE, augment = False) val_generator = data_generator(dataset_val, model.config, shuffle=True, batch_size=model.config.BATCH_SIZE, augment=False) model.config.display() return [model, dataset_train, dataset_val, train_generator, val_generator, config] ##------------------------------------------------------------------------------------ ## Old Shapes TESTING ##------------------------------------------------------------------------------------ def prep_oldshapes_test(init_with = None, FCN_layers = False, batch_sz = 5, epoch_steps = 4, folder_name= "mrcnn_oldshape_test_logs"): import mrcnn.shapes as shapes MODEL_DIR = os.path.join(MODEL_PATH, folder_name) # MODEL_DIR = os.path.join(MODEL_PATH, "mrcnn_development_logs") # Build configuration object ----------------------------------------------- config = shapes.ShapesConfig() config.BATCH_SIZE = batch_sz # Batch size is 2 (# GPUs * images/GPU). config.IMAGES_PER_GPU = batch_sz # Must match BATCH_SIZE config.STEPS_PER_EPOCH = epoch_steps config.FCN_INPUT_SHAPE = config.IMAGE_SHAPE[0:2] # Build shape dataset ----------------------------------------------- dataset_test = shapes.ShapesDataset() dataset_test.load_shapes(500, config.IMAGE_SHAPE[0], config.IMAGE_SHAPE[1]) dataset_test.prepare() # Recreate the model in inference mode try : del model print('delete model is successful') gc.collect() except: pass KB.clear_session() model = modellib.MaskRCNN(mode="inference", config=config, model_dir=MODEL_DIR, FCN_layers = FCN_layers ) print(' COCO Model Path : ', COCO_MODEL_PATH) print(' Checkpoint folder Path: ', MODEL_DIR) print(' Model Parent Path : ', MODEL_PATH) print(' Resent Model Path : ', RESNET_MODEL_PATH) load_model(model, init_with = init_with) test_generator = data_generator(dataset_test, model.config, shuffle=True, batch_size=model.config.BATCH_SIZE, augment = False) model.config.display() return [model, dataset_test, test_generator, config] ##------------------------------------------------------------------------------------ ## New Shapes TESTING ##------------------------------------------------------------------------------------ def prep_newshapes_test(init_with = 'last', FCN_layers = False, batch_sz = 5, epoch_steps = 4,folder_name= "mrcnn_newshape_test_logs"): import mrcnn.new_shapes as new_shapes MODEL_DIR = os.path.join(MODEL_PATH, folder_name) # Build configuration object ----------------------------------------------- config = new_shapes.NewShapesConfig() config.BATCH_SIZE = batch_sz # Batch size is 2 (# GPUs * images/GPU). config.IMAGES_PER_GPU = batch_sz # Must match BATCH_SIZE config.STEPS_PER_EPOCH = epoch_steps config.FCN_INPUT_SHAPE = config.IMAGE_SHAPE[0:2] # Build shape dataset ----------------------------------------------- # Training dataset dataset_test = new_shapes.NewShapesDataset() dataset_test.load_shapes(3000, config.IMAGE_SHAPE[0], config.IMAGE_SHAPE[1]) dataset_test.prepare() # Recreate the model in inference mode try : del model print('delete model is successful') gc.collect() except: pass KB.clear_session() model = modellib.MaskRCNN(mode="inference", config=config, model_dir=MODEL_DIR, FCN_layers = FCN_layers ) print(' COCO Model Path : ', COCO_MODEL_PATH) print(' Checkpoint folder Path: ', MODEL_DIR) print(' Model Parent Path : ', MODEL_PATH) print(' Resent Model Path : ', RESNET_MODEL_PATH) load_model(model, init_with = init_with) test_generator = data_generator(dataset_test, model.config, shuffle=True, batch_size=model.config.BATCH_SIZE, augment = False) model.config.display() return [model, dataset_test, test_generator, config] ##------------------------------------------------------------------------------------ ## New Shapes TRAINING ##------------------------------------------------------------------------------------ def prep_newshapes_train(init_with = "last", FCN_layers= False, batch_sz =5, epoch_steps = 4, folder_name= "mrcnn_newshape_training_logs"): import mrcnn.new_shapes as new_shapes MODEL_DIR = os.path.join(MODEL_PATH, folder_name) # Build configuration object ----------------------------------------------- config = new_shapes.NewShapesConfig() config.BATCH_SIZE = batch_sz # Batch size is 2 (# GPUs * images/GPU). config.IMAGES_PER_GPU = batch_sz # Must match BATCH_SIZE config.STEPS_PER_EPOCH = epoch_steps config.FCN_INPUT_SHAPE = config.IMAGE_SHAPE[0:2] # Build shape dataset ----------------------------------------------- # Training dataset dataset_train = new_shapes.NewShapesDataset() dataset_train.load_shapes(3000, config.IMAGE_SHAPE[0], config.IMAGE_SHAPE[1]) dataset_train.prepare() # Validation dataset dataset_val = new_shapes.NewShapesDataset() dataset_val.load_shapes(500, config.IMAGE_SHAPE[0], config.IMAGE_SHAPE[1]) dataset_val.prepare() try : del model print('delete model is successful') gc.collect() except: pass KB.clear_session() model = modellib.MaskRCNN(mode="training", config=config, model_dir=MODEL_DIR,FCN_layers = FCN_layers) print('MODEL_PATH : ', MODEL_PATH) print('COCO_MODEL_PATH : ', COCO_MODEL_PATH) print('RESNET_MODEL_PATH : ', RESNET_MODEL_PATH) print('MODEL_DIR : ', MODEL_DIR) print('Last Saved Model : ', model.find_last()) load_model(model, init_with = 'last') train_generator = data_generator(dataset_train, model.config, shuffle=True, batch_size=model.config.BATCH_SIZE, augment = False) config.display() return [model, dataset_train, train_generator, config] ##------------------------------------------------------------------------------------ ## LOAD MODEL ##------------------------------------------------------------------------------------ def load_model(model, init_with = None): ''' methods to load weights 1 - load a specific file 2 - find a last checkpoint in a specific folder 3 - use init_with keyword ''' # Which weights to start with? print('-----------------------------------------------') print(' Load model with init parm: ', init_with) # print(' find last chkpt :', model.find_last()) # print(' n) print('-----------------------------------------------') ## 1- look for a specific weights file ## Load trained weights (fill in path to trained weights here) # model_path = 'E:\\Models\\mrcnn_logs\\shapes20180428T1819\\mask_rcnn_shapes_5784.h5' # print(' model_path : ', model_path ) # print("Loading weights from ", model_path) # model.load_weights(model_path, by_name=True) # print('Load weights complete') # ## 2- look for last checkpoint file in a specific folder (not working correctly) # model.config.LAST_EPOCH_RAN = 5784 # model.model_dir = 'E:\\Models\\mrcnn_logs\\shapes20180428T1819' # last_model_found = model.find_last() # print(' last model in MODEL_DIR: ', last_model_found) # # loc= model.load_weights(model.find_last()[1], by_name=True) # # print('Load weights complete :', loc) ## 3- Use init_with keyword ## Which weights to start with? # init_with = "last" # imagenet, coco, or last if init_with == "imagenet": # loc=model.load_weights(model.get_imagenet_weights(), by_name=True) loc=model.load_weights(RESNET_MODEL_PATH, by_name=True) elif init_with == "coco": # Load weights trained on MS COCO, but skip layers that # are different due to the different number of classes # See README for instructions to download the COCO weights loc=model.load_weights(COCO_MODEL_PATH, by_name=True, exclude=["mrcnn_class_logits", "mrcnn_bbox_fc", "mrcnn_bbox", "mrcnn_mask"]) elif init_with == "last": # Load the last model you trained and continue training, placing checkpouints in same folder loc= model.load_weights(model.find_last()[1], by_name=True) else: assert init_with != "", "Provide path to trained weights" print("Loading weights from ", init_with) loc = model.load_weights(init_with, by_name=True) print('Load weights complete', loc) """ ##------------------------------------------------------------------------------------ ## Old Shapes DEVELOPMENT ##------------------------------------------------------------------------------------ def prep_oldshapes_dev(init_with = None, FCN_layers = False, batch_sz = 5): import mrcnn.shapes as shapes MODEL_DIR = os.path.join(MODEL_PATH, "mrcnn_oldshape_dev_logs") config = build_config(batch_sz = batch_sz) dataset_train = shapes.ShapesDataset() dataset_train.load_shapes(150, config.IMAGE_SHAPE[0], config.IMAGE_SHAPE[1]) dataset_train.prepare() try : del model print('delete model is successful') gc.collect() except: pass KB.clear_session() model = modellib.MaskRCNN(mode="training", config=config, model_dir=MODEL_DIR, FCN_layers = FCN_layers) print(' COCO Model Path : ', COCO_MODEL_PATH) print(' Checkpoint folder Path: ', MODEL_DIR) print(' Model Parent Path : ', MODEL_PATH) print(' Resent Model Path : ', RESNET_MODEL_PATH) load_model(model, init_with = init_with) train_generator = data_generator(dataset_train, model.config, shuffle=True, batch_size=model.config.BATCH_SIZE, augment = False) model.config.display() return [model, dataset_train, train_generator, config] ##------------------------------------------------------------------------------------ ## New Shapes DEVELOPMENT ##------------------------------------------------------------------------------------ def prep_newshapes_dev(init_with = "last", FCN_layers= False, batch_sz = 5): import mrcnn.new_shapes as new_shapes MODEL_DIR = os.path.join(MODEL_PATH, "mrcnn_newshape_dev_logs") config = build_config(batch_sz = batch_sz, newshapes=True) # Build shape dataset ----------------------------------------------- # Training dataset dataset_train = new_shapes.NewShapesDataset() dataset_train.load_shapes(3000, config.IMAGE_SHAPE[0], config.IMAGE_SHAPE[1]) dataset_train.prepare() # Validation dataset dataset_val = new_shapes.NewShapesDataset() dataset_val.load_shapes(500, config.IMAGE_SHAPE[0], config.IMAGE_SHAPE[1]) dataset_val.prepare() try : del model, train_generator, val_generator, mm gc.collect() except: pass KB.clear_session() model = modellib.MaskRCNN(mode="training", config=config, model_dir=MODEL_DIR,FCN_layers = FCN_layers) print('MODEL_PATH : ', MODEL_PATH) print('COCO_MODEL_PATH : ', COCO_MODEL_PATH) print('RESNET_MODEL_PATH : ', RESNET_MODEL_PATH) print('MODEL_DIR : ', MODEL_DIR) print('Last Saved Model : ', model.find_last()) load_model(model, init_with = 'last') train_generator = data_generator(dataset_train, model.config, shuffle=True, batch_size=model.config.BATCH_SIZE, augment = False) config.display() return [model, dataset_train, train_generator, config] """

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37364d93f41797c2d94de64e1a22fc15b1eaf8b9

809

py

Python

apply/models.py

4yub1k/job-portal-django

1e6df39cd56cc4a9b0a810a0257e3f1c5b103c5d

[ "MIT" ]

null

apply/models.py

4yub1k/job-portal-django

1e6df39cd56cc4a9b0a810a0257e3f1c5b103c5d

[ "MIT" ]

null

apply/models.py

4yub1k/job-portal-django

1e6df39cd56cc4a9b0a810a0257e3f1c5b103c5d

[ "MIT" ]

null

from django.db import models from listings.models import PostJob # Create your models here. class ApplicantForm(models.Model): job=models.CharField(max_length=100) name = models.CharField(max_length=50) email = models.EmailField(max_length=200) mobile = models.CharField(max_length=15) education = models.CharField(max_length=50) exp = models.CharField(max_length=50) resume = models.FileField(upload_to='resume/%Y/%m/%d/') def __str__(self): return self.name class Review(models.Model): name = models.ForeignKey(ApplicantForm, on_delete=models.CASCADE, null=False) reviewd = models.BooleanField(default=False) ratings =models.CharField(max_length=2,default=0) remarks = models.TextField(blank=True) def __str__(self): return self.name.name

38.52381

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0.733004

108

809

5.333333

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0.25

0.21875

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0.154512

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null

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376c2ba2e639b495e4597e5d4756a44025763255

244

py

Python

modules/xia2/command_line/resolutionizer.py

jorgediazjr/dials-dev20191018

77d66c719b5746f37af51ad593e2941ed6fbba17

[ "BSD-3-Clause" ]

null

modules/xia2/command_line/resolutionizer.py

jorgediazjr/dials-dev20191018

77d66c719b5746f37af51ad593e2941ed6fbba17

[ "BSD-3-Clause" ]

null

modules/xia2/command_line/resolutionizer.py

jorgediazjr/dials-dev20191018

77d66c719b5746f37af51ad593e2941ed6fbba17

[ "BSD-3-Clause" ]

1

2020-02-04T15:39:06.000Z

# LIBTBX_PRE_DISPATCHER_INCLUDE_SH export BOOST_ADAPTBX_FPE_DEFAULT=1 from __future__ import absolute_import, division, print_function import sys if __name__ == "__main__": from dials.util.Resolutionizer import run run(sys.argv[1:])

24.4

69

0.79918

34

244

5.117647

0.794118

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0.009434

0.131148

244

9

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3771d8b8b10ada3965f1cd8fc8bc1d0e18b2817c

89

py

Python

networks/__init__.py

maplel/vehicle-counting

c30372a9695fd7b838491461ee787f72c846d0b4

[ "MIT" ]

47

2020-11-08T08:14:22.000Z

2022-03-26T15:18:04.000Z

networks/__init__.py

alikaz3mi/vehicle-counting

2319714acdc8dcb97b0b7a2c87391b94095bf6fa

[ "MIT" ]

16

2020-11-08T09:05:36.000Z

2022-03-22T04:24:37.000Z

networks/__init__.py

alikaz3mi/vehicle-counting

2319714acdc8dcb97b0b7a2c87391b94095bf6fa

[ "MIT" ]

25

2020-11-08T08:14:24.000Z

2022-03-15T07:19:31.000Z

from .yolo import get_model from .detector import Detector from .deepsort import DeepSort

29.666667

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0.842697

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89

5.692308

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0.123596

89

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29.666667

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null

0

1

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1

0

4

3775adf96a68d0edf0da11b01b76bbd7517e3cbf

68

py

Python

lambda-vpc/lambda.py

jeffbrl/terraform-examples

c25aeda07d26f0f12b6a233cab28227fa668b8fa

[ "MIT" ]

6

2019-03-11T19:07:38.000Z

2021-11-08T13:17:55.000Z

lambda-simple/lambda.py

jeffbrl/terraform-examples

c25aeda07d26f0f12b6a233cab28227fa668b8fa

[ "MIT" ]

null

lambda-simple/lambda.py

jeffbrl/terraform-examples

c25aeda07d26f0f12b6a233cab28227fa668b8fa

[ "MIT" ]

7

2019-07-28T13:25:23.000Z

2022-02-21T10:12:14.000Z

def lambda_handler(event, context): print("Hello from Lambda")

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35

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5.333333

0.888889

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0.161765

68

3

36

22.666667

0.842105

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0.253731

0

1

0.5

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0.5

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null

0

1

0

null

0

1

0

1

0

4

806a59c3e5ca274ac7ce3f0a467e29b7924a7c78

130

py

Python

sandbox/measureIntensities.py

soylentdeen/CIAO-commissioning-tools

8cb3d7412106d3b18054df2e82796000df0035bb

[ "MIT" ]

null

sandbox/measureIntensities.py

soylentdeen/CIAO-commissioning-tools

8cb3d7412106d3b18054df2e82796000df0035bb

[ "MIT" ]

null

sandbox/measureIntensities.py

soylentdeen/CIAO-commissioning-tools

8cb3d7412106d3b18054df2e82796000df0035bb

[ "MIT" ]

null

import scipy import pyfits import numpy import VLTTools ciao = VLTTools.VLTConnection(simulate=False) ciao.averageIntensities()

14.444444

45

0.830769

15

130

7.2

0.666667

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130

8

46

16.25

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0.666667

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8084a4f3321bdd6b0f54cc24dd55849061adcdd0

160

py

Python

src/foriforloop.py

Leopold2020/code_repo

24b1a932d77ba1456f8df4978f029dd841c7b177

[ "MIT" ]

null

src/foriforloop.py

Leopold2020/code_repo

24b1a932d77ba1456f8df4978f029dd841c7b177

[ "MIT" ]

null

src/foriforloop.py

Leopold2020/code_repo

24b1a932d77ba1456f8df4978f029dd841c7b177

[ "MIT" ]

null

#Oskar Svedlund #TEINF-20 #2021-09-20 #For i For loop for i in range(1,10): for j in range(1,10): print(i*j, end="\t") print()

16

28

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0.586207

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null

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4

8097a5a678a48a0f40893c973ba73f456ebd2dc0

71

py

Python

mohdAkibUddin.py

mohdAkibUddin/Week2

f8ad361a3d734dbc32d1bcc571a7fe1abf41b481

[ "MIT" ]

null

mohdAkibUddin.py

mohdAkibUddin/Week2

f8ad361a3d734dbc32d1bcc571a7fe1abf41b481

[ "MIT" ]

null

mohdAkibUddin.py

mohdAkibUddin/Week2

f8ad361a3d734dbc32d1bcc571a7fe1abf41b481

[ "MIT" ]

11

2020-09-21T18:23:21.000Z

2020-10-03T18:18:14.000Z

while (True): print("mohammed uddin made changes") print(":D")

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71

4.888889

0.888889

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0.211268

71

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41

23.666667

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0

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null

0

1

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null

0

1

0

1

0

4

809bd2a29d3c3b05833188596c8dc99b707ea9c0

397

py

Python

tests/plugins/test_stv.py

hymer-up/streamlink

f09bf6e04cddc78eceb9ded655f716ef3ee4b84f

[ "BSD-2-Clause" ]

5

2017-03-21T19:43:17.000Z

2018-10-03T14:04:29.000Z

tests/plugins/test_stv.py

hymer-up/streamlink

f09bf6e04cddc78eceb9ded655f716ef3ee4b84f

[ "BSD-2-Clause" ]

7

2016-10-13T23:29:31.000Z

2018-06-28T14:04:32.000Z

tests/plugins/test_stv.py

bumplzz69/streamlink

34abc43875d7663ebafa241573dece272e93d88b

[ "BSD-2-Clause" ]

2

2016-11-24T18:37:33.000Z

2017-03-21T19:43:49.000Z

import unittest from streamlink.plugins.stv import STV class TestPluginSTV(unittest.TestCase): def test_can_handle_url(self): self.assertTrue(STV.can_handle_url('https://player.stv.tv/live')) self.assertTrue(STV.can_handle_url('http://player.stv.tv/live')) def test_can_handle_url_negative(self): self.assertFalse(STV.can_handle_url('http://example.com/live'))

30.538462

73

0.738035

57

397

4.912281

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0.214286

0.160714

0.425

0.207143

0

0.128463

397

12

74

33.083333

0.809249

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0.25

false

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0

4

80a5b928bb6d1053f9027149c1b0bf7b0a002c9f

24

py

Python

butterflow/version.py

changhaitravis/butterflow

d9f92e31a57e800a56b98fa17786144b50d3e3bb

[ "MIT" ]

1,340

2015-01-04T01:43:39.000Z

2022-03-29T04:44:41.000Z

butterflow/version.py

pmorris2012/butterflow

08f09a0c5e70cdd24953c638b8785e6deee9022e

[ "MIT" ]

134

2015-04-05T10:04:02.000Z

2022-02-16T22:34:03.000Z

butterflow/version.py

pmorris2012/butterflow

08f09a0c5e70cdd24953c638b8785e6deee9022e

[ "MIT" ]

116

2015-04-14T17:37:33.000Z

2022-02-19T21:36:22.000Z

__version__ = '0.2.4a4'

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80a7758b26ee685bbdd108bed7010842ba5f6ca5

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py

Python

app/commands.py

Addovej/simple_catalog

3972c57796538958227a924d618df29c456389ab

[ "MIT" ]

null

app/commands.py

Addovej/simple_catalog

3972c57796538958227a924d618df29c456389ab

[ "MIT" ]

null

app/commands.py

Addovej/simple_catalog

3972c57796538958227a924d618df29c456389ab

[ "MIT" ]

null

from flask import current_app as app @app.cli.command('sync-data') def sync_data_cli(): from app.tasks import sync_data print('Sync data was launched') sync_data.apply_async()

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80d33d703d579b6c6f9d8c149124f4f729a68c98

89

py

Python

game_core/app/user/__init__.py

meseta/advent-of-code-2020

a6871b2efa99c38d5d13d872e53a8e9649f8322b

[ "MIT" ]

1

2020-12-30T11:25:17.000Z

game_core/app/user/__init__.py

meseta/advent-of-code-2020

a6871b2efa99c38d5d13d872e53a8e9649f8322b

[ "MIT" ]

13

2020-12-29T19:08:20.000Z

2021-02-01T04:27:36.000Z

game_core/app/user/__init__.py

meseta/advent-of-code-2020

a6871b2efa99c38d5d13d872e53a8e9649f8322b

[ "MIT" ]

1

2020-12-27T19:57:16.000Z

from .user import User from .models import UserData, Source from .sentinels import NoUid

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80f45a78b0c2b125069f786891a8a66588be6f0b

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py

Python

researchutils/chainer/training/__init__.py

keio-ytlab/researchutils

bb3ec467386d43a1e2282ec6d024216ce4dae841

[ "MIT" ]

1

2018-10-25T12:57:38.000Z

researchutils/chainer/training/__init__.py

yuishihara/researchutils

bb3ec467386d43a1e2282ec6d024216ce4dae841

[ "MIT" ]

28

2018-08-25T03:54:30.000Z

2018-10-14T12:09:47.000Z

researchutils/chainer/training/__init__.py

yuishihara/researchutils

bb3ec467386d43a1e2282ec6d024216ce4dae841

[ "MIT" ]

null

from researchutils.chainer.training import extensions

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0.90566

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null

0

1

0

null

0

1

0

1

0

4

03816d13c5acc34622656515ab97d00edc0dbff5

30

py

Python

hilbert/discontinu.py

kiaderouiche/hilbmetrics

c106d86f9cfad5902e0a6aaa0b3e88312910c42a

[ "Apache-2.0" ]

null

hilbert/discontinu.py

kiaderouiche/hilbmetrics

c106d86f9cfad5902e0a6aaa0b3e88312910c42a

[ "Apache-2.0" ]

1

2020-10-07T14:09:50.000Z

hilbert/discontinu.py

kiaderouiche/hilbmetrics

c106d86f9cfad5902e0a6aaa0b3e88312910c42a

[ "Apache-2.0" ]

null

""" Draw discontinu form """

6

20

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1

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null

0

1

0

null

0

1

0

4

03fea0a393e3529279b7c095f4fe579bb316d449

254

py

Python

src/pytest_alembic/plugin/__init__.py

ZipFile/pytest-alembic

f0f761e6ae2cd7764582f8c6e4c15f3623512de9

[ "MIT" ]

47

2020-04-16T20:03:04.000Z

2022-03-23T09:51:01.000Z

src/pytest_alembic/plugin/__init__.py

ZipFile/pytest-alembic

f0f761e6ae2cd7764582f8c6e4c15f3623512de9

[ "MIT" ]

30

2020-06-26T15:46:45.000Z

2022-03-12T17:29:38.000Z

src/pytest_alembic/plugin/__init__.py

ZipFile/pytest-alembic

f0f761e6ae2cd7764582f8c6e4c15f3623512de9

[ "MIT" ]

8

2021-03-04T16:44:22.000Z

2022-01-21T19:16:33.000Z

# flake8: noqa from pytest_alembic.plugin.fixtures import alembic_config, alembic_engine, alembic_runner from pytest_alembic.plugin.hooks import ( pytest_addoption, pytest_collection_modifyitems, pytest_configure, pytest_itemcollected, )

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null

0

1

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ff16761ac6ac64a735ea2b3c625e7fab066e67b4

97

py

Python

Hello-World/Python/hello_world_weiyim.py

RoyalTechie/Hacktoberfest

5a66f81972a3db15d7c48c8d163d8de4df01d0fe

[ "Apache-2.0" ]

1

2021-10-08T17:18:23.000Z

Hello-World/Python/hello_world_weiyim.py

RoyalTechie/Hacktoberfest

5a66f81972a3db15d7c48c8d163d8de4df01d0fe

[ "Apache-2.0" ]

null

Hello-World/Python/hello_world_weiyim.py

RoyalTechie/Hacktoberfest

5a66f81972a3db15d7c48c8d163d8de4df01d0fe

[ "Apache-2.0" ]

null

''' LANGUAGE: Python AUTHOR: Weiyi GITHUB: https://github.com/weiyi-m ''' print("Hello World!")

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ff1b51db173b135f17b515974a4212da0d589ff5

673

py

Python

music/migrations/0003_auto_20170326_1725.py

wilk16/music

1fce992fa3d3b0f00202c4e5db7cdd3129794325

[ "MIT" ]

1

2017-04-27T19:47:52.000Z

music/migrations/0003_auto_20170326_1725.py

wilk16/music

1fce992fa3d3b0f00202c4e5db7cdd3129794325

[ "MIT" ]

null

music/migrations/0003_auto_20170326_1725.py

wilk16/music

1fce992fa3d3b0f00202c4e5db7cdd3129794325

[ "MIT" ]

null

# -*- coding: utf-8 -*- # Generated by Django 1.10.6 on 2017-03-26 17:25 from __future__ import unicode_literals from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('music', '0002_auto_20170325_1658'), ] operations = [ migrations.AlterModelOptions( name='ownedrecord', options={'ordering': ['-purchase_date']}, ), migrations.AlterModelOptions( name='record', options={'ordering': ['-release_date']}, ), migrations.AlterModelOptions( name='track', options={'ordering': ['number']}, ), ]

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207e722020a615f43ed5982780d18acdc6149839

687

py

Python

httprider/core/util_functions.py

iSWORD/http-rider

5d9e5cc8c5166ab58f81d30d21b3ce2497bf09b9

[ "MIT" ]

27

2019-12-20T00:10:28.000Z

2022-03-09T18:04:23.000Z

httprider/core/util_functions.py

iSWORD/http-rider

5d9e5cc8c5166ab58f81d30d21b3ce2497bf09b9

[ "MIT" ]

6

2019-10-13T08:50:21.000Z

2020-06-05T12:23:08.000Z

httprider/core/util_functions.py

iSWORD/http-rider

5d9e5cc8c5166ab58f81d30d21b3ce2497bf09b9

[ "MIT" ]

7

2019-08-10T01:38:31.000Z

2021-08-23T05:28:46.000Z

import base64 from httprider.core.constants import UTF_8_ENCODING def str_to_base64e(arg, url_safe=False): if not arg: return "" if url_safe: return base64.urlsafe_b64encode(bytes(arg, UTF_8_ENCODING)).decode( UTF_8_ENCODING ) else: return base64.b64encode(bytes(arg, UTF_8_ENCODING)).decode(UTF_8_ENCODING) def str_to_base64d(arg, url_safe=False): if not arg: return "" if url_safe: return base64.urlsafe_b64decode(arg).decode(UTF_8_ENCODING) else: return base64.b64decode(arg).decode(UTF_8_ENCODING) utility_func_map = {"base64Encode": str_to_base64e, "base64Decode": str_to_base64d}

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20cf61fa63479f73f04541a938e1a69f0899fa45

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py

Python

async_pluct/__init__.py

lucasts/async-pluct

466e60ed6ed418cd5aa28cae457af61ef6984325

[ "MIT" ]

4

2017-11-08T19:43:05.000Z

2017-11-10T15:03:46.000Z

async_pluct/__init__.py

lucasts/async-pluct

466e60ed6ed418cd5aa28cae457af61ef6984325

[ "MIT" ]

1

2021-06-01T21:20:10.000Z

async_pluct/__init__.py

lucasts/async-pluct

466e60ed6ed418cd5aa28cae457af61ef6984325

[ "MIT" ]

1

2018-05-25T18:02:10.000Z

# Used to mock validate method on tests try: from async_pluct import resource resources = resource except ImportError: pass __version__ = '0.3.4'

17.666667

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null

0

1

0

null

0

1

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4

4549a01687a779349f7bebd7bb8082ab53880b54

85

py

Python

fabric_colors/main.py

fabric-colors/fabric-colors

de43a4e78b87b6fbfecacca1525178dea1827680

[ "BSD-2-Clause" ]

3

2015-01-14T06:45:44.000Z

2016-11-15T13:37:16.000Z

fabric_colors/main.py

fabric-colors/fabric-colors

de43a4e78b87b6fbfecacca1525178dea1827680

[ "BSD-2-Clause" ]

null

fabric_colors/main.py

fabric-colors/fabric-colors

de43a4e78b87b6fbfecacca1525178dea1827680

[ "BSD-2-Clause" ]

null

def main(): """ Main command-line execution loop. """ print "hello!"

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0

1

0

null

0

1

0

1

0

4

45666129dd2cfce487e7cf0c08300367cf69e77e

226

py

Python

service_data/store_data/admin.py

Djalyarim/Test_task

c4f66b8ef50fcde679c4dff62ddee162064f26e0

[ "MIT" ]

1

2022-02-01T08:29:39.000Z

service_data/store_data/admin.py

Djalyarim/Test_task

c4f66b8ef50fcde679c4dff62ddee162064f26e0

[ "MIT" ]

null

service_data/store_data/admin.py

Djalyarim/Test_task

c4f66b8ef50fcde679c4dff62ddee162064f26e0

[ "MIT" ]

null

from django.contrib import admin from .models import UserWeight @admin.register(UserWeight) class UserweightAdmin(admin.ModelAdmin): list_display = ('id', 'user_id', 'day', 'weight') empty_value_display = '-пусто-'

22.6

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1

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458f340ccbc47b26ed840f1fde9b3e90c4681d97

876

py

Python

tests/util.py

cook-health/messaging

1a827b97d9af6e56d55c362b29dd79a6cb373f88

[ "MIT" ]

null

tests/util.py

cook-health/messaging

1a827b97d9af6e56d55c362b29dd79a6cb373f88

[ "MIT" ]

2

2018-03-14T10:42:37.000Z

2018-03-14T11:01:31.000Z

tests/util.py

Seliniux777/Nexmo-nexmo-python

d1d60e8068b1cb23f12507a6ec1cd500285890b5

[ "MIT" ]

1

2020-10-18T09:41:15.000Z

import re import pytest try: from urllib.parse import urlparse except ImportError: from urlparse import urlparse import responses def request_body(): return responses.calls[0].request.body def request_query(): return urlparse(responses.calls[0].request.url).query def request_user_agent(): return responses.calls[0].request.headers['User-Agent'] def request_authorization(): return responses.calls[0].request.headers['Authorization'].decode('utf-8') def request_content_type(): return responses.calls[0].request.headers['Content-Type'] def stub(method, url): responses.add(method, url, body='{"key":"value"}', status=200, content_type='application/json') def assert_re(pattern, string): __tracebackhide__ = True if not re.search(pattern, string): pytest.fail("Cannot find pattern %r in %r" % (pattern, string))

21.365854

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45a8ff6d95e1c12c273882b1f0f7453ab2352319

1,643

py

Python

test/pandas/1_select.py

wull566/tensorflow_demo

c2c45050867cb056b8193eb53466d26b80b0ec13

[ "MIT" ]

2

2019-03-24T12:58:17.000Z

2021-05-18T06:21:21.000Z

test/pandas/1_select.py

wull566/tensorflow_demo

c2c45050867cb056b8193eb53466d26b80b0ec13

[ "MIT" ]

null

test/pandas/1_select.py

wull566/tensorflow_demo

c2c45050867cb056b8193eb53466d26b80b0ec13

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ pandas 学习字典形式的numpy """ from __future__ import print_function import numpy as np import pandas as pd dates = pd.date_range('20130101', periods=6) df = pd.DataFrame(np.arange(24).reshape((6,4)),index=dates, columns=['A','B','C','D']) """ A B C D 2013-01-01 0 1 2 3 2013-01-02 4 5 6 7 2013-01-03 8 9 10 11 2013-01-04 12 13 14 15 2013-01-05 16 17 18 19 2013-01-06 20 21 22 23 """ # print(df['A']) print(df.A) print(df[0:3]) """ A B C D 2013-01-01 0 1 2 3 2013-01-02 4 5 6 7 2013-01-03 8 9 10 11 """ print(df['20130102':'20130104']) """ A B C D 2013-01-02 4 5 6 7 2013-01-03 8 9 10 11 2013-01-04 12 13 14 15 """ print(df.loc['20130102']) """ A 4 B 5 C 6 D 7 Name: 2013-01-02 00:00:00, dtype: int64 """ print(df.loc[:,['A','B']]) """ A B 2013-01-01 0 1 2013-01-02 4 5 2013-01-03 8 9 2013-01-04 12 13 2013-01-05 16 17 2013-01-06 20 21 """ print(df.loc['20130102',['A','B']]) """ A 4 B 5 Name: 2013-01-02 00:00:00, dtype: int64 """ print(df.iloc[3,1]) # 13 print(df.iloc[3:5,1:3]) """ B C 2013-01-04 13 14 2013-01-05 17 18 """ print(df.iloc[[1,3,5],1:3]) """ B C 2013-01-02 5 6 2013-01-04 13 14 2013-01-06 21 22 """ # 根据混合的这两种 ix print(df.ix[:3,['A','C']]) """ A C 2013-01-01 0 2 2013-01-02 4 6 2013-01-03 8 10 """ print(df[df.A>8]) """ A B C D 2013-01-04 12 13 14 15 2013-01-05 16 17 18 19 2013-01-06 20 21 22 23 """

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afd9196a7899334abc62e4ac1a6e707be3e9bbb2

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py

Python

pyipma/__init__.py

joaocps/pyipma

d10e14bed66213c328e5f409d2cd7b0fddffa6e4

[ "MIT" ]

null

pyipma/__init__.py

joaocps/pyipma

d10e14bed66213c328e5f409d2cd7b0fddffa6e4

[ "MIT" ]

null

pyipma/__init__.py

joaocps/pyipma

d10e14bed66213c328e5f409d2cd7b0fddffa6e4

[ "MIT" ]

null

from .consts import * __version__ = '2.0.5'

11.25

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afe0fb0fe777cecce7f8b46ab7a630caecdcd1dd

135

py

Python

wmc/__main__.py

axju/wmc

caa54c3bfe809104c8c65972d116388dcfb066f5

[ "MIT" ]

1

2019-09-16T22:24:23.000Z

wmc/__main__.py

axju/wmc

caa54c3bfe809104c8c65972d116388dcfb066f5

[ "MIT" ]

null

wmc/__main__.py

axju/wmc

caa54c3bfe809104c8c65972d116388dcfb066f5

[ "MIT" ]

null

"""Only the entry point for the python command""" from wmc.cli import main if __name__ == '__main__': # pragma: no cover main()

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afe39d9a773ee94edb0c80681cedc259ff4d532a

735

py

Python

dynamicserialize/dstypes/com/raytheon/uf/common/localization/stream/LocalizationStreamGetRequest.py

mjames-upc/python-awips

e2b05f5587b02761df3b6dd5c6ee1f196bd5f11c

[ "BSD-3-Clause" ]

null

dynamicserialize/dstypes/com/raytheon/uf/common/localization/stream/LocalizationStreamGetRequest.py

mjames-upc/python-awips

e2b05f5587b02761df3b6dd5c6ee1f196bd5f11c

[ "BSD-3-Clause" ]

null

dynamicserialize/dstypes/com/raytheon/uf/common/localization/stream/LocalizationStreamGetRequest.py

mjames-upc/python-awips

e2b05f5587b02761df3b6dd5c6ee1f196bd5f11c

[ "BSD-3-Clause" ]

null

## ## # File auto-generated against equivalent DynamicSerialize Java class import os from dynamicserialize.dstypes.com.raytheon.uf.common.localization.stream import AbstractLocalizationStreamRequest from dynamicserialize.dstypes.com.raytheon.uf.common.auth.user import User class LocalizationStreamGetRequest(AbstractLocalizationStreamRequest): def __init__(self): super(LocalizationStreamGetRequest, self).__init__() self.offset = None self.numBytes = None def getOffset(self): return self.offset def setOffset(self, offset): self.offset = offset def getNumBytes(self): return self.numBytes def setNumBytes(self, numBytes): self.numBytes = numBytes

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null

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4

b32fba3b2a1b186843d40ad148d4d4fdef0edbb2

189

py

Python

Chap03DataStructures/2-2-3.分数.py

royqh1979/programming_with_python

7e1e8f88381151b803b6ae6ebda9809d9cc6664a

[ "MIT" ]

5

2019-03-06T12:28:47.000Z

2022-01-06T14:06:02.000Z

Chap03DataStructures/2-2-3.分数.py

royqh1979/programming_with_python

7e1e8f88381151b803b6ae6ebda9809d9cc6664a

[ "MIT" ]

6

2021-02-02T22:40:49.000Z

2022-03-12T00:27:54.000Z

Chap03DataStructures/2-2-3.分数.py

royqh1979/programming_with_python

7e1e8f88381151b803b6ae6ebda9809d9cc6664a

[ "MIT" ]

4

2019-03-06T14:29:25.000Z

2020-06-02T15:16:40.000Z

from fractions import Fraction f1=Fraction(1,2) f2=f1 print(f1,f2) print(f1 is f2) print(f1.numerator ,f1.denominator) f1.numerator = 3 f3=Fraction(1,3) f1+=f3 print(f1,f2) print(f1 is f2)

15.75

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189

12

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0

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0

null

1

0

null

0

1

0

4

b33449ad57f994a9b1bcc9f11a29cde1d8020790

577

py

Python

loader.py

LTS4/TIGraNet

22ba11b665e8445f1f759c0d13414429d9a03265

[ "MIT" ]

8

2018-08-21T20:58:05.000Z

2020-05-15T03:42:06.000Z

loader.py

LTS4/TIGraNet

22ba11b665e8445f1f759c0d13414429d9a03265

[ "MIT" ]

1

2020-12-24T05:12:14.000Z

2021-03-23T15:04:46.000Z

loader.py

LTS4/TIGraNet

22ba11b665e8445f1f759c0d13414429d9a03265

[ "MIT" ]

3

2018-10-14T14:54:07.000Z

2021-02-28T21:59:22.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- """ Data loader for the PyTorch framework. """ from tqdm import tqdm import os, re import torch import torch.utils.data as data from utils import select class MNIST_bis(data.Dataset): def __init__(self, dataset, size, digits_to_keep, stratified_sampling=True): self.dataset=dataset self.indices=select(dataset, size, digits_to_keep, stratified_sampling) def __len__(self): return len(self.indices) def __getitem__(self, idx): return self.dataset[self.indices[idx]]

23.08

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577

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null

0

null

0

1

0

1

0

4

b34b3b5f61a158f01b335653ba2d517f0b3bc95c

1,015

py

Python

gcompiler/python/delta_infer/subgraphs/__init__.py

didichuxing/delta

31dfebc8f20b7cb282b62f291ff25a87e403cc86

[ "Apache-2.0" ]

1,442

2019-07-09T07:34:28.000Z

2020-11-15T09:52:09.000Z

gcompiler/python/delta_infer/subgraphs/__init__.py

didichuxing/delta

31dfebc8f20b7cb282b62f291ff25a87e403cc86

[ "Apache-2.0" ]

93

2019-07-22T09:20:20.000Z

2020-11-13T01:59:30.000Z

gcompiler/python/delta_infer/subgraphs/__init__.py

didichuxing/delta

31dfebc8f20b7cb282b62f291ff25a87e403cc86

[ "Apache-2.0" ]

296

2019-07-09T07:35:28.000Z

2020-11-16T02:27:51.000Z

from .transformer import * from .common import * #tf.compat.v1.disable_eager_execution() # #batch_size = 40 #seq_length = 200 #hidden_size = 768 #num_attention_heads =12 #size_per_head = int(hidden_size / num_attention_heads) # #layer_input = tf.compat.v1.placeholder(tf.float32, shape=(batch_size*seq_length, hidden_size)) ## Tensor of shape [batch_size, from_seq_length, to_seq_length]. #attention_mask = tf.compat.v1.placeholder(tf.float32, shape=(batch_size, seq_length, seq_length)) # #output_rnn = transformer_cell(input_tensor=layer_input,#tf.reshape(layer_input, [-1, hidden_size]), # attention_mask=attention_mask, # hidden_size=hidden_size, # num_attention_heads=num_attention_heads, # attention_head_size=size_per_head, # batch_size = batch_size, # seq_length = seq_length, # intermediate_size=1280)

42.291667

100

0.635468

121

1,015

4.933884

0.338843

0.120603

0.113903

0.090452

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0.237856

0.177554

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null

0

1

0

1

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1

0

4

2fa394fb34c74e16598d361838124eaea72619af

109

py

Python

backend/services/web/api/files/interface.py

noasck/EduARd

f4a95a92d513b017ff2f0b0c3591207a741b1110

[ "MIT" ]

3

2021-04-16T14:37:47.000Z

2021-06-28T21:13:50.000Z

backend/services/web/api/files/interface.py

noasck/EduARd

f4a95a92d513b017ff2f0b0c3591207a741b1110

[ "MIT" ]

1

2021-04-17T14:45:59.000Z

backend/services/web/api/files/interface.py

noasck/EduARd

f4a95a92d513b017ff2f0b0c3591207a741b1110

[ "MIT" ]

null

from typing_extensions import TypedDict class IFile(TypedDict, total=False): id: int filename: str

15.571429

39

0.743119

14

109

5.714286

0.928571

0

0.192661

109

6

40

18.166667

0.909091

0

1

0

true

0

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0

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1

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null

0

1

0

null

0

1

0

1

0

4

2ff558a5f19db2df344154d8a9eff0fb4b842486

299

py

Python

Dataset/Leetcode/test/53/589.py

kkcookies99/UAST

fff81885aa07901786141a71e5600a08d7cb4868

[ "MIT" ]

null

Dataset/Leetcode/test/53/589.py

kkcookies99/UAST

fff81885aa07901786141a71e5600a08d7cb4868

[ "MIT" ]

null

Dataset/Leetcode/test/53/589.py

kkcookies99/UAST

fff81885aa07901786141a71e5600a08d7cb4868

[ "MIT" ]

null

class Solution: def XXX(self, nums: List[int]) -> int: local_max_sum, global_max_sum = nums[0], nums[0] for num in nums[1:]: local_max_sum = max(num, local_max_sum + num) global_max_sum = max(global_max_sum, local_max_sum) return global_max_sum

33.222222

63

0.622074

47

299

3.617021

0.382979

0.282353

0.258824

0

0.013889

0.277592

299

8

64

37.375

0.773148

0

null

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null

1

0

1

0

null

0

1

0

4

ff25b9dab4a541d3a2c0dc1b783f7e334e3ee6df

364

py

Python

mmhoidet/core/hoi/__init__.py

noobying/mmhoidet

138e3fbf34ecbc66f98ad26b10e08a9d49a61c38

[ "Apache-2.0" ]

2

2021-09-06T13:09:42.000Z

2021-09-15T09:18:00.000Z

mmhoidet/core/hoi/__init__.py

noobying/mmhoidet

138e3fbf34ecbc66f98ad26b10e08a9d49a61c38

[ "Apache-2.0" ]

null

mmhoidet/core/hoi/__init__.py

noobying/mmhoidet

138e3fbf34ecbc66f98ad26b10e08a9d49a61c38

[ "Apache-2.0" ]

null

from .assigners import BaseAssigner, HungarianAssigner from .builder import build_sampler, build_assigner from .samplers import BaseSampler, PseudoSampler, SamplingResult from .transforms import hoi2result __all__ = [ 'BaseAssigner', 'HungarianAssigner', 'build_assigner', 'build_sampler', 'BaseSampler', 'PseudoSampler', 'SamplingResult', 'hoi2result' ]

36.4

75

0.793956

33

364

8.515152

0.484848

0.206406

0.270463

0

0.006192

0.112637

364

9

76

40.444444

0.863777

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null

1

0

1

0

null

0

1

0

4

ff409c4db87ccb558ceb6212ad3bca445cc63841

168

py

Python

Term 2/21/1-file.py

theseana/ajisa

1c92b00acd3fad7c92b8222b5f6a86fc6db4bcae

[ "MIT" ]

null

Term 2/21/1-file.py

theseana/ajisa

1c92b00acd3fad7c92b8222b5f6a86fc6db4bcae

[ "MIT" ]

null

Term 2/21/1-file.py

theseana/ajisa

1c92b00acd3fad7c92b8222b5f6a86fc6db4bcae

[ "MIT" ]

null

file = open('names.txt', 'w') file.write('amirreza\n') file.write('setayesh\n') file.write('artin\n') file.write('iliya\n') file.write('mohammadjavad\n') file.close()

18.666667

29

0.684524

27

168

4.259259

0.481481

0.391304

0.347826

0

0.065476

168

9

30

18.666667

0.732484

0

0.349112

0

1

0

false

0

1

0

null

1

0

1

0

null

0

4

ff5606d8330c5567ad9e9e09631f157af4a919bd

201

py

Python

htkupdate.py

otherbeast/hackers-tool-kit

12991889db1f6843dde82e7da4b4cdfb50740da5

[ "Apache-2.0" ]

393

2019-01-21T05:52:54.000Z

2022-03-29T06:07:04.000Z

htkupdate.py

otherbeast/hackers-tool-kit

12991889db1f6843dde82e7da4b4cdfb50740da5

[ "Apache-2.0" ]

19

2019-02-22T00:49:28.000Z

2021-12-30T20:28:59.000Z

htkupdate.py

otherbeast/hackers-tool-kit

12991889db1f6843dde82e7da4b4cdfb50740da5

[ "Apache-2.0" ]

138

2019-03-15T23:22:19.000Z

2022-03-20T17:19:09.000Z

import os print "UPDATING..." os.system("cd") os.system('cd /root/ && rm -fr hackers-tool-kit && git clone https://github.com/unkn0wnh4ckr/hackers-tool-kit && echo "[UPDATED]: Restart Your Terminal"')

50.25

155

0.701493

30

201

4.7

0.766667

0.113475

0.141844

0

0.011111

0.104478

201

4

155

50.25

0.772222

0

0.25

0.767327

0

null

0

0.25

null

0.25

0

null

0

1

0

1

0

null

0

1

0

4

ff57741d06b201b53f9b59f8f8fe670385ec9aec

198

py

Python

patreonmanager/apps.py

crydotsnake/djangogirls

0e764294085d6d7d3c4f61a7fe36f91640abedcd

[ "BSD-3-Clause" ]

446

2015-01-04T20:58:26.000Z

2022-03-30T23:08:26.000Z

patreonmanager/apps.py

serenasensini/TheRedCode_Docker-per-Django-e-Postgres

78a2ca1f09ab956a6936d14a5fd99336ff39f472

[ "BSD-3-Clause" ]

649

2015-01-09T23:42:14.000Z

2022-03-31T17:27:19.000Z

patreonmanager/apps.py

serenasensini/TheRedCode_Docker-per-Django-e-Postgres

78a2ca1f09ab956a6936d14a5fd99336ff39f472

[ "BSD-3-Clause" ]

319

2015-01-06T20:58:42.000Z

2022-03-30T06:29:04.000Z

from django.apps import AppConfig from django.utils.translation import gettext_lazy as _ class PatreonManagerConfig(AppConfig): name = 'patreonmanager' verbose_name = _("Patreon Manager")

24.75

54

0.782828

22

198

6.863636

0.772727

0.13245

0

0.146465

198

7

55

28.285714

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false

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1

0

null

0

1

0

1

0

4

ff95cfb4e34bef3bca6c8137953c87f8b4fd0840

121

py

Python

006/006.py

ridvanaltun/project-euler-solutions

2a413d06de1402435d4e5ac8442cd0ab4e465bf4

[ "MIT" ]

null

006/006.py

ridvanaltun/project-euler-solutions

2a413d06de1402435d4e5ac8442cd0ab4e465bf4

[ "MIT" ]

null

006/006.py

ridvanaltun/project-euler-solutions

2a413d06de1402435d4e5ac8442cd0ab4e465bf4

[ "MIT" ]

null

sq_sum, sum = 0, 0 for i in range(1, 101): sq_sum = sq_sum + (i * i) sum = sum + i print((sum * sum) - sq_sum)

15.125

29

0.528926

25

121

2.4

0.4

0.333333

0.266667

0

0.071429

0.305785

121

7

30

17.285714

0.642857

0

1

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false

0

0.2

1

0

null

1

0

1

0

null

0

4

4440f487f379a731540f734b1c318940cea0c7e4

29

py

Python

cross_compile/__init__.py

aws-ros-dev/cross_compile

a0cb03e7ed0d4fa2e637ea94af57c20042bc8bd9

[ "Apache-2.0" ]

2

2019-06-18T22:23:37.000Z

2019-10-08T18:42:28.000Z

cross_compile/__init__.py

aws-ros-dev/cross_compile

a0cb03e7ed0d4fa2e637ea94af57c20042bc8bd9

[ "Apache-2.0" ]

16

2019-08-06T22:11:09.000Z

2021-06-02T02:45:19.000Z

cross_compile/__init__.py

aws-ros-dev/cross_compile

a0cb03e7ed0d4fa2e637ea94af57c20042bc8bd9

[ "Apache-2.0" ]

null

"""Cross Compile package."""

14.5

28

0.655172

3

29

6.333333

1

0

0.103448

29

1

29

0.730769

0.758621

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

null

0

1

0

4

448b9cdfbd54af23caa8644685b2841179fe4d75

239

py

Python

firecares/firecares_core/ext/invitations/adapters.py

FireCARES/firecares

aa708d441790263206dd3a0a480eb6ca9031439d

[ "MIT" ]

12

2016-01-30T02:28:35.000Z

2019-05-29T15:49:56.000Z

firecares/firecares_core/ext/invitations/adapters.py

FireCARES/firecares

aa708d441790263206dd3a0a480eb6ca9031439d

[ "MIT" ]

455

2015-07-27T20:21:56.000Z

2022-03-11T23:26:20.000Z

firecares/firecares_core/ext/invitations/adapters.py

FireCARES/firecares

aa708d441790263206dd3a0a480eb6ca9031439d

[ "MIT" ]

14

2015-07-29T09:45:53.000Z

2020-10-21T20:03:17.000Z

from invitations.adapters import BaseInvitationsAdapter from registration.signals import user_registered class DepartmentInvitationsAdapter(BaseInvitationsAdapter): def get_user_signed_up_signal(self): return user_registered

29.875

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24

239

8.208333

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0.142132

0

0.117155

239

7

60

34.142857

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0

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false

0

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null

0

1

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null

0

1

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4

4495366c3c6fa97f0940d198cabe5dd5baee3692

60

py

Python

dl_in_iot_course/l05_tflite_delegate/__init__.py

antmicro/dl-in-iot-course

2072b88c97c8f643de6055ee7e3b1506303dab98

[ "Apache-2.0" ]

null

dl_in_iot_course/l05_tflite_delegate/__init__.py

antmicro/dl-in-iot-course

2072b88c97c8f643de6055ee7e3b1506303dab98

[ "Apache-2.0" ]

1

2021-11-09T08:47:50.000Z

dl_in_iot_course/l05_tflite_delegate/__init__.py

antmicro/dl-in-iot-course

2072b88c97c8f643de6055ee7e3b1506303dab98

[ "Apache-2.0" ]

2

2021-11-04T19:52:21.000Z

2021-11-05T18:58:44.000Z

""" Module containing tasks regarding TFLite delegates. """

15

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60

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1

0

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60

3

52

20

0.865385

0.85

0

null

0

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0

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1

null

true

0

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1

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0

1

0

1

0

null

0

1

0

4

923e6e3961f511d62da01bc03cc5bb9d98a31c78

80

py

Python

get_parks.py

Matzexxxxx/Monocle

af2b1dd163a33c3ea3bb4bdacf37622df65f4b8e

[ "MIT" ]

21

2017-11-08T12:56:31.000Z

2021-08-19T17:56:35.000Z

get_parks.py

Matzexxxxx/Monocle

af2b1dd163a33c3ea3bb4bdacf37622df65f4b8e

[ "MIT" ]

5

2017-12-16T10:11:35.000Z

2018-03-21T09:30:25.000Z

get_parks.py

Matzexxxxx/Monocle

af2b1dd163a33c3ea3bb4bdacf37622df65f4b8e

[ "MIT" ]

33

2017-12-11T12:30:42.000Z

2018-04-10T01:48:38.000Z

from monocle.parks import * with Parks() as park: park.reset_parks(True)

11.428571

27

0.7

12

80

4.583333

0.75

0

0.2

80

6

28

13.333333

0.859375

0

1

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true

0

0.333333

0

0.333333

0

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0

null

0

1

0

null

0

1

0

1

0

4

926204a877eb44abcc4ae6601aad47f8065816ac

721

py

Python

acrofestival/booking/forms.py

ivoruetsche/acrofestival

7ec798a577fbd66f9b0503a009e13cebe264afd1

[ "MIT" ]

null

acrofestival/booking/forms.py

ivoruetsche/acrofestival

7ec798a577fbd66f9b0503a009e13cebe264afd1

[ "MIT" ]

4

2020-06-06T01:28:01.000Z

2021-06-10T22:37:34.000Z

acrofestival/booking/forms.py

ivoruetsche/acrofestival

7ec798a577fbd66f9b0503a009e13cebe264afd1

[ "MIT" ]

1

2020-03-02T22:12:21.000Z

from django import forms class UrbanAcroForm(forms.Form): name = forms.CharField(required=True) address = forms.CharField(required=True) phone = forms.CharField(required=True) email = forms.EmailField(required=True) option = forms.CharField(required=False) comment = forms.CharField(required=False) class WinterAcroForm(forms.Form): name = forms.CharField(required=False) address = forms.CharField(required=False) phone = forms.CharField(required=False) email = forms.CharField(required=False) option = forms.CharField(required=False) allergies = forms.CharField(required=False) donation = forms.CharField(required=False) date = forms.DateField(required=False)

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1

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null

0

1

0

4

927cce0ce2eabfab247e5f0e2c9384845f1ce34a

64

py

Python

sequence_field/exceptions.py

gnrfan/django-sequence-field

466f609a08ef39203336ca912dae57d2e12a4a30

[ "BSD-3-Clause" ]

15

2015-06-03T03:50:31.000Z

2021-06-14T15:14:02.000Z

sequence_field/exceptions.py

gnrfan/django-sequence-field

466f609a08ef39203336ca912dae57d2e12a4a30

[ "BSD-3-Clause" ]

2

2015-03-05T15:25:28.000Z

2017-01-01T10:11:22.000Z

sequence_field/exceptions.py

gnrfan/django-sequence-field

466f609a08ef39203336ca912dae57d2e12a4a30

[ "BSD-3-Clause" ]

20

2015-01-08T00:33:40.000Z

2021-09-30T16:02:22.000Z

# Exceptions class SequenceFieldException(Exception): pass

12.8

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2baa20c0aa6b23dad4a344a86a36d26e489a3d0c

330

py

Python

backend/project404_t8/project404_t8/router.py

peterweckend/group-project-cmput404

d59912dbe0252868452a2e142e4c20f953792740

[ "MIT" ]

5

2019-02-22T21:15:48.000Z

2019-03-16T22:59:17.000Z

backend/project404_t8/project404_t8/router.py

cjlee1/group-project-cmput404

791ac00494b1005d5b3792492060806bcddc5cf6

[ "MIT" ]

66

2019-03-13T07:03:42.000Z

2022-03-11T23:41:00.000Z

backend/project404_t8/project404_t8/router.py

cjlee1/group-project-cmput404

791ac00494b1005d5b3792492060806bcddc5cf6

[ "MIT" ]

7

2019-01-25T21:09:23.000Z

2019-07-20T16:11:33.000Z

from rest_framework import routers import API.viewsets as Viewsets # these are the API methods api_router = routers.SimpleRouter(trailing_slash=False) api_router.register(r'posts', Viewsets.PostsViewSet) api_router.register(r'author', Viewsets.AuthorViewSet) api_router.register(r'friendrequest', Viewsets.FriendRequestViewSet)

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1

0

null

0

1

0

4

2bc3cd8f8392faf88888db3fbe677503fb340c38

3,996

py

Python

show_data.py

liriqingone/IP_Agent

f562dfe1e4096ed8496767cffa2596704e285861

[ "Apache-2.0" ]

null

show_data.py

liriqingone/IP_Agent

f562dfe1e4096ed8496767cffa2596704e285861

[ "Apache-2.0" ]

null

show_data.py

liriqingone/IP_Agent

f562dfe1e4096ed8496767cffa2596704e285861

[ "Apache-2.0" ]

null

# coding: utf-8 # In[5]: import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns import pymssql import re # In[8]: try: con = pymssql.connect(host='welldatadb.c9rukeih98lt.us-west-1.rds.amazonaws.com',user='welltitled',password='Welltitled888', database='WellDataDB',charset="utf8") sql = "select d.id,d.date,d.is_download,d.case_id from welldata_project p left join welldata_case c on p.id=c.project_id left join dbo.welldata_downloadfile d on c.name_en=d.case_id where p.name_en='USBank'" dfGSE = pd.read_sql(con=con, sql=sql) print dfGSE.head() sns.catplot(x="case_id", kind="count", palette="ch:.25", data=dfGSE) print(plt.show()) except pymssql.Error as e: print e print 0.392157*2.750408e+08 # # `OrigYear` and `wac` # In[40]: # get_ipython().run_cell_magic(u'time', u'', u"\ndfSumm_wac = \\\ndfGSE[['origyear', 'histdate', 'wac', 'fico', 'vsmm', 'curbal']] \\\n.assign(wac = lambda row: 0.5 * np.round(row.wac / 0.5) \\\n ,fico = lambda row: 100 * np.round(row.fico / 100) \\\n ,vsmm = lambda row: row.vsmm * row.curbal \\\n ) \\\n.groupby(['origyear', 'histdate', 'wac'], as_index = False) \\\n.agg({'vsmm': 'sum' \\\n , 'curbal': 'sum'}) \\\n.assign(vsmm = lambda row: row.vsmm / row.curbal \\\n ,histdate = lambda row: pd.to_datetime(row.histdate)) \\") # # # # In[44]: # # # sns.FacetGrid(data = dfSumm_wac.query('origyear > 2009 & wac > 3 & wac < 6.5'), col = 'origyear' # , row = 'wac', # despine = False, height = 3) \ # .map_dataframe(plt.plot, 'histdate', 'vsmm') # # # # # `Origyear` and `wac` and `fico` # # # # 下边 `arrWAC`和 `arrFICO` 是用户的选择。格式是： # # # # `arrWAC = np.array([最小值,最大值,宽度值]) # # # # 这个的目的是尽量保留数据，把筛选留到最后 # # # In[153]: # # # get_ipython().run_cell_magic(u'time', u'', u"\narrWAC = np.array([3.5, 6.5, 0.5])\narrFICO = np.array([500, 800, 100])\n\ndfSumm_wac_fico = \\\ndfGSE[['origyear', 'histdate', 'wac', 'fico', 'vsmm', 'curbal', 'cnt']] \\\n.assign(wac = lambda row: arrWAC[2] * \\\n np.round( \\\n np.where(row.wac < arrWAC[0] \\\n , arrWAC[0]\n ,np.where(row.wac > arrWAC[1]\n , arrWAC[1]\n , row.wac) \\\n ) \\\n / arrWAC[2]) \\\n ,fico = lambda row: arrFICO[2] * \\\n np.round( \\\n np.where(row.fico < arrFICO[0] \\\n , arrFICO[0] \\\n , np.where(row.fico > arrFICO[1] \\\n , arrFICO[1] \\\n , row.fico))\\\n / arrFICO[2]) \\\n ,vsmm = lambda row: row.vsmm * row.curbal \\\n ) \\\n.groupby(['origyear', 'histdate', 'wac', 'fico'], as_index = False) \\\n.agg({'vsmm': 'sum' \\\n , 'curbal': 'sum'\\\n , 'cnt': 'sum'}) \\\n.assign(vsmm = lambda row: row.vsmm / row.curbal \\\n ,histdate = lambda row: pd.to_datetime(row.histdate)) \\") # # # # In[154]: # # # dfSumm_wac_fico_orig = dfSumm_wac_fico[['origyear', 'histdate', 'wac', 'fico', 'curbal', 'cnt']] .groupby(['origyear', 'histdate', 'wac', 'fico'], as_index = False) .agg('sum') .groupby(['origyear', 'wac', 'fico'], as_index = False) .agg('max') .rename(index = str, columns = {'curbal': 'curbal_orig', 'cnt': 'cnt_orig'}) .drop(['histdate'], axis = 1) # # # # # In[155]: # # # dfSumm_wac_fico = dfSumm_wac_fico .merge(dfSumm_wac_fico_orig # , left_on = ['origyear', 'wac', 'fico'] # , right_on = ['origyear', 'wac', 'fico']) \ # # # # In[161]: # # # sns.FacetGrid(data = dfSumm_wac_fico .query('origyear > 2009 & origyear < 2014 & cnt_orig > 20000') # , col = 'origyear' # , row = 'wac', hue = 'fico' # , sharey = False # ,despine = False, height = 3) \ # .map_dataframe(sns.lineplot, 'histdate', 'vsmm', lw = 3)

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2bdc73fe69b3ab66102d99083cd1e9e02dbbcd5d

188

py

Python

yambopy/plot/spectra.py

QU-XIAO/yambopy

ff65a4f90c1bfefe642ebc61e490efe781709ff9

[ "BSD-3-Clause" ]

21

2016-04-07T20:53:29.000Z

2021-05-14T08:06:02.000Z

yambopy/plot/spectra.py

alexmoratalla/yambopy

8ec0e1e18868ccaadb3eab36c55e6a47021e257d

[ "BSD-3-Clause" ]

22

2016-06-14T22:29:47.000Z

2021-09-16T15:36:26.000Z

yambopy/plot/spectra.py

alexmoratalla/yambopy

8ec0e1e18868ccaadb3eab36c55e6a47021e257d

[ "BSD-3-Clause" ]

15

2016-06-14T18:40:57.000Z

2021-08-07T13:17:43.000Z

class YamboSpectra(): """ Class to show optical absorption spectra """ def __init__(self,energies,data): self.energies = energies self.data = data

20.888889

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0.590426

19

188

5.631579

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null

0

1

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4

2bed312f5331da546ffb3e61097ef926eb9510a2

240

py

Python

tests/config.py

null-none/fhir-py

f5e7e13f88188c944696c146954f823be922cbeb

[ "MIT" ]

null

tests/config.py

null-none/fhir-py

f5e7e13f88188c944696c146954f823be922cbeb

[ "MIT" ]

null

tests/config.py

null-none/fhir-py

f5e7e13f88188c944696c146954f823be922cbeb

[ "MIT" ]

null

import os from aiohttp import BasicAuth FHIR_SERVER_URL = os.environ.get("FHIR_SERVER_URL", "http://localhost:8080/fhir") FHIR_SERVER_AUTHORIZATION = os.environ.get( "FHIR_SERVER_AUTHORIZATION", BasicAuth("root", "secret").encode() )

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null

0

1

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4

2bf5a286e05214b62beae5fa0cc265910bdbdbbc

548

py

Python

lib/pylint/test/input/func_e0101.py

willemneal/Docky

d3504e1671b4a6557468234c263950bfab461ce4

[ "MIT" ]

3

2018-11-25T01:09:55.000Z

2021-08-24T01:56:36.000Z

lib/pylint/test/input/func_e0101.py

willemneal/Docky

d3504e1671b4a6557468234c263950bfab461ce4

[ "MIT" ]

7

2021-02-08T20:22:15.000Z

2022-03-11T23:19:41.000Z

lib/pylint/test/input/func_e0101.py

willemneal/Docky

d3504e1671b4a6557468234c263950bfab461ce4

[ "MIT" ]

3

2018-11-09T03:38:09.000Z

2020-02-24T06:26:10.000Z

# pylint: disable=R0903 """test __init__ return """ __revision__ = 'yo' class MyClass(object): """dummy class""" def __init__(self): return 1 class MyClass2(object): """dummy class""" def __init__(self): return class MyClass3(object): """dummy class""" def __init__(self): return None class MyClass4(object): """dummy class""" def __init__(self): yield None class MyClass5(object): """dummy class""" def __init__(self): self.callable = lambda: (yield None)

15.222222

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548

35

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null

0

1

0

1

0

4

9200bad3f6b2369900c4fd8dcf5ca12a1bb52381

297

py

Python

erri/python/lesson_47/pokedex.py

TGITS/programming-workouts

799e805ccf3fd0936ec8ac2417f7193b8e9bcb55

[ "MIT" ]

null

erri/python/lesson_47/pokedex.py

TGITS/programming-workouts

799e805ccf3fd0936ec8ac2417f7193b8e9bcb55

[ "MIT" ]

16

2020-05-30T12:38:13.000Z

2022-02-19T09:23:31.000Z

erri/python/lesson_47/pokedex.py

TGITS/programming-workouts

799e805ccf3fd0936ec8ac2417f7193b8e9bcb55

[ "MIT" ]

null

from pokemon import Pokemon class Pokedex: def __init__(self, pokemons=[]): self.pokemons = pokemons def __str__(self): return "pokemons=\n\t" + '\n\t'.join([str(pokemon) for pokemon in self.pokemons]) def add(self, pokemon): self.pokemons.append(pokemon)

21.214286

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297

13

90

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null

1

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null

0

1

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1

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4

a609c292f61df1cc25f65cc25bd963f12412cf06

38

py

Python

venv/lib/python3.6/encodings/kz1048.py

JamesMusyoka/Blog

fdcb51cf4541bbb3b9b3e7a1c3735a0b1f45f0b5

[ "Unlicense" ]

2

2019-04-17T13:35:50.000Z

2021-12-21T00:11:36.000Z

venv/lib/python3.6/encodings/kz1048.py

JamesMusyoka/Blog

fdcb51cf4541bbb3b9b3e7a1c3735a0b1f45f0b5

[ "Unlicense" ]

2

2021-03-31T19:51:24.000Z

2021-06-10T23:05:09.000Z

venv/lib/python3.6/encodings/kz1048.py

JamesMusyoka/Blog

fdcb51cf4541bbb3b9b3e7a1c3735a0b1f45f0b5

[ "Unlicense" ]

2

2019-10-01T08:47:35.000Z

2020-07-11T06:32:16.000Z

/usr/lib/python3.6/encodings/kz1048.py

38

0.815789

7

38

4.428571

1

0

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0

38

1

38

0.657895

0

null

0

null

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1

0

null

0

1

0

null

0

1

0

4

a613753a0f678d9a92eded4c22220a7eb2bde121

1,111

py

Python

tests/test_read_hdf5.py

kyledecker/Heart_Rate_Monitor

858eff6a48097327ba92d516ad94632c3ad66843

[ "MIT" ]

null

tests/test_read_hdf5.py

kyledecker/Heart_Rate_Monitor

858eff6a48097327ba92d516ad94632c3ad66843

[ "MIT" ]

null

tests/test_read_hdf5.py

kyledecker/Heart_Rate_Monitor

858eff6a48097327ba92d516ad94632c3ad66843

[ "MIT" ]

null

def test_read_hdf5(): import os.path import sys sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), os.pardir)) from read_hdf5 import read_hdf5 import os import numpy as np import h5py # Make tmp mat file and save fs = np.uint16(np.array([100])) ecg = np.uint16(np.array([2,3])) pp = np.uint16(np.array([4,5])) f = h5py.File('tmp.h5', 'w') f.create_dataset('fs',data=fs) f.create_dataset('ecg',data=ecg) f.create_dataset('pp',data=pp) f.close() data = read_hdf5('tmp.h5',offset=0,count_read=4,init_flag=0) os.system('rm tmp.h5') assert np.array_equal(data,[2,4,3,5]) # Make tmp mat file and save fs = np.uint16(np.array([100])) ecg = np.uint16(np.array([2,3])) pp = np.uint16(np.array([4,5])) f = h5py.File('tmp.h5', 'w') f.create_dataset('fs',data=fs) f.create_dataset('ecg',data=ecg) f.create_dataset('pp',data=pp) f.close() data_info = read_hdf5('tmp.h5',offset=0,count_read=2,init_flag=1) os.system('rm tmp.h5') assert np.array_equal(data_info,[5*2,100])

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1

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null

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a69084691ead154f1294eda4aa284570b2d2adbf

131

py

Python

joplin/pages/official_documents_collection/apps.py

cityofaustin/joplin

01424e46993e9b1c8e57391d6b7d9448f31d596b

[ "MIT" ]

15

2018-09-27T07:36:30.000Z

2021-08-03T16:01:21.000Z

joplin/pages/official_documents_collection/apps.py

cityofaustin/joplin

01424e46993e9b1c8e57391d6b7d9448f31d596b

[ "MIT" ]

183

2017-11-16T23:30:47.000Z

2020-12-18T21:43:36.000Z

joplin/pages/official_documents_collection/apps.py

cityofaustin/joplin

01424e46993e9b1c8e57391d6b7d9448f31d596b

[ "MIT" ]

12

2017-12-12T22:48:05.000Z

2021-03-01T18:01:24.000Z

from django.apps import AppConfig class OfficialDocumentsCollectionConfig(AppConfig): name = 'official_documents_collection'

21.833333

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null

0

1

0

1

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4

a6bdd1e0026132efc3537733ac817e4914b9e1ef

254

py

Python

NSE/indices.py

GamesBond008/NSE-India-Scrapper

9963d1b99ee5557e61d6be329bf9f50444d2820a

[ "MIT" ]

1

2021-06-01T19:36:42.000Z

NSE/indices.py

GamesBond008/NSE-India-Scrapper

9963d1b99ee5557e61d6be329bf9f50444d2820a

[ "MIT" ]

null

NSE/indices.py

GamesBond008/NSE-India-Scrapper

9963d1b99ee5557e61d6be329bf9f50444d2820a

[ "MIT" ]

null

from ._MarketData import MarketData class Indices(MarketData): def __init__(self,timeout: int=5): super().__init__(timeout) self._BaseURL="https://www.nseindia.com/api/allIndices" def IndicesMarketWatch(self): return self._GrabData(self._BaseURL)

36.285714

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null

0

1

0

1

0

4

a6e6e31b01c2e6112f8d5f7ef425dbbf969157e4

42

py

Python

dingtalk_python_sdk/__init__.py

jiasir/dingtalk-python-sdk

913188347bee8c3537aacefc64f28a0e5ce8f383

[ "MIT" ]

2

2019-05-13T09:41:39.000Z

2021-11-16T11:21:59.000Z

dingtalk_python_sdk/__init__.py

jiasir/dingtalk-python-sdk

913188347bee8c3537aacefc64f28a0e5ce8f383

[ "MIT" ]

null

dingtalk_python_sdk/__init__.py

jiasir/dingtalk-python-sdk

913188347bee8c3537aacefc64f28a0e5ce8f383

[ "MIT" ]

1

2019-03-06T09:42:01.000Z

__all__ = ["robot"] __version__ = '0.0.1'

14

21

0.619048

6

42

3

0.833333

0

0.083333

0.142857

42

2

22

21

0.416667

0

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0

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false

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0

null

0

1

0

1

0

null

0

4

a6e9550e9909c69bce0c6efb10f4faea541c4361

190

py

Python

src/utils.py

eklitzke/icfp08

d0bbd1a0900a744286f8a1efec3ada8dec275431

[ "0BSD" ]

1

2016-05-08T10:38:12.000Z

src/utils.py

eklitzke/icfp08

d0bbd1a0900a744286f8a1efec3ada8dec275431

[ "0BSD" ]

null

src/utils.py

eklitzke/icfp08

d0bbd1a0900a744286f8a1efec3ada8dec275431

[ "0BSD" ]

null

import sys import logging logging.basicConfig(level=logging.DEBUG, format='%(asctime)s %(name)-15s %(levelname)-8s %(message)s', datefmt='%m-%d %H:%M', stream=sys.stderr)

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a6f9e948f059d84524b4285af4dee39e818ba9d7

184

py

Python

python_polar_coding/polar_codes/fast_scan/codec.py

MingxuZhang/python-polar-coding

bfab8e1cdcffaefea8e6d0209b13465fbd7fa936

[ "MIT" ]

2

2021-12-07T09:52:15.000Z

2022-01-06T14:35:37.000Z

python_polar_coding/polar_codes/fast_scan/codec.py

manhduc1811/python-polar-coding

bfab8e1cdcffaefea8e6d0209b13465fbd7fa936

[ "MIT" ]

null

python_polar_coding/polar_codes/fast_scan/codec.py

manhduc1811/python-polar-coding

bfab8e1cdcffaefea8e6d0209b13465fbd7fa936

[ "MIT" ]

4

2020-07-03T14:20:04.000Z

2021-07-04T13:20:40.000Z

from python_polar_coding.polar_codes.rc_scan import RCSCANPolarCodec from .decoder import FastSCANDecoder class FastSCANCodec(RCSCANPolarCodec): decoder_class = FastSCANDecoder

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4

5b25ee0b235628c7ded5d4b5d1800b04d349a836

175

py

Python

python/django/python-hello-world/src/helloapp/views.py

davidponder/cloud-code-samples

9d7f08e48fd8355c31ac428ff660a24bf1bef742

[ "0BSD" ]

319

2019-03-29T02:21:27.000Z

2022-03-12T00:03:32.000Z

python/django/python-hello-world/src/helloapp/views.py

davidponder/cloud-code-samples

9d7f08e48fd8355c31ac428ff660a24bf1bef742

[ "0BSD" ]

779

2019-03-29T16:53:09.000Z

2022-03-31T18:48:08.000Z

python/django/python-hello-world/src/helloapp/views.py

davidponder/cloud-code-samples

9d7f08e48fd8355c31ac428ff660a24bf1bef742

[ "0BSD" ]

182

2019-03-29T14:17:33.000Z

2022-03-14T22:31:12.000Z

from django.shortcuts import render import os def homePageView(request): return render(request, 'homepage.html', context={ "message": "It's running!" })

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null

0

1

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4

5b38e79a377ccb487655e34396ae67ca6d0baf77

312

py

Python

cnn/struct/layer/relu_layer_module.py

hslee1539/GIS_GANs

6901c830b924e59fd06247247db3f925bab26583

[ "MIT" ]

null

cnn/struct/layer/relu_layer_module.py

hslee1539/GIS_GANs

6901c830b924e59fd06247247db3f925bab26583

[ "MIT" ]

null

cnn/struct/layer/relu_layer_module.py

hslee1539/GIS_GANs

6901c830b924e59fd06247247db3f925bab26583

[ "MIT" ]

null

from import_lib import lib from tensor.main_module import Tensor from cnn.struct.layer_module import Layer from ctypes import Structure, c_int, POINTER def createReluLayer(): return lib.cnn_create_relu_layer() #lib.cnn_create_relu_layer.argtypes = (Layer, Layer) lib.cnn_create_relu_layer.restype = Layer

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312

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53

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null

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0

null

0

1

0

1

0

4

5b4947c1df2c2c11a9c526533e38a8c276e1a76d

253

py

Python

nikola/packages/tzlocal/__init__.py

asmeurer/nikola

ea1c651bfed0fd6337f1d22cf8dd99899722912c

[ "MIT" ]

1,901

2015-01-02T02:49:51.000Z

2022-03-30T23:31:35.000Z

nikola/packages/tzlocal/__init__.py

asmeurer/nikola

ea1c651bfed0fd6337f1d22cf8dd99899722912c

[ "MIT" ]

1,755

2015-01-01T08:17:16.000Z

2022-03-24T18:02:22.000Z

nikola/packages/tzlocal/__init__.py

asmeurer/nikola

ea1c651bfed0fd6337f1d22cf8dd99899722912c

[ "MIT" ]

421

2015-01-02T18:06:37.000Z

2022-03-28T23:18:54.000Z

"""Try to figure out what your local timezone is.""" import sys __version__ = "2.0.0-nikola" if sys.platform == "win32": from .win32 import get_localzone, reload_localzone # NOQA else: from .unix import get_localzone, reload_localzone # NOQA

28.111111

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63

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null

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1

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4

5b641bd2268abc885c0f727b04d7a8e03e2bc714

190

py

Python

src/com/ssafy/test/DevMatching2022_2/pro4.py

ehddn5252/AlgorithmStorage

c9b3464029181767c73f7607725cb47d99b7b7f2

[ "MIT" ]

null

src/com/ssafy/test/DevMatching2022_2/pro4.py

ehddn5252/AlgorithmStorage

c9b3464029181767c73f7607725cb47d99b7b7f2

[ "MIT" ]

null

src/com/ssafy/test/DevMatching2022_2/pro4.py

ehddn5252/AlgorithmStorage

c9b3464029181767c73f7607725cb47d99b7b7f2

[ "MIT" ]

null

-- 코드를 입력하세요 SELECT c_p.cart_id as CART_ID ,if(sum(price)>minimum_requirement,0,1) as abused from Cart_products as c_p join coupons as c where c_p.cart_id = c.cart_id group by c_p.cart_id;

27.142857

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3.186047

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80

31.666667

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null

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0

1

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1

0

null

0

1

0

4

5b94a5735d213c448e683218e28b77df98453cc1

135

py

Python

email_validation/urls.py

rezaramadhan/ValidationSystem

1e36f37ee79bd3a8e618dec492e5cfbe83791ebe

[ "BSD-3-Clause" ]

null

email_validation/urls.py

rezaramadhan/ValidationSystem

1e36f37ee79bd3a8e618dec492e5cfbe83791ebe

[ "BSD-3-Clause" ]

null

email_validation/urls.py

rezaramadhan/ValidationSystem

1e36f37ee79bd3a8e618dec492e5cfbe83791ebe

[ "BSD-3-Clause" ]

null

from django.conf.urls import url from . import views urlpatterns = [ url(r'^$', views.validation_form, name='validation_form'), ]

19.285714

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135

5.222222

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135

6

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null

0

1

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5b9f5359a99fc21561af13513dcf810a58d7c4ab

243

py

Python

app/app/monitoring/__init__.py

ARgorithm/Server

b25b593721bab87263c49dddf52066288e45c272

[ "MIT" ]

2

2021-02-24T17:23:46.000Z

2021-03-07T12:43:31.000Z

app/app/monitoring/__init__.py

ARgorithm/Server

b25b593721bab87263c49dddf52066288e45c272

[ "MIT" ]

14

2020-10-18T14:50:43.000Z

2021-06-18T07:35:13.000Z

app/app/monitoring/__init__.py

ARgorithm/Server

b25b593721bab87263c49dddf52066288e45c272

[ "MIT" ]

null

"""The monitoring module deals with prometheus monitoring for server performance as well as logs """ from .logging import logger from .middleware import MonitoringMiddleware from .view import metrics from .performance import PerformanceMonitor

40.5

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40.5

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true

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null

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1

0

1

0

1

0

4

5bae1409b1600cfbb9380afb8dd3c2261008ab22

158

py

Python

bitcoin_client/__init__.py

ryan-shaw/app-bitcoin-new

aa0a27703f8dbe24c6825c86383a2689e7c4c126

[ "Apache-2.0" ]

16

2021-09-25T11:46:17.000Z

2022-03-10T15:47:14.000Z

bitcoin_client/__init__.py

ryan-shaw/app-bitcoin-new

aa0a27703f8dbe24c6825c86383a2689e7c4c126

[ "Apache-2.0" ]

20

2021-09-24T08:51:48.000Z

2022-03-28T20:00:00.000Z

bitcoin_client/__init__.py

ryan-shaw/app-bitcoin-new

aa0a27703f8dbe24c6825c86383a2689e7c4c126

[ "Apache-2.0" ]

26

2021-09-21T07:03:00.000Z

2022-03-26T04:18:49.000Z

# this folder is not meant to be a python package, but adding this file allows pytest tests # to import the test_utils module in the repository's root folder

52.666667

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2

92

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null

0

1

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4

5bd1b15e65873e8009faaac31e7f6a24996fae29

230

py

Python

tests/core/rules/test_collection_rule.py

manoadamro/flapi

e5ed4ebbb49ac88ce842c04ce73d0a97ce3fe00d

[ "MIT" ]

3

2019-01-07T20:20:30.000Z

2019-01-11T11:15:19.000Z

tests/core/rules/test_collection_rule.py

manoadamro/flapi

e5ed4ebbb49ac88ce842c04ce73d0a97ce3fe00d

[ "MIT" ]

null

tests/core/rules/test_collection_rule.py

manoadamro/flapi

e5ed4ebbb49ac88ce842c04ce73d0a97ce3fe00d

[ "MIT" ]

1

2019-01-11T11:15:27.000Z

import unittest from flapi.core.rules import _CollectionRule class CollectionRuleTest(unittest.TestCase): def test_fails(self): rule = _CollectionRule() self.assertRaises(NotImplementedError, rule, "token")

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9

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null

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1

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1

0

4

5be3df800845edec650c50ee6175a3b1022a9f9a

543

py

Python

trac/trac/web/tests/wikisyntax.py

HelionDevPlatform/bloodhound

206b0d9898159fa8297ad1e407d38484fa378354

[ "Apache-2.0" ]

84

2015-01-07T03:42:53.000Z

2022-01-10T11:57:30.000Z

trac/trac/web/tests/wikisyntax.py

HelionDevPlatform/bloodhound

206b0d9898159fa8297ad1e407d38484fa378354

[ "Apache-2.0" ]

1

2021-11-04T12:52:03.000Z

trac/trac/web/tests/wikisyntax.py

HelionDevPlatform/bloodhound

206b0d9898159fa8297ad1e407d38484fa378354

[ "Apache-2.0" ]

35

2015-01-06T11:30:27.000Z

2021-11-10T16:34:52.000Z

import unittest from trac.wiki.tests import formatter TEST_CASES = """ ============================== htdocs: links resolver htdocs:release-1.0.tar.gz [htdocs:release-1.0.tar.gz Release 1.0] ------------------------------ <p> <a href="/chrome/site/release-1.0.tar.gz">htdocs:release-1.0.tar.gz</a> </p> <p> <a href="/chrome/site/release-1.0.tar.gz">Release 1.0</a> </p> ------------------------------ """ def suite(): return formatter.suite(TEST_CASES, file=__file__) if __name__ == '__main__': unittest.main(defaultTest='suite')

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5bf20cefee7f45afee4dc39c0038a1484863aee3

46

py

Python

04_datacamp/solutions/16_solutions.py

HirahTang/datascience_starter_course

a4429db9ae1795eaf52b795d16897466d769c40c

[ "CC0-1.0" ]

3

2020-09-06T06:01:41.000Z

2020-09-23T19:03:04.000Z

02_pandas/solutions/16_solutions.py

glemaitre/smob_paristech_12_2018

b669206f204a3e57e71efb3dd22e2ffbc4e0a309

[ "CC0-1.0" ]

4

2019-02-22T21:37:20.000Z

2019-03-12T13:20:29.000Z

02_pandas/solutions/16_solutions.py

glemaitre/smob_paristech_12_2018

b669206f204a3e57e71efb3dd22e2ffbc4e0a309

[ "CC0-1.0" ]

5

2020-10-26T05:03:09.000Z

2022-03-24T04:22:09.000Z

df.groupby('Pclass')['Fare'].hist(alpha=0.4);

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4

752c81ba27125f6ffa835642cb7d13b228b30ad9

54,220

py

Python

Survival.py

SimplyNate/PySurvival

c49d52b5e3c60d1063d75b0eb7d43a118647d4fc

[ "MIT" ]

null

Survival.py

SimplyNate/PySurvival

c49d52b5e3c60d1063d75b0eb7d43a118647d4fc

[ "MIT" ]

null

Survival.py

SimplyNate/PySurvival

c49d52b5e3c60d1063d75b0eb7d43a118647d4fc

[ "MIT" ]

null

######################################################################################################################## """ Inspired by http://usingpython.com/programs/ 'Crafting Challenge' Game Created by SimplyNate Coding Module - Python Lab Craft the items indicated in the Quests panel to win the game. Hunger ticks down after each input by the amount indicated below. When hunger reaches 0, Health will begin ticking down instead after each input. When health reaches 0, the game ends. Made for the GenCyber Hawaii SecurityX Camp 2018 """ ######################################################################################################################## # Imports dependencies required for drawing the GUI and other functions import tkinter from tkinter import * from tkinter import ttk # Class that initiates data for use in the GUI class class Game: # Function that initializes variables with data def __init__(self): # EDIT VARIABLES BELOW ######################################################################################### # List of commands - Gets displayed in the "Help" menu self.commands = { "i": "see inventory", "c": "see crafting options", "h": "see help", "q": "see quests", "craft [item] [amount]": "craft something from inventory items", "eat [item]": "Eat something from inventory to restore hunger", "gather [item]": "Increase resources in your inventory" } # an inventory of items - Gets listed in the "Inventory" menu # Edit the number values to change your starting amount self.items = { "flint": 50, "grass": 100, "hay": 0, "tree": 100, "log": 0, "sapling": 100, "twig": 0, "boulder": 30, "rock": 0, "pickaxe": 0, "axe": 0, "firepit": 0, "tent": 0, "torch": 0, } # List of Gatherable items # Add items from the items list below to be able to gather different items self.gatherable = [ "flint", "grass", "tree", "sapling", "boulder", ] # Inventory of Food items # Edit the "amount" numbers to change how much you start with. # Edit the "restores" number to change how much each food restores hunger by. self.foods = { "potato": { "amount": 10, "restores": 5 }, "bread": { "amount": 5, "restores": 10 }, "apple": { "amount": 20, "restores": 2 }, "porkchop": { "amount": 5, "restores": 20 } } # rules to make new objects # Change the number values to change how much resources required to craft the item self.craft = { "hay": {"grass": 1}, "twig": {"sapling": 1}, "log": {"axe": 1, "tree": 1}, "axe": {"twig": 3, "flint": 1}, "tent": {"twig": 10, "hay": 15}, "firepit": {"boulder": 5, "log": 3, "twig": 1, "torch": 1}, "torch": {"flint": 1, "grass": 1, "twig": 1}, "pickaxe": {"flint": 2, "twig": 1} } # List of Quests # Add more quests by adding a new entry under here self.quests = [ "Craft a Hay", "Craft a Tent", "Craft a Firepit", ] # Hero Statistics # Change the hunger value to change how much hunger you start with # Change the hungerDecay value to change how quickly or slowly the hunger goes down by # Change the health value to change how much health you start with # Change the healthDecay value to change how quickly or slowly your health goes down by or regenerates by # Change the gatherRate value to change how much resources you get when using the gather command self.hero = { "hunger": 100, "hungerDecay": 5, "health": 20, "healthDecay": 2, "gatherRate": 2, } ######################################################################################################################## # End Recommended Editable Area # ######################################################################################################################## # Class that draws the GUI and runs the game logic and functions class Gui: argument = "" history = [] index = -1 qtimes = 0 itimes = 0 ctimes = 0 htimes = 0 game = Game() g = Game() # Reference variable def __init__(self, master): # Window itself self.master = master master.title("Python Game") master.geometry("960x480") master.resizable(False, False) master.configure(background='black') # Health and Hunger bar themes self.s = ttk.Style() self.s.theme_use('clam') self.s.configure("red.Horizontal.TProgressbar", foreground="red", background="red") self.s.configure("green.Horizontal.TProgressbar", foreground="green", background="green") self.s.configure("yellow.Horizontal.TProgressbar", foreground="yellow", background="yellow") self.s.configure("brown.Horizontal.TProgressbar", foreground="brown", background="brown") # Title Label self.title = Label(master, text="Generic Survival Game", bg="black", fg="white", font=("Impact", 48)) self.title.place(x=180, y=13) # Subtitle Label self.subtitle = Label(master, text="Written entirely in Python", bg="black", fg="white", font=("Georgia", 16)) self.subtitle.place(x=350, y=90) # Another Label below the Subtitle self.instruction = Label(master, text="Survive the Night!", bg="black", fg="white", font=("Georgia", 24)) self.instruction.place(x=340, y=200) # Button that starts or restarts the game self.start = Button(master, text="Start", command=self.startgame, font=("Impact", 28)) self.start.place(x=230, y=350, width=200, height=100) # Button that quits the game self.quit = Button(master, text="Quit", command=quit, font=("Impact", 28)) self.quit.place(x=530, y=350, width=200, height=100) # Label telling where to put command self.command = Label(master, text="Enter Your Command:", bg="black", fg='white') self.command.configure(highlightbackground='white') # Top divider between user entry and rest of game self.div = Label(master, text="", bg="white") # Divider between Label and User entry box self.div2 = Label(master, text="", bg="white") # Where the user types their arguments self.userCommand = Entry(master, bg="black", fg="white", font=("Georgia", 14), borderwidth=0) # Output box that gives user more info self.outbox = Text(master, wrap="word", state="disabled", font=("Georgia", 12)) # Quests "button" self.quests = Label(master, text="[Q]uests", bg="black", fg="white", borderwidth=2, relief="groove") # Inventory "button" self.inventory = Label(master, text="[I]nventory", bg="black", fg="white", borderwidth=2, relief="groove") # Crafting "button" self.crafting = Label(master, text="[C]rafting", bg="black", fg="white", borderwidth=2, relief="groove") # Help "Button" self.help = Label(master, text="[H]elp", bg="black", fg="white", borderwidth=2, relief="groove") # Boxes self.questbox = Text(master, wrap="word", state="disabled", font=("Georgia", 12)) self.inventorybox = Text(master, wrap="word", state="disabled", font=("Georgia", 12)) self.craftbox = Text(master, wrap="word", state="disabled", font=("Georgia", 12)) self.helpbox = Text(master, wrap="word", state="disabled", font=("Georgia", 12)) # Health display self.health = Label(master, text=("Health: " + str(Gui.game.hero["health"])), bg="black", fg="white", font=("Georgia", 16)) # Hunger display self.hunger = Label(master, text="Hunger: " + str(Gui.game.hero["hunger"]), bg="black", fg="white", font=("Georgia", 16)) # Popup notificatin self.popup = Label(master, text="You survived!", bg="black", fg="white", borderwidth=2, relief="groove") # Health Bar self.hbar = ttk.Progressbar(master, orient="horizontal", length=200, mode="determinate") # Hunger Bar self.hungerbar = ttk.Progressbar(master, orient="horizontal", length=200, mode="determinate") def startgame(self): # Get new instance of Game Gui.game = Game() # Place UI Elements self.command.place(x=10, y=448, height=32) self.div.place(y=447, height=1, width=960) self.div2.place(y=448, x=140, height=32) self.userCommand.place(x=150, y=448, height=32, width=820) self.userCommand.focus() # Binds key to perform a specific function self.master.bind('<Return>', self.parse) # Sends data self.master.bind('<Up>', self.get_history_up) # Gets previous args self.master.bind('<Down>', self.get_history_down) # Gets previous args # Places rest of UI self.outbox.place(y=340, width=960, height=106) self.quests.place(x=0, y=300, width=240, height=40) self.inventory.place(x=240, y=300, width=240, height=40) self.crafting.place(x=480, y=300, width=240, height=40) self.help.place(x=720, y=300, width=240, height=40) self.health.place(x=200, y=130) self.hunger.place(x=200, y=160) self.hunger.config(text="Hunger: " + str(Gui.game.hero["hunger"])) self.health.config(text="Health: " + str(Gui.game.hero["health"])) # Forget buttons and instruction self.start.place_forget() self.quit.place_forget() self.instruction.place_forget() self.hbar.place(x=340, y=137) self.hbar.config(style="green.Horizontal.TProgressbar") self.hbar["value"] = Gui.game.hero["health"] self.hbar["maximum"] = Gui.game.hero["health"] self.hungerbar.place(x=340, y=167) self.hungerbar.config(style="brown.Horizontal.TProgressbar") self.hungerbar["value"] = Gui.game.hero["hunger"] self.hungerbar["maximum"] = Gui.game.hero["hunger"] def endgame(self, endtext): Gui.argument = "" Gui.history = [] Gui.index = -1 Gui.qtimes = 0 Gui.itimes = 0 Gui.ctimes = 0 Gui.htimes = 0 Gui.game = Game() # Needed self.start.config(text="Restart") self.start.place(x=230, y=350, width=200, height=100) self.quit.place(x=530, y=350, width=200, height=100) if endtext == "lose": self.instruction.config(text="You have Died!") self.instruction.place(x=370, y=200) elif endtext == "win": self.instruction.config(text="You have Survived!") self.instruction.place(x=330, y=200) self.command.place_forget() self.div.place_forget() self.div2.place_forget() self.userCommand.place_forget() self.outbox.config(state="normal") self.outbox.delete("1.0", END) self.outbox.config(state="disabled") self.outbox.place_forget() self.quests.config(relief="groove", bg="black", fg="white") self.quests.place_forget() self.inventory.config(relief="groove", bg="black", fg="white") self.inventory.place_forget() self.crafting.config(relief="groove", bg="black", fg="white") self.crafting.place_forget() self.help.config(relief="groove", bg="black", fg="white") self.help.place_forget() self.health.place_forget() self.hunger.place_forget() self.inventorybox.place_forget() self.questbox.place_forget() self.helpbox.place_forget() self.craftbox.place_forget() self.hungerbar.place_forget() self.hbar.place_forget() # Gets the text inputted by the user and parses accordingly def parse(self, event): keypress = event # Stores data about keypress, not necessary Gui.index = -1 Gui.argument = self.userCommand.get() self.userCommand.delete(0, 'end') if Gui.argument is not "" and Gui.argument is not " ": Gui.history.insert(0, Gui.argument) if len(Gui.history) > 100: try: Gui.history.remove(-1) except ValueError: pass Gui.argument = Gui.argument.strip().lower() # Normalizes input if "craft" in Gui.argument and len(Gui.argument.split(" ")) > 1: Gui.craft(self, Gui.argument) # Runs rest of game logic elif "eat " in Gui.argument: tokens = Gui.argument.split(" ") self.write_to_outbox("Eating " + tokens[1]) Gui.eat(self, tokens[1]) elif "gather " in Gui.argument: tokens = Gui.argument.split(" ") self.write_to_outbox("Gathering " + tokens[1]) Gui.gather(self, tokens[1]) elif "quests" in Gui.argument or "q" in Gui.argument and len(Gui.argument) == 1: Gui.qtimes += 1 if Gui.qtimes is 1: self.quests.config(relief="sunken", bg="white", fg="black") self.questbox.config(state="normal") self.questbox.delete("1.0", END) a = get_everything(Gui.game.quests) # Different way to do it self.questbox.insert(INSERT, a) self.questbox.place(x=0, y=210, height=100, width=240) self.questbox.config(state="disabled") # Output Box self.write_to_outbox("Opened Quests menu") else: Gui.qtimes = 0 self.quests.config(relief="groove", bg="black", fg="white") self.questbox.place_forget() # Output Box self.write_to_outbox("Closed Quests menu") elif "inventory" in Gui.argument or "i" in Gui.argument and len(Gui.argument) == 1: Gui.itimes += 1 if Gui.itimes is 1: self.inventory.config(relief="sunken", bg="white", fg="black") self.inventorybox.config(state="normal") self.inventorybox.delete("1.0", END) self.inventorybox.insert(INSERT, get_everything(Gui.game.items)) self.inventorybox.place(x=240, y=210, height=100, width=240) self.inventorybox.config(state="disabled") self.write_to_outbox("Opened Inventory menu") else: Gui.itimes = 0 self.inventory.config(relief="groove", bg="black", fg="white") self.inventorybox.place_forget() self.write_to_outbox("Closed Inventory menu") elif "crafting" == Gui.argument or "c" in Gui.argument and len(Gui.argument) == 1: Gui.ctimes += 1 if Gui.ctimes is 1: self.crafting.config(relief="sunken", bg="white", fg="black") self.craftbox.config(state="normal") self.craftbox.delete("1.0", END) self.craftbox.insert(INSERT, get_everything(Gui.game.craft)) self.craftbox.place(x=480, y=210, height=100, width=240) self.craftbox.config(state="disabled") self.write_to_outbox("Opened Crafting menu") else: Gui.ctimes = 0 self.crafting.config(relief="groove", bg="black", fg="white") self.craftbox.place_forget() self.write_to_outbox("Closed Crafting menu") elif "help" in Gui.argument or "h" in Gui.argument and len(Gui.argument) == 1: Gui.htimes += 1 if Gui.htimes is 1: self.help.config(relief="sunken", bg="white", fg="black") self.helpbox.config(state="normal") self.helpbox.delete("1.0", END) self.helpbox.insert(INSERT, get_everything(Gui.game.commands)) self.helpbox.place(x=720, y=210, height=100, width=240) self.helpbox.config(state="disabled") self.write_to_outbox("Opened Help menu") else: Gui.htimes = 0 self.help.config(relief="groove", bg="black", fg="white") self.helpbox.place_forget() self.write_to_outbox("Closed Help menu") else: self.write_to_outbox(Gui.argument + " is not a valid argument") # if the command is not blank if Gui.argument is not "" and Gui.argument is not " ": # Hunger and Health # If hunger is greather than 70% of max if Gui.game.hero["hunger"] >= int(Gui.g.hero["hunger"] * 0.7): # If health is lower than maximum if Gui.game.hero["health"] < Gui.g.hero["health"]: # Regeneration of health Gui.game.hero["health"] += Gui.game.hero["healthDecay"] # If health is greater than maximum if Gui.game.hero["health"] > Gui.g.hero["health"]: # Set health to maximum Gui.game.hero["health"] = Gui.g.hero["health"] if Gui.game.hero["hunger"] > 0 and "eat" not in Gui.argument.lower(): Gui.game.hero["hunger"] -= Gui.game.hero["hungerDecay"] if Gui.game.hero["hunger"] < 0: Gui.game.hero["hunger"] = 0 if Gui.game.hero["hunger"] == 0 and "eat" not in Gui.argument.lower(): if Gui.game.hero["health"] != 0: Gui.game.hero["health"] -= Gui.game.hero["healthDecay"] if Gui.game.hero["health"] < 0: Gui.game.hero["health"] = 0 if Gui.game.hero["health"] == 0: self.endgame("lose") self.hbar["value"] = Gui.game.hero["health"] self.hungerbar["value"] = Gui.game.hero["hunger"] self.hunger.config(text="Hunger: " + str(Gui.game.hero["hunger"])) self.health.config(text="Health: " + str(Gui.game.hero["health"])) if Gui.game.hero["health"] > int(Gui.g.hero["health"] * 0.5): self.hbar.configure(style="green.Horizontal.TProgressbar") elif Gui.game.hero["health"] > int(Gui.g.hero["health"] * 0.25): self.hbar.configure(style="yellow.Horizontal.TProgressbar") else: self.hbar.configure(style="red.Horizontal.TProgressbar") # Function that "returns" previous commands def get_history_up(self, event): keypress = event amt = len(Gui.history) if amt > 0: Gui.index += 1 if Gui.index < amt: self.userCommand.delete(0, 'end') self.userCommand.insert(0, Gui.history[Gui.index]) else: Gui.index = amt-1 self.userCommand.delete(0, 'end') self.userCommand.insert(0, Gui.history[Gui.index]) # Function that "returns" previous commands (backwards) def get_history_down(self, event): keypress = event if len(Gui.history) > 0: Gui.index -= 1 if Gui.index >= 0: self.userCommand.delete(0, 'end') self.userCommand.insert(0, Gui.history[Gui.index]) if Gui.index <= -1: Gui.index = -1 self.userCommand.delete(0, 'end') def write_to_outbox(self, text): text = text + "\n" self.outbox.config(state="normal") self.outbox.insert(END, text) self.outbox.config(state="disabled") self.outbox.see(tkinter.END) def eat(self, item): if item in Gui.game.foods.keys() and Gui.game.foods[item]["amount"] > 0: self.write_to_outbox("Restored " + str(Gui.game.foods[item]["restores"]) + " hunger") if Gui.game.hero["hunger"] != 100: Gui.game.hero["hunger"] += Gui.game.foods[item]["restores"] if Gui.game.hero["hunger"] > 100: Gui.game.hero["hunger"] = 100 self.hunger.config(text="Hunger: " + str(Gui.game.hero["hunger"])) Gui.game.foods[item]["amount"] -= 1 self.inventorybox.config(state="normal") self.inventorybox.delete("1.0", END) self.inventorybox.insert(INSERT, get_everything(Gui.game.items)) self.inventorybox.config(state="disabled") else: self.write_to_outbox(item + " is not an edible item") if Gui.game.hero["hunger"] > 0: Gui.game.hero["hunger"] -= Gui.game.hero["hungerDecay"] if Gui.game.hero["hunger"] < 0: Gui.game.hero["hunger"] = 0 self.hunger.config(text="Hunger: " + str(Gui.game.hero["hunger"])) if Gui.game.hero["hunger"] == 0: if Gui.game.hero["health"] != 0: Gui.game.hero["health"] -= Gui.game.hero["healthDecay"] if Gui.game.hero["health"] <= 0: self.endgame("lose") else: self.endgame("lose") self.hungerbar["value"] = Gui.game.hero["hunger"] def gather(self, item): if item in Gui.game.gatherable: self.write_to_outbox("Gathered " + str(Gui.game.hero["gatherRate"]) + " " + item) Gui.game.items[item] += Gui.game.hero["gatherRate"] self.inventorybox.config(state="normal") self.inventorybox.delete("1.0", END) self.inventorybox.insert(INSERT, get_everything(Gui.game.items)) self.inventorybox.config(state="disabled") elif item in Gui.game.foods: self.write_to_outbox("Gathered " + item) Gui.game.foods[item]["amount"] += Gui.game.hero["gatherRate"] self.inventorybox.config(state="normal") self.inventorybox.delete("1.0", END) self.inventorybox.insert(INSERT, get_everything(Gui.game.items)) self.inventorybox.config(state="disabled") else: self.write_to_outbox(item + " is not gatherable") # Method for Crafting items def craft(self, arg): command = arg.split(" ") if len(command) > 1: item = command[1].lower() else: Gui.write_to_outbox(self, "Error: No item specified.") return # If a quantity is defined, try to extract it if len(command) > 2: try: quantity = int(command[2].lower()) except ValueError: Gui.write_to_outbox(self, "Error: Please switch position of item and quantity") return else: quantity = 1 Gui.write_to_outbox(self, "Crafting " + item + ":") if item in Gui.game.craft: # Print item requirements and check if all items are present for i in Gui.game.craft[item]: Gui.write_to_outbox(self, f"{item} requires: {str(Gui.game.craft[item][i] * quantity)} {i}. You have: {str(Gui.game.items[i])}") if (Gui.game.craft[item][i] * quantity) > Gui.game.items[i]: Gui.write_to_outbox(self, "Item cannot be crafted.") return # Remove the items from the inventory for i in Gui.game.craft[item]: Gui.game.items[i] -= Gui.game.craft[item][i] * quantity # Add the new item Gui.game.items[item] += 1 * quantity Gui.remove_quest(self, item) Gui.write_to_outbox(self, f"{item} crafted.\n") self.inventorybox.config(state="normal") self.inventorybox.delete("1.0", END) self.inventorybox.insert(INSERT, get_everything(Gui.game.items)) self.inventorybox.config(state="disabled") if len(Gui.game.quests) == 0: Gui.write_to_outbox(self, "\n**YOU HAVE MANAGED TO SURVIVE!\nWELL DONE!") self.endgame("win") else: Gui.write_to_outbox(self, "Error: That item does not exist in the crafting table.") def remove_quest(self, arg): arg = arg.capitalize() # for i in range(len(Gui.game.quests)): for item in Gui.game.quests[:]: if arg in item: try: Gui.game.quests.remove(item) except ValueError: pass self.questbox.config(state="normal") self.questbox.delete("1.0", END) a = get_everything(Gui.game.quests) # Different way to do it self.questbox.insert(INSERT, a) self.questbox.config(state="disabled") def get_everything(objects): if objects is Gui.game.quests: quest_string = "" for i in range(len(objects)): quest_string += objects[i] + "\n" return quest_string elif objects is Gui.game.craft: recipe_strings = "" for key in objects: recipe_strings += key + " can be made with:\n" for i in Gui.game.craft[key]: recipe_strings += str(Gui.game.craft[key][i]) + " " + i + "\n" recipe_strings += "\n" return recipe_strings elif objects is Gui.game.items: item_strings = "" for key in objects: item_strings += key + "\t: " + str(objects[key]) + "\n" for key in Gui.game.foods: item_strings += key + "\t: " + str(Gui.game.foods[key]["amount"]) + "\n" return item_strings else: object_strings = "" for key in objects: object_strings += key + " : " + str(objects[key]) + "\n" return object_strings ######################################################################################################################## # END LAB 2 ############################################################################################################ ######################################################################################################################## ######################################################################################################################## # START LAB 3 ########################################################################################################## ######################################################################################################################## """ Warning: Do not alter anything below until instructed to do so. Instructions: Attempt to bypass or disable the login box Remember: Undo [CTRL]+[Z] is your friend """ def qeydfsfgdstreygfd(hytedy, ghytjh, fewqe): return hytedy + ghytjh + fewqe from base64 import b64decode as безопасность pdftagrthrsae = "ianm_girstn<8ab6>_u-ecxmeaoplvfybh." лояльность = qeydfsfgdstreygfd(pdftagrthrsae[4],pdftagrthrsae[17],pdftagrthrsae[3])+"ai"+qeydfsfgdstreygfd(pdftagrthrsae[2],pdftagrthrsae[17],"_") Кремль = qeydfsfgdstreygfd(pdftagrthrsae[11],pdftagrthrsae[8],pdftagrthrsae[9])+qeydfsfgdstreygfd(pdftagrthrsae[7]+"i",pdftagrthrsae[10],pdftagrthrsae[5]+">") технологии = qeydfsfgdstreygfd(pdftagrthrsae[20],"",pdftagrthrsae[29]) компьютер = qeydfsfgdstreygfd("",pdftagrthrsae[1],pdftagrthrsae[28]) кодирование = qeydfsfgdstreygfd(технологии,"",компьютер) нарушения = eval(compile(qeydfsfgdstreygfd("c",pdftagrthrsae[26]+"m",pdftagrthrsae[27]+"i")+qeydfsfgdstreygfd(pdftagrthrsae[28],pdftagrthrsae[20],""), Кремль, кодирование)) оценивать = eval(compile(кодирование, Кремль, кодирование)) сила = qeydfsfgdstreygfd(pdftagrthrsae[32]+"y","t",pdftagrthrsae[20]+pdftagrthrsae[8]+".") информационная = оценивать(нарушения(сила+qeydfsfgdstreygfd(pdftagrthrsae[-5]+"ro","m",pdftagrthrsae[-2]+pdftagrthrsae[20])+pdftagrthrsae[22], Кремль, кодирование)) большевик = pdftagrthrsae[18]+pdftagrthrsae[9]+qeydfsfgdstreygfd(pdftagrthrsae[30],pdftagrthrsae[19],pdftagrthrsae[12]) взлом = оценивать(нарушения(qeydfsfgdstreygfd(pdftagrthrsae[(9-1)],pdftagrthrsae[(27-9*(2-(-2)))*-1],pdftagrthrsae[int((2/(1/4))-1)]), Кремль, кодирование)) советский = qeydfsfgdstreygfd(pdftagrthrsae[20],pdftagrthrsae[22],pdftagrthrsae[(17+3)])+pdftagrthrsae[(7*3)] выигрыш = оценивать(нарушения(pdftagrthrsae[4]+qeydfsfgdstreygfd("_"+pdftagrthrsae[2]+pdftagrthrsae[1],pdftagrthrsae[3]+pdftagrthrsae[20],pdftagrthrsae[17]+pdftagrthrsae[4]), Кремль, кодирование)) """ So you want to know what goes on below? The first step is to decode the message in tow. There are many unnecessary marks under the score But only one aligns different than the rest. Once you find the correct mark, Move not more than two forward and not more than three backward, For these nefarious characters Are plotting against you. Fuse all the pieces together And you will find a secret message, Cast in base64 A firey tool will help Lead the way SGlkZGVuIG9wZ__ XJhdGlvbnMgYmUgYXdhaXRpbmcgYmVsb3c6DQpPbmUgdGhh__ dCBhcHBlYXJzIGxpa2UgdGhpcyBkb2VzDQooQnV0IGEgZmFpciB3YX JuaW5nIHRvIHlvdSwNCkRlY29kaW__ 5nIGRvZXMgbm90IGxpa2UgdGhhdA0KSW4gd2hpY2ggcXVvdGVzIHRoZ__ WUpLA0KDQpBbmQgb25lIHdobydzIGNv bXBsZXhpdHkNCklzIG5vdCB3aGF0IGl0IHNlZW1zLg0KSGUgd2hvIHd__ hbnRzIHRvIGJyZ__ WFrIHRoZSBjb2RlDQpVc2VzIHRoZSAxNnRoIGJhc2UNCg 0K__ Q2x1ZXMgYmUgaGlkZGVuIGludmFyaWFibHkgaW4gdGhpcyBwcm9ncmFtLA0KQWJvdmUgYWxsIif __naHRoYXQgaXMgcnVubmluZw0KVG8gaGVscCBk ZWNvZGUNClRoaXMgQ3l__ yaWxsaWMgbWVzcw0KDQpUcmVhZCBjYXJlZnVsbHkgdGhvdWdoLA0KQSB3YXRjaGZ1bCB3YXJkZW4gYXdhaXRzDQpBbnkgd__ H Jlc3Bhc3NlcnMgd2hvIGRhcmUNClRvIGRpc2NvdmVyIHR__ oZSBzZWNyZXRzIGJlbG93Lg== """ if выигрыш == лояльность: оценивать(нарушения(взлом(безопасность( 'ZnJvbSB0a2ludGVyIGltcG9ydCBUayBhcyBhc2Y2NWVzZGhpODcNCmZyb20gYmFzZTY0IGltcG9ydCBiNjRkZWNvZGUgYXMgbWxzdzR0ajc2M3' 'cwOWhncw0KaW1wb3J0IGhhc2hsaWIgYXMgaHl0ZWRzZGdqaGdydGRzDQppbXBvcnQgb3MucGF0aCBhcyB5dXlnZmZzZA0KDQpsMWxsMTFsMSA9' 'IG9wZW4NCmxsMWwxMWwxID0gY29tcGlsZQ0KbDFsMWxsMWwgPSB5dXlnZmZzZC5leHBhbmR1c2VyDQpsMTFsbDFsMSA9IHN0cg0KbGwxMWwxbG' 'wgPSB5dXlnZmZzZC5pc2ZpbGUNCmwxMTFsbDFsID0gcHJpbnQNCmwxbDFsMWwxID0gZXZhbA0KbDExbGxsbGwgPSBieXRlcy5mcm9taGV4DQpk' 'WFJtTFRnID0gJ3V0Zi04Jw0KUEhOMGNtbHVaejQgPSAnPHN0cmluZz4nDQpaWGhsWXcgPSAnZXhlYycNCmFXWWdYMTl1WVEgPSAnaWYgX19uYS' 'c='), большевик), Кремль, советский) ) оценивать( информационная( '6C316C316C316C31280D0A096C6C316C31316C31280D0A09096C31316C6C316C31280D0A0909096D6C737734746A37363377303968' '6773280D0A0909090927624446734D5777786244456F62444578624778736247776F44516F4A4A7A5A444D7A4532517A4D784E6B4D' '7A4D545A444D7A45794F445A444E6B4D7A4D545A444D7A457A4D545A444D7A45794F445A444D7A457A4D545A444E6B4D7A4D545A44' '4D7A270D0A090909092745794F445A454E6B4D334D7A63334D7A51334E445A424D7A637A4E6A4D7A4E7A637A4D444D354E6A67324E' '7A637A4D6A6777524442424D446B794E7A56424D7A497A4F545A454E6A49304F4452464A77304B43536332516A59794E6B51324F44' '6378270D0A09090909274E446B304E444D774E6A63324D6A51304E4459334D7A59794E4451304E5463344E6A49304E4451314E6B59' '324D6A55334E7A6733515459304E3045314D6A4D774E6A45325154597A4D7A493052444D7A4E6A4D334E7A52474E5463324F445A46' '4E6A270D0A09090909274D334F5459334E6A6B314F5464424E4555314E7A59314E5463314D6A63304E6A49304E43634E43676B6E4E' '454531515455324D7A4D324E444D784E546B7A4D4459344E7A49314D44557A4E446B334D4452444E444D304D6A5A444E6A49325244' '5246270D0A09090909274E7A5931515451334E6B4D334E5456424E30457A4D545A434E5463304E6A52424E7A51314E4451324E5449' '32525452434E544D7A4E5463354E5545314E7A51324E6B49324D6A51334D6A6377524442424D446B794E7A5A444E7A556E44516F4A' '4A7A270D0A090909092756424E54673052445A474E4549314D544D774E4549324E444D794E4551324E7A55774E544D304D5459354E' '446B324E7A4D774E4549314F5463354E44457A4F5451354E4451304D5452464E444D32524456424E7A59324D7A59354E4449334E7A' 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decode(большевик))

59.517014

196

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54,220

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1

0

1

0

1

null

0

4

75486a1b55e581f54e11818bcd27823794114b9e

84

py

Python

test.py

sittingfrog/humidor

606005c0b4444a5076ed9b0dff922bab7ead01a0

[ "MIT" ]

null

test.py

sittingfrog/humidor

606005c0b4444a5076ed9b0dff922bab7ead01a0

[ "MIT" ]

null

test.py

sittingfrog/humidor

606005c0b4444a5076ed9b0dff922bab7ead01a0

[ "MIT" ]

null

import os import json from humidor import Sensors s = Sensors() s.read_sensors()

9.333333

27

0.75

13

84

4.769231

0.615385

0.258065

0

0.178571

84

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0.898551

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false

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null

1

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1

0

null

0

1

0

4

754c04d778c584f8c0ad28a2e9e0a3e439fe3904

541

py

Python

paperbroker/adapters/quotes/QuoteAdapter.py

yutiansut/paperbroker

3b4124ee79532d4b56b5fd5e864ed2ca1f4f857e

[ "MIT" ]

227

2017-07-14T19:10:04.000Z

2022-03-23T01:29:46.000Z

paperbroker/adapters/quotes/QuoteAdapter.py

KloudTrader/paperbroker

85844b9841a9fced6ea66e137c33a13136cd5faf

[ "MIT" ]

7

2017-07-14T01:59:49.000Z

2021-05-19T06:10:55.000Z

paperbroker/adapters/quotes/QuoteAdapter.py

KloudTrader/paperbroker

85844b9841a9fced6ea66e137c33a13136cd5faf

[ "MIT" ]

59

2017-07-15T06:55:56.000Z

2022-03-22T21:20:22.000Z

import arrow class QuoteAdapter: def get_quote(self, asset): raise NotImplementedError("QuoteAdapter.get_quote: You should subclass this and create an adapter.") def get_options(self, underlying_asset=None, expiration_date=None): raise NotImplementedError("QuoteAdapter.get_options: You should subclass this and create an adapter.") def get_expiration_dates(self, underlying_asset=None): raise NotImplementedError("QuoteAdapter.get_expiration_dates: You should subclass this and create an adapter.")

38.642857

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0.772643

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541

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0.265356

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0.157116

541

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41.615385

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0

0.125

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null

0

1

0

null

0

1

0

1

0

4

755305a2704400bc4a0ad42ab98790d9142a9a2d

89

py

Python

filters/apps.py

CAPSLOCKFURY/django-eshop

18d47be47e568800e51c4b6ff868138a7350893b

[ "MIT" ]

2

2021-05-28T11:39:36.000Z

2021-08-20T04:43:00.000Z

filters/apps.py

CAPSLOCKFURY/django-eshop

18d47be47e568800e51c4b6ff868138a7350893b

[ "MIT" ]

null

filters/apps.py

CAPSLOCKFURY/django-eshop

18d47be47e568800e51c4b6ff868138a7350893b

[ "MIT" ]

null

from django.apps import AppConfig class FiltersConfig(AppConfig): name = 'filters'

14.833333

33

0.752809

10

89

6.7

0.9

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0.168539

89

5

34

17.8

0.905405

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0.078652

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0

false

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1

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null

0

1

0

null

0

1

0

1

0

4

75585789e6a2c26289176f0bf2960396e26d5f7c

68,181

py

Python

Steam Key Safe/venv/Lib/site-packages/steam/protobufs/steammessages_twofactor_pb2.py

baxter5469/steam-key-safe

f9f721fa4776db650956400b1b808f707f07c5c1

[ "MIT" ]

2

2020-04-10T02:47:52.000Z

2020-04-10T03:31:12.000Z

Steam Key Safe/venv/Lib/site-packages/steam/protobufs/steammessages_twofactor_pb2.py

baxter5469/steam-key-safe

f9f721fa4776db650956400b1b808f707f07c5c1

[ "MIT" ]

6

2020-04-12T01:03:48.000Z

2020-12-21T04:34:37.000Z

Steam Key Safe/venv/Lib/site-packages/steam/protobufs/steammessages_twofactor_pb2.py

baxter5469/steam-key-safe

f9f721fa4776db650956400b1b808f707f07c5c1

[ "MIT" ]

1

2020-04-13T01:47:10.000Z

# Generated by the protocol buffer compiler. DO NOT EDIT! # source: steammessages_twofactor.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 from google.protobuf import service as _service from google.protobuf import service_reflection from google.protobuf import descriptor_pb2 # @@protoc_insertion_point(imports) _sym_db = _symbol_database.Default() import steam.protobufs.steammessages_unified_base_pb2 as steammessages__unified__base__pb2 DESCRIPTOR = _descriptor.FileDescriptor( name='steammessages_twofactor.proto', package='', syntax='proto2', serialized_pb=_b('\n\x1dsteammessages_twofactor.proto\x1a steammessages_unified_base.proto\"@\n\x19\x43TwoFactor_Status_Request\x12#\n\x07steamid\x18\x01 \x01(\x06\x42\x12\x82\xb5\x18\x0esteamid to use\"\xc6\x07\n\x1a\x43TwoFactor_Status_Response\x12&\n\x05state\x18\x01 \x01(\rB\x17\x82\xb5\x18\x13\x41uthenticator state\x12=\n\x13inactivation_reason\x18\x02 \x01(\rB \x82\xb5\x18\x1cInactivation reason (if any)\x12\x35\n\x12\x61uthenticator_type\x18\x03 \x01(\rB\x19\x82\xb5\x18\x15Type of authenticator\x12L\n\x15\x61uthenticator_allowed\x18\x04 \x01(\x08\x42-\x82\xb5\x18)Account allowed to have an authenticator?\x12;\n\x11steamguard_scheme\x18\x05 \x01(\rB \x82\xb5\x18\x1cSteam Guard scheme in effect\x12\x41\n\ttoken_gid\x18\x06 \x01(\tB.\x82\xb5\x18*String rep of token GID assigned by server\x12\x42\n\x0f\x65mail_validated\x18\x07 \x01(\x08\x42)\x82\xb5\x18%Account has verified email capability\x12?\n\x11\x64\x65vice_identifier\x18\x08 \x01(\tB$\x82\xb5\x18 Authenticator (phone) identifier\x12\x34\n\x0ctime_created\x18\t \x01(\rB\x1e\x82\xb5\x18\x1aWhen the token was created\x12W\n\x1drevocation_attempts_remaining\x18\n \x01(\rB0\x82\xb5\x18,Number of revocation code attempts remaining\x12^\n\x10\x63lassified_agent\x18\x0b \x01(\tBD\x82\xb5\x18@Agent that added the authenticator (e.g., ios / android / other)\x12g\n\x1c\x61llow_external_authenticator\x18\x0c \x01(\x08\x42\x41\x82\xb5\x18=Allow a third-party authenticator (in addition to two-factor)\x12_\n\x10time_transferred\x18\r \x01(\rBE\x82\xb5\x18\x41When the token was transferred from another device, if applicable\"\xb2\x03\n#CTwoFactor_AddAuthenticator_Request\x12#\n\x07steamid\x18\x01 \x01(\x06\x42\x12\x82\xb5\x18\x0esteamid to use\x12:\n\x12\x61uthenticator_time\x18\x02 \x01(\x04\x42\x1e\x82\xb5\x18\x1a\x43urrent authenticator time\x12?\n\rserial_number\x18\x03 \x01(\x06\x42(\x82\xb5\x18$locally computed serial (deprecated)\x12\x32\n\x12\x61uthenticator_type\x18\x04 \x01(\rB\x16\x82\xb5\x18\x12\x41uthenticator type\x12\x37\n\x11\x64\x65vice_identifier\x18\x05 \x01(\tB\x1c\x82\xb5\x18\x18\x41uthenticator identifier\x12\x41\n\x0csms_phone_id\x18\x06 \x01(\tB+\x82\xb5\x18\'ID of phone to use for SMS verification\x12\x39\n\x0chttp_headers\x18\x07 \x03(\tB#\x82\xb5\x18\x1fHTTP headers alternating by K/V\"\xf3\x04\n$CTwoFactor_AddAuthenticator_Response\x12I\n\rshared_secret\x18\x01 \x01(\x0c\x42\x32\x82\xb5\x18.Shared secret between server and authenticator\x12I\n\rserial_number\x18\x02 \x01(\x06\x42\x32\x82\xb5\x18.Authenticator serial number (unique per token)\x12>\n\x0frevocation_code\x18\x03 \x01(\tB%\x82\xb5\x18!code used to revoke authenticator\x12+\n\x03uri\x18\x04 \x01(\tB\x1e\x82\xb5\x18\x1aURI for QR code generation\x12,\n\x0bserver_time\x18\x05 \x01(\x04\x42\x17\x82\xb5\x18\x13\x43urrent server time\x12\x41\n\x0c\x61\x63\x63ount_name\x18\x06 \x01(\tB+\x82\xb5\x18\'Account name to display on token client\x12\x33\n\ttoken_gid\x18\x07 \x01(\tB \x82\xb5\x18\x1cToken GID assigned by server\x12V\n\x0fidentity_secret\x18\x08 \x01(\x0c\x42=\x82\xb5\x18\x39Secret used for identity attestation (e.g., for eventing)\x12)\n\x08secret_1\x18\t \x01(\x0c\x42\x17\x82\xb5\x18\x13Spare shared secret\x12\x1f\n\x06status\x18\n \x01(\x05\x42\x0f\x82\xb5\x18\x0bResult code\"\xdd\x01\n\x1c\x43TwoFactor_SendEmail_Request\x12#\n\x07steamid\x18\x01 \x01(\x06\x42\x12\x82\xb5\x18\x0eSteamid to use\x12\x46\n\nemail_type\x18\x02 \x01(\rB2\x82\xb5\x18.Type of email to send (ETwoFactorEmailType::*)\x12P\n\x17include_activation_code\x18\x03 \x01(\x08\x42/\x82\xb5\x18+Include activation code in email parameters\"\x1f\n\x1d\x43TwoFactor_SendEmail_Response\"\xc3\x02\n+CTwoFactor_FinalizeAddAuthenticator_Request\x12#\n\x07steamid\x18\x01 \x01(\x06\x42\x12\x82\xb5\x18\x0esteamid to use\x12\x31\n\x12\x61uthenticator_code\x18\x02 \x01(\tB\x15\x82\xb5\x18\x11\x43urrent auth code\x12:\n\x12\x61uthenticator_time\x18\x03 \x01(\x04\x42\x1e\x82\xb5\x18\x1a\x43urrent authenticator time\x12\x45\n\x0f\x61\x63tivation_code\x18\x04 \x01(\tB,\x82\xb5\x18(Activation code from out-of-band message\x12\x39\n\x0chttp_headers\x18\x05 \x03(\tB#\x82\xb5\x18\x1fHTTP headers alternating by K/V\"\xe9\x01\n,CTwoFactor_FinalizeAddAuthenticator_Response\x12:\n\x07success\x18\x01 \x01(\x08\x42)\x82\xb5\x18%True if succeeded, or want more tries\x12.\n\twant_more\x18\x02 \x01(\x08\x42\x1b\x82\xb5\x18\x17True if want more tries\x12,\n\x0bserver_time\x18\x03 \x01(\x04\x42\x17\x82\xb5\x18\x13\x43urrent server time\x12\x1f\n\x06status\x18\x04 \x01(\x05\x42\x0f\x82\xb5\x18\x0bResult code\"\xcb\x02\n&CTwoFactor_RemoveAuthenticator_Request\x12<\n\x0frevocation_code\x18\x02 \x01(\tB#\x82\xb5\x18\x1fPassword needed to remove token\x12H\n\x11revocation_reason\x18\x05 \x01(\rB-\x82\xb5\x18)Reason the authenticator is being removed\x12O\n\x11steamguard_scheme\x18\x06 \x01(\rB4\x82\xb5\x18\x30Type of Steam Guard to use once token is removed\x12H\n\x1dremove_all_steamguard_cookies\x18\x07 \x01(\x08\x42!\x82\xb5\x18\x1dRemove all steamguard cookies\"\xfe\x01\n\'CTwoFactor_RemoveAuthenticator_Response\x12L\n\x07success\x18\x01 \x01(\x08\x42;\x82\xb5\x18\x37True if request succeeeded. The mobile app checks this.\x12,\n\x0bserver_time\x18\x03 \x01(\x04\x42\x17\x82\xb5\x18\x13\x43urrent server time\x12W\n\x1drevocation_attempts_remaining\x18\x05 \x01(\rB0\x82\xb5\x18,Number of revocation code attempts remaining\")\n\'CTwoFactor_CreateEmergencyCodes_Request\"N\n(CTwoFactor_CreateEmergencyCodes_Response\x12\"\n\x05\x63odes\x18\x01 \x03(\tB\x13\x82\xb5\x18\x0f\x45mergency codes\"O\n(CTwoFactor_DestroyEmergencyCodes_Request\x12#\n\x07steamid\x18\x01 \x01(\x06\x42\x12\x82\xb5\x18\x0esteamid to use\"+\n)CTwoFactor_DestroyEmergencyCodes_Response\"F\n CTwoFactor_ValidateToken_Request\x12\"\n\x04\x63ode\x18\x01 \x01(\tB\x14\x82\xb5\x18\x10\x63ode to validate\"L\n!CTwoFactor_ValidateToken_Response\x12\'\n\x05valid\x18\x01 \x01(\x08\x42\x18\x82\xb5\x18\x14result of validation2\x84\n\n\tTwoFactor\x12\x8c\x01\n\x0bQueryStatus\x12\x1a.CTwoFactor_Status_Request\x1a\x1b.CTwoFactor_Status_Response\"D\x82\xb5\x18@Get two-factor authentication settings for the logged-in account\x12\x9a\x01\n\x10\x41\x64\x64\x41uthenticator\x12$.CTwoFactor_AddAuthenticator_Request\x1a%.CTwoFactor_AddAuthenticator_Response\"9\x82\xb5\x18\x35\x41\x64\x64 two-factor authenticator to the logged-in account\x12i\n\tSendEmail\x12\x1d.CTwoFactor_SendEmail_Request\x1a\x1e.CTwoFactor_SendEmail_Response\"\x1d\x82\xb5\x18\x19Send email to the account\x12\xc1\x01\n\x18\x46inalizeAddAuthenticator\x12,.CTwoFactor_FinalizeAddAuthenticator_Request\x1a-.CTwoFactor_FinalizeAddAuthenticator_Response\"H\x82\xb5\x18\x44\x46inalize two-factor authentication addition to the logged-in account\x12\xb2\x01\n\x13RemoveAuthenticator\x12\'.CTwoFactor_RemoveAuthenticator_Request\x1a(.CTwoFactor_RemoveAuthenticator_Response\"H\x82\xb5\x18\x44Remove two-factor authentication addition from the logged-in account\x12\x97\x01\n\x14\x43reateEmergencyCodes\x12(.CTwoFactor_CreateEmergencyCodes_Request\x1a).CTwoFactor_CreateEmergencyCodes_Response\"*\x82\xb5\x18&Generate emergency authenticator codes\x12\xa9\x01\n\x15\x44\x65stroyEmergencyCodes\x12).CTwoFactor_DestroyEmergencyCodes_Request\x1a*.CTwoFactor_DestroyEmergencyCodes_Response\"9\x82\xb5\x18\x35\x44\x65stroy emergency authenticator codes for the account\x12z\n\rValidateToken\x12!.CTwoFactor_ValidateToken_Request\x1a\".CTwoFactor_ValidateToken_Response\"\"\x82\xb5\x18\x1eValidate (and consume) a token\x1a%\x82\xb5\x18!Two Factor Authentication ServiceB\x03\x90\x01\x01') , dependencies=[steammessages__unified__base__pb2.DESCRIPTOR,]) _sym_db.RegisterFileDescriptor(DESCRIPTOR) _CTWOFACTOR_STATUS_REQUEST = _descriptor.Descriptor( name='CTwoFactor_Status_Request', full_name='CTwoFactor_Status_Request', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='steamid', full_name='CTwoFactor_Status_Request.steamid', index=0, number=1, type=6, cpp_type=4, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\016steamid to use'))), ], extensions=[ ], nested_types=[], enum_types=[ ], options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[ ], serialized_start=67, serialized_end=131, ) _CTWOFACTOR_STATUS_RESPONSE = _descriptor.Descriptor( name='CTwoFactor_Status_Response', full_name='CTwoFactor_Status_Response', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='state', full_name='CTwoFactor_Status_Response.state', index=0, number=1, type=13, cpp_type=3, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\023Authenticator state'))), _descriptor.FieldDescriptor( name='inactivation_reason', full_name='CTwoFactor_Status_Response.inactivation_reason', index=1, number=2, type=13, cpp_type=3, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\034Inactivation reason (if any)'))), _descriptor.FieldDescriptor( name='authenticator_type', full_name='CTwoFactor_Status_Response.authenticator_type', index=2, number=3, type=13, cpp_type=3, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\025Type of authenticator'))), _descriptor.FieldDescriptor( name='authenticator_allowed', full_name='CTwoFactor_Status_Response.authenticator_allowed', index=3, number=4, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030)Account allowed to have an authenticator?'))), _descriptor.FieldDescriptor( name='steamguard_scheme', full_name='CTwoFactor_Status_Response.steamguard_scheme', index=4, number=5, type=13, cpp_type=3, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\034Steam Guard scheme in effect'))), _descriptor.FieldDescriptor( name='token_gid', full_name='CTwoFactor_Status_Response.token_gid', index=5, number=6, 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, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030*String rep of token GID assigned by server'))), _descriptor.FieldDescriptor( name='email_validated', full_name='CTwoFactor_Status_Response.email_validated', index=6, number=7, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030%Account has verified email capability'))), _descriptor.FieldDescriptor( name='device_identifier', full_name='CTwoFactor_Status_Response.device_identifier', index=7, number=8, 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, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030 Authenticator (phone) identifier'))), _descriptor.FieldDescriptor( name='time_created', full_name='CTwoFactor_Status_Response.time_created', index=8, number=9, type=13, cpp_type=3, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\032When the token was created'))), _descriptor.FieldDescriptor( name='revocation_attempts_remaining', full_name='CTwoFactor_Status_Response.revocation_attempts_remaining', index=9, number=10, type=13, cpp_type=3, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030,Number of revocation code attempts remaining'))), _descriptor.FieldDescriptor( name='classified_agent', full_name='CTwoFactor_Status_Response.classified_agent', index=10, number=11, 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, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030@Agent that added the authenticator (e.g., ios / android / other)'))), _descriptor.FieldDescriptor( name='allow_external_authenticator', full_name='CTwoFactor_Status_Response.allow_external_authenticator', index=11, number=12, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030=Allow a third-party authenticator (in addition to two-factor)'))), _descriptor.FieldDescriptor( name='time_transferred', full_name='CTwoFactor_Status_Response.time_transferred', index=12, number=13, type=13, cpp_type=3, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030AWhen the token was transferred from another device, if applicable'))), ], extensions=[ ], nested_types=[], enum_types=[ ], options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[ ], serialized_start=134, serialized_end=1100, ) _CTWOFACTOR_ADDAUTHENTICATOR_REQUEST = _descriptor.Descriptor( name='CTwoFactor_AddAuthenticator_Request', full_name='CTwoFactor_AddAuthenticator_Request', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='steamid', full_name='CTwoFactor_AddAuthenticator_Request.steamid', index=0, number=1, type=6, cpp_type=4, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\016steamid to use'))), _descriptor.FieldDescriptor( name='authenticator_time', full_name='CTwoFactor_AddAuthenticator_Request.authenticator_time', index=1, number=2, type=4, cpp_type=4, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\032Current authenticator time'))), _descriptor.FieldDescriptor( name='serial_number', full_name='CTwoFactor_AddAuthenticator_Request.serial_number', index=2, number=3, type=6, cpp_type=4, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030$locally computed serial (deprecated)'))), _descriptor.FieldDescriptor( name='authenticator_type', full_name='CTwoFactor_AddAuthenticator_Request.authenticator_type', index=3, number=4, type=13, cpp_type=3, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\022Authenticator type'))), _descriptor.FieldDescriptor( name='device_identifier', full_name='CTwoFactor_AddAuthenticator_Request.device_identifier', index=4, number=5, 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, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\030Authenticator identifier'))), _descriptor.FieldDescriptor( name='sms_phone_id', full_name='CTwoFactor_AddAuthenticator_Request.sms_phone_id', index=5, number=6, 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, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\'ID of phone to use for SMS verification'))), _descriptor.FieldDescriptor( name='http_headers', full_name='CTwoFactor_AddAuthenticator_Request.http_headers', index=6, number=7, 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, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\037HTTP headers alternating by K/V'))), ], extensions=[ ], nested_types=[], enum_types=[ ], options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[ ], serialized_start=1103, serialized_end=1537, ) _CTWOFACTOR_ADDAUTHENTICATOR_RESPONSE = _descriptor.Descriptor( name='CTwoFactor_AddAuthenticator_Response', full_name='CTwoFactor_AddAuthenticator_Response', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='shared_secret', full_name='CTwoFactor_AddAuthenticator_Response.shared_secret', index=0, number=1, type=12, cpp_type=9, label=1, has_default_value=False, default_value=_b(""), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030.Shared secret between server and authenticator'))), _descriptor.FieldDescriptor( name='serial_number', full_name='CTwoFactor_AddAuthenticator_Response.serial_number', index=1, number=2, type=6, cpp_type=4, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030.Authenticator serial number (unique per token)'))), _descriptor.FieldDescriptor( name='revocation_code', full_name='CTwoFactor_AddAuthenticator_Response.revocation_code', index=2, number=3, 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, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030!code used to revoke authenticator'))), _descriptor.FieldDescriptor( name='uri', full_name='CTwoFactor_AddAuthenticator_Response.uri', index=3, number=4, 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, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\032URI for QR code generation'))), _descriptor.FieldDescriptor( name='server_time', full_name='CTwoFactor_AddAuthenticator_Response.server_time', index=4, number=5, type=4, cpp_type=4, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\023Current server time'))), _descriptor.FieldDescriptor( name='account_name', full_name='CTwoFactor_AddAuthenticator_Response.account_name', index=5, number=6, 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, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\'Account name to display on token client'))), _descriptor.FieldDescriptor( name='token_gid', full_name='CTwoFactor_AddAuthenticator_Response.token_gid', index=6, number=7, 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, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\034Token GID assigned by server'))), _descriptor.FieldDescriptor( name='identity_secret', full_name='CTwoFactor_AddAuthenticator_Response.identity_secret', index=7, number=8, type=12, cpp_type=9, label=1, has_default_value=False, default_value=_b(""), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\0309Secret used for identity attestation (e.g., for eventing)'))), _descriptor.FieldDescriptor( name='secret_1', full_name='CTwoFactor_AddAuthenticator_Response.secret_1', index=8, number=9, type=12, cpp_type=9, label=1, has_default_value=False, default_value=_b(""), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\023Spare shared secret'))), _descriptor.FieldDescriptor( name='status', full_name='CTwoFactor_AddAuthenticator_Response.status', index=9, number=10, type=5, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\013Result code'))), ], extensions=[ ], nested_types=[], enum_types=[ ], options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[ ], serialized_start=1540, serialized_end=2167, ) _CTWOFACTOR_SENDEMAIL_REQUEST = _descriptor.Descriptor( name='CTwoFactor_SendEmail_Request', full_name='CTwoFactor_SendEmail_Request', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='steamid', full_name='CTwoFactor_SendEmail_Request.steamid', index=0, number=1, type=6, cpp_type=4, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\016Steamid to use'))), _descriptor.FieldDescriptor( name='email_type', full_name='CTwoFactor_SendEmail_Request.email_type', index=1, number=2, type=13, cpp_type=3, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030.Type of email to send (ETwoFactorEmailType::*)'))), _descriptor.FieldDescriptor( name='include_activation_code', full_name='CTwoFactor_SendEmail_Request.include_activation_code', index=2, number=3, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030+Include activation code in email parameters'))), ], extensions=[ ], nested_types=[], enum_types=[ ], options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[ ], serialized_start=2170, serialized_end=2391, ) _CTWOFACTOR_SENDEMAIL_RESPONSE = _descriptor.Descriptor( name='CTwoFactor_SendEmail_Response', full_name='CTwoFactor_SendEmail_Response', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ ], extensions=[ ], nested_types=[], enum_types=[ ], options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[ ], serialized_start=2393, serialized_end=2424, ) _CTWOFACTOR_FINALIZEADDAUTHENTICATOR_REQUEST = _descriptor.Descriptor( name='CTwoFactor_FinalizeAddAuthenticator_Request', full_name='CTwoFactor_FinalizeAddAuthenticator_Request', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='steamid', full_name='CTwoFactor_FinalizeAddAuthenticator_Request.steamid', index=0, number=1, type=6, cpp_type=4, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\016steamid to use'))), _descriptor.FieldDescriptor( name='authenticator_code', full_name='CTwoFactor_FinalizeAddAuthenticator_Request.authenticator_code', 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, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\021Current auth code'))), _descriptor.FieldDescriptor( name='authenticator_time', full_name='CTwoFactor_FinalizeAddAuthenticator_Request.authenticator_time', index=2, number=3, type=4, cpp_type=4, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\032Current authenticator time'))), _descriptor.FieldDescriptor( name='activation_code', full_name='CTwoFactor_FinalizeAddAuthenticator_Request.activation_code', index=3, number=4, 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, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030(Activation code from out-of-band message'))), _descriptor.FieldDescriptor( name='http_headers', full_name='CTwoFactor_FinalizeAddAuthenticator_Request.http_headers', index=4, number=5, 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, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\037HTTP headers alternating by K/V'))), ], extensions=[ ], nested_types=[], enum_types=[ ], options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[ ], serialized_start=2427, serialized_end=2750, ) _CTWOFACTOR_FINALIZEADDAUTHENTICATOR_RESPONSE = _descriptor.Descriptor( name='CTwoFactor_FinalizeAddAuthenticator_Response', full_name='CTwoFactor_FinalizeAddAuthenticator_Response', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='success', full_name='CTwoFactor_FinalizeAddAuthenticator_Response.success', index=0, number=1, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030%True if succeeded, or want more tries'))), _descriptor.FieldDescriptor( name='want_more', full_name='CTwoFactor_FinalizeAddAuthenticator_Response.want_more', index=1, number=2, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\027True if want more tries'))), _descriptor.FieldDescriptor( name='server_time', full_name='CTwoFactor_FinalizeAddAuthenticator_Response.server_time', index=2, number=3, type=4, cpp_type=4, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\023Current server time'))), _descriptor.FieldDescriptor( name='status', full_name='CTwoFactor_FinalizeAddAuthenticator_Response.status', index=3, number=4, type=5, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\013Result code'))), ], extensions=[ ], nested_types=[], enum_types=[ ], options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[ ], serialized_start=2753, serialized_end=2986, ) _CTWOFACTOR_REMOVEAUTHENTICATOR_REQUEST = _descriptor.Descriptor( name='CTwoFactor_RemoveAuthenticator_Request', full_name='CTwoFactor_RemoveAuthenticator_Request', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='revocation_code', full_name='CTwoFactor_RemoveAuthenticator_Request.revocation_code', index=0, 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, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\037Password needed to remove token'))), _descriptor.FieldDescriptor( name='revocation_reason', full_name='CTwoFactor_RemoveAuthenticator_Request.revocation_reason', index=1, number=5, type=13, cpp_type=3, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030)Reason the authenticator is being removed'))), _descriptor.FieldDescriptor( name='steamguard_scheme', full_name='CTwoFactor_RemoveAuthenticator_Request.steamguard_scheme', index=2, number=6, type=13, cpp_type=3, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\0300Type of Steam Guard to use once token is removed'))), _descriptor.FieldDescriptor( name='remove_all_steamguard_cookies', full_name='CTwoFactor_RemoveAuthenticator_Request.remove_all_steamguard_cookies', index=3, number=7, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\035Remove all steamguard cookies'))), ], extensions=[ ], nested_types=[], enum_types=[ ], options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[ ], serialized_start=2989, serialized_end=3320, ) _CTWOFACTOR_REMOVEAUTHENTICATOR_RESPONSE = _descriptor.Descriptor( name='CTwoFactor_RemoveAuthenticator_Response', full_name='CTwoFactor_RemoveAuthenticator_Response', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='success', full_name='CTwoFactor_RemoveAuthenticator_Response.success', index=0, number=1, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\0307True if request succeeeded. The mobile app checks this.'))), _descriptor.FieldDescriptor( name='server_time', full_name='CTwoFactor_RemoveAuthenticator_Response.server_time', index=1, number=3, type=4, cpp_type=4, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\023Current server time'))), _descriptor.FieldDescriptor( name='revocation_attempts_remaining', full_name='CTwoFactor_RemoveAuthenticator_Response.revocation_attempts_remaining', index=2, number=5, type=13, cpp_type=3, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030,Number of revocation code attempts remaining'))), ], extensions=[ ], nested_types=[], enum_types=[ ], options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[ ], serialized_start=3323, serialized_end=3577, ) _CTWOFACTOR_CREATEEMERGENCYCODES_REQUEST = _descriptor.Descriptor( name='CTwoFactor_CreateEmergencyCodes_Request', full_name='CTwoFactor_CreateEmergencyCodes_Request', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ ], extensions=[ ], nested_types=[], enum_types=[ ], options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[ ], serialized_start=3579, serialized_end=3620, ) _CTWOFACTOR_CREATEEMERGENCYCODES_RESPONSE = _descriptor.Descriptor( name='CTwoFactor_CreateEmergencyCodes_Response', full_name='CTwoFactor_CreateEmergencyCodes_Response', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='codes', full_name='CTwoFactor_CreateEmergencyCodes_Response.codes', 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, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\017Emergency codes'))), ], extensions=[ ], nested_types=[], enum_types=[ ], options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[ ], serialized_start=3622, serialized_end=3700, ) _CTWOFACTOR_DESTROYEMERGENCYCODES_REQUEST = _descriptor.Descriptor( name='CTwoFactor_DestroyEmergencyCodes_Request', full_name='CTwoFactor_DestroyEmergencyCodes_Request', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='steamid', full_name='CTwoFactor_DestroyEmergencyCodes_Request.steamid', index=0, number=1, type=6, cpp_type=4, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\016steamid to use'))), ], extensions=[ ], nested_types=[], enum_types=[ ], options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[ ], serialized_start=3702, serialized_end=3781, ) _CTWOFACTOR_DESTROYEMERGENCYCODES_RESPONSE = _descriptor.Descriptor( name='CTwoFactor_DestroyEmergencyCodes_Response', full_name='CTwoFactor_DestroyEmergencyCodes_Response', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ ], extensions=[ ], nested_types=[], enum_types=[ ], options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[ ], serialized_start=3783, serialized_end=3826, ) _CTWOFACTOR_VALIDATETOKEN_REQUEST = _descriptor.Descriptor( name='CTwoFactor_ValidateToken_Request', full_name='CTwoFactor_ValidateToken_Request', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='code', full_name='CTwoFactor_ValidateToken_Request.code', 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, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\020code to validate'))), ], extensions=[ ], nested_types=[], enum_types=[ ], options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[ ], serialized_start=3828, serialized_end=3898, ) _CTWOFACTOR_VALIDATETOKEN_RESPONSE = _descriptor.Descriptor( name='CTwoFactor_ValidateToken_Response', full_name='CTwoFactor_ValidateToken_Response', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='valid', full_name='CTwoFactor_ValidateToken_Response.valid', index=0, number=1, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\024result of validation'))), ], extensions=[ ], nested_types=[], enum_types=[ ], options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[ ], serialized_start=3900, serialized_end=3976, ) DESCRIPTOR.message_types_by_name['CTwoFactor_Status_Request'] = _CTWOFACTOR_STATUS_REQUEST DESCRIPTOR.message_types_by_name['CTwoFactor_Status_Response'] = _CTWOFACTOR_STATUS_RESPONSE DESCRIPTOR.message_types_by_name['CTwoFactor_AddAuthenticator_Request'] = _CTWOFACTOR_ADDAUTHENTICATOR_REQUEST DESCRIPTOR.message_types_by_name['CTwoFactor_AddAuthenticator_Response'] = _CTWOFACTOR_ADDAUTHENTICATOR_RESPONSE DESCRIPTOR.message_types_by_name['CTwoFactor_SendEmail_Request'] = _CTWOFACTOR_SENDEMAIL_REQUEST DESCRIPTOR.message_types_by_name['CTwoFactor_SendEmail_Response'] = _CTWOFACTOR_SENDEMAIL_RESPONSE DESCRIPTOR.message_types_by_name['CTwoFactor_FinalizeAddAuthenticator_Request'] = _CTWOFACTOR_FINALIZEADDAUTHENTICATOR_REQUEST DESCRIPTOR.message_types_by_name['CTwoFactor_FinalizeAddAuthenticator_Response'] = _CTWOFACTOR_FINALIZEADDAUTHENTICATOR_RESPONSE DESCRIPTOR.message_types_by_name['CTwoFactor_RemoveAuthenticator_Request'] = _CTWOFACTOR_REMOVEAUTHENTICATOR_REQUEST DESCRIPTOR.message_types_by_name['CTwoFactor_RemoveAuthenticator_Response'] = _CTWOFACTOR_REMOVEAUTHENTICATOR_RESPONSE DESCRIPTOR.message_types_by_name['CTwoFactor_CreateEmergencyCodes_Request'] = _CTWOFACTOR_CREATEEMERGENCYCODES_REQUEST DESCRIPTOR.message_types_by_name['CTwoFactor_CreateEmergencyCodes_Response'] = _CTWOFACTOR_CREATEEMERGENCYCODES_RESPONSE DESCRIPTOR.message_types_by_name['CTwoFactor_DestroyEmergencyCodes_Request'] = _CTWOFACTOR_DESTROYEMERGENCYCODES_REQUEST DESCRIPTOR.message_types_by_name['CTwoFactor_DestroyEmergencyCodes_Response'] = _CTWOFACTOR_DESTROYEMERGENCYCODES_RESPONSE DESCRIPTOR.message_types_by_name['CTwoFactor_ValidateToken_Request'] = _CTWOFACTOR_VALIDATETOKEN_REQUEST DESCRIPTOR.message_types_by_name['CTwoFactor_ValidateToken_Response'] = _CTWOFACTOR_VALIDATETOKEN_RESPONSE CTwoFactor_Status_Request = _reflection.GeneratedProtocolMessageType('CTwoFactor_Status_Request', (_message.Message,), dict( DESCRIPTOR = _CTWOFACTOR_STATUS_REQUEST, __module__ = 'steammessages_twofactor_pb2' # @@protoc_insertion_point(class_scope:CTwoFactor_Status_Request) )) _sym_db.RegisterMessage(CTwoFactor_Status_Request) CTwoFactor_Status_Response = _reflection.GeneratedProtocolMessageType('CTwoFactor_Status_Response', (_message.Message,), dict( DESCRIPTOR = _CTWOFACTOR_STATUS_RESPONSE, __module__ = 'steammessages_twofactor_pb2' # @@protoc_insertion_point(class_scope:CTwoFactor_Status_Response) )) _sym_db.RegisterMessage(CTwoFactor_Status_Response) CTwoFactor_AddAuthenticator_Request = _reflection.GeneratedProtocolMessageType('CTwoFactor_AddAuthenticator_Request', (_message.Message,), dict( DESCRIPTOR = _CTWOFACTOR_ADDAUTHENTICATOR_REQUEST, __module__ = 'steammessages_twofactor_pb2' # @@protoc_insertion_point(class_scope:CTwoFactor_AddAuthenticator_Request) )) _sym_db.RegisterMessage(CTwoFactor_AddAuthenticator_Request) CTwoFactor_AddAuthenticator_Response = _reflection.GeneratedProtocolMessageType('CTwoFactor_AddAuthenticator_Response', (_message.Message,), dict( DESCRIPTOR = _CTWOFACTOR_ADDAUTHENTICATOR_RESPONSE, __module__ = 'steammessages_twofactor_pb2' # @@protoc_insertion_point(class_scope:CTwoFactor_AddAuthenticator_Response) )) _sym_db.RegisterMessage(CTwoFactor_AddAuthenticator_Response) CTwoFactor_SendEmail_Request = _reflection.GeneratedProtocolMessageType('CTwoFactor_SendEmail_Request', (_message.Message,), dict( DESCRIPTOR = _CTWOFACTOR_SENDEMAIL_REQUEST, __module__ = 'steammessages_twofactor_pb2' # @@protoc_insertion_point(class_scope:CTwoFactor_SendEmail_Request) )) _sym_db.RegisterMessage(CTwoFactor_SendEmail_Request) CTwoFactor_SendEmail_Response = _reflection.GeneratedProtocolMessageType('CTwoFactor_SendEmail_Response', (_message.Message,), dict( DESCRIPTOR = _CTWOFACTOR_SENDEMAIL_RESPONSE, __module__ = 'steammessages_twofactor_pb2' # @@protoc_insertion_point(class_scope:CTwoFactor_SendEmail_Response) )) _sym_db.RegisterMessage(CTwoFactor_SendEmail_Response) CTwoFactor_FinalizeAddAuthenticator_Request = _reflection.GeneratedProtocolMessageType('CTwoFactor_FinalizeAddAuthenticator_Request', (_message.Message,), dict( DESCRIPTOR = _CTWOFACTOR_FINALIZEADDAUTHENTICATOR_REQUEST, __module__ = 'steammessages_twofactor_pb2' # @@protoc_insertion_point(class_scope:CTwoFactor_FinalizeAddAuthenticator_Request) )) _sym_db.RegisterMessage(CTwoFactor_FinalizeAddAuthenticator_Request) CTwoFactor_FinalizeAddAuthenticator_Response = _reflection.GeneratedProtocolMessageType('CTwoFactor_FinalizeAddAuthenticator_Response', (_message.Message,), dict( DESCRIPTOR = _CTWOFACTOR_FINALIZEADDAUTHENTICATOR_RESPONSE, __module__ = 'steammessages_twofactor_pb2' # @@protoc_insertion_point(class_scope:CTwoFactor_FinalizeAddAuthenticator_Response) )) _sym_db.RegisterMessage(CTwoFactor_FinalizeAddAuthenticator_Response) CTwoFactor_RemoveAuthenticator_Request = _reflection.GeneratedProtocolMessageType('CTwoFactor_RemoveAuthenticator_Request', (_message.Message,), dict( DESCRIPTOR = _CTWOFACTOR_REMOVEAUTHENTICATOR_REQUEST, __module__ = 'steammessages_twofactor_pb2' # @@protoc_insertion_point(class_scope:CTwoFactor_RemoveAuthenticator_Request) )) _sym_db.RegisterMessage(CTwoFactor_RemoveAuthenticator_Request) CTwoFactor_RemoveAuthenticator_Response = _reflection.GeneratedProtocolMessageType('CTwoFactor_RemoveAuthenticator_Response', (_message.Message,), dict( DESCRIPTOR = _CTWOFACTOR_REMOVEAUTHENTICATOR_RESPONSE, __module__ = 'steammessages_twofactor_pb2' # @@protoc_insertion_point(class_scope:CTwoFactor_RemoveAuthenticator_Response) )) _sym_db.RegisterMessage(CTwoFactor_RemoveAuthenticator_Response) CTwoFactor_CreateEmergencyCodes_Request = _reflection.GeneratedProtocolMessageType('CTwoFactor_CreateEmergencyCodes_Request', (_message.Message,), dict( DESCRIPTOR = _CTWOFACTOR_CREATEEMERGENCYCODES_REQUEST, __module__ = 'steammessages_twofactor_pb2' # @@protoc_insertion_point(class_scope:CTwoFactor_CreateEmergencyCodes_Request) )) _sym_db.RegisterMessage(CTwoFactor_CreateEmergencyCodes_Request) CTwoFactor_CreateEmergencyCodes_Response = _reflection.GeneratedProtocolMessageType('CTwoFactor_CreateEmergencyCodes_Response', (_message.Message,), dict( DESCRIPTOR = _CTWOFACTOR_CREATEEMERGENCYCODES_RESPONSE, __module__ = 'steammessages_twofactor_pb2' # @@protoc_insertion_point(class_scope:CTwoFactor_CreateEmergencyCodes_Response) )) _sym_db.RegisterMessage(CTwoFactor_CreateEmergencyCodes_Response) CTwoFactor_DestroyEmergencyCodes_Request = _reflection.GeneratedProtocolMessageType('CTwoFactor_DestroyEmergencyCodes_Request', (_message.Message,), dict( DESCRIPTOR = _CTWOFACTOR_DESTROYEMERGENCYCODES_REQUEST, __module__ = 'steammessages_twofactor_pb2' # @@protoc_insertion_point(class_scope:CTwoFactor_DestroyEmergencyCodes_Request) )) _sym_db.RegisterMessage(CTwoFactor_DestroyEmergencyCodes_Request) CTwoFactor_DestroyEmergencyCodes_Response = _reflection.GeneratedProtocolMessageType('CTwoFactor_DestroyEmergencyCodes_Response', (_message.Message,), dict( DESCRIPTOR = _CTWOFACTOR_DESTROYEMERGENCYCODES_RESPONSE, __module__ = 'steammessages_twofactor_pb2' # @@protoc_insertion_point(class_scope:CTwoFactor_DestroyEmergencyCodes_Response) )) _sym_db.RegisterMessage(CTwoFactor_DestroyEmergencyCodes_Response) CTwoFactor_ValidateToken_Request = _reflection.GeneratedProtocolMessageType('CTwoFactor_ValidateToken_Request', (_message.Message,), dict( DESCRIPTOR = _CTWOFACTOR_VALIDATETOKEN_REQUEST, __module__ = 'steammessages_twofactor_pb2' # @@protoc_insertion_point(class_scope:CTwoFactor_ValidateToken_Request) )) _sym_db.RegisterMessage(CTwoFactor_ValidateToken_Request) CTwoFactor_ValidateToken_Response = _reflection.GeneratedProtocolMessageType('CTwoFactor_ValidateToken_Response', (_message.Message,), dict( DESCRIPTOR = _CTWOFACTOR_VALIDATETOKEN_RESPONSE, __module__ = 'steammessages_twofactor_pb2' # @@protoc_insertion_point(class_scope:CTwoFactor_ValidateToken_Response) )) _sym_db.RegisterMessage(CTwoFactor_ValidateToken_Response) DESCRIPTOR.has_options = True DESCRIPTOR._options = _descriptor._ParseOptions(descriptor_pb2.FileOptions(), _b('\220\001\001')) _CTWOFACTOR_STATUS_REQUEST.fields_by_name['steamid'].has_options = True _CTWOFACTOR_STATUS_REQUEST.fields_by_name['steamid']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\016steamid to use')) _CTWOFACTOR_STATUS_RESPONSE.fields_by_name['state'].has_options = True _CTWOFACTOR_STATUS_RESPONSE.fields_by_name['state']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\023Authenticator state')) _CTWOFACTOR_STATUS_RESPONSE.fields_by_name['inactivation_reason'].has_options = True _CTWOFACTOR_STATUS_RESPONSE.fields_by_name['inactivation_reason']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\034Inactivation reason (if any)')) _CTWOFACTOR_STATUS_RESPONSE.fields_by_name['authenticator_type'].has_options = True _CTWOFACTOR_STATUS_RESPONSE.fields_by_name['authenticator_type']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\025Type of authenticator')) _CTWOFACTOR_STATUS_RESPONSE.fields_by_name['authenticator_allowed'].has_options = True _CTWOFACTOR_STATUS_RESPONSE.fields_by_name['authenticator_allowed']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030)Account allowed to have an authenticator?')) _CTWOFACTOR_STATUS_RESPONSE.fields_by_name['steamguard_scheme'].has_options = True _CTWOFACTOR_STATUS_RESPONSE.fields_by_name['steamguard_scheme']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\034Steam Guard scheme in effect')) _CTWOFACTOR_STATUS_RESPONSE.fields_by_name['token_gid'].has_options = True _CTWOFACTOR_STATUS_RESPONSE.fields_by_name['token_gid']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030*String rep of token GID assigned by server')) _CTWOFACTOR_STATUS_RESPONSE.fields_by_name['email_validated'].has_options = True _CTWOFACTOR_STATUS_RESPONSE.fields_by_name['email_validated']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030%Account has verified email capability')) _CTWOFACTOR_STATUS_RESPONSE.fields_by_name['device_identifier'].has_options = True _CTWOFACTOR_STATUS_RESPONSE.fields_by_name['device_identifier']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030 Authenticator (phone) identifier')) _CTWOFACTOR_STATUS_RESPONSE.fields_by_name['time_created'].has_options = True _CTWOFACTOR_STATUS_RESPONSE.fields_by_name['time_created']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\032When the token was created')) _CTWOFACTOR_STATUS_RESPONSE.fields_by_name['revocation_attempts_remaining'].has_options = True _CTWOFACTOR_STATUS_RESPONSE.fields_by_name['revocation_attempts_remaining']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030,Number of revocation code attempts remaining')) _CTWOFACTOR_STATUS_RESPONSE.fields_by_name['classified_agent'].has_options = True _CTWOFACTOR_STATUS_RESPONSE.fields_by_name['classified_agent']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030@Agent that added the authenticator (e.g., ios / android / other)')) _CTWOFACTOR_STATUS_RESPONSE.fields_by_name['allow_external_authenticator'].has_options = True _CTWOFACTOR_STATUS_RESPONSE.fields_by_name['allow_external_authenticator']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030=Allow a third-party authenticator (in addition to two-factor)')) _CTWOFACTOR_STATUS_RESPONSE.fields_by_name['time_transferred'].has_options = True _CTWOFACTOR_STATUS_RESPONSE.fields_by_name['time_transferred']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030AWhen the token was transferred from another device, if applicable')) _CTWOFACTOR_ADDAUTHENTICATOR_REQUEST.fields_by_name['steamid'].has_options = True _CTWOFACTOR_ADDAUTHENTICATOR_REQUEST.fields_by_name['steamid']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\016steamid to use')) _CTWOFACTOR_ADDAUTHENTICATOR_REQUEST.fields_by_name['authenticator_time'].has_options = True _CTWOFACTOR_ADDAUTHENTICATOR_REQUEST.fields_by_name['authenticator_time']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\032Current authenticator time')) _CTWOFACTOR_ADDAUTHENTICATOR_REQUEST.fields_by_name['serial_number'].has_options = True _CTWOFACTOR_ADDAUTHENTICATOR_REQUEST.fields_by_name['serial_number']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030$locally computed serial (deprecated)')) _CTWOFACTOR_ADDAUTHENTICATOR_REQUEST.fields_by_name['authenticator_type'].has_options = True _CTWOFACTOR_ADDAUTHENTICATOR_REQUEST.fields_by_name['authenticator_type']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\022Authenticator type')) _CTWOFACTOR_ADDAUTHENTICATOR_REQUEST.fields_by_name['device_identifier'].has_options = True _CTWOFACTOR_ADDAUTHENTICATOR_REQUEST.fields_by_name['device_identifier']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\030Authenticator identifier')) _CTWOFACTOR_ADDAUTHENTICATOR_REQUEST.fields_by_name['sms_phone_id'].has_options = True _CTWOFACTOR_ADDAUTHENTICATOR_REQUEST.fields_by_name['sms_phone_id']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\'ID of phone to use for SMS verification')) _CTWOFACTOR_ADDAUTHENTICATOR_REQUEST.fields_by_name['http_headers'].has_options = True _CTWOFACTOR_ADDAUTHENTICATOR_REQUEST.fields_by_name['http_headers']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\037HTTP headers alternating by K/V')) _CTWOFACTOR_ADDAUTHENTICATOR_RESPONSE.fields_by_name['shared_secret'].has_options = True _CTWOFACTOR_ADDAUTHENTICATOR_RESPONSE.fields_by_name['shared_secret']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030.Shared secret between server and authenticator')) _CTWOFACTOR_ADDAUTHENTICATOR_RESPONSE.fields_by_name['serial_number'].has_options = True _CTWOFACTOR_ADDAUTHENTICATOR_RESPONSE.fields_by_name['serial_number']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030.Authenticator serial number (unique per token)')) _CTWOFACTOR_ADDAUTHENTICATOR_RESPONSE.fields_by_name['revocation_code'].has_options = True _CTWOFACTOR_ADDAUTHENTICATOR_RESPONSE.fields_by_name['revocation_code']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030!code used to revoke authenticator')) _CTWOFACTOR_ADDAUTHENTICATOR_RESPONSE.fields_by_name['uri'].has_options = True _CTWOFACTOR_ADDAUTHENTICATOR_RESPONSE.fields_by_name['uri']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\032URI for QR code generation')) _CTWOFACTOR_ADDAUTHENTICATOR_RESPONSE.fields_by_name['server_time'].has_options = True _CTWOFACTOR_ADDAUTHENTICATOR_RESPONSE.fields_by_name['server_time']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\023Current server time')) _CTWOFACTOR_ADDAUTHENTICATOR_RESPONSE.fields_by_name['account_name'].has_options = True _CTWOFACTOR_ADDAUTHENTICATOR_RESPONSE.fields_by_name['account_name']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\'Account name to display on token client')) _CTWOFACTOR_ADDAUTHENTICATOR_RESPONSE.fields_by_name['token_gid'].has_options = True _CTWOFACTOR_ADDAUTHENTICATOR_RESPONSE.fields_by_name['token_gid']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\034Token GID assigned by server')) _CTWOFACTOR_ADDAUTHENTICATOR_RESPONSE.fields_by_name['identity_secret'].has_options = True _CTWOFACTOR_ADDAUTHENTICATOR_RESPONSE.fields_by_name['identity_secret']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\0309Secret used for identity attestation (e.g., for eventing)')) _CTWOFACTOR_ADDAUTHENTICATOR_RESPONSE.fields_by_name['secret_1'].has_options = True _CTWOFACTOR_ADDAUTHENTICATOR_RESPONSE.fields_by_name['secret_1']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\023Spare shared secret')) _CTWOFACTOR_ADDAUTHENTICATOR_RESPONSE.fields_by_name['status'].has_options = True _CTWOFACTOR_ADDAUTHENTICATOR_RESPONSE.fields_by_name['status']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\013Result code')) _CTWOFACTOR_SENDEMAIL_REQUEST.fields_by_name['steamid'].has_options = True _CTWOFACTOR_SENDEMAIL_REQUEST.fields_by_name['steamid']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\016Steamid to use')) _CTWOFACTOR_SENDEMAIL_REQUEST.fields_by_name['email_type'].has_options = True _CTWOFACTOR_SENDEMAIL_REQUEST.fields_by_name['email_type']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030.Type of email to send (ETwoFactorEmailType::*)')) _CTWOFACTOR_SENDEMAIL_REQUEST.fields_by_name['include_activation_code'].has_options = True _CTWOFACTOR_SENDEMAIL_REQUEST.fields_by_name['include_activation_code']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030+Include activation code in email parameters')) _CTWOFACTOR_FINALIZEADDAUTHENTICATOR_REQUEST.fields_by_name['steamid'].has_options = True _CTWOFACTOR_FINALIZEADDAUTHENTICATOR_REQUEST.fields_by_name['steamid']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\016steamid to use')) _CTWOFACTOR_FINALIZEADDAUTHENTICATOR_REQUEST.fields_by_name['authenticator_code'].has_options = True _CTWOFACTOR_FINALIZEADDAUTHENTICATOR_REQUEST.fields_by_name['authenticator_code']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\021Current auth code')) _CTWOFACTOR_FINALIZEADDAUTHENTICATOR_REQUEST.fields_by_name['authenticator_time'].has_options = True _CTWOFACTOR_FINALIZEADDAUTHENTICATOR_REQUEST.fields_by_name['authenticator_time']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\032Current authenticator time')) _CTWOFACTOR_FINALIZEADDAUTHENTICATOR_REQUEST.fields_by_name['activation_code'].has_options = True _CTWOFACTOR_FINALIZEADDAUTHENTICATOR_REQUEST.fields_by_name['activation_code']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030(Activation code from out-of-band message')) _CTWOFACTOR_FINALIZEADDAUTHENTICATOR_REQUEST.fields_by_name['http_headers'].has_options = True _CTWOFACTOR_FINALIZEADDAUTHENTICATOR_REQUEST.fields_by_name['http_headers']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\037HTTP headers alternating by K/V')) _CTWOFACTOR_FINALIZEADDAUTHENTICATOR_RESPONSE.fields_by_name['success'].has_options = True _CTWOFACTOR_FINALIZEADDAUTHENTICATOR_RESPONSE.fields_by_name['success']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030%True if succeeded, or want more tries')) _CTWOFACTOR_FINALIZEADDAUTHENTICATOR_RESPONSE.fields_by_name['want_more'].has_options = True _CTWOFACTOR_FINALIZEADDAUTHENTICATOR_RESPONSE.fields_by_name['want_more']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\027True if want more tries')) _CTWOFACTOR_FINALIZEADDAUTHENTICATOR_RESPONSE.fields_by_name['server_time'].has_options = True _CTWOFACTOR_FINALIZEADDAUTHENTICATOR_RESPONSE.fields_by_name['server_time']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\023Current server time')) _CTWOFACTOR_FINALIZEADDAUTHENTICATOR_RESPONSE.fields_by_name['status'].has_options = True _CTWOFACTOR_FINALIZEADDAUTHENTICATOR_RESPONSE.fields_by_name['status']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\013Result code')) _CTWOFACTOR_REMOVEAUTHENTICATOR_REQUEST.fields_by_name['revocation_code'].has_options = True _CTWOFACTOR_REMOVEAUTHENTICATOR_REQUEST.fields_by_name['revocation_code']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\037Password needed to remove token')) _CTWOFACTOR_REMOVEAUTHENTICATOR_REQUEST.fields_by_name['revocation_reason'].has_options = True _CTWOFACTOR_REMOVEAUTHENTICATOR_REQUEST.fields_by_name['revocation_reason']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030)Reason the authenticator is being removed')) _CTWOFACTOR_REMOVEAUTHENTICATOR_REQUEST.fields_by_name['steamguard_scheme'].has_options = True _CTWOFACTOR_REMOVEAUTHENTICATOR_REQUEST.fields_by_name['steamguard_scheme']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\0300Type of Steam Guard to use once token is removed')) _CTWOFACTOR_REMOVEAUTHENTICATOR_REQUEST.fields_by_name['remove_all_steamguard_cookies'].has_options = True _CTWOFACTOR_REMOVEAUTHENTICATOR_REQUEST.fields_by_name['remove_all_steamguard_cookies']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\035Remove all steamguard cookies')) _CTWOFACTOR_REMOVEAUTHENTICATOR_RESPONSE.fields_by_name['success'].has_options = True _CTWOFACTOR_REMOVEAUTHENTICATOR_RESPONSE.fields_by_name['success']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\0307True if request succeeeded. The mobile app checks this.')) _CTWOFACTOR_REMOVEAUTHENTICATOR_RESPONSE.fields_by_name['server_time'].has_options = True _CTWOFACTOR_REMOVEAUTHENTICATOR_RESPONSE.fields_by_name['server_time']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\023Current server time')) _CTWOFACTOR_REMOVEAUTHENTICATOR_RESPONSE.fields_by_name['revocation_attempts_remaining'].has_options = True _CTWOFACTOR_REMOVEAUTHENTICATOR_RESPONSE.fields_by_name['revocation_attempts_remaining']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030,Number of revocation code attempts remaining')) _CTWOFACTOR_CREATEEMERGENCYCODES_RESPONSE.fields_by_name['codes'].has_options = True _CTWOFACTOR_CREATEEMERGENCYCODES_RESPONSE.fields_by_name['codes']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\017Emergency codes')) _CTWOFACTOR_DESTROYEMERGENCYCODES_REQUEST.fields_by_name['steamid'].has_options = True _CTWOFACTOR_DESTROYEMERGENCYCODES_REQUEST.fields_by_name['steamid']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\016steamid to use')) _CTWOFACTOR_VALIDATETOKEN_REQUEST.fields_by_name['code'].has_options = True _CTWOFACTOR_VALIDATETOKEN_REQUEST.fields_by_name['code']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\020code to validate')) _CTWOFACTOR_VALIDATETOKEN_RESPONSE.fields_by_name['valid'].has_options = True _CTWOFACTOR_VALIDATETOKEN_RESPONSE.fields_by_name['valid']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\202\265\030\024result of validation')) _TWOFACTOR = _descriptor.ServiceDescriptor( name='TwoFactor', full_name='TwoFactor', file=DESCRIPTOR, index=0, options=_descriptor._ParseOptions(descriptor_pb2.ServiceOptions(), _b('\202\265\030!Two Factor Authentication Service')), serialized_start=3979, serialized_end=5263, methods=[ _descriptor.MethodDescriptor( name='QueryStatus', full_name='TwoFactor.QueryStatus', index=0, containing_service=None, input_type=_CTWOFACTOR_STATUS_REQUEST, output_type=_CTWOFACTOR_STATUS_RESPONSE, options=_descriptor._ParseOptions(descriptor_pb2.MethodOptions(), _b('\202\265\030@Get two-factor authentication settings for the logged-in account')), ), _descriptor.MethodDescriptor( name='AddAuthenticator', full_name='TwoFactor.AddAuthenticator', index=1, containing_service=None, input_type=_CTWOFACTOR_ADDAUTHENTICATOR_REQUEST, output_type=_CTWOFACTOR_ADDAUTHENTICATOR_RESPONSE, options=_descriptor._ParseOptions(descriptor_pb2.MethodOptions(), _b('\202\265\0305Add two-factor authenticator to the logged-in account')), ), _descriptor.MethodDescriptor( name='SendEmail', full_name='TwoFactor.SendEmail', index=2, containing_service=None, input_type=_CTWOFACTOR_SENDEMAIL_REQUEST, output_type=_CTWOFACTOR_SENDEMAIL_RESPONSE, options=_descriptor._ParseOptions(descriptor_pb2.MethodOptions(), _b('\202\265\030\031Send email to the account')), ), _descriptor.MethodDescriptor( name='FinalizeAddAuthenticator', full_name='TwoFactor.FinalizeAddAuthenticator', index=3, containing_service=None, input_type=_CTWOFACTOR_FINALIZEADDAUTHENTICATOR_REQUEST, output_type=_CTWOFACTOR_FINALIZEADDAUTHENTICATOR_RESPONSE, options=_descriptor._ParseOptions(descriptor_pb2.MethodOptions(), _b('\202\265\030DFinalize two-factor authentication addition to the logged-in account')), ), _descriptor.MethodDescriptor( name='RemoveAuthenticator', full_name='TwoFactor.RemoveAuthenticator', index=4, containing_service=None, input_type=_CTWOFACTOR_REMOVEAUTHENTICATOR_REQUEST, output_type=_CTWOFACTOR_REMOVEAUTHENTICATOR_RESPONSE, options=_descriptor._ParseOptions(descriptor_pb2.MethodOptions(), _b('\202\265\030DRemove two-factor authentication addition from the logged-in account')), ), _descriptor.MethodDescriptor( name='CreateEmergencyCodes', full_name='TwoFactor.CreateEmergencyCodes', index=5, containing_service=None, input_type=_CTWOFACTOR_CREATEEMERGENCYCODES_REQUEST, output_type=_CTWOFACTOR_CREATEEMERGENCYCODES_RESPONSE, options=_descriptor._ParseOptions(descriptor_pb2.MethodOptions(), _b('\202\265\030&Generate emergency authenticator codes')), ), _descriptor.MethodDescriptor( name='DestroyEmergencyCodes', full_name='TwoFactor.DestroyEmergencyCodes', index=6, containing_service=None, input_type=_CTWOFACTOR_DESTROYEMERGENCYCODES_REQUEST, output_type=_CTWOFACTOR_DESTROYEMERGENCYCODES_RESPONSE, options=_descriptor._ParseOptions(descriptor_pb2.MethodOptions(), _b('\202\265\0305Destroy emergency authenticator codes for the account')), ), _descriptor.MethodDescriptor( name='ValidateToken', full_name='TwoFactor.ValidateToken', index=7, containing_service=None, input_type=_CTWOFACTOR_VALIDATETOKEN_REQUEST, output_type=_CTWOFACTOR_VALIDATETOKEN_RESPONSE, options=_descriptor._ParseOptions(descriptor_pb2.MethodOptions(), _b('\202\265\030\036Validate (and consume) a token')), ), ]) TwoFactor = service_reflection.GeneratedServiceType('TwoFactor', (_service.Service,), dict( DESCRIPTOR = _TWOFACTOR, __module__ = 'steammessages_twofactor_pb2' )) TwoFactor_Stub = service_reflection.GeneratedServiceStubType('TwoFactor_Stub', (TwoFactor,), dict( DESCRIPTOR = _TWOFACTOR, __module__ = 'steammessages_twofactor_pb2' )) # @@protoc_insertion_point(module_scope)

60.337168

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0.796468

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68,181

6.200266

0.057962

0.027791

0.066785

0.089814

0.824314

0.724097

0.674798

0.633112

0.582037

0.527118

0

0.051193

0.091506

68,181

1,129

7,573

60.390611

0.776016

0.019932

0

0.55311

1

0.011483

0.285999

0.180401

0

1

0

false

0.002871

0.008612

0

0.008612

0

null

0

1

0

1

0

1

0

null

0

4

755c918e2b3983c8a538846e6f8956c9a8b29af1

245

py

Python

rpython/translator/cli/test/test_range.py

kantai/passe-pypy-taint-tracking

b60a3663f8fe89892dc182c8497aab97e2e75d69

[ "MIT" ]

2

2016-07-06T23:30:20.000Z

2017-05-30T15:59:31.000Z

rpython/translator/cli/test/test_range.py

kantai/passe-pypy-taint-tracking

b60a3663f8fe89892dc182c8497aab97e2e75d69

[ "MIT" ]

null

rpython/translator/cli/test/test_range.py

kantai/passe-pypy-taint-tracking

b60a3663f8fe89892dc182c8497aab97e2e75d69

[ "MIT" ]

2

2020-07-09T08:14:22.000Z

2021-01-15T18:01:25.000Z

import py from rpython.translator.cli.test.runtest import CliTest from rpython.rtyper.test.test_rrange import BaseTestRrange class TestCliRange(CliTest, BaseTestRrange): def test_rlist_range(self): pass # it doesn't make sense here

30.625

58

0.791837

34

245

5.617647

0.735294

0.115183

0

0.142857

245

7

59

35

0.909524

0.106122

0

1

0.166667

false

0.166667

0.5

0

0.833333

0

null

0

1

0

null

0

1

0

1

0

4

f329fde281d55efd7c63f2af1252431e6f963b07

50

py

Python

tests/__init__.py

Informasjonsforvaltning/jsonschematordf

dfeb039411b5a9797ad3b7769e0dd3489abc5502

[ "Apache-2.0" ]

null

tests/__init__.py

Informasjonsforvaltning/jsonschematordf

dfeb039411b5a9797ad3b7769e0dd3489abc5502

[ "Apache-2.0" ]

24

2021-08-19T08:33:39.000Z

2021-10-06T07:43:28.000Z

tests/__init__.py

Informasjonsforvaltning/jsonschematordf

dfeb039411b5a9797ad3b7769e0dd3489abc5502

[ "Apache-2.0" ]

null

"""Test suite for the jsonschematordf package."""

25

49

0.74

6

50

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0

0.12

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1

50

0.840909

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0

1

0

null

0

1

0

4

f3336e010473b7a299957a7c7773a12a5ea46d38

157

py

Python

tests/conftest.py

mt3o/injectable

0ffc5c758b63d9391134cd822158e1846999b404

[ "MIT" ]

71

2018-02-05T04:12:27.000Z

2022-02-15T23:08:16.000Z

tests/conftest.py

Euraxluo/injectable

74e640f0911480fb06fa97c1a468c3863541c0fd

[ "MIT" ]

104

2018-02-06T23:37:36.000Z

2021-08-25T04:50:15.000Z

tests/conftest.py

Euraxluo/injectable

74e640f0911480fb06fa97c1a468c3863541c0fd

[ "MIT" ]

13

2019-02-10T18:52:50.000Z

2022-01-26T17:12:35.000Z

import pytest from testfixtures import LogCapture @pytest.fixture(autouse=True) def log_capture(): with LogCapture() as capture: yield capture

17.444444

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157

6.105263

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157

8

36

19.625

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1

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true

0

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1

0

null

0

1

0

1

0

4

f354a99befa71d5bc60e342f2839f7c5d0fd0ed2

401

py

Python

g.py

HakimdarC/Dice-Game

96377891e43df68911fa841204c8d1854d068088

[ "MIT" ]

null

g.py

HakimdarC/Dice-Game

96377891e43df68911fa841204c8d1854d068088

[ "MIT" ]

null

g.py

HakimdarC/Dice-Game

96377891e43df68911fa841204c8d1854d068088

[ "MIT" ]

null

import random m=1 n=6 roll_again="yes" while roll_again=="yes" or roll_again=="y": print("Rolling the dice....") print("the values are..") print(random.randint(m,n)) print(random.randint(m,n)) print(random.randint(m,n)) break roll_again = input("enter yes or y to continue...Roll the dices again?") print(random.randint(m,n)) print(random.randint(m,n)) print(random.randint(m,n))

25.0625

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0.683292

69

401

3.913043

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0.244444

0.4

0.422222

0.444444

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0.005764

0.134663

401

15

74

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0

0.4

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1

0

false

0

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0

null

1

0

null

0

1

0

4

f36bbf06a2fee123d5868f458818a488d5cdfe22

32

py

Python

pysedm/dask/__init__.py

MickaelRigault/pysedm

5d34d3a6b48eb3bbb7ba9d89b88b4b5b1ff09624

[ "Apache-2.0" ]

5

2018-03-16T14:58:09.000Z

2019-11-25T15:57:14.000Z

pysedm/dask/__init__.py

MickaelRigault/pysedm

5d34d3a6b48eb3bbb7ba9d89b88b4b5b1ff09624

[ "Apache-2.0" ]

9

2018-02-13T17:02:17.000Z

2020-09-15T11:43:37.000Z

pysedm/dask/__init__.py

MickaelRigault/pysedm

5d34d3a6b48eb3bbb7ba9d89b88b4b5b1ff09624

[ "Apache-2.0" ]

4

2018-03-16T14:58:14.000Z

2022-02-07T20:02:58.000Z

""" Dask scripts for pysedm """

16

31

0.625

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5

1

0

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1

32

0.769231

0.71875

0

null

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null

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null

0

null

1

null

true

0

null

1

0

null

0

1

0

null

0

1

0

4

f3ad39a1e9e6605b2b9a4db24488edb7e7bc809b

87

py

Python

boleto/apps.py

feliperuhland/home

cfe84ec7243faec8c0cf5cc4afb1db76aebb86f9

[ "MIT" ]

null

boleto/apps.py

feliperuhland/home

cfe84ec7243faec8c0cf5cc4afb1db76aebb86f9

[ "MIT" ]

null

boleto/apps.py

feliperuhland/home

cfe84ec7243faec8c0cf5cc4afb1db76aebb86f9

[ "MIT" ]

null

from django.apps import AppConfig class BoletoConfig(AppConfig): name = 'boleto'

14.5

33

0.747126

10

87

6.5

0.9

0

0.172414

87

5

34

17.4

0.902778

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0.068966

0

1

0

false

0

0.333333

0

1

0

1

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null

0

1

0

null

0

1

0

1

0

4

f3c4cd31cade4e1b3f0dbf54c546f501767d472f

198

py

Python

academic_helper/management/commands/dev_init.py

AviH0/coursist

3db05e8a168be33d2f03b9e082ee4779d80be7c7

[ "MIT" ]

6

2020-06-26T12:09:10.000Z

2021-12-18T11:44:55.000Z

academic_helper/management/commands/dev_init.py

AviH0/coursist

3db05e8a168be33d2f03b9e082ee4779d80be7c7

[ "MIT" ]

89

2020-06-02T11:42:57.000Z

2021-06-10T19:09:09.000Z

academic_helper/management/commands/dev_init.py

AviH0/coursist

3db05e8a168be33d2f03b9e082ee4779d80be7c7

[ "MIT" ]

14

2020-06-26T12:08:34.000Z

2021-04-20T10:59:45.000Z

from django.core.management import BaseCommand from academic_helper.management.init_data import create_all class Command(BaseCommand): def handle(self, *args, **options): create_all()

24.75

59

0.767677

25

198

5.92

0.76

0.121622

0

0.146465

198

7

60

28.285714

0.87574

0

1

0.2

false

0

0.4

0

0.8

0

1

0

null

0

1

0

null

0

1

0

1

0

4

f3c5ec66234aca2dce7301f5e29b899eff21bf61

76

py

Python

src/dna-center/dna-center-authenticate.py

fernando28024/git-clone-https-github.com-CiscoDevNet-devasc-code-examples

589dbd5d34f67f9b823159b731844432977b6490

[ "BSD-3-Clause" ]

43

2020-08-01T03:01:53.000Z

2022-02-17T12:43:27.000Z

src/dna-center/dna-center-authenticate.py

fernando28024/git-clone-https-github.com-CiscoDevNet-devasc-code-examples

589dbd5d34f67f9b823159b731844432977b6490

[ "BSD-3-Clause" ]

2

2021-04-20T17:13:39.000Z

2021-09-23T23:35:12.000Z

src/dna-center/dna-center-authenticate.py

grelleum/devasc-code-examples

589dbd5d34f67f9b823159b731844432977b6490

[ "BSD-3-Clause" ]

14

2020-08-02T00:07:43.000Z

2022-03-15T22:25:39.000Z

# Fill in this file with the code from the DNA Center authenticate exercise

38

75

0.802632

13

76

4.692308

0.923077

0

0.184211

76

1

76

0.983871

0.960526

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

1

0

null

0

1

0

4

45ebdac3524066e7f27b37c7714af6daef248dfc

240

py

Python

ToxicSense/ToxicSense/urls.py

adamwespiser/toxic-twitter

d9d804ffa231ae1f7b5b188d4beaff1a521d9f27

[ "MIT" ]

7

2018-12-16T07:14:35.000Z

2022-02-27T03:58:51.000Z

ToxicSense/ToxicSense/urls.py

adamwespiser/toxic-twitter

d9d804ffa231ae1f7b5b188d4beaff1a521d9f27

[ "MIT" ]

1

2019-12-17T19:26:46.000Z

ToxicSense/ToxicSense/urls.py

adamwespiser/toxic-twitter

d9d804ffa231ae1f7b5b188d4beaff1a521d9f27

[ "MIT" ]

3

2019-07-15T05:11:21.000Z

2022-02-27T04:02:25.000Z

from django.contrib import admin from django.contrib.staticfiles.urls import staticfiles_urlpatterns from django.urls import include, path urlpatterns = [ path("", include("clientapp.urls")), ] urlpatterns += staticfiles_urlpatterns()

26.666667

67

0.7875

27

240

6.925926

0.407407

0.160428

0.181818

0

0.1125

240

9

68

26.666667

0.877934

0

0.058091

0

1

0

false

0

0.428571

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0.428571

0

1

0

null

0

1

0

1

0

null

0

1

0

4

45f365799338ddd03fef83273cefbaad5a6921c9

94

py

Python

categories/settings/settings.py

visuable/vktoolspython

8e30ef1ad175c721e4eedf37ec32a77a5af984d0

[ "MIT" ]

null

categories/settings/settings.py

visuable/vktoolspython

8e30ef1ad175c721e4eedf37ec32a77a5af984d0

[ "MIT" ]

null

categories/settings/settings.py

visuable/vktoolspython

8e30ef1ad175c721e4eedf37ec32a77a5af984d0

[ "MIT" ]

null

class Settings: params = () def __init__(self, params): self.params = params

15.666667

31

0.595745

10

94

5.2

0.6

0.384615

0

0.297872

94

5

32

18.8

0.787879

0

1

0.25

false

0

0.75

0

1

0

null

1

0

1

0

null

0

1

0

4

3455b1955d2acc2dab4b57985cd45af0fbcdd700

27

py

Python

dockerspawner/_version.py

JocelynDelalande/dockerspawner

d1f27e2855d2cefbdb25b29cc069b9ca69d564e3

[ "BSD-3-Clause" ]

1

2021-01-28T17:22:25.000Z

dockerspawner/_version.py

JocelynDelalande/dockerspawner

d1f27e2855d2cefbdb25b29cc069b9ca69d564e3

[ "BSD-3-Clause" ]

null

dockerspawner/_version.py

JocelynDelalande/dockerspawner

d1f27e2855d2cefbdb25b29cc069b9ca69d564e3

[ "BSD-3-Clause" ]

1

2018-07-25T16:11:06.000Z

__version__ = '0.12.0.dev'

13.5

26

0.666667

5

27

2.8

0.8

0

0.166667

0.111111

27

1

27

0.416667

0

0.37037

0

1

0

false

0

1

0

null

0

1

0

1

0

null

0

4

347c81c8c4f4daeb6c84c94cebb5501201c3d014

126

py

Python

src/generate-random-int.py

MRN-Code/coinstac-example-computation-bisect-converge

761515c865ed635946620968da8e3bfbab632b7a

[ "MIT" ]

null

src/generate-random-int.py

MRN-Code/coinstac-example-computation-bisect-converge

761515c865ed635946620968da8e3bfbab632b7a

[ "MIT" ]

2

2016-06-08T02:03:27.000Z

2016-10-03T23:11:00.000Z

src/generate-random-int.py

MRN-Code/coinstac-example-computation-bisect-converge

761515c865ed635946620968da8e3bfbab632b7a

[ "MIT" ]

1

2021-02-08T03:00:52.000Z

import sys from random import randint myint = randint(1, 100) print myint sys.stderr.write('My random # was ' + str(myint))

15.75

49

0.722222

20

126

4.55

0.7

0

0.038095

0.166667

126

7

50

18

0.828571

0

0.126984

0

null

0

0.4

null

0.2

1

0

null

0

1

0

null

0

1

0

1

0

4

ca9ef425f203639ff23bebc4718d840ebe0dc154

388

py

Python

pyfilter/src/filters/__init__.py

zkscpqm/pyfilter

39c284681ec6f377059907b75346028d99cbdd4c

[ "MIT" ]

null

pyfilter/src/filters/__init__.py

zkscpqm/pyfilter

39c284681ec6f377059907b75346028d99cbdd4c

[ "MIT" ]

1

2021-04-28T18:40:13.000Z

pyfilter/src/filters/__init__.py

zkscpqm/pyfilter

39c284681ec6f377059907b75346028d99cbdd4c

[ "MIT" ]

null

from pyfilter.src.filters.base_filter import _BaseFilter from pyfilter.src.filters.any_match_filter import _AnyMatchFilter from pyfilter.src.filters.all_match_filter import _AllMatchFilter from pyfilter.src.filters.regex_match_filter import _RegexMatchFilter BaseFilter = _BaseFilter AnyMatchFilter = _AnyMatchFilter AllMatchFilter = _AllMatchFilter RegexMatchFilter = _RegexMatchFilter

38.8

69

0.884021

43

388

7.627907

0.348837

0.146341

0.182927

0.268293

0

0.074742

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9

70

43.111111

0.913649

0

1

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false

0

0.5

0

0.5

0

null

0

1

0

1

0

null

0

1

0

4

cab6f3d40e01507ff20d8b05edf9989c7da0065c

906

py

Python

cli/psym/graphql/input/service_type_create_data.py

danielrh135568/symphony-1

54c92a0f8775d1a837ab7c7bd6a08ccd906d28a4

[ "BSD-3-Clause" ]

null

cli/psym/graphql/input/service_type_create_data.py

danielrh135568/symphony-1

54c92a0f8775d1a837ab7c7bd6a08ccd906d28a4

[ "BSD-3-Clause" ]

12

2022-02-14T04:20:30.000Z

2022-03-28T04:20:17.000Z

cli/psym/graphql/input/service_type_create_data.py

danielrh135568/symphony-1

54c92a0f8775d1a837ab7c7bd6a08ccd906d28a4

[ "BSD-3-Clause" ]

1

2022-02-24T21:47:51.000Z

#!/usr/bin/env python3 # @generated AUTOGENERATED file. Do not Change! from dataclasses import dataclass, field as _field from functools import partial from ...config import custom_scalars, datetime from numbers import Number from typing import Any, AsyncGenerator, Dict, List, Generator, Optional from dataclasses_json import DataClassJsonMixin, config from gql_client.runtime.enum_utils import enum_field_metadata from ..enum.discovery_method import DiscoveryMethod from ..input.property_type_input import PropertyTypeInput from ..input.service_endpoint_definition_input import ServiceEndpointDefinitionInput @dataclass(frozen=True) class ServiceTypeCreateData(DataClassJsonMixin): name: str hasCustomer: bool properties: Optional[List[PropertyTypeInput]] = None endpoints: Optional[List[ServiceEndpointDefinitionInput]] = None discoveryMethod: Optional[DiscoveryMethod] = None

34.846154

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906

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0

0.001247

0.11479

906

25

85

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0.073951

0

1

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1

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true

0

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0.941176

0

null

0

1

0

null

0

1

0

1

0

1

0

4

cad352c0ee2da88f233720dd9035312793238d9b

128

py

Python

tests/druid_import_test.py

moshebeeri/datap

9ff99bb435728cd69f2589e3ee858a06768ea85e

[ "Apache-2.0" ]

null

tests/druid_import_test.py

moshebeeri/datap

9ff99bb435728cd69f2589e3ee858a06768ea85e

[ "Apache-2.0" ]

null

tests/druid_import_test.py

moshebeeri/datap

9ff99bb435728cd69f2589e3ee858a06768ea85e

[ "Apache-2.0" ]

null

from service.druid import Druid class TestImport: def test_it(self): druid = Druid(start=None, end=None) assert True

18.285714

39

0.71875

19

128

4.789474

0.789474

0

0.195313

128

7

40

18.285714

0.883495

0

0.2

1

0.2

false

0

0.4

0

0.8

0

1

0

null

0

1

0

null

0

1

0

1

0

4

cade8ff7daa98befdd0853d4ec61c4994673256a

184

py

Python

alwahaj/core/views/solutions.py

haidarzxc/alwahaj

113ece9c713a6e6d04b5f9804885ed6a5337e404

[ "MIT" ]

null

alwahaj/core/views/solutions.py

haidarzxc/alwahaj

113ece9c713a6e6d04b5f9804885ed6a5337e404

[ "MIT" ]

12

2020-02-12T00:30:39.000Z

2022-03-11T23:49:30.000Z

alwahaj/core/views/solutions.py

haidarzxc/alwahaj

113ece9c713a6e6d04b5f9804885ed6a5337e404

[ "MIT" ]

null

from django.shortcuts import render from django.http import HttpResponse def solutionsView(request): context=dict(x=1) return render(request,"pages/solutions.html",context)

20.444444

57

0.777174

24

184

5.958333

0.75

0.13986

0

0.00625

0.130435

184

9

57

20.444444

0.8875

0

0.108108

0

1

0.2

false

0

0.4

0

0.8

0

1

0

null

0

1

0

null

0

1

0

1

0

4

caf87ffe18c8dd2b3ed1b12b61911c042ab343c5

34

py

Python

api/tests.py

PrynsTag/oneBarangay

6a8d56003d85b8385e91f5c5d81208619023c1ee

[ "Apache-2.0" ]

null

api/tests.py

PrynsTag/oneBarangay

6a8d56003d85b8385e91f5c5d81208619023c1ee

[ "Apache-2.0" ]

96

2021-08-28T12:37:02.000Z

2022-03-23T04:25:12.000Z

api/tests.py

PrynsTag/oneBarangay

6a8d56003d85b8385e91f5c5d81208619023c1ee

[ "Apache-2.0" ]

null

"""Create your api tests here."""

17

33

0.647059

5

34

4.4

1

0

0.147059

34

1

34

0.758621

0.794118

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

null

0

1

0

4

cafc41228d764fbb4c8e04bd84332419504fc329

150

py

Python

Level 1/Math with Python/7) Geometry/Question 4.py

aaravdave/YoungWonks

92f1de88f5c46744bc7229153af4392afa8f6353

[ "MIT" ]

null

Level 1/Math with Python/7) Geometry/Question 4.py

aaravdave/YoungWonks

92f1de88f5c46744bc7229153af4392afa8f6353

[ "MIT" ]

null

Level 1/Math with Python/7) Geometry/Question 4.py

aaravdave/YoungWonks

92f1de88f5c46744bc7229153af4392afa8f6353

[ "MIT" ]

null

from math import sqrt x, y, a, b = list(map(int, input('Enter \'x, y, a, b\': ').split(', '))) print(round(sqrt(((x - a) ** 2) + ((y - b) ** 2)), 2))

37.5

72

0.48

28

150

2.571429

0.607143

0.138889

0.083333

0.111111

0

0.024793

0.193333

150

3

73

50

0.570248

0

0.073333

0

1

0

true

0

0.333333

0

0.333333

1

0

null

0

1

0

null

0

1

0

1

0

4

1b0aaadcb1eb6855d01a9e5bbebdbbb9748a0d63

217

py

Python

main/frontend.py

IFRCGo/ifrcgo-api

c1c3e0cf1076ab48d03db6aaf7a00f8485ca9e1a

[ "MIT" ]

null

main/frontend.py

IFRCGo/ifrcgo-api

c1c3e0cf1076ab48d03db6aaf7a00f8485ca9e1a

[ "MIT" ]

null

main/frontend.py

IFRCGo/ifrcgo-api

c1c3e0cf1076ab48d03db6aaf7a00f8485ca9e1a

[ "MIT" ]

null

from django.conf import settings def get_project_url(id): return f'https://{settings.FRONTEND_URL}/three-w/{id}/' def get_flash_update_url(id): return f'https://{settings.FRONTEND_URL}/flash-update/{id}/'

21.7

64

0.728111

34

217

4.441176

0.529412

0.07947

0.145695

0.15894

0.476821

0

0.110599

217

9

65

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1b41e9945c6c839c2b6547f47be6d98b19247e76

314

py

Python

tests/image/test_util.py

kungfuai/kaishi

e20360170ccac2111cab61fcd71b81be3c2a7468

[ "MIT" ]

10

2020-04-01T16:46:25.000Z

2021-02-09T15:56:42.000Z

tests/image/test_util.py

kungfuai/kaishi

e20360170ccac2111cab61fcd71b81be3c2a7468

[ "MIT" ]

14

2020-03-23T13:32:35.000Z

2021-12-07T19:30:23.000Z

tests/image/test_util.py

kungfuai/kaishi

e20360170ccac2111cab61fcd71b81be3c2a7468

[ "MIT" ]

2

2020-08-14T07:23:06.000Z

2021-12-06T18:20:42.000Z

from kaishi.image.util import validate_image_header def test_validate_image_header(): invalid_file = "tests/data/image/empty_unsupported_extension.gif" valid_file = "tests/data/image/sample.jpg" assert validate_image_header(invalid_file) is False assert validate_image_header(valid_file) is True

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1b425500770d391ac96c7cb6e196a4ccf6b4b2b5

106

py

Python

codesignal/arcade/python/intro_51_delete_digit.py

tinesife94/random

b802924dce4635ae074d30dc03962d4301bd6d8b

[ "MIT" ]

null

codesignal/arcade/python/intro_51_delete_digit.py

tinesife94/random

b802924dce4635ae074d30dc03962d4301bd6d8b

[ "MIT" ]

null

codesignal/arcade/python/intro_51_delete_digit.py

tinesife94/random

b802924dce4635ae074d30dc03962d4301bd6d8b

[ "MIT" ]

null

def solution(n): s = str(n) return max(int('{}{}'.format(s[:i], s[i+1:])) for i in range(len(s)))

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1b56962e5a6d5a63085f2158e015e7d133280d2e

82

py

Python

0-notes/job-search/Cracking the Coding Interview/C14Databases/questions/14.5-question.py

eengineergz/Lambda

1fe511f7ef550aed998b75c18a432abf6ab41c5f

[ "MIT" ]

null

0-notes/job-search/Cracking the Coding Interview/C14Databases/questions/14.5-question.py

eengineergz/Lambda

1fe511f7ef550aed998b75c18a432abf6ab41c5f

[ "MIT" ]

null

0-notes/job-search/Cracking the Coding Interview/C14Databases/questions/14.5-question.py

eengineergz/Lambda

1fe511f7ef550aed998b75c18a432abf6ab41c5f

[ "MIT" ]

null

# 14.5 Denormalization # What is denormalization? # Explain the pros and cons.

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1b7b2c0fc016dac1b8ba6644993ed164d22d1e22

156

py

Python

Chapter07/function_with_variable_length.py

kaushalkumarshah/Learn-Python-in-7-Days

2663656767c8959ace836f0c0e272f3e501bbe6e

[ "MIT" ]

12

2018-07-09T16:20:31.000Z

2022-03-21T22:52:15.000Z

Chapter07/function_with_variable_length.py

kaushalkumarshah/Learn-Python-in-7-Days

2663656767c8959ace836f0c0e272f3e501bbe6e

[ "MIT" ]

null

Chapter07/function_with_variable_length.py

kaushalkumarshah/Learn-Python-in-7-Days

2663656767c8959ace836f0c0e272f3e501bbe6e

[ "MIT" ]

19

2018-01-09T12:49:06.000Z

2021-11-23T08:05:55.000Z

def variable_argument( var1, *vari): print "Out-put is",var1 for var in vari: print var variable_argument(60) variable_argument(100,90,40,50,60)

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156

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null

0

1

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4

1b8169f6eb4ed6fdca2017473451e62866f4dddf

72

py

Python

osmaxx/clipping_area/__init__.py

tyrasd/osmaxx

da4454083d17b2ef8b0623cad62e39992b6bd52a

[ "MIT" ]

27

2015-03-30T14:17:26.000Z

2022-02-19T17:30:44.000Z

osmaxx/clipping_area/__init__.py

tyrasd/osmaxx

da4454083d17b2ef8b0623cad62e39992b6bd52a

[ "MIT" ]

483

2015-03-09T16:58:03.000Z

2022-03-14T09:29:06.000Z

osmaxx/clipping_area/__init__.py

tyrasd/osmaxx

da4454083d17b2ef8b0623cad62e39992b6bd52a

[ "MIT" ]

6

2015-04-07T07:38:30.000Z

2020-04-01T12:45:53.000Z

default_app_config = 'osmaxx.clipping_area.apps.ClippingGeometryConfig'

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null

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1bad40a6c2690a5a87273e36adcd88ebfffca7a8

357

py

Python

investment_dashboard/portfolio/models.py

mjenrungrot/investment-dashboard

89b296a635ee3c29171f7bf88cc8e49250981637

[ "MIT" ]

null

investment_dashboard/portfolio/models.py

mjenrungrot/investment-dashboard

89b296a635ee3c29171f7bf88cc8e49250981637

[ "MIT" ]

4

2017-12-19T08:39:10.000Z

2017-12-20T10:59:38.000Z

investment_dashboard/portfolio/models.py

mjenrungrot/investment-dashboard

89b296a635ee3c29171f7bf88cc8e49250981637

[ "MIT" ]

null

from django.db import models # Create your models here. class PortfolioTransaction(models.Model): datetime = models.DateTimeField() equityType = models.CharField(max_length=10) equityName = models.CharField(max_length=30) units = models.DecimalField(max_digits=20, decimal_places=10) currency = models.CharField(max_length=10)

35.7

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357

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357

9

67

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null

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1

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1

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null

0

1

0

4

9415bfc2b8cf89dc33ab8191d5c33c608b5083d7

717

py

Python

database/users.py

obeyurfate/GitWithMe

d316814eac17ce9b704b4dd98b8af59ab931b131

[ "Unlicense" ]

1

2021-04-02T14:41:51.000Z

database/users.py

obeyurfate/GitWithMe

d316814eac17ce9b704b4dd98b8af59ab931b131

[ "Unlicense" ]

null

database/users.py

obeyurfate/GitWithMe

d316814eac17ce9b704b4dd98b8af59ab931b131

[ "Unlicense" ]

null

import sqlalchemy from flask_login import UserMixin from sqlalchemy import orm from sqlalchemy_serializer import SerializerMixin from .db_sess import SqlAlchemyBase class User(SqlAlchemyBase, UserMixin, SerializerMixin): __tablename__ = 'users' id = sqlalchemy.Column(sqlalchemy.Integer, primary_key=True, autoincrement=True) nickname = sqlalchemy.Column(sqlalchemy.String) description = sqlalchemy.Column(sqlalchemy.String, nullable=True) groups = orm.relation("Groups", secondary="groups_to_users", backref="user") github = sqlalchemy.Column(sqlalchemy.String) icon = sqlalchemy.Column(sqlalchemy.String)

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9422f13be98f523048aa61ab11428eaf41ad9a8c

87

py

Python

apps/codigo/apps.py

IngMachine/compiladores

d8cd2cde29af09188037e7627fc63403a322f5c7

[ "Apache-2.0" ]

null

apps/codigo/apps.py

IngMachine/compiladores

d8cd2cde29af09188037e7627fc63403a322f5c7

[ "Apache-2.0" ]

null

apps/codigo/apps.py

IngMachine/compiladores

d8cd2cde29af09188037e7627fc63403a322f5c7

[ "Apache-2.0" ]

null

from django.apps import AppConfig class CodigoConfig(AppConfig): name = 'codigo'

14.5

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943728d83ca7e0f84d7f541a3c187e7872c2261b

200

py

Python

backend/apps/sampleapp/serializers.py

domasx2/django-angular-docker-seed

5c1ad6d62d179c9cb5cdbf7b1254576efa63b2fb

[ "Unlicense" ]

32

2015-04-27T02:01:59.000Z

2021-04-06T10:19:42.000Z

backend/apps/sampleapp/serializers.py

domasx2/django-angular-docker-seed

5c1ad6d62d179c9cb5cdbf7b1254576efa63b2fb

[ "Unlicense" ]

14

2015-03-21T08:20:34.000Z

2016-02-15T07:07:39.000Z

backend/apps/sampleapp/serializers.py

domasx2/django-angular-docker-seed

5c1ad6d62d179c9cb5cdbf7b1254576efa63b2fb

[ "Unlicense" ]

21

2015-03-18T18:40:12.000Z

2021-03-16T22:12:44.000Z

from rest_framework import serializers from .models import Task class TaskSerializer(serializers.HyperlinkedModelSerializer): class Meta: model = Task read_only_fields = ('slug',)

28.571429

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