Datasets:

PatrickHaller

/

the-stack-python-100k

like 0

ae1c314367eda94502f3250be74ea3c895585272

py

Python

tutorials/stock-wallet/microservices/wallet/src/commands/__init__.py

bhardwajRahul/minos-python

bad7a280ad92680abdeab01d1214688279cf6316

2022-01-24T14:55:30.000Z

2022-03-25T12:06:17.000Z

tutorials/stock-wallet/microservices/wallet/src/commands/__init__.py

bhardwajRahul/minos-python

bad7a280ad92680abdeab01d1214688279cf6316

2022-01-24T14:54:31.000Z

2022-03-31T09:31:09.000Z

tutorials/stock-wallet/microservices/wallet/src/commands/__init__.py

bhardwajRahul/minos-python

bad7a280ad92680abdeab01d1214688279cf6316

2022-02-06T17:25:58.000Z

2022-03-27T04:50:29.000Z

from .services import ( WalletCommandService, )

91b98f54e60a18955447366f3b8df0621aa0d797

py

Python

openr/py/openr/utils/tests/serializer_tests.py

jamesbj/openr

878f7e74ec91b9407110e64e6d8a9f9499a335d5

2019-02-26T06:36:29.000Z

2019-02-26T06:36:29.000Z

openr/py/openr/utils/tests/serializer_tests.py

flintfinally/openr

a5abf937f75ca757c9d4c17093c3d89ffbb28e09

openr/py/openr/utils/tests/serializer_tests.py

flintfinally/openr

a5abf937f75ca757c9d4c17093c3d89ffbb28e09

#!/usr/bin/env python3 # # Copyright (c) 2014-present, Facebook, Inc. # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. # from __future__ import absolute_import, division, print_function, unicode_literals import random import string import unittest from builtins import range from openr.Lsdb import ttypes as lsdb_types from openr.utils import serializer from thrift.protocol.TJSONProtocol import TJSONProtocolFactory class TestSerialization(unittest.TestCase): def test_reverse_equality(self): for _ in range(100): thrift_obj = lsdb_types.PrefixDatabase() random_string = "".join(random.choice(string.digits) for _ in range(10)) thrift_obj.thisNodeName = random_string raw_msg = serializer.serialize_thrift_object(thrift_obj) recovered_obj = serializer.deserialize_thrift_object( raw_msg, lsdb_types.PrefixDatabase ) self.assertEqual(thrift_obj, recovered_obj) for _ in range(100): thrift_obj = lsdb_types.PrefixDatabase() random_string = "".join(random.choice(string.digits) for _ in range(10)) thrift_obj.thisNodeName = random_string raw_msg = serializer.serialize_thrift_object( thrift_obj, TJSONProtocolFactory ) recovered_obj = serializer.deserialize_thrift_object( raw_msg, lsdb_types.PrefixDatabase, TJSONProtocolFactory ) self.assertEqual(thrift_obj, recovered_obj) def test_thrifttype_sensitivity(self): thrift_obj = lsdb_types.PrefixDatabase() thrift_obj.thisNodeName = "some node" raw_msg = serializer.serialize_thrift_object(thrift_obj) recovered_obj = serializer.deserialize_thrift_object( raw_msg, lsdb_types.PrefixEntry ) self.assertTrue(thrift_obj != recovered_obj) def test_exception_handling(self): thrift_obj = lsdb_types.PrefixDatabase() thrift_obj.thisNodeName = "some node" raw_msg = serializer.serialize_thrift_object(thrift_obj) # should raise exception due to inconsistency of protocol factor with self.assertRaises(Exception): serializer.deserialize_thrift_object( raw_msg, lsdb_types.PrefixDatabase, TJSONProtocolFactory )

c8903add0c99f574198791b79f48983a3adc5052

py

Python

molecule/alternative/tests/test_alternative.py

m1cka/ansible-grafana

3cd6f9d3414ef5cf8ed6def8bcad1d654e2cce9e

2017-11-27T18:07:59.000Z

2022-03-28T09:15:42.000Z

molecule/alternative/tests/test_alternative.py

m1cka/ansible-grafana

3cd6f9d3414ef5cf8ed6def8bcad1d654e2cce9e

2017-12-02T20:00:47.000Z

2022-03-30T06:25:37.000Z

molecule/alternative/tests/test_alternative.py

m1cka/ansible-grafana

3cd6f9d3414ef5cf8ed6def8bcad1d654e2cce9e

2017-12-14T22:01:40.000Z

2022-03-31T18:41:19.000Z

import os import testinfra.utils.ansible_runner testinfra_hosts = testinfra.utils.ansible_runner.AnsibleRunner( os.environ['MOLECULE_INVENTORY_FILE']).get_hosts('all') def test_directories(host): dirs = [ "/etc/grafana", "/var/log/grafana", "/var/lib/grafana", "/var/lib/grafana/dashboards", "/var/lib/grafana/plugins", "/var/lib/grafana/plugins/raintank-worldping-app" ] files = [ "/etc/grafana/grafana.ini", "/etc/grafana/ldap.toml" ] for directory in dirs: d = host.file(directory) assert d.is_directory assert d.exists for file in files: f = host.file(file) assert f.exists assert f.is_file def test_service(host): s = host.service("grafana-server") # assert s.is_enabled assert s.is_running def test_packages(host): p = host.package("grafana") assert p.is_installed assert p.version == "6.2.5" def test_socket(host): assert host.socket("tcp://127.0.0.1:3000").is_listening def test_alternative_yum_repo(host): if host.system_info.distribution in ['centos', 'redhat', 'fedora']: f = host.file("/etc/yum.repos.d/alternative.grafana.yum.repo") assert f.exists def test_custom_auth_option(host): f = host.file("/etc/grafana/grafana.ini") assert f.contains("login_maximum_inactive_lifetime_days = 42")

052215bcbd2fb3b15cbab4b53fd49cdd0329c447

py

Python

torchvision/utils.py

gpleiss/vision

7a975284a88a3c722a92a7962180781df2acfa68

2019-10-29T02:38:55.000Z

2020-12-17T07:54:42.000Z

torchvision/utils.py

fmassa/vision-1

7a975284a88a3c722a92a7962180781df2acfa68

torchvision/utils.py

fmassa/vision-1

7a975284a88a3c722a92a7962180781df2acfa68

import torch import math irange = range def make_grid(tensor, nrow=8, padding=2, normalize=False, range=None, scale_each=False): """ Given a 4D mini-batch Tensor of shape (B x C x H x W), or a list of images all of the same size, makes a grid of images normalize=True will shift the image to the range (0, 1), by subtracting the minimum and dividing by the maximum pixel value. if range=(min, max) where min and max are numbers, then these numbers are used to normalize the image. scale_each=True will scale each image in the batch of images separately rather than computing the (min, max) over all images. [Example usage is given in this notebook](https://gist.github.com/anonymous/bf16430f7750c023141c562f3e9f2a91) """ # if list of tensors, convert to a 4D mini-batch Tensor if isinstance(tensor, list): tensorlist = tensor numImages = len(tensorlist) size = torch.Size(torch.Size([long(numImages)]) + tensorlist[0].size()) tensor = tensorlist[0].new(size) for i in irange(numImages): tensor[i].copy_(tensorlist[i]) if tensor.dim() == 2: # single image H x W tensor = tensor.view(1, tensor.size(0), tensor.size(1)) if tensor.dim() == 3: # single image if tensor.size(0) == 1: # if single-channel, convert to 3-channel tensor = torch.cat((tensor, tensor, tensor), 0) return tensor if tensor.dim() == 4 and tensor.size(1) == 1: # single-channel images tensor = torch.cat((tensor, tensor, tensor), 1) if normalize is True: if range is not None: assert isinstance(range, tuple), \ "range has to be a tuple (min, max) if specified. min and max are numbers" def norm_ip(img, min, max): img.clamp_(min=min, max=max) img.add_(-min).div_(max - min) def norm_range(t, range): if range is not None: norm_ip(t, range[0], range[1]) else: norm_ip(t, t.min(), t.max()) if scale_each is True: for t in tensor: # loop over mini-batch dimension norm_range(t, range) else: norm_range(tensor, range) # make the mini-batch of images into a grid nmaps = tensor.size(0) xmaps = min(nrow, nmaps) ymaps = int(math.ceil(float(nmaps) / xmaps)) height, width = int(tensor.size(2) + padding), int(tensor.size(3) + padding) grid = tensor.new(3, height * ymaps, width * xmaps).fill_(0) k = 0 for y in irange(ymaps): for x in irange(xmaps): if k >= nmaps: break grid.narrow(1, y * height + 1 + padding // 2, height - padding)\ .narrow(2, x * width + 1 + padding // 2, width - padding)\ .copy_(tensor[k]) k = k + 1 return grid def save_image(tensor, filename, nrow=8, padding=2, normalize=False, range=None, scale_each=False): """ Saves a given Tensor into an image file. If given a mini-batch tensor, will save the tensor as a grid of images by calling `make_grid`. All options after `filename` are passed through to `make_grid`. Refer to it's documentation for more details """ from PIL import Image tensor = tensor.cpu() grid = make_grid(tensor, nrow=nrow, padding=padding, normalize=normalize, range=range, scale_each=scale_each) ndarr = grid.mul(255).byte().transpose(0, 2).transpose(0, 1).numpy() im = Image.fromarray(ndarr) im.save(filename)

3d525a6aae3aa9b7e9040c0ce82aa571165c0b2f

py

Python

main.py

Eclipi/PaDiM-EfficientNet

738830a11db5bcb725407e9323177d565667a0f9

main.py

Eclipi/PaDiM-EfficientNet

738830a11db5bcb725407e9323177d565667a0f9

main.py

Eclipi/PaDiM-EfficientNet

738830a11db5bcb725407e9323177d565667a0f9

# 1. import module ## utils import random import os from random import sample import numpy as np import pickle import time import sys import copy import argparse import warnings import matplotlib import matplotlib.pyplot as plt from PIL import Image from tqdm import tqdm from collections import OrderedDict from sklearn.metrics import roc_auc_score, roc_curve, f1_score, accuracy_score, recall_score, precision_score, confusion_matrix, precision_recall_curve from scipy.ndimage import gaussian_filter from skimage import morphology from skimage.segmentation import mark_boundaries ## torch module import torch import torch.nn as nn import torch.nn.functional as F from torch.utils.data import DataLoader ## eff model from efficient_modified import EfficientNetModified ## mvtec datasets import datasets.mvtec as mvtec ## filter warnings warnings.filterwarnings('ignore') # 2. choose device use_cuda = torch.cuda.is_available() device = torch.device('cuda' if use_cuda else 'cpu') # 3. argparse def parse_args(): parser = argparse.ArgumentParser('PaDiM Parameters') parser.add_argument('-d', '--data_path', type=str, required=True, help='mvtec data location') parser.add_argument('-s', '--save_path', type=str, required=True, help='inference model & data location') parser.add_argument('-a', '--arch', type=str, choices=['b0', 'b1', 'b4', 'b7'], default='b4') parser.add_argument('-b', '--batch_size', type=int, default=32) parser.add_argument('--training', type=lambda s: s.lower() in ['true', '1']) parser.add_argument('--seed', type=int, default=1024) parser.add_argument('--resize', type=int, default=256) parser.add_argument('--cropsize', type=int, default=224) parser.add_argument('--model_print', type=bool, default=False) parser.add_argument('--img_print', type=bool, default=False) return parser.parse_args() # epoch, random_select size def create_seed(filters): random.seed(args.seed) torch.manual_seed(args.seed) if use_cuda: torch.cuda.manual_seed_all(args.seed) def embedding_concat(x, y): B, C1, H1, W1 = x.size() _, C2, H2, W2 = y.size() s = int(H1/H2) x = F.unfold(x, kernel_size=s, dilation=1, stride=s) x = x.view(B, C1, -1, H2, W2) z = torch.zeros(B, C1 + C2, x.size(2), H2, W2) for i in range(x.size(2)): z[:, :, i, :, :] = torch.cat((x[:, :, i, :, :], y), 1) z = z.view(B, -1, H2 * W2) z = F.fold(z, kernel_size=s, output_size=(H1, W1), stride=s) return z def show_feat_list(model, size=(1, 3, 224, 224)): sample_inputs = torch.zeros(size) feat_list = model.extract_entire_features(sample_inputs) for i, feat in enumerate(feat_list, 0): print(i, feat.shape) def denormalize(img): mean = torch.tensor([0.485, 0.456, 0.406]) std = torch.tensor([0.229, 0.224, 0.225]) return img.mul_(std).add_(mean) def show_sample_images(dataloader, n, class_name): x, _, _ = next(iter(dataloader)) if x == None: print('[error] dataloader empty!') return if n > args.batch_size: print('[error] n exceeds batch size!') return rows = n//4 cols = 4 axes = [] fig = plt.figure(figsize=(20, 20), dpi=200) for i in range(rows*cols): axes.append(fig.add_subplot(rows, cols, i+1)) title = '%s subplot %d' % (class_name, i) axes[-1].set_title(title) axes[-1].imshow(denormalize(x[i].permute(1, 2, 0))) fig.tight_layout() pic_save_path = os.path.join(args.save_path, 'sample_%s' % (class_name)) fig.savefig(pic_save_path, dpi=200) plt.show() def calc_covinv(embedding_vectors, H, W, C): for i in range(H * W): yield np.linalg.inv(np.cov(embedding_vectors[:, :, i].numpy(), rowvar=False) + 0.01 * np.identity(C)) def plot_fig(test_img, scores, gts, threshold, save_dir, class_name): num = len(scores) vmax = scores.max() * 255. vmin = scores.min() * 255. mean = np.array([0.485, 0.456, 0.406]) std = np.array([0.229, 0.224, 0.225]) for i in range(num): img = test_img[i] img = (((img.transpose(1, 2, 0) * std) + mean) * 255.).astype(np.uint8) # denormalize gt = gts[i].transpose(1, 2, 0).squeeze() heat_map = scores[i] * 255 mask = scores[i] mask[mask > threshold] = 1 mask[mask <= threshold] = 0 kernel = morphology.disk(4) mask = morphology.opening(mask, kernel) mask *= 255 vis_img = mark_boundaries(img, mask, color=(1, 0, 0), mode='thick') fig_img, ax_img = plt.subplots(1, 5, figsize=(12, 3)) fig_img.subplots_adjust(right=0.9) norm = matplotlib.colors.Normalize(vmin=vmin, vmax=vmax) for ax_i in ax_img: ax_i.axes.xaxis.set_visible(False) ax_i.axes.yaxis.set_visible(False) ax_img[0].imshow(img) ax_img[0].title.set_text('Image') ax_img[1].imshow(gt, cmap='gray') ax_img[1].title.set_text('GroundTruth') ax = ax_img[2].imshow(heat_map, cmap='jet', norm=norm) ax_img[2].imshow(img, cmap='gray', interpolation='none') ax_img[2].imshow(heat_map, cmap='jet', alpha=0.5, interpolation='none') ax_img[2].title.set_text('Predicted heat map') ax_img[3].imshow(mask, cmap='gray') ax_img[3].title.set_text('Predicted mask') ax_img[4].imshow(vis_img) ax_img[4].title.set_text('Segmentation result') left = 0.92 bottom = 0.15 width = 0.015 height = 1 - 2 * bottom rect = [left, bottom, width, height] cbar_ax = fig_img.add_axes(rect) cb = plt.colorbar(ax, shrink=0.6, cax=cbar_ax, fraction=0.046) cb.ax.tick_params(labelsize=8) font = { 'family': 'serif', 'color': 'black', 'weight': 'normal', 'size': 8, } cb.set_label('Anomaly Score', fontdict=font) fig_img.savefig(os.path.join(save_dir, class_name + '_{}'.format(i)), dpi=100) plt.close() def main(): # make directory for saving data os.makedirs(os.path.join(args.save_path, 'model_pkl_%s' % name), exist_ok=True) # capture ROCAUC score fig, ax = plt.subplots(1, 2, figsize=(20, 10)) fig_img_rocauc = ax[0] fig_pixel_rocauc = ax[1] total_roc_auc = [] total_pixel_roc_auc = [] if args.arch == 'b0': block_num = torch.tensor([3, 5, 11]) # b0 filters = (24 + 40 + 112) # 176 elif args.arch == 'b1': # block_num = torch.tensor([3, 6, 9]) # b1 first, 24 + 40 + 80 # block_num = torch.tensor([4, 7, 13]) # b1 medium 24 + 40 + 112 block_num = torch.tensor([5, 8, 16]) # b1 last 24 + 40 + 112 filters = (24 + 40 + 112) # 176 elif args.arch == 'b4': # block_num = torch.tensor([3, 7, 11]) # b4 (32 + 56 + 112) block_num = torch.tensor([3, 7, 17]) # b4 (32 + 56 + 160) # block_num = torch.tensor([5, 9, 13]) # (32 + 56 + 112) # block_num = torch.tensor([5, 9, 20]) # b4 (32 + 56 + 160) # block_num = torch.tensor([6, 10, 22]) # b4 (32 + 56 + 160) filters = (32 + 56 + 160) # 248 elif args.arch == 'b7': block_num = torch.tensor([11, 18, 38]) # b7 (48 + 80 + 224) # last # block_num = torch.tensor([5, 12, 29]) # b7 (48 + 80 + 224) # first # block_num = torch.tensor([8, 15, 33]) # medium filters = (48 + 80 + 224) # 352 ''' The number of filters is so small that I want to take the entire filter, not randomly. So I'm going to delete the random code this time. ''' create_seed(filters) # model attach to device eff_model.to(device) print('training: ', args.training) for k, class_name in enumerate(mvtec.CLASS_NAMES): train_dataset = mvtec.MVTecDataset(args.data_path, class_name=class_name, is_train=True) train_dataloader = DataLoader(train_dataset, batch_size=args.batch_size, pin_memory=True) test_dataset = mvtec.MVTecDataset(args.data_path, class_name=class_name, is_train=False) test_dataloader = DataLoader(test_dataset, batch_size=args.batch_size, pin_memory=True) train_outputs = OrderedDict([('layer1', []), ('layer2', []), ('layer3', [])]) test_outputs = OrderedDict([('layer1', []), ('layer2', []), ('layer3', [])]) # plt.show vscode not work, so save fig if args.img_print: show_sample_images(train_dataloader, args.batch_size // 8, class_name) # model_path train_feature_filepath = os.path.join(args.save_path, 'model_pkl_%s' % name, 'train_%s.pkl' % class_name) if args.training: if os.path.exists(train_feature_filepath): os.remove(train_feature_filepath) eff_model.eval() for (x, _, _) in tqdm(train_dataloader, '%d | feature extraction | train | %s |' % (k, class_name)): with torch.no_grad(): feats = eff_model.extract_features(x.to(device), block_num.to(device)) # If you want to see the shape of the feature... # for i, feat in enumerate(feats): # print("layer {} feature's shape: {}".format(i, feat.shape)) for k, v in zip(train_outputs.keys(), feats): train_outputs[k].append(v.cpu().detach()) for k, v in train_outputs.items(): train_outputs[k] = torch.cat(v, 0) embedding_vectors = train_outputs['layer1'] for layer_name in ['layer2', 'layer3']: embedding_vectors = embedding_concat(embedding_vectors, train_outputs[layer_name]) B, C, H, W = embedding_vectors.size() print("embedding vector's size: {}, {}, {}, {}".format(B, C, H, W)) embedding_vectors = embedding_vectors.view(B, C, H * W) mean = torch.mean(embedding_vectors, dim=0).numpy() cov_inv = torch.zeros(C, C, H * W).numpy() I = np.identity(C) # It's done with generator, but it doesn't matter what you do because there's not much memory difference. # cc = calc_covinv(embedding_vectors, H, W, C) # for i, value in enumerate(cc): # cov_inv[:, :, i] = value for i in range(H * W): cov_inv[:, :, i] = np.linalg.inv(np.cov(embedding_vectors[:, :, i].numpy(), rowvar=False) + 0.01 * I) # save learned distribution train_outputs = [mean, cov_inv] with open(train_feature_filepath, 'wb') as f: pickle.dump(train_outputs, f, protocol=pickle.HIGHEST_PROTOCOL) else: if not os.path.exists(train_feature_filepath): print('train set feature file not exists: {}'.format(train_feature_filepath)) else: print('load train set feat file from %s' % train_feature_filepath) with open(train_feature_filepath, 'rb') as f: train_outputs = pickle.load(f) gt_list = [] gt_mask_list = [] test_imgs = [] # If you pass without picking a feature # Depending on eval, the method of calculating bn, dropout etc varies. eff_model.eval() for (x, y, mask) in tqdm(test_dataloader, '| feature extraction | test | %s |' % class_name): test_imgs.extend(x.cpu().detach().numpy()) gt_list.extend(y.cpu().detach().numpy()) gt_mask_list.extend(mask.cpu().detach().numpy()) with torch.no_grad(): feats = eff_model.extract_features(x.to(device), block_num.to(device)) for k, v in zip(test_outputs.keys(), feats): test_outputs[k].append(v.cpu().detach()) for k, v in test_outputs.items(): test_outputs[k] = torch.cat(v, 0) embedding_vectors = test_outputs['layer1'] for layer_name in ['layer2', 'layer3']: embedding_vectors = embedding_concat(embedding_vectors, test_outputs[layer_name]) inference_start = time.time() B, C, H, W = embedding_vectors.size() embedding_vectors = embedding_vectors.view(B, C, H * W).to(device) mean = torch.Tensor(train_outputs[0]).to(device) cov_inv = torch.Tensor(train_outputs[1]).to(device) dist_list = torch.zeros(size=(H*W, B)) for i in range(H*W): delta = embedding_vectors[:, :, i] - mean[:, i] m_dist = torch.sqrt(torch.diag(torch.mm(torch.mm(delta, cov_inv[:, :, i]), delta.t()))) dist_list[i] = m_dist dist_list = dist_list.transpose(1, 0).view(B, H, W) score_map = F.interpolate(dist_list.unsqueeze(1), size=x.size(2), mode='bilinear', align_corners=False).squeeze().cpu().numpy() for i in range(score_map.shape[0]): score_map[i] = gaussian_filter(score_map[i], sigma=4) inference_time = time.time() - inference_start print('{} inference time: {:.3f}'.format(class_name, inference_time)) # Normalization max_score = score_map.max() min_score = score_map.min() scores = (score_map - min_score) / (max_score - min_score) # calculate image-level ROC AUC score img_scores = scores.reshape(scores.shape[0], -1).max(axis=1) gt_list = np.asarray(gt_list) fpr, tpr, img_thresholds = roc_curve(gt_list, img_scores) img_roc_auc = roc_auc_score(gt_list, img_scores) total_roc_auc.append(img_roc_auc) print('image ROCAUC: %.3f' % (img_roc_auc)) fig_img_rocauc.plot(fpr, tpr, label='%s img_ROCAUC: %.3f' % (class_name, img_roc_auc)) # get optimal threshold based Label distances = (tpr - 1.) ** 2 + fpr ** 2 img_threshold = img_thresholds[np.argmin(distances)] gt_mask = np.asarray(gt_mask_list) precision, recall, thresholds = precision_recall_curve(gt_mask.flatten(), scores.flatten()) a = 2 * precision * recall b = precision + recall f1 = np.divide(a, b, out=np.zeros_like(a), where=b != 0) threshold = thresholds[np.argmax(f1)] # label, mask two types threshold print('label based threshold: {:.3f}, pixel based threshold: {:.3f}'.format(img_threshold, threshold)) # calculate per-pixel level ROCAUC fpr, tpr, _ = roc_curve(gt_mask.flatten(), scores.flatten()) per_pixel_rocauc = roc_auc_score(gt_mask.flatten(), scores.flatten()) total_pixel_roc_auc.append(per_pixel_rocauc) print('pixel ROCAUC: %.3f' % (per_pixel_rocauc)) fig_pixel_rocauc.plot(fpr, tpr, label='%s ROCAUC: %.3f' % (class_name, per_pixel_rocauc)) save_dir = args.save_path + '/' + f'pictures_efficientnet-{args.arch}' os.makedirs(save_dir, exist_ok=True) plot_fig(test_imgs, scores, gt_mask_list, threshold, save_dir, class_name) # class, image ROCAUC, pixel ROCAUC, inference_time with open(args.save_path + '/' + f'efficientnet-{args.arch}-lst.txt', "a") as f: f.write('{}-{:.3f}-{:.3f}-{:.3f}\n'.format(class_name, img_roc_auc, per_pixel_rocauc, inference_time)) print('Average ROCAUC: %.3f' % np.mean(total_roc_auc)) fig_img_rocauc.title.set_text('Average image ROCAUC: %.3f' % np.mean(total_roc_auc)) fig_img_rocauc.legend(loc="lower right") print('Average pixel ROCUAC: %.3f' % np.mean(total_pixel_roc_auc)) fig_pixel_rocauc.title.set_text('Average pixel ROCAUC: %.3f' % np.mean(total_pixel_roc_auc)) fig_pixel_rocauc.legend(loc="lower right") fig.tight_layout() fig.savefig(os.path.join(args.save_path, '%s_lst_roc_curve.png' % name), dpi=100) if __name__ == '__main__': args = parse_args() name = 'efficientnet-{}'.format(args.arch) eff_model = EfficientNetModified.from_pretrained(name) if args.model_print: print(eff_model) #show_feat_list(eff_model) main() # print(dir(eff_model)) # test code # for i, (name, layer) in enumerate(eff_model.named_modules()): # print(i, name) # for i, block in enumerate(eff_model._blocks): # print(i)

06fec3cb86fc17f43dd3990090d847ea8835629a

py

Python

PMBANet_TOPO/dataset.py

Alina-Mingchi/TOPO_final

a8983006929b60bda0ed1d2e9a9130427b628431

PMBANet_TOPO/dataset.py

Alina-Mingchi/TOPO_final

a8983006929b60bda0ed1d2e9a9130427b628431

PMBANet_TOPO/dataset.py

Alina-Mingchi/TOPO_final

a8983006929b60bda0ed1d2e9a9130427b628431

import torch.utils.data as data import torch import numpy as np import os from os import listdir from os.path import join from PIL import Image import random from random import randrange def is_image_file(filename): return any(filename.endswith(extension) for extension in [".png", ".jpg", ".jpeg"]) def load_img(filepath): #img = Image.open(filepath).convert('RGB') ############## img = Image.open(filepath) #img = Image.open(filepath) #y, _, _ = img.split() return img def get_patch(img_in, img_tar, patch_size, scale, ix=-1, iy=-1): (c, ih, iw) = img_in.shape ####print('input:', ih, iw) (th, tw) = (scale * ih, scale * iw) patch_mult = scale #if len(scale) > 1 else 1 tp = patch_mult * patch_size ip = tp // scale if ix == -1: ix = random.randrange(0, iw - ip + 1) if iy == -1: iy = random.randrange(0, ih - ip + 1) (tx, ty) = (scale * ix, scale * iy) img_in = img_in[:, iy:iy + ip, ix:ix + ip] print('get_patch', img_tar.size(), ty, ty+tp, tx, tx+tp) img_tar = img_tar[:, ty:ty + tp, tx:tx + tp] info_patch = { 'ix': ix, 'iy': iy, 'ip': ip, 'tx': tx, 'ty': ty, 'tp': tp} ####print('after', img_tar.size()) return img_in, img_tar, info_patch def augment(img_in,img_color, img_tar, flip_h=True, rot=True): info_aug = {'flip_h': False, 'flip_v': False, 'trans': False} if random.random() < 0.5 and flip_h: ####print('<-------------->', img_tar.size()) img_in = torch.from_numpy(img_in.numpy()[:, :, ::-1].copy()) img_color = torch.from_numpy(img_color.numpy()[:, :, ::-1].copy()) img_tar = torch.from_numpy(img_tar.numpy()[:, :, ::-1].copy()) info_aug['flip_h'] = True if rot: if random.random() < 0.5: img_in = torch.from_numpy(img_in.numpy()[:, ::-1, :].copy()) img_color = torch.from_numpy(img_color.numpy()[:, ::-1, :].copy()) img_tar = torch.from_numpy(img_tar.numpy()[:, ::-1, :].copy()) info_aug['flip_v'] = True if random.random() < 0.5: img_in = torch.FloatTensor(np.transpose(img_in.numpy(),(0,2,1))) img_color = torch.FloatTensor(np.transpose(img_color.numpy(),(0,2,1))) img_tar = torch.FloatTensor(np.transpose(img_tar.numpy(),(0,2,1))) info_aug['trans'] = True return img_in,img_color,img_tar, info_aug class DatasetFromFolder(data.Dataset): def __init__(self, image_dir, lr_dir, rgb_dir, patch_size, upscale_factor, dataset, data_augmentation, input_transform=None, input_rgb_transform=None, target_transform=None): super(DatasetFromFolder, self).__init__() self.image_filenames = [join(image_dir, x) for x in listdir(image_dir) if is_image_file(x)] self.lr_dir = lr_dir self.rgb_dir = rgb_dir self.patch_size = patch_size self.upscale_factor = upscale_factor self.dataset = dataset self.input_transform = input_transform self.input_rgb_transform = input_rgb_transform self.target_transform = target_transform self.data_augmentation = data_augmentation def __getitem__(self, index): target = load_img(self.image_filenames[index]) print(self.image_filenames[index], target.size) _, file = os.path.split(self.image_filenames[index]) ##### print('<==============>', self.dataset) if self.dataset == 'DIV2K_train_LR_aug_x8': input_lr = load_img(os.path.join(self.lr_dir,os.path.splitext(file)[0]+'x8.png')) # elif self.dataset == 'DIV2K_train_LR_aug_x4': # input = load_img(os.path.join(self.lr_dir,os.path.splitext(file)[0]+'x4.png')) # elif self.dataset == 'DIV2K_train_LR_aug_x2': # input = load_img(os.path.join(self.lr_dir,os.path.splitext(file)[0]+'x2.png')) # elif self.dataset == 'DIV2K_train_LR_difficult': # input = load_img(os.path.join(self.lr_dir,os.path.splitext(file)[0]+'x4d.png')) # elif self.dataset == 'DIV2K_train_LR_mild': # input = load_img(os.path.join(self.lr_dir,os.path.splitext(file)[0]+'x4m.png')) # elif self.dataset == 'DIV2K_train_LR_wild': # set=randrange(1, 5) # input = load_img(os.path.join(self.lr_dir,os.path.splitext(file)[0]+'x4w'+str(set)+'.png')) # ##### # elif self.dataset == 'DIV2K_train_LR_bicubicX2/X2/': # ##### print('--------', self.lr_dir, os.path.splitext(file)[0]) # input = load_img(os.path.join(self.lr_dir,os.path.splitext(file)[0]+'x2.png')) # #### print(os.path.join(self.lr_dir,os.path.splitext(file)[0]+'x2.png')) # elif self.dataset == 'DIV2K_train_LR_bicubicX4/X4/': # ##### print('--------', self.lr_dir, os.path.splitext(file)[0]) # input = load_img(os.path.join(self.lr_dir,os.path.splitext(file)[0]+'x4.png')) # #### print(os.path.join(self.lr_dir,os.path.splitext(file)[0]+'x2.png')) # elif self.dataset == 'DIV2K_train_LR_unknownX2/X2/': # ##### print('--------', self.lr_dir, os.path.splitext(file)[0]) # input = load_img(os.path.join(self.lr_dir,os.path.splitext(file)[0]+'x2.png')) # #### print(os.path.join(self.lr_dir,os.path.splitext(file)[0]+'x2.png')) # elif self.dataset == 'DIV2K_train_LR_unknownX3/X3/': # ##### print('--------', self.lr_dir, os.path.splitext(file)[0]) # input = load_img(os.path.join(self.lr_dir,os.path.splitext(file)[0]+'x3.png')) # #### print(os.path.join(self.lr_dir,os.path.splitext(file)[0]+'x2.png')) # elif self.dataset == 'DIV2K_train_LR_unknownX4/X4/': # ##### print('--------', self.lr_dir, os.path.splitext(file)[0]) # input = load_img(os.path.join(self.lr_dir,os.path.splitext(file)[0]+'x4.png')) # #### print(os.path.join(self.lr_dir,os.path.splitext(file)[0]+'x2.png')) # elif self.dataset == 'DIV2K_train_LR_bicubicX3/X3/': # ##### print('--------', self.lr_dir, os.path.splitext(file)[0]) # input = load_img(os.path.join(self.lr_dir,os.path.splitext(file)[0]+'x3.png')) # #### print(os.path.join(self.lr_dir,os.path.splitext(file)[0]+'x2.png')) ###################### elif self.dataset == 'depth_map/data/L_pic128_x2/': input_lr = load_img(os.path.join(self.lr_dir,os.path.splitext(file)[0]+'.png')) #print(os.path.join(self.lr_dir,os.path.splitext(file)[0]+'.png')) ###################### #######*** elif self.dataset == 'train_all_depth_x8/': input_rgb = load_img(os.path.join(self.rgb_dir, os.path.splitext(file)[0]+'.png')) input_lr = load_img(os.path.join(self.lr_dir,os.path.splitext(file)[0]+'.png')) elif self.dataset == 'train_all_depth_x4/': input_rgb = load_img(os.path.join(self.rgb_dir, os.path.splitext(file)[0]+'.png')) input_lr = load_img(os.path.join(self.lr_dir,os.path.splitext(file)[0]+'.png')) elif self.dataset == 'train_all_depth_x2/': input_rgb = load_img(os.path.join(self.rgb_dir, os.path.splitext(file)[0]+'.png')) input_lr = load_img(os.path.join(self.lr_dir,os.path.splitext(file)[0]+'.png')) elif self.dataset == 'train_all_depth_x16/': input_rgb = load_img(os.path.join(self.rgb_dir, os.path.splitext(file)[0]+'.png')) input_lr = load_img(os.path.join(self.lr_dir,os.path.splitext(file)[0]+'.png')) elif self.dataset == 'train_x8/': input_rgb = load_img(os.path.join('/Users/felicity/Desktop/Super-resolution-drones-depth-images/PBMANet_TOPO/data/train_x8_rgb', os.path.splitext(file)[0]+'.png')) print('RGB: '+str(input_rgb.shape)) input_lr = load_img(os.path.join('/Users/felicity/Desktop/Super-resolution-drones-depth-images/PBMANet_TOPO/data/train_x8_lr',os.path.splitext(file)[0]+'.png')) print('lr: '+ str(input_lr.shape)) # if self.input_rgb_transform: # input_rgb = self.input_rgb_transform(input_rgb) # if self.input_transform: # input_lr = self.input_transform(input) # if self.target_transform: # target = self.target_transform(target) # print('target:', target.size()) # print('target:', target.size()) if self.data_augmentation: input_lr,input_rgb,target, _ = augment(input_lr,input_rgb,target) return input_rgb, input_lr, target def __len__(self): return len(self.image_filenames) class DatasetFromFolderEval(data.Dataset): def __init__(self, lr_dir,rgb_dir, input_transform=None,input_rgb_transform=None,target_transform=None): super(DatasetFromFolderEval, self).__init__() self.image_filenames = [join(lr_dir, x) for x in listdir(lr_dir) if is_image_file(x)] self.input_transform = input_transform self.input_rgb_transform = input_rgb_transform self.target_transform = target_transform self.lr_dir = lr_dir self.rgb_dir = rgb_dir def __getitem__(self, index): input_lr = load_img(self.image_filenames[index]) _, file = os.path.split(self.image_filenames[index]) if self.lr_dir == './data/testx8/': input_rgb = load_img(os.path.join(self.rgb_dir,os.path.splitext(file)[0]+'.png')) if self.input_transform: input_lr = self.input_transform(input_lr) if self.input_rgb_transform: input_rgb = self.input_rgb_transform(input_rgb) return input_lr, input_rgb, file def __len__(self): return len(self.image_filenames)

2d4c8ae85ec8a72c2b240c3bd32e5e9359f4002a

py

Python

sdk/python/pulumi_azure_nextgen/network/v20180601/connection_monitor.py

test-wiz-sec/pulumi-azure-nextgen

20a695af0d020b34b0f1c336e1b69702755174cc

sdk/python/pulumi_azure_nextgen/network/v20180601/connection_monitor.py

test-wiz-sec/pulumi-azure-nextgen

20a695af0d020b34b0f1c336e1b69702755174cc

sdk/python/pulumi_azure_nextgen/network/v20180601/connection_monitor.py

test-wiz-sec/pulumi-azure-nextgen

20a695af0d020b34b0f1c336e1b69702755174cc

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi SDK Generator. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union from ... import _utilities, _tables from . import outputs from ._inputs import * __all__ = ['ConnectionMonitor'] class ConnectionMonitor(pulumi.CustomResource): def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, auto_start: Optional[pulumi.Input[bool]] = None, connection_monitor_name: Optional[pulumi.Input[str]] = None, destination: Optional[pulumi.Input[pulumi.InputType['ConnectionMonitorDestinationArgs']]] = None, location: Optional[pulumi.Input[str]] = None, monitoring_interval_in_seconds: Optional[pulumi.Input[int]] = None, network_watcher_name: Optional[pulumi.Input[str]] = None, resource_group_name: Optional[pulumi.Input[str]] = None, source: Optional[pulumi.Input[pulumi.InputType['ConnectionMonitorSourceArgs']]] = None, tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None, __props__=None, __name__=None, __opts__=None): """ Information about the connection monitor. :param str resource_name: The name of the resource. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[bool] auto_start: Determines if the connection monitor will start automatically once created. :param pulumi.Input[str] connection_monitor_name: The name of the connection monitor. :param pulumi.Input[pulumi.InputType['ConnectionMonitorDestinationArgs']] destination: Describes the destination of connection monitor. :param pulumi.Input[str] location: Connection monitor location. :param pulumi.Input[int] monitoring_interval_in_seconds: Monitoring interval in seconds. :param pulumi.Input[str] network_watcher_name: The name of the Network Watcher resource. :param pulumi.Input[str] resource_group_name: The name of the resource group containing Network Watcher. :param pulumi.Input[pulumi.InputType['ConnectionMonitorSourceArgs']] source: Describes the source of connection monitor. :param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: Connection monitor tags. """ if __name__ is not None: warnings.warn("explicit use of __name__ is deprecated", DeprecationWarning) resource_name = __name__ if __opts__ is not None: warnings.warn("explicit use of __opts__ is deprecated, use 'opts' instead", DeprecationWarning) opts = __opts__ if opts is None: opts = pulumi.ResourceOptions() if not isinstance(opts, pulumi.ResourceOptions): raise TypeError('Expected resource options to be a ResourceOptions instance') if opts.version is None: opts.version = _utilities.get_version() if opts.id is None: if __props__ is not None: raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource') __props__ = dict() __props__['auto_start'] = auto_start if connection_monitor_name is None: raise TypeError("Missing required property 'connection_monitor_name'") __props__['connection_monitor_name'] = connection_monitor_name if destination is None: raise TypeError("Missing required property 'destination'") __props__['destination'] = destination __props__['location'] = location __props__['monitoring_interval_in_seconds'] = monitoring_interval_in_seconds if network_watcher_name is None: raise TypeError("Missing required property 'network_watcher_name'") __props__['network_watcher_name'] = network_watcher_name if resource_group_name is None: raise TypeError("Missing required property 'resource_group_name'") __props__['resource_group_name'] = resource_group_name if source is None: raise TypeError("Missing required property 'source'") __props__['source'] = source __props__['tags'] = tags __props__['etag'] = None __props__['monitoring_status'] = None __props__['name'] = None __props__['provisioning_state'] = None __props__['start_time'] = None __props__['type'] = None alias_opts = pulumi.ResourceOptions(aliases=[pulumi.Alias(type_="azure-nextgen:network/latest:ConnectionMonitor"), pulumi.Alias(type_="azure-nextgen:network/v20171001:ConnectionMonitor"), pulumi.Alias(type_="azure-nextgen:network/v20171101:ConnectionMonitor"), pulumi.Alias(type_="azure-nextgen:network/v20180101:ConnectionMonitor"), pulumi.Alias(type_="azure-nextgen:network/v20180201:ConnectionMonitor"), pulumi.Alias(type_="azure-nextgen:network/v20180401:ConnectionMonitor"), pulumi.Alias(type_="azure-nextgen:network/v20180701:ConnectionMonitor"), pulumi.Alias(type_="azure-nextgen:network/v20180801:ConnectionMonitor"), pulumi.Alias(type_="azure-nextgen:network/v20181001:ConnectionMonitor"), pulumi.Alias(type_="azure-nextgen:network/v20181101:ConnectionMonitor"), pulumi.Alias(type_="azure-nextgen:network/v20181201:ConnectionMonitor"), pulumi.Alias(type_="azure-nextgen:network/v20190201:ConnectionMonitor"), pulumi.Alias(type_="azure-nextgen:network/v20190401:ConnectionMonitor"), pulumi.Alias(type_="azure-nextgen:network/v20190601:ConnectionMonitor"), pulumi.Alias(type_="azure-nextgen:network/v20190701:ConnectionMonitor"), pulumi.Alias(type_="azure-nextgen:network/v20190801:ConnectionMonitor"), pulumi.Alias(type_="azure-nextgen:network/v20190901:ConnectionMonitor"), pulumi.Alias(type_="azure-nextgen:network/v20191101:ConnectionMonitor"), pulumi.Alias(type_="azure-nextgen:network/v20191201:ConnectionMonitor"), pulumi.Alias(type_="azure-nextgen:network/v20200301:ConnectionMonitor"), pulumi.Alias(type_="azure-nextgen:network/v20200401:ConnectionMonitor"), pulumi.Alias(type_="azure-nextgen:network/v20200501:ConnectionMonitor"), pulumi.Alias(type_="azure-nextgen:network/v20200601:ConnectionMonitor"), pulumi.Alias(type_="azure-nextgen:network/v20200701:ConnectionMonitor")]) opts = pulumi.ResourceOptions.merge(opts, alias_opts) super(ConnectionMonitor, __self__).__init__( 'azure-nextgen:network/v20180601:ConnectionMonitor', resource_name, __props__, opts) @staticmethod def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions] = None) -> 'ConnectionMonitor': """ Get an existing ConnectionMonitor resource's state with the given name, id, and optional extra properties used to qualify the lookup. :param str resource_name: The unique name of the resulting resource. :param pulumi.Input[str] id: The unique provider ID of the resource to lookup. :param pulumi.ResourceOptions opts: Options for the resource. """ opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = dict() return ConnectionMonitor(resource_name, opts=opts, __props__=__props__) @property @pulumi.getter(name="autoStart") def auto_start(self) -> pulumi.Output[Optional[bool]]: """ Determines if the connection monitor will start automatically once created. """ return pulumi.get(self, "auto_start") @property @pulumi.getter def destination(self) -> pulumi.Output['outputs.ConnectionMonitorDestinationResponse']: """ Describes the destination of connection monitor. """ return pulumi.get(self, "destination") @property @pulumi.getter def etag(self) -> pulumi.Output[Optional[str]]: return pulumi.get(self, "etag") @property @pulumi.getter def location(self) -> pulumi.Output[Optional[str]]: """ Connection monitor location. """ return pulumi.get(self, "location") @property @pulumi.getter(name="monitoringIntervalInSeconds") def monitoring_interval_in_seconds(self) -> pulumi.Output[Optional[int]]: """ Monitoring interval in seconds. """ return pulumi.get(self, "monitoring_interval_in_seconds") @property @pulumi.getter(name="monitoringStatus") def monitoring_status(self) -> pulumi.Output[Optional[str]]: """ The monitoring status of the connection monitor. """ return pulumi.get(self, "monitoring_status") @property @pulumi.getter def name(self) -> pulumi.Output[str]: """ Name of the connection monitor. """ return pulumi.get(self, "name") @property @pulumi.getter(name="provisioningState") def provisioning_state(self) -> pulumi.Output[Optional[str]]: """ The provisioning state of the connection monitor. """ return pulumi.get(self, "provisioning_state") @property @pulumi.getter def source(self) -> pulumi.Output['outputs.ConnectionMonitorSourceResponse']: """ Describes the source of connection monitor. """ return pulumi.get(self, "source") @property @pulumi.getter(name="startTime") def start_time(self) -> pulumi.Output[Optional[str]]: """ The date and time when the connection monitor was started. """ return pulumi.get(self, "start_time") @property @pulumi.getter def tags(self) -> pulumi.Output[Optional[Mapping[str, str]]]: """ Connection monitor tags. """ return pulumi.get(self, "tags") @property @pulumi.getter def type(self) -> pulumi.Output[str]: """ Connection monitor type. """ return pulumi.get(self, "type") def translate_output_property(self, prop): return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop def translate_input_property(self, prop): return _tables.SNAKE_TO_CAMEL_CASE_TABLE.get(prop) or prop

314822762e3da09eb0492e6232daa27551cb69d0

py

Python

dataset/augmentation.py

Creearc/Codenrock-New-Year-ML-Battle

38dc89e45482cf6668cc0abe46f07b9d69db32a2

2022-01-07T16:57:34.000Z

2022-01-07T16:57:34.000Z

dataset/augmentation.py

Creearc/Codenrock-New-Year-ML-Battle

38dc89e45482cf6668cc0abe46f07b9d69db32a2

dataset/augmentation.py

Creearc/Codenrock-New-Year-ML-Battle

38dc89e45482cf6668cc0abe46f07b9d69db32a2

import os import cv2 import numpy as np ###################################################################### ''' PARAMETERS ''' ###################################################################### path = 'train' img_shape = (448, 448) split_matrix = (4, 4) ###################################################################### imgs = os.listdir(path) ##img_name = imgs[2] ##img = cv2.imread('{}/{}'.format(path, img_name)) ## ##img = cv2.resize(img, img_shape, cv2.INTER_AREA) ##out = img.copy() ## ##hist = [] ##for i in range(3): ## h = cv2.calcHist([img], [i], None, [256], [0,256]) ## hist.append(h) ## print(np.median(h)) ## ##path_shape = (img_shape[0] // split_matrix[0], ## img_shape[1] // split_matrix[1]) ## ##for x in range(split_matrix[0]): ## for y in range(split_matrix[1]): ## cv2.rectangle(img, ## (x * path_shape[0], y * path_shape[1]), ## ((x + 1) * path_shape[0], (y + 1) * path_shape[1]), ## (255, 0, 0), 1) ## ## ## ## ##cv2.imshow('', img) ##cv2.waitKey(0) ##cv2.destroyAllWindows() dataset_path = 'train_s' f = open('smth_wild.csv', 'w') for folder in os.listdir(dataset_path): for file in os.listdir('{}/{}'.format(dataset_path, folder)): img = cv2.imread('{}/{}/{}'.format(dataset_path, folder, file), cv2.IMREAD_COLOR) f.write('{}'.format(folder)) for i in range(3): h = cv2.calcHist([img], [i], None, [256], [0,256]) h = np.median(h) f.write(';{}'.format(h)) f.write('\n') f.close()

ba6df220730e0dc296eb8bac4db80bf6a043bafb

py

Python

plenum/test/checkpoints/test_checkpoint_stabilization_after_catchup.py

ken-ebert/indy-plenum

7578798df456d06ffe24f488452fab3f1b3f00f1

plenum/test/checkpoints/test_checkpoint_stabilization_after_catchup.py

ken-ebert/indy-plenum

7578798df456d06ffe24f488452fab3f1b3f00f1

plenum/test/checkpoints/test_checkpoint_stabilization_after_catchup.py

ken-ebert/indy-plenum

7578798df456d06ffe24f488452fab3f1b3f00f1

from plenum.test import waits from plenum.test.helper import sdk_send_random_and_check from plenum.test.node_catchup.helper import waitNodeDataEquality from plenum.test.pool_transactions.helper import sdk_add_new_steward_and_node from plenum.test.test_node import checkNodesConnected CHK_FREQ = 5 LOG_SIZE = 3 * CHK_FREQ def test_second_checkpoint_after_catchup_can_be_stabilized( chkFreqPatched, looper, txnPoolNodeSet, sdk_wallet_steward, sdk_wallet_client, sdk_pool_handle, tdir, tconf, allPluginsPath): _, new_node = sdk_add_new_steward_and_node( looper, sdk_pool_handle, sdk_wallet_steward, 'EpsilonSteward', 'Epsilon', tdir, tconf, allPluginsPath=allPluginsPath) txnPoolNodeSet.append(new_node) looper.run(checkNodesConnected(txnPoolNodeSet)) waitNodeDataEquality(looper, new_node, *txnPoolNodeSet[:-1], exclude_from_check="check_last_ordered_3pc_backup") # Epsilon did not participate in ordering of the batch with EpsilonSteward # NYM transaction and the batch with Epsilon NODE transaction. # Epsilon got these transactions via catch-up. master_replica = new_node.replicas._master_replica assert len(master_replica._checkpointer._checkpoint_state) == 0 assert len(master_replica._checkpointer._stashed_recvd_checkpoints) == 0 assert master_replica.h == 2 assert master_replica.H == 17 sdk_send_random_and_check(looper, txnPoolNodeSet, sdk_pool_handle, sdk_wallet_client, 1) for replica in new_node.replicas.values(): assert len(replica._checkpointer._checkpoint_state) == 1 assert len(replica._checkpointer._stashed_recvd_checkpoints) == 0 assert replica.h == 2 assert replica.H == 17 sdk_send_random_and_check(looper, txnPoolNodeSet, sdk_pool_handle, sdk_wallet_client, 6) stabilization_timeout = \ waits.expectedTransactionExecutionTime(len(txnPoolNodeSet)) looper.runFor(stabilization_timeout) for replica in new_node.replicas.values(): assert len(replica._checkpointer._checkpoint_state) == 2 keys_iter = iter(replica._checkpointer._checkpoint_state) assert next(keys_iter) == (3, 5) assert replica._checkpointer._checkpoint_state[3, 5].seqNo == 5 assert replica._checkpointer._checkpoint_state[3, 5].digest is None assert replica._checkpointer._checkpoint_state[3, 5].isStable is False assert next(keys_iter) == (6, 10) assert replica._checkpointer._checkpoint_state[6, 10].seqNo == 9 assert replica._checkpointer._checkpoint_state[6, 10].digest is None assert replica._checkpointer._checkpoint_state[6, 10].isStable is False # nothing is stashed since it's ordered during catch-up assert len(replica._checkpointer._stashed_recvd_checkpoints) == 0 assert replica.h == 2 assert replica.H == 17 sdk_send_random_and_check(looper, txnPoolNodeSet, sdk_pool_handle, sdk_wallet_client, 1) looper.runFor(stabilization_timeout) for replica in new_node.replicas.values(): assert len(replica._checkpointer._checkpoint_state) == 1 keys_iter = iter(replica._checkpointer._checkpoint_state) assert next(keys_iter) == (6, 10) assert replica._checkpointer._checkpoint_state[6, 10].seqNo == 10 assert replica._checkpointer._checkpoint_state[6, 10].digest is not None assert replica._checkpointer._checkpoint_state[6, 10].isStable is True assert len(replica._checkpointer._stashed_recvd_checkpoints) == 0 assert replica.h == 10 assert replica.H == 25

15f26f126f2c6165dd849cf156c3f7af1e2d95bf

py

Python

Django/users/urls.py

zarif007/Blog-site

e20e3f73fedbd7acb2f3d22398c36f3dcd4f88b4

2021-03-15T22:28:26.000Z

2021-03-15T22:28:26.000Z

Django/users/urls.py

zarif007/Blog-site

e20e3f73fedbd7acb2f3d22398c36f3dcd4f88b4

Django/users/urls.py

zarif007/Blog-site

e20e3f73fedbd7acb2f3d22398c36f3dcd4f88b4

from django.urls import path from .views import CustomUserCreate, BlacklistTokenUpdateView app_name = 'users' urlpatterns = [ path('create/', CustomUserCreate.as_view(), name="create_user"), path('logout/blacklist/', BlacklistTokenUpdateView.as_view(), name='blacklist') ]

fc4f9f615f696fd7084207acf75320326b2d95e4

py

Python

Ene-Jun-2021/perez-gutierrez-julio-cesar/Examen Extraordinario/Ejercicio-2/triangulo.py

jarmarj/DAS_Sistemas

36c876673e7abae503cc137c3f66585a0e45ed79

2017-09-26T09:36:32.000Z

2022-03-19T18:05:25.000Z

Ene-Jun-2021/perez-gutierrez-julio-cesar/Examen Extraordinario/Ejercicio-2/triangulo.py

jarmarj/DAS_Sistemas

36c876673e7abae503cc137c3f66585a0e45ed79

2017-09-11T05:06:12.000Z

2022-02-14T04:44:04.000Z

Ene-Jun-2021/perez-gutierrez-julio-cesar/Examen Extraordinario/Ejercicio-2/triangulo.py

jarmarj/DAS_Sistemas

36c876673e7abae503cc137c3f66585a0e45ed79

2017-09-01T00:10:08.000Z

2022-03-19T18:05:12.000Z

from abc import ABC, abstractmethod class Triangulo(ABC): def __init__(self, angulo1, angulo2, angulo3, lado1, lado2, lado3): self.angulo1 = angulo1 self.angulo2 = angulo2 self.angulo3 = angulo3 self.lado1 = lado1 self.lado2 = lado2 self.lado3 = lado3 self.num_de_lados = 3 def __str__(self): return f"Angulo 1 = {self.angulo1}\nAngulo 2 = {self.angulo2}\n" \ f"Angulo 3 = {self.angulo3}\nLado 1 = {self.lado1}\n" \ f"Lado 2 = {self.lado2}\nLado 3 = {self.lado3}\nValidar : {self.verificar_triangulo()}" def verificar_angulos(self): return self.angulo1 + self.angulo2 + self.angulo3 == 180 @abstractmethod def verificar_triangulo(self) -> bool: pass class Equilatero(Triangulo): def verificar_triangulo(self): return self.lado1 == self.lado2 and self.lado2 == self.lado3 class Isoceles(Triangulo): def verificar_triangulo(self): if self.lado1 == self.lado2 and self.lado2 == self.lado3: return False elif(self.lado1==self.lado2 or self.lado1==self.lado3 or self.lado2==self.lado3): return True else: return False class Escaleno(Triangulo): def verificar_triangulo(self): if self.lado1 == self.lado2 and self.lado2 == self.lado3: return False elif(self.lado1 == self.lado2 or self.lado1 == self.lado3 or self.lado2 == self.lado3): return False elif (self.lado1 != self.lado2 or self.lado1 != self.lado3 or self.lado2 != self.lado3): return True if __name__ == '__main__': ################_angulos_#_lados_########### equilatero = Equilatero(90,60,30,10,10,10) iso = Isoceles(90,60,30,10,10,20) escaleno = Escaleno(90,60,30,10,20,30) print(equilatero.verificar_triangulo()) print(iso.verificar_triangulo()) print(escaleno.verificar_triangulo())

faab1fe2cdc7f651b0aa16e5d9b11d5c4991baac

py

Python

app_venv/Lib/site-packages/mysql/connector/cursor_cext.py

orlandofv/sianna

f07dd6dbc62a9604f31ab800e482e62f14fba766

app_venv/Lib/site-packages/mysql/connector/cursor_cext.py

orlandofv/sianna

f07dd6dbc62a9604f31ab800e482e62f14fba766

app_venv/Lib/site-packages/mysql/connector/cursor_cext.py

orlandofv/sianna

f07dd6dbc62a9604f31ab800e482e62f14fba766

# Copyright (c) 2014, 2021, Oracle and/or its affiliates. # # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License, version 2.0, as # published by the Free Software Foundation. # # This program is also distributed with certain software (including # but not limited to OpenSSL) that is licensed under separate terms, # as designated in a particular file or component or in included license # documentation. The authors of MySQL hereby grant you an # additional permission to link the program and your derivative works # with the separately licensed software that they have included with # MySQL. # # Without limiting anything contained in the foregoing, this file, # which is part of MySQL Connector/Python, is also subject to the # Universal FOSS Exception, version 1.0, a copy of which can be found at # http://oss.oracle.com/licenses/universal-foss-exception. # # This program is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the GNU General Public License, version 2.0, for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to the Free Software Foundation, Inc., # 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA """Cursor classes using the C Extension """ from collections import namedtuple import re import weakref from _mysql_connector import MySQLInterfaceError # pylint: disable=F0401,E0611 from .abstracts import (MySQLConnectionAbstract, MySQLCursorAbstract, NAMED_TUPLE_CACHE) from . import errors from .errorcode import CR_NO_RESULT_SET from .cursor import ( RE_PY_PARAM, RE_SQL_INSERT_STMT, RE_SQL_ON_DUPLICATE, RE_SQL_COMMENT, RE_SQL_INSERT_VALUES, RE_SQL_SPLIT_STMTS, RE_SQL_FIND_PARAM ) ERR_NO_RESULT_TO_FETCH = "No result set to fetch from" class _ParamSubstitutor(object): """ Substitutes parameters into SQL statement. """ def __init__(self, params): self.params = params self.index = 0 def __call__(self, matchobj): index = self.index self.index += 1 try: return self.params[index] except IndexError: raise errors.ProgrammingError( "Not enough parameters for the SQL statement") @property def remaining(self): """Returns number of parameters remaining to be substituted""" return len(self.params) - self.index class CMySQLCursor(MySQLCursorAbstract): """Default cursor for interacting with MySQL using C Extension""" _raw = False _buffered = False _raw_as_string = False def __init__(self, connection): """Initialize""" MySQLCursorAbstract.__init__(self) self._insert_id = 0 self._warning_count = 0 self._warnings = None self._affected_rows = -1 self._rowcount = -1 self._nextrow = (None, None) self._executed = None self._executed_list = [] self._stored_results = [] if not isinstance(connection, MySQLConnectionAbstract): raise errors.InterfaceError(errno=2048) self._cnx = weakref.proxy(connection) def reset(self, free=True): """Reset the cursor When free is True (default) the result will be freed. """ self._rowcount = -1 self._nextrow = None self._affected_rows = -1 self._insert_id = 0 self._warning_count = 0 self._warnings = None self._warnings = None self._warning_count = 0 self._description = None self._executed_list = [] if free and self._cnx: self._cnx.free_result() super(CMySQLCursor, self).reset() def _check_executed(self): """Check if the statement has been executed. Raises an error if the statement has not been executed. """ if self._executed is None: raise errors.InterfaceError(ERR_NO_RESULT_TO_FETCH) def _fetch_warnings(self): """Fetch warnings Fetch warnings doing a SHOW WARNINGS. Can be called after getting the result. Returns a result set or None when there were no warnings. Raises errors.Error (or subclass) on errors. Returns list of tuples or None. """ warnings = [] try: # force freeing result self._cnx.consume_results() _ = self._cnx.cmd_query("SHOW WARNINGS") warnings = self._cnx.get_rows()[0] self._cnx.consume_results() except MySQLInterfaceError as exc: raise errors.get_mysql_exception(msg=exc.msg, errno=exc.errno, sqlstate=exc.sqlstate) except Exception as err: raise errors.InterfaceError( "Failed getting warnings; {0}".format(str(err))) if warnings: return warnings return None def _handle_warnings(self): """Handle possible warnings after all results are consumed""" if self._cnx.get_warnings is True and self._warning_count: self._warnings = self._fetch_warnings() def _handle_result(self, result): """Handles the result after statement execution""" if 'columns' in result: self._description = result['columns'] self._rowcount = 0 self._handle_resultset() else: self._insert_id = result['insert_id'] self._warning_count = result['warning_count'] self._affected_rows = result['affected_rows'] self._rowcount = -1 self._handle_warnings() if self._cnx.raise_on_warnings is True and self._warnings: raise errors.get_mysql_exception(*self._warnings[0][1:3]) def _handle_resultset(self): """Handle a result set""" pass def _handle_eof(self): """Handle end of reading the result Raises an errors.Error on errors. """ self._warning_count = self._cnx.warning_count self._handle_warnings() if self._cnx.raise_on_warnings is True and self._warnings: raise errors.get_mysql_exception(*self._warnings[0][1:3]) if not self._cnx.more_results: self._cnx.free_result() def _execute_iter(self): """Generator returns MySQLCursor objects for multiple statements Deprecated: use nextset() method directly. This method is only used when multiple statements are executed by the execute() method. It uses zip() to make an iterator from the given query_iter (result of MySQLConnection.cmd_query_iter()) and the list of statements that were executed. """ executed_list = RE_SQL_SPLIT_STMTS.split(self._executed) i = 0 self._executed = executed_list[i] yield self while True: try: if not self.nextset(): raise StopIteration except errors.InterfaceError as exc: # Result without result set if exc.errno != CR_NO_RESULT_SET: raise except StopIteration: return i += 1 try: self._executed = executed_list[i].strip() except IndexError: self._executed = executed_list[0] yield self return def execute(self, operation, params=(), multi=False): """Execute given statement using given parameters Deprecated: The multi argument is not needed and nextset() should be used to handle multiple result sets. """ if not operation: return None if not self._cnx or self._cnx.is_closed(): raise errors.ProgrammingError("Cursor is not connected", 2055) self._cnx.handle_unread_result() stmt = '' self.reset() try: if isinstance(operation, str): stmt = operation.encode(self._cnx.python_charset) else: stmt = operation except (UnicodeDecodeError, UnicodeEncodeError) as err: raise errors.ProgrammingError(str(err)) if params: prepared = self._cnx.prepare_for_mysql(params) if isinstance(prepared, dict): for key, value in prepared.items(): stmt = stmt.replace("%({0})s".format(key).encode(), value) elif isinstance(prepared, (list, tuple)): psub = _ParamSubstitutor(prepared) stmt = RE_PY_PARAM.sub(psub, stmt) if psub.remaining != 0: raise errors.ProgrammingError( "Not all parameters were used in the SQL statement") try: result = self._cnx.cmd_query(stmt, raw=self._raw, buffered=self._buffered, raw_as_string=self._raw_as_string) except MySQLInterfaceError as exc: raise errors.get_mysql_exception(msg=exc.msg, errno=exc.errno, sqlstate=exc.sqlstate) self._executed = stmt self._handle_result(result) if multi: return self._execute_iter() return None def _batch_insert(self, operation, seq_params): """Implements multi row insert""" def remove_comments(match): """Remove comments from INSERT statements. This function is used while removing comments from INSERT statements. If the matched string is a comment not enclosed by quotes, it returns an empty string, else the string itself. """ if match.group(1): return "" return match.group(2) tmp = re.sub(RE_SQL_ON_DUPLICATE, '', re.sub(RE_SQL_COMMENT, remove_comments, operation)) matches = re.search(RE_SQL_INSERT_VALUES, tmp) if not matches: raise errors.InterfaceError( "Failed rewriting statement for multi-row INSERT. " "Check SQL syntax." ) fmt = matches.group(1).encode(self._cnx.python_charset) values = [] try: stmt = operation.encode(self._cnx.python_charset) for params in seq_params: tmp = fmt prepared = self._cnx.prepare_for_mysql(params) if isinstance(prepared, dict): for key, value in prepared.items(): tmp = tmp.replace("%({0})s".format(key).encode(), value) elif isinstance(prepared, (list, tuple)): psub = _ParamSubstitutor(prepared) tmp = RE_PY_PARAM.sub(psub, tmp) if psub.remaining != 0: raise errors.ProgrammingError( "Not all parameters were used in the SQL statement") values.append(tmp) if fmt in stmt: stmt = stmt.replace(fmt, b','.join(values), 1) self._executed = stmt return stmt return None except (UnicodeDecodeError, UnicodeEncodeError) as err: raise errors.ProgrammingError(str(err)) except Exception as err: raise errors.InterfaceError( "Failed executing the operation; %s" % err) def executemany(self, operation, seq_params): """Execute the given operation multiple times""" if not operation or not seq_params: return None if not self._cnx: raise errors.ProgrammingError("Cursor is not connected") self._cnx.handle_unread_result() if not isinstance(seq_params, (list, tuple)): raise errors.ProgrammingError( "Parameters for query must be list or tuple.") # Optimize INSERTs by batching them if re.match(RE_SQL_INSERT_STMT, operation): if not seq_params: self._rowcount = 0 return None stmt = self._batch_insert(operation, seq_params) if stmt is not None: self._executed = stmt return self.execute(stmt) rowcnt = 0 try: for params in seq_params: self.execute(operation, params) try: while True: if self._description: rowcnt += len(self._cnx.get_rows()[0]) else: rowcnt += self._affected_rows if not self.nextset(): break except StopIteration: # No more results pass except (ValueError, TypeError) as err: raise errors.ProgrammingError( "Failed executing the operation; {0}".format(err)) self._rowcount = rowcnt return None @property def description(self): """Returns description of columns in a result""" return self._description @property def rowcount(self): """Returns the number of rows produced or affected""" if self._rowcount == -1: return self._affected_rows return self._rowcount @property def lastrowid(self): """Returns the value generated for an AUTO_INCREMENT column""" return self._insert_id def close(self): """Close the cursor The result will be freed. """ if not self._cnx: return False self._cnx.handle_unread_result() self._warnings = None self._cnx = None return True def callproc(self, procname, args=()): """Calls a stored procedure with the given arguments""" if not procname or not isinstance(procname, str): raise ValueError("procname must be a string") if not isinstance(args, (tuple, list)): raise ValueError("args must be a sequence") argfmt = "@_{name}_arg{index}" self._stored_results = [] try: argnames = [] argtypes = [] if args: for idx, arg in enumerate(args): argname = argfmt.format(name=procname, index=idx + 1) argnames.append(argname) if isinstance(arg, tuple): argtypes.append(" CAST({0} AS {1})".format(argname, arg[1])) self.execute("SET {0}=%s".format(argname), (arg[0],)) else: argtypes.append(argname) self.execute("SET {0}=%s".format(argname), (arg,)) call = "CALL {0}({1})".format(procname, ','.join(argnames)) result = self._cnx.cmd_query(call, raw=self._raw, raw_as_string=self._raw_as_string) results = [] while self._cnx.result_set_available: result = self._cnx.fetch_eof_columns() # pylint: disable=W0212 if isinstance(self, (CMySQLCursorDict, CMySQLCursorBufferedDict)): cursor_class = CMySQLCursorBufferedDict elif isinstance(self, (CMySQLCursorNamedTuple, CMySQLCursorBufferedNamedTuple)): cursor_class = CMySQLCursorBufferedNamedTuple elif self._raw: cursor_class = CMySQLCursorBufferedRaw else: cursor_class = CMySQLCursorBuffered cur = cursor_class(self._cnx._get_self()) cur._executed = "(a result of {0})".format(call) cur._handle_result(result) # pylint: enable=W0212 results.append(cur) self._cnx.next_result() self._stored_results = results self._handle_eof() if argnames: self.reset() # Create names aliases to be compatible with namedtuples args = [ "{} AS {}".format(name, alias) for name, alias in zip(argtypes, [arg.lstrip("@_") for arg in argnames]) ] select = "SELECT {}".format(",".join(args)) self.execute(select) return self.fetchone() return tuple() except errors.Error: raise except Exception as err: raise errors.InterfaceError( "Failed calling stored routine; {0}".format(err)) def nextset(self): """Skip to the next available result set""" if not self._cnx.next_result(): self.reset(free=True) return None self.reset(free=False) if not self._cnx.result_set_available: eof = self._cnx.fetch_eof_status() self._handle_result(eof) raise errors.InterfaceError(errno=CR_NO_RESULT_SET) self._handle_result(self._cnx.fetch_eof_columns()) return True def fetchall(self): """Returns all rows of a query result set Returns a list of tuples. """ self._check_executed() if not self._cnx.unread_result: return [] rows = self._cnx.get_rows() if self._nextrow and self._nextrow[0]: rows[0].insert(0, self._nextrow[0]) if not rows[0]: self._handle_eof() return [] self._rowcount += len(rows[0]) self._handle_eof() #self._cnx.handle_unread_result() return rows[0] def fetchmany(self, size=1): """Returns the next set of rows of a result set""" self._check_executed() if self._nextrow and self._nextrow[0]: rows = [self._nextrow[0]] size -= 1 else: rows = [] if size and self._cnx.unread_result: rows.extend(self._cnx.get_rows(size)[0]) if size: if self._cnx.unread_result: self._nextrow = self._cnx.get_row() if self._nextrow and not self._nextrow[0] and \ not self._cnx.more_results: self._cnx.free_result() else: self._nextrow = (None, None) if not rows: self._handle_eof() return [] self._rowcount += len(rows) return rows def fetchone(self): """Returns next row of a query result set""" self._check_executed() row = self._nextrow if not row and self._cnx.unread_result: row = self._cnx.get_row() if row and row[0]: self._nextrow = self._cnx.get_row() if not self._nextrow[0] and not self._cnx.more_results: self._cnx.free_result() else: self._handle_eof() return None self._rowcount += 1 return row[0] def __iter__(self): """Iteration over the result set Iteration over the result set which calls self.fetchone() and returns the next row. """ return iter(self.fetchone, None) def stored_results(self): """Returns an iterator for stored results This method returns an iterator over results which are stored when callproc() is called. The iterator will provide MySQLCursorBuffered instances. Returns a iterator. """ for i in range(len(self._stored_results)): yield self._stored_results[i] self._stored_results = [] def __next__(self): """Iteration over the result set Used for iterating over the result set. Calls self.fetchone() to get the next row. Raises StopIteration when no more rows are available. """ try: row = self.fetchone() except errors.InterfaceError: raise StopIteration if not row: raise StopIteration return row @property def column_names(self): """Returns column names This property returns the columns names as a tuple. Returns a tuple. """ if not self.description: return () return tuple([d[0] for d in self.description]) @property def statement(self): """Returns the executed statement This property returns the executed statement. When multiple statements were executed, the current statement in the iterator will be returned. """ try: return self._executed.strip().decode('utf8') except AttributeError: return self._executed.strip() @property def with_rows(self): """Returns whether the cursor could have rows returned This property returns True when column descriptions are available and possibly also rows, which will need to be fetched. Returns True or False. """ if self.description: return True return False def __str__(self): fmt = "{class_name}: {stmt}" if self._executed: try: executed = self._executed.decode('utf-8') except AttributeError: executed = self._executed if len(executed) > 40: executed = executed[:40] + '..' else: executed = '(Nothing executed yet)' return fmt.format(class_name=self.__class__.__name__, stmt=executed) class CMySQLCursorBuffered(CMySQLCursor): """Cursor using C Extension buffering results""" def __init__(self, connection): """Initialize""" super(CMySQLCursorBuffered, self).__init__(connection) self._rows = None self._next_row = 0 def _handle_resultset(self): """Handle a result set""" self._rows = self._cnx.get_rows()[0] self._next_row = 0 self._rowcount = len(self._rows) self._handle_eof() def reset(self, free=True): """Reset the cursor to default""" self._rows = None self._next_row = 0 super(CMySQLCursorBuffered, self).reset(free=free) def _fetch_row(self): """Returns the next row in the result set Returns a tuple or None. """ row = None try: row = self._rows[self._next_row] except IndexError: return None else: self._next_row += 1 return row def fetchall(self): self._check_executed() res = self._rows[self._next_row:] self._next_row = len(self._rows) return res def fetchmany(self, size=1): self._check_executed() res = [] cnt = size or self.arraysize while cnt > 0: cnt -= 1 row = self._fetch_row() if row: res.append(row) else: break return res def fetchone(self): self._check_executed() return self._fetch_row() class CMySQLCursorRaw(CMySQLCursor): """Cursor using C Extension return raw results""" _raw = True class CMySQLCursorBufferedRaw(CMySQLCursorBuffered): """Cursor using C Extension buffering raw results""" _raw = True class CMySQLCursorDict(CMySQLCursor): """Cursor using C Extension returning rows as dictionaries""" _raw = False def fetchone(self): """Returns all rows of a query result set """ row = super(CMySQLCursorDict, self).fetchone() if row: return dict(zip(self.column_names, row)) return None def fetchmany(self, size=1): """Returns next set of rows as list of dictionaries""" res = super(CMySQLCursorDict, self).fetchmany(size=size) return [dict(zip(self.column_names, row)) for row in res] def fetchall(self): """Returns all rows of a query result set as list of dictionaries""" res = super(CMySQLCursorDict, self).fetchall() return [dict(zip(self.column_names, row)) for row in res] class CMySQLCursorBufferedDict(CMySQLCursorBuffered): """Cursor using C Extension buffering and returning rows as dictionaries""" _raw = False def _fetch_row(self): row = super(CMySQLCursorBufferedDict, self)._fetch_row() if row: return dict(zip(self.column_names, row)) return None def fetchall(self): res = super(CMySQLCursorBufferedDict, self).fetchall() return [dict(zip(self.column_names, row)) for row in res] class CMySQLCursorNamedTuple(CMySQLCursor): """Cursor using C Extension returning rows as named tuples""" def _handle_resultset(self): """Handle a result set""" super(CMySQLCursorNamedTuple, self)._handle_resultset() # pylint: disable=W0201 columns = tuple(self.column_names) try: self.named_tuple = NAMED_TUPLE_CACHE[columns] except KeyError: self.named_tuple = namedtuple('Row', columns) NAMED_TUPLE_CACHE[columns] = self.named_tuple # pylint: enable=W0201 def fetchone(self): """Returns all rows of a query result set """ row = super(CMySQLCursorNamedTuple, self).fetchone() if row: return self.named_tuple(*row) return None def fetchmany(self, size=1): """Returns next set of rows as list of named tuples""" res = super(CMySQLCursorNamedTuple, self).fetchmany(size=size) if not res: return [] return [self.named_tuple(*res[0])] def fetchall(self): """Returns all rows of a query result set as list of named tuples""" res = super(CMySQLCursorNamedTuple, self).fetchall() return [self.named_tuple(*row) for row in res] class CMySQLCursorBufferedNamedTuple(CMySQLCursorBuffered): """Cursor using C Extension buffering and returning rows as named tuples""" def _handle_resultset(self): super(CMySQLCursorBufferedNamedTuple, self)._handle_resultset() # pylint: disable=W0201 self.named_tuple = namedtuple('Row', self.column_names) # pylint: enable=W0201 def _fetch_row(self): row = super(CMySQLCursorBufferedNamedTuple, self)._fetch_row() if row: return self.named_tuple(*row) return None def fetchall(self): res = super(CMySQLCursorBufferedNamedTuple, self).fetchall() return [self.named_tuple(*row) for row in res] class CMySQLCursorPrepared(CMySQLCursor): """Cursor using MySQL Prepared Statements""" def __init__(self, connection): super(CMySQLCursorPrepared, self).__init__(connection) self._rows = None self._rowcount = 0 self._next_row = 0 self._binary = True self._stmt = None def _handle_eof(self): """Handle EOF packet""" self._nextrow = (None, None) self._handle_warnings() if self._cnx.raise_on_warnings is True and self._warnings: raise errors.get_mysql_exception( self._warnings[0][1], self._warnings[0][2]) def _fetch_row(self, raw=False): """Returns the next row in the result set Returns a tuple or None. """ if not self._stmt or not self._stmt.have_result_set: return None row = None if self._nextrow == (None, None): (row, eof) = self._cnx.get_row( binary=self._binary, columns=self.description, raw=raw, prep_stmt=self._stmt) else: (row, eof) = self._nextrow if row: self._nextrow = self._cnx.get_row( binary=self._binary, columns=self.description, raw=raw, prep_stmt=self._stmt) eof = self._nextrow[1] if eof is not None: self._warning_count = eof["warning_count"] self._handle_eof() if self._rowcount == -1: self._rowcount = 1 else: self._rowcount += 1 if eof: self._warning_count = eof["warning_count"] self._handle_eof() return row def callproc(self, procname, args=None): """Calls a stored procedue Not supported with CMySQLCursorPrepared. """ raise errors.NotSupportedError() def close(self): """Close the cursor This method will try to deallocate the prepared statement and close the cursor. """ if self._stmt: self.reset() self._cnx.cmd_stmt_close(self._stmt) self._stmt = None super(CMySQLCursorPrepared, self).close() def reset(self, free=True): """Resets the prepared statement.""" if self._stmt: self._cnx.cmd_stmt_reset(self._stmt) super(CMySQLCursorPrepared, self).reset(free=free) def execute(self, operation, params=None, multi=False): # multi is unused """Prepare and execute a MySQL Prepared Statement This method will prepare the given operation and execute it using the given parameters. If the cursor instance already had a prepared statement, it is first closed. """ if not operation: return if not self._cnx or self._cnx.is_closed(): raise errors.ProgrammingError("Cursor is not connected", 2055) self._cnx.handle_unread_result(prepared=True) if operation is not self._executed: if self._stmt: self._cnx.cmd_stmt_close(self._stmt) self._executed = operation try: if not isinstance(operation, bytes): charset = self._cnx.charset if charset == "utf8mb4": charset = "utf8" operation = operation.encode(charset) except (UnicodeDecodeError, UnicodeEncodeError) as err: raise errors.ProgrammingError(str(err)) # need to convert %s to ? before sending it to MySQL if b"%s" in operation: operation = re.sub(RE_SQL_FIND_PARAM, b"?", operation) try: self._stmt = self._cnx.cmd_stmt_prepare(operation) except errors.Error: self._executed = None self._stmt = None raise self._cnx.cmd_stmt_reset(self._stmt) if self._stmt.param_count > 0 and not params: return elif params: if not isinstance(params, (tuple, list)): raise errors.ProgrammingError( errno=1210, msg=f"Incorrect type of argument: {type(params).__name__}({params})" ", it must be of type tuple or list the argument given to " "the prepared statement") if self._stmt.param_count != len(params): raise errors.ProgrammingError( errno=1210, msg="Incorrect number of arguments executing prepared " "statement") if params is None: params = () res = self._cnx.cmd_stmt_execute(self._stmt, *params) if res: self._handle_result(res) def executemany(self, operation, seq_params): """Prepare and execute a MySQL Prepared Statement many times This method will prepare the given operation and execute with each tuple found the list seq_params. If the cursor instance already had a prepared statement, it is first closed. """ rowcnt = 0 try: for params in seq_params: self.execute(operation, params) if self.with_rows: self.fetchall() rowcnt += self._rowcount except (ValueError, TypeError) as err: raise errors.InterfaceError( "Failed executing the operation; {error}".format(error=err)) except: # Raise whatever execute() raises raise self._rowcount = rowcnt def fetchone(self): """Returns next row of a query result set Returns a tuple or None. """ self._check_executed() return self._fetch_row() or None def fetchmany(self, size=None): """Returns the next set of rows of a result set Returns a list of tuples. """ self._check_executed() res = [] cnt = size or self.arraysize while cnt > 0 and self._stmt.have_result_set: cnt -= 1 row = self._fetch_row() if row: res.append(row) return res def fetchall(self): """Returns all rows of a query result set Returns a list of tuples. """ self._check_executed() if not self._stmt.have_result_set: return [] rows = self._cnx.get_rows(prep_stmt=self._stmt) if self._nextrow and self._nextrow[0]: rows[0].insert(0, self._nextrow[0]) if not rows[0]: self._handle_eof() return [] self._rowcount += len(rows[0]) self._handle_eof() return rows[0]

c5bd00fff979b3eaa3d11b34d77d95a965f81c41

py

Python

garagei/torch/modules/reshape.py

artberryx/LSD

99ee081de2502b4d13c140b474f772db8a5f92fe

2022-02-01T03:02:24.000Z

2022-02-10T12:54:05.000Z

garagei/torch/modules/reshape.py

artberryx/LSD

99ee081de2502b4d13c140b474f772db8a5f92fe

garagei/torch/modules/reshape.py

artberryx/LSD

99ee081de2502b4d13c140b474f772db8a5f92fe

2022-02-03T03:33:25.000Z

2022-02-10T12:54:07.000Z

import numpy as np import torch from torch import nn class ReshapeModule(nn.Module): def __init__(self, shape): super().__init__() self.shape = shape def forward(self, x): assert np.prod(x.shape[1:]) == np.prod(self.shape) return x.reshape(-1, *self.shape) class ViewModule(nn.Module): def __init__(self, shape): super().__init__() self.shape = shape def forward(self, x): assert np.prod(x.shape[1:]) == np.prod(self.shape) return x.view(-1, *self.shape)

3184b705d9f26228049b22da9a076f6d64ebdbd4

py

Python

src/dat_analysis/analysis_tools/__init__.py

TimChild/dat_analysis

2902e5cb2f2823a1c7a26faf6b3b6dfeb7633c73

2021-03-07T03:17:13.000Z

2021-03-07T03:17:16.000Z

src/dat_analysis/analysis_tools/__init__.py

TimChild/dat_analysis

2902e5cb2f2823a1c7a26faf6b3b6dfeb7633c73

2021-03-09T00:00:52.000Z

2021-03-09T00:00:52.000Z

src/dat_analysis/analysis_tools/__init__.py

TimChild/dat_analysis

2902e5cb2f2823a1c7a26faf6b3b6dfeb7633c73

from .csq_mapping import calculate_csq_map, calculate_csq_mapped_avg, setup_csq_dat, csq_map_data from . import entropy from . import general_fitting from .nrg import NrgUtil, NRGParams, nrg_func, NRGData, NRG_func_generator

c1cae7f307a37de6781d271c72c5c68d63533d14

py

Python

allennlp/modules/scalar_mix.py

cl-tohoku/allennlp

dffb549a3fe613913e97a34772e56a000a6a2860

allennlp/modules/scalar_mix.py

cl-tohoku/allennlp

dffb549a3fe613913e97a34772e56a000a6a2860

allennlp/modules/scalar_mix.py

cl-tohoku/allennlp

dffb549a3fe613913e97a34772e56a000a6a2860

from typing import List import torch from torch.nn import ParameterList, Parameter from allennlp.common.checks import ConfigurationError class ScalarMix(torch.nn.Module): """ Computes a parameterised scalar mixture of N tensors, ``mixture = gamma * sum(s_k * tensor_k)`` where ``s = softmax(w)``, with ``w`` and ``gamma`` scalar parameters. In addition, if ``do_layer_norm=True`` then apply layer normalization to each tensor before weighting. """ def __init__(self, mixture_size: int, do_layer_norm: bool = False) -> None: super(ScalarMix, self).__init__() self.mixture_size = mixture_size self.do_layer_norm = do_layer_norm self.scalar_parameters = ParameterList([Parameter(torch.FloatTensor([0.0])) for _ in range(mixture_size)]) self.gamma = Parameter(torch.FloatTensor([1.0])) def forward(self, tensors: List[torch.Tensor], # pylint: disable=arguments-differ mask: torch.Tensor = None) -> torch.Tensor: """ Compute a weighted average of the ``tensors``. The input tensors an be any shape with at least two dimensions, but must all be the same shape. When ``do_layer_norm=True``, the ``mask`` is required input. If the ``tensors`` are dimensioned ``(dim_0, ..., dim_{n-1}, dim_n)``, then the ``mask`` is dimensioned ``(dim_0, ..., dim_{n-1})``, as in the typical case with ``tensors`` of shape ``(batch_size, timesteps, dim)`` and ``mask`` of shape ``(batch_size, timesteps)``. When ``do_layer_norm=False`` the ``mask`` is ignored. """ if len(tensors) != self.mixture_size: raise ConfigurationError("{} tensors were passed, but the module was initialized to " "mix {} tensors.".format(len(tensors), self.mixture_size)) def _do_layer_norm(tensor, broadcast_mask, num_elements_not_masked): tensor_masked = tensor * broadcast_mask mean = torch.sum(tensor_masked) / num_elements_not_masked variance = torch.sum(((tensor_masked - mean) * broadcast_mask)**2) / num_elements_not_masked return (tensor - mean) / torch.sqrt(variance + 1E-12) normed_weights = torch.nn.functional.softmax(torch.cat([parameter for parameter in self.scalar_parameters]), dim=0) normed_weights = torch.split(normed_weights, split_size=1) if not self.do_layer_norm: pieces = [] for weight, tensor in zip(normed_weights, tensors): pieces.append(weight * tensor) return self.gamma * sum(pieces) else: mask_float = mask.float() broadcast_mask = mask_float.unsqueeze(-1) input_dim = tensors[0].size(-1) num_elements_not_masked = torch.sum(mask_float) * input_dim pieces = [] for weight, tensor in zip(normed_weights, tensors): pieces.append(weight * _do_layer_norm(tensor, broadcast_mask, num_elements_not_masked)) return self.gamma * sum(pieces)

7eeddfa1ac02b302b51c4f5aea219b75c58fd1e5

py

Python

certbot/certbot/_internal/cert_manager.py

lpmitchell/certbot

fe0c0dc3ae6c25c6087e51717a223f38a9b23d2f

2016-05-06T19:49:59.000Z

2022-03-31T20:01:47.000Z

certbot/certbot/_internal/cert_manager.py

nicholascioli/certbot

86406ab63aebf463ca4aa0381a55ddeb91231cd2

2016-05-06T19:26:43.000Z

2022-03-31T20:27:38.000Z

certbot/certbot/_internal/cert_manager.py

nicholascioli/certbot

86406ab63aebf463ca4aa0381a55ddeb91231cd2

2016-05-06T19:51:23.000Z

2022-03-31T21:01:36.000Z

"""Tools for managing certificates.""" import datetime import logging import re import traceback from typing import Any from typing import Callable from typing import Iterable from typing import List from typing import Optional from typing import Tuple from typing import TypeVar from typing import Union import pytz from certbot import configuration from certbot import crypto_util from certbot import errors from certbot import ocsp from certbot import util from certbot._internal import storage from certbot.compat import os from certbot.display import util as display_util logger = logging.getLogger(__name__) ################### # Commands ################### def update_live_symlinks(config: configuration.NamespaceConfig) -> None: """Update the certificate file family symlinks to use archive_dir. Use the information in the config file to make symlinks point to the correct archive directory. .. note:: This assumes that the installation is using a Reverter object. :param config: Configuration. :type config: :class:`certbot._internal.configuration.NamespaceConfig` """ for renewal_file in storage.renewal_conf_files(config): storage.RenewableCert(renewal_file, config, update_symlinks=True) def rename_lineage(config: configuration.NamespaceConfig) -> None: """Rename the specified lineage to the new name. :param config: Configuration. :type config: :class:`certbot._internal.configuration.NamespaceConfig` """ certname = get_certnames(config, "rename")[0] new_certname = config.new_certname if not new_certname: code, new_certname = display_util.input_text( "Enter the new name for certificate {0}".format(certname), force_interactive=True) if code != display_util.OK or not new_certname: raise errors.Error("User ended interaction.") lineage = lineage_for_certname(config, certname) if not lineage: raise errors.ConfigurationError("No existing certificate with name " "{0} found.".format(certname)) storage.rename_renewal_config(certname, new_certname, config) display_util.notification("Successfully renamed {0} to {1}." .format(certname, new_certname), pause=False) def certificates(config: configuration.NamespaceConfig) -> None: """Display information about certs configured with Certbot :param config: Configuration. :type config: :class:`certbot._internal.configuration.NamespaceConfig` """ parsed_certs = [] parse_failures = [] for renewal_file in storage.renewal_conf_files(config): try: renewal_candidate = storage.RenewableCert(renewal_file, config) crypto_util.verify_renewable_cert(renewal_candidate) parsed_certs.append(renewal_candidate) except Exception as e: # pylint: disable=broad-except logger.warning("Renewal configuration file %s produced an " "unexpected error: %s. Skipping.", renewal_file, e) logger.debug("Traceback was:\n%s", traceback.format_exc()) parse_failures.append(renewal_file) # Describe all the certs _describe_certs(config, parsed_certs, parse_failures) def delete(config: configuration.NamespaceConfig) -> None: """Delete Certbot files associated with a certificate lineage.""" certnames = get_certnames(config, "delete", allow_multiple=True) msg = ["The following certificate(s) are selected for deletion:\n"] for certname in certnames: msg.append(" * " + certname) msg.append( "\nWARNING: Before continuing, ensure that the listed certificates are not being used " "by any installed server software (e.g. Apache, nginx, mail servers). Deleting a " "certificate that is still being used will cause the server software to stop working. " "See https://certbot.org/deleting-certs for information on deleting certificates safely." ) msg.append("\nAre you sure you want to delete the above certificate(s)?") if not display_util.yesno("\n".join(msg), default=True): logger.info("Deletion of certificate(s) canceled.") return for certname in certnames: storage.delete_files(config, certname) display_util.notify("Deleted all files relating to certificate {0}." .format(certname)) ################### # Public Helpers ################### def lineage_for_certname(cli_config: configuration.NamespaceConfig, certname: str) -> Optional[storage.RenewableCert]: """Find a lineage object with name certname.""" configs_dir = cli_config.renewal_configs_dir # Verify the directory is there util.make_or_verify_dir(configs_dir, mode=0o755) try: renewal_file = storage.renewal_file_for_certname(cli_config, certname) except errors.CertStorageError: return None try: return storage.RenewableCert(renewal_file, cli_config) except (errors.CertStorageError, IOError): logger.debug("Renewal conf file %s is broken.", renewal_file) logger.debug("Traceback was:\n%s", traceback.format_exc()) return None def domains_for_certname(config: configuration.NamespaceConfig, certname: str) -> Optional[List[str]]: """Find the domains in the cert with name certname.""" lineage = lineage_for_certname(config, certname) return lineage.names() if lineage else None def find_duplicative_certs(config: configuration.NamespaceConfig, domains: List[str]) -> Tuple[Optional[storage.RenewableCert], Optional[storage.RenewableCert]]: """Find existing certs that match the given domain names. This function searches for certificates whose domains are equal to the `domains` parameter and certificates whose domains are a subset of the domains in the `domains` parameter. If multiple certificates are found whose names are a subset of `domains`, the one whose names are the largest subset of `domains` is returned. If multiple certificates' domains are an exact match or equally sized subsets, which matching certificates are returned is undefined. :param config: Configuration. :type config: :class:`certbot._internal.configuration.NamespaceConfig` :param domains: List of domain names :type domains: `list` of `str` :returns: lineages representing the identically matching cert and the largest subset if they exist :rtype: `tuple` of `storage.RenewableCert` or `None` """ def update_certs_for_domain_matches(candidate_lineage: storage.RenewableCert, rv: Tuple[Optional[storage.RenewableCert], Optional[storage.RenewableCert]] ) -> Tuple[Optional[storage.RenewableCert], Optional[storage.RenewableCert]]: """Return cert as identical_names_cert if it matches, or subset_names_cert if it matches as subset """ # TODO: Handle these differently depending on whether they are # expired or still valid? identical_names_cert, subset_names_cert = rv candidate_names = set(candidate_lineage.names()) if candidate_names == set(domains): identical_names_cert = candidate_lineage elif candidate_names.issubset(set(domains)): # This logic finds and returns the largest subset-names cert # in the case where there are several available. if subset_names_cert is None: subset_names_cert = candidate_lineage elif len(candidate_names) > len(subset_names_cert.names()): subset_names_cert = candidate_lineage return (identical_names_cert, subset_names_cert) init: Tuple[Optional[storage.RenewableCert], Optional[storage.RenewableCert]] = (None, None) return _search_lineages(config, update_certs_for_domain_matches, init) def _archive_files(candidate_lineage: storage.RenewableCert, filetype: str) -> Optional[List[str]]: """ In order to match things like: /etc/letsencrypt/archive/example.com/chain1.pem. Anonymous functions which call this function are eventually passed (in a list) to `match_and_check_overlaps` to help specify the acceptable_matches. :param `.storage.RenewableCert` candidate_lineage: Lineage whose archive dir is to be searched. :param str filetype: main file name prefix e.g. "fullchain" or "chain". :returns: Files in candidate_lineage's archive dir that match the provided filetype. :rtype: list of str or None """ archive_dir = candidate_lineage.archive_dir pattern = [os.path.join(archive_dir, f) for f in os.listdir(archive_dir) if re.match("{0}[0-9]*.pem".format(filetype), f)] if pattern: return pattern return None def _acceptable_matches() -> List[Union[Callable[[storage.RenewableCert], str], Callable[[storage.RenewableCert], Optional[List[str]]]]]: """ Generates the list that's passed to match_and_check_overlaps. Is its own function to make unit testing easier. :returns: list of functions :rtype: list """ return [lambda x: x.fullchain_path, lambda x: x.cert_path, lambda x: _archive_files(x, "cert"), lambda x: _archive_files(x, "fullchain")] def cert_path_to_lineage(cli_config: configuration.NamespaceConfig) -> str: """ If config.cert_path is defined, try to find an appropriate value for config.certname. :param `configuration.NamespaceConfig` cli_config: parsed command line arguments :returns: a lineage name :rtype: str :raises `errors.Error`: If the specified cert path can't be matched to a lineage name. :raises `errors.OverlappingMatchFound`: If the matched lineage's archive is shared. """ acceptable_matches = _acceptable_matches() match = match_and_check_overlaps(cli_config, acceptable_matches, lambda x: cli_config.cert_path, lambda x: x.lineagename) return match[0] def match_and_check_overlaps(cli_config: configuration.NamespaceConfig, acceptable_matches: Iterable[Union[ Callable[[storage.RenewableCert], str], Callable[[storage.RenewableCert], Optional[List[str]]]]], match_func: Callable[[storage.RenewableCert], str], rv_func: Callable[[storage.RenewableCert], str]) -> List[str]: """ Searches through all lineages for a match, and checks for duplicates. If a duplicate is found, an error is raised, as performing operations on lineages that have their properties incorrectly duplicated elsewhere is probably a bad idea. :param `configuration.NamespaceConfig` cli_config: parsed command line arguments :param list acceptable_matches: a list of functions that specify acceptable matches :param function match_func: specifies what to match :param function rv_func: specifies what to return """ def find_matches(candidate_lineage: storage.RenewableCert, return_value: List[str], acceptable_matches: Iterable[Union[ Callable[[storage.RenewableCert], str], Callable[[storage.RenewableCert], Optional[List[str]]]]]) -> List[str]: """Returns a list of matches using _search_lineages.""" acceptable_matches_resolved = [func(candidate_lineage) for func in acceptable_matches] acceptable_matches_rv: List[str] = [] for item in acceptable_matches_resolved: if isinstance(item, list): acceptable_matches_rv += item elif item: acceptable_matches_rv.append(item) match = match_func(candidate_lineage) if match in acceptable_matches_rv: return_value.append(rv_func(candidate_lineage)) return return_value matched: List[str] = _search_lineages(cli_config, find_matches, [], acceptable_matches) if not matched: raise errors.Error("No match found for cert-path {0}!".format(cli_config.cert_path)) elif len(matched) > 1: raise errors.OverlappingMatchFound() return matched def human_readable_cert_info(config: configuration.NamespaceConfig, cert: storage.RenewableCert, skip_filter_checks: bool = False) -> Optional[str]: """ Returns a human readable description of info about a RenewableCert object""" certinfo = [] checker = ocsp.RevocationChecker() if config.certname and cert.lineagename != config.certname and not skip_filter_checks: return None if config.domains and not set(config.domains).issubset(cert.names()): return None now = pytz.UTC.fromutc(datetime.datetime.utcnow()) reasons = [] if cert.is_test_cert: reasons.append('TEST_CERT') if cert.target_expiry <= now: reasons.append('EXPIRED') elif checker.ocsp_revoked(cert): reasons.append('REVOKED') if reasons: status = "INVALID: " + ", ".join(reasons) else: diff = cert.target_expiry - now if diff.days == 1: status = "VALID: 1 day" elif diff.days < 1: status = "VALID: {0} hour(s)".format(diff.seconds // 3600) else: status = "VALID: {0} days".format(diff.days) valid_string = "{0} ({1})".format(cert.target_expiry, status) serial = format(crypto_util.get_serial_from_cert(cert.cert_path), 'x') certinfo.append(" Certificate Name: {}\n" " Serial Number: {}\n" " Key Type: {}\n" " Domains: {}\n" " Expiry Date: {}\n" " Certificate Path: {}\n" " Private Key Path: {}".format( cert.lineagename, serial, cert.private_key_type, " ".join(cert.names()), valid_string, cert.fullchain, cert.privkey)) return "".join(certinfo) def get_certnames(config: configuration.NamespaceConfig, verb: str, allow_multiple: bool = False, custom_prompt: Optional[str] = None) -> List[str]: """Get certname from flag, interactively, or error out.""" certname = config.certname if certname: certnames = [certname] else: filenames = storage.renewal_conf_files(config) choices = [storage.lineagename_for_filename(name) for name in filenames] if not choices: raise errors.Error("No existing certificates found.") if allow_multiple: if not custom_prompt: prompt = "Which certificate(s) would you like to {0}?".format(verb) else: prompt = custom_prompt code, certnames = display_util.checklist( prompt, choices, cli_flag="--cert-name", force_interactive=True) if code != display_util.OK: raise errors.Error("User ended interaction.") else: if not custom_prompt: prompt = "Which certificate would you like to {0}?".format(verb) else: prompt = custom_prompt code, index = display_util.menu( prompt, choices, cli_flag="--cert-name", force_interactive=True) if code != display_util.OK or index not in range(0, len(choices)): raise errors.Error("User ended interaction.") certnames = [choices[index]] return certnames ################### # Private Helpers ################### def _report_lines(msgs: Iterable[str]) -> str: """Format a results report for a category of single-line renewal outcomes""" return " " + "\n ".join(str(msg) for msg in msgs) def _report_human_readable(config: configuration.NamespaceConfig, parsed_certs: Iterable[storage.RenewableCert]) -> str: """Format a results report for a parsed cert""" certinfo = [] for cert in parsed_certs: cert_info = human_readable_cert_info(config, cert) if cert_info is not None: certinfo.append(cert_info) return "\n".join(certinfo) def _describe_certs(config: configuration.NamespaceConfig, parsed_certs: Iterable[storage.RenewableCert], parse_failures: Iterable[str]) -> None: """Print information about the certs we know about""" out: List[str] = [] notify = out.append if not parsed_certs and not parse_failures: notify("No certificates found.") else: if parsed_certs: match = "matching " if config.certname or config.domains else "" notify("Found the following {0}certs:".format(match)) notify(_report_human_readable(config, parsed_certs)) if parse_failures: notify("\nThe following renewal configurations " "were invalid:") notify(_report_lines(parse_failures)) display_util.notification("\n".join(out), pause=False, wrap=False) T = TypeVar('T') def _search_lineages(cli_config: configuration.NamespaceConfig, func: Callable[..., T], initial_rv: T, *args: Any) -> T: """Iterate func over unbroken lineages, allowing custom return conditions. Allows flexible customization of return values, including multiple return values and complex checks. :param `configuration.NamespaceConfig` cli_config: parsed command line arguments :param function func: function used while searching over lineages :param initial_rv: initial return value of the function (any type) :returns: Whatever was specified by `func` if a match is found. """ configs_dir = cli_config.renewal_configs_dir # Verify the directory is there util.make_or_verify_dir(configs_dir, mode=0o755) rv = initial_rv for renewal_file in storage.renewal_conf_files(cli_config): try: candidate_lineage = storage.RenewableCert(renewal_file, cli_config) except (errors.CertStorageError, IOError): logger.debug("Renewal conf file %s is broken. Skipping.", renewal_file) logger.debug("Traceback was:\n%s", traceback.format_exc()) continue rv = func(candidate_lineage, rv, *args) return rv

6455f39ce870b33430a7b3c16277cec12f106bf7

py

Python

Python-3.5.5/Lib/logging/__init__.py

it315/PSPNet-Keras-tensorflow

876448d9c44a8ca475cf0f60f69eb3c72651be87

2018-12-18T18:04:28.000Z

2021-04-23T07:31:13.000Z

Python-3.5.5/Lib/logging/__init__.py

it315/PSPNet-Keras-tensorflow

876448d9c44a8ca475cf0f60f69eb3c72651be87

2021-12-14T20:56:36.000Z

2021-12-20T14:45:34.000Z

Python-3.5.10/Lib/logging/__init__.py

AtriCZE23/POe-full

89be2fda5747e44764a62ba5e358d8c9309fbf0a

2019-06-22T14:16:57.000Z

2021-12-29T22:04:42.000Z

# Copyright 2001-2016 by Vinay Sajip. All Rights Reserved. # # Permission to use, copy, modify, and distribute this software and its # documentation for any purpose and without fee is hereby granted, # provided that the above copyright notice appear in all copies and that # both that copyright notice and this permission notice appear in # supporting documentation, and that the name of Vinay Sajip # not be used in advertising or publicity pertaining to distribution # of the software without specific, written prior permission. # VINAY SAJIP DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE, INCLUDING # ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL # VINAY SAJIP BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR # ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER # IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT # OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. """ Logging package for Python. Based on PEP 282 and comments thereto in comp.lang.python. Copyright (C) 2001-2016 Vinay Sajip. All Rights Reserved. To use, simply 'import logging' and log away! """ import sys, os, time, io, traceback, warnings, weakref, collections from string import Template __all__ = ['BASIC_FORMAT', 'BufferingFormatter', 'CRITICAL', 'DEBUG', 'ERROR', 'FATAL', 'FileHandler', 'Filter', 'Formatter', 'Handler', 'INFO', 'LogRecord', 'Logger', 'LoggerAdapter', 'NOTSET', 'NullHandler', 'StreamHandler', 'WARN', 'WARNING', 'addLevelName', 'basicConfig', 'captureWarnings', 'critical', 'debug', 'disable', 'error', 'exception', 'fatal', 'getLevelName', 'getLogger', 'getLoggerClass', 'info', 'log', 'makeLogRecord', 'setLoggerClass', 'warn', 'warning', 'getLogRecordFactory', 'setLogRecordFactory', 'lastResort'] try: import threading except ImportError: #pragma: no cover threading = None __author__ = "Vinay Sajip <vinay_sajip@red-dove.com>" __status__ = "production" # The following module attributes are no longer updated. __version__ = "0.5.1.2" __date__ = "07 February 2010" #--------------------------------------------------------------------------- # Miscellaneous module data #--------------------------------------------------------------------------- # #_startTime is used as the base when calculating the relative time of events # _startTime = time.time() # #raiseExceptions is used to see if exceptions during handling should be #propagated # raiseExceptions = True # # If you don't want threading information in the log, set this to zero # logThreads = True # # If you don't want multiprocessing information in the log, set this to zero # logMultiprocessing = True # # If you don't want process information in the log, set this to zero # logProcesses = True #--------------------------------------------------------------------------- # Level related stuff #--------------------------------------------------------------------------- # # Default levels and level names, these can be replaced with any positive set # of values having corresponding names. There is a pseudo-level, NOTSET, which # is only really there as a lower limit for user-defined levels. Handlers and # loggers are initialized with NOTSET so that they will log all messages, even # at user-defined levels. # CRITICAL = 50 FATAL = CRITICAL ERROR = 40 WARNING = 30 WARN = WARNING INFO = 20 DEBUG = 10 NOTSET = 0 _levelToName = { CRITICAL: 'CRITICAL', ERROR: 'ERROR', WARNING: 'WARNING', INFO: 'INFO', DEBUG: 'DEBUG', NOTSET: 'NOTSET', } _nameToLevel = { 'CRITICAL': CRITICAL, 'ERROR': ERROR, 'WARN': WARNING, 'WARNING': WARNING, 'INFO': INFO, 'DEBUG': DEBUG, 'NOTSET': NOTSET, } def getLevelName(level): """ Return the textual representation of logging level 'level'. If the level is one of the predefined levels (CRITICAL, ERROR, WARNING, INFO, DEBUG) then you get the corresponding string. If you have associated levels with names using addLevelName then the name you have associated with 'level' is returned. If a numeric value corresponding to one of the defined levels is passed in, the corresponding string representation is returned. Otherwise, the string "Level %s" % level is returned. """ # See Issues #22386, #27937 and #29220 for why it's this way result = _levelToName.get(level) if result is not None: return result result = _nameToLevel.get(level) if result is not None: return result return "Level %s" % level def addLevelName(level, levelName): """ Associate 'levelName' with 'level'. This is used when converting levels to text during message formatting. """ _acquireLock() try: #unlikely to cause an exception, but you never know... _levelToName[level] = levelName _nameToLevel[levelName] = level finally: _releaseLock() if hasattr(sys, '_getframe'): currentframe = lambda: sys._getframe(3) else: #pragma: no cover def currentframe(): """Return the frame object for the caller's stack frame.""" try: raise Exception except Exception: return sys.exc_info()[2].tb_frame.f_back # # _srcfile is used when walking the stack to check when we've got the first # caller stack frame, by skipping frames whose filename is that of this # module's source. It therefore should contain the filename of this module's # source file. # # Ordinarily we would use __file__ for this, but frozen modules don't always # have __file__ set, for some reason (see Issue #21736). Thus, we get the # filename from a handy code object from a function defined in this module. # (There's no particular reason for picking addLevelName.) # _srcfile = os.path.normcase(addLevelName.__code__.co_filename) # _srcfile is only used in conjunction with sys._getframe(). # To provide compatibility with older versions of Python, set _srcfile # to None if _getframe() is not available; this value will prevent # findCaller() from being called. You can also do this if you want to avoid # the overhead of fetching caller information, even when _getframe() is # available. #if not hasattr(sys, '_getframe'): # _srcfile = None def _checkLevel(level): if isinstance(level, int): rv = level elif str(level) == level: if level not in _nameToLevel: raise ValueError("Unknown level: %r" % level) rv = _nameToLevel[level] else: raise TypeError("Level not an integer or a valid string: %r" % level) return rv #--------------------------------------------------------------------------- # Thread-related stuff #--------------------------------------------------------------------------- # #_lock is used to serialize access to shared data structures in this module. #This needs to be an RLock because fileConfig() creates and configures #Handlers, and so might arbitrary user threads. Since Handler code updates the #shared dictionary _handlers, it needs to acquire the lock. But if configuring, #the lock would already have been acquired - so we need an RLock. #The same argument applies to Loggers and Manager.loggerDict. # if threading: _lock = threading.RLock() else: #pragma: no cover _lock = None def _acquireLock(): """ Acquire the module-level lock for serializing access to shared data. This should be released with _releaseLock(). """ if _lock: _lock.acquire() def _releaseLock(): """ Release the module-level lock acquired by calling _acquireLock(). """ if _lock: _lock.release() #--------------------------------------------------------------------------- # The logging record #--------------------------------------------------------------------------- class LogRecord(object): """ A LogRecord instance represents an event being logged. LogRecord instances are created every time something is logged. They contain all the information pertinent to the event being logged. The main information passed in is in msg and args, which are combined using str(msg) % args to create the message field of the record. The record also includes information such as when the record was created, the source line where the logging call was made, and any exception information to be logged. """ def __init__(self, name, level, pathname, lineno, msg, args, exc_info, func=None, sinfo=None, **kwargs): """ Initialize a logging record with interesting information. """ ct = time.time() self.name = name self.msg = msg # # The following statement allows passing of a dictionary as a sole # argument, so that you can do something like # logging.debug("a %(a)d b %(b)s", {'a':1, 'b':2}) # Suggested by Stefan Behnel. # Note that without the test for args[0], we get a problem because # during formatting, we test to see if the arg is present using # 'if self.args:'. If the event being logged is e.g. 'Value is %d' # and if the passed arg fails 'if self.args:' then no formatting # is done. For example, logger.warning('Value is %d', 0) would log # 'Value is %d' instead of 'Value is 0'. # For the use case of passing a dictionary, this should not be a # problem. # Issue #21172: a request was made to relax the isinstance check # to hasattr(args[0], '__getitem__'). However, the docs on string # formatting still seem to suggest a mapping object is required. # Thus, while not removing the isinstance check, it does now look # for collections.Mapping rather than, as before, dict. if (args and len(args) == 1 and isinstance(args[0], collections.Mapping) and args[0]): args = args[0] self.args = args self.levelname = getLevelName(level) self.levelno = level self.pathname = pathname try: self.filename = os.path.basename(pathname) self.module = os.path.splitext(self.filename)[0] except (TypeError, ValueError, AttributeError): self.filename = pathname self.module = "Unknown module" self.exc_info = exc_info self.exc_text = None # used to cache the traceback text self.stack_info = sinfo self.lineno = lineno self.funcName = func self.created = ct self.msecs = (ct - int(ct)) * 1000 self.relativeCreated = (self.created - _startTime) * 1000 if logThreads and threading: self.thread = threading.get_ident() self.threadName = threading.current_thread().name else: # pragma: no cover self.thread = None self.threadName = None if not logMultiprocessing: # pragma: no cover self.processName = None else: self.processName = 'MainProcess' mp = sys.modules.get('multiprocessing') if mp is not None: # Errors may occur if multiprocessing has not finished loading # yet - e.g. if a custom import hook causes third-party code # to run when multiprocessing calls import. See issue 8200 # for an example try: self.processName = mp.current_process().name except Exception: #pragma: no cover pass if logProcesses and hasattr(os, 'getpid'): self.process = os.getpid() else: self.process = None def __str__(self): return '<LogRecord: %s, %s, %s, %s, "%s">'%(self.name, self.levelno, self.pathname, self.lineno, self.msg) __repr__ = __str__ def getMessage(self): """ Return the message for this LogRecord. Return the message for this LogRecord after merging any user-supplied arguments with the message. """ msg = str(self.msg) if self.args: msg = msg % self.args return msg # # Determine which class to use when instantiating log records. # _logRecordFactory = LogRecord def setLogRecordFactory(factory): """ Set the factory to be used when instantiating a log record. :param factory: A callable which will be called to instantiate a log record. """ global _logRecordFactory _logRecordFactory = factory def getLogRecordFactory(): """ Return the factory to be used when instantiating a log record. """ return _logRecordFactory def makeLogRecord(dict): """ Make a LogRecord whose attributes are defined by the specified dictionary, This function is useful for converting a logging event received over a socket connection (which is sent as a dictionary) into a LogRecord instance. """ rv = _logRecordFactory(None, None, "", 0, "", (), None, None) rv.__dict__.update(dict) return rv #--------------------------------------------------------------------------- # Formatter classes and functions #--------------------------------------------------------------------------- class PercentStyle(object): default_format = '%(message)s' asctime_format = '%(asctime)s' asctime_search = '%(asctime)' def __init__(self, fmt): self._fmt = fmt or self.default_format def usesTime(self): return self._fmt.find(self.asctime_search) >= 0 def format(self, record): return self._fmt % record.__dict__ class StrFormatStyle(PercentStyle): default_format = '{message}' asctime_format = '{asctime}' asctime_search = '{asctime' def format(self, record): return self._fmt.format(**record.__dict__) class StringTemplateStyle(PercentStyle): default_format = '${message}' asctime_format = '${asctime}' asctime_search = '${asctime}' def __init__(self, fmt): self._fmt = fmt or self.default_format self._tpl = Template(self._fmt) def usesTime(self): fmt = self._fmt return fmt.find('$asctime') >= 0 or fmt.find(self.asctime_format) >= 0 def format(self, record): return self._tpl.substitute(**record.__dict__) BASIC_FORMAT = "%(levelname)s:%(name)s:%(message)s" _STYLES = { '%': (PercentStyle, BASIC_FORMAT), '{': (StrFormatStyle, '{levelname}:{name}:{message}'), '$': (StringTemplateStyle, '${levelname}:${name}:${message}'), } class Formatter(object): """ Formatter instances are used to convert a LogRecord to text. Formatters need to know how a LogRecord is constructed. They are responsible for converting a LogRecord to (usually) a string which can be interpreted by either a human or an external system. The base Formatter allows a formatting string to be specified. If none is supplied, the default value of "%s(message)" is used. The Formatter can be initialized with a format string which makes use of knowledge of the LogRecord attributes - e.g. the default value mentioned above makes use of the fact that the user's message and arguments are pre- formatted into a LogRecord's message attribute. Currently, the useful attributes in a LogRecord are described by: %(name)s Name of the logger (logging channel) %(levelno)s Numeric logging level for the message (DEBUG, INFO, WARNING, ERROR, CRITICAL) %(levelname)s Text logging level for the message ("DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL") %(pathname)s Full pathname of the source file where the logging call was issued (if available) %(filename)s Filename portion of pathname %(module)s Module (name portion of filename) %(lineno)d Source line number where the logging call was issued (if available) %(funcName)s Function name %(created)f Time when the LogRecord was created (time.time() return value) %(asctime)s Textual time when the LogRecord was created %(msecs)d Millisecond portion of the creation time %(relativeCreated)d Time in milliseconds when the LogRecord was created, relative to the time the logging module was loaded (typically at application startup time) %(thread)d Thread ID (if available) %(threadName)s Thread name (if available) %(process)d Process ID (if available) %(message)s The result of record.getMessage(), computed just as the record is emitted """ converter = time.localtime def __init__(self, fmt=None, datefmt=None, style='%'): """ Initialize the formatter with specified format strings. Initialize the formatter either with the specified format string, or a default as described above. Allow for specialized date formatting with the optional datefmt argument (if omitted, you get the ISO8601 format). Use a style parameter of '%', '{' or '$' to specify that you want to use one of %-formatting, :meth:`str.format` (``{}``) formatting or :class:`string.Template` formatting in your format string. .. versionchanged:: 3.2 Added the ``style`` parameter. """ if style not in _STYLES: raise ValueError('Style must be one of: %s' % ','.join( _STYLES.keys())) self._style = _STYLES[style][0](fmt) self._fmt = self._style._fmt self.datefmt = datefmt default_time_format = '%Y-%m-%d %H:%M:%S' default_msec_format = '%s,%03d' def formatTime(self, record, datefmt=None): """ Return the creation time of the specified LogRecord as formatted text. This method should be called from format() by a formatter which wants to make use of a formatted time. This method can be overridden in formatters to provide for any specific requirement, but the basic behaviour is as follows: if datefmt (a string) is specified, it is used with time.strftime() to format the creation time of the record. Otherwise, the ISO8601 format is used. The resulting string is returned. This function uses a user-configurable function to convert the creation time to a tuple. By default, time.localtime() is used; to change this for a particular formatter instance, set the 'converter' attribute to a function with the same signature as time.localtime() or time.gmtime(). To change it for all formatters, for example if you want all logging times to be shown in GMT, set the 'converter' attribute in the Formatter class. """ ct = self.converter(record.created) if datefmt: s = time.strftime(datefmt, ct) else: t = time.strftime(self.default_time_format, ct) s = self.default_msec_format % (t, record.msecs) return s def formatException(self, ei): """ Format and return the specified exception information as a string. This default implementation just uses traceback.print_exception() """ sio = io.StringIO() tb = ei[2] # See issues #9427, #1553375. Commented out for now. #if getattr(self, 'fullstack', False): # traceback.print_stack(tb.tb_frame.f_back, file=sio) traceback.print_exception(ei[0], ei[1], tb, None, sio) s = sio.getvalue() sio.close() if s[-1:] == "\n": s = s[:-1] return s def usesTime(self): """ Check if the format uses the creation time of the record. """ return self._style.usesTime() def formatMessage(self, record): return self._style.format(record) def formatStack(self, stack_info): """ This method is provided as an extension point for specialized formatting of stack information. The input data is a string as returned from a call to :func:`traceback.print_stack`, but with the last trailing newline removed. The base implementation just returns the value passed in. """ return stack_info def format(self, record): """ Format the specified record as text. The record's attribute dictionary is used as the operand to a string formatting operation which yields the returned string. Before formatting the dictionary, a couple of preparatory steps are carried out. The message attribute of the record is computed using LogRecord.getMessage(). If the formatting string uses the time (as determined by a call to usesTime(), formatTime() is called to format the event time. If there is exception information, it is formatted using formatException() and appended to the message. """ record.message = record.getMessage() if self.usesTime(): record.asctime = self.formatTime(record, self.datefmt) s = self.formatMessage(record) if record.exc_info: # Cache the traceback text to avoid converting it multiple times # (it's constant anyway) if not record.exc_text: record.exc_text = self.formatException(record.exc_info) if record.exc_text: if s[-1:] != "\n": s = s + "\n" s = s + record.exc_text if record.stack_info: if s[-1:] != "\n": s = s + "\n" s = s + self.formatStack(record.stack_info) return s # # The default formatter to use when no other is specified # _defaultFormatter = Formatter() class BufferingFormatter(object): """ A formatter suitable for formatting a number of records. """ def __init__(self, linefmt=None): """ Optionally specify a formatter which will be used to format each individual record. """ if linefmt: self.linefmt = linefmt else: self.linefmt = _defaultFormatter def formatHeader(self, records): """ Return the header string for the specified records. """ return "" def formatFooter(self, records): """ Return the footer string for the specified records. """ return "" def format(self, records): """ Format the specified records and return the result as a string. """ rv = "" if len(records) > 0: rv = rv + self.formatHeader(records) for record in records: rv = rv + self.linefmt.format(record) rv = rv + self.formatFooter(records) return rv #--------------------------------------------------------------------------- # Filter classes and functions #--------------------------------------------------------------------------- class Filter(object): """ Filter instances are used to perform arbitrary filtering of LogRecords. Loggers and Handlers can optionally use Filter instances to filter records as desired. The base filter class only allows events which are below a certain point in the logger hierarchy. For example, a filter initialized with "A.B" will allow events logged by loggers "A.B", "A.B.C", "A.B.C.D", "A.B.D" etc. but not "A.BB", "B.A.B" etc. If initialized with the empty string, all events are passed. """ def __init__(self, name=''): """ Initialize a filter. Initialize with the name of the logger which, together with its children, will have its events allowed through the filter. If no name is specified, allow every event. """ self.name = name self.nlen = len(name) def filter(self, record): """ Determine if the specified record is to be logged. Is the specified record to be logged? Returns 0 for no, nonzero for yes. If deemed appropriate, the record may be modified in-place. """ if self.nlen == 0: return True elif self.name == record.name: return True elif record.name.find(self.name, 0, self.nlen) != 0: return False return (record.name[self.nlen] == ".") class Filterer(object): """ A base class for loggers and handlers which allows them to share common code. """ def __init__(self): """ Initialize the list of filters to be an empty list. """ self.filters = [] def addFilter(self, filter): """ Add the specified filter to this handler. """ if not (filter in self.filters): self.filters.append(filter) def removeFilter(self, filter): """ Remove the specified filter from this handler. """ if filter in self.filters: self.filters.remove(filter) def filter(self, record): """ Determine if a record is loggable by consulting all the filters. The default is to allow the record to be logged; any filter can veto this and the record is then dropped. Returns a zero value if a record is to be dropped, else non-zero. .. versionchanged:: 3.2 Allow filters to be just callables. """ rv = True for f in self.filters: if hasattr(f, 'filter'): result = f.filter(record) else: result = f(record) # assume callable - will raise if not if not result: rv = False break return rv #--------------------------------------------------------------------------- # Handler classes and functions #--------------------------------------------------------------------------- _handlers = weakref.WeakValueDictionary() #map of handler names to handlers _handlerList = [] # added to allow handlers to be removed in reverse of order initialized def _removeHandlerRef(wr): """ Remove a handler reference from the internal cleanup list. """ # This function can be called during module teardown, when globals are # set to None. It can also be called from another thread. So we need to # pre-emptively grab the necessary globals and check if they're None, # to prevent race conditions and failures during interpreter shutdown. acquire, release, handlers = _acquireLock, _releaseLock, _handlerList if acquire and release and handlers: acquire() try: if wr in handlers: handlers.remove(wr) finally: release() def _addHandlerRef(handler): """ Add a handler to the internal cleanup list using a weak reference. """ _acquireLock() try: _handlerList.append(weakref.ref(handler, _removeHandlerRef)) finally: _releaseLock() class Handler(Filterer): """ Handler instances dispatch logging events to specific destinations. The base handler class. Acts as a placeholder which defines the Handler interface. Handlers can optionally use Formatter instances to format records as desired. By default, no formatter is specified; in this case, the 'raw' message as determined by record.message is logged. """ def __init__(self, level=NOTSET): """ Initializes the instance - basically setting the formatter to None and the filter list to empty. """ Filterer.__init__(self) self._name = None self.level = _checkLevel(level) self.formatter = None # Add the handler to the global _handlerList (for cleanup on shutdown) _addHandlerRef(self) self.createLock() def get_name(self): return self._name def set_name(self, name): _acquireLock() try: if self._name in _handlers: del _handlers[self._name] self._name = name if name: _handlers[name] = self finally: _releaseLock() name = property(get_name, set_name) def createLock(self): """ Acquire a thread lock for serializing access to the underlying I/O. """ if threading: self.lock = threading.RLock() else: #pragma: no cover self.lock = None def acquire(self): """ Acquire the I/O thread lock. """ if self.lock: self.lock.acquire() def release(self): """ Release the I/O thread lock. """ if self.lock: self.lock.release() def setLevel(self, level): """ Set the logging level of this handler. level must be an int or a str. """ self.level = _checkLevel(level) def format(self, record): """ Format the specified record. If a formatter is set, use it. Otherwise, use the default formatter for the module. """ if self.formatter: fmt = self.formatter else: fmt = _defaultFormatter return fmt.format(record) def emit(self, record): """ Do whatever it takes to actually log the specified logging record. This version is intended to be implemented by subclasses and so raises a NotImplementedError. """ raise NotImplementedError('emit must be implemented ' 'by Handler subclasses') def handle(self, record): """ Conditionally emit the specified logging record. Emission depends on filters which may have been added to the handler. Wrap the actual emission of the record with acquisition/release of the I/O thread lock. Returns whether the filter passed the record for emission. """ rv = self.filter(record) if rv: self.acquire() try: self.emit(record) finally: self.release() return rv def setFormatter(self, fmt): """ Set the formatter for this handler. """ self.formatter = fmt def flush(self): """ Ensure all logging output has been flushed. This version does nothing and is intended to be implemented by subclasses. """ pass def close(self): """ Tidy up any resources used by the handler. This version removes the handler from an internal map of handlers, _handlers, which is used for handler lookup by name. Subclasses should ensure that this gets called from overridden close() methods. """ #get the module data lock, as we're updating a shared structure. _acquireLock() try: #unlikely to raise an exception, but you never know... if self._name and self._name in _handlers: del _handlers[self._name] finally: _releaseLock() def handleError(self, record): """ Handle errors which occur during an emit() call. This method should be called from handlers when an exception is encountered during an emit() call. If raiseExceptions is false, exceptions get silently ignored. This is what is mostly wanted for a logging system - most users will not care about errors in the logging system, they are more interested in application errors. You could, however, replace this with a custom handler if you wish. The record which was being processed is passed in to this method. """ if raiseExceptions and sys.stderr: # see issue 13807 t, v, tb = sys.exc_info() try: sys.stderr.write('--- Logging error ---\n') traceback.print_exception(t, v, tb, None, sys.stderr) sys.stderr.write('Call stack:\n') # Walk the stack frame up until we're out of logging, # so as to print the calling context. frame = tb.tb_frame while (frame and os.path.dirname(frame.f_code.co_filename) == __path__[0]): frame = frame.f_back if frame: traceback.print_stack(frame, file=sys.stderr) else: # couldn't find the right stack frame, for some reason sys.stderr.write('Logged from file %s, line %s\n' % ( record.filename, record.lineno)) # Issue 18671: output logging message and arguments try: sys.stderr.write('Message: %r\n' 'Arguments: %s\n' % (record.msg, record.args)) except Exception: sys.stderr.write('Unable to print the message and arguments' ' - possible formatting error.\nUse the' ' traceback above to help find the error.\n' ) except OSError: #pragma: no cover pass # see issue 5971 finally: del t, v, tb class StreamHandler(Handler): """ A handler class which writes logging records, appropriately formatted, to a stream. Note that this class does not close the stream, as sys.stdout or sys.stderr may be used. """ terminator = '\n' def __init__(self, stream=None): """ Initialize the handler. If stream is not specified, sys.stderr is used. """ Handler.__init__(self) if stream is None: stream = sys.stderr self.stream = stream def flush(self): """ Flushes the stream. """ self.acquire() try: if self.stream and hasattr(self.stream, "flush"): self.stream.flush() finally: self.release() def emit(self, record): """ Emit a record. If a formatter is specified, it is used to format the record. The record is then written to the stream with a trailing newline. If exception information is present, it is formatted using traceback.print_exception and appended to the stream. If the stream has an 'encoding' attribute, it is used to determine how to do the output to the stream. """ try: msg = self.format(record) stream = self.stream stream.write(msg) stream.write(self.terminator) self.flush() except Exception: self.handleError(record) class FileHandler(StreamHandler): """ A handler class which writes formatted logging records to disk files. """ def __init__(self, filename, mode='a', encoding=None, delay=False): """ Open the specified file and use it as the stream for logging. """ #keep the absolute path, otherwise derived classes which use this #may come a cropper when the current directory changes self.baseFilename = os.path.abspath(filename) self.mode = mode self.encoding = encoding self.delay = delay if delay: #We don't open the stream, but we still need to call the #Handler constructor to set level, formatter, lock etc. Handler.__init__(self) self.stream = None else: StreamHandler.__init__(self, self._open()) def close(self): """ Closes the stream. """ self.acquire() try: try: if self.stream: try: self.flush() finally: stream = self.stream self.stream = None if hasattr(stream, "close"): stream.close() finally: # Issue #19523: call unconditionally to # prevent a handler leak when delay is set StreamHandler.close(self) finally: self.release() def _open(self): """ Open the current base file with the (original) mode and encoding. Return the resulting stream. """ return open(self.baseFilename, self.mode, encoding=self.encoding) def emit(self, record): """ Emit a record. If the stream was not opened because 'delay' was specified in the constructor, open it before calling the superclass's emit. """ if self.stream is None: self.stream = self._open() StreamHandler.emit(self, record) class _StderrHandler(StreamHandler): """ This class is like a StreamHandler using sys.stderr, but always uses whatever sys.stderr is currently set to rather than the value of sys.stderr at handler construction time. """ def __init__(self, level=NOTSET): """ Initialize the handler. """ Handler.__init__(self, level) @property def stream(self): return sys.stderr _defaultLastResort = _StderrHandler(WARNING) lastResort = _defaultLastResort #--------------------------------------------------------------------------- # Manager classes and functions #--------------------------------------------------------------------------- class PlaceHolder(object): """ PlaceHolder instances are used in the Manager logger hierarchy to take the place of nodes for which no loggers have been defined. This class is intended for internal use only and not as part of the public API. """ def __init__(self, alogger): """ Initialize with the specified logger being a child of this placeholder. """ self.loggerMap = { alogger : None } def append(self, alogger): """ Add the specified logger as a child of this placeholder. """ if alogger not in self.loggerMap: self.loggerMap[alogger] = None # # Determine which class to use when instantiating loggers. # def setLoggerClass(klass): """ Set the class to be used when instantiating a logger. The class should define __init__() such that only a name argument is required, and the __init__() should call Logger.__init__() """ if klass != Logger: if not issubclass(klass, Logger): raise TypeError("logger not derived from logging.Logger: " + klass.__name__) global _loggerClass _loggerClass = klass def getLoggerClass(): """ Return the class to be used when instantiating a logger. """ return _loggerClass class Manager(object): """ There is [under normal circumstances] just one Manager instance, which holds the hierarchy of loggers. """ def __init__(self, rootnode): """ Initialize the manager with the root node of the logger hierarchy. """ self.root = rootnode self.disable = 0 self.emittedNoHandlerWarning = False self.loggerDict = {} self.loggerClass = None self.logRecordFactory = None def getLogger(self, name): """ Get a logger with the specified name (channel name), creating it if it doesn't yet exist. This name is a dot-separated hierarchical name, such as "a", "a.b", "a.b.c" or similar. If a PlaceHolder existed for the specified name [i.e. the logger didn't exist but a child of it did], replace it with the created logger and fix up the parent/child references which pointed to the placeholder to now point to the logger. """ rv = None if not isinstance(name, str): raise TypeError('A logger name must be a string') _acquireLock() try: if name in self.loggerDict: rv = self.loggerDict[name] if isinstance(rv, PlaceHolder): ph = rv rv = (self.loggerClass or _loggerClass)(name) rv.manager = self self.loggerDict[name] = rv self._fixupChildren(ph, rv) self._fixupParents(rv) else: rv = (self.loggerClass or _loggerClass)(name) rv.manager = self self.loggerDict[name] = rv self._fixupParents(rv) finally: _releaseLock() return rv def setLoggerClass(self, klass): """ Set the class to be used when instantiating a logger with this Manager. """ if klass != Logger: if not issubclass(klass, Logger): raise TypeError("logger not derived from logging.Logger: " + klass.__name__) self.loggerClass = klass def setLogRecordFactory(self, factory): """ Set the factory to be used when instantiating a log record with this Manager. """ self.logRecordFactory = factory def _fixupParents(self, alogger): """ Ensure that there are either loggers or placeholders all the way from the specified logger to the root of the logger hierarchy. """ name = alogger.name i = name.rfind(".") rv = None while (i > 0) and not rv: substr = name[:i] if substr not in self.loggerDict: self.loggerDict[substr] = PlaceHolder(alogger) else: obj = self.loggerDict[substr] if isinstance(obj, Logger): rv = obj else: assert isinstance(obj, PlaceHolder) obj.append(alogger) i = name.rfind(".", 0, i - 1) if not rv: rv = self.root alogger.parent = rv def _fixupChildren(self, ph, alogger): """ Ensure that children of the placeholder ph are connected to the specified logger. """ name = alogger.name namelen = len(name) for c in ph.loggerMap.keys(): #The if means ... if not c.parent.name.startswith(nm) if c.parent.name[:namelen] != name: alogger.parent = c.parent c.parent = alogger #--------------------------------------------------------------------------- # Logger classes and functions #--------------------------------------------------------------------------- class Logger(Filterer): """ Instances of the Logger class represent a single logging channel. A "logging channel" indicates an area of an application. Exactly how an "area" is defined is up to the application developer. Since an application can have any number of areas, logging channels are identified by a unique string. Application areas can be nested (e.g. an area of "input processing" might include sub-areas "read CSV files", "read XLS files" and "read Gnumeric files"). To cater for this natural nesting, channel names are organized into a namespace hierarchy where levels are separated by periods, much like the Java or Python package namespace. So in the instance given above, channel names might be "input" for the upper level, and "input.csv", "input.xls" and "input.gnu" for the sub-levels. There is no arbitrary limit to the depth of nesting. """ def __init__(self, name, level=NOTSET): """ Initialize the logger with a name and an optional level. """ Filterer.__init__(self) self.name = name self.level = _checkLevel(level) self.parent = None self.propagate = True self.handlers = [] self.disabled = False def setLevel(self, level): """ Set the logging level of this logger. level must be an int or a str. """ self.level = _checkLevel(level) def debug(self, msg, *args, **kwargs): """ Log 'msg % args' with severity 'DEBUG'. To pass exception information, use the keyword argument exc_info with a true value, e.g. logger.debug("Houston, we have a %s", "thorny problem", exc_info=1) """ if self.isEnabledFor(DEBUG): self._log(DEBUG, msg, args, **kwargs) def info(self, msg, *args, **kwargs): """ Log 'msg % args' with severity 'INFO'. To pass exception information, use the keyword argument exc_info with a true value, e.g. logger.info("Houston, we have a %s", "interesting problem", exc_info=1) """ if self.isEnabledFor(INFO): self._log(INFO, msg, args, **kwargs) def warning(self, msg, *args, **kwargs): """ Log 'msg % args' with severity 'WARNING'. To pass exception information, use the keyword argument exc_info with a true value, e.g. logger.warning("Houston, we have a %s", "bit of a problem", exc_info=1) """ if self.isEnabledFor(WARNING): self._log(WARNING, msg, args, **kwargs) def warn(self, msg, *args, **kwargs): warnings.warn("The 'warn' method is deprecated, " "use 'warning' instead", DeprecationWarning, 2) self.warning(msg, *args, **kwargs) def error(self, msg, *args, **kwargs): """ Log 'msg % args' with severity 'ERROR'. To pass exception information, use the keyword argument exc_info with a true value, e.g. logger.error("Houston, we have a %s", "major problem", exc_info=1) """ if self.isEnabledFor(ERROR): self._log(ERROR, msg, args, **kwargs) def exception(self, msg, *args, exc_info=True, **kwargs): """ Convenience method for logging an ERROR with exception information. """ self.error(msg, *args, exc_info=exc_info, **kwargs) def critical(self, msg, *args, **kwargs): """ Log 'msg % args' with severity 'CRITICAL'. To pass exception information, use the keyword argument exc_info with a true value, e.g. logger.critical("Houston, we have a %s", "major disaster", exc_info=1) """ if self.isEnabledFor(CRITICAL): self._log(CRITICAL, msg, args, **kwargs) fatal = critical def log(self, level, msg, *args, **kwargs): """ Log 'msg % args' with the integer severity 'level'. To pass exception information, use the keyword argument exc_info with a true value, e.g. logger.log(level, "We have a %s", "mysterious problem", exc_info=1) """ if not isinstance(level, int): if raiseExceptions: raise TypeError("level must be an integer") else: return if self.isEnabledFor(level): self._log(level, msg, args, **kwargs) def findCaller(self, stack_info=False): """ Find the stack frame of the caller so that we can note the source file name, line number and function name. """ f = currentframe() #On some versions of IronPython, currentframe() returns None if #IronPython isn't run with -X:Frames. if f is not None: f = f.f_back rv = "(unknown file)", 0, "(unknown function)", None while hasattr(f, "f_code"): co = f.f_code filename = os.path.normcase(co.co_filename) if filename == _srcfile: f = f.f_back continue sinfo = None if stack_info: sio = io.StringIO() sio.write('Stack (most recent call last):\n') traceback.print_stack(f, file=sio) sinfo = sio.getvalue() if sinfo[-1] == '\n': sinfo = sinfo[:-1] sio.close() rv = (co.co_filename, f.f_lineno, co.co_name, sinfo) break return rv def makeRecord(self, name, level, fn, lno, msg, args, exc_info, func=None, extra=None, sinfo=None): """ A factory method which can be overridden in subclasses to create specialized LogRecords. """ rv = _logRecordFactory(name, level, fn, lno, msg, args, exc_info, func, sinfo) if extra is not None: for key in extra: if (key in ["message", "asctime"]) or (key in rv.__dict__): raise KeyError("Attempt to overwrite %r in LogRecord" % key) rv.__dict__[key] = extra[key] return rv def _log(self, level, msg, args, exc_info=None, extra=None, stack_info=False): """ Low-level logging routine which creates a LogRecord and then calls all the handlers of this logger to handle the record. """ sinfo = None if _srcfile: #IronPython doesn't track Python frames, so findCaller raises an #exception on some versions of IronPython. We trap it here so that #IronPython can use logging. try: fn, lno, func, sinfo = self.findCaller(stack_info) except ValueError: # pragma: no cover fn, lno, func = "(unknown file)", 0, "(unknown function)" else: # pragma: no cover fn, lno, func = "(unknown file)", 0, "(unknown function)" if exc_info: if isinstance(exc_info, BaseException): exc_info = (type(exc_info), exc_info, exc_info.__traceback__) elif not isinstance(exc_info, tuple): exc_info = sys.exc_info() record = self.makeRecord(self.name, level, fn, lno, msg, args, exc_info, func, extra, sinfo) self.handle(record) def handle(self, record): """ Call the handlers for the specified record. This method is used for unpickled records received from a socket, as well as those created locally. Logger-level filtering is applied. """ if (not self.disabled) and self.filter(record): self.callHandlers(record) def addHandler(self, hdlr): """ Add the specified handler to this logger. """ _acquireLock() try: if not (hdlr in self.handlers): self.handlers.append(hdlr) finally: _releaseLock() def removeHandler(self, hdlr): """ Remove the specified handler from this logger. """ _acquireLock() try: if hdlr in self.handlers: self.handlers.remove(hdlr) finally: _releaseLock() def hasHandlers(self): """ See if this logger has any handlers configured. Loop through all handlers for this logger and its parents in the logger hierarchy. Return True if a handler was found, else False. Stop searching up the hierarchy whenever a logger with the "propagate" attribute set to zero is found - that will be the last logger which is checked for the existence of handlers. """ c = self rv = False while c: if c.handlers: rv = True break if not c.propagate: break else: c = c.parent return rv def callHandlers(self, record): """ Pass a record to all relevant handlers. Loop through all handlers for this logger and its parents in the logger hierarchy. If no handler was found, output a one-off error message to sys.stderr. Stop searching up the hierarchy whenever a logger with the "propagate" attribute set to zero is found - that will be the last logger whose handlers are called. """ c = self found = 0 while c: for hdlr in c.handlers: found = found + 1 if record.levelno >= hdlr.level: hdlr.handle(record) if not c.propagate: c = None #break out else: c = c.parent if (found == 0): if lastResort: if record.levelno >= lastResort.level: lastResort.handle(record) elif raiseExceptions and not self.manager.emittedNoHandlerWarning: sys.stderr.write("No handlers could be found for logger" " \"%s\"\n" % self.name) self.manager.emittedNoHandlerWarning = True def getEffectiveLevel(self): """ Get the effective level for this logger. Loop through this logger and its parents in the logger hierarchy, looking for a non-zero logging level. Return the first one found. """ logger = self while logger: if logger.level: return logger.level logger = logger.parent return NOTSET def isEnabledFor(self, level): """ Is this logger enabled for level 'level'? """ if self.manager.disable >= level: return False return level >= self.getEffectiveLevel() def getChild(self, suffix): """ Get a logger which is a descendant to this one. This is a convenience method, such that logging.getLogger('abc').getChild('def.ghi') is the same as logging.getLogger('abc.def.ghi') It's useful, for example, when the parent logger is named using __name__ rather than a literal string. """ if self.root is not self: suffix = '.'.join((self.name, suffix)) return self.manager.getLogger(suffix) class RootLogger(Logger): """ A root logger is not that different to any other logger, except that it must have a logging level and there is only one instance of it in the hierarchy. """ def __init__(self, level): """ Initialize the logger with the name "root". """ Logger.__init__(self, "root", level) _loggerClass = Logger class LoggerAdapter(object): """ An adapter for loggers which makes it easier to specify contextual information in logging output. """ def __init__(self, logger, extra): """ Initialize the adapter with a logger and a dict-like object which provides contextual information. This constructor signature allows easy stacking of LoggerAdapters, if so desired. You can effectively pass keyword arguments as shown in the following example: adapter = LoggerAdapter(someLogger, dict(p1=v1, p2="v2")) """ self.logger = logger self.extra = extra def process(self, msg, kwargs): """ Process the logging message and keyword arguments passed in to a logging call to insert contextual information. You can either manipulate the message itself, the keyword args or both. Return the message and kwargs modified (or not) to suit your needs. Normally, you'll only need to override this one method in a LoggerAdapter subclass for your specific needs. """ kwargs["extra"] = self.extra return msg, kwargs # # Boilerplate convenience methods # def debug(self, msg, *args, **kwargs): """ Delegate a debug call to the underlying logger. """ self.log(DEBUG, msg, *args, **kwargs) def info(self, msg, *args, **kwargs): """ Delegate an info call to the underlying logger. """ self.log(INFO, msg, *args, **kwargs) def warning(self, msg, *args, **kwargs): """ Delegate a warning call to the underlying logger. """ self.log(WARNING, msg, *args, **kwargs) def warn(self, msg, *args, **kwargs): warnings.warn("The 'warn' method is deprecated, " "use 'warning' instead", DeprecationWarning, 2) self.warning(msg, *args, **kwargs) def error(self, msg, *args, **kwargs): """ Delegate an error call to the underlying logger. """ self.log(ERROR, msg, *args, **kwargs) def exception(self, msg, *args, exc_info=True, **kwargs): """ Delegate an exception call to the underlying logger. """ self.log(ERROR, msg, *args, exc_info=exc_info, **kwargs) def critical(self, msg, *args, **kwargs): """ Delegate a critical call to the underlying logger. """ self.log(CRITICAL, msg, *args, **kwargs) def log(self, level, msg, *args, **kwargs): """ Delegate a log call to the underlying logger, after adding contextual information from this adapter instance. """ if self.isEnabledFor(level): msg, kwargs = self.process(msg, kwargs) self.logger._log(level, msg, args, **kwargs) def isEnabledFor(self, level): """ Is this logger enabled for level 'level'? """ if self.logger.manager.disable >= level: return False return level >= self.getEffectiveLevel() def setLevel(self, level): """ Set the specified level on the underlying logger. """ self.logger.setLevel(level) def getEffectiveLevel(self): """ Get the effective level for the underlying logger. """ return self.logger.getEffectiveLevel() def hasHandlers(self): """ See if the underlying logger has any handlers. """ return self.logger.hasHandlers() root = RootLogger(WARNING) Logger.root = root Logger.manager = Manager(Logger.root) #--------------------------------------------------------------------------- # Configuration classes and functions #--------------------------------------------------------------------------- def basicConfig(**kwargs): """ Do basic configuration for the logging system. This function does nothing if the root logger already has handlers configured. It is a convenience method intended for use by simple scripts to do one-shot configuration of the logging package. The default behaviour is to create a StreamHandler which writes to sys.stderr, set a formatter using the BASIC_FORMAT format string, and add the handler to the root logger. A number of optional keyword arguments may be specified, which can alter the default behaviour. filename Specifies that a FileHandler be created, using the specified filename, rather than a StreamHandler. filemode Specifies the mode to open the file, if filename is specified (if filemode is unspecified, it defaults to 'a'). format Use the specified format string for the handler. datefmt Use the specified date/time format. style If a format string is specified, use this to specify the type of format string (possible values '%', '{', '$', for %-formatting, :meth:`str.format` and :class:`string.Template` - defaults to '%'). level Set the root logger level to the specified level. stream Use the specified stream to initialize the StreamHandler. Note that this argument is incompatible with 'filename' - if both are present, 'stream' is ignored. handlers If specified, this should be an iterable of already created handlers, which will be added to the root handler. Any handler in the list which does not have a formatter assigned will be assigned the formatter created in this function. Note that you could specify a stream created using open(filename, mode) rather than passing the filename and mode in. However, it should be remembered that StreamHandler does not close its stream (since it may be using sys.stdout or sys.stderr), whereas FileHandler closes its stream when the handler is closed. .. versionchanged:: 3.2 Added the ``style`` parameter. .. versionchanged:: 3.3 Added the ``handlers`` parameter. A ``ValueError`` is now thrown for incompatible arguments (e.g. ``handlers`` specified together with ``filename``/``filemode``, or ``filename``/``filemode`` specified together with ``stream``, or ``handlers`` specified together with ``stream``. """ # Add thread safety in case someone mistakenly calls # basicConfig() from multiple threads _acquireLock() try: if len(root.handlers) == 0: handlers = kwargs.pop("handlers", None) if handlers is None: if "stream" in kwargs and "filename" in kwargs: raise ValueError("'stream' and 'filename' should not be " "specified together") else: if "stream" in kwargs or "filename" in kwargs: raise ValueError("'stream' or 'filename' should not be " "specified together with 'handlers'") if handlers is None: filename = kwargs.pop("filename", None) mode = kwargs.pop("filemode", 'a') if filename: h = FileHandler(filename, mode) else: stream = kwargs.pop("stream", None) h = StreamHandler(stream) handlers = [h] dfs = kwargs.pop("datefmt", None) style = kwargs.pop("style", '%') if style not in _STYLES: raise ValueError('Style must be one of: %s' % ','.join( _STYLES.keys())) fs = kwargs.pop("format", _STYLES[style][1]) fmt = Formatter(fs, dfs, style) for h in handlers: if h.formatter is None: h.setFormatter(fmt) root.addHandler(h) level = kwargs.pop("level", None) if level is not None: root.setLevel(level) if kwargs: keys = ', '.join(kwargs.keys()) raise ValueError('Unrecognised argument(s): %s' % keys) finally: _releaseLock() #--------------------------------------------------------------------------- # Utility functions at module level. # Basically delegate everything to the root logger. #--------------------------------------------------------------------------- def getLogger(name=None): """ Return a logger with the specified name, creating it if necessary. If no name is specified, return the root logger. """ if name: return Logger.manager.getLogger(name) else: return root def critical(msg, *args, **kwargs): """ Log a message with severity 'CRITICAL' on the root logger. If the logger has no handlers, call basicConfig() to add a console handler with a pre-defined format. """ if len(root.handlers) == 0: basicConfig() root.critical(msg, *args, **kwargs) fatal = critical def error(msg, *args, **kwargs): """ Log a message with severity 'ERROR' on the root logger. If the logger has no handlers, call basicConfig() to add a console handler with a pre-defined format. """ if len(root.handlers) == 0: basicConfig() root.error(msg, *args, **kwargs) def exception(msg, *args, exc_info=True, **kwargs): """ Log a message with severity 'ERROR' on the root logger, with exception information. If the logger has no handlers, basicConfig() is called to add a console handler with a pre-defined format. """ error(msg, *args, exc_info=exc_info, **kwargs) def warning(msg, *args, **kwargs): """ Log a message with severity 'WARNING' on the root logger. If the logger has no handlers, call basicConfig() to add a console handler with a pre-defined format. """ if len(root.handlers) == 0: basicConfig() root.warning(msg, *args, **kwargs) def warn(msg, *args, **kwargs): warnings.warn("The 'warn' function is deprecated, " "use 'warning' instead", DeprecationWarning, 2) warning(msg, *args, **kwargs) def info(msg, *args, **kwargs): """ Log a message with severity 'INFO' on the root logger. If the logger has no handlers, call basicConfig() to add a console handler with a pre-defined format. """ if len(root.handlers) == 0: basicConfig() root.info(msg, *args, **kwargs) def debug(msg, *args, **kwargs): """ Log a message with severity 'DEBUG' on the root logger. If the logger has no handlers, call basicConfig() to add a console handler with a pre-defined format. """ if len(root.handlers) == 0: basicConfig() root.debug(msg, *args, **kwargs) def log(level, msg, *args, **kwargs): """ Log 'msg % args' with the integer severity 'level' on the root logger. If the logger has no handlers, call basicConfig() to add a console handler with a pre-defined format. """ if len(root.handlers) == 0: basicConfig() root.log(level, msg, *args, **kwargs) def disable(level): """ Disable all logging calls of severity 'level' and below. """ root.manager.disable = level def shutdown(handlerList=_handlerList): """ Perform any cleanup actions in the logging system (e.g. flushing buffers). Should be called at application exit. """ for wr in reversed(handlerList[:]): #errors might occur, for example, if files are locked #we just ignore them if raiseExceptions is not set try: h = wr() if h: try: h.acquire() h.flush() h.close() except (OSError, ValueError): # Ignore errors which might be caused # because handlers have been closed but # references to them are still around at # application exit. pass finally: h.release() except: # ignore everything, as we're shutting down if raiseExceptions: raise #else, swallow #Let's try and shutdown automatically on application exit... import atexit atexit.register(shutdown) # Null handler class NullHandler(Handler): """ This handler does nothing. It's intended to be used to avoid the "No handlers could be found for logger XXX" one-off warning. This is important for library code, which may contain code to log events. If a user of the library does not configure logging, the one-off warning might be produced; to avoid this, the library developer simply needs to instantiate a NullHandler and add it to the top-level logger of the library module or package. """ def handle(self, record): """Stub.""" def emit(self, record): """Stub.""" def createLock(self): self.lock = None # Warnings integration _warnings_showwarning = None def _showwarning(message, category, filename, lineno, file=None, line=None): """ Implementation of showwarnings which redirects to logging, which will first check to see if the file parameter is None. If a file is specified, it will delegate to the original warnings implementation of showwarning. Otherwise, it will call warnings.formatwarning and will log the resulting string to a warnings logger named "py.warnings" with level logging.WARNING. """ if file is not None: if _warnings_showwarning is not None: _warnings_showwarning(message, category, filename, lineno, file, line) else: s = warnings.formatwarning(message, category, filename, lineno, line) logger = getLogger("py.warnings") if not logger.handlers: logger.addHandler(NullHandler()) logger.warning("%s", s) def captureWarnings(capture): """ If capture is true, redirect all warnings to the logging package. If capture is False, ensure that warnings are not redirected to logging but to their original destinations. """ global _warnings_showwarning if capture: if _warnings_showwarning is None: _warnings_showwarning = warnings.showwarning warnings.showwarning = _showwarning else: if _warnings_showwarning is not None: warnings.showwarning = _warnings_showwarning _warnings_showwarning = None

03f90baae4ff70e1b31d69e4414b67e45ce1f979

py

Python

discord/abc.py

curiositIy/discord.py

f6a74f74a7aed0879fc086805eae8873e745d0ea

2021-12-21T16:30:30.000Z

2021-12-21T16:30:30.000Z

discord/abc.py

curiositIy/discord.py

f6a74f74a7aed0879fc086805eae8873e745d0ea

discord/abc.py

curiositIy/discord.py

f6a74f74a7aed0879fc086805eae8873e745d0ea

""" The MIT License (MIT) Copyright (c) 2015-present Rapptz Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. """ from __future__ import annotations import copy import time import asyncio from datetime import datetime from typing import ( Any, AsyncIterator, Callable, Dict, Iterable, List, Optional, TYPE_CHECKING, Protocol, Sequence, Tuple, TypeVar, Union, overload, runtime_checkable, ) from .object import OLDEST_OBJECT, Object from .context_managers import Typing from .enums import ChannelType from .errors import ClientException from .mentions import AllowedMentions from .permissions import PermissionOverwrite, Permissions from .role import Role from .invite import Invite from .file import File from .http import handle_message_parameters from .voice_client import VoiceClient, VoiceProtocol from .sticker import GuildSticker, StickerItem from . import utils __all__ = ( 'Snowflake', 'User', 'PrivateChannel', 'GuildChannel', 'Messageable', 'Connectable', ) T = TypeVar('T', bound=VoiceProtocol) if TYPE_CHECKING: from typing_extensions import Self from .client import Client from .user import ClientUser from .asset import Asset from .state import ConnectionState from .guild import Guild from .member import Member from .channel import CategoryChannel from .embeds import Embed from .message import Message, MessageReference, PartialMessage from .channel import TextChannel, DMChannel, GroupChannel, PartialMessageable, VoiceChannel from .threads import Thread from .enums import InviteTarget from .ui.view import View from .types.channel import ( PermissionOverwrite as PermissionOverwritePayload, Channel as ChannelPayload, GuildChannel as GuildChannelPayload, OverwriteType, ) from .types.snowflake import ( SnowflakeList, ) PartialMessageableChannel = Union[TextChannel, VoiceChannel, Thread, DMChannel, PartialMessageable] MessageableChannel = Union[PartialMessageableChannel, GroupChannel] SnowflakeTime = Union["Snowflake", datetime] MISSING = utils.MISSING class _Undefined: def __repr__(self) -> str: return 'see-below' _undefined: Any = _Undefined() async def _single_delete_strategy(messages: Iterable[Message], *, reason: Optional[str] = None): for m in messages: await m.delete() async def _purge_helper( channel: Union[Thread, TextChannel, VoiceChannel], *, limit: Optional[int] = 100, check: Callable[[Message], bool] = MISSING, before: Optional[SnowflakeTime] = None, after: Optional[SnowflakeTime] = None, around: Optional[SnowflakeTime] = None, oldest_first: Optional[bool] = False, bulk: bool = True, reason: Optional[str] = None, ) -> List[Message]: if check is MISSING: check = lambda m: True iterator = channel.history(limit=limit, before=before, after=after, oldest_first=oldest_first, around=around) ret: List[Message] = [] count = 0 minimum_time = int((time.time() - 14 * 24 * 60 * 60) * 1000.0 - 1420070400000) << 22 strategy = channel.delete_messages if bulk else _single_delete_strategy async for message in iterator: if count == 100: to_delete = ret[-100:] await strategy(to_delete, reason=reason) count = 0 await asyncio.sleep(1) if not check(message): continue if message.id < minimum_time: # older than 14 days old if count == 1: await ret[-1].delete() elif count >= 2: to_delete = ret[-count:] await strategy(to_delete, reason=reason) count = 0 strategy = _single_delete_strategy count += 1 ret.append(message) # Some messages remaining to poll if count >= 2: # more than 2 messages -> bulk delete to_delete = ret[-count:] await strategy(to_delete, reason=reason) elif count == 1: # delete a single message await ret[-1].delete() return ret @runtime_checkable class Snowflake(Protocol): """An ABC that details the common operations on a Discord model. Almost all :ref:`Discord models <discord_api_models>` meet this abstract base class. If you want to create a snowflake on your own, consider using :class:`.Object`. Attributes ----------- id: :class:`int` The model's unique ID. """ __slots__ = () id: int @runtime_checkable class User(Snowflake, Protocol): """An ABC that details the common operations on a Discord user. The following implement this ABC: - :class:`~discord.User` - :class:`~discord.ClientUser` - :class:`~discord.Member` This ABC must also implement :class:`~discord.abc.Snowflake`. Attributes ----------- name: :class:`str` The user's username. discriminator: :class:`str` The user's discriminator. bot: :class:`bool` If the user is a bot account. system: :class:`bool` If the user is a system account. """ __slots__ = () name: str discriminator: str bot: bool system: bool @property def display_name(self) -> str: """:class:`str`: Returns the user's display name.""" raise NotImplementedError @property def mention(self) -> str: """:class:`str`: Returns a string that allows you to mention the given user.""" raise NotImplementedError @property def avatar(self) -> Optional[Asset]: """Optional[:class:`~discord.Asset`]: Returns an Asset that represents the user's avatar, if present.""" raise NotImplementedError @property def default_avatar(self) -> Asset: """:class:`~discord.Asset`: Returns the default avatar for a given user. This is calculated by the user's discriminator.""" raise NotImplementedError @property def display_avatar(self) -> Asset: """:class:`~discord.Asset`: Returns the user's display avatar. For regular users this is just their default avatar or uploaded avatar. .. versionadded:: 2.0 """ raise NotImplementedError def mentioned_in(self, message: Message) -> bool: """Checks if the user is mentioned in the specified message. Parameters ----------- message: :class:`~discord.Message` The message to check if you're mentioned in. Returns ------- :class:`bool` Indicates if the user is mentioned in the message. """ raise NotImplementedError @runtime_checkable class PrivateChannel(Snowflake, Protocol): """An ABC that details the common operations on a private Discord channel. The following implement this ABC: - :class:`~discord.DMChannel` - :class:`~discord.GroupChannel` This ABC must also implement :class:`~discord.abc.Snowflake`. Attributes ----------- me: :class:`~discord.ClientUser` The user presenting yourself. """ __slots__ = () me: ClientUser class _Overwrites: __slots__ = ('id', 'allow', 'deny', 'type') ROLE = 0 MEMBER = 1 def __init__(self, data: PermissionOverwritePayload) -> None: self.id: int = int(data['id']) self.allow: int = int(data.get('allow', 0)) self.deny: int = int(data.get('deny', 0)) self.type: OverwriteType = data['type'] def _asdict(self) -> PermissionOverwritePayload: return { 'id': self.id, 'allow': str(self.allow), 'deny': str(self.deny), 'type': self.type, } def is_role(self) -> bool: return self.type == 0 def is_member(self) -> bool: return self.type == 1 class GuildChannel: """An ABC that details the common operations on a Discord guild channel. The following implement this ABC: - :class:`~discord.TextChannel` - :class:`~discord.VoiceChannel` - :class:`~discord.CategoryChannel` - :class:`~discord.StageChannel` - :class:`~discord.ForumChannel` This ABC must also implement :class:`~discord.abc.Snowflake`. Attributes ----------- name: :class:`str` The channel name. guild: :class:`~discord.Guild` The guild the channel belongs to. position: :class:`int` The position in the channel list. This is a number that starts at 0. e.g. the top channel is position 0. """ __slots__ = () id: int name: str guild: Guild type: ChannelType position: int category_id: Optional[int] _state: ConnectionState _overwrites: List[_Overwrites] if TYPE_CHECKING: def __init__(self, *, state: ConnectionState, guild: Guild, data: GuildChannelPayload): ... def __str__(self) -> str: return self.name @property def _sorting_bucket(self) -> int: raise NotImplementedError def _update(self, guild: Guild, data: Dict[str, Any]) -> None: raise NotImplementedError async def _move( self, position: int, parent_id: Optional[Any] = None, lock_permissions: bool = False, *, reason: Optional[str], ) -> None: if position < 0: raise ValueError('Channel position cannot be less than 0.') http = self._state.http bucket = self._sorting_bucket channels: List[GuildChannel] = [c for c in self.guild.channels if c._sorting_bucket == bucket] channels.sort(key=lambda c: c.position) try: # remove ourselves from the channel list channels.remove(self) except ValueError: # not there somehow lol return else: index = next((i for i, c in enumerate(channels) if c.position >= position), len(channels)) # add ourselves at our designated position channels.insert(index, self) payload = [] for index, c in enumerate(channels): d: Dict[str, Any] = {'id': c.id, 'position': index} if parent_id is not _undefined and c.id == self.id: d.update(parent_id=parent_id, lock_permissions=lock_permissions) payload.append(d) await http.bulk_channel_update(self.guild.id, payload, reason=reason) async def _edit(self, options: Dict[str, Any], reason: Optional[str]) -> Optional[ChannelPayload]: try: parent = options.pop('category') except KeyError: parent_id = _undefined else: parent_id = parent and parent.id try: options['rate_limit_per_user'] = options.pop('slowmode_delay') except KeyError: pass try: rtc_region = options.pop('rtc_region') except KeyError: pass else: options['rtc_region'] = None if rtc_region is None else str(rtc_region) try: video_quality_mode = options.pop('video_quality_mode') except KeyError: pass else: options['video_quality_mode'] = int(video_quality_mode) lock_permissions = options.pop('sync_permissions', False) try: position = options.pop('position') except KeyError: if parent_id is not _undefined: if lock_permissions: category = self.guild.get_channel(parent_id) if category: options['permission_overwrites'] = [c._asdict() for c in category._overwrites] options['parent_id'] = parent_id elif lock_permissions and self.category_id is not None: # if we're syncing permissions on a pre-existing channel category without changing it # we need to update the permissions to point to the pre-existing category category = self.guild.get_channel(self.category_id) if category: options['permission_overwrites'] = [c._asdict() for c in category._overwrites] else: await self._move(position, parent_id=parent_id, lock_permissions=lock_permissions, reason=reason) overwrites = options.get('overwrites', None) if overwrites is not None: perms = [] for target, perm in overwrites.items(): if not isinstance(perm, PermissionOverwrite): raise TypeError(f'Expected PermissionOverwrite received {perm.__class__.__name__}') allow, deny = perm.pair() payload = { 'allow': allow.value, 'deny': deny.value, 'id': target.id, } if isinstance(target, Role): payload['type'] = _Overwrites.ROLE else: payload['type'] = _Overwrites.MEMBER perms.append(payload) options['permission_overwrites'] = perms try: ch_type = options['type'] except KeyError: pass else: if not isinstance(ch_type, ChannelType): raise TypeError('type field must be of type ChannelType') options['type'] = ch_type.value if options: return await self._state.http.edit_channel(self.id, reason=reason, **options) def _fill_overwrites(self, data: GuildChannelPayload) -> None: self._overwrites = [] everyone_index = 0 everyone_id = self.guild.id for index, overridden in enumerate(data.get('permission_overwrites', [])): overwrite = _Overwrites(overridden) self._overwrites.append(overwrite) if overwrite.type == _Overwrites.MEMBER: continue if overwrite.id == everyone_id: # the @everyone role is not guaranteed to be the first one # in the list of permission overwrites, however the permission # resolution code kind of requires that it is the first one in # the list since it is special. So we need the index so we can # swap it to be the first one. everyone_index = index # do the swap tmp = self._overwrites if tmp: tmp[everyone_index], tmp[0] = tmp[0], tmp[everyone_index] @property def changed_roles(self) -> List[Role]: """List[:class:`~discord.Role`]: Returns a list of roles that have been overridden from their default values in the :attr:`~discord.Guild.roles` attribute.""" ret = [] g = self.guild for overwrite in filter(lambda o: o.is_role(), self._overwrites): role = g.get_role(overwrite.id) if role is None: continue role = copy.copy(role) role.permissions.handle_overwrite(overwrite.allow, overwrite.deny) ret.append(role) return ret @property def mention(self) -> str: """:class:`str`: The string that allows you to mention the channel.""" return f'<#{self.id}>' @property def jump_url(self) -> str: """:class:`str`: Returns a URL that allows the client to jump to the channel. .. versionadded:: 2.0 """ return f'https://discord.com/channels/{self.guild.id}/{self.id}' @property def created_at(self) -> datetime: """:class:`datetime.datetime`: Returns the channel's creation time in UTC.""" return utils.snowflake_time(self.id) def overwrites_for(self, obj: Union[Role, User]) -> PermissionOverwrite: """Returns the channel-specific overwrites for a member or a role. Parameters ----------- obj: Union[:class:`~discord.Role`, :class:`~discord.abc.User`] The role or user denoting whose overwrite to get. Returns --------- :class:`~discord.PermissionOverwrite` The permission overwrites for this object. """ if isinstance(obj, User): predicate = lambda p: p.is_member() elif isinstance(obj, Role): predicate = lambda p: p.is_role() else: predicate = lambda p: True for overwrite in filter(predicate, self._overwrites): if overwrite.id == obj.id: allow = Permissions(overwrite.allow) deny = Permissions(overwrite.deny) return PermissionOverwrite.from_pair(allow, deny) return PermissionOverwrite() @property def overwrites(self) -> Dict[Union[Role, Member], PermissionOverwrite]: """Returns all of the channel's overwrites. This is returned as a dictionary where the key contains the target which can be either a :class:`~discord.Role` or a :class:`~discord.Member` and the value is the overwrite as a :class:`~discord.PermissionOverwrite`. Returns -------- Dict[Union[:class:`~discord.Role`, :class:`~discord.Member`], :class:`~discord.PermissionOverwrite`] The channel's permission overwrites. """ ret = {} for ow in self._overwrites: allow = Permissions(ow.allow) deny = Permissions(ow.deny) overwrite = PermissionOverwrite.from_pair(allow, deny) target = None if ow.is_role(): target = self.guild.get_role(ow.id) elif ow.is_member(): target = self.guild.get_member(ow.id) # TODO: There is potential data loss here in the non-chunked # case, i.e. target is None because get_member returned nothing. # This can be fixed with a slight breaking change to the return type, # i.e. adding discord.Object to the list of it # However, for now this is an acceptable compromise. if target is not None: ret[target] = overwrite return ret @property def category(self) -> Optional[CategoryChannel]: """Optional[:class:`~discord.CategoryChannel`]: The category this channel belongs to. If there is no category then this is ``None``. """ return self.guild.get_channel(self.category_id) # type: ignore # These are coerced into CategoryChannel @property def permissions_synced(self) -> bool: """:class:`bool`: Whether or not the permissions for this channel are synced with the category it belongs to. If there is no category then this is ``False``. .. versionadded:: 1.3 """ if self.category_id is None: return False category = self.guild.get_channel(self.category_id) return bool(category and category.overwrites == self.overwrites) def permissions_for(self, obj: Union[Member, Role], /) -> Permissions: """Handles permission resolution for the :class:`~discord.Member` or :class:`~discord.Role`. This function takes into consideration the following cases: - Guild owner - Guild roles - Channel overrides - Member overrides - Member timeout If a :class:`~discord.Role` is passed, then it checks the permissions someone with that role would have, which is essentially: - The default role permissions - The permissions of the role used as a parameter - The default role permission overwrites - The permission overwrites of the role used as a parameter .. versionchanged:: 2.0 The object passed in can now be a role object. .. versionchanged:: 2.0 ``obj`` parameter is now positional-only. Parameters ---------- obj: Union[:class:`~discord.Member`, :class:`~discord.Role`] The object to resolve permissions for. This could be either a member or a role. If it's a role then member overwrites are not computed. Returns ------- :class:`~discord.Permissions` The resolved permissions for the member or role. """ # The current cases can be explained as: # Guild owner get all permissions -- no questions asked. Otherwise... # The @everyone role gets the first application. # After that, the applied roles that the user has in the channel # (or otherwise) are then OR'd together. # After the role permissions are resolved, the member permissions # have to take into effect. # After all that is done.. you have to do the following: # If manage permissions is True, then all permissions are set to True. # The operation first takes into consideration the denied # and then the allowed. if self.guild.owner_id == obj.id: return Permissions.all() default = self.guild.default_role base = Permissions(default.permissions.value) # Handle the role case first if isinstance(obj, Role): base.value |= obj._permissions if base.administrator: return Permissions.all() # Apply @everyone allow/deny first since it's special try: maybe_everyone = self._overwrites[0] if maybe_everyone.id == self.guild.id: base.handle_overwrite(allow=maybe_everyone.allow, deny=maybe_everyone.deny) except IndexError: pass if obj.is_default(): return base overwrite = utils.get(self._overwrites, type=_Overwrites.ROLE, id=obj.id) if overwrite is not None: base.handle_overwrite(overwrite.allow, overwrite.deny) return base roles = obj._roles get_role = self.guild.get_role # Apply guild roles that the member has. for role_id in roles: role = get_role(role_id) if role is not None: base.value |= role._permissions # Guild-wide Administrator -> True for everything # Bypass all channel-specific overrides if base.administrator: return Permissions.all() # Apply @everyone allow/deny first since it's special try: maybe_everyone = self._overwrites[0] if maybe_everyone.id == self.guild.id: base.handle_overwrite(allow=maybe_everyone.allow, deny=maybe_everyone.deny) remaining_overwrites = self._overwrites[1:] else: remaining_overwrites = self._overwrites except IndexError: remaining_overwrites = self._overwrites denies = 0 allows = 0 # Apply channel specific role permission overwrites for overwrite in remaining_overwrites: if overwrite.is_role() and roles.has(overwrite.id): denies |= overwrite.deny allows |= overwrite.allow base.handle_overwrite(allow=allows, deny=denies) # Apply member specific permission overwrites for overwrite in remaining_overwrites: if overwrite.is_member() and overwrite.id == obj.id: base.handle_overwrite(allow=overwrite.allow, deny=overwrite.deny) break # if you can't send a message in a channel then you can't have certain # permissions as well if not base.send_messages: base.send_tts_messages = False base.mention_everyone = False base.embed_links = False base.attach_files = False # if you can't read a channel then you have no permissions there if not base.read_messages: denied = Permissions.all_channel() base.value &= ~denied.value if obj.is_timed_out(): # Timeout leads to every permission except VIEW_CHANNEL and READ_MESSAGE_HISTORY # being explicitly denied # N.B.: This *must* come last, because it's a conclusive mask base.value &= Permissions._timeout_mask() return base async def delete(self, *, reason: Optional[str] = None) -> None: """|coro| Deletes the channel. You must have :attr:`~discord.Permissions.manage_channels` permission to use this. Parameters ----------- reason: Optional[:class:`str`] The reason for deleting this channel. Shows up on the audit log. Raises ------- ~discord.Forbidden You do not have proper permissions to delete the channel. ~discord.NotFound The channel was not found or was already deleted. ~discord.HTTPException Deleting the channel failed. """ await self._state.http.delete_channel(self.id, reason=reason) @overload async def set_permissions( self, target: Union[Member, Role], *, overwrite: Optional[Union[PermissionOverwrite, _Undefined]] = ..., reason: Optional[str] = ..., ) -> None: ... @overload async def set_permissions( self, target: Union[Member, Role], *, reason: Optional[str] = ..., **permissions: Optional[bool], ) -> None: ... async def set_permissions( self, target: Union[Member, Role], *, overwrite: Any = _undefined, reason: Optional[str] = None, **permissions: Optional[bool], ) -> None: r"""|coro| Sets the channel specific permission overwrites for a target in the channel. The ``target`` parameter should either be a :class:`~discord.Member` or a :class:`~discord.Role` that belongs to guild. The ``overwrite`` parameter, if given, must either be ``None`` or :class:`~discord.PermissionOverwrite`. For convenience, you can pass in keyword arguments denoting :class:`~discord.Permissions` attributes. If this is done, then you cannot mix the keyword arguments with the ``overwrite`` parameter. If the ``overwrite`` parameter is ``None``, then the permission overwrites are deleted. You must have the :attr:`~discord.Permissions.manage_roles` permission to use this. .. note:: This method *replaces* the old overwrites with the ones given. Examples ---------- Setting allow and deny: :: await message.channel.set_permissions(message.author, read_messages=True, send_messages=False) Deleting overwrites :: await channel.set_permissions(member, overwrite=None) Using :class:`~discord.PermissionOverwrite` :: overwrite = discord.PermissionOverwrite() overwrite.send_messages = False overwrite.read_messages = True await channel.set_permissions(member, overwrite=overwrite) .. versionchanged:: 2.0 This function will now raise :exc:`TypeError` instead of ``InvalidArgument``. Parameters ----------- target: Union[:class:`~discord.Member`, :class:`~discord.Role`] The member or role to overwrite permissions for. overwrite: Optional[:class:`~discord.PermissionOverwrite`] The permissions to allow and deny to the target, or ``None`` to delete the overwrite. \*\*permissions A keyword argument list of permissions to set for ease of use. Cannot be mixed with ``overwrite``. reason: Optional[:class:`str`] The reason for doing this action. Shows up on the audit log. Raises ------- ~discord.Forbidden You do not have permissions to edit channel specific permissions. ~discord.HTTPException Editing channel specific permissions failed. ~discord.NotFound The role or member being edited is not part of the guild. TypeError The ``overwrite`` parameter was invalid or the target type was not :class:`~discord.Role` or :class:`~discord.Member`. ValueError The ``overwrite`` parameter and ``positions`` parameters were both unset. """ http = self._state.http if isinstance(target, User): perm_type = _Overwrites.MEMBER elif isinstance(target, Role): perm_type = _Overwrites.ROLE else: raise ValueError('target parameter must be either Member or Role') if overwrite is _undefined: if len(permissions) == 0: raise ValueError('No overwrite provided.') try: overwrite = PermissionOverwrite(**permissions) except (ValueError, TypeError): raise TypeError('Invalid permissions given to keyword arguments.') else: if len(permissions) > 0: raise TypeError('Cannot mix overwrite and keyword arguments.') # TODO: wait for event if overwrite is None: await http.delete_channel_permissions(self.id, target.id, reason=reason) elif isinstance(overwrite, PermissionOverwrite): (allow, deny) = overwrite.pair() await http.edit_channel_permissions( self.id, target.id, str(allow.value), str(deny.value), perm_type, reason=reason ) else: raise TypeError('Invalid overwrite type provided.') async def _clone_impl( self, base_attrs: Dict[str, Any], *, name: Optional[str] = None, reason: Optional[str] = None, ) -> Self: base_attrs['permission_overwrites'] = [x._asdict() for x in self._overwrites] base_attrs['parent_id'] = self.category_id base_attrs['name'] = name or self.name guild_id = self.guild.id cls = self.__class__ data = await self._state.http.create_channel(guild_id, self.type.value, reason=reason, **base_attrs) obj = cls(state=self._state, guild=self.guild, data=data) # temporarily add it to the cache self.guild._channels[obj.id] = obj # type: ignore # obj is a GuildChannel return obj async def clone(self, *, name: Optional[str] = None, reason: Optional[str] = None) -> Self: """|coro| Clones this channel. This creates a channel with the same properties as this channel. You must have the :attr:`~discord.Permissions.manage_channels` permission to do this. .. versionadded:: 1.1 Parameters ------------ name: Optional[:class:`str`] The name of the new channel. If not provided, defaults to this channel name. reason: Optional[:class:`str`] The reason for cloning this channel. Shows up on the audit log. Raises ------- ~discord.Forbidden You do not have the proper permissions to create this channel. ~discord.HTTPException Creating the channel failed. Returns -------- :class:`.abc.GuildChannel` The channel that was created. """ raise NotImplementedError @overload async def move( self, *, beginning: bool, offset: int = MISSING, category: Optional[Snowflake] = MISSING, sync_permissions: bool = MISSING, reason: Optional[str] = MISSING, ) -> None: ... @overload async def move( self, *, end: bool, offset: int = MISSING, category: Optional[Snowflake] = MISSING, sync_permissions: bool = MISSING, reason: str = MISSING, ) -> None: ... @overload async def move( self, *, before: Snowflake, offset: int = MISSING, category: Optional[Snowflake] = MISSING, sync_permissions: bool = MISSING, reason: str = MISSING, ) -> None: ... @overload async def move( self, *, after: Snowflake, offset: int = MISSING, category: Optional[Snowflake] = MISSING, sync_permissions: bool = MISSING, reason: str = MISSING, ) -> None: ... async def move(self, **kwargs: Any) -> None: """|coro| A rich interface to help move a channel relative to other channels. If exact position movement is required, ``edit`` should be used instead. You must have the :attr:`~discord.Permissions.manage_channels` permission to do this. .. note:: Voice channels will always be sorted below text channels. This is a Discord limitation. .. versionadded:: 1.7 .. versionchanged:: 2.0 This function will now raise :exc:`TypeError` or :exc:`ValueError` instead of ``InvalidArgument``. Parameters ------------ beginning: :class:`bool` Whether to move the channel to the beginning of the channel list (or category if given). This is mutually exclusive with ``end``, ``before``, and ``after``. end: :class:`bool` Whether to move the channel to the end of the channel list (or category if given). This is mutually exclusive with ``beginning``, ``before``, and ``after``. before: :class:`~discord.abc.Snowflake` The channel that should be before our current channel. This is mutually exclusive with ``beginning``, ``end``, and ``after``. after: :class:`~discord.abc.Snowflake` The channel that should be after our current channel. This is mutually exclusive with ``beginning``, ``end``, and ``before``. offset: :class:`int` The number of channels to offset the move by. For example, an offset of ``2`` with ``beginning=True`` would move it 2 after the beginning. A positive number moves it below while a negative number moves it above. Note that this number is relative and computed after the ``beginning``, ``end``, ``before``, and ``after`` parameters. category: Optional[:class:`~discord.abc.Snowflake`] The category to move this channel under. If ``None`` is given then it moves it out of the category. This parameter is ignored if moving a category channel. sync_permissions: :class:`bool` Whether to sync the permissions with the category (if given). reason: :class:`str` The reason for the move. Raises ------- ValueError An invalid position was given. TypeError A bad mix of arguments were passed. Forbidden You do not have permissions to move the channel. HTTPException Moving the channel failed. """ if not kwargs: return beginning, end = kwargs.get('beginning'), kwargs.get('end') before, after = kwargs.get('before'), kwargs.get('after') offset = kwargs.get('offset', 0) if sum(bool(a) for a in (beginning, end, before, after)) > 1: raise TypeError('Only one of [before, after, end, beginning] can be used.') bucket = self._sorting_bucket parent_id = kwargs.get('category', MISSING) # fmt: off channels: List[GuildChannel] if parent_id not in (MISSING, None): parent_id = parent_id.id channels = [ ch for ch in self.guild.channels if ch._sorting_bucket == bucket and ch.category_id == parent_id ] else: channels = [ ch for ch in self.guild.channels if ch._sorting_bucket == bucket and ch.category_id == self.category_id ] # fmt: on channels.sort(key=lambda c: (c.position, c.id)) try: # Try to remove ourselves from the channel list channels.remove(self) except ValueError: # If we're not there then it's probably due to not being in the category pass index = None if beginning: index = 0 elif end: index = len(channels) elif before: index = next((i for i, c in enumerate(channels) if c.id == before.id), None) elif after: index = next((i + 1 for i, c in enumerate(channels) if c.id == after.id), None) if index is None: raise ValueError('Could not resolve appropriate move position') channels.insert(max((index + offset), 0), self) payload = [] lock_permissions = kwargs.get('sync_permissions', False) reason = kwargs.get('reason') for index, channel in enumerate(channels): d = {'id': channel.id, 'position': index} if parent_id is not MISSING and channel.id == self.id: d.update(parent_id=parent_id, lock_permissions=lock_permissions) payload.append(d) await self._state.http.bulk_channel_update(self.guild.id, payload, reason=reason) async def create_invite( self, *, reason: Optional[str] = None, max_age: int = 0, max_uses: int = 0, temporary: bool = False, unique: bool = True, target_type: Optional[InviteTarget] = None, target_user: Optional[User] = None, target_application_id: Optional[int] = None, ) -> Invite: """|coro| Creates an instant invite from a text or voice channel. You must have the :attr:`~discord.Permissions.create_instant_invite` permission to do this. Parameters ------------ max_age: :class:`int` How long the invite should last in seconds. If it's 0 then the invite doesn't expire. Defaults to ``0``. max_uses: :class:`int` How many uses the invite could be used for. If it's 0 then there are unlimited uses. Defaults to ``0``. temporary: :class:`bool` Denotes that the invite grants temporary membership (i.e. they get kicked after they disconnect). Defaults to ``False``. unique: :class:`bool` Indicates if a unique invite URL should be created. Defaults to True. If this is set to ``False`` then it will return a previously created invite. reason: Optional[:class:`str`] The reason for creating this invite. Shows up on the audit log. target_type: Optional[:class:`.InviteTarget`] The type of target for the voice channel invite, if any. .. versionadded:: 2.0 target_user: Optional[:class:`User`] The user whose stream to display for this invite, required if `target_type` is `TargetType.stream`. The user must be streaming in the channel. .. versionadded:: 2.0 target_application_id:: Optional[:class:`int`] The id of the embedded application for the invite, required if `target_type` is `TargetType.embedded_application`. .. versionadded:: 2.0 Raises ------- ~discord.HTTPException Invite creation failed. ~discord.NotFound The channel that was passed is a category or an invalid channel. Returns -------- :class:`~discord.Invite` The invite that was created. """ data = await self._state.http.create_invite( self.id, reason=reason, max_age=max_age, max_uses=max_uses, temporary=temporary, unique=unique, target_type=target_type.value if target_type else None, target_user_id=target_user.id if target_user else None, target_application_id=target_application_id, ) return Invite.from_incomplete(data=data, state=self._state) async def invites(self) -> List[Invite]: """|coro| Returns a list of all active instant invites from this channel. You must have :attr:`~discord.Permissions.manage_channels` to get this information. Raises ------- ~discord.Forbidden You do not have proper permissions to get the information. ~discord.HTTPException An error occurred while fetching the information. Returns ------- List[:class:`~discord.Invite`] The list of invites that are currently active. """ state = self._state data = await state.http.invites_from_channel(self.id) guild = self.guild return [Invite(state=state, data=invite, channel=self, guild=guild) for invite in data] class Messageable: """An ABC that details the common operations on a model that can send messages. The following classes implement this ABC: - :class:`~discord.TextChannel` - :class:`~discord.VoiceChannel` - :class:`~discord.DMChannel` - :class:`~discord.GroupChannel` - :class:`~discord.PartialMessageable` - :class:`~discord.User` - :class:`~discord.Member` - :class:`~discord.ext.commands.Context` - :class:`~discord.Thread` """ __slots__ = () _state: ConnectionState async def _get_channel(self) -> MessageableChannel: raise NotImplementedError @overload async def send( self, content: Optional[str] = ..., *, tts: bool = ..., embed: Embed = ..., file: File = ..., stickers: Sequence[Union[GuildSticker, StickerItem]] = ..., delete_after: float = ..., nonce: Union[str, int] = ..., allowed_mentions: AllowedMentions = ..., reference: Union[Message, MessageReference, PartialMessage] = ..., mention_author: bool = ..., view: View = ..., suppress_embeds: bool = ..., ) -> Message: ... @overload async def send( self, content: Optional[str] = ..., *, tts: bool = ..., embed: Embed = ..., files: Sequence[File] = ..., stickers: Sequence[Union[GuildSticker, StickerItem]] = ..., delete_after: float = ..., nonce: Union[str, int] = ..., allowed_mentions: AllowedMentions = ..., reference: Union[Message, MessageReference, PartialMessage] = ..., mention_author: bool = ..., view: View = ..., suppress_embeds: bool = ..., ) -> Message: ... @overload async def send( self, content: Optional[str] = ..., *, tts: bool = ..., embeds: Sequence[Embed] = ..., file: File = ..., stickers: Sequence[Union[GuildSticker, StickerItem]] = ..., delete_after: float = ..., nonce: Union[str, int] = ..., allowed_mentions: AllowedMentions = ..., reference: Union[Message, MessageReference, PartialMessage] = ..., mention_author: bool = ..., view: View = ..., suppress_embeds: bool = ..., ) -> Message: ... @overload async def send( self, content: Optional[str] = ..., *, tts: bool = ..., embeds: Sequence[Embed] = ..., files: Sequence[File] = ..., stickers: Sequence[Union[GuildSticker, StickerItem]] = ..., delete_after: float = ..., nonce: Union[str, int] = ..., allowed_mentions: AllowedMentions = ..., reference: Union[Message, MessageReference, PartialMessage] = ..., mention_author: bool = ..., view: View = ..., suppress_embeds: bool = ..., ) -> Message: ... async def send( self, content: Optional[str] = None, *, tts: bool = False, embed: Optional[Embed] = None, embeds: Optional[Sequence[Embed]] = None, file: Optional[File] = None, files: Optional[Sequence[File]] = None, stickers: Optional[Sequence[Union[GuildSticker, StickerItem]]] = None, delete_after: Optional[float] = None, nonce: Optional[Union[str, int]] = None, allowed_mentions: Optional[AllowedMentions] = None, reference: Optional[Union[Message, MessageReference, PartialMessage]] = None, mention_author: Optional[bool] = None, view: Optional[View] = None, suppress_embeds: bool = False, ) -> Message: """|coro| Sends a message to the destination with the content given. The content must be a type that can convert to a string through ``str(content)``. If the content is set to ``None`` (the default), then the ``embed`` parameter must be provided. To upload a single file, the ``file`` parameter should be used with a single :class:`~discord.File` object. To upload multiple files, the ``files`` parameter should be used with a :class:`list` of :class:`~discord.File` objects. **Specifying both parameters will lead to an exception**. To upload a single embed, the ``embed`` parameter should be used with a single :class:`~discord.Embed` object. To upload multiple embeds, the ``embeds`` parameter should be used with a :class:`list` of :class:`~discord.Embed` objects. **Specifying both parameters will lead to an exception**. .. versionchanged:: 2.0 This function will now raise :exc:`TypeError` or :exc:`ValueError` instead of ``InvalidArgument``. Parameters ------------ content: Optional[:class:`str`] The content of the message to send. tts: :class:`bool` Indicates if the message should be sent using text-to-speech. embed: :class:`~discord.Embed` The rich embed for the content. embeds: List[:class:`~discord.Embed`] A list of embeds to upload. Must be a maximum of 10. .. versionadded:: 2.0 file: :class:`~discord.File` The file to upload. files: List[:class:`~discord.File`] A list of files to upload. Must be a maximum of 10. nonce: :class:`int` The nonce to use for sending this message. If the message was successfully sent, then the message will have a nonce with this value. delete_after: :class:`float` If provided, the number of seconds to wait in the background before deleting the message we just sent. If the deletion fails, then it is silently ignored. allowed_mentions: :class:`~discord.AllowedMentions` Controls the mentions being processed in this message. If this is passed, then the object is merged with :attr:`~discord.Client.allowed_mentions`. The merging behaviour only overrides attributes that have been explicitly passed to the object, otherwise it uses the attributes set in :attr:`~discord.Client.allowed_mentions`. If no object is passed at all then the defaults given by :attr:`~discord.Client.allowed_mentions` are used instead. .. versionadded:: 1.4 reference: Union[:class:`~discord.Message`, :class:`~discord.MessageReference`, :class:`~discord.PartialMessage`] A reference to the :class:`~discord.Message` to which you are replying, this can be created using :meth:`~discord.Message.to_reference` or passed directly as a :class:`~discord.Message`. You can control whether this mentions the author of the referenced message using the :attr:`~discord.AllowedMentions.replied_user` attribute of ``allowed_mentions`` or by setting ``mention_author``. .. versionadded:: 1.6 mention_author: Optional[:class:`bool`] If set, overrides the :attr:`~discord.AllowedMentions.replied_user` attribute of ``allowed_mentions``. .. versionadded:: 1.6 view: :class:`discord.ui.View` A Discord UI View to add to the message. .. versionadded:: 2.0 stickers: Sequence[Union[:class:`~discord.GuildSticker`, :class:`~discord.StickerItem`]] A list of stickers to upload. Must be a maximum of 3. .. versionadded:: 2.0 suppress_embeds: :class:`bool` Whether to suppress embeds for the message. This sends the message without any embeds if set to ``True``. .. versionadded:: 2.0 Raises -------- ~discord.HTTPException Sending the message failed. ~discord.Forbidden You do not have the proper permissions to send the message. ValueError The ``files`` or ``embeds`` list is not of the appropriate size. TypeError You specified both ``file`` and ``files``, or you specified both ``embed`` and ``embeds``, or the ``reference`` object is not a :class:`~discord.Message`, :class:`~discord.MessageReference` or :class:`~discord.PartialMessage`. Returns --------- :class:`~discord.Message` The message that was sent. """ channel = await self._get_channel() state = self._state content = str(content) if content is not None else None previous_allowed_mention = state.allowed_mentions if stickers is not None: sticker_ids: SnowflakeList = [sticker.id for sticker in stickers] else: sticker_ids = MISSING if reference is not None: try: reference_dict = reference.to_message_reference_dict() except AttributeError: raise TypeError('reference parameter must be Message, MessageReference, or PartialMessage') from None else: reference_dict = MISSING if view and not hasattr(view, '__discord_ui_view__'): raise TypeError(f'view parameter must be View not {view.__class__!r}') if suppress_embeds: from .message import MessageFlags # circular import flags = MessageFlags._from_value(4) else: flags = MISSING with handle_message_parameters( content=content, tts=tts, file=file if file is not None else MISSING, files=files if files is not None else MISSING, embed=embed if embed is not None else MISSING, embeds=embeds if embeds is not None else MISSING, nonce=nonce, allowed_mentions=allowed_mentions, message_reference=reference_dict, previous_allowed_mentions=previous_allowed_mention, mention_author=mention_author, stickers=sticker_ids, view=view, flags=flags, ) as params: data = await state.http.send_message(channel.id, params=params) ret = state.create_message(channel=channel, data=data) if view: state.store_view(view, ret.id) if delete_after is not None: await ret.delete(delay=delete_after) return ret def typing(self) -> Typing: """Returns an asynchronous context manager that allows you to send a typing indicator to the destination for an indefinite period of time, or 10 seconds if the context manager is called using ``await``. Example Usage: :: async with channel.typing(): # simulate something heavy await asyncio.sleep(20) await channel.send('Done!') Example Usage: :: await channel.typing() # Do some computational magic for about 10 seconds await channel.send('Done!') .. versionchanged:: 2.0 This no longer works with the ``with`` syntax, ``async with`` must be used instead. .. versionchanged:: 2.0 Added functionality to ``await`` the context manager to send a typing indicator for 10 seconds. """ return Typing(self) async def fetch_message(self, id: int, /) -> Message: """|coro| Retrieves a single :class:`~discord.Message` from the destination. Parameters ------------ id: :class:`int` The message ID to look for. Raises -------- ~discord.NotFound The specified message was not found. ~discord.Forbidden You do not have the permissions required to get a message. ~discord.HTTPException Retrieving the message failed. Returns -------- :class:`~discord.Message` The message asked for. """ channel = await self._get_channel() data = await self._state.http.get_message(channel.id, id) return self._state.create_message(channel=channel, data=data) async def pins(self) -> List[Message]: """|coro| Retrieves all messages that are currently pinned in the channel. .. note:: Due to a limitation with the Discord API, the :class:`.Message` objects returned by this method do not contain complete :attr:`.Message.reactions` data. Raises ------- ~discord.HTTPException Retrieving the pinned messages failed. Returns -------- List[:class:`~discord.Message`] The messages that are currently pinned. """ channel = await self._get_channel() state = self._state data = await state.http.pins_from(channel.id) return [state.create_message(channel=channel, data=m) for m in data] async def history( self, *, limit: Optional[int] = 100, before: Optional[SnowflakeTime] = None, after: Optional[SnowflakeTime] = None, around: Optional[SnowflakeTime] = None, oldest_first: Optional[bool] = None, ) -> AsyncIterator[Message]: """Returns an :term:`asynchronous iterator` that enables receiving the destination's message history. You must have :attr:`~discord.Permissions.read_message_history` permissions to use this. Examples --------- Usage :: counter = 0 async for message in channel.history(limit=200): if message.author == client.user: counter += 1 Flattening into a list: :: messages = [message async for message in channel.history(limit=123)] # messages is now a list of Message... All parameters are optional. Parameters ----------- limit: Optional[:class:`int`] The number of messages to retrieve. If ``None``, retrieves every message in the channel. Note, however, that this would make it a slow operation. before: Optional[Union[:class:`~discord.abc.Snowflake`, :class:`datetime.datetime`]] Retrieve messages before this date or message. If a datetime is provided, it is recommended to use a UTC aware datetime. If the datetime is naive, it is assumed to be local time. after: Optional[Union[:class:`~discord.abc.Snowflake`, :class:`datetime.datetime`]] Retrieve messages after this date or message. If a datetime is provided, it is recommended to use a UTC aware datetime. If the datetime is naive, it is assumed to be local time. around: Optional[Union[:class:`~discord.abc.Snowflake`, :class:`datetime.datetime`]] Retrieve messages around this date or message. If a datetime is provided, it is recommended to use a UTC aware datetime. If the datetime is naive, it is assumed to be local time. When using this argument, the maximum limit is 101. Note that if the limit is an even number then this will return at most limit + 1 messages. oldest_first: Optional[:class:`bool`] If set to ``True``, return messages in oldest->newest order. Defaults to ``True`` if ``after`` is specified, otherwise ``False``. Raises ------ ~discord.Forbidden You do not have permissions to get channel message history. ~discord.HTTPException The request to get message history failed. Yields ------- :class:`~discord.Message` The message with the message data parsed. """ async def _around_strategy(retrieve, around, limit): if not around: return [] around_id = around.id if around else None data = await self._state.http.logs_from(channel.id, retrieve, around=around_id) return data, None, limit async def _after_strategy(retrieve, after, limit): after_id = after.id if after else None data = await self._state.http.logs_from(channel.id, retrieve, after=after_id) if data: if limit is not None: limit -= len(data) after = Object(id=int(data[0]['id'])) return data, after, limit async def _before_strategy(retrieve, before, limit): before_id = before.id if before else None data = await self._state.http.logs_from(channel.id, retrieve, before=before_id) if data: if limit is not None: limit -= len(data) before = Object(id=int(data[-1]['id'])) return data, before, limit if isinstance(before, datetime): before = Object(id=utils.time_snowflake(before, high=False)) if isinstance(after, datetime): after = Object(id=utils.time_snowflake(after, high=True)) if isinstance(around, datetime): around = Object(id=utils.time_snowflake(around)) if oldest_first is None: reverse = after is not None else: reverse = oldest_first after = after or OLDEST_OBJECT predicate = None if around: if limit is None: raise ValueError('history does not support around with limit=None') if limit > 101: raise ValueError("history max limit 101 when specifying around parameter") # Strange Discord quirk limit = 100 if limit == 101 else limit strategy, state = _around_strategy, around if before and after: predicate = lambda m: after.id < int(m['id']) < before.id elif before: predicate = lambda m: int(m['id']) < before.id elif after: predicate = lambda m: after.id < int(m['id']) elif reverse: strategy, state = _after_strategy, after if before: predicate = lambda m: int(m['id']) < before.id else: strategy, state = _before_strategy, before if after and after != OLDEST_OBJECT: predicate = lambda m: int(m['id']) > after.id channel = await self._get_channel() while True: retrieve = min(100 if limit is None else limit, 100) if retrieve < 1: return data, state, limit = await strategy(retrieve, state, limit) # Terminate loop on next iteration; there's no data left after this if len(data) < 100: limit = 0 if reverse: data = reversed(data) if predicate: data = filter(predicate, data) for raw_message in data: yield self._state.create_message(channel=channel, data=raw_message) class Connectable(Protocol): """An ABC that details the common operations on a channel that can connect to a voice server. The following implement this ABC: - :class:`~discord.VoiceChannel` - :class:`~discord.StageChannel` """ __slots__ = () _state: ConnectionState def _get_voice_client_key(self) -> Tuple[int, str]: raise NotImplementedError def _get_voice_state_pair(self) -> Tuple[int, int]: raise NotImplementedError async def connect( self, *, timeout: float = 60.0, reconnect: bool = True, cls: Callable[[Client, Connectable], T] = VoiceClient, self_deaf: bool = False, self_mute: bool = False, ) -> T: """|coro| Connects to voice and creates a :class:`~discord.VoiceClient` to establish your connection to the voice server. This requires :attr:`~discord.Intents.voice_states`. Parameters ----------- timeout: :class:`float` The timeout in seconds to wait for the voice endpoint. reconnect: :class:`bool` Whether the bot should automatically attempt a reconnect if a part of the handshake fails or the gateway goes down. cls: Type[:class:`~discord.VoiceProtocol`] A type that subclasses :class:`~discord.VoiceProtocol` to connect with. Defaults to :class:`~discord.VoiceClient`. self_mute: :class:`bool` Indicates if the client should be self-muted. .. versionadded:: 2.0 self_deaf: :class:`bool` Indicates if the client should be self-deafened. .. versionadded:: 2.0 Raises ------- asyncio.TimeoutError Could not connect to the voice channel in time. ~discord.ClientException You are already connected to a voice channel. ~discord.opus.OpusNotLoaded The opus library has not been loaded. Returns -------- :class:`~discord.VoiceProtocol` A voice client that is fully connected to the voice server. """ key_id, _ = self._get_voice_client_key() state = self._state if state._get_voice_client(key_id): raise ClientException('Already connected to a voice channel.') client = state._get_client() voice: T = cls(client, self) if not isinstance(voice, VoiceProtocol): raise TypeError('Type must meet VoiceProtocol abstract base class.') state._add_voice_client(key_id, voice) try: await voice.connect(timeout=timeout, reconnect=reconnect, self_deaf=self_deaf, self_mute=self_mute) except asyncio.TimeoutError: try: await voice.disconnect(force=True) except Exception: # we don't care if disconnect failed because connection failed pass raise # re-raise return voice

6855f86f9dbd0042e9b7c5f640f4f0a0337b4b1e

py

Python

feedparser/api.py

alexreg/feedparser

8a2a71b72d5b1454d456759d6270fa1dd18dfc4f

2015-01-06T07:56:10.000Z

2022-03-31T11:20:18.000Z

feedparser/api.py

alexreg/feedparser

8a2a71b72d5b1454d456759d6270fa1dd18dfc4f

2015-02-02T22:32:10.000Z

2022-03-29T21:45:56.000Z

feedparser/api.py

alexreg/feedparser

8a2a71b72d5b1454d456759d6270fa1dd18dfc4f

2015-02-03T21:28:01.000Z

2022-03-24T23:01:41.000Z

# The public API for feedparser # Copyright 2010-2021 Kurt McKee <contactme@kurtmckee.org> # Copyright 2002-2008 Mark Pilgrim # All rights reserved. # # This file is a part of feedparser. # # Redistribution and use in source and binary forms, with or without modification, # are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright notice, # this list of conditions and the following disclaimer. # * Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation # and/or other materials provided with the distribution. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS 'AS IS' # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. import datetime import io import time from typing import Dict, List, Union import urllib.error import urllib.parse import xml.sax from .datetimes import registerDateHandler, _parse_date from .encodings import convert_to_utf8 from .html import BaseHTMLProcessor from . import http from .mixin import XMLParserMixin from .parsers.loose import LooseXMLParser from .parsers.strict import StrictXMLParser from .parsers.json import JSONParser from .sanitizer import replace_doctype from .urls import convert_to_idn, make_safe_absolute_uri from .util import FeedParserDict # List of preferred XML parsers, by SAX driver name. These will be tried first, # but if they're not installed, Python will keep searching through its own list # of pre-installed parsers until it finds one that supports everything we need. PREFERRED_XML_PARSERS = ["drv_libxml2"] _XML_AVAILABLE = True SUPPORTED_VERSIONS = { '': 'unknown', 'rss090': 'RSS 0.90', 'rss091n': 'RSS 0.91 (Netscape)', 'rss091u': 'RSS 0.91 (Userland)', 'rss092': 'RSS 0.92', 'rss093': 'RSS 0.93', 'rss094': 'RSS 0.94', 'rss20': 'RSS 2.0', 'rss10': 'RSS 1.0', 'rss': 'RSS (unknown version)', 'atom01': 'Atom 0.1', 'atom02': 'Atom 0.2', 'atom03': 'Atom 0.3', 'atom10': 'Atom 1.0', 'atom': 'Atom (unknown version)', 'cdf': 'CDF', 'json1': 'JSON feed 1', } def _open_resource(url_file_stream_or_string, etag, modified, agent, referrer, handlers, request_headers, result): """URL, filename, or string --> stream This function lets you define parsers that take any input source (URL, pathname to local or network file, or actual data as a string) and deal with it in a uniform manner. Returned object is guaranteed to have all the basic stdio read methods (read, readline, readlines). Just .close() the object when you're done with it. If the etag argument is supplied, it will be used as the value of an If-None-Match request header. If the modified argument is supplied, it can be a tuple of 9 integers (as returned by gmtime() in the standard Python time module) or a date string in any format supported by feedparser. Regardless, it MUST be in GMT (Greenwich Mean Time). It will be reformatted into an RFC 1123-compliant date and used as the value of an If-Modified-Since request header. If the agent argument is supplied, it will be used as the value of a User-Agent request header. If the referrer argument is supplied, it will be used as the value of a Referer[sic] request header. If handlers is supplied, it is a list of handlers used to build a urllib2 opener. if request_headers is supplied it is a dictionary of HTTP request headers that will override the values generated by FeedParser. :return: A bytes object. """ if hasattr(url_file_stream_or_string, 'read'): return url_file_stream_or_string.read() if isinstance(url_file_stream_or_string, str) \ and urllib.parse.urlparse(url_file_stream_or_string)[0] in ('http', 'https', 'ftp', 'file', 'feed'): return http.get(url_file_stream_or_string, etag, modified, agent, referrer, handlers, request_headers, result) # try to open with native open function (if url_file_stream_or_string is a filename) try: with open(url_file_stream_or_string, 'rb') as f: data = f.read() except (IOError, UnicodeEncodeError, TypeError, ValueError): # if url_file_stream_or_string is a str object that # cannot be converted to the encoding returned by # sys.getfilesystemencoding(), a UnicodeEncodeError # will be thrown # If url_file_stream_or_string is a string that contains NULL # (such as an XML document encoded in UTF-32), TypeError will # be thrown. pass else: return data # treat url_file_stream_or_string as string if not isinstance(url_file_stream_or_string, bytes): return url_file_stream_or_string.encode('utf-8') return url_file_stream_or_string class LooseFeedParser(LooseXMLParser, XMLParserMixin, BaseHTMLProcessor): pass class StrictFeedParser(StrictXMLParser, XMLParserMixin, xml.sax.handler.ContentHandler): pass def parse( url_file_stream_or_string, etag: str = None, modified: Union[str, datetime.datetime, time.struct_time] = None, agent: str = None, referrer: str = None, handlers: List = None, request_headers: Dict[str, str] = None, response_headers: Dict[str, str] = None, resolve_relative_uris: bool = None, sanitize_html: bool = None, ) -> FeedParserDict: """Parse a feed from a URL, file, stream, or string. :param url_file_stream_or_string: File-like object, URL, file path, or string. Both byte and text strings are accepted. If necessary, encoding will be derived from the response headers or automatically detected. Note that strings may trigger network I/O or filesystem access depending on the value. Wrap an untrusted string in a :class:`io.StringIO` or :class:`io.BytesIO` to avoid this. Do not pass untrusted strings to this function. When a URL is not passed the feed location to use in relative URL resolution should be passed in the ``Content-Location`` response header (see ``response_headers`` below). :param etag: HTTP ``ETag`` request header. :param modified: HTTP ``Last-Modified`` request header. :param agent: HTTP ``User-Agent`` request header, which defaults to the value of :data:`feedparser.USER_AGENT`. :param referrer: HTTP ``Referer`` [sic] request header. :param handlers: A list of handlers that will be passed to urllib2. :param request_headers: A mapping of HTTP header name to HTTP header value to add to the request, overriding internally generated values. :param response_headers: A mapping of HTTP header name to HTTP header value. Multiple values may be joined with a comma. If a HTTP request was made, these headers override any matching headers in the response. Otherwise this specifies the entirety of the response headers. :param resolve_relative_uris: Should feedparser attempt to resolve relative URIs absolute ones within HTML content? Defaults to the value of :data:`feedparser.RESOLVE_RELATIVE_URIS`, which is ``True``. :param sanitize_html: Should feedparser skip HTML sanitization? Only disable this if you know what you are doing! Defaults to the value of :data:`feedparser.SANITIZE_HTML`, which is ``True``. """ # Avoid a cyclic import. if not agent: import feedparser agent = feedparser.USER_AGENT if sanitize_html is None: import feedparser sanitize_html = bool(feedparser.SANITIZE_HTML) if resolve_relative_uris is None: import feedparser resolve_relative_uris = bool(feedparser.RESOLVE_RELATIVE_URIS) result = FeedParserDict( bozo=False, entries=[], feed=FeedParserDict(), headers={}, ) try: data = _open_resource(url_file_stream_or_string, etag, modified, agent, referrer, handlers, request_headers, result) except urllib.error.URLError as error: result.update({ 'bozo': True, 'bozo_exception': error, }) return result if not data: return result # overwrite existing headers using response_headers result['headers'].update(response_headers or {}) data = convert_to_utf8(result['headers'], data, result) use_json_parser = result['content-type'] == 'application/json' use_strict_parser = result['encoding'] and True or False if not use_json_parser: result['version'], data, entities = replace_doctype(data) # Ensure that baseuri is an absolute URI using an acceptable URI scheme. contentloc = result['headers'].get('content-location', '') href = result.get('href', '') baseuri = make_safe_absolute_uri(href, contentloc) or make_safe_absolute_uri(contentloc) or href baselang = result['headers'].get('content-language', None) if isinstance(baselang, bytes) and baselang is not None: baselang = baselang.decode('utf-8', 'ignore') if not _XML_AVAILABLE: use_strict_parser = False feed_parser: Union[JSONParser, StrictFeedParser, LooseFeedParser] if use_json_parser: result['version'] = None feed_parser = JSONParser(baseuri, baselang, 'utf-8') try: feed_parser.feed(data) except Exception as e: result['bozo'] = 1 result['bozo_exception'] = e elif use_strict_parser: # Initialize the SAX parser. feed_parser = StrictFeedParser(baseuri, baselang, 'utf-8') feed_parser.resolve_relative_uris = resolve_relative_uris feed_parser.sanitize_html = sanitize_html saxparser = xml.sax.make_parser(PREFERRED_XML_PARSERS) saxparser.setFeature(xml.sax.handler.feature_namespaces, 1) try: # Disable downloading external doctype references, if possible. saxparser.setFeature(xml.sax.handler.feature_external_ges, 0) except xml.sax.SAXNotSupportedException: pass saxparser.setContentHandler(feed_parser) saxparser.setErrorHandler(feed_parser) source = xml.sax.xmlreader.InputSource() source.setByteStream(io.BytesIO(data)) try: saxparser.parse(source) except xml.sax.SAXException as e: result['bozo'] = 1 result['bozo_exception'] = feed_parser.exc or e use_strict_parser = False # The loose XML parser will be tried if the JSON parser was not used, # and if the strict XML parser was not used (or if if it failed). if not use_json_parser and not use_strict_parser: feed_parser = LooseFeedParser(baseuri, baselang, 'utf-8', entities) feed_parser.resolve_relative_uris = resolve_relative_uris feed_parser.sanitize_html = sanitize_html feed_parser.feed(data.decode('utf-8', 'replace')) result['feed'] = feed_parser.feeddata result['entries'] = feed_parser.entries result['version'] = result['version'] or feed_parser.version if isinstance(feed_parser, JSONParser): result['namespaces'] = {} else: result['namespaces'] = feed_parser.namespaces_in_use return result

8fa28b82796ff1989b56b27c5b05c4106998d5bb

py

Python

code/utiy.py

hjl2626/stu_manager

63b44dd0810d9dc324b458667ebd3f939b5ad5d3

2018-07-29T10:53:41.000Z

2018-07-29T10:53:41.000Z

code/utiy.py

hjl2626/stu_manager

63b44dd0810d9dc324b458667ebd3f939b5ad5d3

code/utiy.py

hjl2626/stu_manager

63b44dd0810d9dc324b458667ebd3f939b5ad5d3

import random def _get_name(): num = random.randint(2, 3) name = ''.join([chr(random.randint(0x4E00, 0x8377)) for i in range(num)]) if num == 2: return name[0] + ' ' + name[1] return name def _get_id(): return ''.join(random.sample('0123456789', 6)) def _get_sco(): return '%.1f' % (random.randint(10, 100)) def _get_sex(): return ['男', '女'][random.randint(0, 1)] def init_write_file(filename, num=50, flag=False): result = [] for i in range(num): if flag: tmp = ','.join([_get_id(), _get_name(), _get_sex(), _get_sco(), _get_sco(), _get_sco(), '0.0', '0.0']) else: tmp = ','.join([_get_id(), _get_name(), _get_sex(), _get_sco(), _get_sco(), _get_sco()]) result.append(tmp) with open(filename, 'w+') as f: f.write('\n'.join(result)) def print_menu(): print('*************************************************************************************************') print('\t\t1.创建文件 \t\t\t\t 2.显示记录 \t\t\t\t 3.编辑记录') print('\t\t4.增加记录 \t\t\t\t 5.删除记录 \t\t\t\t 6.查询记录') print('\t\t7.排序记录 \t\t\t\t 8.统计记录 \t\t\t\t 0.退出') print('*************************************************************************************************') def plot(name, xdata, ydata): """ Simple demo of a horizontal bar chart. """ import matplotlib.pyplot as plt plt.figure(figsize=(10,6)) import matplotlib.pyplot as plt y_pos = range(1, 11) plt.barh(y_pos, xdata, align='center', alpha=0.9) plt.yticks(y_pos, ydata) plt.xlabel('人数') plt.title(name) plt.show()

e5fa59c737fc8afbd1e405f75796b4a44d8e2d93

py

Python

qmla/exploration_strategies/demos/__init__.py

Evan1415/QMLA

4521f7c08456a4494aed7c1b78d8ded5ea40f3d8

qmla/exploration_strategies/demos/__init__.py

Evan1415/QMLA

4521f7c08456a4494aed7c1b78d8ded5ea40f3d8

qmla/exploration_strategies/demos/__init__.py

Evan1415/QMLA

4521f7c08456a4494aed7c1b78d8ded5ea40f3d8

from qmla.exploration_strategies.demos.example import * from qmla.exploration_strategies.demos.analytical import * from qmla.exploration_strategies.demos.demo_lattices import * from qmla.exploration_strategies.demos.learning_probes import * from qmla.exploration_strategies.demos.heuristics import * from qmla.exploration_strategies.demos.demo_genetic_algorithm import * from qmla.exploration_strategies.demos.obj_fncs import * from qmla.exploration_strategies.demos.bayes_factor_by_fscore import *

64f09598975a9b23a6c64953c59d0644495239a1

py

Python

setup.py

JohnCrickett/CommandoMaths

278e178762cd8b0cac96fe3fc15a80212cff33c6

setup.py

JohnCrickett/CommandoMaths

278e178762cd8b0cac96fe3fc15a80212cff33c6

setup.py

JohnCrickett/CommandoMaths

278e178762cd8b0cac96fe3fc15a80212cff33c6

from setuptools import setup, find_packages setup( name='CommandoMaths', author='John Crickett', author_email='john@trivialbusiness.co.uk', version='0.1.0.dev', packages=find_packages(), package_data={'commandomaths': ['resources/*']}, license='See LICENSE', long_description=open('README.md').read(), install_requires=["pygame >= 1.9.2a0" ], entry_points={ 'console_scripts': [ 'commandomaths=commandomaths.main:main', ], }, zip_safe=True, include_package_data=True )

6bfa812b2856d854dcc773e9c780bf84d1ced696

py

Python

neural-tangents/examples/util.py

DarrenZhang01/Neural_Tangents_TensorFlow

2fd360c8b1b8c9106044034f6a8b5c2734db9c3d

2020-12-25T17:37:13.000Z

2022-01-03T17:00:23.000Z

neural-tangents/examples/util.py

DarrenZhang01/TensorFlow_GSoC

2fd360c8b1b8c9106044034f6a8b5c2734db9c3d

2020-07-18T18:57:54.000Z

2020-08-17T13:58:46.000Z

neural-tangents/examples/util.py

DarrenZhang01/Neural_Tangents_TensorFlow

2fd360c8b1b8c9106044034f6a8b5c2734db9c3d

2021-08-16T19:00:06.000Z

2021-08-16T19:00:06.000Z

# Copyright 2019 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """A set of utility operations for running examples. """ from tensorflow.python.ops import numpy_ops as np def _accuracy(y, y_hat): """Compute the accuracy of the predictions with respect to one-hot labels.""" return np.mean(np.argmax(y, axis=1) == np.argmax(y_hat, axis=1)) def print_summary(name, labels, net_p, lin_p, loss): """Print summary information comparing a network with its linearization.""" print('\nEvaluating Network on {} data.'.format(name)) print('---------------------------------------') print('Network Accuracy = {}'.format(_accuracy(net_p, labels))) print('Network Loss = {}'.format(loss(net_p, labels))) if lin_p is not None: print('Linearization Accuracy = {}'.format(_accuracy(lin_p, labels))) print('Linearization Loss = {}'.format(loss(lin_p, labels))) print('RMSE of predictions: {}'.format( np.sqrt(np.mean((net_p - lin_p) ** 2)))) print('---------------------------------------')

1d189508960364e96144da62b95c6b63cb331e62

py

Python

homeassistant/components/comfoconnect/sensor.py

zcmosz/home-assistant

58b4efe880478f8c66f0bf53e957268667301220

2021-01-10T05:35:53.000Z

2021-01-10T05:35:53.000Z

homeassistant/components/comfoconnect/sensor.py

zcmosz/home-assistant

58b4efe880478f8c66f0bf53e957268667301220

homeassistant/components/comfoconnect/sensor.py

zcmosz/home-assistant

58b4efe880478f8c66f0bf53e957268667301220

2020-02-24T16:17:42.000Z

2020-02-24T16:17:42.000Z

"""Platform to control a Zehnder ComfoAir Q350/450/600 ventilation unit.""" import logging from pycomfoconnect import ( SENSOR_BYPASS_STATE, SENSOR_DAYS_TO_REPLACE_FILTER, SENSOR_FAN_EXHAUST_DUTY, SENSOR_FAN_EXHAUST_FLOW, SENSOR_FAN_EXHAUST_SPEED, SENSOR_FAN_SUPPLY_DUTY, SENSOR_FAN_SUPPLY_FLOW, SENSOR_FAN_SUPPLY_SPEED, SENSOR_HUMIDITY_EXHAUST, SENSOR_HUMIDITY_EXTRACT, SENSOR_HUMIDITY_OUTDOOR, SENSOR_HUMIDITY_SUPPLY, SENSOR_POWER_CURRENT, SENSOR_TEMPERATURE_EXHAUST, SENSOR_TEMPERATURE_EXTRACT, SENSOR_TEMPERATURE_OUTDOOR, SENSOR_TEMPERATURE_SUPPLY, ) import voluptuous as vol from homeassistant.components.sensor import PLATFORM_SCHEMA from homeassistant.const import ( ATTR_DEVICE_CLASS, CONF_RESOURCES, DEVICE_CLASS_HUMIDITY, DEVICE_CLASS_POWER, DEVICE_CLASS_TEMPERATURE, POWER_WATT, TEMP_CELSIUS, TIME_DAYS, TIME_HOURS, ) import homeassistant.helpers.config_validation as cv from homeassistant.helpers.dispatcher import async_dispatcher_connect from homeassistant.helpers.entity import Entity from . import DOMAIN, SIGNAL_COMFOCONNECT_UPDATE_RECEIVED, ComfoConnectBridge ATTR_AIR_FLOW_EXHAUST = "air_flow_exhaust" ATTR_AIR_FLOW_SUPPLY = "air_flow_supply" ATTR_BYPASS_STATE = "bypass_state" ATTR_CURRENT_HUMIDITY = "current_humidity" ATTR_CURRENT_TEMPERATURE = "current_temperature" ATTR_DAYS_TO_REPLACE_FILTER = "days_to_replace_filter" ATTR_EXHAUST_FAN_DUTY = "exhaust_fan_duty" ATTR_EXHAUST_FAN_SPEED = "exhaust_fan_speed" ATTR_EXHAUST_HUMIDITY = "exhaust_humidity" ATTR_EXHAUST_TEMPERATURE = "exhaust_temperature" ATTR_OUTSIDE_HUMIDITY = "outside_humidity" ATTR_OUTSIDE_TEMPERATURE = "outside_temperature" ATTR_POWER_CURRENT = "power_usage" ATTR_SUPPLY_FAN_DUTY = "supply_fan_duty" ATTR_SUPPLY_FAN_SPEED = "supply_fan_speed" ATTR_SUPPLY_HUMIDITY = "supply_humidity" ATTR_SUPPLY_TEMPERATURE = "supply_temperature" _LOGGER = logging.getLogger(__name__) ATTR_ICON = "icon" ATTR_ID = "id" ATTR_LABEL = "label" ATTR_MULTIPLIER = "multiplier" ATTR_UNIT = "unit" SENSOR_TYPES = { ATTR_CURRENT_TEMPERATURE: { ATTR_DEVICE_CLASS: DEVICE_CLASS_TEMPERATURE, ATTR_LABEL: "Inside Temperature", ATTR_UNIT: TEMP_CELSIUS, ATTR_ICON: "mdi:thermometer", ATTR_ID: SENSOR_TEMPERATURE_EXTRACT, ATTR_MULTIPLIER: 0.1, }, ATTR_CURRENT_HUMIDITY: { ATTR_DEVICE_CLASS: DEVICE_CLASS_HUMIDITY, ATTR_LABEL: "Inside Humidity", ATTR_UNIT: "%", ATTR_ICON: "mdi:water-percent", ATTR_ID: SENSOR_HUMIDITY_EXTRACT, }, ATTR_OUTSIDE_TEMPERATURE: { ATTR_DEVICE_CLASS: DEVICE_CLASS_TEMPERATURE, ATTR_LABEL: "Outside Temperature", ATTR_UNIT: TEMP_CELSIUS, ATTR_ICON: "mdi:thermometer", ATTR_ID: SENSOR_TEMPERATURE_OUTDOOR, ATTR_MULTIPLIER: 0.1, }, ATTR_OUTSIDE_HUMIDITY: { ATTR_DEVICE_CLASS: DEVICE_CLASS_HUMIDITY, ATTR_LABEL: "Outside Humidity", ATTR_UNIT: "%", ATTR_ICON: "mdi:water-percent", ATTR_ID: SENSOR_HUMIDITY_OUTDOOR, }, ATTR_SUPPLY_TEMPERATURE: { ATTR_DEVICE_CLASS: DEVICE_CLASS_TEMPERATURE, ATTR_LABEL: "Supply Temperature", ATTR_UNIT: TEMP_CELSIUS, ATTR_ICON: "mdi:thermometer", ATTR_ID: SENSOR_TEMPERATURE_SUPPLY, ATTR_MULTIPLIER: 0.1, }, ATTR_SUPPLY_HUMIDITY: { ATTR_DEVICE_CLASS: DEVICE_CLASS_HUMIDITY, ATTR_LABEL: "Supply Humidity", ATTR_UNIT: "%", ATTR_ICON: "mdi:water-percent", ATTR_ID: SENSOR_HUMIDITY_SUPPLY, }, ATTR_SUPPLY_FAN_SPEED: { ATTR_DEVICE_CLASS: None, ATTR_LABEL: "Supply Fan Speed", ATTR_UNIT: "rpm", ATTR_ICON: "mdi:fan", ATTR_ID: SENSOR_FAN_SUPPLY_SPEED, }, ATTR_SUPPLY_FAN_DUTY: { ATTR_DEVICE_CLASS: None, ATTR_LABEL: "Supply Fan Duty", ATTR_UNIT: "%", ATTR_ICON: "mdi:fan", ATTR_ID: SENSOR_FAN_SUPPLY_DUTY, }, ATTR_EXHAUST_FAN_SPEED: { ATTR_DEVICE_CLASS: None, ATTR_LABEL: "Exhaust Fan Speed", ATTR_UNIT: "rpm", ATTR_ICON: "mdi:fan", ATTR_ID: SENSOR_FAN_EXHAUST_SPEED, }, ATTR_EXHAUST_FAN_DUTY: { ATTR_DEVICE_CLASS: None, ATTR_LABEL: "Exhaust Fan Duty", ATTR_UNIT: "%", ATTR_ICON: "mdi:fan", ATTR_ID: SENSOR_FAN_EXHAUST_DUTY, }, ATTR_EXHAUST_TEMPERATURE: { ATTR_DEVICE_CLASS: DEVICE_CLASS_TEMPERATURE, ATTR_LABEL: "Exhaust Temperature", ATTR_UNIT: TEMP_CELSIUS, ATTR_ICON: "mdi:thermometer", ATTR_ID: SENSOR_TEMPERATURE_EXHAUST, ATTR_MULTIPLIER: 0.1, }, ATTR_EXHAUST_HUMIDITY: { ATTR_DEVICE_CLASS: DEVICE_CLASS_HUMIDITY, ATTR_LABEL: "Exhaust Humidity", ATTR_UNIT: "%", ATTR_ICON: "mdi:water-percent", ATTR_ID: SENSOR_HUMIDITY_EXHAUST, }, ATTR_AIR_FLOW_SUPPLY: { ATTR_DEVICE_CLASS: None, ATTR_LABEL: "Supply airflow", ATTR_UNIT: f"m³/{TIME_HOURS}", ATTR_ICON: "mdi:fan", ATTR_ID: SENSOR_FAN_SUPPLY_FLOW, }, ATTR_AIR_FLOW_EXHAUST: { ATTR_DEVICE_CLASS: None, ATTR_LABEL: "Exhaust airflow", ATTR_UNIT: f"m³/{TIME_HOURS}", ATTR_ICON: "mdi:fan", ATTR_ID: SENSOR_FAN_EXHAUST_FLOW, }, ATTR_BYPASS_STATE: { ATTR_DEVICE_CLASS: None, ATTR_LABEL: "Bypass State", ATTR_UNIT: "%", ATTR_ICON: "mdi:camera-iris", ATTR_ID: SENSOR_BYPASS_STATE, }, ATTR_DAYS_TO_REPLACE_FILTER: { ATTR_DEVICE_CLASS: None, ATTR_LABEL: "Days to replace filter", ATTR_UNIT: TIME_DAYS, ATTR_ICON: "mdi:calendar", ATTR_ID: SENSOR_DAYS_TO_REPLACE_FILTER, }, ATTR_POWER_CURRENT: { ATTR_DEVICE_CLASS: DEVICE_CLASS_POWER, ATTR_LABEL: "Power usage", ATTR_UNIT: POWER_WATT, ATTR_ICON: "mdi:flash", ATTR_ID: SENSOR_POWER_CURRENT, }, } PLATFORM_SCHEMA = PLATFORM_SCHEMA.extend( { vol.Optional(CONF_RESOURCES, default=[]): vol.All( cv.ensure_list, [vol.In(SENSOR_TYPES)] ) } ) def setup_platform(hass, config, add_entities, discovery_info=None): """Set up the ComfoConnect fan platform.""" ccb = hass.data[DOMAIN] sensors = [] for resource in config[CONF_RESOURCES]: sensors.append( ComfoConnectSensor( name=f"{ccb.name} {SENSOR_TYPES[resource][ATTR_LABEL]}", ccb=ccb, sensor_type=resource, ) ) add_entities(sensors, True) class ComfoConnectSensor(Entity): """Representation of a ComfoConnect sensor.""" def __init__(self, name, ccb: ComfoConnectBridge, sensor_type) -> None: """Initialize the ComfoConnect sensor.""" self._ccb = ccb self._sensor_type = sensor_type self._sensor_id = SENSOR_TYPES[self._sensor_type][ATTR_ID] self._name = name async def async_added_to_hass(self): """Register for sensor updates.""" _LOGGER.debug( "Registering for sensor %s (%d)", self._sensor_type, self._sensor_id ) async_dispatcher_connect( self.hass, SIGNAL_COMFOCONNECT_UPDATE_RECEIVED.format(self._sensor_id), self._handle_update, ) await self.hass.async_add_executor_job( self._ccb.comfoconnect.register_sensor, self._sensor_id ) def _handle_update(self, value): """Handle update callbacks.""" _LOGGER.debug( "Handle update for sensor %s (%d): %s", self._sensor_type, self._sensor_id, value, ) self._ccb.data[self._sensor_id] = round( value * SENSOR_TYPES[self._sensor_type].get(ATTR_MULTIPLIER, 1), 2 ) self.schedule_update_ha_state() @property def state(self): """Return the state of the entity.""" try: return self._ccb.data[self._sensor_id] except KeyError: return None @property def should_poll(self) -> bool: """Do not poll.""" return False @property def unique_id(self): """Return a unique_id for this entity.""" return f"{self._ccb.unique_id}-{self._sensor_type}" @property def name(self): """Return the name of the sensor.""" return self._name @property def icon(self): """Return the icon to use in the frontend.""" return SENSOR_TYPES[self._sensor_type][ATTR_ICON] @property def unit_of_measurement(self): """Return the unit of measurement of this entity.""" return SENSOR_TYPES[self._sensor_type][ATTR_UNIT] @property def device_class(self): """Return the device_class.""" return SENSOR_TYPES[self._sensor_type][ATTR_DEVICE_CLASS]

92329787e1f78fa2b395079190e18bf60385017d

py

Python

anadama2/grid/grid.py

biobakery/anadama2

39f791c8d8588ff92202c115c8e8e7fc25153c93

2020-06-08T22:10:48.000Z

2021-07-27T13:57:43.000Z

anadama2/grid/grid.py

biobakery/anadama2

39f791c8d8588ff92202c115c8e8e7fc25153c93

anadama2/grid/grid.py

biobakery/anadama2

39f791c8d8588ff92202c115c8e8e7fc25153c93

2020-09-10T08:29:22.000Z

2020-09-10T08:29:22.000Z

# -*- coding: utf-8 -*- import os import sys import threading try: import Queue except ImportError: import queue as Queue import time import tempfile import string import logging import itertools import re import six from .. import runners from .. import picklerunner from ..helpers import format_command from ..helpers import file_size if os.name == 'posix' and sys.version_info[0] < 3: import subprocess32 as subprocess else: import subprocess class GridJobRequires(object): """Defines the resources required for a task on the grid. :param time: Wall clock time in minutes. :type time: int :param mem: RAM Usage in MB (8*1024*1024 bits). :type mem: int :param cores: CPU cores. :type cores: int :param partition: grid partition. :type partition: string """ def __init__(self, time, mem, cores, partition, docker_image, depends=None): # if time is not an int, try to format the equation if not str(time).isdigit(): self.time = format_command(time, depends=depends, cores=cores) else: self.time = int(time) # if memory is not an int, try to format the equation if not str(mem).isdigit(): self.mem = format_command(mem, depends=depends, cores=cores) else: self.mem = int(mem) self.cores = int(cores) self.partition = partition self.docker_image = docker_image class Grid(object): """ Base Grid Workflow manager class """ def __init__(self, name, worker, queue, tmpdir, benchmark_on=None, max_time=None, max_mem=None): self.name = name self.worker = worker self.queue = queue self.tmpdir = tmpdir self.max_time = None self.max_mem = None try: if max_time: self.max_time = int(max_time) except ValueError: print("ERROR: Please provide an integer for the max time: {}".format(max_time)) try: if max_mem: self.max_mem = int(max_mem) except ValueError: print("ERROR: Please provide an integer for the max memory: {}".format(max_mem)) # create the folder if it does not already exist for temp directory if not os.path.isdir(self.tmpdir): os.makedirs(self.tmpdir) self.task_data = dict() def _get_grid_task_settings(self, kwargs, depends): """ Get the resources required to run this task on the grid """ # check for the required keywords requires=[] for key in ["time","mem","cores"]: try: requires.append(kwargs[key]) except KeyError: raise KeyError(key+" is a required keyword argument for a grid task") # check for optional keyword try: requires.append(kwargs["partition"]) except KeyError: requires.append(None) try: requires.append(kwargs["docker_image"]) except KeyError: requires.append(None) requires+=[depends] jobrequires=GridJobRequires(*requires) # add the max time and memory overrides jobrequires.time=[jobrequires.time,self.max_time] jobrequires.mem=[jobrequires.mem,self.max_mem] return (jobrequires, self.tmpdir) def do(self, task, **kwargs): """Accepts the following extra arguments: :param time: The maximum time in minutes allotted to run the command :type time: int :param mem: The maximum memory in megabytes allocated to run the command :type mem: int :param cores: The number of CPU cores allocated to the job :type cores: int :param partition: The grid partition to send this job to :type partition: str """ self.add_task(task, **kwargs) def add_task(self, task, **kwargs): """Accepts the following extra arguments: :keyword time: The maximum time in minutes allotted to run the command :type time: int :keyword mem: The maximum memory in megabytes allocated to run the command :type mem: int :keyword cores: The number of CPU cores allocated to the job :type cores: int :keyword partition: The grid partition to send this job to :type partition: str """ self.task_data[task.task_no] = self._get_grid_task_settings(kwargs, task.depends) def runner(self, workflow, jobs=1, grid_jobs=1): runner = runners.GridRunner(workflow) runner.add_worker(runners.ParallelLocalWorker, name="local", rate=jobs, default=True) runner.add_worker(self.worker, name=self.name, rate=grid_jobs) runner.routes.update(( ( task_no, (self.name, list(extra)+[self.queue, workflow._reporter]) ) for task_no, extra in six.iteritems(self.task_data) )) return runner class GridQueue(object): def __init__(self, partition, benchmark_on=None): # check for short/long partitions if not isinstance(partition, list): partition = [x.strip() for x in partition.split(",")] try: self.partition_short, self.partition_long, self.partition_cutoff = partition self.partition_cutoff = int(self.partition_cutoff) except ValueError: self.partition_short = partition[0] self.partition_long = partition[0] self.partition_cutoff = 0 # this is the refresh rate for checking the queue, in seconds self.refresh_rate = 10*60 # this is the rate for checking the job status, in seconds self.check_job_rate = 60 # this is the number of minutes to wait if there is an time out # socket error returned from the scheduler when running a command self.timeout_sleep = 5*60 # this is the number of times to retry after a timeout error self.timeout_retry_max = 3 # this is the number of seconds to wait after job submission self.submit_sleep = 5 # this is the last time the queue was checked self.last_check = time.time() self.sacct = None # create a lock for jobs in queue self.lock_status = threading.Lock() self.lock_submit = threading.Lock() # set if benchmarking should be run self.benchmark_on = benchmark_on @staticmethod def submit_command(grid_script): raise NotImplementedError def submit_template(self): raise NotImplementedError def job_failed(self,status): raise NotImplementedError def job_stopped(self,status): raise NotImplementedError def refresh_queue_status(self): raise NotImplementedError def job_memkill(self, status, jobid, memory): return False def job_timeout(self, status, jobid, time): return False def get_job_status_from_stderr(self, error_file, grid_job_status, grid_jobid): return grid_job_status def get_partition(self, time, partition): """ Get the partition for the task based on the time requested """ # if a partition is already set for the task, use that partition if not partition is None: return partition if time > self.partition_cutoff: return self.partition_long else: return self.partition_short def get_queue_status(self, refresh=None): """ Get the queue accounting stats """ # lock to prevent race conditions with status update self.lock_status.acquire() # check the last time the queue was captured and refresh if set current_time = time.time() if ( current_time - self.last_check > self.refresh_rate ) or refresh or self.sacct is None: self.last_check = current_time logging.info("Getting latest queue info to refresh job status") self.sacct = self.refresh_queue_status() self.lock_status.release() return self.sacct def get_all_stats_for_jobid(self,jobid): """ Get all the stats for a specific job id """ # use the existing stats, to get the information for the jobid try: job_stats=list(filter(lambda x: x[0].startswith(jobid),self.get_queue_status())) except IndexError: job_stats=[] # if the job stats are not found for the job, return an NA state if not job_stats: job_stats=[[jobid,"Pending","NA","NA","NA"]] return job_stats def get_job_status(self, jobid): """ Check the status of the job """ info=self.get_all_stats_for_jobid(jobid) return info[0][1] def record_benchmark(self, jobid, task_number, reporter): """ Check the benchmarking stats of the grid id """ # check if benchmarking is set to off if not self.benchmark_on: logging.info("Benchmarking is set to off") return reporter.task_grid_status(task_number,jobid,"Getting benchmarking data") status, cpus, elapsed, memory = self.get_benchmark(jobid) logging.info("Benchmark information for job id %s:\nElapsed Time: %s \nCores: %s\nMemory: %s MB", task_number, elapsed, cpus, memory) reporter.task_grid_status(task_number,jobid,"Final status of "+status) def get_benchmark(self, jobid, wait=None): """ Get the benchmarking data for the jobid """ # if the job is not shown to have finished running then # wait for the next queue refresh status=self.get_job_status(jobid) if wait or not (self.job_stopped(status) or self.job_failed(status)): wait_time = abs(self.refresh_rate - (time.time() - self.last_check)) + 10 time.sleep(wait_time) info=self.get_all_stats_for_jobid(jobid) try: status=info[0][1] except IndexError: status="Unknown" try: cpus=info[0][2] except IndexError: cpus="NA" try: elapsed=info[0][3] except IndexError: elapsed="NA" # get the memory max from the batch line which is the second line of output try: memory=info[0][4] except IndexError: memory="NA" if "K" in memory: # if memory is in KB, convert to MB memory="{:.1f}".format(float(memory.replace("K",""))/1024.0) elif "M" in memory: memory="{:.1f}".format(float(memory.replace("M",""))) elif "G" in memory: # if memory is in GB, convert to MB memory="{:.1f}".format(float(memory.replace("G",""))*1024.0) return status, cpus, elapsed, memory def run_grid_command(self,command): """ Run the grid command and check for errors """ error=None if six.callable(command): try: logging.debug("Running grid submit command") stdout=command() except StandardError as err: error=err.output stdout=error or "error" else: try: logging.debug("Running grid command: %s"," ".join(command)) stdout=subprocess.check_output(command, stderr=subprocess.STDOUT).decode('utf-8') except subprocess.CalledProcessError as err: error=err.output.decode('utf-8') stdout=error or "error" timeout_error=False if error and "error" in error and "Socket timed out on send/recv operation" in error: # check for a socket timeout error timeout_error=True return stdout, timeout_error def run_grid_command_resubmit(self,command): """ Run this grid command, check for error, resubmit if needed """ # run the grid command stdout, timeout_error = self.run_grid_command(command) # retry if timeout error present after wait resubmissions = 0 if timeout_error and resubmissions < self.timeout_retry_max: resubmissions+=1 # wait before retrying logging.warning("Unable to run grid command, waiting and retrying") time.sleep(self.timeout_sleep) stdout, timeout_error = self.run_grid_command(command) return stdout @staticmethod def job_submission_failed(jobid): """ Check if the job failed in submission and did not get an id """ return True if not jobid.isdigit() else False @staticmethod def get_job_id_from_submit_output(stdout): try: # search for the decimal job id at any location in stdout jobid=re.findall(r'\d+',stdout)[0] except IndexError: jobid="error" return jobid def submit_job(self,grid_script): """ Submit the grid jobs and return the grid job id """ # lock so only one task submits jobs to the queue at a time self.lock_submit.acquire() # submit the job and get the grid id logging.debug("Submitting job to grid") stdout=self.run_grid_command_resubmit(self.submit_command(grid_script)) # get the job id from the stdout jobid=self.get_job_id_from_submit_output(stdout) # check the jobid for a submission failed if self.job_submission_failed(jobid): logging.error("Unable to submit job to queue: "+stdout) # pause for the scheduler time.sleep(self.submit_sleep) self.lock_submit.release() return jobid def create_grid_script(self,partition,cpus,minutes,memory,command,taskid,dir,docker_image): """ Create a grid script from the template also creating temp stdout and stderr files """ # create temp files for stdout, stderr, and return code handle_out, out_file=tempfile.mkstemp(suffix=".out",prefix="task_"+str(taskid)+"_",dir=dir) os.close(handle_out) handle_err, error_file=tempfile.mkstemp(suffix=".err",prefix="task_"+str(taskid)+"_",dir=dir) os.close(handle_err) handle_rc, rc_file=tempfile.mkstemp(suffix=".rc",prefix="task_"+str(taskid)+"_",dir=dir) os.close(handle_rc) # add the remaining sections to the bash template bash_template = string.Template("\n".join(["#!/bin/bash "] + self.submit_template() + ["${command}", "${rc_command}"])) # convert the minutes to the time string "HH:MM:00" hours, remaining_minutes = divmod(minutes, 60) time = "{:02d}:{:02d}:00".format(hours, remaining_minutes) bash=bash_template.substitute(partition=partition,cpus=cpus,time=time, memory=memory,command=command,output=out_file,error=error_file,rc_command="export RC=$? ; echo $RC > "+rc_file+" ; bash -c 'exit $RC'") file_handle, new_file=tempfile.mkstemp(suffix=".bash",prefix="task_"+str(taskid)+"_",dir=dir) os.write(file_handle,bytearray(bash, 'utf-8')) os.close(file_handle) return new_file, out_file, error_file, rc_file class GridWorker(threading.Thread): """ Base Grid Worker class """ def __init__(self, work_q, result_q, lock, reporter): super(GridWorker, self).__init__() self.daemon = True self.logger = runners.logger self.work_q = work_q self.result_q = result_q self.lock = lock self.reporter = reporter @staticmethod def appropriate_q_class(*args, **kwargs): return six.moves.queue.Queue(*args, **kwargs) @staticmethod def appropriate_lock(): return threading.Lock() def run(self): return runners.worker_run_loop(self.work_q, self.result_q, self.run_task_by_type, self.reporter, self.lock) @classmethod def run_task_by_type(cls, task, extra): # if any of the tasks are a function, then use pickle interface if list(filter(six.callable,task.actions)): return cls.run_task_function(task, extra) else: return cls.run_task_command(task, extra) @classmethod def run_task_function(cls, task, extra): (perf, tmpdir, grid_queue, reporter) = extra # create a script to run the python function pickle_script = picklerunner.PickleScript(task, tmpdir, "task_"+str(task.task_no)) pickle_task = pickle_script.create_task() # run the task as a command result = cls.run_task_command(pickle_task, extra) # decode the result result = pickle_script.result(result) return result @classmethod def run_task_command(cls, task, extra): (perf, tmpdir, grid_queue, reporter) = extra # report the task has started reporter.task_running(task.task_no) # create a script and stdout/stderr files for this task commands="\n".join(task.actions) logging.info("Running commands for task id %s:\n%s", task.task_no, commands) resubmission = 0 cores, time, memory, partition, docker_image = perf.cores, perf.time, perf.mem, perf.partition, perf.docker_image jobid, out_file, error_file, rc_file = cls.submit_grid_job(cores, time, memory, partition, tmpdir, commands, task, grid_queue, reporter, docker_image) # monitor job if submission was successful result, job_final_status = cls.check_submission_then_monitor_grid_job(grid_queue, task, jobid, out_file, error_file, rc_file, reporter) # if a timeout or memory max, resubmit at most three times while ( grid_queue.job_timeout(job_final_status, jobid, time) or grid_queue.job_memkill(job_final_status, jobid, memory) ) and resubmission < 3: resubmission+=1 # increase the memory or the time if grid_queue.job_timeout(job_final_status, jobid, time): time = "({})*2".format(time) if isinstance(time,str) else time*2 logging.info("Resubmission number %s of grid job for task id %s with 2x more time: %s minutes", resubmission, task.task_no, time) reporter.task_grid_status(task.task_no,jobid,"Resubmitting due to time out") elif grid_queue.job_memkill(job_final_status, jobid, memory): memory = "({})*2".format(memory) if isinstance(memory,str) else memory*2 logging.info("Resubmission number %s of grid job for task id %s with 2x more memory: %s MB", resubmission, task.task_no, memory) reporter.task_grid_status(task.task_no,jobid,"Resubmitting due to max memory") jobid, out_file, error_file, rc_file = cls.submit_grid_job(cores, time, memory, partition, tmpdir, commands, task, grid_queue, reporter, docker_image) # monitor job if submission was successful result, job_final_status = cls.check_submission_then_monitor_grid_job(grid_queue, task, jobid, out_file, error_file, rc_file, reporter) # get the benchmarking data if the job was submitted if not grid_queue.job_submission_failed(jobid): grid_queue.record_benchmark(jobid, task.task_no, reporter) return result @classmethod def submit_grid_job(cls, cores, time, memory, partition, tmpdir, commands, task, grid_queue, reporter, docker_image): # evaluate the time/memory requests for the job time, memory = cls.evaluate_resource_requests(time, memory) # get the partition for the task current_partition = grid_queue.get_partition(time, partition) # create the grid bash script grid_script, out_file, error_file, rc_file = grid_queue.create_grid_script(current_partition, cores, time, memory, commands, task.task_no, tmpdir, docker_image) logging.info("Created grid files for task id %s: %s, %s, %s, %s", task.task_no, grid_script, out_file, error_file, rc_file) # submit the job jobid = grid_queue.submit_job(grid_script) logging.info("Submitted job for task id %s: grid id %s", task.task_no, jobid) if not grid_queue.job_submission_failed(jobid): reporter.task_grid_status(task.task_no,jobid,"Submitted") return jobid, out_file, error_file, rc_file @staticmethod def log_grid_output(taskid, file, file_type): """ Write the grid stdout/stderr files to the log """ try: lines=open(file).readlines() except EnvironmentError: lines=[] logging.info("Grid %s from task id %s:\n%s",taskid, file_type, "".join(lines)) @staticmethod def get_return_code(file): """ Read the return code from the file """ try: line=open(file).readline().rstrip() except (EnvironmentError, TypeError): line="" return line @staticmethod def evaluate_resource_requests(time,mem): """ Evaluate the time/memory requests for the grid job, allowing for ints or formulas """ # allow for optional max time and memory if not isinstance(time,list): time=[time] if not isinstance(mem,list): mem=[mem] try: time[0]=eval(str(time[0])) except TypeError: raise TypeError("Unable to evaluate time request for task: "+ time) try: mem[0]=eval(str(mem[0])) except TypeError: raise TypeError("Unable to evaluate memory request for task: "+ mem) # check for override with max if time[-1] and time[0] > time[-1]: logging.info("Using override of max time from {0} reset to {1}".format(time[0],time[-1])) time=time[-1] else: time=time[0] if mem[-1] and mem[0] > mem[-1]: logging.info("Using override of max mem from {0} reset to {1}".format(mem[0],mem[-1])) mem=mem[-1] else: mem=mem[0] return time, mem @classmethod def check_submission_then_monitor_grid_job(cls, grid_queue, task, grid_jobid, out_file, error_file, rc_file, reporter): # monitor job if submission was successful if not grid_queue.job_submission_failed(grid_jobid): result, job_final_status = cls.monitor_grid_job(grid_queue, task, grid_jobid, out_file, error_file, rc_file, reporter) else: job_final_status = "SUBMIT FAILED" # get the anadama task result result=runners._get_task_result(task) # add the extra error result = result._replace(error=str(result.error)+"Unable to submit job to queue.") return result, job_final_status @classmethod def monitor_grid_job(cls, grid_queue, task, grid_jobid, out_file, error_file, rc_file, reporter): # poll to check for status grid_job_status=None for tries in itertools.count(1): # only check status at intervals time.sleep(grid_queue.check_job_rate) # check the queue stats grid_job_status = grid_queue.get_job_status(grid_jobid) reporter.task_grid_status_polling(task.task_no,grid_jobid,grid_job_status) logging.info("Status for job id %s with grid id %s is %s",task.task_no, grid_jobid,grid_job_status) if grid_queue.job_stopped(grid_job_status): logging.info("Grid status for job id %s shows it has stopped",task.task_no) break # check if the return code file is written if rc_file and os.path.getsize(rc_file) > 0: logging.info("Return code file for job id %s shows it has stopped",task.task_no) break # check if a grid error is written to the output file grid_job_status = grid_queue.get_job_status_from_stderr(error_file, grid_job_status, grid_jobid) # write the stdout and stderr to the log if out_file: cls.log_grid_output(task.task_no, out_file, "standard output") if error_file: cls.log_grid_output(task.task_no, error_file, "standard error") if rc_file: cls.log_grid_output(task.task_no, rc_file, "return code") # check the return code extra_error="" return_code=cls.get_return_code(rc_file) if return_code and not return_code == "0": extra_error="\nReturn Code Error: " + return_code # check the queue status if grid_queue.job_failed(grid_job_status): extra_error+="\nGrid Status Error: " + grid_job_status # get the anadama task result result=runners._get_task_result(task) # add the extra error if found if extra_error: result = result._replace(error=str(result.error)+extra_error) return result, grid_job_status

12703fc35cb13a111f4d4a67edef7f21df62d678

py

Python

src/rubrix/sdk/models/text_classification_record_inputs.py

sakares/rubrix

791ffb29815b5d24f2bbbb0fa422f85f8b30098f

src/rubrix/sdk/models/text_classification_record_inputs.py

sakares/rubrix

791ffb29815b5d24f2bbbb0fa422f85f8b30098f

src/rubrix/sdk/models/text_classification_record_inputs.py

sakares/rubrix

791ffb29815b5d24f2bbbb0fa422f85f8b30098f

# coding=utf-8 # Copyright 2021-present, the Recognai S.L. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import Any, Dict, List, Type, TypeVar, Union, cast import attr from ..types import Unset T = TypeVar("T", bound="TextClassificationRecordInputs") @attr.s(auto_attribs=True) class TextClassificationRecordInputs: """ """ additional_properties: Dict[str, Union[str, List[str]]] = attr.ib( init=False, factory=dict ) def to_dict(self) -> Dict[str, Any]: field_dict: Dict[str, Any] = {} for prop_name, prop in self.additional_properties.items(): if isinstance(prop, list): field_dict[prop_name] = prop else: field_dict[prop_name] = prop field_dict.update({}) return field_dict @classmethod def from_dict(cls: Type[T], src_dict: Dict[str, Any]) -> T: d = src_dict.copy() text_classification_record_inputs = cls() additional_properties = {} for prop_name, prop_dict in d.items(): def _parse_additional_property(data: Any) -> Union[str, List[str]]: data = None if isinstance(data, Unset) else data additional_property: Union[str, List[str]] try: additional_property = cast(List[str], data) return additional_property except: # noqa: E722 pass return cast(Union[str, List[str]], data) additional_property = _parse_additional_property(prop_dict) additional_properties[prop_name] = additional_property text_classification_record_inputs.additional_properties = additional_properties return text_classification_record_inputs @property def additional_keys(self) -> List[str]: return list(self.additional_properties.keys()) def __getitem__(self, key: str) -> Union[str, List[str]]: return self.additional_properties[key] def __setitem__(self, key: str, value: Union[str, List[str]]) -> None: self.additional_properties[key] = value def __delitem__(self, key: str) -> None: del self.additional_properties[key] def __contains__(self, key: str) -> bool: return key in self.additional_properties

7be89b1878e37a1d27590ceff7093e0168541eab

py

Python

ibsng/handler/group/del_group.py

ParspooyeshFanavar/pyibsng

d48bcf4f25e3f23461528bf0ff8870cc3d537444

2018-03-06T10:16:36.000Z

2021-12-05T12:43:10.000Z

ibsng/handler/group/del_group.py

ParspooyeshFanavar/pyibsng

d48bcf4f25e3f23461528bf0ff8870cc3d537444

2018-03-06T10:27:08.000Z

2022-01-02T15:21:27.000Z

ibsng/handler/group/del_group.py

ParspooyeshFanavar/pyibsng

d48bcf4f25e3f23461528bf0ff8870cc3d537444

2018-01-06T16:28:31.000Z

2018-09-17T19:47:19.000Z

"""Delete group API method.""" from ibsng.handler.handler import Handler class delGroup(Handler): """Delete group method class.""" def control(self): """Validate inputs after setup method. :return: None :rtype: None """ self.is_valid(self.group_name, str) def setup(self, group_name): """Setup required parameters. :param str group_name: group name :return: None :rtype: None """ self.group_name = group_name

a91aace4b4b73696a2072b4c6c0785803cdba80a

py

Python

PythonCode/ModelPredictorModule.py

janw23/Ballance

f085d2d03c31a8e3be74d4c82300f571cc3cad65

2019-05-16T21:24:24.000Z

2019-05-19T15:24:03.000Z

PythonCode/ModelPredictorModule.py

janw23/Ballance

f085d2d03c31a8e3be74d4c82300f571cc3cad65

PythonCode/ModelPredictorModule.py

janw23/Ballance

f085d2d03c31a8e3be74d4c82300f571cc3cad65

import math import MathModule as MM #modul przeiwdywania przyszlej pozycji kulki class ModelPredictor: eta = 0.00001 #stala 'niedokladnosci' obliczen modelDelay = 0.2 #czas opoznienia reakcji rzeczywistego urzadzenia gravity = 9.81 anglePerServoSignal = 7.5 * 0.001 * math.pi / 180 #kat nachylenia plyty przypadajacy na sygnal pozycji serw boardSize = 1 / 0.2 #dlugosc boku platformy servo_speed = 10000 #'prawdziwa' szybkosc ruchu serw friction_static = (0.04 * gravity) ** 2 #wspolczynnik tarcia statycznego friction_dynamic = 0.002 * gravity #wspolczynnik tarcia dynamicznego def __init__(self): self.position = [0.1, 0.1] self.velocity = [0.0, 0.0] self.servo = [0.0, 0.0] self.servo_target = [0.0, 0.0] #docelowa pozycja serwa #self.signal_delayer = MM.SignalDelay(4, (0.5, 0.5)) def GetPosition(self): return tuple(self.position) #self.signal_delayer.get() def Reset(self): self.position = [0.5, 0.5] self.velocity = [0.0, 0.0] def SetServos(self, values): self.servo_target[0] = values[0] self.servo_target[1] = -values[1] #aktualizuje pozycje serw def updateServos(self, deltaTime): for i in range(2): #tylko 2 serwa movement_dir = MM.sign(self.servo_target[i] - self.servo[i]) self.servo[i] += ModelPredictor.servo_speed * movement_dir * deltaTime if movement_dir > 0: self.servo[i] = min(self.servo[i], self.servo_target[i]) elif movement_dir < 0: self.servo[i] = max(self.servo[i], self.servo_target[i]) self.servo[i] = round(self.servo[i]) def update(self, deltaTime): ModelPredictor.updateServos(self, deltaTime) accel_x = math.sin(self.servo[0] * ModelPredictor.anglePerServoSignal) * ModelPredictor.gravity accel_y = math.sin(self.servo[1] * ModelPredictor.anglePerServoSignal) * ModelPredictor.gravity #jesli kulka jest nieruchoma if MM.sqrMagnitude(self.velocity) <= ModelPredictor.eta: #jesli sila dzialajaca na kulke jest za mala, zeby pokonac sile tarcia statycznego if MM.sqrMagnitude(accel_x, accel_y) <= ModelPredictor.friction_static: accel_x = 0 accel_y = 0 #jesli kulka jest ruchoma else: #aplikowanie tarcia dynamicznego direction = MM.normalized(self.velocity) accel_x -= direction[0] * ModelPredictor.friction_dynamic accel_y -= direction[1] * ModelPredictor.friction_dynamic self.velocity[0] += accel_x * deltaTime self.velocity[1] += accel_y * deltaTime self.position[0] += self.velocity[0] * ModelPredictor.boardSize * deltaTime self.position[1] += self.velocity[1] * ModelPredictor.boardSize * deltaTime #self.signal_delayer.push(tuple(self.position)) #self.signal_delayer.tick()

d9fa2107c380e2b8cf924c8b99282e9f1a5ef054

py

Python

home/migrations/0018_auto_20210806_1501.py

SoumyaRanjanPatnaik/Health-And-Safety-Dashboard

ad5ba26f280db0f199a0a4598959c0ec0b4d6515

2021-07-17T07:55:48.000Z

2021-07-17T07:55:48.000Z

home/migrations/0018_auto_20210806_1501.py

SoumyaRanjanPatnaik/Health-And-Safety-Dashboard

ad5ba26f280db0f199a0a4598959c0ec0b4d6515

home/migrations/0018_auto_20210806_1501.py

SoumyaRanjanPatnaik/Health-And-Safety-Dashboard

ad5ba26f280db0f199a0a4598959c0ec0b4d6515

# Generated by Django 3.2.5 on 2021-08-06 15:01 from django.db import migrations, models import django.utils.timezone class Migration(migrations.Migration): dependencies = [ ('home', '0017_auto_20210806_1420'), ] operations = [ migrations.AddField( model_name='log', name='date', field=models.DateTimeField(auto_now_add=True, default=django.utils.timezone.now), preserve_default=False, ), migrations.AddField( model_name='log', name='time', field=models.DateTimeField(auto_now_add=True, default=django.utils.timezone.now), preserve_default=False, ), ]

8f73d94fd3cdcb5b3078226c573445355eeb58ae

py

Python

bcs-ui/backend/resources/workloads/statefulset/formatter.py

laodiu/bk-bcs

2a956a42101ff6487ff521fb3ef429805bfa7e26

2019-06-25T03:20:46.000Z

2022-03-31T12:14:33.000Z

bcs-ui/backend/resources/workloads/statefulset/formatter.py

laodiu/bk-bcs

2a956a42101ff6487ff521fb3ef429805bfa7e26

2019-06-27T06:03:44.000Z

2022-03-31T12:10:01.000Z

bcs-ui/backend/resources/workloads/statefulset/formatter.py

laodiu/bk-bcs

2a956a42101ff6487ff521fb3ef429805bfa7e26

2019-06-25T03:26:05.000Z

2022-03-31T07:52:03.000Z

# -*- coding: utf-8 -*- """ Tencent is pleased to support the open source community by making 蓝鲸智云PaaS平台社区版 (BlueKing PaaS Community Edition) available. Copyright (C) 2017-2021 THL A29 Limited, a Tencent company. All rights reserved. Licensed under the MIT License (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://opensource.org/licenses/MIT Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ from typing import Dict from backend.resources.workloads.common.formatter import WorkloadFormatter class StatefulSetFormatter(WorkloadFormatter): """ StatefulSet 格式化 """ def format_dict(self, resource_dict: Dict) -> Dict: return self.format_common_dict(resource_dict)

e0406c39fb352e91c6d82c7d9b9c7e3a34e61d8a

py

Python

train.py

neeleshnegi/chat-bot

1800cc0896ca30f573a3c8baa9719685908c1d5b

train.py

neeleshnegi/chat-bot

1800cc0896ca30f573a3c8baa9719685908c1d5b

train.py

neeleshnegi/chat-bot

1800cc0896ca30f573a3c8baa9719685908c1d5b

import numpy as np import tensorflow as tf import argparse import time, datetime import os import pickle import sys from utils import TextLoader from model import Model def main(): assert sys.version_info >= (3, 3), \ "Must be run in Python 3.3 or later. You are running {}".format(sys.version) parser = argparse.ArgumentParser() parser.add_argument('--data_dir', type=str, default='data/scotus', help='data directory containing input.txt') parser.add_argument('--save_dir', type=str, default='models/new_save', help='directory for checkpointed models (load from here if one is already present)') parser.add_argument('--block_size', type=int, default=2048, help='number of cells per block') parser.add_argument('--num_blocks', type=int, default=3, help='number of blocks per layer') parser.add_argument('--num_layers', type=int, default=3, help='number of layers') parser.add_argument('--model', type=str, default='gru', help='rnn, gru, lstm or nas') parser.add_argument('--batch_size', type=int, default=40, help='minibatch size') parser.add_argument('--seq_length', type=int, default=40, help='RNN sequence length') parser.add_argument('--num_epochs', type=int, default=50, help='number of epochs') parser.add_argument('--save_every', type=int, default=5000, help='save frequency') parser.add_argument('--grad_clip', type=float, default=5., help='clip gradients at this value') parser.add_argument('--learning_rate', type=float, default=1e-5, help='learning rate') parser.add_argument('--decay_rate', type=float, default=0.975, help='how much to decay the learning rate') parser.add_argument('--decay_steps', type=int, default=100000, help='how often to decay the learning rate') parser.add_argument('--set_learning_rate', type=float, default=-1, help='reset learning rate to this value (if greater than zero)') args = parser.parse_args() train(args) def train(args): data_loader = TextLoader(args.data_dir, args.batch_size, args.seq_length) args.vocab_size = data_loader.vocab_size load_model = False if not os.path.exists(args.save_dir): print("Creating directory %s" % args.save_dir) os.mkdir(args.save_dir) elif (os.path.exists(os.path.join(args.save_dir, 'config.pkl'))): ckpt = tf.train.get_checkpoint_state(args.save_dir) if ckpt and ckpt.model_checkpoint_path: with open(os.path.join(args.save_dir, 'config.pkl'), 'rb') as f: saved_args = pickle.load(f) args.block_size = saved_args.block_size args.num_blocks = saved_args.num_blocks args.num_layers = saved_args.num_layers args.model = saved_args.model print("Found a previous checkpoint. Overwriting model description arguments to:") print(" model: {}, block_size: {}, num_blocks: {}, num_layers: {}".format( saved_args.model, saved_args.block_size, saved_args.num_blocks, saved_args.num_layers)) load_model = True with open(os.path.join(args.save_dir, 'config.pkl'), 'wb') as f: pickle.dump(args, f) with open(os.path.join(args.save_dir, 'chars_vocab.pkl'), 'wb') as f: pickle.dump((data_loader.chars, data_loader.vocab), f) print("Building the model") model = Model(args) print("Total trainable parameters: {:,d}".format(model.trainable_parameter_count())) os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' config = tf.ConfigProto(log_device_placement=False) with tf.Session(config=config) as sess: tf.global_variables_initializer().run() saver = tf.train.Saver(model.save_variables_list(), max_to_keep=3) if (load_model): print("Loading saved parameters") saver.restore(sess, ckpt.model_checkpoint_path) global_epoch_fraction = sess.run(model.global_epoch_fraction) global_seconds_elapsed = sess.run(model.global_seconds_elapsed) if load_model: print("Resuming from global epoch fraction {:.3f}," " total trained time: {}, learning rate: {}".format( global_epoch_fraction, datetime.timedelta(seconds=float(global_seconds_elapsed)), sess.run(model.lr))) if (args.set_learning_rate > 0): sess.run(tf.assign(model.lr, args.set_learning_rate)) print("Reset learning rate to {}".format(args.set_learning_rate)) data_loader.cue_batch_pointer_to_epoch_fraction(global_epoch_fraction) initial_batch_step = int((global_epoch_fraction - int(global_epoch_fraction)) * data_loader.total_batch_count) epoch_range = (int(global_epoch_fraction), args.num_epochs + int(global_epoch_fraction)) writer = tf.summary.FileWriter(args.save_dir, graph=tf.get_default_graph()) outputs = [model.cost, model.final_state, model.train_op, model.summary_op] global_step = epoch_range[0] * data_loader.total_batch_count + initial_batch_step avg_loss = 0 avg_steps = 0 try: for e in range(*epoch_range): state = sess.run(model.zero_state) batch_range = (initial_batch_step, data_loader.total_batch_count) initial_batch_step = 0 for b in range(*batch_range): global_step += 1 if global_step % args.decay_steps == 0: current_learning_rate = sess.run(model.lr) current_learning_rate *= args.decay_rate sess.run(tf.assign(model.lr, current_learning_rate)) print("Decayed learning rate to {}".format(current_learning_rate)) start = time.time() x, y = data_loader.next_batch() feed = {model.input_data: x, model.targets: y} model.add_state_to_feed_dict(feed, state) train_loss, state, _, summary = sess.run(outputs, feed) elapsed = time.time() - start global_seconds_elapsed += elapsed writer.add_summary(summary, e * batch_range[1] + b + 1) if avg_steps < 100: avg_steps += 1 avg_loss = 1 / avg_steps * train_loss + (1 - 1 / avg_steps) * avg_loss print("{:,d} / {:,d} (epoch {:.3f} / {}), loss {:.3f} (avg {:.3f}), {:.3f}s" \ .format(b, batch_range[1], e + b / batch_range[1], epoch_range[1], train_loss, avg_loss, elapsed)) if (e * batch_range[1] + b + 1) % args.save_every == 0 \ or (e == epoch_range[1] - 1 and b == batch_range[1] - 1): save_model(sess, saver, model, args.save_dir, global_step, data_loader.total_batch_count, global_seconds_elapsed) except KeyboardInterrupt: print() finally: writer.flush() global_step = e * data_loader.total_batch_count + b save_model(sess, saver, model, args.save_dir, global_step, data_loader.total_batch_count, global_seconds_elapsed) def save_model(sess, saver, model, save_dir, global_step, steps_per_epoch, global_seconds_elapsed): global_epoch_fraction = float(global_step) / float(steps_per_epoch) checkpoint_path = os.path.join(save_dir, 'model.ckpt') print("Saving model to {} (epoch fraction {:.3f})...".format(checkpoint_path, global_epoch_fraction), end='', flush=True) sess.run(tf.assign(model.global_epoch_fraction, global_epoch_fraction)) sess.run(tf.assign(model.global_seconds_elapsed, global_seconds_elapsed)) saver.save(sess, checkpoint_path, global_step = global_step) print("\rSaved model to {} (epoch fraction {:.3f}). ".format(checkpoint_path, global_epoch_fraction)) if __name__ == '__main__': main()

1c97eba874cd83943cd9affa8bb930ddb4922376

py

Python

tests/unit/fake_data_root/openstack/var/lib/juju/agents/unit-neutron-openvswitch-1/charm/hooks/neutron_ovs_context.py

KellenRenshaw/hotsos

e3fc51ab7f8af606a5846a3486a7fda23d761583

2016-04-17T04:00:56.000Z

2020-08-19T12:03:18.000Z

tests/unit/fake_data_root/openstack/var/lib/juju/agents/unit-neutron-openvswitch-1/charm/hooks/neutron_ovs_context.py

KellenRenshaw/hotsos

e3fc51ab7f8af606a5846a3486a7fda23d761583

2021-10-01T18:18:17.000Z

2022-03-29T12:23:20.000Z

tests/unit/fake_data_root/openstack/var/lib/juju/agents/unit-neutron-openvswitch-1/charm/hooks/neutron_ovs_context.py

KellenRenshaw/hotsos

e3fc51ab7f8af606a5846a3486a7fda23d761583

2016-03-23T11:12:19.000Z

2021-12-17T18:24:16.000Z

# Copyright 2016 Canonical Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import uuid from charmhelpers.core.hookenv import ( config, log, relation_get, relation_ids, related_units, unit_get, network_get_primary_address, ) from charmhelpers.core.host import ( CompareHostReleases, is_container, lsb_release, write_file, ) from charmhelpers.contrib.openstack import context from charmhelpers.contrib.openstack.utils import ( get_host_ip, ) from charmhelpers.contrib.network.ip import ( get_address_in_network, ) from charmhelpers.contrib.openstack.context import ( OSContextGenerator, NeutronAPIContext, ) from charmhelpers.contrib.openstack.utils import ( os_release, CompareOpenStackReleases, ) IPTABLES_HYBRID = 'iptables_hybrid' OPENVSWITCH = 'openvswitch' VALID_FIREWALL_DRIVERS = (IPTABLES_HYBRID, OPENVSWITCH) NFG_LOG_RATE_LIMIT_MIN = 100 NFG_LOG_BURST_LIMIT_MIN = 25 def _get_firewall_driver(ovs_ctxt): ''' Determine the firewall driver to use based on configuration, OpenStack and Ubuntu releases. @returns str: firewall driver to use for OpenvSwitch ''' driver = config('firewall-driver') or IPTABLES_HYBRID release = lsb_release()['DISTRIB_CODENAME'] if driver not in VALID_FIREWALL_DRIVERS: return IPTABLES_HYBRID if driver == IPTABLES_HYBRID and ovs_ctxt['enable_nsg_logging']: msg = "NSG logging can not be enabled - need to set " \ "firewall driver to 'openvswitch' explicitly" log(msg, "WARN") if (driver == OPENVSWITCH and CompareHostReleases(release) < 'xenial'): # NOTE(jamespage): Switch back to iptables_hybrid for # Ubuntu releases prior to Xenial due # to requirements for Linux >= 4.4 and # Open vSwitch >= 2.5 return IPTABLES_HYBRID return driver def get_nsg_log_path(desired_nsg_log_path): if not desired_nsg_log_path: # None means "we need to use syslog" - no need # to check anything on filesystem return None dst_dir, _ = os.path.split(desired_nsg_log_path) path_exists = os.path.exists(dst_dir) if not path_exists: log( "Desired NSG log directory {} not exists! " "falling back to syslog".format(dst_dir), "WARN" ) return None if path_exists and os.path.isdir(desired_nsg_log_path): log( "Desired NSG log path {} should be file, not directory! " "falling back to syslog".format(desired_nsg_log_path), "WARN" ) return None return desired_nsg_log_path def validate_nfg_log_path(desired_nfg_log_path): if not desired_nfg_log_path: # None means "we need to use syslog" - no need # to check anything on filesystem return None dst_dir, _ = os.path.split(desired_nfg_log_path) path_exists = os.path.exists(dst_dir) if not path_exists: log( "Desired NFG log directory {} not exists! " "falling back to syslog".format(dst_dir), "WARN" ) return None if path_exists and os.path.isdir(desired_nfg_log_path): log( "Desired NFG log path {} should be file, not directory! " "falling back to syslog".format(desired_nfg_log_path), "WARN" ) return None return desired_nfg_log_path class OVSPluginContext(context.NeutronContext): interfaces = [] @property def plugin(self): return 'ovs' @property def network_manager(self): return 'neutron' @property def neutron_security_groups(self): if config('disable-security-groups'): return False neutron_api_settings = NeutronAPIContext()() return neutron_api_settings['neutron_security_groups'] def disable_mlockall(self): ''' Determine if Open vSwitch use of mlockall() should be disabled If the disable-mlockall config option is unset, mlockall will be disabled if running in a container and will default to enabled if not running in a container. ''' disable_mlockall = config('disable-mlockall') if disable_mlockall is None: disable_mlockall = False if is_container(): disable_mlockall = True cmp_release = CompareOpenStackReleases( os_release('neutron-common', base='icehouse')) return (cmp_release >= 'mitaka' and disable_mlockall) def ovs_ctxt(self): # In addition to generating config context, ensure the OVS service # is running and the OVS bridge exists. Also need to ensure # local_ip points to actual IP, not hostname. ovs_ctxt = super(OVSPluginContext, self).ovs_ctxt() if not ovs_ctxt: return {} conf = config() fallback = get_host_ip(unit_get('private-address')) if config('os-data-network'): # NOTE: prefer any existing use of config based networking ovs_ctxt['local_ip'] = \ get_address_in_network(config('os-data-network'), fallback) else: # NOTE: test out network-spaces support, then fallback try: ovs_ctxt['local_ip'] = get_host_ip( network_get_primary_address('data') ) except NotImplementedError: ovs_ctxt['local_ip'] = fallback neutron_api_settings = NeutronAPIContext()() ovs_ctxt['neutron_security_groups'] = self.neutron_security_groups ovs_ctxt['l2_population'] = neutron_api_settings['l2_population'] ovs_ctxt['distributed_routing'] = neutron_api_settings['enable_dvr'] ovs_ctxt['extension_drivers'] = neutron_api_settings[ 'extension_drivers'] ovs_ctxt['overlay_network_type'] = \ neutron_api_settings['overlay_network_type'] ovs_ctxt['polling_interval'] = neutron_api_settings['polling_interval'] ovs_ctxt['rpc_response_timeout'] = \ neutron_api_settings['rpc_response_timeout'] ovs_ctxt['report_interval'] = neutron_api_settings['report_interval'] # TODO: We need to sort out the syslog and debug/verbose options as a # general context helper ovs_ctxt['use_syslog'] = conf['use-syslog'] ovs_ctxt['verbose'] = conf['verbose'] ovs_ctxt['debug'] = conf['debug'] ovs_ctxt['prevent_arp_spoofing'] = conf['prevent-arp-spoofing'] ovs_ctxt['enable_dpdk'] = conf['enable-dpdk'] ovs_ctxt['keepalived_healthcheck_interval'] = \ conf['keepalived-healthcheck-interval'] ovs_ctxt['disable_mlockall'] = self.disable_mlockall() net_dev_mtu = neutron_api_settings.get('network_device_mtu') if net_dev_mtu: # neutron.conf ovs_ctxt['network_device_mtu'] = net_dev_mtu # ml2 conf ovs_ctxt['veth_mtu'] = net_dev_mtu mappings = config('bridge-mappings') if mappings: ovs_ctxt['bridge_mappings'] = ','.join(mappings.split()) sriov_mappings = config('sriov-device-mappings') if sriov_mappings: ovs_ctxt['sriov_device_mappings'] = ( ','.join(sriov_mappings.split()) ) enable_sriov = config('enable-sriov') if enable_sriov: ovs_ctxt['enable_sriov'] = True sriov_numvfs = config('sriov-numvfs') if sriov_numvfs: try: if sriov_numvfs != 'auto': int(sriov_numvfs) except ValueError: ovs_ctxt['sriov_vfs_list'] = sriov_numvfs else: ovs_ctxt['sriov_vfs_blanket'] = sriov_numvfs flat_providers = config('flat-network-providers') if flat_providers: ovs_ctxt['network_providers'] = ','.join(flat_providers.split()) vlan_ranges = config('vlan-ranges') if vlan_ranges: ovs_ctxt['vlan_ranges'] = ','.join(vlan_ranges.split()) ovs_ctxt['enable_nsg_logging'] = \ neutron_api_settings['enable_nsg_logging'] ovs_ctxt['nsg_log_output_base'] = get_nsg_log_path( config('security-group-log-output-base') ) ovs_ctxt['nsg_log_rate_limit'] = \ config('security-group-log-rate-limit') ovs_ctxt['nsg_log_burst_limit'] = \ config('security-group-log-burst-limit') ovs_ctxt['firewall_driver'] = _get_firewall_driver(ovs_ctxt) if ovs_ctxt['firewall_driver'] != OPENVSWITCH: ovs_ctxt['enable_nsg_logging'] = False ovs_ctxt['of_inactivity_probe'] = config('of-inactivity-probe') return ovs_ctxt class ZoneContext(OSContextGenerator): def __call__(self): """Return the 'default_availability_zone' from the principal that this ovs unit is attached to (as a subordinate) :returns: {} if no relation set, or {'availability_zone': availability_zone from principal relation} """ # as ovs is a subordinate charm, it should only have one relation to # its principal charm. Thus we can take the 1st (only) element in each # list. rids = relation_ids('neutron-plugin') ctxt = {} if rids: rid = rids[0] units = related_units(rid) if units: availability_zone = relation_get( 'default_availability_zone', rid=rid, unit=units[0]) if availability_zone: ctxt['availability_zone'] = availability_zone return ctxt class L3AgentContext(OSContextGenerator): def __call__(self): neutron_api_settings = NeutronAPIContext()() ctxt = {} if neutron_api_settings['enable_dvr']: use_dvr_snat = config('use-dvr-snat') agent_mode = 'dvr_snat' if use_dvr_snat else 'dvr' ctxt['agent_mode'] = agent_mode ctxt['use_l3ha'] = neutron_api_settings.get('enable_l3ha', False) if not config('ext-port'): ctxt['external_configuration_new'] = True else: ctxt['agent_mode'] = 'legacy' ctxt['enable_nfg_logging'] = ( neutron_api_settings['enable_nfg_logging'] ) ctxt['nfg_log_output_base'] = validate_nfg_log_path( config('firewall-group-log-output-base') ) ctxt['nfg_log_rate_limit'] = config( 'firewall-group-log-rate-limit' ) if ctxt['nfg_log_rate_limit'] is not None: ctxt['nfg_log_rate_limit'] = max( ctxt['nfg_log_rate_limit'], NFG_LOG_RATE_LIMIT_MIN ) ctxt['nfg_log_burst_limit'] = config( 'firewall-group-log-burst-limit' ) if ctxt['nfg_log_burst_limit'] is not None: ctxt['nfg_log_burst_limit'] = max( ctxt['nfg_log_burst_limit'], NFG_LOG_BURST_LIMIT_MIN ) l3_extension_plugins = neutron_api_settings.get( 'l3_extension_plugins', []) ctxt['l3_extension_plugins'] = ','.join(l3_extension_plugins) return ctxt SHARED_SECRET = "/etc/neutron/secret.txt" def get_shared_secret(): secret = None if not os.path.exists(SHARED_SECRET): secret = str(uuid.uuid4()) write_file(SHARED_SECRET, secret, perms=0o400) else: os.chmod(SHARED_SECRET, 0o400) with open(SHARED_SECRET, 'r') as secret_file: secret = secret_file.read().strip() return secret class SharedSecretContext(OSContextGenerator): def __call__(self): if NeutronAPIContext()()['enable_dvr'] or \ config('enable-local-dhcp-and-metadata'): ctxt = { 'shared_secret': get_shared_secret(), } else: ctxt = {} return ctxt class RemoteRestartContext(OSContextGenerator): def __init__(self, interfaces=None): self.interfaces = interfaces or ['neutron-plugin'] def __call__(self): rids = [] for interface in self.interfaces: rids.extend(relation_ids(interface)) ctxt = {} for rid in rids: for unit in related_units(rid): remote_data = relation_get( rid=rid, unit=unit) for k, v in remote_data.items(): if k.startswith('restart-trigger'): restart_key = k.replace('-', '_') try: ctxt[restart_key].append(v) except KeyError: ctxt[restart_key] = [v] for restart_key in ctxt.keys(): ctxt[restart_key] = '-'.join(sorted(ctxt[restart_key])) return ctxt class APIIdentityServiceContext(context.IdentityServiceContext): def __init__(self): super(APIIdentityServiceContext, self).__init__(rel_name='neutron-plugin-api') def __call__(self): ctxt = super(APIIdentityServiceContext, self).__call__() if not ctxt: return for rid in relation_ids('neutron-plugin-api'): for unit in related_units(rid): rdata = relation_get(rid=rid, unit=unit) ctxt['region'] = rdata.get('region') if ctxt['region']: return ctxt return ctxt

fb7f9fb3181dee5ddc7af47e73dea54387c79eba

py

Python

anjay.py

eechzaan/project

f842f5ceb0e7653c51e410413371a08e3b1b0f62

anjay.py

eechzaan/project

f842f5ceb0e7653c51e410413371a08e3b1b0f62

anjay.py

eechzaan/project

f842f5ceb0e7653c51e410413371a08e3b1b0f62

for i in range(10): print()

1919e4dc4a184f40fb2eb92a78e8ef67161a93b0

py

Python

compilation/threshold-write-image.py

stcastle/shell-detection

cdc49190deae7310db66e56574b6737771821f31

2018-03-01T01:14:21.000Z

2020-04-17T08:49:32.000Z

compilation/threshold-write-image.py

SamTCastle/shell-detection

cdc49190deae7310db66e56574b6737771821f31

compilation/threshold-write-image.py

SamTCastle/shell-detection

cdc49190deae7310db66e56574b6737771821f31

''' threshold_write_image.py Experiment with OpenCV for FITS files. http://docs.opencv.org/trunk/doc/py_tutorials/py_tutorials.html Author: S.T. Castle Created: 20150226 ''' import numpy as np import cv2 import os.path def threshold(name, orig_img, blockSize, c): ''' name: original filename of the image orig_img: The image to be processed. blockSize: size of the pixel neighborhood for adaptive thresholding. c: Correction to apply before thresholding. ''' img = np.ndarray.copy(orig_img) # Copy of original for smoothing. # Split filename and extension. spl = os.path.splitext(name) name_pref = spl[0] # Name prefix. # Apply a smoothing filter. # Bilateral blur. img = cv2.bilateralFilter(img,9,75,75) name_pref = name_pref + '_smooth=bilat' # Adaptive mean threshold. am_name = name_pref + '_adapt-mean-thr' + spl[1] am_thr = cv2.adaptiveThreshold(img,255,cv2.ADAPTIVE_THRESH_MEAN_C,\ cv2.THRESH_BINARY,blockSize,c) cv2.imwrite(am_name, am_thr) # Adaptive Gaussian threshold. ag_name = name_pref + '_adapt-gauss-thr' + spl[1] ag_thr = cv2.adaptiveThreshold(img,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,\ cv2.THRESH_BINARY,blockSize,c) cv2.imwrite(ag_name, ag_thr) # Otsu's threshold. ot_name = name_pref + '_otsu-thr' + spl[1] ret, ot_thr = cv2.threshold(img,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU) cv2.imwrite(ot_name, ot_thr)

468200722d3d4631d4cdf89007406ce8f257b074

py

Python

tensorflow_datasets/question_answering/ai2_arc.py

ChAnYaNG97/datasets

0a45e2ea98716d325fc1c5e5494f2575f3bdb908

2020-10-11T19:15:49.000Z

2020-10-11T19:15:49.000Z

tensorflow_datasets/question_answering/ai2_arc.py

ChAnYaNG97/datasets

0a45e2ea98716d325fc1c5e5494f2575f3bdb908

2021-02-23T20:16:05.000Z

2021-02-23T20:16:05.000Z

tensorflow_datasets/question_answering/ai2_arc.py

ChAnYaNG97/datasets

0a45e2ea98716d325fc1c5e5494f2575f3bdb908

2020-08-03T20:19:12.000Z

2020-08-03T20:19:12.000Z

# coding=utf-8 # Copyright 2020 The TensorFlow Datasets Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ai2_arc dataset.""" import json import os import tensorflow as tf import tensorflow_datasets.public_api as tfds _CITATION = """ @article{allenai:arc, author = {Peter Clark and Isaac Cowhey and Oren Etzioni and Tushar Khot and Ashish Sabharwal and Carissa Schoenick and Oyvind Tafjord}, title = {Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge}, journal = {arXiv:1803.05457v1}, year = {2018}, } """ _DESCRIPTION = """ A new dataset of 7,787 genuine grade-school level, multiple-choice science questions, assembled to encourage research in advanced question-answering. The dataset is partitioned into a Challenge Set and an Easy Set, where the former contains only questions answered incorrectly by both a retrieval-based algorithm and a word co-occurrence algorithm. We are also including a corpus of over 14 million science sentences relevant to the task, and an implementation of three neural baseline models for this dataset. We pose ARC as a challenge to the community. """ _HOMEPAGE = "https://allenai.org/data/arc" _URL = "https://ai2-datasets.s3-us-west-2.amazonaws.com/arc/ARC-V1-Feb2018.zip" class Ai2ArcConfig(tfds.core.BuilderConfig): """BuilderConfig for Ai2ARC.""" def __init__(self, **kwargs): """BuilderConfig for Ai2Arc. Args: **kwargs: keyword arguments forwarded to super. """ super(Ai2ArcConfig, self).__init__( version=tfds.core.Version("1.0.0"), **kwargs) class Ai2Arc(tfds.core.GeneratorBasedBuilder): """The AI2 ARC dataset.""" BUILDER_CONFIGS = [ Ai2ArcConfig( name="ARC-Challenge", description="""\ Challenge Set of 2590 "hard" questions (those that both a retrieval and a co-occurrence method fail to answer correctly) """, ), Ai2ArcConfig( name="ARC-Easy", description="""\ Easy Set of 5197 questions for the ARC Challenge. """, ), ] def _info(self): # Most questions have four possible answers, but a few have five. options = ["A", "B", "C", "D", "E"] return tfds.core.DatasetInfo( builder=self, # This is the description that will appear on the datasets page. description=_DESCRIPTION, features=tfds.features.FeaturesDict({ "id": tfds.features.Text(), "question": tfds.features.Text(), "choices": tfds.features.Sequence({ "text": tfds.features.Text(), "label": tfds.features.ClassLabel(names=options) }), "answerKey": tfds.features.ClassLabel(names=options), }), supervised_keys=None, homepage=_HOMEPAGE, citation=_CITATION, ) def _split_generators(self, dl_manager): """Returns SplitGenerators.""" dl_dir = dl_manager.download_and_extract(_URL) data_dir = os.path.join(dl_dir, "ARC-V1-Feb2018-2") base_path = os.path.join(data_dir, self.builder_config.name) return [ tfds.core.SplitGenerator( name=tfds.Split.TRAIN, gen_kwargs={ "filepath": os.path.join(base_path, self.builder_config.name + "-Train.jsonl") }, ), tfds.core.SplitGenerator( name=tfds.Split.VALIDATION, gen_kwargs={ "filepath": os.path.join(base_path, self.builder_config.name + "-Dev.jsonl") }, ), tfds.core.SplitGenerator( name=tfds.Split.TEST, gen_kwargs={ "filepath": os.path.join(base_path, self.builder_config.name + "-Test.jsonl") }, ), ] def _generate_examples(self, filepath: str): """Yields examples. Compatible with huggingface's `nlp` format.""" # Generally labels are in the format "A", "B", "C", "D" but sometimes # they are in the format "1", "2", "3", "4". We convert the later to the # former for consistency. n_to_l = dict(zip("1 2 3 4 5".split(), "A B C D E".split())) with tf.io.gfile.GFile(filepath) as f: for row in f: data = json.loads(row) answerkey = n_to_l.get(data["answerKey"], data["answerKey"]) id_ = data["id"] question = data["question"]["stem"] choices = data["question"]["choices"] text_choices = [choice["text"] for choice in choices] label_choices = [ n_to_l.get(choice["label"], choice["label"]) for choice in choices ] yield id_, { "id": id_, "answerKey": answerkey, "question": question, "choices": { "text": text_choices, "label": label_choices }, }

1b864ad8e87cd52750a6e0ff60f762519a00f3fa

py

Python

speeches/behaviors/models.py

danesjenovdan/parlaspeeches

5431a167a483d1d67c380417a9d0b85ccec96249

speeches/behaviors/models.py

danesjenovdan/parlaspeeches

5431a167a483d1d67c380417a9d0b85ccec96249

2020-02-12T00:33:20.000Z

2021-06-10T20:09:24.000Z

speeches/behaviors/models.py

danesjenovdan/parlaspeeches

5431a167a483d1d67c380417a9d0b85ccec96249

2017-08-26T11:24:19.000Z

2017-08-26T11:24:19.000Z

from django.contrib.contenttypes.fields import GenericForeignKey from django.contrib.contenttypes.models import ContentType from django.core.exceptions import ValidationError from django.core.validators import RegexValidator from django.db import models from django.utils.translation import ugettext_lazy as _ from model_utils.fields import AutoCreatedField, AutoLastModifiedField from autoslug import AutoSlugField from datetime import datetime from taggit.managers import TaggableManager __author__ = 'guglielmo' class GenericRelatable(models.Model): """ An abstract class that provides the possibility of generic relations """ content_type = models.ForeignKey(ContentType, related_name='%(app_label)s_%(class)s_related') object_id = models.PositiveIntegerField() content_object = GenericForeignKey('content_type', 'object_id') class Meta: abstract = True class Timestampable(models.Model): """ An abstract base class model that provides self-updating ``created`` and ``modified`` fields. """ created_at = AutoCreatedField(_('creation time')) updated_at = AutoLastModifiedField(_('last modification time')) class Meta: abstract = True class Versionable(models.Model): """ An abstract base class model that provides versioning fields ``valid_from`` and ``valid_to`` """ valid_from = models.DateTimeField( help_text=_('row valid from'), blank=True, null=True, default=None ) valid_to = models.DateTimeField( help_text=_('row valid to'), blank=True, null=True, default=None ) class Meta: abstract = True class Permalinkable(models.Model): """ An abstract base class model that provides a unique slug, and the methods necessary to handle the permalink """ from django.utils.text import slugify slug = AutoSlugField( populate_from=lambda instance: instance.slug_source, unique=True, slugify=slugify ) class Meta: abstract = True def get_url_kwargs(self, **kwargs): kwargs.update(getattr(self, 'url_kwargs', {})) return kwargs @models.permalink def get_absolute_url(self): url_kwargs = self.get_url_kwargs(slug=self.slug) return (self.url_name, (), url_kwargs) class Taggable(models.Model): tags = TaggableManager() class Meta: abstract = True

7579009cc06014b7ad847f5bf5f07d1a9ad97df6

py

Python

ddtrace/contrib/dogpile_cache/region.py

mykytarudenko/new-project

e06a912382239739dd3f93b54d545b9506102372

ddtrace/contrib/dogpile_cache/region.py

mykytarudenko/new-project

e06a912382239739dd3f93b54d545b9506102372

2021-07-26T01:22:38.000Z

2022-03-21T19:20:53.000Z

ddtrace/contrib/dogpile_cache/region.py

mykytarudenko/new-project

e06a912382239739dd3f93b54d545b9506102372

import dogpile from ddtrace.ext import SpanTypes from ...constants import SPAN_MEASURED_KEY from ...pin import Pin def _wrap_get_create(func, instance, args, kwargs): pin = Pin.get_from(dogpile.cache) if not pin or not pin.enabled(): return func(*args, **kwargs) key = args[0] with pin.tracer.trace("dogpile.cache", resource="get_or_create", span_type=SpanTypes.CACHE) as span: span.set_tag(SPAN_MEASURED_KEY) span.set_tag("key", key) span.set_tag("region", instance.name) span.set_tag("backend", instance.actual_backend.__class__.__name__) return func(*args, **kwargs) def _wrap_get_create_multi(func, instance, args, kwargs): pin = Pin.get_from(dogpile.cache) if not pin or not pin.enabled(): return func(*args, **kwargs) keys = args[0] with pin.tracer.trace("dogpile.cache", resource="get_or_create_multi", span_type="cache") as span: span.set_tag(SPAN_MEASURED_KEY) span.set_tag("keys", keys) span.set_tag("region", instance.name) span.set_tag("backend", instance.actual_backend.__class__.__name__) return func(*args, **kwargs)

b74943a2017281116723e5750d9d310fe7ff23bb

py

Python

spiders/spider/spider/settings.py

dingzhaohan/deep_research

8d4373e32d75a348368d46eca2dd1b26d8d93c4b

2020-02-20T01:57:20.000Z

2020-09-12T12:43:46.000Z

spiders/spider/spider/settings.py

dingzhaohan/deep_research

8d4373e32d75a348368d46eca2dd1b26d8d93c4b

spiders/spider/spider/settings.py

dingzhaohan/deep_research

8d4373e32d75a348368d46eca2dd1b26d8d93c4b

# -*- coding: utf-8 -*- # Scrapy settings for spider project # # For simplicity, this file contains only settings considered important or # commonly used. You can find more settings consulting the documentation: # # https://docs.scrapy.org/en/latest/topics/settings.html # https://docs.scrapy.org/en/latest/topics/downloader-middleware.html # https://docs.scrapy.org/en/latest/topics/spider-middleware.html BOT_NAME = 'spider' SPIDER_MODULES = ['spider.spiders'] NEWSPIDER_MODULE = 'spider.spiders' # Crawl responsibly by identifying yourself (and your website) on the user-agent #USER_AGENT = 'spider (+http://www.yourdomain.com)' # Obey robots.txt rules ROBOTSTXT_OBEY = True # Configure maximum concurrent requests performed by Scrapy (default: 16) #CONCURRENT_REQUESTS = 32 # Configure a delay for requests for the same website (default: 0) # See https://docs.scrapy.org/en/latest/topics/settings.html#download-delay # See also autothrottle settings and docs #DOWNLOAD_DELAY = 3 # The download delay setting will honor only one of: #CONCURRENT_REQUESTS_PER_DOMAIN = 16 #CONCURRENT_REQUESTS_PER_IP = 16 # Disable cookies (enabled by default) #COOKIES_ENABLED = False # Disable Telnet Console (enabled by default) #TELNETCONSOLE_ENABLED = False # Override the default request headers: #DEFAULT_REQUEST_HEADERS = { # 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8', # 'Accept-Language': 'en', #} # Enable or disable spider middlewares # See https://docs.scrapy.org/en/latest/topics/spider-middleware.html #SPIDER_MIDDLEWARES = { # 'spider.middlewares.SpiderSpiderMiddleware': 543, #} # Enable or disable downloader middlewares # See https://docs.scrapy.org/en/latest/topics/downloader-middleware.html #DOWNLOADER_MIDDLEWARES = { # 'spider.middlewares.SpiderDownloaderMiddleware': 543, #} # Enable or disable extensions # See https://docs.scrapy.org/en/latest/topics/extensions.html #EXTENSIONS = { # 'scrapy.extensions.telnet.TelnetConsole': None, #} # Configure item pipelines # See https://docs.scrapy.org/en/latest/topics/item-pipeline.html ITEM_PIPELINES = { 'spider.pipelines.SpiderPipeline': 300, } # Enable and configure the AutoThrottle extension (disabled by default) # See https://docs.scrapy.org/en/latest/topics/autothrottle.html #AUTOTHROTTLE_ENABLED = True # The initial download delay #AUTOTHROTTLE_START_DELAY = 5 # The maximum download delay to be set in case of high latencies #AUTOTHROTTLE_MAX_DELAY = 60 # The average number of requests Scrapy should be sending in parallel to # each remote server #AUTOTHROTTLE_TARGET_CONCURRENCY = 1.0 # Enable showing throttling stats for every response received: #AUTOTHROTTLE_DEBUG = False # Enable and configure HTTP caching (disabled by default) # See https://docs.scrapy.org/en/latest/topics/downloader-middleware.html#httpcache-middleware-settings #HTTPCACHE_ENABLED = True #HTTPCACHE_EXPIRATION_SECS = 0 #HTTPCACHE_DIR = 'httpcache' #HTTPCACHE_IGNORE_HTTP_CODES = [] #HTTPCACHE_STORAGE = 'scrapy.extensions.httpcache.FilesystemCacheStorage'

f62487cb8e45be0449175fff4320b71c15ad3da1

py

Python

libestatecassandra/estate_cassandra.py

mpeuster/estate

4cb94201e8110f09ac72c54e7d282e8c38aee415

2021-04-28T05:13:30.000Z

2021-04-28T05:13:30.000Z

libestatecassandra/estate_cassandra.py

mpeuster/estate

4cb94201e8110f09ac72c54e7d282e8c38aee415

libestatecassandra/estate_cassandra.py

mpeuster/estate

4cb94201e8110f09ac72c54e7d282e8c38aee415

""" libestate prototype using cassandra as backend idea: test different consistency models """ from cassandra import ConsistencyLevel from cassandra.cqlengine import columns from cassandra.cqlengine import connection from cassandra.cqlengine import management from cassandra.cqlengine import models from cassandra.cqlengine import CQLEngineException # define the model class KeyValueItem(models.Model): """ We use cassandras map column type to store the values of one key for all instances as well as the latest one. latest field has index = -1 """ key = columns.Text(primary_key=True) #instance = columns.Integer(primary_key=True) #value = columns.Text() data = columns.Map(columns.Integer, columns.Text) def __repr__(self): return 'KeyValueItem.%s' % (self.key) def __str__(self): return self.__repr__() class estate(object): def __init__(self, instance_id): self.instance_id = int(instance_id) # setup cassadnra connection connection.setup(['127.0.0.1'], "estate1", consistency=ConsistencyLevel.ALL) # TODO move this to an indipened reset DB script if self.instance_id == 0: print "FLUSH DB" # flush database by first node (for development) management.drop_keyspace("estate1") # create needed tables management.create_keyspace_simple("estate1", replication_factor=1, durable_writes=True) management.sync_table(KeyValueItem) print "ES: Initialized estate for instance: %s" % self.instance_id def set(self, k, s): print "ES: SET k=%s s=%s" % (str(k), str(s)) # always update map field "latest" (-1) to contain the latest value for this key KeyValueItem.objects(key=k).update(data__update={-1: str(s), self.instance_id: str(s)}) return True def get(self, k): print "ES: GET k=%s" % (str(k)) try: kvi = KeyValueItem.get(key=k) val = str(kvi.data.get(self.instance_id)) return val if val != "None" else "ES_NONE" except CQLEngineException: return "ES_NONE" def delete(self, k): """ Deletes item of this instance for the given key. Since cassadnra maps do only support add and updata, we set the value to None. Attention: This will also set the "latest" map field to None. """ print "ES: DEL k=%s" % (str(k)) try: #kvi = KeyValueItem.get(key=k) #kvi.delete() KeyValueItem.objects(key=k).update(data__update={-1: None, self.instance_id: None}) return True except CQLEngineException: return False def _get_all_replicas(self, k): try: # return all map items, except of the latest field return [v for i, v in KeyValueItem.get(key=k).data.iteritems() if i >= 0] except CQLEngineException: return [] def _get_newest_replica(self, k): try: # return all map items, except of the latest field return KeyValueItem.get(key=k).data.get(-1) except CQLEngineException: return "ES_NONE" def get_global(self, k, red_func): print "ES: GET_GLOBAL k=%s f=%s" % (str(k), str(red_func)) if red_func is not None: # custom red function return red_func(self._get_all_replicas(k)) return self._get_newest_replica(k) # return newest replica

f59e76b7bf9c99da6263ebfe38308b234796e431

py

Python

ML_and_DL/loss_fucntion.py

AshfakYeafi/AI_practice_code

3d8a0b9382f5903e840ce59218ebb95ca962ab01

ML_and_DL/loss_fucntion.py

AshfakYeafi/AI_practice_code

3d8a0b9382f5903e840ce59218ebb95ca962ab01

ML_and_DL/loss_fucntion.py

AshfakYeafi/AI_practice_code

3d8a0b9382f5903e840ce59218ebb95ca962ab01

import numpy as np y=np.array([1,0,1,1,1,0,0,1,0,1]) y_predict=np.array([1,1,1,0,1,0,0,1,0,0]) # −(ylog(p)+(1−y)log(1−p)) ellipsis = 1e-15 def log_loss(y,y_predict): ellipsis=1e-15 y_predict = np.array([max(i, ellipsis) for i in y_predict]) y_predict = np.array([min(i, 1 - ellipsis) for i in y_predict]) return sum(-(y*np.log(y_predict)+(1-y)*np.log(1-y_predict)))/len(y) print(log_loss(y,y_predict))

4eec0382de1df8770e61f26998ee6b937f362553

py

Python

moderngl_window/context/sdl2/keys.py

DavideRuzza/moderngl-window

e9debc6ed4a1899aa83c0da2320e03b0c2922b80

2019-11-11T23:14:28.000Z

2022-03-29T08:37:03.000Z

moderngl_window/context/sdl2/keys.py

DavideRuzza/moderngl-window

e9debc6ed4a1899aa83c0da2320e03b0c2922b80

2019-10-31T20:31:45.000Z

2022-03-23T15:01:41.000Z

moderngl_window/context/sdl2/keys.py

DavideRuzza/moderngl-window

e9debc6ed4a1899aa83c0da2320e03b0c2922b80

2019-12-12T16:14:10.000Z

2022-01-18T22:58:21.000Z

# flake8: noqa E741 import sdl2 from moderngl_window.context.base import BaseKeys class Keys(BaseKeys): """ Namespace mapping SDL2 specific key constants """ ACTION_PRESS = sdl2.SDL_KEYDOWN ACTION_RELEASE = sdl2.SDL_KEYUP ESCAPE = sdl2.SDLK_ESCAPE SPACE = sdl2.SDLK_SPACE ENTER = sdl2.SDLK_RETURN PAGE_UP = sdl2.SDLK_PAGEUP PAGE_DOWN = sdl2.SDLK_PAGEDOWN LEFT = sdl2.SDLK_LEFT RIGHT = sdl2.SDLK_RIGHT UP = sdl2.SDLK_UP DOWN = sdl2.SDLK_DOWN TAB = sdl2.SDLK_TAB COMMA = sdl2.SDLK_COMMA MINUS = sdl2.SDLK_MINUS PERIOD = sdl2.SDLK_PERIOD SLASH = sdl2.SDLK_SLASH SEMICOLON = sdl2.SDLK_SEMICOLON EQUAL = sdl2.SDLK_EQUALS LEFT_BRACKET = sdl2.SDLK_LEFTBRACKET RIGHT_BRACKET = sdl2.SDLK_RIGHTBRACKET BACKSLASH = sdl2.SDLK_BACKSLASH BACKSPACE = sdl2.SDLK_BACKSPACE INSERT = sdl2.SDLK_INSERT DELETE = sdl2.SDLK_DELETE HOME = sdl2.SDLK_HOME END = sdl2.SDLK_END CAPS_LOCK = sdl2.SDLK_CAPSLOCK F1 = sdl2.SDLK_F1 F2 = sdl2.SDLK_F2 F3 = sdl2.SDLK_F3 F4 = sdl2.SDLK_F4 F5 = sdl2.SDLK_F5 F6 = sdl2.SDLK_F6 F7 = sdl2.SDLK_F7 F8 = sdl2.SDLK_F8 F9 = sdl2.SDLK_F9 F10 = sdl2.SDLK_F10 F11 = sdl2.SDLK_F11 F12 = sdl2.SDLK_F12 NUMBER_0 = sdl2.SDLK_0 NUMBER_1 = sdl2.SDLK_1 NUMBER_2 = sdl2.SDLK_2 NUMBER_3 = sdl2.SDLK_3 NUMBER_4 = sdl2.SDLK_4 NUMBER_5 = sdl2.SDLK_5 NUMBER_6 = sdl2.SDLK_6 NUMBER_7 = sdl2.SDLK_7 NUMBER_8 = sdl2.SDLK_8 NUMBER_9 = sdl2.SDLK_9 NUMPAD_0 = sdl2.SDLK_KP_0 NUMPAD_1 = sdl2.SDLK_KP_1 NUMPAD_2 = sdl2.SDLK_KP_2 NUMPAD_3 = sdl2.SDLK_KP_3 NUMPAD_4 = sdl2.SDLK_KP_4 NUMPAD_5 = sdl2.SDLK_KP_5 NUMPAD_6 = sdl2.SDLK_KP_6 NUMPAD_7 = sdl2.SDLK_KP_7 NUMPAD_8 = sdl2.SDLK_KP_8 NUMPAD_9 = sdl2.SDLK_KP_9 A = sdl2.SDLK_a B = sdl2.SDLK_b C = sdl2.SDLK_c D = sdl2.SDLK_d E = sdl2.SDLK_e F = sdl2.SDLK_f G = sdl2.SDLK_g H = sdl2.SDLK_h I = sdl2.SDLK_i J = sdl2.SDLK_j K = sdl2.SDLK_k L = sdl2.SDLK_l M = sdl2.SDLK_m N = sdl2.SDLK_n O = sdl2.SDLK_o P = sdl2.SDLK_p Q = sdl2.SDLK_q R = sdl2.SDLK_r S = sdl2.SDLK_s T = sdl2.SDLK_t U = sdl2.SDLK_u V = sdl2.SDLK_v W = sdl2.SDLK_w X = sdl2.SDLK_x Y = sdl2.SDLK_y Z = sdl2.SDLK_z

164c03509ca80665fb3bd5a5d50998bd1fd59ebc

py

Python

src/core_backend/utils/json_print.py

jhchen3121/wechat_shop

c9d9ad009df1e5bb0eb23ca8d830dd5c15df5328

src/core_backend/utils/json_print.py

jhchen3121/wechat_shop

c9d9ad009df1e5bb0eb23ca8d830dd5c15df5328

2021-01-28T21:18:27.000Z

2022-03-25T19:10:01.000Z

src/core_backend/utils/json_print.py

jhchen3121/wechat_shop

c9d9ad009df1e5bb0eb23ca8d830dd5c15df5328

#!/usr/bin/env python # -*- coding: utf-8 -*- from __future__ import unicode_literals from __future__ import absolute_import import json from core_backend.utils.json import CustomerEncoder from pygments import highlight from pygments.lexers import JsonLexer from pygments.formatters import TerminalFormatter def json_pretty_print(j_obj): json_str = json.dumps(j_obj, cls=CustomerEncoder, indent=4, ensure_ascii=False, encoding="utf8") print(highlight(json_str, JsonLexer(), TerminalFormatter()).encode("utf8"))

44e1ab3156b8fe736839b72898a10a169b47b337

py

Python

kornia/losses/divergence.py

ChristophReich1996/kornia

35f955b46e8015da1cb9faa28c6943ec2b09cc2a

kornia/losses/divergence.py

ChristophReich1996/kornia

35f955b46e8015da1cb9faa28c6943ec2b09cc2a

kornia/losses/divergence.py

ChristophReich1996/kornia

35f955b46e8015da1cb9faa28c6943ec2b09cc2a

r"""Losses based on the divergence between probability distributions.""" import torch import torch.nn.functional as F def _kl_div_2d(p: torch.Tensor, q: torch.Tensor) -> torch.Tensor: # D_KL(P || Q) batch, chans, height, width = p.shape unsummed_kl = F.kl_div( q.reshape(batch * chans, height * width).log(), p.reshape(batch * chans, height * width), reduction='none', ) kl_values = unsummed_kl.sum(-1).view(batch, chans) return kl_values def _js_div_2d(p: torch.Tensor, q: torch.Tensor) -> torch.Tensor: # JSD(P || Q) m = 0.5 * (p + q) return 0.5 * _kl_div_2d(p, m) + 0.5 * _kl_div_2d(q, m) # TODO: add this to the main module def _reduce_loss(losses: torch.Tensor, reduction: str) -> torch.Tensor: if reduction == 'none': return losses return torch.mean(losses) if reduction == 'mean' else torch.sum(losses) def js_div_loss_2d(input: torch.Tensor, target: torch.Tensor, reduction: str = 'mean'): r"""Calculates the Jensen-Shannon divergence loss between heatmaps. Args: input (torch.Tensor): the input tensor with shape :math:`(B, N, H, W)`. target (torch.Tensor): the target tensor with shape :math:`(B, N, H, W)`. reduction (string, optional): Specifies the reduction to apply to the output: `none` | `mean` | `sum`. `none`: no reduction will be applied, `mean`: the sum of the output will be divided by the number of elements in the output, `sum`: the output will be summed. Default: `mean`. Examples: >>> input = torch.full((1, 1, 2, 4), 0.125) >>> loss = js_div_loss_2d(input, input) >>> loss.item() 0.0 """ return _reduce_loss(_js_div_2d(target, input), reduction) def kl_div_loss_2d(input: torch.Tensor, target: torch.Tensor, reduction: str = 'mean'): r"""Calculates the Kullback-Leibler divergence loss between heatmaps. Args: input (torch.Tensor): the input tensor with shape :math:`(B, N, H, W)`. target (torch.Tensor): the target tensor with shape :math:`(B, N, H, W)`. reduction (string, optional): Specifies the reduction to apply to the output: `none` | `mean` | `sum`. `none`: no reduction will be applied, `mean`: the sum of the output will be divided by the number of elements in the output, `sum`: the output will be summed. Default: `mean`. Examples: >>> input = torch.full((1, 1, 2, 4), 0.125) >>> loss = js_div_loss_2d(input, input) >>> loss.item() 0.0 """ return _reduce_loss(_kl_div_2d(target, input), reduction)

44ac336ed558349611b5be390683c1fe716d316f

py

Python

djAidESILV/shopping_cart/models.py

Kulumbaf/AidESILV

04dad828048edffdd3662b24c415edce22fd3ea3

djAidESILV/shopping_cart/models.py

Kulumbaf/AidESILV

04dad828048edffdd3662b24c415edce22fd3ea3

djAidESILV/shopping_cart/models.py

Kulumbaf/AidESILV

04dad828048edffdd3662b24c415edce22fd3ea3

from __future__ import unicode_literals from django.db import models from account.models import UserProfile from products.models import Product class OrderItem(models.Model): product = models.OneToOneField(Product, on_delete=models.SET_NULL, null=True) is_ordered = models.BooleanField(default=False) date_added = models.DateTimeField(auto_now=True) date_ordered = models.DateTimeField(null=True) def __str__(self): return self.product.name class Order(models.Model): ref_code = models.CharField(max_length=15) owner = models.ForeignKey(UserProfile, on_delete=models.SET_NULL, null=True) is_ordered = models.BooleanField(default=False) items = models.ManyToManyField(OrderItem) date_ordered = models.DateTimeField(auto_now=True) def get_cart_items(self): return self.items.all() def get_cart_total(self): return sum([item.product.price for item in self.items.all()]) def __str__(self): return '{0} - {1}'.format(self.owner, self.ref_code)

9835c10978719609d7260513131261f1b1a0579d

py

Python

scripts/create_fakes.py

lbanner/osf.io

1898ef0ff8bd91713e94c60e7463b5f81ac62caa

scripts/create_fakes.py

lbanner/osf.io

1898ef0ff8bd91713e94c60e7463b5f81ac62caa

scripts/create_fakes.py

lbanner/osf.io

1898ef0ff8bd91713e94c60e7463b5f81ac62caa

# -*- coding: utf-8 -*- """Fake data generator. To use: 1. Install fake-factory. pip install fake-factory 2. Create your OSF user account 3. Run the script, passing in your username (email). :: python -m scripts.create_fakes --user fred@cos.io This will create 3 fake public projects, each with 3 fake contributors (with you as the creator). """ from __future__ import print_function import sys import argparse import logging from modularodm.query.querydialect import DefaultQueryDialect as Q from faker import Factory from framework.auth import Auth from website.app import init_app from website import models, security from framework.auth import utils from tests.factories import UserFactory, ProjectFactory, NodeFactory from faker.providers import BaseProvider class Sciencer(BaseProvider): # Science term Faker Provider created by @csheldonhess # https://github.com/csheldonhess/FakeConsumer/blob/master/faker/providers/science.py word_list = ('abiosis', 'abrade', 'absorption', 'acceleration', 'accumulation', 'acid', 'acidic', 'activist', 'adaptation', 'agonistic', 'agrarian', 'airborne', 'alchemist', 'alignment', 'allele', 'alluvial', 'alveoli', 'ambiparous', 'amphibian', 'amplitude', 'analysis', 'ancestor', 'anodize', 'anomaly', 'anther', 'antigen', 'apiary', 'apparatus', 'application', 'approximation', 'aquatic', 'aquifer', 'arboreal', 'archaeology', 'artery', 'assessment', 'asteroid', 'atmosphere', 'atomic', 'atrophy', 'attenuate', 'aven', 'aviary', 'axis', 'bacteria', 'balance', 'bases', 'biome', 'biosphere', 'black hole', 'blight', 'buoyancy', 'calcium', 'canopy', 'capacity', 'capillary', 'carapace', 'carcinogen', 'catalyst', 'cauldron', 'celestial', 'cells', 'centigrade', 'centimeter', 'centrifugal', 'chemical reaction', 'chemicals', 'chemistry', 'chlorophyll', 'choked', 'chromosome', 'chronic', 'churn', 'classification', 'climate', 'cloud', 'comet', 'composition', 'compound', 'compression', 'condensation', 'conditions', 'conduction', 'conductivity', 'conservation', 'constant', 'constellation', 'continental', 'convection', 'convention', 'cool', 'core', 'cosmic', 'crater', 'creature', 'crepuscular', 'crystals', 'cycle', 'cytoplasm', 'dampness', 'data', 'decay', 'decibel', 'deciduous', 'defoliate', 'density', 'denude', 'dependency', 'deposits', 'depth', 'desiccant', 'detritus', 'development', 'digestible', 'diluted', 'direction', 'disappearance', 'discovery', 'dislodge', 'displace', 'dissection', 'dissolution', 'dissolve', 'distance', 'diurnal', 'diverse', 'doldrums', 'dynamics', 'earthquake', 'eclipse', 'ecology', 'ecosystem', 'electricity', 'elements', 'elevation', 'embryo', 'endangered', 'endocrine', 'energy', 'entropy', 'environment', 'enzyme', 'epidermis', 'epoch', 'equilibrium', 'equine', 'erosion', 'essential', 'estuary', 'ethical', 'evaporation', 'event', 'evidence', 'evolution', 'examination', 'existence', 'expansion', 'experiment', 'exploration ', 'extinction', 'extreme', 'facet', 'fault', 'fauna', 'feldspar', 'fermenting', 'fission', 'fissure', 'flora', 'flourish', 'flowstone', 'foliage', 'food chain', 'forage', 'force', 'forecast', 'forensics', 'formations', 'fossil fuel', 'frequency', 'friction', 'fungi', 'fusion', 'galaxy', 'gastric', 'geo-science', 'geothermal', 'germination', 'gestation', 'global', 'gravitation', 'green', 'greenhouse effect', 'grotto', 'groundwater', 'habitat', 'heat', 'heavens', 'hemisphere', 'hemoglobin', 'herpetologist', 'hormones', 'host', 'humidity', 'hyaline', 'hydrogen', 'hydrology', 'hypothesis', 'ichthyology', 'illumination', 'imagination', 'impact of', 'impulse', 'incandescent', 'indigenous', 'inertia', 'inevitable', 'inherit', 'inquiry', 'insoluble', 'instinct', 'instruments', 'integrity', 'intelligence', 'interacts with', 'interdependence', 'interplanetary', 'invertebrate', 'investigation', 'invisible', 'ions', 'irradiate', 'isobar', 'isotope', 'joule', 'jungle', 'jurassic', 'jutting', 'kilometer', 'kinetics', 'kingdom', 'knot', 'laser', 'latitude', 'lava', 'lethal', 'life', 'lift', 'light', 'limestone', 'lipid', 'lithosphere', 'load', 'lodestone', 'luminous', 'luster', 'magma', 'magnet', 'magnetism', 'mangrove', 'mantle', 'marine', 'marsh', 'mass', 'matter', 'measurements', 'mechanical', 'meiosis', 'meridian', 'metamorphosis', 'meteor', 'microbes', 'microcosm', 'migration', 'millennia', 'minerals', 'modulate', 'moisture', 'molecule', 'molten', 'monograph', 'monolith', 'motion', 'movement', 'mutant', 'mutation', 'mysterious', 'natural', 'navigable', 'navigation', 'negligence', 'nervous system', 'nesting', 'neutrons', 'niche', 'nocturnal', 'nuclear energy', 'numerous', 'nurture', 'obsidian', 'ocean', 'oceanography', 'omnivorous', 'oolites (cave pearls)', 'opaque', 'orbit', 'organ', 'organism', 'ornithology', 'osmosis', 'oxygen', 'paleontology', 'parallax', 'particle', 'penumbra', 'percolate', 'permafrost', 'permutation', 'petrify', 'petrograph', 'phenomena', 'physical property', 'planetary', 'plasma', 'polar', 'pole', 'pollination', 'polymer', 'population', 'precipitation', 'predator', 'prehensile', 'preservation', 'preserve', 'pressure', 'primate', 'pristine', 'probe', 'process', 'propagation', 'properties', 'protected', 'proton', 'pulley', 'qualitative data', 'quantum', 'quark', 'quarry', 'radiation', 'radioactivity', 'rain forest', 'ratio', 'reaction', 'reagent', 'realm', 'redwoods', 'reeds', 'reflection', 'refraction', 'relationships between', 'reptile', 'research', 'resistance', 'resonate', 'rookery', 'rubble', 'runoff', 'salinity', 'sandbar', 'satellite', 'saturation', 'scientific investigation', 'scientist\'s', 'sea floor', 'season', 'sedentary', 'sediment', 'sedimentary', 'seepage', 'seismic', 'sensors', 'shard', 'similarity', 'solar', 'soluble', 'solvent', 'sonic', 'sound', 'source', 'species', 'spectacular', 'spectrum', 'speed', 'sphere', 'spring', 'stage', 'stalactite', 'stalagmites', 'stimulus', 'substance', 'subterranean', 'sulfuric acid', 'surface', 'survival', 'swamp', 'sylvan', 'symbiosis', 'symbol', 'synergy', 'synthesis', 'taiga', 'taxidermy', 'technology', 'tectonics', 'temperate', 'temperature', 'terrestrial', 'thermals', 'thermometer', 'thrust', 'torque', 'toxin', 'trade winds', 'pterodactyl', 'transformation tremors', 'tropical', 'umbra', 'unbelievable', 'underwater', 'unearth', 'unique', 'unite', 'unity', 'universal', 'unpredictable', 'unusual', 'ursine', 'vacuole', 'valuable', 'vapor', 'variable', 'variety', 'vast', 'velocity', 'ventifact', 'verdant', 'vespiary', 'viable', 'vibration', 'virus', 'viscosity', 'visible', 'vista', 'vital', 'vitreous', 'volt', 'volume', 'vulpine', 'wave', 'wax', 'weather', 'westerlies', 'wetlands', 'whitewater', 'xeriscape', 'xylem', 'yield', 'zero-impact', 'zone', 'zygote', 'achieving', 'acquisition of', 'an alternative', 'analysis of', 'approach toward', 'area', 'aspects of', 'assessment of', 'assuming', 'authority', 'available', 'benefit of', 'circumstantial', 'commentary', 'components', 'concept of', 'consistent', 'corresponding', 'criteria', 'data', 'deduction', 'demonstrating', 'derived', 'distribution', 'dominant', 'elements', 'equation', 'estimate', 'evaluation', 'factors', 'features', 'final', 'function', 'initial', 'instance ', 'interpretation of', 'maintaining ', 'method', 'perceived', 'percent', 'period', 'positive', 'potential', 'previous', 'primary', 'principle', 'procedure', 'process', 'range', 'region', 'relevant', 'required', 'research', 'resources', 'response', 'role', 'section', 'select', 'significant ', 'similar', 'source', 'specific', 'strategies', 'structure', 'theory', 'transfer', 'variables', 'corvidae', 'passerine', 'Pica pica', 'Chinchilla lanigera', 'Nymphicus hollandicus', 'Melopsittacus undulatus', ) def science_word(cls): """ :example 'Lorem' """ return cls.random_element(cls.word_list) def science_words(cls, nb=3): """ Generate an array of random words :example array('Lorem', 'ipsum', 'dolor') :param nb how many words to return """ return [cls.science_word() for _ in range(0, nb)] def science_sentence(cls, nb_words=6, variable_nb_words=True): """ Generate a random sentence :example 'Lorem ipsum dolor sit amet.' :param nb_words around how many words the sentence should contain :param variable_nb_words set to false if you want exactly $nbWords returned, otherwise $nbWords may vary by +/-40% with a minimum of 1 """ if nb_words <= 0: return '' if variable_nb_words: nb_words = cls.randomize_nb_elements(nb_words) words = cls.science_words(nb_words) words[0] = words[0].title() return " ".join(words) + '.' def science_sentences(cls, nb=3): """ Generate an array of sentences :example array('Lorem ipsum dolor sit amet.', 'Consectetur adipisicing eli.') :param nb how many sentences to return :return list """ return [cls.science_sentence() for _ in range(0, nb)] def science_paragraph(cls, nb_sentences=3, variable_nb_sentences=True): """ Generate a single paragraph :example 'Sapiente sunt omnis. Ut pariatur ad autem ducimus et. Voluptas rem voluptas sint modi dolorem amet.' :param nb_sentences around how many sentences the paragraph should contain :param variable_nb_sentences set to false if you want exactly $nbSentences returned, otherwise $nbSentences may vary by +/-40% with a minimum of 1 :return string """ if nb_sentences <= 0: return '' if variable_nb_sentences: nb_sentences = cls.randomize_nb_elements(nb_sentences) return " ".join(cls.science_sentences(nb_sentences)) def science_paragraphs(cls, nb=3): """ Generate an array of paragraphs :example array($paragraph1, $paragraph2, $paragraph3) :param nb how many paragraphs to return :return array """ return [cls.science_paragraph() for _ in range(0, nb)] def science_text(cls, max_nb_chars=200): """ Generate a text string. Depending on the $maxNbChars, returns a string made of words, sentences, or paragraphs. :example 'Sapiente sunt omnis. Ut pariatur ad autem ducimus et. Voluptas rem voluptas sint modi dolorem amet.' :param max_nb_chars Maximum number of characters the text should contain (minimum 5) :return string """ text = [] if max_nb_chars < 5: raise ValueError('text() can only generate text of at least 5 characters') if max_nb_chars < 25: # join words while not text: size = 0 # determine how many words are needed to reach the $max_nb_chars once; while size < max_nb_chars: word = (' ' if size else '') + cls.science_word() text.append(word) size += len(word) text.pop() text[0] = text[0][0].upper() + text[0][1:] last_index = len(text) - 1 text[last_index] += '.' elif max_nb_chars < 100: # join sentences while not text: size = 0 # determine how many sentences are needed to reach the $max_nb_chars once while size < max_nb_chars: sentence = (' ' if size else '') + cls.science_sentence() text.append(sentence) size += len(sentence) text.pop() else: # join paragraphs while not text: size = 0 # determine how many paragraphs are needed to reach the $max_nb_chars once while size < max_nb_chars: paragraph = ('\n' if size else '') + cls.science_paragraph() text.append(paragraph) size += len(paragraph) text.pop() return "".join(text) logger = logging.getLogger(__name__) logging.basicConfig(level=logging.ERROR) fake = Factory.create() fake.add_provider(Sciencer) def create_fake_user(): email = fake.email() name = fake.name() parsed = utils.impute_names(name) user = UserFactory.build(username=email, fullname=name, is_registered=True, is_claimed=True, verification_key=security.random_string(15), date_registered=fake.date_time(), emails=[email], **parsed ) user.set_password('faker123') user.save() logger.info('Created user: {0} <{1}>'.format(user.fullname, user.username)) return user def parse_args(): parser = argparse.ArgumentParser(description='Create fake data.') parser.add_argument('-u', '--user', dest='user', required=True) parser.add_argument('--nusers', dest='n_users', type=int, default=3) parser.add_argument('--nprojects', dest='n_projects', type=int, default=3) parser.add_argument('--ncomponents', dest='n_components', type=int, default=0) parser.add_argument('-p', '--privacy', dest="privacy", type=str, default='private', choices=['public', 'private']) parser.add_argument('-n', '--name', dest='name', type=str, default=None) parser.add_argument('-t', '--tags', dest='n_tags', type=int, default=5) parser.add_argument('--presentation', dest='presentation_name', type=str, default=None) return parser.parse_args() def create_fake_project(creator, n_users, privacy, n_components, name, n_tags, presentation_name): auth = Auth(user=creator) project_title = name if name else fake.science_sentence() project = ProjectFactory.build(title=project_title, description=fake.science_paragraph(), creator=creator) project.set_privacy(privacy) for _ in range(n_users): contrib = create_fake_user() project.add_contributor(contrib, auth=auth) for _ in range(n_components): NodeFactory(project=project, title=fake.science_sentence(), description=fake.science_paragraph(), creator=creator) for _ in range(n_tags): project.add_tag(fake.science_word(), auth=auth) if presentation_name is not None: project.add_tag(presentation_name, auth=auth) project.add_tag('poster', auth=auth) project.save() logger.info('Created project: {0}'.format(project.title)) return project def main(): args = parse_args() creator = models.User.find(Q('username', 'eq', args.user))[0] for i in range(args.n_projects): name = args.name + str(i) if args.name else '' create_fake_project(creator, args.n_users, args.privacy, args.n_components, name, args.n_tags, args.presentation_name) print('Created {n} fake projects.'.format(n=args.n_projects)) sys.exit(0) if __name__ == '__main__': app = init_app('website.settings', set_backends=True, routes=True) main()

11ccd1f6ff25eb6ec3298ee5dc66c04fb6232f1c

py

Python

TemaLib/tema/coverage/dummycoverage.py

tema-tut/tema-tg

9c3f119c8bf5cc565e6a3e8e9e6205037e326d89

TemaLib/tema/coverage/dummycoverage.py

tema-tut/tema-tg

9c3f119c8bf5cc565e6a3e8e9e6205037e326d89

TemaLib/tema/coverage/dummycoverage.py

tema-tut/tema-tg

9c3f119c8bf5cc565e6a3e8e9e6205037e326d89

2021-03-27T21:27:32.000Z

2021-03-27T21:27:32.000Z

#!/usr/bin/env python # Copyright (c) 2006-2010 Tampere University of Technology # # Permission is hereby granted, free of charge, to any person obtaining # a copy of this software and associated documentation files (the # "Software"), to deal in the Software without restriction, including # without limitation the rights to use, copy, modify, merge, publish, # distribute, sublicense, and/or sell copies of the Software, and to # permit persons to whom the Software is furnished to do so, subject to # the following conditions: # # The above copyright notice and this permission notice shall be # included in all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, # EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF # MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND # NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE # LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION # OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION # WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. """ Dummy coverage implements requirement interface but gives always zero percentage. This can be used for test runs that should run without stopping condition. """ # tema libraries: import tema.coverage.coverage as coverage # python standard import re class DummyCoverage(coverage.CoverageStorage, coverage.Requirement): def __init__(self, reqstr, **kw): # CoverageStorage constructor is skipped here # because there should be no need to use this as # a coverage storage. That is, this coverage should # not be used with guidance algorithms that calculate # forward. If it still is, a warning is given self._warning_given = 0 if reqstr!="": self.log("WARNING: DummyCoverage got a non-empty coverage requirement: '%s'" % reqstr) self.log("Initialized") def setParameter(self, parametername, value ): print __doc__ raise Exception("Invalid parameter '%s' for dummycoverage." % parametername) # CoverageStorage interface: def markExecuted(self,transition): # Mark execute is quite ok. We just do not care. pass def push(self): # Push and pop are bad. Someone is trying to see if # percentage gets better after a few steps. It won't, # because this gives always zero percentage. Therefore, # give warning. if self._warning_given == 0: self._warning_given = 1 self.log("WARNING: DummyCoverage is used with too smart guidance algorithm.") def pop(self): # see push() self.push() # Requirement interface: def getPercentage(self): return 0.0 def pickDataValue(self,set_of_possible_values): # This coverage requirement does not favor any particular data # values. return None CoverageRequirement = DummyCoverage

bf23af668967713752b766aaa38ef65787a2a859

py

Python

backend/test_34087/urls.py

crowdbotics-apps/test-34087

e04f7ebd844388dae45dbf765e988716c3a0aeb5

backend/test_34087/urls.py

crowdbotics-apps/test-34087

e04f7ebd844388dae45dbf765e988716c3a0aeb5

backend/test_34087/urls.py

crowdbotics-apps/test-34087

e04f7ebd844388dae45dbf765e988716c3a0aeb5

"""test_34087 URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/2.2/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: path('', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home') Including another URLconf 1. Import the include() function: from django.urls import include, path 2. Add a URL to urlpatterns: path('blog/', include('blog.urls')) """ from django.contrib import admin from django.urls import path, include, re_path from django.views.generic.base import TemplateView from allauth.account.views import confirm_email from rest_framework import permissions from drf_yasg.views import get_schema_view from drf_yasg import openapi urlpatterns = [ path("", include("home.urls")), path("accounts/", include("allauth.urls")), path("modules/", include("modules.urls")), path("api/v1/", include("home.api.v1.urls")), path("admin/", admin.site.urls), path("users/", include("users.urls", namespace="users")), path("rest-auth/", include("rest_auth.urls")), # Override email confirm to use allauth's HTML view instead of rest_auth's API view path("rest-auth/registration/account-confirm-email/<str:key>/", confirm_email), path("rest-auth/registration/", include("rest_auth.registration.urls")), ] admin.site.site_header = "Test" admin.site.site_title = "Test Admin Portal" admin.site.index_title = "Test Admin" # swagger api_info = openapi.Info( title="Test API", default_version="v1", description="API documentation for Test App", ) schema_view = get_schema_view( api_info, public=True, permission_classes=(permissions.IsAuthenticated,), ) urlpatterns += [ path("api-docs/", schema_view.with_ui("swagger", cache_timeout=0), name="api_docs") ] urlpatterns += [path("", TemplateView.as_view(template_name='index.html'))] urlpatterns += [re_path(r"^(?:.*)/?$", TemplateView.as_view(template_name='index.html'))]

58d5048ecfe35af7e845e7b271b9793f82e1e34e

py

Python

cvpods/layers/swap_align2nat.py

hanqiu-hq/cvpods

597fa669151fdad87c250fa118a9e3a555f4fb5e

cvpods/layers/swap_align2nat.py

hanqiu-hq/cvpods

597fa669151fdad87c250fa118a9e3a555f4fb5e

cvpods/layers/swap_align2nat.py

hanqiu-hq/cvpods

597fa669151fdad87c250fa118a9e3a555f4fb5e

# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved from torch import nn from torch.autograd import Function from torch.autograd.function import once_differentiable from cvpods import _C class _SwapAlign2Nat(Function): @staticmethod def forward(ctx, X, lambda_val, pad_val): ctx.lambda_val = lambda_val ctx.input_shape = X.size() Y = _C.swap_align2nat_forward(X, lambda_val, pad_val) return Y @staticmethod @once_differentiable def backward(ctx, gY): lambda_val = ctx.lambda_val bs, ch, h, w = ctx.input_shape gX = _C.swap_align2nat_backward(gY, lambda_val, bs, ch, h, w) return gX, None, None swap_align2nat = _SwapAlign2Nat.apply class SwapAlign2Nat(nn.Module): """ The op `SwapAlign2Nat` described in https://arxiv.org/abs/1903.12174. Given an input tensor that predicts masks of shape (N, C=VxU, H, W), apply the op, it will return masks of shape (N, V'xU', H', W') where the unit lengths of (V, U) and (H, W) are swapped, and the mask representation is transformed from aligned to natural. Args: lambda_val (int): the relative unit length ratio between (V, U) and (H, W), as we always have larger unit lengths for (V, U) than (H, W), lambda_val is always >= 1. pad_val (float): padding value for the values falling outside of the input tensor, default set to -6 as sigmoid(-6) is ~0, indicating that is no masks outside of the tensor. """ def __init__(self, lambda_val, pad_val=-6.0): super(SwapAlign2Nat, self).__init__() self.lambda_val = lambda_val self.pad_val = pad_val def forward(self, X): return swap_align2nat(X, self.lambda_val, self.pad_val) def extra_repr(self): tmpstr = "lambda_val=" + str(self.lambda_val) tmpstr += ", pad_val=" + str(self.pad_val) return tmpstr

57fb13d098052cbca253144c458e8a54087f07f8

py

Python

pyzoo/zoo/ray/raycontext.py

taogeanton2/analytics-zoo

e111f445486d971c6dbc3bfde313cff9842cc163

2020-05-18T02:37:10.000Z

2020-05-18T02:37:10.000Z

pyzoo/zoo/ray/raycontext.py

taogeanton2/analytics-zoo

e111f445486d971c6dbc3bfde313cff9842cc163

pyzoo/zoo/ray/raycontext.py

taogeanton2/analytics-zoo

e111f445486d971c6dbc3bfde313cff9842cc163

# # Copyright 2018 Analytics Zoo Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # import os import re import random import signal import multiprocessing from pyspark import BarrierTaskContext from zoo.ray.process import session_execute, ProcessMonitor from zoo.ray.utils import is_local from zoo.ray.utils import resource_to_bytes class JVMGuard: """ The registered pids would be put into the killing list of Spark Executor. """ @staticmethod def register_pids(pids): import traceback try: from zoo.common.utils import callZooFunc import zoo callZooFunc("float", "jvmGuardRegisterPids", pids) except Exception as err: print(traceback.format_exc()) print("Cannot sucessfully register pid into JVMGuard") for pid in pids: os.kill(pid, signal.SIGKILL) raise err class RayServiceFuncGenerator(object): """ This should be a pickable class. """ def _prepare_env(self): modified_env = os.environ.copy() cwd = os.getcwd() modified_env["PATH"] = "{}/{}:{}".format(cwd, "/".join(self.python_loc.split("/")[:-1]), os.environ["PATH"]) modified_env.pop("MALLOC_ARENA_MAX", None) modified_env.pop("RAY_BACKEND_LOG_LEVEL", None) # Unset all MKL setting as Analytics Zoo would give default values when init env. # Running different programs may need different configurations. modified_env.pop("intra_op_parallelism_threads", None) modified_env.pop("inter_op_parallelism_threads", None) modified_env.pop("OMP_NUM_THREADS", None) modified_env.pop("KMP_BLOCKTIME", None) modified_env.pop("KMP_AFFINITY", None) modified_env.pop("KMP_SETTINGS", None) if self.env: # Add in env argument if any MKL setting is needed. modified_env.update(self.env) if self.verbose: print("Executing with these environment setting:") for pair in modified_env.items(): print(pair) print("The $PATH is: {}".format(modified_env["PATH"])) return modified_env def __init__(self, python_loc, redis_port, ray_node_cpu_cores, password, object_store_memory, verbose=False, env=None, extra_params=None): """object_store_memory: integer in bytes""" self.env = env self.python_loc = python_loc self.redis_port = redis_port self.password = password self.ray_node_cpu_cores = ray_node_cpu_cores self.ray_exec = self._get_ray_exec() self.object_store_memory = object_store_memory self.extra_params = extra_params self.verbose = verbose # _mxnet_worker and _mxnet_server are resource tags for distributed MXNet training only # in order to diff worker from server. # This is useful to allocate workers and servers in the cluster. # Leave some reserved custom resources free to avoid unknown crash due to resources. self.labels = \ """--resources='{"_mxnet_worker": %s, "_mxnet_server": %s, "_reserved": %s}' """ \ % (1, 1, 2) def gen_stop(self): def _stop(iter): command = "{} stop".format(self.ray_exec) print("Start to end the ray services: {}".format(command)) session_execute(command=command, fail_fast=True) return iter return _stop @staticmethod def _enrich_command(command, object_store_memory, extra_params): if object_store_memory: command = command + "--object-store-memory {} ".format(str(object_store_memory)) if extra_params: for pair in extra_params.items(): command = command + " --{} {} ".format(pair[0], pair[1]) return command def _gen_master_command(self): command = "{} start --head " \ "--include-webui true --redis-port {} " \ "--redis-password {} --num-cpus {} {}". \ format(self.ray_exec, self.redis_port, self.password, self.ray_node_cpu_cores, self.labels) return RayServiceFuncGenerator._enrich_command(command=command, object_store_memory=self.object_store_memory, extra_params=self.extra_params) @staticmethod def _get_raylet_command(redis_address, ray_exec, password, ray_node_cpu_cores, labels="", object_store_memory=None, extra_params=None): command = "{} start --address {} --redis-password {} --num-cpus {} {} ".format( ray_exec, redis_address, password, ray_node_cpu_cores, labels) return RayServiceFuncGenerator._enrich_command(command=command, object_store_memory=object_store_memory, extra_params=extra_params) def _start_ray_node(self, command, tag): modified_env = self._prepare_env() print("Starting {} by running: {}".format(tag, command)) process_info = session_execute(command=command, env=modified_env, tag=tag) JVMGuard.register_pids(process_info.pids) import ray.services as rservices process_info.node_ip = rservices.get_node_ip_address() return process_info def _get_ray_exec(self): python_bin_dir = "/".join(self.python_loc.split("/")[:-1]) return "{}/python {}/ray".format(python_bin_dir, python_bin_dir) def gen_ray_start(self): def _start_ray_services(iter): tc = BarrierTaskContext.get() # The address is sorted by partitionId according to the comments # Partition 0 is the Master task_addrs = [taskInfo.address for taskInfo in tc.getTaskInfos()] print(task_addrs) master_ip = task_addrs[0].split(":")[0] print("current address {}".format(task_addrs[tc.partitionId()])) print("master address {}".format(master_ip)) redis_address = "{}:{}".format(master_ip, self.redis_port) process_info = None if tc.partitionId() == 0: print("partition id is : {}".format(tc.partitionId())) process_info = self._start_ray_node(command=self._gen_master_command(), tag="ray-master") process_info.master_addr = redis_address tc.barrier() if tc.partitionId() != 0: print("partition id is : {}".format(tc.partitionId())) process_info = self._start_ray_node( command=RayServiceFuncGenerator._get_raylet_command( redis_address=redis_address, ray_exec=self.ray_exec, password=self.password, ray_node_cpu_cores=self.ray_node_cpu_cores, labels=self.labels, object_store_memory=self.object_store_memory, extra_params=self.extra_params), tag="raylet") yield process_info return _start_ray_services class RayContext(object): def __init__(self, sc, redis_port=None, password="123456", object_store_memory=None, verbose=False, env=None, extra_params=None): """ The RayContext would initiate a ray cluster on top of the configuration of SparkContext. After creating RayContext, call the init method to set up the cluster. - For Spark local mode: The total available cores is equal to Spark local cores. - For Spark cluster mode: The number of raylets is equal to number of executors. The number of available cores for each raylet is equal to executor cores. :param sc: An instance of SparkContext. :param redis_port: redis port for the "head" node. The value would be randomly picked if not specified. :param password: Password for the redis. Default to be "123456" if not specified. :param object_store_memory: The memory size for ray object_store in string. This can be specified in bytes(b), kilobytes(k), megabytes(m) or gigabytes(g). For example, 50b, 100k, 250m, 30g. :param verbose: True for more logs when starting ray. Default is False. :param env: The environment variable dict for running ray processes. Default is None. :param extra_params: The key value dict for extra options to launch ray. For example, extra_params={"temp-dir": "/tmp/ray/"} """ assert sc is not None, "sc cannot be None, please create a SparkContext first" self.sc = sc self.stopped = False self.is_local = is_local(sc) self.verbose = verbose self.redis_password = password self.object_store_memory = resource_to_bytes(object_store_memory) self.ray_processesMonitor = None self.env = env self.extra_params = extra_params if self.is_local: self.num_ray_nodes = 1 self.ray_node_cpu_cores = self._get_spark_local_cores() # For Spark local mode, directly call ray.init() and ray.shutdown(). # ray.shutdown() would clear up all the ray related processes. # Ray Manager is only needed for Spark cluster mode to monitor ray processes. else: self.num_ray_nodes = int(self.sc.getConf().get("spark.executor.instances")) self.ray_node_cpu_cores = int(self.sc.getConf().get("spark.executor.cores")) self.python_loc = os.environ['PYSPARK_PYTHON'] self.redis_port = random.randint(10000, 65535) if not redis_port else redis_port self.ray_service = RayServiceFuncGenerator( python_loc=self.python_loc, redis_port=self.redis_port, ray_node_cpu_cores=self.ray_node_cpu_cores, password=self.redis_password, object_store_memory=self.object_store_memory, verbose=self.verbose, env=self.env, extra_params=self.extra_params) self._gather_cluster_ips() from bigdl.util.common import init_executor_gateway print("Start to launch the JVM guarding process") init_executor_gateway(sc) print("JVM guarding process has been successfully launched") def _gather_cluster_ips(self): total_cores = int(self.num_ray_nodes) * int(self.ray_node_cpu_cores) def info_fn(iter): tc = BarrierTaskContext.get() task_addrs = [taskInfo.address.split(":")[0] for taskInfo in tc.getTaskInfos()] yield task_addrs tc.barrier() ips = self.sc.range(0, total_cores, numSlices=total_cores).barrier().mapPartitions(info_fn).collect() return ips[0] def stop(self): if self.stopped: print("This instance has been stopped.") return import ray ray.shutdown() if not self.is_local: if not self.ray_processesMonitor: print("Please start the runner first before closing it") else: self.ray_processesMonitor.clean_fn() self.stopped = True def purge(self): """ Invoke ray stop to clean ray processes. """ if self.stopped: print("This instance has been stopped.") return if self.is_local: import ray ray.shutdown() else: self.sc.range(0, self.num_ray_nodes, numSlices=self.num_ray_nodes).barrier().mapPartitions( self.ray_service.gen_stop()).collect() self.stopped = True def _get_spark_local_cores(self): local_symbol = re.match(r"local\[(.*)\]", self.sc.master).group(1) if local_symbol == "*": return multiprocessing.cpu_count() else: return int(local_symbol) def init(self, driver_cores=0): """ Initiate the ray cluster. :param driver_cores: The number of cores for the raylet on driver for Spark cluster mode. Default is 0 and in this case the local driver wouldn't have any ray workload. """ self.stopped = False if self.is_local: if self.env: os.environ.update(self.env) import ray ray.init(num_cpus=self.ray_node_cpu_cores, object_store_memory=self.object_store_memory, resources=self.extra_params) else: self._start_cluster() self._start_driver(num_cores=driver_cores) def _start_cluster(self): print("Start to launch ray on cluster") ray_rdd = self.sc.range(0, self.num_ray_nodes, numSlices=self.num_ray_nodes) process_infos = ray_rdd.barrier().mapPartitions( self.ray_service.gen_ray_start()).collect() self.ray_processesMonitor = ProcessMonitor(process_infos, self.sc, ray_rdd, self, verbose=self.verbose) self.redis_address = self.ray_processesMonitor.master.master_addr return self def _start_restricted_worker(self, num_cores, node_ip_address): extra_param = {"node-ip-address": node_ip_address} if self.extra_params is not None: extra_param.update(self.extra_params) command = RayServiceFuncGenerator._get_raylet_command( redis_address=self.redis_address, ray_exec="ray ", password=self.redis_password, ray_node_cpu_cores=num_cores, object_store_memory=self.object_store_memory, extra_params=extra_param) print("Executing command: {}".format(command)) process_info = session_execute(command=command, fail_fast=True) ProcessMonitor.register_shutdown_hook(pgid=process_info.pgid) def _start_driver(self, num_cores=0): print("Start to launch ray driver on local") import ray if not self.is_local: import ray.services node_ip = ray.services.get_node_ip_address(self.redis_address) self._start_restricted_worker(num_cores=num_cores, node_ip_address=node_ip) ray.shutdown() ray.init(address=self.redis_address, redis_password=self.ray_service.password, node_ip_address=node_ip) else: ray.shutdown() ray.init(address=self.redis_address, redis_password=self.ray_service.password)

fd02b1f55bd7f591a8c2f654b1d53ca75fd4e963

py

Python

alipay/aop/api/domain/AlipayOpenXwbtesttomsgapiSyncModel.py

snowxmas/alipay-sdk-python-all

96870ced60facd96c5bce18d19371720cbda3317

2018-08-27T16:49:32.000Z

2021-12-29T04:34:12.000Z

alipay/aop/api/domain/AlipayOpenXwbtesttomsgapiSyncModel.py

snowxmas/alipay-sdk-python-all

96870ced60facd96c5bce18d19371720cbda3317

2018-09-29T06:43:00.000Z

2021-09-02T03:27:32.000Z

alipay/aop/api/domain/AlipayOpenXwbtesttomsgapiSyncModel.py

snowxmas/alipay-sdk-python-all

96870ced60facd96c5bce18d19371720cbda3317

2018-08-27T16:59:26.000Z

2022-03-25T10:08:15.000Z

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

43e7468c37ee21f3be050bab3fc7c2c58568418c

py

Python

mat.py

arcangelo7/Meta-Analyser-Tool

c6fc6bb1870a10efe39634687b388946dd8bb762

mat.py

arcangelo7/Meta-Analyser-Tool

c6fc6bb1870a10efe39634687b388946dd8bb762

mat.py

arcangelo7/Meta-Analyser-Tool

c6fc6bb1870a10efe39634687b388946dd8bb762

# -*- coding: utf-8 -*- # Copyright (c) 2019, # Silvio Peroni <silvio.peroni@unibo.it> # # Permission to use, copy, modify, and/or distribute this software for any purpose # with or without fee is hereby granted, provided that the above copyright notice # and this permission notice appear in all copies. # # THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH # REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND # FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT, INDIRECT, # OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, # DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS # ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS # SOFTWARE. # The following importing line is used to include in the definition of this class # the particular functions implemented by a group. The 'my_test_group' module specified # here is just a placeholder, since it defines only the signature of the various # functions but it returns always None. from acari import * class MetaAnalyserTool(object): def __init__(self, metadata_file_path): self.data = process_metadata(metadata_file_path) def get_ids(self, str_value, field_set): return do_get_ids(self.data, str_value, field_set) def get_by_id(self, id, field_set): return do_get_by_id(self.data, id, field_set) def filter(self, field_value_list): return do_filter(self.data, field_value_list) def coauthor_graph(self, author_id, level): return do_coauthor_graph(self.data, author_id, level) def author_network(self): return do_author_network(self.data) def retrieve_tree_of_venues(self, no_ids): return do_retrieve_tree_of_venues(self.data, no_ids)

40ee4ed83f923065e39ef73fb28892862e50e4db

py

Python

quantnode/structures.py

quantnode/quantnode-client

64c849d1f79e1113b7d408a6727284c3ff5b054e

2015-03-07T18:57:11.000Z

2015-03-07T18:57:11.000Z

quantnode/structures.py

quantnode/quantnode-client

64c849d1f79e1113b7d408a6727284c3ff5b054e

quantnode/structures.py

quantnode/quantnode-client

64c849d1f79e1113b7d408a6727284c3ff5b054e

class DotDict(dict): """ A 'dotted dictionary' Simple dictionary wrapper that overrides the dot operator with dictionary getter and setter so that instance['value'] is equivalent to instance.value """ def __setattr__(self, k, v): if k[0] == '_' or k in self.__dict__: return super(DotDict, self).__setattr__(k, v) else: self[k] = v def __getattr__(self, k): if k[0] == '_': raise AttributeError(k) try: return self[k] except KeyError, err: # return None return float('nan') # raise AttributeError(*err.args) @staticmethod def ToDotDict(data): """ Recurisvely transforms a dict to a dotted dictionary """ if isinstance(data, dict): for k, v in data.iteritems(): if isinstance(v, dict): data[k] = DotDict(v) DotDict.ToDotDict(data[k]) elif isinstance(v, list): data[k] = [DotDict.ToDotDict(i) for i in v] elif isinstance(data, list): return [DotDict.ToDotDict(i) for i in data] else: return data return DotDict(data)

77e86dadcc855cf0ba381407dc2772546becf579

py

Python

interview_qns/linked_list.py

shiram/learning-django-rest

3e6d26212429c423851cef047eea3c7a820e8e58

interview_qns/linked_list.py

shiram/learning-django-rest

3e6d26212429c423851cef047eea3c7a820e8e58

interview_qns/linked_list.py

shiram/learning-django-rest

3e6d26212429c423851cef047eea3c7a820e8e58

class Node: def __init__(self, dataval=None): self.dataval = dataval self.nextval = None class SingleLinkedList: def __init__(self): self.headval = None def traverse(self): printval = self.headval list_string = "" while printval is not None: list_string += str(printval.dataval) + "->" printval = printval.nextval list_string += "(Next Node)" print(list_string) def valueExists(self, value): current_value = self.headval while current_value is not None: if (current_value.dataval == value) and (type(current_value.dataval) == type(value)): return True current_value = current_value.nextval return False def insertAtBegin(self, newdata): newNode = Node(newdata) newNode.nextval = self.headval self.headval = newNode def insertAtEnd(self, newdata): newNode = Node(newdata) if self.headval is None: self.headval = newNode return last = self.headval while(last.nextval): last = last.nextval last.nextval = newNode def insertInbetween(self, middle_node, newdata): if middle_node is None: print("The mentioned node is absent") return newNode = Node(newdata) newNode.nextval = middle_node.nextval middle_node.nextval = newNode def removeNode(self, remove_key): headVal = self.headval if (headVal is not None): if (headVal.dataval == remove_key): self.headval = headVal.nextval headVal = None return while headVal is not None: if headVal.dataval == remove_key: break prev = headVal headVal = headVal.nextval if headVal == None: return prev.nextval = headVal.nextval headVal = None list1 = SingleLinkedList() list1.headval = Node("Mon") e2, e3, e4 = Node("Tue"), Node("Wed"), Node("Thur") list1.headval.nextval = e2 e2.nextval = e3 e3.nextval = e4 list1.insertAtBegin("Sun") list1.insertAtEnd("Fri") list1.insertInbetween(e3.nextval, "WhatDay") list1.insertAtEnd(1) list1.insertAtBegin(0) list1.insertAtEnd(False) list1.removeNode("WhatDay") list1.traverse() if list1.valueExists(False): print("Value Exists") else: print("Value Does not Exist")

abdce969985bba575a471cbd06f1f9eaccbc27eb

py

Python

.pc/hg-updates.diff/Lib/tkinter/test/test_tkinter/test_variables.py

Hadron/python

73137f499ed658169f49273eee46845e3b53e800

2016-05-28T18:51:54.000Z

2022-03-20T17:30:31.000Z

.pc/hg-updates.diff/Lib/tkinter/test/test_tkinter/test_variables.py

Hadron/python

73137f499ed658169f49273eee46845e3b53e800

2016-05-29T00:24:56.000Z

2020-07-13T11:56:58.000Z

.pc/hg-updates.diff/Lib/tkinter/test/test_tkinter/test_variables.py

Hadron/python

73137f499ed658169f49273eee46845e3b53e800

2015-05-29T17:18:53.000Z

2022-01-15T14:06:44.000Z

import unittest from tkinter import (Variable, StringVar, IntVar, DoubleVar, BooleanVar, Tcl, TclError) class Var(Variable): _default = "default" side_effect = False def set(self, value): self.side_effect = True super().set(value) class TestBase(unittest.TestCase): def setUp(self): self.root = Tcl() def tearDown(self): del self.root class TestVariable(TestBase): def info_exists(self, *args): return self.root.getboolean(self.root.call("info", "exists", *args)) def test_default(self): v = Variable(self.root) self.assertEqual("", v.get()) self.assertRegex(str(v), r"^PY_VAR(\d+)$") def test_name_and_value(self): v = Variable(self.root, "sample string", "varname") self.assertEqual("sample string", v.get()) self.assertEqual("varname", str(v)) def test___del__(self): self.assertFalse(self.info_exists("varname")) v = Variable(self.root, "sample string", "varname") self.assertTrue(self.info_exists("varname")) del v self.assertFalse(self.info_exists("varname")) def test_dont_unset_not_existing(self): self.assertFalse(self.info_exists("varname")) v1 = Variable(self.root, name="name") v2 = Variable(self.root, name="name") del v1 self.assertFalse(self.info_exists("name")) # shouldn't raise exception del v2 self.assertFalse(self.info_exists("name")) def test___eq__(self): # values doesn't matter, only class and name are checked v1 = Variable(self.root, name="abc") v2 = Variable(self.root, name="abc") self.assertEqual(v1, v2) v3 = Variable(self.root, name="abc") v4 = StringVar(self.root, name="abc") self.assertNotEqual(v3, v4) def test_invalid_name(self): with self.assertRaises(TypeError): Variable(self.root, name=123) def test_null_in_name(self): with self.assertRaises(ValueError): Variable(self.root, name='var\x00name') with self.assertRaises(ValueError): self.root.globalsetvar('var\x00name', "value") with self.assertRaises(ValueError): self.root.globalsetvar(b'var\x00name', "value") with self.assertRaises(ValueError): self.root.setvar('var\x00name', "value") with self.assertRaises(ValueError): self.root.setvar(b'var\x00name', "value") def test_initialize(self): v = Var(self.root) self.assertFalse(v.side_effect) v.set("value") self.assertTrue(v.side_effect) class TestStringVar(TestBase): def test_default(self): v = StringVar(self.root) self.assertEqual("", v.get()) def test_get(self): v = StringVar(self.root, "abc", "name") self.assertEqual("abc", v.get()) self.root.globalsetvar("name", "value") self.assertEqual("value", v.get()) def test_get_null(self): v = StringVar(self.root, "abc\x00def", "name") self.assertEqual("abc\x00def", v.get()) self.root.globalsetvar("name", "val\x00ue") self.assertEqual("val\x00ue", v.get()) class TestIntVar(TestBase): def test_default(self): v = IntVar(self.root) self.assertEqual(0, v.get()) def test_get(self): v = IntVar(self.root, 123, "name") self.assertEqual(123, v.get()) self.root.globalsetvar("name", "345") self.assertEqual(345, v.get()) def test_invalid_value(self): v = IntVar(self.root, name="name") self.root.globalsetvar("name", "value") with self.assertRaises((ValueError, TclError)): v.get() self.root.globalsetvar("name", "345.0") with self.assertRaises((ValueError, TclError)): v.get() class TestDoubleVar(TestBase): def test_default(self): v = DoubleVar(self.root) self.assertEqual(0.0, v.get()) def test_get(self): v = DoubleVar(self.root, 1.23, "name") self.assertAlmostEqual(1.23, v.get()) self.root.globalsetvar("name", "3.45") self.assertAlmostEqual(3.45, v.get()) def test_get_from_int(self): v = DoubleVar(self.root, 1.23, "name") self.assertAlmostEqual(1.23, v.get()) self.root.globalsetvar("name", "3.45") self.assertAlmostEqual(3.45, v.get()) self.root.globalsetvar("name", "456") self.assertAlmostEqual(456, v.get()) def test_invalid_value(self): v = DoubleVar(self.root, name="name") self.root.globalsetvar("name", "value") with self.assertRaises((ValueError, TclError)): v.get() class TestBooleanVar(TestBase): def test_default(self): v = BooleanVar(self.root) self.assertIs(v.get(), False) def test_get(self): v = BooleanVar(self.root, True, "name") self.assertIs(v.get(), True) self.root.globalsetvar("name", "0") self.assertIs(v.get(), False) self.root.globalsetvar("name", 42 if self.root.wantobjects() else 1) self.assertIs(v.get(), True) self.root.globalsetvar("name", 0) self.assertIs(v.get(), False) self.root.globalsetvar("name", "on") self.assertIs(v.get(), True) def test_set(self): true = 1 if self.root.wantobjects() else "1" false = 0 if self.root.wantobjects() else "0" v = BooleanVar(self.root, name="name") v.set(True) self.assertEqual(self.root.globalgetvar("name"), true) v.set("0") self.assertEqual(self.root.globalgetvar("name"), false) v.set(42) self.assertEqual(self.root.globalgetvar("name"), true) v.set(0) self.assertEqual(self.root.globalgetvar("name"), false) v.set("on") self.assertEqual(self.root.globalgetvar("name"), true) def test_invalid_value_domain(self): false = 0 if self.root.wantobjects() else "0" v = BooleanVar(self.root, name="name") with self.assertRaises(TclError): v.set("value") self.assertEqual(self.root.globalgetvar("name"), false) self.root.globalsetvar("name", "value") with self.assertRaises(ValueError): v.get() self.root.globalsetvar("name", "1.0") with self.assertRaises(ValueError): v.get() tests_gui = (TestVariable, TestStringVar, TestIntVar, TestDoubleVar, TestBooleanVar) if __name__ == "__main__": from test.support import run_unittest run_unittest(*tests_gui)

8053e20f581178fa1653e5a1574177dd5703ab87

py

Python

mlsim/__init__.py

brownsarahm/ml-sim

26c03e65c57a2ffc3784e6e79eaa76100f0bf391

2020-11-18T21:08:17.000Z

2020-11-18T21:08:17.000Z

mlsim/__init__.py

brownsarahm/ml-sim

26c03e65c57a2ffc3784e6e79eaa76100f0bf391

2020-02-11T21:37:03.000Z

2020-07-09T01:10:49.000Z

mlsim/__init__.py

brownsarahm/ml-sim

26c03e65c57a2ffc3784e6e79eaa76100f0bf391

from . import anomaly from . import bias __all__ = ['bias','anomaly']

89c0bf36d7b103b646df6f313f741523feb852e7

py

Python

agent0/common/vec_env/__init__.py

zhoubin-me/agent0

1184827077e43dfa63e1f24a004fcc6c3e3d5130

agent0/common/vec_env/__init__.py

zhoubin-me/agent0

1184827077e43dfa63e1f24a004fcc6c3e3d5130

agent0/common/vec_env/__init__.py

zhoubin-me/agent0

1184827077e43dfa63e1f24a004fcc6c3e3d5130

from .dummy_vec_env import DummyVecEnv from .shmem_vec_env import ShmemVecEnv from .subproc_vec_env import SubprocVecEnv from .vec_env import AlreadySteppingError, NotSteppingError, VecEnv, VecEnvWrapper, VecEnvObservationWrapper, \ CloudpickleWrapper from .vec_frame_stack import VecFrameStack from .vec_monitor import VecMonitor from .vec_remove_dict_obs import VecExtractDictObs __all__ = ['AlreadySteppingError', 'NotSteppingError', 'VecEnv', 'VecEnvWrapper', 'VecEnvObservationWrapper', 'CloudpickleWrapper', 'DummyVecEnv', 'ShmemVecEnv', 'SubprocVecEnv', 'VecFrameStack', 'VecMonitor', 'VecNormalize', 'VecExtractDictObs']

465d237f4c57946c16db4d17be14dff3747dbb86

py

Python

conans/test/functional/deploy_test.py

amatoshka/conan

c2726e8c255adb120b5f7bdee9e3ec0bc90f1d7a

conans/test/functional/deploy_test.py

amatoshka/conan

c2726e8c255adb120b5f7bdee9e3ec0bc90f1d7a

2018-02-22T21:28:04.000Z

2018-09-28T13:51:47.000Z

conans/test/functional/deploy_test.py

amatoshka/conan

c2726e8c255adb120b5f7bdee9e3ec0bc90f1d7a

import os import unittest from parameterized.parameterized import parameterized from conans.model.manifest import FileTreeManifest from conans.test.utils.test_files import temp_folder from conans.test.utils.tools import TestClient from conans.util.files import load, mkdir class DeployTest(unittest.TestCase): @parameterized.expand([(True, ), (False, )]) def deploy_test(self, deploy_to_abs): client = TestClient() libconanfile = """from conans import ConanFile from conans.tools import save class Lib(ConanFile): exports_sources = "*" def build(self): save("mylib.dll", "mydll") def package(self): self.copy("*") def deploy(self): self.output.info("Lib deploy()") """ client.save({"conanfile.py": libconanfile, "License.md": "lib license", "otherfile": ""}) client.run("create . Lib/0.1@user/testing") self.assertNotIn("Lib deploy()", client.out) if deploy_to_abs: dll_folder = temp_folder() mkdir(dll_folder) else: dll_folder = "" conanfile = """from conans import ConanFile from conans.tools import save class Pkg(ConanFile): requires = "Lib/0.1@user/testing" def build(self): save("myapp.exe", "myexe") def package(self): self.copy("*") def deploy(self): self.output.info("Pkg deploy()") self.copy("*.exe") self.copy_deps("*.dll", dst="%s") """ % dll_folder.replace("\\", "/") client.save({"conanfile.py": conanfile}) client.run("create . Pkg/0.1@user/testing") self.assertNotIn("deploy()", client.out) def test_install_in(folder): client.current_folder = temp_folder() client.run("install Pkg/0.1@user/testing --install-folder=%s" % folder) self.assertIn("Pkg/0.1@user/testing deploy(): Copied 1 '.dll' file: mylib.dll", client.out) self.assertIn("Pkg/0.1@user/testing deploy(): Copied 1 '.exe' file: myapp.exe", client.out) deploy_manifest = FileTreeManifest.loads(load(os.path.join(client.current_folder, folder, "deploy_manifest.txt"))) app = os.path.abspath(os.path.join(client.current_folder, folder, "myapp.exe")) if deploy_to_abs: lib = os.path.join(dll_folder, "mylib.dll") else: lib = os.path.abspath(os.path.join(client.current_folder, folder, "mylib.dll")) self.assertEqual(sorted([app, lib]), sorted(deploy_manifest.file_sums.keys())) self.assertEqual(load(app), "myexe") self.assertEqual(load(lib), "mydll") test_install_in("./") test_install_in("other_install_folder")

a4490577576ace8a7ae9268ce013320822db68f4

py

Python

build/lib/gains/tests/test_density.py

BeckResearchLab/gains

fa3e008cfd129b1a0411368306eaad0b62309ae0

2018-06-21T16:58:32.000Z

2019-12-11T19:57:37.000Z

build/lib/gains/tests/test_density.py

BeckResearchLab/gains

fa3e008cfd129b1a0411368306eaad0b62309ae0

2018-04-04T16:19:27.000Z

2018-05-23T01:46:18.000Z

build/lib/gains/tests/test_density.py

BeckResearchLab/gains

fa3e008cfd129b1a0411368306eaad0b62309ae0

2018-07-12T05:05:04.000Z

2019-12-03T05:39:45.000Z

from __future__ import absolute_import, division, print_function import gains as genetic from rdkit.Chem import AllChem as Chem from rdkit.ML.Descriptors.MoleculeDescriptors import\ MolecularDescriptorCalculator as calculator import numpy as np import unittest import datetime from math import exp import random class GuessIonTests(unittest.TestCase): geneSet = genetic.generate_geneset() df = genetic.load_data("saltInfo.csv") df = df['anion_SMILES'].unique() ohPickMe = random.sample(range(df.shape[0]), 1) anion = Chem.MolFromSmiles(df[ohPickMe[0]]) def test_1_density(self): target = random.sample(range(800, 1500), 1)[0] self.guess_password(target) def test_benchmark(self): genetic.Benchmark.run(self.test_1_density) def guess_password(self, target): startTime = datetime.datetime.now() def fnGetFitness(genes): return get_fitness(self.anion, genes, target) def fnDisplay(candidate, mutation): display(candidate, mutation, startTime) def fnShowIon(genes, target, mutation_attempts): show_ion(genes, target, mutation_attempts) optimalFitness = 0.99 best = genetic.get_best(fnGetFitness, optimalFitness, self.geneSet, fnDisplay, fnShowIon, target) return best def display(candidate, mutation, startTime): timeDiff = datetime.datetime.now() - startTime print("{}\t{}\t{}\t{}".format(candidate.Genes, candidate.Fitness, mutation, timeDiff)) def get_fitness(anion, genes, target): cation = Chem.MolFromSmiles(genes) model = genetic.load_data("density_nn_model.sav", pickleFile=True) deslist = genetic.load_data("density_nn_model_descriptors.csv") feature_vector = [] with genetic.suppress_stdout_stderr(): for item in deslist: if "anion" in item: feature_vector.append(calculator([item.partition('-') [0]]).CalcDescriptors(anion)[0]) elif "cation" in item: feature_vector.append(calculator([item.partition('-') [0]]).CalcDescriptors(cation)[0]) elif "Temperature_K" in item: feature_vector.append(298.15) elif "Pressure_kPa" in item: feature_vector.append(101.325) else: print("unknown descriptor in list: %s" % item) features_normalized = (feature_vector - deslist.iloc[0].values) /\ deslist.iloc[1].values prediction = exp(model.predict(np.array(features_normalized). reshape(1, -1))[0]) error = abs((prediction - target) / target) return 1 - error def show_ion(genes, target, mutation_attempts): mol = Chem.MolFromSmiles(genes) print("{}\t{}".format("number of atoms: ", mol.GetNumAtoms())) print("{}\t{}".format("mutation attempts: ", mutation_attempts)) print("within 1%% of target density: %s (kg/m) " % target) if __name__ == '__main__': unittest.main()

a2e21cd55be4823e8cb56fa538e704d8c1c46fbd

py

Python

python/paddle/fluid/executor.py

mamingjie-China/Paddle

91d2f1e3e6e51142a74a43d0673a8feff056c39b

2021-12-20T09:44:25.000Z

2021-12-20T09:44:25.000Z

python/paddle/fluid/executor.py

Janayt/Paddle

68c6160e639be38c57a7dd831f7b841b33e92676

python/paddle/fluid/executor.py

Janayt/Paddle

68c6160e639be38c57a7dd831f7b841b33e92676

# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import print_function import logging import os import multiprocessing import sys import warnings import numpy as np from .wrapped_decorator import signature_safe_contextmanager import six from .data_feeder import convert_dtype from .framework import Program, default_main_program, Variable, Operator, convert_np_dtype_to_dtype_ from . import core from . import compiler from .. import compat as cpt from .trainer_factory import TrainerFactory from .trainer_factory import FetchHandlerMonitor import copy __all__ = ['Executor', 'global_scope', 'scope_guard'] g_scope = core.Scope() InferNativeConfig = core.NativeConfig InferAnalysisConfig = core.AnalysisConfig def global_scope(): """ :api_attr: Static Graph Get the global/default scope instance. There are a lot of APIs use :code:`global_scope` as its default value, e.g., :code:`Executor.run` Returns: Scope: The global/default scope instance. Examples: .. code-block:: python import paddle.fluid as fluid import numpy fluid.global_scope().var("data").get_tensor().set(numpy.ones((2, 2)), fluid.CPUPlace()) numpy.array(fluid.global_scope().find_var("data").get_tensor()) """ return g_scope def _switch_scope(scope): global g_scope ex = g_scope g_scope = scope return ex @signature_safe_contextmanager def scope_guard(scope): """ :api_attr: Static Graph This function switches scope through python `with` statement. Scope records the mapping between variable names and variables ( :ref:`api_guide_Variable` ), similar to brackets in programming languages. If this function is not invoked, all variables and variable names are recorded in the default global scope. When users need to create variables with the same name, they need to switch scopes through this function if they do not want the mapping of variables with the same name to be overwritten. After switching through the `with` statement, all variables created in the `with` block will be assigned to a new scope. Parameters: scope: The new scope. Returns: None Examples: .. code-block:: python import paddle.fluid as fluid import numpy new_scope = fluid.Scope() with fluid.scope_guard(new_scope): fluid.global_scope().var("data").get_tensor().set(numpy.ones((2, 2)), fluid.CPUPlace()) numpy.array(new_scope.find_var("data").get_tensor()) """ ex = _switch_scope(scope) try: yield finally: _switch_scope(ex) def as_numpy(tensor): """ Convert a Tensor to a numpy.ndarray, its only support Tensor without LoD information. For higher dimensional sequence data, please use LoDTensor directly. Examples: .. code-block:: python import paddle.fluid as fluid import numpy new_scope = fluid.Scope() with fluid.scope_guard(new_scope): fluid.global_scope().var("data").get_tensor().set(numpy.ones((2, 2)), fluid.CPUPlace()) tensor = new_scope.find_var("data").get_tensor() fluid.executor.as_numpy(tensor) # or numpy.array(new_scope.find_var("data").get_tensor()) Args: tensor(Variable): a instance of Tensor Returns: numpy.ndarray """ if isinstance(tensor, core.LoDTensorArray): return [as_numpy(t) for t in tensor] if isinstance(tensor, list): return [as_numpy(t) for t in tensor] assert isinstance(tensor, core.LoDTensor) lod = tensor.lod() if len(lod) > 0: raise RuntimeError("Some of your fetched tensors hold LoD information. \ They can not be completely cast to Python ndarray. \ Please set the parameter 'return_numpy' as 'False' to \ return LoDTensor itself directly.") if tensor._is_initialized(): return np.array(tensor) else: return None def dtype_is_compatible_with(first, second): """ Returns True if the first dtype can be compatible the second one. Currently, we require the two dtype's have to be same. Args: dtype (np.dtype|VarType|str): The type of data: float32, int64, etc. Returns: True if the two types are same. """ if not isinstance(first, core.VarDesc.VarType): first = convert_np_dtype_to_dtype_(first) if not isinstance(second, core.VarDesc.VarType): second = convert_np_dtype_to_dtype_(second) return first == second def dimension_is_compatible_with(first, second): """ Returns True if the two dimensions are compatible. A dimension is compatible with the other if: 1. The length of the dimensions are same. 2. Each non-negative number of the two dimensions are same. 3. For negative number or 'None' in a dimension, it means unknown so it is compatible with any number. Args: first (list/tuple): integers representing shape. "None" or negative number means unknown. second (list/tuple): integers representing shape. "None" or negative number means unknown. Returns: True if the two dimensions are compatible. """ dim_len = len(first) if dim_len != len(second): return False for i in range(dim_len): if first[i] is None or first[i] < 0: continue if second[i] is None or second[i] < 0: continue if first[i] != second[i]: return False return True def check_feed_shape_type(var, feed, num_places=1): """ Returns True if the variable doesn't require feed check or it is compatible with the shape and have same dtype as the fed value. A dimension is compatible with the other if: 1. The length of the dimensions are same. 2. Each non-negative number of the two dimensions are same. 3. For negative number or 'None' in a dimension, it means unknown so it is compatible with any number. Args: var (Variable): the Variable object feed (LoDTensor): the fed value, which must be a LoDTensor num_places: an integer value indicating the number of places. ParallelExecutor will divide data into devices (CPU/GPU) evenly. Returns: True if the shape and dtype of variable is compatible with the feed value Raises: ValueError: if the shape or dtype of the variable is not compatible with the feed value """ if var.desc.need_check_feed(): diff_shape = core.diff_tensor_shape(feed, var.desc, num_places) if diff_shape is not None: raise ValueError( 'The fed Variable %r should have dimensions = %d, shape = ' '%r, but received fed shape %r on each device' % (var.name, len(var.shape), var.shape, diff_shape)) if not dtype_is_compatible_with(feed._dtype(), var.dtype): var_dtype_format = convert_dtype(var.dtype) if isinstance( var.dtype, core.VarDesc.VarType) else var.dtype feed_dtype_format = convert_dtype(feed._dtype()) if isinstance( feed._dtype(), core.VarDesc.VarType) else feed._dtype() raise ValueError( 'The data type of fed Variable %r must be %r, but received %r' % (var.name, var_dtype_format, feed_dtype_format)) return True def has_feed_operators(block, feed_targets, feed_holder_name): """ Check whether the block already has feed operators. Return false if the block does not have any feed operators. If some feed operators have been prepended to the block, check that the info contained in these feed operators matches the feed_targets and feed_holder_name. Raise exception when any mismatch is found. Return true when the block has feed operators with matching info. Args: block: a block instance (typically global block of a program) feed_targets: a dictionary of {feed_target_name: feed_target_data} feed_holder_name: the name of the variable that holds the data of all feed targets. The type of this feed_holder variable is FEED_MINIBATCH, which is essentially vector<LoDTensor>. Returns: A boolean value that indicates whether a block has feed operators that match the info contained in feed_targets and feed_holder_name. """ feed_count = 0 for op in block.ops: if op.desc.type() == 'feed': feed_count += 1 assert op.desc.input('X')[0] == feed_holder_name feed_target_name = op.desc.output('Out')[0] if feed_target_name not in feed_targets: raise Exception("'feed_targets' does not have {} variable". format(feed_target_name)) else: break if feed_count > 0 and feed_count != len(feed_targets): raise Exception( "Feed operators in program desc do not match 'feed_targets'") return feed_count > 0 def has_fetch_operators(block, fetch_targets, fetch_holder_name): """ Check whether the block already has fetch operators. Return false if the block does not have any fetch operators. If some fetch operators have been appended to the block, check that the info contained in these fetch operators matches the fetch_targets and fetch_holder_name. Raise exception when any mismatch is found. Return true when the block has fetch operators with matching info. Args: block: a block instance (typically global block of a program) fetch_targets: a dictionary of {fetch_target_name: fetch_target_data} fetch_holder_name: the name of the variable that holds the data of all fetch targets. The type of this fetch_holder variable is FETCH_LIST, which is essentially vector<LoDTensor>. Return: A boolean value that indicates whether a block has fetch operators that match the info contained in fetch_targets and fetch_holder_name. """ fetch_count = 0 for op in block.ops: if op.desc.type() == 'fetch': fetch_count += 1 assert op.desc.output('Out')[0] == fetch_holder_name fetch_target_name = op.desc.input('X')[0] if fetch_target_name not in [ var.desc.name() for var in fetch_targets ]: raise Exception("'fetch_targets' does not have {} variable". format(fetch_target_name)) idx = op.desc.attr('col') assert fetch_target_name == fetch_targets[idx].desc.name() if fetch_count > 0 and fetch_count != len(fetch_targets): raise Exception( "Fetch operators in program desc do not match 'fetch_targets'") return fetch_count > 0 def _fetch_var(name, scope=None, return_numpy=True): """ Fetch the value of the variable with the given name from the given scope. Args: name(str): name of the variable. Typically, only persistable variables can be found in the scope used for running the program. scope(core.Scope|None): scope object. It should be the scope where you pass to Executor.run() when running your program. If None, global_scope() will be used. Default None. return_numpy(bool): whether convert the tensor to numpy.ndarray. Default True. Returns: LodTensor|numpy.ndarray """ assert isinstance(name, six.string_types) if scope is None: scope = global_scope() assert isinstance(scope, core._Scope) var = scope.find_var(_to_name_str(name)) assert var is not None, ( "Cannot find " + name + " in scope. Perhaps you need to make the" " variable persistable by using var.persistable = True in your" " program.") tensor = var.get_tensor() if return_numpy: tensor = as_numpy(tensor) return tensor def _to_name_str(var): def _to_str(var): if isinstance(var, Variable): return var.desc.name() elif isinstance(var, str): return var elif isinstance(var, six.string_types): return str(var) elif isinstance(var, Operator): return str(id(var)) else: raise TypeError(str(var) + " should be Variable, Operator or str") # NOTEz(zhiqiu): The item in fetch_list may be tuple returned by Optimizer.minimize(), # see comments in _split_optimize_ops_in_fetch_list for more details. if isinstance(var, tuple): var = var[0] if isinstance(var, list): s = [_to_str(item) for item in var] return ','.join(s) else: return _to_str(var) def _get_strong_program_cache_key(program, feed, fetch_list): return str(id(program)) + _get_program_cache_key(feed, fetch_list) def _get_program_cache_key(feed, fetch_list): feed_var_names = [] if isinstance(feed, dict): feed_var_names = list(feed.keys()) elif isinstance(feed, list) or isinstance(feed, tuple): for i, each in enumerate(feed): feed_var_names += list(each.keys()) fetch_var_names = list(map(_to_name_str, fetch_list)) return str(feed_var_names + fetch_var_names) def _as_lodtensor(data, place, dtype=None): """ Convert numpy.ndarray to Tensor, its only support Tensor without LoD information. For higher dimensional sequence data, please use LoDTensor directly. Examples: >>> import paddle.fluid as fluid >>> place = fluid.CPUPlace() >>> exe = fluid.executor(place) >>> data = np.array(size=(100, 200, 300)) >>> np_outs = map(lambda x: fluid.executor._as_lodtensor(x, place), data) >>> ... Args: data(numpy.ndarray|list|tuple|scalar): a instance of array, scalar, list or tuple data(core.Place): the place of created tensor dtype(core.VarDesc.VarType|str): the expected data type of created tensor Returns: LoDTensor """ #NOTE(zhiqiu): convert python builtin, like float, int, and list, to numpy ndarray if not isinstance(data, np.ndarray): assert dtype is not None, 'The dtype should be given when feed data is not np.ndarray' dtype = convert_dtype(dtype) if isinstance( dtype, core.VarDesc.VarType) else dtype if np.isscalar(data): data = np.array([data]).astype(dtype) elif isinstance(data, (list, tuple)): data = np.array(data) if data.dtype == np.object: raise TypeError( "\n\tFaild to convert input data to a regular ndarray :\n\t* Usually " "this means the input data contains nested lists with different lengths. " "Please consider using 'fluid.create_lod_tensor' to convert it to a LoD-Tensor." ) data = data.astype(dtype) else: raise TypeError( "Convert data of type {} to Tensor is not supported".format( type(data))) # convert numpy.ndarray to tensor tensor = core.LoDTensor() tensor.set(data, place) return tensor class FetchHandler(object): def __init__(self, var_dict=None, period_secs=60): assert var_dict != None self.var_dict = var_dict self.period_secs = period_secs def handler(self, res_dict): for key in res_dict: if type(res_dict[key]) is np.ndarray: sys.stdout.write("{}[0]: {} ".format(key, res_dict[key][0])) sys.stdout.write("\n") @staticmethod def help(): print(""" class FetchHandlerExample(FetchHandler): def handler(self, res_dict): print(res_dict["auc"]) print("auc: {}, {}".format(res_dict["auc"], time.ctime())) auc = Variable() var_dict = {"auc": auc} handler = FetchHandlerExample(var_dict=var_dict) """) class Executor(object): """ :api_attr: Static Graph An Executor in Python, supports single/multiple-GPU running, and single/multiple-CPU running. Args: place(fluid.CPUPlace()|fluid.CUDAPlace(n)|None): This parameter represents which device the executor runs on. When this parameter is None, PaddlePaddle will set the default device according to its installation version. If Paddle is CPU version, the default device would be set to `CPUPlace()` . If Paddle is GPU version, the default device would be set to `CUDAPlace(0)` . Default is None. Returns: Executor Examples: .. code-block:: python import paddle.fluid as fluid import paddle.fluid.compiler as compiler import numpy import os # Set place explicitly. # use_cuda = True # place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() # exe = fluid.Executor(place) # If you don't set place, PaddlePaddle sets the default device. exe = fluid.Executor() train_program = fluid.Program() startup_program = fluid.Program() with fluid.program_guard(train_program, startup_program): data = fluid.data(name='X', shape=[None, 1], dtype='float32') hidden = fluid.layers.fc(input=data, size=10) loss = fluid.layers.mean(hidden) fluid.optimizer.SGD(learning_rate=0.01).minimize(loss) # Run the startup program once and only once. # Not need to optimize/compile the startup program. startup_program.random_seed=1 exe.run(startup_program) # Run the main program directly without compile. x = numpy.random.random(size=(10, 1)).astype('float32') loss_data, = exe.run(train_program, feed={"X": x}, fetch_list=[loss.name]) # Or, compiled the program and run. See `CompiledProgram` # for more detail. # NOTE: If you use CPU to run the program or Paddle is # CPU version, you need to specify the CPU_NUM, otherwise, # fluid will use all the number of the logic core as # the CPU_NUM, in that case, the batch size of the input # should be greater than CPU_NUM, if not, the process will be # failed by an exception. # Set place explicitly. # if not use_cuda: # os.environ['CPU_NUM'] = str(2) # If you don't set place and PaddlePaddle is CPU version os.environ['CPU_NUM'] = str(2) compiled_prog = compiler.CompiledProgram( train_program).with_data_parallel( loss_name=loss.name) loss_data, = exe.run(compiled_prog, feed={"X": x}, fetch_list=[loss.name]) """ def __init__(self, place=None): if place is None: if core.is_compiled_with_cuda(): self.place = core.CUDAPlace(0) else: self.place = core.CPUPlace() else: self.place = place self.program_caches = dict() self.ctx_caches = dict() self.scope_caches = dict() self.var_caches = dict() self.pruned_program_caches = dict() p = core.Place() p.set_place(self.place) self._default_executor = core.Executor(p) self._closed = False self.pruned_program_scope_caches = dict() def _get_scope_cache(self, program_cache_key): return self.scope_caches.get(program_cache_key, None) def _get_ctx_cache(self, program_cache_key): return self.ctx_caches.get(program_cache_key, None) def _get_program_cache(self, program_cache_key): return self.program_caches.get(program_cache_key, None) def _add_program_cache(self, program_cache_key, program): self.program_caches[program_cache_key] = program def _get_pruned_program_cache(self, program_cache_key): return self.pruned_program_caches.get(program_cache_key, None) def _add_pruned_program_cache(self, program_cache_key, program): self.pruned_program_caches[program_cache_key] = program def _get_pruned_program_scope_cache(self, program_cache_key): return self.pruned_program_scope_caches.get(program_cache_key, None) def _add_pruned_program_scope_cache(self, program_cache_key, program): self.pruned_program_scope_caches[program_cache_key] = program def _add_ctx_cache(self, ctx_cache_key, ctx): self.ctx_caches[ctx_cache_key] = ctx def _add_scope_cache(self, scope_cache_key, scope): self.scope_caches[scope_cache_key] = scope def _add_feed_fetch_ops(self, program, feed, fetch_list, feed_var_name, fetch_var_name): tmp_program = program.clone() global_block = tmp_program.global_block() if feed_var_name in global_block.vars: feed_var = global_block.var(feed_var_name) else: feed_var = global_block.create_var( name=feed_var_name, type=core.VarDesc.VarType.FEED_MINIBATCH, persistable=True) if fetch_var_name in global_block.vars: fetch_var = global_block.var(fetch_var_name) else: fetch_var = global_block.create_var( name=fetch_var_name, type=core.VarDesc.VarType.FETCH_LIST, persistable=True) # prepend feed operators if not has_feed_operators(global_block, feed, feed_var_name): for i, name in enumerate(feed): if global_block.has_var(name): out = global_block.var(name) global_block._prepend_op( type='feed', inputs={'X': [feed_var]}, outputs={'Out': [out]}, attrs={'col': i}) else: warnings.warn( "The variable %s is not found in program. It is not declared or is pruned." % name) # append fetch_operators if not has_fetch_operators(global_block, fetch_list, fetch_var_name): for i, var in enumerate(fetch_list): assert isinstance(var, Variable) or isinstance( var, six.string_types), ( "Wrong type for fetch_list[%s]: %s" % (i, type(var))) global_block.append_op( type='fetch', inputs={'X': [var]}, outputs={'Out': [fetch_var]}, attrs={'col': i}) return tmp_program def _feed_data(self, program, feed, feed_var_name, scope): # feed var to framework global_block = program.global_block() for op in global_block.ops: if op.desc.type() == 'feed': feed_target_name = op.desc.output('Out')[0] cur_feed = feed[feed_target_name] var = global_block.var(feed_target_name) if not isinstance(cur_feed, core.LoDTensor): cur_feed = _as_lodtensor(cur_feed, self.place, var.dtype) check_feed_shape_type(var, cur_feed) idx = op.desc.attr('col') core.set_feed_variable(scope, cur_feed, feed_var_name, idx) else: break def _fetch_data(self, fetch_list, fetch_var_name, scope): outs = [ core.get_fetch_variable(scope, fetch_var_name, i) for i in six.moves.range(len(fetch_list)) ] return outs def _split_optimize_ops_in_fetch_list(self, fetch_list): """ Split optimize_ops from fetch_list, which provided to specify program prunning. Args: fetch_list(list): The original fetch_list. Possible types of fetch_list are: fetch_list = ['loss'] fetch_list = [[sgd, sgd], 'loss'] fetch_list = [([sgd, sgd], [(param, grad)]), 'loss'] Returns: optimize_ops(list): The optimize operators splited from fetch_list. fetch_list(list): The updated fetch_list which does not contain optimize operators. """ _optimize_ops = [] _fetch_list = [] def _get_targets(_optimize_ops, _fetch_list, item): if isinstance(item, Operator): if item._is_optimize_op(): _optimize_ops.append(item) else: raise TypeError( "The operator in fetch_list is not an optimize_op") elif isinstance(item, Variable) or isinstance( item, str) or isinstance(item, six.string_types): _fetch_list.append(item) else: raise TypeError( "The item in fetch_list should be str, variable or optimize_op, but recieved %s.", type(item)) for item in fetch_list: # NOTE(zhiqiu): to support (optimizer_ops, param_and_grads) and optimizer_ops in fetch_list # we should handle tuple and list in fetch_list. # TODO(zhiqiu): find a better way to handle that. if isinstance(item, list): for i in item: _get_targets(_optimize_ops, _fetch_list, i) elif isinstance(item, tuple): for i in item[0]: _get_targets(_optimize_ops, _fetch_list, i) else: _get_targets(_optimize_ops, _fetch_list, item) return _fetch_list, _optimize_ops def _prune_program(self, program, feed=None, fetch_list=None, optimize_ops=None): """ Prune operators and variables which are not needed to generate :code:`fetch_list` and optimize operators. Prune operators and variables which are needed to generate variables to be feeded. Notes: This is a very low level API. Users should not use this API directly. Args: program(Program): the origin program feed(list|dict): feed dict or list. fetch_list(list|Variable): A list of variables need to be fetched optimize_ops(list[Operator]): A list of optimizer operators Returns: Program: A new, pruned program. """ compiled = isinstance(program, compiler.CompiledProgram) if compiled: if program._program: origin_program = program._program else: warnings.warn( "The program holds no _program, maybe it is constructed by graph, which can't be pruned yet." ) return else: origin_program = program feed_names = [] if isinstance(feed, dict): feed_names = list(feed.keys()) elif isinstance(feed, list) or isinstance(feed, tuple): for i, each in enumerate(feed): feed_names += list(each.keys()) # if optimize_ops is [], all optimize ops in the program is used. if not optimize_ops: for block in origin_program.blocks: for op in block.ops: if op._is_optimize_op(): optimize_ops.append(op) targets = fetch_list + optimize_ops pruned_program = origin_program._prune_with_input(feed_names, targets) if compiled: # for compiled program, update the underlying program, re-generate graph, # and reset the flag so it can be compiled again. program._program = pruned_program program._graph = core.Graph(pruned_program.desc) program._compiled = False else: program = pruned_program return program def _update_feed(self, program, feed): """ Update the feed dict, remove the feed item which is pruned in program. Notes: This is a very low level API. Users should not use this API directly. Args: program(Program): the pruned program. feed(list|dict): feed dict or list. Returns: feed:(list|dict) updated feed. """ compiled = isinstance(program, compiler.CompiledProgram) if compiled: if program._program: global_block = program._program.global_block() else: warnings.warn( "The program holds no _program, maybe it is constructed by graph." ) else: global_block = program.global_block() if isinstance(feed, dict): for feed_name in list(feed.keys()): if not global_block.has_var(feed_name): feed.pop(feed_name) warnings.warn( "The variable %s is not found in program. It is not declared or is pruned." % feed_name) elif isinstance(feed, list) or isinstance(feed, tuple): for i, each in enumerate(feed): for feed_name in list(each.keys()): if not global_block.has_var(feed_name): each.pop(feed_name) warnings.warn( "The variable %s is not found in program. It is not declared or is pruned." % feed_name) return feed ''' TODO(typhoonzero): Define "no longer use" meaning? Can user create a new Executor for the same program and run? TODO(panyx0718): Why ParallelExecutor doesn't have close? ''' def close(self): """ Close the executor. This interface is used for distributed training (PServers mode). This executor can not be used after calling the interface, because this interface releases resources associated with the current Trainer. Returns: None Examples: .. code-block:: python import paddle.fluid as fluid cpu = fluid.CPUPlace() exe = fluid.Executor(cpu) # execute training or testing exe.close() """ if not self._closed: self._default_executor.close() self._closed = True def _run_parallel(self, program, scope, feed, fetch_list, fetch_var_name, return_numpy, return_merged): exe = program._executor # TODO(zhenghuihuang): quantization uses Graph in CompiledProgram # instead of program. We will add support for checking Vars in Graph need_check_feed = program._program is not None if need_check_feed: global_block = program._program.global_block() if isinstance(feed, dict): feed_tensor_dict = dict() for feed_name in feed: feed_tensor = feed[feed_name] var = global_block.var(feed_name) if need_check_feed else None if not isinstance(feed_tensor, core.LoDTensor): # always set to CPU place, since the tensor need to be split # it is fast in CPU feed_tensor = _as_lodtensor(feed[feed_name], core.CPUPlace(), var.dtype if var else None) if need_check_feed: check_feed_shape_type(var, feed_tensor, exe.device_count()) feed_tensor_dict[feed_name] = feed_tensor exe.feed_and_split_tensor_into_local_scopes(feed_tensor_dict) elif isinstance(feed, list) or isinstance(feed, tuple): res = list() for i, each in enumerate(feed): if not isinstance(each, dict): raise TypeError( "Each element of feed list should be a dict") res_dict = dict() for feed_name in each: tensor = each[feed_name] var = global_block.var( feed_name) if need_check_feed else None if not isinstance(tensor, core.LoDTensor): tensor = _as_lodtensor(each[feed_name], program._places[i], var.dtype if var else None) if need_check_feed: check_feed_shape_type(var, tensor) res_dict[feed_name] = tensor res.append(res_dict) exe.feed_tensors_into_local_scopes(res) fetch_var_names = list(map(_to_name_str, fetch_list)) tensors = exe.run(fetch_var_names, return_merged)._move_to_list() return as_numpy(tensors) if return_numpy else tensors def run(self, program=None, feed=None, fetch_list=None, feed_var_name='feed', fetch_var_name='fetch', scope=None, return_numpy=True, use_program_cache=False, return_merged=True, use_prune=False): """ Run the specified :code:`Program` or :code:`CompiledProgram`. It should be noted that the executor will execute all the operators in :code:`Program` or :code:`CompiledProgram` without pruning some operators of the :code:`Program` or :code:`CompiledProgram` according to fetch_list. And you could specify the scope to store the :code:`Variables` during the executor running if the scope is not set, the executor will use the global scope, i.e. :code:`fluid.global_scope()`. Args: program(Program|CompiledProgram): This parameter represents the :code:`Program` or :code:`CompiledProgram` to be executed. If this parameter is not provided, that parameter is None, the program will be set to :code:`fluid.default_main_program()`. The default is None. feed(list|dict): This parameter represents the input variables of the model. If it is single card training, the feed is dict type, and if it is multi-card training, the parameter feed can be dict or list type variable. If the parameter type is dict, the data in the feed will be split and sent to multiple devices (CPU/GPU), that is to say, the input data will be evenly sent to different devices, so you should make sure the number of samples of the current mini-batch must be greater than the number of places; if the parameter type is list, those data are copied directly to each device, so the length of this list should be equal to the number of places. The default is None. fetch_list(list): This parameter represents the variables that need to be returned after the model runs. The default is None. feed_var_name(str): This parameter represents the name of the input variable of the feed operator. The default is "feed". fetch_var_name(str): This parameter represents the name of the output variable of the fetch operator. The default is "fetch". scope(Scope): the scope used to run this program, you can switch it to different scope. default is :code:`fluid.global_scope()` return_numpy(bool): This parameter indicates whether convert the fetched variables (the variable specified in the fetch list) to numpy.ndarray. if it is False, the type of the return value is a list of :code:`LoDTensor`. The default is True. use_program_cache(bool): This parameter indicates whether the input :code:`Program` is cached. If the parameter is True, the model may run faster in the following cases: the input program is :code:`fluid.Program`, and the parameters(program, feed variable name and fetch_list variable) of this interface remains unchanged during running. The default is False. return_merged(bool): This parameter indicates whether fetched variables (the variables specified in the fetch list) should be merged according to the execution device dimension. If :code:`return_merged` is False, the type of the return value is a two-dimensional list of :code:`Tensor` / :code:`LoDTensorArray` ( :code:`return_numpy` is False) or a two-dimensional list of :code:`numpy.ndarray` ( :code:`return_numpy` is True). If :code:`return_merged` is True, the type of the return value is an one-dimensional list of :code:`Tensor` / :code:`LoDTensorArray` ( :code:`return_numpy` is False) or an one-dimensional list of :code:`numpy.ndarray` ( :code:`return_numpy` is True). Please see Examples 2 for more details. If the lengths of fetched results are variant, please set :code:`return_merged` as False, which denotes that the fetched results will not be merged. The default is True, but it is just for the compatibility, and may use False as default value in the future version. use_prune(bool): This parameter indicates whether the input :code:`Program` will be pruned. If the parameter is True, the program will be pruned accroding to the given feed and fetch_list, which means the operators and variables in program that generate :code:`feed` and are not needed to generate :code:`fetch_list` will be pruned. The default is False, which means the program will not pruned and all the operators and variables will be executed during running. Note that if the tuple returned from :code:`Optimizer.minimize()` is passed to :code:`fetch_list`, :code:`use_prune` will be overrided to True, and the program will be pruned. Returns: List: The fetched result list. NOTES: 1. If it is multi-card running and the feed parameter is dict type, the input data will be evenly sent to different cards. For example, using two GPUs to run the model, the input sample number is 3, that is, [0, 1, 2], the sample number on GPU0 is 1, that is, [0], and the sample number on GPU1 is 2, that is, [1, 2]. If the number of samples is less than the number of devices, the program will throw an exception, so when running the model, you should make sure that the number of samples of the last batch of the data set should be greater than the number of CPU cores or GPU cards, if it is less than, it is recommended that the batch be discarded. 2. If the number of CPU cores or GPU cards available is greater than 1, the fetch results are spliced together in dimension 0 for the same variable values (variables in fetch_list) on different devices. Examples 1: .. code-block:: python import paddle.fluid as fluid import numpy # First create the Executor. place = fluid.CPUPlace() # fluid.CUDAPlace(0) exe = fluid.Executor(place) data = fluid.data(name='X', shape=[None, 1], dtype='float32') hidden = fluid.layers.fc(input=data, size=10) loss = fluid.layers.mean(hidden) adam = fluid.optimizer.Adam() adam.minimize(loss) i = fluid.layers.zeros(shape=[1], dtype='int64') array = fluid.layers.array_write(x=loss, i=i) # Run the startup program once and only once. exe.run(fluid.default_startup_program()) x = numpy.random.random(size=(10, 1)).astype('float32') loss_val, array_val = exe.run(feed={'X': x}, fetch_list=[loss.name, array.name]) print(array_val) # [array([0.02153828], dtype=float32)] Examples 2: .. code-block:: python import paddle.fluid as fluid import numpy as np # First create the Executor. place = fluid.CUDAPlace(0) exe = fluid.Executor(place) data = fluid.data(name='X', shape=[None, 1], dtype='float32') class_dim = 2 prediction = fluid.layers.fc(input=data, size=class_dim) loss = fluid.layers.mean(prediction) adam = fluid.optimizer.Adam() adam.minimize(loss) # Run the startup program once and only once. exe.run(fluid.default_startup_program()) build_strategy = fluid.BuildStrategy() binary = fluid.CompiledProgram(fluid.default_main_program()).with_data_parallel( loss_name=loss.name, build_strategy=build_strategy) batch_size = 6 x = np.random.random(size=(batch_size, 1)).astype('float32') # Set return_merged as False to fetch unmerged results: unmerged_prediction, = exe.run(binary, feed={'X': x}, fetch_list=[prediction.name], return_merged=False) # If the user uses two GPU cards to run this python code, the printed result will be # (2, 3, class_dim). The first dimension value of the printed result is the number of used # GPU cards, and the second dimension value is the quotient of batch_size and the # number of used GPU cards. print("The unmerged prediction shape: {}".format(np.array(unmerged_prediction).shape)) print(unmerged_prediction) # Set return_merged as True to fetch merged results: merged_prediction, = exe.run(binary, feed={'X': x}, fetch_list=[prediction.name], return_merged=True) # If the user uses two GPU cards to run this python code, the printed result will be # (6, class_dim). The first dimension value of the printed result is the batch_size. print("The merged prediction shape: {}".format(np.array(merged_prediction).shape)) print(merged_prediction) # Out: # The unmerged prediction shape: (2, 3, 2) # [array([[-0.37620035, -0.19752218], # [-0.3561043 , -0.18697084], # [-0.24129935, -0.12669306]], dtype=float32), array([[-0.24489994, -0.12858354], # [-0.49041364, -0.25748932], # [-0.44331917, -0.23276259]], dtype=float32)] # The merged prediction shape: (6, 2) # [[-0.37789783 -0.19921964] # [-0.3577645 -0.18863106] # [-0.24274671 -0.12814042] # [-0.24635398 -0.13003758] # [-0.49232286 -0.25939852] # [-0.44514108 -0.2345845 ]] """ try: return self._run_impl( program=program, feed=feed, fetch_list=fetch_list, feed_var_name=feed_var_name, fetch_var_name=fetch_var_name, scope=scope, return_numpy=return_numpy, use_program_cache=use_program_cache, use_prune=use_prune, return_merged=return_merged) except Exception as e: six.reraise(*sys.exc_info()) def _run_impl(self, program, feed, fetch_list, feed_var_name, fetch_var_name, scope, return_numpy, use_program_cache, return_merged, use_prune): if self._closed: raise RuntimeError("Attempted to use a closed Executor") use_default_main_program = program is None if program is None: program = default_main_program() if isinstance(program, Program) and \ len(program.global_block().ops) == 0: if use_default_main_program: error_info = "Now you are using default_main_program, "\ "but there are no operators in the program to be executed. "\ "Please ensure you create model correctly or you can pass "\ "the Program or the CompiledProgram manually." else: error_info = "There are no operators in the program to be executed. "\ "If you pass Program manually, please use fluid.program_guard "\ "to ensure the current Program is being used." warnings.warn(error_info) if scope is None: scope = global_scope() if fetch_list is not None: if isinstance(fetch_list, Variable) or isinstance( fetch_list, str) or isinstance(fetch_list, six.string_types): fetch_list = [fetch_list] assert isinstance(fetch_list, tuple) or isinstance(fetch_list, list), \ "Currently , The fetch_list type only should be list or tuple, \n"\ "but the input type is {}. For more information please refer to \n"\ "the executor.run(...).".format(type(fetch_list)) else: fetch_list = [] # use_prune can be overrided by putting optimize_ops in fetch_list _origin_fetch_list = fetch_list _origin_program = program fetch_list, optimize_ops = self._split_optimize_ops_in_fetch_list( fetch_list) if optimize_ops: use_prune = True if use_prune: cache_key = _get_strong_program_cache_key(program, feed, _origin_fetch_list) cached_pruned_program = self._get_pruned_program_cache(cache_key) if cached_pruned_program is None: if isinstance(program, compiler.CompiledProgram): program_scope_cache = self._get_pruned_program_scope_cache( str(id(_origin_program))) # copy the original program, so it can be cached. program = copy.copy(program) # share the local scopes for same original CompiledProgram. program._share_vars_from = program_scope_cache if self._get_pruned_program_scope_cache( str(id(_origin_program))) is None: self._add_pruned_program_scope_cache( str(id(_origin_program)), program) pruned_program = self._prune_program(program, feed, fetch_list, optimize_ops) self._add_pruned_program_cache(cache_key, pruned_program) else: pruned_program = cached_pruned_program feed = self._update_feed(pruned_program, feed) program = pruned_program compiled = isinstance(program, compiler.CompiledProgram) # For backward compatibility, run directly. if not compiled: # In distributed training, the compiled program is saved in Program._graph has_compiled_graph = isinstance(program._graph, compiler.CompiledProgram) if has_compiled_graph: program._graph._compile(scope, self.place) # _graph in program does not support inference since the _graph is optimized # through optimizer.minimize function and should not be used as inference graph # assert not program._graph._is_inference return self._run_parallel( program._graph, scope=scope, feed=feed, fetch_list=fetch_list, fetch_var_name=fetch_var_name, return_numpy=return_numpy, return_merged=return_merged) return self._run_program( program, feed=feed, fetch_list=fetch_list, feed_var_name=feed_var_name, fetch_var_name=fetch_var_name, scope=scope, return_numpy=return_numpy, use_program_cache=use_program_cache) program._compile(scope, self.place) if program._is_inference: return self._run_inference(program._executor, feed) else: return self._run_parallel( program, scope=scope, feed=feed, fetch_list=fetch_list, fetch_var_name=fetch_var_name, return_numpy=return_numpy, return_merged=return_merged) def _run_program(self, program, feed, fetch_list, feed_var_name, fetch_var_name, scope, return_numpy, use_program_cache): if feed is None: feed = {} elif isinstance(feed, (list, tuple)): assert len(feed) == 1, "Not compiled with data parallel" feed = feed[0] if not isinstance(feed, dict): raise TypeError( "feed requires dict as its Parameter. But you passed in %s" % (type(feed))) assert program is not None, "The program should not be Empty" if not isinstance(program, Program): raise TypeError( "Executor requires Program as its Parameter. But you passed in %s" % (type(program))) if use_program_cache: cache_key = _get_strong_program_cache_key(program, feed, fetch_list) cached_program = self._get_program_cache(cache_key) cached_ctx = self._get_ctx_cache(cache_key) cached_scope = self._get_scope_cache(cache_key) if cached_program is None: cached_program = self._add_feed_fetch_ops( program=program, feed=feed, fetch_list=fetch_list, feed_var_name=feed_var_name, fetch_var_name=fetch_var_name) self._add_program_cache(cache_key, cached_program) fetch_list_str = list(map(_to_name_str, fetch_list)) cached_ctx = self._default_executor.prepare( cached_program.desc, 0, fetch_list_str, False) # currently, we cache program, vars, sub_scope here # we suppose that in a life cycle of training, a user # will not create many programs. So, here the basic # rule of caching is to cache all unseen (program, var, scope) # when a user use use_program_cache. cached_scope = scope.new_scope() self._default_executor.create_variables(cached_program.desc, cached_scope, 0) self._add_ctx_cache(cache_key, cached_ctx) self._add_scope_cache(cache_key, cached_scope) program = cached_program ctx = cached_ctx scope = cached_scope else: program = self._add_feed_fetch_ops( program=program, feed=feed, fetch_list=fetch_list, feed_var_name=feed_var_name, fetch_var_name=fetch_var_name) self._feed_data(program, feed, feed_var_name, scope) if not use_program_cache: self._default_executor.run(program.desc, scope, 0, True, True, fetch_var_name) else: self._default_executor.run_prepared_ctx(ctx, scope, False, False, False) arr = scope.find_var(fetch_var_name).get_fetch_list() tensors = arr._move_to_list() if return_numpy: return as_numpy(tensors) else: return tensors def _run_inference(self, exe, feed): return exe.run(feed) def _dump_debug_info(self, program=None, trainer=None): with open(str(id(program)) + "_train_desc.prototxt", "w") as fout: fout.write(str(trainer)) if program._fleet_opt and "fleet_desc" in program._fleet_opt: with open("fleet_desc.prototxt", "w") as fout: fout.write(str(program._fleet_opt["fleet_desc"])) def _adjust_pipeline_resource(self, pipeline_opt, dataset, pipeline_num): filelist_length = len(dataset.dataset.get_filelist()) if filelist_length < pipeline_num: pipeline_num = filelist_length print( "Pipeline training: setting the pipeline num to %d is enough because there are only %d files" % (filelist_length, filelist_length)) if filelist_length < pipeline_num * pipeline_opt["concurrency_list"][0]: print( "Pipeline training: setting the 1st element in concurrency_list to %d is enough because there are only %d files" % (filelist_length // pipeline_num, filelist_length)) pipeline_opt["concurrency_list"][ 0] = filelist_length // pipeline_num dataset.set_thread(pipeline_opt["concurrency_list"][0] * pipeline_num) return pipeline_num def _prepare_trainer(self, program=None, dataset=None, scope=None, thread=0, debug=False, fetch_list=None, fetch_info=None, print_period=100): is_heter = 0 if not program._fleet_opt is None: if program._fleet_opt.get("worker_class", "") == "HeterCpuWorker": is_heter = 1 if program._fleet_opt("trainer", "") == "HeterXpuTrainer": is_heter = 1 if scope is None: scope = global_scope() if fetch_list is None: fetch_list = [] if fetch_info is None: fetch_info = [] assert len(fetch_list) == len(fetch_info) compiled = isinstance(program, compiler.CompiledProgram) if is_heter: from paddle.fluid.incubate.fleet.parameter_server.pslib import fleet from paddle.fluid.incubate.fleet.utils.fleet_util import FleetUtil fu = FleetUtil() ret = fu.split_program_by_device(program) if not compiled: # TODO: Need a better way to distinguish and specify different execution mode if program._pipeline_opt: trainer = TrainerFactory()._create_trainer( program._pipeline_opt) else: trainer = TrainerFactory()._create_trainer(program._fleet_opt) trainer._set_thread_barrier(program._is_distributed) trainer._set_program(program) if is_heter: trainer._set_heter_info(ret) else: if program._pipeline_opt: trainer = TrainerFactory()._create_trainer( program.program._pipeline_opt) else: trainer = TrainerFactory()._create_trainer( program.program._fleet_opt) trainer._set_program(program.program) if thread <= 0: if dataset.thread_num <= 0: raise RuntimeError( "You should set thread num first, either in Dataset" "or in Executor.train_from_dataset") else: trainer._set_thread(dataset.thread_num) else: trainer._set_thread(thread) trainer._set_debug(debug) trainer._set_fetch_var_and_info(fetch_list, fetch_info, print_period) return scope, trainer def _run_from_dataset(self, program=None, dataset=None, scope=None, thread=0, is_infer=False, debug=False, fetch_list=None, fetch_info=None, print_period=100, fetch_handler=None): if program._pipeline_opt is not None: import paddle if dataset is not None: raise RuntimeError("dataset should be None for pipeline mode") # The following fake dataset is created to call # the _prepare_trainer api, and it is meaningless. data_vars = [] for var in program.global_block().vars.values(): if var.is_data: data_vars.append(var) dataset = paddle.fluid.DatasetFactory().create_dataset( 'FileInstantDataset') dataset.set_batch_size(1) dataset.set_thread(1) dataset.set_filelist(['None']) dataset.set_use_var(data_vars) else: if dataset is None: raise RuntimeError("dataset is need and should be initialized") dataset._prepare_to_run() scope, trainer = self._prepare_trainer( program=program, dataset=dataset, scope=scope, thread=thread, debug=debug, fetch_list=fetch_list, fetch_info=fetch_info, print_period=print_period) trainer._set_infer(is_infer) trainer._gen_trainer_desc() self._dump_debug_info(program=program, trainer=trainer) dataset._dynamic_adjust_before_train(trainer.proto_desc.thread_num) trainer_instance = self._default_executor.init_for_dataset( program.desc, trainer._desc(), scope, dataset.dataset) if fetch_handler is not None: scope0 = trainer_instance.get_worker_scope(0) fetch_monitor = FetchHandlerMonitor(scope0, fetch_handler) fetch_monitor.start() self._default_executor.run_from_dataset(trainer_instance) fetch_monitor.stop() self._default_executor.release_trainer(trainer_instance) else: self._default_executor.run_from_dataset(trainer_instance) self._default_executor.release_trainer(trainer_instance) dataset._dynamic_adjust_after_train() dataset._finish_to_run() return None def infer_from_dataset(self, program=None, dataset=None, scope=None, thread=0, debug=False, fetch_list=None, fetch_info=None, print_period=100, fetch_handler=None): """ Infer from a pre-defined Dataset. Dataset is defined in paddle.fluid.dataset. Given a program, either a program or compiled program, infer_from_dataset will consume all data samples in dataset. Input scope can be given by users. By default, scope is global_scope(). The total number of thread run in training is `thread`. Thread number used in training will be minimum value of threadnum in Dataset and the value of thread in this interface. Debug can be set so that executor will display Run-Time for all operators and the throughputs of current infer task. The document of infer_from_dataset is almost the same as train_from_dataset, except that in distributed training, push gradients will be disabled in infer_from_dataset. infer_from_dataset() can be used for evaluation in multi-threadvery easily. Args: program(Program|CompiledProgram): the program that needs to be run, if not provided, then default_main_program (not compiled) will be used. dataset(paddle.fluid.Dataset): dataset created outside this function, a user should provide a well-defined dataset before calling this function. Please check the document of Dataset if needed. default is None scope(Scope): the scope used to run this program, you can switch it to different scope for each run. default is global_scope thread(int): number of thread a user wants to run in this function. Default is 0, which means using thread num of dataset debug(bool): whether a user wants to run infer_from_dataset, default is False fetch_list(Variable List): fetch variable list, each variable will be printed during training, default is None fetch_info(String List): print information for each variable, default is None print_period(int): the number of mini-batches for each print, default is 100 fetch_handler(FetchHandler): a user define class for fetch output. Returns: None Examples: .. code-block:: python import paddle.fluid as fluid place = fluid.CPUPlace() # you can set place = fluid.CUDAPlace(0) to use gpu exe = fluid.Executor(place) x = fluid.data(name="x", shape=[None, 10, 10], dtype="int64") y = fluid.data(name="y", shape=[None, 1], dtype="int64", lod_level=1) dataset = fluid.DatasetFactory().create_dataset() dataset.set_use_var([x, y]) dataset.set_thread(1) filelist = [] # you should set your own filelist, e.g. filelist = ["dataA.txt"] dataset.set_filelist(filelist) exe.run(fluid.default_startup_program()) exe.infer_from_dataset(program=fluid.default_main_program(), dataset=dataset) """ return self._run_from_dataset(program, dataset, scope, thread, True, debug, fetch_list, fetch_info, print_period, fetch_handler) def start_heter_trainer(self, program=None, scope=None, debug=False, fetch_list=None, fetch_info=None, print_period=100, fetch_handler=None): return self._start_heter_trainer(program, scope, False, debug, fetch_list, fetch_info, print_period, fetch_handler) def _start_heter_trainer(self, program=None, scope=None, is_infer=False, debug=False, fetch_list=None, fetch_info=None, print_period=100, fetch_handler=None): scope, trainer = self._prepare_trainer( program=program, dataset=None, scope=scope, thread=1, debug=debug, fetch_list=fetch_list, fetch_info=fetch_info, print_period=print_period) trainer._set_infer(is_infer) trainer._gen_trainer_desc() self._dump_debug_info(program=program, trainer=trainer) trainer_instance = self._default_executor.init_for_dataset( program.desc, trainer._desc(), scope, None) #if fetch_handler is not None: # scope0 = trainer_instance.get_worker_scope(0) # fetch_monitor = FetchHandlerMonitor(scope0, fetch_handler) # fetch_monitor.start() # self._default_executor.run_from_dataset(trainer_instance) # fetch_monitor.stop() # self._default_executor.release_trainer(trainer_instance) #else: self._default_executor.run_from_dataset(trainer_instance) #self._default_executor.release_trainer(trainer_instance) return trainer_instance def train_from_dataset(self, program=None, dataset=None, scope=None, thread=0, debug=False, fetch_list=None, fetch_info=None, print_period=100, fetch_handler=None): """ Train from a pre-defined Dataset. Dataset is defined in paddle.fluid.dataset. Given a program, either a program or compiled program, train_from_dataset will consume all data samples in dataset. Input scope can be given by users. By default, scope is global_scope(). The total number of thread run in training is `thread`. Thread number used in training will be minimum value of threadnum in Dataset and the value of thread in this interface. Debug can be set so that executor will display Run-Time for all operators and the throughputs of current training task. Note: train_from_dataset will destroy all resources created within executor for each run. Args: program(Program|CompiledProgram): the program that needs to be run, if not provided, then default_main_program (not compiled) will be used. dataset(paddle.fluid.Dataset): dataset created outside this function, a user should provide a well-defined dataset before calling this function. Please check the document of Dataset if needed. scope(Scope): the scope used to run this program, you can switch it to different scope for each run. default is global_scope thread(int): number of thread a user wants to run in this function. Default is 0, which means using thread num of dataset debug(bool): whether a user wants to run train_from_dataset fetch_list(Variable List): fetch variable list, each variable will be printed during training fetch_info(String List): print information for each variable, its length should be equal to fetch_list print_period(int): the number of mini-batches for each print, default is 100 fetch_handler(FetchHandler): a user define class for fetch output. Returns: None Examples: .. code-block:: python import paddle.fluid as fluid place = fluid.CPUPlace() # you can set place = fluid.CUDAPlace(0) to use gpu exe = fluid.Executor(place) x = fluid.data(name="x", shape=[None, 10, 10], dtype="int64") y = fluid.data(name="y", shape=[None, 1], dtype="int64", lod_level=1) dataset = fluid.DatasetFactory().create_dataset() dataset.set_use_var([x, y]) dataset.set_thread(1) filelist = [] # you should set your own filelist, e.g. filelist = ["dataA.txt"] dataset.set_filelist(filelist) exe.run(fluid.default_startup_program()) exe.train_from_dataset(program=fluid.default_main_program(), dataset=dataset) """ return self._run_from_dataset(program, dataset, scope, thread, False, debug, fetch_list, fetch_info, print_period, fetch_handler)

dfa885c2348eb8d5c4c3ee99f858412efa1e82bd

py

Python

tests/conftest.py

oterrier/openapi-python-client

ca8acdbe34b11584143b78afc130684f0690d5bf

2022-03-02T08:15:26.000Z

2022-03-02T08:15:26.000Z

tests/conftest.py

oterrier/openapi-python-client

ca8acdbe34b11584143b78afc130684f0690d5bf

tests/conftest.py

oterrier/openapi-python-client

ca8acdbe34b11584143b78afc130684f0690d5bf

from typing import Any, Callable, Dict import pytest from openapi_python_client.parser.properties import ( AnyProperty, DateProperty, DateTimeProperty, EnumProperty, FileProperty, IntProperty, ListProperty, ModelProperty, Property, StringProperty, UnionProperty, ) @pytest.fixture def model_property_factory() -> Callable[..., ModelProperty]: """ This fixture surfaces in the test as a function which manufactures ModelProperties with defaults. You can pass the same params into this as the ModelProperty constructor to override defaults. """ from openapi_python_client.parser.properties import Class def _factory(**kwargs): kwargs = _common_kwargs(kwargs) kwargs = { "description": "", "class_info": Class(name="MyClass", module_name="my_module"), "required_properties": [], "optional_properties": [], "relative_imports": set(), "additional_properties": False, "python_name": "", **kwargs, } return ModelProperty(**kwargs) return _factory @pytest.fixture def enum_property_factory() -> Callable[..., EnumProperty]: """ This fixture surfaces in the test as a function which manufactures EnumProperties with defaults. You can pass the same params into this as the EnumProerty constructor to override defaults. """ from openapi_python_client.parser.properties import Class def _factory(**kwargs): kwargs = _common_kwargs(kwargs) kwargs = { "class_info": Class(name=kwargs["name"], module_name=kwargs["name"]), "values": {}, "value_type": str, **kwargs, } return EnumProperty(**kwargs) return _factory @pytest.fixture def property_factory() -> Callable[..., Property]: """ This fixture surfaces in the test as a function which manufactures Properties with defaults. You can pass the same params into this as the Property constructor to override defaults. """ def _factory(**kwargs): kwargs = _common_kwargs(kwargs) return Property(**kwargs) return _factory @pytest.fixture def any_property_factory() -> Callable[..., AnyProperty]: """ This fixture surfaces in the test as a function which manufactures AnyProperty with defaults. You can pass the same params into this as the AnyProperty constructor to override defaults. """ def _factory(**kwargs): kwargs = _common_kwargs(kwargs) return AnyProperty(**kwargs) return _factory @pytest.fixture def string_property_factory() -> Callable[..., StringProperty]: """ This fixture surfaces in the test as a function which manufactures StringProperties with defaults. You can pass the same params into this as the StringProperty constructor to override defaults. """ def _factory(**kwargs): kwargs = _common_kwargs(kwargs) return StringProperty(**kwargs) return _factory @pytest.fixture def int_property_factory() -> Callable[..., IntProperty]: """ This fixture surfaces in the test as a function which manufactures StringProperties with defaults. You can pass the same params into this as the StringProperty constructor to override defaults. """ def _factory(**kwargs): kwargs = _common_kwargs(kwargs) return IntProperty(**kwargs) return _factory @pytest.fixture def date_time_property_factory() -> Callable[..., DateTimeProperty]: """ This fixture surfaces in the test as a function which manufactures DateTimeProperties with defaults. You can pass the same params into this as the DateTimeProperty constructor to override defaults. """ def _factory(**kwargs): kwargs = _common_kwargs(kwargs) return DateTimeProperty(**kwargs) return _factory @pytest.fixture def date_property_factory() -> Callable[..., DateProperty]: """ This fixture surfaces in the test as a function which manufactures DateProperties with defaults. You can pass the same params into this as the DateProperty constructor to override defaults. """ def _factory(**kwargs): kwargs = _common_kwargs(kwargs) return DateProperty(**kwargs) return _factory @pytest.fixture def file_property_factory() -> Callable[..., FileProperty]: """ This fixture surfaces in the test as a function which manufactures FileProperties with defaults. You can pass the same params into this as the FileProperty constructor to override defaults. """ def _factory(**kwargs): kwargs = _common_kwargs(kwargs) return FileProperty(**kwargs) return _factory @pytest.fixture def list_property_factory(string_property_factory) -> Callable[..., ListProperty]: """ This fixture surfaces in the test as a function which manufactures ListProperties with defaults. You can pass the same params into this as the ListProperty constructor to override defaults. """ def _factory(**kwargs): kwargs = _common_kwargs(kwargs) if "inner_property" not in kwargs: kwargs["inner_property"] = string_property_factory() return ListProperty(**kwargs) return _factory @pytest.fixture def union_property_factory(date_time_property_factory, string_property_factory) -> Callable[..., UnionProperty]: """ This fixture surfaces in the test as a function which manufactures UnionProperties with defaults. You can pass the same params into this as the UnionProperty constructor to override defaults. """ def _factory(**kwargs): kwargs = _common_kwargs(kwargs) if "inner_properties" not in kwargs: kwargs["inner_properties"] = [date_time_property_factory(), string_property_factory()] return UnionProperty(**kwargs) return _factory def _common_kwargs(kwargs: Dict[str, Any]) -> Dict[str, Any]: kwargs = { "name": "test", "required": True, "nullable": False, "default": None, **kwargs, } if not kwargs.get("python_name"): kwargs["python_name"] = kwargs["name"] return kwargs

32f11132095e12efea852f9cbbc4009e5275c1d6

py

Python

classes/realsense.py

snavas/PyMote

9ac51251abbc943fcd36fbb58ff5c3031d375c14

2021-01-09T13:51:49.000Z

2022-02-22T20:32:34.000Z

classes/realsense.py

snavas/GECCO

9ac51251abbc943fcd36fbb58ff5c3031d375c14

2020-11-20T11:06:34.000Z

2022-03-12T00:54:17.000Z

classes/realsense.py

snavas/PyMote

9ac51251abbc943fcd36fbb58ff5c3031d375c14

#from device import Device from classes.device import Device import pyrealsense2 as rs import numpy as np import cv2 class RealSense(Device): #pipeline = rs.pipeline() def getcolorintrinsics(self): return self.color_intr def getdepthintrinsics(self): return self.depth_intr def getirintrinsics(self): return self.ir_intr # overriding abstract method def __init__(self, id, depth, ir): ctx = rs.context() devices = ctx.query_devices() print("<*> Connected devices: ") print(*devices, sep="\n") # TODO: Posible improvement to better query devices: #>> > d = ctx.load_device("C:\\Users\\local_admin\\Documents\\20180212_000327.bag") #>> > s = d.query_sensors()[0] #>> > s.get_stream_profiles()[0] # Configure depth and color streams self.pipeline = rs.pipeline() config = rs.config() if len(devices) > 0: print("<*> Using device: ", id) config.enable_device(id) else: print("<*> Realsense device not found, loading: ", id) # Tell config that we will use a recorded device from filem to be used by the pipeline through playback. rs.config.enable_device_from_file(config, id) config.enable_stream(rs.stream.color, 1280, 720, rs.format.rgb8, 30) if depth: config.enable_stream(rs.stream.depth, 1280, 720, rs.format.z16, 30) if ir: config.enable_stream(rs.stream.infrared, 1280, 720) # Start streaming profile = self.pipeline.start(config) self.color_intr = profile.get_stream(rs.stream.color).as_video_stream_profile().get_intrinsics() if ir: self.ir_intr = profile.get_stream(rs.stream.infrared).as_video_stream_profile().get_intrinsics() if depth: self.depth_intr = profile.get_stream(rs.stream.depth).as_video_stream_profile().get_intrinsics() # Getting the depth sensor's depth scale (see rs-align example for explanation) depth_sensor = profile.get_device().first_depth_sensor() self.depth_scale = depth_sensor.get_depth_scale() print("<*> Depth Scale is: ", self.depth_scale) try: # increase lase power laser_range = depth_sensor.get_option_range(rs.option.laser_power) depth_sensor.set_option(rs.option.laser_power, laser_range.max) # turn off auto-exposure depth_sensor.set_option(rs.option.enable_auto_exposure, False) except Exception as e: pass # We will be removing the background of objects more than # clipping_distance_in_meters meters away clipping_distance_in_meters = 1.15 # 1 meter self.clipping_distance = clipping_distance_in_meters / self.depth_scale # Create an align object # rs.align allows us to perform alignment of depth frames to others frames # The "align_to" is the stream type to which we plan to align depth frames. if ir: align_to = rs.stream.depth else: align_to = rs.stream.color self.align = rs.align(align_to) for index in range(5): self.pipeline.wait_for_frames() def getdepthscale(self): return self.depth_scale # overriding abstract method # Streaming loop def getstream(self): # Wait for a coherent pair of frames: depth and color frames = self.pipeline.wait_for_frames() # frames.get_depth_frame() is a 640x360 depth image # depth_frame = frames.get_depth_frame() # color_frame = frames.get_color_frame() aligned_frames = self.align.process(frames) aligned_depth_frame = aligned_frames.get_depth_frame() # aligned_depth_frame is a 640x480 depth image aligned_color_frame = aligned_frames.get_color_frame() #if not depth_frame or not color_frame: # continue # Convert images to numpy arrays depth_image = np.asanyarray(aligned_depth_frame.get_data()) color_image = np.asanyarray(aligned_color_frame.get_data()) # Apply colormap on depth image (image must be converted to 8-bit per pixel first) depth_colormap = cv2.applyColorMap(cv2.convertScaleAbs(depth_image, alpha=0.03), cv2.COLORMAP_JET) # Stack both images horizontally # images = np.hstack((color_image, depth_colormap)) return depth_colormap def getcolorstream(self): frames = self.pipeline.wait_for_frames() aligned_color_frame = self.align.process(frames).get_color_frame() color_image = np.asanyarray(aligned_color_frame.get_data()) return color_image def getirstream(self): frames = self.pipeline.wait_for_frames() aligned_infrared_frame = self.align.process(frames).get_infrared_frame() infrared_image = np.asanyarray(aligned_infrared_frame.get_data()) return infrared_image def getdepthstream(self): frames = self.pipeline.wait_for_frames() aligned_depth_frame = self.align.process(frames).get_depth_frame() depth_image = np.asanyarray(aligned_depth_frame.get_data()) #scaled_image = cv2.convertScaleAbs(depth_image, alpha=0.03) return depth_image def getdepthcolormap(self): depth_image = self.getdepthstream() depth_colormap = cv2.applyColorMap(cv2.convertScaleAbs(depth_image, alpha=0.03), cv2.COLORMAP_JET) return depth_colormap def getsegmentedstream(self): frames = self.pipeline.wait_for_frames() aligned_frames = self.align.process(frames) aligned_depth_frame = aligned_frames.get_depth_frame() color_frame = aligned_frames.get_color_frame() depth_image = np.asanyarray(aligned_depth_frame.get_data()) color_image = np.asanyarray(color_frame.get_data()) grey_color = 255 depth_image_3d = np.dstack((depth_image, depth_image, depth_image)) # depth image is 1 channel, color is 3 channels bg_removed = np.where((depth_image_3d > self.clipping_distance) | (depth_image_3d <= 0), grey_color, color_image) #depth_colormap = cv2.applyColorMap(cv2.convertScaleAbs(depth_image, alpha=0.03), cv2.COLORMAP_JET) return bg_removed def stop(self): self.pipeline.stop() def restart(self): self.pipeline.start()

d4425032785310871a42971b84fc330ff517f757

py

Python

tests/simple_pages_test.py

EDS435/IS219-Advanced

06f3c358a781d4d41b13716536e20260228a2e29

2022-03-04T16:05:47.000Z

2022-03-04T16:05:47.000Z

tests/simple_pages_test.py

EDS435/IS219-Advanced

06f3c358a781d4d41b13716536e20260228a2e29

2022-03-02T20:18:51.000Z

2022-03-02T20:18:51.000Z

tests/simple_pages_test.py

EDS435/IS219-Advanced

06f3c358a781d4d41b13716536e20260228a2e29

2022-02-23T18:37:17.000Z

2022-02-23T21:17:32.000Z

"""This test the homepage""" def test_request_main_menu_links(client): """This makes the index page""" response = client.get("/") assert response.status_code == 200 assert b'<li><a href="/page/about">About</a></li>' in response.data assert b'<li><a href="/page/welcome">Welcome</a></li>' in response.data def test_request_about(client): """This makes the index page""" response = client.get("/page/about") assert response.status_code == 200 assert b"About Page" in response.data def test_request_welcome(client): """This makes the index page""" response = client.get("/page/welcome") assert response.status_code == 200 assert b"Welcome Page" in response.data def test_request_page_not_found(client): """This makes the index page""" response = client.get("/page/page5") assert response.status_code == 404

804673849be60263f7484601d158a27e8ad2e092

py

Python

ectyper/loggingFunctions.py

phac-nml/ecoli_serotyping

52293255ca02dd9631d637265ac7d6a5d7bfbb82

2017-01-19T18:05:23.000Z

2021-07-26T22:16:42.000Z

ectyper/loggingFunctions.py

phac-nml/ecoli_serotyping

52293255ca02dd9631d637265ac7d6a5d7bfbb82

2017-07-19T17:26:43.000Z

2022-01-14T19:19:39.000Z

ectyper/loggingFunctions.py

phac-nml/ecoli_serotyping

52293255ca02dd9631d637265ac7d6a5d7bfbb82

2017-01-19T18:21:17.000Z

2022-03-07T20:46:12.000Z

import logging def create_logger(): """ Create the logger for ectyper :return: The root logger for the program """ log = logging.getLogger('ectyper') formatter = logging.Formatter( '%(asctime)s %(name)-12s %(levelname)-8s %(message)s') log.setLevel(logging.DEBUG) # define a Handler which writes INFO messages or higher to the sys.stderr console = logging.StreamHandler() console.setFormatter(formatter) #console.setLevel(logging.INFO) log.addHandler(console) return log

f12c8f5a1f9e57fc3e186b8331f1d0a2a45f18bb

py

Python

python/src/leetcode/2020/20.py

ccampo133/coding-challenges

618b51d2d6f6946d3756c68b0008a178d80952ae

python/src/leetcode/2020/20.py

ccampo133/coding-challenges

618b51d2d6f6946d3756c68b0008a178d80952ae

python/src/leetcode/2020/20.py

ccampo133/coding-challenges

618b51d2d6f6946d3756c68b0008a178d80952ae

class Solution: def isValid(self, s: str) -> bool: closed = { ')': '(', '}': '{', ']': '[' } stack = [] for c in s: if c in closed: if len(stack) == 0: return False prev = stack.pop() if closed[c] != prev: return False else: stack.append(c) return len(stack) == 0 if __name__ == '__main__': soln = Solution() s1 = '()' s2 = '()[]{}' s3 = '(]' s4 = '([)]' s5 = '{[]}' assert soln.isValid(s1) assert soln.isValid(s2) assert not soln.isValid(s3) assert not soln.isValid(s4) assert soln.isValid(s5)

4d2b25f9cde689a7c1b019eed38b4011963ea06e

py

Python

nets-in-progress/8r-1c-srv6-pm/isis8d.py

x-Ultra/rose-srv6-tutorial

fb1a074d0490234eeec20f491bd39ceb91156994

2020-06-28T15:33:48.000Z

2021-10-15T00:18:49.000Z

nets-in-progress/8r-1c-srv6-pm/isis8d.py

x-Ultra/rose-srv6-tutorial

fb1a074d0490234eeec20f491bd39ceb91156994

2020-05-12T22:44:06.000Z

2022-01-04T02:11:45.000Z

nets-in-progress/8r-1c-srv6-pm/isis8d.py

x-Ultra/rose-srv6-tutorial

fb1a074d0490234eeec20f491bd39ceb91156994

2020-06-16T10:32:27.000Z

2021-11-27T14:34:01.000Z

#!/usr/bin/python # pylint: disable=missing-module-docstring # pylint: disable=missing-function-docstring # pylint: disable=missing-class-docstring import os import shutil import sys from argparse import ArgumentParser import python_hosts from dotenv import load_dotenv from mininet.cli import CLI # from mininet.link import Link from mininet.log import setLogLevel from mininet.net import Mininet # from mininet.topo import Topo from mininet.node import Host, OVSBridge from mininet.util import dumpNodeConnections from time import sleep # BASEDIR = "/home/user/mytests/ospf3routers/nodeconf/" BASEDIR = os.getcwd() + "/nodeconf/" OUTPUT_PID_TABLE_FILE = "/tmp/pid_table_file.txt" PRIVDIR = '/var/priv' # Path of the file containing the entries (ip-hostname) # to be added to /etc/hosts ETC_HOSTS_FILE = './etc-hosts' # Define whether to add Mininet nodes to /etc/hosts file or not ADD_ETC_HOSTS = True # Define whether to start the node managers on the routers or not START_NODE_MANAGERS = False # Load environment variables from .env file load_dotenv() # Get node manager path NODE_MANAGER_PATH = os.getenv('NODE_MANAGER_PATH', None) if NODE_MANAGER_PATH is not None: NODE_MANAGER_PATH = os.path.join(NODE_MANAGER_PATH, 'srv6_manager.py') # Get gRPC server port NODE_MANAGER_GRPC_PORT = os.getenv('NODE_MANAGER_GRPC_PORT', None) class BaseNode(Host): def __init__(self, name, *args, **kwargs): dirs = [PRIVDIR] Host.__init__(self, name, privateDirs=dirs, *args, **kwargs) self.dir = "/tmp/%s" % name self.nets = [] if not os.path.exists(self.dir): os.makedirs(self.dir) def config(self, **kwargs): # pylint: disable=arguments-differ # Init steps Host.config(self, **kwargs) # Iterate over the interfaces # first = True for intf in self.intfs.values(): # Remove any configured address self.cmd('ifconfig %s 0' % intf.name) # # For the first one, let's configure the mgmt address # if first: # first = False # self.cmd('ip a a %s dev %s' %(kwargs['mgmtip'], intf.name)) # let's write the hostname in /var/mininet/hostname self.cmd("echo '" + self.name + "' > " + PRIVDIR + "/hostname") if os.path.isfile(BASEDIR + self.name + "/start.sh"): self.cmd('source %s' % BASEDIR + self.name + "/start.sh") def cleanup(self): def remove_if_exists(filename): if os.path.exists(filename): os.remove(filename) Host.cleanup(self) # Rm dir if os.path.exists(self.dir): shutil.rmtree(self.dir) remove_if_exists(BASEDIR + self.name + "/zebra.pid") remove_if_exists(BASEDIR + self.name + "/zebra.log") remove_if_exists(BASEDIR + self.name + "/zebra.sock") remove_if_exists(BASEDIR + self.name + "/isis8d.pid") remove_if_exists(BASEDIR + self.name + "/isis8d.log") remove_if_exists(BASEDIR + self.name + "/isisd.log") remove_if_exists(BASEDIR + self.name + "/isisd.pid") remove_if_exists(OUTPUT_PID_TABLE_FILE) # if os.path.exists(BASEDIR+self.name+"/zebra.pid"): # os.remove(BASEDIR+self.name+"/zebra.pid") # if os.path.exists(BASEDIR+self.name+"/zebra.log"): # os.remove(BASEDIR+self.name+"/zebra.log") # if os.path.exists(BASEDIR+self.name+"/zebra.sock"): # os.remove(BASEDIR+self.name+"/zebra.sock") # if os.path.exists(BASEDIR+self.name+"/ospfd.pid"): # os.remove(BASEDIR+self.name+"/ospfd.pid") # if os.path.exists(BASEDIR+self.name+"/ospfd.log"): # os.remove(BASEDIR+self.name+"/ospfd.log") # if os.path.exists(OUTPUT_PID_TABLE_FILE): # os.remove(OUTPUT_PID_TABLE_FILE) class Router(BaseNode): def __init__(self, name, *args, **kwargs): BaseNode.__init__(self, name, *args, **kwargs) def config(self, **kwargs): # pylint: disable=arguments-differ # Init steps BaseNode.config(self, **kwargs) # Start node managers if START_NODE_MANAGERS: self.cmd('python %s --grpc-port %s &' % (NODE_MANAGER_PATH, NODE_MANAGER_GRPC_PORT)) class Switch(OVSBridge): def __init__(self, name, *args, **kwargs): # dirs = [PRIVDIR] OVSBridge.__init__(self, name, *args, **kwargs) self.dir = "/tmp/%s" % name self.nets = [] if not os.path.exists(self.dir): os.makedirs(self.dir) def config(self, **kwargs): # pylint: disable=arguments-differ # Init steps OVSBridge.config(self, **kwargs) # Iterate over the interfaces for intf in self.intfs.values(): # Remove any configured address self.cmd('ifconfig %s 0' % intf.name) # # For the first one, let's configure the mgmt address # if first: # first = False # self.cmd('ip a a %s dev %s' %(kwargs['mgmtip'], intf.name)) # let's write the hostname in /var/mininet/hostname self.cmd("echo '" + self.name + "' > " + PRIVDIR + "/hostname") if os.path.isfile(BASEDIR + self.name + "/start.sh"): self.cmd('source %s' % BASEDIR + self.name + "/start.sh") def cleanup(self): # def remove_if_exists(filename): # if os.path.exists(filename): # os.remove(filename) OVSBridge.cleanup(self) # Rm dir if os.path.exists(self.dir): shutil.rmtree(self.dir) # the add_link function creates a link and assigns the interface names # as node1-node2 and node2-node1 def add_link(my_net, node1, node2): my_net.addLink(node1, node2, intfName1=node1.name + '-' + node2.name, intfName2=node2.name + '-' + node1.name) def create_topo(my_net): # pylint: disable=invalid-name, too-many-locals, too-many-statements h11 = my_net.addHost(name='h11', cls=BaseNode) h12 = my_net.addHost(name='h12', cls=BaseNode) h13 = my_net.addHost(name='h13', cls=BaseNode) h31 = my_net.addHost(name='h31', cls=BaseNode) h32 = my_net.addHost(name='h32', cls=BaseNode) h33 = my_net.addHost(name='h33', cls=BaseNode) h51 = my_net.addHost(name='h51', cls=BaseNode) h52 = my_net.addHost(name='h52', cls=BaseNode) h53 = my_net.addHost(name='h53', cls=BaseNode) h81 = my_net.addHost(name='h81', cls=BaseNode) h82 = my_net.addHost(name='h82', cls=BaseNode) h83 = my_net.addHost(name='h83', cls=BaseNode) hdc1 = my_net.addHost(name='hdc1', cls=BaseNode) hdc2 = my_net.addHost(name='hdc2', cls=BaseNode) hdc3 = my_net.addHost(name='hdc3', cls=BaseNode) controller = my_net.addHost(name='controller', cls=BaseNode, sshd=False, inNamespace=False) r1 = my_net.addHost(name='r1', cls=Router) r2 = my_net.addHost(name='r2', cls=Router) r3 = my_net.addHost(name='r3', cls=Router) r4 = my_net.addHost(name='r4', cls=Router) r5 = my_net.addHost(name='r5', cls=Router) r6 = my_net.addHost(name='r6', cls=Router) r7 = my_net.addHost(name='r7', cls=Router) r8 = my_net.addHost(name='r8', cls=Router) # note that if the interface names are not provided, # the order of adding link will determine the # naming of the interfaces (e.g. on r1: r1-eth0, r1-eth1, r1-eth2...) # it is possible to provide names as follows # Link(h1, r1, intfName1='h1-eth0', intfName2='r1-eth0') # the add_link function creates a link and assigns the interface names # as node1-node2 and node2-node1 # hosts of r1 add_link(my_net, h11, r1) add_link(my_net, h12, r1) add_link(my_net, h13, r1) # r1 - r2 add_link(my_net, r1, r2) # datacenters of r2 add_link(my_net, hdc1, r2) # r2 - r3 add_link(my_net, r2, r3) # r2 - r7 add_link(my_net, r2, r7) # hosts of r3 add_link(my_net, h31, r3) add_link(my_net, h32, r3) add_link(my_net, h33, r3) # r3 - r4 add_link(my_net, r3, r4) # r4 - r5 add_link(my_net, r4, r5) # r4 - r6 add_link(my_net, r4, r6) # hosts of r5 add_link(my_net, h51, r5) add_link(my_net, h52, r5) add_link(my_net, h53, r5) # datacenters of r5 add_link(my_net, hdc3, r5) # r5 - r6 add_link(my_net, r5, r6) # r6 - r7 add_link(my_net, r6, r7) # r6 - r8 add_link(my_net, r6, r8) # r7 - r8 add_link(my_net, r7, r8) # hosts of r8 add_link(my_net, h81, r8) add_link(my_net, h82, r8) add_link(my_net, h83, r8) # datacenters of r8 add_link(my_net, hdc2, r8) # Create the mgmt switch sw = my_net.addSwitch(name='sw', cls=Switch, dpid='1') # Create a link between mgmt switch and controller add_link(my_net, controller, sw) # Connect all the routers to the management network add_link(my_net, r1, sw) add_link(my_net, r2, sw) add_link(my_net, r3, sw) add_link(my_net, r4, sw) add_link(my_net, r5, sw) add_link(my_net, r6, sw) add_link(my_net, r7, sw) add_link(my_net, r8, sw) def add_nodes_to_etc_hosts(): # Get /etc/hosts etc_hosts = python_hosts.hosts.Hosts() # Import host-ip mapping defined in etc-hosts file count = etc_hosts.import_file(ETC_HOSTS_FILE) # Print results count = count['add_result']['ipv6_count'] + \ count['add_result']['ipv4_count'] print('*** Added %s entries to /etc/hosts\n' % count) def remove_nodes_from_etc_hosts(net): print('*** Removing entries from /etc/hosts\n') # Get /etc/hosts etc_hosts = python_hosts.hosts.Hosts() for host in net.hosts: # Remove all the nodes from /etc/hosts etc_hosts.remove_all_matching(name=str(host)) # Remove entries related to the management network # These entries are in the form *.m (e.g. r1.m, controller.m) # therefore they are not removed during the previous loop for host in net.hosts: etc_hosts.remove_all_matching(name='%s.m' % host) # Write changes to /etc/hosts etc_hosts.write() def stop_all(): # Clean Mininet emulation environment os.system('sudo mn -c') # Kill all the started daemons os.system('sudo killall zebra isisd') def extract_host_pid(dumpline): temp = dumpline[dumpline.find('pid=') + 4:] return int(temp[:len(temp) - 2]) def simple_test(): global choosed_sender, choosed_reflector "Create and test a simple network" # topo = RoutersTopo() # net = Mininet(topo=topo, build=False, controller=None) net = Mininet(topo=None, build=False, controller=None) create_topo(net) net.build() net.start() print("Dumping host connections") dumpNodeConnections(net.hosts) # print "Testing network connectivity" # net.pingAll() with open(OUTPUT_PID_TABLE_FILE, "w") as file: for host in net.hosts: file.write("%s %d\n" % (host, extract_host_pid(repr(host)))) # Add Mininet nodes to /etc/hosts if ADD_ETC_HOSTS: add_nodes_to_etc_hosts() # This waiting time is needed because after several tests we noticed that # it is needed some time to let each node of the topology to be set up correctly print("Waiting the components of the topology to set up correctly (40 seconds)") sleep(40) CLI(net) # Remove Mininet nodes from /etc/hosts if ADD_ETC_HOSTS: remove_nodes_from_etc_hosts(net) net.stop() stop_all() def parse_arguments(): # Get parser parser = ArgumentParser( description='Emulation of a Mininet topology (8 routers running ' 'IS-IS, 1 controller out-of-band' ) parser.add_argument( '--start-node-managers', dest='start_node_managers', action='store_true', default=False, help='Define whether to start node manager on routers or not' ) parser.add_argument( '--no-etc-hosts', dest='add_etc_hosts', action='store_false', default=True, help='Define whether to add Mininet nodes to /etc/hosts file or not' ) # Parse input parameters args = parser.parse_args() # Return the arguments return args def __main(): global ADD_ETC_HOSTS # pylint: disable=global-statement global START_NODE_MANAGERS # pylint: disable=global-statement global NODE_MANAGER_GRPC_PORT # pylint: disable=global-statement global net global choosed_sender, choosed_reflector # Parse command-line arguments args = parse_arguments() # Define whether to start node manager on routers or not START_NODE_MANAGERS = args.start_node_managers if START_NODE_MANAGERS: if NODE_MANAGER_PATH is None: print('Error: --start-node-managers requires NODE_MANAGER_PATH ' 'variable') print('NODE_MANAGER_PATH variable not set in .env file\n') sys.exit(-2) if not os.path.exists(NODE_MANAGER_PATH): print('Error: --start-node-managers requires NODE_MANAGER_PATH ' 'variable') print('NODE_MANAGER_PATH defined in .env file ' 'points to a non existing folder\n') sys.exit(-2) if NODE_MANAGER_GRPC_PORT is None: print('Error: --start-node-managers requires ' 'NODE_MANAGER_GRPC_PORT variable') print('NODE_MANAGER_GRPC_PORT variable not set in .env file\n') sys.exit(-2) # Define whether to add Mininet nodes to /etc/hosts file or not ADD_ETC_HOSTS = args.add_etc_hosts # Tell mininet to print useful information setLogLevel('info') simple_test() if __name__ == '__main__': __main()

b1f9a4604433639a386c822a7d132a063e8b45cb

py

Python

xlsxwriter/test/worksheet/test_sparkline11.py

yxwlr995/-Python-Pandas-XlsxWriter

cd28c1b968795b67f3013c49a0e02ffda5898163

xlsxwriter/test/worksheet/test_sparkline11.py

yxwlr995/-Python-Pandas-XlsxWriter

cd28c1b968795b67f3013c49a0e02ffda5898163

xlsxwriter/test/worksheet/test_sparkline11.py

yxwlr995/-Python-Pandas-XlsxWriter

cd28c1b968795b67f3013c49a0e02ffda5898163

############################################################################### # # Tests for XlsxWriter. # # Copyright (c), 2013, John McNamara, jmcnamara@cpan.org # import unittest from ..compatibility import StringIO from ..helperfunctions import _xml_to_list from ...worksheet import Worksheet class TestAssembleWorksheet(unittest.TestCase): """ Test assembling a complete Worksheet file. """ def test_assemble_xml_file(self): """Test writing a worksheet with no cell data.""" self.maxDiff = None fh = StringIO() worksheet = Worksheet() worksheet._set_filehandle(fh) worksheet.select() worksheet.name = 'Sheet1' worksheet.excel_version = 2010 data = [-2, 2, 3, -1, 0] worksheet.write_row('A1', data) worksheet.write_row('A2', data) worksheet.write_row('A3', data) worksheet.write_row('A4', [1, 2, 3, 4, 5]) # Set up sparklines. worksheet.add_sparkline('F1', {'range': 'A1:E1', 'max': 0.5, 'min':-0.5, 'axis': True, 'reverse': True, 'empty_cells': 'zero', 'weight': 0.25, 'high_point': True, 'low_point': True, 'negative_points': True, 'first_point': True, 'last_point': True, 'markers': True, }) worksheet.add_sparkline('F2', {'range': 'A2:E2', 'max': 'group', 'min': 'group', 'empty_cells': 'connect', 'weight': 2.25, }) worksheet.add_sparkline('F3', {'range': 'A3:E3', 'max': 'group', 'min': '0', 'show_hidden': True, 'weight': 6, 'date_axis': 'A4:E4', }) worksheet._assemble_xml_file() exp = _xml_to_list(""" <?xml version="1.0" encoding="UTF-8" standalone="yes"?> <worksheet xmlns="http://schemas.openxmlformats.org/spreadsheetml/2006/main" xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships" xmlns:mc="http://schemas.openxmlformats.org/markup-compatibility/2006" xmlns:x14ac="http://schemas.microsoft.com/office/spreadsheetml/2009/9/ac" mc:Ignorable="x14ac"> <dimension ref="A1:E4"/> <sheetViews> <sheetView tabSelected="1" workbookViewId="0"/> </sheetViews> <sheetFormatPr defaultRowHeight="15" x14ac:dyDescent="0.25"/> <sheetData> <row r="1" spans="1:5" x14ac:dyDescent="0.25"> <c r="A1"> <v>-2</v> </c> <c r="B1"> <v>2</v> </c> <c r="C1"> <v>3</v> </c> <c r="D1"> <v>-1</v> </c> <c r="E1"> <v>0</v> </c> </row> <row r="2" spans="1:5" x14ac:dyDescent="0.25"> <c r="A2"> <v>-2</v> </c> <c r="B2"> <v>2</v> </c> <c r="C2"> <v>3</v> </c> <c r="D2"> <v>-1</v> </c> <c r="E2"> <v>0</v> </c> </row> <row r="3" spans="1:5" x14ac:dyDescent="0.25"> <c r="A3"> <v>-2</v> </c> <c r="B3"> <v>2</v> </c> <c r="C3"> <v>3</v> </c> <c r="D3"> <v>-1</v> </c> <c r="E3"> <v>0</v> </c> </row> <row r="4" spans="1:5" x14ac:dyDescent="0.25"> <c r="A4"> <v>1</v> </c> <c r="B4"> <v>2</v> </c> <c r="C4"> <v>3</v> </c> <c r="D4"> <v>4</v> </c> <c r="E4"> <v>5</v> </c> </row> </sheetData> <pageMargins left="0.7" right="0.7" top="0.75" bottom="0.75" header="0.3" footer="0.3"/> <extLst> <ext xmlns:x14="http://schemas.microsoft.com/office/spreadsheetml/2009/9/main" uri="{05C60535-1F16-4fd2-B633-F4F36F0B64E0}"> <x14:sparklineGroups xmlns:xm="http://schemas.microsoft.com/office/excel/2006/main"> <x14:sparklineGroup manualMin="0" lineWeight="6" dateAxis="1" displayEmptyCellsAs="gap" displayHidden="1" minAxisType="custom" maxAxisType="group"> <x14:colorSeries theme="4" tint="-0.499984740745262"/> <x14:colorNegative theme="5"/> <x14:colorAxis rgb="FF000000"/> <x14:colorMarkers theme="4" tint="-0.499984740745262"/> <x14:colorFirst theme="4" tint="0.39997558519241921"/> <x14:colorLast theme="4" tint="0.39997558519241921"/> <x14:colorHigh theme="4"/> <x14:colorLow theme="4"/> <xm:f>Sheet1!A4:E4</xm:f> <x14:sparklines> <x14:sparkline> <xm:f>Sheet1!A3:E3</xm:f> <xm:sqref>F3</xm:sqref> </x14:sparkline> </x14:sparklines> </x14:sparklineGroup> <x14:sparklineGroup lineWeight="2.25" displayEmptyCellsAs="span" minAxisType="group" maxAxisType="group"> <x14:colorSeries theme="4" tint="-0.499984740745262"/> <x14:colorNegative theme="5"/> <x14:colorAxis rgb="FF000000"/> <x14:colorMarkers theme="4" tint="-0.499984740745262"/> <x14:colorFirst theme="4" tint="0.39997558519241921"/> <x14:colorLast theme="4" tint="0.39997558519241921"/> <x14:colorHigh theme="4"/> <x14:colorLow theme="4"/> <x14:sparklines> <x14:sparkline> <xm:f>Sheet1!A2:E2</xm:f> <xm:sqref>F2</xm:sqref> </x14:sparkline> </x14:sparklines> </x14:sparklineGroup> <x14:sparklineGroup manualMax="0.5" manualMin="-0.5" lineWeight="0.25" markers="1" high="1" low="1" first="1" last="1" negative="1" displayXAxis="1" minAxisType="custom" maxAxisType="custom" rightToLeft="1"> <x14:colorSeries theme="4" tint="-0.499984740745262"/> <x14:colorNegative theme="5"/> <x14:colorAxis rgb="FF000000"/> <x14:colorMarkers theme="4" tint="-0.499984740745262"/> <x14:colorFirst theme="4" tint="0.39997558519241921"/> <x14:colorLast theme="4" tint="0.39997558519241921"/> <x14:colorHigh theme="4"/> <x14:colorLow theme="4"/> <x14:sparklines> <x14:sparkline> <xm:f>Sheet1!A1:E1</xm:f> <xm:sqref>F1</xm:sqref> </x14:sparkline> </x14:sparklines> </x14:sparklineGroup> </x14:sparklineGroups> </ext> </extLst> </worksheet> """) got = _xml_to_list(fh.getvalue()) self.assertEqual(got, exp) if __name__ == '__main__': unittest.main()

fbdf99fa9089d23f97064fb031bacc1f156ee4d0

py

Python

nf_ea_com_bnop_source/b_code/migrations/nf_ea_com_to_bnop/migrators/ea_subtyping_connectors_migrator.py

boro-alpha/nf_ea_com_bnop

21a500bc84304b2709a5a58dbea1d69cfc048f7e

nf_ea_com_bnop_source/b_code/migrations/nf_ea_com_to_bnop/migrators/ea_subtyping_connectors_migrator.py

boro-alpha/nf_ea_com_bnop

21a500bc84304b2709a5a58dbea1d69cfc048f7e

nf_ea_com_bnop_source/b_code/migrations/nf_ea_com_to_bnop/migrators/ea_subtyping_connectors_migrator.py

boro-alpha/nf_ea_com_bnop

21a500bc84304b2709a5a58dbea1d69cfc048f7e

from bnop_source.b_code.bnop_facades import BnopFacades from bnop_source.b_code.core.object_model.bnop_repositories import BnopRepositories from bnop_source.b_code.core.object_model.objects.bnop_objects import BnopObjects from boro_common_source.ckids.boro_object_ckids import BoroObjectCkIds from nf_common_source.code.constants.standard_constants import DEFAULT_NULL_VALUE from nf_common_source.code.nf.types.nf_column_types import NfColumnTypes from nf_common_source.code.services.dataframe_service.dataframe_mergers import inner_merge_dataframes from nf_ea_common_tools_source.b_code.nf_ea_common.common_knowledge.ea_connector_types import EaConnectorTypes from nf_ea_common_tools_source.b_code.services.general.nf_ea.com.common_knowledge.collection_types.nf_ea_com_collection_types import NfEaComCollectionTypes from nf_ea_common_tools_source.b_code.services.general.nf_ea.com.common_knowledge.column_types.nf_ea_com_column_types import NfEaComColumnTypes from nf_ea_common_tools_source.b_code.services.general.nf_ea.com.nf_ea_com_universes import NfEaComUniverses SUBTYPE_UML_NAMES_COLUMN = \ 'subtype_uml_names' SUPERTYPE_UML_NAMES_COLUMN = \ 'supertype_uml_names' def migrate_ea_connectors_in_scope_of_subtyping_pattern( nf_ea_com_universe: NfEaComUniverses, bnop_repository: BnopRepositories): subtyping_ea_connectors = \ __get_subtyping_connectors( nf_ea_com_universe=nf_ea_com_universe) __migrate_subtyping_connectors( ea_connectors=subtyping_ea_connectors, bnop_repository=bnop_repository) def __get_subtyping_connectors( nf_ea_com_universe: NfEaComUniverses) \ -> list: ea_connectors = \ nf_ea_com_universe.nf_ea_com_registry.dictionary_of_collections[NfEaComCollectionTypes.EA_CONNECTORS] ea_classifiers = \ nf_ea_com_universe.nf_ea_com_registry.dictionary_of_collections[NfEaComCollectionTypes.EA_CLASSIFIERS] subtyping_ea_connectors = \ ea_connectors[ea_connectors[ NfEaComColumnTypes.CONNECTORS_ELEMENT_TYPE_NAME.column_name] == EaConnectorTypes.GENERALIZATION.type_name] subtyping_ea_connectors_with_uml_names_dataframe = \ inner_merge_dataframes( master_dataframe=subtyping_ea_connectors, master_dataframe_key_columns=[ NfEaComColumnTypes.ELEMENTS_CLIENT_PLACE2_END_CONNECTORS.column_name], merge_suffixes=['', '_type_uml_names'], foreign_key_dataframe=ea_classifiers, foreign_key_dataframe_fk_columns=[NfColumnTypes.NF_UUIDS.column_name], foreign_key_dataframe_other_column_rename_dictionary= { NfEaComColumnTypes.EXPLICIT_OBJECTS_EA_OBJECT_NAME.column_name: SUPERTYPE_UML_NAMES_COLUMN }) subtyping_ea_connectors_with_uml_names_dataframe = \ inner_merge_dataframes( master_dataframe=subtyping_ea_connectors_with_uml_names_dataframe, master_dataframe_key_columns=[ NfEaComColumnTypes.ELEMENTS_SUPPLIER_PLACE1_END_CONNECTORS.column_name], merge_suffixes=['', '_instance_uml_names'], foreign_key_dataframe=ea_classifiers, foreign_key_dataframe_fk_columns=[NfColumnTypes.NF_UUIDS.column_name], foreign_key_dataframe_other_column_rename_dictionary= { NfEaComColumnTypes.EXPLICIT_OBJECTS_EA_OBJECT_NAME.column_name: SUBTYPE_UML_NAMES_COLUMN }) subtyping_ea_connectors_with_uml_names_dataframe.fillna( value=DEFAULT_NULL_VALUE, inplace=True) typing_ea_connectors_with_uml_names = \ subtyping_ea_connectors_with_uml_names_dataframe.to_dict( orient='records') return \ typing_ea_connectors_with_uml_names def __migrate_subtyping_connectors( ea_connectors: list, bnop_repository: BnopRepositories): for ea_connector in ea_connectors: __migrate_subtyping_connector( ea_connector=ea_connector, bnop_repository=bnop_repository) def __migrate_subtyping_connector( bnop_repository: BnopRepositories, ea_connector: dict): subtyping_tuple_nf_uuid = \ ea_connector[NfColumnTypes.NF_UUIDS.column_name] subtype_nf_uuid = \ ea_connector[NfEaComColumnTypes.ELEMENTS_SUPPLIER_PLACE1_END_CONNECTORS.column_name] subtype_uml_name = \ ea_connector[SUBTYPE_UML_NAMES_COLUMN] supertype_nf_uuid = \ ea_connector[NfEaComColumnTypes.ELEMENTS_CLIENT_PLACE2_END_CONNECTORS.column_name] supertype_uml_name = \ ea_connector[SUPERTYPE_UML_NAMES_COLUMN] if subtype_nf_uuid in BnopObjects.registry_keyed_on_uuid: bnop_subtype = \ BnopObjects.registry_keyed_on_uuid[subtype_nf_uuid] else: bnop_subtype = \ BnopFacades.create_bnop_object( object_uuid=subtype_nf_uuid, owning_repository_uuid=bnop_repository.uuid, presentation_name=subtype_uml_name) if supertype_nf_uuid in BnopObjects.registry_keyed_on_uuid: bnop_supertype = \ BnopObjects.registry_keyed_on_uuid[supertype_nf_uuid] else: bnop_supertype = \ BnopFacades.create_bnop_type( type_uuid=supertype_nf_uuid, owning_repository_uuid=bnop_repository.uuid, presentation_name=supertype_uml_name) BnopFacades.create_bnop_tuple_from_two_placed_objects( tuple_uuid=subtyping_tuple_nf_uuid, placed1_object=bnop_supertype, placed2_object=bnop_subtype, immutable_minor_composition_couple_type_boro_object_ckid=BoroObjectCkIds.SuperSubTypes, owning_repository_uuid=bnop_repository.uuid)

e4098159c8d77581a4206fd7e90ebd21515f728a

py

Python

utils.py

hengshuangliu/Practice-of-JSRG

90a13004d5e7e37bab0ea3f58d53b9250d97adc8

utils.py

hengshuangliu/Practice-of-JSRG

90a13004d5e7e37bab0ea3f58d53b9250d97adc8

utils.py

hengshuangliu/Practice-of-JSRG

90a13004d5e7e37bab0ea3f58d53b9250d97adc8

#! usr/bin/env python """ Created on Fri May 20 09:09:27 2016 merge images and extract a part of them randomly. @author: shuang """ #import tensorflow as tf import numpy as np import os import random import shutil import PIL.Image from cStringIO import StringIO from IPython.display import clear_output, Image, display import time #---------------------------------------Configure--------------------------------------------------------- #local parameters debug=False # 1 for merge: merge files in the two directory into destination directory. # 2 for random_extract: # 3 for displayArray: RUN_FUNCTION=2 # merge function arguments. IMAGE_DIR1='old.data/pic0529/pic_y2' IMAGE_DIR2='old.data/pic0529/pic_y3' MERGE_DIR='old.data/pic0529/pic_y' # random_extract parameters. SOURCE_DIR='data/multi_pic/pic_no/pic' EXTRACT_DIR='data/multi_pic/pic_no/pic_19' EXTRACT_NUM=250 SUFFIX_LIST=[] # if string_list is empty, no file limited. for example, suffix=['.txt'] IF_CP=False # if IF_CP=True, copy file, otherwise move file. #--------------------------------------Functions--------------------------------------------------------- def merge(merge_dir=MERGE_DIR, image_dir1=IMAGE_DIR1, image_dir2=IMAGE_DIR2): """ Merge files in the two directory into destination directory. Args: merge_dir: destination directory,string. image_dir1: string. image_dir2: string. Returns: bool, True for success, and False for fail. """ print 'current work directory:',os.getcwd() dir1_filename_list=[] dir2_filename_list=[] try: if not os.path.exists(image_dir1): raise ValueError('%s is not exist.'%image_dir1) if not os.path.exists(image_dir2): raise ValueError('%s is not exist.'%image_dir2) dir1_filename_list=os.listdir(image_dir1) dir2_filename_list=os.listdir(image_dir2) same_filename=[] if len(dir1_filename_list)==0: raise ValueError('%s is empty.'%image_dir1) if len(dir2_filename_list)==0: raise ValueError('%s is empty'%image_dir2) for filename in dir1_filename_list: if filename in dir2_filename_list: same_filename.append(filename) if not os.path.exists(merge_dir): print 'merge_dir:',merge_dir,' is not exist.' os.mkdir(merge_dir) print 'merge_dir:',merge_dir,'is created.' if len(same_filename)>0: print 'those file have same name in %s and %s'%(image_dir1,image_dir2) print same_filename if_rename=raw_input('rename them or give up merge them? (r=rename,g=give up):') if if_rename=='r': for f in dir1_filename_list: shutil.copy(os.path.join(image_dir1,f),merge_dir) if f in same_filename: os.rename(os.path.join(merge_dir,f), os.path.join(merge_dir,'(1)'+f)) for f2 in dir2_filename_list: shutil.copy(os.path.join(image_dir2,f2),merge_dir) if f2 in same_filename: os.rename(os.path.join(merge_dir,f2), os.path.join(merge_dir,'(2)'+f2)) elif if_rename=='g': for f3 in dir1_filename_list: if f3 not in same_filename: shutil.copy(os.path.join(image_dir1,f3),merge_dir) for f4 in dir2_filename_list: if f4 not in same_filename: shutil.copy(os.path.join(image_dir2,f4),merge_dir) else: raise ValueError('Error input: r=rename,g=give up') else: for f5 in dir1_filename_list: shutil.copy(os.path.join(image_dir1,f5),merge_dir) for f6 in dir2_filename_list: shutil.copy(os.path.join(image_dir2,f6),merge_dir) except ValueError as e: print e return False print 'merge success.' return True def random_extract(src_dir=SOURCE_DIR, dest_dir=EXTRACT_DIR, num=EXTRACT_NUM, suffix_list=SUFFIX_LIST, if_copy=False): """ randomly extract some files in source directory. Args: src_dir: source directory,string. dest_dir: destnation diretory, if not exist, creat it. string. num: numbers of file you want to copy or move,integer. suffix_list: suffix for your wanted file, string list. if_copy: if True, copy file form src_dir to dst_dir. Returns: """ print 'current work directory:',os.getcwd() filename_list=[] try: if not os.path.exists(src_dir): raise ValueError('SOURCE_DIR:%s is not exist.'%SOURCE_DIR) else: file_list=os.listdir(src_dir) if len(suffix_list)==0: filename_list=file_list else: if len(file_list)==0: print 'no file in ',src_dir return False else: for filename in file_list: if os.path.splitext(filename)[1] in suffix_list: filename_list.append(filename) # random copy or cut files. if len(filename_list) <= num: raise ValueError('extract numbers error:%d should be less than files in %s'%(num,src_dir)) else: if not os.path.exists(dest_dir): print 'dest_dir:',dest_dir,' is not exist.' os.mkdir(dest_dir) print 'dest_dir:',dest_dir,'is created.' random.shuffle(filename_list) for i in range(num): if if_copy: shutil.copy(os.path.join(src_dir,filename_list[i]), dest_dir) else: shutil.move(os.path.join(src_dir,filename_list[i]), dest_dir) except ValueError as e: print e return False print 'great work' return True def displayArray(a, fmt='jpeg', rng=[0,255]): """Display an array as a picture. Args: a: object with array interface. """ a = (a - rng[0])/float(rng[1] - rng[0])*255 a = np.uint8(np.clip(a, 0, 255)) f = StringIO() PIL.Image.fromarray(a).save(f, fmt) display(Image(data=f.getvalue())) time.sleep(5) clear_output return True def test_display(): # Initial Conditions -- some rain drops hit a pond N=500 data = np.zeros([N, N], dtype="float32") # Some rain drops hit a pond at random points for n in range(40): a,b = np.random.randint(0, N, 2) data[a,b] = np.random.uniform() displayArray(data,rng=[-0.1,0.1]) print 'great work' return True def main(): if debug: print 'debuging' test_display() else: if RUN_FUNCTION==1: merge() elif RUN_FUNCTION==2: random_extract() elif RUN_FUNCTION==3: displayArray() else: print 'RUN_FUNCTION setup error:' if __name__=='__main__': main()

24f2ea84b32f8d7b9aae3334f00cb8f687cfe5b7

py

Python

deploy-aks/azext_aks_deploy/dev/aks/up.py

atbagga/cli-extension-aks-up

0e28e4120a2eecac825a3d4fd11539ec922b1895

deploy-aks/azext_aks_deploy/dev/aks/up.py

atbagga/cli-extension-aks-up

0e28e4120a2eecac825a3d4fd11539ec922b1895

2019-12-19T08:18:59.000Z

2020-01-28T09:54:47.000Z

deploy-aks/azext_aks_deploy/dev/aks/up.py

atbagga/cli-extension-aks-up

0e28e4120a2eecac825a3d4fd11539ec922b1895

2019-12-09T06:03:04.000Z

2020-01-02T10:40:39.000Z

# -------------------------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # -------------------------------------------------------------------------------------------- from knack.log import get_logger from knack.util import CLIError from azext_aks_deploy.dev.common.git import resolve_repository from azext_aks_deploy.dev.common.github_api_helper import (Files, get_work_flow_check_runID, push_files_to_repository, get_languages_for_repo, get_github_pat_token, get_default_branch, check_file_exists) from azext_aks_deploy.dev.common.github_workflow_helper import poll_workflow_status, get_new_workflow_yaml_name from azext_aks_deploy.dev.common.github_azure_secrets import get_azure_credentials from azext_aks_deploy.dev.common.kubectl import get_deployment_IP_port from azext_aks_deploy.dev.common.const import (CHECKIN_MESSAGE_AKS, APP_NAME_DEFAULT, APP_NAME_PLACEHOLDER, ACR_PLACEHOLDER, RG_PLACEHOLDER, PORT_NUMBER_DEFAULT, CLUSTER_PLACEHOLDER, RELEASE_PLACEHOLDER, RELEASE_NAME) from azext_aks_deploy.dev.aks.docker_helm_template import get_docker_templates, get_helm_charts logger = get_logger(__name__) aks_token_prefix = "AksAppUpCLIExt_" # pylint: disable=too-many-statements def aks_deploy(aks_cluster=None, acr=None, repository=None, port=None, branch_name=None, skip_secrets_generation=False, do_not_wait=False): """Build and Deploy to AKS via GitHub actions :param aks_cluster: Name of the cluster to select for deployment. :type aks_cluster: str :param acr: Name of the Azure Container Registry to be used for pushing the image. :type acr: str :param repository: GitHub repository URL e.g. https://github.com/azure/azure-cli. :type repository: str :param port: Port on which your application runs. Default is 8080 :type port:str :param branch_name: New branch name to be created to check in files and raise a PR :type branch_name:str :param skip_secrets_generation : Skip generation of Azure credentials. :type skip_secrets_generation: bool :param do_not_wait : Do not wait for workflow completion. :type do_not_wait bool """ repo_name, repository = resolve_repository(repository) get_github_pat_token(token_prefix=aks_token_prefix + repo_name, display_warning=True) logger.warning('Setting up your workflow.') languages = get_languages_for_repo(repo_name) if not languages: raise CLIError('Language detection failed for this repository.') language = choose_supported_language(languages) if language: logger.warning('%s repository detected.', language) else: logger.debug('Languages detected : %s', languages) raise CLIError('The languages in this repository are not yet supported from up command.') from azext_aks_deploy.dev.common.azure_cli_resources import (get_aks_details, get_acr_details, configure_aks_credentials) cluster_details = get_aks_details(aks_cluster) logger.debug(cluster_details) acr_details = get_acr_details(acr) logger.debug(acr_details) print('') files = [] if port is None: port = PORT_NUMBER_DEFAULT if 'Dockerfile' not in languages.keys(): # check in docker file and docker ignore docker_files = get_docker_templates(language, port) if docker_files: files = files + docker_files else: logger.warning('Using the Dockerfile found in the repository %s', repo_name) if 'Smarty' not in languages.keys(): # check in helm charts helm_charts = get_helm_charts(language, acr_details, port) if helm_charts: files = files + helm_charts # create azure service principal and display json on the screen for user to configure it as Github secrets if not skip_secrets_generation: get_azure_credentials() print('') workflow_files = get_yaml_template_for_repo(cluster_details, acr_details, repo_name) if workflow_files: files = files + workflow_files # File checkin for file_name in files: logger.debug("Checkin file path: %s", file_name.path) logger.debug("Checkin file content: %s", file_name.content) default_branch = get_default_branch(repo_name) workflow_commit_sha = push_files_to_repository( repo_name=repo_name, default_branch=default_branch, files=files, branch_name=branch_name, message=CHECKIN_MESSAGE_AKS) if workflow_commit_sha: print('Creating workflow...') check_run_id = get_work_flow_check_runID(repo_name, workflow_commit_sha) workflow_url = 'https://github.com/{repo_id}/runs/{checkID}'.format(repo_id=repo_name, checkID=check_run_id) print('GitHub Action workflow has been created - {}'.format(workflow_url)) if not do_not_wait: poll_workflow_status(repo_name, check_run_id) configure_aks_credentials(cluster_details['name'], cluster_details['resourceGroup']) deployment_ip, port = get_deployment_IP_port(RELEASE_NAME, language) print('Your app is deployed at: http://{ip}:{port}'.format(ip=deployment_ip, port=port)) def get_yaml_template_for_repo(cluster_details, acr_details, repo_name): files_to_return = [] github_workflow_path = '.github/workflows/' # Read template file yaml_file_name = 'main.yml' workflow_yaml = github_workflow_path + yaml_file_name if check_file_exists(repo_name, workflow_yaml): yaml_file_name = get_new_workflow_yaml_name() workflow_yaml = github_workflow_path + yaml_file_name from azext_aks_deploy.dev.resources.resourcefiles import DEPLOY_TO_AKS_TEMPLATE files_to_return.append(Files(path=workflow_yaml, content=DEPLOY_TO_AKS_TEMPLATE .replace(APP_NAME_PLACEHOLDER, APP_NAME_DEFAULT) .replace(ACR_PLACEHOLDER, acr_details['name']) .replace(CLUSTER_PLACEHOLDER, cluster_details['name']) .replace(RELEASE_PLACEHOLDER, RELEASE_NAME) .replace(RG_PLACEHOLDER, cluster_details['resourceGroup']))) return files_to_return def choose_supported_language(languages): # check if one of top three languages are supported or not list_languages = list(languages.keys()) first_language = list_languages[0] if first_language in ('JavaScript', 'Java', 'Python'): return first_language if len(list_languages) >= 1 and list_languages[1] in ('JavaScript', 'Java', 'Python'): return list_languages[1] if len(list_languages) >= 2 and list_languages[2] in ('JavaScript', 'Java', 'Python'): return list_languages[2] return None

c25277d771538e45826dca99de727dec34e39427

py

Python

stweet/file_reader/read_from_file.py

enginbozaba/stweet-twitter-api

060250e00a01ae53c2ca12954719b5efc918e132

stweet/file_reader/read_from_file.py

enginbozaba/stweet-twitter-api

060250e00a01ae53c2ca12954719b5efc918e132

stweet/file_reader/read_from_file.py

enginbozaba/stweet-twitter-api

060250e00a01ae53c2ca12954719b5efc918e132

"""Methods to read tweets from files.""" import json from typing import List import pandas as pd from ..model.tweet import Tweet def read_from_csv(file_path: str) -> List[Tweet]: """Method to read tweets from csv file.""" df = pd.read_csv(file_path, dtype={ 'quoted_status_id_str': str, 'in_reply_to_status_id_str': str, 'in_reply_to_user_id_str': str }) df.quoted_status_id_str.fillna('', inplace=True) df.quoted_status_short_url.fillna('', inplace=True) df.quoted_status_expand_url.fillna('', inplace=True) df.in_reply_to_status_id_str.fillna('', inplace=True) df.in_reply_to_user_id_str.fillna('', inplace=True) df.hashtags.fillna('', inplace=True) df.urls.fillna('', inplace=True) df.mentions.fillna('', inplace=True) return [Tweet.create_tweet_from_flat_dict(row) for _, row in df.iterrows()] def read_from_json_lines(file_path: str) -> List[Tweet]: """Method to read tweets from csv file.""" file = open(file_path, 'r') return [Tweet.create_tweet_from_dict(json.loads(line)) for line in file.readlines()]

fe2db0c6670d327cca552e693aecbce693d02c3e

py

Python

utils/handle_modes.py

Djack1010/sciba_malwareImg2smali

2bbe1a90b16abdb99a7ac3a3d31cbb84808d8e68

utils/handle_modes.py

Djack1010/sciba_malwareImg2smali

2bbe1a90b16abdb99a7ac3a3d31cbb84808d8e68

utils/handle_modes.py

Djack1010/sciba_malwareImg2smali

2bbe1a90b16abdb99a7ac3a3d31cbb84808d8e68

2021-04-21T02:33:20.000Z

2021-04-21T02:33:20.000Z

import tensorflow as tf from utils.generic_utils import print_log import utils.config as config import os import datetime import time from utils.analyzing_data import multiclass_analysis import pickle import cv2 import numpy as np def get_label(file_path): # convert the path to a list of path components parts = tf.strings.split(file_path, os.path.sep) # The second to last is the class-directory # cast to float32 for one_hot encode (otherwise TRUE/FALSE tensor) return tf.cast(parts[-2] == config.CLASS_NAMES, tf.float32) def decode_img(img): # convert the compressed string to a 3D uint8 tensor img = tf.image.decode_png(img, channels=config.CHANNELS) # tf.image.decode_jpeg(img, channels=CHANNELS) # Use `convert_image_dtype` to convert to floats in the [0,1] range. return tf.image.convert_image_dtype(img, tf.float32) def process_path(file_path, model_input='images', data_type='images'): label = get_label(file_path) # load the raw data from the file as a string img = tf.io.read_file(file_path) if data_type == 'images': img = decode_img(img) # resize the image to the desired size. img = tf.image.resize(img, [config.IMG_DIM, config.IMG_DIM]) if model_input == 'vectors': # flatten the data to vector img = tf.reshape(img, [-1]) return img, label def process_path_vector(file_paths): vectors = [] labels = [] for fp in file_paths: label = get_label(fp).numpy() vector = cv2.cvtColor(cv2.imread(fp.numpy().decode("utf-8")), cv2.COLOR_BGR2GRAY).flatten() vectors.append(vector) labels.append(label) return tf.data.Dataset.from_tensor_slices((np.array(vectors), np.array(labels))) def prepare_for_training(ds, batch_size, cache=True, shuffle_buffer_size=1000, loop=False): """ cache: If isinstance(cache, str), then represents the name of a directory on the filesystem to use for caching elements in this Dataset. Otherwise, the dataset will be cached in memory. """ # IF it is a small dataset, only load it once and keep it in memory. # OTHERWISE use `.cache(filename)` to cache preprocessing work for datasets that don't fit in memory. if cache: if isinstance(cache, str): ds = ds.cache(cache) else: ds = ds.cache() ds = ds.shuffle(buffer_size=shuffle_buffer_size) # Repeat forever if loop: ds = ds.repeat() ds = ds.batch(batch_size) # `prefetch` lets the dataset fetch batches in the background while the model # is training. ds = ds.prefetch(buffer_size=config.AUTOTUNE) return ds def prepare_ds(caching, my_set, cache_name, batch_train): if caching: # delete previous cache files and store for this execution caching_file_base = config.main_path + "temp/" for f in os.listdir(caching_file_base): if "{}.tfcache".format(cache_name) in f: os.remove(caching_file_base + f) set_ds = prepare_for_training(my_set, batch_size=batch_train, cache=caching_file_base + "{}.tfcache".format(cache_name)) else: set_ds = prepare_for_training(my_set, batch_size=batch_train) return set_ds def get_ds(name_ds, ds_info): # Load filepaths file_paths = ds_info[name_ds] # Create tf.Dataset from filepaths file_paths_ds = tf.data.Dataset.from_tensor_slices(file_paths).shuffle(len(file_paths)) return file_paths_ds def initialization(arguments, class_info, ds_info, model_class): # GLOBAL SETTINGS config.AUTOTUNE = tf.data.experimental.AUTOTUNE config.CLASS_NAMES = class_info['class_names'] config.BATCH_SIZE = arguments.batch_size config.DATA_REQ = model_class.input_type print("LOADING AND PRE-PROCESSING DATA") try: # STATS size_train, size_val, size_test = class_info['train_size'], class_info['val_size'], class_info['test_size'] class_names, nclasses = class_info['class_names'], class_info['n_classes'] # Print information on log # EXECUTION Info mode_info = "load_model = {}".format(arguments.load_model) if arguments.load_model is not None \ else "mode = {}".format(arguments.mode) print_log("INFO EXECUTION:" "\n{}\nmodel = {}\ndataset = {}" "\noutput_model = {}\nepochs = {}\nbatch_size = {}\ncaching = {}" "\nmodel_input_type = {}" "\n----------------" .format(mode_info, arguments.model, arguments.dataset, arguments.output_model, arguments.epochs, arguments.batch_size, arguments.caching, config.DATA_REQ)) # DATA Info print_log("INFO DATA:" "\nnum_classes = {}\nclass_names= {}\nSize train-val-test= {}-{}-{}" "\ndata_type = {}\nsize_{}" .format(nclasses, class_names, size_train, size_val, size_test, ds_info['ds_type'], "img = {}x{}".format(config.IMG_DIM, config.CHANNELS) if config.DATA_REQ == "images" else "vec = {}".format(config.VECTOR_DIM))) for ds_class in class_names: print_log("{} : {}-{}-{} -> {}".format(ds_class, class_info['info'][ds_class]['TRAIN'], class_info['info'][ds_class]['VAL'], class_info['info'][ds_class]['TEST'], class_info['info'][ds_class]['TOT'])) print_log("----------------") except KeyError as e: print("KeyError: {}".format(e)) print("POSSIBLE FIX: run 'python main.py -m DATA -d {}'".format(arguments.dataset)) exit() def train_val(arguments, model, ds_info): # Create tf.Dataset from ds_info e filepaths train_paths_ds, val_paths_ds = get_ds('train_paths', ds_info), get_ds('val_paths', ds_info) # -------------- TRAINING and VALIDATION part -------------------- # Use Dataset.map to create a dataset of image, label pairs # Set `num_parallel_calls` so multiple images are loaded/processed in parallel. lab_train_ds = train_paths_ds.map(lambda x: process_path(x, model_input=config.DATA_REQ, data_type=ds_info['ds_type']), num_parallel_calls=config.AUTOTUNE) lab_val_ds = val_paths_ds.map(lambda x: process_path(x, model_input=config.DATA_REQ, data_type=ds_info['ds_type']), num_parallel_calls=config.AUTOTUNE) # Caching dataset in memory for big dataset (IF arguments.caching is set) train_ds, val_ds = prepare_ds(arguments.caching, lab_train_ds, "train", arguments.batch_size),\ prepare_ds(arguments.caching, lab_val_ds, "val", arguments.batch_size) print_log('Start Training for {} epochs '.format(arguments.epochs), print_on_screen=True) # Initialize callbacks for Tensorboard log_fit = config.main_path + "results/tensorboard/fit/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S") tensorboard_callback_fit = tf.keras.callbacks.TensorBoard(log_dir=log_fit, histogram_freq=1) train_results = model.fit(x=train_ds, batch_size=arguments.batch_size, epochs=arguments.epochs, validation_data=val_ds, callbacks=[tensorboard_callback_fit]) print_log("\ttrain_loss:{} \n\ttrain_acc:{} \n\ttrain_prec:{} \n\ttrain_rec:{} \n" "\tval_loss:{} \n\tval_acc:{} \n\tval_prec:{} \n\tval_rec:{}" .format(train_results.history['loss'], train_results.history['prec'], train_results.history['rec'], train_results.history['acc'], train_results.history['val_loss'], train_results.history['val_acc'], train_results.history['val_prec'], train_results.history['val_rec'])) del train_ds, val_ds def train_test(arguments, model, class_info, ds_info): # Create tf.Dataset from ds_info e filepaths final_training_paths_ds = get_ds('final_training_paths', ds_info) # -------------- FINAL TRAINING and TEST part -------------------- # Use Dataset.map to create a dataset of image, label pairs # Set `num_parallel_calls` so multiple images are loaded/processed in parallel. lab_final_train_ds = final_training_paths_ds.map(lambda x: process_path(x, model_input=config.DATA_REQ, data_type=ds_info['ds_type']), num_parallel_calls=config.AUTOTUNE) # NB The batch_size for testing is set to 1 to make easier the calculation of the performance results fin_train_ds = prepare_ds(arguments.caching, lab_final_train_ds, "fin_tr", arguments.batch_size) # Train the model over the entire total_training set and then test print_log('Start Final Training for {} epochs '.format(arguments.epochs), print_on_screen=True) start_training = time.perf_counter() final_train_results = model.fit(x=fin_train_ds, batch_size=arguments.batch_size, epochs=arguments.epochs) end_training = time.perf_counter() print_log("\ttrain_loss:{} \n\ttrain_acc:{} \n\ttrain_prec:{} \n\ttrain_rec:{} \n" .format(final_train_results.history['loss'], final_train_results.history['prec'], final_train_results.history['rec'], final_train_results.history['acc'])) print_log("FINAL TRAINING TIME: {} ".format(str(datetime.timedelta(seconds=end_training - start_training)))) del fin_train_ds # Test the trained model over the test set test(arguments, model, class_info, ds_info) def test(arguments, model, class_info, ds_info): # Create tf.Dataset from ds_info e filepaths test_paths_ds = get_ds('test_paths', ds_info) # -------------- TEST part -------------------- # Use Dataset.map to create a dataset of image, label pairs # Set `num_parallel_calls` so multiple images are loaded/processed in parallel. lab_test_ds = test_paths_ds.map(lambda x: process_path(x, model_input=config.DATA_REQ, data_type=ds_info['ds_type']), num_parallel_calls=config.AUTOTUNE) # NB The batch size for test is set to 1 to make easier the calculation of the performance results test_ds = prepare_ds(arguments.caching, lab_test_ds, "test", 1) # Test the trained model over the test set print_log('Start Test', print_on_screen=True) results = model.evaluate(test_ds) print_log("\ttest loss:{} \n\ttest accuracy:{}".format(results[0], results[1]), print_on_screen=True) print_log("\tPrec:{} \n\tRecall:{}".format(results[2], results[3]), print_on_screen=True) try: # F-measure calculated as (2 * Prec * Recall)/(Prec + Recall) print_log("\tF-Measure:{} \n\tAUC:{}" .format((2 * results[2] * results[3]) / (results[2] + results[3]), results[4]), print_on_screen=True) except ZeroDivisionError: print_log("\tF-Measure:{} \n\tAUC:{}" .format("Error", results[4]), print_on_screen=True) # TODO: split evaluation and prediction in two phases -> at the moment, the test set is first used by model.evaluate # to get cumulative information, and then is again used by model.predict to get per class information, thus, the # test process is repeated two times! print("Calculating performances per class, it may take a while...") cm, results_classes, to_print = multiclass_analysis(model, test_ds, class_info['class_names'], save_fig=config.main_path + "results/figures/CM_{}" .format(config.timeExec)) print_log("Results per classes", print_on_screen=True) print_log(to_print, print_on_screen=True) del test_ds def save_model(arguments, model): model_path = config.main_path + 'models_saved/{}_m{}' \ .format(arguments.output_model, arguments.model) # save model and architecture to single file tf.keras.models.save_model(model, model_path, overwrite=False) with open(model_path + '.info', 'wb') \ as filehandle: store_data = {"CLASS_NAMES": config.CLASS_NAMES, "CHANNELS": config.CHANNELS, "IMG_DIM": config.IMG_DIM} pickle.dump(store_data, filehandle) print_log("Model, Weights and Info saved to 'models_saved/{}_m{}[.info]'" .format(arguments.output_model, arguments.model), print_on_screen=True) def load_model(arguments, required_img, required_chan, required_numClasses): """ The required_img_chan args is (None,None) when the model is loaded with no specific request on the img size. That is the case of the apply_gradcam, when we want just to test the model, while in main.py we could also specify an --image_size arguments and then check if the loaded model fits this requirements """ print("LOADING MODEL") model_path = config.main_path + 'models_saved/{}_m{}'\ .format(arguments.load_model, arguments.model) if not os.path.isdir(model_path): print("Model not found in {}, exiting...".format(model_path)) exit() model = tf.keras.models.load_model(model_path, compile=False) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['acc', tf.keras.metrics.Precision(name="prec"), tf.keras.metrics.Recall(name="rec"), tf.keras.metrics.AUC(name='auc')]) with open(model_path + ".info", 'rb') \ as filehandle: stored_data = pickle.load(filehandle) class_names = stored_data["CLASS_NAMES"] channel = stored_data["CHANNELS"] img_dim = stored_data["IMG_DIM"] if (required_img is not None and img_dim != required_img) or \ (required_chan is not None and channel != required_chan) or \ (required_numClasses is not None and len(class_names) != required_numClasses): print("IMG_DIM, CHANNELS and/or number of output classes DIFFERS from the required! Exiting...") print("Asking img size with {}x{} on a {}-class classification problem, but model {}x{} and outputs on " "{}-class, exiting...".format(required_img, required_chan, required_numClasses, img_dim, channel, len(class_names))) exit() return model def save_weights(arguments, model): print("SAVING WEIGHTS") model.save_weights(config.main_path + 'models_saved/{}_m{}_weights'.format(arguments.output_model, arguments.model)) def load_weights(arguments, model): print("LOADING WEIGHTS") model.load_weights(config.main_path + 'models_saved/{}_m{}_weights'.format(arguments.load_model, arguments.model)) return model

0a2649be8d41ac38c2172f78249af3222274b850

py

Python

cpt2wgt.py

kevincao91/Tools

545901c682c20cd06256156dadc75b8e4e7df88c

cpt2wgt.py

kevincao91/Tools

545901c682c20cd06256156dadc75b8e4e7df88c

cpt2wgt.py

kevincao91/Tools

545901c682c20cd06256156dadc75b8e4e7df88c

import pickle import os,sys cptPath=sys.argv[1] wgtPath=cptPath with open(cptPath,'rb') as f: data = pickle.load(f,encoding='latin1') keys = data['blobs'].keys() # needs = ['conv','res','fpn',] not_needs = ['fc1000','momentum'] output_dic={'blobs':{}} print('filtered out:') for key in keys: keep = True # for need in needs: # if key.startswith(need): # keep=True for not_need in not_needs: if not_need in key: keep=False break if keep: # if 'score' in key: # print(key) output_dic['blobs'][key] = data['blobs'][key] #print(key) else: print(' - '+key) #print(output_dic['blobs'].keys()) with open(wgtPath,'wb') as f: pickle.dump(output_dic,f,protocol=0)

9b7b6c73bab3ea09a06d02c91d959d9f4990dd59

py

Python

utils/http_helper.py

BlockVigil/ethvigil-cli

80247260a6b20224f6528edbfee5bdee19baf842

utils/http_helper.py

BlockVigil/ethvigil-cli

80247260a6b20224f6528edbfee5bdee19baf842

2019-08-06T10:16:04.000Z

2020-07-23T17:16:27.000Z

utils/http_helper.py

BlockVigil/ethvigil-cli

80247260a6b20224f6528edbfee5bdee19baf842

2019-08-07T19:55:58.000Z

2019-08-07T19:55:58.000Z