xszheng2020/the_stack_dedup_python_hits_5 · Datasets at Hugging Face

0347ce2c699c5e1ec571bccc902e6b648616eb2c

53

py

Python

project/test/Backend/pages/api/__init__.py

fael07/DRF-Project

f65b4177e56e7209d2369ba9d6d81bfe00321052

[ "MIT" ]

null

project/test/Backend/pages/api/__init__.py

fael07/DRF-Project

f65b4177e56e7209d2369ba9d6d81bfe00321052

[ "MIT" ]

null

project/test/Backend/pages/api/__init__.py

fael07/DRF-Project

f65b4177e56e7209d2369ba9d6d81bfe00321052

[ "MIT" ]

null

from ...class_models.list import TestModelDataListApi

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5

0356e139e2b81cb6158b675d2913aeabc45c3748

131

py

Python

week6/function_list.py

melphick/pybasics

68508d10b7509943b629b3c627252de60b6a5744

[ "Apache-2.0" ]

null

week6/function_list.py

melphick/pybasics

68508d10b7509943b629b3c627252de60b6a5744

[ "Apache-2.0" ]

null

week6/function_list.py

melphick/pybasics

68508d10b7509943b629b3c627252de60b6a5744

[ "Apache-2.0" ]

null

#!/usr/bin/python def f1(a_list): a_list = [] a_list.append("bar") print a_list a = range(10) print a f1(a) print a

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5

ceeaa77147b15728a1249aab97187c113b4c9d37

59

py

Python

node_manager_fkie/src/node_manager_fkie/editor/__init__.py

ahoarau/multimaster_fkie

82bf341423bd3c2a15005c85eca9de5747cb8069

[ "BSD-3-Clause" ]

1

2020-03-10T06:32:51.000Z

node_manager_fkie/src/node_manager_fkie/editor/__init__.py

ahoarau/multimaster_fkie

82bf341423bd3c2a15005c85eca9de5747cb8069

[ "BSD-3-Clause" ]

1

2018-04-20T13:03:34.000Z

node_manager_fkie/src/node_manager_fkie/editor/__init__.py

ahoarau/multimaster_fkie

82bf341423bd3c2a15005c85eca9de5747cb8069

[ "BSD-3-Clause" ]

1

2018-11-07T03:37:23.000Z

from .editor import Editor from .text_edit import TextEdit

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1

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5

3018be304e4c88570da6295a8e7a6a579831ac98

325

py

Python

myexceptions.py

carloshernangarrido/whatsapp_sender

1489bc6cf12e1557e6e85a5ed2f15e4ba3b86a19

[ "MIT" ]

null

myexceptions.py

carloshernangarrido/whatsapp_sender

1489bc6cf12e1557e6e85a5ed2f15e4ba3b86a19

[ "MIT" ]

null

myexceptions.py

carloshernangarrido/whatsapp_sender

1489bc6cf12e1557e6e85a5ed2f15e4ba3b86a19

[ "MIT" ]

null

### Exceptions ### E1 = "Hay un problema con este número" E2 = """ **************************************************************************** *** No encontré el archivo. Recordá que debe estar dentro de mi carpeta. *** **************************************************************************** """

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5

3019b61980e68a75e379d6f84dc8be166146691b

141

py

Python

1. Dive into Python/1.21. Exercise Grading in CENG314.py

ahmetutkuozkan/my_ceng240_exercises_solutions

167bb9938515870ec1f01853933edc3b55937bff

[ "MIT" ]

null

1. Dive into Python/1.21. Exercise Grading in CENG314.py

ahmetutkuozkan/my_ceng240_exercises_solutions

167bb9938515870ec1f01853933edc3b55937bff

[ "MIT" ]

null

1. Dive into Python/1.21. Exercise Grading in CENG314.py

ahmetutkuozkan/my_ceng240_exercises_solutions

167bb9938515870ec1f01853933edc3b55937bff

[ "MIT" ]

null

g1 = int(input()); g2 = int(input()); g3 = int(input()); g4 = int(input()); g5 = int(input()); print((g1+g2+g3+g4+g5-min(g1,g2,g3,g4,g5))/8)

70.5

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5

3045f8cef687c8b569284017ff047114def1211a

234

py

Python

api/invitations/exceptions.py

liobrdev/simplekanban

ececbe15cd34aa53e7d37564879a8c14827e0ebb

[ "MIT" ]

null

api/invitations/exceptions.py

liobrdev/simplekanban

ececbe15cd34aa53e7d37564879a8c14827e0ebb

[ "MIT" ]

null

api/invitations/exceptions.py

liobrdev/simplekanban

ececbe15cd34aa53e7d37564879a8c14827e0ebb

[ "MIT" ]

null

from django.db import IntegrityError class InvitedAlreadyMember(IntegrityError): def __init__(self, email): self.email = email def __str__(self): return f'<{self.email}> is already a member of this project.'

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5

305bac82d1c20fb14ff96c24e4759b9c235a7aaa

42

py

Python

watersnake/__init__.py

bjhockley/watersnake

a06d2eb8dec0207ee329dc6ab574ba1a347e9522

[ "MIT" ]

null

watersnake/__init__.py

bjhockley/watersnake

a06d2eb8dec0207ee329dc6ab574ba1a347e9522

[ "MIT" ]

null

watersnake/__init__.py

bjhockley/watersnake

a06d2eb8dec0207ee329dc6ab574ba1a347e9522

[ "MIT" ]

null

"""module __init__ file for watersnake"""

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1

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5

307847be6d58b8759f76299b89b86382c867cbb0

265

py

Python

learn_uwsgi/dev.py

Carsten-Leue/learn-uwsgi

0363b81da20d4faed29a80126cc5c9a1e2035cfc

[ "MIT" ]

null

learn_uwsgi/dev.py

Carsten-Leue/learn-uwsgi

0363b81da20d4faed29a80126cc5c9a1e2035cfc

[ "MIT" ]

null

learn_uwsgi/dev.py

Carsten-Leue/learn-uwsgi

0363b81da20d4faed29a80126cc5c9a1e2035cfc

[ "MIT" ]

null

from learn_uwsgi.iplink.mock import createIpLinkMock from learn_uwsgi.iplink.shell import createIpLinkShell from logging import Logger from learn_uwsgi.routes import create_routes def create_dev_routes(logger: Logger): create_routes(createIpLinkMock(logger))

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5

061f99144deb6063d2449993e477f2e34c061f5f

17,905

py

Python

lib/datasets/cityscapes.py

scenarios/VAE-2

3dfbaeea9fa29f88805e728cd26ce35cb5586b1c

[ "MIT" ]

5

2021-01-28T15:43:06.000Z

2021-12-31T02:55:11.000Z

lib/datasets/cityscapes.py

scenarios/VAE-2

3dfbaeea9fa29f88805e728cd26ce35cb5586b1c

[ "MIT" ]

null

lib/datasets/cityscapes.py

scenarios/VAE-2

3dfbaeea9fa29f88805e728cd26ce35cb5586b1c

[ "MIT" ]

null

# ------------------------------------------------------------------------------ # Copyright (c) Microsoft # Licensed under the MIT License. # Written by Ke Sun (sunk@mail.ustc.edu.cn) # ------------------------------------------------------------------------------ import os import logging import cv2 import numpy as np from PIL import Image import torch from torch.nn import functional as F from .base_dataset import BaseDataset from zipfile import ZipFile class Cityscapes(BaseDataset): def __init__(self, root, list_path, num_samples=None, num_classes=19, multi_scale=True, flip=True, ignore_label=-1, base_size=2048, crop_size=(512, 1024), center_crop_test=False, downsample_rate=1, scale_factor=16, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]): super(Cityscapes, self).__init__(ignore_label, base_size, crop_size, downsample_rate, scale_factor, mean, std,) self.root = root self.list_path = list_path self.num_classes = num_classes self.class_weights = torch.FloatTensor([0.8373, 0.918, 0.866, 1.0345, 1.0166, 0.9969, 0.9754, 1.0489, 0.8786, 1.0023, 0.9539, 0.9843, 1.1116, 0.9037, 1.0865, 1.0955, 1.0865, 1.1529, 1.0507]).cuda() self.multi_scale = multi_scale self.flip = flip self.center_crop_test = center_crop_test self.img_list = [line.strip().split() for line in open(root+list_path)] self.files = self.read_files() if num_samples: self.files = self.files[:num_samples] self.label_mapping = {-1: ignore_label, 0: ignore_label, 1: ignore_label, 2: ignore_label, 3: ignore_label, 4: ignore_label, 5: ignore_label, 6: ignore_label, 7: 0, 8: 1, 9: ignore_label, 10: ignore_label, 11: 2, 12: 3, 13: 4, 14: ignore_label, 15: ignore_label, 16: ignore_label, 17: 5, 18: ignore_label, 19: 6, 20: 7, 21: 8, 22: 9, 23: 10, 24: 11, 25: 12, 26: 13, 27: 14, 28: 15, 29: ignore_label, 30: ignore_label, 31: 16, 32: 17, 33: 18} def read_files(self): files = [] if 'test' in self.list_path: for item in self.img_list: image_path = item name = os.path.splitext(os.path.basename(image_path[0]))[0] files.append({ "img": image_path[0], "name": name, }) else: for item in self.img_list: image_path, label_path = item name = os.path.splitext(os.path.basename(label_path))[0] files.append({ "img": image_path, "label": label_path, "name": name, "weight": 1 }) return files def convert_label(self, label, inverse=False): temp = label.copy() if inverse: for v, k in self.label_mapping.items(): label[temp == k] = v else: for k, v in self.label_mapping.items(): label[temp == k] = v return label def __getitem__(self, index): item = self.files[index] name = item["name"] image = cv2.imread(os.path.join(self.root,'cityscapes',item["img"]), cv2.IMREAD_COLOR) size = image.shape if 'test' in self.list_path: image = self.input_transform(image) image = image.transpose((2, 0, 1)) return image.copy(), np.array(size), name label = cv2.imread(os.path.join(self.root,'cityscapes',item["label"]), cv2.IMREAD_GRAYSCALE) label = self.convert_label(label) image, label = self.gen_sample(image, label, self.multi_scale, self.flip, self.center_crop_test) return image.copy(), label.copy(), np.array(size), name def multi_scale_inference(self, model, image, scales=[1], flip=False): batch, _, ori_height, ori_width = image.size() assert batch == 1, "only supporting batchsize 1." image = image.numpy()[0].transpose((1,2,0)).copy() stride_h = np.int(self.crop_size[0] * 1.0) stride_w = np.int(self.crop_size[1] * 1.0) final_pred = torch.zeros([1, self.num_classes, ori_height,ori_width]).cuda() for scale in scales: new_img = self.multi_scale_aug(image=image, rand_scale=scale, rand_crop=False) height, width = new_img.shape[:-1] if scale <= 1.0: new_img = new_img.transpose((2, 0, 1)) new_img = np.expand_dims(new_img, axis=0) new_img = torch.from_numpy(new_img) preds = self.inference(model, new_img, flip) preds = preds[:, :, 0:height, 0:width] else: new_h, new_w = new_img.shape[:-1] rows = np.int(np.ceil(1.0 * (new_h - self.crop_size[0]) / stride_h)) + 1 cols = np.int(np.ceil(1.0 * (new_w - self.crop_size[1]) / stride_w)) + 1 preds = torch.zeros([1, self.num_classes, new_h,new_w]).cuda() count = torch.zeros([1,1, new_h, new_w]).cuda() for r in range(rows): for c in range(cols): h0 = r * stride_h w0 = c * stride_w h1 = min(h0 + self.crop_size[0], new_h) w1 = min(w0 + self.crop_size[1], new_w) h0 = max(int(h1 - self.crop_size[0]), 0) w0 = max(int(w1 - self.crop_size[1]), 0) crop_img = new_img[h0:h1, w0:w1, :] crop_img = crop_img.transpose((2, 0, 1)) crop_img = np.expand_dims(crop_img, axis=0) crop_img = torch.from_numpy(crop_img) pred = self.inference(model, crop_img, flip) preds[:,:,h0:h1,w0:w1] += pred[:,:, 0:h1-h0, 0:w1-w0] count[:,:,h0:h1,w0:w1] += 1 preds = preds / count preds = preds[:,:,:height,:width] preds = F.upsample(preds, (ori_height, ori_width), mode='bilinear') final_pred += preds return final_pred def get_palette(self, n): palette = [0] * (n * 3) for j in range(0, n): lab = j palette[j * 3 + 0] = 0 palette[j * 3 + 1] = 0 palette[j * 3 + 2] = 0 i = 0 while lab: palette[j * 3 + 0] |= (((lab >> 0) & 1) << (7 - i)) palette[j * 3 + 1] |= (((lab >> 1) & 1) << (7 - i)) palette[j * 3 + 2] |= (((lab >> 2) & 1) << (7 - i)) i += 1 lab >>= 3 return palette def save_pred(self, preds, sv_path, name): palette = self.get_palette(256) preds = preds.cpu().numpy().copy() preds = np.asarray(np.argmax(preds, axis=1), dtype=np.uint8) for i in range(preds.shape[0]): pred = self.convert_label(preds[i], inverse=True) save_img = Image.fromarray(pred) save_img.putpalette(palette) save_img.save(os.path.join(sv_path, name[i]+'.png')) class CityscapesSequence(BaseDataset): def __init__(self, root, list_path, num_samples=None, num_classes=19, multi_scale=True, flip=True, ignore_label=-1, base_size=2048, crop_size=(512, 1024), center_crop_test=False, downsample_rate=1, scale_factor=16, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], clip_length = 3, clip_num = 3, random_pos = True, image_tmpl = '{:06d}_leftImg8bit.png', fixed_length = None, is_baseline = None): super(CityscapesSequence, self).__init__(ignore_label, base_size, crop_size, downsample_rate, scale_factor, mean, std, ) self.root = root self.list_path = list_path self.num_classes = num_classes self.class_weights = torch.FloatTensor([0.8373, 0.918, 0.866, 1.0345, 1.0166, 0.9969, 0.9754, 1.0489, 0.8786, 1.0023, 0.9539, 0.9843, 1.1116, 0.9037, 1.0865, 1.0955, 1.0865, 1.1529, 1.0507]).cuda() self.clip_length = clip_length self.clip_num = clip_num self.multi_scale = multi_scale self.flip = flip self.center_crop_test = center_crop_test self.random_pos = random_pos self.image_tmpl = image_tmpl self.sequence_list = [line.strip() for line in open(list_path)] self.files = self.read_files() if num_samples: self.files = self.files[:num_samples] self.label_mapping = {-1: ignore_label, 0: ignore_label, 1: ignore_label, 2: ignore_label, 3: ignore_label, 4: ignore_label, 5: ignore_label, 6: ignore_label, 7: 0, 8: 1, 9: ignore_label, 10: ignore_label, 11: 2, 12: 3, 13: 4, 14: ignore_label, 15: ignore_label, 16: ignore_label, 17: 5, 18: ignore_label, 19: 6, 20: 7, 21: 8, 22: 9, 23: 10, 24: 11, 25: 12, 26: 13, 27: 14, 28: 15, 29: ignore_label, 30: ignore_label, 31: 16, 32: 17, 33: 18} def read_files(self): files = [] for item in self.sequence_list: sequence_path = item name = os.path.splitext(os.path.basename(sequence_path))[0] files.append({ "seq": sequence_path, "name": name, }) return files def convert_label(self, label, inverse=False): temp = label.copy() if inverse: for v, k in self.label_mapping.items(): label[temp == k] = v else: for k, v in self.label_mapping.items(): label[temp == k] = v return label def _load_image(self, idx, zip_f): try: im = Image.open(zip_f.open(self.image_tmpl.format(idx))).convert('RGB') except Exception as e: new_idx = idx - 1 if idx > 0 else idx + 1 logging.error('Failed to open {}, open {} instead'.format(self.image_tmpl.format(idx), self.image_tmpl.format(new_idx))) im = Image.open(zip_f.open(self.image_tmpl.format(new_idx))).convert('RGB') return im def get(self, path): images = list() with ZipFile(os.path.join(self.root, path), mode='r') as zip_f: sample_pos = np.random.randint(0, max(1, 30 - self.clip_length * self.clip_num + 1)) if self.random_pos \ else max(0, 30 - self.clip_length * self.clip_num - 1) for p in range(sample_pos, sample_pos + self.clip_length * self.clip_num): seg_imgs = np.asarray(self._load_image(p, zip_f).resize((self.crop_size[1], self.crop_size[0])), dtype=np.float32) images.append(seg_imgs) return images def input_transform(self, sequence): sequence = np.concatenate(sequence, axis=-1) sequence = sequence / 255.0 sequence -= self.mean * self.clip_length * self.clip_num sequence /= self.std * self.clip_length * self.clip_num return sequence def __getitem__(self, index): item = self.files[index] name = item["name"] sequence = self.get(item['seq']) sequence = np.transpose(self.input_transform(sequence), (2, 0, 1)) sequences = [sequence[i * (self.clip_length * 3) : (i+1) * (self.clip_length * 3)].copy() for i in range(0, self.clip_num)] return sequences, name def multi_scale_inference(self, model, image, scales=[1], flip=False): batch, _, ori_height, ori_width = image.size() assert batch == 1, "only supporting batchsize 1." image = image.numpy()[0].transpose((1, 2, 0)).copy() stride_h = np.int(self.crop_size[0] * 1.0) stride_w = np.int(self.crop_size[1] * 1.0) final_pred = torch.zeros([1, self.num_classes, ori_height, ori_width]).cuda() for scale in scales: new_img = self.multi_scale_aug(image=image, rand_scale=scale, rand_crop=False) height, width = new_img.shape[:-1] if scale <= 1.0: new_img = new_img.transpose((2, 0, 1)) new_img = np.expand_dims(new_img, axis=0) new_img = torch.from_numpy(new_img) preds = self.inference(model, new_img, flip) preds = preds[:, :, 0:height, 0:width] else: new_h, new_w = new_img.shape[:-1] rows = np.int(np.ceil(1.0 * (new_h - self.crop_size[0]) / stride_h)) + 1 cols = np.int(np.ceil(1.0 * (new_w - self.crop_size[1]) / stride_w)) + 1 preds = torch.zeros([1, self.num_classes, new_h, new_w]).cuda() count = torch.zeros([1, 1, new_h, new_w]).cuda() for r in range(rows): for c in range(cols): h0 = r * stride_h w0 = c * stride_w h1 = min(h0 + self.crop_size[0], new_h) w1 = min(w0 + self.crop_size[1], new_w) h0 = max(int(h1 - self.crop_size[0]), 0) w0 = max(int(w1 - self.crop_size[1]), 0) crop_img = new_img[h0:h1, w0:w1, :] crop_img = crop_img.transpose((2, 0, 1)) crop_img = np.expand_dims(crop_img, axis=0) crop_img = torch.from_numpy(crop_img) pred = self.inference(model, crop_img, flip) preds[:, :, h0:h1, w0:w1] += pred[:, :, 0:h1 - h0, 0:w1 - w0] count[:, :, h0:h1, w0:w1] += 1 preds = preds / count preds = preds[:, :, :height, :width] preds = F.upsample(preds, (ori_height, ori_width), mode='bilinear') final_pred += preds return final_pred def get_palette(self, n): palette = [0] * (n * 3) for j in range(0, n): lab = j palette[j * 3 + 0] = 0 palette[j * 3 + 1] = 0 palette[j * 3 + 2] = 0 i = 0 while lab: palette[j * 3 + 0] |= (((lab >> 0) & 1) << (7 - i)) palette[j * 3 + 1] |= (((lab >> 1) & 1) << (7 - i)) palette[j * 3 + 2] |= (((lab >> 2) & 1) << (7 - i)) i += 1 lab >>= 3 return palette def save_pred(self, preds, sv_path, name): palette = self.get_palette(256) preds = preds.cpu().numpy().copy() preds = np.asarray(np.argmax(preds, axis=1), dtype=np.uint8) for i in range(preds.shape[0]): pred = self.convert_label(preds[i], inverse=True) save_img = Image.fromarray(pred) save_img.putpalette(palette) save_img.save(os.path.join(sv_path, name[i] + '.png')) if __name__ == '__main__': import sys sys.path.insert(0, "/home/yzzhou/workspace/code/video-prediction/lib/datasets/") from base_dataset import BaseDataset train_dataset = CityscapesSequence( root='/data/yizhou/cityscape/leftImg8bit_sequence_resized_zip/', list_path='/data/yizhou/cityscape/test_list.text', num_samples=None, num_classes=3, multi_scale=False, flip=False, base_size=512, crop_size=(256, 512)) trainloader = torch.utils.data.DataLoader( train_dataset, batch_size=1, shuffle=False, num_workers=1, pin_memory=True, drop_last=True) for i_iter, s in enumerate(trainloader): st, s2t, s3t, name = s im = Image.fromarray(np.transpose(np.uint8(st[0][0:3]), (1, 2, 0))) im.save('test.png')

41.16092

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17,905

3.732349

0.136133

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0.026706

0.014466

0.753833

0.728116

0.70413

0.690529

0.665554

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0.068303

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17,905

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133

41.25576

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0

1

0

1

0

1

0

null

0

5

0651363901f8a71dd2c42c685e0a44470afbd9cd

103

py

Python

search-insert-position/Solution.6587756.py

rahul-ramadas/leetcode

6c84c2333a613729361c5cdb63dc3fc80203b340

[ "MIT" ]

null

search-insert-position/Solution.6587756.py

rahul-ramadas/leetcode

6c84c2333a613729361c5cdb63dc3fc80203b340

[ "MIT" ]

1

2016-09-11T22:26:17.000Z

2016-09-13T01:49:48.000Z

search-insert-position/Solution.6587756.py

rahul-ramadas/leetcode

6c84c2333a613729361c5cdb63dc3fc80203b340

[ "MIT" ]

null

class Solution: def searchInsert(self, A, target): return bisect.bisect_left(A, target)

25.75

45

0.669903

13

103

5.230769

0.769231

0.205882

0

0.23301

103

3

46

34.333333

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0

1

0.333333

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0

0.333333

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0

1

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1

0

1

0

null

0

1

0

1

0

5

06737af4719e3bc932b559060aaa9f99f62c96d2

132

py

Python

pinry/pins/admin.py

Jenso/ProjectY

267a349ce45d3399ecac71d81b09db4d6943a329

[ "BSD-2-Clause", "Unlicense" ]

null

pinry/pins/admin.py

Jenso/ProjectY

267a349ce45d3399ecac71d81b09db4d6943a329

[ "BSD-2-Clause", "Unlicense" ]

null

pinry/pins/admin.py

Jenso/ProjectY

267a349ce45d3399ecac71d81b09db4d6943a329

[ "BSD-2-Clause", "Unlicense" ]

null

from django.contrib import admin from pinry.pins.models import Category, Pin admin.site.register(Category) admin.site.register(Pin)

26.4

43

0.825758

20

132

5.45

0.6

0.165138

0.311927

0

0.083333

132

4

44

33

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0

true

0

0.5

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null

0

1

0

1

0

5

ebf46c046d0a6084a9884b8af2a208157180556b

91

py

Python

comments/admin.py

etovrodeya/hotel_project2

0f81856b48baeae1b63f994f796cedd68ad10b03

[ "MIT" ]

1

2020-07-29T20:16:17.000Z

comments/admin.py

etovrodeya/hotel_project2

0f81856b48baeae1b63f994f796cedd68ad10b03

[ "MIT" ]

null

comments/admin.py

etovrodeya/hotel_project2

0f81856b48baeae1b63f994f796cedd68ad10b03

[ "MIT" ]

null

from django.contrib import admin from .models import Comment admin.site.register(Comment)

18.2

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91

5.769231

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0.10989

91

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33

22.75

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null

0

1

0

1

0

1

0

5

231a7fbf7a63544baf0b98f6a3f17bc0547c4ae5

712

py

Python

music-app/music/models.py

izzywrubel/COMP333-project-2

00a7f2b2bd3df24f7509d10327568cc6a812ece5

[ "MIT" ]

null

music-app/music/models.py

izzywrubel/COMP333-project-2

00a7f2b2bd3df24f7509d10327568cc6a812ece5

[ "MIT" ]

null

music-app/music/models.py

izzywrubel/COMP333-project-2

00a7f2b2bd3df24f7509d10327568cc6a812ece5

[ "MIT" ]

null

from django.db import models class artists(models.Model): song = models.CharField(primary_key = True, max_length=200) artist = models.CharField(max_length=200) class ratings(models.Model): id = models.IntegerField(primary_key = True) username = models.CharField(max_length=200) song = models.CharField(max_length=200) rating = models.IntegerField(default=0) class users(models.Model): username = models.CharField(primary_key = True, max_length=200) password = models.CharField(max_length=200) class genres(models.Model): song = models.CharField(max_length=200) artist = models.CharField(primary_key = True, max_length=200) genre = models.CharField(max_length=200)

33.904762

67

0.744382

95

712

5.442105

0.294737

0.261122

0.208897

0.27853

0.659574

0.504836

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0

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712

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1

0

null

0

1

0

1

0

5

2354ef5b72940e31c6fa59d92fcdd07418771edb

244

py

Python

Chapter5_module_package_program/Section5.3_module_and_import/report.py

skatsuta/introducing-python

945fc84ba58aaa2602e454890c8c6f26e403660e

[ "MIT" ]

null

Chapter5_module_package_program/Section5.3_module_and_import/report.py

skatsuta/introducing-python

945fc84ba58aaa2602e454890c8c6f26e403660e

[ "MIT" ]

null

Chapter5_module_package_program/Section5.3_module_and_import/report.py

skatsuta/introducing-python

945fc84ba58aaa2602e454890c8c6f26e403660e

[ "MIT" ]

null

"""This module provides the weather report.""" def get_description(): """Returns a random weather.""" from random import choice possibilities = ['rain', 'snow', 'sleet', 'fog', 'sun', 'who knows'] return choice(possibilities)

27.111111

72

0.655738

28

244

5.678571

0.857143

0.238994

0

0.184426

244

8

73

30.5

0.798995

0.270492

0

0.167665

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0.25

false

0

0.25

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0.75

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1

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null

1

0

1

0

null

0

1

0

1

0

5

236dfca3b37cc5c1191e5631c0d4f408c064e660

1,450

py

Python

sudoku/four_pyramid.py

billsioros/sudoku-generator-n-solver

0a6488cdd6541b98898cdd43cb4266b289e98a83

[ "MIT" ]

null

sudoku/four_pyramid.py

billsioros/sudoku-generator-n-solver

0a6488cdd6541b98898cdd43cb4266b289e98a83

[ "MIT" ]

null

sudoku/four_pyramid.py

billsioros/sudoku-generator-n-solver

0a6488cdd6541b98898cdd43cb4266b289e98a83

[ "MIT" ]

null

from pulp import * from sudoku import classic class FourPyramidSudokuLP(classic.SudokuLP): def __init__(self, matrix): super().__init__(matrix) for k in range(1, self.n + 1): self += lpSum([ lpSum([ self.x[r - 1][c - 1][k - 1] for c in range(self.m + r, self.n - r + 1) ]) for r in range(1, self.m + 1) ]) == 1, f"in pyramid 1 only one {k + 1}" for k in range(1, self.n + 1): self += lpSum([ lpSum([ self.x[r - 1][c - 1][k - 1] for r in range(1 + c, self.n - self.m + 1 - c + 1) ]) for c in range(1, self.m + 1) ]) == 1, f"in pyramid 2 only one {k + 1}" for k in range(1, self.n + 1): self += lpSum([ lpSum([ self.x[r - 1][c - 1][k - 1] for c in range(self.n + self.m - 1 - r, r - self.m + 1) ]) for r in range(self.n - self.m + 1, self.n + 1) ]) == 1, f"in pyramid 3 only one {k + 1}" for k in range(1, self.n + 1): self += lpSum([ lpSum([ self.x[r - 1][c - 1][k - 1] for r in range(self.n + self.m + 1 - c, c - 1 + 1) ]) for c in range(self.n - self.m + 1, self.n + 1) ]) == 1, f"in pyramid 4 only one {k + 1}"

33.72093

75

0.393103

221

1,450

2.542986

0.140271

0.149466

0.085409

0.128114

0.752669

0.743772

0.709964

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0.062262

0.457241

1,450

42

76

34.52381

0.651842

0

0.484848

0

0.08

0

1

0.030303

false

0

0.060606

0

0.121212

0

null

0

1

0

null

0

5

88bf88705521642d5ab41822892d7ec98f7eef3a

40

py

Python

python/basic-python/guided_exercises/ch1_variables/hello_world.py

codingandcommunity/rise_high

042d07cee1119b46f723a9c763b8ee3d0fc4ac2c

[ "MIT" ]

2

2019-08-12T23:19:48.000Z

2019-08-15T00:24:01.000Z

python/basic-python/guided_exercises/ch1_variables/hello_world.py

codingandcommunity/rise_high

042d07cee1119b46f723a9c763b8ee3d0fc4ac2c

[ "MIT" ]

null

python/basic-python/guided_exercises/ch1_variables/hello_world.py

codingandcommunity/rise_high

042d07cee1119b46f723a9c763b8ee3d0fc4ac2c

[ "MIT" ]

null

# print "Hello, World!" to the terminal

20

39

0.7

6

40

4.666667

1

0

0.175

40

1

40

0.848485

0.925

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

1

0

null

0

1

0

5

88ca41dafcab03a6dfbb0899b17daf1859a479f9

65

py

Python

mrc_insar_common/model/__init__.py

UAMRC-3vG/mrc_insar_common

89171e6387cccc07d08ef802b0f0f807eca09b1b

[ "Apache-2.0" ]

1

2022-02-16T03:55:34.000Z

mrc_insar_common/model/__init__.py

UAMRC-3vG/MRC-InSAR-Common

89171e6387cccc07d08ef802b0f0f807eca09b1b

[ "Apache-2.0" ]

null

mrc_insar_common/model/__init__.py

UAMRC-3vG/MRC-InSAR-Common

89171e6387cccc07d08ef802b0f0f807eca09b1b

[ "Apache-2.0" ]

null

from .dncnn.dncnn import DnCNN from .unet.unet_model import UNet

21.666667

33

0.815385

11

65

4.727273

0.454545

0

0.123077

65

2

34

32.5

0.912281

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

88db744aa5201da6ac36f1c55f23dcb67aa30c05

106

py

Python

fibonacci.py

simatei/CS-problems

b9b3aac98745680a89f7097a1147367cc934fbca

[ "MIT" ]

null

fibonacci.py

simatei/CS-problems

b9b3aac98745680a89f7097a1147367cc934fbca

[ "MIT" ]

null

fibonacci.py

simatei/CS-problems

b9b3aac98745680a89f7097a1147367cc934fbca

[ "MIT" ]

null

def fib1(n: int) -> int: # base case if n < 2: return n return fib1(n-1) + fib1(n-2)

15.142857

32

0.481132

19

106

2.684211

0.526316

0.294118

0

0.089552

0.367925

106

6

33

17.666667

0.671642

0.084906

0

1

0.25

false

0

0.75

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

88f1a171328ab52d5df3b53f9d432ec88316ed9e

109

py

Python

coding_intereview/1323. Maximum 69 Number.py

purusharthmalik/Python-Bootcamp

2ed1cf886d1081de200b0fdd4cb4e28008c7e3d1

[ "MIT" ]

2

2020-10-03T16:38:10.000Z

2021-06-03T11:01:59.000Z

coding_intereview/1323. Maximum 69 Number.py

purusharthmalik/Python-Bootcamp

2ed1cf886d1081de200b0fdd4cb4e28008c7e3d1

[ "MIT" ]

null

coding_intereview/1323. Maximum 69 Number.py

purusharthmalik/Python-Bootcamp

2ed1cf886d1081de200b0fdd4cb4e28008c7e3d1

[ "MIT" ]

1

2020-10-03T16:38:02.000Z

class Solution: def maximum69Number(self, num: int) -> int: return str(num).replace('6', '9', 1)

27.25

47

0.605505

15

109

4.4

0.866667

0

0.058824

0.220183

109

3

48

36.333333

0.717647

0

0.018349

0

1

0.333333

false

0

0.333333

1

0

1

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null

0

1

0

null

0

1

0

1

0

5

88fd73d60abfea79f73ed106a42ce76d3603cae3

103

py

Python

src/metric/__init__.py

markkua/ImpliCity

2bf80ae1a05a530e0d405ce2057ab5b9c57ea21a

[ "MIT" ]

17

2022-02-21T12:25:05.000Z

2022-03-23T20:37:37.000Z

src/metric/__init__.py

markkua/ImpliCity

2bf80ae1a05a530e0d405ce2057ab5b9c57ea21a

[ "MIT" ]

null

src/metric/__init__.py

markkua/ImpliCity

2bf80ae1a05a530e0d405ce2057ab5b9c57ea21a

[ "MIT" ]

2

2022-02-21T21:58:35.000Z

2022-03-15T18:26:32.000Z

# encoding: utf-8 # Author: Bingxin Ke # Created: 2021/10/8 from .metrics import * from .iou import *

14.714286

22

0.68932

16

103

4.4375

0.8125

0

0.095238

0.184466

103

6

23

17.166667

0.75

0.514563

0

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true

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0

null

0

1

0

1

0

1

0

5

0022b85501c961d030f6f096a7a20b04e251c53c

227

py

Python

components/collector/src/source_collectors/testng/test_cases.py

kargaranamir/quality-time

1c427c61bee9d31c3526f0a01be2218a7e167c23

[ "Apache-2.0" ]

33

2016-01-20T07:35:48.000Z

2022-03-14T09:20:51.000Z

components/collector/src/source_collectors/testng/test_cases.py

kargaranamir/quality-time

1c427c61bee9d31c3526f0a01be2218a7e167c23

[ "Apache-2.0" ]

2,410

2016-01-22T18:13:01.000Z

2022-03-31T16:57:34.000Z

components/collector/src/source_collectors/testng/test_cases.py

kargaranamir/quality-time

1c427c61bee9d31c3526f0a01be2218a7e167c23

[ "Apache-2.0" ]

21

2016-01-16T11:49:23.000Z

2022-01-14T21:53:22.000Z

"""TestNG test cases collector.""" from .tests import TestNGTests # pylint: disable=no-name-in-module class TestNGTestCases(TestNGTests): # pylint: disable=too-few-public-methods """Collector for TestNG test cases."""

28.375

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0.735683

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227

5.964286

0.75

0.11976

0.179641

0

0.132159

227

7

78

32.428571

0.847716

0.594714

0

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true

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null

0

1

0

1

0

1

0

5

cc51453db04d31f3ae0c71202f2fbab8b7c97640

206

py

Python

SbisFiles/__init__.py

PesyCorm/AutomationFiles

3afe7cd28e6b472bd822c0974386591408f0d62d

[ "MIT" ]

null

SbisFiles/__init__.py

PesyCorm/AutomationFiles

3afe7cd28e6b472bd822c0974386591408f0d62d

[ "MIT" ]

null

SbisFiles/__init__.py

PesyCorm/AutomationFiles

3afe7cd28e6b472bd822c0974386591408f0d62d

[ "MIT" ]

null

from .DemoAuth import AuthPage from .SbisAccord import AccordSectionSelector from .TasksSection import TasksSectionSelector from .TasksOnMe import TasksPageManagement from .ExecPanel import ControlExecPanel

41.2

46

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9.1

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206

5

47

41.2

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true

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aebd5a9230f0c5c3cb2a10502bb77eedd3dbf9e7

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Python

trident/models/__init__.py

cronin4392/trident

1c1eb01bcde861496ce83e265ff071fc9bcb9db2

[ "MIT" ]

68

2020-11-13T06:40:52.000Z

2022-03-28T12:40:59.000Z

trident/models/__init__.py

cronin4392/trident

1c1eb01bcde861496ce83e265ff071fc9bcb9db2

[ "MIT" ]

1

2021-08-15T17:06:35.000Z

2021-11-10T04:42:52.000Z

trident/models/__init__.py

cronin4392/trident

1c1eb01bcde861496ce83e265ff071fc9bcb9db2

[ "MIT" ]

11

2020-11-24T13:14:16.000Z

2021-12-26T07:41:29.000Z

"""trident models""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from trident.backend.common import get_backend if get_backend()=='pytorch': from . import pytorch_vgg as vgg from . import pytorch_resnet as resnet from . import pytorch_senet as senet from . import pytorch_densenet as densenet from . import pytorch_efficientnet as efficientnet #from . import pytorch_efficientnetv2 as efficientnet_v2 from . import pytorch_mobilenet as mobilenet from . import pytorch_deeplab as deeplab from . import pytorch_arcfacenet as arcfacenet from . import pytorch_mtcnn as mtcnn from . import pytorch_rfbnet as rfbnet from . import pytorch_ssd as ssd from . import pytorch_yolo as yolo from . import pytorch_embedded as embedded from . import pytorch_inception as inception from . import pytorch_visual_transformer as visual_transformer elif get_backend()=='tensorflow': from . import tensorflow_vgg as vgg from . import tensorflow_resnet as resnet from . import tensorflow_efficientnet as efficientnet from . import tensorflow_densenet as densenet from . import tensorflow_mobilenet as mobilenet from . import tensorflow_deeplab as deeplab from . import tensorflow_mtcnn as mtcnn #__all__ = ['vgg','resnet','densenet','efficientnet','mobilenet','gan','deeplab','arcfacenet','mtcnn','rfbnet','ssd','yolo']

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Python

testsuite/modulegraph-dir/multi_level_star_import.py

xoviat/modulegraph2

766d00bdb40e5b2fe206b53a87b1bce3f9dc9c2a

[ "MIT" ]

9

2020-03-22T14:48:01.000Z

2021-05-30T12:18:12.000Z

testsuite/modulegraph-dir/multi_level_star_import.py

xoviat/modulegraph2

766d00bdb40e5b2fe206b53a87b1bce3f9dc9c2a

[ "MIT" ]

15

2020-01-06T10:02:32.000Z

2021-05-28T12:22:44.000Z

testsuite/modulegraph-dir/multi_level_star_import.py

ronaldoussoren/modulegraph2

b6ab1766b0098651b51083235ff8a18a5639128b

[ "MIT" ]

4

2020-05-10T18:51:41.000Z

2021-04-07T14:03:12.000Z

from pkg_a import *

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py

Python

models/build_model_3d.py

AdityaNG/LEAStereo

2a17c0f7501b8f8e75709e66fd5504900b758c9f

[ "MIT" ]

186

2020-11-30T06:52:26.000Z

2022-03-31T12:41:37.000Z

models/build_model_3d.py

AdityaNG/LEAStereo

2a17c0f7501b8f8e75709e66fd5504900b758c9f

[ "MIT" ]

31

2020-12-01T07:10:28.000Z

2022-02-23T12:28:19.000Z

models/build_model_3d.py

bhlarson/LEAStereo

e3e474703e5ba4009832908dac8af02188e63b03

[ "MIT" ]

40

2020-11-30T11:22:47.000Z

2022-03-10T01:37:37.000Z

import torch.nn as nn import torch.nn.functional as F import models.cell_level_search_3d as cell_level_search from models.genotypes_3d import PRIMITIVES from models.operations_3d import * from models.decoding_formulas import Decoder import pdb class AutoMatching(nn.Module): def __init__(self, num_layers, filter_multiplier=8, block_multiplier=2, step=3, cell=cell_level_search.Cell): super(AutoMatching, self).__init__() self.cells = nn.ModuleList() self._num_layers = num_layers self._step = step self._block_multiplier = block_multiplier self._filter_multiplier = filter_multiplier self._initialize_alphas_betas() f_initial = int(self._filter_multiplier) self._num_end = f_initial * self._block_multiplier print('Matching Net block_multiplier:{0}'.format(block_multiplier)) print('Matching Net filter_multiplier:{0}'.format(filter_multiplier)) print('Matching Net f_initial:{0}'.format(f_initial)) self.stem0 = ConvBR(self._num_end*2, self._num_end, 3, stride=1, padding=1) for i in range(self._num_layers): if i == 0: cell1 = cell(self._step, self._block_multiplier, -1, None, f_initial, None, self._filter_multiplier) cell2 = cell(self._step, self._block_multiplier, -1, f_initial, None, None, self._filter_multiplier * 2) self.cells += [cell1] self.cells += [cell2] elif i == 1: cell1 = cell(self._step, self._block_multiplier, f_initial, None, self._filter_multiplier, self._filter_multiplier * 2, self._filter_multiplier) cell2 = cell(self._step, self._block_multiplier, -1, self._filter_multiplier, self._filter_multiplier * 2, None, self._filter_multiplier * 2) cell3 = cell(self._step, self._block_multiplier, -1, self._filter_multiplier * 2, None, None, self._filter_multiplier * 4) self.cells += [cell1] self.cells += [cell2] self.cells += [cell3] elif i == 2: cell1 = cell(self._step, self._block_multiplier, self._filter_multiplier, None, self._filter_multiplier, self._filter_multiplier * 2, self._filter_multiplier) cell2 = cell(self._step, self._block_multiplier, self._filter_multiplier * 2, self._filter_multiplier, self._filter_multiplier * 2, self._filter_multiplier * 4, self._filter_multiplier * 2) cell3 = cell(self._step, self._block_multiplier, -1, self._filter_multiplier * 2, self._filter_multiplier * 4, None, self._filter_multiplier * 4) cell4 = cell(self._step, self._block_multiplier, -1, self._filter_multiplier * 4, None, None, self._filter_multiplier * 8) self.cells += [cell1] self.cells += [cell2] self.cells += [cell3] self.cells += [cell4] elif i == 3: cell1 = cell(self._step, self._block_multiplier, self._filter_multiplier, None, self._filter_multiplier, self._filter_multiplier * 2, self._filter_multiplier) cell2 = cell(self._step, self._block_multiplier, self._filter_multiplier * 2, self._filter_multiplier, self._filter_multiplier * 2, self._filter_multiplier * 4, self._filter_multiplier * 2) cell3 = cell(self._step, self._block_multiplier, self._filter_multiplier * 4, self._filter_multiplier * 2, self._filter_multiplier * 4, self._filter_multiplier * 8, self._filter_multiplier * 4) cell4 = cell(self._step, self._block_multiplier, -1, self._filter_multiplier * 4, self._filter_multiplier * 8, None, self._filter_multiplier * 8) self.cells += [cell1] self.cells += [cell2] self.cells += [cell3] self.cells += [cell4] else: cell1 = cell(self._step, self._block_multiplier, self._filter_multiplier, None, self._filter_multiplier, self._filter_multiplier * 2, self._filter_multiplier) cell2 = cell(self._step, self._block_multiplier, self._filter_multiplier * 2, self._filter_multiplier, self._filter_multiplier * 2, self._filter_multiplier * 4, self._filter_multiplier * 2) cell3 = cell(self._step, self._block_multiplier, self._filter_multiplier * 4, self._filter_multiplier * 2, self._filter_multiplier * 4, self._filter_multiplier * 8, self._filter_multiplier * 4) cell4 = cell(self._step, self._block_multiplier, self._filter_multiplier * 8, self._filter_multiplier * 4, self._filter_multiplier * 8, None, self._filter_multiplier * 8) self.cells += [cell1] self.cells += [cell2] self.cells += [cell3] self.cells += [cell4] self.last_3 = ConvBR(self._num_end, 1, 3, 1, 1, bn=False, relu=False) self.last_6 = ConvBR(self._num_end*2 , self._num_end, 1, 1, 0) self.last_12 = ConvBR(self._num_end*4 , self._num_end*2, 1, 1, 0) self.last_24 = ConvBR(self._num_end*8 , self._num_end*4, 1, 1, 0) def forward(self, x): self.level_3 = [] self.level_6 = [] self.level_12 = [] self.level_24 = [] stem = self.stem0(x) self.level_3.append(stem) count = 0 normalized_betas = torch.randn(self._num_layers, 4, 3).cuda() # Softmax on alphas and betas if torch.cuda.device_count() > 1: #print('1') img_device = torch.device('cuda', x.get_device()) normalized_alphas = F.softmax(self.alphas.to(device=img_device), dim=-1) # normalized_betas[layer][ith node][0 : ➚, 1: ➙, 2 : ➘] for layer in range(len(self.betas)): if layer == 0: normalized_betas[layer][0][1:] = F.softmax(self.betas[layer][0][1:].to(device=img_device), dim=-1) * (2/3) elif layer == 1: normalized_betas[layer][0][1:] = F.softmax(self.betas[layer][0][1:].to(device=img_device), dim=-1) * (2/3) normalized_betas[layer][1] = F.softmax(self.betas[layer][1].to(device=img_device), dim=-1) elif layer == 2: normalized_betas[layer][0][1:] = F.softmax(self.betas[layer][0][1:].to(device=img_device), dim=-1) * (2/3) normalized_betas[layer][1] = F.softmax(self.betas[layer][1].to(device=img_device), dim=-1) normalized_betas[layer][2] = F.softmax(self.betas[layer][2].to(device=img_device), dim=-1) else: normalized_betas[layer][0][1:] = F.softmax(self.betas[layer][0][1:].to(device=img_device), dim=-1) * (2/3) normalized_betas[layer][1] = F.softmax(self.betas[layer][1].to(device=img_device), dim=-1) normalized_betas[layer][2] = F.softmax(self.betas[layer][2].to(device=img_device), dim=-1) normalized_betas[layer][3][:2] = F.softmax(self.betas[layer][3][:1].to(device=img_device), dim=-1) * (2/3) else: normalized_alphas = F.softmax(self.alphas, dim=-1) for layer in range(len(self.betas)): if layer == 0: normalized_betas[layer][0][1:] = F.softmax(self.betas[layer][0][1:], dim=-1) * (2/3) elif layer == 1: normalized_betas[layer][0][1:] = F.softmax(self.betas[layer][0][1:], dim=-1) * (2/3) normalized_betas[layer][1] = F.softmax(self.betas[layer][1], dim=-1) elif layer == 2: normalized_betas[layer][0][1:] = F.softmax(self.betas[layer][0][1:], dim=-1) * (2/3) normalized_betas[layer][1] = F.softmax(self.betas[layer][1], dim=-1) normalized_betas[layer][2] = F.softmax(self.betas[layer][2], dim=-1) else: normalized_betas[layer][0][1:] = F.softmax(self.betas[layer][0][1:], dim=-1) * (2/3) normalized_betas[layer][1] = F.softmax(self.betas[layer][1], dim=-1) normalized_betas[layer][2] = F.softmax(self.betas[layer][2], dim=-1) normalized_betas[layer][3][:2] = F.softmax(self.betas[layer][3][:2], dim=-1) * (2/3) for layer in range(self._num_layers): if layer == 0: level3_new, = self.cells[count](None, None, self.level_3[-1], None, normalized_alphas) count += 1 level6_new, = self.cells[count](None, self.level_3[-1], None, None, normalized_alphas) count += 1 level3_new = normalized_betas[layer][0][1] * level3_new level6_new = normalized_betas[layer][0][2] * level6_new self.level_3.append(level3_new) self.level_6.append(level6_new) elif layer == 1: level3_new_1, level3_new_2 = self.cells[count](self.level_3[-2], None, self.level_3[-1], self.level_6[-1], normalized_alphas) count += 1 level3_new = normalized_betas[layer][0][1] * level3_new_1 + normalized_betas[layer][1][0] * level3_new_2 level6_new_1, level6_new_2 = self.cells[count](None, self.level_3[-1], self.level_6[-1], None, normalized_alphas) count += 1 level6_new = normalized_betas[layer][0][2] * level6_new_1 + normalized_betas[layer][1][2] * level6_new_2 level12_new, = self.cells[count](None, self.level_6[-1], None, None, normalized_alphas) level12_new = normalized_betas[layer][1][2] * level12_new count += 1 self.level_3.append(level3_new) self.level_6.append(level6_new) self.level_12.append(level12_new) elif layer == 2: level3_new_1, level3_new_2 = self.cells[count](self.level_3[-2], None, self.level_3[-1], self.level_6[-1], normalized_alphas) count += 1 level3_new = normalized_betas[layer][0][1] * level3_new_1 + normalized_betas[layer][1][0] * level3_new_2 level6_new_1, level6_new_2, level6_new_3 = self.cells[count](self.level_6[-2], self.level_3[-1], self.level_6[-1], self.level_12[-1], normalized_alphas) count += 1 level6_new = normalized_betas[layer][0][2] * level6_new_1 + normalized_betas[layer][1][1] * level6_new_2 + normalized_betas[layer][2][ 0] * level6_new_3 level12_new_1, level12_new_2 = self.cells[count](None, self.level_6[-1], self.level_12[-1], None, normalized_alphas) count += 1 level12_new = normalized_betas[layer][1][2] * level12_new_1 + normalized_betas[layer][2][1] * level12_new_2 level24_new, = self.cells[count](None, self.level_12[-1], None, None, normalized_alphas) level24_new = normalized_betas[layer][2][2] * level24_new count += 1 self.level_3.append(level3_new) self.level_6.append(level6_new) self.level_12.append(level12_new) self.level_24.append(level24_new) elif layer == 3: level3_new_1, level3_new_2 = self.cells[count](self.level_3[-2], None, self.level_3[-1], self.level_6[-1], normalized_alphas) count += 1 level3_new = normalized_betas[layer][0][1] * level3_new_1 + normalized_betas[layer][1][0] * level3_new_2 level6_new_1, level6_new_2, level6_new_3 = self.cells[count](self.level_6[-2], self.level_3[-1], self.level_6[-1], self.level_12[-1], normalized_alphas) count += 1 level6_new = normalized_betas[layer][0][2] * level6_new_1 + normalized_betas[layer][1][1] * level6_new_2 + normalized_betas[layer][2][ 0] * level6_new_3 level12_new_1, level12_new_2, level12_new_3 = self.cells[count](self.level_12[-2], self.level_6[-1], self.level_12[-1], self.level_24[-1], normalized_alphas) count += 1 level12_new = normalized_betas[layer][1][2] * level12_new_1 + normalized_betas[layer][2][1] * level12_new_2 + normalized_betas[layer][3][ 0] * level12_new_3 level24_new_1, level24_new_2 = self.cells[count](None, self.level_12[-1], self.level_24[-1], None, normalized_alphas) count += 1 level24_new = normalized_betas[layer][2][2] * level24_new_1 + normalized_betas[layer][3][1] * level24_new_2 self.level_3.append(level3_new) self.level_6.append(level6_new) self.level_12.append(level12_new) self.level_24.append(level24_new) else: level3_new_1, level3_new_2 = self.cells[count](self.level_3[-2], None, self.level_3[-1], self.level_6[-1], normalized_alphas) count += 1 level3_new = normalized_betas[layer][0][1] * level3_new_1 + normalized_betas[layer][1][0] * level3_new_2 level6_new_1, level6_new_2, level6_new_3 = self.cells[count](self.level_6[-2], self.level_3[-1], self.level_6[-1], self.level_12[-1], normalized_alphas) count += 1 level6_new = normalized_betas[layer][0][2] * level6_new_1 + normalized_betas[layer][1][1] * level6_new_2 + normalized_betas[layer][2][ 0] * level6_new_3 level12_new_1, level12_new_2, level12_new_3 = self.cells[count](self.level_12[-2], self.level_6[-1], self.level_12[-1], self.level_24[-1], normalized_alphas) count += 1 level12_new = normalized_betas[layer][1][2] * level12_new_1 + normalized_betas[layer][2][1] * level12_new_2 + normalized_betas[layer][3][ 0] * level12_new_3 level24_new_1, level24_new_2 = self.cells[count](self.level_24[-2], self.level_12[-1], self.level_24[-1], None, normalized_alphas) count += 1 level24_new = normalized_betas[layer][2][2] * level24_new_1 + normalized_betas[layer][3][1] * level24_new_2 self.level_3.append(level3_new) self.level_6.append(level6_new) self.level_12.append(level12_new) self.level_24.append(level24_new) self.level_3 = self.level_3[-2:] self.level_6 = self.level_6[-2:] self.level_12 = self.level_12[-2:] self.level_24 = self.level_24[-2:] #define upsampling d, h, w = stem.size()[2], stem.size()[3], stem.size()[4] upsample_6 = nn.Upsample(size=stem.size()[2:], mode='trilinear', align_corners=True) upsample_12 = nn.Upsample(size=[d//2, h//2, w//2], mode='trilinear', align_corners=True) upsample_24 = nn.Upsample(size=[d//4, h//4, w//4], mode='trilinear', align_corners=True) result_3 = self.last_3(self.level_3[-1]) result_6 = self.last_3(upsample_6(self.last_6(self.level_6[-1]))) result_12 = self.last_3(upsample_6(self.last_6(upsample_12(self.last_12(self.level_12[-1]))))) result_24 = self.last_3(upsample_6(self.last_6(upsample_12(self.last_12(self.last_24(self.level_24[-1])))))) sum_matching_map =result_3 + result_6 + result_12 + result_24 return sum_matching_map def _initialize_alphas_betas(self): k = sum(1 for i in range(self._step) for n in range(2 + i)) num_ops = len(PRIMITIVES) alphas = (1e-3 * torch.randn(k, num_ops)).clone().detach().requires_grad_(True) betas = (1e-3 * torch.randn(self._num_layers, 4, 3)).clone().detach().requires_grad_(True) self._arch_parameters = [ alphas, betas, ] self._arch_param_names = [ 'alphas', 'betas', ] [self.register_parameter(name, torch.nn.Parameter(param)) for name, param in zip(self._arch_param_names, self._arch_parameters)] def arch_parameters(self): return [param for name, param in self.named_parameters() if name in self._arch_param_names] def weight_parameters(self): return [param for name, param in self.named_parameters() if name not in self._arch_param_names] def genotype(self): decoder = Decoder(self.alphas_cell, self._block_multiplier, self._step) return decoder.genotype_decode()

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aee884a148f55eccdc9f5baa38829f7715f42086

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wsgi

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app.wsgi

taps1197/Traahi

52765e26b844169349de7c5a13da8edcbd6e7d47

[ "MIT" ]

1

2019-03-29T11:38:03.000Z

app.wsgi

taps1197/Traahi

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[ "MIT" ]

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app.wsgi

taps1197/Traahi

52765e26b844169349de7c5a13da8edcbd6e7d47

[ "MIT" ]

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import sys sys.path.insert(0,'/var/www/html/blockMakers') from blockMakers import app as application

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py

Python

eip_bridge/src/eip_bridge/__init__.py

marip8/eip_bridge

b1ec48d2a16ed7a861f9d2e6473a3a339b82a1c8

[ "Apache-2.0" ]

null

eip_bridge/src/eip_bridge/__init__.py

marip8/eip_bridge

b1ec48d2a16ed7a861f9d2e6473a3a339b82a1c8

[ "Apache-2.0" ]

null

eip_bridge/src/eip_bridge/__init__.py

marip8/eip_bridge

b1ec48d2a16ed7a861f9d2e6473a3a339b82a1c8

[ "Apache-2.0" ]

null

from eip_bridge import *

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Python

src/fingerflow/matcher/VerifyNet/verify_net_train_experimental.py

jakubarendac/fingerflow

a0a53259ec575704d19ae0ae770335536e567583

[ "MIT" ]

null

src/fingerflow/matcher/VerifyNet/verify_net_train_experimental.py

jakubarendac/fingerflow

a0a53259ec575704d19ae0ae770335536e567583

[ "MIT" ]

null

src/fingerflow/matcher/VerifyNet/verify_net_train_experimental.py

jakubarendac/fingerflow

a0a53259ec575704d19ae0ae770335536e567583

[ "MIT" ]

null

# pylint: skip-file import tensorflow as tf from . import constants, utils def get_verify_net_model(precision, verify_net_path=None): embedding_network = get_embeddings_model(precision) input_1 = tf.keras.Input(utils.get_input_shape(precision)) # x1 = tf.keras.layers.ZeroPadding2D((0, 7))(input_1) # x1 = tf.keras.layers.ZeroPadding2D((22, 22))(x1) # x1 = tf.keras.layers.Conv2D(3, (3, 3), padding='same')(x1) input_2 = tf.keras.Input(utils.get_input_shape(precision)) # x2 = tf.keras.layers.ZeroPadding2D((0, 7))(input_1) # x2 = tf.keras.layers.ZeroPadding2D((22, 22))(x2) # x2 = tf.keras.layers.Conv2D(3, (3, 3), padding='same')(x2) tower_1 = embedding_network(input_1) tower_2 = embedding_network(input_2) merge_layer = tf.keras.layers.Lambda(utils.euclidean_distance)([tower_1, tower_2]) normal_layer = tf.keras.layers.BatchNormalization()(merge_layer) output_layer = tf.keras.layers.Dense(1, activation="sigmoid")(normal_layer) siamese_network = tf.keras.Model(inputs=[input_1, input_2], outputs=output_layer) siamese_network.compile( loss=utils.verify_net_loss(constants.MARGIN), optimizer=tf.keras.optimizers.Adam(0.001), metrics=["accuracy"]) if verify_net_path: siamese_network.load_weights(verify_net_path) print(f'Verify net weights loaded from {verify_net_path}') return siamese_network def get_embeddings_model(precision): switcher = { 15: build_15_minutiae_model, 20: build_20_minutiae_model } inputs = tf.keras.Input(shape=(utils.get_input_shape(precision))) x = tf.keras.layers.BatchNormalization()(inputs) outputs = switcher.get(precision)(x) # return outputs # # x = tf.keras.layers.Dense(32, activation='relu')(x) # # x = tf.keras.layers.Dense(64, activation='sigmoid')(x) # # x = tf.keras.layers.Dense(128, activation='relu')(inputs) # # x = tf.keras.layers.Dense(128, # # kernel_regularizer=tf.keras.regularizers.l1(l1=0.001))(x) # # x = tf.keras.layers.PReLU()(x) # # x = tf.keras.layers.Dropout(0.1)(x) # # something good # # x = tf.keras.layers.Conv1D(64, 3, activation=tf.keras.layers.PReLU(), # # kernel_regularizer=tf.keras.regularizers.l2(l2=0))(x) # # x = tf.keras.layers.BatchNormalization()(x) # # # x = tf.keras.layers.PReLU()(x) # # # x = tf.keras.layers.Dropout(0.2)(x) # # # x = tf.keras.layers.MaxPooling1D(2)(x) # # # x = tf.keras.layers.Conv1D(128, 3)(x) # # # # x = tf.keras.layers.PReLU()(x) # # # x = tf.keras.layers.Dropout(0.2)(x) # # # x = tf.keras.layers.MaxPooling1D(2)(x) # # x = tf.keras.layers.Conv1D(64, 3, activation=tf.keras.layers.PReLU(), # # kernel_regularizer=tf.keras.regularizers.l2(l2=0))(x) # # x = tf.keras.layers.BatchNormalization()(x) # # # x = tf.keras.layers.PReLU()(x) # # # x = tf.keras.layers.Dropout(0.2)(x) # # # x = tf.keras.layers.MaxPooling1D(2)(x) # # x = tf.keras.layers.Dense(32, activation=tf.keras.layers.PReLU())(x) # # something good # # x = tf.keras.layers.PReLU()(x) # # x = tf.keras.layers.Dropout(0.1)(x) # # x = tf.keras.layers.Dense(128, # # kernel_regularizer=tf.keras.regularizers.l1(l1=0.001))(x) # # x = tf.keras.layers.PReLU()(x) # # x = tf.keras.layers.Dropout(0.1)(x) # # x = tf.keras.layers.Dense(128, activation='sigmoid')(x) # # x = tf.keras.layers.Dense(64, activation='sigmoid')(x) # x = tf.keras.layers.Dense(64, activation=tf.keras.layers.PReLU())(x) # x = tf.keras.layers.Dense(5, activation=tf.keras.layers.PReLU())(x) # x = tf.keras.layers.Flatten()(x) # # outputs = tf.keras.layers.Dense(5, activation='relu')(x) # # x = tf.keras.layers.Conv1D(64, 3)(x) # # x = tf.keras.layers.PReLU()(x) # # x = tf.keras.layers.MaxPooling1D(2)(x) embedding_network = tf.keras.Model(inputs, outputs) embedding_network.summary() #model = ResNet50() return embedding_network def KerasResNet50(): base_model = tf.keras.applications.ResNet50( weights=None, include_top=False, input_shape=(64, 64, 3), classifier_activation="softmax") x = base_model.output x = tf.keras.layers.GlobalAveragePooling2D()(x) x = tf.keras.layers.Dropout(0.2)(x) predictions = tf.keras.layers.Dense(256, activation='softmax')(x) # base_model.trainable = False model = tf.keras.Model(inputs=base_model.input, outputs=predictions) model.summary() return model # def MergeModel(input_shape): # X_input = tf.keras.layers.Input(input_shape) # # X = tf.keras.layers.ZeroPadding2D((3, 3))(X_input) # # Zero-Padding # X = tf.keras.layers.ZeroPadding2D((0, 7))(X_input) # X = tf.keras.layers.ZeroPadding2D((25, 25))(X) # X = tf.keras.layers.Conv2D(3, (3, 3), padding='same')(X) # preprocessing_model = tf.keras.Model(inputs=X_input, outputs=X) # keras_model = KerasModel() # concatenated = tf.keras.layers.merge.concatenate([model1_out, model2_out]) def build_15_minutiae_model(x): x = tf.keras.layers.Conv1D(64, 3, activation="relu")(x) x = tf.keras.layers.MaxPooling1D(2)(x) x = tf.keras.layers.Dropout(0.2)(x) x = tf.keras.layers.Conv1D(64, 3, activation="relu")(x) x = tf.keras.layers.MaxPooling1D(2)(x) x = tf.keras.layers.Dropout(0.2)(x) return x def build_20_minutiae_model(x): # x = tf.keras.layers.Conv1D(64, 3, activation="relu")(x) # # x = tf.keras.layers.MaxPooling1D(2)(x) # # x = tf.keras.layers.Dropout(0.2)(x) # x = tf.keras.layers.Conv1D(128, 3, activation="relu", # kernel_regularizer=tf.keras.regularizers.l2(l2=0))(x) #x = tf.keras.layers.MaxPooling1D(1)(x) #x = tf.keras.layers.Dropout(0.2)(x) # x = tf.keras.layers.Conv1D(128, 3, activation="relu")(x) # x = tf.keras.layers.Conv1D(256, 3, activation="relu")(x) # x = tf.keras.layers.Conv1D(128, 3, activation="relu")(x) # # x = tf.keras.layers.MaxPooling1D(2)(x) # x = tf.keras.layers.Conv1D(16, 15, activation="relu")(x) # x = tf.keras.layers.MaxPooling1D(1, strides=2)(x) # x = tf.keras.layers.Conv1D(32, 10, strides=5, activation="relu")(x) # x = tf.keras.layers.Conv1D(27, 5, strides=1, activation="relu")(x) # x = tf.keras.layers.Conv1D(227, 11, strides=4, activation="relu")(x) # x = tf.keras.layers.MaxPooling1D(2)(x) # x = tf.keras.layers.Dropout(0.2)(x) # x = tf.keras.layers.Conv1D(8, 3, activation="relu")(x) # x = tf.keras.layers.Conv1D(256, 3, activation="relu", # kernel_regularizer=tf.keras.regularizers.l2(l2=0))(x) #x = tf.keras.layers.MaxPooling1D(1)(x) #x = tf.keras.layers.Dropout(0.1)(x) # x = tf.keras.layers.Conv1D(512, 3, activation="relu", # kernel_regularizer=tf.keras.regularizers.l2(l2=0))(x) #x = tf.keras.layers.MaxPooling1D(1)(x) #x = tf.keras.layers.Dropout(0.1)(x) # x = tf.keras.layers.Conv1D(256, 3, activation="relu", # kernel_regularizer=tf.keras.regularizers.l2(l2=0))(x) #x = tf.keras.layers.MaxPooling1D(1)(x) #x = tf.keras.layers.Dropout(0.1)(x) #x = tf.keras.layers.Conv1D(128, 3, activation="relu")(x) #x = tf.keras.layers.MaxPooling1D(1)(x) #x = tf.keras.layers.Dropout(0.1)(x) x = tf.keras.layers.ZeroPadding2D((0, (6, 5)))(x) # X = tf.keras.layers.ZeroPadding2D((25, 25))(X) # x = tf.keras.layers.Conv2D(1, (3,3), # kernel_regularizer=tf.keras.regularizers.l2(l2=0.001), # activation="relu")(x) #x = tf.keras.layers.MaxPooling1D(1)(x) #x = tf.keras.layers.Dropout(0.2)(x) # x = tf.keras.layers.Dense(1024, activation='sigmoid')(x) #x = tf.keras.layers.Dense(256, activation='sigmoid')(x) # x = tf.keras.layers.Dropout(0.1)(x) #x = tf.keras.layers.Flatten()(x) x = tf.keras.layers.Conv1D(64, 3, 2, activation="relu")(x) #x = tf.keras.layers.MaxPooling2D(2)(x) #x = tf.keras.layers.Dropout(0.1)(x) x = tf.keras.layers.Conv1D(64, 3, 2, activation="relu")(x) x = tf.keras.layers.MaxPooling2D(2)(x) #x = tf.keras.layers.Dropout(0.1)(x) # x = tf.keras.layers.Dense(5, activation='sigmoid')(x) x = tf.keras.layers.Dense(256, kernel_regularizer=tf.keras.regularizers.l2(l2=0.0001), activation='relu')(x) x = tf.keras.layers.Dropout(0.6)(x) x = tf.keras.layers.Dense(128, kernel_regularizer=tf.keras.regularizers.l2(l2=0.0001), activation='relu')(x) x = tf.keras.layers.Dropout(0.6)(x) x = tf.keras.layers.Flatten()(x) x = tf.keras.layers.Dense(10, activation='sigmoid')(x) # x = tf.keras.layers.Dropout(0.1)(x) return x def identity_block(X, f, filters, stage, block): """ Implementation of the identity block as defined in Figure 3 Arguments: X -- input tensor of shape (m, n_H_prev, n_W_prev, n_C_prev) f -- integer, specifying the shape of the middle CONV's window for the main path filters -- python list of integers, defining the number of filters in the CONV layers of the main path stage -- integer, used to name the layers, depending on their position in the network block -- string/character, used to name the layers, depending on their position in the network Returns: X -- output of the identity block, tensor of shape (n_H, n_W, n_C) """ # defining name basis conv_name_base = 'res' + str(stage) + block + '_branch' bn_name_base = 'bn' + str(stage) + block + '_branch' # Retrieve Filters F1, F2, F3 = filters # Save the input value. You'll need this later to add back to the main path. X_shortcut = X # First component of main path X = tf.keras.layers.Conv2D(filters=F1, kernel_size=(1, 1), strides=(1, 1), padding='valid', name=conv_name_base + '2a')(X) X = tf.keras.layers.BatchNormalization(name=bn_name_base + '2a')(X) X = tf.keras.layers.Activation('relu')(X) # Second component of main path (≈3 lines) X = tf.keras.layers.Conv2D(filters=F2, kernel_size=(f, f), strides=(1, 1), padding='same', name=conv_name_base + '2b')(X) X = tf.keras.layers.BatchNormalization(name=bn_name_base + '2b')(X) X = tf.keras.layers.Activation('relu')(X) # Third component of main path (≈2 lines) X = tf.keras.layers.Conv2D(filters=F3, kernel_size=(1, 1), strides=(1, 1), padding='valid', name=conv_name_base + '2c')(X) X = tf.keras.layers.BatchNormalization(name=bn_name_base + '2c')(X) # Final step: Add shortcut value to main path, and pass it through a RELU activation (≈2 lines) X = tf.keras.layers.Add()([X, X_shortcut]) X = tf.keras.layers.Activation('relu')(X) return X def convolutional_block(X, f, filters, stage, block, s=2): """ Implementation of the convolutional block as defined in Figure 4 Arguments: X -- input tensor of shape (m, n_H_prev, n_W_prev, n_C_prev) f -- integer, specifying the shape of the middle CONV's window for the main path filters -- python list of integers, defining the number of filters in the CONV layers of the main path stage -- integer, used to name the layers, depending on their position in the network block -- string/character, used to name the layers, depending on their position in the network s -- Integer, specifying the stride to be used Returns: X -- output of the convolutional block, tensor of shape (n_H, n_W, n_C) """ # defining name basis conv_name_base = 'res' + str(stage) + block + '_branch' bn_name_base = 'bn' + str(stage) + block + '_branch' # Retrieve Filters F1, F2, F3 = filters # Save the input value X_shortcut = X ##### MAIN PATH ##### # First component of main path X = tf.keras.layers.Conv2D(F1, (1, 1), strides=(s, s), name=conv_name_base + '2a')(X) # X = tf.keras.layers.Dropout(0.2) X = tf.keras.layers.BatchNormalization(name=bn_name_base + '2a')(X) X = tf.keras.layers.Activation('relu')(X) # Second component of main path (≈3 lines) X = tf.keras.layers.Conv2D( filters=F2, kernel_size=(f, f), strides=(1, 1), padding='same', name=conv_name_base + '2b')(X) # X = tf.keras.layers.Dropout(0.2) X = tf.keras.layers.BatchNormalization(name=bn_name_base + '2b')(X) X = tf.keras.layers.Activation('relu')(X) # Third component of main path (≈2 lines) X = tf.keras.layers.Conv2D( filters=F3, kernel_size=(1, 1), strides=(1, 1), padding='valid', name=conv_name_base + '2c')(X) # X = tf.keras.layers.Dropout(0.2) X = tf.keras.layers.BatchNormalization(name=bn_name_base + '2c')(X) # SHORTCUT PATH #### (≈2 lines) X_shortcut = tf.keras.layers.Conv2D( filters=F3, kernel_size=(1, 1), strides=(s, s), padding='valid', name=conv_name_base + '1')(X_shortcut) X_shortcut = tf.keras.layers.BatchNormalization( name=bn_name_base + '1')(X_shortcut) # Final step: Add shortcut value to main path, and pass it through a RELU activation (≈2 lines) X = tf.keras.layers.Add()([X, X_shortcut]) X = tf.keras.layers.Activation('relu')(X) return X # https://github.com/priya-dwivedi/Deep-Learning/blob/master/resnet_keras/Residual_Network_Keras.ipynb # https://towardsdatascience.com/understanding-and-coding-a-resnet-in-keras-446d7ff84d33 def ResNet50(input_shape=(20, 6, 1)): """ Implementation of the popular ResNet50 the following architecture: CONV1D -> BATCHNORM -> RELU -> MAXPOOL -> CONVBLOCK -> IDBLOCK*2 -> CONVBLOCK -> IDBLOCK*3 -> CONVBLOCK -> IDBLOCK*5 -> CONVBLOCK -> IDBLOCK*2 -> AVGPOOL -> TOPLAYER Arguments: input_shape -- shape of the images of the dataset classes -- integer, number of classes Returns: model -- a Model() instance in Keras """ # Define the input as a tensor with shape input_shape X_input = tf.keras.layers.Input(input_shape) # X = tf.keras.layers.ZeroPadding2D((3, 3))(X_input) # Zero-Padding X = tf.keras.layers.ZeroPadding2D((0, 7))(X_input) X = tf.keras.layers.ZeroPadding2D((25, 25))(X) X = tf.keras.layers.Conv2D(3, (3, 3), padding='same')(X) # Stage 1 X = tf.keras.layers.Conv2D(64, (7, 7), strides=(2, 2), name='conv1', kernel_initializer=tf.keras.initializers.glorot_uniform(seed=0))(X) X = tf.keras.layers.Dropout(0.1)(X) X = tf.keras.layers.BatchNormalization(axis=3, name='bn_conv1')(X) X = tf.keras.layers.Activation('relu')(X) X = tf.keras.layers.MaxPooling2D((3, 3), strides=(2, 2))(X) X = tf.keras.layers.Conv2D(64, (3, 3), strides=(1, 1), name='conv2', kernel_initializer=tf.keras.initializers.glorot_uniform(seed=0))(X) # X = tf.keras.layers.Activation('relu')(X) # Stage 2 #X = convolutional_block(X, f=3, filters=[64, 64, 256], stage=2, block='a', s=1) #X = identity_block(X, 3, [64, 64, 256], stage=2, block='b') #X = identity_block(X, 3, [64, 64, 256], stage=2, block='c') # # ### START CODE HERE ### # # # Stage 3 (≈4 lines) # X = convolutional_block(X, f=3, filters=[128, 128, 512], stage=3, block='a', s=2) # X = identity_block(X, 3, [128, 128, 512], stage=3, block='b') # X = identity_block(X, 3, [128, 128, 512], stage=3, block='c') # X = identity_block(X, 3, [128, 128, 512], stage=3, block='d') # # # Stage 4 (≈6 lines) # X = convolutional_block(X, f=3, filters=[256, 256, 1024], stage=4, block='a', s=2) # X = identity_block(X, 3, [256, 256, 1024], stage=4, block='b') # X = identity_block(X, 3, [256, 256, 1024], stage=4, block='c') # X = identity_block(X, 3, [256, 256, 1024], stage=4, block='d') # X = identity_block(X, 3, [256, 256, 1024], stage=4, block='e') # X = identity_block(X, 3, [256, 256, 1024], stage=4, block='f') # # # Stage 5 (≈3 lines) # X = convolutional_block(X, f=3, filters=[512, 512, 2048], stage=5, block='a', s=2) # X = identity_block(X, 3, [512, 512, 2048], stage=5, block='b') # X = identity_block(X, 3, [512, 512, 2048], stage=5, block='c') # AVGPOOL (≈1 line). Use "X = AveragePooling1D(...)(X)" # X = tf.keras.layers.AveragePooling2D((2, 2), name="avg_pool")(X) ### END CODE HERE ### # output layer X = tf.keras.layers.Flatten()(X) X = tf.keras.layers.Dense( 16, activation='relu', name='fc' + str(16), #kernel_regularization = tf.keras.regularizers.l2(l2=0.001), kernel_initializer=tf.keras.initializers.glorot_uniform(seed=0))(X) #X = tf.keras.layers.Dropout(0.1)(X) # Create model model = tf.keras.Model(inputs=X_input, outputs=X, name='ResNet50') print("summary => ", model.summary()) return model

37.949451

106

0.618579

2,556

17,267

4.103678

0.103678

0.122795

0.197064

0.184193

0.774907

0.742206

0.708456

0.691391

0.64601

0.60921

0

0.053855

0.214977

17,267

454

107

38.03304

0.719144

0.541843

0

0.437956

0

0.036564

0

1

0.058394

false

0

0.014599

0

0.131387

0.014599

0

null

0

1

0

1

0

1

0

null

0

5

9dba1f1f826948da80bc258fa359068d19d1c196

970

py

Python

_site/Forritun/Forritun 1/testaroni tima.py

EinarK2/einark2.github.io

ec121871d381fe62e29573e67b57baf80f31e90d

[ "CC-BY-4.0" ]

null

_site/Forritun/Forritun 1/testaroni tima.py

EinarK2/einark2.github.io

ec121871d381fe62e29573e67b57baf80f31e90d

[ "CC-BY-4.0" ]

null

_site/Forritun/Forritun 1/testaroni tima.py

EinarK2/einark2.github.io

ec121871d381fe62e29573e67b57baf80f31e90d

[ "CC-BY-4.0" ]

1

2018-09-12T15:12:34.000Z

#testaroni tala=[401, 406, 408, 410, 410, 411, 413, 414, 414, 416, 422, 423, 423, 425, 425, 425, 427, 427, 429, 430, 431, 440, 442, 444, 445, 446, 448, 452, 454, 454, 454, 458, 460, 461, 461, 461, 463, 464, 464, 465, 466, 467, 478, 482, 483, 484, 485, 487, 487, 489, 490, 491, 491, 491, 492, 496, 496, 498, 498, 498, 500, 500, 502, 504, 507, 508, 508, 511, 514, 517, 518, 519, 519, 523, 525, 526, 534, 535, 536, 543, 544, 545, 546, 547, 552, 554, 554, 555, 556, 556, 557, 560, 562, 563, 563, 564, 565, 567, 569, 569, 569, 571, 572, 573, 575, 575, 577, 578, 578, 580, 580, 580, 580, 581, 582, 586, 587, 587, 588, 589, 591, 591, 593, 594, 596, 597, 601, 601, 601, 601, 601, 605, 605, 605, 607, 607, 608, 610, 611, 611, 613, 614, 617, 617, 621, 621, 624, 625, 628, 628, 629, 629, 631, 632, 636, 637, 638, 639, 641, 644, 644, 647, 649, 652, 652, 654, 654, 655, 656, 659, 660, 660, 662, 665, 666, 669, 673, 682, 683, 687, 690, 690, 692, 697, 697, 699, 699] print(sum(tala))

194

940

0.604124

192

970

3.052083

0.729167

0.040956

0.046075

0.040956

0

0.719231

0.195876

970

4

941

242.5

0.032051

0.009278

0

1

0

false

0

0.5

0

null

0

1

0

1

0

1

0

null

0

1

0

5

3b031973d209945661d54d63d398e421573bf003

20

py

Python

src/__init__.py

mrdlp/Ontology-Population-with-Web-Scraping

1d3bc24dcb74be0fe11e7c7509aa5f9932233dd0

[ "MIT" ]

null

src/__init__.py

mrdlp/Ontology-Population-with-Web-Scraping

1d3bc24dcb74be0fe11e7c7509aa5f9932233dd0

[ "MIT" ]

null

src/__init__.py

mrdlp/Ontology-Population-with-Web-Scraping

1d3bc24dcb74be0fe11e7c7509aa5f9932233dd0

[ "MIT" ]

null

#this is a first try

20

0.75

5

20

3

1

0

0.2

20

1

20

0.9375

0.95

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

1

0

null

0

1

0

5

d177e320f045b46e9a86a4c092aa56852188fe67

59

py

Python

tools/Polygraphy/polygraphy/backend/common/__init__.py

martellz/TensorRT

f182e83b30b5d45aaa3f9a041ff8b3ce83e366f4

[ "Apache-2.0" ]

4

2021-04-16T13:49:38.000Z

2022-01-16T08:58:07.000Z

tools/Polygraphy/polygraphy/backend/common/__init__.py

martellz/TensorRT

f182e83b30b5d45aaa3f9a041ff8b3ce83e366f4

[ "Apache-2.0" ]

null

tools/Polygraphy/polygraphy/backend/common/__init__.py

martellz/TensorRT

f182e83b30b5d45aaa3f9a041ff8b3ce83e366f4

[ "Apache-2.0" ]

2

2021-02-04T14:46:10.000Z

2021-02-04T14:56:08.000Z

from polygraphy.backend.common.loader import BytesFromPath

29.5

58

0.881356

7

59

7.428571

1

0

0.067797

59

1

59

0.945455

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

d1830178d78d1efb4f5dea41fd8038f2403c0bef

112

py

Python

packages/jsii-rosetta/test/translations/comments/interleave_single_line_comments_with_function_call.py

NGL321/jsii

a31ebf5ef676391d97f2286edc21e5859c38c96c

[ "Apache-2.0" ]

1,639

2019-07-05T07:21:00.000Z

2022-03-31T09:55:01.000Z

packages/jsii-rosetta/test/translations/comments/interleave_single_line_comments_with_function_call.py

NGL321/jsii

a31ebf5ef676391d97f2286edc21e5859c38c96c

[ "Apache-2.0" ]

2,704

2019-07-01T23:10:28.000Z

2022-03-31T23:40:12.000Z

packages/jsii-rosetta/test/translations/comments/interleave_single_line_comments_with_function_call.py

NGL321/jsii

a31ebf5ef676391d97f2286edc21e5859c38c96c

[ "Apache-2.0" ]

146

2019-07-02T14:36:25.000Z

2022-03-26T00:21:27.000Z

some_function(arg1, # A comment before arg2 arg2="string", # A comment before arg3 arg3="boo" )

16

27

0.625

15

112

4.6

0.666667

0.231884

0.405797

0

0.060976

0.267857

112

7

28

16

0.780488

0.383929

0

0.134328

0

1

0

true

0

1

0

null

1

0

1

0

null

0

1

0

5

d184470930fb9ef30eb1dfb0cf90d6541d4c6a11

43

py

Python

vernon/summers2005/__main__.py

pkgw/vernon

9dd52d813722d0932195723cf8c37a5dd2fd0d25

[ "MIT" ]

null

vernon/summers2005/__main__.py

pkgw/vernon

9dd52d813722d0932195723cf8c37a5dd2fd0d25

[ "MIT" ]

null

vernon/summers2005/__main__.py

pkgw/vernon

9dd52d813722d0932195723cf8c37a5dd2fd0d25

[ "MIT" ]

1

2020-12-05T06:05:40.000Z

from . import summarize summarize().show()

14.333333

23

0.744186

5

43

6.4

0.8

0

0.116279

43

2

24

21.5

0.842105

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

5

d19f53e7c6b215bf96e844a57c88e556dc065849

73

py

Python

captain_hook/services/base/events/__init__.py

brantje/captain_hook

dde076a96afffa2235b7d8d01d47c4e61099c6b6

[ "Apache-2.0" ]

1

2017-01-07T16:22:05.000Z

captain_hook/services/base/events/__init__.py

brantje/captain_hook

dde076a96afffa2235b7d8d01d47c4e61099c6b6

[ "Apache-2.0" ]

3

2017-02-27T00:34:19.000Z

2017-02-27T14:25:44.000Z

captain_hook/services/base/events/__init__.py

brantje/telegram-github-bot

dde076a96afffa2235b7d8d01d47c4e61099c6b6

[ "Apache-2.0" ]

null

from __future__ import absolute_import from .base_event import BaseEvent

24.333333

38

0.876712

10

73

5.8

0.7

0

0.109589

73

2

39

36.5

0.892308

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

ae134f522864342441d0466e5437cf4a8df35157

705

py

Python

diskpy/ICgen/__init__.py

langfzac/diskpy

3b0f4fdc7f1fea21efdd3ab55bbf362181c7a3c4

[ "MIT" ]

4

2016-03-25T18:09:39.000Z

2020-03-10T09:27:41.000Z

diskpy/ICgen/__init__.py

langfzac/diskpy

3b0f4fdc7f1fea21efdd3ab55bbf362181c7a3c4

[ "MIT" ]

21

2015-07-20T21:56:45.000Z

2017-09-16T23:01:15.000Z

diskpy/ICgen/__init__.py

langfzac/diskpy

3b0f4fdc7f1fea21efdd3ab55bbf362181c7a3c4

[ "MIT" ]

4

2015-08-07T22:03:12.000Z

2021-02-19T16:30:17.000Z

# -*- coding: utf-8 -*- """ Created on Fri Aug 7 10:11:48 2015 @author: ibackus """ from ICgen import IC, load import AddBinary, binary, binaryUtils, calc_temp, calc_velocity, \ ICgen_settings, ICgen_utils, make_sigma, \ make_snapshotBinary, make_snapshot, make_snapshotSType, pos_class, \ sigma_profile, vertical_solver from rhosolver import rhosolver, loadrho __all__ = ['IC', 'load', 'AddBinary', 'binary', 'binaryUtils', 'calc_temp', 'calc_velocity', 'ICgen_settings', 'ICgen_utils', 'make_sigma', 'make_snapshotBinary', 'make_snapshot', 'make_snapshotSType', 'pos_class', 'sigma_profile', 'vertical_solver', 'rhosolver', 'loadrho']

29.375

74

0.680851

80

705

5.675

0.5

0.026432

0.114537

0.132159

0.700441

0

0.020942

0.187234

705

23

75

30.652174

0.771379

0.107801

0

0.309179

0

1

0

false

0

0.25

0

0.25

0

null

0

1

0

null

0

5

ae5702cbf7d34170815f9d815c74f6fb052ca673

73

py

Python

pyha/cores/packet/__init__.py

gasparka/pyha

60d9bbfd6075e7548d670d05317d64bc2a1a19ee

[ "Apache-2.0" ]

6

2017-05-18T18:57:07.000Z

2020-08-06T11:23:34.000Z

pyha/cores/packet/__init__.py

gasparka/pyha

60d9bbfd6075e7548d670d05317d64bc2a1a19ee

[ "Apache-2.0" ]

607

2017-05-10T12:51:54.000Z

2022-03-31T18:08:15.000Z

pyha/cores/packet/__init__.py

gasparka/pyha

60d9bbfd6075e7548d670d05317d64bc2a1a19ee

[ "Apache-2.0" ]

1

2019-03-20T13:57:46.000Z

from .crc16 import CRC16 from .header_correlator import HeaderCorrelator

24.333333

47

0.863014

9

73

6.888889

0.666667

0

0.061538

0.109589

73

2

48

36.5

0.892308

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

ae9bf5c79674fa3f110bb10b95216faff91f1343

76

py

Python

sacrerouge/datasets/duc_tac/duc2004/__init__.py

danieldeutsch/decomposed-rouge

0d723be8e3359f0bdcc9c7940336800895e46dbb

[ "Apache-2.0" ]

81

2020-07-10T15:45:08.000Z

2022-03-30T12:19:11.000Z

sacrerouge/datasets/duc_tac/duc2004/__init__.py

danieldeutsch/decomposed-rouge

0d723be8e3359f0bdcc9c7940336800895e46dbb

[ "Apache-2.0" ]

29

2020-08-03T21:50:45.000Z

2022-02-23T14:34:16.000Z

sacrerouge/datasets/duc_tac/duc2004/__init__.py

danieldeutsch/decomposed-rouge

0d723be8e3359f0bdcc9c7940336800895e46dbb

[ "Apache-2.0" ]

7

2020-08-14T09:54:08.000Z

2022-03-30T12:19:25.000Z

from sacrerouge.datasets.duc_tac.duc2004.subcommand import DUC2004Subcommand

76

0.907895

9

76

7.555556

1

0

0.109589

0.039474

76

1

76

0.821918

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

ae9e90cb0cda8da5d0da0d14eedc75eb0c359ea9

203

py

Python

output/models/ms_data/datatypes/facets/unsigned_short/unsigned_short_min_inclusive005_xsd/__init__.py

tefra/xsdata-w3c-tests

b6b6a4ac4e0ab610e4b50d868510a8b7105b1a5f

[ "MIT" ]

1

2021-08-14T17:59:21.000Z

output/models/ms_data/datatypes/facets/unsigned_short/unsigned_short_min_inclusive005_xsd/__init__.py

tefra/xsdata-w3c-tests

b6b6a4ac4e0ab610e4b50d868510a8b7105b1a5f

[ "MIT" ]

4

2020-02-12T21:30:44.000Z

2020-04-15T20:06:46.000Z

output/models/ms_data/datatypes/facets/unsigned_short/unsigned_short_min_inclusive005_xsd/__init__.py

tefra/xsdata-w3c-tests

b6b6a4ac4e0ab610e4b50d868510a8b7105b1a5f

[ "MIT" ]

null

from output.models.ms_data.datatypes.facets.unsigned_short.unsigned_short_min_inclusive005_xsd.unsigned_short_min_inclusive005 import ( FooType, Test, ) __all__ = [ "FooType", "Test", ]

20.3

135

0.753695

24

203

5.833333

0.666667

0.278571

0.228571

0.4

0

0.034682

0.147783

203

9

136

22.555556

0.774566

0

0.054187

0

1

0

false

0

0.125

0

0.125

0

1

0

null

1

0

1

0

null

0

5

881d037b3fd5697bbac007352270a46871233414

80

py

Python

mytest.py

iPhone2018/bkapp

21e30122b8fdaecba2f1d6bbc349e2a67d866c22

[ "Apache-2.0" ]

null

mytest.py

iPhone2018/bkapp

21e30122b8fdaecba2f1d6bbc349e2a67d866c22

[ "Apache-2.0" ]

5

2019-11-07T07:03:55.000Z

2021-06-10T22:09:28.000Z

mytest.py

iPhone2018/bkapp

21e30122b8fdaecba2f1d6bbc349e2a67d866c22

[ "Apache-2.0" ]

null

s = "2.12 1.86 1.81 3/982 17267\n" s = s.replace("\n", "").split(" ")[1] print s

26.666667

37

0.525

19

80

2.210526

0.684211

0

0.283582

0.1625

80

3

38

26.666667

0.343284

0

0.382716

0

null

0

null

0.333333

1

0

null

0

1

0

1

0

1

0

null

0

1

0

5

882bf0ef2ddf53307445dd3e3611e5f2c90370d7

1,268

py

Python

main.py

Iangecko/arbys

5b2e4b4e511d5721d6a1cc447b8fbf9be43fa909

[ "MIT" ]

null

main.py

Iangecko/arbys

5b2e4b4e511d5721d6a1cc447b8fbf9be43fa909

[ "MIT" ]

null

main.py

Iangecko/arbys

5b2e4b4e511d5721d6a1cc447b8fbf9be43fa909

[ "MIT" ]

null

from client import client from key import token # To load new modules, copy/paste the line below, uncommented, with X filled in for the name of your file # from modules import X from modules import about from modules import ares from modules import beef from modules import call from modules import chicken from modules import cond from modules import cqdx from modules import emoji_stats from modules import exec from modules import exit from modules import fivenine from modules import ham from modules import help from modules import htm from modules import info from modules import join_leave_msgs from modules import logstat from modules import markov from modules import mc from modules import message_log from modules import morse from modules import music from modules import n2yo from modules import nou from modules import ntp from modules import phonehand from modules import ping from modules import pingreact from modules import relay from modules import roles from modules import spaceman from modules import stats from modules import thiccbeef from modules import thiccom from modules import thiccseal from modules import time from modules import tubez from modules import units from modules import unmorse from modules import uwu client.run(token)

25.36

105

0.836751

199

1,268

5.311558

0.341709

0.426679

0.659413

0.041627

0

0.000935

0.156151

1,268

49

106

25.877551

0.986916

0.09858

0

1

0

true

0

0.976744

0

0.976744

0

null

1

0

null

0

1

0

1

0

1

0

5

88340ce6bd4e7d86f02425b3444ad02587cf331b

78

py

Python

sample/sample.py

eaybek/brainduck

f45dea58a39dc543d9bbf9cdc4732cbdd8f7c0ea

[ "MIT" ]

null

sample/sample.py

eaybek/brainduck

f45dea58a39dc543d9bbf9cdc4732cbdd8f7c0ea

[ "MIT" ]

null

sample/sample.py

eaybek/brainduck

f45dea58a39dc543d9bbf9cdc4732cbdd8f7c0ea

[ "MIT" ]

null

from brainduck.brainduck import Brainduck class Brainduck(object): pass

13

41

0.782051

9

78

6.777778

0.666667

0

0.166667

78

5

42

15.6

0.938462

0

1

0

true

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

5

887f8b04819f37d165c87e09b1e6033da5cdebe0

351

py

Python

code/tools/gen_checkpoint.py

santomon/taskonomy

4b22087a2686172b21b61589831061e7a386fe36

[ "MIT" ]

789

2018-03-21T05:28:38.000Z

2022-03-29T19:32:47.000Z

code/tools/gen_checkpoint.py

santomon/taskonomy

4b22087a2686172b21b61589831061e7a386fe36

[ "MIT" ]

46

2018-05-03T07:11:10.000Z

2022-03-11T23:26:03.000Z

code/tools/gen_checkpoint.py

santomon/taskonomy

4b22087a2686172b21b61589831061e7a386fe36

[ "MIT" ]

152

2018-03-24T10:20:44.000Z

2022-02-09T02:38:10.000Z

ckpt_string = 'model_checkpoint_path: "/home/ubuntu/s3/model_log_final/{task}/logs/slim-train/time/model.ckpt-{step}"\nall_model_checkpoint_paths: "/home/ubuntu/s3/model_log_final/{task}/logs/slim-train/time/model.ckpt-{step}"' with open("checkpoint", "w") as text_file: print(ckpt_string.format(task="keypoint3d", step="112830"), file=text_file)

87.75

227

0.769231

55

351

4.672727

0.509091

0.077821

0.093385

0.132296

0.459144

0

0.026946

0.048433

351

3

228

117

0.742515

0

0.333333

0.678063

0.595442

0

1

0

false

0

0.333333

0

null

0

1

0

1

0

1

null

0

5

88a33772a285013cc309453a69a781ac80f43100

268

py

Python

quake/server/worker.py

It4innovations/quake

a57f37e5c871e0c7c00b84aef638b925ef96690a

[ "MIT" ]

1

2021-03-26T14:23:44.000Z

quake/server/worker.py

It4innovations/quake

a57f37e5c871e0c7c00b84aef638b925ef96690a

[ "MIT" ]

null

quake/server/worker.py

It4innovations/quake

a57f37e5c871e0c7c00b84aef638b925ef96690a

[ "MIT" ]

null

class Worker: def __init__(self, worker_id, hostname): self.worker_id = worker_id self.hostname = hostname self.ds_connection = None self.tasks = set() def __repr__(self): return "<Worker id={}>".format(self.worker_id)

26.8

54

0.619403

33

268

4.636364

0.454545

0.261438

0.235294

0

0.268657

268

9

55

29.777778

0.780612

0

0.052239

0

1

0.25

false

0

0.125

0.5

0

null

1

0

1

0

null

0

1

0

1

0

5

ee14c20cb31444294bf49ed6a884974ffa5e6dd2

83

py

Python

tirma/models/__init__.py

sergevkim/ImageTranslation

b90f71b6abf0950569e6567ed67cb4bb9f99eaaf

[ "MIT" ]

1

2020-11-28T18:35:31.000Z

tirma/models/__init__.py

sergevkim/ImageTranslation

b90f71b6abf0950569e6567ed67cb4bb9f99eaaf

[ "MIT" ]

null

tirma/models/__init__.py

sergevkim/ImageTranslation

b90f71b6abf0950569e6567ed67cb4bb9f99eaaf

[ "MIT" ]

null

from .cycle_gan import CycleGAN from .pix2pix_translator import Pix2PixTranslator

20.75

49

0.86747

10

83

7

0.8

0

0.027027

0.108434

83

3

50

27.666667

0.918919

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

ee41ed3d586de4ced7f9b519426a38c70d13d085

224

py

Python

spotty/deployment/utils/cli.py

greglira/spotty

0b5073621ba8e19be75b6f9701e6c9971b6d17fb

[ "MIT" ]

246

2018-09-03T09:09:48.000Z

2020-07-18T21:07:15.000Z

spotty/deployment/utils/cli.py

greglira/spotty

0b5073621ba8e19be75b6f9701e6c9971b6d17fb

[ "MIT" ]

42

2018-10-09T19:41:56.000Z

2020-06-15T22:55:58.000Z

spotty/deployment/utils/cli.py

greglira/spotty

0b5073621ba8e19be75b6f9701e6c9971b6d17fb

[ "MIT" ]

27

2018-10-09T22:16:40.000Z

2020-06-08T22:26:00.000Z

import shlex def shlex_join(split_command: list): """Return a shell-escaped string from *split_command*. Copy-pasted from the Python 3.8 code. """ return ' '.join(shlex.quote(arg) for arg in split_command)

24.888889

62

0.696429

34

224

4.470588

0.705882

0.236842

0

0.01105

0.191964

224

8

63

28

0.828729

0.397321

0

0.008333

0

1

0.333333

false

0

0.333333

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

5

ee81e68223163d241cbf2ecc1c0257b17be08804

189

py

Python

prl/utils/__init__.py

sliwy/prl

0e4bfa5578d11890d21932f535b095f2657ed4ff

[ "MIT" ]

51

2020-02-12T08:57:50.000Z

2022-03-14T13:27:40.000Z

prl/utils/__init__.py

sliwy/prl

0e4bfa5578d11890d21932f535b095f2657ed4ff

[ "MIT" ]

4

2021-03-19T10:47:07.000Z

2022-03-12T00:14:39.000Z

prl/utils/__init__.py

sliwy/prl

0e4bfa5578d11890d21932f535b095f2657ed4ff

[ "MIT" ]

4

2020-03-04T07:03:24.000Z

2022-03-14T13:27:43.000Z

from prl.utils.misc import colors from prl.utils.utils import timeit from prl.utils.loggers import ( time_logger, memory_logger, agent_logger, misc_logger, nn_logger, )

18.9

34

0.730159

27

189

4.925926

0.481481

0.157895

0.270677

0

0.201058

189

9

35

21

0.880795

0

1

0

true

0

0.333333

0

0.333333

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

ee8f577665e1b983ed81ddb097fdcd5fe554ea2f

328

py

Python

sparkplug/timereporters/base.py

freshbooks/sparkplug

4f4fe38655a93cdee602019de2a75cd3d320408c

[ "MIT" ]

null

sparkplug/timereporters/base.py

freshbooks/sparkplug

4f4fe38655a93cdee602019de2a75cd3d320408c

[ "MIT" ]

11

2015-04-16T18:34:31.000Z

2021-05-07T14:19:57.000Z

sparkplug/timereporters/base.py

freshbooks/sparkplug

4f4fe38655a93cdee602019de2a75cd3d320408c

[ "MIT" ]

1

2019-03-14T12:52:44.000Z

import datetime def _milliseconds(timedelta): return timedelta.total_seconds() * 1000 class Base(object): def __init__(self): pass def append_wait(self, delta, tags=None): pass def append_exec(self, delta, tags=None): pass def append_erro(self, delta, tags=None): pass

16.4

44

0.640244

41

328

4.902439

0.536585

0.104478

0.19403

0.253731

0.402985

0.298507

0

0.016598

0.265244

328

19

45

17.263158

0.817427

0

0.333333

0

1

0.416667

false

0.333333

0.083333

0.666667

0

null

0

1

0

null

0

1

0

1

0

1

0

5

4e66849f114be35de8a8317ef834f4045fce8dde

16,953

bzl

Python

3rdparty/workspace.bzl

digital-peace-talks/argument-analysis-research

587b52bedb79a0c9497b8c39ccc70edf4d165249

[ "MIT" ]

null

3rdparty/workspace.bzl

digital-peace-talks/argument-analysis-research

587b52bedb79a0c9497b8c39ccc70edf4d165249

[ "MIT" ]

null

3rdparty/workspace.bzl

digital-peace-talks/argument-analysis-research

587b52bedb79a0c9497b8c39ccc70edf4d165249

[ "MIT" ]

null

# Do not edit. bazel-deps autogenerates this file from dependencies.yaml. def _jar_artifact_impl(ctx): jar_name = "%s.jar" % ctx.name ctx.download( output=ctx.path("jar/%s" % jar_name), url=ctx.attr.urls, sha256=ctx.attr.sha256, executable=False ) src_name="%s-sources.jar" % ctx.name srcjar_attr="" has_sources = len(ctx.attr.src_urls) != 0 if has_sources: ctx.download( output=ctx.path("jar/%s" % src_name), url=ctx.attr.src_urls, sha256=ctx.attr.src_sha256, executable=False ) srcjar_attr ='\n srcjar = ":%s",' % src_name build_file_contents = """ package(default_visibility = ['//visibility:public']) java_import( name = 'jar', tags = ['maven_coordinates={artifact}'], jars = ['{jar_name}'],{srcjar_attr} ) filegroup( name = 'file', srcs = [ '{jar_name}', '{src_name}' ], visibility = ['//visibility:public'] )\n""".format(artifact = ctx.attr.artifact, jar_name = jar_name, src_name = src_name, srcjar_attr = srcjar_attr) ctx.file(ctx.path("jar/BUILD"), build_file_contents, False) return None jar_artifact = repository_rule( attrs = { "artifact": attr.string(mandatory = True), "sha256": attr.string(mandatory = True), "urls": attr.string_list(mandatory = True), "src_sha256": attr.string(mandatory = False, default=""), "src_urls": attr.string_list(mandatory = False, default=[]), }, implementation = _jar_artifact_impl ) def jar_artifact_callback(hash): src_urls = [] src_sha256 = "" source=hash.get("source", None) if source != None: src_urls = [source["url"]] src_sha256 = source["sha256"] jar_artifact( artifact = hash["artifact"], name = hash["name"], urls = [hash["url"]], sha256 = hash["sha256"], src_urls = src_urls, src_sha256 = src_sha256 ) native.bind(name = hash["bind"], actual = hash["actual"]) def list_dependencies(): return [ {"artifact": "com.fasterxml.jackson.core:jackson-annotations:2.9.0", "lang": "java", "sha1": "07c10d545325e3a6e72e06381afe469fd40eb701", "sha256": "45d32ac61ef8a744b464c54c2b3414be571016dd46bfc2bec226761cf7ae457a", "repository": "http://central.maven.org/maven2/", "url": "http://central.maven.org/maven2/com/fasterxml/jackson/core/jackson-annotations/2.9.0/jackson-annotations-2.9.0.jar", "name": "com_fasterxml_jackson_core_jackson_annotations", "actual": "@com_fasterxml_jackson_core_jackson_annotations//jar", "bind": "jar/com/fasterxml/jackson/core/jackson_annotations"}, {"artifact": "com.fasterxml.jackson.core:jackson-core:2.9.7", "lang": "java", "sha1": "4b7f0e0dc527fab032e9800ed231080fdc3ac015", "sha256": "9e5bc0efabd9f0cac5c1fdd9ae35b16332ed22a0ee19a356de370a18a8cb6c84", "repository": "http://central.maven.org/maven2/", "url": "http://central.maven.org/maven2/com/fasterxml/jackson/core/jackson-core/2.9.7/jackson-core-2.9.7.jar", "name": "com_fasterxml_jackson_core_jackson_core", "actual": "@com_fasterxml_jackson_core_jackson_core//jar", "bind": "jar/com/fasterxml/jackson/core/jackson_core"}, {"artifact": "com.fasterxml.jackson.core:jackson-databind:2.9.7", "lang": "java", "sha1": "e6faad47abd3179666e89068485a1b88a195ceb7", "sha256": "675376decfc070b039d2be773a97002f1ee1e1346d95bd99feee0d56683a92bf", "repository": "http://central.maven.org/maven2/", "url": "http://central.maven.org/maven2/com/fasterxml/jackson/core/jackson-databind/2.9.7/jackson-databind-2.9.7.jar", "name": "com_fasterxml_jackson_core_jackson_databind", "actual": "@com_fasterxml_jackson_core_jackson_databind//jar", "bind": "jar/com/fasterxml/jackson/core/jackson_databind"}, {"artifact": "com.fasterxml.jackson.module:jackson-module-kotlin:2.9.7", "lang": "kotlin", "sha1": "9ec9b84e8af4c4f31efcbc5c21e34da8021419f1", "sha256": "5b313b299717156ee883ef37774f709c8c9942b395edcc1d13368e52a786be28", "repository": "http://central.maven.org/maven2/", "url": "http://central.maven.org/maven2/com/fasterxml/jackson/module/jackson-module-kotlin/2.9.7/jackson-module-kotlin-2.9.7.jar", "name": "com_fasterxml_jackson_module_jackson_module_kotlin", "actual": "@com_fasterxml_jackson_module_jackson_module_kotlin//jar:file", "bind": "jar/com/fasterxml/jackson/module/jackson_module_kotlin"}, {"artifact": "io.javalin:javalin:2.3.0", "lang": "kotlin", "sha1": "73836e9cf29f978e47817584f9cee86b5e1f4c09", "sha256": "3571e83863e1f163854f1b2ee3cbfc1336fcbdfa595ec9c2ed8ab8bfa792e5f4", "repository": "http://central.maven.org/maven2/", "url": "http://central.maven.org/maven2/io/javalin/javalin/2.3.0/javalin-2.3.0.jar", "name": "io_javalin_javalin", "actual": "@io_javalin_javalin//jar:file", "bind": "jar/io/javalin/javalin"}, {"artifact": "javax.servlet:javax.servlet-api:3.1.0", "lang": "java", "sha1": "3cd63d075497751784b2fa84be59432f4905bf7c", "sha256": "af456b2dd41c4e82cf54f3e743bc678973d9fe35bd4d3071fa05c7e5333b8482", "repository": "http://central.maven.org/maven2/", "url": "http://central.maven.org/maven2/javax/servlet/javax.servlet-api/3.1.0/javax.servlet-api-3.1.0.jar", "name": "javax_servlet_javax_servlet_api", "actual": "@javax_servlet_javax_servlet_api//jar", "bind": "jar/javax/servlet/javax_servlet_api"}, {"artifact": "org.eclipse.jetty.websocket:websocket-api:9.4.12.v20180830", "lang": "java", "sha1": "97d6376f70ae6c01112325c5254e566af118bc75", "sha256": "6f7ecb42601058ffe4a6c19c5340cac3ebf0f83e2e252b457558f104238278e3", "repository": "http://central.maven.org/maven2/", "url": "http://central.maven.org/maven2/org/eclipse/jetty/websocket/websocket-api/9.4.12.v20180830/websocket-api-9.4.12.v20180830.jar", "name": "org_eclipse_jetty_websocket_websocket_api", "actual": "@org_eclipse_jetty_websocket_websocket_api//jar", "bind": "jar/org/eclipse/jetty/websocket/websocket_api"}, {"artifact": "org.eclipse.jetty.websocket:websocket-client:9.4.12.v20180830", "lang": "java", "sha1": "75880b6a90a6eda83fdbfc20a42f23eade4b975d", "sha256": "97c6882c858a75776773eaccc01739757c4e9f60a51613878c1f2b2ba03d91af", "repository": "http://central.maven.org/maven2/", "url": "http://central.maven.org/maven2/org/eclipse/jetty/websocket/websocket-client/9.4.12.v20180830/websocket-client-9.4.12.v20180830.jar", "name": "org_eclipse_jetty_websocket_websocket_client", "actual": "@org_eclipse_jetty_websocket_websocket_client//jar", "bind": "jar/org/eclipse/jetty/websocket/websocket_client"}, {"artifact": "org.eclipse.jetty.websocket:websocket-common:9.4.12.v20180830", "lang": "java", "sha1": "33997cdafbabb3ffd6947a5c33057f967e10535b", "sha256": "3c35aefa720c51e09532c16fdbfaaebd1af3e07dee699dacaba8e0ab0adf88e5", "repository": "http://central.maven.org/maven2/", "url": "http://central.maven.org/maven2/org/eclipse/jetty/websocket/websocket-common/9.4.12.v20180830/websocket-common-9.4.12.v20180830.jar", "name": "org_eclipse_jetty_websocket_websocket_common", "actual": "@org_eclipse_jetty_websocket_websocket_common//jar", "bind": "jar/org/eclipse/jetty/websocket/websocket_common"}, {"artifact": "org.eclipse.jetty.websocket:websocket-server:9.4.12.v20180830", "lang": "java", "sha1": "fadf609aec6026cb25f25b6bc0b979821f849fd7", "sha256": "7b1bd39006be8c32d7426a119567d860b3e4a3dc3c01a5c91326450bb0213a03", "repository": "http://central.maven.org/maven2/", "url": "http://central.maven.org/maven2/org/eclipse/jetty/websocket/websocket-server/9.4.12.v20180830/websocket-server-9.4.12.v20180830.jar", "name": "org_eclipse_jetty_websocket_websocket_server", "actual": "@org_eclipse_jetty_websocket_websocket_server//jar", "bind": "jar/org/eclipse/jetty/websocket/websocket_server"}, {"artifact": "org.eclipse.jetty.websocket:websocket-servlet:9.4.12.v20180830", "lang": "java", "sha1": "8d212616b6ea21b96152ff202c2f53fdca8b8b53", "sha256": "8d43e0882759ecd093bd1a5a0ef2b4db38ac279212488a34edb8d7de7c45cc4d", "repository": "http://central.maven.org/maven2/", "url": "http://central.maven.org/maven2/org/eclipse/jetty/websocket/websocket-servlet/9.4.12.v20180830/websocket-servlet-9.4.12.v20180830.jar", "name": "org_eclipse_jetty_websocket_websocket_servlet", "actual": "@org_eclipse_jetty_websocket_websocket_servlet//jar", "bind": "jar/org/eclipse/jetty/websocket/websocket_servlet"}, {"artifact": "org.eclipse.jetty:jetty-client:9.4.12.v20180830", "lang": "java", "sha1": "1d329d68f31dce13135243c06013aaf6f708f7e7", "sha256": "62efbbfda88cd4f7644242c4b4df8f3b0a671bfeafea7682dabe00352ba07db7", "repository": "http://central.maven.org/maven2/", "url": "http://central.maven.org/maven2/org/eclipse/jetty/jetty-client/9.4.12.v20180830/jetty-client-9.4.12.v20180830.jar", "name": "org_eclipse_jetty_jetty_client", "actual": "@org_eclipse_jetty_jetty_client//jar", "bind": "jar/org/eclipse/jetty/jetty_client"}, {"artifact": "org.eclipse.jetty:jetty-http:9.4.12.v20180830", "lang": "java", "sha1": "1341796dde4e16df69bca83f3e87688ba2e7d703", "sha256": "20547da653be9942cc63f57e632a732608559aebde69753bc7312cfe16e8d9c0", "repository": "http://central.maven.org/maven2/", "url": "http://central.maven.org/maven2/org/eclipse/jetty/jetty-http/9.4.12.v20180830/jetty-http-9.4.12.v20180830.jar", "name": "org_eclipse_jetty_jetty_http", "actual": "@org_eclipse_jetty_jetty_http//jar", "bind": "jar/org/eclipse/jetty/jetty_http"}, {"artifact": "org.eclipse.jetty:jetty-io:9.4.12.v20180830", "lang": "java", "sha1": "e93f5adaa35a9a6a85ba130f589c5305c6ecc9e3", "sha256": "ab1784abbb9e0ed0869ab6568fe46f1faa79fb5e948cf96450daecd9d27ba1db", "repository": "http://central.maven.org/maven2/", "url": "http://central.maven.org/maven2/org/eclipse/jetty/jetty-io/9.4.12.v20180830/jetty-io-9.4.12.v20180830.jar", "name": "org_eclipse_jetty_jetty_io", "actual": "@org_eclipse_jetty_jetty_io//jar", "bind": "jar/org/eclipse/jetty/jetty_io"}, {"artifact": "org.eclipse.jetty:jetty-security:9.4.12.v20180830", "lang": "java", "sha1": "299e0602a9c0b753ba232cc1c1dda72ddd9addcf", "sha256": "513184970c785ac830424a9c62c2fadfa77a630f44aa0bdd792f00aaa092887e", "repository": "http://central.maven.org/maven2/", "url": "http://central.maven.org/maven2/org/eclipse/jetty/jetty-security/9.4.12.v20180830/jetty-security-9.4.12.v20180830.jar", "name": "org_eclipse_jetty_jetty_security", "actual": "@org_eclipse_jetty_jetty_security//jar", "bind": "jar/org/eclipse/jetty/jetty_security"}, {"artifact": "org.eclipse.jetty:jetty-server:9.4.12.v20180830", "lang": "java", "sha1": "b0f25df0d32a445fd07d5f16fff1411c16b888fa", "sha256": "4833644e5c5a09bbddc85f75c53e0c8ed750de120ba248fffd8508028528252d", "repository": "http://central.maven.org/maven2/", "url": "http://central.maven.org/maven2/org/eclipse/jetty/jetty-server/9.4.12.v20180830/jetty-server-9.4.12.v20180830.jar", "name": "org_eclipse_jetty_jetty_server", "actual": "@org_eclipse_jetty_jetty_server//jar", "bind": "jar/org/eclipse/jetty/jetty_server"}, {"artifact": "org.eclipse.jetty:jetty-servlet:9.4.12.v20180830", "lang": "java", "sha1": "4c1149328eda9fa39a274262042420f66d9ffd5f", "sha256": "7310d4cccf8abf27fde0c3f1a32e19c75fe33c6f1ab558f0704d915f0f01cb07", "repository": "http://central.maven.org/maven2/", "url": "http://central.maven.org/maven2/org/eclipse/jetty/jetty-servlet/9.4.12.v20180830/jetty-servlet-9.4.12.v20180830.jar", "name": "org_eclipse_jetty_jetty_servlet", "actual": "@org_eclipse_jetty_jetty_servlet//jar", "bind": "jar/org/eclipse/jetty/jetty_servlet"}, {"artifact": "org.eclipse.jetty:jetty-util:9.4.12.v20180830", "lang": "java", "sha1": "cb4ccec9bd1fe4b10a04a0fb25d7053c1050188a", "sha256": "60ad53e118a3e7d10418b155b9944d90b2e4e4c732e53ef4f419473288d3f48c", "repository": "http://central.maven.org/maven2/", "url": "http://central.maven.org/maven2/org/eclipse/jetty/jetty-util/9.4.12.v20180830/jetty-util-9.4.12.v20180830.jar", "name": "org_eclipse_jetty_jetty_util", "actual": "@org_eclipse_jetty_jetty_util//jar", "bind": "jar/org/eclipse/jetty/jetty_util"}, {"artifact": "org.eclipse.jetty:jetty-webapp:9.4.12.v20180830", "lang": "java", "sha1": "a3e119df2da04fcf5aa290c8c35c5b310ce2dcd1", "sha256": "5301e412a32bf7dddcfad458d952179597c61f8fd531c265873562725c3d4646", "repository": "http://central.maven.org/maven2/", "url": "http://central.maven.org/maven2/org/eclipse/jetty/jetty-webapp/9.4.12.v20180830/jetty-webapp-9.4.12.v20180830.jar", "name": "org_eclipse_jetty_jetty_webapp", "actual": "@org_eclipse_jetty_jetty_webapp//jar", "bind": "jar/org/eclipse/jetty/jetty_webapp"}, {"artifact": "org.eclipse.jetty:jetty-xml:9.4.12.v20180830", "lang": "java", "sha1": "e9f1874e9b5edd498f2fe7cd0904405da07cc300", "sha256": "5b8298ab3d43ddaf0941d41f51b82c8ae23a247da055fa161b752ab9495155ed", "repository": "http://central.maven.org/maven2/", "url": "http://central.maven.org/maven2/org/eclipse/jetty/jetty-xml/9.4.12.v20180830/jetty-xml-9.4.12.v20180830.jar", "name": "org_eclipse_jetty_jetty_xml", "actual": "@org_eclipse_jetty_jetty_xml//jar", "bind": "jar/org/eclipse/jetty/jetty_xml"}, {"artifact": "org.jetbrains.kotlin:kotlin-reflect:1.2.51", "lang": "java", "sha1": "36b719a7b84452dd13eeec979d8c82bfb765c57d", "sha256": "129f42c1ad5c3958856ecf2b2dadcd76e24a0b9b7f85aa2aba383616fcc49c7d", "repository": "http://central.maven.org/maven2/", "url": "http://central.maven.org/maven2/org/jetbrains/kotlin/kotlin-reflect/1.2.51/kotlin-reflect-1.2.51.jar", "name": "org_jetbrains_kotlin_kotlin_reflect", "actual": "@org_jetbrains_kotlin_kotlin_reflect//jar", "bind": "jar/org/jetbrains/kotlin/kotlin_reflect"}, {"artifact": "org.jetbrains.kotlin:kotlin-stdlib-common:1.2.71", "lang": "java", "sha1": "ba18ca1aa0e40eb6f1865b324af2f4cbb691c1ec", "sha256": "63999687ff2fce8a592dd180ffbbf8f1d21c26b4044c55cdc74ff3cf3b3cf328", "repository": "http://central.maven.org/maven2/", "url": "http://central.maven.org/maven2/org/jetbrains/kotlin/kotlin-stdlib-common/1.2.71/kotlin-stdlib-common-1.2.71.jar", "name": "org_jetbrains_kotlin_kotlin_stdlib_common", "actual": "@org_jetbrains_kotlin_kotlin_stdlib_common//jar", "bind": "jar/org/jetbrains/kotlin/kotlin_stdlib_common"}, {"artifact": "org.jetbrains.kotlin:kotlin-stdlib-jdk7:1.2.71", "lang": "java", "sha1": "4ce93f539e2133f172f1167291a911f83400a5d0", "sha256": "b136bd61b240e07d4d92ce00d3bd1dbf584400a7bf5f220c2f3cd22446858082", "repository": "http://central.maven.org/maven2/", "url": "http://central.maven.org/maven2/org/jetbrains/kotlin/kotlin-stdlib-jdk7/1.2.71/kotlin-stdlib-jdk7-1.2.71.jar", "name": "org_jetbrains_kotlin_kotlin_stdlib_jdk7", "actual": "@org_jetbrains_kotlin_kotlin_stdlib_jdk7//jar", "bind": "jar/org/jetbrains/kotlin/kotlin_stdlib_jdk7"}, {"artifact": "org.jetbrains.kotlin:kotlin-stdlib-jdk8:1.2.71", "lang": "java", "sha1": "5470d1f752cd342edb77e1062bac07e838d2cea4", "sha256": "ac3c8abf47790b64b4f7e2509a53f0c145e061ac1612a597520535d199946ea9", "repository": "http://central.maven.org/maven2/", "url": "http://central.maven.org/maven2/org/jetbrains/kotlin/kotlin-stdlib-jdk8/1.2.71/kotlin-stdlib-jdk8-1.2.71.jar", "name": "org_jetbrains_kotlin_kotlin_stdlib_jdk8", "actual": "@org_jetbrains_kotlin_kotlin_stdlib_jdk8//jar", "bind": "jar/org/jetbrains/kotlin/kotlin_stdlib_jdk8"}, # duplicates in org.jetbrains.kotlin:kotlin-stdlib promoted to 1.2.71 # - org.jetbrains.kotlin:kotlin-reflect:1.2.51 wanted version 1.2.51 # - org.jetbrains.kotlin:kotlin-stdlib-jdk8:1.2.71 wanted version 1.2.71 {"artifact": "org.jetbrains.kotlin:kotlin-stdlib:1.2.71", "lang": "java", "sha1": "d9717625bb3c731561251f8dd2c67a1011d6764c", "sha256": "4c895c270b87f5fec2a2796e1d89c15407ee821de961527c28588bb46afbc68b", "repository": "http://central.maven.org/maven2/", "url": "http://central.maven.org/maven2/org/jetbrains/kotlin/kotlin-stdlib/1.2.71/kotlin-stdlib-1.2.71.jar", "name": "org_jetbrains_kotlin_kotlin_stdlib", "actual": "@org_jetbrains_kotlin_kotlin_stdlib//jar", "bind": "jar/org/jetbrains/kotlin/kotlin_stdlib"}, {"artifact": "org.jetbrains:annotations:13.0", "lang": "java", "sha1": "919f0dfe192fb4e063e7dacadee7f8bb9a2672a9", "sha256": "ace2a10dc8e2d5fd34925ecac03e4988b2c0f851650c94b8cef49ba1bd111478", "repository": "http://central.maven.org/maven2/", "url": "http://central.maven.org/maven2/org/jetbrains/annotations/13.0/annotations-13.0.jar", "name": "org_jetbrains_annotations", "actual": "@org_jetbrains_annotations//jar", "bind": "jar/org/jetbrains/annotations"}, {"artifact": "org.slf4j:slf4j-api:1.7.25", "lang": "java", "sha1": "da76ca59f6a57ee3102f8f9bd9cee742973efa8a", "sha256": "18c4a0095d5c1da6b817592e767bb23d29dd2f560ad74df75ff3961dbde25b79", "repository": "http://central.maven.org/maven2/", "url": "http://central.maven.org/maven2/org/slf4j/slf4j-api/1.7.25/slf4j-api-1.7.25.jar", "name": "org_slf4j_slf4j_api", "actual": "@org_slf4j_slf4j_api//jar", "bind": "jar/org/slf4j/slf4j_api"}, ] def maven_dependencies(callback = jar_artifact_callback): for hash in list_dependencies(): callback(hash)

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null

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1

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null

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5

4e779751446e43b6a57dd34b97f035ba3270117f

117

py

Python

keras_contrib/layers/noise.py

WiproOpenSourcePractice/keras-contrib

3e77ba234f46b82997271996946b731bc774fb9f

[ "MIT" ]

11

2019-03-23T13:23:49.000Z

2022-01-20T07:57:56.000Z

keras_contrib/layers/noise.py

WiproOpenSourcePractice/keras-contrib

3e77ba234f46b82997271996946b731bc774fb9f

[ "MIT" ]

1

2021-06-18T23:07:54.000Z

2021-07-13T21:43:51.000Z

keras_contrib/layers/noise.py

WiproOpenSourcePractice/keras-contrib

3e77ba234f46b82997271996946b731bc774fb9f

[ "MIT" ]

11

2017-07-06T14:11:51.000Z

2021-08-21T23:18:20.000Z

from __future__ import absolute_import from keras.engine import Layer from .. import backend as K import numpy as np

23.4

38

0.820513

19

117

4.789474

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0.153846

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0

1

0

1

0

1

0

5

4ea3bc6d97a5e08e9785fbd729d7caa84df4d848

11,475

py

Python

plugins/good-morning/main.py

fz6m/tomon-naixue

dfbdd69836f26d160cece34e204f9fb2ed731607

[ "MIT" ]

3

2020-08-23T17:43:09.000Z

2020-08-31T04:43:42.000Z

plugins/good-morning/main.py

fz6m/tomon-naixue

dfbdd69836f26d160cece34e204f9fb2ed731607

[ "MIT" ]

null

plugins/good-morning/main.py

fz6m/tomon-naixue

dfbdd69836f26d160cece34e204f9fb2ed731607

[ "MIT" ]

null

import random from .config import goodMorningInstructionSet, goodNightInstructionSet from .utils import Tools, Status, Utils, TimeUtils, GoodMorningModel async def mainProgram(bot, userQQ, userGroup, msg, nickname, cid): # Good morning match if Tools.commandMatch(msg, goodMorningInstructionSet): sendMsg = await goodMorningInformation(userQQ, userGroup, nickname) await bot.send_text( cid = cid, content = sendMsg ) return # Good night detection if Tools.commandMatch(msg, goodNightInstructionSet): sendMsg = await goodNightInformation(userQQ, userGroup, nickname) await bot.send_text( cid = cid, content = sendMsg ) return async def userRegistration(userQQ, model): registrationStructure = { 'qq': userQQ, 'model': model, 'time': TimeUtils.getTheCurrentTime(), 'accurateTime': TimeUtils.getAccurateTimeNow() } await Utils.userInformationWriting(userQQ, registrationStructure) return Status.SUCCESS async def createACheckInPool(userGroup, model): signInPoolStructure = { 'qun': userGroup, 'time': TimeUtils.getTheCurrentTime(), 'accurateTime': TimeUtils.getAccurateTimeNow(), 'userList': [], 'number': 0 } await Utils.groupWrite(str(userGroup) + '-' + model, signInPoolStructure) return Status.SUCCESS async def addToCheckInPoolAndGetRanking(userQQ, userGroup, model): if model == GoodMorningModel.MORNING_MODEL.value: # Check if there is a check-in pool content = await Utils.groupRead(str(userGroup) + '-' + model) if content == Status.FAILURE: # Create a check-in pool await createACheckInPool(userGroup, model) content = await Utils.groupRead(str(userGroup) + '-' + model) # Check if the pool has expired if content['time'] != TimeUtils.getTheCurrentTime(): # Expired, rebuild the pool await createACheckInPool(userGroup, model) content = await Utils.groupRead(str(userGroup) + '-' + model) # Add users to the check-in pool user = await Utils.userInformationReading(userQQ) content['userList'].append(user) content['number'] += 1 await Utils.groupWrite(str(userGroup) + '-' + model, content) return content['number'] if model == GoodMorningModel.NIGHT_MODEL.value: # Check if there is a check-in pool content = await Utils.groupRead(str(userGroup) + '-' + model) if content == Status.FAILURE: # Create a check-in pool await createACheckInPool(userGroup, model) content = await Utils.groupRead(str(userGroup) + '-' + model) # Check if the pool has expired hourNow = TimeUtils.getTheCurrentHour() expiryId = False if content['time'] != TimeUtils.getTheCurrentTime(): if TimeUtils.judgeTimeDifference(content['accurateTime']) < 24: if hourNow >= 12: expiryId = True else: expiryId = True if expiryId: # Expired, rebuild the pool await createACheckInPool(userGroup, model) content = await Utils.groupRead(str(userGroup) + '-' + model) # Add users to the check-in pool user = await Utils.userInformationReading(userQQ) content['userList'].append(user) content['number'] += 1 await Utils.groupWrite(str(userGroup) + '-' + model, content) return content['number'] async def goodMorningInformation(userQQ, userGroup, nickname): # Check if registered registered = await Utils.userInformationReading(userQQ) send = Tools.at(userQQ) if registered == Status.FAILURE: # registered await userRegistration(userQQ, GoodMorningModel.MORNING_MODEL.value) # Add to check-in pool and get ranking rank = await addToCheckInPoolAndGetRanking(userQQ, userGroup, GoodMorningModel.MORNING_MODEL.value) send += (await Utils.extractRandomWords(GoodMorningModel.MORNING_MODEL.value, nickname) + '\n' + (await Utils.extractConfigurationInformationAccordingToSpecifiedParameters('suffix', GoodMorningModel.MORNING_MODEL.value)).replace(r'{number}', str(rank))) return send # Already registered if registered['model'] == GoodMorningModel.MORNING_MODEL.value: # too little time if TimeUtils.judgeTimeDifference(registered['accurateTime']) <= 4: send += await Utils.extractConfigurationInformationAccordingToSpecifiedParameters('triggered', GoodMorningModel.MORNING_MODEL.value) return send # Good morning no twice a day if registered['time'] != TimeUtils.getTheCurrentTime(): await userRegistration(userQQ, GoodMorningModel.MORNING_MODEL.value) rank = await addToCheckInPoolAndGetRanking(userQQ, userGroup, GoodMorningModel.MORNING_MODEL.value) send += (await Utils.extractRandomWords(GoodMorningModel.MORNING_MODEL.value, nickname) + '\n' + (await Utils.extractConfigurationInformationAccordingToSpecifiedParameters('suffix', GoodMorningModel.MORNING_MODEL.value)).replace(r'{number}', str(rank))) return send if registered['model'] == GoodMorningModel.NIGHT_MODEL.value: sleepingTime = TimeUtils.judgeTimeDifference(registered['accurateTime']) # too little time if sleepingTime <= 4: send += await Utils.extractConfigurationInformationAccordingToSpecifiedParameters('unable_to_trigger', GoodMorningModel.MORNING_MODEL.value) return send # Sleep time cannot exceed 24 hours await userRegistration(userQQ, GoodMorningModel.MORNING_MODEL.value) if sleepingTime < 24: send += await Utils.extractRandomWords(GoodMorningModel.MORNING_MODEL.value, nickname) # Calculate Wake Up Ranking rank = await addToCheckInPoolAndGetRanking(userQQ, userGroup, GoodMorningModel.MORNING_MODEL.value) send += ((await Utils.extractConfigurationInformationAccordingToSpecifiedParameters('suffix', GoodMorningModel.MORNING_MODEL.value)).replace(r'{number}', str(rank)) + '\n') # Calculate precise sleep time sleepPreciseTime = TimeUtils.calculateTheElapsedTimeCombination(registered['accurateTime']) if sleepPreciseTime[0] >= 9: send += TimeUtils.replaceHourMinuteAndSecond(sleepPreciseTime, (await Utils.readConfiguration(GoodMorningModel.MORNING_MODEL.value))['sleeping_time'][1]['content']) elif sleepPreciseTime[0] >= 7: send += TimeUtils.replaceHourMinuteAndSecond(sleepPreciseTime, (await Utils.readConfiguration(GoodMorningModel.MORNING_MODEL.value))['sleeping_time'][0]['content']) else: send += TimeUtils.replaceHourMinuteAndSecond(sleepPreciseTime, (await Utils.readConfiguration(GoodMorningModel.MORNING_MODEL.value))['too_little_sleep']) else: rank = await addToCheckInPoolAndGetRanking(userQQ, userGroup, GoodMorningModel.MORNING_MODEL.value) send += (await Utils.extractRandomWords(GoodMorningModel.MORNING_MODEL.value, nickname) + '\n' + (await Utils.extractConfigurationInformationAccordingToSpecifiedParameters('suffix', GoodMorningModel.MORNING_MODEL.value)).replace(r'{number}', str(rank))) return send return Status.FAILURE async def goodNightInformation(userQQ, userGroup, nickname): # Check if registered registered = await Utils.userInformationReading(userQQ) send = Tools.at(userQQ) if registered == Status.FAILURE: # registered await userRegistration(userQQ, GoodMorningModel.NIGHT_MODEL.value) # Add to check-in pool and get ranking rank = await addToCheckInPoolAndGetRanking(userQQ, userGroup, GoodMorningModel.NIGHT_MODEL.value) send += (await Utils.extractRandomWords(GoodMorningModel.NIGHT_MODEL.value, nickname) + '\n' + (await Utils.extractConfigurationInformationAccordingToSpecifiedParameters('suffix', GoodMorningModel.NIGHT_MODEL.value)).replace(r'{number}', str(rank))) return send # Already registered if registered['model'] == GoodMorningModel.NIGHT_MODEL.value: # too little time if TimeUtils.judgeTimeDifference(registered['accurateTime']) <= 4: send += await Utils.extractConfigurationInformationAccordingToSpecifiedParameters('triggered', GoodMorningModel.NIGHT_MODEL.value) return send # Two good nights can not be less than 12 hours if TimeUtils.judgeTimeDifference(registered['accurateTime']) >= 12: await userRegistration(userQQ, GoodMorningModel.NIGHT_MODEL.value) rank = await addToCheckInPoolAndGetRanking(userQQ, userGroup, GoodMorningModel.NIGHT_MODEL.value) send += (await Utils.extractRandomWords(GoodMorningModel.NIGHT_MODEL.value, nickname) + '\n' + (await Utils.extractConfigurationInformationAccordingToSpecifiedParameters('suffix', GoodMorningModel.NIGHT_MODEL.value)).replace(r'{number}', str(rank))) return send if registered['model'] == GoodMorningModel.MORNING_MODEL.value: soberTime = TimeUtils.judgeTimeDifference(registered['accurateTime']) # too little time if soberTime <= 4: send += await Utils.extractConfigurationInformationAccordingToSpecifiedParameters('unable_to_trigger', GoodMorningModel.NIGHT_MODEL.value) return send # sober time cannot exceed 24 hours await userRegistration(userQQ, GoodMorningModel.NIGHT_MODEL.value) if soberTime < 24: send += await Utils.extractRandomWords(GoodMorningModel.NIGHT_MODEL.value, nickname) rank = await addToCheckInPoolAndGetRanking(userQQ, userGroup, GoodMorningModel.NIGHT_MODEL.value) send += ((await Utils.extractConfigurationInformationAccordingToSpecifiedParameters('suffix', GoodMorningModel.NIGHT_MODEL.value)).replace(r'{number}', str(rank)) + '\n') soberAccurateTime = TimeUtils.calculateTheElapsedTimeCombination(registered['accurateTime']) if soberAccurateTime[0] >= 12: send += TimeUtils.replaceHourMinuteAndSecond(soberAccurateTime, (await Utils.readConfiguration(GoodMorningModel.NIGHT_MODEL.value))['working_hours'][2]['content']) else: send += TimeUtils.replaceHourMinuteAndSecond(soberAccurateTime, random.choice((await Utils.readConfiguration(GoodMorningModel.NIGHT_MODEL.value))['working_hours'])['content']) else: rank = await addToCheckInPoolAndGetRanking(userQQ, userGroup, GoodMorningModel.NIGHT_MODEL.value) send += (await Utils.extractRandomWords(GoodMorningModel.NIGHT_MODEL.value, nickname) + '\n' + (await Utils.extractConfigurationInformationAccordingToSpecifiedParameters('suffix', GoodMorningModel.NIGHT_MODEL.value)).replace(r'{number}', str(rank))) return send return Status.FAILURE

53.621495

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4ebbd4818dc7bb79d00626d86f42c008d5f1a870

7,984

py

Python

test/unit_tests/providers/test_slideshare.py

ourresearch/total-impact-webapp

ab0d011dc783491bc85aadc2dc9c0f204e59429e

[ "MIT" ]

4

2015-10-22T10:11:01.000Z

2017-06-04T18:08:28.000Z

test/unit_tests/providers/test_slideshare.py

Impactstory/total-impact-webapp

ab0d011dc783491bc85aadc2dc9c0f204e59429e

[ "MIT" ]

2

2015-01-11T05:45:59.000Z

2015-02-11T20:37:05.000Z

test/unit_tests/providers/test_slideshare.py

Impactstory/total-impact-webapp

ab0d011dc783491bc85aadc2dc9c0f204e59429e

[ "MIT" ]

3

2015-01-10T03:23:13.000Z

2015-10-11T15:49:41.000Z

from test.unit_tests.providers import common from test.unit_tests.providers.common import ProviderTestCase from totalimpact.providers.provider import Provider, ProviderContentMalformedError from totalimpact.providers import provider from test.utils import http import os import collections from nose.tools import assert_equals, raises, nottest, assert_true datadir = os.path.join(os.path.split(__file__)[0], "../../../extras/sample_provider_pages/slideshare") SAMPLE_EXTRACT_MEMBER_ITEMS_PAGE = os.path.join(datadir, "members") SAMPLE_EXTRACT_METRICS_PAGE = os.path.join(datadir, "metrics") SAMPLE_EXTRACT_ALIASES_PAGE = os.path.join(datadir, "aliases") SAMPLE_EXTRACT_BIBLIO_PAGE = os.path.join(datadir, "biblio") TEST_URL = "http://www.slideshare.net/cavlec/manufacturing-serendipity-12176916" TEST_URL2 = "www.slideshare.net/hpiwowar/right-time-right-place-to-change-the-world" TEST_SLIDESHARE_USER = "cavlec" class TestSlideshare(ProviderTestCase): provider_name = "slideshare" testitem_members = "cavlec" testitem_aliases = ("url", TEST_URL) testitem_metrics = ("url", TEST_URL) testitem_biblio = ("url", TEST_URL) def setUp(self): ProviderTestCase.setUp(self) def test_is_relevant_alias(self): # ensure that it matches an appropriate ids assert_equals(self.provider.is_relevant_alias(self.testitem_aliases), True) assert_equals(self.provider.is_relevant_alias(("github", "egonw,cdk")), False) def test_extract_members(self): f = open(SAMPLE_EXTRACT_MEMBER_ITEMS_PAGE, "r") members = self.provider._extract_members(f.read(), TEST_SLIDESHARE_USER) assert_equals(len(members), 36) assert_true('url', u'http://www.slideshare.net/cavlec/avoiding-heronway' in members) def test_extract_biblio(self): f = open(SAMPLE_EXTRACT_BIBLIO_PAGE, "r") ret = self.provider._extract_biblio(f.read()) assert_equals(ret, {'username': u'cavlec', 'title': u'Manufacturing Serendipity', 'repository': 'Slideshare', 'created': u'Tue Mar 27 10:10:11 -0500 2012'}) def test_extract_aliases(self): # ensure that the dryad reader can interpret an xml doc appropriately f = open(SAMPLE_EXTRACT_ALIASES_PAGE, "r") aliases = self.provider._extract_aliases(f.read()) assert_equals(aliases, [('title', u'Manufacturing Serendipity')]) def test_extract_metrics_success(self): f = open(SAMPLE_EXTRACT_METRICS_PAGE, "r") metrics_dict = self.provider._extract_metrics(f.read()) assert_equals(metrics_dict["slideshare:views"], 337) assert_equals(metrics_dict["slideshare:downloads"], 4) @http def test_metrics(self): metrics_dict = self.provider.metrics([self.testitem_metrics]) expected = {'slideshare:downloads': (4, 'http://www.slideshare.net/cavlec/manufacturing-serendipity-12176916'), 'slideshare:views': (543, 'http://www.slideshare.net/cavlec/manufacturing-serendipity-12176916'), 'slideshare:favorites': (2, 'http://www.slideshare.net/cavlec/manufacturing-serendipity-12176916')} print metrics_dict for key in expected: assert metrics_dict[key][0] >= expected[key][0], [key, metrics_dict[key], expected[key]] assert metrics_dict[key][1] == expected[key][1], [key, metrics_dict[key], expected[key]] @http def test_provider_import(self): test_tabs = {"account_name": "cavlec", "standard_urls_input": TEST_URL2} members = provider.import_products("slideshare", test_tabs) print members expected = [('url', u'https://www.slideshare.net/hpiwowar/right-time-right-place-to-change-the-world'), ('url', u'http://www.slideshare.net/cavlec/week8-5557551'), ('url', u'http://www.slideshare.net/cavlec/canoe-the-open-content-rapids'), ('url', u'http://www.slideshare.net/cavlec/so-you-think-you-know-libraries'), ('url', u'http://www.slideshare.net/cavlec/what-we-organize'), ('url', u'http://www.slideshare.net/cavlec/escapar-la-carrera-de-la-reina'), ('url', u'http://www.slideshare.net/cavlec/librarians-love-data'), ('url', u'http://www.slideshare.net/cavlec/even-the-loons-are-licensed'), ('url', u'http://www.slideshare.net/cavlec/institutional-repositories-rebirth-of-the-phoenix'), ('url', u'http://www.slideshare.net/cavlec/manufacturing-serendipity-12176916'), ('url', u'http://www.slideshare.net/cavlec/canoe-the-open-content-rapids-2862487'), ('url', u'http://www.slideshare.net/cavlec/encryption-27779361'), ('url', u'http://www.slideshare.net/cavlec/who-owns-our-work-notes'), ('url', u'http://www.slideshare.net/cavlec/rdf-rda-and-other-tlas'), ('url', u'http://www.slideshare.net/cavlec/whats-driving-open-access'), ('url', u'http://www.slideshare.net/cavlec/manufacturing-serendipity'), ('url', u'http://www.slideshare.net/cavlec/i-own-copyright-so-i-pwn-you'), ('url', u'http://www.slideshare.net/cavlec/paying-forit'), ('url', u'http://www.slideshare.net/cavlec/grab-a-bucket-its-raining-data'), ('url', u'http://www.slideshare.net/cavlec/soylent-semantic-web-is-people-with-notes'), ('url', u'http://www.slideshare.net/cavlec/who-owns-our-work'), ('url', u'http://www.slideshare.net/cavlec/soylent-semanticweb-is-people'), ('url', u'http://www.slideshare.net/cavlec/open-sesame-and-other-open-movements'), ('url', u'http://www.slideshare.net/cavlec/digital-preservation-and-institutional-repositories'), ('url', u'http://www.slideshare.net/cavlec/a-successful-failure-community-requirements-gathering-for-dspace'), ('url', u'http://www.slideshare.net/cavlec/project-management-16606291'), ('url', u'http://www.slideshare.net/cavlec/databases-markup-and-regular-expressions'), ('url', u'http://www.slideshare.net/cavlec/solving-problems-with-web-20'), ('url', u'http://www.slideshare.net/cavlec/educators-together'), ('url', u'http://www.slideshare.net/cavlec/le-ir-cest-mort-vive-le-ir'), ('url', u'http://www.slideshare.net/cavlec/open-content'), ('url', u'http://www.slideshare.net/cavlec/so-are-we-winning-yet'), ('url', u'http://www.slideshare.net/cavlec/save-the-cows-data-curation-for-the-rest-of-us-1533252'), ('url', u'http://www.slideshare.net/cavlec/grab-a-bucket-its-raining-data-2134106'), ('url', u'http://www.slideshare.net/cavlec/nsa-27779364'), ('url', u'http://www.slideshare.net/cavlec/is-this-big-data-which-i-see-before-me'), ('url', u'http://www.slideshare.net/cavlec/escaping-the-red-queens-race-with-open-access'), ('url', u'http://www.slideshare.net/cavlec/research-data-and-scholarly-communication'), ('url', u'http://www.slideshare.net/cavlec/so-arewewinningyet-notes'), ('url', u'http://www.slideshare.net/cavlec/week13-5972690'), ('url', u'http://www.slideshare.net/cavlec/privacy-inlibs'), ('url', u'http://www.slideshare.net/cavlec/marc-and-bibframe-linking-libraries-and-archives'), ('url', u'http://www.slideshare.net/cavlec/frbr-and-rda'), ('url', u'http://www.slideshare.net/cavlec/the-social-journal'), ('url', u'http://www.slideshare.net/cavlec/occupy-copyright'), ('url', u'http://www.slideshare.net/cavlec/research-data-and-scholarly-communication-16366049'), ('url', u'http://www.slideshare.net/cavlec/what-youre-up-against'), ('url', u'http://www.slideshare.net/cavlec/escaping-datageddon'), ('url', u'http://www.slideshare.net/cavlec/risk-management-and-auditing'), ('url', u'http://www.slideshare.net/cavlec/the-canonically-bad-digital-humanities-proposal'), ('url', u'http://www.slideshare.net/cavlec/data-and-the-law'), ('url', u'http://www.slideshare.net/cavlec/ejournals-and-open-access'), ('url', u'http://www.slideshare.net/cavlec/preservation-and-institutional-repositories-for-the-digital-arts-and-humanities'), ('url', u'http://www.slideshare.net/cavlec/avoiding-heronway'), ('url', u'http://www.slideshare.net/cavlec/taming-the-monster-digital-preservation-planning-and-implementation-tools'), ('url', u'http://www.slideshare.net/cavlec/library-linked-data')] for member in expected: assert(member in members)

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4ebc5860728f6bf2e085520108e5d15a38534a71

221

py

Python

cmp_telegram_pusher/src/controllers/interfaces/MessageRegistrar.py

andrii-z4i/xmind-telegram

82e50ae0ada048b87a2c082bbdd4510e02cb3694

[ "MIT" ]

null

cmp_telegram_pusher/src/controllers/interfaces/MessageRegistrar.py

andrii-z4i/xmind-telegram

82e50ae0ada048b87a2c082bbdd4510e02cb3694

[ "MIT" ]

16

2018-05-07T09:42:56.000Z

2018-11-19T06:05:51.000Z

cmp_telegram_pusher/src/controllers/interfaces/MessageRegistrar.py

andrii-z4i/xmind-telegram

82e50ae0ada048b87a2c082bbdd4510e02cb3694

[ "MIT" ]

null

from abc import ABC, abstractmethod from shared.model import MessageContainer class MessageRegistrar(ABC): @abstractmethod def store_message(self, message_body: str) -> None: raise NotImplementedError()

24.555556

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null

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5

14e0de81c17877378bf1b55e3760f43a8dd0f9f3

432

py

Python

task5.py

missKatiaPunter/python-coursework

80c8760ad2d337a8cdac6d64f30fd429b67d7d99

[ "MIT" ]

null

task5.py

missKatiaPunter/python-coursework

80c8760ad2d337a8cdac6d64f30fd429b67d7d99

[ "MIT" ]

null

task5.py

missKatiaPunter/python-coursework

80c8760ad2d337a8cdac6d64f30fd429b67d7d99

[ "MIT" ]

null

# Task 5 # You have found a mystery function; all you know are some of its inputs/outputs: # mystery_num(300) ==> returns 2 # mystery_num(6996) ==> returns 4 # mystery_num(666) ==> returns 3 # mystery_num(90783) ==> returns 4 # mystery_num(1233321457) ==> returns 0 # mystery_num(81234) ==> returns 2 # mystery_num(89282350306) ==> returns 8 # mystery_num(3479283469) ==> returns 5 # Write the function. def mystery_num(): pass

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null

0

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5

14e2b60e2f696e2cff3f74e7af5f80b7ec1f35a4

85

py

Python

__init__.py

sesquideus/scalyca

ae1e8bcf4dbbdf1b653e0dd89a842a202cbbc624

[ "MIT" ]

null

__init__.py

sesquideus/scalyca

ae1e8bcf4dbbdf1b653e0dd89a842a202cbbc624

[ "MIT" ]

null

__init__.py

sesquideus/scalyca

ae1e8bcf4dbbdf1b653e0dd89a842a202cbbc624

[ "MIT" ]

null

from .scalyca import Scala, Scalyca from .utilities import ReadableDir, WriteableDir

28.333333

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null

0

1

0

1

0

1

0

5

14f30a7b6a9d5232b5d721cd14792a63df21179d

102

py

Python

src/chapter1.py

andyliumathematics/princeton-calculus

9be5f4038a67b90f9844d1c9d4592dc2a3bf4647

[ "Apache-2.0" ]

null

src/chapter1.py

andyliumathematics/princeton-calculus

9be5f4038a67b90f9844d1c9d4592dc2a3bf4647

[ "Apache-2.0" ]

null

src/chapter1.py

andyliumathematics/princeton-calculus

9be5f4038a67b90f9844d1c9d4592dc2a3bf4647

[ "Apache-2.0" ]

null

# %% from sympy import Symbol from sympy import plot x = Symbol('x') f = x**2 plot(f,(x,-4,2)) # %%

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0947223e4288e72867f9482453beae975c34c6a8

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py

Python

judge/settings/__init__.py

shan18/Online-Judge

b03e1df9eaa91957b635b6527f4abf5509495b56

[ "MIT" ]

1

2020-07-26T20:54:53.000Z

judge/settings/__init__.py

shan18/Online-Judge

b03e1df9eaa91957b635b6527f4abf5509495b56

[ "MIT" ]

null

judge/settings/__init__.py

shan18/Online-Judge

b03e1df9eaa91957b635b6527f4abf5509495b56

[ "MIT" ]

null

from .production import * # from .local import *

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0956151bd7ce7420fb63ab57e2d787ee0beae934

153

py

Python

compiled/construct/valid_fail_contents.py

smarek/ci_targets

c5edee7b0901fd8e7f75f85245ea4209b38e0cb3

[ "MIT" ]

4

2017-04-08T12:55:11.000Z

2020-12-05T21:09:31.000Z

compiled/construct/valid_fail_contents.py

smarek/ci_targets

c5edee7b0901fd8e7f75f85245ea4209b38e0cb3

[ "MIT" ]

7

2018-04-23T01:30:33.000Z

2020-10-30T23:56:14.000Z

compiled/construct/valid_fail_contents.py

smarek/ci_targets

c5edee7b0901fd8e7f75f85245ea4209b38e0cb3

[ "MIT" ]

6

2017-04-08T11:41:14.000Z

2020-10-30T22:47:31.000Z

from construct import * from construct.lib import * valid_fail_contents = Struct( 'foo' / FixedSized(2, GreedyBytes), ) _schema = valid_fail_contents

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null

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095aafdd4b924c03f4b72c04afaa459f0064ed5b

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py

Python

cloverapi/__init__.py

jmphilli/clover-api-python

e67f7578e7d46fb4753b10f3827fe5684f4678a6

[ "MIT" ]

5

2018-08-02T18:40:51.000Z

2022-03-04T17:13:55.000Z

cloverapi/__init__.py

jmphilli/clover-api-python

e67f7578e7d46fb4753b10f3827fe5684f4678a6

[ "MIT" ]

2

2018-12-13T15:51:30.000Z

2020-05-26T02:29:47.000Z

cloverapi/__init__.py

jmphilli/clover-api-python

e67f7578e7d46fb4753b10f3827fe5684f4678a6

[ "MIT" ]

11

2018-12-12T19:22:48.000Z

2021-02-02T00:48:16.000Z

from cloverapi.cloverapi_client import CloverApiClient

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5

1189d28af4a59e7a26e3152176a77da4b0778007

237

py

Python

backend/app/__init__.py

Sirius-ctrl/COMP90024-Project2-Distributed-Twitter-Analyser

7b19bb3bb995bec981a3d159e5e4e853361341b0

[ "Apache-2.0" ]

9

2020-08-09T14:31:48.000Z

2022-03-15T09:41:28.000Z

backend/app/__init__.py

Sirius-ctrl/COMP90024-Project2-Distributed-Twitter-Analyser

7b19bb3bb995bec981a3d159e5e4e853361341b0

[ "Apache-2.0" ]

null

backend/app/__init__.py

Sirius-ctrl/COMP90024-Project2-Distributed-Twitter-Analyser

7b19bb3bb995bec981a3d159e5e4e853361341b0

[ "Apache-2.0" ]

8

2020-06-30T12:37:55.000Z

2022-03-03T11:12:23.000Z

""" Author: XuLin Yang & Renjie Meng Student id: 904904 & 877396 Date: 2020-4-24 01:16:19 Description: creates the application object as an instance of class Flask imported from the flask package. """ from app import aurin

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null

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1

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1

0

1

0

5

11e102a796adaf58782ed3f3edc74ff89132e10d

1,068

py

Python

End2EndPurification/blocking_times.py

mercari/quantum-entanglement-purification-simulator

94b6616216589ef017e415227e7cdf61a6f8b6b0

[ "MIT" ]

null

End2EndPurification/blocking_times.py

mercari/quantum-entanglement-purification-simulator

94b6616216589ef017e415227e7cdf61a6f8b6b0

[ "MIT" ]

null

End2EndPurification/blocking_times.py

mercari/quantum-entanglement-purification-simulator

94b6616216589ef017e415227e7cdf61a6f8b6b0

[ "MIT" ]

null

class BlockingTimes: LIMIT = 1000000 def __init__(self, bloking_time_int_node, blocking_time_end_node) -> None: self.blocking_time_int_node = bloking_time_int_node self.blocking_time_end_node = blocking_time_end_node def __repr__(self): return "(b_time_interm_node:"+ '{:.5g}'.format(self.blocking_time_int_node) +", b_time_end_node:"+ '{:.5g}'.format(self.blocking_time_end_node) + ")" @staticmethod def merge_blocking_times(bt_left, bt_right): return BlockingTimes(bt_left.blocking_time_int_node+bt_right.blocking_time_int_node, bt_left.blocking_time_end_node + bt_right.blocking_time_end_node) @staticmethod def add_blocking_times(self, blocking_time_int_node, blocking_time_end_node): return BlockingTimes(self.blocking_time_int_node + blocking_time_int_node, self.blocking_time_end_node + blocking_time_end_node) @staticmethod def multiply_blocking_times(self, multiplier): return BlockingTimes(self.blocking_time_int_node * multiplier, self.blocking_time_end_node * multiplier)

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11f8b49c41268945b9ec8cc9da45b0cf2b608dde

548

py

Python

PyOpenGL-3.0.2/OpenGL/raw/GL/NV/texture_barrier.py

frederica07/Dragon_Programming_Process

c0dff2e20c1be6db5adc6f9977efae8f7f888ef5

[ "BSD-2-Clause" ]

null

PyOpenGL-3.0.2/OpenGL/raw/GL/NV/texture_barrier.py

frederica07/Dragon_Programming_Process

c0dff2e20c1be6db5adc6f9977efae8f7f888ef5

[ "BSD-2-Clause" ]

null

PyOpenGL-3.0.2/OpenGL/raw/GL/NV/texture_barrier.py

frederica07/Dragon_Programming_Process

c0dff2e20c1be6db5adc6f9977efae8f7f888ef5

[ "BSD-2-Clause" ]

null

'''Autogenerated by get_gl_extensions script, do not edit!''' from OpenGL import platform as _p, constants as _cs, arrays from OpenGL.GL import glget import ctypes EXTENSION_NAME = 'GL_NV_texture_barrier' def _f( function ): return _p.createFunction( function,_p.GL,'GL_NV_texture_barrier',False) @_f @_p.types(None,) def glTextureBarrierNV( ):pass def glInitTextureBarrierNV(): '''Return boolean indicating whether this extension is available''' from OpenGL import extensions return extensions.hasGLExtension( EXTENSION_NAME )

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5

01137d37e254794642b22504259bd11e043d0fc3

4,061

py

Python

car/TF_RefineDet_CIDI3/model/VGG.py

donghaiwang/VisualTracking_DRL

8fbe03f3b56a90ebd53173a2d367f49e52c25a4c

[ "Apache-2.0" ]

4

2018-12-07T12:47:13.000Z

2021-12-21T08:46:50.000Z

car/TF_RefineDet_CIDI3/model/VGG.py

donghaiwang/VisualTracking_DRL

8fbe03f3b56a90ebd53173a2d367f49e52c25a4c

[ "Apache-2.0" ]

null

car/TF_RefineDet_CIDI3/model/VGG.py

donghaiwang/VisualTracking_DRL

8fbe03f3b56a90ebd53173a2d367f49e52c25a4c

[ "Apache-2.0" ]

2

2018-10-29T09:29:19.000Z

2021-12-21T08:46:52.000Z

# -*- coding: UTF-8 -*- """ vgg(base network)->RefineDet_tf for vechile detection. @author: xie wei """ from model.layers_group import * slim = tf.contrib.slim def VGG(inputs,name,training=True,w_summary=True,keep_prob = 1.0,use_bn = False, reuse = False): with tf.variable_scope(name,reuse=reuse): end_points_collection = name + '_end_logits' if (use_bn): conv1_1 = conv_bn_relu(inputs, 64, 3, 1,'SAME', training, w_summary, name='conv1_1') conv1_2 = conv_bn_relu(conv1_1, 64, 3, 1, 'SAME', training, w_summary, name='conv1_2') pool1 = pool(conv1_2,2, 2, 'max', name='pool1') conv2_1 = conv_bn_relu(pool1, 128, 3, 1, 'SAME', training, w_summary, name='conv2_1') conv2_2 = conv_bn_relu(conv2_1, 128, 3, 1, 'SAME', training, w_summary, name='conv2_2') pool2 = pool(conv2_2, 2, 2, 'max', name='pool2') conv3_1 = conv_bn_relu(pool2, 256, 3, 1, 'SAME', training, w_summary, name='conv3_1') conv3_2 = conv_bn_relu(conv3_1, 256, 3, 1, 'SAME', training, w_summary, name='conv3_2') conv3_3 = conv_bn_relu(conv3_2, 256, 3, 1, 'SAME', training, w_summary, name='conv3_3') pool3 = pool(conv3_3, 2, 2, 'max', name='pool3') conv4_1 = conv_bn_relu(pool3, 512, 3, 1, 'SAME', training, w_summary, name='conv4_1') conv4_2 = conv_bn_relu(conv4_1, 512, 3, 1, 'SAME', training, w_summary, name='conv4_2') conv4_3 = conv_bn_relu(conv4_2, 512, 3, 1, 'SAME', training, w_summary, name='conv4_3') pool4 = pool(conv4_3, 2, 2, 'max', name='pool4') conv5_1 = conv_bn_relu(pool4, 512, 3, 1, 'SAME', training, w_summary, name='conv5_1') conv5_2 = conv_bn_relu(conv5_1, 512, 3, 1, 'SAME', training, w_summary, name='conv5_2') conv5_3 = conv_bn_relu(conv5_2, 512, 3, 1, 'SAME', training, w_summary, name='conv5_3') pool5 = pool(conv5_3, 2, 2, 'max', name='pool5') else: conv1_1 = conv_relu(inputs, 64, 3, 1, 'SAME', training, w_summary, bias=False,name='conv1_1') conv1_2 = conv_relu(conv1_1, 64, 3, 1, 'SAME', training, w_summary, bias=False,name='conv1_2') pool1 = pool(conv1_2, 2, 2, 'max', name='pool1') conv2_1 = conv_relu(pool1, 128, 3, 1, 'SAME', training, w_summary, bias=False,name='conv2_1') conv2_2 = conv_relu(conv2_1, 128, 3, 1, 'SAME', training, w_summary, bias=False,name='conv2_2') pool2 = pool(conv2_2, 2, 2, 'max', name='pool2') conv3_1 = conv_relu(pool2, 256, 3, 1, 'SAME', training, w_summary, bias=False,name='conv3_1') conv3_2 = conv_relu(conv3_1, 256, 3, 1, 'SAME', training, w_summary, bias=False,name='conv3_2') conv3_3 = conv_relu(conv3_2, 256, 3, 1, 'SAME', training, w_summary, bias=False,name='conv3_3') pool3 = pool(conv3_3, 2, 2, 'max', name='pool3') conv4_1 = conv_relu(pool3, 512, 3, 1, 'SAME', training, w_summary, bias=False,name='conv4_1') conv4_2 = conv_relu(conv4_1, 512, 3, 1, 'SAME', training, w_summary, bias=False,name='conv4_2') conv4_3 = conv_relu(conv4_2, 512, 3, 1, 'SAME', training, w_summary, bias=False,name='conv4_3') pool4 = pool(conv4_3, 2, 2, 'max', name='pool4') conv5_1 = conv_relu(pool4, 512, 3, 1, 'SAME', training, w_summary, name='conv5_1') conv5_2 = conv_relu(conv5_1, 512, 3, 1, 'SAME', training, w_summary, name='conv5_2') conv5_3 = conv_relu(conv5_2, 512, 3, 1, 'SAME', training, w_summary, name='conv5_3') pool5 = pool(conv5_3, 2, 2, 'max', name='pool5') fc6 = astrous_conv_relu(pool5,1024,3,3,'SAME',w_summary,training,True,name='fc6') fc7 = conv_relu(fc6, 1024, 1, 1, 'SAME', w_summary, training, True, name='fc7') end_logits = slim.utils.convert_collection_to_dict(end_points_collection) end_logits['conv4_3'] = conv4_3 end_logits['conv5_3'] = conv5_3 end_logits['fc7'] = fc7 return end_logits

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5

0153b9d466f4ce802dc638ae6cfe0fca086a1d7b

149

py

Python

tests/benchmark/includes.py

maguec/RediSearch

c6ecf9de36b7aa5f3603ead7c8fc18c330882668

[ "MIT", "Ruby", "Apache-2.0", "BSD-3-Clause" ]

2,098

2019-05-13T09:11:54.000Z

2022-03-31T06:24:50.000Z

tests/benchmark/includes.py

maguec/RediSearch

c6ecf9de36b7aa5f3603ead7c8fc18c330882668

[ "MIT", "Ruby", "Apache-2.0", "BSD-3-Clause" ]

1,659

2019-05-13T07:55:29.000Z

2022-03-31T02:42:57.000Z

tests/benchmark/includes.py

maguec/RediSearch

c6ecf9de36b7aa5f3603ead7c8fc18c330882668

[ "MIT", "Ruby", "Apache-2.0", "BSD-3-Clause" ]

227

2019-05-17T07:54:49.000Z

2022-03-28T03:50:19.000Z

import sys import os try: sys.path.insert(0, os.path.join(os.path.dirname(__file__), "../../deps/readies")) import paella except: pass

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5

0153c219e223f1ad14a430c3b20b3bef90024de9

232

py

Python

tests/fakes/fake_rml_mapper.py

meaningfy-ws/ted-xml-2-rdf

ac26a19f3761b7cf79d79a46be6323b658f067eb

[ "Apache-2.0" ]

1

2022-03-21T12:32:52.000Z

tests/fakes/fake_rml_mapper.py

meaningfy-ws/ted-xml-2-rdf

ac26a19f3761b7cf79d79a46be6323b658f067eb

[ "Apache-2.0" ]

24

2022-02-10T10:43:56.000Z

2022-03-29T12:36:21.000Z

tests/fakes/fake_rml_mapper.py

meaningfy-ws/ted-sws

d1e351eacb2900f84ec7edc457e49d8202fbaff5

[ "Apache-2.0" ]

null

import pathlib from ted_sws.notice_transformer.adapters.rml_mapper import RMLMapperABC, SerializationFormat class FakeRMLMapper(RMLMapperABC): def execute(self, package_path: pathlib.Path) -> str: return "RDF result"

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null

0

1

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5

0165969bb12452f1b5637a68c6aed00efe056f00

3,626

py

Python

z2/part3/updated_part2_batch/jm/parser_errors_2/565102744.py

kozakusek/ipp-2020-testy

09aa008fa53d159672cc7cbf969a6b237e15a7b8

[ "MIT" ]

1

2020-04-16T12:13:47.000Z

z2/part3/updated_part2_batch/jm/parser_errors_2/565102744.py

kozakusek/ipp-2020-testy

09aa008fa53d159672cc7cbf969a6b237e15a7b8

[ "MIT" ]

18

2020-03-06T17:50:15.000Z

2020-05-19T14:58:30.000Z

z2/part3/updated_part2_batch/jm/parser_errors_2/565102744.py

kozakusek/ipp-2020-testy

09aa008fa53d159672cc7cbf969a6b237e15a7b8

[ "MIT" ]

18

2020-03-06T17:45:13.000Z

2020-06-09T19:18:31.000Z

from part1 import ( gamma_board, gamma_busy_fields, gamma_delete, gamma_free_fields, gamma_golden_move, gamma_golden_possible, gamma_move, gamma_new, ) """ scenario: test_random_actions uuid: 565102744 """ """ random actions, total chaos """ board = gamma_new(4, 5, 4, 3) assert board is not None assert gamma_move(board, 1, 2, 1) == 1 assert gamma_move(board, 1, 0, 4) == 1 assert gamma_move(board, 2, 3, 0) == 1 assert gamma_move(board, 2, 2, 0) == 1 assert gamma_move(board, 3, 4, 1) == 0 assert gamma_move(board, 3, 0, 1) == 1 assert gamma_move(board, 4, 1, 3) == 1 assert gamma_move(board, 4, 1, 0) == 1 assert gamma_move(board, 1, 3, 2) == 1 assert gamma_move(board, 1, 0, 1) == 0 assert gamma_busy_fields(board, 1) == 3 board905986942 = gamma_board(board) assert board905986942 is not None assert board905986942 == ("1...\n" ".4..\n" "...1\n" "3.1.\n" ".422\n") del board905986942 board905986942 = None assert gamma_move(board, 2, 2, 1) == 0 assert gamma_move(board, 2, 2, 1) == 0 assert gamma_move(board, 3, 2, 2) == 1 assert gamma_move(board, 4, 2, 0) == 0 assert gamma_golden_possible(board, 4) == 1 assert gamma_move(board, 1, 0, 0) == 0 assert gamma_move(board, 1, 2, 2) == 0 assert gamma_free_fields(board, 1) == 5 assert gamma_move(board, 2, 3, 2) == 0 assert gamma_move(board, 2, 1, 4) == 1 assert gamma_move(board, 3, 3, 4) == 1 assert gamma_move(board, 3, 0, 2) == 1 assert gamma_move(board, 4, 4, 2) == 0 assert gamma_move(board, 4, 1, 4) == 0 assert gamma_move(board, 1, 0, 2) == 0 assert gamma_move(board, 2, 3, 0) == 0 assert gamma_move(board, 2, 3, 2) == 0 assert gamma_busy_fields(board, 2) == 3 assert gamma_move(board, 3, 3, 2) == 0 assert gamma_move(board, 3, 1, 1) == 1 assert gamma_move(board, 4, 0, 0) == 1 assert gamma_move(board, 1, 1, 3) == 0 assert gamma_move(board, 2, 4, 2) == 0 assert gamma_move(board, 2, 3, 2) == 0 assert gamma_move(board, 3, 2, 2) == 0 assert gamma_move(board, 3, 2, 2) == 0 assert gamma_move(board, 4, 3, 2) == 0 assert gamma_move(board, 4, 3, 0) == 0 assert gamma_move(board, 1, 1, 0) == 0 assert gamma_move(board, 2, 3, 2) == 0 assert gamma_golden_move(board, 3, 1, 2) == 0 assert gamma_move(board, 4, 1, 3) == 0 assert gamma_move(board, 1, 1, 3) == 0 assert gamma_move(board, 1, 0, 0) == 0 assert gamma_move(board, 2, 2, 1) == 0 assert gamma_move(board, 3, 2, 1) == 0 assert gamma_busy_fields(board, 3) == 5 assert gamma_move(board, 4, 0, 3) == 1 assert gamma_move(board, 4, 2, 4) == 1 assert gamma_move(board, 1, 1, 4) == 0 assert gamma_move(board, 1, 0, 0) == 0 assert gamma_move(board, 2, 3, 2) == 0 assert gamma_move(board, 2, 0, 0) == 0 assert gamma_move(board, 3, 3, 3) == 1 assert gamma_move(board, 3, 1, 0) == 0 assert gamma_move(board, 4, 2, 3) == 1 assert gamma_move(board, 4, 0, 2) == 0 assert gamma_move(board, 1, 1, 3) == 0 assert gamma_move(board, 1, 2, 2) == 0 assert gamma_move(board, 2, 2, 1) == 0 assert gamma_golden_possible(board, 2) == 1 board365356545 = gamma_board(board) assert board365356545 is not None assert board365356545 == ("1243\n" "4443\n" "3.31\n" "331.\n" "4422\n") del board365356545 board365356545 = None assert gamma_move(board, 3, 1, 3) == 0 assert gamma_move(board, 3, 2, 2) == 0 assert gamma_move(board, 4, 2, 1) == 0 assert gamma_free_fields(board, 4) == 2 assert gamma_move(board, 1, 2, 2) == 0 assert gamma_move(board, 2, 1, 3) == 0 assert gamma_move(board, 3, 2, 1) == 0 assert gamma_move(board, 3, 0, 2) == 0 assert gamma_move(board, 4, 1, 3) == 0 assert gamma_move(board, 4, 3, 2) == 0 gamma_delete(board)

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null

0

1

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5

01699a7bc043086f4b1a7dd3ccdbbc60336984f2

81

py

Python

configs/snippets/person_test.py

trunkclub/ontology_etl

097985be505469258ee6c831e789f64fb804f091

[ "MIT" ]

null

configs/snippets/person_test.py

trunkclub/ontology_etl

097985be505469258ee6c831e789f64fb804f091

[ "MIT" ]

null

configs/snippets/person_test.py

trunkclub/ontology_etl

097985be505469258ee6c831e789f64fb804f091

[ "MIT" ]

null

def person_test(x): return isinstance(x, dict) and 'demographic_data' in x

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0

null

0

1

0

1

0

5

6d8463279d1fd30035686d5a87eb901a846a235f

131

py

Python

test-ui.py

eliteraspberries/python-ui

f09156ac7b63d37488c5f65d4acd883d4bea5a32

[ "0BSD" ]

null

test-ui.py

eliteraspberries/python-ui

f09156ac7b63d37488c5f65d4acd883d4bea5a32

[ "0BSD" ]

null

test-ui.py

eliteraspberries/python-ui

f09156ac7b63d37488c5f65d4acd883d4bea5a32

[ "0BSD" ]

null

#!/usr/bin/env python def test_import(): import ui print(ui.__version__) if __name__ == '__main__': test_import()

10.916667

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27

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1

0

1

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5

6db21f19e578c4cc807810c5713b692d634bb1a0

29

py

Python

readalongs/_version.py

ReadAlongs/Studio

3fd89f49466bbed99b6cabaf071b0605e63d1fdc

[ "MIT" ]

16

2020-05-27T18:09:04.000Z

2022-03-16T17:40:57.000Z

readalongs/_version.py

ReadAlongs/Studio

3fd89f49466bbed99b6cabaf071b0605e63d1fdc

[ "MIT" ]

77

2020-03-31T16:07:15.000Z

2022-03-17T14:22:51.000Z

readalongs/_version.py

ReadAlongs/Studio

3fd89f49466bbed99b6cabaf071b0605e63d1fdc

[ "MIT" ]

7

2021-05-04T17:38:57.000Z

2022-03-25T09:07:23.000Z

__version__ = "0.2.20211122"

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0

1

0

1

0

1

0

null

0

5

6db762539a39aca2b870a7a5e6e2567334c4fd70

81

py

Python

ifstat/errors.py

aviramc/ifstat

51285c387d5821794b2bdbd4e73d8396ab916baf

[ "Apache-2.0" ]

null

ifstat/errors.py

aviramc/ifstat

51285c387d5821794b2bdbd4e73d8396ab916baf

[ "Apache-2.0" ]

null

ifstat/errors.py

aviramc/ifstat

51285c387d5821794b2bdbd4e73d8396ab916baf

[ "Apache-2.0" ]

null

class IFStatError(Exception): pass class NoDeviceError(Exception): pass

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null

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1

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5

6dc415488103250a683918a53bae83c67262dfb9

26

py

Python

apps/controllerx/cx_version.py

francoisauclair911/controllerx

00b38cdbddb75e470d1577f1d22c8e99d62e1256

[ "MIT" ]

null

apps/controllerx/cx_version.py

francoisauclair911/controllerx

00b38cdbddb75e470d1577f1d22c8e99d62e1256

[ "MIT" ]

null

apps/controllerx/cx_version.py

francoisauclair911/controllerx

00b38cdbddb75e470d1577f1d22c8e99d62e1256

[ "MIT" ]

null

__version__ = "v4.17.0b1"

13

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3.5

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false

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1

0

null

0

5

6dca4a4128fe68de3a8391eeacaf57697bb0ceb1

107

py

Python

app/fedcv/image_segmentation/model/__init__.py

ray-ruisun/FedML

24ff30d636bb70f64e94e9ca205375033597d3dd

[ "Apache-2.0" ]

null

app/fedcv/image_segmentation/model/__init__.py

ray-ruisun/FedML

24ff30d636bb70f64e94e9ca205375033597d3dd

[ "Apache-2.0" ]

null

app/fedcv/image_segmentation/model/__init__.py

ray-ruisun/FedML

24ff30d636bb70f64e94e9ca205375033597d3dd

[ "Apache-2.0" ]

null

from .deeplabV3_plus import DeepLabV3_plus from .unet import UNet from .transunet import VisionTransformer

26.75

42

0.859813

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107

6.428571

0.5

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0

0.021053

0.11215

107

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35.666667

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1

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true

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null

1

0

1

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null

0

1

0

1

0

5

6df88ee732d0633406d96262c31e8a7428165c84

78

py

Python

spark_minimal_algorithms/examples/__init__.py

kowaalczyk/spark-minimal-algorithms

450e536b46af60056b77f5c2cef195af2bb988bd

[ "MIT" ]

3

2020-06-17T22:41:46.000Z

2021-04-06T06:51:37.000Z

spark_minimal_algorithms/examples/__init__.py

kowaalczyk/spark-minimal-algorithms

450e536b46af60056b77f5c2cef195af2bb988bd

[ "MIT" ]

null

spark_minimal_algorithms/examples/__init__.py

kowaalczyk/spark-minimal-algorithms

450e536b46af60056b77f5c2cef195af2bb988bd

[ "MIT" ]

null

# flake8: noqa from .countifs import Countifs from .tera_sort import TeraSort

19.5

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11

78

5.636364

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0

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78

3

32

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0

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true

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1

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5

0987241dfaf99a6fc4d4d0fc5ec6ff15ee143330

119

py

Python

envoy.dependency.cve_scan/envoy/dependency/cve_scan/exceptions.py

envoyproxy/pytooling

db8b60184f8a61b3184a111b0cfaff4780511b46

[ "Apache-2.0" ]

1

2021-12-09T19:24:48.000Z

envoy.dependency.cve_scan/envoy/dependency/cve_scan/exceptions.py

envoyproxy/pytooling

db8b60184f8a61b3184a111b0cfaff4780511b46

[ "Apache-2.0" ]

392

2021-08-24T15:55:32.000Z

2022-03-28T14:26:22.000Z

envoy.dependency.cve_scan/envoy/dependency/cve_scan/exceptions.py

phlax/abstracts

53fbbee68d1f56effe0ded1ed4e28be870693877

[ "Apache-2.0" ]

3

2021-10-06T13:43:11.000Z

2021-11-29T13:48:56.000Z

class CPEError(Exception): pass class CVEError(Exception): pass class CVECheckError(Exception): pass

9.153846

31

0.697479

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119

6.916667

0.5

0.46988

0.433735

0

0.226891

119

12

32

9.916667

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1

0

null

1

0

null

0

1

0

5

09966618108baaff19e5db40c4e4d7749c7eaefb

82

py

Python

pytwitchchat/__init__.py

benjiJanssens/PyTwitchChat

16aad531c120514469ad472dd35b090869f651f8

[ "MIT" ]

1

2021-05-04T12:31:01.000Z

pytwitchchat/__init__.py

benjiJanssens/PyTwitchChat

16aad531c120514469ad472dd35b090869f651f8

[ "MIT" ]

null

pytwitchchat/__init__.py

benjiJanssens/PyTwitchChat

16aad531c120514469ad472dd35b090869f651f8

[ "MIT" ]

1

2021-05-04T12:15:17.000Z

# noinspection PyUnresolvedReferences from .py_twitch_chat import TwitchChatClient

41

44

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8

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null

0

1

0

1

0

1

0

5

099acdd9fd5c25d8ba82bd94fede238a36d7233c

15,750

py

Python

api/migrations/0001_initial.py

Python-Marketing/django-content-server

16794265c44152a86f99b8548c8e1cb8c890f51a

[ "CC0-1.0" ]

null

api/migrations/0001_initial.py

Python-Marketing/django-content-server

16794265c44152a86f99b8548c8e1cb8c890f51a

[ "CC0-1.0" ]

null

api/migrations/0001_initial.py

Python-Marketing/django-content-server

16794265c44152a86f99b8548c8e1cb8c890f51a

[ "CC0-1.0" ]

null

# Generated by Django 2.2.16 on 2020-10-27 07:52 from django.conf import settings from django.db import migrations, models import django.db.models.deletion import filer.fields.image class Migration(migrations.Migration): initial = True dependencies = [ ('contenttypes', '0002_remove_content_type_name'), ('djangocms_blog', '0002_auto_20200929_2310'), migrations.swappable_dependency(settings.FILER_IMAGE_MODEL), migrations.swappable_dependency(settings.AUTH_USER_MODEL), ('cms', '0022_auto_20180620_1551'), ] operations = [ migrations.CreateModel( name='AllowedDomain', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=75)), ('domain', models.URLField(blank=True)), ('term', models.CharField(blank=True, max_length=75)), ('page_name', models.CharField(default=None, max_length=75)), ('class_names', models.CharField(max_length=150)), ('id_names', models.CharField(max_length=150)), ], ), migrations.CreateModel( name='BeautifulGumtreeQuery', fields=[ ('id', models.AutoField(primary_key=True, serialize=False)), ('term', models.CharField(max_length=75)), ('price_start', models.PositiveIntegerField(blank=True, null=True)), ('price_end', models.PositiveIntegerField(blank=True, null=True)), ('date_created', models.DateTimeField(auto_now_add=True, null=True)), ('date_modified', models.DateTimeField(blank=True, null=True)), ('running', models.BooleanField(default=False)), ], ), migrations.CreateModel( name='GumtreeCategoryLabel', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=75)), ('link', models.CharField(max_length=75)), ('key', models.CharField(max_length=75)), ], ), migrations.CreateModel( name='GumtreeProvince', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=75)), ('link', models.CharField(max_length=75)), ('key', models.CharField(max_length=75)), ], ), migrations.CreateModel( name='Page', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('slug', models.SlugField(unique=True)), ], ), migrations.CreateModel( name='Volunteer', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('date_created', models.DateTimeField(auto_now_add=True, verbose_name='created at')), ('date_modified', models.DateTimeField(auto_now=True, verbose_name='modified at')), ('author', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)), ], ), migrations.CreateModel( name='Video', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('title', models.CharField(max_length=75)), ('description', models.CharField(blank=True, max_length=255)), ('url', models.CharField(max_length=175)), ('uploaded_at', models.DateTimeField(auto_now_add=True)), ('body', models.TextField(blank=True)), ('author', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)), ], ), migrations.CreateModel( name='Testimonial', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('body', models.TextField(blank=True)), ('date_created', models.DateTimeField(auto_now_add=True, verbose_name='created at')), ('date_modified', models.DateTimeField(auto_now=True, verbose_name='modified at')), ('author', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)), ], ), migrations.CreateModel( name='Post', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('title', models.CharField(max_length=75)), ('slug', models.SlugField(unique=True)), ('body', models.TextField(blank=True)), ('link', models.URLField(blank=True)), ('file', models.FileField(upload_to='development/django-content-server/media/Posts')), ('author', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)), ('page', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='api.Page')), ], ), migrations.CreateModel( name='Picture', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('image', models.ImageField(upload_to='')), ('caption', models.TextField(blank=True)), ('author', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)), ], ), migrations.CreateModel( name='PageDetailExtension', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('image', models.ImageField(upload_to='development/django-content-server/media/Posts')), ('description', models.TextField(blank=True)), ('file', models.FileField(upload_to='development/django-content-server/media/Posts')), ('extended_object', models.OneToOneField(editable=False, on_delete=django.db.models.deletion.CASCADE, to='cms.Page')), ('public_extension', models.OneToOneField(editable=False, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='draft_extension', to='api.PageDetailExtension')), ], options={ 'abstract': False, }, ), migrations.CreateModel( name='GumtreeLocation', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=75)), ('link', models.CharField(max_length=75)), ('key', models.CharField(max_length=75)), ('parent', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='api.GumtreeLocation')), ('province', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='api.GumtreeProvince')), ], ), migrations.CreateModel( name='GumtreeCategory', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=75)), ('link', models.CharField(max_length=75)), ('key', models.CharField(max_length=75)), ('label', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='api.GumtreeCategoryLabel')), ], ), migrations.CreateModel( name='Gallery', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('caption', models.TextField(blank=True, default='Change Me')), ('active', models.BooleanField(default=False)), ('blog_post', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='djangocms_blog.Post')), ('image', filer.fields.image.FilerImageField(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='djangocms_blog_post_gallery', to=settings.FILER_IMAGE_MODEL, verbose_name='gallery images')), ], ), migrations.CreateModel( name='ExtendedPage', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('description', models.TextField(blank=True)), ('background_image', models.FileField(upload_to='development/django-content-server/media/Posts')), ('page', models.ForeignKey(editable=False, on_delete=django.db.models.deletion.CASCADE, related_name='extended_fields', to='api.Page', verbose_name='Page')), ], ), migrations.CreateModel( name='Donation', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('amount', models.PositiveIntegerField()), ('date_created', models.DateTimeField(auto_now_add=True, verbose_name='created at')), ('date_modified', models.DateTimeField(auto_now=True, verbose_name='modified at')), ('author', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)), ('charity', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='djangocms_blog.Post')), ], ), migrations.CreateModel( name='Comment', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('body', models.TextField(blank=True)), ('object_id', models.PositiveIntegerField()), ('date_created', models.DateTimeField(auto_now_add=True, verbose_name='created at')), ('date_modified', models.DateTimeField(auto_now=True, verbose_name='modified at')), ('author', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)), ('content_type', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='contenttypes.ContentType')), ], ), migrations.CreateModel( name='BeautifulGumtreeSearch', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('term', models.CharField(max_length=75)), ('link', models.URLField(blank=True)), ('body', models.TextField(blank=True)), ('date_created', models.DateTimeField(auto_now_add=True, verbose_name='created at')), ('date_modified', models.DateTimeField(auto_now=True, verbose_name='modified at')), ('allowed', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='api.AllowedDomain')), ], ), migrations.CreateModel( name='BeautifulGumtreeResult', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('title', models.CharField(max_length=75)), ('subtitle', models.CharField(max_length=75)), ('abstract', models.TextField(blank=True)), ('image', models.CharField(max_length=75)), ('price', models.CharField(max_length=75)), ('cell', models.CharField(max_length=75)), ('email', models.EmailField(max_length=75)), ('date_created', models.DateTimeField(auto_now_add=True, verbose_name='created at')), ('date_modified', models.DateTimeField(auto_now=True, verbose_name='modified at')), ('bgs', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='api.BeautifulGumtreeSearch')), ('category', models.ForeignKey(blank=True, on_delete=django.db.models.deletion.CASCADE, to='api.GumtreeCategory')), ('label', models.ForeignKey(blank=True, on_delete=django.db.models.deletion.CASCADE, to='api.GumtreeCategoryLabel')), ('location', models.ForeignKey(blank=True, on_delete=django.db.models.deletion.CASCADE, to='api.GumtreeLocation')), ('province', models.ForeignKey(blank=True, on_delete=django.db.models.deletion.CASCADE, to='api.GumtreeProvince')), ('query', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='api.BeautifulGumtreeQuery')), ], ), migrations.AddField( model_name='beautifulgumtreequery', name='category', field=models.ManyToManyField(blank=True, to='api.GumtreeCategory'), ), migrations.AddField( model_name='beautifulgumtreequery', name='label', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='api.GumtreeCategoryLabel'), ), migrations.AddField( model_name='beautifulgumtreequery', name='location', field=models.ManyToManyField(blank=True, to='api.GumtreeLocation'), ), migrations.AddField( model_name='beautifulgumtreequery', name='province', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='api.GumtreeProvince'), ), migrations.CreateModel( name='BeautifulGoogleSearch', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('term', models.CharField(max_length=75)), ('link', models.URLField(blank=True)), ('body', models.TextField(blank=True)), ('date_created', models.DateTimeField(auto_now_add=True, verbose_name='created at')), ('date_modified', models.DateTimeField(auto_now=True, verbose_name='modified at')), ('allowed', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='api.AllowedDomain')), ], ), migrations.CreateModel( name='BeautifulGoogleResult', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('title', models.CharField(max_length=75)), ('subtitle', models.CharField(max_length=75)), ('abstract', models.TextField(blank=True)), ('image', models.CharField(max_length=75)), ('date_created', models.DateTimeField(auto_now_add=True, verbose_name='created at')), ('date_modified', models.DateTimeField(auto_now=True, verbose_name='modified at')), ('bgs', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='api.BeautifulGoogleSearch')), ], ), ]

56.25

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0

5

09a6c071ba31783273bba64529ff66fdfa139695

12,694

py

Python

CreateFilesinEOL_HCMnew.py

judysu1983/PythonMBSi

9481bf1409a888c3f8511bcd05718ea81a063fa1

[ "bzip2-1.0.6" ]

null

CreateFilesinEOL_HCMnew.py

judysu1983/PythonMBSi

9481bf1409a888c3f8511bcd05718ea81a063fa1

[ "bzip2-1.0.6" ]

null

CreateFilesinEOL_HCMnew.py

judysu1983/PythonMBSi

9481bf1409a888c3f8511bcd05718ea81a063fa1

[ "bzip2-1.0.6" ]

null

import xml.etree.ElementTree as ETXML import xml.etree.ElementTree as Element import xml.etree.ElementTree as etree import os ##fullfilename=["BusinessProcess.en-US.label.txt", "BusinessProcess.en-US.label.txt"] ##sourcelocation=["[git_OOBAPPs]\HCM\source\metadata\BusinessProcess\BusinessProcess\AxLabelFile\LabelResources\en-US","test"] ##BaseName=["BusinessProcess","testBasename"] ##ExtensionName="label.txt" ##TargetLclLocation= ["BusinessProcess\BusinessProcess\AxLabelFile\LabelResources","test2"] #fullfilename=["PersonnelCore.en-US.label.txt", "HcmPeopleNavigatorControl.en-US.label.txt", "HcmPersonCard.en-US.label.txt", "TaxEngineConfiguration.en-US.label.txt", "TaxEngine.en-US.label.txt", "TaxEngineInterface.en-US.label.txt", "TaxSettlement.en-US.label.txt", "GetStarted.en-US.label.txt", "SysBasicUpgrade.en-US.label.txt"] fullfilename=["TaxSettlement.en-US.label.txt", "TaxEngineInterface.en-US.label.txt", "TaxEngineConfiguration.en-US.label.txt", "TaxEngine.en-US.label.txt", "ElectronicReportingMapping.en-US.label.txt", "ElectronicReportingForAx.en-US.label.txt", "ElectronicReportingCore.en-US.label.txt", "ElectronicReportingPrintManagementIntegration.en-US.label.txt", "ElectronicReporting.en-US.label.txt", "Subledger.en-US.label.txt", "TaxEngineIntegration_SourceDocTypes.en-US.label.txt", "TaxEngineIntegration_SourceDoc.en-US.label.txt", "SourceDocumentation.en-US.label.txt", "AccountingFramework.en-US.label.txt", "Measurement.en-US.label.txt", "SegmentedEntry.en-US.label.txt", "Ledger.en-US.label.txt", "FieldDescriptions_Ledger.en-US.label.txt", "Dimension.en-US.label.txt", "FieldDescriptions_GeneralLedger_Currency.en-US.label.txt", "CurrencyExchange.en-US.label.txt", "Calenden-USs.en-US.label.txt", "BankAccountType.en-US.label.txt", "CDSIntegration.en-US.label.txt", "UserDefinedFields.en-US.label.txt", "UnitOfMeasure.en-US.label.txt", "SysBasicUpgrade.en-US.label.txt", "SysPolicy.en-US.label.txt", "DirectoryUpgrade.en-US.label.txt", "GlobalAddressBook.en-US.label.txt", "Directory_InvoicesCommunication.en-US.label.txt", "ContactPersonManagement.en-US.label.txt", "Processflow.en-US.label.txt", "Hieren-USchicalGridCommon.en-US.label.txt", "GetSten-USted.en-US.label.txt", "ApplicationCommon.en-US.label.txt", "PersonnelUpgrade.en-US.label.txt", "HcmMobile.en-US.label.txt", "Workforce.en-US.label.txt", "TalentClient.en-US.label.txt", "Talent.en-US.label.txt", "Payroll.en-US.label.txt", "HcmGenericProcess.en-US.label.txt", "HcmACA.en-US.label.txt", "HCM.en-US.label.txt", "FieldDescriptions_Hcm.en-US.label.txt", "Compensation.en-US.label.txt", "Benefits.en-US.label.txt", "PersonnelIntegration.en-US.label.txt", "PersonnelCore.en-US.label.txt", "PersonnelBusinessProcess.en-US.label.txt", "HcmOnboen-USd.en-US.label.txt", "Personnel.en-US.label.txt", "Leave.en-US.label.txt", "HumanCapitalMobile.en-US.label.txt", "HumanCapitalManagementIntegration.en-US.label.txt", "HumanCapitalManagement.en-US.label.txt", "HcmPersonCen-USd.en-US.label.txt", "HcmPeopleSeen-USchControl.en-US.label.txt", "HcmPeopleNavigatorControl.en-US.label.txt", "CaseManagement.en-US.label.txt", "BusinessProcess.en-US.label.txt", "UserDefinedApp.en-US.label.txt"] #ModelName=["TaxEngine", "TaxEngine", "TaxEngine", "TaxEngine", "ElectronicReportingMapping", "ElectronicReportingForAx", "ElectronicReportingCore", "ElectronicReporting", "ElectronicReporting", "Subledger", "SourceDocumentationTypes", "SourceDocumentation", "SourceDocumentation", "SourceDocumentation", "Measurement", "Ledger", "Ledger", "Ledger", "Dimensions", "Currency", "Currency", "Calenden-US", "BankTypes", "ApplicationIntegration", "UserDefinedField", "UnitOfMeasure", "SysBasicUpgrade", "Policy", "DirectoryUpgrade", "Directory", "Directory", "ContactPerson", "ApplicationCommon", "ApplicationCommon", "ApplicationCommon", "ApplicationCommon", "PersonnelUpgrade", "PersonnelMobile", "PersonnelManagement", "PersonnelManagement", "PersonnelManagement", "PersonnelManagement", "PersonnelManagement", "PersonnelManagement", "PersonnelManagement", "PersonnelManagement", "PersonnelManagement", "PersonnelManagement", "PersonnelIntegration", "PersonnelCore", "PersonnelBusinessProcess", "PersonnelBusinessProcess", "Personnel", "Leave", "HumanCapitalMobile", "HumanCapitalManagementIntegration", "HumanCapitalManagement", "HumanCapitalManagement", "HumanCapitalManagement", "HumanCapitalManagement", "CaseManagement", "BusinessProcess", "ApplicationExtensibility"] #BaseName=["PersonnelCore", "HcmPeopleNavigatorControl", "HcmPersonCard", "TaxEngineConfiguration", "TaxEngine", "TaxEngineInterface", "TaxSettlement", "GetStarted", "SysBasicUpgrade"] BaseName=["TaxSettlement", "TaxEngineInterface", "TaxEngineConfiguration", "TaxEngine", "ElectronicReportingMapping", "ElectronicReportingForAx", "ElectronicReportingCore", "ElectronicReportingPrintManagementIntegration", "ElectronicReporting", "Subledger", "TaxEngineIntegration_SourceDocTypes", "TaxEngineIntegration_SourceDoc", "SourceDocumentation", "AccountingFramework", "Measurement", "SegmentedEntry", "Ledger", "FieldDescriptions_Ledger", "Dimension", "FieldDescriptions_GeneralLedger_Currency", "CurrencyExchange", "Calenden-USs", "BankAccountType", "CDSIntegration", "UserDefinedFields", "UnitOfMeasure", "SysBasicUpgrade", "SysPolicy", "DirectoryUpgrade", "GlobalAddressBook", "Directory_InvoicesCommunication", "ContactPersonManagement", "Processflow", "Hieren-USchicalGridCommon", "GetSten-USted", "ApplicationCommon", "PersonnelUpgrade", "HcmMobile", "Workforce", "TalentClient", "Talent", "Payroll", "HcmGenericProcess", "HcmACA", "HCM", "FieldDescriptions_Hcm", "Compensation", "Benefits", "PersonnelIntegration", "PersonnelCore", "PersonnelBusinessProcess", "HcmOnboen-USd", "Personnel", "Leave", "HumanCapitalMobile", "HumanCapitalManagementIntegration", "HumanCapitalManagement", "HcmPersonCen-USd", "HcmPeopleSeen-USchControl", "HcmPeopleNavigatorControl", "CaseManagement", "BusinessProcess", "UserDefinedApp"] ExtensionName="label.txt" TargetLclLocation= ["ElectronicReporting\source\Metadata\TaxEngine\TaxEngine\AxLabelFile\LabelResources", "ElectronicReporting\source\Metadata\TaxEngine\TaxEngine\AxLabelFile\LabelResources", "ElectronicReporting\source\Metadata\TaxEngine\TaxEngine\AxLabelFile\LabelResources", "ElectronicReporting\source\Metadata\TaxEngine\TaxEngine\AxLabelFile\LabelResources", "ElectronicReporting\source\Metadata\ElectronicReportingMapping\ElectronicReportingMapping\AxLabelFile\LabelResources", "ElectronicReporting\source\Metadata\ElectronicReportingForAx\ElectronicReportingForAx\AxLabelFile\LabelResources", "ElectronicReporting\source\Metadata\ElectronicReportingCore\ElectronicReportingCore\AxLabelFile\LabelResources", "ElectronicReporting\source\Metadata\ElectronicReporting\ElectronicReporting\AxLabelFile\LabelResources", "ElectronicReporting\source\Metadata\ElectronicReporting\ElectronicReporting\AxLabelFile\LabelResources", "Accounting Foundation\source\Metadata\Subledger\Subledger\AxLabelFile\LabelResources", "Accounting Foundation\source\Metadata\SourceDocumentationTypes\SourceDocumentationTypes\AxLabelFile\LabelResources", "Accounting Foundation\source\Metadata\SourceDocumentation\SourceDocumentation\AxLabelFile\LabelResources", "Accounting Foundation\source\Metadata\SourceDocumentation\SourceDocumentation\AxLabelFile\LabelResources", "Accounting Foundation\source\Metadata\SourceDocumentation\SourceDocumentation\AxLabelFile\LabelResources", "Accounting Foundation\source\Metadata\Measurement\Measurement\AxLabelFile\LabelResources", "Accounting Foundation\source\Metadata\Ledger\Ledger\AxLabelFile\LabelResources", "Accounting Foundation\source\Metadata\Ledger\Ledger\AxLabelFile\LabelResources", "Accounting Foundation\source\Metadata\Ledger\Ledger\AxLabelFile\LabelResources", "Accounting Foundation\source\Metadata\Dimensions\Dimensions\AxLabelFile\LabelResources", "Accounting Foundation\source\Metadata\Currency\Currency\AxLabelFile\LabelResources", "Accounting Foundation\source\Metadata\Currency\Currency\AxLabelFile\LabelResources", "Accounting Foundation\source\Metadata\Calenden-US\Calenden-US\AxLabelFile\LabelResources", "Accounting Foundation\source\Metadata\BankTypes\BankTypes\AxLabelFile\LabelResources", "ApplicationIntegration\source\Metadata\ApplicationIntegration\ApplicationIntegration\AxLabelFile\LabelResources", "ApplicationCommon\source\Metadata\UserDefinedField\UserDefinedField\AxLabelFile\LabelResources", "ApplicationCommon\source\Metadata\UnitOfMeasure\UnitOfMeasure\AxLabelFile\LabelResources", "ApplicationCommon\source\Metadata\SysBasicUpgrade\SysBasicUpgrade\AxLabelFile\LabelResources", "ApplicationCommon\source\Metadata\Policy\Policy\AxLabelFile\LabelResources", "ApplicationCommon\source\Metadata\DirectoryUpgrade\DirectoryUpgrade\AxLabelFile\LabelResources", "ApplicationCommon\source\Metadata\Directory\Directory\AxLabelFile\LabelResources", "ApplicationCommon\source\Metadata\Directory\Directory\AxLabelFile\LabelResources", "ApplicationCommon\source\Metadata\ContactPerson\ContactPerson\AxLabelFile\LabelResources", "ApplicationCommon\source\Metadata\ApplicationCommon\ApplicationCommon\AxLabelFile\LabelResources", "ApplicationCommon\source\Metadata\ApplicationCommon\ApplicationCommon\AxLabelFile\LabelResources", "ApplicationCommon\source\Metadata\ApplicationCommon\ApplicationCommon\AxLabelFile\LabelResources", "ApplicationCommon\source\Metadata\ApplicationCommon\ApplicationCommon\AxLabelFile\LabelResources", "HCM\source\Metadata\PersonnelUpgrade\PersonnelUpgrade\AxLabelFile\LabelResources", "HCM\source\Metadata\PersonnelMobile\PersonnelMobile\AxLabelFile\LabelResources", "HCM\source\Metadata\PersonnelManagement\PersonnelManagement\AxLabelFile\LabelResources", "HCM\source\Metadata\PersonnelManagement\PersonnelManagement\AxLabelFile\LabelResources", "HCM\source\Metadata\PersonnelManagement\PersonnelManagement\AxLabelFile\LabelResources", "HCM\source\Metadata\PersonnelManagement\PersonnelManagement\AxLabelFile\LabelResources", "HCM\source\Metadata\PersonnelManagement\PersonnelManagement\AxLabelFile\LabelResources", "HCM\source\Metadata\PersonnelManagement\PersonnelManagement\AxLabelFile\LabelResources", "HCM\source\Metadata\PersonnelManagement\PersonnelManagement\AxLabelFile\LabelResources", "HCM\source\Metadata\PersonnelManagement\PersonnelManagement\AxLabelFile\LabelResources", "HCM\source\Metadata\PersonnelManagement\PersonnelManagement\AxLabelFile\LabelResources", "HCM\source\Metadata\PersonnelManagement\PersonnelManagement\AxLabelFile\LabelResources", "HCM\source\Metadata\PersonnelIntegration\PersonnelIntegration\AxLabelFile\LabelResources", "HCM\source\Metadata\PersonnelCore\PersonnelCore\AxLabelFile\LabelResources", "HCM\source\Metadata\PersonnelBusinessProcess\PersonnelBusinessProcess\AxLabelFile\LabelResources", "HCM\source\Metadata\PersonnelBusinessProcess\PersonnelBusinessProcess\AxLabelFile\LabelResources", "HCM\source\Metadata\Personnel\Personnel\AxLabelFile\LabelResources", "HCM\source\Metadata\Leave\Leave\AxLabelFile\LabelResources", "HCM\source\Metadata\HumanCapitalMobile\HumanCapitalMobile\AxLabelFile\LabelResources", "HCM\source\Metadata\HumanCapitalManagementIntegration\HumanCapitalManagementIntegration\AxLabelFile\LabelResources", "HCM\source\Metadata\HumanCapitalManagement\HumanCapitalManagement\AxLabelFile\LabelResources", "HCM\source\Metadata\HumanCapitalManagement\HumanCapitalManagement\AxLabelFile\LabelResources", "HCM\source\Metadata\HumanCapitalManagement\HumanCapitalManagement\AxLabelFile\LabelResources", "HCM\source\Metadata\HumanCapitalManagement\HumanCapitalManagement\AxLabelFile\LabelResources", "HCM\source\Metadata\CaseManagement\CaseManagement\AxLabelFile\LabelResources", "HCM\source\Metadata\BusinessProcess\BusinessProcess\AxLabelFile\LabelResources", "HCM\source\Metadata\ApplicationExtensibility\ApplicationExtensibility\AxLabelFile\LabelResources"] root=ETXML.Element('EOL') for i in range(0,63): sub=ETXML.SubElement(root, "File", name=fullfilename[i], parser="[parser.Txt]", noType="Comments") #ETXML.SubElement(sub, "File", name="BusinessProcess.en-US.label.txt" parser="[parser.Txt]" noType="Comments").text='\n' #ETXML.SubElement(sub, "File", name=fullfilename, parser="[parser.Txt]", noType="Comments").text = "\n" ETXML.SubElement(sub, "Var", name="File.Model").text = ModelName[i] ETXML.SubElement(sub, "Var", name="File.BaseName").text = BaseName[i] ETXML.SubElement(sub, "Var", name="File.ExtensionName").text = ExtensionName ETXML.SubElement(sub, "Var", name="File.TargetLclLocation").text = TargetLclLocation[i] rootWrite=ETXML.ElementTree(root) rootWrite.write('aFile.xml')

384.666667

5,893

0.830314

1,144

12,694

9.200175

0.116259

0.058527

0.064133

0.085511

0.55924

0.433444

0.415202

0.395819

0.327316

0

0.000326

0.032771

12,694

32

5,894

396.6875

0.856899

0.186545

0

0.864967

0.782858

0

null

0

0.235294

null

0

null

0

1

0

1

null

0

1

0

5

09dfd1e310666db5edd3c147dc942be9da8f3842

124

py

Python

portfolio/blog/admin.py

xeroCBW/portfolio

1e14e64cd3235ed95918963dc5734881af75a668

[ "MIT" ]

null

portfolio/blog/admin.py

xeroCBW/portfolio

1e14e64cd3235ed95918963dc5734881af75a668

[ "MIT" ]

null

portfolio/blog/admin.py

xeroCBW/portfolio

1e14e64cd3235ed95918963dc5734881af75a668

[ "MIT" ]

null

from django.contrib import admin from .models import Blog # Register your models here. # 注册model admin.site.register(Blog)

17.714286

32

0.790323

18

124

5.444444

0.666667

0

0.137097

124

6

33

20.666667

0.915888

0.274194

0

1

0

true

0

0.666667

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

115039428f128ec659a854e5aed90f4f5e9f9050

43,362

py

Python

fluiddb/data/test/test_object.py

fluidinfo/fluiddb

b5a8c8349f3eaf3364cc4efba4736c3e33b30d96

[ "Apache-2.0" ]

3

2021-05-10T14:41:30.000Z

2021-12-16T05:53:30.000Z

fluiddb/data/test/test_object.py

fluidinfo/fluiddb

b5a8c8349f3eaf3364cc4efba4736c3e33b30d96

[ "Apache-2.0" ]

null

fluiddb/data/test/test_object.py

fluidinfo/fluiddb

b5a8c8349f3eaf3364cc4efba4736c3e33b30d96

[ "Apache-2.0" ]

2

2018-01-24T09:03:21.000Z

2021-06-25T08:34:54.000Z

from uuid import uuid4 from twisted.internet.defer import inlineCallbacks from fluiddb.data.object import ( DirtyObject, ObjectIndex, SearchError, escapeWithWildcards, createDirtyObject, getDirtyObjects, touchObjects) from fluiddb.query.parser import parseQuery from fluiddb.testing.basic import FluidinfoTestCase from fluiddb.testing.resources import ( ConfigResource, IndexResource, DatabaseResource) class ObjectIndexTest(FluidinfoTestCase): resources = [('client', IndexResource()), ('config', ConfigResource())] def setUp(self): super(ObjectIndexTest, self).setUp() self.index = ObjectIndex(self.client) @inlineCallbacks def testUpdateWithoutData(self): """ L{ObjectIndex.update} is effectively a no-op if no values are provided. """ yield self.index.update({}) yield self.index.commit() response = yield self.client.search('*:*') self.assertEqual([], response.results.docs) @inlineCallbacks def testUpdateWithNoneValue(self): """ L{ObjectIndex.update} is creates Solr documents for the specified objects, L{Tag.path}s and C{None} values. """ objectID = uuid4() yield self.index.update({objectID: {u'test/tag': None}}) yield self.index.commit() response = yield self.client.search('*:*') self.assertEqual([{u'fluiddb/id': str(objectID)}], response.results.docs) @inlineCallbacks def testUpdateWithBoolValue(self): """ L{ObjectIndex.update} is creates Solr documents for the specified objects, L{Tag.path}s and C{bool} values. """ objectID = uuid4() yield self.index.update({objectID: {u'test/tag': True}}) yield self.index.commit() response = yield self.client.search('*:*') self.assertEqual([{u'fluiddb/id': str(objectID)}], response.results.docs) @inlineCallbacks def testUpdateWithIntValue(self): """ L{ObjectIndex.update} is creates Solr documents for the specified objects, L{Tag.path}s and C{int} values. """ objectID = uuid4() yield self.index.update({objectID: {u'test/tag': 42}}) yield self.index.commit() response = yield self.client.search('*:*') self.assertEqual([{u'fluiddb/id': str(objectID)}], response.results.docs) @inlineCallbacks def testUpdateWithFloatValue(self): """ L{ObjectIndex.update} is creates Solr documents for the specified objects, L{Tag.path}s and C{float} values. """ objectID = uuid4() yield self.index.update({objectID: {u'test/tag': 42.3}}) yield self.index.commit() response = yield self.client.search('*:*') self.assertEqual([{u'fluiddb/id': str(objectID)}], response.results.docs) @inlineCallbacks def testUpdateWithUnicodeValue(self): """ L{ObjectIndex.update} is creates Solr documents for the specified objects, L{Tag.path}s and C{unicode} values. """ objectID = uuid4() yield self.index.update({objectID: {u'test/tag': u'value'}}) yield self.index.commit() response = yield self.client.search('*:*') self.assertEqual([{u'fluiddb/id': str(objectID)}], response.results.docs) @inlineCallbacks def testUpdateWithSetValue(self): """ L{ObjectIndex.update} is creates Solr documents for the specified objects, L{Tag.path}s and C{list} values. """ objectID = uuid4() yield self.index.update({objectID: {u'test/tag': [u'foo', u'bar']}}) yield self.index.commit() response = yield self.client.search('*:*') self.assertEqual([{u'fluiddb/id': str(objectID)}], response.results.docs) @inlineCallbacks def testUpdateWithBinaryValue(self): """ L{ObjectIndex.update} is creates Solr documents for the specified objects, L{Tag.path}s and binary values. """ objectID = uuid4() yield self.index.update( {objectID: {u'test/tag': {'mime-type': 'text/html', 'file-id': 'index.html', 'size': 123}}}) yield self.index.commit() response = yield self.client.search('*:*') self.assertEqual([{u'fluiddb/id': str(objectID)}], response.results.docs) @inlineCallbacks def testUpdateWithManyValues(self): """ L{ObjectIndex.update} can create or update documents about many objects, L{Tag.path}s and values at once. """ objectID1 = uuid4() objectID2 = uuid4() yield self.index.update({objectID1: {u'test/tag1': u'Hi!'}, objectID2: {u'test/tag2': 42}}) yield self.index.commit() response = yield self.client.search('*:*') self.assertEqual(sorted([{u'fluiddb/id': str(objectID1)}, {u'fluiddb/id': str(objectID2)}]), sorted(response.results.docs)) @inlineCallbacks def testSearchWithoutData(self): """ L{ObjectIndex.search} returns an empty result if there are no documents in the index. """ query = parseQuery(u'test/tag = 5') result = yield self.index.search(query) self.assertEqual(set(), result) @inlineCallbacks def testSearchWithoutMatch(self): """ L{ObjectIndex.search} returns an empty result if no documents in the index match the specified L{Query}. """ yield self.index.update({uuid4(): {u'test/tag': 42}}) yield self.index.commit() query = parseQuery(u'unknown/tag = 5') result = yield self.index.search(query) self.assertEqual(set(), result) @inlineCallbacks def testSearchWithEqualsUnicodeComparison(self): """ L{ObjectIndex.search} can perform C{=} comparisons with C{unicode} values. """ objectID = uuid4() yield self.index.update({objectID: {u'test/unicode': u'value'}, uuid4(): {u'test/unicode': u'another'}}) yield self.index.commit() query = parseQuery('test/unicode = "value"') result = yield self.index.search(query) self.assertEqual(set([objectID]), result) @inlineCallbacks def testSearchWithEqualsWithEmptyValue(self): """ L{ObjectIndex.search} can perform C{=} comparisons with empty strings. """ objectID = uuid4() yield self.index.update({objectID: {u'test/tag': u''}, uuid4(): {u'test/tag': u'devalue'}}) yield self.index.commit() query = parseQuery(u'test/tag = ""') result = yield self.index.search(query) self.assertEqual(set([objectID]), result) @inlineCallbacks def testSearchWithEqualsNullComparison(self): """ L{ObjectIndex.search} can perform C{=} comparisons with C{null} values. """ objectID = uuid4() yield self.index.update({objectID: {u'test/tag': None}, uuid4(): {u'test/tag': u'another'}}) yield self.index.commit() query = parseQuery('test/tag = null') result = yield self.index.search(query) self.assertEqual(set([objectID]), result) @inlineCallbacks def testSearchWithNotEqualsNullComparison(self): """ L{ObjectIndex.search} can perform C{!=} comparisons with C{null} values. """ objectID = uuid4() yield self.index.update({objectID: {u'test/tag': u'value'}, uuid4(): {u'test/tag': None}}) yield self.index.commit() query = parseQuery('test/tag != null') result = yield self.index.search(query) self.assertEqual(set([objectID]), result) @inlineCallbacks def testSearchWithEqualsBoolComparison(self): """ L{ObjectIndex.search} can perform C{=} comparisons with C{bool} values. """ objectID = uuid4() yield self.index.update({objectID: {u'test/int': True}, uuid4(): {u'test/int': False}}) yield self.index.commit() query = parseQuery(u'test/int = true') result = yield self.index.search(query) self.assertEqual(set([objectID]), result) @inlineCallbacks def testSearchWithEqualsIntComparison(self): """ L{ObjectIndex.search} can perform C{=} comparisons with C{int} values. """ objectID = uuid4() yield self.index.update({objectID: {u'test/int': 42}, uuid4(): {u'test/int': 65}}) yield self.index.commit() query = parseQuery(u'test/int = 42') result = yield self.index.search(query) self.assertEqual(set([objectID]), result) @inlineCallbacks def testSearchWithEqualsIntComparisonWithNegative(self): """ L{ObjectIndex.search} can perform C{=} comparisons with negative C{int} values. See bug #827411. """ objectID = uuid4() yield self.index.update({objectID: {u'test/int': -42}, uuid4(): {u'test/int': -65}}) yield self.index.commit() query = parseQuery(u'test/int = -42') result = yield self.index.search(query) self.assertEqual(set([objectID]), result) @inlineCallbacks def testSearchWithEqualsFloatComparison(self): """ L{ObjectIndex.search} can perform C{=} comparisons with C{float} values. """ objectID = uuid4() yield self.index.update({objectID: {u'test/float': 42.3}, uuid4(): {u'test/float': 42.31}}) yield self.index.commit() query = parseQuery(u'test/float = 42.3') result = yield self.index.search(query) self.assertEqual(set([objectID]), result) @inlineCallbacks def testSearchWithEqualsFloatComparisonWithNegative(self): """ L{ObjectIndex.search} can perform C{=} comparisons with negative C{float} values. See bug #827411. """ objectID = uuid4() yield self.index.update({objectID: {u'test/float': -42.3}, uuid4(): {u'test/float': -42.31}}) yield self.index.commit() query = parseQuery(u'test/float = -42.3') result = yield self.index.search(query) self.assertEqual(set([objectID]), result) @inlineCallbacks def testSearchWithEqualsIntAndFloatComparison(self): """ L{ObjectIndex.search} can perform C{=} comparisons with C{float} and C{int} values. """ objectID1 = uuid4() objectID2 = uuid4() objectID3 = uuid4() yield self.index.update({objectID1: {u'test/number': 42.0}, objectID2: {u'test/number': 42}, objectID3: {u'test/number': 48}}) yield self.index.commit() query = parseQuery(u'test/number = 42') result = yield self.index.search(query) self.assertEqual(set([objectID1, objectID2]), result) @inlineCallbacks def testSearchWithEqualsIntAndFloatComparisonWithNegative(self): """ L{ObjectIndex.search} can perform C{=} comparisons with negative C{float} and C{int} values. See bug #827411. """ objectID1 = uuid4() objectID2 = uuid4() objectID3 = uuid4() yield self.index.update({objectID1: {u'test/number': -42.0}, objectID2: {u'test/number': -42}, objectID3: {u'test/number': -48}}) yield self.index.commit() query = parseQuery(u'test/number = -42') result = yield self.index.search(query) self.assertEqual(set([objectID1, objectID2]), result) def testSearchWithEqualsAndFluidDBSlashID(self): """ A L{SearchError} is raised if an C{equals} query is used with the special C{fluiddb/id} virtual tag. """ objectID = uuid4() query = parseQuery(u'fluiddb/id = "%s"' % objectID) return self.assertFailure(self.index.search(query), SearchError) @inlineCallbacks def testSearchWithNotEqualsUnicodeComparison(self): """ L{ObjectIndex.search} can perform C{!=} comparisons with C{unicode} values. """ objectID = uuid4() yield self.index.update({objectID: {u'test/unicode': u'novalue'}, uuid4(): {u'test/unicode': u'value'}}) yield self.index.commit() query = parseQuery(u'test/unicode != "value"') result = yield self.index.search(query) self.assertEqual(set([objectID]), result) @inlineCallbacks def testSearchWithNotEqualsBoolComparison(self): """ L{ObjectIndex.search} can perform C{!=} comparisons with C{bool} values. """ objectID = uuid4() yield self.index.update({objectID: {u'test/bool': True}, uuid4(): {u'test/bool': False}}) yield self.index.commit() query = parseQuery(u'test/bool != False') result = yield self.index.search(query) self.assertEqual(set([objectID]), result) @inlineCallbacks def testSearchWithNotEqualsIntComparison(self): """ L{ObjectIndex.search} can perform C{!=} comparisons with C{int} values. """ objectID = uuid4() yield self.index.update({objectID: {u'test/int': 42}, uuid4(): {u'test/int': 65}}) yield self.index.commit() query = parseQuery(u'test/int != 65') result = yield self.index.search(query) self.assertEqual(set([objectID]), result) @inlineCallbacks def testSearchWithNotEqualsFloatComparison(self): """ L{ObjectIndex.search} can perform C{!=} comparisons with C{float} values. """ objectID = uuid4() yield self.index.update({objectID: {u'test/float': 42.1}, uuid4(): {u'test/float': 65.3}}) yield self.index.commit() query = parseQuery(u'test/float != 65.3') result = yield self.index.search(query) self.assertEqual(set([objectID]), result) def testSearchWithNotEqualsFluidDBSlashIDComparison(self): """ A L{SearchError} is raised if a C{!=} comparison is used with the special C{fluiddb/id} virtual tag. """ objectID = uuid4() query = parseQuery(u'fluiddb/id != "%s"' % objectID) return self.assertFailure(self.index.search(query), SearchError) @inlineCallbacks def testSearchWithLessThanIntComparison(self): """ L{ObjectIndex.search} can perform C{<} comparisons with C{int} values. """ objectID = uuid4() yield self.index.update({objectID: {u'test/int': 42}, uuid4(): {u'test/int': 43}}) yield self.index.commit() query = parseQuery(u'test/int < 43') result = yield self.index.search(query) self.assertEqual(set([objectID]), result) @inlineCallbacks def testSearchWithLessThanFloatComparison(self): """ L{ObjectIndex.search} can perform C{<} comparisons with C{float} values. """ objectID = uuid4() yield self.index.update({objectID: {u'test/float': 42.1}, uuid4(): {u'test/float': 42.2}}) yield self.index.commit() query = parseQuery(u'test/float < 42.2') result = yield self.index.search(query) self.assertEqual(set([objectID]), result) @inlineCallbacks def testSearchWithLessThanIntAndFloatComparison(self): """ L{ObjectIndex.search} can perform C{<} comparisons with C{float} and C{int} values. """ objectID1 = uuid4() objectID2 = uuid4() objectID3 = uuid4() yield self.index.update({objectID1: {u'test/number': 42.1}, objectID2: {u'test/number': 42.2}, objectID3: {u'test/number': 42}}) yield self.index.commit() query = parseQuery(u'test/number < 42.2') result = yield self.index.search(query) self.assertEqual(set([objectID1, objectID3]), result) def testSearchWithLessThanFluidDBSlashIDComparison(self): """ A L{SearchError} is raised if a C{<} comparison is used with the special C{fluiddb/id} virtual tag. """ objectID = uuid4() query = parseQuery(u'fluiddb/id < "%s"' % objectID) return self.assertFailure(self.index.search(query), SearchError) @inlineCallbacks def testSearchWithLessThanOrEqualIntComparison(self): """ L{ObjectIndex.search} can perform C{<=} comparisons with C{int} values. """ objectID = uuid4() yield self.index.update({objectID: {u'test/int': 42}, uuid4(): {u'test/int': 43}}) yield self.index.commit() query = parseQuery(u'test/int <= 42') result = yield self.index.search(query) self.assertEqual(set([objectID]), result) @inlineCallbacks def testSearchWithLessThanOrEqualFloatComparison(self): """ L{ObjectIndex.search} can perform C{<=} comparisons with C{float} values. """ objectID = uuid4() yield self.index.update({objectID: {u'test/float': 42.1}, uuid4(): {u'test/float': 42.11}}) yield self.index.commit() query = parseQuery(u'test/float <= 42.1') result = yield self.index.search(query) self.assertEqual(set([objectID]), result) @inlineCallbacks def testSearchWithLessThanOrEqualIntAndFloatComparison(self): """ L{ObjectIndex.search} can perform C{<=} comparisons with C{float} and C{int} values. """ objectID1 = uuid4() objectID2 = uuid4() objectID3 = uuid4() yield self.index.update({objectID1: {u'test/number': 42.1}, objectID2: {u'test/number': 42.11}, objectID3: {u'test/number': 42}}) yield self.index.commit() query = parseQuery(u'test/number <= 42.1') result = yield self.index.search(query) self.assertEqual(set([objectID1, objectID3]), result) def testSearchWithLessThanOrEqualFluidDBSlashIDComparison(self): """ A L{SearchError} is raised if a C{<=} comparison is used with the special C{fluiddb/id} virtual tag. """ objectID = uuid4() query = parseQuery(u'fluiddb/id <= "%s"' % objectID) return self.assertFailure(self.index.search(query), SearchError) @inlineCallbacks def testSearchWithGreaterThanIntComparison(self): """ L{ObjectIndex.search} can perform C{>} comparisons with C{int} values. """ objectID = uuid4() yield self.index.update({objectID: {u'test/int': 43}, uuid4(): {u'test/int': 42}}) yield self.index.commit() query = parseQuery(u'test/int > 42') result = yield self.index.search(query) self.assertEqual(set([objectID]), result) @inlineCallbacks def testSearchWithGreaterThanFloatComparison(self): """ L{ObjectIndex.search} can perform C{>} comparisons with C{float} values. """ objectID = uuid4() yield self.index.update({objectID: {u'test/float': 42.2}, uuid4(): {u'test/float': 42.1}}) yield self.index.commit() query = parseQuery(u'test/float > 42.1') result = yield self.index.search(query) self.assertEqual(set([objectID]), result) @inlineCallbacks def testSearchWithGreaterThanIntAndFloatComparison(self): """ L{ObjectIndex.search} can perform C{>} comparisons with C{float} and C{int} values. """ objectID1 = uuid4() objectID2 = uuid4() objectID3 = uuid4() yield self.index.update({objectID1: {u'test/number': 42.2}, objectID2: {u'test/number': 42.1}, objectID3: {u'test/number': 43}}) yield self.index.commit() query = parseQuery(u'test/number > 42.1') result = yield self.index.search(query) self.assertEqual(set([objectID1, objectID3]), result) def testSearchWithGreaterThanFluidDBSlashIDComparison(self): """ A L{SearchError} is raised if a C{>} comparison is used with the special C{fluiddb/id} virtual tag. """ objectID = uuid4() query = parseQuery(u'fluiddb/id > "%s"' % objectID) return self.assertFailure(self.index.search(query), SearchError) @inlineCallbacks def testSearchWithGreaterThanOrEqualIntComparison(self): """ L{ObjectIndex.search} can perform C{>=} comparisons with C{int} values. """ objectID = uuid4() yield self.index.update({objectID: {u'test/int': 43}, uuid4(): {u'test/int': 42}}) yield self.index.commit() query = parseQuery(u'test/int >= 43') result = yield self.index.search(query) self.assertEqual(set([objectID]), result) @inlineCallbacks def testSearchWithGreaterThanOrEqualFloatComparison(self): """ L{ObjectIndex.search} can perform C{>=} comparisons with C{float} values. """ objectID = uuid4() yield self.index.update({objectID: {u'test/float': 42.2}, uuid4(): {u'test/float': 42.1}}) yield self.index.commit() query = parseQuery(u'test/float >= 42.2') result = yield self.index.search(query) self.assertEqual(set([objectID]), result) @inlineCallbacks def testSearchWithGreaterThanOrEqualIntAndFloatComparison(self): """ L{ObjectIndex.search} can perform C{>=} comparisons with C{float} and C{int} values. """ objectID1 = uuid4() objectID2 = uuid4() objectID3 = uuid4() yield self.index.update({objectID1: {u'test/number': 42.2}, objectID2: {u'test/number': 42.1}, objectID3: {u'test/number': 43}}) yield self.index.commit() query = parseQuery(u'test/number >= 42.2') result = yield self.index.search(query) self.assertEqual(set([objectID1, objectID3]), result) def testSearchWithGreaterThanOrEqualFluidDBSlashIDComparison(self): """ A L{SearchError} is raised if a C{>=} comparison is used with the special C{fluiddb/id} virtual tag. """ objectID = uuid4() query = parseQuery(u'fluiddb/id > "%s"' % objectID) return self.assertFailure(self.index.search(query), SearchError) @inlineCallbacks def testSearchWithHasNoneValue(self): """ L{ObjectIndex.search} can perform C{has} queries with C{None} values. """ objectID = uuid4() yield self.index.update({objectID: {u'test/tag1': None}, uuid4(): {u'test/tag2': None}}) yield self.index.commit() query = parseQuery(u'has test/tag1') result = yield self.index.search(query) self.assertEqual(set([objectID]), result) @inlineCallbacks def testSearchWithHasBoolValue(self): """ L{ObjectIndex.search} can perform C{has} queries with C{bool} values. """ objectID = uuid4() yield self.index.update({objectID: {u'test/tag1': True}, uuid4(): {u'test/tag2': True}}) yield self.index.commit() query = parseQuery(u'has test/tag1') result = yield self.index.search(query) self.assertEqual(set([objectID]), result) @inlineCallbacks def testSearchWithHasIntValue(self): """ L{ObjectIndex.search} can perform C{has} queries with C{int} values. """ objectID = uuid4() yield self.index.update({objectID: {u'test/tag1': 42}, uuid4(): {u'test/tag2': 42}}) yield self.index.commit() query = parseQuery(u'has test/tag1') result = yield self.index.search(query) self.assertEqual(set([objectID]), result) @inlineCallbacks def testSearchWithHasFloatValue(self): """ L{ObjectIndex.search} can perform C{has} queries with C{float} values. """ objectID = uuid4() yield self.index.update({objectID: {u'test/tag1': 42.1}, uuid4(): {u'test/tag2': 42.2}}) yield self.index.commit() query = parseQuery(u'has test/tag1') result = yield self.index.search(query) self.assertEqual(set([objectID]), result) @inlineCallbacks def testSearchWithHasUnicodeValue(self): """ L{ObjectIndex.search} can perform C{has} queries with C{unicode} values. """ objectID = uuid4() yield self.index.update({objectID: {u'test/tag1': u'value'}, uuid4(): {u'test/tag2': u'value'}}) yield self.index.commit() query = parseQuery(u'has test/tag1') result = yield self.index.search(query) self.assertEqual(set([objectID]), result) @inlineCallbacks def testSearchWithHasSetValue(self): """ L{ObjectIndex.search} can perform C{has} queries with C{list} values. """ objectID = uuid4() yield self.index.update({objectID: {u'test/tag1': [u'foo', u'bar']}, uuid4(): {u'test/tag2': [u'foo', u'bar']}}) yield self.index.commit() query = parseQuery(u'has test/tag1') result = yield self.index.search(query) self.assertEqual(set([objectID]), result) @inlineCallbacks def testSearchWithHasBinaryValue(self): """ L{ObjectIndex.search} can perform C{has} queries with binary values. """ objectID = uuid4() value = {'mime-type': 'text/html', 'file-id': 'index.html', 'size': 7} yield self.index.update({objectID: {u'test/tag1': value}, uuid4(): {u'test/tag2': value}}) yield self.index.commit() query = parseQuery(u'has test/tag1') result = yield self.index.search(query) self.assertEqual(set([objectID]), result) @inlineCallbacks def testSearchWithHasColonInPath(self): """ L{ObjectIndex.search} can perform C{has} queries with paths having a colon. """ objectID = uuid4() value = {'mime-type': 'text/html', 'file-id': 'index.html', 'size': 7} yield self.index.update({objectID: {u'test/one:two': value}, uuid4(): {u'test/tag2': value}}) yield self.index.commit() query = parseQuery(u'has test/one:two') result = yield self.index.search(query) self.assertEqual(set([objectID]), result) @inlineCallbacks def testSearchWithMatches(self): """L{ObjectIndex.search} can perform C{matches} queries.""" objectID = uuid4() yield self.index.update({objectID: {u'test/tag': u'value'}, uuid4(): {u'test/tag': u'devalue'}}) yield self.index.commit() query = parseQuery(u'test/tag matches "value"') result = yield self.index.search(query) self.assertEqual(set([objectID]), result) @inlineCallbacks def testSearchWithMatchesWithEmptyValue(self): """ L{ObjectIndex.search} can perform C{matches} queries with empty strings. """ objectID = uuid4() yield self.index.update({objectID: {u'test/tag': u''}, uuid4(): {u'test/tag': u'devalue'}}) yield self.index.commit() query = parseQuery(u'test/tag matches ""') result = yield self.index.search(query) self.assertEqual(set([objectID]), result) @inlineCallbacks def testSearchWithMatchesIsCaseInsensitive(self): """ L{ObjectIndex.search} performs C{matches} queries case-insensitively. """ objectID1 = uuid4() objectID2 = uuid4() objectID3 = uuid4() yield self.index.update({objectID1: {u'test/tag': u'VALUE'}, objectID2: {u'test/tag': u'value'}, objectID3: {u'test/tag': u'VaLuE'}, uuid4(): {u'test/tag': u'devalue'}}) yield self.index.commit() query = parseQuery(u'test/tag matches "vAlUe"') result = yield self.index.search(query) self.assertEqual(set([objectID1, objectID2, objectID3]), result) @inlineCallbacks def testSearchWithMatchesAndManyTerms(self): """ L{ObjectIndex.search} can match terms with spaces when the C{matches} query is used. """ objectID = uuid4() yield self.index.update( {objectID: {u'test/tag': u'apple orange cherry'}, uuid4(): {u'test/tag': u'value'}}) yield self.index.commit() query = parseQuery(u'test/tag matches "apple orange"') result = yield self.index.search(query) self.assertEqual(set([objectID]), result) @inlineCallbacks def testSearchWithMatchesAndManyTermsIsCaseInsensitive(self): """ L{ObjectIndex.search} can match terms with spaces when the C{matches} query is used. """ objectID1 = uuid4() objectID2 = uuid4() objectID3 = uuid4() yield self.index.update( {objectID1: {u'test/tag': u'APPLE ORANGE CHERRY'}, objectID2: {u'test/tag': u'apple orange cherry'}, objectID3: {u'test/tag': u'apple orange cherry'}, uuid4(): {u'test/tag': u'devalue'}}) yield self.index.commit() query = parseQuery(u'test/tag matches "aPpLe OrAnGe"') result = yield self.index.search(query) self.assertEqual(set([objectID1, objectID2, objectID3]), result) @inlineCallbacks def testSearchWithMatchesAndPunctuation(self): """ L{ObjectIndex.search} can match terms with punctuation when a C{matches} query is used. """ objectID1 = uuid4() objectID2 = uuid4() yield self.index.update( {objectID1: {u'test/tag': u'book: Moby Dick'}, objectID2: {u'test/tag': u'One, Two, Three.'}, uuid4(): {u'test/tag': u'One Book'}}) yield self.index.commit() query = parseQuery( u'test/tag matches "book:" or test/tag matches "One,"') result = yield self.index.search(query) self.assertEqual(set([objectID1, objectID2]), result) @inlineCallbacks def testSearchWithMatchesAndStarWildcard(self): """ L{ObjectIndex.search} can match terms using the '*' wildcard when a C{matches} query is used. """ objectID1 = uuid4() objectID2 = uuid4() yield self.index.update( {objectID1: {u'test/tag': u'book:Moby Dict'}, objectID2: {u'test/tag': u'book:Alice in Wonderland'}, uuid4(): {u'test/tag': u'One Book'}}) yield self.index.commit() query = parseQuery(u'test/tag matches "book:*"') result = yield self.index.search(query) self.assertEqual(set([objectID1, objectID2]), result) @inlineCallbacks def testSearchWithMatchesAndStarWildcardAtTheBegining(self): """ L{ObjectIndex.search} can match terms using the '*' wildcard at the begining of a term when a C{matches} query is used. """ objectID1 = uuid4() objectID2 = uuid4() yield self.index.update( {objectID1: {u'test/tag': u'book:Moby Dick'}, objectID2: {u'test/tag': u'movie:Moby Dick'}, uuid4(): {u'test/tag': u'One Book'}}) yield self.index.commit() query = parseQuery(u'test/tag matches "*moby"') result = yield self.index.search(query) self.assertEqual(set([objectID1, objectID2]), result) @inlineCallbacks def testSearchWithMatchesAndQuestionMarkWildcard(self): """ L{ObjectIndex.search} can match terms using the '?' wildcard when a C{matches} query is used. """ objectID1 = uuid4() objectID2 = uuid4() yield self.index.update( {objectID1: {u'test/tag': u'red stone'}, objectID2: {u'test/tag': u'get rid of the body'}, uuid4(): {u'test/tag': u'run, forest, run'}}) yield self.index.commit() query = parseQuery(u'test/tag matches "r?d"') result = yield self.index.search(query) self.assertEqual(set([objectID1, objectID2]), result) @inlineCallbacks def testSearchWithMatchesAndFuzzySearch(self): """ L{ObjectIndex.search} can match fuzzy terms using the '~' wildcard when a C{matches} query is used. """ objectID1 = uuid4() objectID2 = uuid4() yield self.index.update( {objectID1: {u'test/tag': u'fuzzy search'}, objectID2: {u'test/tag': u'wuzzy term'}, uuid4(): {u'test/tag': u'not related term'}}) yield self.index.commit() query = parseQuery(u'test/tag matches "fuzzy~"') result = yield self.index.search(query) self.assertEqual(set([objectID1, objectID2]), result) @inlineCallbacks def testSearchWithMatchesAndEscapedWildcars(self): """ L{ObjectIndex.search} can match terms with '*', '?' and '~' using character escaping. """ objectID1 = uuid4() objectID2 = uuid4() yield self.index.update( {objectID1: {u'test/tag': u'Is that man blue?'}, objectID2: {u'test/tag': u'Syntax: *remark*'}, uuid4(): {u'test/tag': u'Blue and remarkable'}}) yield self.index.commit() query = parseQuery( u'test/tag matches "blue\?" or test/tag matches "\*remark\*"') result = yield self.index.search(query) self.assertEqual(set([objectID1, objectID2]), result) def testSearchWithMatchesAndFluidDBSlashID(self): """ A L{SearchError} is raised if a C{matches} query is used with the special C{fluiddb/id} virtual tag. """ objectID = uuid4() query = parseQuery(u'fluiddb/id matches "%s"' % objectID) return self.assertFailure(self.index.search(query), SearchError) @inlineCallbacks def testSearchWithContains(self): """L{ObjectIndex.search} can perform C{contains} queries.""" objectID = uuid4() yield self.index.update({objectID: {u'test/tag': [u'foo', u'bar']}, uuid4(): {u'test/tag': [u'baz']}}) yield self.index.commit() query = parseQuery(u'test/tag contains "foo"') result = yield self.index.search(query) self.assertEqual(set([objectID]), result) @inlineCallbacks def testSearchWithContainsAndTermWithWhitespace(self): """ L{ObjectIndex.search} can perform C{contains} queries with terms that include whitespace. """ objectID = uuid4() yield self.index.update({objectID: {u'test/tag': [u'foo bar', u'baz']}, uuid4(): {u'test/tag': [u'quux']}}) yield self.index.commit() query = parseQuery(u'test/tag contains "foo bar"') result = yield self.index.search(query) self.assertEqual(set([objectID]), result) def testSearchWithContainsAndFluidDBSlashID(self): """ A L{SearchError} is raised if a C{contains} query is used with the special C{fluiddb/id} virtual tag. """ objectID = uuid4() query = parseQuery(u'fluiddb/id contains "%s"' % objectID) return self.assertFailure(self.index.search(query), SearchError) @inlineCallbacks def testSearchWithOr(self): """L{ObjectIndex.search} can perform C{or} queries.""" objectID1 = uuid4() objectID2 = uuid4() yield self.index.update({objectID1: {u'test/int': 42}, objectID2: {u'test/int': 67}, uuid4(): {u'test/int': 93}}) yield self.index.commit() query = parseQuery(u'test/int = 42 or test/int = 67') result = yield self.index.search(query) self.assertEqual(set([objectID1, objectID2]), result) @inlineCallbacks def testSearchWithOrUnmatched(self): """ L{ObjectIndex.search} only returns objects that match one side of an C{or} query. """ yield self.index.update({uuid4(): {u'test/int': 42}, uuid4(): {u'test/int': 67}}) yield self.index.commit() query = parseQuery(u'test/int = 41 or test/int = 66') result = yield self.index.search(query) self.assertEqual(set([]), result) @inlineCallbacks def testSearchWithAnd(self): """L{ObjectIndex.search} can perform C{and} queries.""" objectID = uuid4() yield self.index.update({objectID: {u'test/int': 42, u'test/unicode': u'value'}, uuid4(): {u'test/int': 67}}) yield self.index.commit() query = parseQuery(u'test/int = 42 and test/unicode = "value"') result = yield self.index.search(query) self.assertEqual(set([objectID]), result) @inlineCallbacks def testSearchWithAndUnmatched(self): """ L{ObjectIndex.search} only returns objects that match both sides of an C{and} query. """ yield self.index.update({uuid4(): {u'test/int': 67, u'test/unicode': u'value'}, uuid4(): {u'test/int': 95}}) yield self.index.commit() query = parseQuery(u'test/int = 42 and test/unicode = "value"') result = yield self.index.search(query) self.assertEqual(set([]), result) @inlineCallbacks def testSearchWithExcept(self): """L{ObjectIndex.search} can perform C{except} queries.""" objectID1 = uuid4() objectID2 = uuid4() yield self.index.update({objectID1: {u'test/int': 42, u'test/unicode': u'value'}, objectID2: {u'test/int': 42, u'test/unicode': u'hello'}}) yield self.index.commit() query = parseQuery(u'test/int = 42 except test/unicode = "value"') result = yield self.index.search(query) self.assertEqual(set([objectID2]), result) @inlineCallbacks def testSearchWithUnicodePath(self): """ L{ObjectIndex.search} can search for paths with unicode characters in them. """ objectID = uuid4() path = u'test/\N{HIRAGANA LETTER A}' yield self.index.update({objectID: {path: u'value'}, uuid4(): {path: u'another'}}) yield self.index.commit() query = parseQuery(u'test/\N{HIRAGANA LETTER A} = "value"') result = yield self.index.search(query) self.assertEqual(set([objectID]), result) @inlineCallbacks def testSearchWithComplexQuery(self): """L{ObjectIndex.search} can handle complex queries.""" objectID = uuid4() yield self.index.update({objectID: {u'test/unicode': u'value', u'test/int': 42, u'test/float': 42.1}}) yield self.index.commit() query = parseQuery(u'test/unicode = "value" and ' u'(test/int = 42 or test/float = 42.1) ' u'except test/unknown = 10') result = yield self.index.search(query) self.assertEqual(set([objectID]), result) class EscapeWithWildcards(FluidinfoTestCase): def testEscapeWithWildcards(self): """ L{escapeWithWildcards} escapes all Lucene especial characters except the wildcards. """ terms = [(r'Hello*World', r'Hello*World'), (r'Hello\*World', r'Hello\*World'), (r'Hello "World"', r'Hello \"World\"'), (r'Hello |&^"~*?', r'Hello \|\&\^\"~*?'), (r'Hello (World)', r'Hello \(World\)'), (r'Hello:World', r'Hello\:World'), (r'Hello\World', r'Hello\\World'), (r'Hello World', r'Hello World'), ] for raw, escaped in terms: self.assertEqual(escaped, escapeWithWildcards(raw)) class CreateObjectTest(FluidinfoTestCase): resources = [('store', DatabaseResource())] def testCreateObject(self): """L{createDirtyObject} creates a new L{DirtyObject}.""" objectID = uuid4() object1 = createDirtyObject(objectID) self.assertEqual(objectID, object1.objectID) def testCreateTagAddsToStore(self): """ L{createDirtyObject} adds the new L{DirtyObject} to the main store. """ objectID = uuid4() object1 = createDirtyObject(objectID) result = self.store.find(DirtyObject, DirtyObject.objectID == objectID) self.assertIdentical(object1, result.one()) class GetObjectsTest(FluidinfoTestCase): resources = [('store', DatabaseResource())] def testGetObjects(self): """ L{getDirtyObjects} returns all L{DirtyObject}s in the database, by default. """ object1 = createDirtyObject(uuid4()) self.assertEqual(object1, getDirtyObjects().one()) def testGetObjectsWithObjectIDs(self): """ When L{DirtyObject.objectID}s are provided L{getDirtyObjects} returns matching L{DirtyObject}s. """ objectID = uuid4() object1 = createDirtyObject(objectID) createDirtyObject(uuid4()) result = getDirtyObjects(objectIDs=[objectID]) self.assertIdentical(object1, result.one()) class TouchObjectsTest(FluidinfoTestCase): resources = [('store', DatabaseResource())] def testTouchObjects(self): """L{touchObjects} adds the objects to the C{dirty_objects} table.""" objectID = uuid4() touchObjects([objectID]) self.assertNotIdentical(None, getDirtyObjects([objectID]).one())

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fede757cb8f63cae1c1695305be6efd36ec7dff5

46

py

Python

src/gui.py

LBRY-Omnibus/lbry-multi-channel-uploader

cee11b82f4dda71418aa84c0fdd707392bd97f3f

[ "Apache-2.0" ]

null

src/gui.py

LBRY-Omnibus/lbry-multi-channel-uploader

cee11b82f4dda71418aa84c0fdd707392bd97f3f

[ "Apache-2.0" ]

2

2022-02-25T20:59:06.000Z

2022-03-11T20:32:13.000Z

src/gui.py

LBRY-Omnibus/lbry-multi-channel-uploader

cee11b82f4dda71418aa84c0fdd707392bd97f3f

[ "Apache-2.0" ]

1

2022-03-11T20:43:53.000Z

# this is going to end up being built in kivy.

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0.73913

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3.4

1

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null

0

1

0

5

fef249334441b817efc539f483477cc3f7b2d0bf

177

py

Python

eplusplus/exception/__init__.py

labeee/EPlusPlus

da6cbd60575146a8f165fb72e165919cd83ddc24

[ "MIT" ]

1

2018-02-06T17:41:12.000Z

eplusplus/exception/__init__.py

labeee/EPlusPlus

da6cbd60575146a8f165fb72e165919cd83ddc24

[ "MIT" ]

null

eplusplus/exception/__init__.py

labeee/EPlusPlus

da6cbd60575146a8f165fb72e165919cd83ddc24

[ "MIT" ]

1

2021-06-29T02:49:59.000Z

from .installException import InstallException from .columnException import ColumnException from .noIdfException import NoIdfException from .noCsvException import NoCsvException

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0.892655

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0.375

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44.25

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null

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0

1

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1

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5

3a0f53d9c18c480fc07ee446c047a9bbd16a6435

275

py

Python

n_klimovych/code_wars/9.3 - Basic subclasses - Adam and Eve.py

kolyasalubov/Lv-639.pythonCore

06f10669a188318884adb00723127465ebdf2907

[ "MIT" ]

null

n_klimovych/code_wars/9.3 - Basic subclasses - Adam and Eve.py

kolyasalubov/Lv-639.pythonCore

06f10669a188318884adb00723127465ebdf2907

[ "MIT" ]

null

n_klimovych/code_wars/9.3 - Basic subclasses - Adam and Eve.py

kolyasalubov/Lv-639.pythonCore

06f10669a188318884adb00723127465ebdf2907

[ "MIT" ]

null

class Human(object): def __init__(self): pass class Man(Human): def __init__(self): super().__init__() class Woman(Human): def __init__(self): super().__init__() def God(): adam = Man() eva = Woman() return [adam,eva]

18.333333

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0.556364

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275

4.15625

0.4375

0.157895

0.24812

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null

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1

0

1

0

5

3a4052903ae0ed6b3dfecbb28ea8d1c41cf14ea6

115

py

Python

pulsemaker/__init__.py

anushkrishnav/pulsemaker

598b0b35569a7b3adb4722d0ebd70dd495e12037

[ "Apache-2.0" ]

14

2021-02-13T03:02:45.000Z

2021-12-13T06:03:53.000Z

pulsemaker/__init__.py

anushkrishnav/pulsemaker

598b0b35569a7b3adb4722d0ebd70dd495e12037

[ "Apache-2.0" ]

null

pulsemaker/__init__.py

anushkrishnav/pulsemaker

598b0b35569a7b3adb4722d0ebd70dd495e12037

[ "Apache-2.0" ]

3

2021-02-08T08:07:21.000Z

2021-12-17T14:16:32.000Z

# __all__ = ['ScheduleEditor', 'PulseDesigner', '...'] from .ScheduleDesigner import * from .PulseDesigner import *

38.333333

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0.721739

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115

8.777778

0.666667

0

0.113043

115

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55

38.333333

0.77451

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0

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0

true

0

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null

0

1

0

1

0

1

0

5

3a513009baf5a320f8dd023af69a29b800673d58

58

py

Python

vocompr/__init__.py

EnzoBnl/vocompr

fdbe5df59a698e232fb4b107aba1d96d4f8dba80

[ "Apache-2.0" ]

null

vocompr/__init__.py

EnzoBnl/vocompr

fdbe5df59a698e232fb4b107aba1d96d4f8dba80

[ "Apache-2.0" ]

5

2019-11-22T22:52:46.000Z

2020-04-21T13:09:36.000Z

vocompr/__init__.py

EnzoBnl/vocompr

fdbe5df59a698e232fb4b107aba1d96d4f8dba80

[ "Apache-2.0" ]

null

from .vocompr import vocompr, unvocompr, compression_rate

29

57

0.844828

7

58

6.857143

0.857143

0

0.103448

58

1

58

0.923077

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true

0

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1

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null

0

1

0

1

0

1

0

5

28c5a3cbc23a3e5cfc7c9e419f4fe6e0fa3da4c3

271

py

Python

config.py

Iggip/LPML-bot

cd96777d0cc2105d3117c1d918eb4d87be81b47b

[ "Apache-2.0" ]

1

2021-04-23T14:15:27.000Z

config.py

Iggip/LPML-bot

cd96777d0cc2105d3117c1d918eb4d87be81b47b

[ "Apache-2.0" ]

null

config.py

Iggip/LPML-bot

cd96777d0cc2105d3117c1d918eb4d87be81b47b

[ "Apache-2.0" ]

null

TOKEN = "YOUR_TOKEN" way = 'YOUR_FILE_WHERE_HOMEWORK_WILL_BE_STORED.dat' students = ['SURNAME NAME', 'SURNAME NAME', 'SURNAME NAME' ...] color = 0x###### bot_activity = 'STATUS' edit_role = 'ROLE_WHICH_CAN_CREATE_AND_EDIT_HOMEWORKS' prefix = 'COMMAND_PREFIX(/, !, ?...)'

33.875

63

0.719557

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271

4.972222

0.75

0.184358

0.201117

0.24581

0

0.004132

0.107011

271

7

64

38.714286

0.735537

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1

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1

0

null

0

1

0

5

28df939866977b795813e9387b91c4ae2ce65025

2,236

py

Python

uc-backbone/select_backbone.py

lovish1234/TPC

10e93eeb0e22e411579cfb9f94fac7870f6e2039

[ "MIT" ]

null

uc-backbone/select_backbone.py

lovish1234/TPC

10e93eeb0e22e411579cfb9f94fac7870f6e2039

[ "MIT" ]

null

uc-backbone/select_backbone.py

lovish1234/TPC

10e93eeb0e22e411579cfb9f94fac7870f6e2039

[ "MIT" ]

null

from resnet_2d3d import * def select_resnet(network, track_running_stats=True, distance_type='certain', radius_type='linear'): param = {'feature_size': 1024} if network == 'resnet8': model = resnet8_2d3d_mini(track_running_stats=track_running_stats, distance_type=distance_type, radius_type=radius_type) if distance_type == 'uncertain': param['feature_size'] = 17 elif distance_type == 'certain': param['feature_size'] = 16 elif network == 'resnet10': model = resnet10_2d3d_mini(track_running_stats=track_running_stats, distance_type=distance_type, radius_type=radius_type) if distance_type == 'uncertain': param['feature_size'] = 17 elif distance_type == 'certain': param['feature_size'] = 16 elif network == 'resnet18': model = resnet18_2d3d_full( track_running_stats=track_running_stats, distance_type=distance_type, radius_type=radius_type) if distance_type == 'uncertain': param['feature_size'] = 257 elif distance_type == 'certain': param['feature_size'] = 256 elif network == 'resnet34': model = resnet34_2d3d_full( track_running_stats=track_running_stats, distance_type=distance_type, radius_type=radius_type) if distance_type == 'uncertain': param['feature_size'] = 257 elif distance_type == 'certain': param['feature_size'] = 256 elif network == 'resnet50': model = resnet50_2d3d_full(track_running_stats=track_running_stats) elif network == 'resnet101': model = resnet101_2d3d_full(track_running_stats=track_running_stats) elif network == 'resnet152': model = resnet152_2d3d_full(track_running_stats=track_running_stats) elif network == 'resnet200': model = resnet200_2d3d_full(track_running_stats=track_running_stats) else: raise IOError('model type is wrong') return model, param

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5.465517

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0.227918

0.138801

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0.307245

2,236

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1

0

1

0

null

0

5

e90330aaf8e6cae28c5171b3603be49d1945e671

5,232

py

Python

program.py

Sanjin84/CompetitionInterface

50ba1c58b874897d1991b6a28816f2424803a0b2

[ "CC0-1.0" ]

null

program.py

Sanjin84/CompetitionInterface

50ba1c58b874897d1991b6a28816f2424803a0b2

[ "CC0-1.0" ]

null

program.py

Sanjin84/CompetitionInterface

50ba1c58b874897d1991b6a28816f2424803a0b2

[ "CC0-1.0" ]

null

import tkinter as tk from tkinter import * root = Tk() root.geometry("1000x600") root.resizable(True,True) root.title("DASHBOARD") def second(): sec= Frame(root,bg='#4472C4').place(relx=0, rely=0, relwidth=1, relheight=1) tt = tk.Label(sec,text="GLOBAL WATCHTOWER SV 21 INTERFACE",font = "Verdana 15 bold",bg="#4472C4").place(rely=0.06,relx=0.3) t2 = tk.Label(sec,text="TO LOG IN YOU MUST ENTER 3 VALID KEYWORDS\nYOU CAN MAKE AS MANY ATTEMPTS AS YOU WISH",font = "Verdana 15 bold",bg="#4472C4").place(rely=0.25,relx=0.25) e1 = Entry(sec, text = 'WORD 1',bg="#70AD47").place(relx=0.1,rely=0.4, relwidth=0.45, relheight=0.1) e2 = Entry(sec, text = 'WORD 2',bg="#70AD47").place(relx=0.1,rely=0.55, relwidth=0.45, relheight=0.1) e3 = Entry(sec, text = 'WORD 3',bg="#70AD47").place(relx=0.1,rely=0.7, relwidth=0.45, relheight=0.1) b1 = Button(sec,text="VALIDATE", command="",bg="gray",font = "Verdana 15 bold").place(relx=0.6,rely=0.4, relwidth=0.2, relheight=0.1) b2 = Button(sec,text="VALIDATE", command="",bg="gray",font = "Verdana 15 bold").place(relx=0.6,rely=0.55,relwidth=0.2, relheight=0.1) b3 = Button(sec,text="VALIDATE", command=third,bg="gray",font = "Verdana 15 bold").place(relx=0.6,rely=0.7,relwidth=0.2, relheight=0.1) def third(): thr= Frame(root,bg='#4472C4').place(relx=0, rely=0, relwidth=1, relheight=1) tt = tk.Label(thr,text="GLOBAL WATCHTOWER SV 21 INTERFACE",font = "Verdana 15 bold",bg='#4472C4').place(relx=0.3,rely=0.06) frame1 = tk.LabelFrame(thr,bd=5,bg='#4472C4').place(relx=0.06,rely=0.35, relwidth=0.25, relheight=0.6) b1 = Button(frame1,text="Launch Virus", command=launch,bg="#70AD47",font = "Arial 20 bold").place(relx=0.06,rely=0.2, relwidth=0.25, relheight=0.1) #t2 = tk.Label(frame1,text="This option launches the virus directly\nin 3,125,673 with number of \ninfected devices doubling every 13 hours."+ #"\n"+"Total collapse in global tech infrastructure in 3 -5 days",font = "Verdana 9",justify=LEFT).place(relx=0.08,rely=0.4, relwidth=0.25, relheight=0.5) frame2 = tk.LabelFrame(thr,bd=5,bg='#4472C4').place(relx=0.4,rely=0.35, relwidth=0.25, relheight=0.6) b2 = Button(frame2,text="Stall Virus", command=stall,bg="#70AD47",font = "Arial 20 bold").place(relx=0.4,rely=0.2,relwidth=0.25, relheight=0.1) #t2 = tk.Label(thr,text="GLOBAL WATCHTOWER SV 21 INTERFACE",font = "Verdana 15 bold").place(rely=0.3,relx=0.4) frame3 = tk.LabelFrame(thr,bd=5,bg='#4472C4').place(relx=0.7,rely=0.35, relwidth=0.25, relheight=0.6) b3 = Button(frame3,text="Destroy Virus", command="",bg="#70AD47",font = "Arial 20 bold").place(relx=0.7,rely=0.2,relwidth=0.25, relheight=0.1) #tt = tk.Label(thr,text="GLOBAL WATCHTOWER SV 21 INTERFACE",font = "Verdana 15 bold").place(rely=0.06,relx=0.3) def launch(): sec= Frame(root,bg='#4472C4').place(relx=0, rely=0, relwidth=1, relheight=1) tt = tk.Label(sec,text="GLOBAL WATCHTOWER SV 21 INTERFACE",font = "Verdana 15 bold",bg="#4472C4").place(rely=0.06,relx=0.3) t2 = tk.Label(sec,text="DESTRUCTION OF THE VIRUS REQUIRES THE USE OF THE FINAL TWO KEYWORDS",font = "Verdana 15 bold",bg="#4472C4").place(relx=0.05,rely=0.25) e1 = Entry(sec, text = 'WORD 1',bg="#70AD47").place(relx=0.1,rely=0.4, relwidth=0.45, relheight=0.1) e2 = Entry(sec, text = 'WORD 2',bg="#70AD47").place(relx=0.1,rely=0.55, relwidth=0.45, relheight=0.1) b1 = Button(sec,text="VALIDATE", command="",bg="gray",font = "Verdana 15 bold").place(relx=0.6,rely=0.4, relwidth=0.2, relheight=0.1) b2 = Button(sec,text="VALIDATE", command="",bg="gray",font = "Verdana 15 bold").place(relx=0.6,rely=0.55,relwidth=0.2, relheight=0.1) def stall(): sec= Frame(root,bg='#4472C4').place(relx=0, rely=0, relwidth=1, relheight=1) tt = tk.Label(sec,text="GLOBAL WATCHTOWER SV 21 INTERFACE",font = "Verdana 15 bold",bg="#4472C4").place(rely=0.06,relx=0.3) t1 = tk.Label(sec,text="VIRUS RELEASE SUSPENDED FOR 5 DAYS!",font = "Verdana 15 bold",bg="#4472C4").place(relx=0.25,rely=0.15) t2 = tk.Label(sec,text="DESTRUCTION OF THE VIRUS REQUIRES THE USE OF THE FINAL TWO KEYWORDS",font = "Verdana 15 bold",bg="#4472C4").place(relx=0.05,rely=0.25) e1 = Entry(sec, text = 'WORD 1',bg="#70AD47").place(relx=0.1,rely=0.4, relwidth=0.45, relheight=0.1) e2 = Entry(sec, text = 'WORD 2',bg="#70AD47").place(relx=0.1,rely=0.55, relwidth=0.45, relheight=0.1) b1 = Button(sec,text="VALIDATE", command="",bg="gray",font = "Verdana 15 bold").place(relx=0.6,rely=0.4, relwidth=0.2, relheight=0.1) b2 = Button(sec,text="VALIDATE", command="",bg="gray",font = "Verdana 15 bold").place(relx=0.6,rely=0.55,relwidth=0.2, relheight=0.1) def raise_frame(frame): frame.tkraise() start = Frame(root,bg='#4472C4').place(relx=0, rely=0, relwidth=1, relheight=1) fr = tk.Label(start,text="GLOBAL WATCHTOWER \n SV 21 INTERFACE",font = "Verdana 30 bold",bg="#4472C4").place(rely=0.1,relx=0.25) e1 = Entry(start, text = 'ENTER TEAM NAME',bg="#70AD47").place(relx=0.1,rely=0.5, relwidth=0.8, relheight=0.15) button = Button(start,text="Enter", command=second,bg="gray",font = "Verdana 15 bold") button.place(rely=0.65,relx=0.1, relwidth=0.8, relheight=0.15) mainloop()

70.702703

179

0.675268

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3.717895

0.143158

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0.087769

0.086636

0.778879

0.750849

0.723386

0.714326

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0.117928

5,232

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180

70.702703

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null

0

1

0

null

0

5

e922f862820a572c1143bb0a95c6ba242c6c2615

2,415

py

Python

run_multi-gpu.py

cloneniu/mini-AlphaStar

b08c48e2c04a384fce5a84245e54ded93c6def4e

[ "Apache-2.0" ]

null

run_multi-gpu.py

cloneniu/mini-AlphaStar

b08c48e2c04a384fce5a84245e54ded93c6def4e

[ "Apache-2.0" ]

null

run_multi-gpu.py

cloneniu/mini-AlphaStar

b08c48e2c04a384fce5a84245e54ded93c6def4e

[ "Apache-2.0" ]

null

import os USED_DEVICES = "2, 3" os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" os.environ["CUDA_VISIBLE_DEVICES"] = USED_DEVICES os.environ["CUDA_LAUNCH_BLOCKING"] = "1" import alphastarmini import torch from alphastarmini.core.arch import entity_encoder from alphastarmini.core.arch import scalar_encoder from alphastarmini.core.arch import spatial_encoder from alphastarmini.core.arch import arch_model from alphastarmini.core.arch import action_type_head from alphastarmini.core.arch import selected_units_head from alphastarmini.core.arch import target_unit_head from alphastarmini.core.arch import delay_head from alphastarmini.core.arch import queue_head from alphastarmini.core.arch import location_head from alphastarmini.core.arch import agent from alphastarmini.core.arch import baseline from alphastarmini.core.sl import load_pickle from alphastarmini.core.rl import action from alphastarmini.core.rl import env_utils from alphastarmini.core.rl import actor from alphastarmini.core.rl import against_computer from alphastarmini.core.rl import pseudo_reward import param as P if __name__ == '__main__': # if we don't add this line, it may cause running time error while in Windows # torch.multiprocessing.freeze_support() print("run init") # ------------------------ # 1. first we transform the replays to pickle # from alphastarmini.core.sl import transform_replay_data # transform_replay_data.test(on_server=P.on_server) # # 2. second we use pickle to do multi-gpu supervised learning from alphastarmini.core.sl import sl_multi_gpu_by_pickle sl_multi_gpu_by_pickle.test(on_server=P.on_server) # # 2. second we use to do supervised learning # from alphastarmini.core.sl import sl_multi_gpu_by_tensor # sl_multi_gpu_by_tensor.test(on_server=P.on_server) # 3. third we use SL model and replays to do reinforcement learning # from alphastarmini.core.rl import rl_train_with_replay # rl_train_with_replay.test(on_server=P.on_server, replay_path=P.replay_path) # ------------------------ # # below is not recommended to use # from alphastarmini.core.sl import analyze_replay_statistic # analyze_replay_statistic.test(on_server=False) # from alphastarmini.core.rl import rl_train_wo_replay # rl_train_wo_replay.test(on_server=False) # against_computer.test(on_server=False) print('run over')

34.5

81

0.775569

355

2,415

5.033803

0.312676

0.228316

0.282037

0.167879

0.556799

0.348069

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0.102966

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0.00337

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0.741935

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1

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null

0

1

0

1

0

5

3a7894a7cf6f4fe2181d5ce9bdc3d914f45d6ad4

134

py

Python

v2_pir/admin.py

ruslan-ok/ruslan

fc402e53d2683581e13f4d6c69a6f21e5c2ca1f8

[ "MIT" ]

null

v2_pir/admin.py

ruslan-ok/ruslan

fc402e53d2683581e13f4d6c69a6f21e5c2ca1f8

[ "MIT" ]

null

v2_pir/admin.py

ruslan-ok/ruslan

fc402e53d2683581e13f4d6c69a6f21e5c2ca1f8

[ "MIT" ]

null

from django.contrib import admin from pir.models import PirTable, PirData admin.site.register(PirTable) admin.site.register(PirData)

22.333333

40

0.828358

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134

5.842105

0.578947

0.162162

0.306306

0

0.089552

134

5

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0.909836

0

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true

0

0.5

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0.5

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0

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1

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null

0

1

0

1

0

5

3a861bb3a65a63855afa1f29ff9c36d3ec3e4767

7,468

py

Python

cl_streaming/process_results.py

x-zho14/bilevel_coresets

9ceb91af966a9a0a53d4f322ec747fa7ce853af9

[ "MIT" ]

46

2020-05-28T11:25:21.000Z

2022-03-30T01:32:27.000Z

cl_streaming/process_results.py

x-zho14/bilevel_coresets

9ceb91af966a9a0a53d4f322ec747fa7ce853af9

[ "MIT" ]

1

2021-11-01T13:48:11.000Z

2021-11-03T17:52:46.000Z

cl_streaming/process_results.py

x-zho14/bilevel_coresets

9ceb91af966a9a0a53d4f322ec747fa7ce853af9

[ "MIT" ]

6

2021-03-29T08:36:19.000Z

2022-01-11T05:53:05.000Z

import numpy as np import json import argparse def get_best_betas(methods, datasets, betas, seeds, buffer_size, save_best=False, path='cl_results', save_path='cl_results/best_betas.txt'): best_betas = {} for method in methods: best_beta_for_method = {} for dataset in datasets: best_acc, best_beta = -1, -1 for beta in betas: res = [] for seed in seeds: with open('{}/{}_{}_{}_{}_{}.txt'.format(path, dataset, method, buffer_size, beta, seed), 'r') as f: data = json.load(f) res.append(data['test_acc']) if len(res) > 0 and np.mean(res) > best_acc: best_acc = np.mean(res) best_beta = beta print(method, dataset, best_beta) best_beta_for_method[dataset] = best_beta best_betas[method] = best_beta_for_method if save_best: with open(save_path, "w") as f: json.dump(best_betas, f, sort_keys=True, indent=4) return best_betas def get_result(method, dataset, beta, seeds, buffer_size, path='cl_results'): res = [] for seed in seeds: with open('{}/{}_{}_{}_{}_{}.txt'.format(path, dataset, method, buffer_size, beta, seed), 'r') as f: data = json.load(f) res.append(data) return res def continual_learning_results(): datasets = ['permmnist', 'splitmnist'] methods = [ 'uniform', 'kmeans_features', 'kmeans_embedding', 'kmeans_grads', 'kcenter_features', 'kcenter_embedding', 'kcenter_grads', 'entropy', 'hardest', 'frcl', 'icarl', 'grad_matching', 'coreset' ] seeds = range(5) betas = [0.01, 0.1, 1.0, 10.0, 100.0, 1000.0] buffer_size = 100 best_betas = get_best_betas(methods, datasets, betas, seeds, buffer_size, save_best=True, path='cl_results', save_path='cl_results/best_betas.txt') print('Continual Learning study\n') print('Method \ Dataset'.ljust(45), end='') for dataset in datasets: print(' ' + dataset.ljust(18), end='') print('') for method in methods: print(method.ljust(43), end='') for dataset in datasets: beta = best_betas[method][dataset] res = get_result(method, dataset, beta, seeds, buffer_size, 'cl_results') res = [r['test_acc'] for r in res] print(' {:.2f} +- {:.2f}'.format(np.mean(res), np.std(res)).ljust(20), end='') print('') def streaming_results(): datasets = ['permmnist', 'splitmnist'] methods = ['reservoir', 'coreset'] seeds = range(5) betas = [0.01, 0.1, 1.0, 10.0, 100.0, 1000.0] buffer_size = 100 best_betas = get_best_betas(methods, datasets, betas, seeds, buffer_size, save_best=True, path='streaming_results', save_path='streaming_results/best_betas.txt') print('Streaming study\n') print('Method \ Dataset'.ljust(45), end='') for dataset in datasets: print(' ' + dataset.ljust(18), end='') print('') for method in methods: print(method.ljust(43), end='') for dataset in datasets: beta = best_betas[method][dataset] res = get_result(method, dataset, beta, seeds, buffer_size, 'streaming_results') res = [r['test_acc'] for r in res] print(' {:.2f} +- {:.2f}'.format(np.mean(res), np.std(res)).ljust(20), end='') print('') def imbalanced_streaming_results(): datasets = ['splitmnistimbalanced'] methods = ['reservoir', 'cbrs', 'coreset'] seeds = range(5) betas = [0.01, 0.1, 1.0, 10.0, 100.0, 1000.0] buffer_size = 100 best_betas = get_best_betas(methods, datasets, betas, seeds, buffer_size, save_best=True, path='streaming_results', save_path='streaming_results/best_betas_imbalanced.txt') print('Streaming study\n') print('Method \ Dataset'.ljust(45), end='') for dataset in datasets: print(' ' + dataset.ljust(18), end='') print('') for method in methods: print(method.ljust(43), end='') for dataset in datasets: beta = best_betas[method][dataset] res = get_result(method, dataset, beta, seeds, buffer_size, 'streaming_results') res = [r['test_acc'] for r in res] print(' {:.2f} +- {:.2f}'.format(np.mean(res), np.std(res)).ljust(20), end='') print('') def splitcifar_results(): datasets = ['splitcifar'] methods = [ 'uniform', 'kmeans_features', 'kmeans_embedding', 'kmeans_grads', 'kcenter_features', 'kcenter_embedding', 'kcenter_grads', 'entropy', 'hardest', 'frcl', 'icarl', 'grad_matching', 'coreset' ] seeds = range(5) betas = [0.01, 0.1, 1.0, 10.0, 100.0, 1000.0] buffer_size = 200 best_betas = get_best_betas(methods, datasets, betas, seeds, buffer_size, save_best=True, path='cl_results', save_path='cl_results/best_betas_splitcifar.txt') print('Streaming study\n') print('Method \ Dataset'.ljust(45), end='') for dataset in datasets: print(' ' + dataset.ljust(18), end='') print('') for method in methods: print(method.ljust(43), end='') for dataset in datasets: beta = best_betas[method][dataset] res = get_result(method, dataset, beta, seeds, buffer_size, 'cl_results') res = [r['test_acc'] for r in res] print(' {:.2f} +- {:.2f}'.format(np.mean(res), np.std(res)).ljust(20), end='') print('') def imbalanced_streaming_cifar_results(): datasets = ['stream_imbalanced_splitcifar'] methods = ['reservoir', 'cbrs', 'coreset'] seeds = range(5) betas = [0.01, 0.1, 1.0, 10.0, 100.0, 1000.0] buffer_size = 200 best_betas = get_best_betas(methods, datasets, betas, seeds, buffer_size, save_best=True, path='streaming_results', save_path='streaming_results/best_betas_imbalanced_cifar.txt') print('Streaming study\n') print('Method \ Dataset'.ljust(45), end='') for dataset in datasets: print(' ' + dataset.ljust(18), end='') print('') for method in methods: print(method.ljust(43), end='') for dataset in datasets: beta = best_betas[method][dataset] res = get_result(method, dataset, beta, seeds, buffer_size, 'streaming_results') res = [r['test_acc'] for r in res] print(' {:.2f} +- {:.2f}'.format(np.mean(res), np.std(res)).ljust(20), end='') print('') if __name__ == "__main__": parser = argparse.ArgumentParser(description='Results processor') parser.add_argument('--exp', default='cl', choices=['cl', 'streaming', 'imbalanced_streaming', 'splitcifar', 'imbalanced_streaming_cifar']) args = parser.parse_args() exp = args.exp if exp == 'cl': continual_learning_results() elif exp == 'streaming': streaming_results() elif exp == 'imbalanced_streaming': imbalanced_streaming_results() elif exp == 'splitcifar': splitcifar_results() elif exp == 'imbalanced_streaming_cifar': imbalanced_streaming_cifar_results() else: raise Exception('Unknown experiment')

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py

Python

wsgi.py

argosopentech/argos-search

7cb7d85d1d76916f272ce2e52cfd6f6856fd1f69

[ "MIT" ]

5

2021-11-05T00:20:45.000Z

2021-12-15T02:49:53.000Z

wsgi.py

argosopentech/argos-search

7cb7d85d1d76916f272ce2e52cfd6f6856fd1f69

[ "MIT" ]

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2021-11-02T11:38:09.000Z

wsgi.py

argosopentech/argos-search

7cb7d85d1d76916f272ce2e52cfd6f6856fd1f69

[ "MIT" ]

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2021-11-05T03:17:54.000Z

from web import app

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tests/__init__.py

tim-fi/swpatterns

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[ "MIT" ]

null

tests/__init__.py

tim-fi/swpatterns

7fe12f9816a0aef8c8cfdd57536ac578a89a83b9

[ "MIT" ]

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2020-03-31T04:13:12.000Z

tests/__init__.py

tim-fi/swpatterns

7fe12f9816a0aef8c8cfdd57536ac578a89a83b9

[ "MIT" ]

null

from .test_composition import * from .test_interface import * from .test_matching import * __all__ = test_composition.__all__ + test_interface.__all__ + test_matching.__all__

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py

Python

plugins/filetime_from_hg/__init__.py

mohnjahoney/website_source

edc86a869b90ae604f32e736d9d5ecd918088e6a

[ "MIT" ]

13

2020-01-27T09:02:25.000Z

2022-01-20T07:45:26.000Z

plugins/filetime_from_hg/__init__.py

mohnjahoney/website_source

edc86a869b90ae604f32e736d9d5ecd918088e6a

[ "MIT" ]

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2020-03-22T06:57:57.000Z

2022-01-24T22:46:42.000Z

plugins/filetime_from_hg/__init__.py

mohnjahoney/website_source

edc86a869b90ae604f32e736d9d5ecd918088e6a

[ "MIT" ]

6

2020-07-10T00:13:30.000Z

2022-01-26T08:22:33.000Z

from .filetime_from_hg import *

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Python

Chapter 05 - Functions/Assignments/5.8 Writing Your Own Value-Returning Functions/51218.py

EllisBarnes00/COP-1000

8509e59e8a566c77295c714ddcb0f557c470358b

[ "Unlicense" ]

null

Chapter 05 - Functions/Assignments/5.8 Writing Your Own Value-Returning Functions/51218.py

EllisBarnes00/COP-1000

8509e59e8a566c77295c714ddcb0f557c470358b

[ "Unlicense" ]

1

2021-06-07T03:55:29.000Z

2021-06-07T03:56:47.000Z

Chapter 05 - Functions/Assignments/5.8 Writing Your Own Value-Returning Functions/51218.py

EllisBarnes00/COP-1000

8509e59e8a566c77295c714ddcb0f557c470358b

[ "Unlicense" ]

null

def max(num1, num2, num3): nums = [num1, num2, num3] nums.sort() return nums[len(nums) - 1] print(max(32, 65, 2))

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py

Python

splitting_image.py

purinisivakrishna/image

550c55807bc92bb81e6c6114d1e79b3b7ff2be42

[ "MIT" ]

null

splitting_image.py

purinisivakrishna/image

550c55807bc92bb81e6c6114d1e79b3b7ff2be42

[ "MIT" ]

null

splitting_image.py

purinisivakrishna/image

550c55807bc92bb81e6c6114d1e79b3b7ff2be42

[ "MIT" ]

null

import split_folders split_folders.ratio("dog_flower", output="image_split", ratio=(0.7, 0.15, 0.15))

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c9149f717d51cd8a808885eb620755d9764bd4e1

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py

Python

Repositories/Python/Basic/format_map_example.py

Dong-gi/Dong-gi.github.io

2c3d083db72e06032a1daf528ee9b175219aa554

[ "MIT" ]

5

2018-02-27T16:19:35.000Z

2020-08-25T13:09:49.000Z

Repositories/Python/Basic/format_map_example.py

Dong-gi/Dong-gi.github.io

2c3d083db72e06032a1daf528ee9b175219aa554

[ "MIT" ]

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2019-03-28T00:36:04.000Z

2021-08-12T01:42:41.000Z

Repositories/Python/Basic/format_map_example.py

Dong-gi/Dong-gi.github.io

2c3d083db72e06032a1daf528ee9b175219aa554

[ "MIT" ]

1

2021-11-28T11:28:29.000Z

# 출처: https://docs.python.org/3/library/stdtypes.html#str.format_map class Default(dict): def __missing__(self, key): return key print('{name} was born in {country}'.format_map(Default(name='Guido'))) # Guido was born in country

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Python

tests/data/nested_gitignore_tests/root/child/b.py

BigNuoLi/black

71e71e5f52e5f6bdeae63cc8c11b1bee44d11c30

[ "MIT" ]

16,110

2019-07-22T21:54:54.000Z

2022-03-31T22:52:39.000Z

tests/data/nested_gitignore_tests/root/child/b.py

marnixah/black-but-usable

83b83d3066d1d857983bfa1a666a409e7255d79d

[ "MIT" ]

1,981

2019-07-22T21:26:16.000Z

2022-03-31T23:14:35.000Z

tests/data/nested_gitignore_tests/root/child/b.py

marnixah/black-but-usable

83b83d3066d1d857983bfa1a666a409e7255d79d

[ "MIT" ]

1,762

2019-07-22T21:23:00.000Z

2022-03-31T06:10:22.000Z

# should be excluded (child/.gitignore)

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c94009b096978f5e41e55cb6cf4b13dfe214aee4

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py

Python

crosswalk/crosswalk/envs/__init__.py

ArayCHN/Pedestrian-navigation

1cd9a6400bbc39200e6b27a2cc7e802418697ef7

[ "MIT" ]

null

crosswalk/crosswalk/envs/__init__.py

ArayCHN/Pedestrian-navigation

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[ "MIT" ]

null

crosswalk/crosswalk/envs/__init__.py

ArayCHN/Pedestrian-navigation

1cd9a6400bbc39200e6b27a2cc7e802418697ef7

[ "MIT" ]

null

from crosswalk.envs.crosswalk_env import CrosswalkEnv

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py

Python

stores/admin.py

ashishkr619/dukaan_main

b236b498b95f62160959b5e84bb642a0be6063b0

[ "MIT" ]

null

stores/admin.py

ashishkr619/dukaan_main

b236b498b95f62160959b5e84bb642a0be6063b0

[ "MIT" ]

null

stores/admin.py

ashishkr619/dukaan_main

b236b498b95f62160959b5e84bb642a0be6063b0

[ "MIT" ]

null

# from django.contrib import admin from django.contrib import admin from .models import Store @admin.register(Store) class StoreAdmin(admin.ModelAdmin): list_display = ['id', 'store_link', 'name', 'address', ] list_filter = ['name', 'address', 'updated_at']

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py

Python

aiohttp_apispec_plugin/__init__.py

ckkz-it/aiohttp-apispec-plugin

41aedb69426c4292a59036bcac14660f31810c1b

[ "MIT" ]

2

2021-01-06T08:21:16.000Z

2021-02-11T09:04:32.000Z

aiohttp_apispec_plugin/__init__.py

ckkz-it/aiohttp-apispec-plugin

41aedb69426c4292a59036bcac14660f31810c1b

[ "MIT" ]

null

aiohttp_apispec_plugin/__init__.py

ckkz-it/aiohttp-apispec-plugin

41aedb69426c4292a59036bcac14660f31810c1b

[ "MIT" ]

null

from aiohttp_apispec_plugin.aiohttp_plugin import AioHttpPlugin

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py

Python

white_matter/wm_recipe/layer_profiles/__init__.py

alex4200/Long-range-micro-connectome

833aad78bc71e49a5059b276e65d3fef21686f9d

[ "BSD-3-Clause" ]

9

2019-05-01T13:12:17.000Z

2021-11-23T10:34:56.000Z

white_matter/wm_recipe/layer_profiles/__init__.py

alex4200/Long-range-micro-connectome

833aad78bc71e49a5059b276e65d3fef21686f9d

[ "BSD-3-Clause" ]

2

2022-02-03T13:56:22.000Z

2022-02-04T07:16:37.000Z

white_matter/wm_recipe/layer_profiles/__init__.py

alex4200/Long-range-micro-connectome

833aad78bc71e49a5059b276e65d3fef21686f9d

[ "BSD-3-Clause" ]

1

2022-02-03T12:05:12.000Z

from .layer_profile_mixer import ProfileMixer, LayerProfiles

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