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

0643deae65bf97584696f33e80afdf35b197abcf

1,677

py

Python

robit/core/alert.py

stratusadv/robit

7e0414d0ed3d98bb2c9a8785bf36961ac08f1d27

[ "MIT" ]

null

robit/core/alert.py

stratusadv/robit

7e0414d0ed3d98bb2c9a8785bf36961ac08f1d27

[ "MIT" ]

1

2021-11-01T18:51:04.000Z

robit/core/alert.py

stratusadv/robit

7e0414d0ed3d98bb2c9a8785bf36961ac08f1d27

[ "MIT" ]

null

import logging from datetime import datetime, timedelta from robit.core.health import Health

37.266667

119

0.627907

06440df8fe03e6136138c3851bd70beb0db8af44

5,823

py

Python

validate_tags/validate_master.py

asurion/Hibernate

b95c68ba8dba6a43baea288ade231944d1719988

[ "Apache-2.0" ]

9

2017-06-06T17:47:57.000Z

2021-08-06T18:30:11.000Z

validate_tags/validate_master.py

asurion/Hibernate

b95c68ba8dba6a43baea288ade231944d1719988

[ "Apache-2.0" ]

null

validate_tags/validate_master.py

asurion/Hibernate

b95c68ba8dba6a43baea288ade231944d1719988

[ "Apache-2.0" ]

null

import datetime import pytz from utils.timezones import time_zones from utils.CalculateHours import calc_hours

35.290909

117

0.493732

0647d34191beaa453acced9a85d7fac8926ac453

110

py

Python

pre_questionnaire/admin.py

AurelienNioche/ModelingMadeEasyApp

dbb738dda204906c5f4b7aeb9c71feea961a4cce

[ "MIT" ]

null

pre_questionnaire/admin.py

AurelienNioche/ModelingMadeEasyApp

dbb738dda204906c5f4b7aeb9c71feea961a4cce

[ "MIT" ]

null

pre_questionnaire/admin.py

AurelienNioche/ModelingMadeEasyApp

dbb738dda204906c5f4b7aeb9c71feea961a4cce

[ "MIT" ]

null

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

18.333333

37

0.845455

0648e18f81ac883f3b49a5656d1320a8eddbf0ed

5,014

py

Python

unitorch/score/voc_map.py

fuliucansheng/UniTorch

47038321593ce4e7eabda555bd58c0cf89482146

[ "MIT" ]

2

2022-02-05T08:52:00.000Z

2022-03-27T07:01:34.000Z

unitorch/score/voc_map.py

Lixin-Qian/unitorch

47038321593ce4e7eabda555bd58c0cf89482146

[ "MIT" ]

null

unitorch/score/voc_map.py

Lixin-Qian/unitorch

47038321593ce4e7eabda555bd58c0cf89482146

[ "MIT" ]

1

2022-03-27T07:01:13.000Z

import numpy as np from collections import defaultdict from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Union def _voc_ap( rec, prec, use_07_metric=False, ): """Compute VOC AP given precision and recall. If use_07_metric is true, uses the VOC 07 11-point method (default:False). """ if use_07_metric: # 11 point metric ap = 0.0 for t in np.arange(0.0, 1.1, 0.1): if np.sum(rec >= t) == 0: p = 0 else: p = np.max(prec[rec >= t]) ap = ap + p / 11.0 else: # correct AP calculation # first append sentinel values at the end mrec = np.concatenate(([0.0], rec, [1.0])) mpre = np.concatenate(([0.0], prec, [0.0])) # compute the precision envelope for i in range(mpre.size - 1, 0, -1): mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i]) # to calculate area under PR curve, look for points # where X axis (recall) changes value i = np.where(mrec[1:] != mrec[:-1])[0] # and sum (\Delta recall) * prec ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) return ap def voc_ap_score( p_bboxes: List[np.ndarray], p_scores: List[np.ndarray], p_classes: List[np.ndarray], gt_bboxes: List[np.ndarray], gt_classes: List[np.ndarray], class_id: int = None, threshold: float = 0.5, ): """ Args: p_bboxes: a list of predict bboxes p_scores: a list of predict score for bbox p_classes: a list of predict class id for bbox gt_bboxes: a list of ground truth bboxes gt_classes: a list of true class id for each true bbox class_id: the class id to compute ap score threshold: the threshold to ap score """ if class_id is not None: gt_bboxes = [gt_bbox[gt_class == class_id] for gt_class, gt_bbox in zip(gt_classes, gt_bboxes)] p_bboxes = [p_bbox[p_class == class_id] for p_class, p_bbox in zip(p_classes, p_bboxes)] p_scores = [p_score[p_class == class_id] for p_class, p_score in zip(p_classes, p_scores)] p_indexes = [np.array([i] * len(p_bboxes[i])) for i in range(len(p_bboxes))] p_bboxes, p_scores, p_indexes = ( np.concatenate(p_bboxes), np.concatenate(p_scores), np.concatenate(p_indexes), ) p_sort_indexes = np.argsort(-p_scores) tp = np.zeros(p_scores.shape[0]) fp = np.zeros(p_scores.shape[0]) gt_bbox_status = defaultdict(set) for idx, p_sort_index in enumerate(p_sort_indexes): p_index = int(p_indexes[p_sort_index]) gt_bbox = gt_bboxes[p_index] p_bbox = p_bboxes[p_sort_index] vmax = -float("inf") jmax = -1 if gt_bbox.size > 0: ixmin = np.maximum(gt_bbox[:, 0], p_bbox[0]) iymin = np.maximum(gt_bbox[:, 1], p_bbox[1]) ixmax = np.minimum(gt_bbox[:, 2], p_bbox[2]) iymax = np.minimum(gt_bbox[:, 3], p_bbox[3]) iw = np.maximum(ixmax - ixmin + 1.0, 0.0) ih = np.maximum(iymax - iymin + 1.0, 0.0) inters = iw * ih uni = ( (p_bbox[2] - p_bbox[0] + 1.0) * (p_bbox[3] - p_bbox[1] + 1.0) + (gt_bbox[:, 2] - gt_bbox[:, 0] + 1.0) * (gt_bbox[:, 3] - gt_bbox[:, 1] + 1.0) - inters ) overlaps = inters / uni vmax = np.max(overlaps) jmax = np.argmax(overlaps) if vmax > threshold: if jmax not in gt_bbox_status[p_index]: tp[idx] = 1 gt_bbox_status[p_index].add(jmax) else: fp[idx] = 1 else: fp[idx] = 1 fp = np.cumsum(fp, axis=0) tp = np.cumsum(tp, axis=0) rec = tp / float(sum([len(gt) for gt in gt_bboxes])) prec = tp / np.maximum(tp + fp, np.finfo(np.float).eps) ap = _voc_ap(rec, prec) return ap def voc_map_score( p_bboxes: List[np.ndarray], p_scores: List[np.ndarray], p_classes: List[np.ndarray], gt_bboxes: List[np.ndarray], gt_classes: List[np.ndarray], ): """ Args: p_bboxes: a list of predict bboxes p_scores: a list of predict score for bbox p_classes: a list of predict class id for bbox gt_bboxes: a list of ground truth bboxes gt_classes: a list of true class id for each true bbox Returns: a avg ap score of all classes in ground truth """ classes = set(list(np.concatenate(gt_classes))) ap_scores = dict() for thres in range(50, 100, 5): ap_scores[thres] = [ voc_ap_score( p_bboxes, p_scores, p_classes, gt_bboxes, gt_classes, c, thres / 100, ) for c in classes ] mAP = {iou: np.mean(x) for iou, x in ap_scores.items()} return np.mean(list(mAP.values()))

33.426667

103

0.553849

06499670b453111a27b20a04dcc90e10b1a775c3

1,418

py

Python

tests/_test_callable_py3.py

youknowone/callable

a31ea23103d7df156c30d990c5c249844ce302f6

[ "BSD-2-Clause-FreeBSD" ]

null

tests/_test_callable_py3.py

youknowone/callable

a31ea23103d7df156c30d990c5c249844ce302f6

[ "BSD-2-Clause-FreeBSD" ]

null

tests/_test_callable_py3.py

youknowone/callable

a31ea23103d7df156c30d990c5c249844ce302f6

[ "BSD-2-Clause-FreeBSD" ]

null

import asyncio from callable import Callable from signature import signature import pytest

28.938776

85

0.568406

06499d2a878e935b1bbb4ea1ae0081bd6e6ed4b7

75

py

Python

.metadata/.plugins/org.python.pydev.shared_interactive_console/history.py

fullerene12/VOTA

3a5cfc1e210ac7ea274537a8d189b54660416599

[ "MIT" ]

null

.metadata/.plugins/org.python.pydev.shared_interactive_console/history.py

fullerene12/VOTA

3a5cfc1e210ac7ea274537a8d189b54660416599

[ "MIT" ]

null

.metadata/.plugins/org.python.pydev.shared_interactive_console/history.py

fullerene12/VOTA

3a5cfc1e210ac7ea274537a8d189b54660416599

[ "MIT" ]

1

2021-08-01T22:39:18.000Z

import sys; print('%s %s' % (sys.executable or sys.platform, sys.version))

37.5

74

0.693333

064ab2c6467200414b067feed1868bd5e05cfaa4

1,293

py

Python

caravel/loaders/_png.py

neurospin/pycaravel

412d37ae1b06afcc9645e01a096bbe2674cfe47a

[ "CECILL-B" ]

null

caravel/loaders/_png.py

neurospin/pycaravel

412d37ae1b06afcc9645e01a096bbe2674cfe47a

[ "CECILL-B" ]

null

caravel/loaders/_png.py

neurospin/pycaravel

412d37ae1b06afcc9645e01a096bbe2674cfe47a

[ "CECILL-B" ]

1

2020-08-27T13:17:00.000Z

# coding: utf-8 ########################################################################## # NSAp - Copyright (C) CEA, 2020 # Distributed under the terms of the CeCILL-B license, as published by # the CEA-CNRS-INRIA. Refer to the LICENSE file or to # http://www.cecill.info/licences/Licence_CeCILL-B_V1-en.html # for details. ########################################################################## """ This module defines the png dataset loader. """ # Third party import import imageio # Package import from .loader_base import LoaderBase

24.865385

74

0.520495

064ad9d1f980f75038d7cfdfdcbb95549772aa8a

92

py

Python

src/auth/__init__.py

MarkStefanovic/todo-api

fb6198511712df853e693787839533f0c9956178

[ "MIT" ]

null

src/auth/__init__.py

MarkStefanovic/todo-api

fb6198511712df853e693787839533f0c9956178

[ "MIT" ]

null

src/auth/__init__.py

MarkStefanovic/todo-api

fb6198511712df853e693787839533f0c9956178

[ "MIT" ]

null

from src.auth.adapter import * from src.auth.domain import * from src.auth.service import *

23

30

0.771739

064ca7c37993e4810c14d5f7e1d0f4a40a067487

8,098

py

Python

video_utils.py

Domhnall-Liopa/Lip2Wav

236ae24cd7945da8a75ddea1cfdc3da271c3c59f

[ "MIT" ]

null

video_utils.py

Domhnall-Liopa/Lip2Wav

236ae24cd7945da8a75ddea1cfdc3da271c3c59f

[ "MIT" ]

null

video_utils.py

Domhnall-Liopa/Lip2Wav

236ae24cd7945da8a75ddea1cfdc3da271c3c59f

[ "MIT" ]

null

import json import random import re import subprocess import tempfile from datetime import timedelta import cv2 import numpy as np import requests from vidaug import augmentors as va # this is a static build from https://www.johnvansickle.com/ffmpeg/old-releases/ffmpeg-4.4.1-i686-static.tar.xz # requires new ffmpeg version for: # - duration of extracted audio == video # - contains x264 codec in build required for clean video frames FFMPEG_PATH = '/opt/lip2wav/ffmpeg-4.4.1-i686-static/ffmpeg' FFPROBE_PATH = '/opt/lip2wav/ffmpeg-4.4.1-i686-static/ffprobe' OLD_FFMPEG_PATH = 'ffmpeg-2.8.15' FFMPEG_OPTIONS = '-hide_banner -loglevel panic' VIDEO_CROP_COMMAND = f'{FFMPEG_PATH} {FFMPEG_OPTIONS} -y -i {{input_video_path}} -ss {{start_time}} -to {{end_time}} -async 1 {{output_video_path}}' VIDEO_INFO_COMMAND = f'{FFMPEG_PATH} -i {{input_video_path}}' VIDEO_DURATION_COMMAND = f'{FFPROBE_PATH} {FFMPEG_OPTIONS} -show_entries format=duration -of default=noprint_wrappers=1:nokey=1 {{video_path}}' VIDEO_TO_AUDIO_COMMAND = f'{{ffmpeg_path}} {FFMPEG_OPTIONS} -threads 1 -y -i {{input_video_path}} -async 1 -ac 1 -vn -acodec pcm_s16le -ar 16000 {{output_audio_path}}' VIDEO_CONVERT_FPS_COMMAND = f'{FFMPEG_PATH} {FFMPEG_OPTIONS} -y -i {{input_video_path}} -strict -2 -filter:v fps=fps={{fps}} {{output_video_path}}' # copies original codecs and metadata (rotation) VIDEO_SPEED_ALTER_COMMAND = f'{FFMPEG_PATH} {FFMPEG_OPTIONS} -y -i {{input_video_path}} -filter_complex "[0:v]setpts={{video_speed}}*PTS[v];[0:a]atempo={{audio_speed}}[a]" -map "[v]" -map "[a]" {{output_video_path}}' VIDEO_REMOVE_AUDIO_COMMAND = f'{FFMPEG_PATH} {FFMPEG_OPTIONS} -y -i {{input_video_path}} -c copy -an {{output_video_path}}' VIDEO_ADD_AUDIO_COMMAND = f'{FFMPEG_PATH} {FFMPEG_OPTIONS} -y -i {{input_video_path}} -i {{input_audio_path}} -strict -2 -c:v copy -c:a aac {{output_video_path}}'

32.785425

216

0.67918

064f53cd615575e4bc66f6d26d74337b90be2852

621

py

Python

aflcov/vis.py

axt/afl-cov-vis

7806fa430113732790563b0f15884a087ebd21ea

[ "BSD-2-Clause" ]

29

2017-11-12T09:35:01.000Z

2022-02-17T09:29:54.000Z

aflcov/vis.py

usc-isi-bass/afl-cov

18e305d101443d8a06c46f9ac080dd45ca13d8bb

[ "BSD-2-Clause" ]

2

2017-11-12T09:40:43.000Z

2018-01-19T10:37:17.000Z

aflcov/vis.py

usc-isi-bass/afl-cov

18e305d101443d8a06c46f9ac080dd45ca13d8bb

[ "BSD-2-Clause" ]

6

2017-11-12T09:50:20.000Z

2022-02-22T06:01:17.000Z

from bingraphvis.base import Content

31.05

130

0.481481

064faa0fae768ef7598b80938b851b966512e6ab

3,418

py

Python

corehq/couchapps/tests/test_all_docs.py

kkrampa/commcare-hq

d64d7cad98b240325ad669ccc7effb07721b4d44

[ "BSD-3-Clause" ]

1

2020-05-05T13:10:01.000Z

corehq/couchapps/tests/test_all_docs.py

kkrampa/commcare-hq

d64d7cad98b240325ad669ccc7effb07721b4d44

[ "BSD-3-Clause" ]

1

2019-12-09T14:00:14.000Z

corehq/couchapps/tests/test_all_docs.py

MaciejChoromanski/commcare-hq

fd7f65362d56d73b75a2c20d2afeabbc70876867

[ "BSD-3-Clause" ]

5

2015-11-30T13:12:45.000Z

2019-07-01T19:27:07.000Z

from __future__ import absolute_import from __future__ import unicode_literals from corehq.dbaccessors.couchapps.all_docs import \ get_all_doc_ids_for_domain_grouped_by_db, get_doc_count_by_type, \ delete_all_docs_by_doc_type, get_doc_count_by_domain_type from dimagi.utils.couch.database import get_db from django.test import TestCase

50.264706

104

0.675834

0650fe2b4a8ec05a4f26bd56fccd6338468b7b46

913

py

Python

parc/instruction.py

kevinyuan/pydgin

9e5dea526a17b23929b2a1d24598154b42323073

[ "BSD-3-Clause" ]

159

2015-02-12T03:28:25.000Z

2022-02-24T22:40:35.000Z

parc/instruction.py

kevinyuan/pydgin

9e5dea526a17b23929b2a1d24598154b42323073

[ "BSD-3-Clause" ]

21

2015-01-31T23:47:26.000Z

2020-12-21T12:41:08.000Z

parc/instruction.py

kevinyuan/pydgin

9e5dea526a17b23929b2a1d24598154b42323073

[ "BSD-3-Clause" ]

40

2015-01-28T21:31:30.000Z

2022-01-25T12:50:23.000Z

#======================================================================= # instruction.py #======================================================================= from pydgin.utils import r_uint

19.020833

72

0.496166

06517cf6f2e451230d0d6eb3711d313d63aa2c66

564

py

Python

docs/examples/dev/_semantic_text_features.py

yacth/autogoal

a55c1534161e850587e2ca3533aa2fd5ae28569e

[ "MIT" ]

null

docs/examples/dev/_semantic_text_features.py

yacth/autogoal

a55c1534161e850587e2ca3533aa2fd5ae28569e

[ "MIT" ]

null

docs/examples/dev/_semantic_text_features.py

yacth/autogoal

a55c1534161e850587e2ca3533aa2fd5ae28569e

[ "MIT" ]

null

from autogoal.contrib import find_classes from autogoal.kb import * from autogoal.kb import build_pipelines, build_pipeline_graph from autogoal.contrib.spacy import SpacyNLP from autogoal.contrib._wrappers import FlagsMerger import logging logging.basicConfig(level=logging.INFO) pipeline_space = build_pipeline_graph( input=List(Sentence()), output=MatrixContinuousDense(), registry=find_classes(), # registry=[SpacyNLP, FlagsMerger], # max_list_depth=1, ) for i in range(10): pipeline = pipeline_space.sample() print(pipeline)

22.56

61

0.77305

0651ab151a7c92bb5c33655beaba51093024c9dc

341

py

Python

opytimizer/spaces/__init__.py

anukaal/opytimizer

5f1ccc0da80e6a4cabd99578fa24cf4f6466f9b9

[ "Apache-2.0" ]

528

2018-10-01T20:00:09.000Z

2022-03-27T11:15:31.000Z

opytimizer/spaces/__init__.py

anukaal/opytimizer

5f1ccc0da80e6a4cabd99578fa24cf4f6466f9b9

[ "Apache-2.0" ]

17

2019-10-30T00:47:03.000Z

2022-03-21T11:39:28.000Z

opytimizer/spaces/__init__.py

anukaal/opytimizer

5f1ccc0da80e6a4cabd99578fa24cf4f6466f9b9

[ "Apache-2.0" ]

35

2018-10-01T20:03:23.000Z

2022-03-20T03:54:15.000Z

"""Customizable space module that provides different search spaces implementations. """ from opytimizer.spaces.boolean import BooleanSpace from opytimizer.spaces.grid import GridSpace from opytimizer.spaces.hyper_complex import HyperComplexSpace from opytimizer.spaces.search import SearchSpace from opytimizer.spaces.tree import TreeSpace

34.1

66

0.859238

0652b6080d711fc812aa3a6054f91161bc0d0a8b

16,913

py

Python

lattpy/spatial.py

dylanljones/lattpy

6779ae7755aaf9e844d63a6f63b5036fb64d9f89

[ "MIT" ]

11

2020-10-29T17:23:02.000Z

2022-02-28T12:25:41.000Z

lattpy/spatial.py

dylanljones/lattpy

6779ae7755aaf9e844d63a6f63b5036fb64d9f89

[ "MIT" ]

7

2021-01-12T13:53:42.000Z

2022-03-29T11:21:58.000Z

lattpy/spatial.py

dylanljones/lattpy

6779ae7755aaf9e844d63a6f63b5036fb64d9f89

[ "MIT" ]

1

2021-10-31T11:15:20.000Z

# coding: utf-8 # # This code is part of lattpy. # # Copyright (c) 2021, Dylan Jones # # This code is licensed under the MIT License. The copyright notice in the # LICENSE file in the root directory and this permission notice shall # be included in all copies or substantial portions of the Software. """Spatial algorithms and data structures.""" import math import numpy as np import itertools import matplotlib.pyplot as plt from scipy.spatial import cKDTree, Voronoi from typing import Iterable, Sequence, Optional, Union from .utils import ArrayLike, min_dtype, chain from .plotting import draw_points, draw_vectors, draw_lines, draw_surfaces __all__ = [ "distance", "interweave", "vindices", "vrange", "cell_size", "cell_volume", "compute_vectors", "compute_neighbors", "KDTree", "VoronoiTree", "WignerSeitzCell", "rx", "ry", "rz", "rotate2d", "rotate3d", "build_periodic_translation_vector" ] def distance(r1: ArrayLike, r2: ArrayLike, decimals: Optional[int] = None) -> float: """ Calculates the euclidian distance bewteen two points. Parameters ---------- r1: array_like First input point. r2: array_like Second input point of matching size. decimals: int, optional Optional decimals to round distance to. Returns ------- distance: float """ dist = math.sqrt(np.sum(np.square(r1 - r2))) if decimals is not None: dist = round(dist, decimals) return dist def interweave(arrays: Sequence[np.ndarray]) -> np.ndarray: """ Interweaves multiple arrays along the first axis Example ------- >>> arr1 = np.array([[1, 1], [3, 3], [5, 5]]) >>> arr2 = np.array([[2, 2], [4, 4], [6, 6]]) >>> interweave([arr1, arr2]) array([[1, 1], [2, 2], [3, 3], [4, 4], [5, 5], [6, 6]]) Parameters ---------- arrays: (M) Sequence of (N, ...) array_like The input arrays to interwave. The shape of all arrays must match. Returns ------- interweaved: (M*N, ....) np.ndarray """ shape = list(arrays[0].shape) shape[0] = sum(x.shape[0] for x in arrays) result = np.empty(shape, dtype=arrays[0].dtype) n = len(arrays) for i, arr in enumerate(arrays): result[i::n] = arr return result def vindices(limits: Iterable[Sequence[int]], sort_axis: Optional[int] = 0, dtype: Optional[Union[int, str, np.dtype]] = None) -> np.ndarray: """ Return an array representing the indices of a d-dimensional grid. Parameters ---------- limits: (D, 2) array_like The limits of the indices for each axis. sort_axis: int, optional Optional axis that is used to sort indices. dtype: int or str or np.dtype, optional Optional data-type for storing the lattice indices. By default the given limits are checked to determine the smallest possible data-type. Returns ------- vectors: (N, D) np.ndarray """ if dtype is None: dtype = min_dtype(limits, signed=True) limits = np.asarray(limits) dim = limits.shape[0] # Create meshgrid reshape grid to array of indices # version 1: # axis = np.meshgrid(*(np.arange(*lim, dtype=dtype) for lim in limits)) # nvecs = np.asarray([np.asarray(a).flatten("F") for a in axis]).T # version 2: # slices = [slice(lim[0], lim[1], 1) for lim in limits] # nvecs = np.mgrid[slices].astype(dtype).reshape(dim, -1).T # version 3: size = limits[:, 1] - limits[:, 0] nvecs = np.indices(size, dtype=dtype).reshape(dim, -1).T + limits[:, 0] # Optionally sort indices along given axis if sort_axis is not None: nvecs = nvecs[np.lexsort(nvecs.T[[sort_axis]])] return nvecs def vrange(start=None, *args, dtype: Optional[Union[int, str, np.dtype]] = None, sort_axis: Optional[int] = 0, **kwargs) -> np.ndarray: """ Return evenly spaced vectors within a given interval. Parameters ---------- start: array_like, optional The starting value of the interval. The interval includes this value. The default start value is 0. stop: array_like The end value of the interval. step: array_like, optional Spacing between values. If `start` and `stop` are sequences and the `step` is a scalar the given step size is used for all dimensions of the vectors. The default step size is 1. sort_axis: int, optional Optional axis that is used to sort indices. dtype: dtype, optional The type of the output array. If `dtype` is not given, infer the data type from the other input arguments. Returns ------- vectors: (N, D) np.ndarray """ # parse arguments if len(args) == 0: stop = start start = np.zeros_like(stop) step = kwargs.get("step", 1.0) elif len(args) == 1: stop = args[0] step = kwargs.get("step", 1.0) else: stop, step = args start = np.atleast_1d(start) stop = np.atleast_1d(stop) if step is None: step = np.ones_like(start) elif not hasattr(step, "__len__"): step = np.ones_like(start) * step # Create grid and reshape to array of vectors slices = [slice(i, f, s) for i, f, s in zip(start, stop, step)] array = np.mgrid[slices].reshape(len(slices), -1).T # Optionally sort array along given axis if sort_axis is not None: array = array[np.lexsort(array.T[[sort_axis]])] return array if dtype is None else array.astype(dtype) def cell_size(vectors: ArrayLike) -> np.ndarray: """ Computes the shape of the box spawned by the given vectors. Parameters ---------- vectors: array_like The basis vectors defining the cell. Returns ------- size: np.ndarray """ max_values = np.max(vectors, axis=0) min_values = np.min(vectors, axis=0) min_values[min_values > 0] = 0 return max_values - min_values def cell_volume(vectors: ArrayLike) -> float: r""" Computes the volume of the unit cell defined by the primitive vectors. The volume of the unit-cell in two and three dimensions is defined by .. math:: V_{2d} = \abs{a_1 \cross a_2}, \quad V_{3d} = a_1 \cdot \abs{a_2 \cross a_3} For higher dimensions the volume is computed using the determinant: .. math:: V_{d} = \sqrt{\det{A A^T}} where .math:`A` is the array of vectors. Parameters ---------- vectors: array_like The basis vectors defining the cell. Returns ------- vol: float """ dim = len(vectors) if dim == 1: v = float(vectors) elif dim == 2: v = np.cross(vectors[0], vectors[1]) elif dim == 3: cross = np.cross(vectors[1], vectors[2]) v = np.dot(vectors[0], cross) else: v = np.sqrt(np.linalg.det(np.dot(vectors.T, vectors))) return abs(v) def compute_vectors(a: float, b: Optional[float] = None, c: Optional[float] = None, alpha: Optional[float] = None, beta: Optional[float] = None, gamma: Optional[float] = None, decimals: Optional[int] = 0) -> np.ndarray: """ Computes lattice vectors by the lengths and angles. """ if b is None and c is None: vectors = [a] elif c is None: alpha = np.deg2rad(alpha) ax = a bx = b * np.cos(alpha) by = b * np.sin(alpha) vectors = np.array([ [ax, 0], [bx, by] ]) else: alpha = np.deg2rad(alpha) beta = np.deg2rad(beta) gamma = np.deg2rad(gamma) ax = a bx = b * np.cos(gamma) by = b * np.sin(gamma) cx = c * np.cos(beta) cy = (abs(c) * abs(b) * np.cos(alpha) - bx * cx) / by cz = np.sqrt(c ** 2 - cx ** 2 - cy ** 2) vectors = np.array([ [ax, 0, 0], [bx, by, 0], [cx, cy, cz] ]) if decimals: vectors = np.round(vectors, decimals=decimals) return vectors # noinspection PyUnresolvedReferences def arange(self, steps, offset=0.): limits = self.limits * (1 + offset) steps = [steps] * self.dim if not hasattr(steps, "__len__") else steps return [np.arange(*lims, step=step) for lims, step in zip(limits, steps)] def linspace(self, nums, offset=0.): limits = self.limits * (1 + offset) nums = [nums] * self.dim if not hasattr(nums, "__len__") else nums return [np.linspace(*lims, num=num) for lims, num in zip(limits, nums)] def meshgrid(self, nums=None, steps=None, offset=0., check=True): if nums is not None: grid = np.array(np.meshgrid(*self.linspace(nums, offset))) elif steps is not None: grid = np.array(np.meshgrid(*self.arange(steps, offset))) else: raise ValueError("Either the number of points or the step size muste be specified") if check: lengths = grid.shape[1:] dims = range(len(lengths)) for item in itertools.product(*[range(n) for n in lengths]): point = np.array([grid[d][item] for d in dims]) if not self.check(point): for d in dims: grid[d][item] = np.nan return grid def symmetry_points(self): origin = np.zeros((1,)) corners = self.vertices.copy() face_centers = None if self.dim == 1: return origin, corners, None, None elif self.dim == 2: edge_centers = np.zeros((len(self.edges), 2)) for i, simplex in enumerate(self.edges): p1, p2 = self.vertices[simplex] edge_centers[i] = p1 + (p2 - p1) / 2 elif self.dim == 3: edge_centers = list() face_centers = list() for i, simplex in enumerate(self.edges): edges = self.vertices[simplex] # compute face centers face_centers.append(np.mean(edges, axis=0)) # compute edge centers for p1, p2 in chain(edges, cycle=True): edge_centers.append(p1 + (p2 - p1) / 2) edge_centers = np.asarray(edge_centers) face_centers = np.asarray(face_centers) else: raise NotImplementedError() return origin, corners, edge_centers, face_centers def rx(theta: float) -> np.ndarray: """X-Rotation matrix.""" sin, cos = np.sin(theta), np.cos(theta) return np.array([[1, 0, 0], [0, cos, -sin], [0, sin, cos]]) def ry(theta: float) -> np.ndarray: """Y-Rotation matrix.""" sin, cos = np.sin(theta), np.cos(theta) return np.array([[cos, 0, sin], [0, 1, 0], [-sin, 0, +cos]]) def rz(theta: float) -> np.ndarray: """Z-Rotation matrix.""" sin, cos = np.sin(theta), np.cos(theta) return np.array([[cos, -sin, 0], [sin, cos, 0], [0, 0, 1]]) def rot(thetax: float = 0., thetay: float = 0., thetaz: float = 0.) -> np.ndarray: """General rotation matrix""" r = np.eye(3) if thetaz: r = np.dot(r, rz(thetaz)) if thetay: r = np.dot(r, ry(thetay)) if thetax: r = np.dot(r, rz(thetax)) return r def rotate2d(a, theta): """Applies the z-rotation matrix to a 2D point""" return np.dot(a, rz(theta)[:2, :2]) def rotate3d(a, thetax=0., thetay=0., thetaz=0.): """Applies the general rotation matrix to a 3D point""" return np.dot(a, rot(thetax, thetay, thetaz))

32.840777

98

0.586945

0653906f675760bbd82805e22137ded59d26086f

1,471

py

Python

test/test_io.py

wiktorn/pyosmium

0517eae40da1347523c72a6cb097a5b6158b25a8

[ "BSD-2-Clause" ]

null

test/test_io.py

wiktorn/pyosmium

0517eae40da1347523c72a6cb097a5b6158b25a8

[ "BSD-2-Clause" ]

null

test/test_io.py

wiktorn/pyosmium

0517eae40da1347523c72a6cb097a5b6158b25a8

[ "BSD-2-Clause" ]

null

21.955224

82

0.636302

06556055c40c35a9d717e9e1b378737090b2df39

302

py

Python

tg_gui/_platform_displayio_/shared.py

TG-Techie/tg-gui

11d0eabeffb36fa43fc90f818624053928c37637

[ "MIT" ]

1

2019-03-20T19:49:14.000Z

tg_gui/_platform_displayio_/shared.py

TG-Techie/tg_gui

11d0eabeffb36fa43fc90f818624053928c37637

[ "MIT" ]

15

2019-02-09T12:24:05.000Z

2021-12-01T17:21:06.000Z

tg_gui/_platform_displayio_/shared.py

TG-Techie/tg-gui

11d0eabeffb36fa43fc90f818624053928c37637

[ "MIT" ]

null

from typing import TYPE_CHECKING if TYPE_CHECKING: from displayio import Shape, Group, TileGrid, Palette, Bitmap, OnDiskBitmap NativeElement = Group | Shape | TileGrid | Palette | Bitmap | OnDiskBitmap NativeContainer = Group else: NativeElement = object NativeContainer = object

27.454545

79

0.748344

0655cb7f1c625d52133699a96d2f35fe1b202f17

13,279

py

Python

pyaccords/pysrc/ec2instanceinfo.py

MarouenMechtri/accords-platform-1

4f950fffd9fbbf911840cc5ad0fe5b5a331edf42

[ "Apache-2.0" ]

1

2015-02-28T21:25:54.000Z

pyaccords/pysrc/ec2instanceinfo.py

MarouenMechtri/accords-platform-1

4f950fffd9fbbf911840cc5ad0fe5b5a331edf42

[ "Apache-2.0" ]

null

pyaccords/pysrc/ec2instanceinfo.py

MarouenMechtri/accords-platform-1

4f950fffd9fbbf911840cc5ad0fe5b5a331edf42

[ "Apache-2.0" ]

null

############################################################################## #copyright 2013, Hamid MEDJAHED (hmedjahed@prologue.fr) Prologue # #Licensed under the Apache License, Version 2.0 (the "License"); # #you may not use this file except in compliance with the License. # #You may obtain a copy of the License at # # # # http://www.apache.org/licenses/LICENSE-2.0 # # # #Unless required by applicable law or agreed to in writing, software # #distributed under the License is distributed on an "AS IS" BASIS, # #WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # #See the License for the specific language governing permissions and # #limitations under the License. # ############################################################################## import HTMLParser data = ''' <table cellspacing="0" class="table table-bordered table-hover table-condensed" id="data"> <thead> <tr> <th class="name">Name</th> <th class="memory">Memory</th> <th class="computeunits"> <abbr title="One EC2 Compute Unit provides the equivalent CPU capacity of a 1.0-1.2 GHz 2007 Opteron or 2007 Xeon processor.">Compute Units</abbr> </th> <th class="storage">Storage</th> <th class="architecture">Architecture</th> <th class="ioperf">I/O Performance</th> <th class="maxips"> <abbr title="Adding additional IPs requires launching the instance in a VPC.">Max IPs</abbr> </th> <th class="apiname">API Name</th> <th class="cost">Linux cost</th> <th class="cost">Windows cost</th> </tr> </thead> <tbody> <tr> <td class="name">M1 Small</td> <td class="memory">1.70 GB</td> <td class="computeunits">1</td> <td class="storage">160 GB</td> <td class="architecture">32/64-bit</td> <td class="ioperf">Moderate</td> <td class="maxips">8</td> <td class="apiname">m1.small</td> <td class="cost" hour_cost="0.060">$0.060 per hour</td> <td class="cost" hour_cost="0.115">$0.115 per hour</td> </tr> <tr> <td class="name">M1 Medium</td> <td class="memory">3.75 GB</td> <td class="computeunits">2</td> <td class="storage">410 GB</td> <td class="architecture">32/64-bit</td> <td class="ioperf">Moderate</td> <td class="maxips">12</td> <td class="apiname">m1.medium</td> <td class="cost" hour_cost="0.12">$0.12 per hour</td> <td class="cost" hour_cost="0.23">$0.23 per hour</td> </tr> <tr> <td class="name">M1 Large</td> <td class="memory">7.50 GB</td> <td class="computeunits">4</td> <td class="storage">850 GB</td> <td class="architecture">64-bit</td> <td class="ioperf">High</td> <td class="maxips">30</td> <td class="apiname">m1.large</td> <td class="cost" hour_cost="0.24">$0.24 per hour</td> <td class="cost" hour_cost="0.46">$0.46 per hour</td> </tr> <tr> <td class="name">M1 Extra Large</td> <td class="memory">15.00 GB</td> <td class="computeunits">8</td> <td class="storage">1690 GB</td> <td class="architecture">64-bit</td> <td class="ioperf">High</td> <td class="maxips">60</td> <td class="apiname">m1.xlarge</td> <td class="cost" hour_cost="0.48">$0.48 per hour</td> <td class="cost" hour_cost="0.92">$0.92 per hour</td> </tr> <tr> <td class="name">Micro</td> <td class="memory">0.60 GB</td> <td class="computeunits">2</td> <td class="storage">0 GB</td> <td class="architecture">32/64-bit</td> <td class="ioperf">Low</td> <td class="maxips">1</td> <td class="apiname">t1.micro</td> <td class="cost" hour_cost="0.02">$0.02 per hour</td> <td class="cost" hour_cost="0.02">$0.02 per hour</td> </tr> <tr> <td class="name">High-Memory Extra Large</td> <td class="memory">17.10 GB</td> <td class="computeunits">6.5</td> <td class="storage">420 GB</td> <td class="architecture">64-bit</td> <td class="ioperf">Moderate</td> <td class="maxips">60</td> <td class="apiname">m2.xlarge</td> <td class="cost" hour_cost="0.41">$0.41 per hour</td> <td class="cost" hour_cost="0.57">$0.57 per hour</td> </tr> <tr> <td class="name">High-Memory Double Extra Large</td> <td class="memory">34.20 GB</td> <td class="computeunits">13</td> <td class="storage">850 GB</td> <td class="architecture">64-bit</td> <td class="ioperf">High</td> <td class="maxips">120</td> <td class="apiname">m2.2xlarge</td> <td class="cost" hour_cost="0.82">$0.82 per hour</td> <td class="cost" hour_cost="1.14">$1.14 per hour</td> </tr> <tr> <td class="name">High-Memory Quadruple Extra Large</td> <td class="memory">68.40 GB</td> <td class="computeunits">26</td> <td class="storage">1690 GB</td> <td class="architecture">64-bit</td> <td class="ioperf">High</td> <td class="maxips">240</td> <td class="apiname">m2.4xlarge</td> <td class="cost" hour_cost="1.64">$1.64 per hour</td> <td class="cost" hour_cost="2.28">$2.28 per hour</td> </tr> <tr> <td class="name">M3 Extra Large</td> <td class="memory">15.00 GB</td> <td class="computeunits">13</td> <td class="storage">0 GB</td> <td class="architecture">64-bit</td> <td class="ioperf">Moderate</td> <td class="maxips">60</td> <td class="apiname">m3.xlarge</td> <td class="cost" hour_cost="0.50">$0.50 per hour</td> <td class="cost" hour_cost="0.98">$0.98 per hour</td> </tr> <tr> <td class="name">M3 Double Extra Large</td> <td class="memory">30.00 GB</td> <td class="computeunits">26</td> <td class="storage">0 GB</td> <td class="architecture">64-bit</td> <td class="ioperf">High</td> <td class="maxips">120</td> <td class="apiname">m3.2xlarge</td> <td class="cost" hour_cost="1.00">$1.00 per hour</td> <td class="cost" hour_cost="1.96">$1.96 per hour</td> </tr> <tr> <td class="name">High-CPU Medium</td> <td class="memory">1.70 GB</td> <td class="computeunits">5</td> <td class="storage">350 GB</td> <td class="architecture">32_64-bit</td> <td class="ioperf">Moderate</td> <td class="maxips">12</td> <td class="apiname">c1.medium</td> <td class="cost" hour_cost="0.145">$0.145 per hour</td> <td class="cost" hour_cost="0.285">$0.285 per hour</td> </tr> <tr> <td class="name">High-CPU Extra Large</td> <td class="memory">7.00 GB</td> <td class="computeunits">20</td> <td class="storage">1690 GB</td> <td class="architecture">64-bit</td> <td class="ioperf">High</td> <td class="maxips">60</td> <td class="apiname">c1.xlarge</td> <td class="cost" hour_cost="0.58">$0.58 per hour</td> <td class="cost" hour_cost="1.14">$1.14 per hour</td> </tr> <tr> <td class="name">Cluster Compute Quadruple Extra Large</td> <td class="memory">23.00 GB</td> <td class="computeunits">33.5</td> <td class="storage">1690 GB</td> <td class="architecture">64-bit</td> <td class="ioperf">Very High</sort></td> <td class="maxips">1</td> <td class="apiname">cc1.4xlarge</td> <td class="cost" hour_cost="1.30">$1.30 per hour</td> <td class="cost" hour_cost="1.61">$1.61 per hour</td> </tr> <tr> <td class="name">Cluster Compute Eight Extra Large</td> <td class="memory">60.50 GB</td> <td class="computeunits">88</td> <td class="storage">3370 GB</td> <td class="architecture">64-bit</td> <td class="ioperf">Very High</sort></td> <td class="maxips">240</td> <td class="apiname">cc2.8xlarge</td> <td class="cost" hour_cost="2.40">$2.40 per hour</td> <td class="cost" hour_cost="2.97">$2.97 per hour</td> </tr> <tr> <td class="name">Cluster GPU Quadruple Extra Large</td> <td class="memory">22.00 GB</td> <td class="computeunits">33.5</td> <td class="storage">1690 GB</td> <td class="architecture">64-bit</td> <td class="ioperf">Very High</sort></td> <td class="maxips">1</td> <td class="apiname">cg1.4xlarge</td> <td class="cost" hour_cost="2.10">$2.10 per hour</td> <td class="cost" hour_cost="2.60">$2.60 per hour</td> </tr> <tr> <td class="name">High I/O Quadruple Extra Large</td> <td class="memory">60.50 GB</td> <td class="computeunits">35</td> <td class="storage">2048 GB</td> <td class="architecture">64-bit</td> <td class="ioperf">Very High</sort></td> <td class="maxips">1</td> <td class="apiname">hi1.4xlarge</td> <td class="cost" hour_cost="3.10">$3.10 per hour</td> <td class="cost" hour_cost="3.58">$3.58 per hour</td> </tr> <tr> <td class="name">High Storage Eight Extra Large</td> <td class="memory">117.00 GB</td> <td class="computeunits">35</td> <td class="storage">48 TB</td> <td class="architecture">64-bit</td> <td class="ioperf">Very High</sort></td> <td class="maxips">1</td> <td class="apiname">hs1.8xlarge</td> <td class="cost" hour_cost="4.600">$4.600 per hour</td> <td class="cost" hour_cost="4.931">$4.931 per hour</td> </tr> <tr> <td class="name">High Memory Cluster Eight Extra Large</td> <td class="memory">244.00 GB</td> <td class="computeunits">88</td> <td class="storage">240 GB</td> <td class="architecture">64-bit</td> <td class="ioperf">Very High</sort></td> <td class="maxips">1</td> <td class="apiname">cr1.8xlarge</td> <td class="cost" hour_cost="3.500">$3.500 per hour</td> <td class="cost" hour_cost="3.831">$3.831 per hour</td> </tr> </tbody> </table> '''

48.112319

158

0.524889

06585c3b0c0000d446eb614d1e5895fa37089822

1,105

py

Python

backend/project_requests/admin.py

mnieber/taskboard

7925342751e2782bd0a0258eb2d43d9ec90ce9d8

[ "MIT" ]

null

backend/project_requests/admin.py

mnieber/taskboard

7925342751e2782bd0a0258eb2d43d9ec90ce9d8

[ "MIT" ]

null

backend/project_requests/admin.py

mnieber/taskboard

7925342751e2782bd0a0258eb2d43d9ec90ce9d8

[ "MIT" ]

null

from django.contrib import admin from django.http import HttpResponseRedirect from django.urls import path from faker import Faker from .models import ProjectRequest from .utils import create_project_request

29.864865

84

0.608145

065a10f929ff09c5fb1f252b5c8f9281c467c8ab

13,477

py

Python

reinforcement_learning/tabular_RL/algorithms/td_zero.py

EliorBenYosef/reinforcement-learning

c694d07044e12c92e08ca8c2ef06b073ca1704d4

[ "MIT" ]

16

2019-09-01T14:12:45.000Z

2022-03-07T03:10:38.000Z

reinforcement_learning/tabular_RL/algorithms/td_zero.py

EliorBenYosef/reinforcement-learning

c694d07044e12c92e08ca8c2ef06b073ca1704d4

[ "MIT" ]

5

2020-11-13T19:08:40.000Z

2022-02-10T04:13:29.000Z

reinforcement_learning/tabular_RL/algorithms/td_zero.py

EliorBenYosef/reinforcement-learning

c694d07044e12c92e08ca8c2ef06b073ca1704d4

[ "MIT" ]

1

2022-03-07T03:10:39.000Z

import numpy as np from gym import wrappers from reinforcement_learning.utils.utils import decrement_eps, EPS_DEC_LINEAR, pickle_save from reinforcement_learning.tabular_RL.utils import init_v, init_q, init_q1_q2, \ max_action_q, max_action_q1_q2, eps_greedy_q, eps_greedy_q1_q2, print_v

34.205584

119

0.549974

0660694db2ddc7b0023f6b169f47cbe6fc31c8a7

916

py

Python

topo.py

rahil-g/gpf

234c22f500283f75454ccba4a12b765be9ddad05

[ "MIT" ]

null

topo.py

rahil-g/gpf

234c22f500283f75454ccba4a12b765be9ddad05

[ "MIT" ]

null

topo.py

rahil-g/gpf

234c22f500283f75454ccba4a12b765be9ddad05

[ "MIT" ]

null

#Author: Rahil Gandotra #This file consists of the custom Mininet topology used for GPF. from mininet.topo import Topo topos = { 'mytopo': ( lambda: MyTopo() ) }

31.586207

75

0.622271

0661b5f4de7b9d1818fd8ebe0cb07e2e58e19d2a

10,819

py

Python

Contents/Libraries/Shared/subliminal_patch/providers/legendastv.py

jippo015/Sub-Zero.bundle

734e0f7128c05c0f639e11e7dfc77daa1014064b

[ "MIT" ]

1,553

2015-11-09T02:17:06.000Z

2022-03-31T20:24:52.000Z

Contents/Libraries/Shared/subliminal_patch/providers/legendastv.py

saiterlz/Sub-Zero.bundle

1a0bb9c3e4be84be35d46672907783363fe5a87b

[ "MIT" ]

691

2015-11-05T21:32:26.000Z

2022-03-17T10:52:45.000Z

Contents/Libraries/Shared/subliminal_patch/providers/legendastv.py

saiterlz/Sub-Zero.bundle

1a0bb9c3e4be84be35d46672907783363fe5a87b

[ "MIT" ]

162

2015-11-06T19:38:55.000Z

2022-03-16T02:42:41.000Z

# coding=utf-8 import logging import rarfile import os from subliminal.exceptions import ConfigurationError from subliminal.providers.legendastv import LegendasTVSubtitle as _LegendasTVSubtitle, \ LegendasTVProvider as _LegendasTVProvider, Episode, Movie, guess_matches, guessit, sanitize, region, type_map, \ raise_for_status, json, SHOW_EXPIRATION_TIME, title_re, season_re, datetime, pytz, NO_VALUE, releases_key, \ SUBTITLE_EXTENSIONS, language_converters from subzero.language import Language logger = logging.getLogger(__name__) def list_subtitles(self, video, languages): season = episode = None if isinstance(video, Episode): titles = [video.series] + video.alternative_series season = video.season episode = video.episode else: titles = [video.title] + video.alternative_titles for title in titles: subtitles = [s for l in languages for s in self.query(l, title, season=season, episode=episode, year=video.year, imdb_id=video.imdb_id)] if subtitles: return subtitles return [] def download_subtitle(self, subtitle): super(LegendasTVProvider, self).download_subtitle(subtitle) subtitle.archive.content = None def get_archives(self, title_id, language_code, title_type, season, episode): return super(LegendasTVProvider, self).get_archives.original(self, title_id, language_code, title_type, season, episode)

41.136882

118

0.574175

06631addf22bfb69f24be36f23cfcd2fff2aa5f2

1,587

py

Python

Position.py

bubakazouba/Robinhood-for-Google-Finance

4e0aa8955e4bc786a8528ea500459f5937f15a96

[ "MIT" ]

5

2017-11-24T08:13:47.000Z

2021-05-05T04:48:30.000Z

Position.py

bubakazouba/Robinhood-for-Google-Finance

4e0aa8955e4bc786a8528ea500459f5937f15a96

[ "MIT" ]

null

Position.py

bubakazouba/Robinhood-for-Google-Finance

4e0aa8955e4bc786a8528ea500459f5937f15a96

[ "MIT" ]

null

import re

44.083333

352

0.63264

066587c08345eadec5ce3298131ac1c2190623fb

15,789

py

Python

app_framework/main_window.py

planktontoolbox/plankton-toolbox

626930120329983fb9419a9aed94712148bac219

[ "MIT" ]

5

2016-12-02T08:24:35.000Z

2021-02-24T08:41:41.000Z

app_framework/main_window.py

planktontoolbox/plankton-toolbox

626930120329983fb9419a9aed94712148bac219

[ "MIT" ]

53

2016-11-14T13:11:41.000Z

2022-01-13T09:28:11.000Z

app_framework/main_window.py

planktontoolbox/plankton-toolbox

626930120329983fb9419a9aed94712148bac219

[ "MIT" ]

1

2020-11-27T01:20:10.000Z

#!/usr/bin/python3 # -*- coding:utf-8 -*- # Project: http://plankton-toolbox.org # Copyright (c) 2010-2018 SMHI, Swedish Meteorological and Hydrological Institute # License: MIT License (see LICENSE.txt or http://opensource.org/licenses/mit). import time import codecs from PyQt5 import QtWidgets from PyQt5 import QtCore import plankton_core import app_framework import app_activities import app_tools import toolbox_utils

44.601695

104

0.611565

0665edd3f6dbbe3c348e0f2328bbc74630f84ac0

481

py

Python

Traits/manual/this.py

marshallmcdonnell/interactive_plotting

35e9a781fa1a7328679794d27e24e194e35c012b

[ "MIT" ]

null

Traits/manual/this.py

marshallmcdonnell/interactive_plotting

35e9a781fa1a7328679794d27e24e194e35c012b

[ "MIT" ]

null

Traits/manual/this.py

marshallmcdonnell/interactive_plotting

35e9a781fa1a7328679794d27e24e194e35c012b

[ "MIT" ]

null

#!/usr/bin/env python # this.py --- Example of This predefined trait from traits.api import HasTraits, This #--------------------------------------- # Extrat fred = Employee() mary = Executive() # The following is OK, because fred's manager can be an # instance of Employee or any subclass. fred.manager = mary # This is also OK, because mary's manager can be an Employee mary.manager = fred

20.041667

60

0.665281

06669e5cbe5823ce5ec6dea9345b3539ee4591b9

1,443

py

Python

two_buckets_and_a_lambda/terraform/lambdas/credentials-lambda.py

chariotsolutions/aws-examples

0c0945966f3e1b118ba5db948d5db3e304bc2ac3

[ "MIT" ]

6

2020-05-20T13:58:35.000Z

2022-02-04T13:25:05.000Z

two_buckets_and_a_lambda/terraform/lambdas/credentials-lambda.py

chariotsolutions/aws-examples

0c0945966f3e1b118ba5db948d5db3e304bc2ac3

[ "MIT" ]

1

2021-09-02T21:19:10.000Z

two_buckets_and_a_lambda/terraform/lambdas/credentials-lambda.py

chariotsolutions/aws-examples

0c0945966f3e1b118ba5db948d5db3e304bc2ac3

[ "MIT" ]

3

2019-11-14T21:03:15.000Z

2022-01-17T19:12:02.000Z

import boto3 import json import logging import os bucket = os.environ['UPLOAD_BUCKET'] role_arn = os.environ['ASSUMED_ROLE_ARN'] sts_client = boto3.client('sts') logger = logging.getLogger(__name__) logger.setLevel(logging.DEBUG)

27.226415

105

0.546778

066b73292bddecff1f5b7768febdea7bae1b1c84

173

py

Python

tests/src/year2017/test_day05b.py

lancelote/advent_of_code

06dda6ca034bc1e86addee7798bb9b2a34ff565b

[ "Unlicense" ]

10

2017-12-11T17:54:52.000Z

2021-12-09T20:16:30.000Z

tests/src/year2017/test_day05b.py

lancelote/advent_of_code

06dda6ca034bc1e86addee7798bb9b2a34ff565b

[ "Unlicense" ]

260

2015-12-09T11:03:03.000Z

2021-12-12T14:32:23.000Z

tests/src/year2017/test_day05b.py

lancelote/advent_of_code

06dda6ca034bc1e86addee7798bb9b2a34ff565b

[ "Unlicense" ]

null

"""2017 - Day 5 Part 2: A Maze of Twisty Trampolines, All Alike tests.""" from src.year2017.day05b import solve

24.714286

73

0.676301

066ba314a42c5ac31373da270a9eb728c97a0653

1,374

py

Python

tests/test_signal.py

etcd/manticore

87073d9985c4ca445217b7b135a6af0a51044b21

[ "Apache-2.0" ]

null

tests/test_signal.py

etcd/manticore

87073d9985c4ca445217b7b135a6af0a51044b21

[ "Apache-2.0" ]

null

tests/test_signal.py

etcd/manticore

87073d9985c4ca445217b7b135a6af0a51044b21

[ "Apache-2.0" ]

1

2021-12-26T12:57:01.000Z

import unittest from manticore.utils.event import Signal

19.628571

56

0.569869

066e5fb8233dc5224d22ebf3b89a9a83782274aa

745

py

Python

TITADOweb/web/migrations/0005_passwordresetcodes.py

KomeilParseh/TITA-DO

714685fa18bfd2ef07f5c0d656927039b05d7997

[ "MIT" ]

9

2020-08-27T10:10:11.000Z

2021-04-21T04:46:15.000Z

TITADOweb/web/migrations/0005_passwordresetcodes.py

mdk1384/TITA-DO-1

714685fa18bfd2ef07f5c0d656927039b05d7997

[ "MIT" ]

2

2020-08-27T12:09:57.000Z

2021-01-05T09:29:19.000Z

TITADOweb/web/migrations/0005_passwordresetcodes.py

mdk1384/TITA-DO-1

714685fa18bfd2ef07f5c0d656927039b05d7997

[ "MIT" ]

2

2020-08-27T10:10:18.000Z

2021-01-01T06:20:20.000Z

# Generated by Django 3.1.4 on 2020-12-27 17:34 from django.db import migrations, models

29.8

114

0.561074

0672220769ef18bb8f7d78e648bf612a87c0cd49

253

py

Python

setup.py

SodakDoubleD/dbprime

76d2824adbe0f10d6ad04a5607a07f36874389c7

[ "MIT" ]

null

setup.py

SodakDoubleD/dbprime

76d2824adbe0f10d6ad04a5607a07f36874389c7

[ "MIT" ]

null

setup.py

SodakDoubleD/dbprime

76d2824adbe0f10d6ad04a5607a07f36874389c7

[ "MIT" ]

null

from distutils.core import setup with open("README.md", "r") as fh: long_description = fh.read() setup( name="dbprime", version="0.1dev", author="Dalton Dirkson", author_email="sodakdoubled@gmail.com", packages=["dbprime",], )

19.461538

42

0.652174

0672274e210ffb823f4cb0faec6bba2fb13a9739

2,491

py

Python

008.py

ThomasB123/Project-Euler

ca6786513f210e79fe55417ed43797ffb24610af

[ "MIT" ]

null

008.py

ThomasB123/Project-Euler

ca6786513f210e79fe55417ed43797ffb24610af

[ "MIT" ]

null

008.py

ThomasB123/Project-Euler

ca6786513f210e79fe55417ed43797ffb24610af

[ "MIT" ]

null

# Largest product in a series ''' The four adjacent digits in the 1000-digit number that have the greatest product are 9 9 8 9 = 5832. 73167176531330624919225119674426574742355349194934 96983520312774506326239578318016984801869478851843 85861560789112949495459501737958331952853208805511 12540698747158523863050715693290963295227443043557 66896648950445244523161731856403098711121722383113 62229893423380308135336276614282806444486645238749 30358907296290491560440772390713810515859307960866 70172427121883998797908792274921901699720888093776 65727333001053367881220235421809751254540594752243 52584907711670556013604839586446706324415722155397 53697817977846174064955149290862569321978468622482 83972241375657056057490261407972968652414535100474 82166370484403199890008895243450658541227588666881 16427171479924442928230863465674813919123162824586 17866458359124566529476545682848912883142607690042 24219022671055626321111109370544217506941658960408 07198403850962455444362981230987879927244284909188 84580156166097919133875499200524063689912560717606 05886116467109405077541002256983155200055935729725 71636269561882670428252483600823257530420752963450 Find the thirteen adjacent digits in the 1000-digit number that have the greatest product. What is the value of this product? ''' # Answer = 23514624000 number = '7316717653133062491922511967442657474235534919493496983520312774506326239578318016984801869478851843858615607891129494954595017379583319528532088055111254069874715852386305071569329096329522744304355766896648950445244523161731856403098711121722383113622298934233803081353362766142828064444866452387493035890729629049156044077239071381051585930796086670172427121883998797908792274921901699720888093776657273330010533678812202354218097512545405947522435258490771167055601360483958644670632441572215539753697817977846174064955149290862569321978468622482839722413756570560574902614079729686524145351004748216637048440319989000889524345065854122758866688116427171479924442928230863465674813919123162824586178664583591245665294765456828489128831426076900422421902267105562632111110937054421750694165896040807198403850962455444362981230987879927244284909188845801561660979191338754992005240636899125607176060588611646710940507754100225698315520005593572972571636269561882670428252483600823257530420752963450' greatest = 1 for x in range(988): product = 1 for y in range(13): product *= int(number[x+y]) greatest = max(greatest,product) print(greatest)

67.324324

1,011

0.934163

067270cf798fc12d58fd8f1dd276c3807b8272a4

4,102

py

Python

tfsnippet/utils/misc.py

Feng37/tfsnippet

70c7dc5c8c8f6314f9d9e44697f90068417db5cd

[ "MIT" ]

null

tfsnippet/utils/misc.py

Feng37/tfsnippet

70c7dc5c8c8f6314f9d9e44697f90068417db5cd

[ "MIT" ]

null

tfsnippet/utils/misc.py

Feng37/tfsnippet

70c7dc5c8c8f6314f9d9e44697f90068417db5cd

[ "MIT" ]

null

import os import re from contextlib import contextmanager import numpy as np import six __all__ = ['humanize_duration', 'camel_to_underscore', 'NOT_SET', 'cached_property', 'clear_cached_property', 'maybe_close', 'iter_files'] def humanize_duration(seconds): """ Format specified time duration as human readable text. Args: seconds: Number of seconds of the time duration. Returns: str: The formatted time duration. """ if seconds < 0: seconds = -seconds suffix = ' ago' else: suffix = '' pieces = [] for uvalue, uname in [(86400, 'day'), (3600, 'hr'), (60, 'min')]: if seconds >= uvalue: val = int(seconds // uvalue) if val > 0: if val > 1: uname += 's' pieces.append('{:d} {}'.format(val, uname)) seconds %= uvalue if seconds > np.finfo(np.float64).eps: pieces.append('{:.4g} sec{}'.format( seconds, 's' if seconds > 1 else '')) elif not pieces: pieces.append('0 sec') return ' '.join(pieces) + suffix def camel_to_underscore(name): """Convert a camel-case name to underscore.""" s1 = re.sub(CAMEL_TO_UNDERSCORE_S1, r'\1_\2', name) return re.sub(CAMEL_TO_UNDERSCORE_S2, r'\1_\2', s1).lower() CAMEL_TO_UNDERSCORE_S1 = re.compile('([^_])([A-Z][a-z]+)') CAMEL_TO_UNDERSCORE_S2 = re.compile('([a-z0-9])([A-Z])') NOT_SET = NotSet() def cached_property(cache_key): """ Decorator to cache the return value of an instance property. .. code-block:: python class MyClass(object): @cached_property('_cached_property'): def cached_property(self): return ... # usage o = MyClass() print(o.cached_property) # fetch the cached value Args: cache_key (str): Attribute name to store the cached value. """ return wrapper def clear_cached_property(instance, cache_key): """ Clear the cached values of specified property. Args: instance: The owner instance of the cached property. cache_key (str): Attribute name to store the cached value. """ if hasattr(instance, cache_key): delattr(instance, cache_key)

25.962025

73

0.573623

0673b5944cf3b730042b94eae2844b3646f79c99

54,598

py

Python

spaic/Backend/Backend.py

ZhejianglabNCRC/SPAIC

5a08328adcc5a197316cf353746bae7ab6865337

[ "Apache-2.0" ]

3

2022-03-01T03:04:25.000Z

2022-03-01T03:07:15.000Z

spaic/Backend/Backend.py

ZhejianglabNCRC/SPAIC

5a08328adcc5a197316cf353746bae7ab6865337

[ "Apache-2.0" ]

null

spaic/Backend/Backend.py

ZhejianglabNCRC/SPAIC

5a08328adcc5a197316cf353746bae7ab6865337

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- """ Created on 2020/8/6 @project: SPAIC @filename: Backend @author: Hong Chaofei @contact: hongchf@gmail.com @description: backend Pytorch, Tensorflow, CUDA, Euler, 2 Runge-Kutta """ from abc import abstractmethod, ABC from collections import OrderedDict from ..Network.BaseModule import BaseModule, VariableAgent from ..Network.DelayQueue import DelayQueue import numpy as np import torch backends = dict() # @abstractmethod # def euler_update(self): # pass # # @abstractmethod # def rk2_update(self): # pass # # @abstractmethod # def reset(self, v, v_reset, u_reset, spike): # ''' # voltage reset # # Parameters # ---------- # v # v_reset # u_reset # spike # # Returns # ------- # v[spike] = v_reset # v[spike] += u_reset # ''' # # @abstractmethod # def reset_u(self, u, u_reset, spike): # ''' # recovery reset # # Parameters # ---------- # u # _reset # spike # # Returns # ------- # u[spike] = u+u_reset # ''' # NotImplementedError() # # @abstractmethod # def next_stage(self, x): # ''' # # Parameters # ---------- # x: list # # Returns # ------- # x[index] # ''' # # @abstractmethod # def izh_v(self, v, u, psp): # ''' # # Parameters # ---------- # v: list # u: list # psp: list # # Returns # ------- # V=V+dt*(0.04*V^2+5*V+140-U+PSP) # ''' # NotImplementedError() # # @abstractmethod # def izh_u(self, a, b, v, u): # ''' # # Parameters # ---------- # a: list # b: list # u: list # v: list # # Returns # ------- # U=U+a*(b*V-U) # ''' # NotImplementedError() def exp(self, x): ''' Args: x(tensor): an n-dimensional torch.Tensor Returns: return exp(x) ''' NotImplementedError() def sin(self, x): ''' Args: x(tensor): an n-dimensional torch.Tensor Returns: return exp(x) ''' NotImplementedError() def cos(self, x): ''' Args: x(tensor): an n-dimensional torch.Tensor Returns: return exp(x) ''' NotImplementedError() def tan(self, x): ''' Args: x(tensor): an n-dimensional torch.Tensor Returns: return exp(x) ''' NotImplementedError() def log(self, x): ''' Args: x(tensor): an n-dimensional torch.Tensor Returns: return exp(x) ''' NotImplementedError() def log2(self, x): ''' Args: x(tensor): an n-dimensional torch.Tensor Returns: return exp(x) ''' NotImplementedError() def log10(self, x): ''' Args: x(tensor): an n-dimensional torch.Tensor Returns: return exp(x) ''' NotImplementedError() # class Darwin_Backend(Backend): # # def __init__(self): # super(Darwin_Backend, self).__init__() # pass

36.447263

218

0.54502

0673b6dfdd8c195674ae3591ed3bb93d152c2801

1,257

py

Python

yuz_egitimi.py

mehdikosaca/yuz_tanima

d2d7828a1f5562d21acde3af8df60ec96a88e7c3

[ "Apache-2.0" ]

2

2021-12-30T06:38:21.000Z

2021-12-30T06:39:24.000Z

yuz_egitimi.py

mehdikosaca/yuz_tanima

d2d7828a1f5562d21acde3af8df60ec96a88e7c3

[ "Apache-2.0" ]

null

yuz_egitimi.py

mehdikosaca/yuz_tanima

d2d7828a1f5562d21acde3af8df60ec96a88e7c3

[ "Apache-2.0" ]

null

import cv2 import numpy as np from PIL import Image import os #Verilerin yolu path = "veriseti" recognizer = cv2.face.LBPHFaceRecognizer_create() detector = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml') #imajlarn alnmas ve etiketlenmesi iin fonksiyon print("\n [INFO] yzler eitiliyor. Birka saniye bekleyin...") yuzler, ids = getImageAndLabels(path) recognizer.train(yuzler,np.array(ids)) #Modeli eitim/eitim dosyasna kaydet recognizer.write("egitim/egitim.yml") #Dikkat! recognizer.save() Raspberry Pi zerinde almyor #Eitilen yz saysn gster ve kodu sonlandr print(f"\n [INFO] {len(np.unique(ids))} yz eitildi. Betik sonlandrlyor...") print(yuzler)

36.970588

97

0.706444

06743547989129e1af7ae30ff01eaf04b4056ad2

1,846

py

Python

hello.py

jferroaq/Tarea7z

013f1f1e8dc3b631be102d6e5731d2ffdffd3657

[ "Apache-2.0" ]

null

hello.py

jferroaq/Tarea7z

013f1f1e8dc3b631be102d6e5731d2ffdffd3657

[ "Apache-2.0" ]

null

hello.py

jferroaq/Tarea7z

013f1f1e8dc3b631be102d6e5731d2ffdffd3657

[ "Apache-2.0" ]

null

import kivy from kivy.app import App from kivy.uix.button import Label from kivy.uix.colorpicker import ColorPicker from kivy.graphics import Color, Ellipse, Triangle from kivy.properties import StringProperty, ObjectProperty if __name__ in ["__main__", "__android__"]: SaludoApp().run()

25.287671

65

0.62026

0674d6e58cd606f3c44fa44647eb41365904b800

356

py

Python

mundo-02/aula13-ex054.py

fabiocoutoaraujo/CursoVideoPython

7e3b6ab89cbbba79f640d12e40f3d1e8c22295cf

[ "MIT" ]

1

2020-04-18T16:39:23.000Z

mundo-02/aula13-ex054.py

fabiocoutoaraujo/CursoVideoPython

7e3b6ab89cbbba79f640d12e40f3d1e8c22295cf

[ "MIT" ]

null

mundo-02/aula13-ex054.py

fabiocoutoaraujo/CursoVideoPython

7e3b6ab89cbbba79f640d12e40f3d1e8c22295cf

[ "MIT" ]

null

from datetime import date maior = menor = 0 atual = date.today().year for c in range(1, 8): nascimento = int(input(f'Em que ano a {c} pessoa nasceu? ')) if atual - nascimento > 20: maior += 1 else: menor += 1 print(f'Ao todo, temos {maior} pessoas maiores de idade!') print(f'Ao todo, temos {menor} pessoas menores de idade!')

29.666667

65

0.63764

0675b9a64430a3b476aa0125ccfd22711ba0b255

6,356

py

Python

Contents/Code/zdfneo.py

typekitrel/abctestard

1df43561327694ba155f513ad152aab51c56ef42

[ "MIT" ]

null

Contents/Code/zdfneo.py

typekitrel/abctestard

1df43561327694ba155f513ad152aab51c56ef42

[ "MIT" ]

null

Contents/Code/zdfneo.py

typekitrel/abctestard

1df43561327694ba155f513ad152aab51c56ef42

[ "MIT" ]

null

# -*- coding: utf-8 -*- # zdfneo.py - Aufruf durch __init__.py/ZDF_get_content # # Die Funktionen dienen zur Auswertung der ZDF-Neo-Seiten # Neo_Base = 'https://www.neo-magazin-royale.de' PREFIX = '/video/ardmediathek2016/zdfneo' #################################################################################################### #------------------------- #------------------------- #################################################################################################### # htmlentities in neo, Zeichen s. http://aurelio.net/bin/python/fix-htmldoc-utf8.py # HTMLParser() versagt hier def unescape_neo(line): line_ret = (line.replace("Ã¶", "").replace("Ã&curren", "").replace("Ã&frac14", "") .replace("Ã\x96", "").replace("Ã\x84", "").replace("Ã\x9c", "") .replace("Ã\x9f", "")) return line_ret

43.834483

112

0.660321

067c09b6f2a552b9aa72b55a71741cdde3c0cbee

8,732

py

Python

luna/mol/atom.py

keiserlab/LUNA

ab2a968550f3f91107f8a91ad00da6aa3e2df68b

[ "MIT" ]

2

2022-03-23T13:34:36.000Z

2022-03-27T22:21:08.000Z

luna/mol/atom.py

keiserlab/LUNA

ab2a968550f3f91107f8a91ad00da6aa3e2df68b

[ "MIT" ]

1

2022-03-22T19:29:50.000Z

luna/mol/atom.py

keiserlab/LUNA

ab2a968550f3f91107f8a91ad00da6aa3e2df68b

[ "MIT" ]

null

import numpy as np from openbabel import openbabel as ob import logging logger = logging.getLogger() def __eq__(self, other): """Overrides the default implementation""" if isinstance(self, other.__class__): return (self.atomic_num == other.atomic_num and np.all(self._coord == other._coord) and self.serial_number == other.serial_number) return False def __ne__(self, other): """Overrides the default implementation""" return not self.__eq__(other) def __hash__(self): """Overrides the default implementation""" return hash((self.atomic_num, tuple(self._coord), self.serial_number)) class ExtendedAtom: """Extend :class:`~luna.MyBio.PDB.Atom.Atom` with additional properties and methods. Parameters ---------- atom : :class:`~luna.MyBio.PDB.Atom.Atom` An atom. nb_info : iterable of `AtomData`, optional A sequence of `AtomData` containing information about atoms covalently bound to ``atom``. atm_grps : iterable of :class:`~luna.groups.AtomGroup`, optional A sequence of atom groups that contain ``atom``. invariants : list or tuple, optional Atomic invariants. """ def add_nb_info(self, nb_info): """ Add `AtomData` objects to ``neighbors_info``.""" self._nb_info = list(set(self._nb_info + list(nb_info))) def add_atm_grps(self, atm_grps): """ Add :class:`~luna.groups.AtomGroup` objects to ``atm_grps``.""" self._atm_grps = list(set(self._atm_grps + list(atm_grps))) def remove_nb_info(self, nb_info): """ Remove `AtomData` objects from ``neighbors_info``.""" self._nb_info = list(set(self._nb_info) - set(nb_info)) def remove_atm_grps(self, atm_grps): """ Remove :class:`~luna.groups.AtomGroup` objects from ``atm_grps``.""" self._atm_grps = list(set(self._atm_grps) - set(atm_grps)) def get_neighbor_info(self, atom): """Get information from a covalently bound atom.""" for info in self._nb_info: if atom.serial_number == info.serial_number: return info def is_neighbor(self, atom): """Check if a given atom is covalently bound to it.""" return atom.serial_number in [i.serial_number for i in self._nb_info] def as_json(self): """Represent the atom as a dict containing the structure id, model id, chain id, residue name, residue id, and atom name. The dict is defined as follows: * ``pdb_id`` (str): structure id; * ``model`` (str): model id; * ``chain`` (str): chain id; * ``res_name`` (str): residue name; * ``res_id`` (tuple): residue id (hetflag, sequence identifier, insertion code); * ``name`` (tuple): atom name (atom name, alternate location). """ full_id = self.get_full_id() return {"pdb_id": full_id[0], "model": full_id[1], "chain": full_id[2], "res_name": self.parent.resname, "res_id": full_id[3], "name": full_id[4]} def __eq__(self, other): """Overrides the default implementation""" if isinstance(self, other.__class__): return self.full_atom_name == other.full_atom_name return False def __ne__(self, other): """Overrides the default implementation""" return not self.__eq__(other) def __hash__(self): """Overrides the default implementation""" return hash(self.full_atom_name)

34.928

122

0.61475

067d4e2d3158aba74160b531385178fe32b82215

1,379

py

Python

src/cogs/example_cog.py

Abaan404/MagmaBot

2149f6ad8a6a1158112ab9efb4dc77c04c3a5f8e

[ "MIT" ]

1

2021-10-03T21:05:45.000Z

src/cogs/example_cog.py

Abaan404/MagmaBot

2149f6ad8a6a1158112ab9efb4dc77c04c3a5f8e

[ "MIT" ]

null

src/cogs/example_cog.py

Abaan404/MagmaBot

2149f6ad8a6a1158112ab9efb4dc77c04c3a5f8e

[ "MIT" ]

null

import discord, itertools from discord.ext import commands, tasks # Lava is not allowed to change the first text PRESENCE_TEXT = itertools.cycle(["lava is cute", "*pushes you against wall* wanna play fortnite amongus?", "with ur mum", "owo.exe", "dangit jelly", "gewrhgkhewghkhfuckoiyo5uo", "MiEWcWAFT?? OWOWO"])

35.358974

199

0.645395

067de1744accb6c29040ae07655261d1c6513b87

33,808

py

Python

Public_accessV3.py

nismod/natural-capital-mapping

f388fee3c2592bf99eb628b4d4743bf9be3d4346

[ "MIT" ]

1

2021-03-31T00:35:00.000Z

Public_accessV3.py

nismod/natural-capital-mapping

f388fee3c2592bf99eb628b4d4743bf9be3d4346

[ "MIT" ]

null

Public_accessV3.py

nismod/natural-capital-mapping

f388fee3c2592bf99eb628b4d4743bf9be3d4346

[ "MIT" ]

2

2020-01-30T09:40:36.000Z

2021-04-16T09:36:40.000Z

# Aim is to apply a multiplier to the natural capital scores to reflect the degree of public access # Challenge is that it is difficult to clip or intersect the complex public access layer with the large and # detailed OSMM-based base map - it takes days to run and then fails. # So here we extract a subset of the base map that excludes gardens and manmade features, to cut the processing load. # Create a public access layer from component datasets and set up a multiplier for recreation # Intersect the public access layer with the subset and merge back into the base map # A separate multiplier can then be applied to all gardens to reflect their private value if required # ----------------------------------------------------------------------------------------------------------------- import time import arcpy import os import MyFunctions arcpy.CheckOutExtension("Spatial") print(''.join(["## Started on : ", time.ctime()])) arcpy.env.overwriteOutput = True # Overwrites files arcpy.env.qualifiedFieldNames = False # Joined fields will be exported without the join table name arcpy.env.XYTolerance = "0.001 Meters" # *** Enter parameters # -------------------- # region = "Arc" # region = "Oxon" region = "NP" # Choice of method that has been used to generate the input files - this determines location and names of input files method = "CROME_PHI" # method = "LERC" # method = "HLU" if region == "Oxon" and method == "HLU": gdbs = [r"D:\cenv0389\Oxon_GIS\Oxon_county\Data\Public_access.gdb"] region_boundary = "Oxfordshire" boundary = "Oxfordshire" base_map = "OSMM_HLU_CR_ALC_Des_GS" area_tag = "Oxon" hab_field = "Interpreted_habitat" # Name of OSMM fields used for interpretation MakeField = "Make" DescGroup = "DescriptiveGroup" DescTerm = "DescriptiveTerm" delete_1 = True elif region == "Arc" or region == "NP" or (region == "Oxon" and method == "CROME_PHI"): if region == "NP": folder = r"M:\urban_development_natural_capital" region_boundary = os.path.join(folder, "Data.gdb\NP_boundary") else: folder = r"D:\cenv0389\OxCamArc\NatCap_Arc_FreeData" region_boundary = os.path.join(folder, "Arc_outline.shp") arcpy.env.workspace = folder if region == "Arc": gdbs = arcpy.ListWorkspaces("*", "FileGDB") # Or comment out previous line and use this format (one row per gdb) if repeating certain gdbs only # gdbs = [] # gdbs.append(os.path.join(folder, "AylesburyVale.gdb")) # gdbs.append(os.path.join(folder, "Chiltern.gdb")) # gdbs.append(os.path.join(folder, "SouthOxfordshire.gdb")) area_tag = "Arc" elif region == "NP": # Remember Leeds not in the list below because already done # "Allerdale.gdb", "Barnsley.gdb", "Barrow-in-Furness.gdb", "Blackburn with Darwen.gdb", "Blackpool.gdb", # "Bolton.gdb", "Bradford.gdb", "Burnley.gdb", "Bury.gdb", "Calderdale.gdb", "Carlisle.gdb", # "Cheshire East.gdb", "Cheshire West and Chester.gdb", "Chorley.gdb", "Copeland.gdb", "County Durham.gdb", # "Craven.gdb", "Darlington.gdb", "Doncaster.gdb", # "East Riding of Yorkshire.gdb", "Eden.gdb", "Fylde.gdb", "Gateshead.gdb", # "Halton.gdb", "Hambleton.gdb", "Harrogate.gdb", "Hartlepool.gdb", "Hyndburn.gdb", "Kirklees.gdb", "Knowsley.gdb", # "Lancaster.gdb", "Liverpool.gdb", "Manchester.gdb", "Middlesbrough.gdb", "Newcastle upon Tyne.gdb", # "North East Lincolnshire.gdb", "North Lincolnshire.gdb", "Northumberland.gdb", "North Tyneside.gdb", "Oldham.gdb", # "Pendle.gdb", "Preston.gdb", "Redcar and Cleveland.gdb", "Ribble Valley.gdb", # "Richmondshire.gdb", "Rochdale.gdb", "Rossendale.gdb", "Rotherham.gdb", "Ryedale.gdb", "Salford.gdb", # "Scarborough.gdb", "Sefton.gdb", "Selby.gdb", "Sheffield.gdb", "South Lakeland.gdb", "South Ribble.gdb", # "South Tyneside.gdb", "St Helens.gdb", "Stockport.gdb", "Stockton-on-Tees.gdb", "Sunderland.gdb", # "Tameside.gdb", "Trafford.gdb", "Wakefield.gdb", "Warrington.gdb", "West Lancashire.gdb", # "Wigan.gdb", "Wirral.gdb", "Wyre.gdb", "York.gdb" gdb_names = ["East Riding of Yorkshire.gdb"] gdbs = [] for gdb_name in gdb_names: gdbs.append(os.path.join(r"M:\urban_development_natural_capital\LADs", gdb_name.replace(" ", ""))) area_tag = "NP" elif region == "Oxon": gdbs = [] LADs = ["Cherwell.gdb", "Oxford.gdb", "SouthOxfordshire.gdb", "ValeofWhiteHorse.gdb", "WestOxfordshire.gdb"] for LAD in LADs: gdbs.append(os.path.join(folder, LAD)) boundary = "boundary" if method == "LERC": base_map = "LERC_ALC_Desig_GS" # Name of OSMM fields used for interpretation MakeField = "make" DescGroup = "DescGroup" DescTerm = "DescTerm" # Do not tidy up by deleting fields containing the string "_1" as there are lots we want to keep in this dataset! delete_1 = False # Feature classes to keep - the others will be deleted if you select 'tidy_workspace' = true keep_fcs = ["boundary", "Designations", "LERC", "LERC_ALC", "LERC_ALC_Desig", "LERC_ALC_Desig_GS", "LERC_ALC_Desig_GS_PA", "OS_Open_GS", "OS_Open_GS_clip", "OSGS", "New_snap_union_sp_delid_elim_del", "Public_access"] else: base_map = "OSMM_CR_PHI_ALC_Desig_GS" # Name of OSMM fields used for interpretation if region == "NP": MakeField = "make" DescGroup = "descriptivegroup" DescTerm = "descriptiveterm" else: MakeField = "Make" DescGroup = "DescriptiveGroup" DescTerm = "DescriptiveTerm" delete_1 = True # Feature classes to keep - the others will be deleted if you select 'tidy_workspace' = true keep_fcs = ["ALC_diss_Union", "boundary", "Designations", "LCM_arable", "LCM_improved_grassland", "OS_Open_GS", "OS_Open_GS_clip", "OSGS", "OSMM", "OSMM_CROME", "OSMM_CROME_PHI", "OSMM_CR_PHI_ALC", "OSMM_CR_PHI_ALC_Desig", "OSMM_CR_PHI_ALC_Desig_GS", "OSMM_CR_PHI_ALC_Desig_GS_PA", "PHI", "Public_access"] hab_field = "Interpreted_habitat" # Source of public access data and gdb where public access layer will be created if region == "Oxon": data_gdb = r"D:\cenv0389\Oxon_GIS\Oxon_county\Data\Public_access.gdb" elif region == "Arc": data_gdb = r"D:\cenv0389\Oxon_GIS\OxCamArc\Data\Public_access.gdb" elif region == "NP": data_gdb = r"M:\urban_development_natural_capital\Public_access.gdb" # Do not delete fid field at end (when all other surplus fields are deleted) as this is now the new name for TOID protected_fields = ["fid"] des_list = ['CountryPk', 'NT', 'NNR', 'LNR', 'DoorstepGn', 'MillenGn', 'RSPB'] des_list_expression = "(((CountryPk + NT + NNR + LNR + MillenGn + DoorstepGn + RSPB) = 0) OR " \ "(CountryPk IS NULL AND NT IS NULL AND NNR IS NULL AND LNR IS NULL AND MillenGn IS NULL AND DoorstepGn IS " \ "NULL AND RSPB IS NULL))" # Table containing info for each input layer - user needs to set it up. Note: we also use OS Greenspace, OS Open Greenspace and # various designations (e.g. nature reserves), but these are already merged into the base map so do not need to be listed in the info table. InfoTable = os.path.join(data_gdb, "PublicAccessFiles") AccessTable_name = "AccessMultipliers" AccessTable = os.path.join(data_gdb, "AccessMultipliers") # Buffer distance for paths buffer_distance = "50 Meters" # Need to dissolve all paths into a single buffer area if networks are complex, otherwise the process may crash dissolve_paths = True # Which stages of the process do we want to run? Useful for debugging or updates create_access_layer = False # These four stages will only be run if create_access_layer is True prep_OSM_paths = True clip_region = True buffer_paths = True merge_paths = True clip_PA_into_LAD_gdb = True # Do not use this if the public access layer is made in the same gdb extract_relevant_polygons = True intersect_access = True # *** note there is currently a temporary correction in the code here that needs to be removed in due course! NT_correction = True # CORRECTION for Northern Powerhouse only sp_and_repair = True interpret_access = True tidy_fields = True # Recommend not using tidy_workspace here but using the separate code Delete_fcs_from_gdb instead - it is safer! # if method == "CROME_PHI" or method == "LERC": # tidy_workspace = False # DO NOT USE THIS FOR OXON HLU method!! It is not set up yet. # else: # tidy_workspace = False # *** End of parameter entry # -------------------------- if create_access_layer: # Create public access layer by merging multiple input files, reading info from a table # Linear features (paths, cycle routes) are converted to a 50m buffer zone # Set up Type, Description and Name field for each file, reading info from InfoTable, and populate by copying existing relevant fields arcpy.env.workspace = data_gdb InAreas = [] InPaths = [] ipath = 0 # First loop through to find max length for Name and Description fields max_NameLen = 0 max_DescLen = 0 cursor = arcpy.SearchCursor(InfoTable) for row in cursor: if dissolve_paths == False or (dissolve_paths == True and row.getValue("Path") == 0): DescLen = row.getValue("DescLength") if DescLen > max_DescLen: max_DescLen = DescLen NameLen = row.getValue("NameLength") if NameLen > max_NameLen: max_NameLen = NameLen # Deal with paths first. # If we are dissolving paths, merge all the path input line files first if dissolve_paths: if merge_paths: cursor = arcpy.SearchCursor(InfoTable) for row in cursor: if row.getValue("Path") == 1: in_file = row.getValue("Filename") if clip_region: print("Clipping " + in_file) arcpy.Clip_analysis(in_file, region_boundary, in_file + "_" + area_tag) if area_tag <> "": in_file = in_file + "_" + area_tag InPaths.append(in_file) print "Merging paths" arcpy.Merge_management(InPaths, "Paths_merge") print("Buffering and dissolving merged paths") arcpy.Buffer_analysis("Paths_merge", "Paths_merge_buffer", buffer_distance, dissolve_option="ALL") # Add PAType print("Adding Type field") MyFunctions.check_and_add_field("Paths_merge_buffer", "PAType", "TEXT", 50) arcpy.CalculateField_management("Paths_merge_buffer", "PAType", "'Path'", "PYTHON_9.3") arcpy.MultipartToSinglepart_management("Paths_merge_buffer", "Paths_merge_buffer_sp") # Now loop through the other areas (and paths if keeping separate) to set up the Type, Description and Name fields cursor = arcpy.SearchCursor(InfoTable) for row in cursor: exit_flag = False in_file = row.getValue("Filename") ShortName = row.getValue("ShortName") print("Processing " + ShortName) Type = row.getValue("Type") Path = row.getValue("Path") NameField = row.getValue("NameField") DescField = row.getValue("DescField") if Path == 1: if dissolve_paths: exit_flag = True else: exit_flag = False if exit_flag == False: if clip_region: print("Clipping " + in_file) arcpy.Clip_analysis(in_file, region_boundary, in_file + "_" + area_tag) if area_tag <> "": in_file = in_file + "_" + area_tag if Path == 1: if buffer_paths: print("Buffering " + in_file) arcpy.Buffer_analysis(in_file, in_file + "_buffer", buffer_distance, dissolve_option="NONE") in_file = in_file + "_buffer" MyFunctions.check_and_repair(in_file) print("Adding Type field") MyFunctions.check_and_add_field(in_file, "PAType", "TEXT", 50) arcpy.CalculateField_management(in_file, "PAType", "'" + Type + "'", "PYTHON_9.3") if DescField: if max_DescLen <= 40: max_DescLen = 40 print("Adding Description field") MyFunctions.check_and_add_field(in_file, "PADescription", "TEXT", max_DescLen) arcpy.CalculateField_management(in_file, "PADescription", "!" + DescField + "!", "PYTHON_9.3") if NameField: print("Adding Name field") MyFunctions.check_and_add_field(in_file, "PAName", "TEXT", max_NameLen) arcpy.CalculateField_management(in_file, "PAName", "!" + NameField + "!", "PYTHON_9.3") # Delete fields that are not needed needed_fields = ["PAType", "PADescription", "PAName"] MyFunctions.delete_fields(in_file, needed_fields, in_file + "_input") if Path: # If this is not the first path dataset, erase it from the others and then append. This way we should avoid overlaps, # provided that paths have been dissolved (as delete_identical method may not work for very large and complex layers # with lots of overlaps). if ipath == 1: arcpy.CopyFeatures_management(in_file + "_input", "Access_paths_merge_1") elif ipath > 1: print ("Erasing " + in_file + "_input from merged paths") try: arcpy.Erase_analysis("Access_paths_merge_" + str(ipath-1), in_file + "_input", "Access_paths_merge_" + str(ipath)) except: print("Erase failed - please try manually in ArcMap and then comment out this section and restart") exit() print ("Appending " + in_file + "_input to merged paths") arcpy.Append_management(["Access_paths_merge_1" + str(ipath)], in_file + "_input", "NO_TEST") else: # Check for any duplicate polygons arcpy.FindIdentical_management(in_file + "_input", "Identical_" + in_file, ["Shape"], output_record_option="ONLY_DUPLICATES") numrows = arcpy.GetCount_management("Identical_" + in_file) if numrows>0: print ("Warning - " + str(numrows) + " duplicate polygons found in " + in_file + "_input. All but one of each shape will be deleted.") arcpy.DeleteIdentical_management(in_file + "_input", ["Shape"]) InAreas.append(in_file + "_input") print("Merging areas: " + ', '.join(InAreas)) arcpy.Merge_management(InAreas, "Access_areas_merge") # Need to convert merged paths to single part otherwise it crashes print ("Converting merged paths to single part") if not dissolve_paths: arcpy.MultipartToSinglepart_management("Access_paths_merge_" + str(ipath), "Paths_merge_buffer_sp") MyFunctions.check_and_repair("Paths_merge_buffer_sp") # Erase any paths that are within the accessible areas or private (military) areas, to reduce the complexity of the merged shapes print ("Erasing paths within areas") arcpy.Merge_management(["Access_areas_merge", "OSM_military"], "Access_areas_to_erase") print " Buffering and dissolving areas to erase (to remove internal slivers and simplify shapes)" arcpy.Buffer_analysis("Access_areas_to_erase", "Access_areas_to_erase_buff_diss", "1 Meters", dissolve_option="ALL") print " Converting to single part" arcpy.MultipartToSinglepart_management("Access_areas_to_erase_buff_diss", "Access_areas_to_erase_buff_diss_sp") MyFunctions.check_and_repair("Access_areas_to_erase_buff_diss_sp") print " Erasing..." try: arcpy.Erase_analysis("Paths_merge_buffer_sp", "Access_areas_to_erase_buff_diss_sp", "Access_paths_erase") except: print("Erase failed but will probably work manually in ArcGIS. Please try this and then restart, commenting out previous steps") exit() print ("Merging paths and areas") arcpy.Merge_management(["Access_areas_merge", "Access_paths_erase"], "Access_merge") print("After merge there are " + str(arcpy.GetCount_management("Access_merge")) + " rows") print ("Dissolving - retaining type, name and description") arcpy.Dissolve_management("Access_merge", "Access_merge_diss", ["PAType", "PADescription", "PAName"], multi_part="SINGLE_PART") print ("Unioning as first step to removing overlaps") try: arcpy.Union_analysis([["Access_merge_diss", 1]], "Access_merge_union", "NO_FID") except: print ("Union failed. Please do manually then comment out preceding steps and restart.") exit() print("After union there are " + str(arcpy.GetCount_management("Access_merge_union")) + " rows") # If description is blank, fill in with Type print ("Filling in missing Descriptions") arcpy.MakeFeatureLayer_management("Access_merge_union", "join_lyr") arcpy.SelectLayerByAttribute_management("join_lyr", where_clause="PADescription IS NULL OR PADescription = ''") arcpy.CalculateField_management("join_lyr", "PADescription", "!PAType!", "PYTHON_9.3") arcpy.Delete_management("join_lyr") # Set up Access multiplier based on Type and Description (join to Access table then copy over source, type and multiplier) print ("Joining to access multiplier") MyFunctions.check_and_add_field("Access_merge_union", "Source", "TEXT", 30) MyFunctions.check_and_add_field("Access_merge_union", "AccessType", "TEXT", 30) MyFunctions.check_and_add_field("Access_merge_union", "AccessMult", "FLOAT", 0) arcpy.MakeFeatureLayer_management("Access_merge_union", "join_lyr2") print ("Adding join") arcpy.AddJoin_management("join_lyr2", "PADescription", AccessTable, "Description", "KEEP_ALL") print("Copying source field") arcpy.CalculateField_management("join_lyr2", "Access_merge_union.Source", "!" + AccessTable_name + ".Source!", "PYTHON_9.3") print ("Copying access type") arcpy.CalculateField_management("join_lyr2", "Access_merge_union.AccessType", "!" + AccessTable_name + ".AccessType!", "PYTHON_9.3") print ("Copying access multiplier") arcpy.CalculateField_management("join_lyr2", "Access_merge_union.AccessMult", "!" + AccessTable_name + ".AccessMult!", "PYTHON_9.3") arcpy.RemoveJoin_management("join_lyr2", AccessTable_name) arcpy.Delete_management("join_lyr2") print("Sorting " + str(arcpy.GetCount_management("Access_merge_union")) + " rows") # Sort by access multiplier (descending) so highest multipliers are at the top arcpy.Sort_management("Access_merge_union", "Access_merge_union_sort", [["AccessMult", "DESCENDING"]]) # Delete identical polygons to remove overlaps but leave the highest access score. For complex path networks this may fail, so # dissolve paths and then do this step only for areas, not paths print ("Deleting overlapping polygons, keeping the one with the highest access score") arcpy.MakeFeatureLayer_management("Access_merge_union_sort", "del_lyr") if dissolve_paths: arcpy.SelectLayerByAttribute_management("del_lyr", where_clause="AccessType <> 'Path'") arcpy.DeleteIdentical_management("del_lyr", ["Shape"]) print("After deleting identical polygons there are " + str(arcpy.GetCount_management("Access_merge_union_sort")) + " rows") arcpy.Delete_management("del_lyr") print ("Dissolving") dissolve_fields = ["PAType", "PADescription", "PAName", "Source", "AccessType", "AccessMult"] arcpy.Dissolve_management("Access_merge_union_sort","Access_merge_union_sort_diss", dissolve_field=dissolve_fields) print("After dissolving there are " + str(arcpy.GetCount_management("Access_merge_union_sort_diss")) + " rows") arcpy.MultipartToSinglepart_management("Access_merge_union_sort_diss", "Public_access") print("After converting to single part there are " + str(arcpy.GetCount_management("Public_access")) + " rows") MyFunctions.check_and_repair("Public_access") for gdb in gdbs: arcpy.env.workspace = gdb numrows = arcpy.GetCount_management(os.path.join(gdb, base_map)) print (''.join(["### Started processing ", gdb, " on ", time.ctime(), ": ", str(numrows), " rows"])) if clip_PA_into_LAD_gdb: # Use this to clip the master copy of the public access layer into each LAD gdb. print(" Clipping public access layer") PA_layer = os.path.join(data_gdb, "Public_access") arcpy.Clip_analysis(PA_layer, boundary, "Public_access") if extract_relevant_polygons: # Select base map polygons that are not 'Manmade' or 'Garden', green space or designated as accessible types, and export to new file print (" Extracting polygons that are not gardens or manmade and have no relevant greenspace or designation attributes") arcpy.MakeFeatureLayer_management(base_map, "sel_lyr") # There was an error here: Amenity grassland had an underscore between the words so would not have been excluded as intended. # Fixed on 1/10/2020. This will have affected all the work done for Blenheim and EA Arc, and updated Oxon map sent to # Nick and Mel end Sept 2020. But makes no difference? Because it simply added either Open or Path # to amenity grassland not in Greenspace (rather than leaving it out), which is later over-ridden to Open for all amenity grassland. expression = hab_field + " NOT IN ('Garden', 'Amenity grassland') AND " + MakeField + " <> 'Manmade' AND " \ "(GreenSpace IS NULL OR GreenSpace = '') AND " + des_list_expression arcpy.SelectLayerByAttribute_management("sel_lyr", where_clause=expression) arcpy.CopyFeatures_management("sel_lyr", "Natural_features") arcpy.Delete_management("sel_lyr") if intersect_access: print (" Erasing and deleting existing greenspace from access layer, to reduce slivers") arcpy.MakeFeatureLayer_management("Public_access", "del_lyr") expression = "PADescription = 'country_park' OR PADescription = 'millennium_green' OR PADescription = 'doorstep_green'" arcpy.SelectLayerByAttribute_management("del_lyr", where_clause=expression) arcpy.DeleteFeatures_management("del_lyr") arcpy.Delete_management("del_lyr") arcpy.MakeFeatureLayer_management(base_map, "sel_lyr2") expression = "GreenSpace IS NOT NULL AND GreenSpace <> ''" arcpy.SelectLayerByAttribute_management("sel_lyr2", where_clause=expression) arcpy.Erase_analysis("Public_access", "sel_lyr2", "Public_access_erase", cluster_tolerance="0.001 Meters") print (" Deleting slivers") arcpy.MultipartToSinglepart_management("Public_access_erase", "Public_access_erase_sp") MyFunctions.delete_by_size("Public_access_erase_sp", 20) print(" Intersect started on " + time.ctime() ) arcpy.Intersect_analysis(["Natural_features", "Public_access_erase_sp"], base_map + "_isect") print(" Intersect completed on " + time.ctime()) print (" Erasing and merging back in") arcpy.Erase_analysis(base_map, base_map + "_isect", base_map + "_isect_erase", cluster_tolerance="0.001 Meters" ) arcpy.Merge_management([base_map + "_isect_erase", base_map + "_isect"], base_map + "_merge") print(" Merge completed on : " + time.ctime()) # *** TEMPORARY Correction for NP because access field was omitted accidentally when I made the designations layer # if NT_correction and region == "NP": # # select NT polygons and spatially join to a dataset containing only the NT access description # print " Correcting by adding in missing NT access field" # arcpy.MakeFeatureLayer_management(base_map + "_merge", "NT_lyr") # arcpy.SelectLayerByAttribute_management("NT_lyr", where_clause="NT = 1") # arcpy.SpatialJoin_analysis("NT_lyr", os.path.join(data_gdb, "NT_access"), "NT_access") # # delete the NT features from the original file and then append the new spatially joined rows back in # arcpy.DeleteFeatures_management("NT_lyr") # arcpy.Delete_management("NT_lyr") # MyFunctions.check_and_add_field(base_map + "_merge", "NT_desc", "TEXT", 20) # arcpy.Append_management("NT_access", base_map + "_merge", "NO_TEST") if sp_and_repair: # Sort by shape so it displays faster print(" Converting to single part and sorting") arcpy.MultipartToSinglepart_management(base_map + "_merge", base_map + "_merge_sp") arcpy.Sort_management(base_map + "_merge_sp", base_map + "_PA", [["SHAPE", "ASCENDING"]], "PEANO") print (" Rows have increased from " + str(numrows) + " to " + str(arcpy.GetCount_management(base_map + "_PA"))) # Check and repair geometry MyFunctions.check_and_repair(base_map + "_PA") if interpret_access: print(" Interpreting accessibility") # Add interpretation for the remaining types of green space # Amenity grassland - from habitat and/or OSGS 'Amenity - residential and business' - assume all is accessible. # Hopefully OSGS amenity excludes most amenity associated with large rural houses but keeps urban green spaces that are usually # accessible by all. Road verges and 'Amenity - transport' currently excluded as they include roundabouts / motorway embankments. arcpy.MakeFeatureLayer_management(base_map + "_PA", "amenity_lyr") expression = hab_field + " = 'Amenity grassland' AND (PAType IS NULL OR PAType = '' OR AccessType = 'Path') " \ "AND " + DescGroup + " NOT LIKE '%Rail%'" arcpy.SelectLayerByAttribute_management("amenity_lyr", where_clause=expression) arcpy.CalculateField_management("amenity_lyr", "PAType", "'Amenity grassland'", "PYTHON_9.3") arcpy.CalculateField_management("amenity_lyr", "PADescription", "'Amenity grassland'", "PYTHON_9.3") arcpy.CalculateField_management("amenity_lyr", "Source", "'Amenity grassland'", "PYTHON_9.3") arcpy.CalculateField_management("amenity_lyr", "AccessType", "'Open'", "PYTHON_9.3") arcpy.CalculateField_management("amenity_lyr", "AccessMult", 1.0, "PYTHON_9.3") # Designated sites, e.g. country parks, millennium and doorstep greens, local and national nature reserves for designation in des_list: arcpy.MakeFeatureLayer_management(base_map + "_PA", "des_lyr") arcpy.SelectLayerByAttribute_management("des_lyr", where_clause=designation + " = 1") numrows = arcpy.GetCount_management("des_lyr") print (" Designation: " + designation + " Rows: " + str(numrows)) if numrows >0: arcpy.CalculateField_management("des_lyr", "PAType", "'" + designation + "'", "PYTHON_9.3") # Special case for National Trust where description states degree of access if designation == "NT": arcpy.CalculateField_management("des_lyr", "PADescription", "!NT_desc!", "PYTHON_9.3") else: arcpy.CalculateField_management("des_lyr", "PADescription", "'" + designation + "'", "PYTHON_9.3") arcpy.AddJoin_management("des_lyr", "PADescription", AccessTable, "Description", "KEEP_ALL") arcpy.CalculateField_management("des_lyr", "Source", "'Designations'", "PYTHON_9.3") arcpy.CalculateField_management("des_lyr", "AccessType", "!" + AccessTable_name + ".AccessType!", "PYTHON_9.3") arcpy.CalculateField_management("des_lyr", "AccessMult", "!" + AccessTable_name + ".AccessMult!", "PYTHON_9.3") arcpy.RemoveJoin_management("des_lyr", AccessTable_name) arcpy.Delete_management("des_lyr") # Green spaces (from OS green space and OS open green space) - correct for Rail in OSGS Amenity residential # Exclude National Trust as that has better information on access, so we don't want to overwrite it # Also exclude arable land (added 4/10/2020 at end of EA work) otherwise incorrect OSGS 'Amenity' over-rides habitat type print " Interpreting green space" arcpy.MakeFeatureLayer_management(base_map + "_PA", "sel_lyr4") expression = hab_field + " NOT IN ('Arable', 'Arable and scattered trees', 'Arable fields, horticulture and temporary grass') " expression = expression + "AND GreenSpace IS NOT NULL AND GreenSpace <> '' " expression = expression + "AND " + DescGroup + " NOT LIKE '%Rail%' AND (NT IS NULL OR NT = 0)" arcpy.SelectLayerByAttribute_management("sel_lyr4", where_clause=expression) if arcpy.GetCount_management("sel_lyr4") > 0: arcpy.CalculateField_management("sel_lyr4", "PAType", "!GreenSpace!", "PYTHON_9.3") arcpy.CalculateField_management("sel_lyr4", "PADescription", "!GreenSpace!", "PYTHON_9.3") arcpy.AddJoin_management("sel_lyr4", "PADescription", AccessTable, "Description", "KEEP_ALL") arcpy.CalculateField_management("sel_lyr4", "Source", "'GreenSpace'", "PYTHON_9.3") arcpy.CalculateField_management("sel_lyr4", "AccessType", "!" + AccessTable_name + ".AccessType!", "PYTHON_9.3") arcpy.CalculateField_management("sel_lyr4", "AccessMult", "!" + AccessTable_name + ".AccessMult!", "PYTHON_9.3") arcpy.RemoveJoin_management("sel_lyr4", AccessTable_name) arcpy.Delete_management("sel_lyr4") # Correction for school grounds from OSGS because playing fields were omitted (this will omit non-urban schools not in OSGS) print " Interpreting schools" arcpy.MakeFeatureLayer_management(base_map + "_PA", "school_lyr") arcpy.SelectLayerByAttribute_management("school_lyr", where_clause="OSGS_priFunc = 'School Grounds'") if arcpy.GetCount_management("school_lyr") > 0: arcpy.CalculateField_management("school_lyr", "PAType", "'School Grounds'", "PYTHON_9.3") arcpy.CalculateField_management("school_lyr", "PADescription", "'School Grounds'", "PYTHON_9.3") arcpy.AddJoin_management("school_lyr", "PADescription", AccessTable, "Description", "KEEP_ALL") arcpy.CalculateField_management("school_lyr", "Source", "'OSGS'", "PYTHON_9.3") arcpy.CalculateField_management("school_lyr", "AccessType", "!" + AccessTable_name + ".AccessType!", "PYTHON_9.3") arcpy.CalculateField_management("school_lyr", "AccessMult", "!" + AccessTable_name + ".AccessMult!", "PYTHON_9.3") arcpy.RemoveJoin_management("school_lyr", AccessTable_name) arcpy.Delete_management("school_lyr") # Add in full accessibility for rivers, lakes, reservoirs, weirs and canals. Correction made 4 Oct 2020. print " Interpreting water" arcpy.MakeFeatureLayer_management(base_map + "_PA", "water_lyr") expression = DescTerm + " IN ('Watercourse', 'Static Water', 'Canal', 'Weir', 'Reservoir')" arcpy.SelectLayerByAttribute_management("water_lyr", where_clause=expression) if arcpy.GetCount_management("water_lyr") > 0: arcpy.CalculateField_management("water_lyr", "PAType", "'Water'", "PYTHON_9.3") arcpy.CalculateField_management("water_lyr", "PADescription", "'Water'", "PYTHON_9.3") arcpy.AddJoin_management("water_lyr", "PADescription", AccessTable, "Description", "KEEP_ALL") arcpy.CalculateField_management("water_lyr", "Source", "'Water'", "PYTHON_9.3") arcpy.CalculateField_management("water_lyr", "AccessType", "!" + AccessTable_name + ".AccessType!", "PYTHON_9.3") arcpy.CalculateField_management("water_lyr", "AccessMult", "!" + AccessTable_name + ".AccessMult!", "PYTHON_9.3") arcpy.RemoveJoin_management("water_lyr", AccessTable_name) arcpy.Delete_management("water_lyr") if tidy_fields: # CAUTION: this deletes any field containing "_1" (if delete_1 is True) as well as those containing _OBJID, # FID_, _FID, BaseID_, _Area, _Relationship unless in list of protected fields print("Tidying up surplus attributes") MyFunctions.tidy_fields(base_map + "_PA", delete_1, protected_fields) # Recommend using the separate code Delete_fcs_from_gdb instead - it is safer! # if tidy_workspace and (method == "CROME_PHI" or method == "LERC"): # Not set up yet for Oxon gdb used for HLU method # print("Tidying workspace") # fcs = arcpy.ListFeatureClasses("*") # delete_fcs = [] # for fc in fcs: # if fc not in keep_fcs and "NatCap_" not in fc: # delete_fcs.append (fc) # # print("Deleting " + fc + " from " + gdb) # if len(delete_fcs) > 0: # arcpy.Delete_management (fc) # if len(delete_fcs) > 0: # print(" Deleted intermediate feature classes: " + ', '.join(delete_fcs)) print(''.join(["Completed " + gdb + " on : ", time.ctime()])) exit()

60.479428

141

0.664458

067f96e0223f4a4ef755767aa7893138791277ee

3,688

py

Python

old_site/import/scraper.py

basilleaf/dailyrings

e970ee7771bd1fb60b07f8208b2ffc4866779e88

[ "MIT" ]

null

old_site/import/scraper.py

basilleaf/dailyrings

e970ee7771bd1fb60b07f8208b2ffc4866779e88

[ "MIT" ]

3

2018-04-27T04:11:13.000Z

2018-04-28T01:26:57.000Z

old_site/import/scraper.py

basilleaf/dailyrings

e970ee7771bd1fb60b07f8208b2ffc4866779e88

[ "MIT" ]

null

""" this script parses the pds-rings press release gallery tree at base_url = "http://pds-rings.seti.org/saturn/cassini/" if an image already exists in the database it is updated to get only the most recent month set latest_month_only to True """ latest_month_only = True # like I was really going to do this monthly # Set up the Django Enviroment for running as shell script from django.core.management import setup_environ import settings setup_environ(settings) # script imports from stripogram import html2text, html2safehtml from priod.daily_image.models import Image from HTMLParser import HTMLParser from urlparse import urlparse import exceptions, urllib2, re base_url = "http://pds-rings.seti.org/saturn/cassini/" # set to strict imports, ie want to know if an url is too long for field from django.db import connection cursor = connection.cursor() cursor.execute("SET SQL_MODE = 'STRICT_ALL_TABLES'") # get all the monthly gallery pages print "scanning " + base_url homepage = urllib2.urlopen(base_url).read() list_pages = re.findall("HREF=\"([0-9]+-[0-9]+)\.html", homepage) # get all the detail pages detail_pages = [] for page_name in list_pages: print "scanning gallery page " + page_name list_page = urllib2.urlopen(base_url + page_name + ".html").read() detail_pages += re.findall("HREF=\"\./(.*)\.html", list_page) if latest_month_only: break # scrape each detail page errors = [] for page_name in detail_pages: url = base_url + page_name + '.html' try: print "opening " + url page = urllib2.urlopen(url).read() except HTTPError: print "failed at " + url errors += [url] print "scraping " + url try: name,title = re.search("<title>(.*)</title>", page).group(1).split(':') name = name.strip() title = title.strip() more_info = "http://pds-rings.seti.org/saturn/cassini/" + name caption = re.search("Original Caption Released with Image:(.*)Image Credit:", page, re.DOTALL | re.UNICODE).group(1).strip() caption = html2safehtml(caption,valid_tags=("p","a","img","br")).strip() credit = re.search("Image Credit:(.*) ", page, re.DOTALL | re.UNICODE).group(1).strip() credit = html2safehtml(credit,valid_tags=("p","a","img")).strip() # find images image_url = re.search("href\t*=\t*\"(.*)\.tif\"", page).group(1) image_url = urlparse(image_url).netloc if not image_url: image_url = base_url else: image_url = 'http://' + image_url + '/' jpg = 'jpeg/' + name.strip() + '.jpg' jpg_mod = 'jpegMod/' + name.strip() + '_modest.jpg' tif = 'tiff/' + name.strip() + '.tif' except: errors += ["could not parse " + url] print "failed " + url continue try: pub_date=Image.objects.get(pk=name).pub_date user_ordered=Image.objects.get(pk=name).user_ordered pub_order=Image.objects.get(pk=name).pub_order except Image.DoesNotExist: pub_date = None user_ordered = False pub_order = None # update db image = Image(name=name,title=title,caption=caption,more_info=more_info,credit=credit,image_url=image_url,jpg=jpg,pub_date=pub_date,user_ordered=user_ordered,pub_order=pub_order) try: image.save() print name + " saved \n" except: print "failed " + url errors += ["could not save to db" + url] print "finished! " print "" if len(errors): print "HTTP Errors could not load the following pages\n" for e in errors: print e + "\n"

33.225225

182

0.639371

068506b54ed89a62c865b814f0418d72003474e6

856

py

Python

packit_dashboard/api/routes.py

lbarcziova/dashboard

6ad1141a475d68b081a4fa2ceec5363678ae4e38

[ "MIT" ]

null

packit_dashboard/api/routes.py

lbarcziova/dashboard

6ad1141a475d68b081a4fa2ceec5363678ae4e38

[ "MIT" ]

null

packit_dashboard/api/routes.py

lbarcziova/dashboard

6ad1141a475d68b081a4fa2ceec5363678ae4e38

[ "MIT" ]

null

from flask import Blueprint, jsonify, request from packit_dashboard.utils import return_json from packit_dashboard.config import API_URL api = Blueprint("api", __name__) # The react frontend will request information here instead of fetching directly # from the main API. # This is because it will be easier to implement caching API requests here. # (Flask-Caching etc)

31.703704

79

0.731308

0687517d0be83d1b02e62dee6d9d45a4dee1e6e1

4,959

py

Python

auth/util.py

prajask/Find-Your-Doctor

a4fc682a1757d83bd62b9daea7476f97db91ffb8

[ "MIT" ]

1

2020-09-10T10:52:21.000Z

auth/util.py

prajask/find-your-doctor

a4fc682a1757d83bd62b9daea7476f97db91ffb8

[ "MIT" ]

null

auth/util.py

prajask/find-your-doctor

a4fc682a1757d83bd62b9daea7476f97db91ffb8

[ "MIT" ]

null

""" Authentication Utility Functions """ from flask import session from models import Patient, Doctor, Degree, database import hashlib, binascii from config import SECRET_KEY

32.201299

154

0.689655

06875fe7ae608373332a8fb7606601d9cb556ce3

267

py

Python

cctbx/eltbx/covalent_radii.py

hbrunie/cctbx_project

2d8cb383d50fe20cdbbe4bebae8ed35fabce61e5

[ "BSD-3-Clause-LBNL" ]

2

2021-03-18T12:31:57.000Z

2022-03-14T06:27:06.000Z

cctbx/eltbx/covalent_radii.py

hbrunie/cctbx_project

2d8cb383d50fe20cdbbe4bebae8ed35fabce61e5

[ "BSD-3-Clause-LBNL" ]

null

cctbx/eltbx/covalent_radii.py

hbrunie/cctbx_project

2d8cb383d50fe20cdbbe4bebae8ed35fabce61e5

[ "BSD-3-Clause-LBNL" ]

1

2021-03-26T12:52:30.000Z

from __future__ import absolute_import, division, print_function import boost.python ext = boost.python.import_ext("cctbx_eltbx_covalent_radii_ext") from cctbx_eltbx_covalent_radii_ext import * boost.python.inject(ext.table_iterator, boost.python.py3_make_iterator)

38.142857

71

0.861423

068765a77334bb8b750cf60699365fc4fc30858b

4,004

py

Python

player.py

akrabio/NBANormalizedStats

de5fb5243e9e26e4d534c319ea984712a908cadc

[ "MIT" ]

null

player.py

akrabio/NBANormalizedStats

de5fb5243e9e26e4d534c319ea984712a908cadc

[ "MIT" ]

null

player.py

akrabio/NBANormalizedStats

de5fb5243e9e26e4d534c319ea984712a908cadc

[ "MIT" ]

null

from nba_py import player from numpy import mean import league from consts import all_time_stats import consts name_to_index = {'season': 1, consts.rebounds: 20, consts.assists: 21, consts.steals: 22, consts.blocks: 23, consts.points: 26} if __name__ == '__main__': # player = Player('michael', 'jordan') player = Player('patrick', 'ewing') print 'Career stats: {}'.format(player.career_stats) print 'Normalized career stats: {}'.format(player.normalized_career_stats) # print player.player_stats

43.521739

130

0.623876

0687810d3ca357eb81c8f40b9ee9e277ec90842e

3,668

py

Python

examples/mag_wmm2015.py

CHEN-Zhaohui/geoist

06a00db3e0ed3d92abf3e45b7b3bfbef6a858a5b

[ "MIT" ]

53

2018-11-17T03:29:55.000Z

2022-03-18T02:36:25.000Z

examples/mag_wmm2015.py

CHEN-Zhaohui/geoist

06a00db3e0ed3d92abf3e45b7b3bfbef6a858a5b

[ "MIT" ]

3

2018-11-28T11:37:51.000Z

2019-01-30T01:52:45.000Z

examples/mag_wmm2015.py

CHEN-Zhaohui/geoist

06a00db3e0ed3d92abf3e45b7b3bfbef6a858a5b

[ "MIT" ]

35

2018-11-17T03:29:57.000Z

2022-03-23T17:57:06.000Z

# -*- coding: utf-8 -*- """ Created on Thu Jan 10 18:34:07 2019 WMM2015WMM.cofpy @author: chens """ import numpy as np from pathlib import Path import xarray import ctypes as ct import sys import datetime from matplotlib.pyplot import figure #libwmm = ct.cdll.LoadLibrary(str('D:\\MyWorks\\WMM2015-master\\wmm15.dll')) libwmm = ct.cdll.LoadLibrary(str('D:\\MyWorks\\WMM2015-master\\noaa.dll')) from geoist.others.scidates import datetime2yeardec dt = datetime.datetime(2012, 7, 12, 12) print(datetime2yeardec(dt)) mag = noaa(45.5, 105.6, 0.2, datetime2yeardec(dt), mod='emm') #print(mag.north.item()) #print(mag.east.item()) #print(mag.down.item()) print("F:",mag.total.item()) #F print("D:",mag.decl.item()) #D print("I:",mag.incl.item()) #I from matplotlib.pyplot import show lon, lat = np.meshgrid(np.arange(-180, 180+10, 10), np.arange(-90, 90+10, 10)) mag = noaa(lat, lon, 0, 2015) plotwmm(mag) show()

33.045045

109

0.571156

0688619f7ef43b02605de1e45f9fd553d9142b12

3,089

py

Python

test/e2e/tests/test_transit_gateway.py

timbyr/ec2-controller

d96d056fdc6ec7d31981f4c14cad8d740f6cf6ec

[ "Apache-2.0" ]

14

2021-08-04T00:21:49.000Z

2022-03-21T01:06:09.000Z

test/e2e/tests/test_transit_gateway.py

timbyr/ec2-controller

d96d056fdc6ec7d31981f4c14cad8d740f6cf6ec

[ "Apache-2.0" ]

48

2021-08-03T19:00:42.000Z

2022-03-31T22:18:42.000Z

test/e2e/tests/test_transit_gateway.py

timbyr/ec2-controller

d96d056fdc6ec7d31981f4c14cad8d740f6cf6ec

[ "Apache-2.0" ]

9

2021-07-22T15:49:43.000Z

2022-03-06T22:24:14.000Z

# Copyright Amazon.com Inc. or its affiliates. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"). You may # not use this file except in compliance with the License. A copy of the # License is located at # # http://aws.amazon.com/apache2.0/ # # or in the "license" file accompanying this file. This file is distributed # on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either # express or implied. See the License for the specific language governing # permissions and limitations under the License. """Integration tests for the Transit Gateway API. """ import boto3 import pytest import time import logging from acktest.resources import random_suffix_name from acktest.k8s import resource as k8s from e2e import service_marker, CRD_GROUP, CRD_VERSION, load_ec2_resource from e2e.replacement_values import REPLACEMENT_VALUES RESOURCE_PLURAL = "transitgateways" ## The long delete wait is required to make sure the TGW can transition out of its "pending" status. ## TGWs are unable to be deleted while in "pending" CREATE_WAIT_AFTER_SECONDS = 90 DELETE_WAIT_AFTER_SECONDS = 10

30.89

100

0.697637

06897ca4a2ea127df4c4fbdc8e71310f23dfe61f

2,862

py

Python

Phase 4/src/search.py

ishaanshah/GameDhaBha

5ab4f13ec7554ba74739d9a149da1154bb09041a

[ "MIT" ]

null

Phase 4/src/search.py

ishaanshah/GameDhaBha

5ab4f13ec7554ba74739d9a149da1154bb09041a

[ "MIT" ]

null

Phase 4/src/search.py

ishaanshah/GameDhaBha

5ab4f13ec7554ba74739d9a149da1154bb09041a

[ "MIT" ]

null

""" Contains all the functions related to the search of enitities in the Database """ from tabulate import tabulate def SearchPlayerByName(cur, con): """ Searches for the provided name's similar occurences in the Player's first and last name """ # Take in the input for the search query search = {} search["pattern"] = input("Enter the player name that you are looking for: ") search["pattern"] = "%" + search["pattern"] + "%" query = """ SELECT * FROM Players WHERE FirstName LIKE %(pattern)s OR LastName LIKE %(pattern)s """ print("\nExecuting") print(query) # Execute query cur.execute(query, search) # Print the output headers = ["Username", "PlayerID", "FirstName", "LastName", "Winnings", "Nationality", "DateOfBirth"] rows = [] while True: res = cur.fetchone() if res is None: break rows.append([ res["Username"], res["PlayerID"], res["FirstName"], res["LastName"], res["Winnings"], res["Nationality"], res["DateOfBirth"] ]) print(tabulate(rows, headers = headers, tablefmt = "orgtbl")) print("") def SearchOrganisationByName(cur, con): """ Searches for an Organisation by the name given. """ # Take in the input for the search query search = {} search["pattern"] = input("Enter the organisation's name that you are looking for: ") search["pattern"] = "%" + search["pattern"] + "%" query = """ SELECT * FROM Organisations WHERE Name LIKE %(pattern)s """ print("\nExecuting") print(query) # Execute query cur.execute(query, search) # Print the output headers = ["OrganisationID", "Name", "Headquarters", "Founded", "Earnings"] rows = [] while True: res = cur.fetchone() if res is None: break rows.append([ res["OrganisationID"], res["Name"], res["Headquarters"], res["Founded"], res["Earnings"] ]) print(tabulate(rows, headers = headers, tablefmt = "orgtbl")) print("")

25.327434

99

0.568484

068a35a559d65ea89371c4e0284f743170c94d8d

15,413

py

Python

machine/qemu/sources/u-boot/test/py/tests/test_efi_fit.py

muddessir/framework

5b802b2dd7ec9778794b078e748dd1f989547265

[ "MIT" ]

1

2021-11-21T19:56:29.000Z

machine/qemu/sources/u-boot/test/py/tests/test_efi_fit.py

muddessir/framework

5b802b2dd7ec9778794b078e748dd1f989547265

[ "MIT" ]

null

machine/qemu/sources/u-boot/test/py/tests/test_efi_fit.py

muddessir/framework

5b802b2dd7ec9778794b078e748dd1f989547265

[ "MIT" ]

null

# SPDX-License-Identifier: GPL-2.0 # Copyright (c) 2019, Cristian Ciocaltea <cristian.ciocaltea@gmail.com> # # Work based on: # - test_net.py # Copyright (c) 2016, NVIDIA CORPORATION. All rights reserved. # - test_fit.py # Copyright (c) 2013, Google Inc. # # Test launching UEFI binaries from FIT images. """ Note: This test relies on boardenv_* containing configuration values to define which network environment is available for testing. Without this, the parts that rely on network will be automatically skipped. For example: # Boolean indicating whether the Ethernet device is attached to USB, and hence # USB enumeration needs to be performed prior to network tests. # This variable may be omitted if its value is False. env__net_uses_usb = False # Boolean indicating whether the Ethernet device is attached to PCI, and hence # PCI enumeration needs to be performed prior to network tests. # This variable may be omitted if its value is False. env__net_uses_pci = True # True if a DHCP server is attached to the network, and should be tested. # If DHCP testing is not possible or desired, this variable may be omitted or # set to False. env__net_dhcp_server = True # A list of environment variables that should be set in order to configure a # static IP. If solely relying on DHCP, this variable may be omitted or set to # an empty list. env__net_static_env_vars = [ ('ipaddr', '10.0.0.100'), ('netmask', '255.255.255.0'), ('serverip', '10.0.0.1'), ] # Details regarding a file that may be read from a TFTP server. This variable # may be omitted or set to None if TFTP testing is not possible or desired. # Additionally, when the 'size' is not available, the file will be generated # automatically in the TFTP root directory, as specified by the 'dn' field. env__efi_fit_tftp_file = { 'fn': 'test-efi-fit.img', # File path relative to TFTP root 'size': 3831, # File size 'crc32': '9fa3f79c', # Checksum using CRC-32 algorithm, optional 'addr': 0x40400000, # Loading address, integer, optional 'dn': 'tftp/root/dir', # TFTP root directory path, optional } """ import os.path import pytest import u_boot_utils as util # Define the parametrized ITS data to be used for FIT images generation. ITS_DATA = ''' /dts-v1/; / { description = "EFI image with FDT blob"; #address-cells = <1>; images { efi { description = "Test EFI"; data = /incbin/("%(efi-bin)s"); type = "%(kernel-type)s"; arch = "%(sys-arch)s"; os = "efi"; compression = "%(efi-comp)s"; load = <0x0>; entry = <0x0>; }; fdt { description = "Test FDT"; data = /incbin/("%(fdt-bin)s"); type = "flat_dt"; arch = "%(sys-arch)s"; compression = "%(fdt-comp)s"; }; }; configurations { default = "config-efi-fdt"; config-efi-fdt { description = "EFI FIT w/ FDT"; kernel = "efi"; fdt = "fdt"; }; config-efi-nofdt { description = "EFI FIT w/o FDT"; kernel = "efi"; }; }; }; ''' # Define the parametrized FDT data to be used for DTB images generation. FDT_DATA = ''' /dts-v1/; / { #address-cells = <1>; #size-cells = <1>; model = "%(sys-arch)s %(fdt_type)s EFI FIT Boot Test"; compatible = "%(sys-arch)s"; reset@0 { compatible = "%(sys-arch)s,reset"; reg = <0 4>; }; }; '''

33.579521

99

0.594693

068bed0bd09441343b0ab11a87d3f70ca8cbcf66

2,234

py

Python

data_dictionary/data_dictionary.py

georgetown-analytics/DC-Bikeshare

9f5a6a3256cff15a29f0dca6e9a9d8098ab2df28

[ "MIT" ]

11

2018-07-01T16:43:05.000Z

2020-07-17T19:08:16.000Z

data_dictionary/data_dictionary.py

noahnewberger/Bikeshare-DC

42676654d103cdaddfb76db76d1eece533251261

[ "MIT" ]

5

2021-02-08T20:21:12.000Z

2021-12-13T19:47:04.000Z

data_dictionary/data_dictionary.py

noahnewberger/Bikeshare-DC

42676654d103cdaddfb76db76d1eece533251261

[ "MIT" ]

5

2018-10-05T19:54:20.000Z

2020-10-27T11:54:09.000Z

#!/usr/bin/env python import report, sys import psycopg2.extras parser = report.get_parser(sys.argv[0]) parser.add_argument('--title', '-t', required=False, dest='title', default="Data Dictionary", help='Report Title') args = parser.parse_args() conn = report.get_connection(args) curs = conn.cursor(cursor_factory=psycopg2.extras.RealDictCursor) tmpl_vars = { 'dictionary': get_dictionary(), 'title': args.title } report.generate_report(tmpl_vars, args) report.generate_csv(tmpl_vars, args)

29.394737

114

0.637422

068d0a9c6eb823b33105c8883388612ae4b08f65

1,112

py

Python

LeetCode/InsertionLL.py

Jaidev810/Competitive-Questions

5d5b28be69e8572e9b4353e9790ee39b56769fc3

[ "MIT" ]

1

2021-02-27T06:12:55.000Z

LeetCode/InsertionLL.py

Jaidev810/Competitive-Questions

5d5b28be69e8572e9b4353e9790ee39b56769fc3

[ "MIT" ]

1

2021-02-02T08:52:17.000Z

2021-02-03T08:19:12.000Z

LeetCode/InsertionLL.py

Jaidev810/Competitive-Questions

5d5b28be69e8572e9b4353e9790ee39b56769fc3

[ "MIT" ]

null

head = takeinputLL() printLL(insertionLL(head))

19.508772

52

0.522482

068db78fb9e1cc510a957bc841fd463a0fc7de6a

2,581

py

Python

migrations/versions/458a7da0c9da_.py

dmiklic/psiholeks-web

68dda07228a53790ab1e797336bb236031a544de

[ "MIT" ]

null

migrations/versions/458a7da0c9da_.py

dmiklic/psiholeks-web

68dda07228a53790ab1e797336bb236031a544de

[ "MIT" ]

1

2018-05-01T09:15:12.000Z

2018-05-01T09:25:03.000Z

migrations/versions/458a7da0c9da_.py

dmiklic/psiholeks-web

68dda07228a53790ab1e797336bb236031a544de

[ "MIT" ]

null

"""empty message Revision ID: 458a7da0c9da Revises: Create Date: 2018-05-01 21:15:27.029811 """ from alembic import op import sqlalchemy as sa # revision identifiers, used by Alembic. revision = '458a7da0c9da' down_revision = None branch_labels = None depends_on = None

46.089286

109

0.720651

068fc5e74266b5c9c2303aed1e80240bd5fd0b7c

573

py

Python

mimic/modalities/MimicLateral.py

Jimmy2027/MoPoE-MIMIC

d167719b0dc7ba002b7421eb82a83e47d2437795

[ "MIT" ]

1

2021-09-30T07:56:46.000Z

mimic/modalities/MimicLateral.py

Jimmy2027/MoPoE-MIMIC

d167719b0dc7ba002b7421eb82a83e47d2437795

[ "MIT" ]

null

mimic/modalities/MimicLateral.py

Jimmy2027/MoPoE-MIMIC

d167719b0dc7ba002b7421eb82a83e47d2437795

[ "MIT" ]

null

import torch import mimic.modalities.utils from mimic.modalities.Modality import ModalityIMG

31.833333

85

0.687609

06911eef246c1772226f520da651c6e6b9337698

40,564

py

Python

src/rogerthat/rpc/rpc.py

goubertbrent/oca-backend

b9f59cc02568aecb55d4b54aec05245790ea25fd

[ "Apache-2.0" ]

null

src/rogerthat/rpc/rpc.py

goubertbrent/oca-backend

b9f59cc02568aecb55d4b54aec05245790ea25fd

[ "Apache-2.0" ]

null

src/rogerthat/rpc/rpc.py

goubertbrent/oca-backend

b9f59cc02568aecb55d4b54aec05245790ea25fd

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # Copyright 2020 Green Valley Belgium NV # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # # @@license_version:1.7@@ import base64 import json import logging import threading import time import traceback import types import uuid from copy import deepcopy from random import choice from types import NoneType from concurrent import futures # @UnresolvedImport from google.appengine.api import urlfetch, memcache from google.appengine.api.apiproxy_stub_map import UserRPC from google.appengine.api.app_identity.app_identity import get_application_id from google.appengine.api.taskqueue import TaskRetryOptions from google.appengine.ext import db, deferred from hyper import HTTP20Connection from jose import jwt from jose.constants import Algorithms from mcfw.cache import set_cache_key from mcfw.consts import MISSING from mcfw.properties import azzert from mcfw.rpc import arguments, returns, check_function_metadata, get_parameter_types, run, get_parameters, \ get_type_details, serialize_value, parse_parameter from rogerthat.consts import DEBUG, HIGH_LOAD_WORKER_QUEUE, FAST_QUEUE from rogerthat.dal.app import get_app_by_id from rogerthat.dal.mobile import get_mobile_settings_cached from rogerthat.dal.rpc_call import get_rpc_capi_backlog_parent_by_account, get_rpc_capi_backlog_parent_by_mobile from rogerthat.models import UserProfile from rogerthat.rpc import users from rogerthat.rpc.models import Mobile, RpcAPIResult, RpcCAPICall, OutStandingFirebaseKick, \ ServiceAPICallback, RpcException from rogerthat.settings import get_server_settings from rogerthat.to.push import PushData from rogerthat.to.system import LogErrorRequestTO from rogerthat.utils import now, privatize from rogerthat.utils.cloud_tasks import create_task, schedule_tasks from rogerthat.utils.crypto import encrypt_for_jabber_cloud, decrypt_from_jabber_cloud from rogerthat.utils.transactions import on_trans_committed _CALL_ACTION_RESEND = 1 _CALL_ACTION_MUST_PROCESS = 2 _CALL_ACTION_DO_NOT_PROCESS = 3 BACKLOG_CONCURRENCY_PROTECTION_INTERVAL = 120 MESSAGE_LINGER_INTERVAL = 3600 * 24 * 20 # 20 days MESSAGE_ALLOWED_FUTURE_TIME_INTERVAL = 3600 * 24 BACKLOG_MESSAGE_RETENTION_INTERVAL = 3600 * 24 + MESSAGE_LINGER_INTERVAL # 21 days BACKLOG_DUPLICATE_AVOIDANCE_RETENTION_INTERVAL = 3600 * 24 # 1 day APPENGINE_APP_ID = get_application_id() DO_NOT_SAVE_RPCCALL_OBJECTS = "DO_NOT_SAVE_RPCCALL_OBJECTS" PERFORM_CALLBACK_SYNCHRONOUS = "PERFORM_CALLBACK_SYNCHRONOUS" SKIP_ACCOUNTS = "SKIP_ACCOUNTS" MOBILE_ACCOUNT = "MOBILE_ACCOUNT" DEFER_KICK = "DEFER_KICK" TARGET_MFR = "TARGET_MFR" API_VERSION = u"av" API_DIRECT_PATH_KEY = u"ap" CALL_ID = u"ci" FUNCTION = u"f" PARAMETERS = u"a" STATUS = u"s" STATUS_SUCCESS = u"success" STATUS_FAIL = u"fail" RESULT = u"r" ERROR = u"e" CALL_TIMESTAMP = u"t" CALL_RESEND_TIMEOUT = 120 DEFAULT_RETENTION = 3600 * 24 MANDATORY_CALL_KEYS_SET = {PARAMETERS, API_VERSION, CALL_ID, FUNCTION} SEND_ACK = 1 IGNORE = 2 PRIORITY_NORMAL = 5 PRIORITY_HIGH = 10 DEFAULT_APPLE_PUSH_MESSAGE = base64.encodestring('{"aps":{"content-available":1}}') CAPI_KEYWORD_ARG_PRIORITY = "_cka_priority_" CAPI_KEYWORD_ARG_APPLE_PUSH_MESSAGE = "_cka_apple_push_message_" CAPI_KEYWORD_PUSH_DATA = '_push_data_' def create_firebase_request(data, is_gcm=False): # type: (dict) -> UserRPC # See https://firebase.google.com/docs/cloud-messaging/http-server-ref settings = get_server_settings() rpc_item = urlfetch.create_rpc(5, None) url = 'https://fcm.googleapis.com/fcm/send' headers = { 'Content-Type': 'application/json', 'Authorization': 'key=%s' % (settings.gcmKey if is_gcm else settings.firebaseKey) } urlfetch.make_fetch_call(rpc_item, url, json.dumps(data), urlfetch.POST, headers) return rpc_item def retry_firebase_request(payload, is_gcm=False): rpc_item = create_firebase_request(payload, is_gcm=is_gcm) response = rpc_item.get_result() # type: urlfetch._URLFetchResult if response.status_code != 200: raise Exception(response.content) apns_cache = APNSCache() kicks = JabberRpcCaller("kick") firebase = FirebaseKicker() api_callbacks = DirectRpcCaller() rpc_items = RpcFinisher() context_threads = ContextFinisher() # @arguments(alias=unicode, accept_sub_types=bool, priority=int, feature_version=Feature) def _store_rpc_api_result_deferred(mobile_key, call_id, result_json, now_): RpcAPIResult(parent=mobile_key, key_name=call_id, result=result_json, timestamp=now_).put() def _ack_all_deferred(mobile_key, call_ids): db.delete_async([db.Key.from_path(RpcAPIResult.kind(), call_id, parent=mobile_key) for call_id in call_ids]) def _validate_capi_call(result_f, error_f, target, alias, f, accept_sub_types=False): check_decorations(result_f) check_decorations(error_f) funcs = result_f, error_f logging.debug(funcs) from rogerthat.rpc.calls import result_mapping if any(filter(lambda fn: "mapping" not in fn.meta or fn.meta["mapping"] not in result_mapping, funcs)): raise ValueError( "Result and error processing functions must have their mapping declared in rogerthat.rpc.calls.result_mapping!") if any(filter(lambda fn: fn.meta["return_type"] != NoneType, funcs)): raise ValueError("Result and error processing functions cannot have return types.") if any(filter(lambda fn: "context" not in fn.meta["kwarg_types"] or fn.meta["kwarg_types"]["context"] != RpcCAPICall, funcs)): raise ValueError( "Result and error processing functions must have a arg 'context' of type rogerthat.rpc.models.RpcCAPICall.") if any(filter(lambda fn: len(fn.meta["kwarg_types"]) != 2, funcs)): raise ValueError("Result and error processing functions must have 2 arguments!") if not accept_sub_types and f.meta["return_type"] != result_f.meta["kwarg_types"]["result"]: raise ValueError("Return value type and result function result argument types do not match!") if accept_sub_types and not issubclass(f.meta["return_type"], result_f.meta["kwarg_types"]["result"]): raise ValueError("Return value type and result function result argument types do not match!") islist = False if not isinstance(target, (users.User, NoneType)): islist = True if not isinstance(target, (list, set)): raise_invalid_target() if any((not isinstance(m, (users.User, NoneType)) for m in target)): raise_invalid_target() from rogerthat.rpc.calls import client_mapping if not alias in client_mapping: raise ValueError("Function is not present in client_mapping") if not "error" in error_f.meta["kwarg_types"] or error_f.meta["kwarg_types"]["error"] in (str, unicode): raise ValueError("Error function must have an error parameter of type string.") return filter(lambda x: x, target) if islist else ([target] if target else []) def check_decorations(f): if not hasattr(f, "meta") or "return_type" not in f.meta or "kwarg_types" not in f.meta: raise ValueError("Function needs to be decorated with argument and return types")

43.852973

200

0.636377

0693b9613a135ff67d5413df7255909db8145fcb

1,131

py

Python

setup.py

Sipondo/ulix-dexflow

de46482fe08e3d600dd5da581f0524b55e5df961

[ "MIT" ]

5

2021-06-25T16:44:38.000Z

2021-12-31T01:29:00.000Z

setup.py

Sipondo/ulix-dexflow

de46482fe08e3d600dd5da581f0524b55e5df961

[ "MIT" ]

null

setup.py

Sipondo/ulix-dexflow

de46482fe08e3d600dd5da581f0524b55e5df961

[ "MIT" ]

1

2021-06-25T20:33:47.000Z

import os, sys, shutil from cx_Freeze import setup, Executable from pathlib import Path # Dependencies are automatically detected, but it might need fine tuning. additional_modules = [] build_exe_options = { "includes": additional_modules, "packages": [ "moderngl", "moderngl_window", "pyglet", "moderngl_window.context.pyglet", "glcontext", "moderngl_window.loaders.texture", "moderngl_window.loaders.program", ], } base = None if sys.platform == "win32": base = "Win32GUI" setup( name="Catchbase", version="1.0", description="Play your fangame", options={"build_exe": build_exe_options}, executables=[Executable(script="game.py", base=base)], ) for x in Path("build").glob("*"): p = x break copytree("resources", str(p / "resources"))

22.62

73

0.622458

0694fc4578a8a2dc3e2046b27ace2ea005f03139

5,332

py

Python

api/login.py

lachouettecoop/inventory-coop-api

583866737522ef473cbc05664835fe50787e2146

[ "MIT" ]

null

api/login.py

lachouettecoop/inventory-coop-api

583866737522ef473cbc05664835fe50787e2146

[ "MIT" ]

17

2020-10-05T19:21:55.000Z

2022-02-13T09:28:44.000Z

api/login.py

lachouettecoop/inventory-coop

c89488ea177b5616f1fe0bb5f149c61ae4bce2d1

[ "MIT" ]

null

import os import time import jwt import ldap from eve.auth import TokenAuth from flask import Blueprint, abort, jsonify, request blueprint = Blueprint("login", __name__) ADMIN_USERS = os.environ.get("ADMIN_USERS", "papanowel@gmail.com") JWT_ALGORITHM = os.environ.get("JWT_ALGORITHM", "HS256") JWT_EXPIRE_OFFSET = os.environ.get("JWT_EXPIRE_OFFSET", 60 * 60 * 12) # 12H JWT_SECRET = os.environ.get("JWT_SECRET") LDAP_SERVER = os.environ.get("LDAP_SERVER", "ldap://ldap.lachouettecoop.fr:389") LDAP_BASE_DN = os.environ.get("LDAP_BASE_DN", "cn=admin,dc=lachouettecoop,dc=fr") LDAP_SEARCH_DN = os.environ.get("LDAP_SEARCH_DN", "dc=lachouettecoop,dc=fr") LDAP_USER_DN = os.environ.get( "LDAP_USER_DN", "cn={},ou=membres,o=lachouettecoop,dc=lachouettecoop,dc=fr" ) LDAP_ADMIN_PASS = os.environ.get("LDAP_ADMIN_PASS") LDAP_SCOPE_SUBTREE = 2

33.325

99

0.61853

069560330062cf15b22f72ebec9dba45c5f2ba00

411

py

Python

tools/pinyin-completion/setup.py

Vayn/dotfiles

5a3e0e71669dd1832df7147b14c2943de6746119

[ "MIT" ]

4

2015-03-25T01:46:39.000Z

2017-04-30T18:04:46.000Z

tools/pinyin-completion/setup.py

Vayn/dotfiles

5a3e0e71669dd1832df7147b14c2943de6746119

[ "MIT" ]

null

tools/pinyin-completion/setup.py

Vayn/dotfiles

5a3e0e71669dd1832df7147b14c2943de6746119

[ "MIT" ]

null

#!/usr/bin/env python # vim: set fileencoding=utf-8 : from distutils.core import setup setup( name="pinyin-comp", version="0.1", description="complete path containing Chinese by pinyin acronym", author="Jekyll Wu", author_email="adaptee@gmail.com", url="http://www.github.com/adaptee/pinyin-comp", packages=['pinyin'], scripts=['pinyin-comp'] , )

27.4

73

0.622871

069859b4e100fade3b9371a57b0661bbf0c77719

1,518

py

Python

DailyCodingProblem/52_Google_LRU.py

RafayAK/CodingPrep

718eccb439db0f6e727806964766a40e8234c8a9

[ "MIT" ]

5

2019-09-07T17:31:17.000Z

2022-03-05T09:59:46.000Z

DailyCodingProblem/52_Google_LRU.py

RafayAK/CodingPrep

718eccb439db0f6e727806964766a40e8234c8a9

[ "MIT" ]

null

DailyCodingProblem/52_Google_LRU.py

RafayAK/CodingPrep

718eccb439db0f6e727806964766a40e8234c8a9

[ "MIT" ]

2

2019-09-07T17:31:24.000Z

2019-10-28T16:10:52.000Z

""" Good morning! Here's your coding interview problem for today. This problem was asked by Google. Implement an LRU (Least Recently Used) cache. It should be able to be initialized with a cache size n, and contain the following methods: set(key, value): sets key to value. If there are already n items in the cache and we are adding a new item, then it should also remove the least recently used item. get(key): gets the value at key. If no such key exists, return null. Each operation should run in O(1) time. """ if __name__ == '__main__': lru_cache = lru(5) assert not lru_cache.get(key='a') lru_cache.set('a', 1) assert lru_cache.get(key='a') == 1 lru_cache.set('b', 2) lru_cache.set('c', 3) lru_cache.set('d', 4) lru_cache.set('f', 6) lru_cache.set('e', 5) assert not lru_cache.get(key='a') assert lru_cache.get('e') == 5

24.885246

91

0.601449

0698ddf547c57339f83986d2ec83f39b1adf80a3

233

py

Python

setup.py

kowalcj0/teeb

15b87d9857510890fbe417e4de473b8b685b3319

[ "BSD-3-Clause" ]

null

setup.py

kowalcj0/teeb

15b87d9857510890fbe417e4de473b8b685b3319

[ "BSD-3-Clause" ]

null

setup.py

kowalcj0/teeb

15b87d9857510890fbe417e4de473b8b685b3319

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- from setuptools import setup # Metadata goes in setup.cfg. These are here for GitHub's dependency graph. setup( name="Teeb", install_requires=["chardet==4.0.0", "send2trash==1.5.0", "wand==0.6.5"], )

25.888889

76

0.656652

069a8d7c6fbb5a8f120cebac621c759b5b2c0718

233

py

Python

article_retrieval/__main__.py

aleph-oh/wikigame-solver

9a7b0a16df41291890e2bbe5903be55b25cef0f4

[ "MIT" ]

null

article_retrieval/__main__.py

aleph-oh/wikigame-solver

9a7b0a16df41291890e2bbe5903be55b25cef0f4

[ "MIT" ]

null

article_retrieval/__main__.py

aleph-oh/wikigame-solver

9a7b0a16df41291890e2bbe5903be55b25cef0f4

[ "MIT" ]

null

#!/usr/bin/env python3 """Constructs article graph.""" from database import clear_db from database.constants import engine from .database_builder import populate_db if __name__ == "__main__": clear_db(engine) populate_db()

21.181818

41

0.759657

069d259501ee0ad7f4c234fd4a9336eca8bab60c

637

py

Python

playlist_organizer/client/deezer/settings.py

perminovs/playlist_organizer

cc5da2b0a031e29235e9ebe982f1062900602dd4

[ "MIT" ]

null

playlist_organizer/client/deezer/settings.py

perminovs/playlist_organizer

cc5da2b0a031e29235e9ebe982f1062900602dd4

[ "MIT" ]

null

playlist_organizer/client/deezer/settings.py

perminovs/playlist_organizer

cc5da2b0a031e29235e9ebe982f1062900602dd4

[ "MIT" ]

null

from pydantic import BaseSettings

25.48

61

0.66876

069dac451eea987083fb0222c0d932e8a5b6741b

2,462

py

Python

services/web/project/routes/api.py

sthe0/test-bot-fullstack

602c876177eb16958748a9e46274533759ff5792

[ "MIT" ]

null

services/web/project/routes/api.py

sthe0/test-bot-fullstack

602c876177eb16958748a9e46274533759ff5792

[ "MIT" ]

null

services/web/project/routes/api.py

sthe0/test-bot-fullstack

602c876177eb16958748a9e46274533759ff5792

[ "MIT" ]

null

# -*- coding: utf-8 -*- from flask import Blueprint, jsonify, request from functools import wraps from sqlalchemy import desc from project.common import app, db, fb_api from project.config import ApiConfig from project.models import Client, Message api = Blueprint('api', __name__)

27.662921

89

0.675467

069dc6e8933ab9b9913ed9fdacb63aac7e39388b

3,830

py

Python

c4/test.py

duilio/c4

6dcde8316603192b0bc713d1bedb94290d123a9d

[ "MIT" ]

16

2016-03-19T16:34:58.000Z

2021-11-07T08:59:53.000Z

c4/test.py

duilio/c4

6dcde8316603192b0bc713d1bedb94290d123a9d

[ "MIT" ]

1

2017-08-27T10:18:39.000Z

2018-02-24T20:55:27.000Z

c4/test.py

duilio/c4

6dcde8316603192b0bc713d1bedb94290d123a9d

[ "MIT" ]

9

2017-02-23T23:14:17.000Z

2020-12-25T12:26:47.000Z

import unittest import numpy as np from c4.board import Board, PLAYER1

42.555556

58

0.232376

069eac769d2ccf455170294707453e0b3dff4035

1,134

py

Python

NLP/3-Advanced-RNN-2/3-1-cnn-rnn-1.py

agarwalanant/ThaparWorkshopANN

fd7858b89ade8a621f30e389721166b222228f02

[ "MIT" ]

16

2019-06-19T05:43:01.000Z

2020-12-01T13:24:55.000Z

NLP/3-Advanced-RNN-2/3-1-cnn-rnn-1.py

agarwalanant/ThaparWorkshopANN

fd7858b89ade8a621f30e389721166b222228f02

[ "MIT" ]

null

NLP/3-Advanced-RNN-2/3-1-cnn-rnn-1.py

agarwalanant/ThaparWorkshopANN

fd7858b89ade8a621f30e389721166b222228f02

[ "MIT" ]

37

2019-06-17T11:53:13.000Z

2020-06-02T13:05:31.000Z

import numpy as np from sklearn.metrics import accuracy_score from keras.datasets import cifar10 from keras.utils import to_categorical from keras.models import Sequential, Model from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, LSTM, Input, Activation, Reshape, concatenate from keras import optimizers (X_train, y_train), (X_test, y_test) = cifar10.load_data() y_train = to_categorical(y_train) y_test = to_categorical(y_test) model = Sequential() model.add(Conv2D(input_shape = (X_train.shape[1], X_train.shape[2], X_train.shape[3]), filters = 50, kernel_size = (3,3), strides = (1,1), padding = 'same')) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size = (2,2))) model.add(Reshape(target_shape = (16*16, 50))) model.add(LSTM(50, return_sequences = False)) model.add(Dense(10)) model.add(Activation('softmax')) adam = optimizers.Adam(lr = 0.001) model.compile(loss = 'categorical_crossentropy', optimizer = adam, metrics = ['accuracy']) history = model.fit(X_train, y_train, epochs = 100, batch_size = 100, verbose = 1) results = model.evaluate(X_test, y_test) print('Test Accuracy: ', results[1])

37.8

157

0.751323

069f831615d2592dffe7e15e0512bd6627e063ca

1,652

py

Python

pyage2/lib/actions.py

kachayev/pyage2

adf87e0deeddaa4301dbcaf4fa7d396a71d129de

[ "Apache-2.0" ]

13

2021-07-09T07:10:42.000Z

2022-03-16T10:38:59.000Z

pyage2/lib/actions.py

kachayev/pyage2

adf87e0deeddaa4301dbcaf4fa7d396a71d129de

[ "Apache-2.0" ]

null

pyage2/lib/actions.py

kachayev/pyage2

adf87e0deeddaa4301dbcaf4fa7d396a71d129de

[ "Apache-2.0" ]

1

2022-01-16T13:24:32.000Z

# Copyright 2021 PyAge2, Oleksii Kachaiev <kachayev@gmail.com>. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS-IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import Union import pyage2.expert.action.action_pb2 as action from pyage2.lib import expert from pyage2.lib.expert import StrategicNumber, ObjectType, TechType

37.545455

84

0.766344

069f9b47635b756c567cad2b645af0001f7d8f95

4,045

py

Python

multi_view_ctrl/grid_element_div.py

imldresden/mcv-displaywall

d08cf6fab869ee03d8b3af203dd0e55b42ab4605

[ "MIT" ]

2

2019-12-12T20:57:37.000Z

2021-09-29T02:59:19.000Z

multi_view_ctrl/grid_element_div.py

imldresden/mcv-displaywall

d08cf6fab869ee03d8b3af203dd0e55b42ab4605

[ "MIT" ]

null

multi_view_ctrl/grid_element_div.py

imldresden/mcv-displaywall

d08cf6fab869ee03d8b3af203dd0e55b42ab4605

[ "MIT" ]

null

from libavg import avg from events.event_dispatcher import EventDispatcher from multi_view_ctrl.grid_element import GridElement from multi_view_ctrl.configurations.grid_element_div_configuration import GridElementDivConfigurations def is_pos_in(self, pos): """ Checks if a given pos lies inside in this grid element div. :param pos: The pos to check for. :type pos: tuple[float, float] :return: Is the given pos in this element? :rtype: bool """ return self.pos[0] <= pos[0] <= self.pos[0] + self.size[0] and self.pos[1] <= pos[1] <= self.pos[1] + self.size[1] def append_child_for_grid(self, node): """ Appends the given node. It also sets the size of the node to the size of this grid element div. :param node: The node to add to this grid element. :type node: Node """ node.size = self._internal_div.size node.view_id = self.grid_id self._internal_div.appendChild(node) self._child_nodes.append(node) def start_listening(self): """ Registers a callback to listen to changes to this grid elemen div. Listeners can register to any number of the provided events. For the required structure of the callbacks see below. """ pass def stop_listening(self): """ Stops listening to an event the listener has registered to previously. The provided callback needs to be the same that was used to listen to the event in the fist place. """ pass

38.52381

135

0.648949

06a1854bfcd9896d019df15af2c9bb29dfe25337

4,152

py

Python

relations-finder/src/relations_finder.py

ahmed91abbas/wiki-relations

9f8d20c512f993cab6065cb2695c996c076b6d13

[ "MIT" ]

5

2021-03-05T12:19:44.000Z

2022-01-05T19:28:44.000Z

relations-finder/src/relations_finder.py

ahmed91abbas/wiki-relations

9f8d20c512f993cab6065cb2695c996c076b6d13

[ "MIT" ]

null

relations-finder/src/relations_finder.py

ahmed91abbas/wiki-relations

9f8d20c512f993cab6065cb2695c996c076b6d13

[ "MIT" ]

2

2021-03-13T12:07:33.000Z

2021-03-30T07:58:38.000Z

import en_core_web_sm from spacy import displacy

37.071429

98

0.575626

06a1e865401185ec36f7fc0f3099d2dfb3463d93

8,597

py

Python

app/users/routes.py

Shikhar-SRJ/DL_Model_APIs

ea26cb415477cf58a2ddd925689b62588bf95b13

[ "MIT" ]

null

app/users/routes.py

Shikhar-SRJ/DL_Model_APIs

ea26cb415477cf58a2ddd925689b62588bf95b13

[ "MIT" ]

null

app/users/routes.py

Shikhar-SRJ/DL_Model_APIs

ea26cb415477cf58a2ddd925689b62588bf95b13

[ "MIT" ]

null

from flask import jsonify, request, Blueprint, current_app, send_file, make_response import tensorflow as tf import numpy as np from app.users import utils import cv2 from app.models import User, Data, Predictions, Coordinates from app import db from werkzeug.security import generate_password_hash, check_password_hash import jwt users = Blueprint('users', __name__)

41.331731

139

0.609282

06a25915be2cf155b3153cd03aaa095116d1e484

352

py

Python

Curso em Video - Exercicios/ex006.py

JefferMarcelino/Aulas-Python

3885b1cfa7f27b90b702ee81fc5eab108c029e52

[ "MIT" ]

2

2021-01-27T19:30:05.000Z

2022-01-10T20:34:53.000Z

Curso em Video - Exercicios/ex006.py

JefferMarcelino/Aulas-Python

3885b1cfa7f27b90b702ee81fc5eab108c029e52

[ "MIT" ]

null

Curso em Video - Exercicios/ex006.py

JefferMarcelino/Aulas-Python

3885b1cfa7f27b90b702ee81fc5eab108c029e52

[ "MIT" ]

null

""" EXERCCIO 006: Dobro, Triplo, Raiz Quadrada Crie um algoritmo que leia um nmero e mostre o seu dobro, triplo e raiz quadrada. """ n = int(input('Digite um nmero: ')) print('O dobro de {} vale {}.'.format(n, (n * 2))) print('O triplo de {} vale {}.'.format(n, (n * 3))) print('A raiz quadrada de {} igual a {:.2f}.'.format(n, pow(n, (1 / 2))))

35.2

82

0.619318

06a27f548fc5e06937812be30ed778ed22cfc7a5

2,494

py

Python

bionic/gcs.py

IDl0T/bionic

8eaa868a2e7af81bb561492c045feb414f7c6326

[ "Apache-2.0" ]

98

2019-08-29T21:38:44.000Z

2022-01-26T04:59:57.000Z

bionic/gcs.py

IDl0T/bionic

8eaa868a2e7af81bb561492c045feb414f7c6326

[ "Apache-2.0" ]

143

2019-09-11T15:32:17.000Z

2021-06-08T21:48:30.000Z

bionic/gcs.py

IDl0T/bionic

8eaa868a2e7af81bb561492c045feb414f7c6326

[ "Apache-2.0" ]

20

2019-09-13T18:13:03.000Z

2021-12-03T19:51:01.000Z

""" Utilities for working with Google Cloud Storage. """ import logging import warnings from .deps.optdep import import_optional_dependency logger = logging.getLogger(__name__) _cached_gcs_fs = None # TODO: Consider using persistence.GcsFilesystem instead of exposing this function. def upload_to_gcs(path, url): """ Copy a local path to GCS URL. """ gcs_fs = get_gcs_fs_without_warnings() if path.is_dir(): gcs_fs.put(str(path), url, recursive=True) else: # If the GCS URL is a folder, we want to write the file in the folder. # There seems to be a bug in fsspec due to which, the file is uploaded # as the url, instead of inside the folder. What this means is, writing # a file c.json to gs://a/b/ would result in file gs://a/b instead of # gs://a/b/c.json. # # The `put` API is supposed to write the file inside the folder but it # strips the ending "/" at the end in fsspec's `_strip_protocol` method. # See https://github.com/intake/filesystem_spec/issues/448 for more # details and tracking this issue. if url.endswith("/"): url = url + path.name gcs_fs.put_file(str(path), url)

38.369231

85

0.676423

06a3f43967e178259c2fded854053a178b218002

208

py

Python

src/utils/const.py

yizhongw/TagNN-PDTB

9b944210bcc3851c65cb479ef705acbb1b45b08f

[ "MIT" ]

14

2018-11-19T02:49:34.000Z

2022-02-18T04:00:31.000Z

src/utils/const.py

lidejian/TreeLSTM-PDTB

3f048d2a3daf3fb5e803037f9344f515d0e71450

[ "MIT" ]

null

src/utils/const.py

lidejian/TreeLSTM-PDTB

3f048d2a3daf3fb5e803037f9344f515d0e71450

[ "MIT" ]

5

2017-12-04T13:29:29.000Z

2018-05-07T08:45:04.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- # author: yizhong # created_at: 17-5-2 5:00 PAD_WORD = '<blank>' UNK_WORD = '<unk>' BOS_WORD = '<s>' EOS_WORD = '</s>' NUM_WORD = '<num>' PUNC_TAG = '<punc>'

16

27

0.586538

06a458881d352d1e5bc5252e5c9354f711ebe5e6

208

py

Python

src_old/tests/scripts/core/ex7.py

toddrme2178/pyccel

deec37503ab0c5d0bcca1a035f7909f7ce8ef653

[ "MIT" ]

null

src_old/tests/scripts/core/ex7.py

toddrme2178/pyccel

deec37503ab0c5d0bcca1a035f7909f7ce8ef653

[ "MIT" ]

null

src_old/tests/scripts/core/ex7.py

toddrme2178/pyccel

deec37503ab0c5d0bcca1a035f7909f7ce8ef653

[ "MIT" ]

null

#coding: utf-8 a = zeros((10,10), double) for i in range(0,10): a[i,i] = 2.0 for i in range(0,9): a[i,i+1] = -1.0 for i in range(0,9): a[i,i+1] = -1.0 n = 5 for i in range(0, n): x = 1

10.947368

26

0.480769

06a83d0998f9996abe66240e832c87433d984bc2

626

py

Python

src/learning_language/views.py

gsi-luis/djangolearning

4cf1e016cfe2910c907a669e518f5233ae04fb12

[ "MIT" ]

1

2020-07-05T18:33:33.000Z

src/learning_language/views.py

gsi-luis/djangolearning

4cf1e016cfe2910c907a669e518f5233ae04fb12

[ "MIT" ]

2

2021-03-30T13:49:58.000Z

2021-06-10T19:43:27.000Z

src/learning_language/views.py

gsi-luis/djangolearning

4cf1e016cfe2910c907a669e518f5233ae04fb12

[ "MIT" ]

null

from django.shortcuts import render from .forms import LanguageForm from learning_django import settings from django.utils import translation

27.217391

76

0.701278

06abef330b43336341fe87f19a5bb8dd00ab85db

252

py

Python

driver/comtypes_gamry_simulate.py

yul69-cell/HELAO

a39372eb385ee93b711443d9cbd56c5ec737ff70

[ "CC0-1.0" ]

null

driver/comtypes_gamry_simulate.py

yul69-cell/HELAO

a39372eb385ee93b711443d9cbd56c5ec737ff70

[ "CC0-1.0" ]

null

driver/comtypes_gamry_simulate.py

yul69-cell/HELAO

a39372eb385ee93b711443d9cbd56c5ec737ff70

[ "CC0-1.0" ]

null

#create cinet and functions like COMError that simulate Gamry #dtaq.Cook is defined to return dummy data when called #import config here and check if a simulation is being run and if so load that simulation .py that overrides functions like dtaq.Cook

50.4

133

0.809524

06ada35b71f676f14ae2a8fbfcb628afacd0c4d8

512

py

Python

oj2.py

YanshuHu/combinatoricsoj2

51fa8cf06042e63642b8407d12de99d22f0e7a3b

[ "Apache-2.0" ]

null

oj2.py

YanshuHu/combinatoricsoj2

51fa8cf06042e63642b8407d12de99d22f0e7a3b

[ "Apache-2.0" ]

null

oj2.py

YanshuHu/combinatoricsoj2

51fa8cf06042e63642b8407d12de99d22f0e7a3b

[ "Apache-2.0" ]

null

if __name__ == '__main__': main()

20.48

40

0.564453

06af2b443d404bade0c4526a7994135505c898f7

737

py

Python

kelte/maths/vector.py

brianbruggeman/rl

6dd8a53da07697ffc87e62aa397be7b3b08f0aa0

[ "MIT" ]

null

kelte/maths/vector.py

brianbruggeman/rl

6dd8a53da07697ffc87e62aa397be7b3b08f0aa0

[ "MIT" ]

null

kelte/maths/vector.py

brianbruggeman/rl

6dd8a53da07697ffc87e62aa397be7b3b08f0aa0

[ "MIT" ]

null

from dataclasses import dataclass, field from .point import Point NONE: Direction = Direction(0, 0) NORTH: Direction = Direction(0, -1) SOUTH: Direction = Direction(0, 1) EAST: Direction = Direction(1, 0) WEST: Direction = Direction(-1, 0) NORTH_EAST: Direction = NORTH + EAST NORTH_WEST: Direction = NORTH + WEST SOUTH_EAST: Direction = SOUTH + EAST SOUTH_WEST: Direction = SOUTH + WEST UP: Direction = Direction(0, -1) DOWN: Direction = Direction(0, 1) RIGHT: Direction = Direction(1, 0) LEFT: Direction = Direction(-1, 0) UP_RIGHT: Direction = UP + RIGHT UP_LEFT: Direction = UP + LEFT DOWN_RIGHT: Direction = DOWN + RIGHT DOWN_LEFT: Direction = DOWN + LEFT

22.333333

40

0.708277

06af865f1a3973785536a7d3858ef8ea324bb911

1,437

py

Python

tests/bugs/core_4158_test.py

FirebirdSQL/firebird-qa

96af2def7f905a06f178e2a80a2c8be4a4b44782

[ "MIT" ]

1

2022-02-05T11:37:13.000Z

tests/bugs/core_4158_test.py

FirebirdSQL/firebird-qa

96af2def7f905a06f178e2a80a2c8be4a4b44782

[ "MIT" ]

1

2021-09-03T11:47:00.000Z

2021-09-03T12:42:10.000Z

tests/bugs/core_4158_test.py

FirebirdSQL/firebird-qa

96af2def7f905a06f178e2a80a2c8be4a4b44782

[ "MIT" ]

1

2021-06-30T14:14:16.000Z

#coding:utf-8 # # id: bugs.core_4158 # title: Regression: LIKE with escape does not work # decription: # tracker_id: CORE-4158 # min_versions: ['2.0.7'] # versions: 2.0.7 # qmid: None import pytest from firebird.qa import db_factory, isql_act, Action # version: 2.0.7 # resources: None substitutions_1 = [] init_script_1 = """ recreate table tab1 ( id int constraint pk_tab1 primary key, val varchar(30) ); insert into tab1 (id, val) values (1, 'abcdef'); insert into tab1 (id, val) values (2, 'abc_ef'); insert into tab1 (id, val) values (3, 'abc%ef'); insert into tab1 (id, val) values (4, 'abc&%ef'); insert into tab1 (id, val) values (5, 'abc&_ef'); """ db_1 = db_factory(page_size=4096, sql_dialect=3, init=init_script_1) test_script_1 = """ set list on; select id, val from tab1 where val like 'abc&%ef' escape '&'; select id, val from tab1 where val like 'abc&_ef' escape '&'; """ act_1 = isql_act('db_1', test_script_1, substitutions=substitutions_1) expected_stdout_1 = """ ID 3 VAL abc%ef ID 2 VAL abc_ef """

26.127273

70

0.592902

06afc4b209dc7b6ac90802b9ff2ce19d8ee2b910

18,430

py

Python

trustyroles/arpd_update/arpd_update.py

hmcguire1/trustyroles

5dbe3d65353538f84f12f3ecef6de2a8cc3f731f

[ "MIT" ]

2

2019-12-16T15:10:13.000Z

2020-02-24T20:13:40.000Z

trustyroles/arpd_update/arpd_update.py

hmcguire1/trustyroles

5dbe3d65353538f84f12f3ecef6de2a8cc3f731f

[ "MIT" ]

null

trustyroles/arpd_update/arpd_update.py

hmcguire1/trustyroles

5dbe3d65353538f84f12f3ecef6de2a8cc3f731f

[ "MIT" ]

1

2019-12-05T01:12:33.000Z

""" arpd_update focuses on easily editing the assume role policy document of a role. """ import os import json import logging import argparse from datetime import datetime from typing import List, Dict, Optional import boto3 # type: ignore from botocore.exceptions import ClientError # type: ignore LOGGER = logging.getLogger("IAM-ROLE-TRUST-POLICY") logging.basicConfig(level=logging.WARNING) PARSER = argparse.ArgumentParser() def _main(): """The _main method can take in a list of ARNs, role to update, and method [get, update, remove, restore].""" PARSER.add_argument( "-a", "--arn", nargs="+", required=False, help="Add new ARNs to trust policy. Takes a comma-seperated list of ARNS.", ) PARSER.add_argument( "-u", "--update_role", type=str, required=True, help="Role for updating trust policy. Takes an role friendly name as string.", ) PARSER.add_argument( "-m", "--method", type=str, required=False, choices=["get", "update", "remove", "restore"], help="Takes choice of method to get, update, or remove.", ) PARSER.add_argument( "-e", "--add_external_id", type=str, required=False, help="Takes an externalId as a string.", ) PARSER.add_argument( "--remove_external_id", action="store_true", required=False, help="Method for removing externalId condition. Takes no arguments", ) PARSER.add_argument( "--json", action="store_true", required=False, help="Add to print json in get method.", ) PARSER.add_argument( "--add_sid", type=str, required=False, help="Add a Sid to trust policy. Takes a string.", ) PARSER.add_argument( "--remove_sid", action="store_true", required=False, help="Remove a Sid from a trust policy. Takes no arguments.", ) PARSER.add_argument( "--backup_policy", type=str, required=False, help="""Creates a backup of previous policy in current directory as <ISO-time>.policy.bk""", ) PARSER.add_argument( "--dir_path", type=str, required=False, help="Path to directory for backup policy. Takes a string", ) PARSER.add_argument( "--file_path", type=str, required=False, help="File for backup policy. Takes a string", ) PARSER.add_argument( "--bucket", type=str, required=False, help="S3 bucket name for backup policy. Takes a string", ) PARSER.add_argument( "--key", type=str, required=False, help="S3 key name for restoring S3 policy. Takes a string", ) args = vars(PARSER.parse_args()) if args["backup_policy"]: if args["backup_policy"] == "local": if args["dir_path"]: dir_path = args["dir_path"] else: dir_path = os.getcwd() bucket = None elif args["backup_policy"] == "s3": bucket = args["bucket"] dir_path = None else: dir_path = os.getcwd() bucket = "" if args["method"] == "update": arpd = update_arn( args["arn"], args["update_role"], dir_path=dir_path, bucket=bucket, backup_policy=args["backup_policy"], ) print(json.dumps(arpd["Statement"][0], indent=4)) elif args["method"] == "remove": arpd = remove_arn( args["arn"], args["update_role"], dir_path=dir_path, bucket=bucket, backup_policy=args["backup_policy"], ) print(json.dumps(arpd["Statement"][0], indent=4)) elif args["method"] == "get": arpd = get_arpd(args["update_role"]) if args["json"]: print(json.dumps(arpd["Statement"][0], indent=4)) else: print(f"\nARNS:") if isinstance(arpd["Statement"][0]["Principal"]["AWS"], list): for arn in arpd["Statement"][0]["Principal"]["AWS"]: print(f" {arn}") else: print(f" {arpd['Statement'][0]['Principal']['AWS']}") print(f"Conditions:") if arpd["Statement"][0]["Condition"]: print(f" {arpd['Statement'][0]['Condition']}") elif args["method"] == "restore" and args["backup_policy"]: if args["backup_policy"].lower() == "local" and args["file_path"]: arpd = restore_from_backup( role_name=args["update_role"], location_type="local", file_path=args["file_path"], ) elif args["backup_policy"].lower() == "s3": arpd = restore_from_backup( role_name=args["update_role"], location_type="s3", file_path="", key=args["key"], bucket=bucket, backup_policy=args["backup_policy"], ) print(json.dumps(arpd["Statement"][0], indent=4)) if args["add_external_id"]: arpd = add_external_id( external_id=args["add_external_id"], role_name=args["update_role"], dir_path=dir_path, bucket=bucket, backup_policy=args["backup_policy"], ) print(json.dumps(arpd["Statement"][0], indent=4)) if args["remove_external_id"]: arpd = remove_external_id( role_name=args["update_role"], dir_path=dir_path, bucket=bucket, backup_policy=args["backup_policy"], ) print(json.dumps(arpd["Statement"][0], indent=4)) if args["add_sid"]: arpd = add_sid( role_name=args["update_role"], sid=args["add_sid"], dir_path=dir_path, bucket=bucket, backup_policy=args["backup_policy"], ) print(json.dumps(arpd["Statement"][0], indent=4)) if args["remove_sid"]: arpd = remove_sid( role_name=args["update_role"], dir_path=dir_path, bucket=bucket, backup_policy=args["backup_policy"], ) print(json.dumps(arpd["Statement"][0], indent=4)) def get_arpd(role_name: str, session=None, client=None) -> Dict: """The get_arpd method takes in a role_name as a string and provides trusted ARNS and Conditions. """ if session: iam_client = session.client("iam") elif client: iam_client = client else: iam_client = boto3.client("iam") role = iam_client.get_role(RoleName=role_name) return role["Role"]["AssumeRolePolicyDocument"] def update_arn( role_name: str, arn_list: List, dir_path: Optional[str], client=None, session=None, backup_policy: Optional[str] = "", bucket: Optional[str] = None, ) -> Dict: """The update_arn method takes a multiple ARNS(arn_list) and a role_name to add to trust policy of suppplied role. """ if session: iam_client = session.client("iam") elif client: iam_client = client else: iam_client = boto3.client("iam") role = iam_client.get_role(RoleName=role_name) arpd = role["Role"]["AssumeRolePolicyDocument"] old_principal_list = arpd["Statement"][0]["Principal"]["AWS"] if backup_policy: if backup_policy.lower() == "local": if dir_path: retain_policy( policy=arpd, role_name=role_name, location_type="local", dir_path=dir_path, ) else: retain_policy(policy=arpd, role_name=role_name, location_type="local") elif backup_policy.lower() == "s3": retain_policy( policy=arpd, role_name=role_name, location_type="s3", bucket=bucket ) if isinstance(old_principal_list, list): for arn in arn_list: arpd["Statement"][0]["Principal"]["AWS"].append(arn) else: old_principal_list = [old_principal_list] for arn in arn_list: arpd["Statement"][0]["Principal"]["AWS"] = old_principal_list arpd["Statement"][0]["Principal"]["AWS"].append(arn) try: iam_client.update_assume_role_policy( RoleName=role_name, PolicyDocument=json.dumps(arpd) ) return arpd except ClientError as error: raise error def remove_arn( role_name: str, arn_list: List, dir_path: Optional[str], session=None, client=None, backup_policy: Optional[str] = "", bucket: Optional[str] = None, ) -> Dict: """The remove_arn method takes in a string or multiple of ARNs and a role_name to remove ARNS from trust policy of supplied role. """ if session: iam_client = session.client("iam") elif client: iam_client = client else: iam_client = boto3.client("iam") role = iam_client.get_role(RoleName=role_name) arpd = role["Role"]["AssumeRolePolicyDocument"] old_principal_list = arpd["Statement"][0]["Principal"]["AWS"] if backup_policy: if backup_policy.lower() == "local": if dir_path: retain_policy( policy=arpd, role_name=role_name, location_type="local", dir_path=dir_path, ) else: retain_policy(policy=arpd, role_name=role_name, location_type="local") elif backup_policy.lower() == "s3": retain_policy( policy=arpd, role_name=role_name, location_type="s3", bucket=bucket ) for arn in arn_list: if arn in old_principal_list: arpd["Statement"][0]["Principal"]["AWS"].remove(arn) try: iam_client.update_assume_role_policy( RoleName=role_name, PolicyDocument=json.dumps(arpd) ) return arpd except ClientError as error: raise error def add_external_id( role_name: str, external_id: str, dir_path: Optional[str], client=None, session=None, backup_policy: Optional[str] = "", bucket: Optional[str] = None, ) -> Dict: """ The add_external_id method takes an external_id and role_name as strings to allow the addition of an externalId condition. """ if session: iam_client = session.client("iam") elif client: iam_client = client else: iam_client = boto3.client("iam") role = iam_client.get_role(RoleName=role_name) arpd = role["Role"]["AssumeRolePolicyDocument"] if backup_policy: if backup_policy.lower() == "local": if dir_path: retain_policy( policy=arpd, role_name=role_name, location_type="local", dir_path=dir_path, ) else: retain_policy(policy=arpd, role_name=role_name, location_type="local") elif backup_policy.lower() == "s3": retain_policy( policy=arpd, role_name=role_name, location_type="s3", bucket=bucket ) arpd["Statement"][0]["Condition"] = { "StringEquals": {"sts:ExternalId": external_id} } try: iam_client.update_assume_role_policy( RoleName=role_name, PolicyDocument=json.dumps(arpd) ) return arpd except ClientError as error: raise error def remove_external_id( role_name: str, dir_path: Optional[str], session=None, client=None, backup_policy: Optional[str] = "", bucket: Optional[str] = None, ) -> Dict: """The remove_external_id method takes a role_name as a string to allow the removal of an externalId condition. """ if session: iam_client = session.client("iam") elif client: iam_client = client else: iam_client = boto3.client("iam") role = iam_client.get_role(RoleName=role_name) arpd = role["Role"]["AssumeRolePolicyDocument"] if backup_policy: if backup_policy.lower() == "local": if dir_path: retain_policy( policy=arpd, role_name=role_name, location_type="local", dir_path=dir_path, ) else: retain_policy(policy=arpd, role_name=role_name, location_type="local") elif backup_policy.lower() == "s3": retain_policy( policy=arpd, role_name=role_name, location_type="s3", bucket=bucket ) arpd["Statement"][0]["Condition"] = {} try: iam_client.update_assume_role_policy( RoleName=role_name, PolicyDocument=json.dumps(arpd) ) return arpd except ClientError as error: raise error def add_sid( role_name: str, sid: str, dir_path: Optional[str], session=None, client=None, backup_policy: str = "", bucket: Optional[str] = None, ) -> Dict: """ The add_sid method adds a statement ID to the assume role policy document """ if session: iam_client = session.client("iam") elif client: iam_client = client else: iam_client = boto3.client("iam") role = iam_client.get_role(RoleName=role_name) arpd = role["Role"]["AssumeRolePolicyDocument"] if backup_policy.lower() == "local": if dir_path: retain_policy( policy=arpd, role_name=role_name, location_type="local", dir_path=dir_path, ) else: retain_policy(policy=arpd, role_name=role_name, location_type="local") elif backup_policy.lower() == "s3": retain_policy( policy=arpd, role_name=role_name, location_type="s3", bucket=bucket ) arpd["Statement"][0]["Sid"] = sid try: iam_client.update_assume_role_policy( RoleName=role_name, PolicyDocument=json.dumps(arpd) ) return arpd except ClientError as ex: raise ex def remove_sid( role_name: str, dir_path: Optional[str], session=None, client=None, backup_policy: str = "", bucket: Optional[str] = None, ) -> Dict: """ The remove_sid method removes the statement ID from the assume role policy document """ if session: iam_client = session.client("iam") elif client: iam_client = client else: iam_client = boto3.client("iam") role = iam_client.get_role(RoleName=role_name) arpd = role["Role"]["AssumeRolePolicyDocument"] if backup_policy.lower() == "local": if dir_path: retain_policy( policy=arpd, role_name=role_name, location_type="local", dir_path=dir_path, ) else: retain_policy(policy=arpd, role_name=role_name, location_type="local") elif backup_policy.lower() == "s3": retain_policy( policy=arpd, role_name=role_name, location_type="s3", bucket=bucket ) if arpd["Statement"][0]["Sid"]: arpd["Statement"][0].pop("Sid") try: iam_client.update_assume_role_policy( RoleName=role_name, PolicyDocument=json.dumps(arpd) ) except ClientError as error: raise error return arpd def retain_policy( role_name: str, policy: Dict, session=None, client=None, location_type: Optional[str] = None, dir_path=os.getcwd(), bucket: Optional[str] = None, ) -> None: """ The retain_policy method creates a backup of previous policy in current directory by default as <ISO-time>.<RoleName>.bk or specified directory for local file or with s3 to specified bucket and key name. """ assert location_type if location_type.lower() == "local": with open( dir_path + "/" + datetime.utcnow().strftime("%Y-%m-%dT%H:%M:%SZ") + f".{role_name}.bk", "w", ) as file: json.dump(policy, file, ensure_ascii=False, indent=4) elif location_type.lower() == "s3": if session: s3_client = session.client("s3") elif client: s3_client = client else: s3_client = boto3.client("s3") try: s3_client.put_object( Bucket=bucket, Key=datetime.utcnow().strftime("%Y-%m-%dT%H:%M:%SZ") + f".{role_name}.bk", Body=json.dumps(policy).encode(), ) except ClientError as error: raise error if __name__ == "__main__": _main()

27.507463

93

0.566522

06aff71efc0dec027a46c0058c117887035af9c9

7,471

py

Python

kartingpros/timetrial.py

Vishvak365/Karting-Pros

1c482cff78e7402c8da8870ff519eea760be4a34

[ "MIT" ]

1

2021-06-28T21:55:18.000Z

kartingpros/timetrial.py

wboyd600/Karting-Pros

4db4b9f075b152dfea79c89640c0bac1becce89b

[ "MIT" ]

17

2020-11-27T14:33:39.000Z

2020-12-08T00:45:18.000Z

kartingpros/timetrial.py

wboyd600/Karting-Pros

4db4b9f075b152dfea79c89640c0bac1becce89b

[ "MIT" ]

1

2021-06-27T20:27:38.000Z

import pygame import time import math import sys from kartingpros import track, mainmenu, car, settings, loadimage from kartingpros.loadimage import _load_image, _load_sound, _load_font import numpy as np from numpy import save from kartingpros.car import Car from pygame.locals import * from pygame import mixer import os

33.959091

99

0.549458

06b0e4b7f2071c5642bd956f75e4b9df9624fc3e

9,079

py

Python

tests/location/test_location_utility.py

questionlp/wwdtm

f3cf3399c22bf19e369e6e0250e7c72de0be3a90

[ "Apache-2.0" ]

null

tests/location/test_location_utility.py

questionlp/wwdtm

f3cf3399c22bf19e369e6e0250e7c72de0be3a90

[ "Apache-2.0" ]

1

2022-01-17T04:25:49.000Z

tests/location/test_location_utility.py

questionlp/wwdtm

f3cf3399c22bf19e369e6e0250e7c72de0be3a90

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # vim: set noai syntax=python ts=4 sw=4: # # Copyright (c) 2018-2021 Linh Pham # wwdtm is released under the terms of the Apache License 2.0 """Testing for object: :py:class:`wwdtm.location.LocationUtility` """ import json from typing import Any, Dict import pytest from wwdtm.location import LocationUtility

38.965665

88

0.702941

06b195aef83b65c429bf30fd2c08ed267c6351f6

2,204

py

Python

test/create_cert.py

finsberg/pytest-tornado

52ba5119310be5385ceed74ef94f4538660e3725

[ "Apache-2.0" ]

123

2015-03-31T17:25:34.000Z

2021-12-16T12:14:38.000Z

test/create_cert.py

finsberg/pytest-tornado

52ba5119310be5385ceed74ef94f4538660e3725

[ "Apache-2.0" ]

53

2015-02-04T06:02:21.000Z

2020-11-25T20:04:52.000Z

test/create_cert.py

finsberg/pytest-tornado

52ba5119310be5385ceed74ef94f4538660e3725

[ "Apache-2.0" ]

43

2015-02-26T05:02:44.000Z

2021-12-17T10:08:44.000Z

# -*- coding: utf-8 -*- """ Create a cert with pyOpenSSL for tests. Heavily based on python-opsi's OPSI.Util.Task.Certificate. Source: https://github.com/opsi-org/python-opsi/blob/stable/OPSI/Util/Task/Certificate.py """ import argparse import os import random import socket from tempfile import NamedTemporaryFile from OpenSSL import crypto try: import secrets except ImportError: secrets = None def createCertificate(path): """ Creates a certificate. """ cert = crypto.X509() cert.get_subject().C = "DE" # Country cert.get_subject().ST = "HE" # State cert.get_subject().L = "Wiesbaden" # Locality cert.get_subject().O = "pytest-tornado" # Organisation cert.get_subject().OU = "Testing Department" # organisational unit cert.get_subject().CN = socket.getfqdn() # common name # As described in RFC5280 this value is required and must be a # positive and unique integer. # Source: http://tools.ietf.org/html/rfc5280#page-19 cert.set_serial_number(random.randint(0, pow(2, 16))) cert.gmtime_adj_notBefore(0) cert.gmtime_adj_notAfter(60 * 60) # Valid 1 hour k = crypto.PKey() k.generate_key(crypto.TYPE_RSA, 2048) cert.set_issuer(cert.get_subject()) cert.set_pubkey(k) cert.set_version(2) cert.sign(k, 'sha512') certcontext = b"".join( ( crypto.dump_certificate(crypto.FILETYPE_PEM, cert), crypto.dump_privatekey(crypto.FILETYPE_PEM, k) ) ) with open(path, "wt") as certfile: certfile.write(certcontext.decode()) try: with NamedTemporaryFile(mode="wb", delete=False) as randfile: randfile.write(randomBytes(512)) command = u"openssl dhparam -rand {tempfile} 512 >> {target}".format( tempfile=randfile.name, target=path ) os.system(command) finally: os.remove(randfile.name) def randomBytes(length): """ Return _length_ random bytes. :rtype: bytes """ if secrets: return secrets.token_bytes(512) else: return os.urandom(512) if __name__ == '__main__': parser = argparse.ArgumentParser(description='Create certificate for testing') parser.add_argument('--cert', dest='cert', default="testcert.pem", help='Name of the certificate') args = parser.parse_args() createCertificate(args.cert)

23.956522

89

0.71824

06b1a7bf9e162d2f1a93b478504af2c68a143b23

680

py

Python

positional_args.py

nickaigi/effective_python_tips

1a68b6eaed2e946b003c0cd0bdea03e79b8e8990

[ "Unlicense" ]

null

positional_args.py

nickaigi/effective_python_tips

1a68b6eaed2e946b003c0cd0bdea03e79b8e8990

[ "Unlicense" ]

null

positional_args.py

nickaigi/effective_python_tips

1a68b6eaed2e946b003c0cd0bdea03e79b8e8990

[ "Unlicense" ]

null

def log(message, *values): """ * operator instructs python to pass items from the sequence as positional arguments Remember: - using the * operator with a generator may cause your program to run out of memory and crash. - adding new positional parameters to functions that accept *args can introduce hard-to-find bugs """ if not values: print(message) else: values_str = ', '.join(str(x) for x in values) print('%s: %s' % (message, values_str)) if __name__ == '__main__': log('My numbers are', 1, 2) log('Hi there') favorites = [7, 33, 99] log('Favorites colors', *favorites)

28.333333

70

0.613235

06b1dfaeb76409a1c03b523aa234e5fabb549ad9

2,942

py

Python

fastmri_recon/models/subclassed_models/updnet.py

samiulshuvo/fastmri-reproducible-benchmark

5ac9ba3e7f1ad859dcf74e7019b574a6bf065eac

[ "MIT" ]

105

2019-09-30T06:05:38.000Z

2022-03-02T09:48:31.000Z

fastmri_recon/models/subclassed_models/updnet.py

samiulshuvo/fastmri-reproducible-benchmark

5ac9ba3e7f1ad859dcf74e7019b574a6bf065eac

[ "MIT" ]

103

2019-09-18T08:30:23.000Z

2022-03-16T22:24:14.000Z

fastmri_recon/models/subclassed_models/updnet.py

samiulshuvo/fastmri-reproducible-benchmark

5ac9ba3e7f1ad859dcf74e7019b574a6bf065eac

[ "MIT" ]

38

2019-09-30T06:05:41.000Z

2022-02-26T14:07:52.000Z

from .unet import UnetComplex from .cross_domain import CrossDomainNet from ..utils.fourier import FFT, IFFT

37.240506

81

0.602311

06b2849360054f2d534889fecd3a7de975d603e4

4,342

py

Python

utilities/misc.py

lebionick/stereo-transformer

6e7df042d917c5ed00d10bd6ddb6f76e90429148

[ "Apache-2.0" ]

410

2020-11-06T02:10:17.000Z

2022-03-25T17:12:24.000Z

utilities/misc.py

lppllppl920/stereo-transformer

f07b1ee8ced1c36e10630401688a06e355056e56

[ "Apache-2.0" ]

55

2020-11-06T10:29:16.000Z

2022-03-30T02:10:10.000Z

utilities/misc.py

lppllppl920/stereo-transformer

f07b1ee8ced1c36e10630401688a06e355056e56

[ "Apache-2.0" ]

72

2020-11-06T07:22:39.000Z

2022-03-19T14:20:38.000Z

# Authors: Zhaoshuo Li, Xingtong Liu, Francis X. Creighton, Russell H. Taylor, and Mathias Unberath # # Copyright (c) 2020. Johns Hopkins University - All rights reserved. import copy import numpy as np import torch import torch.nn as nn def torch_1d_sample(source, sample_points, mode='linear'): """ linearly sample source tensor along the last dimension input: source [N,D1,D2,D3...,Dn] sample_points [N,D1,D2,....,Dn-1,1] output: [N,D1,D2...,Dn-1] """ idx_l = torch.floor(sample_points).long().clamp(0, source.size(-1) - 1) idx_r = torch.ceil(sample_points).long().clamp(0, source.size(-1) - 1) if mode == 'linear': weight_r = sample_points - idx_l weight_l = 1 - weight_r elif mode == 'sum': weight_r = (idx_r != idx_l).int() # we only sum places of non-integer locations weight_l = 1 else: raise Exception('mode not recognized') out = torch.gather(source, -1, idx_l) * weight_l + torch.gather(source, -1, idx_r) * weight_r return out.squeeze(-1) def find_occ_mask(disp_left, disp_right): """ find occlusion map 1 indicates occlusion disp range [0,w] """ w = disp_left.shape[-1] # # left occlusion # find corresponding pixels in target image coord = np.linspace(0, w - 1, w)[None,] # 1xW right_shifted = coord - disp_left # 1. negative locations will be occlusion occ_mask_l = right_shifted <= 0 # 2. wrong matches will be occlusion right_shifted[occ_mask_l] = 0 # set negative locations to 0 right_shifted = right_shifted.astype(np.int) disp_right_selected = np.take_along_axis(disp_right, right_shifted, axis=1) # find tgt disparity at src-shifted locations wrong_matches = np.abs(disp_right_selected - disp_left) > 1 # theoretically, these two should match perfectly wrong_matches[disp_right_selected <= 0.0] = False wrong_matches[disp_left <= 0.0] = False # produce final occ wrong_matches[occ_mask_l] = True # apply case 1 occlusion to case 2 occ_mask_l = wrong_matches # # right occlusion # find corresponding pixels in target image coord = np.linspace(0, w - 1, w)[None,] # 1xW left_shifted = coord + disp_right # 1. negative locations will be occlusion occ_mask_r = left_shifted >= w # 2. wrong matches will be occlusion left_shifted[occ_mask_r] = 0 # set negative locations to 0 left_shifted = left_shifted.astype(np.int) disp_left_selected = np.take_along_axis(disp_left, left_shifted, axis=1) # find tgt disparity at src-shifted locations wrong_matches = np.abs(disp_left_selected - disp_right) > 1 # theoretically, these two should match perfectly wrong_matches[disp_left_selected <= 0.0] = False wrong_matches[disp_right <= 0.0] = False # produce final occ wrong_matches[occ_mask_r] = True # apply case 1 occlusion to case 2 occ_mask_r = wrong_matches return occ_mask_l, occ_mask_r

32.893939

114

0.649931

06b29440122743c4d662f5e0b42777454bfb53b1

2,600

py

Python

tfcli/resources/asg.py

leowa/tfcli

21314feabcb56fe802298a98a66eb4e2a9de8cc7

[ "MIT" ]

null

tfcli/resources/asg.py

leowa/tfcli

21314feabcb56fe802298a98a66eb4e2a9de8cc7

[ "MIT" ]

null

tfcli/resources/asg.py

leowa/tfcli

21314feabcb56fe802298a98a66eb4e2a9de8cc7

[ "MIT" ]

null

from .base import BaseResource class LaunchTemplate(BaseResource): """ launch template resource to generate from current region """ def list_all(self): """list all such kind of resources from AWS :return: list of tupe for a resource (type, name, id) """ ec2 = self.session.client("ec2") items = ec2.describe_launch_templates()["LaunchTemplates"] for item in items: _name = _id = item["LaunchTemplateId"] yield "aws_launch_template", _name, _id

31.707317

87

0.611154

06b306a89a539a3cbfca1d1c817821e2aac7c4eb

28,278

py

Python

BASS-train.py

shlpu/Statlie-Image-Processor

e40355f43f344fd02041bdc8ce57b0ee101c6cdb

[ "Apache-2.0" ]

1

2019-11-23T12:58:09.000Z

BASS-train.py

shlpu/Statlie-Image-Processor

e40355f43f344fd02041bdc8ce57b0ee101c6cdb

[ "Apache-2.0" ]

null

BASS-train.py

shlpu/Statlie-Image-Processor

e40355f43f344fd02041bdc8ce57b0ee101c6cdb

[ "Apache-2.0" ]

3

2019-03-27T00:47:08.000Z

2022-02-05T04:52:48.000Z

import numpy as np import scipy.io from sklearn.metrics import confusion_matrix from random import randint, shuffle from argparse import ArgumentParser from helper import getValidDataset import tensorflow as tf parser = ArgumentParser() parser.add_argument('--data', type=str, default='Indian_pines') parser.add_argument('--patch_size', type=int, default=3) parser.add_argument('--library', type=str, default='tensorflow') opt = parser.parse_args() import os model_directory = os.path.join(os.getcwd(), 'BASSNET_Trained_model/') # Load MATLAB pre-processed image data try: TRAIN = scipy.io.loadmat("./data/" + opt.data + "_Train_patch_" + str(opt.patch_size) + ".mat") VALIDATION = scipy.io.loadmat("./data/" + opt.data + "_Val_patch_" + str(opt.patch_size) + ".mat") TEST = scipy.io.loadmat("./data/" + opt.data + "_Test_patch_" + str(opt.patch_size) + ".mat") except NameError: raise print('--data options are: Indian_pines, Salinas, KSC, Botswana') # Extract data and label from MATLAB file training_data, training_label = TRAIN['train_patch'], TRAIN['train_labels'] validation_data, validation_label = VALIDATION['val_patch'], VALIDATION['val_labels'] test_data, test_label = TEST['test_patch'], TEST['test_labels'] getValidDataset(test_data, test_label) print('\nData input shape') print('training_data shape' + str(training_data.shape)) print('training_label shape' + str(training_label.shape) + '\n') print('testing_data shape' + str(test_data.shape)) print('testing_label shape' + str(test_label.shape) + '\n') SIZE = training_data.shape[0] HEIGHT = training_data.shape[1] WIDTH = training_data.shape[2] BANDS = training_data.shape[3] NUM_PARALLEL_BAND = 10 BAND_SIZE = BANDS / 10 NUM_CLASS = training_label.shape[1] # Helper Functions # Define BASSNET archicture a =8 graph = tf.Graph() with graph.as_default(): img_entry = tf.placeholder(tf.float32, shape=[None, HEIGHT, WIDTH, BANDS], name='img_entry') img_label = tf.placeholder(tf.uint8, shape=[None, NUM_CLASS], name='img_label') image_true_class = tf.argmax(img_label, axis=1, name="img_true_label") prob = tf.placeholder(tf.float32) model = bassnet(statlieImg=img_entry, prob=prob) final_layer = model['block3_dense3'] cross_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(logits=final_layer, labels=img_label) cost = tf.reduce_mean(cross_entropy) # Optimisation function optimizer = tf.train.AdamOptimizer(learning_rate=0.0005).minimize(cost) predict_class = model['predict_class_number'] correction = tf.equal( predict_class, image_true_class) accuracy = tf.reduce_mean(tf.cast(correction, tf.float32)) saver = tf.train.Saver() with tf.Session(graph=graph) as session: writer = tf.summary.FileWriter("BASSNETlogs/", session.graph) if os.path.isdir(model_directory): saver.restore(session, 'BASSNET_Trained_model/') session.run(tf.global_variables_initializer()) total_iterations = 0 train(num_iterations=12000, train_batch_size=200) saver.save(session, model_directory) test() # trainTestSwap(training_data, training_label, test_data, test_label, 1, size=250) print('End session')

47.766892

169

0.607539

ebe8e591a0b9b4b36a891f5a5dfead02a0b973da

2,300

py

Python

hungarian/python/code/point.py

hrutkabence/tutorials

bd76294860804aee8ecda5e1445464506bf02ee0

[ "CC0-1.0" ]

null

hungarian/python/code/point.py

hrutkabence/tutorials

bd76294860804aee8ecda5e1445464506bf02ee0

[ "CC0-1.0" ]

null

hungarian/python/code/point.py

hrutkabence/tutorials

bd76294860804aee8ecda5e1445464506bf02ee0

[ "CC0-1.0" ]

null

#!/usr/bin/env python # -*- coding: UTF-8 -*- import math def PolarP(dist, ang): """ polar to rectangular coordinates returning Point """ return Point(dist * math.cos(ang), dist * math.sin(ang)) if __name__ == "__main__": # tests v = 0.1 A = Point(-100.4627, 52.5957) B = Point(11.0532, 52.5956) dist, bea = (B - A).polar() P1 = A + PolarP(v, bea + math.pi * 3 / 2) P2 = P1 + PolarP(dist, bea) P3 = P2 + PolarP(v, bea + math.pi / 2) P4 = A + PolarP(v, bea +math.pi / 2) print(P1) print(P2) print(P3) print(P4)

25.274725

65

0.513478

ebef4935fe5542a7f33a3a5e4cd173560258a38e

4,588

py

Python

mlmodels/model_tf/misc/tfcode2/CNN/alex-net/alexnet.py

gitter-badger/mlmodels

f08cc9b6ec202d4ad25ecdda2f44487da387569d

[ "MIT" ]

1

2022-03-11T07:57:48.000Z

mlmodels/model_tf/misc/tfcode2/CNN/alex-net/alexnet.py

whitetiger1002/mlmodels

f70f1da7434e8855eed50adc67b49cc169f2ea24

[ "MIT" ]

null

mlmodels/model_tf/misc/tfcode2/CNN/alex-net/alexnet.py

whitetiger1002/mlmodels

f70f1da7434e8855eed50adc67b49cc169f2ea24

[ "MIT" ]

null

#!/usr/bin/env python # coding: utf-8 # In[1]: import time import matplotlib.pyplot as plt import numpy as np import seaborn as sns import tensorflow as tf from scipy.misc import imresize from sklearn.cross_validation import train_test_split import _pickle as cPickle from train import train # In[2]: def unpickle(file): with open(file, "rb") as fo: dict = cPickle.load(fo, encoding="latin1") return dict unique_name = unpickle("cifar-10-batches-py/batches.meta")["label_names"] batches = unpickle("cifar-10-batches-py/data_batch_1") train_X, test_X, train_Y, test_Y = train_test_split( batches["data"], batches["labels"], test_size=0.2 ) # In[3]: BATCH_SIZE = 5 # alexnet original IMG_SIZE = 224 LEARNING_RATE = 0.0001 # In[4]: sess = tf.InteractiveSession() model = Alexnet(IMG_SIZE, len(unique_name), LEARNING_RATE) sess.run(tf.global_variables_initializer()) # In[5]: RESULTS = train( sess, model, 20, BATCH_SIZE, len(unique_name), IMG_SIZE, train_X, test_X, train_Y, test_Y ) # In[13]: sns.set() plt.figure(figsize=(15, 5)) plt.subplot(1, 2, 1) plt.plot(np.arange(len(RESULTS[0])), RESULTS[0], label="entropy cost") plt.legend() plt.subplot(1, 2, 2) plt.plot(np.arange(len(RESULTS[0])), RESULTS[1], label="accuracy training") plt.plot(np.arange(len(RESULTS[0])), RESULTS[2], label="accuracy testing") plt.legend() plt.show() # In[ ]:

37

99

0.649956

ebefcab7987e2949070f887144afd954129e8c65

4,184

py

Python

p8_test/test_local/__init__.py

crazynayan/tpf1

c81a15d88d4d1f3ed2cf043c90782a4b8509ef14

[ "MIT" ]

1

2020-01-27T10:10:40.000Z

p8_test/test_local/__init__.py

crazynayan/tpf1

c81a15d88d4d1f3ed2cf043c90782a4b8509ef14

[ "MIT" ]

4

2019-08-23T05:24:23.000Z

2021-09-16T10:05:55.000Z

p8_test/test_local/__init__.py

crazynayan/tpf1

c81a15d88d4d1f3ed2cf043c90782a4b8509ef14

[ "MIT" ]

null

import random import string import unittest from typing import List, Union, Dict from config import config from p2_assembly.mac2_data_macro import DataMacro from p3_db.test_data import TestData from p3_db.test_data_elements import Pnr from p4_execution.debug import get_debug_loc, add_debug_loc, get_missed_loc from p4_execution.ex5_execute import TpfServer

42.693878

119

0.633843

ebf03515b2af7a1e35e2369f06d9b3087f0c51d9

2,893

py

Python

models/models.py

ahuimanu/vatsimlib

12f1bcd248b2157349304583cbc1b499d9f39be0

[ "MIT" ]

null

models/models.py

ahuimanu/vatsimlib

12f1bcd248b2157349304583cbc1b499d9f39be0

[ "MIT" ]

null

models/models.py

ahuimanu/vatsimlib

12f1bcd248b2157349304583cbc1b499d9f39be0

[ "MIT" ]

null

from dataclasses import dataclass from datetime import datetime from typing import Optional

28.087379

230

0.614587

ebf042ca58b471a448c4adbffa95dc9ab6a93834

360

py

Python

temp-api/app/models.py

TheLongRunSmoke/birdfeeder

6f238c9b8c8abdc866aaf042f79b674714fdaa8c

[ "MIT" ]

null

temp-api/app/models.py

TheLongRunSmoke/birdfeeder

6f238c9b8c8abdc866aaf042f79b674714fdaa8c

[ "MIT" ]

1

2017-11-28T04:26:45.000Z

2017-11-28T04:57:47.000Z

temp-api/app/models.py

TheLongRunSmoke/birdfeeder

6f238c9b8c8abdc866aaf042f79b674714fdaa8c

[ "MIT" ]

null

from app import db

24

80

0.644444

ebf1ffe3b522e31d9f44e5d373462af230e2e497

3,199

py

Python

src/GameController.py

salemalex11/Gomoku

e709bc161a945e5521ea3b234ce8db41d3fd5bfe

[ "MIT" ]

null

src/GameController.py

salemalex11/Gomoku

e709bc161a945e5521ea3b234ce8db41d3fd5bfe

[ "MIT" ]

null

src/GameController.py

salemalex11/Gomoku

e709bc161a945e5521ea3b234ce8db41d3fd5bfe

[ "MIT" ]

3

2019-02-17T22:15:36.000Z

2021-01-04T19:13:52.000Z

# Define imports import pygame from pygame import * import sys import time

36.352273

131

0.449203

ebf22c5792152fe6b5cb3d25a3473aad20996bcf

17,101

py

Python

silverberg/test/test_client.py

TimothyZhang/silverberg

fb93ab68988c6ad6f7a4136d2c5b16b32966d0ca

[ "Apache-2.0" ]

1

2019-09-22T04:00:56.000Z

silverberg/test/test_client.py

TimothyZhang/silverberg

fb93ab68988c6ad6f7a4136d2c5b16b32966d0ca

[ "Apache-2.0" ]

14

2015-01-22T01:00:50.000Z

2017-12-06T03:35:46.000Z

silverberg/test/test_client.py

TimothyZhang/silverberg

fb93ab68988c6ad6f7a4136d2c5b16b32966d0ca

[ "Apache-2.0" ]

4

2015-03-31T19:49:05.000Z

2020-03-03T20:44:32.000Z

# Copyright 2012 Rackspace Hosting, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Test the client.""" import mock from uuid import UUID from twisted.internet import defer from silverberg.client import CQLClient, ConsistencyLevel, TestingCQLClient from silverberg.cassandra import ttypes, Cassandra from silverberg.test.util import BaseTestCase # class FaultTestCase(BaseTestCase): # def setUp(self): # self.client = CqlClient(TCP4ClientEndpoint(reactor, '127.0.0.1', 9160), 'blah') # def test_vers(self): # d = self.client.describe_version() # def printR(r): # print r # d.addCallback(printR) # return d # def test_cql(self): # d = self.client.execute("SELECT * FROM blah;", {}) # def printQ(r): # print r # d.addCallback(printQ) # return d

41.009592

103

0.626513

ebf2bc1d88e8d3404f1439f8fb4400bf3874e4c0

3,386

py

Python

drawer.py

jarekwg/crossword-packer

88f90c16272c2c2f64475dffe3b0aaeec11c0606

[ "MIT" ]

null

drawer.py

jarekwg/crossword-packer

88f90c16272c2c2f64475dffe3b0aaeec11c0606

[ "MIT" ]

null

drawer.py

jarekwg/crossword-packer

88f90c16272c2c2f64475dffe3b0aaeec11c0606

[ "MIT" ]

null

import re from exceptions import WordPlacementConflict from constants import ACROSS, DOWN

52.092308

159

0.560543

ebf45563a2d56576081e640ac1564e55a2546dba

4,200

py

Python

src/analyse/bubble_map.py

timtroendle/geographic-scale

81ec940e10b8e692429797e6a066a177e1508a89

[ "MIT" ]

3

2020-08-19T17:56:22.000Z

2021-08-19T08:52:21.000Z

src/analyse/bubble_map.py

timtroendle/geographic-scale

81ec940e10b8e692429797e6a066a177e1508a89

[ "MIT" ]

null

src/analyse/bubble_map.py

timtroendle/geographic-scale

81ec940e10b8e692429797e6a066a177e1508a89

[ "MIT" ]

null

import numpy as np import shapely import geopandas as gpd import xarray as xr import matplotlib.pyplot as plt import seaborn as sns EPSG_3035_PROJ4 = "+proj=laea +lat_0=52 +lon_0=10 +x_0=4321000 +y_0=3210000 +ellps=GRS80 +units=m +no_defs " GREY = "#C0C0C0" BLUE = "#4F6DB8" YELLOW = "#FABC3C" SUPPLY_TECHS = [ "hydro_reservoir", "hydro_run_of_river", "open_field_pv", "roof_mounted_pv", "wind_offshore", "wind_onshore_competing", "wind_onshore_monopoly" ] DEMAND_TECH = "demand_elec" MAP_MIN_X = 2200000 MAP_MIN_Y = 1400000 MAP_MAX_X = 6300000 MAP_MAX_Y = 5500000 if __name__ == "__main__": bubble_map( path_to_shapes=snakemake.input.shapes, path_to_continent_shape=snakemake.input.continent_shape, scenario=snakemake.wildcards.scenario, colour=snakemake.wildcards.colour, markersize=snakemake.wildcards.markersize, resolution_km=snakemake.params.resolution_km, path_to_results=snakemake.input.results, path_to_output=snakemake.output[0] )

30.882353

113

0.662619

ebf4ac0d537eab2740d82a66c4418235ab9c1ffc

885

py

Python

src/ytbdl/__init__.py

danloveg/dl-album

20d142a0992f61b9d13beceb6abe9a9086e33e6a

[ "MIT" ]

null

src/ytbdl/__init__.py

danloveg/dl-album

20d142a0992f61b9d13beceb6abe9a9086e33e6a

[ "MIT" ]

null

src/ytbdl/__init__.py

danloveg/dl-album

20d142a0992f61b9d13beceb6abe9a9086e33e6a

[ "MIT" ]

null

import os import confuse config = confuse.LazyConfig('ytbdl', None) def get_loaded_config_sources(): ''' Get existing configuration files Returns: (list): A list of (string) paths to configuration files that exist on the file system. Returns an empty list if no configuration files exist ''' config.resolve() return [s.filename for s in config.sources if os.path.exists(s.filename)] def get_main_config_path(): ''' Get the main configuration file path Returns: (str): A path to the configuration file. This path may or may not exist ''' return os.path.join(config.config_dir(), 'config.yaml') def config_exists(): ''' Determine if one or more configuration files exist. Returns: (bool): True if a config file exists, False otherwise ''' return any(get_loaded_config_sources())

26.818182

79

0.673446

ebf5ca4f90a237385342b586d5c1e142847a2572

4,875

py

Python

GUI/my_lib/factory.py

EnviableYapper0/FMachineSchedulerPL

05ba6a2169ee481062b71b917d1f32d26e240eb8

[ "MIT" ]

null

GUI/my_lib/factory.py

EnviableYapper0/FMachineSchedulerPL

05ba6a2169ee481062b71b917d1f32d26e240eb8

[ "MIT" ]

null

GUI/my_lib/factory.py

EnviableYapper0/FMachineSchedulerPL

05ba6a2169ee481062b71b917d1f32d26e240eb8

[ "MIT" ]

null

from . import machine as m from . import machine_calculator as mc from . import my_time as mt

35.326087

106

0.633231

ebfa57fc6af077b8e484bb5107bce4b51e06f9f3

1,898

py

Python

places/models.py

amureki/lunchtime-with-channels

7cf6cb15b88ceefbebd53963ff1e194d8df6c25c

[ "MIT" ]

null

places/models.py

amureki/lunchtime-with-channels

7cf6cb15b88ceefbebd53963ff1e194d8df6c25c

[ "MIT" ]

null

places/models.py

amureki/lunchtime-with-channels

7cf6cb15b88ceefbebd53963ff1e194d8df6c25c

[ "MIT" ]

null

from django.conf import settings from django.db import models from django.utils import timezone from django.utils.translation import ugettext_lazy as _ from django_extensions.db.models import TimeStampedModel from stdimage import StdImageField from stdimage.utils import UploadToUUID

29.2

67

0.615385

ebfc9f2828a65b31b16c43b42091b7e322b73651

2,363

py

Python

models/process_dataset.py

Aremaki/MscProjectNMR

5bb8fb129d5fe326aa73b56cb7c5b01a17aebb0d

[ "MIT" ]

null

models/process_dataset.py

Aremaki/MscProjectNMR

5bb8fb129d5fe326aa73b56cb7c5b01a17aebb0d

[ "MIT" ]

null

models/process_dataset.py

Aremaki/MscProjectNMR

5bb8fb129d5fe326aa73b56cb7c5b01a17aebb0d

[ "MIT" ]

1

2021-07-28T11:18:00.000Z

import tensorflow as tf def shuffle_and_batch_dataset(dataset, batch_size, shuffle_buffer=None): """ This function is used to shuffle and batch the dataset, using shuffle_buffer and batch_size. """ if shuffle_buffer is not None: dataset = dataset.shuffle(shuffle_buffer) dataset = dataset.batch(batch_size) return dataset def split_dataset(dataset, train_prop=0.8, val_prop=0.2): """ This function takes in the loaded TFRecordDataset, and builds training, validation and test TFRecordDataset objects. The test_prop is automatically set up to be equal to 1 - (train_prop + val_prop). """ dataset_size = sum(1 for _ in dataset) train_size = int(train_prop * dataset_size) val_size = int(val_prop * dataset_size) train_dataset = dataset.take(train_size) remaining_dataset = dataset.skip(train_size) val_dataset = remaining_dataset.take(val_size) test_dataset = remaining_dataset.skip(val_size) return train_dataset, val_dataset, test_dataset def process_dataset(dataset, batch_sizes=None, shuffle_buffers=None, train_prop=0.8, val_prop=0.2): """ :param dataset: TFRecordDataset object :param batch_sizes: list of batch_size for train set, validation set and test set :param shuffle_buffers: an integer shuffle_buffer for the train set only :param train_prop: the ratio between the full dataset size and the train set size :param val_prop: the ratio between the full dataset size and the validation set size :return: fully processed train, validation and test TFRecordDataset """ if batch_sizes is None: batch_sizes = [64, 64, 64] if type(shuffle_buffers) != int: return "Error: shuffle_buffers should be an integer" if len(batch_sizes) != 3: return "Error: batch_sizes should have a length of 3." train_dataset, val_dataset, test_dataset = split_dataset(dataset, train_prop, val_prop) train_dataset = shuffle_and_batch_dataset(train_dataset, batch_sizes[0], shuffle_buffers) train_dataset = train_dataset.prefetch(tf.data.experimental.AUTOTUNE) val_dataset = val_dataset.batch(batch_sizes[1]).prefetch(tf.data.experimental.AUTOTUNE) test_dataset = test_dataset.batch(batch_sizes[2]).prefetch(tf.data.experimental.AUTOTUNE) return train_dataset, val_dataset, test_dataset

41.45614

99

0.7427

ebff48e4dc84757244340320b746691161bc3959

1,080

py

Python

bdc_collection_builder/celery/worker.py

rodolfolotte/bdc-collection-builder

62583f6c25bca79e7e1b5503bc6308298838c877

[ "MIT" ]

null

bdc_collection_builder/celery/worker.py

rodolfolotte/bdc-collection-builder

62583f6c25bca79e7e1b5503bc6308298838c877

[ "MIT" ]

null

bdc_collection_builder/celery/worker.py

rodolfolotte/bdc-collection-builder

62583f6c25bca79e7e1b5503bc6308298838c877

[ "MIT" ]

null

# # This file is part of Brazil Data Cube Collection Builder. # Copyright (C) 2019-2020 INPE. # # Brazil Data Cube Collection Builder is free software; you can redistribute it and/or modify it # under the terms of the MIT License; see LICENSE file for more details. # """Defines a structure component to run celery worker.""" # Python Native import logging # 3rdparty from celery.signals import celeryd_after_setup, worker_shutdown # Builder from .. import create_app from ..utils import initialize_factories, finalize_factories from . import create_celery_app app = create_app() celery = create_celery_app(app)

25.714286

96

0.759259