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f7fdb318437a941360b8499d5a8e3f2e59604c63 | 7,565 | py | Python | ALREC_Method/stmarc/train_new_method_v4_for_atd.py | proy3/Abnormal_Trajectory_Classifier | a6b27c6847262e9703a0f3404c85c135415c1d4c | [
"MIT"
] | 6 | 2019-10-29T03:05:14.000Z | 2022-03-18T05:14:25.000Z | ALREC_Method/rene/train_new_method_v4_for_atd.py | proy3/Abnormal_Trajectory_Classifier | a6b27c6847262e9703a0f3404c85c135415c1d4c | [
"MIT"
] | 1 | 2022-03-11T03:49:34.000Z | 2022-03-11T03:49:34.000Z | ALREC_Method/rouen/train_new_method_v4_for_atd.py | proy3/Abnormal_Trajectory_Classifier | a6b27c6847262e9703a0f3404c85c135415c1d4c | [
"MIT"
] | 1 | 2021-12-15T09:21:26.000Z | 2021-12-15T09:21:26.000Z | """
Train Abnormal trajectory detection with deep autoencoder.
"""
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
import ae_utilities as aeu
import input_data as data
import abnormal_data_generation as adg
import dataset_defines as dd
import numpy as np
import os
abspath = os.path.abs... | 49.444444 | 118 | 0.645605 |
f7fdb428b910bb6d6e131170f6089015f3491cb8 | 1,400 | py | Python | memoize/decorator.py | ECrownofFire/chaos | 0cfbb85ab52654967909aef54eff3a0e62b641bd | [
"MIT"
] | 1,804 | 2017-05-23T02:34:27.000Z | 2017-05-26T00:44:44.000Z | memoize/decorator.py | ECrownofFire/chaos | 0cfbb85ab52654967909aef54eff3a0e62b641bd | [
"MIT"
] | 345 | 2017-05-20T23:55:12.000Z | 2017-06-19T07:48:58.000Z | memoize/decorator.py | ECrownofFire/chaos | 0cfbb85ab52654967909aef54eff3a0e62b641bd | [
"MIT"
] | 248 | 2017-05-23T02:00:07.000Z | 2017-05-26T00:00:28.000Z | from functools import wraps
import time
import inspect
from . import helpers
def memoize(ttl_spec, whitelist=None, blacklist=None,
key_fn=helpers._json_keyify, backend=lambda fn: dict(),
get_now=time.time):
""" memoize/cache the decorated function for ttl amount of time """
ttl = hel... | 29.787234 | 80 | 0.566429 |
f7fde8a0153e5f47b6098d1748aa541f4af9a7f2 | 964 | py | Python | vortidplenigilo.py | corcra/esperanto | be8f6eda63c4f20b6f7667a50f9b85d3dba32258 | [
"MIT"
] | 15 | 2015-11-15T15:15:55.000Z | 2018-05-05T19:13:01.000Z | vortidplenigilo.py | corcra/esperanto | be8f6eda63c4f20b6f7667a50f9b85d3dba32258 | [
"MIT"
] | 1 | 2015-11-19T15:11:32.000Z | 2015-11-19T15:12:06.000Z | vortidplenigilo.py | corcra/esperanto | be8f6eda63c4f20b6f7667a50f9b85d3dba32258 | [
"MIT"
] | 2 | 2015-11-28T13:15:35.000Z | 2016-03-03T09:24:17.000Z | #!/usr/bin/env ipython
# coding=utf-8
# This is intended to be run on a cronjob
from __future__ import print_function
import tweepy
from creds import consumer_key, consumer_secret, access_token, access_token_secret
from soup import tweet_soup
from parse_EO_full import eo_to_en
import random
# --- set up API --- #
au... | 27.542857 | 82 | 0.708506 |
f7fe22be8e9933d73e58f8f86a8508c7b07bfe49 | 7,375 | py | Python | glib/glib-2.46.2/glib/glib.py | imx6uldev/depedency_tools | 0748392a4e97ded2a770b6fbcab281dd3fda2db7 | [
"MIT"
] | null | null | null | glib/glib-2.46.2/glib/glib.py | imx6uldev/depedency_tools | 0748392a4e97ded2a770b6fbcab281dd3fda2db7 | [
"MIT"
] | 1 | 2020-10-13T07:38:31.000Z | 2020-10-13T07:38:31.000Z | migrate/glib/glib/glib.py | zhongliangkang/twemproxy41 | 8dc3664145b0fcdd32fa321720235a9db9b3cece | [
"Apache-2.0"
] | 1 | 2020-02-04T15:39:06.000Z | 2020-02-04T15:39:06.000Z | import gdb
import sys
if sys.version_info[0] >= 3:
long = int
# This is not quite right, as local vars may override symname
def read_global_var (symname):
return gdb.selected_frame().read_var(symname)
def g_quark_to_string (quark):
if quark == None:
return None
quark = long(quark)
if quar... | 28.585271 | 122 | 0.532068 |
f7fe38c9a4b8c5796670a8aa33b5cb1b8bbd7c39 | 5,246 | py | Python | src/jetson/Sensors/sensors_simple.py | ichalkiad/VW_challenge | 333222010ecf3d1ca4a0e181239f761c975453e9 | [
"Apache-2.0"
] | 1 | 2017-08-16T08:42:49.000Z | 2017-08-16T08:42:49.000Z | src/jetson/Sensors/sensors_simple.py | ichalkiad/VW_challenge | 333222010ecf3d1ca4a0e181239f761c975453e9 | [
"Apache-2.0"
] | 4 | 2017-08-09T23:01:30.000Z | 2017-08-24T16:44:13.000Z | src/jetson/Sensors/sensors_simple.py | yhalk/vw_challenge_ECR | c1ff50070d0f7367ccfbf473c69e90fd2be5e85e | [
"Apache-2.0"
] | null | null | null | import paho.mqtt.client as mqtt
import ev3dev.ev3 as ev3
import ctypes
import numpy as np
import sys
import cv2
from Sensors.mpu6050.mpu6050 import MPU6050
import smbus
from Sensors.odometry import Odometry
import sys, serial
from serial.tools import list_ports
class Sensor(object):
def __init__(self, *args, **kwa... | 43 | 280 | 0.609989 |
f7fe3f64ab7d9812e1dfd30423018d4e5b38e582 | 11,395 | py | Python | sdf/analytics.py | drgmk/sdf | a44e66a82f876dda079686b32c767370276c38a1 | [
"MIT"
] | 1 | 2020-07-01T15:55:16.000Z | 2020-07-01T15:55:16.000Z | sdf/analytics.py | drgmk/sdf | a44e66a82f876dda079686b32c767370276c38a1 | [
"MIT"
] | 4 | 2017-03-28T19:18:09.000Z | 2021-09-21T08:17:45.000Z | sdf/analytics.py | drgmk/sdf | a44e66a82f876dda079686b32c767370276c38a1 | [
"MIT"
] | 1 | 2020-07-13T19:39:15.000Z | 2020-07-13T19:39:15.000Z | '''Analytic routines for debris disks.'''
import numpy as np
from . import photometry
from . import filter
from . import utils
class BB_Disk(object):
'''A blackbody disk class.
Takes multiple temperatures, the purpose being for use to show
disk properties in parameter spaces such as fractional l... | 37.731788 | 101 | 0.526196 |
f7fe47a4927438bfc838f1831fa8c68f9d1deb1a | 1,873 | py | Python | ginga/mockw/ImageViewCanvasMock.py | Cadair/ginga | 5afdd8824f27c7ae7d8d82b5013b0ff0068bd8b8 | [
"BSD-3-Clause"
] | null | null | null | ginga/mockw/ImageViewCanvasMock.py | Cadair/ginga | 5afdd8824f27c7ae7d8d82b5013b0ff0068bd8b8 | [
"BSD-3-Clause"
] | null | null | null | ginga/mockw/ImageViewCanvasMock.py | Cadair/ginga | 5afdd8824f27c7ae7d8d82b5013b0ff0068bd8b8 | [
"BSD-3-Clause"
] | null | null | null | #
# ImageViewCanvasMock.py -- A Ginga image widget with canvas drawing in mock
# widget set
#
# Eric Jeschke (eric@naoj.org)
#
# Copyright (c) Eric R. Jeschke. All rights reserved.
# This is open-source software licensed under a BSD license.
# Please see the file LICENSE.txt for details.
#
... | 34.054545 | 76 | 0.610785 |
f7fe81eb8b18aea5e9f85788db3c3c72624547db | 1,278 | py | Python | hs_core/tests/api/rest/test_resource_meta.py | hydroshare/hydroshare | bf9888bbe61507aff070b1dfcec2fdec1921468d | [
"BSD-3-Clause"
] | 178 | 2015-01-08T23:03:36.000Z | 2022-03-03T13:56:45.000Z | hs_core/tests/api/rest/test_resource_meta.py | hydroshare/hydroshare | bf9888bbe61507aff070b1dfcec2fdec1921468d | [
"BSD-3-Clause"
] | 4,125 | 2015-01-01T14:26:15.000Z | 2022-03-31T16:38:55.000Z | hs_core/tests/api/rest/test_resource_meta.py | hydroshare/hydroshare | bf9888bbe61507aff070b1dfcec2fdec1921468d | [
"BSD-3-Clause"
] | 53 | 2015-03-15T17:56:51.000Z | 2022-03-17T00:32:16.000Z | import os
import json
import tempfile
import shutil
from lxml import etree
from rest_framework import status
from hs_core.hydroshare import resource
from .base import HSRESTTestCase
class TestResourceMetadata(HSRESTTestCase):
def setUp(self):
super(TestResourceMetadata, self).setUp()
self.rty... | 33.631579 | 81 | 0.656495 |
f7fe983d9f3c9724263133f0b77deed272534129 | 1,764 | py | Python | src/main/py/ltprg/game/color/properties/get_stim_embeddings.py | forkunited/ltprg | 4e40d3571d229023df0f845c68643024e04bc202 | [
"MIT"
] | 11 | 2017-08-03T15:42:19.000Z | 2021-02-04T12:43:35.000Z | src/main/py/ltprg/game/color/properties/get_stim_embeddings.py | forkunited/ltprg | 4e40d3571d229023df0f845c68643024e04bc202 | [
"MIT"
] | null | null | null | src/main/py/ltprg/game/color/properties/get_stim_embeddings.py | forkunited/ltprg | 4e40d3571d229023df0f845c68643024e04bc202 | [
"MIT"
] | 1 | 2021-02-04T12:43:37.000Z | 2021-02-04T12:43:37.000Z | from __future__ import division
import numpy as np
import sys
sys.path.append('../../../../../../../../Packages/mungpy/src/main/py/mung/')
import torch
import torch.nn as nn
from torch.autograd import Variable
import time
from data import DataSet
from alexnet import PartialAlexnet, rgb_to_alexnet_input
from colorspace_... | 33.283019 | 76 | 0.707483 |
f7feac26a36903f73b984dcc0360155b4034e6fc | 8,257 | py | Python | src/tantale/livestatus/parser.py | redref/tantale | 358748e7d4a1d87ee48168a03ed68acefb2b9ca2 | [
"Apache-2.0"
] | null | null | null | src/tantale/livestatus/parser.py | redref/tantale | 358748e7d4a1d87ee48168a03ed68acefb2b9ca2 | [
"Apache-2.0"
] | null | null | null | src/tantale/livestatus/parser.py | redref/tantale | 358748e7d4a1d87ee48168a03ed68acefb2b9ca2 | [
"Apache-2.0"
] | null | null | null | # coding=utf-8
import traceback
import logging
from six import b as bytes
from tantale.livestatus.query import Query
from tantale.livestatus.command import Command
from tantale.livestatus.mapping import *
class Parser(object):
def __init__(self):
self.log = logging.getLogger('tantale.livestatus')
d... | 36.056769 | 77 | 0.475354 |
f7ff31f42e5b26cbf413226fb48b06e483fc6c0e | 95,067 | py | Python | lib/matplotlib/transforms.py | pmarshwx/matplotlib | 12be528dbf2114f7c25abf60de8100cb2d4494af | [
"MIT",
"BSD-3-Clause"
] | null | null | null | lib/matplotlib/transforms.py | pmarshwx/matplotlib | 12be528dbf2114f7c25abf60de8100cb2d4494af | [
"MIT",
"BSD-3-Clause"
] | null | null | null | lib/matplotlib/transforms.py | pmarshwx/matplotlib | 12be528dbf2114f7c25abf60de8100cb2d4494af | [
"MIT",
"BSD-3-Clause"
] | null | null | null | """
matplotlib includes a framework for arbitrary geometric
transformations that is used determine the final position of all
elements drawn on the canvas.
Transforms are composed into trees of :class:`TransformNode` objects
whose actual value depends on their children. When the contents of
children change, their pare... | 34.159899 | 94 | 0.591541 |
f7ff3ca0c18cb91c08e9be2931bae83818bf7916 | 3,768 | py | Python | test/unit/helper/test_helper.py | hanneshauer/python-client | e5909ed4e364d0c980f80e48b3af4acf77bff08e | [
"Apache-2.0"
] | null | null | null | test/unit/helper/test_helper.py | hanneshauer/python-client | e5909ed4e364d0c980f80e48b3af4acf77bff08e | [
"Apache-2.0"
] | null | null | null | test/unit/helper/test_helper.py | hanneshauer/python-client | e5909ed4e364d0c980f80e48b3af4acf77bff08e | [
"Apache-2.0"
] | null | null | null | #!/usr/bin/env python
# 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... | 28.330827 | 82 | 0.556263 |
f7ff72d1afddf700afb1487426bc4bfd6bebd700 | 5,409 | py | Python | lib/datasets/factory.py | denglixi/faster-rcnn.pytorch | 12158fa2ec998ba3733a4696b7a4e08a35c157e3 | [
"MIT"
] | null | null | null | lib/datasets/factory.py | denglixi/faster-rcnn.pytorch | 12158fa2ec998ba3733a4696b7a4e08a35c157e3 | [
"MIT"
] | null | null | null | lib/datasets/factory.py | denglixi/faster-rcnn.pytorch | 12158fa2ec998ba3733a4696b7a4e08a35c157e3 | [
"MIT"
] | null | null | null | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
"""Factory method for easily getting imdbs by name."""
from __future__ ... | 40.669173 | 207 | 0.623405 |
f7ff762a24e5e6fb13bc8f7e21fa0a320773288c | 1,223 | py | Python | ml/models/random_forest.py | refactoring-ai/Machine-Learning | 908d35322a06a7b1709d83f731033a939a864c6b | [
"MIT"
] | 5 | 2020-09-02T19:46:37.000Z | 2021-04-21T15:41:11.000Z | ml/models/random_forest.py | refactoring-ai/Machine-Learning | 908d35322a06a7b1709d83f731033a939a864c6b | [
"MIT"
] | 16 | 2020-08-04T08:07:50.000Z | 2021-03-23T08:15:30.000Z | ml/models/random_forest.py | refactoring-ai/Machine-Learning | 908d35322a06a7b1709d83f731033a939a864c6b | [
"MIT"
] | 1 | 2021-04-17T18:34:47.000Z | 2021-04-17T18:34:47.000Z | from sklearn.ensemble import RandomForestClassifier
from configs import CORE_COUNT, SEED
from ml.models.base import SupervisedMLRefactoringModel
class RandomForestRefactoringModel(SupervisedMLRefactoringModel):
def feature_reduction(self) -> bool:
return False
def params_to_tune(self):
retur... | 34.942857 | 67 | 0.604252 |
f7ffa21c257ba1e17cc8c6621957e39dccd105d8 | 14,480 | py | Python | softlearning/algorithms/sac.py | JiazhengChai/synergy_DRL | c08e78e5fe39d9d46213e1bf07b8dafc2195b05a | [
"MIT"
] | 2 | 2020-01-07T04:12:42.000Z | 2021-12-21T22:25:31.000Z | softlearning/algorithms/sac.py | JiazhengChai/synergy_DRL | c08e78e5fe39d9d46213e1bf07b8dafc2195b05a | [
"MIT"
] | 11 | 2019-11-29T02:59:34.000Z | 2022-03-12T00:07:28.000Z | softlearning/algorithms/sac.py | JiazhengChai/synergy_DRL | c08e78e5fe39d9d46213e1bf07b8dafc2195b05a | [
"MIT"
] | 1 | 2020-04-28T12:06:40.000Z | 2020-04-28T12:06:40.000Z | from collections import OrderedDict
from numbers import Number
import numpy as np
import tensorflow as tf
from tensorflow.python.training import training_util
from .rl_algorithm import RLAlgorithm
def td_target(reward, discount, next_value):
return reward + discount * next_value
class SAC(RLAlgorithm):
""... | 33.364055 | 80 | 0.59261 |
f7ffb892e8610fda995948c4c171e6e76e5d5004 | 9,578 | py | Python | DropDtw-Code/train.py | Crossmdl/Crossmdl | 49f245349cc32f750bc33ef891b2ee90f60317a6 | [
"MIT"
] | null | null | null | DropDtw-Code/train.py | Crossmdl/Crossmdl | 49f245349cc32f750bc33ef891b2ee90f60317a6 | [
"MIT"
] | null | null | null | DropDtw-Code/train.py | Crossmdl/Crossmdl | 49f245349cc32f750bc33ef891b2ee90f60317a6 | [
"MIT"
] | null | null | null | import os
import torch
import argparse
import random
import torch
import numpy as np
import pytorch_lightning as pl
import torchmetrics
from copy import deepcopy, copy
import pickle as pkl
from paths import PROJECT_PATH, WEIGHTS_PATH
from models.nets import EmbeddingsMapping
from models.losses import compute_clust_l... | 48.619289 | 171 | 0.687409 |
f7ffcf3141c9a7705268082eb3e32b2c0285b192 | 840 | py | Python | SmallObjectAugmentation/Helpers.py | riciche/SimpleCVReproduction | 4075de39f9c61f1359668a413f6a5d98903fcf97 | [
"Apache-2.0"
] | 923 | 2020-01-11T06:36:53.000Z | 2022-03-31T00:26:57.000Z | SmallObjectAugmentation/Helpers.py | riciche/SimpleCVReproduction | 4075de39f9c61f1359668a413f6a5d98903fcf97 | [
"Apache-2.0"
] | 25 | 2020-02-27T08:35:46.000Z | 2022-01-25T08:54:19.000Z | SmallObjectAugmentation/Helpers.py | riciche/SimpleCVReproduction | 4075de39f9c61f1359668a413f6a5d98903fcf97 | [
"Apache-2.0"
] | 262 | 2020-01-02T02:19:40.000Z | 2022-03-23T04:56:16.000Z | import glob
import cv2 as cv2
import numpy as np
import matplotlib.pyplot as plt
# import random
import math
from tqdm import tqdm
def load_images(path):
image_list = []
images = glob.glob(path)
for index in range(len(images)):
image = cv2.cvtColor(cv2.imread(images[index]), cv2.COLOR_BGR2RGB)
... | 21.538462 | 74 | 0.672619 |
f7fff262cc16ee1944b9c7c01e1a4feef815e52e | 14,803 | py | Python | sdk/storage/azure-mgmt-storage/azure/mgmt/storage/v2019_04_01/operations/_file_services_operations.py | vincenttran-msft/azure-sdk-for-python | 348b56f9f03eeb3f7b502eed51daf494ffff874d | [
"MIT"
] | 1 | 2022-03-09T08:59:13.000Z | 2022-03-09T08:59:13.000Z | sdk/storage/azure-mgmt-storage/azure/mgmt/storage/v2019_04_01/operations/_file_services_operations.py | vincenttran-msft/azure-sdk-for-python | 348b56f9f03eeb3f7b502eed51daf494ffff874d | [
"MIT"
] | null | null | null | sdk/storage/azure-mgmt-storage/azure/mgmt/storage/v2019_04_01/operations/_file_services_operations.py | vincenttran-msft/azure-sdk-for-python | 348b56f9f03eeb3f7b502eed51daf494ffff874d | [
"MIT"
] | 1 | 2022-03-04T06:21:56.000Z | 2022-03-04T06:21:56.000Z | # coding=utf-8
# --------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for license information.
# Code generated by Microsoft (R) AutoRest Code Generator.
# Changes may ... | 43.538235 | 221 | 0.694656 |
f7ffff8e2c23d4349b4f870e5d0fb30bb2f3b2a3 | 12,427 | py | Python | cleverhans/train.py | iamgroot42/cleverhans | 53da9cd6daf9d7457800831c3eaa75f729a39145 | [
"MIT"
] | 21 | 2019-06-07T17:05:30.000Z | 2022-02-07T03:25:15.000Z | cleverhans/train.py | iamgroot42/cleverhans | 53da9cd6daf9d7457800831c3eaa75f729a39145 | [
"MIT"
] | 7 | 2019-12-16T22:20:01.000Z | 2022-02-10T00:45:21.000Z | cleverhans/train.py | iamgroot42/cleverhans | 53da9cd6daf9d7457800831c3eaa75f729a39145 | [
"MIT"
] | 8 | 2019-06-11T03:06:29.000Z | 2022-01-18T04:18:27.000Z | """
Multi-replica synchronous training
NOTE: This module is much more free to change than many other modules
in CleverHans. CleverHans is very conservative about changes to any
code that affects the output of benchmark tests (attacks, evaluation
methods, etc.). This module provides *model training* functionality
not ... | 40.087097 | 79 | 0.692042 |
790019d09eb81c29d6d6712867240f600e2c9dc0 | 3,343 | py | Python | services/endorser/api/core/config.py | Open-Earth-Foundation/traction | 908b555a7f408a88541b7692d3730e37a297c919 | [
"Apache-2.0"
] | 12 | 2022-01-29T20:30:03.000Z | 2022-03-29T11:46:14.000Z | services/endorser/api/core/config.py | Open-Earth-Foundation/traction | 908b555a7f408a88541b7692d3730e37a297c919 | [
"Apache-2.0"
] | 38 | 2021-11-22T17:52:50.000Z | 2022-03-31T17:52:00.000Z | services/endorser/api/core/config.py | Open-Earth-Foundation/traction | 908b555a7f408a88541b7692d3730e37a297c919 | [
"Apache-2.0"
] | 9 | 2021-11-22T18:05:48.000Z | 2022-03-29T11:25:08.000Z | import logging
import os
from enum import Enum
from functools import lru_cache
from typing import Optional
from pydantic import BaseSettings, PostgresDsn
logger = logging.getLogger(__name__)
class EnvironmentEnum(str, Enum):
PRODUCTION = "production"
LOCAL = "local"
class GlobalConfig(BaseSettings):
T... | 30.669725 | 107 | 0.714029 |
79003124fbb1cb58aae990f0214882ce3dfac658 | 5,878 | py | Python | mdrsl/rule_models/mids/objective_function/mids_objective_function_statistics.py | joschout/Multi-Directional-Rule-Set-Learning | ef0620b115f4e0fd7fba3e752d238a8020c1ca6b | [
"Apache-2.0"
] | 3 | 2020-08-03T19:25:44.000Z | 2021-06-27T22:25:55.000Z | mdrsl/rule_models/mids/objective_function/mids_objective_function_statistics.py | joschout/Multi-Directional-Rule-Set-Learning | ef0620b115f4e0fd7fba3e752d238a8020c1ca6b | [
"Apache-2.0"
] | null | null | null | mdrsl/rule_models/mids/objective_function/mids_objective_function_statistics.py | joschout/Multi-Directional-Rule-Set-Learning | ef0620b115f4e0fd7fba3e752d238a8020c1ca6b | [
"Apache-2.0"
] | 2 | 2020-08-07T22:54:28.000Z | 2021-02-18T06:11:01.000Z | from typing import Optional, Dict
from tabulate import tabulate
import pandas as pd
from mdrsl.utils.value_collection import ValueCollector
class MIDSObjectiveFunctionStatistics:
def __init__(self):
self.last_f0: Optional[int] = None
self.last_f1: Optional[int] = None
self.last_f2: Opti... | 35.409639 | 81 | 0.525859 |
7900355bbe26186ac5dbd81b76fbdbe822cdd10a | 105,956 | py | Python | models/transformer.py | NCTUMLlab/Adversarial-Masking-Transformers-for-Language-Understanding | b43fb91cf99ee3ffaf137cd0be87b67448995c9b | [
"MIT"
] | null | null | null | models/transformer.py | NCTUMLlab/Adversarial-Masking-Transformers-for-Language-Understanding | b43fb91cf99ee3ffaf137cd0be87b67448995c9b | [
"MIT"
] | null | null | null | models/transformer.py | NCTUMLlab/Adversarial-Masking-Transformers-for-Language-Understanding | b43fb91cf99ee3ffaf137cd0be87b67448995c9b | [
"MIT"
] | 1 | 2021-06-01T17:58:43.000Z | 2021-06-01T17:58:43.000Z | # Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the LICENSE file in
# the root directory of this source tree. An additional grant of patent rights
# can be found in the PATENTS file in the same directory.
import math
import numpy... | 45.987847 | 169 | 0.625363 |
79005df4d98ef9f7d776dc6ceae1987c1fcee100 | 8,010 | py | Python | tools/check_target_files_vintf.py | FabriSC/Alioth-SC | bbe9723401b351c2a34b09a30978373d456d20a2 | [
"MIT"
] | null | null | null | tools/check_target_files_vintf.py | FabriSC/Alioth-SC | bbe9723401b351c2a34b09a30978373d456d20a2 | [
"MIT"
] | null | null | null | tools/check_target_files_vintf.py | FabriSC/Alioth-SC | bbe9723401b351c2a34b09a30978373d456d20a2 | [
"MIT"
] | 1 | 2022-03-30T04:47:35.000Z | 2022-03-30T04:47:35.000Z | #!/usr/bin/env python
#
# Copyright (C) 2019 The Android Open Source Project
#
# 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 req... | 32.962963 | 80 | 0.705243 |
79007d5b8ec7c37cab25ce2888d47369bb4eb40a | 10,403 | py | Python | tutorial/deprecated/tutorial_a2c_with_infinite_env/a2c.py | Purple-PI/rlstructures | 9b201b083715bbda2f3534b010c84e11dfc0a1c7 | [
"MIT"
] | 281 | 2021-01-13T14:20:23.000Z | 2022-03-23T08:46:56.000Z | tutorial/deprecated/tutorial_a2c_with_infinite_env/a2c.py | Purple-PI/rlstructures | 9b201b083715bbda2f3534b010c84e11dfc0a1c7 | [
"MIT"
] | 2 | 2021-01-22T23:28:34.000Z | 2021-04-29T22:05:42.000Z | tutorial/deprecated/tutorial_a2c_with_infinite_env/a2c.py | Purple-PI/rlstructures | 9b201b083715bbda2f3534b010c84e11dfc0a1c7 | [
"MIT"
] | 13 | 2021-01-15T14:53:32.000Z | 2022-03-22T11:12:54.000Z | #
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
#
from rlstructures.logger import Logger, TFLogger
from rlstructures import DictTensor, TemporalDictTensor
from rlstructures import logging... | 42.461224 | 136 | 0.612323 |
790086155fac3be5e7a46c4cd0fb18fbd1b1b996 | 3,702 | py | Python | yardstick/dispatcher/http.py | kkltcjk/kklt | 5388eb439616a442dde496ef77ba6b71169369e0 | [
"Apache-2.0"
] | null | null | null | yardstick/dispatcher/http.py | kkltcjk/kklt | 5388eb439616a442dde496ef77ba6b71169369e0 | [
"Apache-2.0"
] | null | null | null | yardstick/dispatcher/http.py | kkltcjk/kklt | 5388eb439616a442dde496ef77ba6b71169369e0 | [
"Apache-2.0"
] | null | null | null | # Copyright 2013 IBM Corp
# 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 appl... | 35.257143 | 79 | 0.603998 |
7900954e9b27e49fd025c56ca1ab18a6e8667b43 | 14,230 | py | Python | train_pose_euler_crop.py | msieb1/LTCN | c9432891327774edf8193e885cc4f10f53fcaa60 | [
"MIT"
] | 1 | 2020-08-21T03:47:33.000Z | 2020-08-21T03:47:33.000Z | train_pose_euler_crop.py | msieb1/LTCN | c9432891327774edf8193e885cc4f10f53fcaa60 | [
"MIT"
] | null | null | null | train_pose_euler_crop.py | msieb1/LTCN | c9432891327774edf8193e885cc4f10f53fcaa60 | [
"MIT"
] | null | null | null | import matplotlib
matplotlib.use('Agg')
import os
from os.path import join
import argparse
import torch
import numpy as np
import pickle
import sys
import datetime
sys.path.append('./utils')
from torch import optim
from torch import nn
from torch import multiprocessing
from torch.optim import lr_scheduler
from torch.a... | 43.650307 | 169 | 0.646381 |
7900f081a429cddbd00caa87f27fe51153cc6f27 | 6,431 | py | Python | Q/questionnaire/q_urls.py | ES-DOC/esdoc-questionnaire | 9301eda375c4046323265b37ba96d94c94bf8b11 | [
"MIT"
] | null | null | null | Q/questionnaire/q_urls.py | ES-DOC/esdoc-questionnaire | 9301eda375c4046323265b37ba96d94c94bf8b11 | [
"MIT"
] | 477 | 2015-01-07T18:22:27.000Z | 2017-07-17T15:05:48.000Z | Q/questionnaire/q_urls.py | ES-DOC/esdoc-questionnaire | 9301eda375c4046323265b37ba96d94c94bf8b11 | [
"MIT"
] | null | null | null | ####################
# ES-DOC CIM Questionnaire
# Copyright (c) 2017 ES-DOC. All rights reserved.
#
# University of Colorado, Boulder
# http://cires.colorado.edu/
#
# This project is distributed according to the terms of the MIT license [http://www.opensource.org/licenses/MIT].
####################
from djan... | 46.941606 | 195 | 0.664438 |
7900fe6b4324af019ea50bf8ebbfcd7c728fc238 | 3,829 | py | Python | stock_trading_backend/agent/neural_network_model.py | iryzhkov/stock-trading-backend | 7161026b7b4deb78a934b66550c85a27c6b32933 | [
"MIT"
] | 1 | 2021-01-27T18:24:02.000Z | 2021-01-27T18:24:02.000Z | stock_trading_backend/agent/neural_network_model.py | iryzhkov/stock-trading-backend | 7161026b7b4deb78a934b66550c85a27c6b32933 | [
"MIT"
] | null | null | null | stock_trading_backend/agent/neural_network_model.py | iryzhkov/stock-trading-backend | 7161026b7b4deb78a934b66550c85a27c6b32933 | [
"MIT"
] | null | null | null | """Polynomial model class used by agents for building stuff.
"""
from torch import nn, optim
import torch
import torch.nn.functional as F
from stock_trading_backend.agent.model import Model
class NNModel(nn.Module):
"""Torch neural network model.
"""
def __init__(self, num_inputs, num_hidden_layers, num... | 34.495495 | 89 | 0.648211 |
790142b269f640eaae6cdf66f9c7a34c83d113b9 | 460 | py | Python | test/test_tiddler_fields_as_strings.py | funkyeah/tiddlyweb | 2346e6c05aa03ae9c8f2687d9ef9e46103267a8e | [
"BSD-3-Clause"
] | null | null | null | test/test_tiddler_fields_as_strings.py | funkyeah/tiddlyweb | 2346e6c05aa03ae9c8f2687d9ef9e46103267a8e | [
"BSD-3-Clause"
] | null | null | null | test/test_tiddler_fields_as_strings.py | funkyeah/tiddlyweb | 2346e6c05aa03ae9c8f2687d9ef9e46103267a8e | [
"BSD-3-Clause"
] | null | null | null | """
Make sure that tiddler fields which are not strings
are stringified, otherwise, the text serialization will
assplode.
"""
from tiddlyweb.serializer import Serializer
from tiddlyweb.model.tiddler import Tiddler
def setup_module(module):
pass
def test_float_field():
tiddler = Tiddler('foo', 'bar')
t... | 20.909091 | 56 | 0.719565 |
7901540d8570ef9a1bfd9dec9917f2c247c1890c | 3,382 | py | Python | app/user/tests/test_user_api.py | reallyusefulengine/django_rest_recipe | 49943404bfe4b6a84c3b4f6d8332f952982b3281 | [
"MIT"
] | null | null | null | app/user/tests/test_user_api.py | reallyusefulengine/django_rest_recipe | 49943404bfe4b6a84c3b4f6d8332f952982b3281 | [
"MIT"
] | null | null | null | app/user/tests/test_user_api.py | reallyusefulengine/django_rest_recipe | 49943404bfe4b6a84c3b4f6d8332f952982b3281 | [
"MIT"
] | null | null | null | from django.test import TestCase
from django.contrib.auth import get_user_model
from django.urls import reverse
from rest_framework.test import APIClient
from rest_framework import status
CREATE_USER_URL = reverse('user:create')
TOKEN_URL = reverse('user:token')
def create_user(**params):
return get_user_model(... | 37.577778 | 77 | 0.63631 |
790154b62d7337a78a9bce3b537685b14b4ca1a2 | 13,300 | py | Python | oscar/lib/python2.7/site-packages/prompt_toolkit/contrib/telnet/server.py | sainjusajan/django-oscar | 466e8edc807be689b0a28c9e525c8323cc48b8e1 | [
"BSD-3-Clause"
] | null | null | null | oscar/lib/python2.7/site-packages/prompt_toolkit/contrib/telnet/server.py | sainjusajan/django-oscar | 466e8edc807be689b0a28c9e525c8323cc48b8e1 | [
"BSD-3-Clause"
] | null | null | null | oscar/lib/python2.7/site-packages/prompt_toolkit/contrib/telnet/server.py | sainjusajan/django-oscar | 466e8edc807be689b0a28c9e525c8323cc48b8e1 | [
"BSD-3-Clause"
] | null | null | null | """
Telnet server.
Example usage::
class MyTelnetApplication(TelnetApplication):
def client_connected(self, telnet_connection):
# Set CLI with simple prompt.
telnet_connection.set_application(
telnet_connection.create_prompt_application(...))
def... | 32.598039 | 98 | 0.60015 |
79015f55f04aee062f85e9234f10a50e4771d3b4 | 178 | py | Python | files/judgeFileType.py | sdyz5210/python | 78f9999f94d92d9ca7fde6f18acec7d3abd422ef | [
"BSD-3-Clause"
] | null | null | null | files/judgeFileType.py | sdyz5210/python | 78f9999f94d92d9ca7fde6f18acec7d3abd422ef | [
"BSD-3-Clause"
] | null | null | null | files/judgeFileType.py | sdyz5210/python | 78f9999f94d92d9ca7fde6f18acec7d3abd422ef | [
"BSD-3-Clause"
] | null | null | null | #!/usr/bin/evn python
# -*- coding: utf-8 -*-
# python version 2.7.6
import magic
mime = magic.Magic(mime=True)
print mime.from_file("/Users/mac/Documents/data/fastq/8.fastq") | 19.777778 | 63 | 0.696629 |
79016cd7b072ebe0a6633862ea0beada1a4ce8ca | 186 | py | Python | dataset_utils/general_utils.py | kareemjano/image_toolbox | ea9e1654142a1492e7e462b4f0a8245f4ee430ae | [
"Apache-2.0"
] | null | null | null | dataset_utils/general_utils.py | kareemjano/image_toolbox | ea9e1654142a1492e7e462b4f0a8245f4ee430ae | [
"Apache-2.0"
] | null | null | null | dataset_utils/general_utils.py | kareemjano/image_toolbox | ea9e1654142a1492e7e462b4f0a8245f4ee430ae | [
"Apache-2.0"
] | null | null | null | from collections import defaultdict
def list_to_map(Xs, ys):
labels_map = defaultdict(list)
for x, y in list(zip(Xs, ys)):
labels_map[y].append(x)
return labels_map | 23.25 | 35 | 0.682796 |
79016e655840dcf25f5b90b8bffc280f87a56c79 | 4,446 | py | Python | pandas_ta/overlap/hilo.py | MyBourse/pandas-ta | 5998e92e39b71cd79a6e75d7c599492181af5f65 | [
"MIT"
] | 2 | 2021-03-30T01:23:14.000Z | 2021-04-02T18:04:51.000Z | pandas_ta/overlap/hilo.py | lukaszbinden/pandas-ta | 98478f8bf049a4c8748d6f3c795f4f335ced05ca | [
"MIT"
] | null | null | null | pandas_ta/overlap/hilo.py | lukaszbinden/pandas-ta | 98478f8bf049a4c8748d6f3c795f4f335ced05ca | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
from numpy import NaN as npNaN
from pandas import DataFrame, Series
# from pandas_ta.overlap.ma import ma
from .ma import ma
from pandas_ta.utils import get_offset, verify_series
def hilo(high, low, close, high_length=None, low_length=None, mamode=None, offset=None, **kwargs):
"""Indicator... | 34.734375 | 111 | 0.65857 |
79017c0607032cf5f29a9166dd2136111da20517 | 3,223 | py | Python | demo.py | ijinmao/CAM-Localization | dfa214be984f77d577dba1065e2c63e0c1b0b82b | [
"MIT"
] | 4 | 2017-09-07T05:55:58.000Z | 2019-09-05T04:02:41.000Z | demo.py | ijinmao/CAM-Localization | dfa214be984f77d577dba1065e2c63e0c1b0b82b | [
"MIT"
] | null | null | null | demo.py | ijinmao/CAM-Localization | dfa214be984f77d577dba1065e2c63e0c1b0b82b | [
"MIT"
] | 1 | 2019-04-02T05:03:25.000Z | 2019-04-02T05:03:25.000Z |
import numpy as np
import cv2
import matplotlib.pylab as plt
from keras.preprocessing.image import load_img
from keras.models import model_from_json
from models import (
create_cam_model, preprocess_image,
get_cam_img
)
# Define CAM conv layer name
CAM_CONV_LAYER = 'cam_conv_layer'
def read_model(model_path, wei... | 25.784 | 97 | 0.730996 |
7901adeebc4eddb9811775a1dd8834093c7ac65d | 2,057 | py | Python | examples/pylab_examples/mri_with_eeg.py | yuvallanger/matplotlib | e0020d318a9a9685594c6bff4631f74599321459 | [
"MIT",
"BSD-3-Clause"
] | 8 | 2017-04-11T08:55:30.000Z | 2022-03-25T04:31:26.000Z | examples/pylab_examples/mri_with_eeg.py | epgauss/matplotlib | c9898ea9a30c67c579ab27cd61b68e2abae0fb0e | [
"MIT",
"BSD-3-Clause"
] | null | null | null | examples/pylab_examples/mri_with_eeg.py | epgauss/matplotlib | c9898ea9a30c67c579ab27cd61b68e2abae0fb0e | [
"MIT",
"BSD-3-Clause"
] | 14 | 2015-10-05T04:15:46.000Z | 2020-06-11T18:06:02.000Z | #!/usr/bin/env python
"""
This now uses the imshow command instead of pcolor which *is much
faster*
"""
from __future__ import division, print_function
import numpy as np
from matplotlib.pyplot import *
from matplotlib.collections import LineCollection
import matplotlib.cbook as cbook
# I use if 1 to break up the di... | 26.037975 | 71 | 0.618376 |
7901bfee3778dd08118d7ec1c5e0e0d9e7c93415 | 1,004 | py | Python | alipay/aop/api/response/AlipayBossFncSettleSettlementbillCreateResponse.py | antopen/alipay-sdk-python-all | 8e51c54409b9452f8d46c7bb10eea7c8f7e8d30c | [
"Apache-2.0"
] | 213 | 2018-08-27T16:49:32.000Z | 2021-12-29T04:34:12.000Z | alipay/aop/api/response/AlipayBossFncSettleSettlementbillCreateResponse.py | antopen/alipay-sdk-python-all | 8e51c54409b9452f8d46c7bb10eea7c8f7e8d30c | [
"Apache-2.0"
] | 29 | 2018-09-29T06:43:00.000Z | 2021-09-02T03:27:32.000Z | alipay/aop/api/response/AlipayBossFncSettleSettlementbillCreateResponse.py | antopen/alipay-sdk-python-all | 8e51c54409b9452f8d46c7bb10eea7c8f7e8d30c | [
"Apache-2.0"
] | 59 | 2018-08-27T16:59:26.000Z | 2022-03-25T10:08:15.000Z | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import json
from alipay.aop.api.response.AlipayResponse import AlipayResponse
from alipay.aop.api.domain.SettlementbillOpenApiDTO import SettlementbillOpenApiDTO
class AlipayBossFncSettleSettlementbillCreateResponse(AlipayResponse):
def __init__(self):
super... | 33.466667 | 120 | 0.74004 |
7901c99ab98015bba242cc58af0f02592b798a4d | 3,920 | py | Python | advbench/lib/meters.py | constrainedlearning/advbench | 68f9f6d77268aad45517ca84d383b996724cc976 | [
"MIT"
] | null | null | null | advbench/lib/meters.py | constrainedlearning/advbench | 68f9f6d77268aad45517ca84d383b996724cc976 | [
"MIT"
] | null | null | null | advbench/lib/meters.py | constrainedlearning/advbench | 68f9f6d77268aad45517ca84d383b996724cc976 | [
"MIT"
] | null | null | null | import time
try:
import wandb
wandb_log=True
except ImportError:
wandb_log=False
import numpy as np
from advbench.lib.plotting import plot_perturbed_wandb
from einops import rearrange
class AverageMeter:
"""Computes and stores the average and current value"""
def __init__(self, avg_mom=0.5):
... | 29.923664 | 135 | 0.53801 |
7901f88050817b7d944fde8456d5af6133e7ce35 | 723 | py | Python | app/main/forms.py | edumorris/pomodoro | cde372be1d5c37dd8221ebd40b684d07fbb472b5 | [
"MIT"
] | null | null | null | app/main/forms.py | edumorris/pomodoro | cde372be1d5c37dd8221ebd40b684d07fbb472b5 | [
"MIT"
] | null | null | null | app/main/forms.py | edumorris/pomodoro | cde372be1d5c37dd8221ebd40b684d07fbb472b5 | [
"MIT"
] | null | null | null | from flask_wtf import FlaskForm
from wtforms import StringField,PasswordField,SubmitField, ValidationError, BooleanField, TextAreaField,SelectField
from wtforms.validators import Required,Email,EqualTo
from ..models import User
class CommentForm(FlaskForm):
comment = TextAreaField('Your comment:', validators=[Requ... | 48.2 | 158 | 0.75657 |
7902045a8bcac920a9e2f9d298a662706cdcfa87 | 7,289 | py | Python | train.py | petersvenningsson/radar-Bayesian-human-motion | 728db0f39c107faccf9d711670177aac74456e3f | [
"MIT"
] | 1 | 2022-02-01T20:42:24.000Z | 2022-02-01T20:42:24.000Z | train.py | petersvenningsson/radar-Bayesian-human-motion | 728db0f39c107faccf9d711670177aac74456e3f | [
"MIT"
] | null | null | null | train.py | petersvenningsson/radar-Bayesian-human-motion | 728db0f39c107faccf9d711670177aac74456e3f | [
"MIT"
] | null | null | null | import argparse
import numpy as np
from sklearn.metrics import accuracy_score, jaccard_score, balanced_accuracy_score
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import dataloader
import track
from classifiers import ObservationsConditionsClassifier
from classifiers import ClassifierComp... | 40.949438 | 189 | 0.664563 |
7902350dee2a3d3b50074578b7417126b6253c64 | 3,756 | py | Python | external/sbml/bindings/python/test/sbml/TestConstraint_newSetters.py | dchandran/evolvenetworks | 072f9e1292552f691a86457ffd16a5743724fb5e | [
"BSD-3-Clause"
] | 1 | 2019-08-22T17:17:41.000Z | 2019-08-22T17:17:41.000Z | external/sbml/bindings/python/test/sbml/TestConstraint_newSetters.py | dchandran/evolvenetworks | 072f9e1292552f691a86457ffd16a5743724fb5e | [
"BSD-3-Clause"
] | null | null | null | external/sbml/bindings/python/test/sbml/TestConstraint_newSetters.py | dchandran/evolvenetworks | 072f9e1292552f691a86457ffd16a5743724fb5e | [
"BSD-3-Clause"
] | null | null | null | #
# @file TestConstraint_newSetters.py
# @brief Constraint unit tests for new set function API
#
# @author Akiya Jouraku (Python conversion)
# @author Sarah Keating
#
# $Id$
# $HeadURL$
#
# This test file was converted from src/sbml/test/TestConstraint_newSetters.c
# with the help of conversion sciprt (ctest_... | 32.947368 | 79 | 0.660011 |
7902482a6fb74642b39229bc8a84f18d19d023ff | 2,777 | py | Python | tests/utils/test_pnc.py | hjmodi/atomic-reactor | 547f3edd28628dc59a98c4928a0ecf280f5983cb | [
"BSD-3-Clause"
] | null | null | null | tests/utils/test_pnc.py | hjmodi/atomic-reactor | 547f3edd28628dc59a98c4928a0ecf280f5983cb | [
"BSD-3-Clause"
] | null | null | null | tests/utils/test_pnc.py | hjmodi/atomic-reactor | 547f3edd28628dc59a98c4928a0ecf280f5983cb | [
"BSD-3-Clause"
] | null | null | null | """
Copyright (c) 2021 Red Hat, Inc
All rights reserved.
This software may be modified and distributed under the terms
of the BSD license. See the LICENSE file for details.
"""
from io import BufferedReader, BytesIO
import pytest
import requests
import responses
from flexmock import flexmock
from atomic_reactor.uti... | 33.457831 | 95 | 0.695355 |
79027676c5ca108e115bc437519299eb7bbd02f4 | 1,713 | py | Python | coffee-maturation/src/models/non_maximum.py | dahem/coffe-images | 2af526c57c08317829e0b99af83b11c9fb9182da | [
"MIT"
] | null | null | null | coffee-maturation/src/models/non_maximum.py | dahem/coffe-images | 2af526c57c08317829e0b99af83b11c9fb9182da | [
"MIT"
] | null | null | null | coffee-maturation/src/models/non_maximum.py | dahem/coffe-images | 2af526c57c08317829e0b99af83b11c9fb9182da | [
"MIT"
] | null | null | null | import numpy as np
def non_max_suppression_fast(boxes, overlapThresh):
# if there are no boxes, return an empty list
if len(boxes) == 0:
return []
# if the boxes are integers, convert them to floats (due to divisions)
if boxes.dtype.kind == "i":
boxes = boxes.astype("float")
# initial... | 24.126761 | 108 | 0.615879 |
79028f78d8a408e97a58606dd5ac6bc08df170f3 | 5,531 | py | Python | igemm_codegen.py | aska-0096/iGEMMgen | cff8507355d86e47f5b099cd9b8a81d94fab93d7 | [
"MIT"
] | 20 | 2020-04-14T14:39:24.000Z | 2022-02-23T19:37:04.000Z | igemm_codegen.py | aska-0096/iGEMMgen | cff8507355d86e47f5b099cd9b8a81d94fab93d7 | [
"MIT"
] | 38 | 2020-04-21T12:23:07.000Z | 2021-12-31T02:26:21.000Z | igemm_codegen.py | aska-0096/iGEMMgen | cff8507355d86e47f5b099cd9b8a81d94fab93d7 | [
"MIT"
] | 9 | 2020-04-20T06:34:16.000Z | 2022-02-23T19:37:06.000Z | ################################################################################
#
# MIT License
#
# Copyright (c) 2020 Advanced Micro Devices, Inc.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# ... | 42.875969 | 196 | 0.685952 |
79029440277e967106f528a1a3f24a2937e0ceee | 27,758 | py | Python | readthedocs/api/v3/serializers.py | mehrdad-khojastefar/readthedocs.org | b958bb8d04c454324d612345890b13af54a19eb6 | [
"MIT"
] | 2,092 | 2019-06-29T07:47:30.000Z | 2022-03-31T14:54:59.000Z | readthedocs/api/v3/serializers.py | mehrdad-khojastefar/readthedocs.org | b958bb8d04c454324d612345890b13af54a19eb6 | [
"MIT"
] | 2,389 | 2019-06-29T04:22:55.000Z | 2022-03-31T22:57:49.000Z | readthedocs/api/v3/serializers.py | mehrdad-khojastefar/readthedocs.org | b958bb8d04c454324d612345890b13af54a19eb6 | [
"MIT"
] | 1,185 | 2019-06-29T21:49:31.000Z | 2022-03-30T09:57:15.000Z | import datetime
import urllib
from django.conf import settings
from django.contrib.auth.models import User
from django.urls import reverse
from django.utils.translation import ugettext as _
from rest_flex_fields import FlexFieldsModelSerializer
from rest_flex_fields.serializers import FlexFieldsSerializerMixin
from re... | 28.180711 | 89 | 0.595468 |
7902958aba3b36b6c985cbc6b8b862ef5e83942c | 9,239 | py | Python | deepchembed/dce.py | hanghu/AutoChemCluster | 2ab4ae996b300a90637b124707905201c89d74d8 | [
"MIT"
] | 2 | 2019-05-15T06:31:35.000Z | 2019-08-31T13:13:21.000Z | deepchembed/dce.py | hanghu/AutoChemCluster | 2ab4ae996b300a90637b124707905201c89d74d8 | [
"MIT"
] | 7 | 2019-05-02T19:01:40.000Z | 2022-02-10T00:11:00.000Z | deepchembed/dce.py | hanghu/AutoChemCluster | 2ab4ae996b300a90637b124707905201c89d74d8 | [
"MIT"
] | 1 | 2019-08-17T11:34:56.000Z | 2019-08-17T11:34:56.000Z | """
DeepChEmbed （DCE) Models
"""
from dimreducer import DeepAutoEncoder
from cluster import KMeansLayer
from cluster import KMeans
from keras import Model
from keras import optimizers
from keras.utils import normalize
import numpy as np
class DCE():
"""
The class to build a deep chemical embedding model.
... | 41.430493 | 79 | 0.573763 |
7902c0eb5175879fdc97f1cf09a1cd5cc8374bfd | 4,994 | py | Python | models/official/unet3d/unet_main.py | tensorflow/tpu-demos | 8aac591077e5781785aa6c22bc400472ba14dada | [
"Apache-2.0"
] | 65 | 2017-07-28T03:47:42.000Z | 2018-02-04T20:54:18.000Z | models/official/unet3d/unet_main.py | tensorflow/tpu-demos | 8aac591077e5781785aa6c22bc400472ba14dada | [
"Apache-2.0"
] | 10 | 2017-08-11T22:55:40.000Z | 2018-02-07T01:11:28.000Z | models/official/unet3d/unet_main.py | tensorflow/tpu-demos | 8aac591077e5781785aa6c22bc400472ba14dada | [
"Apache-2.0"
] | 28 | 2017-07-28T08:20:06.000Z | 2018-01-28T16:28:12.000Z | # Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | 35.928058 | 80 | 0.718662 |
7902e69fa7db7f459fe2c262ca5e329f7363bc68 | 1,353 | py | Python | interpolML/interpolML/model/model.py | MiguelMque/interpolML | 980d55583285ba1d289de69b5c05c65fc34097f5 | [
"MIT"
] | null | null | null | interpolML/interpolML/model/model.py | MiguelMque/interpolML | 980d55583285ba1d289de69b5c05c65fc34097f5 | [
"MIT"
] | null | null | null | interpolML/interpolML/model/model.py | MiguelMque/interpolML | 980d55583285ba1d289de69b5c05c65fc34097f5 | [
"MIT"
] | null | null | null | from typing import Any
from copy import deepcopy
class Model:
def __init__(self, name: str, model, freq: str):
self.name = name
self.model = model
self.freq = freq
self.train = None
self.test = None
self.prediction = None
self.pred_col = "prediction"
... | 27.612245 | 92 | 0.597931 |
79033e329aeeed6995605fb7fa079108c03ba683 | 8,699 | py | Python | awx/main/dispatch/worker/callback.py | Mayses/awx | 35441694a9707d0d2f57c701970db22110091163 | [
"Apache-2.0"
] | 1 | 2021-08-02T10:37:09.000Z | 2021-08-02T10:37:09.000Z | awx/main/dispatch/worker/callback.py | Mayses/awx | 35441694a9707d0d2f57c701970db22110091163 | [
"Apache-2.0"
] | 2 | 2019-03-01T19:08:10.000Z | 2020-03-12T09:14:27.000Z | awx/main/dispatch/worker/callback.py | hostinger/awx | dac01b14e2c04c201a162ea03ef8386d822e3923 | [
"Apache-2.0"
] | 24 | 2020-11-27T08:37:35.000Z | 2021-03-08T13:27:15.000Z | import cProfile
import json
import logging
import os
import pstats
import signal
import tempfile
import time
import traceback
from django.conf import settings
from django.utils.timezone import now as tz_now
from django.db import DatabaseError, OperationalError, connection as django_connection
from django.db.utils impo... | 40.840376 | 136 | 0.550178 |
79034b377e53dbe27bcfc9791d8775eae11ab645 | 4,532 | py | Python | notebook-samples/unsupervised/pred_electricity_consumption.py | MarkMoretto/python-examples-main | 37b8c41d2f175029f4536ca970f037ff19b4e951 | [
"MIT"
] | 1 | 2020-07-21T23:24:25.000Z | 2020-07-21T23:24:25.000Z | notebook-samples/unsupervised/pred_electricity_consumption.py | MarkMoretto/python-examples-main | 37b8c41d2f175029f4536ca970f037ff19b4e951 | [
"MIT"
] | 4 | 2021-06-29T00:38:57.000Z | 2022-01-15T00:22:15.000Z | notebook-samples/unsupervised/pred_electricity_consumption.py | MarkMoretto/python-examples-main | 37b8c41d2f175029f4536ca970f037ff19b4e951 | [
"MIT"
] | null | null | null |
"""
Purpose: Unsupervised learning sampler
Date created: 2020-11-06
Ref repo: https://github.com/White-Link/UnsupervisedScalableRepresentationLearningTimeSeries
Local folder: C:/Users/Work1/Desktop/Info/GitHub/python-examples-main/notebook-samples/unsupervised
Contributor(s):
Mark M.
"""
import os
from pathlib... | 23.978836 | 115 | 0.703442 |
790371ae35706b91f27bf2d89e39770f3441a18b | 4,910 | py | Python | superset/sql_parse.py | gabe-lyons/incubator-superset | 7669cdb8c51bcc3f298aff2a14cbfeea3cbf5f13 | [
"Apache-2.0"
] | 4 | 2018-07-25T17:12:13.000Z | 2020-12-28T10:26:53.000Z | superset/sql_parse.py | ksangeet9ap/incubator-superset | f417172071503e48bdbbe00d8254c204928a5d3e | [
"Apache-2.0"
] | 1 | 2018-02-22T23:29:06.000Z | 2018-02-23T21:44:00.000Z | superset/sql_parse.py | ksangeet9ap/incubator-superset | f417172071503e48bdbbe00d8254c204928a5d3e | [
"Apache-2.0"
] | 4 | 2020-03-07T11:58:42.000Z | 2020-05-26T02:07:27.000Z | # -*- coding: utf-8 -*-
# pylint: disable=C,R,W
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import logging
import sqlparse
from sqlparse.sql import Identifier, IdentifierList
from sqlparse.tokens import Keyword, N... | 34.822695 | 79 | 0.614868 |
79038b3e118983bc62e021442b3e8f2c6f1fa0d7 | 1,003 | py | Python | examples/outlook/send_message.py | stardust85/Office365-REST-Python-Client | cd369c607c7d137a000734e9c5e8f03ae3e3c603 | [
"MIT"
] | null | null | null | examples/outlook/send_message.py | stardust85/Office365-REST-Python-Client | cd369c607c7d137a000734e9c5e8f03ae3e3c603 | [
"MIT"
] | null | null | null | examples/outlook/send_message.py | stardust85/Office365-REST-Python-Client | cd369c607c7d137a000734e9c5e8f03ae3e3c603 | [
"MIT"
] | null | null | null | from office365.graph.graph_client import GraphClient
from settings import settings
def get_token(auth_ctx):
"""Acquire token via client credential flow (ADAL Python library is utilized)"""
token = auth_ctx.acquire_token_with_client_credentials(
"https://graph.microsoft.com",
settings['client_c... | 27.861111 | 84 | 0.612164 |
7903a0d0a7d350892d692d86b8bbd1dc00694d86 | 257 | py | Python | settings/__init__.py | ppold/lambtastic | 29d96f0f111a950a6ecd7af1cdc172addd64de04 | [
"Unlicense"
] | null | null | null | settings/__init__.py | ppold/lambtastic | 29d96f0f111a950a6ecd7af1cdc172addd64de04 | [
"Unlicense"
] | 1 | 2021-06-01T21:53:04.000Z | 2021-06-01T21:53:04.000Z | settings/__init__.py | ppold/lambtastic | 29d96f0f111a950a6ecd7af1cdc172addd64de04 | [
"Unlicense"
] | null | null | null | """ core app configuration """
import os
environment = os.getenv('LAMBTASTIC_ENV', 'development')
if environment == 'testing':
from .testing import *
elif environment == 'production':
from .production import *
else:
from .development import *
| 21.416667 | 56 | 0.696498 |
7903b2cc28f548b837773d028e06bc2268565a94 | 24,145 | py | Python | .venv/Lib/site-packages/rich/pretty.py | jefferdo/gpt-3-client | 7acbc5f518fe3fcb55d0bdcbf93fc87b103b1148 | [
"MIT"
] | null | null | null | .venv/Lib/site-packages/rich/pretty.py | jefferdo/gpt-3-client | 7acbc5f518fe3fcb55d0bdcbf93fc87b103b1148 | [
"MIT"
] | 76 | 2020-07-31T05:33:39.000Z | 2022-03-28T05:04:17.000Z | rich/pretty.py | shyovn/rich | a05a5a1c2f95f25db70ac3657e99f0bab652e2cd | [
"MIT"
] | null | null | null | import builtins
import os
import sys
from array import array
from collections import Counter, defaultdict, deque
from dataclasses import dataclass, fields, is_dataclass
from itertools import islice
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
Iterable,
List,
Optional,
Set,
... | 34.741007 | 134 | 0.566163 |
7903b71f344fdae0aaa535b9d1dc6746718b0d4e | 6,074 | py | Python | models/fdconv1d_lstm/train.py | rovo98/model-unkown-dfa-diagnosis-based-on-running-logs | f80c838dea6a8313165fbf10d64d5dc935cc036c | [
"Apache-2.0"
] | null | null | null | models/fdconv1d_lstm/train.py | rovo98/model-unkown-dfa-diagnosis-based-on-running-logs | f80c838dea6a8313165fbf10d64d5dc935cc036c | [
"Apache-2.0"
] | 4 | 2020-04-30T07:57:42.000Z | 2020-09-27T06:52:00.000Z | models/fdconv1d_lstm/train.py | rovo98/model-unkown-dfa-diagnosis-based-on-running-logs | f80c838dea6a8313165fbf10d64d5dc935cc036c | [
"Apache-2.0"
] | null | null | null | # author rovo98
import os
import tensorflow as tf
from tensorflow.keras.utils import plot_model
from tensorflow.keras.callbacks import EarlyStopping
from model_data_input import load_processed_dataset
from models.fdconv1d_lstm.model import build_fdconv1d_lstm
from models.utils.misc import running_timer
from models.u... | 43.697842 | 114 | 0.689167 |
7903eb73c3f6b1512edfad4b6b076a4433ccc540 | 347 | py | Python | pools/eventlet.py | JohnStarich/python-pool-performance | 5a8428ca95240932e0b1b0d7064bf8020e0b1f2e | [
"MIT"
] | 32 | 2016-08-05T20:54:57.000Z | 2021-11-16T19:28:12.000Z | pools/eventlet.py | ktosiu/python-pool-performance | 5a8428ca95240932e0b1b0d7064bf8020e0b1f2e | [
"MIT"
] | 1 | 2018-10-26T10:43:16.000Z | 2018-10-31T07:37:20.000Z | pools/eventlet.py | ktosiu/python-pool-performance | 5a8428ca95240932e0b1b0d7064bf8020e0b1f2e | [
"MIT"
] | 7 | 2017-03-18T21:27:53.000Z | 2022-02-11T01:40:48.000Z | from pools import PoolTest
import eventlet
class EventletPool(PoolTest):
def init_pool(self, worker_count):
return eventlet.GreenPool(worker_count)
def map(self, work_func, inputs):
return self.pool.imap(work_func, inputs)
def init_network_resource(self):
return eventlet.import_p... | 24.785714 | 58 | 0.731988 |
79040eed931a507c8701c847570903d6715a6f2e | 9,615 | py | Python | arcface/resnet_cbam.py | DerryHub/the-TaobaoLive-Commodity-Identify-Competition | 7e5e5c4fbddd9949fe01810d58bd7994889c007c | [
"MIT"
] | 4 | 2020-08-15T14:49:37.000Z | 2022-01-16T08:34:07.000Z | arcface/resnet_cbam.py | weilin-droid/the-TaobaoLive-Commodity-Identify-Competition | 7e5e5c4fbddd9949fe01810d58bd7994889c007c | [
"MIT"
] | null | null | null | arcface/resnet_cbam.py | weilin-droid/the-TaobaoLive-Commodity-Identify-Competition | 7e5e5c4fbddd9949fe01810d58bd7994889c007c | [
"MIT"
] | 2 | 2021-05-26T05:16:09.000Z | 2021-06-09T09:07:49.000Z | import torch
import torch.nn as nn
import math
from arcface.utils import l2_norm, Flatten, SentVec_TFIDF
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
'resnet50': 'https://download.pytorch.org... | 33.618881 | 87 | 0.562142 |
7904170d3c8142ffcd70d73d75dd5cdaa82082ab | 1,122 | py | Python | scripts/pivot_cluster_day.py | isabella232/allsongsconsidered-poll | f4b63effcf57c6b6680eac9f11a55cd0541e358c | [
"MIT"
] | 3 | 2018-01-04T12:07:28.000Z | 2018-04-10T02:10:27.000Z | scripts/pivot_cluster_day.py | nprapps/allsongsconsidered-poll | f4b63effcf57c6b6680eac9f11a55cd0541e358c | [
"MIT"
] | 1 | 2021-02-24T06:47:12.000Z | 2021-02-24T06:47:12.000Z | scripts/pivot_cluster_day.py | isabella232/allsongsconsidered-poll | f4b63effcf57c6b6680eac9f11a55cd0541e358c | [
"MIT"
] | 3 | 2018-08-03T22:10:04.000Z | 2022-03-23T11:33:55.000Z | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import argparse
import sys
import numpy as np
import pandas as pd
def run(args):
data = pd.read_csv(sys.stdin)
# Find maximum rank value and increase by one to use as a fill_value
# on the pivot with cluster by day
# notfound_value = grouped['rank'].max()... | 29.526316 | 76 | 0.564171 |
79042e70f30245770d9db6b182ee23c020d301ec | 7,960 | py | Python | utils/lib_classifier.py | eddylamhw/trAIner24 | ac7cf1b95a2ecdfc44d11451984b016524ed7657 | [
"MIT"
] | 1 | 2021-11-25T16:32:51.000Z | 2021-11-25T16:32:51.000Z | utils/lib_classifier.py | eddylamhw/trAIner24 | ac7cf1b95a2ecdfc44d11451984b016524ed7657 | [
"MIT"
] | null | null | null | utils/lib_classifier.py | eddylamhw/trAIner24 | ac7cf1b95a2ecdfc44d11451984b016524ed7657 | [
"MIT"
] | null | null | null | '''
This script includes:
1. ClassifierOfflineTrain
This is for offline training. The input data are the processed features.
2. class ClassifierOnlineTest(object)
This is for online testing. The input data are the raw skeletons.
It uses FeatureGenerator to extract features,
and then use ClassifierOffli... | 35.855856 | 97 | 0.629899 |
79043c4a2b502fc7c643b0638228e702db778599 | 2,611 | py | Python | neuro-cli/tests/unit/formatters/test_blob_formatters.py | neuro-inc/platform-client-python | 012e355249ea900b76f9ce4209fb9d029652f9b2 | [
"Apache-2.0"
] | 11 | 2020-10-11T15:38:11.000Z | 2021-11-09T11:29:50.000Z | neuro-cli/tests/unit/formatters/test_blob_formatters.py | neuro-inc/platform-client-python | 012e355249ea900b76f9ce4209fb9d029652f9b2 | [
"Apache-2.0"
] | 611 | 2020-09-30T21:27:52.000Z | 2022-01-10T10:44:44.000Z | neuro-cli/tests/unit/formatters/test_blob_formatters.py | neuro-inc/platform-client-python | 012e355249ea900b76f9ce4209fb9d029652f9b2 | [
"Apache-2.0"
] | 1 | 2020-10-05T15:10:24.000Z | 2020-10-05T15:10:24.000Z | from datetime import datetime
from typing import Any, List, Union
import pytest
from neuro_sdk import BlobCommonPrefix, BlobObject, Bucket, BucketEntry
from neuro_cli.formatters.blob_storage import (
BaseBlobFormatter,
LongBlobFormatter,
SimpleBlobFormatter,
)
class TestBlobFormatter:
buckets: Lis... | 29.011111 | 87 | 0.542704 |
79044aafe60974ffe22e1146ae93d53991ba1bf9 | 23,871 | py | Python | src/nfc/llcp/llc.py | javgh/bitpay-brick | 688cb3403111494bba5f453ea681515e03bf43b4 | [
"MIT"
] | 15 | 2016-11-20T15:38:49.000Z | 2021-08-23T02:59:49.000Z | src/nfc/llcp/llc.py | javgh/bitpay-brick | 688cb3403111494bba5f453ea681515e03bf43b4 | [
"MIT"
] | null | null | null | src/nfc/llcp/llc.py | javgh/bitpay-brick | 688cb3403111494bba5f453ea681515e03bf43b4 | [
"MIT"
] | 3 | 2016-11-21T11:57:13.000Z | 2019-03-24T21:12:41.000Z | # -*- coding: latin-1 -*-
# -----------------------------------------------------------------------------
# Copyright 2009-2011 Stephen Tiedemann <stephen.tiedemann@googlemail.com>
#
# Licensed under the EUPL, Version 1.1 or - as soon they
# will be approved by the European Commission - subsequent
# versions of the EU... | 38.011146 | 79 | 0.548071 |
7904526d4f781baed9cdc8dc4972204a7a65ec51 | 236 | py | Python | some-euler/p31.py | rik0/rk-exempla | 811f859a0980b0636bbafa2656893d988c4d0e32 | [
"MIT"
] | 1 | 2017-02-20T21:04:47.000Z | 2017-02-20T21:04:47.000Z | some-euler/p31.py | rik0/rk-exempla | 811f859a0980b0636bbafa2656893d988c4d0e32 | [
"MIT"
] | null | null | null | some-euler/p31.py | rik0/rk-exempla | 811f859a0980b0636bbafa2656893d988c4d0e32 | [
"MIT"
] | 2 | 2017-02-20T21:04:49.000Z | 2021-05-18T11:29:16.000Z | import constraint
coins = [1, 2, 5, 10, 20, 50, 100, 200]
CSP = constraint.Problem()
for coin in coins:
CSP.addVariable(coin, range(0, 201, coin))
CSP.addConstraint(constraint.ExactSumConstraint(200))
print len(CSP.getSolutions()) | 26.222222 | 53 | 0.724576 |
79045746d76c6cba54bceacab6b61a83a5825c4e | 1,328 | py | Python | components/contrib/_converters/KerasModelHdf5/to_TensorflowSavedModel/component.py | Iuiu1234/pipelines | 1e032f550ce23cd40bfb6827b995248537b07d08 | [
"Apache-2.0"
] | 2,860 | 2018-05-24T04:55:01.000Z | 2022-03-31T13:49:56.000Z | components/contrib/_converters/KerasModelHdf5/to_TensorflowSavedModel/component.py | Iuiu1234/pipelines | 1e032f550ce23cd40bfb6827b995248537b07d08 | [
"Apache-2.0"
] | 7,331 | 2018-05-16T09:03:26.000Z | 2022-03-31T23:22:04.000Z | components/contrib/_converters/KerasModelHdf5/to_TensorflowSavedModel/component.py | Iuiu1234/pipelines | 1e032f550ce23cd40bfb6827b995248537b07d08 | [
"Apache-2.0"
] | 1,359 | 2018-05-15T11:05:41.000Z | 2022-03-31T09:42:09.000Z | from kfp.components import create_component_from_func, InputPath, OutputPath
def keras_convert_hdf5_model_to_tf_saved_model(
model_path: InputPath('KerasModelHdf5'),
converted_model_path: OutputPath('TensorflowSavedModel'),
):
'''Converts Keras HDF5 model to Tensorflow SavedModel format.
Args:
... | 39.058824 | 182 | 0.734187 |
790476bfb1348ab4e0cb7a3cfe0c76769a6bf9e2 | 9,966 | py | Python | mmcls/models/backbones/mobilenet_v2.py | ChaseMonsterAway/mmclassification | 85d26b8eb2fc799599c42ca33831c40707311bd7 | [
"Apache-2.0"
] | null | null | null | mmcls/models/backbones/mobilenet_v2.py | ChaseMonsterAway/mmclassification | 85d26b8eb2fc799599c42ca33831c40707311bd7 | [
"Apache-2.0"
] | null | null | null | mmcls/models/backbones/mobilenet_v2.py | ChaseMonsterAway/mmclassification | 85d26b8eb2fc799599c42ca33831c40707311bd7 | [
"Apache-2.0"
] | null | null | null | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
import torch.utils.checkpoint as cp
from mmcv.cnn import ConvModule
from mmcv.runner import BaseModule
from torch.nn.modules.batchnorm import _BatchNorm
from mmcls.models.utils import make_divisible
from ..builder import BACKBONES
from .base_backbon... | 36.639706 | 173 | 0.553783 |
79048490556fde1b61605b3bb2be4bfa21cfe9d0 | 2,046 | py | Python | src/spaceone/inventory/api/v1/network_type.py | choonho/inventory | cc89757490d28fecb7ffccdfd6f89d4c0aa40da5 | [
"Apache-2.0"
] | null | null | null | src/spaceone/inventory/api/v1/network_type.py | choonho/inventory | cc89757490d28fecb7ffccdfd6f89d4c0aa40da5 | [
"Apache-2.0"
] | null | null | null | src/spaceone/inventory/api/v1/network_type.py | choonho/inventory | cc89757490d28fecb7ffccdfd6f89d4c0aa40da5 | [
"Apache-2.0"
] | null | null | null | from spaceone.api.inventory.v1 import network_type_pb2, network_type_pb2_grpc
from spaceone.core.pygrpc import BaseAPI
class NetworkType(BaseAPI, network_type_pb2_grpc.NetworkTypeServicer):
pb2 = network_type_pb2
pb2_grpc = network_type_pb2_grpc
def create(self, request, context):
params, metada... | 43.531915 | 118 | 0.726784 |
790489aa109a3810fa6f0d208b39f83eb3d71525 | 1,688 | py | Python | kite/venv/lib/python3.7/site-packages/bs4/tests/test_htmlparser.py | pxuanqui/Edge-Assisted-Cart | 2edd1f7023ab0b02f5733e2e9204bac4623eeeac | [
"BSD-3-Clause"
] | 27 | 2019-10-28T05:03:18.000Z | 2021-06-09T00:16:22.000Z | kite/venv/lib/python3.7/site-packages/bs4/tests/test_htmlparser.py | pxuanqui/Edge-Assisted-Cart | 2edd1f7023ab0b02f5733e2e9204bac4623eeeac | [
"BSD-3-Clause"
] | 47 | 2018-11-16T19:18:01.000Z | 2021-12-01T19:40:44.000Z | virtual/lib/python3.6/site-packages/bs4/tests/test_htmlparser.py | catherine244/Reviews | 30138f5ad09a39c1b6866c8bacf3fd0c89abbd00 | [
"MIT"
] | 9 | 2019-11-02T06:44:18.000Z | 2021-11-08T11:46:19.000Z | """Tests to ensure that the html.parser tree builder generates good
trees."""
from pdb import set_trace
import pickle
from bs4.testing import SoupTest, HTMLTreeBuilderSmokeTest
from bs4.builder import HTMLParserTreeBuilder
from bs4.builder._htmlparser import BeautifulSoupHTMLParser
class HTMLParserTreeBuilderSmokeTes... | 35.166667 | 79 | 0.690758 |
79048cff42ce750f3a33344f76f2a01c5367ca07 | 485 | py | Python | wargame/designpatterns/pythonic_orcfighter.py | jeantardelli/wargameRepo | 1e11ae40281f7eafa65ea6e40e045304b20e3824 | [
"MIT"
] | 1 | 2020-12-01T20:30:27.000Z | 2020-12-01T20:30:27.000Z | wargame/designpatterns/pythonic_orcfighter.py | jeantardelli/wargameRepo | 1e11ae40281f7eafa65ea6e40e045304b20e3824 | [
"MIT"
] | null | null | null | wargame/designpatterns/pythonic_orcfighter.py | jeantardelli/wargameRepo | 1e11ae40281f7eafa65ea6e40e045304b20e3824 | [
"MIT"
] | null | null | null | """pythonic_orcfighter
This is one of the different GameUnits that are used in the desing patterns examples.
:copyright: 2020, Jean Tardelli
:license: The MIT license (MIT). See LICENSE file for further details.
"""
from pythonic_abstractgameunit import AbstractGameUnit
class OrcFighter(AbstractGameUnit):
"""Cr... | 30.3125 | 85 | 0.736082 |
7904a3040e8c6bf9902569d51ccd6879143a4351 | 1,963 | py | Python | DailyCodingProblem/84_Amazon_Find_Islands_From_Matrix.py | RafayAK/CodingPrep | 718eccb439db0f6e727806964766a40e8234c8a9 | [
"MIT"
] | 5 | 2019-09-07T17:31:17.000Z | 2022-03-05T09:59:46.000Z | DailyCodingProblem/84_Amazon_Find_Islands_From_Matrix.py | RafayAK/CodingPrep | 718eccb439db0f6e727806964766a40e8234c8a9 | [
"MIT"
] | null | null | null | DailyCodingProblem/84_Amazon_Find_Islands_From_Matrix.py | RafayAK/CodingPrep | 718eccb439db0f6e727806964766a40e8234c8a9 | [
"MIT"
] | 2 | 2019-09-07T17:31:24.000Z | 2019-10-28T16:10:52.000Z | """
This problem was asked by Amazon.
Given a matrix of 1s and 0s, return the number of "islands" in the matrix.
A 1 represents land and 0 represents water, so an island is a group of 1s
that are neighboring whose perimeter is surrounded by water.
For example, this matrix has 4 islands.
1 0 0 0 0
0 0 1 1 0
0 1 1 0 0... | 21.107527 | 74 | 0.496689 |
7904ab641d3683007eb6f39dfe08fafe512112a5 | 4,181 | py | Python | scripts/asmt_merge_vacc_exetera.py | deng113jie/ExeTeraCovid | ee9ec90983d7c2c711962c7fe9ac25251392e41b | [
"Apache-2.0"
] | 3 | 2021-03-23T14:23:06.000Z | 2021-12-29T16:54:42.000Z | scripts/asmt_merge_vacc_exetera.py | deng113jie/ExeTeraCovid | ee9ec90983d7c2c711962c7fe9ac25251392e41b | [
"Apache-2.0"
] | 29 | 2021-02-22T12:12:53.000Z | 2021-09-27T10:52:25.000Z | scripts/asmt_merge_vacc_exetera.py | deng113jie/ExeTeraCovid | ee9ec90983d7c2c711962c7fe9ac25251392e41b | [
"Apache-2.0"
] | 1 | 2021-03-08T15:00:30.000Z | 2021-03-08T15:00:30.000Z | from datetime import datetime
import numpy as np
import exetera.core.session as sess
from exetera.core import dataframe
ADATA = '/home/jd21/data/processed_May17_processed.hdf5'
VDATA = '/home/jd21/data/vacc.0603.h5'
DSTDATA = '/home/jd21/data/full_merge.h5'
def asmt_merge_vacc():
"""
Merge assessment df wi... | 45.945055 | 153 | 0.643387 |
7904b168a4116b84fb89d03c6509bd216b10c0ed | 5,765 | py | Python | py/util/config.py | PurdueMINDS/MCLV-RBM | 46b1f90b52447687983113f37a5ce2c66b8f0465 | [
"Apache-2.0"
] | 4 | 2018-07-21T14:36:09.000Z | 2021-01-27T15:40:04.000Z | py/util/config.py | PurdueMINDS/MCLV-RBM | 46b1f90b52447687983113f37a5ce2c66b8f0465 | [
"Apache-2.0"
] | null | null | null | py/util/config.py | PurdueMINDS/MCLV-RBM | 46b1f90b52447687983113f37a5ce2c66b8f0465 | [
"Apache-2.0"
] | 1 | 2018-07-21T14:36:10.000Z | 2018-07-21T14:36:10.000Z | # Copyright 2017 Bruno Ribeiro, Mayank Kakodkar, Pedro Savarese
#
# 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 applicabl... | 44.346154 | 104 | 0.580225 |
7904b9ec43363d65c6c7499691923c70ca846c82 | 781 | py | Python | setup.oci.py | busunkim96/cc-utils | aa864b1fad3061410907d6b93b8aee8cd25f33b5 | [
"Apache-2.0"
] | 15 | 2018-04-18T13:25:30.000Z | 2022-03-04T09:25:41.000Z | setup.oci.py | busunkim96/cc-utils | aa864b1fad3061410907d6b93b8aee8cd25f33b5 | [
"Apache-2.0"
] | 221 | 2018-04-12T06:29:43.000Z | 2022-03-27T03:01:40.000Z | setup.oci.py | busunkim96/cc-utils | aa864b1fad3061410907d6b93b8aee8cd25f33b5 | [
"Apache-2.0"
] | 29 | 2018-04-11T14:42:23.000Z | 2021-11-09T16:26:32.000Z | import setuptools
import os
own_dir = os.path.abspath(os.path.dirname(__file__))
def requirements():
with open(os.path.join(own_dir, 'requirements.oci.txt')) as f:
for line in f.readlines():
line = line.strip()
if not line or line.startswith('#'):
continue
... | 19.04878 | 66 | 0.583867 |
7904cd5db58cc10f04e8b8ed06a0c5b09d965fe6 | 544 | py | Python | setup.py | akumor/python-rastervectoranalysis | 33370f8d104d3b69ce4c689783818512e7f864f2 | [
"Apache-2.0"
] | null | null | null | setup.py | akumor/python-rastervectoranalysis | 33370f8d104d3b69ce4c689783818512e7f864f2 | [
"Apache-2.0"
] | null | null | null | setup.py | akumor/python-rastervectoranalysis | 33370f8d104d3b69ce4c689783818512e7f864f2 | [
"Apache-2.0"
] | null | null | null | try:
from setuptools import setup
except ImportError:
from distutils.core import setup
config = {
'description': 'Raster Vector Analysis',
'author': 'Jan Kumor',
'url': 'http://github.com/akumor/python-rastervectoranalysis',
'download_url': 'http://github.com/akumor/python-rastervectoranalysis'... | 27.2 | 75 | 0.667279 |
7904e32998681c19d1931c5a6a712a8fc6b22f7e | 26,232 | py | Python | schmidt_funcs.py | johnarban/arban | dcd2d0838f72c39bf3a52aabfa74d6ea28933d02 | [
"MIT"
] | null | null | null | schmidt_funcs.py | johnarban/arban | dcd2d0838f72c39bf3a52aabfa74d6ea28933d02 | [
"MIT"
] | null | null | null | schmidt_funcs.py | johnarban/arban | dcd2d0838f72c39bf3a52aabfa74d6ea28933d02 | [
"MIT"
] | null | null | null | import numpy as np
from PIL import Image, ImageDraw
from scipy import interpolate, ndimage, stats, signal, integrate, misc
from astropy.io import ascii, fits
from astropy.wcs import WCS
from astropy.coordinates import SkyCoord
import astropy.units as u
import astropy.constants as c
import corner as triangle # formerly... | 30.970484 | 116 | 0.564692 |
7904f2d70ef6781d9d24239fa046eef91f73a28c | 2,720 | py | Python | 6/server.py | mesilliac/multitude | eccab96f496217971d19d2a4592fe48ee837fb3e | [
"CC0-1.0"
] | 2 | 2017-08-22T19:11:58.000Z | 2017-10-10T22:14:33.000Z | 6/server.py | mesilliac/multitude | eccab96f496217971d19d2a4592fe48ee837fb3e | [
"CC0-1.0"
] | null | null | null | 6/server.py | mesilliac/multitude | eccab96f496217971d19d2a4592fe48ee837fb3e | [
"CC0-1.0"
] | null | null | null | #!/usr/bin/python
# coding: utf-8
"""A simple webserver."""
# python 2.7 compatibility
from __future__ import print_function, unicode_literals
# based on tornado
import tornado.ioloop
import tornado.web
import tornado.websocket
import sys
import json
def make_app():
"""Create and return the main Tornado web app... | 31.627907 | 74 | 0.606985 |
790519040488b2751b8ca57d8241ea867d38f82f | 6,597 | py | Python | modules/youtube_music.py | mavroudo/jarvis-discord | 918540a67d7ac48584e8efd6a06385ec5228f4d5 | [
"MIT"
] | null | null | null | modules/youtube_music.py | mavroudo/jarvis-discord | 918540a67d7ac48584e8efd6a06385ec5228f4d5 | [
"MIT"
] | null | null | null | modules/youtube_music.py | mavroudo/jarvis-discord | 918540a67d7ac48584e8efd6a06385ec5228f4d5 | [
"MIT"
] | null | null | null | import os
import discord
import youtube_dl as ytdl
class MusicPlayer:
'''
This module is responsible for connecting and disconnecting the bot from a voice channel, downloading songs from
youtube and add them in the queue . Basic music functions like pause, resume, stop and play, in order to give
... | 43.117647 | 120 | 0.621949 |
7905d0bcd302b5858170c94d003ee28831928a26 | 2,464 | py | Python | master/searx-master/searx/engines/duckduckgo_images.py | AlexRogalskiy/DevArtifacts | 931aabb8cbf27656151c54856eb2ea7d1153203a | [
"MIT"
] | 4 | 2018-09-07T15:35:24.000Z | 2019-03-27T09:48:12.000Z | master/searx-master/searx/engines/duckduckgo_images.py | AlexRogalskiy/DevArtifacts | 931aabb8cbf27656151c54856eb2ea7d1153203a | [
"MIT"
] | 371 | 2020-03-04T21:51:56.000Z | 2022-03-31T20:59:11.000Z | master/searx-master/searx/engines/duckduckgo_images.py | AlexRogalskiy/DevArtifacts | 931aabb8cbf27656151c54856eb2ea7d1153203a | [
"MIT"
] | 3 | 2019-06-18T19:57:17.000Z | 2020-11-06T03:55:08.000Z | """
DuckDuckGo (Images)
@website https://duckduckgo.com/
@provide-api yes (https://duckduckgo.com/api),
but images are not supported
@using-api no
@results JSON (site requires js to get images)
@stable no (JSON can change)
@parse url, title, img_src
@todo avoid extra... | 27.076923 | 103 | 0.631088 |
7905e60159629ca5c376e64caf6d3c85fa260c4a | 1,016 | py | Python | src/schemathesis/cli/output/short.py | chr1st1ank/schemathesis | f2e160d56c1fdce9eac7fee5875b209c8944f54a | [
"MIT"
] | 1 | 2021-06-22T20:01:24.000Z | 2021-06-22T20:01:24.000Z | src/schemathesis/cli/output/short.py | RonnyPfannschmidt/schemathesis | 3542d91d2e7402235e7b2dc995ed7017a0265ff6 | [
"MIT"
] | null | null | null | src/schemathesis/cli/output/short.py | RonnyPfannschmidt/schemathesis | 3542d91d2e7402235e7b2dc995ed7017a0265ff6 | [
"MIT"
] | null | null | null | import click
from ...runner import events
from . import default
def handle_after_execution(context: events.ExecutionContext, event: events.AfterExecution) -> None:
context.endpoints_processed += 1
default.display_execution_result(context, event)
if context.endpoints_processed == event.schema.endpoints_co... | 36.285714 | 99 | 0.748031 |
79060b9bb3f6d7260b99d143dbf8615f9dd467fe | 36,469 | py | Python | pyiron_atomistics/vasp/outcar.py | pyiron/pyiron_atomistic | 0cd4c910806f44dfc829ddd642e323efcf7e36d5 | [
"BSD-3-Clause"
] | 14 | 2021-01-18T10:03:56.000Z | 2022-03-01T20:59:35.000Z | pyiron_atomistics/vasp/outcar.py | pyiron/pyiron_atomistics | 0cd4c910806f44dfc829ddd642e323efcf7e36d5 | [
"BSD-3-Clause"
] | 569 | 2018-04-12T06:37:14.000Z | 2022-03-31T18:06:27.000Z | pyiron_atomistics/vasp/outcar.py | pyiron/pyiron_atomistic | 0cd4c910806f44dfc829ddd642e323efcf7e36d5 | [
"BSD-3-Clause"
] | 6 | 2018-10-23T09:48:48.000Z | 2022-02-13T12:13:00.000Z | # coding: utf-8
# Copyright (c) Max-Planck-Institut für Eisenforschung GmbH - Computational Materials Design (CM) Department
# Distributed under the terms of "New BSD License", see the LICENSE file.
from collections import OrderedDict
import numpy as np
import warnings
import scipy.constants
import re
__author__ = "S... | 37.365779 | 129 | 0.56681 |
79062be30a913ab25a06164b9800864bb79d5e79 | 333 | py | Python | tests/test-failinfo_refcount.py | lwllvyb/libfiu-hack | a41612d78fbce5e2a33745837c2ec735cc22fd6e | [
"MIT"
] | null | null | null | tests/test-failinfo_refcount.py | lwllvyb/libfiu-hack | a41612d78fbce5e2a33745837c2ec735cc22fd6e | [
"MIT"
] | null | null | null | tests/test-failinfo_refcount.py | lwllvyb/libfiu-hack | a41612d78fbce5e2a33745837c2ec735cc22fd6e | [
"MIT"
] | null | null | null |
"""
Test that we keep references to failinfo as needed.
"""
import fiu
# Object we'll use for failinfo
finfo = [1, 2, 3]
fiu.enable('p1', failinfo = finfo)
assert fiu.fail('p1')
assert fiu.failinfo('p1') is finfo
finfo_id = id(finfo)
del finfo
assert fiu.failinfo('p1') == [1, 2, 3]
assert id(fiu.failinfo('p1')) ... | 15.136364 | 51 | 0.666667 |
790632599ec0c5e0db7b70a082f0a63ae4a7dec4 | 26,693 | py | Python | lib/python2.7/matplotlib/projections/polar.py | ashley8jain/IITD-complaint-system-web | 21a94601cba710f558d1689b87cfc391a1541c9f | [
"BSD-3-Clause"
] | 1 | 2017-01-25T00:38:48.000Z | 2017-01-25T00:38:48.000Z | lib/python2.7/matplotlib/projections/polar.py | ashley8jain/IITD-complaint-system-web | 21a94601cba710f558d1689b87cfc391a1541c9f | [
"BSD-3-Clause"
] | null | null | null | lib/python2.7/matplotlib/projections/polar.py | ashley8jain/IITD-complaint-system-web | 21a94601cba710f558d1689b87cfc391a1541c9f | [
"BSD-3-Clause"
] | null | null | null | import math
import warnings
import numpy as np
import matplotlib
rcParams = matplotlib.rcParams
from matplotlib.axes import Axes
import matplotlib.axis as maxis
from matplotlib import cbook
from matplotlib import docstring
from matplotlib.patches import Circle
from matplotlib.path import Path
from matplotlib.ticker i... | 35.076216 | 106 | 0.559173 |
790656792cf1a06755ecb98d07fc56893c0250cc | 3,151 | py | Python | tests/test_docs/test_standalone_transaction/test_standalone_transaction.py | valory-xyz/agents-aea | 8f38efa96041b0156ed1ae328178e395dbabf2fc | [
"Apache-2.0"
] | 28 | 2021-10-31T18:54:14.000Z | 2022-03-17T13:10:43.000Z | tests/test_docs/test_standalone_transaction/test_standalone_transaction.py | valory-xyz/agents-aea | 8f38efa96041b0156ed1ae328178e395dbabf2fc | [
"Apache-2.0"
] | 66 | 2021-10-31T11:55:48.000Z | 2022-03-31T06:26:23.000Z | tests/test_docs/test_standalone_transaction/test_standalone_transaction.py | valory-xyz/agents-aea | 8f38efa96041b0156ed1ae328178e395dbabf2fc | [
"Apache-2.0"
] | null | null | null | # -*- coding: utf-8 -*-
# ------------------------------------------------------------------------------
#
# Copyright 2022 Valory AG
# Copyright 2018-2021 Fetch.AI Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# ... | 35.404494 | 95 | 0.671215 |
79070d17a4163f46519228f051f77c1390ac6edb | 1,285 | py | Python | tf2onnx/tflite/LessOptions.py | LoicDagnas/tensorflow-onnx | 6691850e79047d05d85017573170fd8240393b57 | [
"Apache-2.0"
] | 1,473 | 2018-03-16T02:47:33.000Z | 2022-03-31T03:43:52.000Z | tf2onnx/tflite/LessOptions.py | LoicDagnas/tensorflow-onnx | 6691850e79047d05d85017573170fd8240393b57 | [
"Apache-2.0"
] | 1,208 | 2018-03-14T09:58:49.000Z | 2022-03-31T17:56:20.000Z | tf2onnx/tflite/LessOptions.py | LoicDagnas/tensorflow-onnx | 6691850e79047d05d85017573170fd8240393b57 | [
"Apache-2.0"
] | 350 | 2018-04-03T03:48:40.000Z | 2022-03-30T11:23:55.000Z | # SPDX-License-Identifier: Apache-2.0
# automatically generated by the FlatBuffers compiler, do not modify
# namespace: tflite
import flatbuffers
from flatbuffers.compat import import_numpy
np = import_numpy()
class LessOptions(object):
__slots__ = ['_tab']
@classmethod
def GetRootAs(cls, buf, offset=0... | 32.125 | 114 | 0.705837 |
790710a4696737e320d90c8c3b766f346cca7bef | 2,133 | py | Python | bot.py | sagol/umorilibot | 89e4bdc9771c21326768171099ee9872dc40b194 | [
"MIT"
] | 1 | 2021-02-19T11:13:24.000Z | 2021-02-19T11:13:24.000Z | bot.py | sagol/umorilibot | 89e4bdc9771c21326768171099ee9872dc40b194 | [
"MIT"
] | null | null | null | bot.py | sagol/umorilibot | 89e4bdc9771c21326768171099ee9872dc40b194 | [
"MIT"
] | null | null | null | from sources import Sources
from stories import Stories
class Bot():
def __init__(self, config):
self.url = config.get_url()
self.sources = None
self.stories = None
def load(self):
self.sources = Sources(self.url)
self.stories = Stories(self.sources)
... | 34.403226 | 95 | 0.547586 |
79072799f7f744d11592756ce43654976d9a7ea8 | 1,619 | py | Python | tests/test_nexus.py | ghuls/weblogo | 7eab5d1b8a8ec38786fa426af84bd77950835524 | [
"MIT"
] | 108 | 2015-08-21T10:39:22.000Z | 2022-03-04T22:10:49.000Z | tests/test_nexus.py | ghuls/weblogo | 7eab5d1b8a8ec38786fa426af84bd77950835524 | [
"MIT"
] | 60 | 2015-07-21T22:55:52.000Z | 2022-03-24T21:20:00.000Z | tests/test_nexus.py | ghuls/weblogo | 7eab5d1b8a8ec38786fa426af84bd77950835524 | [
"MIT"
] | 40 | 2015-08-04T00:18:23.000Z | 2021-12-30T13:41:54.000Z | #!/usr/bin/env python
import unittest
from weblogo.seq_io._nexus import Nexus
from . import data_stream
class test_nexus(unittest.TestCase):
def test_create(self):
n = Nexus()
self.assertNotEqual(n, None)
def test_parse_f0(self):
f = data_stream("nexus/test_Nexus_input.nex")
... | 23.463768 | 62 | 0.536751 |
7907502c72435035b8773b2e9d3de98a759ac87a | 17,141 | py | Python | evolution/diversity.py | narendasan/neural-mmo | 36a588db0021cccd7275cebef2cbdc5ee8eb40d5 | [
"MIT"
] | null | null | null | evolution/diversity.py | narendasan/neural-mmo | 36a588db0021cccd7275cebef2cbdc5ee8eb40d5 | [
"MIT"
] | null | null | null | evolution/diversity.py | narendasan/neural-mmo | 36a588db0021cccd7275cebef2cbdc5ee8eb40d5 | [
"MIT"
] | null | null | null | from pdb import set_trace as TT
import numpy as np
import scipy
from scipy.spatial import ConvexHull
import skimage
from skimage.morphology import disk
import skbio
global trg_image
trg_image = None
def diversity_calc(config):
div_calc_name = config.FITNESS_METRIC
return get_div_calc(div_calc_name)
def get_div... | 37.589912 | 225 | 0.687066 |
790765f7d394af7b8ea9033521908c3ce8929ca0 | 2,852 | py | Python | defoe/alto/queries/keyword_concordance_by_word.py | kallewesterling/defoe | d72af2f748fd4363a4718c93bb0b0284b8cb1f3e | [
"MIT"
] | 2 | 2022-02-14T12:10:54.000Z | 2022-02-14T12:35:44.000Z | defoe/alto/queries/keyword_concordance_by_word.py | kallewesterling/defoe | d72af2f748fd4363a4718c93bb0b0284b8cb1f3e | [
"MIT"
] | 17 | 2022-02-09T21:46:14.000Z | 2022-02-25T14:55:09.000Z | defoe/alto/queries/keyword_concordance_by_word.py | kallewesterling/defoe | d72af2f748fd4363a4718c93bb0b0284b8cb1f3e | [
"MIT"
] | 1 | 2022-02-14T13:19:08.000Z | 2022-02-14T13:19:08.000Z | """
Gets concordance for keywords and groups by word.
"""
from defoe import query_utils
from defoe.alto.query_utils import get_page_matches
def do_query(archives, config_file=None, logger=None, context=None):
"""
Gets concordance for keywords and groups by word.
config_file must be the path to a configu... | 31 | 75 | 0.547686 |
79077298425f95eefa6233036c8a6843840ee10f | 2,515 | py | Python | plot_conditionals_with_tis.py | samiemostafavi/conditional-latency-probability-prediction | a196f2db8c6f30f8613797b6a23bffd77a01e1e3 | [
"MIT"
] | null | null | null | plot_conditionals_with_tis.py | samiemostafavi/conditional-latency-probability-prediction | a196f2db8c6f30f8613797b6a23bffd77a01e1e3 | [
"MIT"
] | null | null | null | plot_conditionals_with_tis.py | samiemostafavi/conditional-latency-probability-prediction | a196f2db8c6f30f8613797b6a23bffd77a01e1e3 | [
"MIT"
] | null | null | null | import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pyarrow.compute as pc
import matplotlib.pyplot as plt
import seaborn as sns
from pr3d.nonbayesian import ConditionalGammaEVM
# load dataset first
file_addresses = ['dataset_onehop_processed.parquet']
table = pa.concat_tables(
pq.read_tabl... | 31.049383 | 99 | 0.662425 |
79077e9bac0a3bae6b1a07981b053ed053545a65 | 7,146 | py | Python | test/test_spatial_interpolation.py | rgaensler/gcode | c6a6b617a04490dedefb2bae7b596a2e12ab4ab1 | [
"MIT"
] | null | null | null | test/test_spatial_interpolation.py | rgaensler/gcode | c6a6b617a04490dedefb2bae7b596a2e12ab4ab1 | [
"MIT"
] | 314 | 2020-02-26T12:37:17.000Z | 2021-08-02T00:32:32.000Z | test/test_spatial_interpolation.py | rgaensler/gcode | c6a6b617a04490dedefb2bae7b596a2e12ab4ab1 | [
"MIT"
] | 2 | 2020-11-12T16:07:48.000Z | 2020-11-16T09:14:48.000Z | from math import pi, sqrt
from typing import List
import numpy as np
import pytest
from src.kinematics.forward_kinematics import get_tform
from src.prechecks.spatial_interpolation import linear_interpolation, circular_interpolation
@pytest.mark.parametrize("start,end,ds,expected_points",
[
... | 47.64 | 110 | 0.34047 |
79079afb5049c4952a78491f534997124403c2b1 | 999 | py | Python | sdk/communication/azure-communication-networktraversal/azure/communication/networktraversal/_generated/models/_communication_network_traversal_client_enums.py | vincenttran-msft/azure-sdk-for-python | 348b56f9f03eeb3f7b502eed51daf494ffff874d | [
"MIT"
] | 1 | 2022-03-09T08:59:13.000Z | 2022-03-09T08:59:13.000Z | sdk/communication/azure-communication-networktraversal/azure/communication/networktraversal/_generated/models/_communication_network_traversal_client_enums.py | vincenttran-msft/azure-sdk-for-python | 348b56f9f03eeb3f7b502eed51daf494ffff874d | [
"MIT"
] | null | null | null | sdk/communication/azure-communication-networktraversal/azure/communication/networktraversal/_generated/models/_communication_network_traversal_client_enums.py | vincenttran-msft/azure-sdk-for-python | 348b56f9f03eeb3f7b502eed51daf494ffff874d | [
"MIT"
] | 1 | 2022-03-04T06:21:56.000Z | 2022-03-04T06:21:56.000Z | # coding=utf-8
# --------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for license information.
# Code generated by Microsoft (R) AutoRest Code Generator.
# Changes may ... | 43.434783 | 99 | 0.648649 |
7907b99c86f63ebda9f0e3eb4ef5f5e50c0aacaa | 204 | py | Python | maingui/urls.py | edgarceron/agent_console | a75501957722a349c7276e4d199425897f351bc0 | [
"BSD-3-Clause"
] | null | null | null | maingui/urls.py | edgarceron/agent_console | a75501957722a349c7276e4d199425897f351bc0 | [
"BSD-3-Clause"
] | 3 | 2021-03-30T13:46:24.000Z | 2021-09-22T19:18:18.000Z | maingui/urls.py | edgarceron/agent_console | a75501957722a349c7276e4d199425897f351bc0 | [
"BSD-3-Clause"
] | null | null | null | """ Contains the urls for the maingui module"""
from django.urls import path
from . import views
urlpatterns = [
path('', views.index, name='index'),
path('login', views.login, name='login'),
]
| 20.4 | 47 | 0.661765 |
7907d5d8da7a9d75d151f7561ae03dee7c281322 | 10,916 | py | Python | algorithm/python/topological_sort.py | yennanliu/Python_basics | 6a597442d39468295946cefbfb11d08f61424dc3 | [
"Unlicense"
] | null | null | null | algorithm/python/topological_sort.py | yennanliu/Python_basics | 6a597442d39468295946cefbfb11d08f61424dc3 | [
"Unlicense"
] | null | null | null | algorithm/python/topological_sort.py | yennanliu/Python_basics | 6a597442d39468295946cefbfb11d08f61424dc3 | [
"Unlicense"
] | null | null | null | #---------------------------------------------------------------
# ALGORITHM DEMO : TOPLOGICAL SORT
#---------------------------------------------------------------
# Topological Sort is a algorithm can find "ordering" on an "order dependency" graph
# Concept
# https://blog.techbridge.cc/2020/05/10/leetcode-topologica... | 30.322222 | 146 | 0.563576 |
7907e2ef9017c5ca381a123f82ee9a08309d578c | 3,199 | py | Python | server/app/scrapers/maarten.py | damienallen/makelaardij-notify | ea8e37e1b0f867487b90590c5273e7fb25d868cf | [
"MIT"
] | null | null | null | server/app/scrapers/maarten.py | damienallen/makelaardij-notify | ea8e37e1b0f867487b90590c5273e7fb25d868cf | [
"MIT"
] | 15 | 2021-02-13T23:46:28.000Z | 2021-02-25T15:36:08.000Z | server/app/scrapers/maarten.py | damienallen/makelaardij-notify | ea8e37e1b0f867487b90590c5273e7fb25d868cf | [
"MIT"
] | null | null | null | import asyncio
from typing import List
from app.common import SkipListing
from app.scrapers.base import BaseScraper
class MaartenScraper(BaseScraper):
MAKELAARDIJ: str = "maarten"
BASE_URL: str = "https://www.maartenmakelaardij.nl"
# Specific functions
async def extract_object_urls(self, soup) -> L... | 29.897196 | 87 | 0.546733 |
7908015094df0f7d24b375510cc3e85e33122519 | 11,743 | py | Python | PNet/train_pnet.py | mangye16/ReID-Label-Noise | 89aa11f68c275a0bcff232d9a5c3ae152c9276af | [
"MIT"
] | 11 | 2020-04-03T09:01:36.000Z | 2022-03-11T08:12:16.000Z | PNet/train_pnet.py | mangye16/ReID-Label-Noise | 89aa11f68c275a0bcff232d9a5c3ae152c9276af | [
"MIT"
] | null | null | null | PNet/train_pnet.py | mangye16/ReID-Label-Noise | 89aa11f68c275a0bcff232d9a5c3ae152c9276af | [
"MIT"
] | 3 | 2020-12-18T11:53:05.000Z | 2022-01-12T16:35:45.000Z | # -*- coding: UTF-8 -*-
from __future__ import print_function, division
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.optim import lr_scheduler
from torch.autograd import Variable
from torchvision import datasets, models, transforms
from tensorboardX import ... | 38.755776 | 154 | 0.664311 |
790804f388e72af2f2b67453edb3a003c8e8aa74 | 576 | py | Python | test/rules/test_fires_child.py | rileyhazard/SmartVA-Analyze-1 | 0573eeff27d03f54e7506db4f1631c0cd9f54bbb | [
"MIT"
] | 4 | 2019-01-23T12:57:47.000Z | 2020-04-18T17:13:08.000Z | test/rules/test_fires_child.py | rileyhazard/SmartVA-Analyze-1 | 0573eeff27d03f54e7506db4f1631c0cd9f54bbb | [
"MIT"
] | 4 | 2019-01-09T22:10:07.000Z | 2022-02-16T04:57:06.000Z | test/rules/test_fires_child.py | rileyhazard/SmartVA-Analyze-1 | 0573eeff27d03f54e7506db4f1631c0cd9f54bbb | [
"MIT"
] | 11 | 2018-12-11T22:01:13.000Z | 2022-01-07T11:38:02.000Z | from smartva.rules import fires_child as fires
from smartva.data.constants import *
VA = Child
def test_pass():
row = {
VA.BURN: YES,
VA.INJURY_DAYS: 0,
}
assert fires.logic_rule(row) is True
def test_fail_fires():
row = {
VA.BURN: NO,
VA.INJURY_DAYS: 0,
}
... | 15.157895 | 46 | 0.604167 |
79083036c4c19017232d49b3487ba0475de179c0 | 625 | py | Python | indra/tests/test_tas.py | djinnome/indra | 382b7f236e0b1422c96a268ef873530b5e92d48f | [
"BSD-2-Clause"
] | null | null | null | indra/tests/test_tas.py | djinnome/indra | 382b7f236e0b1422c96a268ef873530b5e92d48f | [
"BSD-2-Clause"
] | null | null | null | indra/tests/test_tas.py | djinnome/indra | 382b7f236e0b1422c96a268ef873530b5e92d48f | [
"BSD-2-Clause"
] | null | null | null | from __future__ import absolute_import, print_function, unicode_literals
from builtins import dict, str
from indra.sources.tas.api import _load_data, process_csv
def test_load_data():
data = _load_data()
assert len(data) > 100, len(data)
def test_processor():
tp = process_csv(affinity_class_limit=10)
... | 29.761905 | 72 | 0.7296 |
7908573ec9d313bf3168d98f4cf7ead29d9e6104 | 1,590 | py | Python | tests/test_job_slurm.py | boazbk/mle-scheduler | 4cf83873d9beb75b19b2deb9baf4394931b9624d | [
"MIT"
] | 26 | 2021-11-12T15:06:54.000Z | 2022-03-29T20:42:17.000Z | tests/test_job_slurm.py | boazbk/mle-scheduler | 4cf83873d9beb75b19b2deb9baf4394931b9624d | [
"MIT"
] | 4 | 2021-12-12T20:37:40.000Z | 2022-03-01T10:18:14.000Z | tests/test_job_slurm.py | boazbk/mle-scheduler | 4cf83873d9beb75b19b2deb9baf4394931b9624d | [
"MIT"
] | 1 | 2021-12-13T17:24:02.000Z | 2021-12-13T17:24:02.000Z | from mle_scheduler.cluster.slurm.helpers_launch_slurm import slurm_generate_startup_file
job_arguments = {
"num_logical_cores": 5,
"partition": "standard",
"job_name": "test_job",
"num_gpus": 1,
"gpu_type": "RTX2080",
"env_name": "test_env",
"use_conda_venv": True,
"script": "python run... | 32.44898 | 88 | 0.510063 |
7908689ce4a719ab15bd49a368a87f9cad7c6d61 | 11,740 | py | Python | tensorflow/contrib/lite/python/op_hint.py | tianyapiaozi/tensorflow | fb3ce0467766a8e91f1da0ad7ada7c24fde7a73a | [
"Apache-2.0"
] | 71 | 2017-05-25T16:02:15.000Z | 2021-06-09T16:08:08.000Z | tensorflow/contrib/lite/python/op_hint.py | shrikunjsarda/tensorflow | 7e8927e7af0c51ac20a63bd4eab6ff83df1a39ae | [
"Apache-2.0"
] | 133 | 2017-04-26T16:49:49.000Z | 2019-10-15T11:39:26.000Z | tensorflow/contrib/lite/python/op_hint.py | shrikunjsarda/tensorflow | 7e8927e7af0c51ac20a63bd4eab6ff83df1a39ae | [
"Apache-2.0"
] | 31 | 2018-09-11T02:17:17.000Z | 2021-12-15T10:33:35.000Z | # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | 37.993528 | 80 | 0.725639 |
79088521341c908e9acfbace2f7914850759e997 | 3,642 | py | Python | code_examples/tensorflow/kernel_benchmarks/dense.py | Splendon/examples | ed4a8a01857b6ddca49559141acf5d0986eb01e1 | [
"MIT"
] | null | null | null | code_examples/tensorflow/kernel_benchmarks/dense.py | Splendon/examples | ed4a8a01857b6ddca49559141acf5d0986eb01e1 | [
"MIT"
] | null | null | null | code_examples/tensorflow/kernel_benchmarks/dense.py | Splendon/examples | ed4a8a01857b6ddca49559141acf5d0986eb01e1 | [
"MIT"
] | null | null | null | #!/usr/bin/env python
"""
Benchmark a single Dense layer with no host/device data transfers.
The Items/sec reported at the end of the benchmark is based on wall time.
Run with -h or --help for options.
"""
import inspect
import os
import sys
import tensorflow as tf
from tensorflow.python.ipu import utils
def dense(... | 33.109091 | 87 | 0.635365 |
7908919fd9c2722e099a3815953cf94ccddb5d9a | 439 | py | Python | array/twosum.py | mengyangbai/leetcode | e7a6906ecc5bce665dec5d0f057b302a64d50f40 | [
"MIT"
] | null | null | null | array/twosum.py | mengyangbai/leetcode | e7a6906ecc5bce665dec5d0f057b302a64d50f40 | [
"MIT"
] | null | null | null | array/twosum.py | mengyangbai/leetcode | e7a6906ecc5bce665dec5d0f057b302a64d50f40 | [
"MIT"
] | null | null | null | class Solution:
def twoSum(self, nums, target):
"""
:type nums: List[int]
:type target: int
:rtype: List[int]
"""
lookup = dict(((v, i) for i, v in enumerate(nums)))
return next(( (i+1, lookup.get(target-v)+1)
for i, v in enumerate(nums)
... | 29.266667 | 59 | 0.498861 |
7908a4da11350dcc729f2f370f826d6e172bbe48 | 780 | py | Python | RandomWords.py | Makemeproud/BitcoinGenerator | 10e2864a2254635153c757beece028c85a31e1ca | [
"Apache-2.0"
] | null | null | null | RandomWords.py | Makemeproud/BitcoinGenerator | 10e2864a2254635153c757beece028c85a31e1ca | [
"Apache-2.0"
] | null | null | null | RandomWords.py | Makemeproud/BitcoinGenerator | 10e2864a2254635153c757beece028c85a31e1ca | [
"Apache-2.0"
] | 1 | 2022-02-27T14:57:19.000Z | 2022-02-27T14:57:19.000Z | #!/usr/bin/env python
'''
Pull random words from http://world.std.com/~reinhold/diceware.wordlist.asc
Written 2013 Hal Canary.
Dedicated to the public domain.
'''
import random,math,sys,os
useDevRandom = True
dicewareWordlist = '~/Downloads/diceware.wordlist.asc'
with open(os.path.expanduser(dicewareWordlist)) as f:
... | 28.888889 | 75 | 0.723077 |
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