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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
60648a56773ceecb201aec8a10a45d6b2f493b08 | 2,755 | py | Python | Python/face_detect_camera/managers.py | abondar24/OpenCVBase | 9b23e3b31304e77ad1135d90efb41e3dc069194a | [
"Apache-2.0"
] | null | null | null | Python/face_detect_camera/managers.py | abondar24/OpenCVBase | 9b23e3b31304e77ad1135d90efb41e3dc069194a | [
"Apache-2.0"
] | null | null | null | Python/face_detect_camera/managers.py | abondar24/OpenCVBase | 9b23e3b31304e77ad1135d90efb41e3dc069194a | [
"Apache-2.0"
] | null | null | null | import cv2
import numpy as np
import time
class WindowManager(object):
| 28.697917 | 93 | 0.642468 |
6064dc0f50d6a2d8e20ae50d87b6b6f9606110f6 | 6,937 | py | Python | ELLA/ELLA.py | micaelverissimo/lifelong_ringer | d2e7173ce08d1c087e811f6451cae1cb0e381076 | [
"MIT"
] | null | null | null | ELLA/ELLA.py | micaelverissimo/lifelong_ringer | d2e7173ce08d1c087e811f6451cae1cb0e381076 | [
"MIT"
] | null | null | null | ELLA/ELLA.py | micaelverissimo/lifelong_ringer | d2e7173ce08d1c087e811f6451cae1cb0e381076 | [
"MIT"
] | null | null | null | """ Alpha version of a version of ELLA that plays nicely with sklearn
@author: Paul Ruvolo
"""
from math import log
import numpy as np
from scipy.special import logsumexp
from scipy.linalg import sqrtm, inv, norm
from sklearn.linear_model import LinearRegression, Ridge, LogisticRegression, Lasso
import matplotlib.pyplot as plt
from sklearn.metrics import accuracy_score, explained_variance_score
| 49.198582 | 119 | 0.618135 |
60652bf0a5fb58eb37f612eac700378eff72f02f | 1,970 | py | Python | webhook/utils.py | Myst1c-a/phen-cogs | 672f9022ddbbd9a84b0a05357347e99e64a776fc | [
"MIT"
] | null | null | null | webhook/utils.py | Myst1c-a/phen-cogs | 672f9022ddbbd9a84b0a05357347e99e64a776fc | [
"MIT"
] | null | null | null | webhook/utils.py | Myst1c-a/phen-cogs | 672f9022ddbbd9a84b0a05357347e99e64a776fc | [
"MIT"
] | null | null | null | """
MIT License
Copyright (c) 2020-present phenom4n4n
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
"""
import re
import discord
from redbot.core.commands import Context
USER_MENTIONS = discord.AllowedMentions.none()
USER_MENTIONS.users = True
WEBHOOK_RE = re.compile(
r"discord(?:app)?.com/api/webhooks/(?P<id>[0-9]{17,21})/(?P<token>[A-Za-z0-9\.\-\_]{60,68})"
)
| 35.818182 | 96 | 0.740102 |
606613f1ae0ac5127e78b4dfa447fad203daeafe | 6,736 | py | Python | scripts/generate.py | jwise/pebble-caltrain | 770497cb38205827fee2e4e4cfdd79bcf60ceb65 | [
"MIT"
] | 1 | 2019-06-18T01:17:25.000Z | 2019-06-18T01:17:25.000Z | scripts/generate.py | jwise/pebble-caltrain | 770497cb38205827fee2e4e4cfdd79bcf60ceb65 | [
"MIT"
] | null | null | null | scripts/generate.py | jwise/pebble-caltrain | 770497cb38205827fee2e4e4cfdd79bcf60ceb65 | [
"MIT"
] | null | null | null | __author__ = 'katharine'
import csv
import struct
import time
import datetime
if __name__ == "__main__":
import sys
generate_files(sys.argv[1], sys.argv[2])
| 36.215054 | 138 | 0.532512 |
606629e6c71087f04da2a0bec8e5f2d2e0b13de3 | 3,218 | py | Python | tests/test_is_valid_php_version_file_version.py | gerardroche/sublime-phpunit | 73e96ec5e4ac573c5d5247cf87c38e8243da906b | [
"BSD-3-Clause"
] | 85 | 2015-02-18T00:05:54.000Z | 2022-01-01T12:20:22.000Z | tests/test_is_valid_php_version_file_version.py | gerardroche/sublime-phpunit | 73e96ec5e4ac573c5d5247cf87c38e8243da906b | [
"BSD-3-Clause"
] | 98 | 2015-01-07T22:23:48.000Z | 2021-06-03T19:37:50.000Z | tests/test_is_valid_php_version_file_version.py | gerardroche/sublime-phpunit | 73e96ec5e4ac573c5d5247cf87c38e8243da906b | [
"BSD-3-Clause"
] | 21 | 2015-08-12T01:02:17.000Z | 2021-09-12T09:16:39.000Z | from PHPUnitKit.tests import unittest
from PHPUnitKit.plugin import is_valid_php_version_file_version
| 52.754098 | 75 | 0.757613 |
6066634d419973bf2d50293d1e8b24d66fea6c84 | 3,554 | py | Python | feed/tests/test_consts.py | cul-it/arxiv-rss | 40c0e859528119cc8ba3700312cb8df095d95cdd | [
"MIT"
] | 4 | 2020-06-29T15:05:37.000Z | 2022-02-02T10:28:28.000Z | feed/tests/test_consts.py | arXiv/arxiv-feed | 82923d062e2524df94c22490cf936a988559ce66 | [
"MIT"
] | 12 | 2020-03-06T16:45:00.000Z | 2022-03-02T15:36:14.000Z | feed/tests/test_consts.py | cul-it/arxiv-rss | 40c0e859528119cc8ba3700312cb8df095d95cdd | [
"MIT"
] | 2 | 2020-12-06T16:30:06.000Z | 2021-11-05T12:29:08.000Z | import pytest
from feed.consts import FeedVersion
from feed.utils import randomize_case
from feed.errors import FeedVersionError
# FeedVersion.supported
# FeedVersion.get
| 27.765625 | 69 | 0.652786 |
6066c37cd5157dfddcd5a311c42cacbf5b1656ab | 203 | py | Python | cmsplugin_cascade/migrations/0007_add_proxy_models.py | teklager/djangocms-cascade | adc461f7054c6c0f88bc732aefd03b157df2f514 | [
"MIT"
] | 139 | 2015-01-08T22:27:06.000Z | 2021-08-19T03:36:58.000Z | cmsplugin_cascade/migrations/0007_add_proxy_models.py | teklager/djangocms-cascade | adc461f7054c6c0f88bc732aefd03b157df2f514 | [
"MIT"
] | 286 | 2015-01-02T14:15:14.000Z | 2022-03-22T11:00:12.000Z | cmsplugin_cascade/migrations/0007_add_proxy_models.py | teklager/djangocms-cascade | adc461f7054c6c0f88bc732aefd03b157df2f514 | [
"MIT"
] | 91 | 2015-01-16T15:06:23.000Z | 2022-03-23T23:36:54.000Z | from django.db import migrations, models
| 16.916667 | 66 | 0.679803 |
60685790ed05ececd1e05f44f06a9d6c6808d671 | 1,380 | py | Python | theonionbox/tob/credits.py | ralphwetzel/theonionbox | 9812fce48153955e179755ea7a58413c3bee182f | [
"MIT"
] | 120 | 2015-12-30T09:41:56.000Z | 2022-03-23T02:30:05.000Z | theonionbox/tob/credits.py | nwithan8/theonionbox | 9e51fe0b4d07fc89a8a133fdeceb5f97d5d58713 | [
"MIT"
] | 57 | 2015-12-29T21:55:14.000Z | 2022-01-07T09:48:51.000Z | theonionbox/tob/credits.py | nwithan8/theonionbox | 9e51fe0b4d07fc89a8a133fdeceb5f97d5d58713 | [
"MIT"
] | 17 | 2018-02-05T08:57:46.000Z | 2022-02-28T16:44:41.000Z | Credits = [
('Bootstrap', 'https://getbootstrap.com', 'The Bootstrap team', 'MIT'),
('Bottle', 'http://bottlepy.org', 'Marcel Hellkamp', 'MIT'),
('Cheroot', 'https://github.com/cherrypy/cheroot', 'CherryPy Team', 'BSD 3-Clause "New" or "Revised" License'),
('Click', 'https://github.com/pallets/click', 'Pallets', 'BSD 3-Clause "New" or "Revised" License'),
('ConfigUpdater', 'https://github.com/pyscaffold/configupdater', 'Florian Wilhelm', 'MIT'),
('Glide', 'https://github.com/glidejs/glide', '@jedrzejchalubek', 'MIT'),
('JQuery', 'https://jquery.com', 'The jQuery Foundation', 'MIT'),
('jquery.pep.js', 'http://pep.briangonzalez.org', '@briangonzalez', 'MIT'),
('js-md5', 'https://github.com/emn178/js-md5', '@emn178', 'MIT'),
('PySocks', 'https://github.com/Anorov/PySocks', '@Anorov', 'Custom DAN HAIM'),
('RapydScript-NG', 'https://github.com/kovidgoyal/rapydscript-ng', '@kovidgoyal',
'BSD 2-Clause "Simplified" License'),
('Requests', 'https://requests.kennethreitz.org', 'Kenneth Reitz', 'Apache License, Version 2.0'),
('scrollMonitor', 'https://github.com/stutrek/scrollmonitor', '@stutrek', 'MIT'),
('Smoothie Charts', 'https://github.com/joewalnes/smoothie', '@drewnoakes', 'MIT'),
('stem', 'https://stem.torproject.org', 'Damian Johnson and The Tor Project', 'GNU LESSER GENERAL PUBLIC LICENSE')
]
| 69 | 118 | 0.647101 |
606899616433fe3e3273bc5fab025d75f1c9d731 | 3,953 | py | Python | turorials/Google/projects/01_02_TextClassification/01_02_main.py | Ubpa/LearnTF | 2c9f5d790a9911a860da1e0db4c7bb56a9eee5cb | [
"MIT"
] | null | null | null | turorials/Google/projects/01_02_TextClassification/01_02_main.py | Ubpa/LearnTF | 2c9f5d790a9911a860da1e0db4c7bb56a9eee5cb | [
"MIT"
] | null | null | null | turorials/Google/projects/01_02_TextClassification/01_02_main.py | Ubpa/LearnTF | 2c9f5d790a9911a860da1e0db4c7bb56a9eee5cb | [
"MIT"
] | null | null | null | #----------------
# 01_02
#----------------
# TensorFlow and tf.keras
import tensorflow as tf
from tensorflow import keras
# Helper libraries
import numpy as np
import matplotlib.pyplot as plt
# TensorFlow's version : 1.12.0
print('TensorFlow\'s version : ', tf.__version__)
#----------------
# 1 IMDB
#----------------
imdb = keras.datasets.imdb
(train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=10000)
#----------------
# 2
#----------------
# Training entries: 25000, labels: 25000
print("Training entries: {}, labels: {}".format(len(train_data), len(train_labels)))
print(train_data[0])
# (218, 189)
print(len(train_data[0]), len(train_data[1]))
# A dictionary mapping words to an integer index
word_index = imdb.get_word_index()
# The first indices are reserved
word_index = {k:(v+3) for k,v in word_index.items()}
word_index["<PAD>"] = 0
word_index["<START>"] = 1
word_index["<UNK>"] = 2 # unknown
word_index["<UNUSED>"] = 3
reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])
decode_review(train_data[0])
#----------------
# 3
#----------------
train_data = keras.preprocessing.sequence.pad_sequences(train_data,
value=word_index["<PAD>"],
padding='post',
maxlen=256)
test_data = keras.preprocessing.sequence.pad_sequences(test_data,
value=word_index["<PAD>"],
padding='post',
maxlen=256)
# (256, 256)
print((len(train_data[0]), len(train_data[1])))
print(train_data[0])
#----------------
# 4
#----------------
# input shape is the vocabulary count used for the movie reviews (10,000 words)
vocab_size = 10000
model = keras.Sequential()
model.add(keras.layers.Embedding(vocab_size, 16))
model.add(keras.layers.GlobalAveragePooling1D())
model.add(keras.layers.Dense(16, activation=tf.nn.relu))
model.add(keras.layers.Dense(1, activation=tf.nn.sigmoid))
model.summary()
model.compile(optimizer=tf.train.AdamOptimizer(),
loss='binary_crossentropy',
metrics=['accuracy'])
#----------------
# 5
#----------------
x_val = train_data[:10000]
partial_x_train = train_data[10000:]
y_val = train_labels[:10000]
partial_y_train = train_labels[10000:]
#----------------
# 6
#----------------
history = model.fit(partial_x_train,
partial_y_train,
epochs=40,
batch_size=512,
validation_data=(x_val, y_val),
verbose=1)
#----------------
# 7
#----------------
results = model.evaluate(test_data, test_labels)
print(results)
#----------------
# 8
#----------------
history_dict = history.history
# dict_keys(['loss', 'val_loss', 'val_acc', 'acc'])
print(history_dict.keys())
acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(1, len(acc) + 1)
# loss
# "bo" is for "blue dot"
plt.plot(epochs, loss, 'bo', label='Training loss')
# b is for "solid blue line"
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()
plt.show()
# acc
plt.clf() # clear figure
acc_values = history_dict['acc']
val_acc_values = history_dict['val_acc']
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.legend()
plt.show()
| 24.251534 | 86 | 0.583607 |
6068adea6b26bf93c6fc76af394fa54701dacddb | 6,134 | py | Python | backend/api/urls.py | 12xiaoni/text-label | 7456c5e73d32bcfc81a02be7e0d748f162934d35 | [
"MIT"
] | null | null | null | backend/api/urls.py | 12xiaoni/text-label | 7456c5e73d32bcfc81a02be7e0d748f162934d35 | [
"MIT"
] | null | null | null | backend/api/urls.py | 12xiaoni/text-label | 7456c5e73d32bcfc81a02be7e0d748f162934d35 | [
"MIT"
] | null | null | null | from django.urls import include, path
from .views import (annotation, auto_labeling, comment, example, example_state,
health, label, project, tag, task)
from .views.tasks import category, relation, span, text
urlpatterns_project = [
path(
route='category-types',
view=label.CategoryTypeList.as_view(),
name='category_types'
),
path(
route='category-types/<int:label_id>',
view=label.CategoryTypeDetail.as_view(),
name='category_type'
),
path(
route='span-types',
view=label.SpanTypeList.as_view(),
name='span_types'
),
path(
route='span-types/<int:label_id>',
view=label.SpanTypeDetail.as_view(),
name='span_type'
),
path(
route='category-type-upload',
view=label.CategoryTypeUploadAPI.as_view(),
name='category_type_upload'
),
path(
route='span-type-upload',
view=label.SpanTypeUploadAPI.as_view(),
name='span_type_upload'
),
path(
route='examples',
view=example.ExampleList.as_view(),
name='example_list'
),
path(
route='examples/<int:example_id>',
view=example.ExampleDetail.as_view(),
name='example_detail'
),
path(
route='relation_types',
view=label.RelationTypeList.as_view(),
name='relation_types_list'
),
path(
route='relation_type-upload',
view=label.RelationTypeUploadAPI.as_view(),
name='relation_type-upload'
),
path(
route='relation_types/<int:relation_type_id>',
view=label.RelationTypeDetail.as_view(),
name='relation_type_detail'
),
path(
route='annotation_relations',
view=relation.RelationList.as_view(),
name='relation_types_list'
),
path(
route='annotation_relation-upload',
view=relation.RelationUploadAPI.as_view(),
name='annotation_relation-upload'
),
path(
route='annotation_relations/<int:annotation_relation_id>',
view=relation.RelationDetail.as_view(),
name='annotation_relation_detail'
),
path(
route='approval/<int:example_id>',
view=annotation.ApprovalAPI.as_view(),
name='approve_labels'
),
path(
route='examples/<int:example_id>/categories',
view=category.CategoryListAPI.as_view(),
name='category_list'
),
path(
route='examples/<int:example_id>/categories/<int:annotation_id>',
view=category.CategoryDetailAPI.as_view(),
name='category_detail'
),
path(
route='examples/<int:example_id>/spans',
view=span.SpanListAPI.as_view(),
name='span_list'
),
path(
route='examples/<int:example_id>/spans/<int:annotation_id>',
view=span.SpanDetailAPI.as_view(),
name='span_detail'
),
path(
route='examples/<int:example_id>/texts',
view=text.TextLabelListAPI.as_view(),
name='text_list'
),
path(
route='examples/<int:example_id>/texts/<int:annotation_id>',
view=text.TextLabelDetailAPI.as_view(),
name='text_detail'
),
path(
route='tags',
view=tag.TagList.as_view(),
name='tag_list'
),
path(
route='tags/<int:tag_id>',
view=tag.TagDetail.as_view(),
name='tag_detail'
),
path(
route='examples/<int:example_id>/comments',
view=comment.CommentListDoc.as_view(),
name='comment_list_doc'
),
path(
route='comments',
view=comment.CommentListProject.as_view(),
name='comment_list_project'
),
path(
route='examples/<int:example_id>/comments/<int:comment_id>',
view=comment.CommentDetail.as_view(),
name='comment_detail'
),
path(
route='examples/<int:example_id>/states',
view=example_state.ExampleStateList.as_view(),
name='example_state_list'
),
path(
route='auto-labeling-templates',
view=auto_labeling.AutoLabelingTemplateListAPI.as_view(),
name='auto_labeling_templates'
),
path(
route='auto-labeling-templates/<str:option_name>',
view=auto_labeling.AutoLabelingTemplateDetailAPI.as_view(),
name='auto_labeling_template'
),
path(
route='auto-labeling-configs',
view=auto_labeling.AutoLabelingConfigList.as_view(),
name='auto_labeling_configs'
),
path(
route='auto-labeling-configs/<int:config_id>',
view=auto_labeling.AutoLabelingConfigDetail.as_view(),
name='auto_labeling_config'
),
path(
route='auto-labeling-config-testing',
view=auto_labeling.AutoLabelingConfigTest.as_view(),
name='auto_labeling_config_test'
),
path(
route='examples/<int:example_id>/auto-labeling',
view=auto_labeling.AutoLabelingAnnotation.as_view(),
name='auto_labeling_annotation'
),
path(
route='auto-labeling-parameter-testing',
view=auto_labeling.AutoLabelingConfigParameterTest.as_view(),
name='auto_labeling_parameter_testing'
),
path(
route='auto-labeling-template-testing',
view=auto_labeling.AutoLabelingTemplateTest.as_view(),
name='auto_labeling_template_test'
),
path(
route='auto-labeling-mapping-testing',
view=auto_labeling.AutoLabelingMappingTest.as_view(),
name='auto_labeling_mapping_test'
)
]
urlpatterns = [
path(
route='health',
view=health.Health.as_view(),
name='health'
),
path(
route='projects',
view=project.ProjectList.as_view(),
name='project_list'
),
path(
route='tasks/status/<task_id>',
view=task.TaskStatus.as_view(),
name='task_status'
),
path(
route='projects/<int:project_id>',
view=project.ProjectDetail.as_view(),
name='project_detail'
),
path('projects/<int:project_id>/', include(urlpatterns_project))
]
| 28.798122 | 79 | 0.616074 |
6068f5688ef9ffee1272e2ce66f9f86a9888991e | 4,855 | py | Python | nwbwidgets/test/test_base.py | d-sot/nwb-jupyter-widgets | f9bf5c036c39f29e26b3cdb78198cccfa1b13cef | [
"BSD-3-Clause-LBNL"
] | null | null | null | nwbwidgets/test/test_base.py | d-sot/nwb-jupyter-widgets | f9bf5c036c39f29e26b3cdb78198cccfa1b13cef | [
"BSD-3-Clause-LBNL"
] | null | null | null | nwbwidgets/test/test_base.py | d-sot/nwb-jupyter-widgets | f9bf5c036c39f29e26b3cdb78198cccfa1b13cef | [
"BSD-3-Clause-LBNL"
] | null | null | null | import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from pynwb import TimeSeries
from datetime import datetime
from dateutil.tz import tzlocal
from pynwb import NWBFile
from ipywidgets import widgets
from pynwb.core import DynamicTable
from pynwb.file import Subject
from nwbwidgets.view import default_neurodata_vis_spec
from pynwb import ProcessingModule
from pynwb.behavior import Position, SpatialSeries
from nwbwidgets.base import show_neurodata_base,processing_module, nwb2widget, show_text_fields, \
fig2widget, vis2widget, show_fields, show_dynamic_table, df2accordion, lazy_show_over_data
import unittest
import pytest
| 32.366667 | 98 | 0.622039 |
606923d815b75242b92321d08cd16583deeb515a | 7,987 | py | Python | subliminal/video.py | orikad/subliminal | 5bd87a505f7a4cad2a3a872128110450c69da4f0 | [
"MIT"
] | null | null | null | subliminal/video.py | orikad/subliminal | 5bd87a505f7a4cad2a3a872128110450c69da4f0 | [
"MIT"
] | null | null | null | subliminal/video.py | orikad/subliminal | 5bd87a505f7a4cad2a3a872128110450c69da4f0 | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
from __future__ import division
from datetime import datetime, timedelta
import logging
import os
from guessit import guessit
logger = logging.getLogger(__name__)
#: Video extensions
VIDEO_EXTENSIONS = ('.3g2', '.3gp', '.3gp2', '.3gpp', '.60d', '.ajp', '.asf', '.asx', '.avchd', '.avi', '.bik',
'.bix', '.box', '.cam', '.dat', '.divx', '.dmf', '.dv', '.dvr-ms', '.evo', '.flc', '.fli',
'.flic', '.flv', '.flx', '.gvi', '.gvp', '.h264', '.m1v', '.m2p', '.m2ts', '.m2v', '.m4e',
'.m4v', '.mjp', '.mjpeg', '.mjpg', '.mkv', '.moov', '.mov', '.movhd', '.movie', '.movx', '.mp4',
'.mpe', '.mpeg', '.mpg', '.mpv', '.mpv2', '.mxf', '.nsv', '.nut', '.ogg', '.ogm' '.ogv', '.omf',
'.ps', '.qt', '.ram', '.rm', '.rmvb', '.swf', '.ts', '.vfw', '.vid', '.video', '.viv', '.vivo',
'.vob', '.vro', '.wm', '.wmv', '.wmx', '.wrap', '.wvx', '.wx', '.x264', '.xvid')
class Episode(Video):
"""Episode :class:`Video`.
:param str series: series of the episode.
:param int season: season number of the episode.
:param int episode: episode number of the episode.
:param str title: title of the episode.
:param int year: year of the series.
:param bool original_series: whether the series is the first with this name.
:param int tvdb_id: TVDB id of the episode.
:param \*\*kwargs: additional parameters for the :class:`Video` constructor.
"""
def __repr__(self):
if self.year is None:
return '<%s [%r, %dx%d]>' % (self.__class__.__name__, self.series, self.season, self.episode)
return '<%s [%r, %d, %dx%d]>' % (self.__class__.__name__, self.series, self.year, self.season, self.episode)
class Movie(Video):
"""Movie :class:`Video`.
:param str title: title of the movie.
:param int year: year of the movie.
:param \*\*kwargs: additional parameters for the :class:`Video` constructor.
"""
def __repr__(self):
if self.year is None:
return '<%s [%r]>' % (self.__class__.__name__, self.title)
return '<%s [%r, %d]>' % (self.__class__.__name__, self.title, self.year)
| 35.497778 | 116 | 0.597471 |
6069d2236a48fbb04ece52eba51a580571ed12ab | 2,756 | py | Python | backend/app/migrations/0001_initial.py | juniorosorio47/client-order | ec429436d822d07d0ec1e0be0c2615087eec6e65 | [
"MIT"
] | null | null | null | backend/app/migrations/0001_initial.py | juniorosorio47/client-order | ec429436d822d07d0ec1e0be0c2615087eec6e65 | [
"MIT"
] | null | null | null | backend/app/migrations/0001_initial.py | juniorosorio47/client-order | ec429436d822d07d0ec1e0be0c2615087eec6e65 | [
"MIT"
] | null | null | null | # Generated by Django 3.2.7 on 2021-10-18 23:21
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
| 41.757576 | 142 | 0.588534 |
606baf8442117d47dded1db451079748f48b00b6 | 4,291 | py | Python | nemo/collections/nlp/models/machine_translation/mt_enc_dec_config.py | vadam5/NeMo | 3c5db09539293c3c19a6bb7437011f91261119af | [
"Apache-2.0"
] | 1 | 2021-04-13T20:34:16.000Z | 2021-04-13T20:34:16.000Z | nemo/collections/nlp/models/machine_translation/mt_enc_dec_config.py | vadam5/NeMo | 3c5db09539293c3c19a6bb7437011f91261119af | [
"Apache-2.0"
] | null | null | null | nemo/collections/nlp/models/machine_translation/mt_enc_dec_config.py | vadam5/NeMo | 3c5db09539293c3c19a6bb7437011f91261119af | [
"Apache-2.0"
] | null | null | null | # Copyright (c) 2020, NVIDIA CORPORATION. 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 dataclasses import dataclass
from typing import Any, Optional, Tuple
from omegaconf.omegaconf import MISSING
from nemo.collections.nlp.data.machine_translation.machine_translation_dataset import TranslationDataConfig
from nemo.collections.nlp.models.enc_dec_nlp_model import EncDecNLPModelConfig
from nemo.collections.nlp.modules.common.token_classifier import TokenClassifierConfig
from nemo.collections.nlp.modules.common.tokenizer_utils import TokenizerConfig
from nemo.collections.nlp.modules.common.transformer.transformer import (
NeMoTransformerConfig,
NeMoTransformerEncoderConfig,
)
from nemo.core.config.modelPT import ModelConfig, OptimConfig, SchedConfig
# TODO: Refactor this dataclass to to support more optimizers (it pins the optimizer to Adam-like optimizers).
| 32.022388 | 110 | 0.718714 |
606d3133565f3d0c7f55e0387f7f06dca6adb6f2 | 7,097 | py | Python | shadowsocksr_cli/main.py | MaxSherry/ssr-command-client | e52ea0a74e2a1bbdd7e816e0e2670d66ebdbf159 | [
"MIT"
] | null | null | null | shadowsocksr_cli/main.py | MaxSherry/ssr-command-client | e52ea0a74e2a1bbdd7e816e0e2670d66ebdbf159 | [
"MIT"
] | null | null | null | shadowsocksr_cli/main.py | MaxSherry/ssr-command-client | e52ea0a74e2a1bbdd7e816e0e2670d66ebdbf159 | [
"MIT"
] | null | null | null | """
@author: tyrantlucifer
@contact: tyrantlucifer@gmail.com
@blog: https://tyrantlucifer.com
@file: main.py
@time: 2021/2/18 21:36
@desc: shadowsocksr-cli
"""
import argparse
import traceback
from shadowsocksr_cli.functions import *
if __name__ == "__main__":
main()
| 52.962687 | 118 | 0.683106 |
606d8521b79694dc8353d8aa111b68f4f20bff71 | 9,321 | py | Python | examples/Python 2.7/Client_Complete.py | jcjveraa/EDDN | d0cbae6b7a2cac180dd414cbc324c2d84c867cd8 | [
"BSD-3-Clause"
] | 100 | 2017-07-19T10:11:04.000Z | 2020-07-05T22:07:39.000Z | examples/Python 2.7/Client_Complete.py | Assasinnys/EDDN | f03a0857cf4a2b95903d332697b07cc055c6cee7 | [
"BSD-3-Clause"
] | 61 | 2020-09-28T19:05:10.000Z | 2022-03-22T09:16:08.000Z | examples/Python 2.7/Client_Complete.py | Assasinnys/EDDN | f03a0857cf4a2b95903d332697b07cc055c6cee7 | [
"BSD-3-Clause"
] | 28 | 2020-07-19T22:37:44.000Z | 2022-03-22T09:01:39.000Z | import zlib
import zmq
import simplejson
import sys, os, datetime, time
"""
" Configuration
"""
__relayEDDN = 'tcp://eddn.edcd.io:9500'
#__timeoutEDDN = 600000 # 10 minuts
__timeoutEDDN = 60000 # 1 minut
# Set False to listen to production stream; True to listen to debug stream
__debugEDDN = False;
# Set to False if you do not want verbose logging
__logVerboseFile = os.path.dirname(__file__) + '/Logs_Verbose_EDDN_%DATE%.htm'
#__logVerboseFile = False
# Set to False if you do not want JSON logging
__logJSONFile = os.path.dirname(__file__) + '/Logs_JSON_EDDN_%DATE%.log'
#__logJSONFile = False
# A sample list of authorised softwares
__authorisedSoftwares = [
"EDCE",
"ED-TD.SPACE",
"EliteOCR",
"Maddavo's Market Share",
"RegulatedNoise",
"RegulatedNoise__DJ",
"E:D Market Connector [Windows]"
]
# Used this to excludes yourself for example has you don't want to handle your own messages ^^
__excludedSoftwares = [
'My Awesome Market Uploader'
]
"""
" Start
"""
__oldTime = False
if __name__ == '__main__':
main()
| 40.526087 | 161 | 0.466259 |
606de86a65d9bb68c662f132a9f86b56feda8791 | 672 | py | Python | zad1.py | nadkkka/H8PW | 21b5d28bb42af163e7dad43368d21b550ae66618 | [
"MIT"
] | 6 | 2019-10-20T18:25:28.000Z | 2019-11-17T12:21:42.000Z | zad1.py | nadkkka/H8PW | 21b5d28bb42af163e7dad43368d21b550ae66618 | [
"MIT"
] | null | null | null | zad1.py | nadkkka/H8PW | 21b5d28bb42af163e7dad43368d21b550ae66618 | [
"MIT"
] | 4 | 2019-10-20T18:25:28.000Z | 2019-11-30T19:33:47.000Z |
print (repleace_pattern([1,2,3,1,2,3,4],[1,2,3,4],[9,0]))
| 18.162162 | 58 | 0.383929 |
606dfbab5706a842277bbd2a3b9198129d579201 | 2,249 | py | Python | mycroft/client/enclosure/weather.py | Matjordan/mycroft-core | 8b64930f3b3dae671535fc3b096ce9d846c54f6d | [
"Apache-2.0"
] | null | null | null | mycroft/client/enclosure/weather.py | Matjordan/mycroft-core | 8b64930f3b3dae671535fc3b096ce9d846c54f6d | [
"Apache-2.0"
] | null | null | null | mycroft/client/enclosure/weather.py | Matjordan/mycroft-core | 8b64930f3b3dae671535fc3b096ce9d846c54f6d | [
"Apache-2.0"
] | null | null | null | # Copyright 2017 Mycroft AI 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.
| 34.075758 | 77 | 0.56514 |
606fa44df2b3928dca9a1f9a1a195390a91a5ba6 | 6,698 | py | Python | tests/processing_components/test_image_iterators.py | cnwangfeng/algorithm-reference-library | 9605eb01652fbfcb9ff003cc12b44c84093b7fb1 | [
"Apache-2.0"
] | 22 | 2016-12-14T11:20:07.000Z | 2021-08-13T15:23:41.000Z | tests/processing_components/test_image_iterators.py | cnwangfeng/algorithm-reference-library | 9605eb01652fbfcb9ff003cc12b44c84093b7fb1 | [
"Apache-2.0"
] | 30 | 2017-06-27T09:15:38.000Z | 2020-09-11T18:16:37.000Z | tests/processing_components/test_image_iterators.py | cnwangfeng/algorithm-reference-library | 9605eb01652fbfcb9ff003cc12b44c84093b7fb1 | [
"Apache-2.0"
] | 20 | 2017-07-02T03:45:49.000Z | 2019-12-11T17:19:01.000Z | """Unit tests for image iteration
"""
import logging
import unittest
import numpy
from data_models.polarisation import PolarisationFrame
from processing_components.image.iterators import image_raster_iter, image_channel_iter, image_null_iter
from processing_components.image.operations import create_empty_image_like
from processing_components.simulation.testing_support import create_test_image
log = logging.getLogger(__name__)
if __name__ == '__main__':
unittest.main()
| 49.985075 | 116 | 0.591371 |
606ffbed507972fed40e1f7c61ad9e16979a735d | 1,501 | py | Python | a_other_video/MCL-Motion-Focused-Contrastive-Learning/sts/motion_sts.py | alisure-fork/Video-Swin-Transformer | aa0a31bd4df0ad2cebdcfb2ad53df712fce79809 | [
"Apache-2.0"
] | null | null | null | a_other_video/MCL-Motion-Focused-Contrastive-Learning/sts/motion_sts.py | alisure-fork/Video-Swin-Transformer | aa0a31bd4df0ad2cebdcfb2ad53df712fce79809 | [
"Apache-2.0"
] | null | null | null | a_other_video/MCL-Motion-Focused-Contrastive-Learning/sts/motion_sts.py | alisure-fork/Video-Swin-Transformer | aa0a31bd4df0ad2cebdcfb2ad53df712fce79809 | [
"Apache-2.0"
] | null | null | null | import cv2
import numpy as np
from scipy import ndimage
| 24.209677 | 70 | 0.592938 |
606fff8ef61161b6a42c08c99645ebf6b02a454f | 3,618 | py | Python | tests/test_button.py | MSLNZ/msl-qt | 33abbb4807b54e3a06dbe9c0f9b343802ece9b97 | [
"MIT"
] | 1 | 2018-07-14T23:02:24.000Z | 2018-07-14T23:02:24.000Z | tests/test_button.py | MSLNZ/msl-qt | 33abbb4807b54e3a06dbe9c0f9b343802ece9b97 | [
"MIT"
] | 1 | 2019-08-12T04:52:33.000Z | 2019-08-21T00:06:59.000Z | tests/test_button.py | MSLNZ/msl-qt | 33abbb4807b54e3a06dbe9c0f9b343802ece9b97 | [
"MIT"
] | 1 | 2019-08-05T05:22:47.000Z | 2019-08-05T05:22:47.000Z | import os
import sys
import pytest
from msl.qt import convert, Button, QtWidgets, QtCore, Qt
| 31.46087 | 93 | 0.657546 |
60700c79a8bc5322f89b51ceab88a89b92a4de5b | 499 | py | Python | Exercicios/ex028.py | MateusBarboza99/Python-03- | 9c6df88aaa8ba83d385b92722ed1df5873df3a77 | [
"MIT"
] | null | null | null | Exercicios/ex028.py | MateusBarboza99/Python-03- | 9c6df88aaa8ba83d385b92722ed1df5873df3a77 | [
"MIT"
] | null | null | null | Exercicios/ex028.py | MateusBarboza99/Python-03- | 9c6df88aaa8ba83d385b92722ed1df5873df3a77 | [
"MIT"
] | null | null | null | from random import randint
from time import sleep
computador = randint(0, 5) # Faz o computador "PENSAR"
print('-=-' * 20)
print('Vou Pensar em Um Nmero Entre 0 e 5. Tente Adivinhar Paoca...')
print('-=-' * 20)
jogador = int(input('Em que Nmero eu Pensei? ')) # Jogador tenta Adivinhar
print('PROCESSANDO........')
sleep(3)
if jogador == computador:
print('PARABNS! Voc conseguiu me Vencer Paoca')
else:
print('GANHEI! Eu Pensei no Nmero {} e no no {}!'.format(computador, jogador))
| 35.642857 | 84 | 0.687375 |
6070e1255db727d4dd3901174951be184b84e950 | 2,691 | py | Python | Student Database/input_details.py | manas1410/Miscellaneous-Development | 8ffd2b586cb05b12ed0855d97c3015c8bb2a6c01 | [
"MIT"
] | null | null | null | Student Database/input_details.py | manas1410/Miscellaneous-Development | 8ffd2b586cb05b12ed0855d97c3015c8bb2a6c01 | [
"MIT"
] | null | null | null | Student Database/input_details.py | manas1410/Miscellaneous-Development | 8ffd2b586cb05b12ed0855d97c3015c8bb2a6c01 | [
"MIT"
] | null | null | null | from tkinter import*
import tkinter.font as font
import sqlite3
name2=''
regis2=''
branch2=''
if __name__=='__main__':
main()
| 25.149533 | 106 | 0.536603 |
60720921ca305f9abf0188d61f032d72e5cdb0ce | 16,036 | py | Python | IQS5xx/IQS5xx.py | jakezimmerTHT/py_IQS5xx | 5f90be17ea0429eeeb3726c7647f0b7ad1fb7b06 | [
"MIT"
] | 1 | 2019-02-26T11:56:26.000Z | 2019-02-26T11:56:26.000Z | IQS5xx/IQS5xx.py | jakezimmerTHT/py_IQS5xx | 5f90be17ea0429eeeb3726c7647f0b7ad1fb7b06 | [
"MIT"
] | null | null | null | IQS5xx/IQS5xx.py | jakezimmerTHT/py_IQS5xx | 5f90be17ea0429eeeb3726c7647f0b7ad1fb7b06 | [
"MIT"
] | 1 | 2022-02-22T19:47:26.000Z | 2022-02-22T19:47:26.000Z | import unittest
import time
import logging
logging.basicConfig()
from intelhex import IntelHex
import Adafruit_GPIO.I2C as i2c
from gpiozero import OutputDevice
from gpiozero import DigitalInputDevice
from ctypes import c_uint8, c_uint16, c_uint32, cast, pointer, POINTER
from ctypes import create_string_buffer, Structure
from fcntl import ioctl
import struct
import Adafruit_PureIO.smbus as smbus
from Adafruit_PureIO.smbus import make_i2c_rdwr_data
from IQS5xx_Defs import *
IQS5xx_DEFAULT_ADDRESS = 0x74
IQS5xx_MAX_ADDRESS = 0x78
CHECKSUM_DESCRIPTOR_START = 0x83C0
CHECKSUM_DESCRIPTOR_END = 0x83FF
APP_START_ADDRESS = 0x8400
APP_END_ADDRESS = 0xBDFF #inclusive
NV_SETTINGS_START = 0xBE00
NV_SETTINGS_END = 0xBFFF #inclusive
FLASH_PADDING = 0x00
BLOCK_SIZE = 64
APP_SIZE_BLOCKS = (((APP_END_ADDRESS+1) - APP_START_ADDRESS) / BLOCK_SIZE)
NV_SETTINGS_SIZE_BLOCKS = (((NV_SETTINGS_END+1) - NV_SETTINGS_START) / BLOCK_SIZE)
BL_CMD_READ_VERSION = 0x00
BL_CMD_READ_64_BYTES = 0x01
BL_CMD_EXECUTE_APP = 0x02 # Write only, 0 bytes
BL_CMD_RUN_CRC = 0x03
BL_CRC_FAIL = 0x01
BL_CRC_PASS = 0x00
BL_VERSION = 0x0200
i2c.Device.writeBytes = writeBytes
i2c.Device.readBytes = readBytes
i2c.Device.writeRawListReadRawList = writeRawListReadRawList
i2c.Device.writeBytes_16BitAddress = writeBytes_16BitAddress
i2c.Device.readBytes_16BitAddress = readBytes_16BitAddress
i2c.Device.readByte_16BitAddress = readByte_16BitAddress
i2c.Device.writeByte_16BitAddress = writeByte_16BitAddress
class TestIQS5xx(unittest.TestCase):
hexFile = "IQS550_B000_Trackpad_40_15_2_2_BL.HEX"
possibleAddresses = [0x74, 0x75, 0x76, 0x77]
desiredAddress = 0x74
device = None
# @unittest.skip
# def test_update(self):
# self.__class__.device.updateFirmware(self.__class__.hexFile)
#
# @unittest.skip
# def test_update_and_changeaddress(self):
# newAddy = 0x77
# self.__class__.device.updateFirmware(self.__class__.hexFile, newDeviceAddress=newAddy)
# self.assertEqual(self.__class__.device.address, newAddy)
# time.sleep(0.1)
# self.assertTrue(self.__class__.device.bootloaderAvailable())
if __name__ == '__main__':
unittest.main()
| 41.760417 | 175 | 0.692816 |
60730c8aa9c4f71059543c43f3553248624a3054 | 2,322 | py | Python | code/loader/lock.py | IBCNServices/StardogStreamReasoning | 646db9cec7bd06ac8bfa75952b9a41773f35544d | [
"Apache-2.0"
] | 5 | 2020-04-08T22:55:17.000Z | 2021-07-30T12:12:45.000Z | code/loader/lock.py | IBCNServices/StardogStreamReasoning | 646db9cec7bd06ac8bfa75952b9a41773f35544d | [
"Apache-2.0"
] | 1 | 2021-04-29T21:58:32.000Z | 2021-05-04T08:05:11.000Z | code/loader/lock.py | IBCNServices/StardogStreamReasoning | 646db9cec7bd06ac8bfa75952b9a41773f35544d | [
"Apache-2.0"
] | 3 | 2020-06-12T13:48:08.000Z | 2021-07-23T11:24:27.000Z | import threading
| 31.808219 | 75 | 0.736434 |
6073572f31a3babd2ff3a1985183c4320d96810e | 5,627 | py | Python | src/pyfmodex/channel_group.py | Loodoor/UnamedPy | 7d154c3a652992b3c1f28050f0353451f57b2a2d | [
"MIT"
] | 1 | 2017-02-21T16:46:21.000Z | 2017-02-21T16:46:21.000Z | src/pyfmodex/channel_group.py | Loodoor/UnamedPy | 7d154c3a652992b3c1f28050f0353451f57b2a2d | [
"MIT"
] | 1 | 2017-02-21T17:57:05.000Z | 2017-02-22T11:28:51.000Z | src/pyfmodex/channel_group.py | Loodoor/UnamedPy | 7d154c3a652992b3c1f28050f0353451f57b2a2d | [
"MIT"
] | null | null | null | from .fmodobject import *
from .globalvars import dll as _dll
from .globalvars import get_class
def get_group(self, idx):
grp_ptr = c_void_p()
self._call_fmod("FMOD_ChannelGroup_GetGroup", idx)
return ChannelGroup(grp_ptr)
def get_spectrum(self, numvalues, channeloffset, window):
arr = c_float * numvalues
arri = arr()
self._call_fmod("FMOD_ChannelGroup_GetSpectrum", byref(arri), numvalues, channeloffset, window)
return list(arri)
def get_wave_data(self, numvalues, channeloffset):
arr = c_float * numvalues
arri = arr()
self._call_fmod("FMOD_ChannelGroup_GetWaveData", byref(arri), numvalues, channeloffset)
return list(arri)
def override_3d_attributes(self, pos=0, vel=0):
self._call_fmod("FMOD_ChannelGroup_Override3DAttributes", pos, vel)
def override_frequency(self, freq):
self._call_fmod("FMOD_ChannelGroup_OverrideFrequency", c_float(freq))
def override_pan(self, pan):
self._call_fmod("FMOD_ChannelGroup_OverridePan", c_float(pan))
def override_reverb_properties(self, props):
check_type(props, REVERB_CHANNELPROPERTIES)
self._call_fmod("FMOD_ChannelGroup_OverrideReverbProperties", props)
def override_speaker_mix(self, frontleft, frontright, center, lfe, backleft, backright, sideleft, sideright):
self._call_fmod("FMOD_ChannelGroup_OverrideSpeakerMix", frontleft, frontright, center, lfe, backleft, backright,
sideleft, sideright)
def override_volume(self, vol):
self._call_fmod("FMOD_ChannelGroup_OverrideVolume", c_float(vol))
def release(self):
self._call_fmod("FMOD_ChannelGroup_Release")
def stop(self):
self._call_fmod("FMOD_ChannelGroup_Stop")
| 31.61236 | 120 | 0.675671 |
60736b2c5b81bc8177746e07c1771f09afc46a66 | 2,214 | py | Python | program.py | siddhi117/ADB_Homework | 1751b3cc2d5ec1584efdf7f8961507bc29179e49 | [
"MIT"
] | null | null | null | program.py | siddhi117/ADB_Homework | 1751b3cc2d5ec1584efdf7f8961507bc29179e49 | [
"MIT"
] | null | null | null | program.py | siddhi117/ADB_Homework | 1751b3cc2d5ec1584efdf7f8961507bc29179e49 | [
"MIT"
] | null | null | null | import sqlite3
from bottle import route, run,debug,template,request,redirect
debug(True)
run(reloader=True)
| 27.333333 | 93 | 0.584914 |
60761440b9fc5de896572a3624d9ef4c6e6c7759 | 3,246 | py | Python | pipeline/metadata/maxmind.py | censoredplanet/censoredplanet-analysis | f5e5d82f890e47599bc0baa9a9390f3c5147a6f7 | [
"Apache-2.0"
] | 6 | 2021-06-02T11:15:12.000Z | 2022-03-04T12:09:35.000Z | pipeline/metadata/maxmind.py | censoredplanet/censoredplanet-analysis | f5e5d82f890e47599bc0baa9a9390f3c5147a6f7 | [
"Apache-2.0"
] | 24 | 2021-04-13T18:07:29.000Z | 2022-03-25T20:26:27.000Z | pipeline/metadata/maxmind.py | censoredplanet/censoredplanet-analysis | f5e5d82f890e47599bc0baa9a9390f3c5147a6f7 | [
"Apache-2.0"
] | 2 | 2021-06-02T11:30:21.000Z | 2021-08-20T12:17:12.000Z | """Module to initialize Maxmind databases and lookup IP metadata."""
import logging
import os
from typing import Optional, Tuple, NamedTuple
import geoip2.database
from pipeline.metadata.mmdb_reader import mmdb_reader
MAXMIND_CITY = 'GeoLite2-City.mmdb'
MAXMIND_ASN = 'GeoLite2-ASN.mmdb'
# Tuple(netblock, asn, as_name, country)
# ex: ("1.0.0.1/24", 13335, "CLOUDFLARENET", "AU")
MaxmindReturnValues = NamedTuple('MaxmindReturnValues',
[('netblock', Optional[str]), ('asn', int),
('as_name', Optional[str]),
('country', Optional[str])])
| 30.336449 | 78 | 0.663586 |
6076505608a2c5b23c1f2f1d1636e9aacc345050 | 1,105 | py | Python | examples/plot_graph.py | huyvo/gevent-websocket-py3.5 | b2eb3b5cfb020ac976ac0970508589020dce03ad | [
"Apache-2.0"
] | null | null | null | examples/plot_graph.py | huyvo/gevent-websocket-py3.5 | b2eb3b5cfb020ac976ac0970508589020dce03ad | [
"Apache-2.0"
] | null | null | null | examples/plot_graph.py | huyvo/gevent-websocket-py3.5 | b2eb3b5cfb020ac976ac0970508589020dce03ad | [
"Apache-2.0"
] | null | null | null | from __future__ import print_function
"""
This example generates random data and plots a graph in the browser.
Run it using Gevent directly using:
$ python plot_graph.py
Or with an Gunicorn wrapper:
$ gunicorn -k "geventwebsocket.gunicorn.workers.GeventWebSocketWorker" \
plot_graph:resource
"""
import gevent
import random
from geventwebsocket import WebSocketServer, WebSocketApplication, Resource
from geventwebsocket._compat import range_type
resource = Resource([
('/', static_wsgi_app),
('/data', PlotApplication)
])
if __name__ == "__main__":
server = WebSocketServer(('', 8000), resource, debug=True)
server.serve_forever()
| 25.113636 | 76 | 0.700452 |
60765090bf7eb3ddb56eaccfac94b3add8ca8a04 | 844 | py | Python | nas_big_data/combo/best/combo_4gpu_8_agebo/predict.py | deephyper/NASBigData | 18f083a402b80b1d006eada00db7287ff1802592 | [
"BSD-2-Clause"
] | 3 | 2020-08-07T12:05:12.000Z | 2021-04-05T19:38:37.000Z | nas_big_data/combo/best/combo_2gpu_8_agebo/predict.py | deephyper/NASBigData | 18f083a402b80b1d006eada00db7287ff1802592 | [
"BSD-2-Clause"
] | 2 | 2020-07-17T14:44:12.000Z | 2021-04-04T14:52:11.000Z | nas_big_data/combo/best/combo_4gpu_8_agebo/predict.py | deephyper/NASBigData | 18f083a402b80b1d006eada00db7287ff1802592 | [
"BSD-2-Clause"
] | 1 | 2021-03-28T01:49:21.000Z | 2021-03-28T01:49:21.000Z | import os
import numpy as np
import tensorflow as tf
from nas_big_data.combo.load_data import load_data_npz_gz
from deephyper.nas.run.util import create_dir
from deephyper.nas.train_utils import selectMetric
os.environ["CUDA_VISIBLE_DEVICES"] = ",".join([str(i) for i in range(4)])
HERE = os.path.dirname(os.path.abspath(__file__))
fname = HERE.split("/")[-1]
output_dir = "logs"
create_dir(output_dir)
X_test, y_test = load_data_npz_gz(test=True)
dependencies = {
"r2":selectMetric("r2")
}
model = tf.keras.models.load_model(f"best_model_{fname}.h5", custom_objects=dependencies)
model.compile(
metrics=["mse", "mae", selectMetric("r2")]
)
score = model.evaluate(X_test, y_test)
score_names = ["loss", "mse", "mae", "r2"]
print("score:")
output = " ".join([f"{sn}:{sv:.3f}" for sn,sv in zip(score_names, score)])
print(output) | 26.375 | 89 | 0.722749 |
6077a342275cffb372223916fb877af7a30c823c | 14,744 | py | Python | ship/utils/utilfunctions.py | duncan-r/SHIP | 2c4c22c77f9c18ea545d3bce70a36aebbd18256a | [
"MIT"
] | 6 | 2016-04-10T17:32:44.000Z | 2022-03-13T18:41:21.000Z | ship/utils/utilfunctions.py | duncan-r/SHIP | 2c4c22c77f9c18ea545d3bce70a36aebbd18256a | [
"MIT"
] | 19 | 2017-06-23T08:21:53.000Z | 2017-07-26T08:23:03.000Z | ship/utils/utilfunctions.py | duncan-r/SHIP | 2c4c22c77f9c18ea545d3bce70a36aebbd18256a | [
"MIT"
] | 6 | 2016-10-26T16:04:38.000Z | 2019-04-25T23:55:06.000Z | """
Summary:
Utility Functions that could be helpful in any part of the API.
All functions that are likely to be called across a number of classes
and Functions in the API should be grouped here for convenience.
Author:
Duncan Runnacles
Created:
01 Apr 2016
Copyright:
Duncan Runnacles 2016
TODO: This module, like a lot of other probably, needs reviewing for how
'Pythonic' t is. There are a lot of places where generators,
comprehensions, maps, etc should be used to speed things up and make
them a bit clearer.
More importantly there are a lot of places using '==' compare that
should be using 'in' etc. This could cause bugs and must be fixed
soon.
Updates:
"""
from __future__ import unicode_literals
import re
import os
import operator
import logging
logger = logging.getLogger(__name__)
"""logging references with a __name__ set to this module."""
# def resolveSeDecorator(se_vals, path):
# """Decorator function for replacing Scen/Evt placholders.
#
# Checks fro scenario and event placeholders in the return value of a
# function and replaces them with corresponding values if found.
#
# Args:
# se_vals(dict): standard scenario/event dictionary in the format:
# {'scenario': {
# """
# def seDecorator(func):
# def seWrapper(*args, **kwargs):
# result = func(*args, **kwargs)
#
# if '~' in result:
# # Check for scenarion stuff
# for key, val in self.se_vals['scenario'].items():
# temp = '~' + key + '~'
# if temp in result:
# result = result.replace(temp, val)
# # Check for event stuff
# for key, val in self.se_vals['event'].items():
# temp = '~' + key + '~'
# if temp in result:
# result = result.replace(temp, val)
# return result
# return seWrapper
# return seDecorator
def formatFloat(value, no_of_dps, ignore_empty_str=True):
"""Format a float as a string to given number of decimal places.
Args:
value(float): the value to format.
no_of_dps(int): number of decimal places to format to.
ignore_empty_str(True): return a stripped blank string if set to True.
Return:
str - the formatted float.
Raises:
ValueError - if value param is not type float.
"""
if ignore_empty_str and not isNumeric(value) and str(value).strip() == '':
return str(value).strip()
if not isNumeric(value):
raise ValueError
decimal_format = '%0.' + str(no_of_dps) + 'f'
value = decimal_format % float(value)
return value
def checkFileType(file_path, ext):
"""Checks a file to see that it has the right extension.
Args:
file_path (str): The file path to check.
ext (List): list containing the extension types to match the file
against.
Returns:
True if the extension matches the ext variable given or False if not.
"""
file_ext = os.path.splitext(file_path)[1]
logger.info('File ext = ' + file_ext)
for e in ext:
if e == file_ext:
return True
else:
return False
def isNumeric(s):
"""Tests if string is a number or not.
Simply tries to convert it and catches the error if launched.
Args:
s (str): string to test number compatibility.
Returns:
Bool - True if number. False if not.
"""
try:
float(s)
return True
except (ValueError, TypeError):
return False
def isString(value):
"""Tests a given value to see if it is an instance of basestring or not.
Note:
This function should be used whenever testing this as it accounts for
both Python 2.7+ and 3.2+ variations of string.
Args:
value: the variable to test.
Returns:
Bool - True if value is a unicode str (basestring type)
"""
try:
return isinstance(value, basestring)
except NameError:
return isinstance(value, str)
# if not isinstance(value, basestring):
# return False
#
# return True
def isList(value):
"""Test a given value to see if it is a list or not.
Args:
value: the variable to test for list type.
Returns:
True if value is of type list; False otherwise.
"""
if not isinstance(value, list):
return False
return True
def arrayToString(self, str_array):
"""Convert a list to a String
Creates one string by adding each part of the array to one string using
', '.join()
Args:
str_array (List): to convert into single string.
Returns:
str - representaion of the array joined together.
Raises:
ValueError: if not contents of list are instances of basestring.
"""
if not isinstance(str_array[0], basestring):
raise ValueError('Array values are not strings')
out_string = ''
out_string = ', '.join(str_array)
return out_string
def findSubstringInList(substr, the_list):
"""Returns a list containing the indices that a substring was found at.
Uses a generator to quickly find all indices that str appears in.
Args:
substr (str): the sub string to search for.
the_list (List): a list containing the strings to search.
Returns:
tuple - containing:
* a list with the indices that the substring was found in
(this list can be empty if no matches were found).
* an integer containing the number of elements it was found in.
"""
indices = [i for i, s in enumerate(the_list) if substr in s]
return indices, len(indices)
def findMax(val1, val2):
"""Returns tuple containing min, max of two values
Args:
val1: first integer or float.
val2: second integer or float.
Returns:
tuple - containing:
* lower value
* higher value
* False if not same or True if the same.
"""
if val1 == val2:
return val1, val2, True
elif val1 > val2:
return val2, val1, False
else:
return val1, val2, False
def fileExtensionWithoutPeriod(filepath, name_only=False):
"""Extracts the extension without '.' from filepath.
The extension will always be converted to lower case before returning.
Args:
filepath (str): A full filepath if name_only=False. Otherwise a file
name with extension if name_only=True.
name_only (bool): True if filepath is only filename.extension.
"""
if name_only:
file, ext = os.path.splitext(filepath)
else:
path, filename = os.path.split(filepath)
file, ext = os.path.splitext(filename)
ext = ext[1:]
return ext.lower()
def findWholeWord(w):
"""Find a whole word amoungst a string."""
return re.compile(r'\b({0})\b'.format(w), flags=re.IGNORECASE).search
def convertRunOptionsToSEDict(options):
"""Converts tuflow command line options to scenario/event dict.
Tuflow uses command line option (e.g. -s1 blah -e1 blah) to set scenario
values which can either be provided on the command line or through the
FMP run form. The TuflowLoader can use these arguments but requires a
slightly different setup.
This function converts the command line string into the scenarion and
event dictionary expected by the TuflowLoader.
Args:
options(str): command line options.
Return:
dict - {'scenario': {'s1': blah}, 'event': {'e1': blah}}
Raises:
AttributeError: if both -s and -s1 or -e and -e1 occurr in the options
string. -x and -x1 are treated as the same variable by tuflow and
one of the values would be ignored.
"""
if ' -s ' in options and ' -s1 ' in options:
raise AttributeError
if ' -e ' in options and ' -e2 ' in options:
raise AttributeError
outvals = {'scenario': {}, 'event': {}}
vals = options.split(" ")
for i in range(len(vals)):
if vals[i].startswith('-s'):
outvals['scenario'][vals[i][1:]] = vals[i + 1]
elif vals[i].startswith('-e'):
outvals['event'][vals[i][1:]] = vals[i + 1]
return outvals
def getSEResolvedFilename(filename, se_vals):
"""Replace a tuflow placeholder filename with the scenario/event values.
Replaces all of the placholder values (e.g. ~s1~_~e1~) in a tuflow
filename with the corresponding values provided in the run options string.
If the run options flags are not found in the filename their values will
be appended to the end of the string.
The setup of the returned filename is always the same:
- First replace all placeholders with corresponding flag values.
- s1 == s and e1 == e.
- Append additional e values to end with '_' before first and '+' before others.
- Append additional s values to end with '_' before first and '+' before others.
Args:
filename(str): the filename to update.
se_vals(str): the run options string containing the 's' and
'e' flags and their corresponding values.
Return:
str - the updated filename.
"""
if not 'scenario' in se_vals.keys():
se_vals['scenario'] = {}
if not 'event' in se_vals.keys():
se_vals['event'] = {}
# Format the key value pairs into a list and combine the scenario and
# event list together and sort them into e, e1, e2, s, s1, s2 order.
scen_keys = ['-' + a for a in se_vals['scenario'].keys()]
scen_vals = se_vals['scenario'].values()
event_keys = ['-' + a for a in se_vals['event'].keys()]
event_vals = se_vals['event'].values()
scen = [list(a) for a in zip(scen_keys, scen_vals)]
event = [list(a) for a in zip(event_keys, event_vals)]
se_vals = scen + event
vals = sorted(se_vals, key=operator.itemgetter(0))
# Build a new filename by replacing or adding the flag values
outname = filename
in_e = False
for v in vals:
placeholder = ''.join(['~', v[0][1:], '~'])
if placeholder in filename:
outname = outname.replace(placeholder, v[1])
elif v[0] == '-e1' and '~e~' in filename and not '-e' in se_vals:
outname = outname.replace('~e~', v[1])
elif v[0] == '-s1' and '~s~' in filename and not '-s' in se_vals:
outname = outname.replace('~s~', v[1])
# DEBUG - CHECK THIS IS TRUE!
elif v[0] == '-e' and '~e1~' in filename:
outname = outname.replace('~e1~', v[1])
elif v[0] == '-s' and '~s1~' in filename:
outname = outname.replace('~s1~', v[1])
else:
if v[0].startswith('-e'):
if not in_e:
prefix = '_'
else:
prefix = '+'
in_e = True
elif v[0].startswith('-s'):
if in_e:
prefix = '_'
else:
prefix = '+'
in_e = False
outname += prefix + v[1]
return outname
def enum(*sequential, **named):
"""Creates a new enum using the values handed to it.
Taken from Alec Thomas on StackOverflow:
http://stackoverflow.com/questions/36932/how-can-i-represent-an-enum-in-python
Examples:
Can be created and accessed using:
>>> Numbers = enum('ZERO', 'ONE', 'TWO')
>>> Numbers.ZERO
0
>>> Numbers.ONE
1
Or reverse the process o get the name from the value:
>>> Numbers.reverse_mapping['three']
'THREE'
"""
enums = dict(zip(sequential, range(len(sequential))), **named)
reverse = dict((value, key) for key, value in enums.items())
enums['reverse_mapping'] = reverse
return type(str('Enum'), (), enums)
| 30.151329 | 89 | 0.573928 |
6077af38c7d93f92258618a8fca241844841e829 | 3,636 | py | Python | src/ansible_navigator/ui_framework/content_defs.py | goneri/ansible-navigator | 59c5c4e9758404bcf363face09cf46c325b01ad3 | [
"Apache-2.0"
] | null | null | null | src/ansible_navigator/ui_framework/content_defs.py | goneri/ansible-navigator | 59c5c4e9758404bcf363face09cf46c325b01ad3 | [
"Apache-2.0"
] | null | null | null | src/ansible_navigator/ui_framework/content_defs.py | goneri/ansible-navigator | 59c5c4e9758404bcf363face09cf46c325b01ad3 | [
"Apache-2.0"
] | null | null | null | """Definitions of UI content objects."""
from dataclasses import asdict
from dataclasses import dataclass
from enum import Enum
from typing import Dict
from typing import Generic
from typing import TypeVar
from ..utils.compatibility import TypeAlias
from ..utils.serialize import SerializationFormat
T = TypeVar("T") # pylint:disable=invalid-name # https://github.com/PyCQA/pylint/pull/5221
DictType: TypeAlias = Dict[str, T]
| 32.756757 | 98 | 0.667217 |
6077b8cff40a612dbe4bda3b40ee9c7455ae0910 | 1,244 | py | Python | FWCore/MessageService/test/u28_cerr_cfg.py | SWuchterl/cmssw | 769b4a7ef81796579af7d626da6039dfa0347b8e | [
"Apache-2.0"
] | 6 | 2017-09-08T14:12:56.000Z | 2022-03-09T23:57:01.000Z | FWCore/MessageService/test/u28_cerr_cfg.py | SWuchterl/cmssw | 769b4a7ef81796579af7d626da6039dfa0347b8e | [
"Apache-2.0"
] | 545 | 2017-09-19T17:10:19.000Z | 2022-03-07T16:55:27.000Z | FWCore/MessageService/test/u28_cerr_cfg.py | SWuchterl/cmssw | 769b4a7ef81796579af7d626da6039dfa0347b8e | [
"Apache-2.0"
] | 14 | 2017-10-04T09:47:21.000Z | 2019-10-23T18:04:45.000Z | # u28_cerr_cfg.py:
#
# Non-regression test configuration file for MessageLogger service:
# distinct threshold level for linked destination, where
#
import FWCore.ParameterSet.Config as cms
process = cms.Process("TEST")
import FWCore.Framework.test.cmsExceptionsFatal_cff
process.options = FWCore.Framework.test.cmsExceptionsFatal_cff.options
process.load("FWCore.MessageService.test.Services_cff")
process.MessageLogger = cms.Service("MessageLogger",
categories = cms.untracked.vstring('preEventProcessing'),
destinations = cms.untracked.vstring('cerr'),
statistics = cms.untracked.vstring('cerr_stats'),
cerr_stats = cms.untracked.PSet(
threshold = cms.untracked.string('WARNING'),
output = cms.untracked.string('cerr')
),
u28_output = cms.untracked.PSet(
threshold = cms.untracked.string('INFO'),
noTimeStamps = cms.untracked.bool(True),
preEventProcessing = cms.untracked.PSet(
limit = cms.untracked.int32(0)
)
)
)
process.maxEvents = cms.untracked.PSet(
input = cms.untracked.int32(3)
)
process.source = cms.Source("EmptySource")
process.sendSomeMessages = cms.EDAnalyzer("UnitTestClient_A")
process.p = cms.Path(process.sendSomeMessages)
| 29.619048 | 70 | 0.728296 |
607a424b8a6541dc8b215105306da525113497c5 | 2,001 | py | Python | content/browse/utils.py | Revibe-Music/core-services | 6b11cf16ad2c35d948f3a5c0e7a161e5b7cfc1b2 | [
"MIT"
] | 2 | 2022-01-24T23:30:18.000Z | 2022-01-26T00:21:22.000Z | content/browse/utils.py | Revibe-Music/core-services | 6b11cf16ad2c35d948f3a5c0e7a161e5b7cfc1b2 | [
"MIT"
] | null | null | null | content/browse/utils.py | Revibe-Music/core-services | 6b11cf16ad2c35d948f3a5c0e7a161e5b7cfc1b2 | [
"MIT"
] | null | null | null | """
Created:04 Mar. 2020
Author: Jordan Prechac
"""
from revibe._helpers import const
from administration.utils import retrieve_variable
from content.models import Song, Album, Artist
from content.serializers import v1 as cnt_ser_v1
# -----------------------------------------------------------------------------
# _DEFAULT_LIMIT = 50
# limit_variable = retrieve_variable()
# try:
# limit_variable = int(limit_variable)
# _DEFAULT_LIMIT = max(min(limit_variable, 100), 10)
# except ValueError as ve:
# print("Could not read browse section default limit variable")
# print(ve)
_name = "name"
_type = "type"
_results = "results"
_endpoint = "endpoint" | 34.5 | 127 | 0.710645 |
607a5c3dca22f0d225966ee1a7f786fad681858e | 4,433 | py | Python | Segmentation/model.py | vasetrendafilov/ComputerVision | 5fcbe57fb1609ef44733aed0fab8c69d71fae21f | [
"MIT"
] | null | null | null | Segmentation/model.py | vasetrendafilov/ComputerVision | 5fcbe57fb1609ef44733aed0fab8c69d71fae21f | [
"MIT"
] | null | null | null | Segmentation/model.py | vasetrendafilov/ComputerVision | 5fcbe57fb1609ef44733aed0fab8c69d71fae21f | [
"MIT"
] | null | null | null | """
Authors: Elena Vasileva, Zoran Ivanovski
E-mail: elenavasileva95@gmail.com, mars@feit.ukim.edu.mk
Course: Mashinski vid, FEEIT, Spring 2021
Date: 09.03.2021
Description: function library
model operations: construction, loading, saving
Python version: 3.6
"""
# python imports
from keras.layers import Conv2D, Conv2DTranspose, MaxPool2D, UpSampling2D, Input, Concatenate
from keras.models import Model, model_from_json
def load_model(model_path, weights_path):
"""
loads a pre-trained model configuration and calculated weights
:param model_path: path of the serialized model configuration file (.json) [string]
:param weights_path: path of the serialized model weights file (.h5) [string]
:return: model - keras model object
"""
# --- load model configuration ---
json_file = open(model_path, 'r')
model_json = json_file.read()
json_file.close()
model = model_from_json(model_json) # load model architecture
model.load_weights(weights_path) # load weights
return model
def construct_model_unet_orig(input_shape):
"""
construct semantic segmentation model architecture (encoder-decoder)
:param input_shape: list of input dimensions (height, width, depth) [tuple]
:return: model - Keras model object
"""
input = Input(shape=input_shape)
# --- encoder ---
conv1 = Conv2D(filters=64, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(input)
conv11 = Conv2D(filters=64, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(conv1)
pool1 = MaxPool2D(pool_size=(2, 2))(conv11)
conv2 = Conv2D(filters=128, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(pool1)
conv22 = Conv2D(filters=128, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(conv2)
pool2 = MaxPool2D(pool_size=(2, 2))(conv22)
conv3 = Conv2D(filters=256, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(pool2)
conv33 = Conv2D(filters=256, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(conv3)
pool3 = MaxPool2D(pool_size=(2, 2))(conv33)
conv4 = Conv2D(filters=512, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(pool3)
conv44 = Conv2D(filters=512, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(conv4)
pool4 = MaxPool2D(pool_size=(2, 2))(conv44)
# --- decoder ---
conv5 = Conv2D(filters=1024, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(pool4)
conv55 = Conv2D(filters=512, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(conv5)
up1 = UpSampling2D(size=(2, 2))(conv55)
merge1 = Concatenate(axis=3)([conv44, up1])
deconv1 = Conv2DTranspose(filters=512, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(merge1)
deconv11 = Conv2DTranspose(filters=256, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(deconv1)
up2 = UpSampling2D(size=(2, 2))(deconv11)
merge2 = Concatenate(axis=3)([conv33, up2])
deconv2 = Conv2DTranspose(filters=256, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(merge2)
deconv22 = Conv2DTranspose(filters=128, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(deconv2)
up3 = UpSampling2D(size=(2, 2))(deconv22)
merge3 = Concatenate(axis=3)([conv22, up3])
deconv3 = Conv2DTranspose(filters=128, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(merge3)
deconv33 = Conv2DTranspose(filters=64, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(deconv3)
up4 = UpSampling2D(size=(2, 2))(deconv33)
merge4 = Concatenate(axis=3)([conv11, up4])
deconv4 = Conv2DTranspose(filters=64, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(merge4)
deconv44 = Conv2DTranspose(filters=64, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(deconv4)
output = Conv2DTranspose(filters=input_shape[2], kernel_size=1, padding='same', activation='sigmoid')(deconv44)
model = Model(input=input, output=output)
return model
| 47.666667 | 134 | 0.723438 |
607a8097d7dacf319776f076e01a66d964066e6e | 631 | py | Python | Day24_Python/part1.py | Rog3rSm1th/PolyglotOfCode | a70f50b5c882139727cbdf75144a8346cb6c538b | [
"MIT"
] | 7 | 2021-03-23T14:08:01.000Z | 2021-05-17T16:24:16.000Z | Day24_Python/part1.py | Rog3rSm1th/PolyglotOfCode | a70f50b5c882139727cbdf75144a8346cb6c538b | [
"MIT"
] | null | null | null | Day24_Python/part1.py | Rog3rSm1th/PolyglotOfCode | a70f50b5c882139727cbdf75144a8346cb6c538b | [
"MIT"
] | 2 | 2021-04-29T22:03:02.000Z | 2022-01-18T15:55:42.000Z | #!/usr/bin/env python3
#-*- coding: utf-8 -*-
from itertools import combinations
packages = [int(num) for num in open('input.txt')]
print(solve(packages, 3)) | 30.047619 | 74 | 0.62916 |
607b94247f45130f250e70bb74d679462531a2da | 5,921 | py | Python | generate-album.py | atomicparade/photo-album | 437bc18bb00da5ce27216d03b48b78d60a0ad3fd | [
"CC0-1.0",
"Unlicense"
] | null | null | null | generate-album.py | atomicparade/photo-album | 437bc18bb00da5ce27216d03b48b78d60a0ad3fd | [
"CC0-1.0",
"Unlicense"
] | null | null | null | generate-album.py | atomicparade/photo-album | 437bc18bb00da5ce27216d03b48b78d60a0ad3fd | [
"CC0-1.0",
"Unlicense"
] | null | null | null | import configparser
import math
import re
import urllib
from pathlib import Path
from PIL import Image
if __name__ == '__main__':
main()
| 22.861004 | 161 | 0.570174 |
607c6c12c8adb56d644bb03375a7a0619419eb1b | 4,385 | py | Python | tests/test_sne_truth.py | LSSTDESC/sims_TruthCatalog | 348f5d231997eed387aaa6e3fd4218c126e14cdb | [
"BSD-3-Clause"
] | 2 | 2020-02-04T22:59:41.000Z | 2020-03-19T00:17:09.000Z | tests/test_sne_truth.py | LSSTDESC/sims_TruthCatalog | 348f5d231997eed387aaa6e3fd4218c126e14cdb | [
"BSD-3-Clause"
] | 7 | 2020-02-10T21:59:19.000Z | 2021-04-27T16:31:26.000Z | tests/test_sne_truth.py | LSSTDESC/sims_TruthCatalog | 348f5d231997eed387aaa6e3fd4218c126e14cdb | [
"BSD-3-Clause"
] | null | null | null | """
Unit tests for SNIa truth catalog code.
"""
import os
import unittest
import sqlite3
import numpy as np
import pandas as pd
from desc.sims_truthcatalog import SNeTruthWriter, SNSynthPhotFactory
if __name__ == '__main__':
unittest.main()
| 41.367925 | 77 | 0.580844 |
607dfd1e508e9c983974056179f2dfae1594aa2a | 10,053 | py | Python | testsite/management/commands/load_test_transactions.py | gikoluo/djaodjin-saas | badd7894ac327191008a1b3a0ebd0d07b55908c3 | [
"BSD-2-Clause"
] | null | null | null | testsite/management/commands/load_test_transactions.py | gikoluo/djaodjin-saas | badd7894ac327191008a1b3a0ebd0d07b55908c3 | [
"BSD-2-Clause"
] | null | null | null | testsite/management/commands/load_test_transactions.py | gikoluo/djaodjin-saas | badd7894ac327191008a1b3a0ebd0d07b55908c3 | [
"BSD-2-Clause"
] | null | null | null | # Copyright (c) 2018, DjaoDjin inc.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice,
# this list of conditions and the following disclaimer.
# 2. Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED
# TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR
# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
# OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR
# OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF
# ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import datetime, logging, random
from django.conf import settings
from django.core.management.base import BaseCommand
from django.db.utils import IntegrityError
from django.template.defaultfilters import slugify
from django.utils.timezone import utc
from saas.backends.razorpay_processor import RazorpayBackend
from saas.models import Plan, Transaction, get_broker
from saas.utils import datetime_or_now
from saas.settings import PROCESSOR_ID
LOGGER = logging.getLogger(__name__)
| 34.90625 | 80 | 0.552372 |
607e7c80f119c1c9c55adbd71e291f8ae17a8b06 | 2,801 | py | Python | seq2seq_pt/s2s/xutils.py | magic282/SEASS | b780bf45b47d15145a148e5992bcd157c119d338 | [
"MIT"
] | 36 | 2018-05-25T01:09:21.000Z | 2022-01-25T02:45:18.000Z | seq2seq_pt/s2s/xutils.py | magic282/SEASS | b780bf45b47d15145a148e5992bcd157c119d338 | [
"MIT"
] | 11 | 2018-06-30T14:19:21.000Z | 2021-03-19T01:27:09.000Z | seq2seq_pt/s2s/xutils.py | magic282/SEASS | b780bf45b47d15145a148e5992bcd157c119d338 | [
"MIT"
] | 10 | 2018-06-06T03:15:51.000Z | 2022-01-25T02:45:44.000Z | import sys
import struct
| 47.474576 | 87 | 0.593717 |
608009fe5a0b2fb99700c8345bb126060be7366e | 4,219 | py | Python | pml-services/pml_storage.py | Novartis/Project-Mona-Lisa | f8fcef5b434470e2a17e97fceaef46615eda1b31 | [
"Apache-2.0"
] | 3 | 2017-10-17T14:49:54.000Z | 2021-01-12T23:37:33.000Z | pml-services/pml_storage.py | Novartis/Project-Mona-Lisa | f8fcef5b434470e2a17e97fceaef46615eda1b31 | [
"Apache-2.0"
] | 10 | 2019-12-16T20:37:22.000Z | 2021-05-21T14:35:39.000Z | pml-services/pml_storage.py | Novartis/Project-Mona-Lisa | f8fcef5b434470e2a17e97fceaef46615eda1b31 | [
"Apache-2.0"
] | 1 | 2018-09-12T17:06:18.000Z | 2018-09-12T17:06:18.000Z | # Copyright 2017 Novartis Institutes for BioMedical Research 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.
from __future__ import print_function
import boto3
from boto3.dynamodb.conditions import Key
from random import randint
import os
import base64
| 35.754237 | 584 | 0.576677 |
6081b2d6561823daafb84e5aca2d32f42f1d920c | 3,196 | py | Python | binan.py | Nightleaf0512/PythonCryptoCurriencyPriceChecker | 9531d4a6978d280b4ca759d7ba24d3edf77fe5b2 | [
"CC0-1.0"
] | null | null | null | binan.py | Nightleaf0512/PythonCryptoCurriencyPriceChecker | 9531d4a6978d280b4ca759d7ba24d3edf77fe5b2 | [
"CC0-1.0"
] | null | null | null | binan.py | Nightleaf0512/PythonCryptoCurriencyPriceChecker | 9531d4a6978d280b4ca759d7ba24d3edf77fe5b2 | [
"CC0-1.0"
] | null | null | null | from binance.client import Client
import PySimpleGUI as sg
api_key = "your_binance_apikey"
secret_key = "your_binance_secretkey"
client = Client(api_key=api_key, api_secret=secret_key)
# price
# 24h percentchange
# GUI
sg.theme('DefaultNoMoreNagging')
# Tabs
# Layout
layout = [[sg.Text("Crypto Currencies", font=("Arial", 10, 'bold'))],
[sg.TabGroup([[sg.Tab("BTC", tabs("BTC"), border_width="18"), sg.Tab("XRP", tabs("XRP"), border_width="18"), sg.Tab("DOGE", tabs("DOGE"), border_width="18")]])]]
window = sg.Window("NightLeaf Crypto", layout)
a_list =["BTC", "XRP", "DOGE"]
coin_window(*a_list)
| 42.613333 | 172 | 0.595432 |
608211636fe7f97cd14766030248070475866342 | 1,026 | py | Python | saleor/graphql/ushop/bulk_mutations.py | nlkhagva/saleor | 0d75807d08ac49afcc904733724ac870e8359c10 | [
"CC-BY-4.0"
] | null | null | null | saleor/graphql/ushop/bulk_mutations.py | nlkhagva/saleor | 0d75807d08ac49afcc904733724ac870e8359c10 | [
"CC-BY-4.0"
] | 1 | 2022-02-15T03:31:12.000Z | 2022-02-15T03:31:12.000Z | saleor/graphql/ushop/bulk_mutations.py | nlkhagva/ushop | abf637eb6f7224e2d65d62d72a0c15139c64bb39 | [
"CC-BY-4.0"
] | null | null | null | import graphene
from ...unurshop.ushop import models
from ..core.mutations import BaseBulkMutation, ModelBulkDeleteMutation
| 28.5 | 87 | 0.660819 |
608242d64b36989e754c60c8ef5cc3a72e5f5595 | 3,180 | py | Python | src/main/NLP/STRING_MATCH/scopus_ha_module_match.py | alvinajacquelyn/COMP0016_2 | fd57706a992e1e47af7c802320890e93a15fc0c7 | [
"MIT"
] | null | null | null | src/main/NLP/STRING_MATCH/scopus_ha_module_match.py | alvinajacquelyn/COMP0016_2 | fd57706a992e1e47af7c802320890e93a15fc0c7 | [
"MIT"
] | null | null | null | src/main/NLP/STRING_MATCH/scopus_ha_module_match.py | alvinajacquelyn/COMP0016_2 | fd57706a992e1e47af7c802320890e93a15fc0c7 | [
"MIT"
] | null | null | null | import os, sys, re
import json
import pandas as pd
import pymongo
from main.LOADERS.publication_loader import PublicationLoader
from main.MONGODB_PUSHERS.mongodb_pusher import MongoDbPusher
from main.NLP.PREPROCESSING.preprocessor import Preprocessor
| 42.972973 | 136 | 0.610377 |
60848af0afddec7b1d22b291ac9bd888d5799291 | 642 | py | Python | tools/urls.py | Cyberdeep/archerysec | a4b1a0c4f736bd70bdea693c7a7c479a69bb0f7d | [
"BSD-3-Clause"
] | null | null | null | tools/urls.py | Cyberdeep/archerysec | a4b1a0c4f736bd70bdea693c7a7c479a69bb0f7d | [
"BSD-3-Clause"
] | null | null | null | tools/urls.py | Cyberdeep/archerysec | a4b1a0c4f736bd70bdea693c7a7c479a69bb0f7d | [
"BSD-3-Clause"
] | 1 | 2018-08-12T17:29:35.000Z | 2018-08-12T17:29:35.000Z | # _
# /\ | |
# / \ _ __ ___| |__ ___ _ __ _ _
# / /\ \ | '__/ __| '_ \ / _ \ '__| | | |
# / ____ \| | | (__| | | | __/ | | |_| |
# /_/ \_\_| \___|_| |_|\___|_| \__, |
# __/ |
# |___/
# Copyright (C) 2017-2018 ArcherySec
# This file is part of ArcherySec Project.
from django.conf.urls import url
from tools import views
app_name = 'tools'
urlpatterns = [
url(r'^sslscan/$',
views.sslscan,
name='sslscan'),
url(r'^sslscan_result/$',
views.sslscan_result,
name='sslscan_result'),
]
| 25.68 | 43 | 0.426791 |
6084e8784a7c8fb58869bc711fe87b2383807ac6 | 7,484 | py | Python | api/vm/base/utils.py | erigones/esdc-ce | 2e39211a8f5132d66e574d3a657906c7d3c406fe | [
"Apache-2.0"
] | 97 | 2016-11-15T14:44:23.000Z | 2022-03-13T18:09:15.000Z | api/vm/base/utils.py | erigones/esdc-ce | 2e39211a8f5132d66e574d3a657906c7d3c406fe | [
"Apache-2.0"
] | 334 | 2016-11-17T19:56:57.000Z | 2022-03-18T10:45:53.000Z | api/vm/base/utils.py | erigones/esdc-ce | 2e39211a8f5132d66e574d3a657906c7d3c406fe | [
"Apache-2.0"
] | 33 | 2017-01-02T16:04:13.000Z | 2022-02-07T19:20:24.000Z | from core.celery.config import ERIGONES_TASK_USER
from que.tasks import execute, get_task_logger
from vms.models import SnapshotDefine, Snapshot, BackupDefine, Backup, IPAddress
logger = get_task_logger(__name__)
def is_vm_missing(vm, msg):
"""
Check failed command output and return True if VM is not on compute node.
"""
check_str = vm.hostname + ': No such zone configured'
return check_str in msg
def vm_delete_snapshots_of_removed_disks(vm):
"""
This helper function deletes snapshots for VM with changing disk IDs. Bug #chili-363
++ Bug #chili-220 - removing snapshot and backup definitions for removed disks.
"""
removed_disk_ids = [Snapshot.get_real_disk_id(i) for i in vm.create_json_update_disks().get('remove_disks', [])]
if removed_disk_ids:
Snapshot.objects.filter(vm=vm, disk_id__in=removed_disk_ids).delete()
SnapshotDefine.objects.filter(vm=vm, disk_id__in=removed_disk_ids).delete()
Backup.objects.filter(vm=vm, disk_id__in=removed_disk_ids, last=True).update(last=False)
BackupDefine.objects.filter(vm=vm, disk_id__in=removed_disk_ids).delete()
return removed_disk_ids
def _reset_allowed_ip_usage(vm, ip):
"""Helper function used below. It sets the IP usage back to VM [1] only if other VMs, which use the address in
allowed_ips are in notcreated state."""
if all(other_vm.is_notcreated() for other_vm in ip.vms.exclude(uuid=vm.uuid)):
ip.usage = IPAddress.VM
ip.save()
def _is_ip_ok(ip_queryset, vm_ip, vm_network_uuid):
"""Helper function used below. Return True if vm_ip (string) is "dhcp" or is found in the IPAddress queryset
and has the expected usage flag and subnet uuid."""
if vm_ip == 'dhcp':
return True
return any(ip.ip == vm_ip and ip.subnet.uuid == vm_network_uuid and ip.usage == IPAddress.VM_REAL
for ip in ip_queryset)
def vm_update_ipaddress_usage(vm):
"""
This helper function is responsible for updating IPAddress.usage and IPAddress.vm of server IPs (#chili-615,1029),
by removing association from IPs that, are not set on any NIC and:
- when a VM is deleted all IP usages are set to IPAddress.VM (in DB) and
- when a VM is created or updated all IP usages are set to IPAddress.VM_REAL (on hypervisor) and
Always call this function _only_ after vm.json_active is synced with vm.json!!!
In order to properly understand this code you have understand the association between an IPAddress and Vm model.
This function may raise a ValueError if the VM and IP address were not properly associated (e.g. via vm_define_nic).
"""
current_ips = set(vm.json_active_get_ips(primary_ips=True, allowed_ips=False))
current_ips.update(vm.json_get_ips(primary_ips=True, allowed_ips=False))
current_allowed_ips = set(vm.json_active_get_ips(primary_ips=False, allowed_ips=True))
current_allowed_ips.update(vm.json_get_ips(primary_ips=False, allowed_ips=True))
# Return old IPs back to IP pool, so they can be used again
vm.ipaddress_set.exclude(ip__in=current_ips).update(vm=None, usage=IPAddress.VM)
# Remove association of removed vm.allowed_ips
for ip in vm.allowed_ips.exclude(ip__in=current_allowed_ips):
ip.vms.remove(vm)
_reset_allowed_ip_usage(vm, ip)
if vm.is_notcreated():
# Server was deleted from hypervisor
vm.ipaddress_set.filter(usage=IPAddress.VM_REAL).update(usage=IPAddress.VM)
for ip in vm.allowed_ips.filter(usage=IPAddress.VM_REAL):
_reset_allowed_ip_usage(vm, ip)
return
# Server was updated or created
vm.ipaddress_set.filter(usage=IPAddress.VM).update(usage=IPAddress.VM_REAL)
vm.allowed_ips.filter(usage=IPAddress.VM).update(usage=IPAddress.VM_REAL)
# The VM configuration may be changed directly on the hypervisor, thus the VM could have
# new NICs and IP addresses which configuration bypassed our API - issue #168.
vm_ips = vm.ipaddress_set.select_related('subnet').filter(usage=IPAddress.VM_REAL)
vm_allowed_ips = vm.allowed_ips.select_related('subnet').filter(usage=IPAddress.VM_REAL)
# For issue #168 we have to check the VM<->IPAddress association in a loop for each NIC, because we need to
# match the NIC.network_uuid with a Subnet.
for nic_id, nic in enumerate(vm.json_active_get_nics(), 1):
network_uuid = nic.get('network_uuid', None)
if network_uuid:
ip = nic.get('ip', '')
allowed_ips = nic.get('allowed_ips', [])
if ip:
logger.debug('VM: %s | NIC ID: %s | NIC network: %s | IP address: %s', vm, nic_id, network_uuid, ip)
if not _is_ip_ok(vm_ips, ip, network_uuid):
raise ValueError('VM %s NIC ID %s IP address %s is not properly associated with VM!' %
(vm, nic_id, ip))
for ip in allowed_ips:
logger.debug('VM: %s | NIC ID: %s | NIC network: %s | IP address: %s', vm, nic_id, network_uuid, ip)
if not _is_ip_ok(vm_allowed_ips, ip, network_uuid):
raise ValueError('VM %s NIC ID %s allowed IP address %s is not properly associated with VM!' %
(vm, nic_id, ip))
else:
raise ValueError('VM %s NIC ID %s does not have a network uuid!' % (vm, nic_id))
def vm_deploy(vm, force_stop=False):
"""
Internal API call used for finishing VM deploy;
Actually cleaning the json and starting the VM.
"""
if force_stop: # VM is running without OS -> stop
cmd = 'vmadm stop %s -F >/dev/null 2>/dev/null; vmadm get %s 2>/dev/null' % (vm.uuid, vm.uuid)
else: # VM is stopped and deployed -> start
cmd = 'vmadm start %s >/dev/null 2>/dev/null; vmadm get %s 2>/dev/null' % (vm.uuid, vm.uuid)
msg = 'Deploy server'
lock = 'vmadm deploy ' + vm.uuid
meta = {
'output': {
'returncode': 'returncode',
'stderr': 'message',
'stdout': 'json'
},
'replace_stderr': ((vm.uuid, vm.hostname),),
'msg': msg, 'vm_uuid': vm.uuid
}
callback = ('api.vm.base.tasks.vm_deploy_cb', {'vm_uuid': vm.uuid})
return execute(ERIGONES_TASK_USER, None, cmd, meta=meta, lock=lock, callback=callback,
queue=vm.node.fast_queue, nolog=True, ping_worker=False, check_user_tasks=False)
def vm_reset(vm):
"""
Internal API call used for VM reboots in emergency situations.
"""
cmd = 'vmadm stop %s -F; vmadm start %s' % (vm.uuid, vm.uuid)
return execute(ERIGONES_TASK_USER, None, cmd, callback=False, queue=vm.node.fast_queue, nolog=True,
check_user_tasks=False)
def vm_update(vm):
"""
Internal API used for updating VM if there were changes in json detected.
"""
logger.info('Running PUT vm_manage(%s), because something (vnc port?) has changed', vm)
from api.vm.base.views import vm_manage
from api.utils.request import get_dummy_request
from api.utils.views import call_api_view
request = get_dummy_request(vm.dc, method='PUT', system_user=True)
res = call_api_view(request, 'PUT', vm_manage, vm.hostname)
if res.status_code == 201:
logger.warn('PUT vm_manage(%s) was successful: %s', vm, res.data)
else:
logger.error('PUT vm_manage(%s) failed: %s (%s): %s', vm, res.status_code, res.status_text, res.data)
| 45.084337 | 120 | 0.67504 |
60855d2a5eca63351c8e4dd3352e1f4b94d4ebb3 | 1,133 | py | Python | 993_Cousins-in-Binary-Tree.py | Coalin/Daily-LeetCode-Exercise | a064dcdc3a82314be4571d342c4807291a24f69f | [
"MIT"
] | 3 | 2018-07-05T05:51:10.000Z | 2019-05-04T08:35:44.000Z | 993_Cousins-in-Binary-Tree.py | Coalin/Daily-LeetCode-Exercise | a064dcdc3a82314be4571d342c4807291a24f69f | [
"MIT"
] | null | null | null | 993_Cousins-in-Binary-Tree.py | Coalin/Daily-LeetCode-Exercise | a064dcdc3a82314be4571d342c4807291a24f69f | [
"MIT"
] | null | null | null | # Definition for a binary tree node.
# class TreeNode:
# def __init__(self, val=0, left=None, right=None):
# self.val = val
# self.left = left
# self.right = right
| 30.621622 | 75 | 0.485437 |
6085668853e75c8ad16430458a509e22fa3b1078 | 5,433 | py | Python | docker-images/taigav2/taiga-back/tests/integration/test_tasks_tags.py | mattcongy/itshop | 6be025a9eaa7fe7f495b5777d1f0e5a3184121c9 | [
"MIT"
] | 1 | 2017-05-29T19:01:06.000Z | 2017-05-29T19:01:06.000Z | docker-images/taigav2/taiga-back/tests/integration/test_tasks_tags.py | mattcongy/itshop | 6be025a9eaa7fe7f495b5777d1f0e5a3184121c9 | [
"MIT"
] | null | null | null | docker-images/taigav2/taiga-back/tests/integration/test_tasks_tags.py | mattcongy/itshop | 6be025a9eaa7fe7f495b5777d1f0e5a3184121c9 | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
# Copyright (C) 2014-2016 Andrey Antukh <niwi@niwi.nz>
# Copyright (C) 2014-2016 Jess Espino <jespinog@gmail.com>
# Copyright (C) 2014-2016 David Barragn <bameda@dbarragan.com>
# Copyright (C) 2014-2016 Alejandro Alonso <alejandro.alonso@kaleidos.net>
# Copyright (C) 2014-2016 Anler Hernndez <hello@anler.me>
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as
# published by the Free Software Foundation, either version 3 of the
# License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
from unittest import mock
from collections import OrderedDict
from django.core.urlresolvers import reverse
from taiga.base.utils import json
from .. import factories as f
import pytest
pytestmark = pytest.mark.django_db
| 33.537037 | 99 | 0.679735 |
6085b11c27e78fe767fa371d2b33135501f31145 | 679 | py | Python | pytorchocr/postprocess/cls_postprocess.py | satchelwu/PaddleOCR2Pytorch | 6941565cfd4c45470cc3bf9d434c8c32267a33ef | [
"Apache-2.0"
] | 3 | 2021-04-23T12:31:07.000Z | 2021-11-17T04:39:38.000Z | pytorchocr/postprocess/cls_postprocess.py | satchelwu/PaddleOCR2Pytorch | 6941565cfd4c45470cc3bf9d434c8c32267a33ef | [
"Apache-2.0"
] | null | null | null | pytorchocr/postprocess/cls_postprocess.py | satchelwu/PaddleOCR2Pytorch | 6941565cfd4c45470cc3bf9d434c8c32267a33ef | [
"Apache-2.0"
] | 1 | 2022-03-24T03:31:34.000Z | 2022-03-24T03:31:34.000Z | import torch
| 33.95 | 62 | 0.60972 |
6088008352658e01cb8328f084aae6c78e40e074 | 5,717 | py | Python | inference_folder.py | aba-ai-learning/Single-Human-Parsing-LIP | b1c0c91cef34dabf598231127886b669838fc085 | [
"MIT"
] | null | null | null | inference_folder.py | aba-ai-learning/Single-Human-Parsing-LIP | b1c0c91cef34dabf598231127886b669838fc085 | [
"MIT"
] | null | null | null | inference_folder.py | aba-ai-learning/Single-Human-Parsing-LIP | b1c0c91cef34dabf598231127886b669838fc085 | [
"MIT"
] | null | null | null | #!/usr/local/bin/python3
# -*- coding: utf-8 -*-
import os
import argparse
import logging
import numpy as np
from PIL import Image
import matplotlib
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
from torchvision import transforms
import cv2
import tqdm
from net.pspnet import PSPNet
models = {
'squeezenet': lambda: PSPNet(sizes=(1, 2, 3, 6), psp_size=512, deep_features_size=256, backend='squeezenet'),
'densenet': lambda: PSPNet(sizes=(1, 2, 3, 6), psp_size=1024, deep_features_size=512, backend='densenet'),
'resnet18': lambda: PSPNet(sizes=(1, 2, 3, 6), psp_size=512, deep_features_size=256, backend='resnet18'),
'resnet34': lambda: PSPNet(sizes=(1, 2, 3, 6), psp_size=512, deep_features_size=256, backend='resnet34'),
'resnet50': lambda: PSPNet(sizes=(1, 2, 3, 6), psp_size=2048, deep_features_size=1024, backend='resnet50'),
'resnet101': lambda: PSPNet(sizes=(1, 2, 3, 6), psp_size=2048, deep_features_size=1024, backend='resnet101'),
'resnet152': lambda: PSPNet(sizes=(1, 2, 3, 6), psp_size=2048, deep_features_size=1024, backend='resnet152')
}
parser = argparse.ArgumentParser(description="Pyramid Scene Parsing Network")
parser.add_argument('--models-path', type=str, default='./checkpoints',
help='Path for storing model snapshots')
parser.add_argument('--backend', type=str,
default='densenet', help='Feature extractor')
parser.add_argument('--num-classes', type=int,
default=20, help="Number of classes.")
args = parser.parse_args()
if __name__ == '__main__':
main()
| 36.414013 | 113 | 0.575477 |
608859a6a315773abcd6cc904b0d54529fd39c40 | 870 | py | Python | src/random_policy.py | shuvoxcd01/Policy-Evaluation | 6bfdfdaa67e1dd67edb75fcf5b4664f2584345ac | [
"Apache-2.0"
] | null | null | null | src/random_policy.py | shuvoxcd01/Policy-Evaluation | 6bfdfdaa67e1dd67edb75fcf5b4664f2584345ac | [
"Apache-2.0"
] | null | null | null | src/random_policy.py | shuvoxcd01/Policy-Evaluation | 6bfdfdaa67e1dd67edb75fcf5b4664f2584345ac | [
"Apache-2.0"
] | null | null | null | from src.gridworld_mdp import GridWorld
| 31.071429 | 79 | 0.651724 |
6088d8cdd329f8f2bb36a7c2566daad3bd603e75 | 6,013 | py | Python | sktime/classification/feature_based/_summary_classifier.py | Rubiel1/sktime | 2fd2290fb438224f11ddf202148917eaf9b73a87 | [
"BSD-3-Clause"
] | 1 | 2021-09-08T14:24:52.000Z | 2021-09-08T14:24:52.000Z | sktime/classification/feature_based/_summary_classifier.py | Rubiel1/sktime | 2fd2290fb438224f11ddf202148917eaf9b73a87 | [
"BSD-3-Clause"
] | null | null | null | sktime/classification/feature_based/_summary_classifier.py | Rubiel1/sktime | 2fd2290fb438224f11ddf202148917eaf9b73a87 | [
"BSD-3-Clause"
] | null | null | null | # -*- coding: utf-8 -*-
"""Summary Classifier.
Pipeline classifier using the basic summary statistics and an estimator.
"""
__author__ = ["MatthewMiddlehurst"]
__all__ = ["SummaryClassifier"]
import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sktime.base._base import _clone_estimator
from sktime.classification.base import BaseClassifier
from sktime.transformations.series.summarize import SummaryTransformer
| 32.327957 | 82 | 0.617163 |
608959b12d0989d6fd9417a960a75d31ceab0a32 | 461 | py | Python | Topics/Submitting data/POST Request With Several Keys/main.py | valenciarichards/hypernews-portal | 0b6c4d8aefe4f8fc7dc90d6542716e98f52515b3 | [
"MIT"
] | 1 | 2021-07-26T03:06:14.000Z | 2021-07-26T03:06:14.000Z | Topics/Submitting data/POST Request With Several Keys/main.py | valenciarichards/hypernews-portal | 0b6c4d8aefe4f8fc7dc90d6542716e98f52515b3 | [
"MIT"
] | null | null | null | Topics/Submitting data/POST Request With Several Keys/main.py | valenciarichards/hypernews-portal | 0b6c4d8aefe4f8fc7dc90d6542716e98f52515b3 | [
"MIT"
] | null | null | null | from django.shortcuts import redirect
from django.views import View
| 27.117647 | 56 | 0.590022 |
608982b20a5decc4b9d80d0fe548b89804a688a8 | 705 | py | Python | pypeln/thread/api/to_iterable_thread_test.py | quarckster/pypeln | f4160d0f4d4718b67f79a0707d7261d249459a4b | [
"MIT"
] | 1,281 | 2018-09-20T05:35:27.000Z | 2022-03-30T01:29:48.000Z | pypeln/thread/api/to_iterable_thread_test.py | webclinic017/pypeln | 5231806f2cac9d2019dacbbcf913484fd268b8c1 | [
"MIT"
] | 78 | 2018-09-18T20:38:12.000Z | 2022-03-30T20:16:02.000Z | pypeln/thread/api/to_iterable_thread_test.py | webclinic017/pypeln | 5231806f2cac9d2019dacbbcf913484fd268b8c1 | [
"MIT"
] | 88 | 2018-09-24T10:46:14.000Z | 2022-03-28T09:34:50.000Z | import typing as tp
from unittest import TestCase
import hypothesis as hp
from hypothesis import strategies as st
import pypeln as pl
import cytoolz as cz
MAX_EXAMPLES = 10
T = tp.TypeVar("T")
| 23.5 | 46 | 0.721986 |
608996de745b7d9dc600ef6a4f86e57b6a5da416 | 410,082 | py | Python | app/datastore/test.py | sem-onyalo/dnn-training-monitoring-flask | 6a81e06a6871b0d6e890f9fd92f4ab79ac8ee639 | [
"MIT"
] | null | null | null | app/datastore/test.py | sem-onyalo/dnn-training-monitoring-flask | 6a81e06a6871b0d6e890f9fd92f4ab79ac8ee639 | [
"MIT"
] | null | null | null | app/datastore/test.py | sem-onyalo/dnn-training-monitoring-flask | 6a81e06a6871b0d6e890f9fd92f4ab79ac8ee639 | [
"MIT"
] | null | null | null | import json
from app.model import TrainMetrics, TrainPlots
TEST_SUMMARY = '''
{
"Epoch":10,
"Real Accuracy":"1%",
"Fake Accuracy":"99%",
"Elapsed Time":"0:06:30.957221"
}
'''
TEST_HYPERPARAMETERS = '''
{
"Epochs": 100,
"Batch Size": 256,
"Eval Frequency": 10,
"Batches Per Epoch": 234,
"Half Batch": 128,
"Latent Dim": 100,
"Conv Filters": [256, 256],
"Conv Layer Kernel Size": "(3, 3)",
"Conv Transpose Filters": [256, 256],
"Conv Transpose Layer Kernel Size": "(4, 4)",
"Generator Input Filters": 256,
"Generator Output Layer Kernel Size": "(7, 7)",
"Adam Learning Rate": 0.0002,
"Adam Beta 1": 0.5,
"Kernel Init Std Dev": 0.02,
"Leaky Relu Alpha": 0.2,
"Dropout Rate": 0.4
}
'''
TEST_PLOT_LOSS = "data:image/png;base64,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"
TEST_PLOT_IMAGE = 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"
TEST_PLOT_TARGET = "data:image/png;base64,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" | 5,467.76 | 189,814 | 0.965924 |
608a1240bebe926df3b7d5f1923157bb4c477433 | 1,614 | py | Python | pybinsim/pose.py | fkleinTUI/pyBinSim | 5320f62422e0a92154272f8167b87cabdcafe27f | [
"MIT"
] | null | null | null | pybinsim/pose.py | fkleinTUI/pyBinSim | 5320f62422e0a92154272f8167b87cabdcafe27f | [
"MIT"
] | null | null | null | pybinsim/pose.py | fkleinTUI/pyBinSim | 5320f62422e0a92154272f8167b87cabdcafe27f | [
"MIT"
] | null | null | null | import logging
from collections import namedtuple
logger = logging.getLogger("pybinsim.Pose")
| 32.938776 | 103 | 0.669145 |
608acb157c66cbf8e5a515ca2945d9dde9e8ed86 | 337 | py | Python | morphocut_server/extensions.py | madelinetharp/morphocut-server | a82ad5916adbd168816f7b26432b4a98d978c299 | [
"MIT"
] | null | null | null | morphocut_server/extensions.py | madelinetharp/morphocut-server | a82ad5916adbd168816f7b26432b4a98d978c299 | [
"MIT"
] | 1 | 2019-08-14T20:07:53.000Z | 2019-08-14T20:07:53.000Z | morphocut_server/extensions.py | madelinetharp/morphocut-server | a82ad5916adbd168816f7b26432b4a98d978c299 | [
"MIT"
] | 2 | 2019-11-28T13:10:28.000Z | 2021-11-19T20:37:19.000Z |
from flask_sqlalchemy import SQLAlchemy
from flask_redis import FlaskRedis
from flask_migrate import Migrate
# from flask_rq2 import RQ
from rq import Queue
from morphocut_server.worker import redis_conn
database = SQLAlchemy()
redis_store = FlaskRedis()
migrate = Migrate()
redis_queue = Queue(connection=redis_conn)
flask_rq = None
| 22.466667 | 46 | 0.824926 |
608b4a53b9f9505194dcc39105a821f7c54562c8 | 12,881 | py | Python | acq4/drivers/ThorlabsMFC1/tmcm.py | aleonlein/acq4 | 4b1fcb9ad2c5e8d4595a2b9cf99d50ece0c0f555 | [
"MIT"
] | 1 | 2020-06-04T17:04:53.000Z | 2020-06-04T17:04:53.000Z | acq4/drivers/ThorlabsMFC1/tmcm.py | aleonlein/acq4 | 4b1fcb9ad2c5e8d4595a2b9cf99d50ece0c0f555 | [
"MIT"
] | 24 | 2016-09-27T17:25:24.000Z | 2017-03-02T21:00:11.000Z | acq4/drivers/ThorlabsMFC1/tmcm.py | sensapex/acq4 | 9561ba73caff42c609bd02270527858433862ad8 | [
"MIT"
] | 4 | 2016-10-19T06:39:36.000Z | 2019-09-30T21:06:45.000Z | from __future__ import print_function
"""
Low-level serial communication for Trinamic TMCM-140-42-SE controller
(used internally for the Thorlabs MFC1)
"""
import serial, struct, time, collections
try:
# this is nicer because it provides deadlock debugging information
from acq4.util.Mutex import RecursiveMutex as RLock
except ImportError:
from threading import RLock
try:
from ..SerialDevice import SerialDevice, TimeoutError, DataError
except ValueError:
## relative imports not allowed when running from command prompt, so
## we adjust sys.path when running the script for testing
if __name__ == '__main__':
import sys, os
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
from SerialDevice import SerialDevice, TimeoutError, DataError
COMMANDS = {
'rol': 2,
'ror': 1,
'mvp': 4,
'mst': 3,
'rfs': 13,
'sco': 30,
'cco': 32,
'gco': 31,
'sap': 5,
'gap': 6,
'stap': 7,
'rsap': 8,
'sgp': 9,
'ggp': 10,
'stgp': 11,
'rsgp': 12,
'sio': 14,
'gio': 15,
'calc': 19,
'comp': 20,
'jc': 21,
'ja': 22,
'csub': 23,
'rsub': 24,
'wait': 27,
'stop': 28,
'sco': 30,
'gco': 31,
'cco': 32,
'calcx': 33,
'aap': 34,
'agp': 35,
'aco': 39,
'sac': 29,
'stop_application': 128,
'run_application': 129,
'step_application': 130,
'reset_application': 131,
'start_download': 132,
'stop_download': 133,
'get_application_status': 135,
'get_firmware_version': 136,
'restore_factory_settings': 137,
}
PARAMETERS = { # negative values indicate read-only parameters
'target_position': 0,
'actual_position': 1,
'target_speed': 2,
'actual_speed': 3,
'maximum_speed': 4,
'maximum_acceleration': 5,
'maximum_current': 6,
'standby_current': 7,
'target_pos_reached': 8,
'ref_switch_status': 9,
'right_limit_switch_status': 10,
'left_limit_switch_status': 11,
'right_limit_switch_disable': 12,
'left_limit_switch_disable': 13,
'minimum_speed': -130,
'acceleration': -135,
'ramp_mode': 138,
'microstep_resolution': 140,
'soft_stop_flag': 149,
'ramp_divisor': 153,
'pulse_divisor': 154,
'referencing_mode': 193,
'referencing_search_speed': 194,
'referencing_switch_speed': 195,
'distance_end_switches': 196,
'mixed_decay_threshold': 203,
'freewheeling': 204,
'stall_detection_threshold': 205,
'actual_load_value': 206,
'driver_error_flags': -208,
'encoder_position': 209,
'encoder_prescaler': 210,
'fullstep_threshold': 211,
'maximum_encoder_deviation': 212,
'power_down_delay': 214,
'absolute_encoder_value': -215,
}
GLOBAL_PARAMETERS = {
'eeprom_magic': 64,
'baud_rate': 65,
'serial_address': 66,
'ascii_mode': 67,
'eeprom_lock': 73,
'auto_start_mode': 77,
'tmcl_code_protection': 81,
'coordinate_storage': 84,
'tmcl_application_status': 128,
'download_mode': 129,
'tmcl_program_counter': 130,
'tick_timer': 132,
'random_number': -133,
}
OPERATORS = {
'add': 0,
'sub': 1,
'mul': 2,
'div': 3,
'mod': 4,
'and': 5,
'or': 6,
'xor': 7,
'not': 8,
'load': 9,
'swap': 10,
}
CONDITIONS = {
'ze': 0,
'nz': 1,
'eq': 2,
'ne': 3,
'gt': 4,
'ge': 5,
'lt': 6,
'le': 7,
'eto': 8,
'eal': 9,
'esd': 12,
}
STATUS = {
1: "Wrong checksum",
2: "Invalid command",
3: "Wrong type",
4: "Invalid value",
5: "Configuration EEPROM locked",
6: "Command not available",
}
| 27.760776 | 98 | 0.559273 |
608ba712fb9a01badbb4b12d5b51c50071dede95 | 981 | py | Python | tests/generators/ios/test_core_data.py | brianleungwh/signals | d28d2722d681d390ebd21cd668d0b19f2f184451 | [
"MIT"
] | 3 | 2016-02-04T22:58:03.000Z | 2017-12-15T13:37:47.000Z | tests/generators/ios/test_core_data.py | brianleungwh/signals | d28d2722d681d390ebd21cd668d0b19f2f184451 | [
"MIT"
] | 37 | 2015-08-28T20:17:23.000Z | 2021-12-13T19:48:49.000Z | tests/generators/ios/test_core_data.py | brianleungwh/signals | d28d2722d681d390ebd21cd668d0b19f2f184451 | [
"MIT"
] | 6 | 2016-01-12T18:51:27.000Z | 2016-10-19T10:32:45.000Z | import unittest
from signals.generators.ios.core_data import get_current_version, get_core_data_from_folder
| 49.05 | 91 | 0.768603 |
608c22777b56d9cf666ec3250a4f4ccb1c127d26 | 848 | py | Python | mcmc/plot_graph.py | hudalao/mcmc | 148d9fbb9ebd85ee5bfd3601d80ebbd96bc25791 | [
"MIT"
] | null | null | null | mcmc/plot_graph.py | hudalao/mcmc | 148d9fbb9ebd85ee5bfd3601d80ebbd96bc25791 | [
"MIT"
] | null | null | null | mcmc/plot_graph.py | hudalao/mcmc | 148d9fbb9ebd85ee5bfd3601d80ebbd96bc25791 | [
"MIT"
] | null | null | null | # commend the lines for plotting using
import matplotlib.pyplot as plt
import networkx as nx
# nx.draw_networkx_nodes(G[time_point],pos,node_size=200)
# edges
# nx.draw_networkx_edges(G[time_point],pos,edgelist=elarge,width=3)
# nx.draw_networkx_edges(G[time_point],pos,edgelist=esmall,width=3,alpha=0.5,edge_color='b',style='dashed')
# labels
# nx.draw_networkx_labels(G[time_point],pos,font_size=10,font_family='sans-serif')
| 33.92 | 110 | 0.681604 |
608d2fef138f592a57bb71b64db9742a86c572f7 | 293 | py | Python | Number Theory/Sieve_of_Eratosthenes.py | mishrakeshav/Competitive-Programming | b25dcfeec0fb9a9c71bf3a05644b619f4ca83dd2 | [
"MIT"
] | 2 | 2020-06-25T21:10:32.000Z | 2020-12-10T06:53:45.000Z | Number Theory/Sieve_of_Eratosthenes.py | mishrakeshav/Competitive-Programming | b25dcfeec0fb9a9c71bf3a05644b619f4ca83dd2 | [
"MIT"
] | null | null | null | Number Theory/Sieve_of_Eratosthenes.py | mishrakeshav/Competitive-Programming | b25dcfeec0fb9a9c71bf3a05644b619f4ca83dd2 | [
"MIT"
] | 3 | 2020-05-15T14:17:09.000Z | 2021-07-25T13:18:20.000Z | from sys import stdin
input = stdin.readline
N = int(input())
primes = [1]*(N+1)
primes[0] = 0
primes[1] = 0
for i in range(2,int(N**0.5)+1):
if primes[i]:
for j in range(i*i,N+1,i):
primes[j] = 0
for i in range(N+1):
if primes[i]:
print(i,end = " ")
| 19.533333 | 34 | 0.522184 |
608e2946d7df6fc1e0129b19ce4192449ba804b9 | 6,118 | py | Python | powerline/lib/tree_watcher.py | kruton/powerline | f6ddb95da5f41b8285cffd1d17c1ef46dc08a7d6 | [
"MIT"
] | 19 | 2015-09-01T20:49:16.000Z | 2022-01-08T22:13:23.000Z | powerline/lib/tree_watcher.py | kruton/powerline | f6ddb95da5f41b8285cffd1d17c1ef46dc08a7d6 | [
"MIT"
] | null | null | null | powerline/lib/tree_watcher.py | kruton/powerline | f6ddb95da5f41b8285cffd1d17c1ef46dc08a7d6 | [
"MIT"
] | 6 | 2019-04-25T03:42:35.000Z | 2020-06-05T15:25:23.000Z | # vim:fileencoding=utf-8:noet
from __future__ import (unicode_literals, absolute_import, print_function)
__copyright__ = '2013, Kovid Goyal <kovid at kovidgoyal.net>'
__docformat__ = 'restructuredtext en'
import sys
import os
import errno
from time import sleep
from powerline.lib.monotonic import monotonic
from powerline.lib.inotify import INotify, INotifyError
def realpath(path):
return os.path.abspath(os.path.realpath(path))
if __name__ == '__main__':
w = INotifyTreeWatcher(sys.argv[-1])
w()
print ('Monitoring', sys.argv[-1], 'press Ctrl-C to stop')
try:
while True:
if w():
print (sys.argv[-1], 'changed')
sleep(1)
except KeyboardInterrupt:
raise SystemExit(0)
| 27.936073 | 152 | 0.698431 |
60903b5f4352258ad0e0ec223250ecb5c743a43e | 3,642 | py | Python | python/qisys/test/fake_interact.py | PrashantKumar-sudo/qibuild | a16ce425cf25127ceff29507feeeeca37af23351 | [
"BSD-3-Clause"
] | null | null | null | python/qisys/test/fake_interact.py | PrashantKumar-sudo/qibuild | a16ce425cf25127ceff29507feeeeca37af23351 | [
"BSD-3-Clause"
] | null | null | null | python/qisys/test/fake_interact.py | PrashantKumar-sudo/qibuild | a16ce425cf25127ceff29507feeeeca37af23351 | [
"BSD-3-Clause"
] | null | null | null | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright (c) 2012-2019 SoftBank Robotics. All rights reserved.
# Use of this source code is governed by a BSD-style license (see the COPYING file).
""" Fake Interact """
from __future__ import absolute_import
from __future__ import unicode_literals
from __future__ import print_function
def ask_choice(self, choices, message, **_unused):
""" Ask Choice """
print("::", message)
for choice in choices:
print("* ", choice)
answer = self._get_answer(message, choices)
print(">", answer)
return answer
def ask_yes_no(self, message, default=False):
""" Ask Yes / No """
print("::", message,)
if default:
print("(Y/n)")
else:
print("(y/N)")
answer = self._get_answer(message, default=default)
print(">", answer)
return answer
def ask_path(self, message):
""" Ask Path """
print("::", message)
answer = self._get_answer(message)
print(">", answer)
return answer
def ask_string(self, message):
""" Ask String """
print("::", message)
answer = self._get_answer(message)
print(">", answer)
return answer
def ask_program(self, message):
""" Ask Program """
print("::", message)
answer = self._get_answer(message)
print(">", answer)
return answer
def get_editor(self):
""" Return the Editor """
return self.editor
def _get_answer(self, message, choices=None, default=None):
""" Get an Answer """
question = dict()
question['message'] = message
question['choices'] = choices
question['default'] = default
self.questions.append(question)
if self.answers_type == "dict":
return self.find_answer(message, choices=choices, default=default)
self.answer_index += 1
return self.answers[self.answer_index]
| 31.396552 | 84 | 0.556013 |
6090c304e6f8f9a2666ec59c479f530bc3d45c1f | 7,727 | py | Python | muselsl/cli.py | kowalej/muse-lsl | 9086f2588bee3b2858b0ff853b7a08cdcd0e7612 | [
"BSD-3-Clause"
] | 2 | 2020-12-04T15:01:19.000Z | 2021-11-20T23:05:38.000Z | muselsl/cli.py | fahad101/muse-lsl | 32aced5eb29db8834cbffd8533607e8d32cfa2b7 | [
"BSD-3-Clause"
] | null | null | null | muselsl/cli.py | fahad101/muse-lsl | 32aced5eb29db8834cbffd8533607e8d32cfa2b7 | [
"BSD-3-Clause"
] | 1 | 2020-12-03T21:28:01.000Z | 2020-12-03T21:28:01.000Z | #!/usr/bin/python
import sys
import argparse
| 52.209459 | 140 | 0.533325 |
609290208240ce63b7e6295f7ddcd5f772b8a453 | 2,420 | py | Python | src/quocspyside2interface/gui/freegradients/GeneralSettingsNM.py | Quantum-OCS/QuOCS-pyside2interface | 69436666a67da6884aed1ddd087b7062dcd2ad90 | [
"Apache-2.0"
] | 1 | 2021-03-27T17:41:16.000Z | 2021-03-27T17:41:16.000Z | src/quocspyside2interface/gui/freegradients/GeneralSettingsNM.py | Quantum-OCS/QuOCS-pyside2interface | 69436666a67da6884aed1ddd087b7062dcd2ad90 | [
"Apache-2.0"
] | null | null | null | src/quocspyside2interface/gui/freegradients/GeneralSettingsNM.py | Quantum-OCS/QuOCS-pyside2interface | 69436666a67da6884aed1ddd087b7062dcd2ad90 | [
"Apache-2.0"
] | null | null | null | # ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
# Copyright 2021- QuOCS Team
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
from qtpy import QtWidgets
from quocspyside2interface.gui.uiclasses.GeneralSettingsNMUI import Ui_Form
from quocspyside2interface.gui.freegradients.StoppingCriteriaNM import StoppingCriteriaNM
from quocspyside2interface.logic.OptimalAlgorithmDictionaries.NelderMeadDictionary import NelderMeadDictionary
| 48.4 | 110 | 0.704132 |
60931ff29f096eec340bc02f5f6b68402207873f | 5,803 | py | Python | pulsar/datadog_checks/pulsar/check.py | divyamamgai/integrations-extras | 8c40a9cf870578687cc224ee91d3c70cd3a435a4 | [
"BSD-3-Clause"
] | 158 | 2016-06-02T16:25:31.000Z | 2022-03-16T15:55:14.000Z | pulsar/datadog_checks/pulsar/check.py | divyamamgai/integrations-extras | 8c40a9cf870578687cc224ee91d3c70cd3a435a4 | [
"BSD-3-Clause"
] | 554 | 2016-03-15T17:39:12.000Z | 2022-03-31T10:29:16.000Z | pulsar/datadog_checks/pulsar/check.py | divyamamgai/integrations-extras | 8c40a9cf870578687cc224ee91d3c70cd3a435a4 | [
"BSD-3-Clause"
] | 431 | 2016-05-13T15:33:13.000Z | 2022-03-31T10:06:46.000Z | from datadog_checks.base import ConfigurationError, OpenMetricsBaseCheck
EVENT_TYPE = SOURCE_TYPE_NAME = 'pulsar'
| 58.616162 | 117 | 0.693607 |
6093e41de9ab4c93dd30e9d6abbd45ef1d80f2ad | 214 | py | Python | Chapter11/publish_horoscope1_in_another_ipns.py | HowToBeCalculated/Hands-On-Blockchain-for-Python-Developers | f9634259dd3dc509f36a5ccf3a5182c0d2ec79c4 | [
"MIT"
] | 62 | 2019-03-18T04:41:41.000Z | 2022-03-31T05:03:13.000Z | Chapter11/publish_horoscope1_in_another_ipns.py | HowToBeCalculated/Hands-On-Blockchain-for-Python-Developers | f9634259dd3dc509f36a5ccf3a5182c0d2ec79c4 | [
"MIT"
] | 2 | 2020-06-14T21:56:03.000Z | 2022-01-07T05:32:01.000Z | Chapter11/publish_horoscope1_in_another_ipns.py | HowToBeCalculated/Hands-On-Blockchain-for-Python-Developers | f9634259dd3dc509f36a5ccf3a5182c0d2ec79c4 | [
"MIT"
] | 42 | 2019-02-22T03:10:36.000Z | 2022-02-20T04:47:04.000Z | import ipfsapi
c = ipfsapi.connect()
peer_id = c.key_list()['Keys'][1]['Id']
c.name_publish('QmYjYGKXqo36GDt6f6qvp9qKAsrc72R9y88mQSLvogu8Ub', key='another_key')
result = c.cat('/ipns/' + peer_id)
print(result)
| 19.454545 | 83 | 0.728972 |
6093e7dfd679097b8c74873a9180892df2d03fa2 | 1,756 | py | Python | magie/window.py | NiumXp/Magie | f0dc1d135274b5453fde659ba46f09f4b303c099 | [
"MIT"
] | 1 | 2020-10-26T17:16:52.000Z | 2020-10-26T17:16:52.000Z | magie/window.py | NiumXp/Magie | f0dc1d135274b5453fde659ba46f09f4b303c099 | [
"MIT"
] | null | null | null | magie/window.py | NiumXp/Magie | f0dc1d135274b5453fde659ba46f09f4b303c099 | [
"MIT"
] | null | null | null | import pygame
def get_width(self) -> int:
"""Returns the widget of the window."""
if self.surface:
return self.surface.get_width()
return self.initial_dimension[0]
def get_height(self) -> int:
"""Returns the height of the window."""
if self.surface:
return self.surface.get_height()
return self.initial_dimension[1]
def get_size(self) -> tuple:
"""Returns the size of the size."""
if self.surface:
return self.surface.get_size()
return self.initial_dimension
def build(self):
"""Build the window."""
self.surface = pygame.display.set_mode(self.initial_dimension)
self.set_title(self.initial_title)
return self
| 27.4375 | 71 | 0.574601 |
6093fc893ebcd37ea362c87f16107050ab4d6124 | 1,179 | py | Python | tests/optimize/test_newton_raphson_hypo.py | dwillmer/fastats | 5915423714b32ed7e953e1e3a311fe50c3f30943 | [
"MIT"
] | 26 | 2017-07-17T09:19:53.000Z | 2021-11-30T01:36:56.000Z | tests/optimize/test_newton_raphson_hypo.py | dwillmer/fastats | 5915423714b32ed7e953e1e3a311fe50c3f30943 | [
"MIT"
] | 320 | 2017-09-02T16:26:25.000Z | 2021-07-28T05:19:49.000Z | tests/optimize/test_newton_raphson_hypo.py | dwillmer/fastats | 5915423714b32ed7e953e1e3a311fe50c3f30943 | [
"MIT"
] | 13 | 2017-07-06T19:02:29.000Z | 2020-01-22T11:36:34.000Z |
from hypothesis import given, assume, settings
from hypothesis.strategies import floats
from numpy import cos
from pytest import approx
from fastats.optimise.newton_raphson import newton_raphson
nr_func = newton_raphson(1, 1e-6, root=func, return_callable=True)
nr_cos = newton_raphson(0.5, 1e-6, root=cos_func, return_callable=True)
if __name__ == '__main__':
import pytest
pytest.main([__file__])
| 21.436364 | 71 | 0.676845 |
6096d49f97f28ce9400a93ea2f44d5ab24de77e4 | 2,701 | py | Python | faceRecognition.py | sequery/Face-Recognition-Project | 84d29322228e140c3d18c9c4d169819375a8e256 | [
"MIT"
] | 2 | 2020-12-23T13:39:54.000Z | 2021-01-11T15:34:29.000Z | faceRecognition.py | sequery/Face-Recognition-Project | 84d29322228e140c3d18c9c4d169819375a8e256 | [
"MIT"
] | null | null | null | faceRecognition.py | sequery/Face-Recognition-Project | 84d29322228e140c3d18c9c4d169819375a8e256 | [
"MIT"
] | null | null | null | import cv2
import os
import numpy as np
# This module contains all common functions that are called in tester.py file
# Given an image below function returns rectangle for face detected alongwith gray scale image
# Given a directory below function returns part of gray_img which is face alongwith its label/ID
# Below function trains haar classifier and takes faces,faceID returned by previous function as its arguments
# Below function draws bounding boxes around detected face in image
# Below function writes name of person for detected label
| 41.553846 | 120 | 0.66716 |
60985f194b3e048052f9cb34471c7382107a1768 | 2,704 | py | Python | evaluation/datasets/build_dataset_images.py | hsiehkl/pdffigures2 | 9ff2978a097f3d500dcb840d31587c26d994cb68 | [
"Apache-2.0"
] | 296 | 2016-06-19T02:41:09.000Z | 2022-03-10T05:46:08.000Z | evaluation/datasets/build_dataset_images.py | hsiehkl/pdffigures2 | 9ff2978a097f3d500dcb840d31587c26d994cb68 | [
"Apache-2.0"
] | 37 | 2016-07-07T00:11:53.000Z | 2022-03-18T07:27:32.000Z | evaluation/datasets/build_dataset_images.py | hsiehkl/pdffigures2 | 9ff2978a097f3d500dcb840d31587c26d994cb68 | [
"Apache-2.0"
] | 81 | 2016-07-06T23:51:21.000Z | 2022-03-19T13:50:25.000Z | import argparse
from os import listdir, mkdir
from os.path import join, isdir
from subprocess import call
import sys
import datasets
from shutil import which
"""
Script to use pdftoppm to turn the pdfs into single images per page
"""
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Cache rasterized page images for a dataset')
parser.add_argument("dataset", choices=datasets.DATASETS.keys(), help="target dataset")
parser.add_argument("color", choices=["gray", "color"], help="kind of images to render")
args = parser.parse_args()
dataset = datasets.get_dataset(args.dataset)
print("Running on dataset: " + dataset.name)
if args.color == "gray":
get_images(dataset.pdf_dir, dataset.page_images_gray_dir,
dataset.IMAGE_DPI, True)
elif args.color == "color":
get_images(dataset.pdf_dir, dataset.page_images_color_dir,
dataset.COLOR_IMAGE_DPI, False)
else:
exit(1)
| 36.053333 | 94 | 0.616864 |
60989896ae8b3f69efc7d2350add8f6f19d85669 | 220 | py | Python | sympy/physics/__init__.py | utkarshdeorah/sympy | dcdf59bbc6b13ddbc329431adf72fcee294b6389 | [
"BSD-3-Clause"
] | 1 | 2018-11-20T11:40:30.000Z | 2018-11-20T11:40:30.000Z | sympy/physics/__init__.py | utkarshdeorah/sympy | dcdf59bbc6b13ddbc329431adf72fcee294b6389 | [
"BSD-3-Clause"
] | 14 | 2018-02-08T10:11:03.000Z | 2019-04-16T10:32:46.000Z | sympy/physics/__init__.py | utkarshdeorah/sympy | dcdf59bbc6b13ddbc329431adf72fcee294b6389 | [
"BSD-3-Clause"
] | 1 | 2022-02-04T13:50:29.000Z | 2022-02-04T13:50:29.000Z | """
A module that helps solving problems in physics.
"""
from . import units
from .matrices import mgamma, msigma, minkowski_tensor, mdft
__all__ = [
'units',
'mgamma', 'msigma', 'minkowski_tensor', 'mdft',
]
| 16.923077 | 60 | 0.677273 |
609c0b0efb59d40f1ab4f4aa98dc75bbea0cab6a | 23 | py | Python | py.py | avr8082/Hadoop | 64b2036e752ac01b9e2256e20b659b1b56a274c9 | [
"Apache-2.0"
] | null | null | null | py.py | avr8082/Hadoop | 64b2036e752ac01b9e2256e20b659b1b56a274c9 | [
"Apache-2.0"
] | null | null | null | py.py | avr8082/Hadoop | 64b2036e752ac01b9e2256e20b659b1b56a274c9 | [
"Apache-2.0"
] | null | null | null | printf("Hello world")
| 11.5 | 22 | 0.695652 |
609c58346f33ce4d4cdeec7d9b46908849047083 | 21,070 | py | Python | build/python-env/lib/python2.7/site-packages/elasticsearch/client/xpack/ml.py | imiMoisesEducation/beatcookie-discbot | 59c8be23346d8d2fc1777a2b08856df88e2ae5c2 | [
"Apache-2.0"
] | 1 | 2021-05-11T12:09:58.000Z | 2021-05-11T12:09:58.000Z | build/python-env/lib/python2.7/site-packages/elasticsearch/client/xpack/ml.py | imiMoisesEducation/beatcookie-discbot | 59c8be23346d8d2fc1777a2b08856df88e2ae5c2 | [
"Apache-2.0"
] | null | null | null | build/python-env/lib/python2.7/site-packages/elasticsearch/client/xpack/ml.py | imiMoisesEducation/beatcookie-discbot | 59c8be23346d8d2fc1777a2b08856df88e2ae5c2 | [
"Apache-2.0"
] | 2 | 2020-01-13T17:51:02.000Z | 2020-07-24T17:50:44.000Z | from elasticsearch.client.utils import NamespacedClient, query_params, _make_path, SKIP_IN_PATH
| 43 | 102 | 0.64205 |
609c675a647587e5e1ba2913f0c1ade0647fff7d | 17,264 | py | Python | src/oci/service_catalog/service_catalog_client_composite_operations.py | LaudateCorpus1/oci-python-sdk | b0d3ce629d5113df4d8b83b7a6502b2c5bfa3015 | [
"Apache-2.0",
"BSD-3-Clause"
] | null | null | null | src/oci/service_catalog/service_catalog_client_composite_operations.py | LaudateCorpus1/oci-python-sdk | b0d3ce629d5113df4d8b83b7a6502b2c5bfa3015 | [
"Apache-2.0",
"BSD-3-Clause"
] | null | null | null | src/oci/service_catalog/service_catalog_client_composite_operations.py | LaudateCorpus1/oci-python-sdk | b0d3ce629d5113df4d8b83b7a6502b2c5bfa3015 | [
"Apache-2.0",
"BSD-3-Clause"
] | null | null | null | # coding: utf-8
# Copyright (c) 2016, 2022, Oracle and/or its affiliates. All rights reserved.
# This software is dual-licensed to you under the Universal Permissive License (UPL) 1.0 as shown at https://oss.oracle.com/licenses/upl or Apache License 2.0 as shown at http://www.apache.org/licenses/LICENSE-2.0. You may choose either license.
import oci # noqa: F401
from oci.util import WAIT_RESOURCE_NOT_FOUND # noqa: F401
| 54.460568 | 245 | 0.703313 |
609c86e4469f43e38a1d6ec5480ccb39cd4fde7a | 1,776 | py | Python | vision/_file_utils.py | BrianOfrim/boja | 6571fbbfb7f015e96e80e822d9dc96b4636b4119 | [
"MIT"
] | 7 | 2020-01-27T18:39:02.000Z | 2022-02-14T13:23:40.000Z | vision/_file_utils.py | a428tm/boja | 6571fbbfb7f015e96e80e822d9dc96b4636b4119 | [
"MIT"
] | 1 | 2021-06-02T00:55:25.000Z | 2021-06-02T00:55:25.000Z | vision/_file_utils.py | a428tm/boja | 6571fbbfb7f015e96e80e822d9dc96b4636b4119 | [
"MIT"
] | 6 | 2020-01-28T21:28:23.000Z | 2020-12-28T14:35:06.000Z | from typing import List
import os
import re
| 29.6 | 85 | 0.628378 |
609cf5344b535f0910e1e3a47704f4e750a0c25d | 5,355 | py | Python | vaccine_allocation/epi_simulations.py | COVID-IWG/epimargin-studies | 7d4a78e2e6713c6a0aea2cd2440529153e9a635d | [
"MIT"
] | null | null | null | vaccine_allocation/epi_simulations.py | COVID-IWG/epimargin-studies | 7d4a78e2e6713c6a0aea2cd2440529153e9a635d | [
"MIT"
] | null | null | null | vaccine_allocation/epi_simulations.py | COVID-IWG/epimargin-studies | 7d4a78e2e6713c6a0aea2cd2440529153e9a635d | [
"MIT"
] | null | null | null | import dask
import numpy as np
import pandas as pd
from epimargin.models import Age_SIRVD
from epimargin.utils import annually, normalize, percent, years
from studies.vaccine_allocation.commons import *
from tqdm import tqdm
import warnings
warnings.filterwarnings("error")
num_sims = 1000
simulation_range = 1 * years
phi_points = [_ * percent * annually for _ in (25, 50, 100, 200)]
simulation_initial_conditions = pd.read_csv(data/f"all_india_coalesced_scaling_Apr15.csv")\
.drop(columns = ["Unnamed: 0"])\
.set_index(["state", "district"])
rerun_states = ["Telangana", "Uttarakhand", "Jharkhand", "Arunachal Pradesh", "Nagaland", "Sikkim"] + coalesce_states
districts_to_run = simulation_initial_conditions
num_age_bins = 7
seed = 0
MORTALITY = [6, 5, 4, 3, 2, 1, 0]
CONTACT = [1, 2, 3, 4, 0, 5, 6]
CONSUMPTION = [4, 5, 6, 3, 2, 1, 0]
if __name__ == "__main__":
distribute = False
if distribute:
with dask.config.set({"scheduler.allowed-failures": 1}):
client = dask.distributed.Client(n_workers = 1, threads_per_worker = 1)
print(client.dashboard_link)
with dask.distributed.get_task_stream(client) as ts:
futures = []
for district in districts_to_run.itertuples():
futures.append(client.submit(process, district, key = ":".join(district[0])))
dask.distributed.progress(futures)
else:
failures = []
for t in tqdm(districts_to_run.itertuples(), total = len(districts_to_run)):
process(t)
# try:
# process(t)
# except Exception as e:
# failures.append((e, t))
for failure in failures:
print(failure)
| 45 | 150 | 0.597572 |
609d2577b79fe5b4822db1b8e2ab1b0bb0027683 | 1,975 | py | Python | src/core/agent_state.py | nandofioretto/py_dcop | fb2dbc97b69360f5d1fb67d84749e44afcdf48c3 | [
"Apache-2.0"
] | 4 | 2018-08-06T08:55:36.000Z | 2018-09-28T12:54:21.000Z | src/core/agent_state.py | nandofioretto/py_dcop | fb2dbc97b69360f5d1fb67d84749e44afcdf48c3 | [
"Apache-2.0"
] | null | null | null | src/core/agent_state.py | nandofioretto/py_dcop | fb2dbc97b69360f5d1fb67d84749e44afcdf48c3 | [
"Apache-2.0"
] | null | null | null | '''Every agent has an agent state, which is its local view of the world'''
import numpy as np
import itertools | 38.72549 | 86 | 0.663797 |
609dc8066e4ed58454e785ac9407ba710dd24391 | 32 | py | Python | johnny_cache/__init__.py | Sonictherocketman/cache-proxy | 75650fb143b365e922c03f87e388c5710ad21799 | [
"MIT"
] | 3 | 2019-07-23T02:33:04.000Z | 2021-05-25T16:57:24.000Z | johnny_cache/__init__.py | Sonictherocketman/cache-proxy | 75650fb143b365e922c03f87e388c5710ad21799 | [
"MIT"
] | null | null | null | johnny_cache/__init__.py | Sonictherocketman/cache-proxy | 75650fb143b365e922c03f87e388c5710ad21799 | [
"MIT"
] | null | null | null | from .server import app # noqa
| 16 | 31 | 0.71875 |
609f2b3abd12396a9fb221b6c6ef204f6a133c95 | 218 | py | Python | python_packages_static/flopy/mf6/__init__.py | usgs/neversink_workflow | acd61435b8553e38d4a903c8cd7a3afc612446f9 | [
"CC0-1.0"
] | 351 | 2015-01-03T15:18:48.000Z | 2022-03-31T09:46:43.000Z | python_packages_static/flopy/mf6/__init__.py | usgs/neversink_workflow | acd61435b8553e38d4a903c8cd7a3afc612446f9 | [
"CC0-1.0"
] | 1,256 | 2015-01-15T21:10:42.000Z | 2022-03-31T22:43:06.000Z | python_packages_static/flopy/mf6/__init__.py | usgs/neversink_workflow | acd61435b8553e38d4a903c8cd7a3afc612446f9 | [
"CC0-1.0"
] | 553 | 2015-01-31T22:46:48.000Z | 2022-03-31T17:43:35.000Z | # imports
from . import coordinates
from . import data
from .modflow import *
from . import utils
from .data import mfdatascalar, mfdatalist, mfdataarray
from .mfmodel import MFModel
from .mfbase import ExtFileAction
| 21.8 | 55 | 0.798165 |
609ff6c4c10fb31262c0f09062e4b789c62b7f8c | 360 | py | Python | tests/__init__.py | issuu/jmespath | 7480643df8ad0c99269b1fa6b9e793582bcb7efe | [
"MIT"
] | null | null | null | tests/__init__.py | issuu/jmespath | 7480643df8ad0c99269b1fa6b9e793582bcb7efe | [
"MIT"
] | null | null | null | tests/__init__.py | issuu/jmespath | 7480643df8ad0c99269b1fa6b9e793582bcb7efe | [
"MIT"
] | null | null | null | import sys
# The unittest module got a significant overhaul
# in 2.7, so if we're in 2.6 we can use the backported
# version unittest2.
if sys.version_info[:2] == (2, 6):
import unittest2 as unittest
import simplejson as json
from ordereddict import OrderedDict
else:
import unittest
import json
from collections import OrderedDict
| 21.176471 | 54 | 0.725 |
60a02b70b3feadeb7f3f6ef29e2296758c121c15 | 2,782 | py | Python | src/routes/scoring.py | jtillman20/cfb-data-api | 69bcae225e4fa0616eb526bd608e20ace17f1816 | [
"MIT"
] | null | null | null | src/routes/scoring.py | jtillman20/cfb-data-api | 69bcae225e4fa0616eb526bd608e20ace17f1816 | [
"MIT"
] | null | null | null | src/routes/scoring.py | jtillman20/cfb-data-api | 69bcae225e4fa0616eb526bd608e20ace17f1816 | [
"MIT"
] | null | null | null | from typing import Union
from flask import request
from flask_restful import Resource
from exceptions import InvalidRequestError
from models import Scoring
from utils import flask_response, rank, sort
def secondary_sort(attr: str, side_of_ball: str) -> tuple:
"""
Determine the secondary sort attribute and order when the
primary sort attribute has the same value.
Args:
attr (str): The primary sort attribute
side_of_ball (str): Offense or defense
Returns:
tuple: Secondary sort attribute and sort order
"""
if attr == 'points_per_game':
secondary_attr = 'games'
elif attr in ['points', 'relative_points_per_game']:
secondary_attr = 'points_per_game'
else:
secondary_attr = attr
return secondary_attr, side_of_ball == 'offense'
| 30.571429 | 74 | 0.626528 |
60a1ad6402e8e4a36b677d266c4a8212a4c64c09 | 15,419 | py | Python | WebPortal/gbol_portal/vars.py | ZFMK/GermanBarcodeofLife | f9e9af699ba3f5b3d0d537819a8463c4162d7e82 | [
"Apache-2.0"
] | null | null | null | WebPortal/gbol_portal/vars.py | ZFMK/GermanBarcodeofLife | f9e9af699ba3f5b3d0d537819a8463c4162d7e82 | [
"Apache-2.0"
] | null | null | null | WebPortal/gbol_portal/vars.py | ZFMK/GermanBarcodeofLife | f9e9af699ba3f5b3d0d537819a8463c4162d7e82 | [
"Apache-2.0"
] | null | null | null | import configparser
c = configparser.ConfigParser()
c.read("production.ini")
config = {}
config['host'] = c['dboption']['chost']
config['port'] = int(c['dboption']['cport'])
config['user'] = c['dboption']['cuser']
config['pw'] = c['dboption']['cpw']
config['db'] = c['dboption']['cdb']
config['homepath'] = c['option']['home']
config['hosturl'] = c['option']['hosturl']
config['news'] = c['news']
config['smtp'] = {}
config['smtp']['sender'] = c['option']['smtp-sender']
config['smtp']['server'] = c['option']['smtp']
config['collection_table'] = {}
config['collection_table']['template'] = c['option']['template_collection_sheet']
config['collection_table']['ordered'] = c['option']['collection_table_ordered']
config['collection_table']['filled'] = c['option']['collection_table_filled']
config['dwb'] = {}
config['dwb']['name_suffix'] = c['option']['dwb_name_suffix']
config['dwb']['connection_string'] = c['option']['dwb_connection_string']
config['dwb']['use_dwb'] = int(c['option']['use_dwb'])
if not c.has_option('option', 'dev_group'):
log.critical('Option `dev_group` is not defined in production.ini!\nPlease add at least one email to the list.')
raise NameError('Option `dev_group` is not defined in production.ini!\nPlease add at least one email to the list.')
config['dev_group'] = c['option']['dev_group']
taxon_ids = """100408, 100430, 100431, 100451, 100453, 3000243, 3100522, 3200125,
3200126, 4000014, 4402020, 4403366, 4403382, 4403383, 4404012,
4404135, 4404679, 4405947, 4406565, 4407062, 4408012, 5000093,
5000095, 5000203, 5009403, 5009532, 5100497, 5200013, 5210014,
5220011, 5400004, 5401236, 5413793, 5416518, 5416650, 5426341,
5428084, 5428327, 5428727, 5428849, 5428977, 5429029, 5429176,
5429405, 5430460, 5431215"""
states = {'de': ["Europa",
"Baden-Wrttemberg",
"Bayern",
"Berlin",
"Brandenburg",
"Bremen",
"Hamburg",
"Hessen",
"Mecklenburg-Vorpommern",
"Niedersachsen",
"Nordrhein-Westfalen",
"Rheinland-Pfalz",
"Saarland",
"Sachsen",
"Sachsen-Anhalt",
"Schleswig-Holstein",
"Thringen"],
'en': ["Europe",
"Baden-Wrttemberg",
"Bavaria",
"Berlin",
"Brandenburg",
"Bremen",
"Hamburg",
"Hesse",
"Mecklenburg-Vorpommern",
"Lower Saxony",
"North Rhine Westphalia",
"RhinelandPalatinate",
"Saarland",
"Saxony",
"Saxony-Anhalt",
"Schleswig-Holstein",
"Thuringia"]}
messages = {}
messages['results'] = {}
messages['results']['choose_taxa'] = {'de': '- Bitte wählen Sie ein Taxon aus -',
'en': '- Please choose a taxon -'}
messages['results']['choose_states'] = {'de': '- Bitte wählen Sie ein Bundesland aus -',
'en': '- Please choose a state -'}
messages['news_edit'] = {'de': ' Bearbeiten ', 'en': ' Edit '}
messages['news_reset'] = {'de': " Zurücksetzen ", 'en': " Reset "}
messages['news_reset_html'] = {'de': "<h2><strong>Titel</strong></h2><p>Inhalt</p>",
'en': "<h2><strong>Title</strong></h2><p>Content</p>"}
messages['news_message_saved'] = {'de': "News gespeichert!", 'en': "News saved!"}
messages['news_message_updated'] = {'de': "News bearbeitet!", 'en': "News updated!"}
messages['news_message_empty'] = {'de': "Bitte geben Sie Titel und Inhalt des neuen Newsbeitrages ein!",
'en': "Please enter title and content of the news posting!"}
messages['news_cancel'] = {'de': " Abbrechen ", 'en': " Cancel "}
messages['contact'] = {'de': 'Bitte berprfen Sie die eingegebenen Daten.', 'en': 'Please check the data entered.'}
messages['contact_send'] = {'de': 'Die Mail wurde versandt!', 'en': 'Send mail was successful!'}
messages['letter_sender'] = {'de': 'Absender', 'en': 'Sender'}
messages['letter_send_to'] = {'de': 'Empfnger', 'en': 'Send to'}
messages['letter_order_no'] = {'de': 'Auftragsnummer {0}', 'en': 'Order no. {0}'}
messages['letter_no_samples'] = {'de': 'Anzahl Proben: {0}', 'en': 'No. samples: {0}'}
messages['letter_body1'] = {'de': 'Hinweis: Bitte drucken Sie das Anschreiben aus oder notieren Sie alternativ die ',
'en': 'Please print this cover letter or write the'}
messages['letter_body2'] = {'de': 'Auftragsnummer auf einem Zettel und legen diesen dem Probenpaket bei.',
'en': 'order number on a slip and send it together with your parcel '
'containing the samples.'}
messages['pls_select'] = {'de': 'Bitte whlen', 'en': 'Please select'}
messages['wrong_credentials'] = {'de': 'Falscher Benutzer oder Passwort!', 'en': 'Wrong user or password!'}
messages['still_locked'] = {'de': 'Sie wurden noch nicht von einem Koordinator freigeschaltet!',
'en': 'Your account must be unlocked by the Administrator!'}
messages['required_fields'] = {'de': 'Bitte alle Pflichtfelder ausfllen!',
'en': 'Please fill out all required fields!'}
messages['username_present'] = {'de': 'Nutzername schon vorhanden, bitte whlen Sie einen anderen.',
'en': 'Username already present, please choose another one.'}
messages['user_created'] = {'de': 'Ihre Registrierungsanfrage wird bearbeitet. Sie werden in Krze eine Email '
'Benachichtigung zum Stand Ihrer Freigabe fr das GBOL Webportal erhalten.',
'en': 'User created. Please wait for unlock of your account by the administrator.'}
messages['reg_exp_mail_subject'] = {'de': 'Ihre Registrierung beim GBOL Webportal',
'en': 'Your Registration at GBOL Webportal'}
messages['reg_exp_mail_body'] = {'de': 'Hallo {salutation} {title} {vorname} {nachname},\n\n'
'wir haben Ihre Registrierung fr die taxonomische Expertise {expertisename} '
'erhalten und an die entsprechenden Koordinatoren weitergeleitet.\n\n'
'Viele Gre\nIhr GBOL Team',
'en': 'Hello {salutation} {title} {vorname} {nachname},\n\n'
'We have received Your registration for the taxonomic expertise {3} and '
'have send them to the corresponding GBOL-taxon coordinators.\n\n'
'Best regards,\nYour GBOL team'}
messages['reg_exp_chg_mail_body'] = {'de': 'Hallo {tk_user},\n\n{req_user} hat sich fr die Expertise {expertisename} '
'registriert.\nBitte prfen Sie die Angaben und zertifizieren die '
'Expertise anschlieend.\n\nViele Gre\nIhr GBOL Team',
'en': 'Hello {tk_user},\n\n{req_user} applies for the taxonomic expertise '
'{expertisename}.\nPlease check the data and approve or decline the request.'
'\n\nBest regards, Your GBOL team'}
messages['reg_exp_accept'] = {'de': """Hallo {3} {1} {2},
die Expertise {0} in Ihrem GBOL Konto wurde erfolgreich von einem Koordinator freigegeben.
Viele Gre
Ihr GBOL Team
""", 'en': """Hello {3} {1} {2}
The expertise {0} of your GBOL account has been approved by the coordinator.
Best regards,
The GBOL Team
"""}
messages['reg_exp_decline'] = {'de': """Hallo {3} {1} {2},
die Expertise {0} in Ihrem GBOL Konto wurde von einem Koordinator abgelehnt.
Sie knnen sich bei Fragen im Kontakt-Bereich bei uns melden.
Viele Gre
Ihr GBOL Team
""", 'en': """Hello {3} {1} {2}
The expertise {0} of your GBOL account has been refused by the coordinator.
If You have any questions regarding the GBOL approval process, please send us a note in the contact area.
We will answer Your inquiry as soon as possible.
Best regards,
The GBOL Team
"""}
messages['pwd_forgot_email_body'] = {'de': """{0},
eine Anfrage zum Zurcksetzen des Passworts fr Ihr Benutzerkonto auf
dem German Barcode of Life Webportal wurde gestellt.
Sie knnen Ihr Passwort mit einem Klick auf folgenden Link ndern:
http://{1}/sammeln/change-password?link={2}
Ihr Benutzername lautet: {3}
Dieser Link kann nur einmal verwendet werden und leitet Sie zu einer Seite,
auf der Sie ein neues Passwort festlegen knnen. Er ist einen Tag lang gltig
und luft automatisch aus, falls Sie ihn nicht verwenden.
Viele Gre
Das Team von German Barcode of Life""",
'en': """{0},
a request for password reset for your useraccount on the
German Barcode of Life webportal has been posed.
You can change your password with the following link:
http://{1}/sammeln/change-password?link={2}
Your user name is: {3}
Please note: this link can only be used once. The link will direct you to a
website where you can enter a new password.
The link is valid for one day.
Best wishes,
Your team from German Barcode of Life"""}
messages['pwd_forgot_email_subject'] = {'de': 'Neue Login-Daten fr {0} auf German Barcode of Life',
'en': 'New login data for your user {0} on German Barcode of '
'Life webportal'}
messages['pwd_forgot_sent'] = {'de': 'Das Passwort und weitere Hinweise wurden an '
'die angegebene Email-Adresse gesendet.',
'en': 'The password and further tips werde sent to your email address.'}
messages['pwd_forgot_not_found'] = {'de': 'Es wurde kein Benutzer mit eingegebenem Namen bzw. Email gefunden.',
'en': 'No user found with the name or the email entered.'}
messages['pwd_unmatch'] = {'de': 'Die beiden Passwrter stimmen nicht berein.', 'en': 'Passwords do not match.'}
messages['pwd_saved'] = {'de': 'Neues Passwort gespeichert.', 'en': 'New password saved'}
messages['pwd__link_used'] = {'de': 'Link wurde bereits benutzt.', 'en': 'The link has been used already'}
messages['pwd__link_invalid'] = {'de': 'Kein gltiger Link.', 'en': 'Link invalid'}
messages['pwd__link_timeout'] = {'de': 'Link ist nicht mehr gltig.', 'en': 'Link has timed out'}
messages['order_success'] = {'de': 'Danke, Ihre Bestellung wurde entgegengenommen.',
'en': 'Thank You, the order has been received.'}
messages['order_info_missing'] = {'de': 'Bitte fllen Sie alle Felder aus.', 'en': 'Please fill out all fields.'}
messages['edt_no_passwd'] = {'de': 'Bitte geben Sie Ihr Passwort an, um das Benutzerprofil zu ndern.',
'en': 'Please provide your password in order to change the userprofile.'}
messages['edt_passwd_wrong'] = {'de': 'Falsches Passwort.', 'en': 'Wrong password.'}
messages['edt_passwd_mismatch'] = {'de': 'Die beiden neuen Passwrter stimmen nicht berein.',
'en': 'Both new passwords do not match.'}
messages['edt_success'] = {'de': 'Benutzerprofil erfolgreich gendert', 'en': 'Userprofile updated.'}
messages['err_upload'] = {'de': 'Ein Fehler ist beim Hochladen der Sammeltabelle aufgetreten. '
'Bitte schicken Sie Ihre Sammeltabelle per E-Mail an den Taxonkoordinator.',
'en': 'An error occured when uploading the collection sheet. Please sent it to the '
'taxon coordinator via e-mail.'}
messages['succ_upload'] = {'de': 'Die Sammeltabelle wurde erfolgreich hochgeladen!',
'en': 'Collection sheet uploaded successfully!'}
messages['download'] = {'de': 'Herunterladen', 'en': 'Download'}
messages['cert'] = {'de': 'zertifiziert', 'en': 'certified'}
messages['subm'] = {'de': 'beantragt', 'en': 'submitted'}
messages['select'] = {'de': 'Auswahl', 'en': 'Please select'}
messages['robot'] = {'de': 'Registrierung konnte nicht durchgefhrt werden!', 'en': 'Could not process registration!'}
messages['email_reg_subject'] = {'de': 'GBOL Registrierung', 'en': 'GBOL Registration'}
messages['email_reg_body'] = {'de': """"Hallo {4} {2} {3}
ihr GBOL Konto {0} wurde erfolgreich von einem Koordinator freigegeben.
Sie knnen sich nun im dem Experten-Bereich anmelden.
Viele Gre
Ihr GBOL Team
""", 'en': """Hello {4} {2} {3}
Your GBOL account has been approved by the coordinator.
You can now login into the expert area.
Best regards,
The GBOL Team
"""}
messages['email_reg_body_decline'] = {'de': """"Hallo {4} {2} {3}
ihr GBOL Konto {0} wurde von einem Koordinator abgelehnt.
Sie knnen sich bei Fragen im Kontakt-Bereich von GBOL bei uns melden.
Best regards,
Ihr GBOL Team
""", 'en': """Hello {4} {2} {3}
Your GBoL account has been refused by the coordinator.
If You have any questions regarding the GBoL approval process, please send us a note in the contact area.
We will answer Your inquiry as soon as possible.
Best regards,
The GBOL Team
"""}
messages['states'] = {'de': {'raw': 'Neu', 'cooking': 'in Arbeit', 'done': 'Fertig'},
'en': {'raw': 'New', 'cooking': 'in progress', 'done': 'Done'}}
messages['error'] = {'de': 'Keine Ergebnisse gefunden', 'en': 'Nothing found'}
messages['coord'] = {'de': 'Koordinaten (lat/lon)', 'en': 'Coordinates (lat/lon)'}
messages['taxon'] = {'de': 'Taxon', 'en': 'Higher taxon'}
messages['ncoll'] = {'en': 'Not Collected', 'de': 'Nicht gesammelt'}
messages['nbar'] = {'en': 'No Barcode', 'de': 'Kein Barcode'}
messages['barc'] = {'en': 'Barcode', 'de': 'Barcode'}
messages['pub_updated'] = {'en': 'Publication updated!', 'de': 'Publikation bearbeitet!'}
messages['pub_saved'] = {'en': 'Publication saved!', 'de': 'Publikation gespeichert!'}
messages['pub_error'] = {'en': 'Please enter title and content of the publications posting!',
'de': 'Bitte geben Sie Titel und Inhalt des neuen Publikationsbeitrages ein!'}
messages['mail_req_body'] = """Guten Tag {0},
eine Bestellung fr Versandmaterial wurde auf dem GBOL-Portal abgesendet.
Gesendet am {1}
Bestellung:
Material: {2}
Anzahl Verpackungseinheiten: {3}
Taxonomische Gruppe: {4}
Nummer erstes Sammelrhrchen: {5}
Nummer letztes Sammelrhrchen: {6}
Absender:
{name}
{street}
{city}
{country}
Email: {email}
"""
# -- In case of an error one of these messages are send to the dev_group specified in production.ini
messages['error'] = {}
messages['error']['order_processing'] = """
Eine Bestellung fr Versandmaterial konnte nicht verarbeitet werden:
Bestellzeit: {1}
Koordinator (User-id): {0}
Mglicher Trasaktions-Key: {9}
Bestellung:
Material: {2}
Anzahl Verpackungseinheiten: {3}
Taxonomische Gruppe (ID): {4}
Nummer erstes Sammelrhrchen: {5}
Nummer letztes Sammelrhrchen: {6}
Bestellt von:
User-ID: {7}
Name: {8}
Fehler:
{10}
"""
| 48.335423 | 120 | 0.613075 |
60a325d8698400a67c71d72f6db2f8d62fcf36f0 | 1,798 | py | Python | challenges/Backend Challenge/pendulum_sort.py | HernandezDerekJ/Interview | 202d78767d452ecfe8c6220180d7ed53b1104231 | [
"MIT"
] | null | null | null | challenges/Backend Challenge/pendulum_sort.py | HernandezDerekJ/Interview | 202d78767d452ecfe8c6220180d7ed53b1104231 | [
"MIT"
] | null | null | null | challenges/Backend Challenge/pendulum_sort.py | HernandezDerekJ/Interview | 202d78767d452ecfe8c6220180d7ed53b1104231 | [
"MIT"
] | null | null | null | """
Coderpad solution
"""
if __name__ == "__main__":
arr = [5,1,3,6,2,4]
pend(arr)
arr = [5, 1, 3, 2, 4]
pend(arr)
arr = [10, 4, 1, 5, 4, 3, 7, 9]
pend(arr)
| 31 | 102 | 0.375417 |
60a3e925a35adb27a53e4a197d8b807d37a073ac | 13,745 | py | Python | swagger_server/controllers/threadFactory.py | garagonc/optimization-framework | 1ca57699d6a3f2f98dcaea96430e75c3f847b49f | [
"Apache-2.0"
] | null | null | null | swagger_server/controllers/threadFactory.py | garagonc/optimization-framework | 1ca57699d6a3f2f98dcaea96430e75c3f847b49f | [
"Apache-2.0"
] | null | null | null | swagger_server/controllers/threadFactory.py | garagonc/optimization-framework | 1ca57699d6a3f2f98dcaea96430e75c3f847b49f | [
"Apache-2.0"
] | null | null | null | import os
import configparser
import json
import time
from IO.inputConfigParser import InputConfigParser
from IO.redisDB import RedisDB
from optimization.ModelException import MissingKeysException
from optimization.controllerDiscrete import OptControllerDiscrete
from optimization.controllerMpc import OptControllerMPC
from optimization.controllerStochasticTestMulti import OptControllerStochastic
#from optimization.controllerStochasticTestPebble import OptControllerStochastic
from prediction.machineLearning import MachineLearning
from prediction.prediction import Prediction
from prediction.pvPrediction import PVPrediction
from utils_intern.constants import Constants
from utils_intern.messageLogger import MessageLogger
| 56.102041 | 120 | 0.587923 |
60a44344bf403a9af637f117e79a2de34161a730 | 8,141 | py | Python | ptrace/oop/math_tests.py | xann16/py-path-tracing | 609dbe6b80580212bd9d8e93afb6902091040d7a | [
"MIT"
] | null | null | null | ptrace/oop/math_tests.py | xann16/py-path-tracing | 609dbe6b80580212bd9d8e93afb6902091040d7a | [
"MIT"
] | null | null | null | ptrace/oop/math_tests.py | xann16/py-path-tracing | 609dbe6b80580212bd9d8e93afb6902091040d7a | [
"MIT"
] | null | null | null | """Unit tests for math-oriented common classes."""
import unittest
import math
import numpy as np
from .vector import Vec3, OrthonormalBasis
from .raycast_base import Ray
from .camera import Camera
if __name__ == '__main__':
unittest.main()
| 35.550218 | 81 | 0.607051 |
60a44dedd81ec9521c352eb02db132ee4f4bcd33 | 2,336 | py | Python | tests/integration/condition__browser__have_url_test.py | kianku/selene | 5361938e4f34d6cfae6df3aeca80e06a3e657d8c | [
"MIT"
] | null | null | null | tests/integration/condition__browser__have_url_test.py | kianku/selene | 5361938e4f34d6cfae6df3aeca80e06a3e657d8c | [
"MIT"
] | null | null | null | tests/integration/condition__browser__have_url_test.py | kianku/selene | 5361938e4f34d6cfae6df3aeca80e06a3e657d8c | [
"MIT"
] | null | null | null | # MIT License
#
# Copyright (c) 2015-2020 Iakiv Kramarenko
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
import os
import pytest
from selene import have
from selene.core.exceptions import TimeoutException
start_page = 'file://' + os.path.abspath(os.path.dirname(__file__)) + '/../resources/start_page.html'
| 37.079365 | 101 | 0.771404 |
60a47f3e241e5f669273b6d0c92cbb5374b0f349 | 3,970 | py | Python | cohesity_management_sdk/models/scheduling_policy.py | chandrashekar-cohesity/management-sdk-python | 9e6ec99e8a288005804b808c4e9b19fd204e3a8b | [
"Apache-2.0"
] | 1 | 2021-01-07T20:36:22.000Z | 2021-01-07T20:36:22.000Z | cohesity_management_sdk/models/scheduling_policy.py | chandrashekar-cohesity/management-sdk-python | 9e6ec99e8a288005804b808c4e9b19fd204e3a8b | [
"Apache-2.0"
] | null | null | null | cohesity_management_sdk/models/scheduling_policy.py | chandrashekar-cohesity/management-sdk-python | 9e6ec99e8a288005804b808c4e9b19fd204e3a8b | [
"Apache-2.0"
] | null | null | null | # -*- coding: utf-8 -*-
# Copyright 2019 Cohesity Inc.
import cohesity_management_sdk.models.continuous_schedule
import cohesity_management_sdk.models.daily_schedule
import cohesity_management_sdk.models.monthly_schedule
import cohesity_management_sdk.models.rpo_schedule
| 43.152174 | 203 | 0.688917 |
60a5c5419df750bc2b6508ae2377a189aff71a1a | 3,575 | py | Python | networking_mlnx/eswitchd/cli/ebrctl.py | mail2nsrajesh/networking-mlnx | 9051eac0c2bc6abf3c8790e01917e405dc479922 | [
"Apache-2.0"
] | null | null | null | networking_mlnx/eswitchd/cli/ebrctl.py | mail2nsrajesh/networking-mlnx | 9051eac0c2bc6abf3c8790e01917e405dc479922 | [
"Apache-2.0"
] | null | null | null | networking_mlnx/eswitchd/cli/ebrctl.py | mail2nsrajesh/networking-mlnx | 9051eac0c2bc6abf3c8790e01917e405dc479922 | [
"Apache-2.0"
] | null | null | null | #!/usr/bin/python
# Copyright 2013 Mellanox Technologies, Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
# implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import sys
from networking_mlnx.eswitchd.cli import conn_utils
from networking_mlnx.eswitchd.cli import exceptions
client = conn_utils.ConnUtil()
def parse():
"""Main method that manages supported CLI commands.
The actions that are supported throught the CLI are:
write-sys, del-port, allocate-port and add-port
Each action is matched with method that should handle it
e.g. write-sys action is matched with write_sys method
"""
parser = argparse.ArgumentParser(prog='ebrctl')
parser.add_argument('action', action='store_true')
parent_parser = argparse.ArgumentParser(add_help=False)
parent_parser.add_argument('vnic_mac')
parent_parser.add_argument('device_id')
parent_parser.add_argument('fabric')
parent_parser.add_argument('vnic_type')
subparsers = parser.add_subparsers()
parser_add_port = subparsers.add_parser('add-port',
parents=[parent_parser])
parser_add_port.add_argument('dev_name')
parser_add_port.set_defaults(func=add_port)
parser_add_port = subparsers.add_parser('allocate-port',
parents=[parent_parser])
parser_add_port.set_defaults(func=allocate_port)
parser_del_port = subparsers.add_parser('del-port')
parser_del_port.set_defaults(func=del_port)
parser_del_port.add_argument('fabric')
parser_del_port.add_argument('vnic_mac')
parser_write_sys = subparsers.add_parser('write-sys')
parser_write_sys.set_defaults(func=write_sys)
parser_write_sys.add_argument('path')
parser_write_sys.add_argument('value')
args = parser.parse_args()
args.func(args)
| 30.555556 | 73 | 0.683636 |
60a620c9bc97c103d049f3c1d9096836751f9133 | 161 | py | Python | survos/core/__init__.py | paskino/SuRVoS | e01e784442e2e9f724826cdb70f3a50c034c6455 | [
"Apache-2.0"
] | 22 | 2016-09-30T08:04:42.000Z | 2022-03-05T07:24:18.000Z | survos/core/__init__.py | paskino/SuRVoS | e01e784442e2e9f724826cdb70f3a50c034c6455 | [
"Apache-2.0"
] | 81 | 2016-11-21T15:32:14.000Z | 2022-02-20T00:22:27.000Z | survos/core/__init__.py | paskino/SuRVoS | e01e784442e2e9f724826cdb70f3a50c034c6455 | [
"Apache-2.0"
] | 6 | 2018-11-22T10:19:59.000Z | 2022-02-04T06:15:48.000Z |
from .launcher import Launcher
from .model import DataModel
from .layers import LayerManager
from .labels import LabelManager
from .singleton import Singleton
| 20.125 | 32 | 0.832298 |
60a739a78fd701a33047ec6e80de5f418140cf06 | 2,567 | py | Python | src/FinanceLib/analysis.py | Chahat-M/FinanceLib | 0428779220a97e7fe0ad35a50207b737059b9c86 | [
"MIT"
] | 3 | 2021-06-20T14:55:16.000Z | 2021-12-29T03:55:34.000Z | src/FinanceLib/analysis.py | Chahat-M/FinanceLib | 0428779220a97e7fe0ad35a50207b737059b9c86 | [
"MIT"
] | null | null | null | src/FinanceLib/analysis.py | Chahat-M/FinanceLib | 0428779220a97e7fe0ad35a50207b737059b9c86 | [
"MIT"
] | 1 | 2021-06-26T05:39:08.000Z | 2021-06-26T05:39:08.000Z | from typing import List, Union
import numpy as np
import pandas_datareader as pdr
import pandas as pd
import matplotlib.pyplot as plt
def rsi(symbol :str ,name :str, date :str) -> None :
"""
Calculates and visualises the Relative Stock Index on a Stock of the company.
Parameters:
symbol(str) : Symbol of the company from https://in.finance.yahoo.com/
name(str) : Name of the company
date(str) : start date of historical data in the format (YYYY,M,D)
Returns:
Return type: void
Example:
rsi('GOOG','Google','2020,01,01')
"""
ticker : str = pdr.get_data_yahoo(symbol, date)
delta : List[float] = ticker['Close'].diff()
up : int = delta.clip(lower=0)
down : int = -1*delta.clip(upper=0)
ema_up : Union[bool,float]= up.ewm(com=13, adjust=False).mean()
ema_down : Union[bool,float] = down.ewm(com=13, adjust=False).mean()
rs : float = ema_up/ema_down
ticker['RSI'] = 100 - (100/(1 + rs))
ticker : list = ticker.iloc[14:]
print(ticker)
fig, (ax1, ax2) = plt.subplots(2)
ax1.get_xaxis().set_visible(False)
fig.suptitle(name)
ticker['Close'].plot(ax=ax1)
ax1.set_ylabel('Price ($)')
ticker['RSI'].plot(ax=ax2)
ax2.set_ylim(0,100)
ax2.axhline(30, color='r', linestyle='--')
ax2.axhline(70, color='r', linestyle='--')
ax2.set_ylabel('RSI')
plt.show()
def volatility(symbol :str, date :str) ->None:
"""
Measures and visualizes the Volatility of a Stock by calculating the Average True Range(ATR)
Parameters:
symbol(str) : Symbol of the company from https://in.finance.yahoo.com/
date(str) : start date of historical data in the format (YYYY,M,D)
Returns:
Return type: void
Example:
volatility('GOOG','2020,01,01')
"""
data : str = pdr.get_data_yahoo(symbol,date)
data.head()
high_low : Union[int,float]= data['High'] - data['Low']
high_cp : List[float] = np.abs(data['High'] - data['Close'].shift())
low_cp : List[float]= np.abs(data['Low'] - data['Close'].shift())
df : List[str] = pd.concat([high_low, high_cp, low_cp], axis=1)
true_range : float= np.max(df, axis=1)
average_true_range : float= true_range.rolling(14).mean()
average_true_range
true_range.rolling(14).sum()/14
fig, ax = plt.subplots()
average_true_range.plot(ax=ax)
ax2 : Union[bool,float]= data['Close'].plot(ax=ax, secondary_y=True, alpha=.3)
ax.set_ylabel("ATR")
ax2.set_ylabel("Price")
plt.show() | 34.689189 | 96 | 0.626023 |
60aaca2558f26dc382fc17ea023f8e47287579c5 | 26,892 | py | Python | datastore/core/test/test_basic.py | jbenet/datastore | 6c0e9f39b844788ca21c5cf613c625a72fb20c20 | [
"MIT"
] | 1 | 2015-05-24T13:11:16.000Z | 2015-05-24T13:11:16.000Z | datastore/core/test/test_basic.py | jbenet/datastore | 6c0e9f39b844788ca21c5cf613c625a72fb20c20 | [
"MIT"
] | null | null | null | datastore/core/test/test_basic.py | jbenet/datastore | 6c0e9f39b844788ca21c5cf613c625a72fb20c20 | [
"MIT"
] | null | null | null |
import unittest
import logging
from ..basic import DictDatastore
from ..key import Key
from ..query import Query
if __name__ == '__main__':
unittest.main()
| 27.553279 | 77 | 0.643611 |
60aacb1ea53997b926d5fc68404773f2cbe78055 | 852 | py | Python | rooms/models.py | Neisvestney/SentSyncServer | 45e9572b6c9b274ed2cbad28749fcb2154c98757 | [
"MIT"
] | null | null | null | rooms/models.py | Neisvestney/SentSyncServer | 45e9572b6c9b274ed2cbad28749fcb2154c98757 | [
"MIT"
] | null | null | null | rooms/models.py | Neisvestney/SentSyncServer | 45e9572b6c9b274ed2cbad28749fcb2154c98757 | [
"MIT"
] | null | null | null | from django.db import models
| 25.818182 | 82 | 0.597418 |
60ae0a74f0e5e8766035e68de7d7b1a1a948d0fa | 321 | py | Python | colbert/parameters.py | techthiyanes/ColBERT | 6493193b98d95595f15cfc375fed2f0b24df4f83 | [
"MIT"
] | 421 | 2020-06-03T05:30:00.000Z | 2022-03-31T13:10:42.000Z | colbert/parameters.py | xrr233/ColBERT | 88a5ecd8aa7dca70d0d52ab51422cb06c843fb4e | [
"MIT"
] | 87 | 2020-08-07T10:07:56.000Z | 2022-03-30T03:49:16.000Z | colbert/parameters.py | xrr233/ColBERT | 88a5ecd8aa7dca70d0d52ab51422cb06c843fb4e | [
"MIT"
] | 111 | 2020-06-28T03:02:14.000Z | 2022-03-15T05:56:24.000Z | import torch
DEVICE = torch.device("cuda")
SAVED_CHECKPOINTS = [32*1000, 100*1000, 150*1000, 200*1000, 300*1000, 400*1000]
SAVED_CHECKPOINTS += [10*1000, 20*1000, 30*1000, 40*1000, 50*1000, 60*1000, 70*1000, 80*1000, 90*1000]
SAVED_CHECKPOINTS += [25*1000, 50*1000, 75*1000]
SAVED_CHECKPOINTS = set(SAVED_CHECKPOINTS)
| 32.1 | 102 | 0.725857 |
60ae786acbdf645e9d5e08b60bc5b3249a338b60 | 6,496 | py | Python | jarvis/stats.py | aburgd/sheila | 556cf3e4a6992b8ba609ba281f5a3657cd91e709 | [
"MIT"
] | null | null | null | jarvis/stats.py | aburgd/sheila | 556cf3e4a6992b8ba609ba281f5a3657cd91e709 | [
"MIT"
] | null | null | null | jarvis/stats.py | aburgd/sheila | 556cf3e4a6992b8ba609ba281f5a3657cd91e709 | [
"MIT"
] | null | null | null | #!/usr/bin/env python3
###############################################################################
# Module Imports
###############################################################################
import pyscp
import textwrap
from dominate import tags as dt
from . import core, lex, ext
###############################################################################
# Templates
###############################################################################
CHART = """
google.charts.setOnLoadCallback({name});
function {name}() {{
var data = new google.visualization.arrayToDataTable([
{data}
]);
var options = {options};
var chart = new google.visualization.{class_name}(
document.getElementById('{name}'));
chart.draw(data, options);
}}
"""
USER = """
[[html]]
<base target="_parent" />
<style type="text/css">
@import url(http://scp-stats.wdfiles.com/local--theme/scp-stats/style.css);
</style>
<script type="text/javascript" src="https://www.gstatic.com/charts/loader.js">
</script>
<script type="text/javascript">
google.charts.load('current', {{'packages':['table', 'corechart']}});
{summary_table}
{articles_chart}
{articles_table}
</script>
<div id="summary_table"></div>
<div id="articles_chart"></div>
<div style="clear: both;"></div>
<h4>Articles</h4>
<div id="articles_table"></div>
[[/html]]
"""
###############################################################################
# Helper Functions
###############################################################################
###############################################################################
# Chart Classes
###############################################################################
###############################################################################
def update_user(name):
wiki = pyscp.wikidot.Wiki('scp-stats')
wiki.auth(core.config.wiki.name, core.config.wiki.password)
p = wiki('user:' + name.lower())
pages = sorted(
core.pages.related(name),
key=lambda x: (x.metadata[name].date, x.created))
pages = ext.PageView(pages)
if not pages.articles:
return lex.not_found.author
data = USER.format(
summary_table=SummaryTable(pages.primary(name), name).render(),
articles_chart=ArticlesChart(pages.articles, name).render(),
articles_table=ArticlesTable(
[p for p in pages if p.tags], name).render())
p.create(data, title=name, comment='automated update')
return p.url
| 28.121212 | 79 | 0.475985 |
60aeca0f1277fa05a855d2d1630e9fb88e8caff2 | 124 | py | Python | entry.py | Allenyou1126/allenyou-acme.sh | 2f5fa606cb0a66ded49d75a98d0dc47adc68c87c | [
"Apache-2.0"
] | null | null | null | entry.py | Allenyou1126/allenyou-acme.sh | 2f5fa606cb0a66ded49d75a98d0dc47adc68c87c | [
"Apache-2.0"
] | null | null | null | entry.py | Allenyou1126/allenyou-acme.sh | 2f5fa606cb0a66ded49d75a98d0dc47adc68c87c | [
"Apache-2.0"
] | null | null | null | #!/usr/bin/env python3
import json
from allenyoucert import Cert
main()
| 11.272727 | 29 | 0.645161 |
60afd06ba545d034d33e457e4d731a2d4625fc23 | 340 | py | Python | dynts/lib/fallback/simplefunc.py | quantmind/dynts | 21ac57c648bfec402fa6b1fe569496cf098fb5e8 | [
"BSD-3-Clause"
] | 57 | 2015-02-10T13:42:06.000Z | 2022-03-28T14:48:36.000Z | dynts/lib/fallback/simplefunc.py | quantmind/dynts | 21ac57c648bfec402fa6b1fe569496cf098fb5e8 | [
"BSD-3-Clause"
] | 1 | 2016-11-01T07:43:05.000Z | 2016-11-01T07:43:05.000Z | dynts/lib/fallback/simplefunc.py | quantmind/dynts | 21ac57c648bfec402fa6b1fe569496cf098fb5e8 | [
"BSD-3-Clause"
] | 17 | 2015-05-08T04:09:19.000Z | 2021-08-02T19:24:52.000Z |
from .common import *
| 20 | 31 | 0.297059 |
60b09b84c56168890b3b6bd6e6a345dc11ac1a37 | 3,552 | py | Python | week9/finance/application.py | lcsm29/edx-harvard-cs50 | 283f49bd6a9e4b8497e2b397d766b64527b4786b | [
"MIT"
] | null | null | null | week9/finance/application.py | lcsm29/edx-harvard-cs50 | 283f49bd6a9e4b8497e2b397d766b64527b4786b | [
"MIT"
] | null | null | null | week9/finance/application.py | lcsm29/edx-harvard-cs50 | 283f49bd6a9e4b8497e2b397d766b64527b4786b | [
"MIT"
] | null | null | null | import os
from cs50 import SQL
from flask import Flask, flash, redirect, render_template, request, session
from flask_session import Session
from tempfile import mkdtemp
from werkzeug.exceptions import default_exceptions, HTTPException, InternalServerError
from werkzeug.security import check_password_hash, generate_password_hash
from helpers import apology, login_required, lookup, usd
# Configure application
app = Flask(__name__)
# Ensure templates are auto-reloaded
app.config["TEMPLATES_AUTO_RELOAD"] = True
# Ensure responses aren't cached
# Custom filter
app.jinja_env.filters["usd"] = usd
# Configure session to use filesystem (instead of signed cookies)
app.config["SESSION_FILE_DIR"] = mkdtemp()
app.config["SESSION_PERMANENT"] = False
app.config["SESSION_TYPE"] = "filesystem"
Session(app)
# Configure CS50 Library to use SQLite database
db = SQL("sqlite:///finance.db")
# Make sure API key is set
if not os.environ.get("API_KEY"):
raise RuntimeError("API_KEY not set")
def errorhandler(e):
"""Handle error"""
if not isinstance(e, HTTPException):
e = InternalServerError()
return apology(e.name, e.code)
# Listen for errors
for code in default_exceptions:
app.errorhandler(code)(errorhandler)
| 24.839161 | 100 | 0.675113 |
60b0bb8f2315cabad512414bb05d03c08dd3d690 | 882 | py | Python | users/forms.py | iurykrieger96/alura-django | d8ed9998ccbc629127b2c6ca3ed3798da9a578f3 | [
"MIT"
] | null | null | null | users/forms.py | iurykrieger96/alura-django | d8ed9998ccbc629127b2c6ca3ed3798da9a578f3 | [
"MIT"
] | null | null | null | users/forms.py | iurykrieger96/alura-django | d8ed9998ccbc629127b2c6ca3ed3798da9a578f3 | [
"MIT"
] | null | null | null | from django import forms
from django.contrib.auth.models import User
from django.forms.utils import ErrorList
| 32.666667 | 90 | 0.679138 |
60b1ecdd22ff60b0f76eb9a849e552628f551e26 | 6,547 | py | Python | python/manager.py | Kiku-Reise/vsmart | dd8cf84816da8734e72dbb46c07694f561597648 | [
"Apache-2.0"
] | null | null | null | python/manager.py | Kiku-Reise/vsmart | dd8cf84816da8734e72dbb46c07694f561597648 | [
"Apache-2.0"
] | null | null | null | python/manager.py | Kiku-Reise/vsmart | dd8cf84816da8734e72dbb46c07694f561597648 | [
"Apache-2.0"
] | null | null | null | from telethon.sync import TelegramClient
from telethon.errors.rpcerrorlist import PhoneNumberBannedError
import pickle, os
from colorama import init, Fore
from time import sleep
init()
n = Fore.RESET
lg = Fore.LIGHTGREEN_EX
r = Fore.RED
w = Fore.WHITE
cy = Fore.CYAN
ye = Fore.YELLOW
colors = [lg, r, w, cy, ye]
try:
import requests
except ImportError:
print(f'{lg}[i] Installing module - requests...{n}')
os.system('pip install requests')
while True:
clr()
banner()
print(lg+'[1] Add new accounts'+n)
print(lg+'[2] Filter all banned accounts'+n)
print(lg+'[3] Delete specific accounts'+n)
print(lg+'[4] Update your Astra'+n)
print(lg+'[5] Quit'+n)
a = int(input('\nEnter your choice: '))
if a == 1:
new_accs = []
with open('vars.txt', 'ab') as g:
number_to_add = int(input(f'\n{lg} [~] Enter number of accounts to add: {r}'))
for i in range(number_to_add):
phone_number = str(input(f'\n{lg} [~] Enter Phone Number: {r}'))
parsed_number = ''.join(phone_number.split())
pickle.dump([parsed_number], g)
new_accs.append(parsed_number)
print(f'\n{lg} [i] Saved all accounts in vars.txt')
clr()
print(f'\n{lg} [*] Logging in from new accounts\n')
for number in new_accs:
c = TelegramClient(f'sessions/{number}', 3910389 , '86f861352f0ab76a251866059a6adbd6')
c.start(number)
print(f'{lg}[+] Login successful')
c.disconnect()
input(f'\n Press enter to goto main menu...')
g.close()
elif a == 2:
accounts = []
banned_accs = []
h = open('vars.txt', 'rb')
while True:
try:
accounts.append(pickle.load(h))
except EOFError:
break
h.close()
if len(accounts) == 0:
print(r+'[!] There are no accounts! Please add some and retry')
sleep(3)
else:
for account in accounts:
phone = str(account[0])
client = TelegramClient(f'sessions/{phone}', 3910389 , '86f861352f0ab76a251866059a6adbd6')
client.connect()
if not client.is_user_authorized():
try:
client.send_code_request(phone)
#client.sign_in(phone, input('[+] Enter the code: '))
print(f'{lg}[+] {phone} is not banned{n}')
except PhoneNumberBannedError:
print(r+str(phone) + ' is banned!'+n)
banned_accs.append(account)
if len(banned_accs) == 0:
print(lg+'Congrats! No banned accounts')
input('\nPress enter to goto main menu...')
else:
for m in banned_accs:
accounts.remove(m)
with open('vars.txt', 'wb') as k:
for a in accounts:
Phone = a[0]
pickle.dump([Phone], k)
k.close()
print(lg+'[i] All banned accounts removed'+n)
input('\nPress enter to goto main menu...')
elif a == 3:
accs = []
f = open('vars.txt', 'rb')
while True:
try:
accs.append(pickle.load(f))
except EOFError:
break
f.close()
i = 0
print(f'{lg}[i] Choose an account to delete\n')
for acc in accs:
print(f'{lg}[{i}] {acc[0]}{n}')
i += 1
index = int(input(f'\n{lg}[+] Enter a choice: {n}'))
phone = str(accs[index][0])
session_file = phone + '.session'
if os.name == 'nt':
os.system(f'del sessions\\{session_file}')
else:
os.system(f'rm sessions/{session_file}')
del accs[index]
f = open('vars.txt', 'wb')
for account in accs:
pickle.dump(account, f)
print(f'\n{lg}[+] Account Deleted{n}')
input(f'\nPress enter to goto main menu...')
f.close()
elif a == 4:
# thanks to github.com/th3unkn0n for the snippet below
print(f'\n{lg}[i] Checking for updates...')
try:
# https://raw.githubusercontent.com/Cryptonian007/Astra/main/version.txt
version = requests.get('https://raw.githubusercontent.com/Cryptonian007/Astra/main/version.txt')
except:
print(f'{r} You are not connected to the internet')
print(f'{r} Please connect to the internet and retry')
exit()
if float(version.text) > 1.1:
prompt = str(input(f'{lg}[~] Update available[Version {version.text}]. Download?[y/n]: {r}'))
if prompt == 'y' or prompt == 'yes' or prompt == 'Y':
print(f'{lg}[i] Downloading updates...')
if os.name == 'nt':
os.system('del add.py')
os.system('del manager.py')
else:
os.system('rm add.py')
os.system('rm manager.py')
#os.system('del scraper.py')
os.system('curl -l -O https://raw.githubusercontent.com/Cryptonian007/Astra/main/add.py')
os.system('curl -l -O https://raw.githubusercontent.com/Cryptonian007/Astra/main/manager.py')
print(f'{lg}[*] Updated to version: {version.text}')
input('Press enter to exit...')
exit()
else:
print(f'{lg}[!] Update aborted.')
input('Press enter to goto main menu...')
else:
print(f'{lg}[i] Your Astra is already up to date')
input('Press enter to goto main menu...')
elif a == 5:
clr()
banner()
exit()
| 36.780899 | 109 | 0.494578 |