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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0184348ba0c172bce71500036c4f9c8c8e4b28b8 | 449 | py | Python | src/leagues/migrations/0015_rotomultileagues_domain.py | sfernandezf/analytics-yahoofantasy | 6242599b903e4b8a7f9c56892ba26591a441b8fb | [
"Apache-2.0"
] | null | null | null | src/leagues/migrations/0015_rotomultileagues_domain.py | sfernandezf/analytics-yahoofantasy | 6242599b903e4b8a7f9c56892ba26591a441b8fb | [
"Apache-2.0"
] | 6 | 2020-03-15T03:32:06.000Z | 2022-01-13T03:46:05.000Z | src/leagues/migrations/0015_rotomultileagues_domain.py | sfernandezf/analytics-yahoofantasy | 6242599b903e4b8a7f9c56892ba26591a441b8fb | [
"Apache-2.0"
] | null | null | null | # Generated by Django 3.0.3 on 2021-05-07 01:58
from django.db import migrations, models
| 23.631579 | 97 | 0.63029 |
01858b30db6e28ba009ea21f05772bc48958908b | 7,329 | py | Python | ThingiBrowser/api/AbstractApiClient.py | BohunkG4mer/ThingiBrowser | 1cb541a6798b125231f64699a13f7bbc25d2b0f0 | [
"MIT"
] | 1 | 2019-11-15T20:06:16.000Z | 2019-11-15T20:06:16.000Z | ThingiBrowser/api/AbstractApiClient.py | BohunkG4mer/ThingiBrowser | 1cb541a6798b125231f64699a13f7bbc25d2b0f0 | [
"MIT"
] | null | null | null | ThingiBrowser/api/AbstractApiClient.py | BohunkG4mer/ThingiBrowser | 1cb541a6798b125231f64699a13f7bbc25d2b0f0 | [
"MIT"
] | null | null | null | # Copyright (c) 2020 Chris ter Beke.
# Thingiverse plugin is released under the terms of the LGPLv3 or higher.
import logging
from typing import List, Callable, Any, Tuple, Optional
from abc import ABC, abstractmethod
from PyQt5.QtCore import QUrl
from PyQt5.QtNetwork import QNetworkAccessManager, QNetworkReply, QNetworkRequest
from UM.Logger import Logger # type: ignore
from .ApiHelper import ApiHelper
from .JsonObject import Thing, ThingFile, Collection, ApiError
def _addCallback(self, reply: QNetworkReply,
on_finished: Callable[[Any], Any],
on_failed: Optional[Callable[[Optional[ApiError]], Any]] = None,
parser: Optional[Callable[[QNetworkReply], Tuple[int, Any]]] = None) -> None:
"""
Creates a callback function so that it includes the parsing of the response into the correct model.
The callback is added to the 'finished' signal of the reply.
:param reply: The reply that should be listened to.
:param on_finished: The callback in case the request is successful.
:param on_failed: The callback in case the request fails.
:param parser: A custom parser for the response data, defaults to a JSON parser.
"""
self._anti_gc_callbacks.append(parse)
reply.finished.connect(parse) # type: ignore
| 41.88 | 116 | 0.669532 |
0186c6f9ccb6910901110026b5550d4363a11f93 | 110 | py | Python | tests/collagen/utils/__init__.py | newskylabs/newskylabs-collagen | 3e2e331605745e6709f57dce8730ceb9ceaa002c | [
"Apache-2.0"
] | null | null | null | tests/collagen/utils/__init__.py | newskylabs/newskylabs-collagen | 3e2e331605745e6709f57dce8730ceb9ceaa002c | [
"Apache-2.0"
] | null | null | null | tests/collagen/utils/__init__.py | newskylabs/newskylabs-collagen | 3e2e331605745e6709f57dce8730ceb9ceaa002c | [
"Apache-2.0"
] | null | null | null | from . import test_conversion
from . import test_generic
from . import test_idxgz
from . import test_settings
| 22 | 29 | 0.818182 |
01886702340a7e2614a1ba8467c3b9e447627ac9 | 2,545 | py | Python | test/prettifier.py | ZachJHansen/Dist_Mem_GAPBS | 4c7d702c641e860e6a28f2957cec1509ce5f9893 | [
"BSD-3-Clause"
] | null | null | null | test/prettifier.py | ZachJHansen/Dist_Mem_GAPBS | 4c7d702c641e860e6a28f2957cec1509ce5f9893 | [
"BSD-3-Clause"
] | 3 | 2021-08-15T18:49:36.000Z | 2021-08-15T18:56:21.000Z | test/prettifier.py | ZachJHansen/Dist_Mem_GAPBS | 4c7d702c641e860e6a28f2957cec1509ce5f9893 | [
"BSD-3-Clause"
] | null | null | null | import subprocess
import time
import os
import sys
commands = generate_synthetic_commands(kernel)
commands = commands + generate_real_commands(kernel)
commands = commands + generate_el_commands(kernel)
prettify(commands)
| 34.863014 | 127 | 0.502554 |
01886934cd30258f1bbd5bc214cc5822c242b7c6 | 504 | py | Python | general/kneeOsteoarthritisDataset/KneeDataset_utility.py | duennbart/masterthesis_VAE | 1a161bc5c234acc0a021d84cde8cd69e784174e1 | [
"BSD-3-Clause"
] | 14 | 2020-06-28T15:38:48.000Z | 2021-12-05T01:49:50.000Z | general/kneeOsteoarthritisDataset/KneeDataset_utility.py | duennbart/masterthesis_VAE | 1a161bc5c234acc0a021d84cde8cd69e784174e1 | [
"BSD-3-Clause"
] | null | null | null | general/kneeOsteoarthritisDataset/KneeDataset_utility.py | duennbart/masterthesis_VAE | 1a161bc5c234acc0a021d84cde8cd69e784174e1 | [
"BSD-3-Clause"
] | 3 | 2020-06-28T15:38:49.000Z | 2022-02-13T22:04:34.000Z | import numpy as np
from kneeOsteoarthritisDataset.KneeOsteoarthritsDataset import KneeOsteoarthritsDataset
data_path = '/home/biomech/Documents/OsteoData/KneeXrayData/ClsKLData/kneeKL299'
kneeosteo = KneeOsteoarthritsDataset(data_path = data_path)
imgs, labels = kneeosteo.load_imgs()
rand_idx = np.random.randint(low=0,high=len(labels))
img = imgs[rand_idx]
label = labels[rand_idx]
kneeosteo.plot_img(img,label)
counter = 0
for item in labels:
if item == 2:
counter += 1
print(counter) | 26.526316 | 87 | 0.781746 |
018a6d7e60dff23b8dbdde473c02f7669ed70154 | 1,551 | py | Python | src/main.py | ubirch/pycom-bootloader | 6510eb34ec198ef74e15bb757bb67e88aaf15b74 | [
"Apache-2.0"
] | null | null | null | src/main.py | ubirch/pycom-bootloader | 6510eb34ec198ef74e15bb757bb67e88aaf15b74 | [
"Apache-2.0"
] | null | null | null | src/main.py | ubirch/pycom-bootloader | 6510eb34ec198ef74e15bb757bb67e88aaf15b74 | [
"Apache-2.0"
] | null | null | null | print("SD bootloader")
import pycom
import time
import machine
import sys
import uos
#paths for the code/lib/mount locations
SD_MOUNTPOINT = '/sd'
CODE_PATH = '/sd/src'
LIB_PATH = '/sd/src/lib'
#LED colors
#for errors (full brightness)
C_YELLOW = 0xffff00
C_RED = 0xff0000
C_PURPLE = 0xff00ff
C_BLUE = 0x0000ff
#for normal boot (dimmed)
C_WHITE_DIM = 0x030303
C_RED_DIM = 0x060000
### Functions ###
### Main Code ###
pycom.heartbeat(False)
pycom.rgbled(C_RED_DIM)
if mount_sd():
print("booting from SD")
pycom.rgbled(C_WHITE_DIM)
#add code and lib dir on sd to import path
sys.path.append(CODE_PATH)
sys.path.append(LIB_PATH)
#change working dir to code directory
try:
uos.chdir(CODE_PATH)
except Exception:
print("could not change to code folder:")
print(CODE_PATH)
endless_blink(C_PURPLE,C_RED)
print("sys.path:")
print(sys.path)
print("uos.getcwd():")
print(uos.getcwd())
#execute code from SD
try:
execfile('main.py')
except Exception:
print("could not execute main.py")
endless_blink(C_BLUE,C_RED)
#sd was not mounted
print("no SD found")
endless_blink(C_YELLOW,C_RED)
| 20.142857 | 49 | 0.654417 |
018d436c5de06e246b9aa736124eb44597086ab1 | 122,321 | py | Python | gui.py | michael11892/picoscopeDataLogger | f50d26f33b5a42731e52261e52c6fe8574ca2bbc | [
"MIT"
] | 3 | 2021-07-02T10:34:16.000Z | 2021-07-03T10:27:22.000Z | gui.py | michael11892/picoscopeDataLogger | f50d26f33b5a42731e52261e52c6fe8574ca2bbc | [
"MIT"
] | null | null | null | gui.py | michael11892/picoscopeDataLogger | f50d26f33b5a42731e52261e52c6fe8574ca2bbc | [
"MIT"
] | null | null | null | import os
import sys
import copy
from math import gcd
from driver_config_macros import *
from data_capture_macros import *
from signal_generator_macros import *
from power_operation_macros import *
from capture_config_macros import *
from trig_config_macros import *
from data_processing_macros import *
from PyQt5 import QtCore, QtGui, QtWidgets
import matplotlib
matplotlib.use('Qt5Agg')
from matplotlib import pyplot as plt
from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg
from matplotlib.figure import Figure
app_ = QtWidgets.QApplication(sys.argv)
w, h = app_.primaryScreen().size().width(), app_.primaryScreen().size().height()
#Screen Ratio Corrections
gcd_ = gcd(w, h)
resRatio = w/h
baseRatio = 16/9
wR = w/(gcd_*16) #Ratio Correction
hR = h/(gcd_*9) #Ratio Correction
widthRatio = 1#1/wR #Full Width Ratio Correction
heightRatio = 1#1/hR #Full Height Ratio Correction
app_.exit()
conditionList = {'conditionTab': [], 'extConditionLabel': [], 'aConditionLabel': [], 'bConditionLabel': [], 'cConditionLabel': [], 'dConditionLabel': [],
'aStateComboBox': [], 'bStateComboBox': [], 'cStateComboBox': [], 'dStateComboBox': [], 'extStateComboBox': [], 'stateLabel': []}
runList = {'runTab': [], 'stackedWidget': [], 'captureTab': []}
if __name__ == "__main__":
import sys
app = QtWidgets.QApplication(sys.argv)
MainWindow = QtWidgets.QMainWindow()
ui = Ui_MainWindow()
ui.setupUi(MainWindow)
MainWindow.show()
sys.exit(app.exec_())
| 65.412299 | 357 | 0.692931 |
018d64e411b9a079532721baad7937f619846f0d | 187 | py | Python | tests/test_main.py | ZhuYuJin/cgroup-parser | 7132791c496dc87af04d0458ad1f820eac8a8f0f | [
"Apache-2.0"
] | null | null | null | tests/test_main.py | ZhuYuJin/cgroup-parser | 7132791c496dc87af04d0458ad1f820eac8a8f0f | [
"Apache-2.0"
] | null | null | null | tests/test_main.py | ZhuYuJin/cgroup-parser | 7132791c496dc87af04d0458ad1f820eac8a8f0f | [
"Apache-2.0"
] | null | null | null | import cgroup_parser
| 20.777778 | 36 | 0.780749 |
018e37a3271bbe0ac811dfe2f2b0248dd13424ad | 5,123 | py | Python | tests/ut/python/dataset_deprecated/test_map.py | httpsgithu/mindspore | c29d6bb764e233b427319cb89ba79e420f1e2c64 | [
"Apache-2.0"
] | 1 | 2022-02-23T09:13:43.000Z | 2022-02-23T09:13:43.000Z | tests/ut/python/dataset_deprecated/test_map.py | 949144093/mindspore | c29d6bb764e233b427319cb89ba79e420f1e2c64 | [
"Apache-2.0"
] | null | null | null | tests/ut/python/dataset_deprecated/test_map.py | 949144093/mindspore | c29d6bb764e233b427319cb89ba79e420f1e2c64 | [
"Apache-2.0"
] | null | null | null | # Copyright 2022 Huawei Technologies Co., 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 pytest
import mindspore.dataset as ds
from mindspore.dataset.transforms import c_transforms
from mindspore.dataset.transforms import py_transforms
import mindspore.dataset.vision.c_transforms as c_vision
import mindspore.dataset.vision.py_transforms as py_vision
DATA_DIR = "../data/dataset/testPK/data"
def test_map_c_transform_exception():
"""
Feature: test c error op def
Description: op defined like c_vision.HWC2CHW
Expectation: success
"""
data_set = ds.ImageFolderDataset(DATA_DIR, num_parallel_workers=1, shuffle=True)
train_image_size = 224
mean = [0.485 * 255, 0.456 * 255, 0.406 * 255]
std = [0.229 * 255, 0.224 * 255, 0.225 * 255]
# define map operations
random_crop_decode_resize_op = c_vision.RandomCropDecodeResize(train_image_size,
scale=(0.08, 1.0),
ratio=(0.75, 1.333))
random_horizontal_flip_op = c_vision.RandomHorizontalFlip(prob=0.5)
normalize_op = c_vision.Normalize(mean=mean, std=std)
hwc2chw_op = c_vision.HWC2CHW # exception
data_set = data_set.map(operations=random_crop_decode_resize_op, input_columns="image", num_parallel_workers=1)
data_set = data_set.map(operations=random_horizontal_flip_op, input_columns="image", num_parallel_workers=1)
data_set = data_set.map(operations=normalize_op, input_columns="image", num_parallel_workers=1)
with pytest.raises(ValueError) as info:
data_set = data_set.map(operations=hwc2chw_op, input_columns="image", num_parallel_workers=1)
assert "Parameter operations's element of method map should be a " in str(info.value)
# compose exception
with pytest.raises(ValueError) as info:
c_transforms.Compose([
c_vision.RandomCropDecodeResize(train_image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
c_vision.RandomHorizontalFlip,
c_vision.Normalize(mean=mean, std=std),
c_vision.HWC2CHW()])
assert " should be a " in str(info.value)
# randomapply exception
with pytest.raises(ValueError) as info:
c_transforms.RandomApply([
c_vision.RandomCropDecodeResize,
c_vision.RandomHorizontalFlip(prob=0.5),
c_vision.Normalize(mean=mean, std=std),
c_vision.HWC2CHW()])
assert " should be a " in str(info.value)
# randomchoice exception
with pytest.raises(ValueError) as info:
c_transforms.RandomChoice([
c_vision.RandomCropDecodeResize(train_image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
c_vision.RandomHorizontalFlip(prob=0.5),
c_vision.Normalize,
c_vision.HWC2CHW()])
assert " should be a " in str(info.value)
def test_map_py_transform_exception():
"""
Feature: test python error op def
Description: op defined like py_vision.RandomHorizontalFlip
Expectation: success
"""
data_set = ds.ImageFolderDataset(DATA_DIR, num_parallel_workers=1, shuffle=True)
# define map operations
decode_op = py_vision.Decode()
random_horizontal_flip_op = py_vision.RandomHorizontalFlip # exception
to_tensor_op = py_vision.ToTensor()
trans = [decode_op, random_horizontal_flip_op, to_tensor_op]
with pytest.raises(ValueError) as info:
data_set = data_set.map(operations=trans, input_columns="image", num_parallel_workers=1)
assert "Parameter operations's element of method map should be a " in str(info.value)
# compose exception
with pytest.raises(ValueError) as info:
py_transforms.Compose([
py_vision.Decode,
py_vision.RandomHorizontalFlip(),
py_vision.ToTensor()])
assert " should be a " in str(info.value)
# randomapply exception
with pytest.raises(ValueError) as info:
py_transforms.RandomApply([
py_vision.Decode(),
py_vision.RandomHorizontalFlip,
py_vision.ToTensor()])
assert " should be a " in str(info.value)
# randomchoice exception
with pytest.raises(ValueError) as info:
py_transforms.RandomChoice([
py_vision.Decode(),
py_vision.RandomHorizontalFlip(),
py_vision.ToTensor])
assert " should be a " in str(info.value)
if __name__ == '__main__':
test_map_c_transform_exception()
test_map_py_transform_exception()
| 40.65873 | 115 | 0.68007 |
018ea4db5dccbf0d3e4ad515400df46535657771 | 1,295 | py | Python | tests/test_structural/test_proxy.py | TrendingTechnology/python-patterns-1 | 426482f58b86a0a7525e303444338e9bb73698de | [
"BSD-3-Clause"
] | 2 | 2021-09-23T16:41:42.000Z | 2021-11-20T11:54:42.000Z | tests/test_structural/test_proxy.py | TrendingTechnology/python-patterns-1 | 426482f58b86a0a7525e303444338e9bb73698de | [
"BSD-3-Clause"
] | null | null | null | tests/test_structural/test_proxy.py | TrendingTechnology/python-patterns-1 | 426482f58b86a0a7525e303444338e9bb73698de | [
"BSD-3-Clause"
] | null | null | null | # -*- coding: utf-8 -*-
# ********************************************************
# Author and developer: Aleksandr Suvorov
# --------------------------------------------------------
# Licensed: BSD 3-Clause License (see LICENSE for details)
# --------------------------------------------------------
# Url: https://github.com/smartlegion/
# --------------------------------------------------------
# Donate: https://smartlegion.github.io/donate
# --------------------------------------------------------
# Copyright 2021 Aleksandr Suvorov
# ========================================================
| 38.088235 | 62 | 0.467954 |
018eb361eddd592309fff69045cb98d9066ea2e0 | 39,647 | py | Python | hallo/test/modules/channel_control/test_de_operator.py | joshcoales/Hallo | 17145d8f76552ecd4cbc5caef8924bd2cf0cbf24 | [
"MIT"
] | 1 | 2018-05-19T22:27:20.000Z | 2018-05-19T22:27:20.000Z | hallo/test/modules/channel_control/test_de_operator.py | joshcoales/Hallo | 17145d8f76552ecd4cbc5caef8924bd2cf0cbf24 | [
"MIT"
] | 75 | 2015-09-26T18:07:18.000Z | 2022-01-04T07:15:11.000Z | hallo/test/modules/channel_control/test_de_operator.py | SpangleLabs/Hallo | 17145d8f76552ecd4cbc5caef8924bd2cf0cbf24 | [
"MIT"
] | 1 | 2021-04-10T12:02:47.000Z | 2021-04-10T12:02:47.000Z | from hallo.events import EventMessage, EventMode
from hallo.server import Server
from hallo.test.server_mock import ServerMock
| 41.042443 | 90 | 0.70762 |
018ecde16201a4f4c059f4251f120ee69a80438a | 7,685 | py | Python | overlays/holo-nixpkgs/hpos-admin/hpos-admin.py | samrose/holo-nixpkgs | 057c92fcef9934d1ba2310e77579b78e61271a59 | [
"MIT"
] | null | null | null | overlays/holo-nixpkgs/hpos-admin/hpos-admin.py | samrose/holo-nixpkgs | 057c92fcef9934d1ba2310e77579b78e61271a59 | [
"MIT"
] | null | null | null | overlays/holo-nixpkgs/hpos-admin/hpos-admin.py | samrose/holo-nixpkgs | 057c92fcef9934d1ba2310e77579b78e61271a59 | [
"MIT"
] | null | null | null | from base64 import b64encode
from flask import Flask, jsonify, request
from functools import reduce
from gevent import subprocess, pywsgi, queue, socket, spawn, lock
from gevent.subprocess import CalledProcessError
from hashlib import sha512
from pathlib import Path
from tempfile import mkstemp
import json
import os
import subprocess
import toml
import requests
import asyncio
import websockets
PROFILES_TOML_PATH = '/etc/nixos/hpos-admin-features.toml'
app = Flask(__name__)
rebuild_queue = queue.PriorityQueue()
state_lock = lock.Semaphore()
# Toggling HPOS features
def read_profiles():
if Path(PROFILES_TOML_PATH).is_file():
return toml.load(PROFILES_TOML_PATH)
else:
return {}
def write_profiles(profiles):
with open(PROFILES_TOML_PATH, 'w') as f:
f.write(toml.dumps(profiles))
def set_feature_state(profile, feature, enable = True):
profiles = read_profiles()
profiles.update({
profile: {
'features': {
feature: {
'enable': enable
}
}
}
})
write_profiles(profiles)
return jsonify({
'enabled': enable
})
def hosted_happs():
conductor_config = toml.load('/var/lib/holochain-conductor/conductor-config.toml')
return [dna for dna in conductor_config['dnas'] if dna['holo-hosted']]
def hosted_instances():
conductor_config = toml.load('/var/lib/holochain-conductor/conductor-config.toml')
return [instance for instance in conductor_config['instances'] if instance['holo-hosted']]
TRAFFIC_NULL_STATE = {'start_date': None, 'total_zome_calls':0, 'value': []}
def unix_socket(path):
sock = socket.socket(socket.AF_UNIX, socket.SOCK_STREAM)
if os.path.exists(path):
os.remove(path)
sock.bind(path)
sock.listen()
return sock
if __name__ == '__main__':
spawn(rebuild_worker)
pywsgi.WSGIServer(unix_socket('/run/hpos-admin.sock'), app).serve_forever()
| 28.462963 | 180 | 0.645413 |
019038b5e29201ae48ff890d61729392b8611ea1 | 1,050 | py | Python | Backend/clother/admin/utils.py | t3ddyss/Clother | ca7bfd2a830cb36cf7ba62782498636e58f9ea17 | [
"MIT"
] | 21 | 2021-04-21T10:36:12.000Z | 2021-10-18T10:23:38.000Z | Backend/clother/admin/utils.py | t3ddyss/Clother | ca7bfd2a830cb36cf7ba62782498636e58f9ea17 | [
"MIT"
] | 1 | 2021-06-04T15:19:35.000Z | 2021-06-04T15:19:35.000Z | Backend/clother/admin/utils.py | t3ddyss/Clother | ca7bfd2a830cb36cf7ba62782498636e58f9ea17 | [
"MIT"
] | 1 | 2022-03-03T02:50:37.000Z | 2022-03-03T02:50:37.000Z | import datetime
import math
import random
import time
from ..users.models import User
| 26.25 | 81 | 0.662857 |
01949a8453b27509b378298375545c88dd880612 | 525 | py | Python | vkmodels/objects/addresses.py | deknowny/vk-api-py-models | 6760c9395b39efd2a987251893b418a61eefbdca | [
"MIT"
] | null | null | null | vkmodels/objects/addresses.py | deknowny/vk-api-py-models | 6760c9395b39efd2a987251893b418a61eefbdca | [
"MIT"
] | null | null | null | vkmodels/objects/addresses.py | deknowny/vk-api-py-models | 6760c9395b39efd2a987251893b418a61eefbdca | [
"MIT"
] | null | null | null | import dataclasses
import enum
import typing
from vkmodels.bases.object import ObjectBase
| 22.826087 | 45 | 0.689524 |
0196af5b9fce69fa6d92fe89461a5b8fdf7588ed | 3,413 | py | Python | test/test_caching.py | ORNL/curifactory | f8be235b7fa7b91cc86f61d610d7093075b89d1f | [
"BSD-3-Clause"
] | 4 | 2022-01-25T18:27:49.000Z | 2022-03-30T22:57:04.000Z | test/test_caching.py | ORNL/curifactory | f8be235b7fa7b91cc86f61d610d7093075b89d1f | [
"BSD-3-Clause"
] | 1 | 2022-03-05T19:10:42.000Z | 2022-03-07T18:00:49.000Z | test/test_caching.py | ORNL/curifactory | f8be235b7fa7b91cc86f61d610d7093075b89d1f | [
"BSD-3-Clause"
] | null | null | null | import curifactory as cf
import json
import os
import pytest
from stages.cache_stages import filerefcacher_stage, filerefcacher_stage_multifile
# TODO: necessary? configured_test_manager already does this
def test_filerefcacher_stores_multiple_paths(configured_test_manager, clear_stage_run):
"""FileReferenceCacher should correctly store a list of files in the saved json."""
r = cf.Record(configured_test_manager, cf.ExperimentArgs(name="test"))
filerefcacher_stage_multifile(r)
argshash = r.args.hash
expected_list = [
os.path.join(
configured_test_manager.cache_path,
f"test_{argshash}_filerefcacher_stage_multifile_my_files/thing{i}",
)
for i in range(5)
]
with open(
os.path.join(
configured_test_manager.cache_path,
f"test_{argshash}_filerefcacher_stage_multifile_output_paths.json",
),
"r",
) as infile:
filelist = json.load(infile)
assert filelist == expected_list
for filename in filelist:
assert os.path.exists(filename)
def test_filerefcacher_shortcircuits(configured_test_manager, clear_stage_run):
"""FileReferenceCacher should short-circuit if all files in the filelist already exist."""
r0 = cf.Record(configured_test_manager, cf.ExperimentArgs(name="test"))
filerefcacher_stage_multifile(r0)
ran_path = os.path.join(configured_test_manager.cache_path, "stage_ran")
assert os.path.exists(ran_path)
os.remove(ran_path)
r1 = cf.Record(configured_test_manager, cf.ExperimentArgs(name="test"))
filerefcacher_stage_multifile(r1)
assert not os.path.exists(ran_path)
def test_filerefcacher_runs_when_file_missing(configured_test_manager, clear_stage_run):
"""FileReferenceCacher should _not_ short-circuit if any of the files in the filelist are missing."""
r0 = cf.Record(configured_test_manager, cf.ExperimentArgs(name="test"))
filerefcacher_stage_multifile(r0)
ran_path = os.path.join(configured_test_manager.cache_path, "stage_ran")
assert os.path.exists(ran_path)
os.remove(ran_path)
os.remove(
os.path.join(
configured_test_manager.cache_path,
f"test_{r0.args.hash}_filerefcacher_stage_multifile_my_files/thing1",
)
)
r1 = cf.Record(configured_test_manager, cf.ExperimentArgs(name="test"))
filerefcacher_stage_multifile(r1)
assert os.path.exists(ran_path)
| 32.504762 | 105 | 0.714914 |
0196ed4e4760ab9bf312a9416801f8b71d0a5124 | 2,471 | py | Python | downloader/download.py | inverthermit/sec_edgar_analysis | ffdf43b30ab53b0a024790757c8ef0c989acf67a | [
"MIT"
] | 1 | 2018-02-03T00:28:53.000Z | 2018-02-03T00:28:53.000Z | downloader/download.py | inverthermit/sec_edgar_analysis | ffdf43b30ab53b0a024790757c8ef0c989acf67a | [
"MIT"
] | null | null | null | downloader/download.py | inverthermit/sec_edgar_analysis | ffdf43b30ab53b0a024790757c8ef0c989acf67a | [
"MIT"
] | null | null | null | import urllib
import time
from multiprocessing.dummy import Pool as ThreadPool
excelFolder = 'F://SecExcelDownload2/'
compListUrl = 'C://Users/l1111/Desktop/AlphaCapture/downloadFileUrl.txt'
successFile = excelFolder+'/success.txt'
failFile = excelFolder+'/fail.txt'
logFile = excelFolder+'/log.txt'
downloadedList = getAlreadyDownload()
fastMultiThread()
# slowSingleThread()
| 28.402299 | 108 | 0.583974 |
0197d01c354f66f49415a9ef3d542eb61ea7a772 | 20,495 | py | Python | HOST/py/tc_TcpEcho.py | cloudFPGA/cFp_HelloKale | 949f8c3005d2824b8bc65345b77ea97bd0b6e692 | [
"Apache-2.0"
] | null | null | null | HOST/py/tc_TcpEcho.py | cloudFPGA/cFp_HelloKale | 949f8c3005d2824b8bc65345b77ea97bd0b6e692 | [
"Apache-2.0"
] | 6 | 2022-01-22T10:04:18.000Z | 2022-02-01T21:28:19.000Z | HOST/py/tc_TcpEcho.py | cloudFPGA/cFp_HelloKale | 949f8c3005d2824b8bc65345b77ea97bd0b6e692 | [
"Apache-2.0"
] | null | null | null | #/*
# * Copyright 2016 -- 2021 IBM Corporation
# *
# * 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.
# *
# *****************************************************************************
# * @file : tc_TcpEcho.py
# * @brief : A multi-threaded script to send and receive traffic on the
# * TCP connection of an FPGA module.
# *
# * System: : cloudFPGA
# * Component : cFp_BringUp/ROLE
# * Language : Python 3
# *
# *****************************************************************************
# ### REQUIRED PYTHON PACKAGES ################################################
import argparse
import datetime
import errno
import filecmp
import socket
import threading
import time
# ### REQUIRED TESTCASE MODULES ###############################################
from tc_utils import *
# ### GLOBAL VARIABLES ########################################################
gEchoRxPath = './echoRx.dat'
gEchoTxPath = './echoTx.dat'
def tcp_tx(sock, message, count, verbose=False):
"""TCP Tx Thread.
:param sock, the socket to send to.
:param message, the random string to sent.
:param count, the number of segments to send.
:param verbose, enables verbosity.
:return None"""
if verbose:
print("The following message of %d bytes will be sent out %d times:\n Message=%s\n" %
(len(message), count, message.decode('ascii')))
# Create a Tx Reference File
echoTxFile = open(gEchoTxPath, 'w')
if count <= 1000:
loop = 0
while loop < count:
echoTxFile.write(message.decode('ascii'))
loop += 1
# Start Data Transmission
loop = 0
startTime = datetime.datetime.now()
while loop < count:
try:
sock.sendall(message)
finally:
pass
loop += 1
endTime = datetime.datetime.now()
elapseTime = endTime - startTime;
bandwidth = len(message) * 8 * count * 1.0 / (elapseTime.total_seconds() * 1024 * 1024)
print("##################################################")
print("#### TCP TX DONE with bandwidth = %6.1f Mb/s ####" % bandwidth)
print("##################################################")
print()
# Close the Tx Reference File
echoTxFile.close()
# Push a few more bytes to force the FPGA to flush its buffers
try:
sock.sendall(message)
finally:
pass
def tcp_rx(sock, message, count, verbose):
"""TCP Rx Thread.
:param sock, the socket to receive from.
:param message, the expected string message to be received.
:param count, the number of segment to receive.
:param verbose, enables verbosity.
:return None"""
# Create an Rx Test File
echoRxFile = open(gEchoRxPath, 'w')
# Start Data Reception
loop = 0
rxBytes = 0
expectedBytes = count*len(message)
startTime = datetime.datetime.now()
while rxBytes < expectedBytes:
try:
data = sock.recv(expectedBytes - rxBytes)
rxBytes += len(data)
if count <= 1000:
echoRxFile.write(data.decode('ascii'))
except socket.error as exc:
print("[EXCEPTION] Socket error while receiving :: %s" % exc)
else:
if verbose:
print("Loop=%d | RxBytes=%d" % (loop, rxBytes))
loop += 1
endTime = datetime.datetime.now()
elapseTime = endTime - startTime
bandwidth = len(message) * 8 * count * 1.0 / (elapseTime.total_seconds() * 1024 * 1024)
print("##################################################")
print("#### TCP RX DONE with bandwidth = %6.1f Mb/s ####" % bandwidth)
print("##################################################")
print()
# Close the Rx Test File
echoRxFile.close()
def waitUntilSocketPairCanBeReused(ipFpga, portFpga):
"""Check and wait until the a socket pair can be reused.
[INFO] When a client or a server initiates an active close, then the same destination socket
(i.e. the same IP address / TCP port number) cannot be re-used immediately because
of security issues. Therefore, a closed connection must linger in a 'TIME_WAIT' or
'FIN_WAIT' state for as long as 2xMSL (Maximum Segment Lifetime), which corresponds
to twice the time a TCP segment might exist in the internet system. The MSL is
arbitrarily defined to be 2 minutes long.
:param ipFpga: the IP address of FPGA.
:param portFpga: the TCP port of the FPGA.
:return: nothing
"""
wait = True
# NETSTAT example: rc = os.system("netstat | grep '10.12.200.163:8803' | grep TIME_WAIT")
cmdStr = "netstat | grep \'" + str(ipFpga) + ":" + str(portFpga) + "\' | grep \'TIME_WAIT\|FIN_WAIT\' "
while wait:
rc = os.system(cmdStr)
if rc == 0:
print("[INFO] Cannot reuse this socket as long as it is in the \'TIME_WAIT\' or \'FIN_WAIT\' state.")
print(" Let's sleep for 5 sec...")
time.sleep(5)
else:
wait = False
def tcp_txrx_loop(sock, message, count, verbose=False):
"""TCP Tx-Rx Single-Thread Loop.
:param sock The socket to send/receive to/from.
:param message The message string to sent.
:param count The number of segments send.
:param verbose Enables verbosity.
:return None"""
if verbose:
print("[INFO] The following message of %d bytes will be sent out %d times:\n Message=%s\n" %
(len(message), count, message.decode('ascii')))
nrErr = 0
txMssgCnt = 0
rxMssgCnt = 0
rxByteCnt = 0
txStream = ""
rxStream = ""
# Init the Tx reference stream
for i in range(count):
txStream = txStream + message.decode('ascii')
startTime = datetime.datetime.now()
while rxByteCnt < (count * len(message)):
if txMssgCnt < count:
# Send a new message
# ------------------------
try:
tcpSock.sendall(message)
txMssgCnt += 1
finally:
pass
# Receive a segment
# --------------------
try:
data = tcpSock.recv(len(message))
rxByteCnt += len(data)
rxMssgCnt += 1
if verbose:
print("%d:%s" % (rxMssgCnt, data.decode('ascii')))
except IOError as e:
# On non blocking connections - when there are no incoming data, error is going to be
# raised. Some operating systems will indicate that using AGAIN, and some using
# WOULDBLOCK error code. We are going to check for both - if one of them - that's
# expected, means no incoming data, continue as normal. If we got different error code,
# something happened
if e.errno != errno.EAGAIN and e.errno != errno.EWOULDBLOCK:
print('[ERROR] Socket reading error: {}'.format(str(e)))
exit(1)
# We just did not receive anything
continue
except socket.error as exc:
# Any other exception
print("[EXCEPTION] Socket error while receiving :: %s" % exc)
# exit(1)
finally:
pass
rxStream = rxStream + data.decode('ascii')
endTime = datetime.datetime.now()
if verbose:
print("\n")
# Compare Tx and Rx stream
if rxStream != txStream:
print(" KO | Received stream = %s" % data.decode('ascii'))
print(" | Expected stream = %s" % rxStream)
nrErr += 1
elif verbose:
print(" OK | Received %d bytes in %d messages." % (rxByteCnt, rxMssgCnt))
elapseTime = endTime - startTime;
bandwidth = len(message) * 8 * count * 1.0 / (elapseTime.total_seconds() * 1024 * 1024)
print("[INFO] Transferred a total of %d bytes." % rxByteCnt)
print("#####################################################")
print("#### TCP Tx/Rx DONE with bandwidth = %6.1f Mb/s ####" % bandwidth)
print("#####################################################")
print()
def tcp_txrx_ramp(sock, message, count, verbose=False):
"""TCP Tx-Rx Single-Thread Ramp.
:param sock The socket to send/receive to/from.
:param message The message string to sent.
:param count The number of segments to send.
:param verbose Enables verbosity.
:return None"""
if verbose:
print("[INFO] The following message of %d bytes will be sent out incrementally %d times:\n Message=%s\n" %
(len(message), count, message.decode('ascii')))
nrErr = 0
loop = 0
rxByteCnt = 0
startTime = datetime.datetime.now()
while loop < count:
i = 1
while i <= len(message):
subMsg = message[0:i]
# Send datagram
# -------------------
try:
tcpSock.sendall(subMsg)
finally:
pass
# Receive datagram
# -------------------
try:
data = tcpSock.recv(len(subMsg))
rxByteCnt += len(data)
if data == subMsg:
if verbose:
print("Loop=%d | RxBytes=%d" % (loop, len(data)))
else:
print("Loop=%d | RxBytes=%d" % (loop, len(data)))
print(" KO | Received Message=%s" % data.decode('ascii'))
print(" | Expecting Message=%s" % subMsg)
nrErr += 1
except IOError as e:
# On non blocking connections - when there are no incoming data, error is going to be raised
# Some operating systems will indicate that using AGAIN, and some using WOULDBLOCK error code
# We are going to check for both - if one of them - that's expected, means no incoming data,
# continue as normal. If we got different error code - something happened
if e.errno != errno.EAGAIN and e.errno != errno.EWOULDBLOCK:
print('[ERROR] Socket reading error: {}'.format(str(e)))
exit(1)
# We just did not receive anything
continue
except socket.error as exc:
# Any other exception
print("[EXCEPTION] Socket error while receiving :: %s" % exc)
# exit(1)
finally:
pass
i += 1
loop += 1
endTime = datetime.datetime.now()
elapseTime = endTime - startTime
bandwidth = (rxByteCnt * 8 * count * 1.0) / (elapseTime.total_seconds() * 1024 * 1024)
megaBytes = (rxByteCnt * 1.0) / (1024 * 1024 * 1.0)
print("[INFO] Transferred a total of %.1f MB." % megaBytes)
print("#####################################################")
print("#### TCP Tx/Rx DONE with bandwidth = %6.1f Mb/s ####" % bandwidth)
print("#####################################################")
print()
###############################################################################
# #
# MAIN #
# #
###############################################################################
rc = 0
# STEP-1: Parse the command line strings into Python objects
# -----------------------------------------------------------------------------
parser = argparse.ArgumentParser(description='A script to send/receive TCP data to/from an FPGA module.')
parser.add_argument('-fi', '--fpga_ipv4', type=str, default='',
help='The destination IPv4 address of the FPGA (a.k.a image_ip / e.g. 10.12.200.163)')
parser.add_argument('-fp', '--fpga_port', type=int, default=8803,
help='The TCP destination port of the FPGA (default is 8803)')
parser.add_argument('-ii', '--inst_id', type=int, default=0,
help='The instance ID assigned by the cloudFPGA Resource Manager (range is 1-32)')
parser.add_argument('-lc', '--loop_count', type=int, default=10,
help='The number of times to run run the test (default is 10)')
parser.add_argument('-mi', '--mngr_ipv4', type=str, default='10.12.0.132',
help='The IP address of the cloudFPGA Resource Manager (default is 10.12.0.132)')
parser.add_argument('-mp', '--mngr_port', type=int, default=8080,
help='The TCP port of the cloudFPGA Resource Manager (default is 8080)')
parser.add_argument('-mt', '--multi_threading', action="store_true",
help='Enable multi_threading')
parser.add_argument('-sd', '--seed', type=int, default=-1,
help='The initial number to seed the pseudo-random number generator.')
parser.add_argument('-sz', '--size', type=int, default=-1,
help='The size of the segment to generate.')
parser.add_argument('-un', '--user_name', type=str, default='',
help='A user name as used to log in ZYC2 (.e.g \'fab\')')
parser.add_argument('-up', '--user_passwd', type=str, default='',
help='The ZYC2 password attached to the user name')
parser.add_argument('-v', '--verbose', action="store_true",
help='Enable verbosity')
args = parser.parse_args()
if args.user_name == '' or args.user_passwd == '':
print("\nWARNING: You must provide a ZYC2 user name and the corresponding password for this script to execute.\n")
exit(1)
# STEP-2a: Retrieve the IP address of the FPGA module (this will be the SERVER)
# ------------------------------------------------------------------------------
ipFpga = getFpgaIpv4(args)
# STEP-2b: Retrieve the instance Id assigned by the cloudFPGA Resource Manager
# -----------------------------------------------------------------------------
instId = getInstanceId(args)
# STEP-2c: Retrieve the IP address of the cF Resource Manager
# -----------------------------------------------------------------------------
ipResMngr = getResourceManagerIpv4(args)
# STEP-3a: Retrieve the TCP port of the FPGA server
# -----------------------------------------------------------------------------
portFpga = getFpgaPort(args)
# STEP-3b: Retrieve the TCP port of the cloudFPGA Resource Manager
# -----------------------------------------------------------------------------
portResMngr = getResourceManagerPort(args)
# STEP-?: Configure the application registers
# -----------------------------------------------------------------------------
# TODO print("\nNow: Configuring the application registers.")
# TODO tcpEchoPathThruMode = (0x0 << 4) # See DIAG_CTRL_2 register
# STEP-4: Trigger the FPGA role to restart (i.e. perform SW reset of the role)
# -----------------------------------------------------------------------------
restartApp(instId, ipResMngr, portResMngr, args.user_name, args.user_passwd)
# STEP-5: Ping the FPGA
# -----------------------------------------------------------------------------
pingFpga(ipFpga)
# STEP-6a: Set the FPGA socket association
# -----------------------------------------------------------------------------
tcpDP = 8803 # 8803=0x2263 and 0x6322=25378
fpgaAssociation = (str(ipFpga), tcpDP)
# STEP-6b: Set the HOST socket association (optional)
# Info: Linux selects a source port from an ephemeral port range, which by
# default is a set to range from 32768 to 61000. You can check it
# with the command:
# > cat /proc/sys/net/ipv4/ip_local_port_range
# If we want to force the source port ourselves, we must use the
# "bind before connect" trick.
# -----------------------------------------------------------------------------
if 0:
tcpSP = tcpDP + 49152 # 8803 + 0xC000
hostAssociation = (ipSaStr, tcpSP)
# STEP-7: Wait until the current socket can be reused
# -----------------------------------------------------------------------------
if 0:
waitUntilSocketPairCanBeReused(ipFpga, portFpga)
# STEP-8a: Create a TCP/IP socket for the TCP/IP connection
# -----------------------------------------------------------------------------
try:
tcpSock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
except Exception as exc:
print("[EXCEPTION] %s" % exc)
exit(1)
# Step-8b: Allow this socket to be re-used and disable the Nagle's algorithm
# ----------------------------------------------------------------------------
tcpSock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
tcpSock.setsockopt(socket.IPPROTO_TCP, socket.TCP_NODELAY, True)
# STEP-8c: Bind before connect (optional).
# This trick enables us to ask the kernel to select a specific source IP and
# source PORT by calling bind() before calling connect().
# -----------------------------------------------------------------------------
if 0:
try:
tcpSock.bind(hostAssociation)
print('Binding the socket address of the HOST to {%s, %d}' % hostAssociation)
except Exception as exc:
print("[EXCEPTION] %s" % exc)
exit(1)
# STEP-9: Connect to the remote FPGA
# -----------------------------------------------------------------------------
try:
tcpSock.connect(fpgaAssociation)
except Exception as exc:
print("[EXCEPTION] %s" % exc)
exit(1)
else:
print('\nSuccessful connection with socket address of FPGA at {%s, %d} \n' % fpgaAssociation)
# STEP-10: Setup the test
# -------------------------------
print("[INFO] Testcase `%s` is run with:" % (os.path.basename(__file__)))
seed = args.seed
if seed == -1:
seed = random.randint(0, 100000)
random.seed(seed)
print("\t\t seed = %d" % seed)
size = args.size
if size == -1:
size = random.randint(1, ZYC2_MSS)
elif size > ZYC2_MSS:
print('\nERROR: ')
print("[ERROR] This test-case expects the transfer of segment which are less or equal to MSS (.i.e %d bytes).\n" % ZYC2_MSS)
exit(1)
print("\t\t size = %d" % size)
count = args.loop_count
print("\t\t loop = %d" % count)
if seed % 1:
message = str_static_gen(size)
else:
message = str_rand_gen(size)
verbose = args.verbose
print("[INFO] This testcase is sending traffic from HOST-to-FPGA and back from FPGA-to-HOST.")
if args.multi_threading:
print("[INFO] This run is executed in multi-threading mode.\n")
# STEP-11: Create Rx and Tx threads
# ----------------------------------
tx_thread = threading.Thread(target=tcp_tx, args=(tcpSock, message, count, args.verbose))
rx_thread = threading.Thread(target=tcp_rx, args=(tcpSock, message, count, args.verbose))
# STEP-12: Start the threads
# ---------------------------
tx_thread.start()
rx_thread.start()
# STEP-13: Wait for threads to terminate
# ----------------------------------------
tx_thread.join()
rx_thread.join()
# STEP-14: Compare Rx and Tx files
# ----------------------------------------
result = filecmp.cmp(gEchoTxPath, gEchoRxPath, shallow=False)
if not result:
print("\n[ERROR] Rx file \'%s\' differs from Tx file \'%s\'.\n" % (gEchoRxPath, gEchoTxPath))
rc = 1
else:
os.remove(gEchoRxPath)
os.remove(gEchoTxPath)
else:
print("[INFO] The run is executed in single-threading mode.\n")
# STEP-11: Set the socket in non-blocking mode
# ----------------------------------------------
tcpSock.setblocking(False)
tcpSock.settimeout(5)
if seed == 0:
tcp_txrx_ramp(tcpSock, message, count, args.verbose)
else:
tcp_txrx_loop(tcpSock, message, count, args.verbose)
# STEP-14: Close socket
# -----------------------
time.sleep(2)
tcpSock.close()
exit(rc)
| 40.99 | 128 | 0.528178 |
0199b3ed945c2771be748d98e1dc7b130caca848 | 2,961 | py | Python | ngram.py | lojikil/lojifacts | 88e2318e0b6c6cb5eb270002abc4b4195e5b85b1 | [
"0BSD"
] | null | null | null | ngram.py | lojikil/lojifacts | 88e2318e0b6c6cb5eb270002abc4b4195e5b85b1 | [
"0BSD"
] | null | null | null | ngram.py | lojikil/lojifacts | 88e2318e0b6c6cb5eb270002abc4b4195e5b85b1 | [
"0BSD"
] | null | null | null | import json
import re
import random
if __name__ == "__main__":
data, gramindex = {}, NGram()
gramind3x = NGram(size=3)
gram1ndex = NGram(size=1)
with open('dump', 'r') as f:
data = json.load(f)
for tweet in data['tweet']:
gramindex.index(tweet)
gramind3x.index(tweet)
gram1ndex.index(tweet)
print gramindex.wordindex.keys()[0:10]
print gramind3x.wordindex.keys()[0:10]
print gram1ndex.wordindex.keys()[0:10]
print gramindex.gram_stats()
print gramind3x.gram_stats()
print gram1ndex.gram_stats()
print gramindex.chain.items()[0:10]
print gramind3x.chain.items()[0:10]
print gram1ndex.chain.items()[0:10]
print gramindex.random_gram()
print gramind3x.random_gram()
print gram1ndex.random_gram()
| 25.09322 | 73 | 0.549814 |
019ba56645c86cd5f76a825624bb4c712e44806d | 967 | py | Python | ymir/command/mir/scm/__init__.py | Zhang-SJ930104/ymir | dd6481be6f229ade4cf8fba64ef44a15357430c4 | [
"Apache-2.0"
] | 64 | 2021-11-15T03:48:00.000Z | 2022-03-25T07:08:46.000Z | ymir/command/mir/scm/__init__.py | Zhang-SJ930104/ymir | dd6481be6f229ade4cf8fba64ef44a15357430c4 | [
"Apache-2.0"
] | 35 | 2021-11-23T04:14:35.000Z | 2022-03-26T09:03:43.000Z | ymir/command/mir/scm/__init__.py | Aryalfrat/ymir | d4617ed00ef67a77ab4e1944763f608bface4be6 | [
"Apache-2.0"
] | 57 | 2021-11-11T10:15:40.000Z | 2022-03-29T07:27:54.000Z | import os
from mir.scm.cmd import CmdScm
from mir.tools.code import MirCode
from mir.tools.errors import MirRuntimeError
def Scm(root_dir: str, scm_executable: str = None) -> CmdScm:
"""Returns SCM instance that corresponds to a repo at the specified
path.
Args:
root_dir (str): path to a root directory of the repo.
scm_excutable(str): "git".
Returns:
mir.scm.cmd.BaseScm: SCM instance.
"""
if scm_executable not in ["git"]:
raise MirRuntimeError(error_code=MirCode.RC_CMD_INVALID_ARGS,
error_message=f"args error: expected git, not {scm_executable}")
if not os.path.exists(root_dir):
os.makedirs(root_dir)
if not os.path.isdir(root_dir):
raise MirRuntimeError(error_code=MirCode.RC_CMD_INVALID_ARGS,
error_message=f"can not create dir: {root_dir}")
return CmdScm(root_dir, scm_executable)
| 35.814815 | 94 | 0.646329 |
6d67721cacf03f0b19c2edfa7d94b286095c3b16 | 724 | py | Python | readability_transformers/features/lf/utils.py | OneTheta/readability-transformers | 3c122c98a90c67add8eafad16563b269d5e3124a | [
"Apache-2.0"
] | 1 | 2022-01-26T10:55:59.000Z | 2022-01-26T10:55:59.000Z | readability_transformers/features/lf/utils.py | OneTheta/readability-transformers | 3c122c98a90c67add8eafad16563b269d5e3124a | [
"Apache-2.0"
] | null | null | null | readability_transformers/features/lf/utils.py | OneTheta/readability-transformers | 3c122c98a90c67add8eafad16563b269d5e3124a | [
"Apache-2.0"
] | 2 | 2021-10-14T22:53:57.000Z | 2022-01-26T10:53:32.000Z | """
Software: LingFeat - Comprehensive Linguistic Features for Readability Assessment
Page: utils.py
License: CC-BY-SA 4.0
Original Author: Bruce W. Lee () @brucewlee
Affiliation 1: LXPER AI, Seoul, South Korea
Affiliation 2: University of Pennsylvania, PA, USA
Contributing Author: -
Affiliation : -
"""
import re
import math | 22.625 | 81 | 0.638122 |
6d6893d2b301e5ade9e14d6da20a25931ea35482 | 416 | py | Python | pretix_eventparts/migrations/0007_alter_eventpart_category.py | bockstaller/pretix-eventparts | b5cb8f89cb86677facc0509f9a36cf9359c94534 | [
"Apache-2.0"
] | null | null | null | pretix_eventparts/migrations/0007_alter_eventpart_category.py | bockstaller/pretix-eventparts | b5cb8f89cb86677facc0509f9a36cf9359c94534 | [
"Apache-2.0"
] | null | null | null | pretix_eventparts/migrations/0007_alter_eventpart_category.py | bockstaller/pretix-eventparts | b5cb8f89cb86677facc0509f9a36cf9359c94534 | [
"Apache-2.0"
] | null | null | null | # Generated by Django 3.2.8 on 2021-10-31 23:57
from django.db import migrations, models
| 21.894737 | 63 | 0.615385 |
6d69e794272f0966fc5025bf7ae39b7bd8cdeaea | 1,173 | py | Python | src/ex05/bwfilter.py | satvik007/Scanner_OP | c146f67e3851cd537d62989842abfee7d34de2c0 | [
"MIT"
] | null | null | null | src/ex05/bwfilter.py | satvik007/Scanner_OP | c146f67e3851cd537d62989842abfee7d34de2c0 | [
"MIT"
] | null | null | null | src/ex05/bwfilter.py | satvik007/Scanner_OP | c146f67e3851cd537d62989842abfee7d34de2c0 | [
"MIT"
] | 1 | 2021-05-10T10:14:27.000Z | 2021-05-10T10:14:27.000Z | # Usage:
# python bwfilter.py --input=./data/test1.jpg
import cv2
import numpy as np
import argparse
if __name__=='__main__':
args = parse_args()
img = cv2.imread(args.input, 0)
bwimg = bwfilter(img)
show_img(bwimg)
cv2.imwrite('bwimg.png', bwimg) | 26.066667 | 105 | 0.6948 |
6d6f7df41b42997a8642b881e20683971aa08d5d | 1,065 | py | Python | resotolib/test/test_graph_extensions.py | someengineering/resoto | ee17313f5376e9797ed305e7fdb62d40139a6608 | [
"Apache-2.0"
] | 126 | 2022-01-13T18:22:03.000Z | 2022-03-31T11:03:14.000Z | resotolib/test/test_graph_extensions.py | someengineering/resoto | ee17313f5376e9797ed305e7fdb62d40139a6608 | [
"Apache-2.0"
] | 110 | 2022-01-13T22:27:55.000Z | 2022-03-30T22:26:50.000Z | resotolib/test/test_graph_extensions.py | someengineering/resoto | ee17313f5376e9797ed305e7fdb62d40139a6608 | [
"Apache-2.0"
] | 8 | 2022-01-15T10:28:16.000Z | 2022-03-30T16:38:21.000Z | from networkx import DiGraph
from pytest import fixture
from resotolib.graph.graph_extensions import dependent_node_iterator
| 26.625 | 68 | 0.605634 |
6d70bb0cad490e48129e211fe47c97af4f46b256 | 693 | py | Python | django/user/models.py | andreyvpng/askme | 65139c347a6b80f0a660ca24d6dd864e4531903a | [
"Apache-2.0"
] | 2 | 2018-10-29T09:37:47.000Z | 2019-11-28T14:11:12.000Z | django/user/models.py | andreyvpng/askme | 65139c347a6b80f0a660ca24d6dd864e4531903a | [
"Apache-2.0"
] | null | null | null | django/user/models.py | andreyvpng/askme | 65139c347a6b80f0a660ca24d6dd864e4531903a | [
"Apache-2.0"
] | 2 | 2018-09-18T14:09:46.000Z | 2019-11-28T14:11:14.000Z | from django.contrib.auth.models import AbstractUser
from django.db import models
from django.urls import reverse
| 23.1 | 58 | 0.613276 |
6d72243ededbe77bc217b7d87bcc872254da5cff | 3,806 | py | Python | cross_sums.py | minddrive/random_math | b5dececaf48ec80d8250d0f5fde0485e1b9e73c2 | [
"MIT"
] | null | null | null | cross_sums.py | minddrive/random_math | b5dececaf48ec80d8250d0f5fde0485e1b9e73c2 | [
"MIT"
] | null | null | null | cross_sums.py | minddrive/random_math | b5dececaf48ec80d8250d0f5fde0485e1b9e73c2 | [
"MIT"
] | null | null | null | #!/usr/bin/env python3.4
import functools
if __name__ == '__main__':
doz_sums = CrossSums('0123456789XE')
print('Sums totalling 15:')
for ds in doz_sums.filter(total='15'):
print(' ', ds)
print('\nSums containing addends 3-X inclusive:')
for ds in doz_sums.filter(addends='3456789X'):
print(' ', ds)
print('\nSums containing ten addends:')
for ds in doz_sums.filter(num_addends=10):
print(' ', ds)
print('\nSums totaling 1X with five addends including 2 and 3:')
for ds in doz_sums.filter(total='1X', num_addends=5, addends='23'):
print(' ', ds)
| 27.781022 | 77 | 0.607725 |
6d7355fa775ea3bb8dea2a5a98443123ea1e47bf | 1,177 | py | Python | functions.py | XomaDev/asteroid-bot | 2e0743fc3c51027b54b8f2e9aedf632395fdbc31 | [
"Apache-2.0"
] | null | null | null | functions.py | XomaDev/asteroid-bot | 2e0743fc3c51027b54b8f2e9aedf632395fdbc31 | [
"Apache-2.0"
] | 2 | 2021-05-12T05:37:24.000Z | 2021-06-02T05:39:21.000Z | functions.py | XomaDev/asteroid-bot | 2e0743fc3c51027b54b8f2e9aedf632395fdbc31 | [
"Apache-2.0"
] | 5 | 2021-05-12T11:39:09.000Z | 2021-10-06T06:49:05.000Z | import base64
import re
| 25.586957 | 197 | 0.514019 |
6d739c512a7425dd3bf1c30daa1d7ca1dc743fab | 377 | py | Python | slidingWindow/maximumSubarrayOfSizeK.py | YasinEhsan/interview-prep | ed9f95af5a37b05304e45b41511068b6f72533e7 | [
"Apache-2.0"
] | 11 | 2019-05-02T22:27:01.000Z | 2020-10-30T08:43:02.000Z | slidingWindow/maximumSubarrayOfSizeK.py | YasinEhsan/interview-prep | ed9f95af5a37b05304e45b41511068b6f72533e7 | [
"Apache-2.0"
] | null | null | null | slidingWindow/maximumSubarrayOfSizeK.py | YasinEhsan/interview-prep | ed9f95af5a37b05304e45b41511068b6f72533e7 | [
"Apache-2.0"
] | 3 | 2019-11-01T01:35:01.000Z | 2020-01-11T18:00:39.000Z | # may 30 20
| 23.5625 | 46 | 0.657825 |
6d768c2ea44a94e626129ce1ad7462b5def358ad | 1,697 | py | Python | docsrc/source/_static/Practice Problem Solutions/Connecting Python and Excel/xlwings/capm_returns/capm_returns.py | whoopnip/fin-model-course | e6c5ae313bba601c4aca0f334818b61cc0393118 | [
"MIT"
] | 5 | 2020-08-29T15:28:39.000Z | 2021-12-01T16:53:25.000Z | docsrc/source/_static/Practice Problem Solutions/Connecting Python and Excel/xlwings/capm_returns/capm_returns.py | whoopnip/fin-model-course | e6c5ae313bba601c4aca0f334818b61cc0393118 | [
"MIT"
] | 16 | 2020-02-26T16:03:47.000Z | 2021-06-15T15:17:37.000Z | docsrc/source/_static/Practice Problem Solutions/Connecting Python and Excel/xlwings/capm_returns/capm_returns.py | whoopnip/fin-model-course | e6c5ae313bba601c4aca0f334818b61cc0393118 | [
"MIT"
] | 3 | 2021-01-22T19:38:36.000Z | 2021-09-28T08:14:00.000Z | import xlwings as xw
import random
import pandas as pd
def capm(risk_free, beta, market_ret, epsilon):
return risk_free + beta * (market_ret - risk_free) + epsilon
def capm_auto_epsilon(risk_free, beta, market_ret, epsilon_stdev):
epsilon = random.normalvariate(0, epsilon_stdev)
return capm(risk_free, beta, market_ret, epsilon)
| 27.819672 | 112 | 0.659988 |
6d769b7ba35986193281813c7384ebbac51f27b4 | 1,547 | py | Python | src/config.py | shousper/pancake-hipchat-bot | a4aaaa6ff0d33daad1cae356a0f26fcbc64cce71 | [
"MIT"
] | null | null | null | src/config.py | shousper/pancake-hipchat-bot | a4aaaa6ff0d33daad1cae356a0f26fcbc64cce71 | [
"MIT"
] | null | null | null | src/config.py | shousper/pancake-hipchat-bot | a4aaaa6ff0d33daad1cae356a0f26fcbc64cce71 | [
"MIT"
] | null | null | null | # -*- coding: UTF-8 -*-
import ConfigParser
import os
import inspect
| 27.625 | 108 | 0.550743 |
6d7de936a991106b4fb0cef936e0e2db3b670915 | 192 | py | Python | visionlib/face/__init__.py | sumeshmn/Visionlib | c543ee038d6d1dcf9d88a8d7a782addd998e6036 | [
"MIT"
] | null | null | null | visionlib/face/__init__.py | sumeshmn/Visionlib | c543ee038d6d1dcf9d88a8d7a782addd998e6036 | [
"MIT"
] | null | null | null | visionlib/face/__init__.py | sumeshmn/Visionlib | c543ee038d6d1dcf9d88a8d7a782addd998e6036 | [
"MIT"
] | null | null | null | from .detection import FDetector
from .haar_detector import HaarDetector
from .hog_detector import Hog_detector
from .mtcnn_detector import MTCNNDetector
from .dnn_detector import DnnDetector
| 32 | 41 | 0.869792 |
6d7f27d4b21a33d924da32d2c0841520bdc52d0d | 4,635 | py | Python | shapey/utils/customdataset.py | njw0709/ShapeY | f2272f799fe779c3e4b3d0d06e88ecde9e4b039c | [
"MIT"
] | 1 | 2022-03-22T17:19:57.000Z | 2022-03-22T17:19:57.000Z | shapey/utils/customdataset.py | njw0709/ShapeY | f2272f799fe779c3e4b3d0d06e88ecde9e4b039c | [
"MIT"
] | null | null | null | shapey/utils/customdataset.py | njw0709/ShapeY | f2272f799fe779c3e4b3d0d06e88ecde9e4b039c | [
"MIT"
] | null | null | null | import torchvision.datasets as datasets
from torch.utils.data import Dataset
from itertools import combinations
import math
import psutil
| 32.1875 | 82 | 0.63754 |
6d810ebbec71bcab0af3dd833414d6e1bddc9b62 | 2,160 | py | Python | iaga/utils.py | bbengfort/solar-tempest | c21b5bf2716f752cb823bac2f01b6531f248dc66 | [
"MIT"
] | 1 | 2018-08-07T21:25:48.000Z | 2018-08-07T21:25:48.000Z | iaga/utils.py | bbengfort/solar-tempest | c21b5bf2716f752cb823bac2f01b6531f248dc66 | [
"MIT"
] | null | null | null | iaga/utils.py | bbengfort/solar-tempest | c21b5bf2716f752cb823bac2f01b6531f248dc66 | [
"MIT"
] | 1 | 2019-06-23T14:22:04.000Z | 2019-06-23T14:22:04.000Z | # iaga.utils
# Utility functions and helpers
#
# Author: Benjamin Bengfort <benjamin@bengfort.com>
# Created: Wed Aug 08 12:00:44 2018 -0400
#
# ID: utils.py [] benjamin@bengfort.com $
"""
Utility functions and helpers
"""
##########################################################################
## Imports
##########################################################################
import time
from functools import wraps
##########################################################################
## Decorators
##########################################################################
def memoized(fget):
"""
Return a property attribute for new-style classes that only calls its
getter on the first access. The result is stored and on subsequent
accesses is returned, preventing the need to call the getter any more.
Parameters
----------
fget: function
The getter method to memoize for subsequent access.
See also
--------
python-memoized-property
`python-memoized-property <https://github.com/estebistec/python-memoized-property>`_
"""
attr_name = '_{0}'.format(fget.__name__)
return property(fget_memoized)
##########################################################################
## Timer Class
##########################################################################
| 26.341463 | 92 | 0.498611 |
6d816723bee564519a5e4db1c3f8cf7df4142b1b | 760 | py | Python | Scripts5/Script38.py | jonfisik/ScriptsPython | 1d15221b3a41a06a189e3e04a5241fa63df9cf3f | [
"MIT"
] | 1 | 2020-09-05T22:25:36.000Z | 2020-09-05T22:25:36.000Z | Scripts5/Script38.py | jonfisik/ScriptsPython | 1d15221b3a41a06a189e3e04a5241fa63df9cf3f | [
"MIT"
] | null | null | null | Scripts5/Script38.py | jonfisik/ScriptsPython | 1d15221b3a41a06a189e3e04a5241fa63df9cf3f | [
"MIT"
] | null | null | null | '''Faa um programa que receba dois nmeros, compare e diga quem maior. Mostrando a msn qual valor maior ou menor. OU diga que os valores so iguais.'''
print('=+=+=+=+=+=+=+=+='*3)
titulo = 'COMPARANDO NMEROS MAIOR, MENOR E IGUAL'
print('{:^51}'.format(titulo))
print('-----------------'*3)
num1 = float(input('Digite o primeiro nmero: '))
num2 = float(input('Digite o segundo nmero: '))
if num1 == num2:
print('Os nmeros {} e {} so iguais.'.format(num1,num2))
elif num1 > num2:
print('O nmero {} \033[1;31mMAIOR\033[m que {}.'.format(num1, num2))
elif num1 < num2:
print('O nmero {} \033[1;34mMAIOR\033[m que {}.'.format(num2, num1))
else:
print('Voc precisa digitar um nmero. Tente novamente.')
print('=+=+=+=+=+=+=+=+='*3)
| 44.705882 | 156 | 0.626316 |
6d82b1a75112fd7c369738ca11d64f3111ccd886 | 234 | py | Python | Ch3/same_name3.py | dmdinh22/ATBS | 3ddd331757cc434faa5f27997b178f8a39e3b5d2 | [
"MIT"
] | null | null | null | Ch3/same_name3.py | dmdinh22/ATBS | 3ddd331757cc434faa5f27997b178f8a39e3b5d2 | [
"MIT"
] | null | null | null | Ch3/same_name3.py | dmdinh22/ATBS | 3ddd331757cc434faa5f27997b178f8a39e3b5d2 | [
"MIT"
] | null | null | null |
eggs = 42 # this is the global var
spam()
print(eggs)
| 16.714286 | 42 | 0.619658 |
6d82b1b31f8d0b6b84847768e733ad87e9b8137d | 19,761 | py | Python | ehlit/writer/dump.py | lefta/reflex-prototype | 9d9a34e222d9782815da529a8e2daa575c7c3eba | [
"MIT"
] | 1 | 2019-03-29T14:06:00.000Z | 2019-03-29T14:06:00.000Z | ehlit/writer/dump.py | lefta/ehlit-prototype | 9d9a34e222d9782815da529a8e2daa575c7c3eba | [
"MIT"
] | null | null | null | ehlit/writer/dump.py | lefta/ehlit-prototype | 9d9a34e222d9782815da529a8e2daa575c7c3eba | [
"MIT"
] | null | null | null | # Copyright 2017-2019 Cedric Legrand
#
# 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 (including the next
# paragraph) 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 logging
from typing import Callable, cast, List, Sequence, Union
from ehlit.parser.c_header import CDefine, CMacroFunction, CAnyType
from ehlit.parser.ast import (
Alias, AnonymousArray, Array, ArrayAccess, Assignment, AST, BoolValue, Cast, Char,
ClassMethod, ClassProperty, CompoundIdentifier, Condition, ControlStructure, DecimalNumber,
Declaration, Dtor, EhClass, EhEnum, EhUnion, EnumField, Expression, ForDoLoop, FunctionCall,
Function, FunctionType, HeapAlloc, HeapDealloc, Identifier, Include, Import, InitializationList,
Namespace, Node, NullValue, Number, Operator, PrefixOperatorValue, ReferenceToType,
ReferenceToValue, Return, Sizeof, Statement, String, Struct, SuffixOperatorValue, SwitchCase,
SwitchCaseBody, SwitchCaseTest, Symbol, TemplatedIdentifier, VariableAssignment,
VariableDeclaration
)
IndentedFnType = Callable[['DumpWriter', Union[Node, str]], None]
def dumpExpression(self, expr: Union[Node, str], is_next: bool) -> None:
expr = cast(Expression, expr)
self.print_node_list('Expression', expr.contents, is_next)
def dumpInitializationList(self, node: Union[Node, str], is_next: bool) -> None:
node = cast(InitializationList, node)
self.print_node_list('InitializerList', node.contents, is_next)
def dumpDoWhileLoop(self, node: Union[Node, str], is_next: bool) -> None:
self.dumpControlStructure(node, is_next)
def dumpCompoundIdentifier(self, node: Union[Node, str], is_next: bool) -> None:
node = cast(CompoundIdentifier, node)
self.increment_prefix(is_next)
self.dump('CompoundIdentifier')
self.dump_qualifiers(node)
i = 0
while i < len(node.elems):
self.print_node(node.elems[i], i < len(node.elems) - 1)
i += 1
self.decrement_prefix()
| 36.730483 | 100 | 0.621325 |
6d833ac6a830a6bbcae3005bb5acb8b96a7801b5 | 1,859 | py | Python | tutorials/04_Tutorial_Boolean.py | lmidolo/samplemaker | 8211af0e4cea60aea8f5720d5ff0ee532c442123 | [
"BSD-3-Clause"
] | null | null | null | tutorials/04_Tutorial_Boolean.py | lmidolo/samplemaker | 8211af0e4cea60aea8f5720d5ff0ee532c442123 | [
"BSD-3-Clause"
] | null | null | null | tutorials/04_Tutorial_Boolean.py | lmidolo/samplemaker | 8211af0e4cea60aea8f5720d5ff0ee532c442123 | [
"BSD-3-Clause"
] | null | null | null | # -*- coding: utf-8 -*-
"""
04_Tutorial_Boolean
"""
# In this tutorial we learn how to do boolean operations between groups of
# polygons
# Let's import basic stuff
import samplemaker.layout as smlay # used for layout
import samplemaker.makers as sm # used for drawing
# Create a simple mask layout
themask = smlay.Mask("04_Tutorial_Boolean")
# Empty geometry
geomE = sm.GeomGroup()
# Let's make a large box
box0 = sm.make_rect(0,0,100,100,layer=1)
# And some text, because text is complex polygons!
text0 = sm.make_text(0, 0, "DIFF", 10, 2,angle=30,to_poly=True,layer=1)
# Let's take the boolean difference box-text
bdiff = box0.copy() # Note that boolean operations alter the original element so we need to make a copy first
bdiff.boolean_difference(text0, 1, 1)
# The first integer is the layer from which you should subtract and the second is the subtracted layer
# Now bdiff is box-text
geomE+=bdiff
# Now let's try intersection (AND operation)
# Let's use two overlapping texts, slighlty larger
text1 = sm.make_text(0,0,"DIFF",11,3,angle=30,to_poly=True,layer=1)
text1.boolean_intersection(text0, 1, 1)
text1.translate(100, 0)
geomE+=text1
# XOR is also quite useful, only keeps parts that are not in both
text2 = sm.make_text(50,0,"XOR",10,1,angle=0,to_poly=True,layer=1)
text2.boolean_xor(box0, 1, 1)
text2.translate(200, 0)
geomE+=text2
# Trapezoid slicing, useful for some e-beam export
trapz = text2.copy()
trapz.trapezoids(1)
trapz.translate(150, 0)
geomE+=trapz
# Union, we could re-unite all trapezoids in the previous
uni1 = trapz.copy()
uni1.boolean_union(1)
uni1.translate(150, 0)
geomE+=uni1
# Just for fun, outlining the last result
out1 = uni1.copy()
out1.poly_outlining(1, 1)
out1.translate(150, 0)
geomE+=out1
# Let's add all to main cell
themask.addToMainCell(geomE)
# Export to GDS
themask.exportGDS()
# Finished! | 26.557143 | 109 | 0.743948 |
6d86c4391d19b15cd170a51c7f4c70bd4a42f337 | 265 | py | Python | app/table.py | aamnv/fred-cli | 86810e93242a90dd8ed1fbbd8999275dbd1da1cc | [
"MIT"
] | 8 | 2020-08-28T15:15:14.000Z | 2021-02-02T07:54:02.000Z | app/table.py | aamnv/fred-cli | 86810e93242a90dd8ed1fbbd8999275dbd1da1cc | [
"MIT"
] | null | null | null | app/table.py | aamnv/fred-cli | 86810e93242a90dd8ed1fbbd8999275dbd1da1cc | [
"MIT"
] | 1 | 2021-03-05T10:27:32.000Z | 2021-03-05T10:27:32.000Z | from tabulate import tabulate
| 22.083333 | 68 | 0.754717 |
6d890c163247c06d98f4d952e4bc7e2fd20b6485 | 2,472 | py | Python | tests/test_version.py | jparsai/cvejob | 8f9462a1ecdf1d4de877ac5f44e772239ffcb379 | [
"Apache-2.0"
] | 8 | 2019-09-25T14:45:28.000Z | 2021-11-08T10:30:03.000Z | tests/test_version.py | jparsai/cvejob | 8f9462a1ecdf1d4de877ac5f44e772239ffcb379 | [
"Apache-2.0"
] | 113 | 2018-07-10T12:58:16.000Z | 2020-12-09T22:33:15.000Z | tests/test_version.py | jparsai/cvejob | 8f9462a1ecdf1d4de877ac5f44e772239ffcb379 | [
"Apache-2.0"
] | 12 | 2018-07-10T11:00:02.000Z | 2021-01-27T12:19:56.000Z | """Test cvejob.version.BenevolentVersion."""
from cvejob.version import BenevolentVersion
def test_version_basic():
"""Test basic behavior."""
assert BenevolentVersion('1') == BenevolentVersion('1')
assert BenevolentVersion('1') != BenevolentVersion('2')
assert BenevolentVersion('1') < BenevolentVersion('2')
assert BenevolentVersion('1') <= BenevolentVersion('2')
assert BenevolentVersion('1') > BenevolentVersion('0')
assert BenevolentVersion('1') >= BenevolentVersion('0')
assert BenevolentVersion(None) != BenevolentVersion('')
assert BenevolentVersion(None) == BenevolentVersion(None)
assert BenevolentVersion('0') != BenevolentVersion('')
assert BenevolentVersion('') == BenevolentVersion('')
assert BenevolentVersion(1) == BenevolentVersion(1)
assert BenevolentVersion('Final.RELEASE') == BenevolentVersion('Final.RELEASE')
def test_version_trailing_zeros():
"""Test with trailing zeros."""
assert BenevolentVersion('1.0.0.0.0') == BenevolentVersion('1.0')
assert BenevolentVersion('1.0.1') != BenevolentVersion('1.0.0')
assert BenevolentVersion('1.1.0') < BenevolentVersion('1.2.0')
assert BenevolentVersion('1.1.0') <= BenevolentVersion('1.2.0')
assert BenevolentVersion('1.2.1.1') > BenevolentVersion('1.2.0')
assert BenevolentVersion('1.2.1.1') >= BenevolentVersion('1.2.1.0')
def test_version_complex():
"""More complex tests."""
assert BenevolentVersion('0.3m') == BenevolentVersion('0.3.0')
assert BenevolentVersion('0.3m1') == BenevolentVersion('0.3')
assert BenevolentVersion('0.3-SNAPSHOT-1') == BenevolentVersion('0.3')
assert BenevolentVersion('1.2.Final') == BenevolentVersion('1.2.0')
assert BenevolentVersion('1.2.Final.RELEASE') == BenevolentVersion('1.2.0')
def test_version_exact():
"""Test exact version."""
assert '1.5.0.RELEASE-1' == BenevolentVersion('1.5.0.RELEASE-1').exact
def test_version_loose():
"""Test loose version."""
assert '1.5' == BenevolentVersion('1.5.0.RELEASE-1').loose
def test_hash():
"""Test hashing."""
s = {
BenevolentVersion('1.0'),
BenevolentVersion('1'),
BenevolentVersion(None)
}
assert len(s) == 2
def test_repr():
"""Basic test for the __repr__ method."""
v = BenevolentVersion('1.0')
assert v.__repr__() == "BenevolentVersion('1.0')"
v = BenevolentVersion('1.2.3')
assert v.__repr__() == "BenevolentVersion('1.2.3')"
| 36.352941 | 83 | 0.677184 |
6d8a66fbf9d684f0b5b7c285749ed54196898dec | 1,211 | py | Python | day6.py | aslttml/30days-of-code | be6c894f8df4913413b7e6d9a6b0585e5884d35d | [
"MIT"
] | null | null | null | day6.py | aslttml/30days-of-code | be6c894f8df4913413b7e6d9a6b0585e5884d35d | [
"MIT"
] | null | null | null | day6.py | aslttml/30days-of-code | be6c894f8df4913413b7e6d9a6b0585e5884d35d | [
"MIT"
] | null | null | null | #!/bin/python3
import math
import os
import random
import re
import sys
import string
if __name__ == '__main__':
try:
t = int(input().strip())
except:
print('Invalid input.')
if t>=1 and t<=10:
for a0 in range(t):
s = input().strip()
index = 0
if len(s)>=2 and len(s)<=10000:
while index<len(s):
#Loop should quit when it reaches the last character, which has an index of (length-1)
if index<2:
odd = s[index]
even = s[index + 1]
elif index>=2:
odd = odd + s[index]
#If string length is an odd number loop should stop at the even index
#Trying to add another character will give an IndexError
if index<len(s)-1:
even = even + s[index + 1]
index = index + 2
print(odd + ' ' + even)
else:
print('Constraint error. String is either too long or too short.')
a0 = a0 + 1
else:
print('Constraint error.')
| 31.051282 | 102 | 0.456647 |
6d8cd75b30233cb95ea2e1005dd56109d735bde6 | 2,377 | py | Python | FindAndReplaceByProjectWithExclusions.py | Zlatov/FindAndReplaceByProjectWithExclusions | f1209696d960bd1471420ed18f4e71e03b3df1b5 | [
"MIT"
] | null | null | null | FindAndReplaceByProjectWithExclusions.py | Zlatov/FindAndReplaceByProjectWithExclusions | f1209696d960bd1471420ed18f4e71e03b3df1b5 | [
"MIT"
] | null | null | null | FindAndReplaceByProjectWithExclusions.py | Zlatov/FindAndReplaceByProjectWithExclusions | f1209696d960bd1471420ed18f4e71e03b3df1b5 | [
"MIT"
] | null | null | null | import sublime, sublime_plugin
import os
import json
| 34.449275 | 120 | 0.715187 |
6d8df2d3b1dcbda991e01aea09990e08fc942cf3 | 1,566 | py | Python | schnell-nautilus.py | umangrajpara99/OpenInSchnell | 5d48be8741f130471c892f1e77f19b9dad70a882 | [
"MIT"
] | null | null | null | schnell-nautilus.py | umangrajpara99/OpenInSchnell | 5d48be8741f130471c892f1e77f19b9dad70a882 | [
"MIT"
] | null | null | null | schnell-nautilus.py | umangrajpara99/OpenInSchnell | 5d48be8741f130471c892f1e77f19b9dad70a882 | [
"MIT"
] | null | null | null | # Schnell Nautilus Extension
#
# Place me in ~/.local/share/nautilus-python/extensions/,
# ensure you have python-nautilus package, restrart Nautilus, and enjoy :)
from gi import require_version
require_version('Gtk', '3.0')
require_version('Nautilus', '3.0')
from gi.repository import Nautilus, GObject
from subprocess import call
import os
# path to schnell
schnell = 'schnell'
# what name do you want to see in the context menu?
schnellname = 'Schnell'
# always create new window?
NEWWINDOW = False
| 27.473684 | 74 | 0.637292 |
6d8fdc15326338e43a53a51f9d3225823820ab40 | 74,548 | py | Python | appengine/components/components/prpc/discovery/service_prpc_pb2.py | stefb965/luci-py | e0a8a5640c4104e5c90781d833168aa8a8d1f24d | [
"Apache-2.0"
] | null | null | null | appengine/components/components/prpc/discovery/service_prpc_pb2.py | stefb965/luci-py | e0a8a5640c4104e5c90781d833168aa8a8d1f24d | [
"Apache-2.0"
] | null | null | null | appengine/components/components/prpc/discovery/service_prpc_pb2.py | stefb965/luci-py | e0a8a5640c4104e5c90781d833168aa8a8d1f24d | [
"Apache-2.0"
] | 1 | 2020-07-05T19:54:40.000Z | 2020-07-05T19:54:40.000Z | # Generated by the pRPC protocol buffer compiler plugin. DO NOT EDIT!
# source: service.proto
import base64
from google.protobuf import descriptor_pb2
# Includes description of the service.proto and all of its transitive
# dependencies. Includes source code info.
FILE_DESCRIPTOR_SET = descriptor_pb2.FileDescriptorSet()
FILE_DESCRIPTOR_SET.ParseFromString(base64.b64decode(
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_INDEX = {
f.name: {
'descriptor': f,
'services': {s.name: s for s in f.service},
}
for f in FILE_DESCRIPTOR_SET.file
}
DiscoveryServiceDescription = {
'file_descriptor_set': FILE_DESCRIPTOR_SET,
'file_descriptor': _INDEX[u'service.proto']['descriptor'],
'service_descriptor': _INDEX[u'service.proto']['services'][u'Discovery'],
}
| 79.47548 | 80 | 0.911493 |
6d9062da7cb5be608d72b13c37aef7c0131a8035 | 1,891 | py | Python | Cogs/StaticMethods.py | pajratbej/hetman | 1da634cdb94221bb81ceb0c29467cccce640bbb6 | [
"MIT"
] | 2 | 2019-12-19T17:11:29.000Z | 2020-02-22T17:55:13.000Z | Cogs/StaticMethods.py | pajratbej/hetman | 1da634cdb94221bb81ceb0c29467cccce640bbb6 | [
"MIT"
] | 5 | 2019-12-08T21:42:12.000Z | 2022-03-11T23:58:29.000Z | Cogs/StaticMethods.py | pajratbej/hetman | 1da634cdb94221bb81ceb0c29467cccce640bbb6 | [
"MIT"
] | null | null | null | from pymongo import MongoClient
import random as r
import os
client = MongoClient(os.environ["MONGO_LAB"])
db = client.get_database("hetmanbot")
collection = db['data_base']
| 31.516667 | 100 | 0.589635 |
6d9276de4eb719b6800f06060463d545ce0e50b7 | 702 | py | Python | article/admin.py | SeddonShen/TimePill | 8b2c4dc2c129f440d67e1dba1ab16591057b65f7 | [
"Apache-2.0"
] | 4 | 2021-12-26T04:39:06.000Z | 2021-12-29T16:57:36.000Z | article/admin.py | SeddonShen/TimePill | 8b2c4dc2c129f440d67e1dba1ab16591057b65f7 | [
"Apache-2.0"
] | null | null | null | article/admin.py | SeddonShen/TimePill | 8b2c4dc2c129f440d67e1dba1ab16591057b65f7 | [
"Apache-2.0"
] | null | null | null | from django.contrib import admin
# Register your models here.
from . import models
from .models import Article, Comment
# title = title,
# content = content,
# square_open = square_open,
# expire_time = expire_time,
# status = status,
# author_id_id = user_id,
# diary_type = diary_type,
# admin.site.register(Article, ArticleAdmin)
admin.site.register(Comment)
admin.site.register(Article)
| 25.071429 | 108 | 0.668091 |
6d961a7fca3206cd16ef0e5d9d5a6b6cd7a06634 | 32,545 | py | Python | neurokit2/ecg/ecg_findpeaks.py | vansjyo/NeuroKit | 238cd3d89467f7922c68a3a4c1f44806a8466922 | [
"MIT"
] | null | null | null | neurokit2/ecg/ecg_findpeaks.py | vansjyo/NeuroKit | 238cd3d89467f7922c68a3a4c1f44806a8466922 | [
"MIT"
] | null | null | null | neurokit2/ecg/ecg_findpeaks.py | vansjyo/NeuroKit | 238cd3d89467f7922c68a3a4c1f44806a8466922 | [
"MIT"
] | null | null | null | # - * - coding: utf-8 - * -
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import scipy.signal
from ..signal import signal_smooth
from ..signal import signal_zerocrossings
def ecg_findpeaks(ecg_cleaned, sampling_rate=1000, method="neurokit", show=False):
"""Find R-peaks in an ECG signal.
Low-level function used by `ecg_peaks()` to identify R-peaks in an ECG signal using a different set of algorithms. See `ecg_peaks()` for details.
Parameters
----------
ecg_cleaned : list, array or Series
The cleaned ECG channel as returned by `ecg_clean()`.
sampling_rate : int
The sampling frequency of `ecg_signal` (in Hz, i.e., samples/second).
Defaults to 1000.
method : string
The algorithm to be used for R-peak detection. Can be one of 'neurokit' (default),
'pamtompkins1985', 'hamilton2002', 'christov2004', 'gamboa2008', 'elgendi2010', 'engzeemod2012', 'kalidas2017', 'martinez2003' or 'rodrigues2020'.
show : bool
If True, will return a plot to visualizing the thresholds used in the
algorithm. Useful for debugging.
Returns
-------
info : dict
A dictionary containing additional information, in this case the
samples at which R-peaks occur, accessible with the key "ECG_R_Peaks".
See Also
--------
ecg_clean, signal_fixpeaks, ecg_peaks, ecg_rate, ecg_process, ecg_plot
Examples
--------
>>> import neurokit2 as nk
>>>
>>> ecg = nk.ecg_simulate(duration=10, sampling_rate=1000)
>>> cleaned = nk.ecg_clean(ecg, sampling_rate=1000)
>>> info = nk.ecg_findpeaks(cleaned)
>>> nk.events_plot(info["ECG_R_Peaks"], cleaned)
>>>
>>> # Different methods
>>> neurokit = nk.ecg_findpeaks(nk.ecg_clean(ecg, method="neurokit"), method="neurokit")
>>> pantompkins1985 = nk.ecg_findpeaks(nk.ecg_clean(ecg, method="pantompkins1985"), method="pantompkins1985")
>>> hamilton2002 = nk.ecg_findpeaks(nk.ecg_clean(ecg, method="hamilton2002"), method="hamilton2002")
>>> christov2004 = nk.ecg_findpeaks(cleaned, method="christov2004")
>>> gamboa2008 = nk.ecg_findpeaks(nk.ecg_clean(ecg, method="gamboa2008"), method="gamboa2008")
>>> elgendi2010 = nk.ecg_findpeaks(nk.ecg_clean(ecg, method="elgendi2010"), method="elgendi2010")
>>> engzeemod2012 = nk.ecg_findpeaks(nk.ecg_clean(ecg, method="engzeemod2012"), method="engzeemod2012")
>>> kalidas2017 = nk.ecg_findpeaks(nk.ecg_clean(ecg, method="kalidas2017"), method="kalidas2017")
>>> martinez2003 = nk.ecg_findpeaks(cleaned, method="martinez2003")
>>>
>>> # Visualize
>>> nk.events_plot([neurokit["ECG_R_Peaks"],
pantompkins1985["ECG_R_Peaks"],
hamilton2002["ECG_R_Peaks"],
christov2004["ECG_R_Peaks"],
gamboa2008["ECG_R_Peaks"],
elgendi2010["ECG_R_Peaks"],
engzeemod2012["ECG_R_Peaks"],
kalidas2017["ECG_R_Peaks"]],
martinez2003["ECG_R_Peaks"]], cleaned)
References
--------------
- Gamboa, H. (2008). Multi-modal behavioral biometrics based on hci and electrophysiology. PhD ThesisUniversidade.
- W. Zong, T. Heldt, G.B. Moody, and R.G. Mark. An open-source algorithm to detect onset of arterial blood pressure pulses. In Computers in Cardiology, 2003, pages 259262, 2003.
- Hamilton, Open Source ECG Analysis Software Documentation, E.P.Limited, 2002.
- Jiapu Pan and Willis J. Tompkins. A Real-Time QRS Detection Algorithm. In: IEEE Transactions on Biomedical Engineering BME-32.3 (1985), pp. 230236.
- C. Zeelenberg, A single scan algorithm for QRS detection and feature extraction, IEEE Comp. in Cardiology, vol. 6, pp. 37-42, 1979
- A. Lourenco, H. Silva, P. Leite, R. Lourenco and A. Fred, "Real Time Electrocardiogram Segmentation for Finger Based ECG Biometrics", BIOSIGNALS 2012, pp. 49-54, 2012.
"""
# Try retrieving right column
if isinstance(ecg_cleaned, pd.DataFrame):
try:
ecg_cleaned = ecg_cleaned["ECG_Clean"]
except NameError:
try:
ecg_cleaned = ecg_cleaned["ECG_Raw"]
except NameError:
ecg_cleaned = ecg_cleaned["ECG"]
method = method.lower() # remove capitalised letters
# Run peak detection algorithm
if method in ["nk", "nk2", "neurokit", "neurokit2"]:
rpeaks = _ecg_findpeaks_neurokit(ecg_cleaned, sampling_rate,
show=show)
elif method in ["pantompkins", "pantompkins1985"]:
rpeaks = _ecg_findpeaks_pantompkins(ecg_cleaned, sampling_rate)
elif method in ["gamboa2008", "gamboa"]:
rpeaks = _ecg_findpeaks_gamboa(ecg_cleaned, sampling_rate)
elif method in ["ssf", "slopesumfunction", "zong", "zong2003"]:
rpeaks = _ecg_findpeaks_ssf(ecg_cleaned, sampling_rate)
elif method in ["hamilton", "hamilton2002"]:
rpeaks = _ecg_findpeaks_hamilton(ecg_cleaned, sampling_rate)
elif method in ["christov", "christov2004"]:
rpeaks = _ecg_findpeaks_christov(ecg_cleaned, sampling_rate)
elif method in ["engzee", "engzee2012", "engzeemod", "engzeemod2012"]:
rpeaks = _ecg_findpeaks_engzee(ecg_cleaned, sampling_rate)
elif method in ["elgendi", "elgendi2010"]:
rpeaks = _ecg_findpeaks_elgendi(ecg_cleaned, sampling_rate)
elif method in ["kalidas2017", "swt", "kalidas", "kalidastamil", "kalidastamil2017"]:
rpeaks = _ecg_findpeaks_kalidas(ecg_cleaned, sampling_rate)
elif method in ["martinez2003", "martinez"]:
rpeaks = _ecg_findpeaks_WT(ecg_cleaned, sampling_rate)
elif method in ["rodrigues2020", "rodrigues", "asi"]:
rpeaks = _ecg_findpeaks_rodrigues(ecg_cleaned, sampling_rate)
else:
raise ValueError("NeuroKit error: ecg_findpeaks(): 'method' should be "
"one of 'neurokit' or 'pamtompkins'.")
# Prepare output.
info = {"ECG_R_Peaks": rpeaks}
return info
# =============================================================================
# NeuroKit
# =============================================================================
def _ecg_findpeaks_neurokit(signal, sampling_rate=1000, smoothwindow=.1, avgwindow=.75,
gradthreshweight=1.5, minlenweight=0.4, mindelay=0.3,
show=False):
"""
All tune-able parameters are specified as keyword arguments. The `signal`
must be the highpass-filtered raw ECG with a lowcut of .5 Hz.
"""
if show is True:
fig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1, sharex=True)
# Compute the ECG's gradient as well as the gradient threshold. Run with
# show=True in order to get an idea of the threshold.
grad = np.gradient(signal)
absgrad = np.abs(grad)
smooth_kernel = int(np.rint(smoothwindow * sampling_rate))
avg_kernel = int(np.rint(avgwindow * sampling_rate))
smoothgrad = signal_smooth(absgrad, kernel="boxcar", size=smooth_kernel)
avggrad = signal_smooth(smoothgrad, kernel="boxcar", size=avg_kernel)
gradthreshold = gradthreshweight * avggrad
mindelay = int(np.rint(sampling_rate * mindelay))
if show is True:
ax1.plot(signal)
ax2.plot(smoothgrad)
ax2.plot(gradthreshold)
# Identify start and end of QRS complexes.
qrs = smoothgrad > gradthreshold
beg_qrs = np.where(np.logical_and(np.logical_not(qrs[0:-1]), qrs[1:]))[0]
end_qrs = np.where(np.logical_and(qrs[0:-1], np.logical_not(qrs[1:])))[0]
# Throw out QRS-ends that precede first QRS-start.
end_qrs = end_qrs[end_qrs > beg_qrs[0]]
# Identify R-peaks within QRS (ignore QRS that are too short).
num_qrs = min(beg_qrs.size, end_qrs.size)
min_len = np.mean(end_qrs[:num_qrs] - beg_qrs[:num_qrs]) * minlenweight
peaks = [0]
for i in range(num_qrs):
beg = beg_qrs[i]
end = end_qrs[i]
len_qrs = end - beg
if len_qrs < min_len:
continue
if show is True:
ax2.axvspan(beg, end, facecolor="m", alpha=0.5)
# Find local maxima and their prominence within QRS.
data = signal[beg:end]
locmax, props = scipy.signal.find_peaks(data, prominence=(None, None))
if locmax.size > 0:
# Identify most prominent local maximum.
peak = beg + locmax[np.argmax(props["prominences"])]
# Enforce minimum delay between peaks.
if peak - peaks[-1] > mindelay:
peaks.append(peak)
peaks.pop(0)
if show is True:
ax1.scatter(peaks, signal[peaks], c="r")
peaks = np.asarray(peaks).astype(int) # Convert to int
return peaks
# =============================================================================
# Pan & Tompkins (1985)
# =============================================================================
def _ecg_findpeaks_pantompkins(signal, sampling_rate=1000):
"""
From https://github.com/berndporr/py-ecg-detectors/
- Jiapu Pan and Willis J. Tompkins. A Real-Time QRS Detection Algorithm.
In: IEEE Transactions on Biomedical Engineering BME-32.3 (1985), pp. 230236.
"""
diff = np.diff(signal)
squared = diff * diff
N = int(0.12 * sampling_rate)
mwa = _ecg_findpeaks_MWA(squared, N)
mwa[:int(0.2 * sampling_rate)] = 0
mwa_peaks = _ecg_findpeaks_peakdetect(mwa, sampling_rate)
mwa_peaks = np.array(mwa_peaks, dtype='int')
return mwa_peaks
# =============================================================================
# Hamilton (2002)
# =============================================================================
def _ecg_findpeaks_hamilton(signal, sampling_rate=1000):
"""
From https://github.com/berndporr/py-ecg-detectors/
- Hamilton, Open Source ECG Analysis Software Documentation, E.P.Limited, 2002.
"""
diff = abs(np.diff(signal))
b = np.ones(int(0.08 * sampling_rate))
b = b/int(0.08 * sampling_rate)
a = [1]
ma = scipy.signal.lfilter(b, a, diff)
ma[0:len(b) * 2] = 0
n_pks = []
n_pks_ave = 0.0
s_pks = []
s_pks_ave = 0.0
QRS = [0]
RR = []
RR_ave = 0.0
th = 0.0
i = 0
idx = []
peaks = []
for i in range(len(ma)):
if i > 0 and i < len(ma) - 1:
if ma[i-1] < ma[i] and ma[i + 1] < ma[i]:
peak = i
peaks.append(i)
if ma[peak] > th and (peak-QRS[-1]) > 0.3 * sampling_rate:
QRS.append(peak)
idx.append(i)
s_pks.append(ma[peak])
if len(n_pks) > 8:
s_pks.pop(0)
s_pks_ave = np.mean(s_pks)
if RR_ave != 0.0:
if QRS[-1]-QRS[-2] > 1.5 * RR_ave:
missed_peaks = peaks[idx[-2] + 1:idx[-1]]
for missed_peak in missed_peaks:
if missed_peak - peaks[idx[-2]] > int(0.360 * sampling_rate) and ma[missed_peak] > 0.5 * th:
QRS.append(missed_peak)
QRS.sort()
break
if len(QRS) > 2:
RR.append(QRS[-1]-QRS[-2])
if len(RR) > 8:
RR.pop(0)
RR_ave = int(np.mean(RR))
else:
n_pks.append(ma[peak])
if len(n_pks) > 8:
n_pks.pop(0)
n_pks_ave = np.mean(n_pks)
th = n_pks_ave + 0.45 * (s_pks_ave-n_pks_ave)
i += 1
QRS.pop(0)
QRS = np.array(QRS, dtype='int')
return QRS
# =============================================================================
# Slope Sum Function (SSF) - Zong et al. (2003)
# =============================================================================
def _ecg_findpeaks_ssf(signal, sampling_rate=1000, threshold=20, before=0.03, after=0.01):
"""
From https://github.com/PIA-Group/BioSPPy/blob/e65da30f6379852ecb98f8e2e0c9b4b5175416c3/biosppy/signals/ecg.py#L448
- W. Zong, T. Heldt, G.B. Moody, and R.G. Mark. An open-source algorithm to detect onset of arterial blood pressure pulses. In Computers in
Cardiology, 2003, pages 259262, 2003.
"""
# TODO: Doesn't really seems to work
# convert to samples
winB = int(before * sampling_rate)
winA = int(after * sampling_rate)
Rset = set()
length = len(signal)
# diff
dx = np.diff(signal)
dx[dx >= 0] = 0
dx = dx ** 2
# detection
idx, = np.nonzero(dx > threshold)
idx0 = np.hstack(([0], idx))
didx = np.diff(idx0)
# search
sidx = idx[didx > 1]
for item in sidx:
a = item - winB
if a < 0:
a = 0
b = item + winA
if b > length:
continue
r = np.argmax(signal[a:b]) + a
Rset.add(r)
# output
rpeaks = list(Rset)
rpeaks.sort()
rpeaks = np.array(rpeaks, dtype='int')
return rpeaks
# =============================================================================
# Christov (2004)
# =============================================================================
def _ecg_findpeaks_christov(signal, sampling_rate=1000):
"""
From https://github.com/berndporr/py-ecg-detectors/
- Ivaylo I. Christov, Real time electrocardiogram QRS detection using combined adaptive threshold, BioMedical Engineering OnLine 2004, vol. 3:28, 2004.
"""
total_taps = 0
b = np.ones(int(0.02 * sampling_rate))
b = b/int(0.02 * sampling_rate)
total_taps += len(b)
a = [1]
MA1 = scipy.signal.lfilter(b, a, signal)
b = np.ones(int(0.028 * sampling_rate))
b = b/int(0.028 * sampling_rate)
total_taps += len(b)
a = [1]
MA2 = scipy.signal.lfilter(b, a, MA1)
Y = []
for i in range(1, len(MA2)-1):
diff = abs(MA2[i + 1]-MA2[i-1])
Y.append(diff)
b = np.ones(int(0.040 * sampling_rate))
b = b/int(0.040 * sampling_rate)
total_taps += len(b)
a = [1]
MA3 = scipy.signal.lfilter(b, a, Y)
MA3[0:total_taps] = 0
ms50 = int(0.05 * sampling_rate)
ms200 = int(0.2 * sampling_rate)
ms1200 = int(1.2 * sampling_rate)
ms350 = int(0.35 * sampling_rate)
M = 0
newM5 = 0
M_list = []
MM = []
M_slope = np.linspace(1.0, 0.6, ms1200-ms200)
F = 0
F_list = []
R = 0
RR = []
Rm = 0
R_list = []
MFR = 0
MFR_list = []
QRS = []
for i in range(len(MA3)):
# M
if i < 5 * sampling_rate:
M = 0.6 * np.max(MA3[:i + 1])
MM.append(M)
if len(MM) > 5:
MM.pop(0)
elif QRS and i < QRS[-1] + ms200:
newM5 = 0.6 * np.max(MA3[QRS[-1]:i])
if newM5 > 1.5 * MM[-1]:
newM5 = 1.1 * MM[-1]
elif QRS and i == QRS[-1] + ms200:
if newM5 == 0:
newM5 = MM[-1]
MM.append(newM5)
if len(MM) > 5:
MM.pop(0)
M = np.mean(MM)
elif QRS and i > QRS[-1] + ms200 and i < QRS[-1] + ms1200:
M = np.mean(MM) * M_slope[i-(QRS[-1] + ms200)]
elif QRS and i > QRS[-1] + ms1200:
M = 0.6 * np.mean(MM)
# F
if i > ms350:
F_section = MA3[i-ms350:i]
max_latest = np.max(F_section[-ms50:])
max_earliest = np.max(F_section[:ms50])
F = F + ((max_latest-max_earliest)/150.0)
# R
if QRS and i < QRS[-1] + int((2.0/3.0 * Rm)):
R = 0
elif QRS and i > QRS[-1] + int((2.0/3.0 * Rm)) and i < QRS[-1] + Rm:
dec = (M-np.mean(MM))/1.4
R = 0 + dec
MFR = M + F + R
M_list.append(M)
F_list.append(F)
R_list.append(R)
MFR_list.append(MFR)
if not QRS and MA3[i] > MFR:
QRS.append(i)
elif QRS and i > QRS[-1] + ms200 and MA3[i] > MFR:
QRS.append(i)
if len(QRS) > 2:
RR.append(QRS[-1] - QRS[-2])
if len(RR) > 5:
RR.pop(0)
Rm = int(np.mean(RR))
QRS.pop(0)
QRS = np.array(QRS, dtype='int')
return QRS
# =============================================================================
# Gamboa (2008)
# =============================================================================
def _ecg_findpeaks_gamboa(signal, sampling_rate=1000, tol=0.002):
"""
From https://github.com/PIA-Group/BioSPPy/blob/e65da30f6379852ecb98f8e2e0c9b4b5175416c3/biosppy/signals/ecg.py#L834
- Gamboa, H. (2008). Multi-modal behavioral biometrics based on hci and electrophysiology. PhD ThesisUniversidade.
"""
# convert to samples
v_100ms = int(0.1 * sampling_rate)
v_300ms = int(0.3 * sampling_rate)
hist, edges = np.histogram(signal, 100, density=True)
TH = 0.01
F = np.cumsum(hist)
v0 = edges[np.nonzero(F > TH)[0][0]]
v1 = edges[np.nonzero(F < (1 - TH))[0][-1]]
nrm = max([abs(v0), abs(v1)])
norm_signal = signal / float(nrm)
d2 = np.diff(norm_signal, 2)
b = np.nonzero((np.diff(np.sign(np.diff(-d2)))) == -2)[0] + 2
b = np.intersect1d(b, np.nonzero(-d2 > tol)[0])
if len(b) < 3:
rpeaks = []
else:
b = b.astype('float')
rpeaks = []
previous = b[0]
for i in b[1:]:
if i - previous > v_300ms:
previous = i
rpeaks.append(np.argmax(signal[int(i):int(i + v_100ms)]) + i)
rpeaks = sorted(list(set(rpeaks)))
rpeaks = np.array(rpeaks, dtype='int')
return rpeaks
# =============================================================================
# Engzee Modified (2012)
# =============================================================================
def _ecg_findpeaks_engzee(signal, sampling_rate=1000):
"""
From https://github.com/berndporr/py-ecg-detectors/
- C. Zeelenberg, A single scan algorithm for QRS detection and feature extraction, IEEE Comp. in Cardiology, vol. 6, pp. 37-42, 1979
- A. Lourenco, H. Silva, P. Leite, R. Lourenco and A. Fred, "Real Time Electrocardiogram Segmentation for Finger Based ECG Biometrics", BIOSIGNALS 2012, pp. 49-54, 2012.
"""
engzee_fake_delay = 0
diff = np.zeros(len(signal))
for i in range(4, len(diff)):
diff[i] = signal[i]-signal[i-4]
ci = [1, 4, 6, 4, 1]
low_pass = scipy.signal.lfilter(ci, 1, diff)
low_pass[:int(0.2 * sampling_rate)] = 0
ms200 = int(0.2 * sampling_rate)
ms1200 = int(1.2 * sampling_rate)
ms160 = int(0.16 * sampling_rate)
neg_threshold = int(0.01 * sampling_rate)
M = 0
M_list = []
neg_m = []
MM = []
M_slope = np.linspace(1.0, 0.6, ms1200-ms200)
QRS = []
r_peaks = []
counter = 0
thi_list = []
thi = False
thf_list = []
thf = False
for i in range(len(low_pass)):
# M
if i < 5 * sampling_rate:
M = 0.6 * np.max(low_pass[:i + 1])
MM.append(M)
if len(MM) > 5:
MM.pop(0)
elif QRS and i < QRS[-1] + ms200:
newM5 = 0.6 * np.max(low_pass[QRS[-1]:i])
if newM5 > 1.5 * MM[-1]:
newM5 = 1.1 * MM[-1]
elif QRS and i == QRS[-1] + ms200:
MM.append(newM5)
if len(MM) > 5:
MM.pop(0)
M = np.mean(MM)
elif QRS and i > QRS[-1] + ms200 and i < QRS[-1] + ms1200:
M = np.mean(MM) * M_slope[i-(QRS[-1] + ms200)]
elif QRS and i > QRS[-1] + ms1200:
M = 0.6 * np.mean(MM)
M_list.append(M)
neg_m.append(-M)
if not QRS and low_pass[i] > M:
QRS.append(i)
thi_list.append(i)
thi = True
elif QRS and i > QRS[-1] + ms200 and low_pass[i] > M:
QRS.append(i)
thi_list.append(i)
thi = True
if thi and i < thi_list[-1] + ms160:
if low_pass[i] < -M and low_pass[i-1] > -M:
# thf_list.append(i)
thf = True
if thf and low_pass[i] < -M:
thf_list.append(i)
counter += 1
elif low_pass[i] > -M and thf:
counter = 0
thi = False
thf = False
elif thi and i > thi_list[-1] + ms160:
counter = 0
thi = False
thf = False
if counter > neg_threshold:
unfiltered_section = signal[thi_list[-1] - int(0.01 * sampling_rate):i]
r_peaks.append(engzee_fake_delay + np.argmax(unfiltered_section) + thi_list[-1] - int(0.01 * sampling_rate))
counter = 0
thi = False
thf = False
r_peaks = np.array(r_peaks, dtype='int')
return r_peaks
# =============================================================================
# Stationary Wavelet Transform (SWT) - Kalidas and Tamil (2017)
# =============================================================================
def _ecg_findpeaks_kalidas(signal, sampling_rate=1000):
"""
From https://github.com/berndporr/py-ecg-detectors/
- Vignesh Kalidas and Lakshman Tamil (2017). Real-time QRS detector using Stationary Wavelet Transform for Automated ECG Analysis. In: 2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE). Uses the Pan and Tompkins thresolding.
"""
# Try loading pywt
try:
import pywt
except ImportError:
raise ImportError("NeuroKit error: ecg_findpeaks(): the 'PyWavelets' "
"module is required for this method to run. ",
"Please install it first (`pip install PyWavelets`).")
swt_level = 3
padding = -1
for i in range(1000):
if (len(signal) + i) % 2 ** swt_level == 0:
padding = i
break
if padding > 0:
signal = np.pad(signal, (0, padding), 'edge')
elif padding == -1:
print("Padding greater than 1000 required\n")
swt_ecg = pywt.swt(signal, 'db3', level=swt_level)
swt_ecg = np.array(swt_ecg)
swt_ecg = swt_ecg[0, 1, :]
squared = swt_ecg * swt_ecg
f1 = 0.01/sampling_rate
f2 = 10/sampling_rate
b, a = scipy.signal.butter(3, [f1 * 2, f2 * 2], btype='bandpass')
filtered_squared = scipy.signal.lfilter(b, a, squared)
filt_peaks = _ecg_findpeaks_peakdetect(filtered_squared, sampling_rate)
filt_peaks = np.array(filt_peaks, dtype='int')
return filt_peaks
# =============================================================================
# Elgendi et al. (2010)
# =============================================================================
def _ecg_findpeaks_elgendi(signal, sampling_rate=1000):
"""
From https://github.com/berndporr/py-ecg-detectors/
- Elgendi, Mohamed & Jonkman, Mirjam & De Boer, Friso. (2010). Frequency Bands Effects on QRS Detection. The 3rd International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS2010). 428-431.
"""
window1 = int(0.12 * sampling_rate)
mwa_qrs = _ecg_findpeaks_MWA(abs(signal), window1)
window2 = int(0.6 * sampling_rate)
mwa_beat = _ecg_findpeaks_MWA(abs(signal), window2)
blocks = np.zeros(len(signal))
block_height = np.max(signal)
for i in range(len(mwa_qrs)):
if mwa_qrs[i] > mwa_beat[i]:
blocks[i] = block_height
else:
blocks[i] = 0
QRS = []
for i in range(1, len(blocks)):
if blocks[i-1] == 0 and blocks[i] == block_height:
start = i
elif blocks[i-1] == block_height and blocks[i] == 0:
end = i-1
if end-start > int(0.08 * sampling_rate):
detection = np.argmax(signal[start:end + 1]) + start
if QRS:
if detection-QRS[-1] > int(0.3 * sampling_rate):
QRS.append(detection)
else:
QRS.append(detection)
QRS = np.array(QRS, dtype='int')
return QRS
# =============================================================================
# Continuous Wavelet Transform (CWT) - Martinez et al. (2003)
# =============================================================================
#
# =============================================================================
# ASI (FSM based 2020)
# =============================================================================
def _ecg_findpeaks_rodrigues(signal, sampling_rate=1000):
"""
Segmenter by Tiago Rodrigues, inspired by on Gutierrez-Rivas (2015) and Sadhukhan (2012).
References
----------
- Gutirrez-Rivas, R., Garca, J. J., Marnane, W. P., & Hernndez, A. (2015). Novel real-time low-complexity QRS complex detector based on adaptive thresholding. IEEE Sensors Journal, 15(10), 6036-6043.
- Sadhukhan, D., & Mitra, M. (2012). R-peak detection algorithm for ECG using double difference and RR interval processing. Procedia Technology, 4, 873-877.
"""
N = int(np.round(3 * sampling_rate/128))
Nd = N-1
Pth = (0.7 * sampling_rate) / 128+2.7
# Pth = 3, optimal for fs = 250 Hz
Rmin = 0.26
rpeaks = []
i = 1
tf = len(signal)
Ramptotal = 0
# Double derivative squared
diff_ecg = [signal[i] - signal[i - Nd] for i in range(Nd, len(signal))]
ddiff_ecg = [diff_ecg[i] - diff_ecg[i - 1] for i in range(1, len(diff_ecg))]
squar = np.square(ddiff_ecg)
# Integrate moving window
b = np.array(np.ones(N))
a = [1]
processed_ecg = scipy.signal.lfilter(b, a, squar)
# R-peak finder FSM
while i < tf - sampling_rate: # ignore last second of recording
# State 1: looking for maximum
tf1 = np.round(i + Rmin*sampling_rate)
Rpeakamp = 0
while i < tf1:
# Rpeak amplitude and position
if processed_ecg[i] > Rpeakamp:
Rpeakamp = processed_ecg[i]
rpeakpos = i + 1
i += 1
Ramptotal = (19 / 20) * Ramptotal + (1 / 20) * Rpeakamp
rpeaks.append(rpeakpos)
# State 2: waiting state
d = tf1 - rpeakpos
tf2 = i + np.round(0.2*2 - d)
while i <= tf2:
i += 1
# State 3: decreasing threshold
Thr = Ramptotal
while processed_ecg[i] < Thr:
Thr = Thr * np.exp(-Pth / sampling_rate)
i += 1
return rpeaks
# =============================================================================
# Utilities
# =============================================================================
def _ecg_findpeaks_MWA(signal, window_size):
"""
From https://github.com/berndporr/py-ecg-detectors/
"""
mwa = np.zeros(len(signal))
sums = np.cumsum(signal)
for i in range(len(signal)):
if i < window_size:
section = signal[0:i]
else:
section = get_mean(i - window_size, i)
if i != 0:
mwa[i] = np.mean(section)
else:
mwa[i] = signal[i]
return mwa
def _ecg_findpeaks_peakdetect(detection, sampling_rate=1000):
"""
From https://github.com/berndporr/py-ecg-detectors/
"""
min_distance = int(0.25 * sampling_rate)
signal_peaks = [0]
noise_peaks = []
SPKI = 0.0
NPKI = 0.0
threshold_I1 = 0.0
threshold_I2 = 0.0
RR_missed = 0
index = 0
indexes = []
missed_peaks = []
peaks = []
for i in range(len(detection)):
if i > 0 and i < len(detection) - 1:
if detection[i-1] < detection[i] and detection[i + 1] < detection[i]:
peak = i
peaks.append(i)
if detection[peak] > threshold_I1 and (peak - signal_peaks[-1]) > 0.3 * sampling_rate:
signal_peaks.append(peak)
indexes.append(index)
SPKI = 0.125 * detection[signal_peaks[-1]] + 0.875 * SPKI
if RR_missed != 0:
if signal_peaks[-1] - signal_peaks[-2] > RR_missed:
missed_section_peaks = peaks[indexes[-2] + 1:indexes[-1]]
missed_section_peaks2 = []
for missed_peak in missed_section_peaks:
if missed_peak - signal_peaks[-2] > min_distance and signal_peaks[-1] - missed_peak > min_distance and detection[missed_peak] > threshold_I2:
missed_section_peaks2.append(missed_peak)
if len(missed_section_peaks2) > 0:
missed_peak = missed_section_peaks2[np.argmax(detection[missed_section_peaks2])]
missed_peaks.append(missed_peak)
signal_peaks.append(signal_peaks[-1])
signal_peaks[-2] = missed_peak
else:
noise_peaks.append(peak)
NPKI = 0.125 * detection[noise_peaks[-1]] + 0.875 * NPKI
threshold_I1 = NPKI + 0.25 * (SPKI - NPKI)
threshold_I2 = 0.5 * threshold_I1
if len(signal_peaks) > 8:
RR = np.diff(signal_peaks[-9:])
RR_ave = int(np.mean(RR))
RR_missed = int(1.66 * RR_ave)
index = index + 1
signal_peaks.pop(0)
return signal_peaks
| 32.940283 | 262 | 0.540636 |
6d96c1cfb476a1c31417724d0d6d9bf4095e9439 | 1,157 | py | Python | tinynn/converter/operators/base.py | www516717402/TinyNeuralNetwork | 23e7931b4377462fad94a9ab0651b6d9a346252d | [
"MIT"
] | 1 | 2022-01-11T06:40:13.000Z | 2022-01-11T06:40:13.000Z | tinynn/converter/operators/base.py | kingkie/TinyNeuralNetwork | 9b4313bbe6fb46d602681b69799e4725eef4d71b | [
"MIT"
] | null | null | null | tinynn/converter/operators/base.py | kingkie/TinyNeuralNetwork | 9b4313bbe6fb46d602681b69799e4725eef4d71b | [
"MIT"
] | 1 | 2021-12-20T07:21:37.000Z | 2021-12-20T07:21:37.000Z | import inspect
import sys
from enum import IntEnum
from tflite.ActivationFunctionType import ActivationFunctionType
from tflite.BuiltinOperator import BuiltinOperator
# In Python 3.6, we cannot make ExtendedOperator derive from IntEnum
if sys.version_info >= (3, 7):
bases = (IntEnum, )
else:
bases = ()
# In Python 3.6, the elements in the parent class are not collected in IntEnum,
# so we have to do that dynamically.
if sys.version_info >= (3, 7):
ExtendedOperator = _ExtendedOperatorBase
else:
ExtendedOperator = IntEnum('ExtendedOperator', dict(
filter(lambda x: not x[0].startswith('__'), inspect.getmembers(_ExtendedOperatorBase))))
FUSE_ACTIVATION_MAP = {BuiltinOperator.RELU: ActivationFunctionType.RELU,
BuiltinOperator.RELU6: ActivationFunctionType.RELU6,
BuiltinOperator.TANH: ActivationFunctionType.TANH}
| 28.925 | 96 | 0.713915 |
6d976cc81d6b3b0e0fa28444c6bbd2374d9c01bb | 169 | py | Python | mkdocs_markmap/__meta__.py | neatc0der/mkdocs-markmap | dff5d6ace8813ef433b54d34e4f7127d04792b89 | [
"MIT"
] | 14 | 2021-01-25T19:46:25.000Z | 2022-02-12T03:35:51.000Z | mkdocs_markmap/__meta__.py | neatc0der/mkdocs-markmap | dff5d6ace8813ef433b54d34e4f7127d04792b89 | [
"MIT"
] | 14 | 2021-01-24T22:01:52.000Z | 2022-02-16T00:56:33.000Z | mkdocs_markmap/__meta__.py | neatc0der/mkdocs-markmap | dff5d6ace8813ef433b54d34e4f7127d04792b89 | [
"MIT"
] | 1 | 2021-11-30T14:39:10.000Z | 2021-11-30T14:39:10.000Z | PACKAGE_NAME = 'mkdocs_markmap'
PROJECT_NAME = PACKAGE_NAME.replace('_', '-')
PROJECT_VERSION = '2.1.3'
OWNER = 'neatc0der'
REPOSITORY_NAME = f'{OWNER}/{PROJECT_NAME}'
| 24.142857 | 45 | 0.727811 |
6d986eb3521f1a36cc7b07b20157b53df24adc51 | 852 | py | Python | ihome/apps/homes/urls.py | Noah-Smith-wgp/rentinghouse | 22ba71aa8b3b0c290b8c01cd2f4dd14bca81d3d3 | [
"MIT"
] | null | null | null | ihome/apps/homes/urls.py | Noah-Smith-wgp/rentinghouse | 22ba71aa8b3b0c290b8c01cd2f4dd14bca81d3d3 | [
"MIT"
] | null | null | null | ihome/apps/homes/urls.py | Noah-Smith-wgp/rentinghouse | 22ba71aa8b3b0c290b8c01cd2f4dd14bca81d3d3 | [
"MIT"
] | null | null | null | from django.conf.urls import url
from rest_framework.routers import DefaultRouter
from apps.homes import views
urlpatterns = [
url(r'^areas/$', views.AreaAPIView.as_view()),
# url(r'^houses/$', views.HouseAPIView.as_view()),
#
url(r'^user/houses/$', views.HouseListView.as_view()),
#
url(r'^houses/index/$', views.HouseIndexView.as_view()),
#
url(r'^houses/(?P<house_id>\d+)/$', views.HouseDetailView.as_view()),
]
router = DefaultRouter()
# #
# router.register(r'houses/index', views.HouseIndexViewSet, basename='index')
# urlpatterns += router.urls
#
router.register(r'houses', views.HouseAPIView, basename='houses')
urlpatterns += router.urls
#
router.register(r'houses/(?P<house_id>\d+)/images', views.HouseImageView, basename='images')
urlpatterns += router.urls
| 29.37931 | 92 | 0.70892 |
6d9999a24bad3d878ecc89ba34c9037a6d5b672e | 646 | py | Python | encryption/validation/ssl_client.py | TheConner/intl-iot | e7f0d7e96392acec900f29eb95cbbf5cb8d8db66 | [
"Apache-2.0"
] | 46 | 2019-09-19T05:03:56.000Z | 2022-03-07T05:55:12.000Z | encryption/validation/ssl_client.py | dng24/intl-iot | 84d46012afce5c7473d0cc9b82dc9e3aef069bbf | [
"Apache-2.0"
] | null | null | null | encryption/validation/ssl_client.py | dng24/intl-iot | 84d46012afce5c7473d0cc9b82dc9e3aef069bbf | [
"Apache-2.0"
] | 23 | 2019-09-18T02:04:59.000Z | 2022-03-07T05:55:13.000Z | import socket
import ssl
import sys
hostname = '127.0.0.1'
if len(sys.argv) < 2:
exit(0)
inputfile = sys.argv[1]
print('\tRead file %s' % inputfile)
# msg = b"HEAD / HTTP /1.0\r\nHost: linuxfr.org\r\n\r\n"
msg = open(inputfile).read()
msg = bytes(msg.encode())
context = ssl.SSLContext(ssl.PROTOCOL_TLS_CLIENT)
context.load_verify_locations('rootCA.pem')
with socket.socket(socket.AF_INET, socket.SOCK_STREAM, 0) as sock:
with context.wrap_socket(sock, server_hostname=hostname) as ssock:
ssock.connect((hostname, 8443))
# cert = ssock.getpeercert()
ssock.sendall(msg)
print('\tSent %s .+' % msg[:10])
| 26.916667 | 70 | 0.676471 |
6da20244b70564cb32df8f9c1bb63836cc6e87c9 | 345 | py | Python | src/game/items.py | pikulet/sandfox | ad1bd15a6d7e6d40799f21ab9b6b5a3ce6590825 | [
"MIT"
] | null | null | null | src/game/items.py | pikulet/sandfox | ad1bd15a6d7e6d40799f21ab9b6b5a3ce6590825 | [
"MIT"
] | null | null | null | src/game/items.py | pikulet/sandfox | ad1bd15a6d7e6d40799f21ab9b6b5a3ce6590825 | [
"MIT"
] | null | null | null | from typing import Callable
| 23 | 65 | 0.657971 |
6da36de83dd56e3ca84e1de8b7ae22701073bf6d | 528 | py | Python | parte2/alternativeq2.py | ronaldbrito/PDS | 58c8f9737e4cc5872a27e7b778a43def5e3e11f4 | [
"MIT"
] | 1 | 2019-03-16T01:49:11.000Z | 2019-03-16T01:49:11.000Z | parte2/alternativeq2.py | heliomeiralins/pds | 58c8f9737e4cc5872a27e7b778a43def5e3e11f4 | [
"MIT"
] | null | null | null | parte2/alternativeq2.py | heliomeiralins/pds | 58c8f9737e4cc5872a27e7b778a43def5e3e11f4 | [
"MIT"
] | null | null | null | import numpy as np
from scipy.misc import imread, imsave
from scipy import ndimage
img = imread('doc1.bmp')
F = np.vectorize(f)
treated_img = F(img)
imsave('treated_doc.bmp', treated_img)
mask = treated_img < treated_img.mean()
label_im, nb_labels = ndimage.label(mask)
sizes = ndimage.sum(mask, label_im, range(nb_labels + 1))
print(nb_labels)
print(sum(sizes > 1))
print(sum(sizes > 2))
print(sum(sizes > 5))
print(sum(sizes > 10))
| 17.6 | 57 | 0.676136 |
6da7a648349b63e6ebd5bddae98e78d24000ce56 | 2,617 | py | Python | module2-sql-for-analysis/insert_rpg_thief.py | KristineYW/DS-Unit-3-Sprint-2-SQL-and-Databases | 4a690cd8e651161296d7aec2af86a56c499d6801 | [
"MIT"
] | null | null | null | module2-sql-for-analysis/insert_rpg_thief.py | KristineYW/DS-Unit-3-Sprint-2-SQL-and-Databases | 4a690cd8e651161296d7aec2af86a56c499d6801 | [
"MIT"
] | null | null | null | module2-sql-for-analysis/insert_rpg_thief.py | KristineYW/DS-Unit-3-Sprint-2-SQL-and-Databases | 4a690cd8e651161296d7aec2af86a56c499d6801 | [
"MIT"
] | null | null | null | import os
from dotenv import load_dotenv
import sqlite3
import psycopg2
from psycopg2.extras import execute_values
load_dotenv() # looks inside the .env file for some env vars
# passes env var values to python var
DB_HOST = os.getenv("DB_HOST", default="OOPS")
DB_NAME = os.getenv("DB_NAME", default="OOPS")
DB_USER = os.getenv("DB_USER", default="OOPS")
DB_PASSWORD = os.getenv("DB_PASSWORD", default="OOPS")
# what is the filepath to connect to our sqlite database?
DB_FILEPATH = os.path.join(os.path.dirname(__file__), "..", "module1-introduction-to-sql", "rpg_db.sqlite3")
if __name__ == "__main__":
#
# EXTRACT (AND MAYBE TRANSFORM IF NECESSARY)
#
sqlite_service = SqliteService_thief()
characters_thief = sqlite_service.fetch_characters_thief()
print(type(characters_thief), len(characters_thief))
print(type(characters_thief[0]), characters_thief[0])
#
# LOAD
#
pg_service = ElephantSQLService_thief()
pg_service.create_characters_thief_table()
pg_service.insert_characters_thief(characters_thief) | 42.209677 | 244 | 0.708445 |
6da810a7e416553569ccec2032142f91db2446a4 | 4,161 | py | Python | xoa_driver/internals/core/commands/px_commands.py | xenadevel/xena-open-automation-python-api | b17e512aa14eee7c51677004b4c91712005edcd0 | [
"Apache-2.0"
] | 1 | 2022-03-18T17:17:59.000Z | 2022-03-18T17:17:59.000Z | xoa_driver/internals/core/commands/px_commands.py | xenadevel/xena-open-automation-python-api | b17e512aa14eee7c51677004b4c91712005edcd0 | [
"Apache-2.0"
] | null | null | null | xoa_driver/internals/core/commands/px_commands.py | xenadevel/xena-open-automation-python-api | b17e512aa14eee7c51677004b4c91712005edcd0 | [
"Apache-2.0"
] | null | null | null | #: L23 Port Transceiver Commands
from dataclasses import dataclass
import typing
from ..protocol.command_builders import (
build_get_request,
build_set_request
)
from .. import interfaces
from ..transporter.token import Token
from ..protocol.fields.data_types import *
from ..protocol.fields.field import XmpField
from ..registry import register_command
from .enums import *
| 33.02381 | 168 | 0.700072 |
6dab7808e0549ea0483294aef2b69179d291af86 | 12,991 | py | Python | nvidia_clara/grpc/metrics_pb2.py | KavinKrishnan/clara-platform-python-client | 05c873b93022de15902adc656cf9735639d57a73 | [
"Apache-2.0"
] | 8 | 2020-10-30T22:45:07.000Z | 2021-09-23T18:22:30.000Z | nvidia_clara/grpc/metrics_pb2.py | KavinKrishnan/clara-platform-python-client | 05c873b93022de15902adc656cf9735639d57a73 | [
"Apache-2.0"
] | 1 | 2020-12-29T23:42:27.000Z | 2020-12-29T23:42:27.000Z | nvidia_clara/grpc/metrics_pb2.py | KavinKrishnan/clara-platform-python-client | 05c873b93022de15902adc656cf9735639d57a73 | [
"Apache-2.0"
] | 4 | 2020-11-03T18:31:49.000Z | 2021-11-09T17:47:12.000Z | # 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.
# -*- coding: utf-8 -*-
# Generated by the protocol buffer compiler. DO NOT EDIT!
# source: nvidia/clara/platform/node-monitor/metrics.proto
import sys
_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
from google.protobuf import descriptor as _descriptor
from google.protobuf import message as _message
from google.protobuf import reflection as _reflection
from google.protobuf import symbol_database as _symbol_database
# @@protoc_insertion_point(imports)
_sym_db = _symbol_database.Default()
from nvidia_clara.grpc import common_pb2 as nvidia_dot_clara_dot_platform_dot_common__pb2
from nvidia_clara.grpc.common_pb2 import *
DESCRIPTOR = _descriptor.FileDescriptor(
name='nvidia/clara/platform/node-monitor/metrics.proto',
package='nvidia.clara.platform.node_monitor',
syntax='proto3',
serialized_options=_b('\n%com.nvidia.clara.platform.nodemonitorZ\004apis\252\002&Nvidia.Clara.Platform.NodeMonitor.Grpc'),
serialized_pb=_b('\n0nvidia/clara/platform/node-monitor/metrics.proto\x12\"nvidia.clara.platform.node_monitor\x1a\"nvidia/clara/platform/common.proto\"\xbb\x02\n\nGpuDetails\x12\x11\n\tdevice_id\x18\x01 \x01(\x05\x12G\n\x04\x64\x61ta\x18\x02 \x01(\x0b\x32\x39.nvidia.clara.platform.node_monitor.GpuDetails.GpuMetrics\x12\x33\n\ttimestamp\x18\x03 \x01(\x0b\x32 .nvidia.clara.platform.Timestamp\x1a\x9b\x01\n\nGpuMetrics\x12\x1a\n\x12memory_utilization\x18\x01 \x01(\x02\x12\x17\n\x0fgpu_utilization\x18\x02 \x01(\x02\x12\x12\n\nfree_bar_1\x18\x03 \x01(\x03\x12\x12\n\nused_bar_1\x18\x04 \x01(\x03\x12\x17\n\x0f\x66ree_gpu_memory\x18\x05 \x01(\x03\x12\x17\n\x0fused_gpu_memory\x18\x06 \x01(\x03\"P\n\x18MonitorGpuMetricsRequest\x12\x34\n\x06header\x18\x01 \x01(\x0b\x32$.nvidia.clara.platform.RequestHeader\"\x97\x01\n\x19MonitorGpuMetricsResponse\x12\x35\n\x06header\x18\x01 \x01(\x0b\x32%.nvidia.clara.platform.ResponseHeader\x12\x43\n\x0bgpu_details\x18\x02 \x03(\x0b\x32..nvidia.clara.platform.node_monitor.GpuDetails2\x97\x01\n\x07Monitor\x12\x8b\x01\n\nGpuMetrics\x12<.nvidia.clara.platform.node_monitor.MonitorGpuMetricsRequest\x1a=.nvidia.clara.platform.node_monitor.MonitorGpuMetricsResponse0\x01\x42V\n%com.nvidia.clara.platform.nodemonitorZ\x04\x61pis\xaa\x02&Nvidia.Clara.Platform.NodeMonitor.GrpcP\x00\x62\x06proto3')
,
dependencies=[nvidia_dot_clara_dot_platform_dot_common__pb2.DESCRIPTOR,],
public_dependencies=[nvidia_dot_clara_dot_platform_dot_common__pb2.DESCRIPTOR,])
_GPUDETAILS_GPUMETRICS = _descriptor.Descriptor(
name='GpuMetrics',
full_name='nvidia.clara.platform.node_monitor.GpuDetails.GpuMetrics',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='memory_utilization', full_name='nvidia.clara.platform.node_monitor.GpuDetails.GpuMetrics.memory_utilization', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=float(0),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='gpu_utilization', full_name='nvidia.clara.platform.node_monitor.GpuDetails.GpuMetrics.gpu_utilization', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=float(0),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='free_bar_1', full_name='nvidia.clara.platform.node_monitor.GpuDetails.GpuMetrics.free_bar_1', index=2,
number=3, type=3, cpp_type=2, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='used_bar_1', full_name='nvidia.clara.platform.node_monitor.GpuDetails.GpuMetrics.used_bar_1', index=3,
number=4, type=3, cpp_type=2, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='free_gpu_memory', full_name='nvidia.clara.platform.node_monitor.GpuDetails.GpuMetrics.free_gpu_memory', index=4,
number=5, type=3, cpp_type=2, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='used_gpu_memory', full_name='nvidia.clara.platform.node_monitor.GpuDetails.GpuMetrics.used_gpu_memory', index=5,
number=6, type=3, cpp_type=2, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=285,
serialized_end=440,
)
_GPUDETAILS = _descriptor.Descriptor(
name='GpuDetails',
full_name='nvidia.clara.platform.node_monitor.GpuDetails',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='device_id', full_name='nvidia.clara.platform.node_monitor.GpuDetails.device_id', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='data', full_name='nvidia.clara.platform.node_monitor.GpuDetails.data', index=1,
number=2, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='timestamp', full_name='nvidia.clara.platform.node_monitor.GpuDetails.timestamp', index=2,
number=3, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[_GPUDETAILS_GPUMETRICS, ],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=125,
serialized_end=440,
)
_MONITORGPUMETRICSREQUEST = _descriptor.Descriptor(
name='MonitorGpuMetricsRequest',
full_name='nvidia.clara.platform.node_monitor.MonitorGpuMetricsRequest',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='header', full_name='nvidia.clara.platform.node_monitor.MonitorGpuMetricsRequest.header', index=0,
number=1, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=442,
serialized_end=522,
)
_MONITORGPUMETRICSRESPONSE = _descriptor.Descriptor(
name='MonitorGpuMetricsResponse',
full_name='nvidia.clara.platform.node_monitor.MonitorGpuMetricsResponse',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='header', full_name='nvidia.clara.platform.node_monitor.MonitorGpuMetricsResponse.header', index=0,
number=1, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='gpu_details', full_name='nvidia.clara.platform.node_monitor.MonitorGpuMetricsResponse.gpu_details', index=1,
number=2, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=525,
serialized_end=676,
)
_GPUDETAILS_GPUMETRICS.containing_type = _GPUDETAILS
_GPUDETAILS.fields_by_name['data'].message_type = _GPUDETAILS_GPUMETRICS
_GPUDETAILS.fields_by_name['timestamp'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._TIMESTAMP
_MONITORGPUMETRICSREQUEST.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._REQUESTHEADER
_MONITORGPUMETRICSRESPONSE.fields_by_name['header'].message_type = nvidia_dot_clara_dot_platform_dot_common__pb2._RESPONSEHEADER
_MONITORGPUMETRICSRESPONSE.fields_by_name['gpu_details'].message_type = _GPUDETAILS
DESCRIPTOR.message_types_by_name['GpuDetails'] = _GPUDETAILS
DESCRIPTOR.message_types_by_name['MonitorGpuMetricsRequest'] = _MONITORGPUMETRICSREQUEST
DESCRIPTOR.message_types_by_name['MonitorGpuMetricsResponse'] = _MONITORGPUMETRICSRESPONSE
_sym_db.RegisterFileDescriptor(DESCRIPTOR)
GpuDetails = _reflection.GeneratedProtocolMessageType('GpuDetails', (_message.Message,), dict(
GpuMetrics = _reflection.GeneratedProtocolMessageType('GpuMetrics', (_message.Message,), dict(
DESCRIPTOR = _GPUDETAILS_GPUMETRICS,
__module__ = 'nvidia.clara.platform.node_monitor.metrics_pb2'
# @@protoc_insertion_point(class_scope:nvidia.clara.platform.node_monitor.GpuDetails.GpuMetrics)
))
,
DESCRIPTOR = _GPUDETAILS,
__module__ = 'nvidia.clara.platform.node_monitor.metrics_pb2'
# @@protoc_insertion_point(class_scope:nvidia.clara.platform.node_monitor.GpuDetails)
))
_sym_db.RegisterMessage(GpuDetails)
_sym_db.RegisterMessage(GpuDetails.GpuMetrics)
MonitorGpuMetricsRequest = _reflection.GeneratedProtocolMessageType('MonitorGpuMetricsRequest', (_message.Message,), dict(
DESCRIPTOR = _MONITORGPUMETRICSREQUEST,
__module__ = 'nvidia.clara.platform.node_monitor.metrics_pb2'
# @@protoc_insertion_point(class_scope:nvidia.clara.platform.node_monitor.MonitorGpuMetricsRequest)
))
_sym_db.RegisterMessage(MonitorGpuMetricsRequest)
MonitorGpuMetricsResponse = _reflection.GeneratedProtocolMessageType('MonitorGpuMetricsResponse', (_message.Message,), dict(
DESCRIPTOR = _MONITORGPUMETRICSRESPONSE,
__module__ = 'nvidia.clara.platform.node_monitor.metrics_pb2'
# @@protoc_insertion_point(class_scope:nvidia.clara.platform.node_monitor.MonitorGpuMetricsResponse)
))
_sym_db.RegisterMessage(MonitorGpuMetricsResponse)
DESCRIPTOR._options = None
_MONITOR = _descriptor.ServiceDescriptor(
name='Monitor',
full_name='nvidia.clara.platform.node_monitor.Monitor',
file=DESCRIPTOR,
index=0,
serialized_options=None,
serialized_start=679,
serialized_end=830,
methods=[
_descriptor.MethodDescriptor(
name='GpuMetrics',
full_name='nvidia.clara.platform.node_monitor.Monitor.GpuMetrics',
index=0,
containing_service=None,
input_type=_MONITORGPUMETRICSREQUEST,
output_type=_MONITORGPUMETRICSRESPONSE,
serialized_options=None,
),
])
_sym_db.RegisterServiceDescriptor(_MONITOR)
DESCRIPTOR.services_by_name['Monitor'] = _MONITOR
# @@protoc_insertion_point(module_scope)
| 44.489726 | 1,331 | 0.760065 |
6dabb24069dd7d2e7f87b22364bf8f6f48e24574 | 964 | py | Python | chapter17/yunqiCrawl/yunqiCrawl/items.py | NetworkRanger/python-spider-project | f501e331a59608d9a321a0d7254fcbcf81b50ec2 | [
"MIT"
] | 1 | 2019-02-08T03:14:17.000Z | 2019-02-08T03:14:17.000Z | chapter17/yunqiCrawl/yunqiCrawl/items.py | NetworkRanger/python-spider-project | f501e331a59608d9a321a0d7254fcbcf81b50ec2 | [
"MIT"
] | null | null | null | chapter17/yunqiCrawl/yunqiCrawl/items.py | NetworkRanger/python-spider-project | f501e331a59608d9a321a0d7254fcbcf81b50ec2 | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
# Define here the models for your scraped items
#
# See documentation in:
# https://doc.scrapy.org/en/latest/topics/items.html
import scrapy
| 28.352941 | 52 | 0.695021 |
6dabb2d9a3beda1b3745a3582f1367443e6ae076 | 4,626 | py | Python | src/data_prep_uci.py | akumesi48/hyper-genetic | 6e1ec16b31bb2259d4a325e08779d5668750a635 | [
"MIT"
] | null | null | null | src/data_prep_uci.py | akumesi48/hyper-genetic | 6e1ec16b31bb2259d4a325e08779d5668750a635 | [
"MIT"
] | null | null | null | src/data_prep_uci.py | akumesi48/hyper-genetic | 6e1ec16b31bb2259d4a325e08779d5668750a635 | [
"MIT"
] | null | null | null | import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.model_selection import StratifiedKFold, KFold
no_of_folds = 3
# Dataset cmc
data_cmc = pd.read_csv("data/cmc.data", header=None)
data_cmc[9] = np.where(data_cmc[9] == 1, 0, 1)
data_cmc_label = data_cmc.pop(9)
x_train_cmc, x_test_cmc, y_train_cmc, y_test_cmc = train_test_split(data_cmc,
data_cmc_label,
random_state=7840,
test_size=0.25)
index_cmc = cv_index(no_of_folds, x_train_cmc, y_train_cmc)
# Dataset SETAP
data_setap = pd.read_csv("data/setap.csv")
data_setap['label'] = np.where(data_setap['label'] == 'A', 0, 1)
data_setap_label = data_setap.pop('label')
x_train_setap, x_test_setap, y_train_setap, y_test_setap = train_test_split(data_setap,
data_setap_label,
random_state=7840,
test_size=0.25)
index_setap = cv_index(no_of_folds, x_train_setap, y_train_setap)
# Dataset audit
data_audit = pd.read_csv("data/audit_risk.csv")
data_audit['LOCATION_ID'] = pd.to_numeric(data_audit['LOCATION_ID'], errors='coerce')
data_audit['LOCATION_ID'] = data_audit['LOCATION_ID'].fillna(data_audit['LOCATION_ID'].mode()[0])
data_audit['Money_Value'] = data_audit['Money_Value'].fillna(data_audit['Money_Value'].mean())
data_audit_label = data_audit.pop('Risk')
x_train_audit, x_test_audit, y_train_audit, y_test_audit = train_test_split(data_audit,
data_audit_label,
random_state=7840,
test_size=0.25,)
index_audit = cv_index(no_of_folds, x_train_audit, y_train_audit)
# Dataset titanic
data_tt = pd.read_csv("data/titanic_train.csv")
data_tt['Age'] = data_tt['Age'].fillna(data_tt['Age'].mean())
data_tt['Embarked'] = data_tt['Embarked'].fillna(data_tt['Embarked'].mode()[0])
data_tt['Pclass'] = data_tt['Pclass'].apply(str)
for col in data_tt.dtypes[data_tt.dtypes == 'object'].index:
for_dummy = data_tt.pop(col)
data_tt = pd.concat([data_tt, pd.get_dummies(for_dummy, prefix=col)], axis=1)
data_tt_labels = data_tt.pop('Survived')
x_train_tt, x_test_tt, y_train_tt, y_test_tt = train_test_split(data_tt,
data_tt_labels,
random_state=7840,
test_size=0.25)
index_tt = cv_index(no_of_folds, x_train_tt, y_train_tt)
# Dataset DotA2
x_train_dota = pd.read_csv("data/dota2Train.csv", header=None)
x_train_dota[0] = np.where(x_train_dota[0] == 1, 1, 0)
y_train_dota = x_train_dota.pop(0)
x_test_dota = pd.read_csv("data/dota2Test.csv", header=None)
x_test_dota[0] = np.where(x_test_dota[0] == 1, 1, 0)
y_test_dota = x_test_dota.pop(0)
index_dota = cv_index(no_of_folds, x_train_dota, y_train_dota)
# for train_index, test_index in skf.split(x_train, y_train):
# train_feature, test_feature = x_train.iloc[train_index], x_train.iloc[test_index]
# train_label, test_label = y_train.iloc[train_index], y_train.iloc[test_index]
# print(train_gbm(train_feature, train_label, test_feature, test_label))
# skf = KFold(5)
# train_index = []
# test_index = []
# index_list = []
# for i, j in skf.split(x_train_cmc, y_train_cmc):
# index_list.append((i, j))
| 47.690722 | 97 | 0.61284 |
6dace628e580ac178333748655d75c5708c2a7c6 | 502 | py | Python | main/views/admin/dashboard/dashboard_controller.py | tiberiucorbu/av-website | f26f44a367d718316442506b130a7034697670b8 | [
"MIT"
] | null | null | null | main/views/admin/dashboard/dashboard_controller.py | tiberiucorbu/av-website | f26f44a367d718316442506b130a7034697670b8 | [
"MIT"
] | null | null | null | main/views/admin/dashboard/dashboard_controller.py | tiberiucorbu/av-website | f26f44a367d718316442506b130a7034697670b8 | [
"MIT"
] | null | null | null | # coding: utf-8
from flask.ext import wtf
import flask
import wtforms
import auth
import config
import model
import util
from main import app
###############################################################################
# Admin Stuff
###############################################################################
| 19.307692 | 79 | 0.482072 |
6dacede88291ab910c867a7b6ae2bd99b6b5522e | 1,564 | py | Python | scrape/views.py | naydeenmonzon/Maritime_Web_Tool | bc8203b9e62b19eaa93bc018f719004269b2eaee | [
"CC0-1.0"
] | null | null | null | scrape/views.py | naydeenmonzon/Maritime_Web_Tool | bc8203b9e62b19eaa93bc018f719004269b2eaee | [
"CC0-1.0"
] | null | null | null | scrape/views.py | naydeenmonzon/Maritime_Web_Tool | bc8203b9e62b19eaa93bc018f719004269b2eaee | [
"CC0-1.0"
] | null | null | null | from datetime import datetime
from django.views.generic.base import TemplateView
from django.shortcuts import get_object_or_404, render
from django.urls import reverse
from .models import MONTH_DICT, MONTH_LIST, YEARS,CARRIER_LIST
from .forms import BSfilterForm
from .main import _init_SCRAPER
def index(request):
return render(request, 'scrape/index.html')
def blanksailing(request):
inital_data = {
'monthF':datetime.now().month,
'monthT':datetime.now().month
}
form = BSfilterForm(request.GET or None, initial=inital_data)
if request.method =='POST':
form = BSfilterForm(request.POST)
if form.is_valid():
data = form.cleaned_data
_init_SCRAPER(data)
# print(data)
else:
print(form.errors)
context = {
'form':form,
'months':MONTH_DICT
}
return render(request,'scrape/blanksailing.html',context)
def vesselroute(request):
return render(request,'scrape/vesselroute.html')
def filter(request):
form = BSfilterForm(request.POST or None)
if form.is_valid():
form.save()
context = {'form':form}
return render(request,'scrape/filter.html',context)
# class BlankSailingView(ListView):
# blanksailing()
# template_name = 'scrape/blanksailing.html'
| 22.028169 | 65 | 0.680307 |
6dad2a388f6001f81a7db3ec98fd61ac8d241fec | 935 | py | Python | ncdoublescrape/__main__.py | hancush/ncdoublescrape | ea64277514ddff04e634bb464dd5ea6bf05226ae | [
"BSD-3-Clause"
] | null | null | null | ncdoublescrape/__main__.py | hancush/ncdoublescrape | ea64277514ddff04e634bb464dd5ea6bf05226ae | [
"BSD-3-Clause"
] | null | null | null | ncdoublescrape/__main__.py | hancush/ncdoublescrape | ea64277514ddff04e634bb464dd5ea6bf05226ae | [
"BSD-3-Clause"
] | null | null | null | import argparse
import importlib
import logging
import sys
logger = logging.getLogger()
COMMAND_MODULES = (
'ncdoublescrape.scrape',
)
if __name__ == '__main__':
main() | 23.375 | 84 | 0.637433 |
6dae00c3441df190db00ecff9c3e1288ac41b72a | 2,971 | py | Python | deleteAllJobs.py | prelert/engine-python | 7a8721fcf718a641acd945300ad9ba95d7cb8e52 | [
"Apache-2.0"
] | 36 | 2015-01-31T22:01:52.000Z | 2019-04-15T14:29:30.000Z | deleteAllJobs.py | prelert/engine-python | 7a8721fcf718a641acd945300ad9ba95d7cb8e52 | [
"Apache-2.0"
] | 2 | 2016-01-13T01:00:58.000Z | 2016-01-13T01:05:44.000Z | deleteAllJobs.py | prelert/engine-python | 7a8721fcf718a641acd945300ad9ba95d7cb8e52 | [
"Apache-2.0"
] | 18 | 2015-03-19T18:43:46.000Z | 2020-05-05T12:28:02.000Z | #!/usr/bin/env python
############################################################################
# #
# Copyright 2014 Prelert 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. #
# #
############################################################################
'''
Delete all the jobs in the Engine API.
Request a list of jobs configured in the API then
delete them one at a time using the job id.
Be careful with this one you can't change your mind afterwards.
'''
import argparse
import sys
import json
import logging
import time
from prelert.engineApiClient import EngineApiClient
# defaults
HOST = 'localhost'
PORT = 8080
BASE_URL = 'engine/v2'
if __name__ == "__main__":
main()
| 34.546512 | 81 | 0.492763 |
6dae65a4786c8a90f1445e3fa12402bbb4864e31 | 614 | py | Python | Codefights/arcade/python-arcade/level-3/23.Two-Teams/Python/test.py | RevansChen/online-judge | ad1b07fee7bd3c49418becccda904e17505f3018 | [
"MIT"
] | 7 | 2017-09-20T16:40:39.000Z | 2021-08-31T18:15:08.000Z | Codefights/arcade/python-arcade/level-3/23.Two-Teams/Python/test.py | RevansChen/online-judge | ad1b07fee7bd3c49418becccda904e17505f3018 | [
"MIT"
] | null | null | null | Codefights/arcade/python-arcade/level-3/23.Two-Teams/Python/test.py | RevansChen/online-judge | ad1b07fee7bd3c49418becccda904e17505f3018 | [
"MIT"
] | null | null | null | # Python3
from solution1 import twoTeams as f
qa = [
([1, 11, 13, 6, 14],
11),
([3, 4],
-1),
([16, 14, 79, 8, 71, 72, 71, 10, 80, 76, 83, 70, 57, 29, 31],
209),
([23, 72, 54, 4, 88, 91, 8, 44],
-38),
([23, 74, 57, 33, 61, 99, 19, 12, 19, 38, 77, 70, 20],
-50)
]
for *q, a in qa:
for i, e in enumerate(q):
print('input{0}: {1}'.format(i + 1, e))
ans = f(*q)
if ans != a:
print(' [failed]')
print(' output:', ans)
print(' expected:', a)
else:
print(' [ok]')
print(' output:', ans)
print()
| 20.466667 | 65 | 0.415309 |
6daefd5d0ce54a9298c815543a7ce9308e437d8f | 5,037 | py | Python | src/lambda_function.py | sd11/react-aws-s3-rekognition | f37ea4ef0242f8c650380ab0c060e0bddb4ff432 | [
"Unlicense"
] | null | null | null | src/lambda_function.py | sd11/react-aws-s3-rekognition | f37ea4ef0242f8c650380ab0c060e0bddb4ff432 | [
"Unlicense"
] | null | null | null | src/lambda_function.py | sd11/react-aws-s3-rekognition | f37ea4ef0242f8c650380ab0c060e0bddb4ff432 | [
"Unlicense"
] | null | null | null | from __future__ import print_function
import boto3
from decimal import Decimal
import json
import urllib
from botocore.vendored import requests
print('Loading function')
rekognition = boto3.client('rekognition')
s3 = boto3.resource("s3")
# --------------- Helper Functions to call Rekognition APIs ------------------
# --------------- Main handler ------------------
def lambda_handler(event, context):
'''Demonstrates S3 trigger that uses
Rekognition APIs to detect faces, labels and index faces in S3 Object.
'''
print("Received event: " + json.dumps(event, indent=2))
# Get the object from the event
bucket = event['Records'][0]['s3']['bucket']['name']
key = urllib.unquote_plus(event['Records'][0]['s3']['object']['key'].encode('utf8'))
try:
# Calls rekognition DetectFaces API to detect faces in S3 object
#response = detect_faces(bucket, key)
# Calls rekognition DetectLabels API to detect labels in S3 object
response = detect_labels(bucket, key)
# Calls rekognition IndexFaces API to detect faces in S3 object and index faces into specified collection
#response = index_faces(bucket, key)
# Print response to console.
print('Detected labels for ' + key)
print()
ingredients = ""
for label in response['Labels']:
encodedName = label['Name'].encode('utf-8')
if len(ingredients):
ingredients = ingredients + ", " + encodedName
else:
ingredients = encodedName
# print ("Label: " + label['Name'])
# print ("Confidence: " + str(label['Confidence']))
# print ("Instances:")
#for instance in label['Instances']:
# print (" Bounding box")
# print (" Top: " + str(instance['BoundingBox']['Top']))
# print (" Left: " + str(instance['BoundingBox']['Left']))
# print (" Width: " + str(instance['BoundingBox']['Width']))
# print (" Height: " + str(instance['BoundingBox']['Height']))
# print (" Confidence: " + str(instance['Confidence']))
# print()
# print ("Parents:")
# for parent in label['Parents']:
# print (" " + parent['Name'])
# print ("----------")
# print ()
recipes = find_recipes(ingredients)
#return recipes
#print(ingredients)
#print(recipes)
recipeResponse = []
for recipe in recipes:
recipeIngredients = []
for usedIngredient in recipe['usedIngredients']:
recipeIngredients.append({
'name': usedIngredient['name'],
'servingSize': str(usedIngredient['amount']) + ' ' + usedIngredient['unit']
})
recipeResponse.append({
'title': recipe['title'],
'image': recipe['image'],
'ingredients': recipeIngredients
})
responseData = { 'ingredients': ingredients, 'recipes': recipeResponse }
if responseData:
print(s3)
obj = s3.Object('groupneuralnetworkrecipebucket1','recipes.json')
obj.put(Body=json.dumps(responseData))
return responseData
except Exception as e:
print(e)
print("Error processing object {} from bucket {}. ".format(key, bucket) +
"Make sure your object and bucket exist and your bucket is in the same region as this function.")
raise e
| 37.036765 | 153 | 0.596188 |
6dafe2f9f75624f4e6bac8d36ece57fee0f45bc2 | 3,836 | py | Python | main_server.py | tenzindayoe/example | edbee1fd1b6cbb55f6b02f82f972c3da46a4dd89 | [
"MIT"
] | null | null | null | main_server.py | tenzindayoe/example | edbee1fd1b6cbb55f6b02f82f972c3da46a4dd89 | [
"MIT"
] | null | null | null | main_server.py | tenzindayoe/example | edbee1fd1b6cbb55f6b02f82f972c3da46a4dd89 | [
"MIT"
] | null | null | null | import socket
import sqlite3
if __name__ == "__main__":
Main().run()
| 29.282443 | 142 | 0.489572 |
6db079b8869b4ec33596b58ada4916db98ed1a2a | 759 | py | Python | data_structures/stacks/implementation.py | karim7262/algorithms-and-datastructures-python | c6c4d1166d07eed549a5f97806222c7a20312d0f | [
"MIT"
] | 1 | 2022-01-07T18:04:26.000Z | 2022-01-07T18:04:26.000Z | data_structures/stacks/implementation.py | karim7262/algorithms-and-datastructures-python | c6c4d1166d07eed549a5f97806222c7a20312d0f | [
"MIT"
] | null | null | null | data_structures/stacks/implementation.py | karim7262/algorithms-and-datastructures-python | c6c4d1166d07eed549a5f97806222c7a20312d0f | [
"MIT"
] | null | null | null | from typing import Any
# There is no point implementing a stack with a linkedlist
# since we'd majorly be interacting with the topmost item
# in the stakc -- this is what arrays are optimized for | 21.685714 | 59 | 0.59025 |
6db2c1ba2f0973e1c395b6375ee992a3997ea0f6 | 2,967 | py | Python | mlib/boot/lang.py | mgroth0/mlib | 0442ed51eab417b6972f885605afd351892a3a9a | [
"MIT"
] | 1 | 2020-06-16T17:26:45.000Z | 2020-06-16T17:26:45.000Z | mlib/boot/lang.py | mgroth0/mlib | 0442ed51eab417b6972f885605afd351892a3a9a | [
"MIT"
] | null | null | null | mlib/boot/lang.py | mgroth0/mlib | 0442ed51eab417b6972f885605afd351892a3a9a | [
"MIT"
] | null | null | null | import collections
import os
from pathlib import Path
enum = enumerate
HOME = str(Path.home())
DESKTOP = os.path.join(HOME, 'Desktop')
DEBUG = True
NORMAL = False
| 20.894366 | 107 | 0.664307 |
6db40aaa8cd6b1e406d9fcd14ef25634a3d1ada0 | 2,274 | py | Python | db/division.py | leaffan/pynhldb | a0cdd56f0c21b866bfe62aa10b3dd205a9ec0ff1 | [
"MIT"
] | 3 | 2017-02-01T15:37:23.000Z | 2017-08-31T20:41:46.000Z | db/division.py | leaffan/pynhldb | a0cdd56f0c21b866bfe62aa10b3dd205a9ec0ff1 | [
"MIT"
] | 41 | 2017-09-13T02:13:21.000Z | 2018-11-07T03:29:39.000Z | db/division.py | leaffan/pynhldb | a0cdd56f0c21b866bfe62aa10b3dd205a9ec0ff1 | [
"MIT"
] | 1 | 2017-03-09T14:58:39.000Z | 2017-03-09T14:58:39.000Z | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import datetime
from .common import Base, session_scope
from .team import Team
| 30.32 | 78 | 0.575198 |
6db421aa4d6562b0233d7c1b87bdca893ef23405 | 1,338 | py | Python | tests/test_config.py | regro/runthis-server | 26d6551560bd6ddabdb9b360ecd327460dfd779a | [
"BSD-3-Clause"
] | 2 | 2019-11-13T23:19:13.000Z | 2019-11-15T21:01:51.000Z | tests/test_config.py | regro/runthis-server | 26d6551560bd6ddabdb9b360ecd327460dfd779a | [
"BSD-3-Clause"
] | null | null | null | tests/test_config.py | regro/runthis-server | 26d6551560bd6ddabdb9b360ecd327460dfd779a | [
"BSD-3-Clause"
] | null | null | null | import pytest
from ruamel import yaml
from runthis.server.config import Config, get_config_from_yaml
DICT_CONFIG_CONTENT = dict(
tty_server="tty-server",
command="myshell",
docker=True,
docker_image="img",
host="8.8.8.8",
certfile="/path/to/cert.pem",
)
| 26.76 | 77 | 0.684604 |
6db4535a87906f105783cb4e0f22471fe703aef0 | 290 | py | Python | src/constants.py | halilyaman/UlasimdaYapayZekaYarismasi | e9f024454470ad6f40653583f3d7f24cdd4f4fd9 | [
"MIT"
] | 1 | 2021-09-23T22:34:12.000Z | 2021-09-23T22:34:12.000Z | src/constants.py | halilyaman/UlasimdaYapayZekaYarismasi | e9f024454470ad6f40653583f3d7f24cdd4f4fd9 | [
"MIT"
] | null | null | null | src/constants.py | halilyaman/UlasimdaYapayZekaYarismasi | e9f024454470ad6f40653583f3d7f24cdd4f4fd9 | [
"MIT"
] | null | null | null | # DISCLAIMER TO CONTEST TEAMS : DO NOT MAKE CHANGES IN THIS FILE.
classes = {
"Tasit": 0,
"Insan": 1,
"UAP": 2,
"UAI": 3,
}
landing_statuses = {
"Inilebilir": "1",
"Inilemez": "0",
"Inis Alani Degil": "-1"
}
base_url = "http://192.168.1.10:3000"
| 19.333333 | 67 | 0.527586 |
6db5c79371243a32af183f73744db734c3cd20b9 | 1,380 | py | Python | ETL/Data_Operations/data_operations.py | rohanharode/DRAW-Drug-Review-Analysis-Work | 89d8df82e1f0b67129727f16c32c038d64af35e2 | [
"MIT"
] | null | null | null | ETL/Data_Operations/data_operations.py | rohanharode/DRAW-Drug-Review-Analysis-Work | 89d8df82e1f0b67129727f16c32c038d64af35e2 | [
"MIT"
] | 2 | 2020-11-08T22:03:32.000Z | 2021-06-27T09:22:31.000Z | ETL/Data_Operations/data_operations.py | rohanharode/Drug-Review-Analysis-Work | 89d8df82e1f0b67129727f16c32c038d64af35e2 | [
"MIT"
] | 1 | 2021-07-08T10:50:36.000Z | 2021-07-08T10:50:36.000Z | from ETL.Data_Preprocessing.data_cleaning_and_filtering import cleaning_and_filtering
from ETL.Data_Transformation.drug_conditions_grouped import group_conditions
from ETL.Data_Transformation.jaccard_similarity import apply_jaccard_similarity
from ETL.Data_Transformation.drug_conditions_fuzzy_matching import fuzzy_matching
from ETL.Data_Transformation.updating_conditions import updating_conditions
from ETL.Data_Transformation.dataset_final_conditions import site_level_final_condition
from ETL.Data_Transformation.drug_final_dataset import site_level_datasets_creation
from ETL.Data_Aggregation.full_merge import final_dataset_creation
all_data_operations() | 37.297297 | 87 | 0.826087 |
6db6887534c339671321ea2ad6c3cae9fe067123 | 2,345 | py | Python | setup.py | Ezbob/dgDynamic | 394de1c138c1517c4cdfead879c43db189752d92 | [
"MIT"
] | null | null | null | setup.py | Ezbob/dgDynamic | 394de1c138c1517c4cdfead879c43db189752d92 | [
"MIT"
] | null | null | null | setup.py | Ezbob/dgDynamic | 394de1c138c1517c4cdfead879c43db189752d92 | [
"MIT"
] | null | null | null | from setuptools import setup
from setuptools.command.install import install
import os
import sys
import atexit
if __name__ == '__main__':
package_name = 'dgdynamic'
excludes = [
'__pycache__',
'StochKit'
]
extras = [
'default_config.ini',
'spim.ocaml',
'stochkit.tar.gz'
]
package_dirs = find_package_dirs(package_name, excludes)
internal_python_paths = {
".".join(p_name.split('/')): p_name
for p_name in package_dirs
}
setup(
cmdclass={'install': CustomInstall},
name=package_name,
version='1.0.0',
description='Dynamic simulation library for the MD graph transformation framework',
url='https://bitbucket.org/Ezben/dgdynamic',
author='Anders Busch',
author_email='andersbusch@gmail.com',
license='MIT',
package_dir=internal_python_paths,
include_package_data=True,
package_data={'': extras},
packages=list(internal_python_paths.keys()),
install_requires=get_requirements(),
zip_safe=False
)
| 33.028169 | 99 | 0.612367 |
6db73ff6f5328cc7f6a179960f5ceb876377c833 | 5,543 | py | Python | Robotics/src/otonomsesli.py | ahmetakif/Voice-Controlled-Raspberry-Pi-Robot | 00dcc15dfbb7441d6403fb0467b2144e8750cc0c | [
"Apache-2.0"
] | 5 | 2019-08-21T08:08:27.000Z | 2021-06-14T06:56:50.000Z | Robotics/src/otonomsesli.py | ahmetakif/Voice-Controlled-Raspberry-Pi-Robot | 00dcc15dfbb7441d6403fb0467b2144e8750cc0c | [
"Apache-2.0"
] | null | null | null | Robotics/src/otonomsesli.py | ahmetakif/Voice-Controlled-Raspberry-Pi-Robot | 00dcc15dfbb7441d6403fb0467b2144e8750cc0c | [
"Apache-2.0"
] | 2 | 2019-08-21T08:16:58.000Z | 2021-04-07T11:56:11.000Z | import os
import RPi.GPIO as gpio
import time
from mesafe import distance
motorhizi = 1
aci2 = aci3 = aci4 = 6
aci = 5.5
in4 = 26
in3 = 4
in2 = 12
in1 = 8
solled = 9
sagled = 11
gpio.setwarnings(False)
print (" ")
print ("otonomgorev yazilimi google speech api sesli komutlari ile robotun otonom hareket etmesi iin yazilmistir")
print (" ")
time.sleep(1)
aci2 = aci3 = aci4 = 6
aci = 5.5
| 20.378676 | 115 | 0.541043 |
6db755c6b9c7ae3c58a95da2aa5e9301523ca9e8 | 632 | py | Python | src/models/discord_users.py | KirtusJ/BirdBot | 4440364caefa6ec9acf1bc7cf38605b1d90de20e | [
"MIT"
] | null | null | null | src/models/discord_users.py | KirtusJ/BirdBot | 4440364caefa6ec9acf1bc7cf38605b1d90de20e | [
"MIT"
] | null | null | null | src/models/discord_users.py | KirtusJ/BirdBot | 4440364caefa6ec9acf1bc7cf38605b1d90de20e | [
"MIT"
] | null | null | null | from sqlalchemy.sql import func
from sqlalchemy import Column, BigInteger, String, DateTime, Boolean
from .model import database | 39.5 | 92 | 0.783228 |
6db800ff348f905ec48118d311341bb879f2f4fd | 2,036 | py | Python | algorithms/timer.py | terratenff/vrp-gen-alg | 3910ff7977a84b03e14c4f500909bcb86e6dd608 | [
"MIT"
] | null | null | null | algorithms/timer.py | terratenff/vrp-gen-alg | 3910ff7977a84b03e14c4f500909bcb86e6dd608 | [
"MIT"
] | null | null | null | algorithms/timer.py | terratenff/vrp-gen-alg | 3910ff7977a84b03e14c4f500909bcb86e6dd608 | [
"MIT"
] | null | null | null | #!/usr/bin/env python
"""
timer.py:
Implementation of a CPU timer that is used as a part of stopping criteria.
"""
import time
def _time():
"""
Convenience function that returns current process time as milliseconds.
"""
return time.process_time() * 1000
| 22.130435 | 81 | 0.562868 |
6db82d6e06cc11fb7b83d45d0342ef4c6c52a44f | 6,093 | py | Python | experiments/livecell/validate_model.py | JonasHell/torch-em | 2e008e0cd2f0ea6681581374fce4f9f47b986d55 | [
"MIT"
] | 13 | 2021-03-09T21:31:09.000Z | 2022-03-21T05:24:26.000Z | experiments/livecell/validate_model.py | JonasHell/torch-em | 2e008e0cd2f0ea6681581374fce4f9f47b986d55 | [
"MIT"
] | 16 | 2021-03-02T23:19:34.000Z | 2022-03-25T19:43:41.000Z | experiments/livecell/validate_model.py | JonasHell/torch-em | 2e008e0cd2f0ea6681581374fce4f9f47b986d55 | [
"MIT"
] | 4 | 2021-05-18T08:29:33.000Z | 2022-02-11T12:16:20.000Z | import argparse
import os
from glob import glob
from pathlib import Path
import imageio
import h5py
import pandas as pd
from bioimageio.core import load_resource_description
from bioimageio.core.prediction import predict_with_padding
from bioimageio.core.prediction_pipeline import create_prediction_pipeline
from elf.evaluation import mean_average_precision
from torch_em.util.segmentation import (connected_components_with_boundaries,
mutex_watershed, size_filter)
from tqdm import tqdm
from xarray import DataArray
try:
import napari
except ImportError:
napari = None
# TODO needs update for live-cell data structure
if __name__ == "__main__":
main()
| 42.02069 | 115 | 0.683243 |
6dba80a9622a3df8b603c41e7552e6d4c8ed3c02 | 23 | py | Python | tests/res/foo.py | lepture/werkzeug | 627ac8370bc5aa3a04ba365b4ebcd32b6a859863 | [
"BSD-3-Clause"
] | 1 | 2019-04-14T19:58:21.000Z | 2019-04-14T19:58:21.000Z | tests/res/foo.py | lepture/werkzeug | 627ac8370bc5aa3a04ba365b4ebcd32b6a859863 | [
"BSD-3-Clause"
] | null | null | null | tests/res/foo.py | lepture/werkzeug | 627ac8370bc5aa3a04ba365b4ebcd32b6a859863 | [
"BSD-3-Clause"
] | null | null | null | from .bar import value
| 11.5 | 22 | 0.782609 |
6dbe230462ef91543db856cb4ff084f41755ca26 | 1,320 | py | Python | src/wa_kat/db/downloader.py | WebArchivCZ/WA-KAT | 719f7607222f5a4d917c535b2da6371184222101 | [
"MIT"
] | 3 | 2017-03-23T12:59:21.000Z | 2017-11-22T08:23:14.000Z | src/wa_kat/db/downloader.py | WebArchivCZ/WA-KAT | 719f7607222f5a4d917c535b2da6371184222101 | [
"MIT"
] | 89 | 2015-06-28T22:10:28.000Z | 2017-01-30T16:06:05.000Z | src/wa_kat/db/downloader.py | WebarchivCZ/WA-KAT | 719f7607222f5a4d917c535b2da6371184222101 | [
"MIT"
] | 1 | 2015-12-17T02:56:59.000Z | 2015-12-17T02:56:59.000Z | #! /usr/bin/env python
# -*- coding: utf-8 -*-
#
# Interpreter version: python 2.7
#
# Imports =====================================================================
import requests
from ..settings import USER_AGENT
from ..settings import REQUEST_TIMEOUT
# Functions & classes =========================================================
def download(url):
"""
Download `url` and return it as utf-8 encoded text.
Args:
url (str): What should be downloaded?
Returns:
str: Content of the page.
"""
headers = {"User-Agent": USER_AGENT}
resp = requests.get(
url,
timeout=REQUEST_TIMEOUT,
headers=headers,
allow_redirects=True,
verify=False,
)
return decode(resp.text, resp.encoding)
| 25.882353 | 79 | 0.537879 |
6dbfcff192ece7ab414ec98a8d97692a952d7bdd | 7,506 | py | Python | main.py | qmn/pershing | ec3cc87d9bfb7ca0cf1da1449d695c36df548309 | [
"BSD-2-Clause"
] | 16 | 2017-05-20T05:30:59.000Z | 2022-02-08T05:41:52.000Z | main.py | qmn/pershing | ec3cc87d9bfb7ca0cf1da1449d695c36df548309 | [
"BSD-2-Clause"
] | null | null | null | main.py | qmn/pershing | ec3cc87d9bfb7ca0cf1da1449d695c36df548309 | [
"BSD-2-Clause"
] | 3 | 2016-09-18T15:55:37.000Z | 2020-12-27T15:36:09.000Z | #!/usr/bin/env python2.7
from __future__ import print_function
import json
import sys
import numpy as np
import os.path
import time
from math import ceil
import argparse
import nbt
from util import blif, cell, cell_library
from placer import placer
from router import router, extractor, minetime
from vis import png
from inserter import inserter
if __name__ == "__main__":
placements = None
dimensions = None
routing = None
# Create parser
parser = argparse.ArgumentParser(description="An automatic place-and-route tool for Minecraft Redstone circuits.")
parser.add_argument('blif', metavar="<input BLIF file>")
parser.add_argument('-o', '--output_dir', metavar="output_directory", dest="output_dir")
parser.add_argument('--library', metavar="library_file", dest="library_file", default="lib/quan.yaml")
parser.add_argument('--placements', metavar="placements_file", dest="placements_file", help="Use this placements file rather than creating one. Must be previously generated from the supplied BLIF.")
parser.add_argument('--routings', metavar="routings_file", dest="routings_file", help="Use this routings file rather than creating one. Must be previously generated from the supplied BLIF and placements JSON.")
parser.add_argument('--world', metavar="world_folder", dest="world_folder", help="Place the extracted redstone circuit layout in this world.")
args = parser.parse_args()
# Load placements, if provided
if args.placements_file is not None:
print("Using placements file:", args.placements_file)
with open(args.placements_file) as f:
placements = json.loads(f.readline())
dimensions = json.loads(f.readline())
# Load library file
with open(args.library_file) as f:
cell_lib = cell_library.load(f)
# Load BLIF
with open(args.blif) as f:
blif = blif.load(f)
# Result directory
if args.output_dir is not None:
if os.path.isabs(args.output_dir):
result_dir = args.output_dir
else:
result_dir = os.path.abspath(args.output_dir)
else:
result_base, _ = os.path.splitext(args.blif)
result_dir = os.path.abspath(result_base + "_result")
# Try making the directory
if not os.path.exists(result_dir):
try:
os.mkdir(result_dir)
print("Made result dir: ", result_dir)
except OSError as e:
print(e)
pregenerated_cells = cell_library.pregenerate_cells(cell_lib, pad=1)
placer = placer.GridPlacer(blif, pregenerated_cells, grid_spacing=5)
start_time = time.time()
print("Started", time.strftime("%c", time.localtime(start_time)))
# PLACE =============================================================
if placements is None:
underline_print("Performing Initial Placement...")
placements, dimensions = placer.initial_placement()
score = placer.score(placements, dimensions)
print("Initial Placement Penalty:", score)
underline_print("Doing Placement...")
# Place cells
T_0 = 250
iterations = 2000
new_placements = placer.simulated_annealing_placement(placements, dimensions, T_0, iterations)
placements, dimensions = placer.shrink(new_placements)
# Place pins and resize
placements += placer.place_pins(dimensions)
placements, dimensions = placer.shrink(placements)
# print(new_placements)
print("Placed", len(placements), "cells")
with open(os.path.join(result_dir, "placements.json"), "w") as f:
json.dump(placements, f)
f.write("\n")
json.dump(dimensions, f)
# Visualize this layout
layout = placer.placement_to_layout(dimensions, placements)
png.layout_to_png(layout, filename_base=os.path.join(result_dir, "composite"))
print("Dimensions:", dimensions)
# ROUTE =============================================================
underline_print("Doing Routing...")
placements, dimensions = placer.shrink(placements)
layout = placer.placement_to_layout(dimensions, placements)
router = router.Router(blif, pregenerated_cells)
# Load routings, if provided
if args.routings_file is not None:
print("Using routings file:", args.routings_file)
with open(args.routings_file) as f:
routing = router.deserialize_routing(f)
if routing is None:
blocks, data = layout
print("Doing initial routing...")
routing = router.initial_routing(placements, blocks.shape)
print("done.")
routing = router.re_route(routing, layout)
# Preserve routing
with open(os.path.join(result_dir, "routing.json"), "w") as f:
router.serialize_routing(routing, dimensions, f)
print("Routed", len(routing), "nets")
# EXTRACT ===========================================================
underline_print("Doing Extraction...")
extractor = extractor.Extractor(blif, pregenerated_cells)
extracted_routing = extractor.extract_routing(routing)
extracted_layout = extractor.extract_layout(extracted_routing, layout)
with open(os.path.join(result_dir, "extraction.json"), "w") as f:
blocks, data = extracted_layout
json.dump(blocks.tolist(), f)
json.dump(data.tolist(), f)
print("Wrote extraction to extraction.json")
# VISUALIZE =========================================================
underline_print("Doing Visualization...")
# Get the pins
pins = placer.locate_circuit_pins(placements)
# png.nets_to_png(layout, routing)
png_fn = os.path.join(result_dir, "layout.png")
png.layout_to_composite(extracted_layout, pins=pins).save(png_fn)
print("Image written to ", png_fn)
# MINETIME =========================================================
underline_print("Doing Timing Analysis with MineTime...")
mt = minetime.MineTime()
path_delays = mt.compute_combinational_delay(placements, extracted_routing, cell_lib)
print("Path delays:")
for path_delay, path in sorted(path_delays, key=lambda x: x[0], reverse=True):
print(path_delay, " ", " -> ".join(path))
print()
crit_delay, crit_path = max(path_delays, key=lambda x: x[0])
print("Critical path delay: {} ticks".format(crit_delay))
print("Minimum period: {:.2f} s".format(crit_delay * 0.05))
print("Maximum frequency: {:.4f} Hz".format(1./(crit_delay * 0.05)))
underline_print("Design Statistics")
blocks, _ = layout
print("Layout size: {} x {} x {}".format(blocks.shape[0], blocks.shape[1], blocks.shape[2]))
print(" Blocks placed: {}".format(sum(blocks.flat != 0)))
print()
print("Total nets: {}".format(len(extracted_routing)))
print(" Segments routed: {}".format(sum(len(net["segments"]) for net in extracted_routing.itervalues())))
print()
end_time = time.time()
print("Finished", time.strftime("%c", time.localtime(end_time)), "(took", ceil(end_time - start_time), "s)")
# INSERTION ========================================================
if args.world_folder is not None:
underline_print("Inserting Design into Minecraft World...")
world = nbt.world.WorldFolder(args.world_folder)
inserter.insert_extracted_layout(world, extracted_layout, offset=(4, 0, 0))
| 37.158416 | 214 | 0.641886 |
6dc096d4b45dd4acd7b5d28912a696afdc093628 | 296 | py | Python | logtest.py | jonathanstrong/log-viewer | 83374de21ce807709217e3fffa87b75265b3edd6 | [
"MIT"
] | 1 | 2017-03-09T01:18:06.000Z | 2017-03-09T01:18:06.000Z | logtest.py | jonathanstrong/log-viewer | 83374de21ce807709217e3fffa87b75265b3edd6 | [
"MIT"
] | null | null | null | logtest.py | jonathanstrong/log-viewer | 83374de21ce807709217e3fffa87b75265b3edd6 | [
"MIT"
] | null | null | null | import logging
import logging.handlers
import time
logger = logging.getLogger(__name__)
handler = logging.handlers.SocketHandler('localhost', 9033)
stream = logging.StreamHandler()
logger.addHandler(handler)
logger.addHandler(stream)
while True:
logger.warning('ping')
time.sleep(.001)
| 22.769231 | 59 | 0.780405 |
6dc161a661b51dac6f78e1e2949123e1dfec52e8 | 5,032 | py | Python | text-builder.py | guskma/text-builder | e9de6178ef5ce71a6f022b7932d40a906200578e | [
"MIT"
] | null | null | null | text-builder.py | guskma/text-builder | e9de6178ef5ce71a6f022b7932d40a906200578e | [
"MIT"
] | null | null | null | text-builder.py | guskma/text-builder | e9de6178ef5ce71a6f022b7932d40a906200578e | [
"MIT"
] | null | null | null | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import print_function
from argparse import ArgumentParser
from collections import OrderedDict
import jinja2
import csv
import sys
import os.path
import re
if __name__ == "__main__":
args = cmd_options()
build_templates(args)
| 29.775148 | 103 | 0.584261 |
6dc1f2e93753dd6196949aaa91494539baeb31b1 | 2,077 | py | Python | hard_grasp.py | bionicdl-sustech/AmphibiousManipulation | 397c7dfef6b4dda178a567c36aabfe0f4b05b821 | [
"MIT"
] | null | null | null | hard_grasp.py | bionicdl-sustech/AmphibiousManipulation | 397c7dfef6b4dda178a567c36aabfe0f4b05b821 | [
"MIT"
] | null | null | null | hard_grasp.py | bionicdl-sustech/AmphibiousManipulation | 397c7dfef6b4dda178a567c36aabfe0f4b05b821 | [
"MIT"
] | null | null | null | import yaml
import os
import sys
import time
import numpy as np
import cv2 as cv
from franka.FrankaController import FrankaController
if __name__ == '__main__':
ROOT = os.path.dirname(os.path.abspath(__file__))
sys.path.append(ROOT)
cfg = read_cfg(ROOT + '/config/grasping _colorseg.yaml')
arm = FrankaController(ROOT + '/config/franka.yaml')
# grasping config
initial_pose = cfg['initial_position']
initial_pose[2] -= 0.3
check_position = cfg['check_position']
drop_position = cfg['drop_position']
grasp_pre_offset = cfg['grasp_prepare_offset']
effector_offset = cfg['effector_offset']
check_threshold = cfg['check_threshold']
attmp_num = cfg['attmp_num']
print("Moving to initial position...")
arm.move_p(initial_pose)
print("Moving to initial position... Done")
stored_exception = None
arm.move_p(initial_pose)
current_num = 0
while current_num < attmp_num:
try:
if stored_exception:
break
target_in_base = drop_position.copy()
target_in_base[2] -= 0.37
prepare_pos = [target_in_base[0], target_in_base[1], target_in_base[2] + grasp_pre_offset + effector_offset, 3.14, 0, 0]
arm.move_p(prepare_pos)
arm.gripperOpen()
arm.move_p([target_in_base[0], target_in_base[1], target_in_base[2] + effector_offset, 3.14, 0, 0])
arm.gripperGrasp(width=0.05, force=2)
time.sleep(0.5)
# Move to check position
# arm.move_p(check_position)
arm.move_p(initial_pose)
# Move to drop position and drop object
arm.move_p(drop_position)
arm.gripperOpen()
# Back to initial position
arm.move_p(initial_pose)
current_num += 1
except KeyboardInterrupt:
stored_exception = sys.exc_info()
cv.destroyAllWindows()
| 25.9625 | 132 | 0.629273 |
6dc61ebe0b329b523bc43f68e083bb96cc389b67 | 2,913 | py | Python | src/server/api.py | Irsutoro/sesame | 142959a6e4c814c72f480b0252a028d8586b77da | [
"MIT"
] | null | null | null | src/server/api.py | Irsutoro/sesame | 142959a6e4c814c72f480b0252a028d8586b77da | [
"MIT"
] | null | null | null | src/server/api.py | Irsutoro/sesame | 142959a6e4c814c72f480b0252a028d8586b77da | [
"MIT"
] | null | null | null | import cherrypy
import sesame
from database import create_database
DATABASE = create_database({'System':'sqlite', 'Database':'testy.db'})
SERVER = None
CHERRYPY_CONFIG = {
'server.socket_host': '127.0.0.1',
'server.socket_port': 8080,
'tools.auth_basic.on': True,
'tools.auth_basic.realm': '127.0.0.1',
'tools.auth_basic.checkpassword': validate_password,
}
if __name__ == '__main__':
WITHOUT_AUTHENTICATION = {'/': {'request.dispatch': cherrypy.dispatch.MethodDispatcher(), 'tools.auth_basic.on': False}}
WITH_AUTHENTICATION = {'/': {'request.dispatch': cherrypy.dispatch.MethodDispatcher()}}
SERVER = sesame.Sesame(DATABASE)
SERVER.create_tables()
SERVER.add_encrypting_algorithm('AES128')
cherrypy.config.update(CHERRYPY_CONFIG)
cherrypy.tree.mount(UserService(), '/api/user', WITH_AUTHENTICATION)
cherrypy.tree.mount(AuthService(), '/api/auth', WITHOUT_AUTHENTICATION)
cherrypy.tree.mount(PasswordService(), '/api/password', WITH_AUTHENTICATION)
cherrypy.engine.start()
cherrypy.engine.block()
| 30.663158 | 124 | 0.6701 |
6dc710027aba9309fc1e58e6facfd13ec0253ff6 | 731 | py | Python | Domain_Knowledge_based/stanfordNER.py | Mount428/Hate-Speech-Detection | f8644844dda954ebd169aeec54cb4c7361d88a09 | [
"MIT"
] | null | null | null | Domain_Knowledge_based/stanfordNER.py | Mount428/Hate-Speech-Detection | f8644844dda954ebd169aeec54cb4c7361d88a09 | [
"MIT"
] | null | null | null | Domain_Knowledge_based/stanfordNER.py | Mount428/Hate-Speech-Detection | f8644844dda954ebd169aeec54cb4c7361d88a09 | [
"MIT"
] | null | null | null | from nltk.tag import StanfordNERTagger
import pandas as pd
from sklearn.metrics import f1_score, confusion_matrix
from loader import Load
train, test = Load('c')
ner = StanfordNERTagger('./stanford-ner-2018-10-16/classifiers/english.all.3class.distsim.crf.ser.gz', './stanford-ner-2018-10-16/stanford-ner.jar')
data = train
data['tweet'] = ner.tag_sents(data['tweet'].str.split(' '))
pred = []
for i, d in data.iterrows():
tweet = d['tweet']
tag = 'IND'
for w in tweet:
if w[1] == 'ORGANIZATION':
tag = 'GRP'
# elif w[1] == 'PEOPLE':
# tag = 'IND'
pred.append(tag)
print(confusion_matrix(data['label'], pred))
print(f1_score(data['label'], pred, average='macro'))
| 22.84375 | 148 | 0.641587 |
6dc79c5bcc11f79748762758e10ea27d0fe9f70f | 41,355 | py | Python | pyrif/FuXi/Rete/Network.py | mpetyx/pyrif | 2f7ba863030d7337bb39ad502d1e09e26ac950d2 | [
"MIT"
] | null | null | null | pyrif/FuXi/Rete/Network.py | mpetyx/pyrif | 2f7ba863030d7337bb39ad502d1e09e26ac950d2 | [
"MIT"
] | null | null | null | pyrif/FuXi/Rete/Network.py | mpetyx/pyrif | 2f7ba863030d7337bb39ad502d1e09e26ac950d2 | [
"MIT"
] | null | null | null | """
====================================================================================
A Rete Network Building and 'Evaluation' Implementation for RDFLib Graphs of
Notation 3 rules.
The DLP implementation uses this network to automatically building RETE
decision trees for OWL forms of DLP
Uses Python hashing mechanism to maximize the efficiency of the built
pattern network.
The network :
- compiles an RDFLib N3 rule graph into AlphaNode and BetaNode instances
- takes a fact (or the removal of a fact, perhaps?) and propagates down,
starting from its alpha nodes
- stores inferred triples in provided triple source (an RDFLib graph) or
a temporary IOMemory Graph by default
"""
from itertools import chain
import sys
import time
from pprint import pprint
try:
from functools import reduce
except ImportError:
pass
try:
from io import StringIO
except ImportError:
from StringIO import StringIO
from .BetaNode import (
BetaNode,
LEFT_MEMORY,
RIGHT_MEMORY,
PartialInstantiation,
)
from .AlphaNode import (
AlphaNode,
BuiltInAlphaNode,
ReteToken,
)
from FuXi.Horn import (
ComplementExpansion,
DATALOG_SAFETY_NONE,
DATALOG_SAFETY_STRICT,
DATALOG_SAFETY_LOOSE,
)
from FuXi.Syntax.InfixOWL import Class
from FuXi.Horn.PositiveConditions import (
Exists,
GetUterm,
Or,
SetOperator,
Uniterm,
)
from FuXi.DLP import (
MapDLPtoNetwork,
non_DHL_OWL_Semantics,
)
from FuXi.DLP.ConditionalAxioms import AdditionalRules
from .Util import (
generateTokenSet,
renderNetwork,
xcombine,
)
from rdflib.graph import (
ConjunctiveGraph,
Graph,
ReadOnlyGraphAggregate,
)
from rdflib.namespace import NamespaceManager
from rdflib import (
BNode,
Literal,
Namespace,
RDF,
RDFS,
URIRef,
Variable,
)
from rdflib import py3compat
from rdflib.util import first
from .ReteVocabulary import RETE_NS
from .RuleStore import (
Formula,
N3Builtin,
N3RuleStore,
)
OWL_NS = Namespace("http://www.w3.org/2002/07/owl#")
Any = None
LOG = Namespace("http://www.w3.org/2000/10/swap/log#")
#From itertools recipes
def any(seq, pred=None):
"""Returns True if pred(x) is true for at least one element in the iterable"""
for elem in filter(pred, seq):
return True
return False
def ComplementExpand(tBoxGraph, complementAnnotation):
complementExpanded = []
for negativeClass in tBoxGraph.subjects(predicate=OWL_NS.complementOf):
containingList = first(tBoxGraph.subjects(RDF.first, negativeClass))
prevLink = None
while containingList:
prevLink = containingList
containingList = first(tBoxGraph.subjects(RDF.rest, containingList))
if prevLink:
for s, p, o in tBoxGraph.triples_choices((None,
[OWL_NS.intersectionOf,
OWL_NS.unionOf],
prevLink)):
if (s, complementAnnotation, None) in tBoxGraph:
continue
_class = Class(s)
complementExpanded.append(s)
print("Added %s to complement expansion" % _class)
ComplementExpansion(_class)
if __name__ == '__main__':
test()
# from FuXi.Rete.Network import iteritems
# from FuXi.Rete.Network import any
# from FuXi.Rete.Network import ComplementExpand
# from FuXi.Rete.Network import HashablePatternList
# from FuXi.Rete.Network import InferredGoal
# from FuXi.Rete.Network import ReteNetwork
| 44.182692 | 132 | 0.58363 |
6dc7ac77f93802122ddb1e47bec4538126c006a2 | 500 | py | Python | gcpy/functions/HTTPFunction.py | MaximBazarov/gcpy | 92c1d31a8133e19a5d2b27dc8f2e0b7e8fe1d609 | [
"MIT"
] | 1 | 2020-07-29T11:20:35.000Z | 2020-07-29T11:20:35.000Z | gcpy/functions/HTTPFunction.py | MaximBazarov/gcpy | 92c1d31a8133e19a5d2b27dc8f2e0b7e8fe1d609 | [
"MIT"
] | null | null | null | gcpy/functions/HTTPFunction.py | MaximBazarov/gcpy | 92c1d31a8133e19a5d2b27dc8f2e0b7e8fe1d609 | [
"MIT"
] | null | null | null | from gcpy.functions.CloudFunction import CloudFunctionTrigger, CloudFunction
from gcpy.utils.binary import binary_decode
| 22.727273 | 76 | 0.7 |
6dcbed133eb3a7b3cdb7874d0063d3eca6ce4f69 | 792 | py | Python | flask_sqlalchemy/app.py | andreeaionescu/graphql-example | ceeff3888ea87312d4df138093d7f6fcaa1ae973 | [
"MIT"
] | null | null | null | flask_sqlalchemy/app.py | andreeaionescu/graphql-example | ceeff3888ea87312d4df138093d7f6fcaa1ae973 | [
"MIT"
] | null | null | null | flask_sqlalchemy/app.py | andreeaionescu/graphql-example | ceeff3888ea87312d4df138093d7f6fcaa1ae973 | [
"MIT"
] | null | null | null | '''
Unlike a RESTful API, there is only a single URL from which GraphQL is accessed.
We are going to use Flask to create a server that expose the GraphQL schema under /graphql and a interface for querying
it easily: GraphiQL (also under /graphql when accessed by a browser).
'''
from flask import Flask
from flask_graphql import GraphQLView
from flask_sqlalchemy.models import db_session
from flask_sqlalchemy.schema import schema, Department
app = Flask(__name__)
app.debug = True
app.add_url_rule(
'/graphql',
view_func=GraphQLView.as_view(
'graphql',
schema=schema,
graphiql=True # for having the GraphiQL interface
)
)
if __name__ == '__main__':
app.run() | 26.4 | 119 | 0.744949 |
6dcd1f0147fa6de6eaa3f0bce8b1c9dccea62eb4 | 1,305 | py | Python | The HackerRank Interview Preparation Kit/6 - Greedy Algorithms/Reverse Shuffle Merge.py | sohammanjrekar/HackerRank | 1f5010133a1ac1e765e855a086053c97d9e958be | [
"MIT"
] | null | null | null | The HackerRank Interview Preparation Kit/6 - Greedy Algorithms/Reverse Shuffle Merge.py | sohammanjrekar/HackerRank | 1f5010133a1ac1e765e855a086053c97d9e958be | [
"MIT"
] | null | null | null | The HackerRank Interview Preparation Kit/6 - Greedy Algorithms/Reverse Shuffle Merge.py | sohammanjrekar/HackerRank | 1f5010133a1ac1e765e855a086053c97d9e958be | [
"MIT"
] | null | null | null | #
# Complete the 'reverseShuffleMerge' function below.
#
# The function is expected to return a STRING.
# The function accepts STRING s as parameter.
#
import os
from collections import defaultdict
if __name__ == '__main__':
fptr = open(os.environ['OUTPUT_PATH'], 'w')
s = input()
result = reverseShuffleMerge(s)
fptr.write(result + '\n')
fptr.close()
| 24.166667 | 73 | 0.596169 |
6dcf6d8a2796f5c3d07f258930f33ef3e7528467 | 4,021 | py | Python | src/skansensor/datacollector.py | fadykuzman/Rodeo-App | 2972b371ed38fad4f93e6afcb699b51cec865510 | [
"BSD-3-Clause"
] | null | null | null | src/skansensor/datacollector.py | fadykuzman/Rodeo-App | 2972b371ed38fad4f93e6afcb699b51cec865510 | [
"BSD-3-Clause"
] | null | null | null | src/skansensor/datacollector.py | fadykuzman/Rodeo-App | 2972b371ed38fad4f93e6afcb699b51cec865510 | [
"BSD-3-Clause"
] | null | null | null | """
This class searches for data in a hierarchy of folders and sorts
them in a list.
Attributes to the data are:
path: path to the raw data file
type: whether from Picarro, DropSense sensors.
data: the data set after reading with the modules:
read_dropsense
read_picarro
Please refer to the documentation of the above data
reading modules to know the data structure of the
resultant datasets
"""
import os
from collections import namedtuple
import skansensor.skansensor as ss
| 36.225225 | 92 | 0.437453 |
6dd2944b2ee1fbe92298d2147e16828378aa049e | 2,794 | py | Python | sonypyapi.py | Johannes-Vitt/sonypiapi | 74c347d8bdb05fe8955dd4478ecfdc19cfd4ec03 | [
"MIT"
] | 2 | 2017-05-12T11:23:48.000Z | 2021-06-12T09:38:24.000Z | sonypyapi.py | Johannes-Vitt/sonypiapi | 74c347d8bdb05fe8955dd4478ecfdc19cfd4ec03 | [
"MIT"
] | null | null | null | sonypyapi.py | Johannes-Vitt/sonypiapi | 74c347d8bdb05fe8955dd4478ecfdc19cfd4ec03 | [
"MIT"
] | null | null | null | #!/usr/bin/python
import sys
import json
import urllib2
import collections
url = "http://192.168.122.1:8080/sony/"
camera_url = url+"camera"
avContent_url = url+"avContent"
command_line_call()
| 29.410526 | 89 | 0.586256 |
6dd2c58e17cfa913515f063e288c4ff6d601590c | 875 | py | Python | pybuildtool/core/context.py | dozymoe/PyBuildTool | d938a8d6335b801e102159e82a6e0002dfaa1b1a | [
"MIT"
] | 5 | 2017-02-10T07:54:49.000Z | 2017-07-11T09:14:26.000Z | pybuildtool/core/context.py | dozymoe/PyBuildTool | d938a8d6335b801e102159e82a6e0002dfaa1b1a | [
"MIT"
] | null | null | null | pybuildtool/core/context.py | dozymoe/PyBuildTool | d938a8d6335b801e102159e82a6e0002dfaa1b1a | [
"MIT"
] | 1 | 2017-05-21T20:35:10.000Z | 2017-05-21T20:35:10.000Z | import os
from waflib import Context, Errors # pylint:disable=import-error
| 31.25 | 76 | 0.637714 |
6dd3236b3617415b5e5c4905e33da2de283601a4 | 3,151 | py | Python | FindJenkins.py | realsanjay/DomainRecon_old | 936f66404c1a4163b119afc06dca03e588c0756e | [
"MIT"
] | 100 | 2018-04-06T13:50:57.000Z | 2018-10-01T14:33:46.000Z | FindJenkins.py | realsanjay/DomainRecon_old | 936f66404c1a4163b119afc06dca03e588c0756e | [
"MIT"
] | 3 | 2018-12-06T04:54:24.000Z | 2021-02-25T14:59:21.000Z | FindJenkins.py | realsanjay/DomainRecon_old | 936f66404c1a4163b119afc06dca03e588c0756e | [
"MIT"
] | 16 | 2018-10-09T13:50:14.000Z | 2021-09-01T06:53:34.000Z | import sys
import re
import requests
import requests.cookies
from requests.packages.urllib3.exceptions import InsecureRequestWarning
requests.packages.urllib3.disable_warnings(InsecureRequestWarning)
from time import sleep
from GlobalVariables import * | 53.40678 | 369 | 0.679467 |
6dd35f03ff9bc671f79e8585b1d7db025e32de94 | 115 | py | Python | tests/conftest.py | techjacker/sitemapgenerator | 512d6ea6f36ac661d3e0b1275a055c381b0ce455 | [
"MIT"
] | null | null | null | tests/conftest.py | techjacker/sitemapgenerator | 512d6ea6f36ac661d3e0b1275a055c381b0ce455 | [
"MIT"
] | null | null | null | tests/conftest.py | techjacker/sitemapgenerator | 512d6ea6f36ac661d3e0b1275a055c381b0ce455 | [
"MIT"
] | null | null | null | import pytest
from pytest_httpbin.plugin import httpbin_ca_bundle
pytest.fixture(autouse=True)(httpbin_ca_bundle)
| 23 | 51 | 0.869565 |
6dd36722d7bc153d838406ca8e22b682264cbef0 | 187 | py | Python | freedom/tests/post_test.py | smbdsbrain/wind_of_freedom | b1b19205175db8c824ab5f4b2e8a8e4e2c5d6873 | [
"WTFPL"
] | null | null | null | freedom/tests/post_test.py | smbdsbrain/wind_of_freedom | b1b19205175db8c824ab5f4b2e8a8e4e2c5d6873 | [
"WTFPL"
] | null | null | null | freedom/tests/post_test.py | smbdsbrain/wind_of_freedom | b1b19205175db8c824ab5f4b2e8a8e4e2c5d6873 | [
"WTFPL"
] | null | null | null | from unittest import mock
| 23.375 | 43 | 0.695187 |
6dd57794c6789b5f5554c5238dd5b5fff9f2b1a6 | 138 | py | Python | Exercise 10/exercise_code/util/__init__.py | CornellLenard/Deep-Learning-Course-Exercises | db32f2b9ab93a50580e93e9dd83be1db7c4c4a19 | [
"MIT"
] | null | null | null | Exercise 10/exercise_code/util/__init__.py | CornellLenard/Deep-Learning-Course-Exercises | db32f2b9ab93a50580e93e9dd83be1db7c4c4a19 | [
"MIT"
] | null | null | null | Exercise 10/exercise_code/util/__init__.py | CornellLenard/Deep-Learning-Course-Exercises | db32f2b9ab93a50580e93e9dd83be1db7c4c4a19 | [
"MIT"
] | null | null | null | """Util functions"""
from .vis_utils import visualizer
from .save_model import save_model
from .Util import checkParams, checkSize, test
| 23 | 46 | 0.797101 |
6dd5f7a4941e24796fe51eb9276d1b79188a16ea | 15,499 | py | Python | au/fixtures/dataset.py | pwais/au2018 | edd224e5fb649b9f0095ffad39b94f72f73e4853 | [
"Apache-2.0"
] | null | null | null | au/fixtures/dataset.py | pwais/au2018 | edd224e5fb649b9f0095ffad39b94f72f73e4853 | [
"Apache-2.0"
] | 3 | 2019-01-05T22:43:37.000Z | 2019-01-26T05:45:01.000Z | au/fixtures/dataset.py | pwais/au2018 | edd224e5fb649b9f0095ffad39b94f72f73e4853 | [
"Apache-2.0"
] | 1 | 2020-05-03T21:10:03.000Z | 2020-05-03T21:10:03.000Z | import io
import os
from collections import OrderedDict
import imageio
import numpy as np
from au import conf
from au.util import create_log
from au import util
##
## Images
##
# @property
# def label_bytes(self):
# if self._label_bytes is '':
# # Read lazily
# if self._cached_label_arr is not '':
# buf = io.BytesIO()
# imageio.imwrite(buf, self._cached_image_arr, format='png')
# self._image_bytes = buf.getvalue()
# elif self._cached_image_fobj is not '':
# self._image_bytes = self._cached_image_fobj.read()
# self._cached_image_fobj = ''
# return self._image_bytes
#
# @property
# def label(self):
# if self._cached_label is '':
# if self.label_encoding == 'json':
#
#
# if self._label is '':
# # Read lazily
# if self._cached_label_arr is not '':
# buf = io.BytesIO()
# imageio.imwrite(buf, self._cached_label_arr, format='png')
# self._label_bytes = buf.getvalue()
# elif self._cached_label_fobj is not '':
# self._label_bytes = self._cached_label_fobj.read()
# self._cached_label_fobj = ''
# return self._label_bytes
def to_debug(self, fname=''):
"""Convenience for dumping an image to a place on disk where the user can
view locally (e.g. using Apple Finder file preview, Ubuntu
image browser, an nginx instance pointed at the folder, etc).
FMI see conf.AU_CACHE_TMP
"""
if self.image_bytes == '':
return None
dest = os.path.join(conf.AU_CACHE_TMP, self.fname())
util.mkdir(conf.AU_CACHE_TMP)
with open(dest, 'wb') as f:
f.write(self.image_bytes)
return dest
## Ops & Utils
import cv2
##
## Tables of images
##
# @classmethod
# def show_stats(cls, spark=None):
#
# @staticmethod
# def write_tf_dataset_to_parquet(
# dataset,
# dest_dir,
#
"""
make a dataset for 1-channel mnist things
make a dataset for our handful of images
try to coerce dataset from mscoco
make one for bbd100k
record activations for mnist
then for mobilenet on bdd100k / mscoco
take note of deeplab inference: https://colab.research.google.com/github/tensorflow/models/blob/master/research/deeplab/deeplab_demo.ipynb#scrollTo=edGukUHXyymr
and we'll wanna add maskrcnn mebbe ?
SPARK_LOCAL_IP=127.0.0.1 $SPARK_HOME/bin/pyspark --packages databricks:tensorframes:0.5.0-s_2.11 --packages databricks:spark-deep-learning:1.2.0-spark2.3-s_2.11
class DatasetFactoryBase(object):
class ParamsBase(object):
def __init__(self):
self.BASE_DIR = ''
@classmethod
def create_dataset(cls):
pass
@classmethod
def get_ctx_for_entry(cls, entry_id):
pass
""" | 28.595941 | 160 | 0.623653 |
6dd85933e9edf9c201a35fe12dd563f0c97ddb8b | 417 | py | Python | Python Fundamentals/Regular Expressions/More Exercises/Task05.py | DonikaChervenkova/SoftUni | bff579c037ec48f39ed193b34bc3502a32e90732 | [
"MIT"
] | 1 | 2022-03-16T10:23:04.000Z | 2022-03-16T10:23:04.000Z | Python Fundamentals/Regular Expressions/More Exercise/Task05.py | IvanTodorovBG/SoftUni | 7b667f6905d9f695ab1484efbb02b6715f6d569e | [
"MIT"
] | null | null | null | Python Fundamentals/Regular Expressions/More Exercise/Task05.py | IvanTodorovBG/SoftUni | 7b667f6905d9f695ab1484efbb02b6715f6d569e | [
"MIT"
] | 1 | 2021-12-04T12:30:57.000Z | 2021-12-04T12:30:57.000Z | import re
title_regex = r'<title>([^<>]*)<\/title>'
info = input()
title = re.findall(title_regex, info)
title = ''.join(title)
print(f"Title: {title}")
body_regex = r'<body>.*<\/body>'
body = re.findall(body_regex, info)
body = ''.join(body)
content_regex = r">([^><]*)<"
content = re.findall(content_regex, body)
content = ''.join(content)
content = content.replace('\\n', '')
print(f'Content: {content}') | 17.375 | 41 | 0.630695 |
6dda593a7a7bdfb7cf3bae36d8a8315e4135dc5a | 230 | py | Python | OMS/helpers/idgenerator.py | RumaisaHabib/orphan-management-system | 4671f40fb2a979e54dec4d12cc522829c2aa739e | [
"MIT"
] | 1 | 2021-12-24T18:14:35.000Z | 2021-12-24T18:14:35.000Z | OMS/helpers/idgenerator.py | RumaisaHabib/orphan-management-system | 4671f40fb2a979e54dec4d12cc522829c2aa739e | [
"MIT"
] | null | null | null | OMS/helpers/idgenerator.py | RumaisaHabib/orphan-management-system | 4671f40fb2a979e54dec4d12cc522829c2aa739e | [
"MIT"
] | null | null | null | import random
| 19.166667 | 63 | 0.521739 |
6ddb9acc38ebe942d14b28143c5d4ded77045159 | 320 | py | Python | code_references/graph.py | nathanShepherd/Intelligent-Interface | 4ab8a223ef6dfaed7cf5ebf61b24ec355d00b593 | [
"MIT"
] | 3 | 2018-03-26T21:08:45.000Z | 2018-11-16T21:16:57.000Z | code_references/graph.py | nathanShepherd/Intelligent-Interface | 4ab8a223ef6dfaed7cf5ebf61b24ec355d00b593 | [
"MIT"
] | null | null | null | code_references/graph.py | nathanShepherd/Intelligent-Interface | 4ab8a223ef6dfaed7cf5ebf61b24ec355d00b593 | [
"MIT"
] | 2 | 2018-03-26T21:08:51.000Z | 2020-05-06T09:22:52.000Z | # Testing various methods to graph with matplotlib
# Developed by Nathan Shepherd
import numpy as np
import matplotlib.pyplot as plt
n = 100
y = [round(np.random.normal(scale=n/10)) for _ in range(n)]
x = [i for i in range(-n, n)]
_y = []
for i in range(-n, n):
_y.append(y.count(i))
plt.plot(x, _y)
plt.show()
| 18.823529 | 59 | 0.671875 |
6ddc0994a3a96b2f0830b2f0c227911b21552e3f | 1,741 | py | Python | scripts/taxonomy_frequency.py | STRIDES-Codes/Exploring-the-Microbiome- | bd29c8c74d8f40a58b63db28815acb4081f20d6b | [
"MIT"
] | null | null | null | scripts/taxonomy_frequency.py | STRIDES-Codes/Exploring-the-Microbiome- | bd29c8c74d8f40a58b63db28815acb4081f20d6b | [
"MIT"
] | null | null | null | scripts/taxonomy_frequency.py | STRIDES-Codes/Exploring-the-Microbiome- | bd29c8c74d8f40a58b63db28815acb4081f20d6b | [
"MIT"
] | 2 | 2021-06-05T07:40:20.000Z | 2021-06-05T08:02:58.000Z | import sys
from Bio import Entrez
from collections import Counter
import pandas as pd
###########################################
###############################################
###############################################
| 31.654545 | 118 | 0.564618 |
6dddd5e456a7ccaaa8659db73eee87ff70f0dccb | 91 | py | Python | 02-array-seq/2_13.py | 393562632/example-code | 4a5da5726408284aed9e01f93a25a0d8dd348fb5 | [
"MIT"
] | null | null | null | 02-array-seq/2_13.py | 393562632/example-code | 4a5da5726408284aed9e01f93a25a0d8dd348fb5 | [
"MIT"
] | null | null | null | 02-array-seq/2_13.py | 393562632/example-code | 4a5da5726408284aed9e01f93a25a0d8dd348fb5 | [
"MIT"
] | null | null | null | weird_board = [['_'] * 3] *3
print(weird_board)
weird_board[0][2] = 'X'
print(weird_board) | 18.2 | 28 | 0.659341 |
6dddfed29dba919369dee25697fc63339866499f | 12,754 | py | Python | gipf/GipfLogic.py | callix2/alphaZero-gipf | fd8dac7606611126d2d14beca0333b53bd5ee995 | [
"MIT"
] | null | null | null | gipf/GipfLogic.py | callix2/alphaZero-gipf | fd8dac7606611126d2d14beca0333b53bd5ee995 | [
"MIT"
] | null | null | null | gipf/GipfLogic.py | callix2/alphaZero-gipf | fd8dac7606611126d2d14beca0333b53bd5ee995 | [
"MIT"
] | null | null | null | '''
Author: Eric P. Nichols
Date: Feb 8, 2008.
Board class.
Board data:
1=white, -1=black, 0=empty
first dim is column , 2nd is row:
pieces[1][7] is the square in column 2,
at the opposite end of the board in row 8.
Squares are stored and manipulated as (x,y) tuples.
x is the column, y is the row.
'''
import numpy as np
| 36.130312 | 148 | 0.509644 |