xszheng2020/the_stack_dedup_python_hits_0 · Datasets at Hugging Face

2176987fdbd61dbebc421fba3bb535bdc9787060

957

py

Python

userlib/utils/__init__.py

WorldMoZara/Pardon-Bot-Community

541a87b0794ff6ecb57531df99b37bab44d86453

[ "MIT" ]

1

2022-01-17T00:51:15.000Z

userlib/utils/__init__.py

WorldMoZara/Pardon-Bot-Community

541a87b0794ff6ecb57531df99b37bab44d86453

[ "MIT" ]

null

userlib/utils/__init__.py

WorldMoZara/Pardon-Bot-Community

541a87b0794ff6ecb57531df99b37bab44d86453

[ "MIT" ]

null

import ctypes import os import re import subprocess QalcExchangeRatesUpdater = ctypes.cdll.LoadLibrary( os.path.join("/".join(__name__.split(".")) if __name__ != "__main__" else ".", "qalc-update-exchange-rates.so") ) qalc_update_exchange_rates = QalcExchangeRatesUpdater._Z20update_exchange_ratev _fchs_conv = [ [r"小?时", r"h"], [r"(分钟?|mi?n?)", r"min"], [r"秒钟?", r"s"], [r"[天日]", r"d"], ] # 字符转换映射关系 def time2sec(time: str) -> int: """Convert time to second(s).""" time_s = time for src, dst in _fchs_conv: # 逐步替换 time_s = re.sub(src, dst, time_s) proc = subprocess.run(["qalc", "%s to s" % time_s], capture_output=True) time_po = proc.stdout.decode() time_po = time_po[time_po.rfind("=") + 1:time_po.rfind("s")].strip() return int(float(time_po)) def ftouch(fp: str): subprocess.run(["touch", fp]) def fgetmtime(fp: str): return os.path.getmtime(fp)

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2176bd40b94b346745b8e60c8cd96eee5c6444d3

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py

Python

project_test/tests/test_005_resolver.py

MtzwOsk/autobreadcrumbs

75e2fd1cc74db790c5fdc924d6fa9410ee7caa45

[ "MIT" ]

null

project_test/tests/test_005_resolver.py

MtzwOsk/autobreadcrumbs

75e2fd1cc74db790c5fdc924d6fa9410ee7caa45

[ "MIT" ]

null

project_test/tests/test_005_resolver.py

MtzwOsk/autobreadcrumbs

75e2fd1cc74db790c5fdc924d6fa9410ee7caa45

[ "MIT" ]

null

# -*- coding: utf-8 -*- import pytest from django.core.urlresolvers import reverse from autobreadcrumbs.resolver import PathBreadcrumbResolver # Autodiscovering is disabled since it allready have be executed # previously, see warning from "test_004_autodiscover.py" # from autobreadcrumbs.discover import autodiscover # autodiscover() @pytest.mark.parametrize("path,segments", [ ('/foo/', [ '/', '/foo/', ]), ('foo/', [ '/', '/foo/', ]), ('/foo', [ '/', '/foo/', ]), (u'/télô/你好/', [ '/', u'/télô/', u'/télô/你好/', ]), ('/foo/bar/', [ '/', '/foo/', '/foo/bar/', ]), ('/foo/bar-mip/flop/', [ '/', '/foo/', '/foo/bar-mip/', '/foo/bar-mip/flop/', ]), ('/foo/bar/foo/zouip/', [ '/', '/foo/', '/foo/bar/', '/foo/bar/foo/', '/foo/bar/foo/zouip/', ]), ]) def test_cut_path_into_segments(settings, path, segments): """Cut a path into segments""" resolver = PathBreadcrumbResolver(settings.ROOT_URLCONF) assert resolver.cut(path) == segments @pytest.mark.parametrize("url,urlcurrent,urltitles", [ ( '/', 'Home', ['Home'] ), ( '/bar/', 'Bar', ['Home', 'Bar'] ), ( '/foo/invisible/chu/', 'Chu', ['Home', 'Foo', 'Chu'] ), ( '/foo/sluggy/plaf/', 'Sluggy', ['Home', 'Foo', 'Sluggy'] ), ( '/foo/date/2016/08/', 'Year month', ['Home', 'Foo', "Year month"] ), ( '/foo/sub/plop/', 'Plop', ['Home', 'Foo', 'Sub', 'Plop'] ), ( '/foo/controlled-true/yep/', 'Control Yep', ['Home', 'Foo', 'Controlled true', 'Control Yep'] ), ( '/foo/controlled-false/nope/', 'Control Nope', ['Home', 'Foo', 'Control Nope'] ), ]) def test_resolving_path(settings, rf, url, urlcurrent, urltitles): """Resolve breadcrumbs from a path""" # Forge a request object from url request = rf.get(url) resolver = PathBreadcrumbResolver(settings.ROOT_URLCONF) results = resolver.resolve(request.path, request=request) elements = results['autobreadcrumbs_elements'] current = results['autobreadcrumbs_current'] #print [str(item.title) for item in elements] assert [item.title for item in elements] == urltitles assert current.title == urlcurrent

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21773447f02a33a437fe0c12beffe1d346edd743

7,480

py

Python

mixer/api.py

ooojustin/mixer.py

9195ecb3f30772c0e982cd92a27f7f56ac05a2a8

[ "MIT" ]

3

2019-08-28T00:51:23.000Z

2020-03-26T01:19:19.000Z

mixer/api.py

ooojustin/mixer.py

9195ecb3f30772c0e982cd92a27f7f56ac05a2a8

[ "MIT" ]

null

mixer/api.py

ooojustin/mixer.py

9195ecb3f30772c0e982cd92a27f7f56ac05a2a8

[ "MIT" ]

null

import aiohttp import dateutil.parser import json from datetime import datetime, timezone, timedelta from enum import Enum from . import exceptions as MixerExceptions from .objects import MixerUser, MixerChannel class RequestMethod(Enum): GET = 0 POST = 1 class MixerAPI: API_URL = "https://mixer.com/api/v1" API_URL_V2 = "https://mixer.com/api/v2" def __init__(self, client_id, client_secret): self.client_id = client_id self.client_secret = client_secret self._session = aiohttp.ClientSession(headers = { "Client-ID": self.client_id }) async def close(self): await self._session.close() async def request(self, method, url, parse_json = False, **kwargs): # pick ... based on request type if method is RequestMethod.GET: ctx_mgr = self._session.get(url, **kwargs) elif method is RequestMethod.POST: data = kwargs.pop("data") ctx_mgr = self._session.post(url, json = data, **kwargs) async with ctx_mgr as response: text = await response.text() # handle specific response codes if response.status == 404: raise MixerExceptions.NotFound(text) if response.status != 200: raise MixerExceptions.WebException(response.status, text) try: # NOTE: the only exception that will be thrown here is in the .json() func return await response.json() if parse_json else text except: raise RuntimeError("Failed to parse json from response.") async def get(self, url, **kwargs): coro = self.request(RequestMethod.GET, url, **kwargs) return await coro async def post(self, url, data, **kwargs): kwargs["data"] = data coro = self.request(RequestMethod.POST, url, **kwargs) return await coro async def get_channel(self, id_or_token): """Retrieves a MixerChannel object from username or channel id. Args: id_or_token (str): Username (or id) of Mixer channel. Returns: :class:`mixer.objects.MixerChannel`: Channel information. """ url = "{}/channels/{}".format(self.API_URL, id_or_token) data = await self.get(url, parse_json = True) channel = MixerChannel(data) channel.set_api(self) return channel async def get_user(self, user_id): """Retrieves a MixerUser object from a user id. Args: user_id (int): The unique id of a Mixer user. Returns: :class:`mixer.objects.MixerUser`: User information. """ url = "{}/users/{}".format(self.API_URL, user_id) data = await self.get(url, parse_json = True) user = MixerUser(data) user.set_api(self) return user async def get_shortcode(self, scope = None): """Makes a request to begin shortcode oauth process. Args: scope (list): A list of scope/permissions to generate token with. Returns: dict: Information to proceed with shortcode oauth process.""" url = "{}/oauth/shortcode".format(self.API_URL) if scope is None: scope = list() data = { "client_id": self.client_id, "client_secret": self.client_secret, "scope": " ".join(scope) } response = await self.post(url, data, parse_json = True) return response async def check_shortcode(self, handle): """Check a shortcode handle to determine it's status. Args: str: Shortcode handle. Returns: dict: Shortcode status information. """ url = "{}/oauth/shortcode/check/{}".format(self.API_URL, handle) response = await self.get(url, parse_json = True) return response async def get_token(self, code_or_token, refresh = False): """Generate/refresh tokens. Args: code_or_token (str): Authorization code or refresh token. refresh (bool): Whether or not a refresh token is provided. Returns: dict: New token(s) + information from server. """ url = "{}/oauth/token".format(self.API_URL) data = { "client_id": self.client_id, "client_secret": self.client_secret } if refresh: data["grant_type"] = "refresh_token" data["refresh_token"] = code_or_token else: data["grant_type"] = "authorization_code" data["code"] = code_or_token response = await self.post(url, data, parse_json = True) return response # https://pastebin.com/n1Kjjphq async def check_token(self, token): """Gets information about an existing token. Args: token (str): An access token or a refresh token. """ url = "{}/oauth/token/introspect".format(self.API_URL) data = { "token": token } response = await self.post(url, data, parse_json = True) return response # https://pastebin.com/SEd6Y2Jz async def get_broadcast(self, channel_id): """Gets an active broadcast on a given chanel. Args: channel_id (int): Unique channel ID number. """ url = "{}/channels/{}/broadcast".format(self.API_URL, channel_id) response = await self.get(url, parse_json = True) return response async def get_uptime(self, channel_id): """Gets the uptime of a channels broadcast. Returns: datetime.timedelta: Duration of the active broadcast. None: If the broadcast is not currently online. """ # get broadcast and make sure it's online broadcast = await self.get_broadcast(channel_id) if "error" in broadcast or not broadcast["online"]: return None # determine the streams start time and current time started = dateutil.parser.parse(broadcast["startedAt"]) now = datetime.now(timezone.utc) # calculate delta and remove microseconds because they're insignificant delta = now - started delta = delta - timedelta(microseconds = delta.microseconds) return delta async def get_leaderboard(self, type, channel_id, limit = 10): # type format: [sparks, embers]-[weekly, monthly, yearly, alltime] url = "{}/leaderboards/{}/channels/{}?limit={}".format(self.API_URL_V2, type, channel_id, limit) response = await self.get(url, parse_json = True) return response async def get_chatters(self, channel_id): url = "{}/chats/{}/users".format(self.API_URL_V2, channel_id) response = await self.get(url, parse_json = True) return response async def get_user_services(self, oauth): # NOTE: requires "user:details:self" scope await oauth.ensure_active() url = "{}/users/{}/links".format(self.API_URL, oauth.user_id) response = await self.get(url, parse_json = True, headers = oauth.header) return response async def get_user_service(self, service, oauth): # NOTE: requires "user:details:self" scope services = await self.get_user_services(oauth) check = lambda d: d["service"] == service filtered = list(filter(check, services)) return filtered[0]["id"] if len(filtered) else None

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217a1c4d9a24091de6f28191afa50d26888bd024

3,983

py

Python

src/baxter_gui/sonar.py

HumaRobotics/baxter_evalutation_gui

eceea236286dd2548287c8df3bc9cae5800ca23e

[ "BSD-2-Clause" ]

2

2017-04-06T06:14:10.000Z

2017-10-05T22:35:58.000Z

src/baxter_gui/sonar.py

HumaRobotics/baxter_evalutation_gui

eceea236286dd2548287c8df3bc9cae5800ca23e

[ "BSD-2-Clause" ]

null

src/baxter_gui/sonar.py

HumaRobotics/baxter_evalutation_gui

eceea236286dd2548287c8df3bc9cae5800ca23e

[ "BSD-2-Clause" ]

2

2017-04-06T06:13:59.000Z

2017-11-06T12:31:53.000Z

#!/usr/bin/env python ########################################################################### # This software is graciously provided by HumaRobotics # under the Simplified BSD License on # github: # HumaRobotics is a trademark of Generation Robots. # www.humarobotics.com # Copyright (c) 2015, Generation Robots. # All rights reserved. # www.generationrobots.com # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # 1. Redistributions of source code must retain the above copyright notice, # this list of conditions and the following disclaimer. # # 2. Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation # and/or other materials provided with the distribution. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, # THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR # PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS # BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF # THE POSSIBILITY OF SUCH DAMAGE. # # The views and conclusions contained in the software and documentation are # those of the authors and should not be interpreted as representing official # policies, either expressed or implied, of the FreeBSD Project. # ############################################################################# import rospy from std_msgs.msg import UInt16 from sensor_msgs.msg import PointCloud from threading import Lock class Sonar: """ Enables or disables the sonar sensors """ def __init__(self): self.state = 0 self.distances = [None for x in xrange(12)] self.mutex = Lock() self.__sonar_pub = rospy.Publisher("/robot/sonar/head_sonar/set_sonars_enabled",UInt16, queue_size=1) self.__sonar_sub = rospy.Subscriber("/robot/sonar/head_sonar/state",PointCloud,self.callback,queue_size=1) def callback(self,msg): with self.mutex: self.distances = [None for x in xrange(12)] for channel in msg.channels: if channel.name == "SensorId": sensors = list(channel.values) if channel.name == "Distance": for i,sensor in enumerate(sensors): self.distances[int(sensor)] = channel.values[i] rospy.sleep(0.2) def getRanges(self): with self.mutex: return self.distances def checkConnection(self): """ Checks if baxter's sonars already subscribed to the publisher """ while not rospy.is_shutdown() and self.__sonar_pub.get_num_connections() < 1: # rospy.logwarn("No subscriber for sonar state found yet") rospy.sleep(0.01) rospy.loginfo("Found a subscriber. Changing sonar state") def enable(self): """ Enables all sonar sensors """ rospy.loginfo("Enable sonar") self.checkConnection() self.state = 4095 self.__sonar_pub.publish(4095) def disable(self): """ Disables all sonar sensors """ rospy.loginfo("Disable sonar") self.checkConnection() self.state = 0 self.__sonar_pub.publish(0) if __name__ == '__main__': rospy.init_node("baxter_sonar")

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null

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217a53f692aa8c3f0575201c87b0e50ccbd4ef2e

1,647

py

Python

Dynamic Programming/LongestCommonSubseq.py

Jh123x/COMP-550-Algorithms-and-Analysis

1bc0b7fb8c48dc52cf89704557795aa87290e88b

[ "MIT" ]

null

Dynamic Programming/LongestCommonSubseq.py

Jh123x/COMP-550-Algorithms-and-Analysis

1bc0b7fb8c48dc52cf89704557795aa87290e88b

[ "MIT" ]

null

Dynamic Programming/LongestCommonSubseq.py

Jh123x/COMP-550-Algorithms-and-Analysis

1bc0b7fb8c48dc52cf89704557795aa87290e88b

[ "MIT" ]

null

from typing import List # Define directions DIAGONOAL = (-1, -1) UP = (0, -1) LEFT = (-1, 0) def trace_table(backtrack_table, seq1, seq2) -> List[str]: """Trace back the backtrack table to find the longest common subsequence""" i, j = len(seq1)-1, len(seq2)-1 lcs = [] while i >= 0 and j >= 0: dx, dy = backtrack_table[i][j] if dx + dy == -2: lcs.append(seq1[i]) # break i += dx j += dy return lcs[::-1] def longest_common_subsequence(seq1, seq2): """Find the longest common subsequence between 2 strings""" count_table = [[0] * (len(seq2)+1) for _ in range(len(seq1)+1)] backtrack_table = [[(0, 0)] * (len(seq2)) for _ in range(len(seq1))] for i in range(1, len(seq1) + 1): for j in range(1, len(seq2) + 1): # If the word is found if (seq1[i-1] == seq2[j-1]): count_table[i][j] = count_table[i-1][j-1] + 1 backtrack_table[i-1][j-1] = DIAGONOAL # If left > up elif count_table[i-1][j] >= count_table[i][j-1]: count_table[i][j] = count_table[i-1][j] backtrack_table[i-1][j-1] = LEFT # Otherwise up else: count_table[i][j] = count_table[i][j-1] backtrack_table[i-1][j-1] = UP return count_table[-1][-1], trace_table(backtrack_table, seq1, seq2) if __name__ == '__main__': X = ["A", "T", "C", "A", "C", "C", "T", "A", "T", "C", "A", "C", "C", "T"] Y = ["A", "T", "A", "A", "C", "T", "A", "T", "A", "A", "C", "T"] print(longest_common_subsequence(X, Y))

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null

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null

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1

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217b4a4e53e620a12cb915d6303bb2ebe3952a08

1,787

py

Python

main.py

gyyang/bio-kvm

1db65ec3250879d16b7906edf4833670d170ec9c

[ "MIT" ]

2

2022-01-01T18:42:50.000Z

2022-03-13T16:20:12.000Z

main.py

gyyang/bio-kvm

1db65ec3250879d16b7906edf4833670d170ec9c

[ "MIT" ]

null

main.py

gyyang/bio-kvm

1db65ec3250879d16b7906edf4833670d170ec9c

[ "MIT" ]

1

2022-03-18T16:16:32.000Z

"""File that summarizes all key results. To train networks in a specific experiment, run in command line python main.py --train experiment_name To analyze results from this experiment python main.py --analyze experiment_name To train and analyze all models quickly, run in command line python main.py --train --analyze To reproduce the results from paper, run python main.py --train --analyze To analyze pretrained networks, run python main.py --analyze To run specific experiments (e.g. orn2pn, vary_pn), run python main.py --train --analyze --experiment orn2pn vary_pn """ import os import platform import sys import argparse from experiment_utils import train_experiment, analyze_experiment parser = argparse.ArgumentParser() parser.add_argument('-d', '--device', help='CUDA device number', default=0, type=int) parser.add_argument('-t', '--train', help='Training', nargs='+', default=[]) parser.add_argument('-a', '--analyze', help='Analyzing', nargs='+', default=[]) parser.add_argument('--no-general', help='No general analysis', dest='general', action='store_false') parser.set_defaults(general=True) args = parser.parse_args() # For running from IDE instead of command line if len(sys.argv) == 1: #no command line parameters passed args.train = [] #add experiments here args.analyze = [] for item in args.__dict__.items(): print(item) os.environ["CUDA_VISIBLE_DEVICES"] = str(args.device) experiments2train = args.train experiments2analyze = args.analyze use_cluster = 'columbia' in platform.node() # on columbia cluster for experiment in experiments2train: train_experiment(experiment, use_cluster=use_cluster) for experiment in experiments2analyze: analyze_experiment(experiment, general=args.general)

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217c9d04d50b6d94a1a684d457eb2c81ff82f9f5

1,216

py

Python

tests/gsfpy3_08/conftest.py

irewolepeter/gsfpy_USM_Implementation

c4614ac3f7d833eb86ea38c7708108b130f96612

[ "MIT" ]

7

2020-07-01T07:12:19.000Z

2022-01-20T20:39:57.000Z

tests/gsfpy3_08/conftest.py

irewolepeter/gsfpy_USM_Implementation

c4614ac3f7d833eb86ea38c7708108b130f96612

[ "MIT" ]

36

2020-06-23T09:10:15.000Z

2022-03-22T10:27:58.000Z

tests/gsfpy3_08/conftest.py

irewolepeter/gsfpy_USM_Implementation

c4614ac3f7d833eb86ea38c7708108b130f96612

[ "MIT" ]

2

2021-02-07T13:21:52.000Z

2021-06-24T19:16:16.000Z

import shutil from dataclasses import dataclass from enum import Enum, auto from pathlib import Path import pytest from pytest_cases import fixture_union class GsfVersion(Enum): V03_08 = auto() @dataclass(frozen=True) class GsfDatafile: gsf_version: GsfVersion path: Path num_beams: int GSF_03_08_DATAFILE = GsfDatafile( GsfVersion.V03_08, Path(__file__).parent.parent / "test_data" / "GSF3_08_test_file.gsf", num_beams=432, ) def _setup_gsf_test_data(src_datafile: GsfDatafile, tmp_path: Path): tmp_path.mkdir(parents=True, exist_ok=True) tmp_datafile_path = tmp_path / src_datafile.path.name shutil.copyfile(src_datafile.path, tmp_datafile_path) yield GsfDatafile( src_datafile.gsf_version, tmp_datafile_path, src_datafile.num_beams ) shutil.rmtree(tmp_path, ignore_errors=True) @pytest.fixture def gsf_test_data_03_08(tmp_path): yield from _setup_gsf_test_data(GSF_03_08_DATAFILE, tmp_path) fixture_union("gsf_test_data", [gsf_test_data_03_08]) @pytest.fixture(params=[GSF_03_08_DATAFILE]) def gsf_test_data(request, tmp_path): src_datafile: GsfDatafile = request.param yield from _setup_gsf_test_data(src_datafile, tmp_path)

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217cdf7669f321ac62a49a7990e683d0c9fcc359

3,041

py

Python

third_party/catapult/dashboard/dashboard/edit_test_owners.py

maidiHaitai/haitaibrowser

a232a56bcfb177913a14210e7733e0ea83a6b18d

[ "BSD-3-Clause" ]

1

2020-09-15T08:43:34.000Z

third_party/catapult/dashboard/dashboard/edit_test_owners.py

maidiHaitai/haitaibrowser

a232a56bcfb177913a14210e7733e0ea83a6b18d

[ "BSD-3-Clause" ]

null

third_party/catapult/dashboard/dashboard/edit_test_owners.py

maidiHaitai/haitaibrowser

a232a56bcfb177913a14210e7733e0ea83a6b18d

[ "BSD-3-Clause" ]

null

# Copyright 2015 The Chromium Authors. All rights reserved. # Use of this source code is governed by a BSD-style license that can be # found in the LICENSE file. """Provides the web interface for adding and removing test owners.""" import json from google.appengine.api import users from dashboard import request_handler from dashboard import test_owner from dashboard import xsrf class EditTestOwnersHandler(request_handler.RequestHandler): """Handles rendering and editing test owners.""" def get(self): """Renders the UI for editing owners. If user is an admin, renders UI with all test suite path and its owners, otherwise renders UI with a list test suite path for the logged in user. """ user = users.get_current_user() if user: if users.is_current_user_admin(): owner_json = self._GetAllOwnerDataJson() else: owner_json = self._GetOwnerDataForUserJson(user) else: self.RenderHtml('result.html', { 'errors': ['Log in to edit test owners.']}) return self.RenderHtml('edit_test_owners.html', {'owner_info': owner_json}) @xsrf.TokenRequired def post(self): """Handles updates of test owners.""" user = users.get_current_user() if not user: self.ReportError('Must be logged in to edit test owners.', status=403) return action = self.request.get('action') test_suite_path = self.request.get('item') if not action or not test_suite_path: self.ReportError('Missing required parameters.', status=403) return owner_email = self.request.get('sub_item') if not users.is_current_user_admin(): owner_email = user.email() test_suite_path = str(test_suite_path) owner_email = str(owner_email) if owner_email else None try: test_owner.ValidateTestSuitePath(test_suite_path) test_owner.ValidateOwnerEmail(owner_email) except ValueError as error: self.ReportError(error.message, status=400) return if action == 'add': test_owner.AddOwner(test_suite_path, owner_email) else: test_owner.RemoveOwner(test_suite_path, owner_email) self.response.out.write('{}') def _GetOwnerDataForUserJson(self, user): """Returns json list of owner data for a user.""" results = [] owner_email = user.email() test_suite_paths = test_owner.GetTestSuitePaths(owner_email) for test_suite_path in sorted(test_suite_paths): results.append({ 'name': test_suite_path, }) return json.dumps(results) def _GetAllOwnerDataJson(self): """Returns json list of all owner data.""" owner_dict = test_owner.GetMasterCachedOwner() results = [] for test_suite_path in sorted(owner_dict): owners = owner_dict[test_suite_path] item = { 'name': test_suite_path, 'sub_items': [] } for owner in owners: item['sub_items'].append({ 'name': owner }) results.append(item) return json.dumps(results)

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null

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217dcf9e3ff97f5c6cd6b8522780f4c3bc9a75ab

1,604

py

Python

google/cloud/websecurityscanner/v1beta/websecurityscanner-v1beta-py/google/cloud/websecurityscanner_v1beta/types/crawled_url.py

googleapis/googleapis-gen

d84824c78563d59b0e58d5664bfaa430e9ad7e7a

[ "Apache-2.0" ]

7

2021-02-21T10:39:41.000Z

2021-12-07T07:31:28.000Z

google/cloud/websecurityscanner/v1beta/websecurityscanner-v1beta-py/google/cloud/websecurityscanner_v1beta/types/crawled_url.py

googleapis/googleapis-gen

d84824c78563d59b0e58d5664bfaa430e9ad7e7a

[ "Apache-2.0" ]

6

2021-02-02T23:46:11.000Z

2021-11-15T01:46:02.000Z

google/cloud/websecurityscanner/v1beta/websecurityscanner-v1beta-py/google/cloud/websecurityscanner_v1beta/types/crawled_url.py

googleapis/googleapis-gen

d84824c78563d59b0e58d5664bfaa430e9ad7e7a

[ "Apache-2.0" ]

4

2021-01-28T23:25:45.000Z

2021-08-30T01:55:16.000Z

# -*- coding: utf-8 -*- # Copyright 2020 Google LLC # # 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 proto # type: ignore __protobuf__ = proto.module( package='google.cloud.websecurityscanner.v1beta', manifest={ 'CrawledUrl', }, ) class CrawledUrl(proto.Message): r"""A CrawledUrl resource represents a URL that was crawled during a ScanRun. Web Security Scanner Service crawls the web applications, following all links within the scope of sites, to find the URLs to test against. Attributes: http_method (str): The http method of the request that was used to visit the URL, in uppercase. url (str): The URL that was crawled. body (str): The body of the request that was used to visit the URL. """ http_method = proto.Field( proto.STRING, number=1, ) url = proto.Field( proto.STRING, number=2, ) body = proto.Field( proto.STRING, number=3, ) __all__ = tuple(sorted(__protobuf__.manifest))

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0.227273

0

null

0

null

0

1

0

218172bb8e01270d10bfb5ce4012d602f95f1b03

4,173

py

Python

src/Chapters/Chapter6.py

Fennec2000GH/Python-Crash-Course

421072c13ec4527405297af5fc7920d963cf0fda

[ "MIT" ]

null

src/Chapters/Chapter6.py

Fennec2000GH/Python-Crash-Course

421072c13ec4527405297af5fc7920d963cf0fda

[ "MIT" ]

null

src/Chapters/Chapter6.py

Fennec2000GH/Python-Crash-Course

421072c13ec4527405297af5fc7920d963cf0fda

[ "MIT" ]

null

# create dictionary alien_0 = {'color': 'green', 'points': 5} print(alien_0['color']) print(alien_0['points']) # assign variable to value in dictionary new_points = alien_0['points'] print(f'You just earned {new_points} points!') print(alien_0) # adding new key-value pair alien_0['x_position'] = 0 alien_0['y_position'] = 25 print(alien_0) # starting with empty dictionary alien_0 = {} alien_0['color'] = 'green' alien_0['points'] = 5 print(alien_0) # modifying values in dictionary alien_0['color'] = 'green' print(f"The alien is {alien_0['color']}.") alien_0['color'] = 'yellow' print(f"The alien is now {alien_0['color']}.") # more practice alien_0 = {'x_position': 0, 'y_position': 25, 'speed': 'medium'} print(f"Original position: {alien_0['x_position']}") # Move the alien to the right. # Determine how far to move the alien based on its current speed. if alien_0['speed'] == 'slow': x_increment = 1 elif alien_0['speed'] == 'medium': x_increment = 2 else: # This must be a fast alien. x_increment = 3 # The new position is the old position plus the increment. v alien_0['x_position'] = alien_0['x_position'] + x_increment print(f"New position: {alien_0['x_position']}") alien_0['speed'] = 'fast' # removing key-value pairs alien_0 = {'color': 'green', 'points': 5} print(alien_0) del alien_0['points'] print(alien_0) # dictionary of similar objects favorite_languages = { 'jen': 'python', 'sarah': 'c', 'edward': 'ruby', 'phil': 'python' } language = favorite_languages['sarah'].title() print(f"Sarah's favorite language is {language}.") # accesssing values in dictionary alien_0 = {'color': 'green', 'speed': 'slow'} point_value = alien_0.get('points', 'No point values assigned.') print(point_value) # looping through dictionary # both keys and values user_0 = { 'username': 'efermi', 'first': 'enrico', 'last': 'fermi' } for key, value in user_0.items(): print(f'\nKey: {key}') print(f'Value: {value}') for name, language in favorite_languages.items(): print(f"{name.title()}'s favorite language is {language.title()}.") # keys only for name in favorite_languages.keys(): print(name.title()) friends = ['phil', 'sarah'] for name in favorite_languages.keys(): print(name.title()) if name in friends: language = favorite_languages[name].title() print(f'\t{name.title()}, I see you love {language}!') if 'erin' not in favorite_languages.keys(): print('Erin, please take our poll!') # sorted keys for name in sorted(favorite_languages.keys()): print(f'{name.title()}, thank you for taking the poll.') # values only print("The following languages have been mentioned:") for language in favorite_languages.values(): print(language.title()) # using set for language in set(favorite_languages.values()): print(language.title()) # nesting # list of dictionaries alien_0 = {'color': 'green', 'points': 5} alien_1 = {'color': 'yellow', 'points': 10} alien_2 = {'color': 'red', 'points': 15} aliens = [alien_0, alien_1, alien_2] for alien in aliens: print(alien) # list in dicitonary pizza = { 'crust': 'thick', 'toppings': ['mushrooms', 'extra cheese'] } print(f"You ordered a {pizza['crust']}-crust pizza with the following toppings:") for topping in pizza['toppings']: print("\t" + topping) favorite_languages = { 'jen': ['python', 'ruby'], 'sarah': ['c'], 'edward': ['ruby', 'go'], 'phil': ['python', 'haskell'] } for name, languages in favorite_languages.items(): print(f"\n{name.title()}'s favorite languages are:") for language in languages: print(f"\t{language.title()}") # dictionary in dictionary users = { 'aeinstein': { 'first': 'albert', 'last': 'einstein', 'location': 'princeton' }, 'mcurie': { 'first': 'marie', 'last': 'curie', 'location': 'paris' } } for username, user_info in users.items(): print(f"\nUsername: {username}") full_name = f"{user_info['first']}" f"{user_info['last']}" location = user_info['location'] print(f"\tFull name: {full_name.title()}") print(f"\tLocation: {location.title()}")

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0.3

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null

0

null

0

1

0

2181c2135fcc99d7c1b53305b3e3de21dd2e227e

6,936

py

Python

MWW-inventory/cooperage.py

ProsperousHeart/Training-Events

71aa6fae8e77f61924619407f34c39b3ace17fe4

[ "MIT" ]

6

2019-04-17T17:41:36.000Z

2021-02-10T20:50:59.000Z

MWW-inventory/cooperage.py

ProsperousHeart/Training-Events

71aa6fae8e77f61924619407f34c39b3ace17fe4

[ "MIT" ]

2

2018-11-27T06:33:06.000Z

2019-02-20T04:35:49.000Z

MWW-inventory/cooperage.py

ProsperousHeart/Personal-Projects

71aa6fae8e77f61924619407f34c39b3ace17fe4

[ "MIT" ]

3

2019-04-23T03:26:55.000Z

2020-04-17T14:38:39.000Z

import cooper_item import logging # https://docs.python.org/3/library/logging.html import pprint import table_mod as tables from money import Money # create_item = cooperidge_item.CooperageItem # https://docs.python.org/3/library/logging.html#logrecord-attributes #fmt = '%(asctime)s | %(levelname)s %(funcname)s at line %(lineno)s: %(message)s' #logging.basicConfig(format=fmt, filename='CooperAppLogs.log', filemode='w', # level=logging.DEBUG, datefmt='%m/%d/%Y %I:%M:%S %p') # fmt = '%(asctime)s | %(levelname)s - %(module)s line %(lineno)d, %(funcName)s: %(message)s' fmt = '%(asctime)s | %(levelname)s - %(module)s.py [%(lineno)d]: %(message)s' # fmt = '%(created)f | %(levelname)s: %(message)s' logging.basicConfig(format=fmt, filename='CooperAppLogs.log', filemode='w', level=logging.DEBUG, datefmt='%m/%d/%Y %I:%M:%S %p') # removing filemode will append logs vs overwrite logger = logging.getLogger(__name__) pp = pprint.PrettyPrinter(indent=4) # base_wood = [{'at_cost': Money(amount=3.65, currency='USA'), base_wood = [{'at_cost': round(3.65, 2), 'hard_vs_soft': 'medium', # 'markup': Money(amount=0.00, currency='USA'), 'markup': 0.00, 'vip_bool': False, 'wood_name': 'birch'}, # {'at_cost': Money(amount=2.50, currency='USA'), {'at_cost': round(2.50, 2), 'hard_vs_soft': 'hard', # 'markup': Money(amount=0.00, currency='USA'), 'markup': round(0.00, 2), 'vip_bool': False, 'wood_name': 'oak'}, # {'at_cost': Money(amount=2.25, currency='USA'), {'at_cost': round(2.25, 2), 'hard_vs_soft': 'soft', # 'markup': Money(amount=0.00, currency='USA'), 'markup': round(0.00, 2), 'vip_bool': False, 'wood_name': 'pine'}] class entry_exit(object): """ This decorator will add logging before and after calling a function. http://python-3-patterns-idioms-test.readthedocs.io/en/latest/PythonDecorators.html#slightly-more-useful """ def __init__(self, func): self.func = func def __call__(self): logger.debug('Starting {} for cooper_item.py file'.format(self.func.__name__)) self.func() logger.debug('Completing {} for cooper_item.py file'.format(self.func.__name__)) # return entry_exit # @entry_exit def create_piece(item_num): """ This function creates a new Cooperidge inventory item. """ new_item = cooper_item.CooperageItem(sum_num=item_num) # logger.debug('Successfully created item {}!'.format(new_item.itemNum)) logger.debug('Returning new cooper_item via create_piece()') # return create_item() return new_item # Create options # - create new cooper_item (cup, sign, furniture, etc) # - create new location (where they take the cups to) def db_manip(sql_cnxn, to_do:str, data, table_name:str=None): """ This function takes in a string (db_name), string (table_name) and data (object). It will then create a new data structure appropriate for the requested table. Current table names: - wood - items """ logger.warning('Check and ensure incoming parameters match expectations') if to_do.lower() == 'add': logger.info('Adding item to DB table: {}'.format(table_name)) tables.add_to_table(sql_cnxn, table_name, data) else: logger.error('Called on a function not yet written!') # viewing options # - all items # - just cups # - just Furniture # - just signs # - wood inventory # - sold # - to sell # - what items are in what location # update cooperage cooperidge_item: # - add final piece number # - update artwork # - update price sold # - update notes # - update _completion_date # - update handle side, staves #, size if Wood_Cup # exportation ability # - export to Excel locally # - export to Google excel # also looking for ability to backup DB locally??? if __name__ == "__main__": """ This function ensures that if this file is run, it is read as a source file. """ logger.debug('Starting __init__ for cooper_app.py file') logger.warning('Need to create a different DB log for JUST changes...') logger.warning('All setter methods will eventually need to also update the DB') # Database needs ... # - DB check & create if none found # - add to DB # - update in DB (per piece, as well as wood resources) logger.debug('Checking for local DB...') sql_cnxn = tables.create_connection('cooperage') tables.check_4_tbls(sql_cnxn, base_wood, cooper_item.CooperageItem()) # item_list = [] piece_num = 1 logger.info('Creating new item...') item = create_piece(piece_num) logger.info('New item created!') print('New item created - adding to DB!') # pp.pprint(item.get_dict()) # replacing internal data pieces with adding to a DB # item_list.append(item) logger.debug('Calling sql_cnxn() for ADD...') # db_manip(sql_cnxn, 'add', 'items', item) rtn_tuple = tables.add_to_table(sql_cnxn, 'items', item.get_txtDict()) if rtn_tuple[0] == True: print('ERROR: There was an error adding item to the DB.') piece_num += 1 print('===' * 5) logger.info('Updating at_cost...') logger.warning('Need to update DB not just local variable.') # item.at_cost(4.69) item.at_cost = 4.69 print("Updated data after changing at_cost to $4.69:\n{}".format(pprint.pformat(item.get_dict()))) print('===' * 5) logger.info('Creating new item...') item = create_piece(piece_num) print('New item created - adding to DB!') # pp.pprint(item.get_dict()) # replacing internal data pieces with adding to a DB # item_list.append(item) logger.debug('Calling sql_cnxn() for ADD...') # db_manip(sql_cnxn, 'add', 'items', item) rtn_tuple = tables.add_to_table(sql_cnxn, 'items', item.get_txtDict()) if rtn_tuple[0] == True: print('ERROR: There was an error adding item to the DB.') piece_num += 1 print('===' * 5) # print("Hardnesses: {}".format([item.woodType.hard_vs_soft for item in item_list])) # print('Need to get Hardnesses of all wood of items in DB') logger.warning('Need to get Hardnesses of all wood of items in DB') # print("Wood Type: {}".format([item.woodType.wood_name for item in item_list])) # print('Need to get names of all wood type of items in DB') logger.warning('Need to get names of all wood type of items in DB') logger.debug('Starting tests for SQL DB calls...') print('Printing full DB:') tables.print_tables(sql_cnxn, log_list=True) sql_cnxn.close() logger.debug('Completing __init__ for cooper_app.py file')

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null

0

null

0

1

0

21827a2ebb42f575b8b66013cc122ecef6030ccc

1,667

py

Python

homework03/main.py

PatrickKalkman/pirplepython

d622ce4541e679d63d56115e3c6b21d39564993c

[ "MIT" ]

null

homework03/main.py

PatrickKalkman/pirplepython

d622ce4541e679d63d56115e3c6b21d39564993c

[ "MIT" ]

null

homework03/main.py

PatrickKalkman/pirplepython

d622ce4541e679d63d56115e3c6b21d39564993c

[ "MIT" ]

null

""" Python Is Easy course @Pirple.com Homework Assignment #3: "If" Statements Patrick Kalkman / patrick@simpletechture.nl Details: Create a function that accepts 3 parameters and checks for equality between any two of them. Your function should return True if 2 or more of the parameters are equal, and false is none of them are equal to any of the others. Extra Credit: Modify your function so that strings can be compared to integers if they are equivalent. For example, if the following values are passed to your function: 6,5,"5" You should modify it so that it returns true instead of false. Hint: there's a built in Python function called "int" that will help you convert strings to Integers. """ def areTwoOrMoreInputsEqual(param1, param2, param3): convertedParam1 = int(param1) convertedParam2 = int(param2) convertedParam3 = int(param3) if convertedParam1 == convertedParam2: return True if convertedParam1 == convertedParam3: return True if convertedParam2 == convertedParam3: return True return False result = areTwoOrMoreInputsEqual(1, 2, 3) print(f"(1,2,3) should return False and is {result}") result = areTwoOrMoreInputsEqual(1, 1, 3) print(f"(1,1,3) should return True and is {result}") result = areTwoOrMoreInputsEqual(1, 2, 2) print(f"(1,2,2) should return True and is {result}") result = areTwoOrMoreInputsEqual(1, 2, 1) print(f"(1,2,1) should return True and is {result}") result = areTwoOrMoreInputsEqual(1, 1, 1) print(f"(1,1,1) should return True and is {result}") result = areTwoOrMoreInputsEqual(1, 2, "1") print(f"(1,2,'1') should return True and is {result}")

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null

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null

0

1

0

21898a7d154a88e2d3cffd20cd2b52c26a47aa0f

4,201

py

Python

compiler.py

Hyper5phere/c-minus-compiler

2b111f98c9f2b0ba7473f169e91e5ef7373c1929

[ "MIT" ]

20

2020-09-20T22:58:58.000Z

2022-03-28T06:35:32.000Z

compiler.py

Hyper5phere/c-minus-compiler

2b111f98c9f2b0ba7473f169e91e5ef7373c1929

[ "MIT" ]

null

compiler.py

Hyper5phere/c-minus-compiler

2b111f98c9f2b0ba7473f169e91e5ef7373c1929

[ "MIT" ]

15

2020-10-04T10:56:10.000Z

2022-03-28T10:11:46.000Z

import os import sys import time import argparse import subprocess as sp script_dir = os.path.dirname(os.path.abspath(__file__)) sys.path.insert(0, os.path.join(script_dir, "modules")) from cparser import Parser from scanner import Scanner, SymbolTableManager from semantic_analyser import SemanticAnalyser from code_gen import CodeGen, MemoryManager # Maximal virtual memory for compiled program process (in bytes). MAX_VIRTUAL_MEMORY = 50 * 1024 * 1024 # 50 MB def limit_virtual_memory(): import resource resource.setrlimit(resource.RLIMIT_AS, (MAX_VIRTUAL_MEMORY, MAX_VIRTUAL_MEMORY)) def compile(args): print("Compiling", args.source_file) SymbolTableManager.init() MemoryManager.init() parser = Parser(args.source_file) start = time.time() parser.parse() stop = time.time() - start print(f"Compilation took {stop:.6f} s") if not SymbolTableManager.error_flag: print("Compilation successful!") else: print("Compilation failed due to the following errors:\n") print(parser.scanner.lexical_errors) print(parser.syntax_errors) print(parser.semantic_analyzer.semantic_errors) if args.abstract_syntax_tree: parser.save_parse_tree() if args.symbol_table: parser.scanner.save_symbol_table() if args.tokens: parser.scanner.save_tokens() if args.error_files: parser.save_syntax_errors() parser.scanner.save_lexical_errors() parser.semantic_analyzer.save_semantic_errors() parser.code_generator.save_output() if args.run and not SymbolTableManager.error_flag: print("Executing compiled program") if os.name == "nt": tester_file = os.path.join(script_dir, "interpreter", "tester_Windows.exe") elif os.name == "posix": tester_file = os.path.join(script_dir, "interpreter", "tester_Linux.out") else: tester_file = os.path.join(script_dir, "interpreter", "tester_Mac.out") output_file = os.path.join(script_dir, "output", "output.txt") output_dir = os.path.dirname(output_file) if os.path.exists(output_file): preexec_fn = limit_virtual_memory if os.name != "nt" else None stderr = sp.PIPE if not args.verbose else None start = time.time() try: tester_output = sp.check_output(tester_file, cwd=output_dir, stderr=stderr, timeout=10, preexec_fn=preexec_fn).decode("utf-8") except sp.TimeoutExpired: print("RuntimeError: Execution timed out!") else: if not args.verbose: tester_output = "\n".join([line.replace("PRINT", "").strip() for line in tester_output.splitlines() if line.startswith("PRINT")]) stop = time.time() - start print(f"Execution took {stop:.6f} s") print("Program output:") print(tester_output) if __name__ == "__main__": parser = argparse.ArgumentParser(description='Simple C Compiler written in Python') parser.add_argument("source_file", help="Path to C source file.") parser.add_argument('-r', '--run', action='store_true', help='Run the output program after compilation.') parser.add_argument('-v', '--verbose', action='store_true', help='Print all used three address codes.') parser.add_argument('-ef', '--error-files', action='store_true', help='Save compilation errors to text files.') parser.add_argument('-ast', '--abstract-syntax-tree', action='store_true', help='Save abstract syntax tree into a text file.') parser.add_argument('-st', '--symbol-table', action='store_true', help='Save symbol table into a text file.') parser.add_argument('-t', '--tokens', action='store_true', help='Save lexed tokens into a text file.') args = parser.parse_args() if not os.path.isabs(args.source_file): args.source_file = os.path.abspath(script_dir) args = parser.parse_args() compile(args)

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null

0

null

0

1

0

218a8e1accf1fbbcec9a82434ec70681538880eb

4,902

py

Python

app/main/views.py

adhoadhi/PITCH

e0e02adf32966bcc622aad4b732e7a7d2db041e5

[ "Unlicense" ]

null

app/main/views.py

adhoadhi/PITCH

e0e02adf32966bcc622aad4b732e7a7d2db041e5

[ "Unlicense" ]

null

app/main/views.py

adhoadhi/PITCH

e0e02adf32966bcc622aad4b732e7a7d2db041e5

[ "Unlicense" ]

null

from flask import render_template, request, redirect, url_for, abort from . import main from flask_login import login_required, current_user from ..models import User, PitchCategory, Pitches, Comments from .forms import UpdateProfile, PitchForm, CommentForm, CategoriesForm from .. import db, photos @main.route('/') def index(): """View root page function that returns index page and the various news sources""" title = 'Home- Welcome to the Pitch Website' categories = PitchCategory.get_categories() return render_template('index.html', title=title, categories=categories) # Route for adding a new pitch @main.route('/category/pitch/new/<int:id>', methods=['GET', 'POST']) @login_required def new_pitch(id): ''' Function to check Pitches form ''' form = PitchForm() category = PitchCategory.query.filter_by(id=id).first() if category is None: abort(404) if form.validate_on_submit(): actual_pitch = form.content.data new_pitch = Pitches(actual_pitch=actual_pitch, user_id=current_user.id, category_id=category.id) new_pitch.save_pitch() return redirect(url_for('.category', id=category.id)) return render_template('new_pitch.html', pitch_form=form, category=category) # Routes for displaying the different pitches @main.route('/category/new',methods=['GET','POST']) @login_required def new_category(): form = CategoriesForm() if form.validate_on_submit(): name = form.name.data new_category = PitchCategory(name=name) new_category.save_category() return redirect(url_for('.index')) title = 'New Pitch Category' return render_template('new_category.html',categories_form=form) @main.route('/category/<int:id>') def category(id): ''' category route function returns a list of pitches in the category chosen ''' category = PitchCategory.query.get(id) if category is None: abort(404) pitches = Pitches.get_pitches(id) return render_template('category.html', category=category, pitches=pitches) @main.route('/pitch/<int:id>', methods=['GET', 'POST']) @login_required def single_pitch(id): ''' Function the returns a single pitch for comment to be added ''' pitches = Pitches.query.get(id) if pitches is None: abort(404) comment = Comments.get_comments(id) return render_template('pitch.html', pitches=pitches, comment=comment) # Routes for user authentication @main.route('/user/<uname>') @login_required def profile(uname): user = User.query.filter_by(username=uname).first() if user is None: abort(404) return render_template("profile/profile.html", user=user) @main.route('/user/<uname>/update', methods=['GET', 'POST']) @login_required def update_profile(uname): user = User.query.filter_by(username=uname).first() if user is None: abort(404) form = UpdateProfile() if form.validate_on_submit(): user.bio = form.bio.data db.session.add(user) db.session.commit() return redirect(url_for('.profile', uname=user.username)) return render_template('profile/update.html', form=form) @main.route('/user/<uname>/update/pic', methods=['POST']) @login_required def update_pic(uname): user = User.query.filter_by(username=uname).first() if 'photo' in request.files: filename = photos.save(request.files['photo']) path = f'photos/{filename}' user.profile_pic_path = path db.session.commit() return redirect(url_for('main.profile', uname=uname)) # Route to add commments. @main.route('/pitch/new/<int:id>', methods=['GET', 'POST']) @login_required def new_comment(id): ''' Function that returns a list of comments for the particular pitch ''' form = CommentForm() pitches = Pitches.query.filter_by(id=id).first() if pitches is None: abort(404) if form.validate_on_submit(): comment_id = form.comment_id.data new_comment = Comments(comment_id=comment_id, user_id=current_user.id, pitches_id=pitches.id) new_comment.save_comment() return redirect(url_for('.category', id=pitches.category_id)) return render_template('comment.html', comment_form=form) @main.route('/like/<id>') @login_required def like(id): if Likes.query.filter(Likes.users_id==current_user.id,Likes.post_id==id).first(): return url_for('main.new_pitch',id=Likes.post_id) Likes(users_id=current_user.id).save() return url_for('main.new_pitch',id=Likes.post_id) @main.route('/dislike/<id>') @login_required def dislike(id): if Dislikes.query.filter(Dislikes.users_id==current_user.id,Dislikes.post_id==id).first(): return url_for('main.new_pitch',id=Dislikes.post_id) Dislikes(users_id=current_user.id,post_id=id).save() return url_for('main.new_pitch',id=Dislikes.post_id)

29.709091

91

0.695838

669

4,902

4.946188

0.16293

0.029918

0.043518

0.036265

0.365065

0.297975

0.22061

0.174977

0.1644

0.139015

0

0.00443

0.171155

4,902

164

92

29.890244

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0.311927

0

null

0

null

0

1

0

218b6c65cc4bb546fc7b94bf21f63dd98d3f4aef

859

py

Python

shifts/urls.py

Beshutaf/shift-report

d8784ba3901fcbbbe20b31d7b9435d8eb808bdc5

[ "MIT" ]

null

shifts/urls.py

Beshutaf/shift-report

d8784ba3901fcbbbe20b31d7b9435d8eb808bdc5

[ "MIT" ]

null

shifts/urls.py

Beshutaf/shift-report

d8784ba3901fcbbbe20b31d7b9435d8eb808bdc5

[ "MIT" ]

null

from django.conf.urls import include, url from django.contrib import admin from django.template.response import TemplateResponse from django.views.generic import TemplateView, RedirectView from django.contrib.staticfiles.views import serve from shifts import settings urlpatterns = [ url(r'^$', TemplateView.as_view(template_name='index.html'), name="home"), url(r'^', include('shift_report.urls')), url(r'^favicon.ico$', RedirectView.as_view(url=settings.STATIC_URL + 'favicon.ico')), url(r'^robots.txt$', lambda request: TemplateResponse( request, template='robots.txt', content_type='text/plain', ) ), url(r'^admin/', admin.site.urls), # url('^', include('django.contrib.auth.urls')), ] if settings.DEBUG: urlpatterns += [ url(r'^(?P<path>.*)$', serve), ]

29.62069

89

0.657742

103

859

5.427184

0.456311

0.042934

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0

0.187427

859

29

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null

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null

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0

218dc3061d5bd2699c54d77094a85e1b5564ed76

3,388

py

Python

src/spinnaker_ros_lsm/venv/lib/python2.7/site-packages/pyNN/neuron/nineml.py

Roboy/LSM_SpiNNaker_MyoArm

04fa1eaf78778edea3ba3afa4c527d20c491718e

[ "BSD-3-Clause" ]

2

2020-11-01T13:22:11.000Z

2020-11-01T13:22:20.000Z

src/spinnaker_ros_lsm/venv/lib/python2.7/site-packages/pyNN/neuron/nineml.py

Roboy/LSM_SpiNNaker_MyoArm

04fa1eaf78778edea3ba3afa4c527d20c491718e

[ "BSD-3-Clause" ]

null

src/spinnaker_ros_lsm/venv/lib/python2.7/site-packages/pyNN/neuron/nineml.py

Roboy/LSM_SpiNNaker_MyoArm

04fa1eaf78778edea3ba3afa4c527d20c491718e

[ "BSD-3-Clause" ]

null

""" Support cell types defined in 9ML with NEURON. Requires the 9ml2nmodl script to be on the path. Classes: NineMLCell - a single neuron instance NineMLCellType - base class for cell types, not used directly Functions: nineml_cell_type - return a new NineMLCellType subclass Constants: NMODL_DIR - subdirectory to which NMODL mechanisms will be written :copyright: Copyright 2006-2013 by the PyNN team, see AUTHORS. :license: CeCILL, see LICENSE for details. """ from __future__ import absolute_import # Not compatible with Python 2.4 import subprocess import neuron from pyNN.nineml.cells import join, _add_prefix, _build_nineml_celltype, NineMLCellType import logging import os h = neuron.h logger = logging.getLogger("PyNN") NMODL_DIR = "nineml_mechanisms" class NineMLCell(object): def __init__(self, **parameters): self.type = parameters.pop("type") self.source_section = h.Section() self.source = getattr(h, self.type.model_name)(0.5, sec=self.source_section) for param, value in parameters.items(): setattr(self.source, param, value) # for recording self.spike_times = h.Vector(0) self.traces = {} self.recording_time = False def __getattr__(self, name): try: return self.__getattribute__(name) except AttributeError: if name in self.type.synapse_types: return self.source # source is also target else: raise AttributeError("'NineMLCell' object has no attribute or synapse type '%s'" % name) def record(self, active): if active: rec = h.NetCon(self.source, None) rec.record(self.spike_times) else: self.spike_times = h.Vector(0) def memb_init(self): # this is a bit of a hack for var in self.type.recordable: if hasattr(self, "%s_init" % var): initial_value = getattr(self, "%s_init" % var) logger.debug("Initialising %s to %g" % (var, initial_value)) setattr(self.source, var, initial_value) def _compile_nmodl(nineml_component, weight_variables): # weight variables should really be within component """ Generate NMODL code for the 9ML component, run "nrnivmodl" and then load the mechanisms into NEURON. """ if not os.path.exists(NMODL_DIR): os.makedirs(NMODL_DIR) cwd = os.getcwd() os.chdir(NMODL_DIR) xml_file = "%s.xml" % nineml_component.name logger.debug("Writing NineML component to %s" % xml_file) nineml_component.write(xml_file) nineml2nmodl = __import__("9ml2nmodl") nineml2nmodl.write_nmodl(xml_file, weight_variables) # weight variables should really come from xml file p = subprocess.check_call(["nrnivmodl"]) os.chdir(cwd) neuron.load_mechanisms(NMODL_DIR) def nineml_cell_type(name, neuron_model, port_map={}, weight_variables={}, **synapse_models): """ Return a new NineMLCellType subclass. """ return _build_nineml_celltype(name, (NineMLCellType,), {'neuron_model': neuron_model, 'synapse_models': synapse_models, 'port_map': port_map, 'weight_variables': weight_variables})

33.88

108

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413

3,388

5.104116

0.394673

0.033207

0.019924

0.042695

0.091082

0.060721

0

0.008839

0.265348

3,388

99

109

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0

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0

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0

1

0.103448

false

0

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0

0.293103

0

null

0

null

0

1

0

218e022befdbcc3b099f59fc82691e5701aaf7ed

693

py

Python

Network/perf.py

LunarWatcher/NN-chatbot-legacy

50e6d124cb3dbab7d79be3cc61b1c4aa45cbcc68

[ "Apache-2.0" ]

null

Network/perf.py

LunarWatcher/NN-chatbot-legacy

50e6d124cb3dbab7d79be3cc61b1c4aa45cbcc68

[ "Apache-2.0" ]

null

Network/perf.py

LunarWatcher/NN-chatbot-legacy

50e6d124cb3dbab7d79be3cc61b1c4aa45cbcc68

[ "Apache-2.0" ]

null

from time import * def ifOrTuple(): boolVal = False t = time() for i in range(10000000): "test" if boolVal else "testFalse" print("Average: {}".format(time() - t)) combined = 0.0 t = time() for i in range(10000000): ("testFalse", "test")[boolVal] print("Average: {}".format(time() - t)) def updateOrManual(): t = time() x = {} for i in range(10000000): x[i] = i print("Average: {}".format(time() - t)) t = time() for i in range(10000000): x.update({i: i}) print("Average: {}".format(time() - t)) if __name__ == "__main__": ifOrTuple() print("####") updateOrManual() print("####")

21

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0.532394

0.402817

0.343662

0

0.068136

0.279942

693

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false

0

0.037037

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0.111111

0.222222

0

null

0

null

0

1

0

218ed007c0e968a23bde2bf831865f422fd7eba5

5,404

py

Python

loris/app/views/fly.py

gucky92/loris

6f54b7d791d473f52690380d71da0acc0352d954

[ "MIT" ]

1

2021-08-01T02:02:54.000Z

loris/app/views/fly.py

gucky92/loris

6f54b7d791d473f52690380d71da0acc0352d954

[ "MIT" ]

null

loris/app/views/fly.py

gucky92/loris

6f54b7d791d473f52690380d71da0acc0352d954

[ "MIT" ]

3

2020-03-31T10:26:46.000Z

2021-08-02T00:12:54.000Z

"""fly specific views """ import os from flask import render_template, request, flash, url_for, redirect, \ send_from_directory, session from functools import wraps from flask_login import current_user, login_user, login_required, logout_user import datajoint as dj import pandas as pd from loris import config from loris.app.app import app from loris.app.templates import form_template, joined_table_template from loris.app.forms.dynamic_form import DynamicForm from loris.app.forms.fixed import ( dynamic_jointablesform, dynamic_settingstableform, LoginForm, PasswordForm, dynamic_tablecreationform ) from loris.app.utils import draw_helper, get_jsontable, user_has_permission from loris.utils import save_join from loris.app.login import User from loris.database.users import grantuser, change_password from loris.io import string_load, string_dump @app.route('/genotype', methods=['GET', 'POST']) @login_required def genotype(): schema = 'subjects' table = 'FlyGenotype' subtable = None edit_url = url_for('genotype') overwrite_url = url_for('genotype') return form_template( schema, table, subtable, edit_url, overwrite_url, page='genotype', override_permissions=True ) @app.route('/stock', methods=['GET', 'POST']) @login_required def stock(): schema = 'subjects' table = 'FlyStock' subtable = None edit_url = url_for('stock') overwrite_url = url_for('stock') return form_template( schema, table, subtable, edit_url, overwrite_url, page='stock', join_tables=[getattr(config['schemata'][schema], 'FlyGenotype')], joined_name='stockgenotype', override_permissions=True, ) @app.route('/cross', methods=['GET', 'POST']) @login_required def cross(): schema = 'subjects' table = 'FlyCross' subtable = None edit_url = url_for('cross') overwrite_url = url_for('cross') load_url = url_for('crossload') return form_template( schema, table, subtable, edit_url, overwrite_url, page='cross', join_tables=[getattr(config['schemata'][schema], 'FlyGenotype')], joined_name='crossgenotype', load_url=load_url ) @app.route('/crossload', methods=['GET', 'POST']) @login_required def crossload(): _id = string_load(request.args.get('_id', string_dump(None))) if _id is None or not isinstance(_id, dict) or 'cross_id' not in _id: flash('No cross_id was given for loading FlyCross', 'error') return redirect(url_for('cross')) # combine tables cross_table = getattr(config['schemata']['subjects'], 'FlyCross') genotype_table = getattr(config['schemata']['subjects'], 'FlyGenotype') # fetch data joined_table = save_join([cross_table, genotype_table]) data = (joined_table & _id).fetch1() image = data['cross_schema'] if image is not None: image = os.path.abspath(image) return render_template( 'pages/crossload.html', cross_id=_id['cross_id'], image=image, experimenter=data['experimenter'], chromosome=f"{data['chr1']}; {data['chr2']}; {data['chr3']}", comments=data['comments'] ) @app.route('/entersubject', methods=['GET', 'POST']) @login_required def entersubject(): schema = 'subjects' table = 'FlySubject' subtable = None edit_url = url_for('entersubject') overwrite_url = url_for('entersubject') return form_template( schema, table, subtable, edit_url, overwrite_url, page='entersubject', join_tables=[getattr(config['schemata'][schema], 'FlyGenotype')], joined_name='subjectgenotype' ) @app.route('/stockgenotype', methods=['GET', 'POST']) @login_required def stockgenotype(): """join various tables in the database """ delete_url = url_for( 'delete', schema='subjects', table='FlyStock', subtable=None) return joined_table_template( ['subjects.fly_genotype', 'subjects.fly_stock'], 'Stock + Genotype Table', 'stock', edit_url=url_for('stock'), delete_url=delete_url ) @app.route('/crossgenotype', methods=['GET', 'POST']) @login_required def crossgenotype(): """join various tables in the database """ delete_url = url_for( 'delete', schema='subjects', table='FlyCross', subtable=None) return joined_table_template( ['subjects.fly_genotype', 'subjects.fly_cross'], 'Cross + Genotype Table', 'cross', edit_url=url_for('cross'), load_url=url_for('crossload'), delete_url=delete_url ) @app.route('/subjectgenotype', methods=['GET', 'POST']) @login_required def subjectgenotype(): """join various tables in the database """ delete_url = url_for( 'delete', schema='subjects', table='FlySubject', subtable=None) return joined_table_template( ['subjects.fly_genotype', 'subjects.fly_subject'], 'Subject + Genotype Table', 'entersubject', edit_url=url_for('entersubject'), delete_url=delete_url ) @app.route('/stockcrossgenotype', methods=['GET', 'POST']) @login_required def stockcrossgenotype(): """join various tables in the database """ return joined_table_template( ['subjects.fly_genotype', 'subjects.fly_stock', 'subjects.fly_cross'], 'Stock + Cross + Genotype Table', '#', )

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0.5

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0

0.000921

0.196151

5,404

186

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0.803867

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false

0.014184

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0

0.248227

0

null

0

null

0

1

0

219016ed9395191a9d92995122f8e4aa0387fd7a

555

py

Python

coroutines/coroutine_finally.py

lisboaxd/python_shards

42c28288da7f08565de43a2f118216c28c07ce92

[ "MIT" ]

null

coroutines/coroutine_finally.py

lisboaxd/python_shards

42c28288da7f08565de43a2f118216c28c07ce92

[ "MIT" ]

null

coroutines/coroutine_finally.py

lisboaxd/python_shards

42c28288da7f08565de43a2f118216c28c07ce92

[ "MIT" ]

null

class DemoException(Exception): '''A kind of exception to demonstrate''' def demonstrate_exc_finally(): print('-> coroutine started') try: while True: try: x = yield except DemoException: print('*** DemoExcepetion handled. Continuining...') else: print('-> Coroutine receied: {!r}'.format(x)) finally: print('-> coroutine ending') coro_finally = demonstrate_exc_finally() next(coro_finally) coro_finally.send(20) coro_finally.send(50)

24.130435

68

0.590991

55

555

5.818182

0.6

0.1375

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0

0.010152

0.29009

555

22

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0.80203

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false

0

0.117647

0.235294

0

null

0

null

0

1

0

21938c91ae398fd3303b1f93d2ae749d6ad8be19

640

py

Python

Lab Tests/2019.1 Lab Test/Solutions/q2a.py

alphatrl/IS111

f09fc47f5321dc4d79b9dde97399109e2a368443

[ "MIT" ]

null

Lab Tests/2019.1 Lab Test/Solutions/q2a.py

alphatrl/IS111

f09fc47f5321dc4d79b9dde97399109e2a368443

[ "MIT" ]

null

Lab Tests/2019.1 Lab Test/Solutions/q2a.py

alphatrl/IS111

f09fc47f5321dc4d79b9dde97399109e2a368443

[ "MIT" ]

null

def get_multiples_of(num_list, n): ''' This function returns the number of numbers in num_list that are multiples of n. If there is no number in num_list that is a multiple of n, the function returns 0. Parameters: - num_list, a list of positive integers; list may be empty - n, a positive integer ''' # write your answer between #start and #end #start number_multiples = 0 if len(num_list) == 0: return number_multiples for number in num_list: if number % n == 0: number_multiples += 1 return number_multiples #end

26.666667

87

0.6125

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640

4.175824

0.450549

0.110526

0.071053

0.068421

0

0.011655

0.329688

640

24

88

26.666667

0.874126

0.496875

0

0.25

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1

0.125

false

0

0.375

0

null

0

null

0

1

0

2195736268951f73a7aaddba531d710135929ebd

8,230

py

Python

nipyapi/nifi/models/connection_diagnostics_snapshot_dto.py

Jimvin/nipyapi

826beac376d4321bd2d69491f09086474c7e7bfb

[ "Apache-2.0" ]

199

2017-08-24T12:19:41.000Z

2022-03-20T14:50:17.000Z

nipyapi/nifi/models/connection_diagnostics_snapshot_dto.py

Jimvin/nipyapi

826beac376d4321bd2d69491f09086474c7e7bfb

[ "Apache-2.0" ]

275

2017-08-28T21:21:49.000Z

2022-03-29T17:57:26.000Z

nipyapi/nifi/models/connection_diagnostics_snapshot_dto.py

Jimvin/nipyapi

826beac376d4321bd2d69491f09086474c7e7bfb

[ "Apache-2.0" ]

73

2017-09-07T10:13:56.000Z

2022-02-28T10:37:21.000Z

# coding: utf-8 """ NiFi Rest API The Rest API provides programmatic access to command and control a NiFi instance in real time. Start and stop processors, monitor queues, query provenance data, and more. Each endpoint below includes a description, definitions of the expected input and output, potential response codes, and the authorizations required to invoke each service. OpenAPI spec version: 1.15.0 Contact: dev@nifi.apache.org Generated by: https://github.com/swagger-api/swagger-codegen.git """ from pprint import pformat from six import iteritems import re class ConnectionDiagnosticsSnapshotDTO(object): """ NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. """ """ Attributes: swagger_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. """ swagger_types = { 'total_flow_file_count': 'int', 'total_byte_count': 'int', 'node_identifier': 'str', 'local_queue_partition': 'LocalQueuePartitionDTO', 'remote_queue_partitions': 'list[RemoteQueuePartitionDTO]' } attribute_map = { 'total_flow_file_count': 'totalFlowFileCount', 'total_byte_count': 'totalByteCount', 'node_identifier': 'nodeIdentifier', 'local_queue_partition': 'localQueuePartition', 'remote_queue_partitions': 'remoteQueuePartitions' } def __init__(self, total_flow_file_count=None, total_byte_count=None, node_identifier=None, local_queue_partition=None, remote_queue_partitions=None): """ ConnectionDiagnosticsSnapshotDTO - a model defined in Swagger """ self._total_flow_file_count = None self._total_byte_count = None self._node_identifier = None self._local_queue_partition = None self._remote_queue_partitions = None if total_flow_file_count is not None: self.total_flow_file_count = total_flow_file_count if total_byte_count is not None: self.total_byte_count = total_byte_count if node_identifier is not None: self.node_identifier = node_identifier if local_queue_partition is not None: self.local_queue_partition = local_queue_partition if remote_queue_partitions is not None: self.remote_queue_partitions = remote_queue_partitions @property def total_flow_file_count(self): """ Gets the total_flow_file_count of this ConnectionDiagnosticsSnapshotDTO. Total number of FlowFiles owned by the Connection :return: The total_flow_file_count of this ConnectionDiagnosticsSnapshotDTO. :rtype: int """ return self._total_flow_file_count @total_flow_file_count.setter def total_flow_file_count(self, total_flow_file_count): """ Sets the total_flow_file_count of this ConnectionDiagnosticsSnapshotDTO. Total number of FlowFiles owned by the Connection :param total_flow_file_count: The total_flow_file_count of this ConnectionDiagnosticsSnapshotDTO. :type: int """ self._total_flow_file_count = total_flow_file_count @property def total_byte_count(self): """ Gets the total_byte_count of this ConnectionDiagnosticsSnapshotDTO. Total number of bytes that make up the content for the FlowFiles owned by this Connection :return: The total_byte_count of this ConnectionDiagnosticsSnapshotDTO. :rtype: int """ return self._total_byte_count @total_byte_count.setter def total_byte_count(self, total_byte_count): """ Sets the total_byte_count of this ConnectionDiagnosticsSnapshotDTO. Total number of bytes that make up the content for the FlowFiles owned by this Connection :param total_byte_count: The total_byte_count of this ConnectionDiagnosticsSnapshotDTO. :type: int """ self._total_byte_count = total_byte_count @property def node_identifier(self): """ Gets the node_identifier of this ConnectionDiagnosticsSnapshotDTO. The Node Identifier that this information pertains to :return: The node_identifier of this ConnectionDiagnosticsSnapshotDTO. :rtype: str """ return self._node_identifier @node_identifier.setter def node_identifier(self, node_identifier): """ Sets the node_identifier of this ConnectionDiagnosticsSnapshotDTO. The Node Identifier that this information pertains to :param node_identifier: The node_identifier of this ConnectionDiagnosticsSnapshotDTO. :type: str """ self._node_identifier = node_identifier @property def local_queue_partition(self): """ Gets the local_queue_partition of this ConnectionDiagnosticsSnapshotDTO. The local queue partition, from which components can pull FlowFiles on this node. :return: The local_queue_partition of this ConnectionDiagnosticsSnapshotDTO. :rtype: LocalQueuePartitionDTO """ return self._local_queue_partition @local_queue_partition.setter def local_queue_partition(self, local_queue_partition): """ Sets the local_queue_partition of this ConnectionDiagnosticsSnapshotDTO. The local queue partition, from which components can pull FlowFiles on this node. :param local_queue_partition: The local_queue_partition of this ConnectionDiagnosticsSnapshotDTO. :type: LocalQueuePartitionDTO """ self._local_queue_partition = local_queue_partition @property def remote_queue_partitions(self): """ Gets the remote_queue_partitions of this ConnectionDiagnosticsSnapshotDTO. :return: The remote_queue_partitions of this ConnectionDiagnosticsSnapshotDTO. :rtype: list[RemoteQueuePartitionDTO] """ return self._remote_queue_partitions @remote_queue_partitions.setter def remote_queue_partitions(self, remote_queue_partitions): """ Sets the remote_queue_partitions of this ConnectionDiagnosticsSnapshotDTO. :param remote_queue_partitions: The remote_queue_partitions of this ConnectionDiagnosticsSnapshotDTO. :type: list[RemoteQueuePartitionDTO] """ self._remote_queue_partitions = remote_queue_partitions def to_dict(self): """ Returns the model properties as a dict """ result = {} for attr, _ in iteritems(self.swagger_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value return result def to_str(self): """ Returns the string representation of the model """ return pformat(self.to_dict()) def __repr__(self): """ For `print` and `pprint` """ return self.to_str() def __eq__(self, other): """ Returns true if both objects are equal """ if not isinstance(other, ConnectionDiagnosticsSnapshotDTO): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """ Returns true if both objects are not equal """ return not self == other

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0.066434

0.539239

0.470474

0.423854

0.281274

0.239899

0.192696

0

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0.281896

8,230

235

479

35.021277

0.869712

0.402916

0

0.074468

0

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0.050374

0

1

0.170213

false

0

0.031915

0

0.351064

0.010638

0

null

0

null

0

1

0

2195ef8dc9eef8e0680f99fe7fef7e19264acba5

11,726

py

Python

blue_st_examples/read_sensors.py

cchangeur/BlueSTSDK_Python

e5c6e4bc5a58680bad0d867633dd9d92012b9baf

[ "BSD-3-Clause" ]

null

blue_st_examples/read_sensors.py

cchangeur/BlueSTSDK_Python

e5c6e4bc5a58680bad0d867633dd9d92012b9baf

[ "BSD-3-Clause" ]

null

blue_st_examples/read_sensors.py

cchangeur/BlueSTSDK_Python

e5c6e4bc5a58680bad0d867633dd9d92012b9baf

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python # IMPORT from __future__ import print_function import sys import os import time from abc import abstractmethod from blue_st_sdk.manager import Manager from blue_st_sdk.manager import ManagerListener from blue_st_sdk.node import NodeListener from blue_st_sdk.feature import FeatureListener from blue_st_sdk.features.audio.adpcm.feature_audio_adpcm import FeatureAudioADPCM from blue_st_sdk.features.audio.adpcm.feature_audio_adpcm_sync import FeatureAudioADPCMSync # PRECONDITIONS # # In case you want to modify the SDK, clone the repository and add the location # of the "BlueSTSDK_Python" folder to the "PYTHONPATH" environment variable. # # On Linux: # export PYTHONPATH=/home/<user>/BlueSTSDK_Python # CONSTANTS # Presentation message. INTRO = """############## # Start Gyro # ##############""" # Bluetooth Scanning time in seconds (optional). SCANNING_TIME_s = 2 #5 # Mac adress to auto connect MAC_AUTO_CONNEXION = "cd:26:fb:e4:6d:f1" # Feature to auto start : "Temperature", "Humidity", "Pressure", "Magnetometer", "Gyroscope", "Accelerometer", "Proximity", "Audio & Sync", "Switch" FEATURE_AUTO_START = ["Gyroscope", "Proximity"] # or ["Temperature", "Humidity", "Pressure"] or [] to use manualy # FUNCTIONS # # Printing intro. # def print_intro(): print('\n' + INTRO + '\n') # INTERFACES # # Implementation of the interface used by the Manager class to notify that a new # node has been discovered or that the scanning starts/stops. # class MyManagerListener(ManagerListener): # # This method is called whenever a discovery process starts or stops. # # @param manager Manager instance that starts/stops the process. # @param enabled True if a new discovery starts, False otherwise. # def on_discovery_change(self, manager, enabled): print('[+] Discovery %s.' % ('started' if enabled else 'stopped')) if not enabled: print() # # This method is called whenever a new node is discovered. # # @param manager Manager instance that discovers the node. # @param node New node discovered. # def on_node_discovered(self, manager, node): print('[+] New device discovered: %s.' % (node.get_name())) # # Implementation of the interface used by the Node class to notify that a node # has updated its status. # class MyNodeListener(NodeListener): # # To be called whenever a node connects to a host. # # @param node Node that has connected to a host. # def on_connect(self, node): print('[+] Device %s connected.' % (node.get_name())) # # To be called whenever a node disconnects from a host. # # @param node Node that has disconnected from a host. # @param unexpected True if the disconnection is unexpected, False otherwise # (called by the user). # def on_disconnect(self, node, unexpected=False): print('[+] Device %s disconnected%s.' % \ (node.get_name(), ' unexpectedly' if unexpected else '')) if unexpected: # Exiting. print('\n[+] Exiting...\n') sys.exit(0) # # Implementation of the interface used by the Feature class to notify that a # feature has updated its data. # class MyFeatureListener(FeatureListener): # # To be called whenever the feature updates its data. # # @param feature Feature that has updated. # @param sample Data extracted from the feature. # def on_update(self, feature, sample): print(feature) #TODO data output (fifo ? ZMQ ?) : timestamp = sample.get_timestamp() if feature.get_name() == "Temperature": out_temp = sample.get_data() elif feature.get_name() == "Humidity": out_temp = sample.get_data() elif feature.get_name() == "Pressure": out_temp = sample.get_data() elif feature.get_name() == "Magnetometer": out_temp_x = sample.get_data()[0] out_temp_y = sample.get_data()[1] out_temp_z = sample.get_data()[2] elif feature.get_name() == "Gyroscope": out_temp_x = sample.get_data()[0] out_temp_y = sample.get_data()[1] out_temp_z = sample.get_data()[2] elif feature.get_name() == "Accelerometer": out_temp_x = sample.get_data()[0] out_temp_y = sample.get_data()[1] out_temp_z = sample.get_data()[2] elif feature.get_name() == "Proximity": out_temp = sample.get_data() elif feature.get_name() == "Audio & Sync": pass elif feature.get_name() == "Switch": pass # MAIN APPLICATION # # Main application. # def main(argv): # Printing intro. print_intro() try: # Creating Bluetooth Manager. manager = Manager.instance() manager_listener = MyManagerListener() manager.add_listener(manager_listener) while True: discovered_devices_once = [] no_connect = True no_feature_select = True feature_selected = [] # Asynchronous discovery of Bluetooth devices. print('[+] Scanning Bluetooth devices...\n') manager.start_discovery() timeout = time.time() + SCANNING_TIME_s while no_connect: time.sleep(0.01) # Getting discovered devices. discovered_devices = manager.get_nodes() i = 1 for device in discovered_devices: if device.get_tag() not in discovered_devices_once: print('[+] %s: [%s]' % (device.get_name(), device.get_tag())) discovered_devices_once.append(device.get_tag()) # Autoconnection management if device.get_tag() == MAC_AUTO_CONNEXION: print('[+] Device MAC address match') no_connect = False choice = i i += 1 # Timeout management if time.time() > timeout: break manager.stop_discovery() # Selecting a device. while no_connect: print('[+] Available Bluetooth devices:') i = 1 for device in discovered_devices: print('[+] %d) %s: [%s]' % (i, device.get_name(), device.get_tag())) i += 1 choice = int(input("\nSelect a device to connect to (\'0\' to quit): ")) if choice >= 0 and choice <= len(discovered_devices): no_connect = False if choice == 0: # Exiting. manager.remove_listener(manager_listener) print('[+] Exiting...\n') sys.exit(0) device = discovered_devices[choice - 1] node_listener = MyNodeListener() device.add_listener(node_listener) # Connecting to the device. print('[+] Connecting to %s...' % (device.get_name())) if not device.connect(): print('[+] Connection failed.\n') continue while True: # Getting features. features = device.get_features() print('\n[+] Features:') i = 1 for feature in features: if feature.get_name() in FEATURE_AUTO_START: print('[+] Feature matching - %s' % (feature.get_name())) choice = i feature_selected.append(i) no_feature_select = False if isinstance(feature, FeatureAudioADPCM): audio_feature = feature print('[+] %d,%d) %s' % (i,i+1, "Audio & Sync")) i+=1 elif isinstance(feature, FeatureAudioADPCMSync): audio_sync_feature = feature else: print('[+] %d) %s' % (i, feature.get_name())) i+=1 # Selecting a feature. while no_feature_select: choice = int(input('\nSelect a feature ''(\'0\' to disconnect): ')) if choice >= 0 and choice <= len(features): feature_selected.append(choice) no_feature_select = False if len(feature_selected) == 0: # Disconnecting from the device. print('\n[+] Disconnecting from %s...' % (device.get_name())) if not device.disconnect(): print('[+] Disconnection failed.\n') continue device.remove_listener(node_listener) # Resetting discovery. manager.reset_discovery() # Going back to the list of devices. break for feature_id in feature_selected: feature = features[feature_id - 1] # Enabling notifications. feature_listener = MyFeatureListener() feature.add_listener(feature_listener) device.enable_notifications(feature) # Handling audio case (both audio features have to be enabled). if isinstance(feature, FeatureAudioADPCM): audio_sync_feature_listener = MyFeatureListener() audio_sync_feature.add_listener(audio_sync_feature_listener) device.enable_notifications(audio_sync_feature) elif isinstance(feature, FeatureAudioADPCMSync): audio_feature_listener = MyFeatureListener() audio_feature.add_listener(audio_feature_listener) device.enable_notifications(audio_feature) # Getting notifications. while True: device.wait_for_notifications(10) #TODO break management # TODO : clean disabling (array of feature/feature_listener) # Disabling notifications. device.disable_notifications(feature) feature.remove_listener(feature_listener) # Handling audio case (both audio features have to be disabled). if isinstance(feature, FeatureAudioADPCM): device.disable_notifications(audio_sync_feature) audio_sync_feature.remove_listener(audio_sync_feature_listener) elif isinstance(feature, FeatureAudioADPCMSync): device.disable_notifications(audio_feature) audio_feature.remove_listener(audio_feature_listener) except KeyboardInterrupt: try: # Exiting. print('\n[+] Exiting...\n') sys.exit(0) except SystemExit: os._exit(0) if __name__ == "__main__": main(sys.argv[1:])

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0.148649

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0.147727

0

null

0

null

0

1

0

219815a06b42358ab44f5f67029d88d169f0dbbc

9,934

py

Python

mnnpy/utils.py

dbrg77/mnnpy

b6fd65aa6c7e2ad72308ba10da810e805b79298f

[ "BSD-3-Clause" ]

null

mnnpy/utils.py

dbrg77/mnnpy

b6fd65aa6c7e2ad72308ba10da810e805b79298f

[ "BSD-3-Clause" ]

null

mnnpy/utils.py

dbrg77/mnnpy

b6fd65aa6c7e2ad72308ba10da810e805b79298f

[ "BSD-3-Clause" ]

1

2018-10-16T01:56:34.000Z

import math import numpy as np from multiprocessing import Pool from scipy.spatial import cKDTree from scipy.linalg import orth from scipy.linalg.interpolative import svd as rsvd from scipy.sparse import issparse from numba import jit, float32, int32, int8 from . import settings from .irlb import lanczos @jit(float32[:](float32[:, :]), nogil=True) def l2_norm(in_matrix): return np.linalg.norm(x=in_matrix, axis=1) @jit(float32[:, :](float32[:, :], float32[:, :]), nogil=True) def scale_rows(in_matrix, scale_vector): return np.divide(in_matrix, scale_vector) @jit(float32[:, :](float32[:, :], float32[:, :])) def kdist(m, n): dist = np.zeros((m.shape[0], n.shape[0]), dtype=np.float32) for i in range(m.shape[0]): for j in range(n.shape[0]): dist[i, j] = np.dot(m[i], n[j]) return dist def transform_input_data(datas, cos_norm_in, cos_norm_out, var_index, var_subset, n_jobs): datas = [data.toarray().astype(np.float32) if issparse(data) else data.astype(np.float32) for data in datas] if var_index is None: raise ValueError('Argument var_index not provideed.') if var_subset is not None: if set(var_subset) - set(var_index) != set(): raise ValueError('Some items in var_subset are not in var_index.') do_subset = True if set(var_index) == set(var_subset): do_subset = False else: do_subset = False same_set = cos_norm_in == cos_norm_out and not do_subset if do_subset: var_sub_index = [list(var_index).index(var) for var in var_subset] in_batches = [data[:, var_sub_index] for data in datas] else: var_sub_index = None in_batches = datas if settings.normalization == 'parallel': with Pool(n_jobs) as p_n: in_scaling = p_n.map(l2_norm, in_batches) else: in_scaling = [l2_norm(b) for b in in_batches] in_scaling = [scaling[:, None] for scaling in in_scaling] if cos_norm_in: if settings.normalization == 'parallel': with Pool(n_jobs) as p_n: in_batches = p_n.starmap(scale_rows, zip(in_batches, in_scaling)) else: in_batches = [scale_rows(a,b) for (a,b) in zip(in_batches, in_scaling)] if cos_norm_out: if not cos_norm_in: if settings.normalization == 'parallel': with Pool(n_jobs) as p_n: out_batches = p_n.starmap(scale_rows, zip(datas, in_scaling)) else: out_batches = [scale_rows(a,b) for (a,b) in zip(datas, in_scaling)] else: if settings.normalization == 'parallel': with Pool(n_jobs) as p_n: out_scaling = p_n.map(l2_norm, datas) else: out_scaling = [l2_norm(d) for d in datas] out_scaling = [scaling[:, None] for scaling in out_scaling] if settings.normalization == 'parallel': with Pool(n_jobs) as p_n: out_batches = p_n.starmap(scale_rows, zip(datas, out_scaling)) else: out_batches = [scale_rows(a,b) for (a,b) in zip(datas, out_scaling)] return in_batches, out_batches, var_sub_index, same_set @jit((float32[:, :], float32[:, :], int8, int8, int8)) def find_mutual_nn(data1, data2, k1, k2, n_jobs): k_index_1 = cKDTree(data1).query(x=data2, k=k1, n_jobs=n_jobs)[1] k_index_2 = cKDTree(data2).query(x=data1, k=k2, n_jobs=n_jobs)[1] mutual_1 = [] mutual_2 = [] for index_2 in range(data2.shape[0]): for index_1 in k_index_1[index_2]: if index_2 in k_index_2[index_1]: mutual_1.append(index_1) mutual_2.append(index_2) return mutual_1, mutual_2 @jit(float32[:, :](float32[:, :], float32[:, :], int32[:], int32[:], float32[:, :], float32)) def compute_correction(data1, data2, mnn1, mnn2, data2_or_raw2, sigma): vect = data1[mnn1] - data2[mnn2] mnn_index, mnn_count = np.unique(mnn2, return_counts=True) vect_reduced = np.zeros((data2.shape[0], vect.shape[1]), dtype=np.float32) for index, ve in zip(mnn2, vect): vect_reduced[index] += ve vect_avg = np.divide(vect_reduced[mnn_index], mnn_count.astype(np.float32)[:, None]) exp_distance = np.exp(-kdist(data2_or_raw2, data2_or_raw2[mnn_index]) / sigma) density = np.sum(exp_distance[mnn_index], axis=0) mult = np.divide(exp_distance, density) total_prob = np.sum(mult, axis=1, keepdims=True) output = np.dot(mult, vect_avg) return np.divide(output, total_prob) def svd_internal(mat, nu, svd_mode, **kwargs): mat = mat.astype(np.float64) if svd_mode == 'svd': svd_out = rsvd(mat, eps_or_k=nu, rand=False) elif svd_mode == 'rsvd': svd_out = rsvd(mat, eps_or_k=nu) elif svd_mode == 'irlb': svd_out = lanczos(mat, nu, **kwargs) else: raise ValueError('The svd_mode must be one of \'rsvd\', \'svd\', \'irlb\'.') return svd_out[0].astype(np.float32), svd_out[1].astype(np.float32), svd_out[2].astype(np.float32) def find_shared_subspace(mat1, mat2, sin_thres=0.05, cos_thres=1 / math.sqrt(2), mat2_vec=False, assume_orthonomal=False, get_angle=True): if mat2_vec: mat2 = mat2[:, None] if not assume_orthonomal: mat1 = orth(mat1) mat2 = orth(mat2) cross_prod = np.dot(mat1.T, mat2) singular = np.linalg.svd(cross_prod) shared = sum(singular[1] > sin_thres) if not get_angle: return None, shared costheta = min(singular[1]) if costheta < cos_thres: theta = math.acos(min(1, costheta)) else: if mat1.shape[1] < mat2.shape[1]: sintheta = np.linalg.norm(x=mat1 - np.dot(mat2, cross_prod.T), ord=2) else: sintheta = np.linalg.norm(x=mat2.T - np.dot(mat1, cross_prod), ord=2) theta = math.asin(min(1, sintheta)) return 180 * theta / math.pi, shared def get_bio_span(exprs, ndim, svd_mode, var_subset=None, **kwargs): centred = exprs - np.mean(exprs, axis=0) if var_subset is not None: subsetter = [True] * centred.shape[1] keeper = [False] * centred.shape[1] for i in var_subset: subsetter[i] = False keeper[i] = True leftovers = centred[:, subsetter].T centred = centred[:, keeper] ndim = min(ndim, *centred.shape) singular = svd_internal(centred.T, ndim, svd_mode, **kwargs) if var_subset is None: return singular[0] output = np.zeros((exprs.shape[1], ndim), dtype=np.float32) output[keeper,] = singular[0] output[subsetter,] = np.divide(np.dot(leftovers, singular[2]), singular[1][range(ndim)]) return output def subtract_bio(*spans, correction, var_subset=None): for span in spans: if var_subset is None: bio_mag = np.dot(correction, span) else: bio_mag = np.dot(correction[:, var_subset], span[var_subset, :]) bio_comp = np.dot(bio_mag, span.T) correction -= bio_comp return correction def adjust_shift_variance(data1, data2, correction, sigma, n_jobs, var_subset=None): if var_subset is not None: vect = correction[:, var_subset] data1 = data1[:, var_subset] data2 = data2[:, var_subset] else: vect = correction with Pool(n_jobs) as p_n: scaling = p_n.starmap(adjust_v_worker(data1, data2, sigma), zip(data2, vect), chunksize=int(data2.shape[0]/n_jobs) + 1) scaling = np.fmax(scaling, 1).astype(np.float32) return correction * scaling[:, None] @jit(float32(float32[:, :], float32[:, :], float32[:], float32[:], float32), nogil=True) def adjust_s_variance(data1, data2, curcell, curvect, sigma): distance1 = np.zeros((data1.shape[0], 2), dtype=np.float32) l2_norm = np.linalg.norm(curvect) grad = np.divide(curvect, l2_norm) curproj = np.dot(grad, curcell) prob2 = 0. totalprob2 = 0. for samecell in data2: sameproj = np.dot(grad, samecell) samedist = sq_dist_to_line(curcell, grad, samecell) sameprob = np.exp(-samedist / sigma) if sameproj <= curproj: prob2 += sameprob totalprob2 += sameprob prob2 /= totalprob2 totalprob1 = 0. for other in range(data1.shape[0]): othercell = data1[other] distance1[other, 0] = np.dot(grad, othercell) otherdist = sq_dist_to_line(curcell, grad, othercell) weight = np.exp(-otherdist / sigma) distance1[other, 1] = weight totalprob1 += weight distance1 = distance1[distance1[:, 0].argsort()] target = prob2 * totalprob1 cumulative = 0. ref_quan = distance1[-1, 0] for i in distance1: cumulative += i[1] if cumulative > target: ref_quan = i[0] break return (ref_quan - curproj) / l2_norm @jit(float32(float32[:], float32[:], float32[:]), nopython=True) def sq_dist_to_line(ref, grad, point): working = ref - point scale = np.dot(working, grad) working = working - grad * scale return np.dot(working, working) class adjust_v_worker(object): def __init__(self, data1, data2, sigma): self.d1 = data1 self.d2 = data2 self.s2 = sigma def __call__(self, curcell, curvect): return adjust_s_variance(self.d1, self.d2, curcell, curvect, self.s2) def get_so_paths(dir_name): dir_name = os.path.join(os.path.dirname(__file__), dir_name) list_dir = os.listdir(dir_name) if os.path.isdir(dir_name) else [] return [os.path.join(dir_name, so_name) for so_name in list_dir if so_name.split('.')[-1] in ['so', 'pyd']] try: from ._utils import _adjust_shift_variance as adjust_shift_variance #print('Cython module loaded!') except ImportError: print('Cython module _utils not initialized. Fallback to python.') pass

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2199fec3c4a94bc3ee9c0ccd244406c9f8fdfe01

15,408

py

Python

pycharmers/utils/soup_utils.py

iwasakishuto/Py-utils

5dc1e7d676811c2239e64f500b734bd508335256

[ "MIT" ]

2

2019-11-13T11:59:30.000Z

2019-11-17T15:44:09.000Z

pycharmers/utils/soup_utils.py

iwasakishuto/Py-utils

5dc1e7d676811c2239e64f500b734bd508335256

[ "MIT" ]

1

2020-09-12T18:00:30.000Z

2020-09-12T18:00:50.000Z

pycharmers/utils/soup_utils.py

iwasakishuto/Py-utils

5dc1e7d676811c2239e64f500b734bd508335256

[ "MIT" ]

1

2021-04-13T16:19:26.000Z

#coding: utf-8 import re import requests from bs4 import BeautifulSoup from .generic_utils import str_strip, handleKeyError def str2soup(string): """Convert strings to soup, and removed extra tags such as ``<html>``, ``<body>``, and ``<head>``. Args: string (str) : strings Returns: bs4.BeautifulSoup : A data structure representing a parsed HTML or XML document. Examples: >>> from pycharmers.utils import str2soup >>> string = "<title>Python-Charmers</title>" >>> type(string) str >>> soup = str2soup(string) >>> soup <title>Python-Charmers</title> >>> type(soup) bs4.BeautifulSoup >>> from bs4 import BeautifulSoup >>> BeautifulSoup(string) <html><head><title>Python-Charmers</title></head></html> """ soup = BeautifulSoup(markup=string, features="html5lib") for attr in ["html", "body", "head"]: if hasattr(soup, attr) and getattr(soup, attr) is not None: getattr(soup, attr).unwrap() return soup def split_section(section, name=None, attrs={}, recursive=True, text=None, **kwargs): """ Split ``bs4.BeautifulSoup``. Args: section (bs4.BeautifulSoup) : A data structure representing a parsed HTML or XML document. name (str) : A filter on tag name. attrs (dict) : A dictionary of filters on attribute values. recursive (bool) : If this is True, ``.find`` will perform a recursive search of this PageElement's children. Otherwise, only the direct children will be considered. text (str) : An inner text. kwargs (dict) : A dictionary of filters on attribute values. Returns: list : A list of elements without filter tag elements. Examples: >>> from bs4 import BeautifulSoup >>> from pycharmers.utils import split_section >>> section = BeautifulSoup(\"\"\" ... <section> ... <div> ... <h2>Title</h2> ... <div> ... <p>aaaaaaaaaaaaaaaaaaaaaa</p> ... <div> ... <img/> ... </div> ... <p>bbbbbbbbbbbbbbbbbbbbbb</p> ... </div> ... </div> ... </section> >>> \"\"\") >>> len(split_section(section, name="img")) 3 >>> split_section(section, name="img") [<section> <div> <h2>Title</h2> <div> <p>aaaaaaaaaaaaaaaaaaaaaa</p> <div> </div></div></div></section>, <img/>, <p>bbbbbbbbbbbbbbbbbbbbbb</p> ] """ str_section = str(section) page_elements = [] delimiters = section.find_all(name=name, attrs=attrs, recursive=recursive, text=text, **kwargs) # Initialization (Prevent occuring an error when for-loop enter continue at the beginning (i=0)) end = 0 for i,delimiter in enumerate(delimiters): str_delimiter = str(delimiter) start = str_section.find(str_delimiter) if start==-1: continue page_elements.append(str2soup(string=str_section[end:start])) page_elements.append(delimiter) end = start + len(str_delimiter) page_elements.append(str2soup(string=str_section[end:])) return page_elements def group_soup_with_head(soup, name=None, attrs={}, recursive=True, text=None, **kwargs): """ Gouping ``bs4.BeautifulSoup`` based on head. Args: section (bs4.BeautifulSoup) : A data structure representing a parsed HTML or XML document. name (str) : A filter on tag name. attrs (dict) : A dictionary of filters on attribute values. recursive (bool) : If this is True, ``.find`` will perform a recursive search of this PageElement's children. Otherwise, only the direct children will be considered. text (str) : An inner text. kwargs (dict) : A dictionary of filters on attribute values. Returns: list : A list of elements without filter tag elements. Examples: >>> from bs4 import BeautifulSoup >>> from pycharmers.utils import group_soup_with_head >>> section = BeautifulSoup(\"\"\" ... <h2>AAA</h2> ... <div> ... <p>aaaaaaaaaaaaaaaaaaaaaa</p> ... </div> ... <h2>BBB</h2> ... <div> ... <p>bbbbbbbbbbbbbbbbbbbbbb</p> ... </div> >>> \"\"\") >>> sections = group_soup_with_head(section, name="h2") >>> len(sections) 2 >>> sections [<section><h2>AAA</h2><div> <p>aaaaaaaaaaaaaaaaaaaaaa</p> </div> </section>, <section><h2>BBB</h2><div> <p>bbbbbbbbbbbbbbbbbbbbbb</p> </div> </section>] """ str_soup = str(soup) sections = [] heads = soup.find_all(name=name, attrs=attrs, recursive=recursive, text=text, **kwargs) # Initialization (Prevent occuring an error when for-loop enter continue at the beginning (i=0)) end = 0; section = BeautifulSoup(markup="", features="lxml").new_tag(name="section") if len(heads)>0: for i,head in enumerate(heads): str_head = str(head) start = str_soup.find(str_head) if start==-1: continue if i>0: body = str2soup(string=str_soup[end:start]) section.append(body) sections.append(section) end = start + len(str_head) section = BeautifulSoup(markup="", features="lxml").new_tag(name="section") section.append(head) body = str2soup(string=str_soup[end:]) section.append(body) sections.append(section) return sections def replace_soup_tag(soup, new_name, new_namespace=None, new_nsprefix=None, new_attrs={}, new_sourceline=None, new_sourcepos=None, new_kwattrs={}, old_name=None, old_attrs={}, old_recursive=True, old_text=None, old_limit=None, old_kwargs={}, **kwargs): """Replace Old tag with New tag. - Args named ``old_XXX`` specifies "How to find old tags" - Args named ``new_XXX`` specifies "How to create new tags" Args: old_name (str) : A filter on tag name. old_attrs (dict) : A dictionary of filters on attribute values. old_recursive (bool) : If this is True, ``.find_all`` will perform a recursive search of this PageElement's children. Otherwise, only the direct children will be considered. old_limit (int) : Stop looking after finding this many results. old_kwargs (dict) : A dictionary of filters on attribute values. new_name (str) : The name of the new Tag. new_namespace (str) : The URI of the new Tag's XML namespace, if any. new_prefix (str) : The prefix for the new Tag's XML namespace, if any. new_attrs (dict) : A dictionary of this Tag's attribute values; can be used instead of `kwattrs` for attributes like 'class' that are reserved words in Python. new_sourceline (str) : The line number where this tag was (purportedly) found in its source document. new_sourcepos (str) : The character position within ``sourceline`` where this tag was (purportedly) found. new_kwattrs (dict) : Keyword arguments for the new Tag's attribute values. Examples: >>> from bs4 import BeautifulSoup >>> from pycharmers.utils import replace_soup_tag >>> section = BeautifulSoup(\"\"\" ... <h2>AAA</h2> ... <div> ... <p>aaaaaaaaaaaaaaaaaaaaaa</p> ... </div> ... <h3>BBB</h3> ... <div> ... <p>bbbbbbbbbbbbbbbbbbbbbb</p> ... </div> >>> \"\"\") >>> section = replace_soup_tag(soup=section, old_name="h3", new_name="h2") >>> section <html><body><h2>AAA</h2> <div> <p>aaaaaaaaaaaaaaaaaaaaaa</p> </div> <h2>BBB</h2> <div> <p>bbbbbbbbbbbbbbbbbbbbbb</p> </div> </body></html> """ for old in soup.find_all(name=old_name, attrs=old_attrs, recursive=old_recursive, text=old_text, limit=old_limit, **old_kwargs): new = BeautifulSoup(markup="", features="lxml").new_tag(name=new_name, namespace=new_namespace, nsprefix=new_nsprefix, attrs=new_attrs, sourceline=new_sourceline, sourcepos=new_sourcepos, **new_kwattrs) new.extend(list(old.children)) old.replace_with(new) return soup def find_target_text(soup, name=None, attrs={}, recursive=True, text=None, default="__NOT_FOUND__", strip=True, **kwargs): """Find target element, and get all child strings from it. Args: soup (bs4.BeautifulSoup) : A data structure representing a parsed HTML or XML document. name (str) : A filter on tag name. attrs (dict) : A dictionary of filters on attribute values. recursive (bool) : If this is True, ``.find`` will perform a recursive search of this PageElement's children. Otherwise, only the direct children will be considered. text (str) : An inner text. default (str) : Default return value if element not found. strip (bool) : Whether to use :func:`str_strip <pycharmers.utils.generic_utils.str_strip>` kwargs (dict) : A dictionary of filters on attribute values. Returns: str : text Examples: >>> from bs4 import BeautifulSoup >>> from pycharmers.utils import find_target_text >>> section = BeautifulSoup(\"\"\" ... <h2>AAA</h2> ... <div> <p>aaaaaaaaaaaaaaaaaaaaaa</p></div> >>> \"\"\") >>> find_target_text(soup=section, name="div") 'aaaaaaaaaaaaaaaaaaaaaa' >>> find_target_text(soup=section, name="div", strip=False) ' aaaaaaaaaaaaaaaaaaaaaa ' >>> find_target_text(soup=section, name="divdiv", default="not found") 'not found' """ target = soup.find(name=name, attrs=attrs, recursive=recursive, text=text, **kwargs) if target is None: text = default else: text = target.text if strip: text = str_strip(string=text) return text def find_all_target_text(soup, name=None, attrs={}, recursive=True, text=None, default="__NOT_FOUND__", strip=True, joint="", **kwargs): """Find target element, and get all child strings from it. Args: soup (bs4.BeautifulSoup) : A data structure representing a parsed HTML or XML document. name (str) : A filter on tag name. attrs (dict) : A dictionary of filters on attribute values. recursive (bool) : If this is True, ``.find`` will perform a recursive search of this PageElement's children. Otherwise, only the direct children will be considered. text (str) : An inner text. default (str) : Default return value if element not found. strip (bool) : Whether to use :func:`str_strip <pycharmers.utils.generic_utils.str_strip>` joint (str) : Inserted between target strings. kwargs (dict) : A dictionary of filters on attribute values. Returns: str : text Examples: >>> from bs4 import BeautifulSoup >>> from pycharmers.utils import find_all_target_text >>> section = BeautifulSoup(\"\"\" ... <div> ... <p class="lang en">Hello</p> ... <p class="lang zh-CN">你好</p> ... <p class="lang es">Hola</p> ... <p class="lang fr">Bonjour</p> ... <p class="lang ja">こんにちは</p> ... </div> >>> \"\"\") >>> find_all_target_text(soup=section, name="p", class_="lang", joint=", ") 'Hello, 你好, Hola, Bonjour, こんにちは' >>> find_all_target_text(soup=section, name="p", class_="es", joint=", ") 'Hola' """ texts = [] for target in soup.find_all(name=name, attrs=attrs, recursive=recursive, text=text, **kwargs): text = target.text if strip: text = str_strip(string=text) texts.append(text) return joint.join(texts) def find_target_id(soup, key, name=None, attrs={}, recursive=True, text=None, default=None, strip=True, **kwargs): """Find target element, and get id from it. Args: soup (bs4.BeautifulSoup) : A data structure representing a parsed HTML or XML document. key (str) : id name. name (str) : A filter on tag name. attrs (dict) : A dictionary of filters on attribute values. recursive (bool) : If this is True, ``.find`` will perform a recursive search of this PageElement's children. Otherwise, only the direct children will be considered. text (str) : An inner text. default (str) : Default return value if element not found. strip (bool) : Whether to use :func:`str_strip <pycharmers.utils.generic_utils.str_strip>` kwargs (dict) : A dictionary of filters on attribute values. Returns: str : text. Examples: >>> from bs4 import BeautifulSoup >>> from pycharmers.utils import find_target_id >>> section = BeautifulSoup(\"\"\" ... <h2>IMAGE</h2> ... <div> ... <img id="apple-touch-icon" src="https://iwasakishuto.github.io/images/apple-touch-icon/Python-Charmers.png"> ... </div> >>> \"\"\") >>> find_target_id(soup=section, name="img", key="id") 'apple-touch-icon' >>> find_target_id(soup=section, name="img", key="src") 'https://iwasakishuto.github.io/images/apple-touch-icon/Python-Charmers.png' """ target = soup.find(name=name, attrs=attrs, recursive=recursive, text=text, **kwargs) if target is None: id_ = default else: id_ = target.get(key=key, default=default) if strip: id_ = str_strip(string=id_) return id_ def get_soup(url, driver=None, features="lxml", timeout=1): """ Scrape and get page source from ``url``. Args: url (str) : URL. driver (WebDriver) : webdriver features (str) : Desirable features of the parser to be used. This may be the name of a specific parser ("lxml", "lxml-xml", "html.parser", or "html5lib") or it may be the type of markup to be used ("html", "html5", "xml"). It's recommended that you name a specific parser, so that Beautiful Soup gives you the same results across platforms and virtual environments. Returns: BeautifulSoup : A data structure representing a parsed HTML or XML document. """ handleKeyError(lst=["lxml", "lxml-xml", "html.parser", "html5lib", "html", "html5", "xml"], features=features) if driver is None: html = requests.get(url=url).content else: from .driver_utils import scrollDown, wait_until_all_elements driver.get(url) wait_until_all_elements(driver=driver, timeout=timeout, verbose=False) scrollDown(driver=driver, verbose=False) html = driver.page_source.encode("utf-8") return BeautifulSoup(markup=html, features=features)

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21a00207837c0b03d9e8ba6b3b0c1c8c83c6a9d4

8,444

py

Python

third_party/chromite/cli/cros/lint_autotest.py

zipated/src

2b8388091c71e442910a21ada3d97ae8bc1845d3

[ "BSD-3-Clause" ]

2,151

2020-04-18T07:31:17.000Z

2022-03-31T08:39:18.000Z

third_party/chromite/cli/cros/lint_autotest.py

cangulcan/src

2b8388091c71e442910a21ada3d97ae8bc1845d3

[ "BSD-3-Clause" ]

395

2020-04-18T08:22:18.000Z

2021-12-08T13:04:49.000Z

third_party/chromite/cli/cros/lint_autotest.py

cangulcan/src

2b8388091c71e442910a21ada3d97ae8bc1845d3

[ "BSD-3-Clause" ]

338

2020-04-18T08:03:10.000Z

2022-03-29T12:33:22.000Z

# -*- coding: utf-8 -*- # Copyright 2017 The Chromium OS Authors. All rights reserved. # Use of this source code is governed by a BSD-style license that can be # found in the LICENSE file. # This module is not automatically loaded by the `cros` helper. The filename # would need a "cros_" prefix to make that happen. It lives here so that it # is alongside the cros_lint.py file. # # For msg namespaces, the 9xxx should generally be reserved for our own use. """A lint module loaded by pylint for Autotest linting. This module patches pylint library functions to suit autotest. This is loaded by pylint directly via the autotest pylintrc file: load-plugins=chromite.cli.cros.lint_autotest """ from __future__ import print_function import re from pylint.checkers import base from pylint.checkers import BaseChecker from pylint.checkers import imports from pylint.checkers import variables from pylint.interfaces import IAstroidChecker import logilab.common.modutils # patch up the logilab module lookup tools to understand autotest_lib.* trash _ffm = logilab.common.modutils.file_from_modpath def file_from_modpath(modpath, paths=None, context_file=None): """Wrapper to eliminate autotest_lib from modpath. Args: modpath: name of module splitted on '.' paths: optional list of paths where module should be searched for. context_file: path to file doing the importing. Returns: The path to the module as returned by the parent method invocation. Raises: ImportError if these is no such module. """ if modpath[0] == "autotest_lib": return _ffm(modpath[1:], paths, context_file) else: return _ffm(modpath, paths, context_file) # patch up pylint import checker to handle our importing magic ROOT_MODULE = 'autotest_lib.' # A list of modules for pylint to ignore, specifically, these modules # are imported for their side-effects and are not meant to be used. _IGNORE_MODULES = ( 'common', 'frontend_test_utils', 'setup_django_environment', 'setup_django_lite_environment', 'setup_django_readonly_environment', 'setup_test_environment', ) def patch_modname(modname): """Patches modname so we can make sense of autotest_lib modules. Args: modname: name of a module, contains '.' Returns: The modname string without the 'autotest_lib.' prefix. For example, patch_modname('autotest_lib.foo.bar') == 'foo.bar' patch_modname('foo.bar') == 'foo.bar' """ if modname.startswith(ROOT_MODULE) or modname.startswith(ROOT_MODULE[:-1]): modname = modname[len(ROOT_MODULE):] return modname def patch_consumed_list(to_consume=None, consumed=None): """Patches the consumed modules list to ignore modules with side effects. Autotest relies on importing certain modules solely for their side effects. Pylint doesn't understand this and flags them as unused, since they're not referenced anywhere in the code. To overcome this we need to transplant said modules into the dictionary of modules pylint has already seen, before pylint checks it. Args: to_consume: a dictionary of names pylint needs to see referenced. consumed: a dictionary of names that pylint has seen referenced. """ if to_consume is None or consumed is None: return for module in _IGNORE_MODULES: if module in to_consume: consumed[module] = to_consume[module] del to_consume[module] # This decorator will be used for monkey patching the built-in pylint classes. def patch_cls(cls): """Sets a method of `cls`.""" def patcher(method): setattr(cls, method.__name__, method) return patcher def CustomizeImportsChecker(): """Modifies stock imports checker to suit autotest.""" cls = imports.ImportsChecker old_visit_from = cls.visit_from @patch_cls(cls) def visit_from(self, node): # pylint: disable=unused-variable node.modname = patch_modname(node.modname) return old_visit_from(self, node) def CustomizeVariablesChecker(): """Modifies stock variables checker to suit autotest.""" cls = variables.VariablesChecker old_visit_module = cls.visit_module @patch_cls(cls) def visit_module(self, node): # pylint: disable=unused-variable """Unflag 'import common'. _to_consume eg: [({to reference}, {referenced}, 'scope type')] Enteries are appended to this list as we drill deeper in scope. If we ever come across a module to ignore, we immediately move it to the consumed list. Args: node: node of the ast we're currently checking. """ old_visit_module(self, node) # pylint: disable=protected-access scoped_names = self._to_consume.pop() patch_consumed_list(scoped_names[0], scoped_names[1]) self._to_consume.append(scoped_names) old_visit_from = cls.visit_from @patch_cls(cls) def visit_from(self, node): # pylint: disable=unused-variable """Patches modnames so pylints understands autotest_lib.""" node.modname = patch_modname(node.modname) return old_visit_from(self, node) def _ShouldSkipArg(arg): """Checks if arg name can be excluded from @param list. Returns: True if the argument given by arg is whitelisted, and does not require a "@param" docstring. """ return arg in ('self', 'cls', 'args', 'kwargs', 'dargs') def ShouldSkipDocstring(node): """Returns whether docstring checks should run on this function node. Args: node: The node under examination. """ # Even plain functions will have a parent, which is the # module they're in, and a frame, which is the context # of said module; They need not however, always have # ancestors. return (node.name in ('run_once', 'initialize', 'cleanup') and hasattr(node.parent.frame(), 'ancestors') and any(ancestor.name == 'base_test' for ancestor in node.parent.frame().ancestors())) def CustomizeDocStringChecker(): """Modifies stock docstring checker to suit Autotest doxygen style.""" cls = base.DocStringChecker @patch_cls(cls) def visit_module(_self, _node): # pylint: disable=unused-variable """Don't visit imported modules when checking for docstrings. Args: node: the node we're visiting. """ old_visit_function = cls.visit_function @patch_cls(cls) def visit_function(self, node): # pylint: disable=unused-variable """Don't request docstrings for commonly overridden autotest functions. Args: node: node of the ast we're currently checking. """ if ShouldSkipDocstring(node): return old_visit_function(self, node) class ParamChecker(BaseChecker): """Checks that each argument has a @param entry in the docstring.""" __implements__ = IAstroidChecker # The numbering for this message matches that of the doc string checker class # in chromite.cli.cros.lint class _MessageCP010(object): """Message for missing @param statements.""" pass name = 'doc_string_param_checker' priority = -1 MSG_ARGS = 'offset:%(offset)i: {%(line)s}' msgs = { 'C9010': ('Docstring for %(func)s needs "@param %(arg)s:"', ('docstring-missing-args'), _MessageCP010), } def visit_function(self, node): """Verify the function's docstrings.""" if node.doc and not ShouldSkipDocstring(node): self._check_all_args_in_doc(node) ARG_DOCSTRING_RGX = re.compile(r'@param ([^:]+)') def _check_all_args_in_doc(self, node): """Teaches pylint to look for @param with each argument. Args: node_type: type of the node we're currently checking. node: node of the ast we're currently checking. """ present_args = set(arg for arg in node.argnames() if not _ShouldSkipArg(arg)) documented_args = set(re.findall(self.ARG_DOCSTRING_RGX, node.doc)) for undocumented in present_args - documented_args: self.add_message('C9010', node=node, line=node.fromlineno, args={'arg': undocumented, 'func': node.name},) def register(linter): """Pylint will call this func when we use the 'load-plugins' invocation. Args: linter: The pylint linter instance for this run. """ # Walk all the classes in this module and register ours. linter.register_checker(ParamChecker(linter)) CustomizeDocStringChecker() CustomizeImportsChecker() CustomizeVariablesChecker() logilab.common.modutils.file_from_modpath = file_from_modpath

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21a0c8636280ad53c71eff8b3260ed75a7d6bbee

8,298

py

Python

MindLink-Eumpy/data_collection_framework/util/EEG_record.py

Breeze1in1drizzle/MindLink-Exploring

24e7d60112754c9fe5faf7b7f9ae255fa1bc4c59

[ "MIT" ]

7

2020-11-19T14:34:50.000Z

2022-02-26T14:16:50.000Z

MindLink-Eumpy/data_collection_framework/util/EEG_record.py

Breeze1in1drizzle/MindLink-Exploring

24e7d60112754c9fe5faf7b7f9ae255fa1bc4c59

[ "MIT" ]

1

2021-08-20T07:30:32.000Z

2021-09-01T07:20:14.000Z

MindLink-Eumpy/data_collection_framework/util/EEG_record.py

Breeze1in1drizzle/MindLink-Exploring

24e7d60112754c9fe5faf7b7f9ae255fa1bc4c59

[ "MIT" ]

2

2021-07-20T08:59:14.000Z

2021-08-10T08:03:56.000Z

# -*- coding: utf-8 -*- """ Created on Thu Oct 18 11:42:40 2018 by Yongrui Huang Modified on Tue Oct 29th 16:16:50 2019 by Ruixin Lee @author: Yongrui Huang """ import sys sys.path.append('../') from data_collection_framework.util import record import configuration import os import platform import time import ctypes from array import * from ctypes import * import multiprocessing if sys.platform.startswith('win32'): import msvcrt elif sys.platform.startswith('linux'): import atexit from select import select from ctypes import * import numpy as np class EEGRecorder(record.AbstractRecorder): ''' This class gives an example of how to use AbsRecorder to record EEG data. Specially, the devices we used is emotiv insight. It should be noted that this code may not work well for your envirnoment, since the device (emotiv insight) is relied on different platform and different version of progarm language. I used windows64 ana python 3.5 for my development. You may noticed that the code here is very complex. However, I just copied the code that emotiv company released and modify just a litte bit, basically. Oct 29 2019 Now we use Emotiv Epoc+ to record EEG signals which has 14 channels. ''' def __init__(self, name): record.AbstractRecorder.__init__(self, name) def record_one_sample(self): ''' This method is supposed to read one data sample from information source. For EEG using emotiv insight, it means recording signal from 5 different channel (i.e, IED_AF3, IED_AF4, IED_T7, IED_T8, IED_Pz) and each channel's PSD features (i.e. theta, alpha, low_beta, high_beta, gamma). 25 features totally to be treated as a sample. Oct 29 2019 Now we use Emotiv Epoc+ with 14 channels to record EEG signals. My mission is to complete the connection method and make it more convenient for others and suitable for all kinds of platforms. ''' #------------------------------------ print("configuration.ROOT_PATH:") print(configuration.ROOT_PATH) print("configuration.ROOT_PATH.") # load_str = configuration.ROOT_PATH + "data_collection_frame/util/win64/edk/1.edk.dll" print("load_str:") # print(load_str) print("load_str.") #-------------------------------------- # self.libEDK = cdll.LoadLibrary(configuration.ROOT_PATH + "data_collection_frame/util/win64/edk.dll") # load sdk self.libEDK = cdll.loadLibrary("win64/edk.dll") # print("self.libEDK") # print(self.libEDK) # print("self.libEDK") # cdll.LoadLibrary()??? # self.libEDK = cdll.LoadLibrary(load_str) # print("self.libEDK") # print(self.libEDK) # print("self.libEDK") # Create an Emotiv Engine Event IEE_EmoEngineEventCreate = self.libEDK.IEE_EmoEngineEventCreate IEE_EmoEngineEventCreate.restype = c_void_p self.eEvent = IEE_EmoEngineEventCreate() IEE_EmoEngineEventGetEmoState = self.libEDK.IEE_EmoEngineEventGetEmoState IEE_EmoEngineEventGetEmoState.argtypes = [c_void_p, c_void_p] IEE_EmoEngineEventGetEmoState.restype = c_int IEE_EmoStateCreate = self.libEDK.IEE_EmoStateCreate IEE_EmoStateCreate.restype = c_void_p eState = IEE_EmoStateCreate() userID = c_uint(0) user = pointer(userID) ready = 0 state = c_int(0) IEE_EngineConnect = self.libEDK.IEE_EngineConnect #add code here IEE_EngineConnect.restype = c_int IEE_EngineConnect.argtypes = [c_void_p] IEE_EngineGetNextEvent = self.libEDK.IEE_EngineGetNextEvent IEE_EngineGetNextEvent.restype = c_int IEE_EngineGetNextEvent.argtypes = [c_void_p] IEE_EmoEngineEventGetUserId = self.libEDK.IEE_EmoEngineEventGetUserId IEE_EmoEngineEventGetUserId.restype = c_int IEE_EmoEngineEventGetUserId.argtypes = [c_void_p , c_void_p] IEE_EmoEngineEventGetType = self.libEDK.IEE_EmoEngineEventGetType IEE_EmoEngineEventGetType.restype = c_int IEE_EmoEngineEventGetType.argtypes = [c_void_p] IEE_EmoEngineEventCreate = self.libEDK.IEE_EmoEngineEventCreate IEE_EmoEngineEventCreate.restype = c_void_p IEE_EmoEngineEventGetEmoState = self.libEDK.IEE_EmoEngineEventGetEmoState IEE_EmoEngineEventGetEmoState.argtypes = [c_void_p, c_void_p] IEE_EmoEngineEventGetEmoState.restype = c_int IEE_EmoStateCreate = self.libEDK.IEE_EmoStateCreate IEE_EmoStateCreate.argtype = c_void_p IEE_EmoStateCreate.restype = c_void_p IEE_FFTSetWindowingType = self.libEDK.IEE_FFTSetWindowingType IEE_FFTSetWindowingType.restype = c_int IEE_FFTSetWindowingType.argtypes = [c_uint, c_void_p] IEE_GetAverageBandPowers = self.libEDK.IEE_GetAverageBandPowers IEE_GetAverageBandPowers.restype = c_int IEE_GetAverageBandPowers.argtypes = [c_uint, c_int, c_void_p, c_void_p, c_void_p, c_void_p, c_void_p] IEE_EngineDisconnect = self.libEDK.IEE_EngineDisconnect IEE_EngineDisconnect.restype = c_int IEE_EngineDisconnect.argtype = c_void_p IEE_EmoStateFree = self.libEDK.IEE_EmoStateFree IEE_EmoStateFree.restype = c_int IEE_EmoStateFree.argtypes = [c_void_p] IEE_EmoEngineEventFree = self.libEDK.IEE_EmoEngineEventFree IEE_EmoEngineEventFree.restype = c_int IEE_EmoEngineEventFree.argtypes = [c_void_p] # finish adding code # init frequency dataf alphaValue = c_double(0) low_betaValue = c_double(0) high_betaValue = c_double(0) gammaValue = c_double(0) thetaValue = c_double(0) alpha = pointer(alphaValue) low_beta = pointer(low_betaValue) high_beta = pointer(high_betaValue) gamma = pointer(gammaValue) theta = pointer(thetaValue) channelList = array('I',[3, 7, 9, 12, 16]) # IED_AF3, IED_AF4, IED_T7, IED_T8, IED_Pz if self.libEDK.IEE_EngineConnect(create_string_buffer(b"Emotiv Systems-5")) != 0: print(self.libEDK.IEE_EngineConnect("Emotiv Systems-5")) print("Emotiv Engine start up failed.") return state = IEE_EngineGetNextEvent(self.eEvent) if state == 0: eventType = IEE_EmoEngineEventGetType(self.eEvent) IEE_EmoEngineEventGetUserId(self.eEvent, user) if eventType == 64: # self.libEDK.IEE_Event_enum.IEE_UserAdded ready = 1 self.libEDK.IEE_FFTSetWindowingType(userID, 1); # 1: self.libEDK.IEE_WindowingTypes_enum.IEE_HAMMING if ready == 1: EEG_row = np.zeros(25) j = 0 for i in channelList: result = c_int(0) result = self.libEDK.IEE_GetAverageBandPowers( userID, i, theta, alpha, low_beta, high_beta, gamma ) if result == 0: # EDK_OK EEG_row[j*5+0], EEG_row[j*5+1], EEG_row[j*5+2], EEG_row[j*5+3], EEG_row[j*5+4] = ( thetaValue.value, alphaValue.value, low_betaValue.value, high_betaValue.value, gammaValue.value ) j += 1 return EEG_row elif state != 0x0600: print("Internal error in Emotiv Engine ! ") else: print('Noe event for EEG device!') def release_resourse_in_one_trial(self): pass if __name__ == '__main__': print("main.start") eeg_recorder = EEGRecorder('EEG') for i in range(100): print("i: %d" % i) print(eeg_recorder.record_one_sample()) print("main.end")

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null

0

null

0

1

0

21a131fadbf256dc2fd90d0c9f6e8868b23e49f2

3,198

py

Python

autophrase/pos_tag.py

QianyangPeng/AutophrasePy

1d34a62fdf96a649f5e06fe69dc74fa8d69fc8a7

[ "Apache-2.0" ]

7

2018-08-11T07:03:07.000Z

2022-03-18T06:33:30.000Z

autophrase/pos_tag.py

QianyangPeng/AutophrasePy

1d34a62fdf96a649f5e06fe69dc74fa8d69fc8a7

[ "Apache-2.0" ]

1

2018-11-01T08:18:00.000Z

autophrase/pos_tag.py

QianyangPeng/AutophrasePy

1d34a62fdf96a649f5e06fe69dc74fa8d69fc8a7

[ "Apache-2.0" ]

1

2020-05-05T12:54:56.000Z

import os from glob import glob from shutil import copyfile from math import floor import sys import threading # from download_parameter_files import download_parameter_files def split_file(num_lines, num_files, raw, tmp): smallfile = None file_num = 0 with open(raw) as bigfile: for lineno, line in enumerate(bigfile): if lineno % num_lines == 0 and file_num != num_files: if smallfile: smallfile.close() small_filename = tmp + "/split_files.{}".format(file_num) file_num += 1 smallfile = open(small_filename, "w") smallfile.write(line) if smallfile: smallfile.close() def one_line_per_word(file): with open(file) as input_file: with open(file + ".token", "w") as out_file: for line in input_file: out_file.write(line.replace(' ', '\n')) def execute_tagger(file, tagger, parfile): command = tagger + " -quiet " + parfile + " < " + file + " > " + file + ".tagged" os.system(command) # with open(file) as token_file: # with open(file + ".tagged", 'w') as tagged: # subprocess.call(command, stdin = token_file, stdout = tagged) def pos_tag(language, num_thread, raw, tmp): root_path = os.path.dirname(os.path.abspath(__file__)) # download_parameter_files(language, root_path) for file in glob(tmp + "/split_files*"): os.remove(file) print("Current step: Splitting files...") with open(raw) as f: num_lines = floor(sum(1 for _ in f)/num_thread) if num_lines <= 0 or num_thread == 1: copyfile(raw, tmp + "/split_files.0") else: split_file(num_lines, num_thread, raw, tmp) for file in glob(tmp + "/split_files.*"): one_line_per_word(file) print("Current step: Tagging...") tagger = None parfile = None if language == "EN": tagger = root_path + "/tools/treetagger/bin/tree-tagger" parfile = root_path + "/tools/treetagger/lib/english-utf8.par" elif language == "FR": tagger = root_path + "/tools/treetagger/bin/tree-tagger" parfile = root_path + "/tools/treetagger/lib/french-utf8.par" elif language == "IT": tagger = root_path + "/tools/treetagger/bin/tree-tagger" parfile = root_path + "/tools/treetagger/lib/italian-utf8.par" elif language == "RU": tagger = root_path + "/tools/treetagger/bin/tree-tagger" parfile = root_path + "/tools/treetagger/lib/russian-utf8.par" elif language == "ES": tagger = root_path + "/tools/treetagger/bin/tree-tagger" parfile = root_path + "/tools/treetagger/lib/spanish-utf8.par" else: sys.exit("[ERROR]: Tree tagger does not support the language.") curent_directory = os.getcwd() # os.chdir(root_path + "/tools/treetagger") thread_list = [] for file in glob(tmp + "/split_files.*.token"): t = threading.Thread(target = execute_tagger, args = (file, tagger, parfile,)) thread_list.append(t) for thread in thread_list: thread.start() for thread in thread_list: thread.join() # os.chdir(curent_directory) print("Current step: Merging...") with open(tmp + "/pos_tags.txt", "w") as outfile: for filenum in range(num_thread): file_name = tmp + "/split_files." + str(filenum) + ".token.tagged" with open(file_name, "r") as infile: outfile.write(infile.read()) for file in glob(tmp + "/split_files*"): os.remove(file)

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0

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3,198

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0.038462

0

null

0

null

0

1

0

21a59aacb4bd81cb3e7cdf393821079b2b85a81e

926

py

Python

app/utils/try.py

yudhik11/StackOverflow_UserQuery

b3c88ed18304078901497a9a0dc99c232e1ffac4

[ "MIT" ]

3

2019-07-21T16:30:27.000Z

2020-03-04T15:03:32.000Z

app/utils/try.py

yudhik11/StackOverflow_UserQuery

b3c88ed18304078901497a9a0dc99c232e1ffac4

[ "MIT" ]

11

2020-01-28T22:51:49.000Z

2022-02-10T09:16:19.000Z

app/utils/try.py

yudhik11/StackOverflow_UserQuery

b3c88ed18304078901497a9a0dc99c232e1ffac4

[ "MIT" ]

2

2019-07-21T12:30:13.000Z

2019-07-31T16:34:07.000Z

import requests, re import numpy as np u='https://api.stackexchange.com/2.2/similar' tag_arr=[] def clean_text(text): text = str(text) text = re.sub(r"[^\w]", " ", text.lower()) return text # for i in range(1,28): for i in range(1,2): print(i) p={'page':str(i), 'pagesize':'100','fromdate':'1388534400','tagged':'javascript;node.js;npm','title':'node how to run node app js', 'order':'desc','sort':'votes','min':'40','site':'stackoverflow','key':'hWdB8OaWM0hGZP3sRV18iA(('} r = requests.get(url = u, params = p) data = r.json() temp = data['items'] for k in temp: # tag_arr.append(k['title'].encode('latin1').decode('utf-8')) tag_arr.append(k['title']) print(tag_arr) #np.savetxt('ques-so.txt', tag_arr, delimiter=',', newline='\n', fmt='%s') with open('ques-tmp.txt', 'w') as f: for item in tag_arr: f.write("%s\n" % clean_text( item)) f.close()

33.071429

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926

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926

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null

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null

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0

21a73b6961e324b2bf36ea8c248c456445c83c7d

832

py

Python

infrastructure/codigos/solver/extraer_datos_yahoo.py

izmfc/MNO_finalproject

5e25ba84708f75e98768e75a681992986efd87fc

[ "RSA-MD" ]

null

infrastructure/codigos/solver/extraer_datos_yahoo.py

izmfc/MNO_finalproject

5e25ba84708f75e98768e75a681992986efd87fc

[ "RSA-MD" ]

61

2020-04-25T01:09:22.000Z

2020-05-29T00:18:46.000Z

infrastructure/codigos/solver/extraer_datos_yahoo.py

izmfc/MNO_finalproject

5e25ba84708f75e98768e75a681992986efd87fc

[ "RSA-MD" ]

4

2020-05-01T19:24:45.000Z

2021-01-23T01:28:44.000Z

import yfinance as yf def extraer_datos_yahoo(stocks, start='2015-01-01', end='2020-04-30'): ''' Funcion para extraer precios al cierre de las acciones mediante yahoo finance de 2015-01-01 a 2020-04-30 params: stocks lista de acciones de las cuales se desea obtener el precio start fecha inicial end fecha final return: base Dataframe de precios por acción (columnas) y día (filas) ''' df_c = yf.download(stocks, start=start, end=end).Close base = df_c['AAPL'].dropna().to_frame() for i in range(0,50): base = base.join(df_c.iloc[:,i].to_frame(), lsuffix='_caller', rsuffix='_other') base = base.drop(columns=['AAPL_caller']) base = base.rename(columns={"AAPL_other": "AAPL"}) base = base.fillna(method='ffill') base = base.fillna(method='bfill') return base

37.818182

106

0.674279

128

832

4.296875

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null

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null

0

1

0

21a81e454b5018f5bfc22e5ccbd3228cb3ba62b6

2,786

py

Python

tests/unit/test_bio_metadata.py

haoyuanli/candig-server

5f58a4ef5f58d9e91afb98d385e9213e0899e51d

[ "Apache-2.0" ]

4

2019-05-08T20:42:10.000Z

2021-08-13T16:39:38.000Z

tests/unit/test_bio_metadata.py

haoyuanli/candig-server

5f58a4ef5f58d9e91afb98d385e9213e0899e51d

[ "Apache-2.0" ]

102

2019-04-03T23:08:46.000Z

2021-11-28T19:41:38.000Z

tests/unit/test_bio_metadata.py

haoyuanli/candig-server

5f58a4ef5f58d9e91afb98d385e9213e0899e51d

[ "Apache-2.0" ]

6

2019-05-08T20:42:16.000Z

2021-08-21T03:15:44.000Z

""" Tests the biodata module """ import unittest import candig.server.datamodel.datasets as datasets import candig.server.exceptions as exceptions import candig.server.datamodel.bio_metadata as bioMetadata import candig.schemas.protocol as protocol class TestIndividuals(unittest.TestCase): """ Tests the Individuals class """ def testToProtocolElement(self): dataset = datasets.Dataset('dataset1') term = protocol.OntologyTerm() term.term = "male genotypic sex" term.term_id = "PATO:0020001" # Write out a valid input print(protocol.toJsonDict(term)) validIndividual = protocol.Individual( name="test", created="2016-05-19T21:00:19Z", updated="2016-05-19T21:00:19Z", sex=term) validIndividual.attributes.attr['test']. \ values.add().string_value = 'test-info' # pass through protocol creation individual = bioMetadata.Individual( dataset, "test") individual.populateFromJson(protocol.toJson(validIndividual)) gaIndividual = individual.toProtocolElement() # Verify elements exist self.assertEqual(gaIndividual.created, validIndividual.created) self.assertEqual(gaIndividual.updated, validIndividual.updated) # Invalid input invalidIndividual = '{"bad:", "json"}' individual = bioMetadata.Individual(dataset, "test") # Should fail self.assertRaises( exceptions.InvalidJsonException, individual.populateFromJson, invalidIndividual) class TestBiosamples(unittest.TestCase): """ Tests the Biosamples class """ def testToProtocolElement(self): dataset = datasets.Dataset('dataset1') # Write out a valid input validBiosample = protocol.Biosample( name="test", created="2016-05-19T21:00:19Z", updated="2016-05-19T21:00:19Z") validBiosample.attributes.attr['test']. \ values.add().string_value = 'test-info' # pass through protocol creation biosample = bioMetadata.Biosample( dataset, "test") biosample.populateFromJson(protocol.toJson(validBiosample)) gaBiosample = biosample.toProtocolElement() # Verify elements exist self.assertEqual(gaBiosample.created, validBiosample.created) self.assertEqual(gaBiosample.updated, validBiosample.updated) # Invalid input invalidBiosample = '{"bad:", "json"}' biosample = bioMetadata.Individual(dataset, "test") # Should fail self.assertRaises( exceptions.InvalidJsonException, biosample.populateFromJson, invalidBiosample)

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7.274194

0.33871

0.026608

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null

0

null

0

1

0

21a84ccfa838eca956c2bc2c4621cd9756bd544c

4,580

py

Python

eval/fig4.py

tk2lab/logbesselk

6ffbc344c0b472d307a556e50de943a380616fb2

[ "Apache-2.0" ]

null

eval/fig4.py

tk2lab/logbesselk

6ffbc344c0b472d307a556e50de943a380616fb2

[ "Apache-2.0" ]

null

eval/fig4.py

tk2lab/logbesselk

6ffbc344c0b472d307a556e50de943a380616fb2

[ "Apache-2.0" ]

null

import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns from matplotlib.colors import ListedColormap from . import common def v_loc(x): return 40*np.log10(x + 1) def x_loc(x): return 40*(np.log10(x) + 1) def main(debug=False): thr_v = 25.0 thr_x = lambda v: 1.6 + 0.5 * np.log(v + 1) name = ['I', 'A', 'S', 'C', 'SCA'] suffix = ['', '', '', '', ''] df = [] for n, s in zip(name, suffix): prec = pd.read_csv(f'results/logk_prec_{n}{s}.csv') prec = prec.groupby(['v', 'x'])['log_err'].mean() prec.name = f'prec_{n}' time = pd.read_csv(f'results/logk_time_{n}{s}.csv') time = time.groupby(['v', 'x'])['time'].mean() time = 1000 * time time.name = f'time_{n}' df += [prec, time] df = pd.concat(df, axis=1) v, x = [np.array(z) for z in zip(*df.index)] for t in ['prec', 'time']: df[f'{t}_SCA'] = df[f'{t}_S'] df.loc[x < thr_x(v), f'{t}_SCA'] = df[f'{t}_S'] df.loc[x >= thr_x(v), f'{t}_SCA'] = df[f'{t}_C'] df.loc[v >= thr_v, f'{t}_SCA'] = df[f'{t}_A'] df['type_prec'] = -1 df['min_prec'] = np.inf df['min_time'] = np.inf for i, name in enumerate(['S', 'C', 'A']): cond = df[f'prec_{name}'] < df['min_prec'] df.loc[cond, 'type_prec'] = i df.loc[cond, 'min_prec'] = df.loc[cond, f'prec_{name}'] df.loc[cond, 'min_time'] = df.loc[cond, f'time_{name}'] df['type_time'] = -1 df['min_prec'] = np.inf df['min_time'] = np.inf for i, name in enumerate(['S', 'C', 'A']): cond = (df[f'prec_{name}'] < 1.) & (df[f'time_{name}'] < df['min_time']) df.loc[cond, 'type_time'] = i df.loc[cond, 'min_prec'] = df.loc[cond, f'prec_{name}'] df.loc[cond, 'min_time'] = df.loc[cond, f'time_{name}'] #type_cmap = ListedColormap(['magenta', 'blue', 'green', 'cyan']) #type_cmap.set_under('white') name = [['type_prec', 'prec_SCA'], ['type_time', 'time_SCA']] #pos = [[[0.1, 0.85], [0.85, 0.1]], [[0.1, 0.1], [0.1, 0.85]]] vmin = [[-1.5, 0], [-1.5, 0]] vmax = [[3.5, 2.8], [3.5, 28]] #cmap = [[type_cmap, 'Reds'], [type_cmap, 'Blues']] cmap = [['Greys', 'Greys'], ['Greys', 'Greys']] fig = common.figure(figsize=(5.5, 4), box=debug) ax = fig.subplots( 2, 2, sharex=True, sharey=True, #gridspec_kw=dict(width_ratios=(1,1,0.15)), ) #ax[0, 2].set_visible(False) #ax[1, 2].set_visible(False) #ax[0, 2] = fig.add_axes([0.93, 0.1, 0.02, 0.4]) #ax[1, 2] = fig.add_axes([0.93, 0.57, 0.02, 0.4]) vticks = [0, 1, 5, 10, 50] xticks = [0.1, 0.5, 1, 5, 10, 50] label = [['a', 'c'], ['b', 'd']] pos = [[[-0.15, 0.8], [-0.1, 0.8]], [[-0.15, 0.8], [-0.1, 0.8]]] for i in range(2): for j in range(2): hm = df[name[i][j]].unstack(0) if j == 0: args = dict(cbar=False) else: args = dict(cbar=True) sns.heatmap(hm, vmin=vmin[i][j], vmax=vmax[i][j], cmap=cmap[i][j], ax=ax[i, j], **args) #sns.heatmap(hm, vmin=vmin[i][j], vmax=vmax[i][j], cmap=cmap[i][j], ax=ax[i, j]) v = np.linspace(0, thr_v, 100) x = thr_x(v) v = v_loc(v) x = x_loc(x) ax[i, j].plot(v, x, c='k') ax[i, j].plot([v_loc(thr_v), v_loc(thr_v)], [x_loc(0.1), x_loc(10**2.1)], c='k') ax[i, j].invert_yaxis() ax[i, j].text(*pos[i][j], label[i][j], transform=ax[i, j].transAxes) ax[i, j].text(v_loc(5), x_loc(0.5), 'S') ax[i, j].text(v_loc(2), x_loc(6), 'C') ax[i, j].text(v_loc(50), x_loc(1), 'A') ax[i, j].set_xticks([v_loc(v) for v in vticks]) ax[i, j].set_xticklabels([f"${k}$" for k in vticks], rotation=0) ax[i, j].xaxis.set_ticks_position('both') ax[i, j].set_yticks([x_loc(x) for x in xticks]) ax[i, j].set_yticklabels([f"${k}$" for k in xticks]) ax[i, j].yaxis.set_ticks_position('both') if i == 1: ax[i, j].set_xlabel('$v$') else: ax[i, j].set_xlabel('') if j == 0: ax[i, j].set_ylabel('$x$') else: ax[i, j].set_ylabel('') #cbar = ax[0, 0].collections[0].colorbar #cbar.set_ticks([0, 10, 20]) #cbar.set_ticklabels([f'${{{l}}}$' for l in [0, 10, 20]]) fig.savefig('figs/fig4.pdf') if __name__ == '__main__': main(debug=False)

35.503876

99

0.4869

792

4,580

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0.191919

0.027243

0.037576

0.026303

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0.286519

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0.218412

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0.020619

0.113402

0

null

0

null

0

1

0

21a89ee632dc825af897834036510c30f557a71f

1,660

py

Python

yt_idv/scene_components/particles.py

sochowski/yt_idv

d840d3f80e6f5afb743f2eb9afce1f576645c731

[ "BSD-3-Clause" ]

3

2021-02-08T21:27:21.000Z

2021-07-09T08:35:37.000Z

yt_idv/scene_components/particles.py

sochowski/yt_idv

d840d3f80e6f5afb743f2eb9afce1f576645c731

[ "BSD-3-Clause" ]

17

2020-12-22T18:45:04.000Z

2022-02-25T20:05:50.000Z

yt_idv/scene_components/particles.py

sochowski/yt_idv

d840d3f80e6f5afb743f2eb9afce1f576645c731

[ "BSD-3-Clause" ]

4

2021-04-02T19:56:56.000Z

2021-09-24T01:22:55.000Z

import math import numpy as np import traitlets from OpenGL import GL from yt_idv.scene_components.base_component import SceneComponent from yt_idv.scene_data.particle_positions import ParticlePositions class ParticleRendering(SceneComponent): name = "particle_rendering" data = traitlets.Instance(ParticlePositions) scale = traitlets.CFloat(1e-3) max_particle_size = traitlets.CFloat(1e-3) def render_gui(self, imgui, renderer, scene): changed = super(ParticleRendering, self).render_gui(imgui, renderer, scene) _, new_value = imgui.slider_float( "Log Scale", math.log10(self.scale), -8.0, 2.0 ) if _: self.scale = 10 ** new_value changed = True imgui.text("Filter Particle Max Size") _, new_value = imgui.slider_float("", 1.0 / self.max_particle_size, 1.0, 100.0) if _: self.max_particle_size = 1.0 / new_value changed = True return changed def draw(self, scene, program): GL.glEnable(GL.GL_CULL_FACE) GL.glCullFace(GL.GL_BACK) GL.glDrawArraysInstanced(GL.GL_TRIANGLE_STRIP, 0, 4, self.data.size) def _set_uniforms(self, scene, shader_program): cam = scene.camera shader_program._set_uniform("scale", self.scale) shader_program._set_uniform("projection", cam.projection_matrix) shader_program._set_uniform("modelview", cam.view_matrix) shader_program._set_uniform("max_particle_size", self.max_particle_size) shader_program._set_uniform( "inv_pmvm", np.linalg.inv(cam.projection_matrix @ cam.view_matrix) )

36.086957

87

0.680723

211

1,660

5.085308

0.379147

0.072693

0.069897

0.107176

0.137931

0.039143

0

0.018634

0.224096

1,660

45

88

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0

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0

0.060241

0

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false

0

0.157895

0

0.394737

0

null

0

null

0

1

0

21a941828c011cb36b4651f7351fe451889403ba

3,332

py

Python

uniplot/cli.py

Titas-In-Cloud/uniplot

0d31f1c4d4d5ac24543d96ec4bfdc2cbf43406c2

[ "MIT" ]

null

uniplot/cli.py

Titas-In-Cloud/uniplot

0d31f1c4d4d5ac24543d96ec4bfdc2cbf43406c2

[ "MIT" ]

null

uniplot/cli.py

Titas-In-Cloud/uniplot

0d31f1c4d4d5ac24543d96ec4bfdc2cbf43406c2

[ "MIT" ]

null

import argparse from os import path from uniplot import plot from . import parse from . import analysis file = open("location.txt", "r") LOC = file.read() file.close() def file_location_configuration(args): """Allows to set the location from where to get the data file""" open("location.txt", "w").close() file = open("location.txt", "r+") location = input("What file would you like to use? Please write the location: ") if path.exists(location): file.write(location) print("Success! File location was scanned.") else: print("Error! File does not exist.") file.close() def dump(args): """Prints a list with all the information about proteins""" for record in parse.uniprot_seqrecords(LOC): print(record) def name_list(args): """Prints a list with the lengths of proteins""" for record in parse.uniprot_seqrecords(LOC): print(record.name) def proteins_average_lenght(args): """Prints the average length of all proteins""" print("Average Length is {}".format(analysis.average_len(parse.uniprot_seqrecords(LOC)))) def bar_plot_average_by_taxa(args): """Gives bar chart with the average length of top level taxa proteins""" av = analysis.average_len_taxa(parse.uniprot_seqrecords(LOC), depth = ()) plot.plot_bar_show(av) def pie_plot_average_by_taxa(args): """Gives pie chart with the average length of top level taxa proteins""" av = analysis.average_len_taxa(parse.uniprot_seqrecords(LOC), depth = ()) plot.plot_pie_show(av) def cli(): """Configures and describes parsing, protein data and help functions""" parser = argparse.ArgumentParser(prog = "uniplot", usage = '%(prog)s [options]') subparsers = parser.add_subparsers(help = "Sub Command Help") subparsers.add_parser("file_location").set_defaults(func = file_location_configuration) subparsers.add_parser("dump").set_defaults(func = dump) subparsers.add_parser("list").set_defaults(func = name_list) subparsers.add_parser("average").set_defaults(func = proteins_average_lenght) subparsers.add_parser("bar_average-by-taxa").set_defaults(func = bar_plot_average_by_taxa) subparsers.add_parser("pie_average-by-taxa").set_defaults(func = pie_plot_average_by_taxa) parser.add_argument('--file_location', help = 'allows the user to set the location of the file' 'that he wants to use') parser.add_argument('--dump', help = 'gives a list with all the information about proteins ' '- protein sequence, ID, name, lenght, description and other ' 'related data') parser.add_argument('--list', help = 'gives a list with only the lenghts of proteins') parser.add_argument('--average', help = 'gives average lenght of all proteins') parser.add_argument('--bar_average-by-taxa', help = 'gives average lenght of proteins categorized ' 'by type in a form of a bar chart') parser.add_argument('--pie_average-by-taxa', help = 'gives average lenght of proteins categorized ' 'by type in a form of a pie chart') args = parser.parse_args() args.func(args)

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0.262443

0.03352

0.048417

0.058194

0.379888

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0.252328

0.219739

0

0.226891

3,332

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0.833851

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0.111111

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0.274232

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false

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0.222222

0.092593

0

null

0

null

0

1

0

21aac5ef63f55e45d1b0efa2a2ae4e72550f661b

2,709

py

Python

tests/utils/adapters/test_source_adapter.py

damare01/novelsave

7896e8393c944e169e3cb52a33ab81ae396dff9f

[ "Apache-2.0" ]

null

tests/utils/adapters/test_source_adapter.py

damare01/novelsave

7896e8393c944e169e3cb52a33ab81ae396dff9f

[ "Apache-2.0" ]

null

tests/utils/adapters/test_source_adapter.py

damare01/novelsave

7896e8393c944e169e3cb52a33ab81ae396dff9f

[ "Apache-2.0" ]

null

import pytest from novelsave_sources import models as sm from novelsave.core import dtos from novelsave.utils.adapters import SourceAdapter @pytest.fixture def source_adapter() -> SourceAdapter: return SourceAdapter() def test_novel_to_internal(source_adapter): test_novel = sm.Novel( title="title", author="author", synopsis=["a nice description"], thumbnail_url="thumbnail", lang="language", url="link", ) expected_novel = dtos.NovelDTO( id=None, title="title", author="author", synopsis="a nice description", thumbnail_url="thumbnail", thumbnail_path=None, lang="language", url="link", last_updated=None, ) actual_novel = source_adapter.novel_to_internal(test_novel) assert expected_novel == actual_novel def test_chapter_to_internal(source_adapter): test_chapter = sm.Chapter( index=1, title="title", paragraphs="paragraphs this is", url="https://", ) expected_chapter = dtos.ChapterDTO( index=1, title="title", content="paragraphs this is", url="https://", ) actual_chapter = source_adapter.chapter_to_internal(test_chapter) assert expected_chapter == actual_chapter def test_chapter_from_internal(source_adapter): test_chapter = dtos.ChapterDTO( index=1, title="title", content="paragraphs this is", url="https://", ) expected_chapter = sm.Chapter( index=1, title="title", paragraphs="paragraphs this is", url="https://", ) actual_chapter = source_adapter.chapter_to_external(test_chapter) assert expected_chapter == actual_chapter def test_chapter_content_to_internal(source_adapter): test_chapter = sm.Chapter( index=1, title="title", paragraphs="paragraphs this is", url="https://", ) expected_chapter = dtos.ChapterDTO( index=-1, title="", url="", ) assert expected_chapter.content is None source_adapter.chapter_content_to_internal(test_chapter, expected_chapter) assert test_chapter.paragraphs == expected_chapter.content def test_metadata_to_internal(source_adapter): test_metadata = sm.Metadata( name="name", value="value", others={"role": "something"}, ) expected_metadata = dtos.MetaDataDTO( name="name", value="value", others={"role": "something"}, namespace="OPF", ) actual_metadata = source_adapter.metadata_to_internal(test_metadata) assert expected_metadata == actual_metadata

24.1875

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291

2,709

5.71134

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0.039711

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0

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2,709

111

79

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0

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0

0.068182

1

0.068182

false

0

0.045455

0.011364

0.125

0

null

0

null

0

1

0

21af623d6e191cda3a4a98ff6894dd3bac159239

570

py

Python

Python/coins_demo.py

Kodiologist/Citematic

03710d08d2a928f0b9bf4a37da056b5946a06c47

[ "Unlicense" ]

1

2016-12-02T20:32:34.000Z

Python/coins_demo.py

Kodiologist/Citematic

03710d08d2a928f0b9bf4a37da056b5946a06c47

[ "Unlicense" ]

null

Python/coins_demo.py

Kodiologist/Citematic

03710d08d2a928f0b9bf4a37da056b5946a06c47

[ "Unlicense" ]

null

#!/usr/bin/env python3 from sys import stderr from os import environ import yaml, cgi from citematic_coins import coins bib_path = environ['DAYLIGHT_BIB_PATH'] with open(bib_path) as o: database = yaml.load(o) print('''<!DOCTYPE html> <html lang="en-US"> <head> <meta charset="UTF-8"> <title>Bibliography in COinS</title> </head> <body>''') for n, x in enumerate(database): print('{} of {} ({})…'.format(n + 1, len(database), x['KEY']), file = stderr) print('<p>{}: {}\n'.format(cgi.escape(x['KEY']), coins(x['csl']))) print('</body></html>')

21.923077

81

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87

570

4.126437

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0.058496

0

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570

25

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0

0.222222

0

0.222222

0

null

0

null

0

1

0

21b2b134eb876a2908c4696de72ff6c0e1d441eb

4,694

py

Python

wsgi/scripts/subinfo_aggrgate.py

AlexeyProskuryakov/rr

dcf598405159a48826f4214fa33abcf57418b680

[ "MIT" ]

null

wsgi/scripts/subinfo_aggrgate.py

AlexeyProskuryakov/rr

dcf598405159a48826f4214fa33abcf57418b680

[ "MIT" ]

null

wsgi/scripts/subinfo_aggrgate.py

AlexeyProskuryakov/rr

dcf598405159a48826f4214fa33abcf57418b680

[ "MIT" ]

null

import logging from Queue import Empty from multiprocessing import Process, Event from multiprocessing import Queue as mQ from threading import Thread from Queue import Queue as Q from time import sleep import praw from wsgi.engine import get_reposts_count from wsgi.scripts import GET_USER_AGENT_R from wsgi.scripts.subinfo_elements import Users, RelationalElements, all_elements from wsgi.scripts.utils import comments_sequence from wsgi.sub_connections import SCStorage log = logging.getLogger("sub_info_agg") CMNT = "comment" ATHR = "author" r = praw.Reddit(user_agent=GET_USER_AGENT_R()) sc_store = SCStorage() def load_recommended(sub_name): recomended_subs = r.get_subreddit_recommendations(sub_name) for r_sub in recomended_subs: sc_store.add_connection(sub_name, r_sub.display_name, ct="recommendation") rr_subs = r.get_subreddit_recommendations(r_sub.display_name) for rr_sub in rr_subs: sc_store.add_connection(r_sub.display_name, rr_sub.display_name, ct="recommendation") def get_sub_users(sub_name, uq): log.info("Start getting users from: %s" % sub_name) sub = r.get_subreddit(sub_name) s_c, c_c = 0, 0 fsbm, esbm = None, None hot = list(sub.get_hot(limit=500)) log.info("Load %s hot posts in %s" % (len(hot), sub_name)) for subm in hot: if sc_store.is_contains(subm.fullname): log.info("%s is contains" % subm.fullname) continue if fsbm is None: fsbm = subm esbm = subm get_reposts_count(subm.url, {"subreddit": subm.subreddit.display_name, "created_utc": subm.created_utc}) if not subm.author: continue su = Users() su.add('author', subm.author.name) for comment in comments_sequence(subm.comments): if comment.author: su.add("comment", comment.author.name) c_c += 1 s_c += 1 log.info("\t%s processed; posts: %s, comments: %s uniques: %s", subm.fullname, s_c, c_c, len(su.all)) sc_store.u_add(subm.fullname) uq.put(su) sub_speed = float(s_c) / abs(esbm.created_utc - fsbm.created_utc) sc_store.set_sub_info(sub_name, {"speed": sub_speed}) def get_subs_from_users(users_queue, sub_queue, event): reddit = praw.Reddit(user_agent=GET_USER_AGENT_R()) while 1: user_name = qget(users_queue) if not user_name: break if sc_store.is_contains(user_name): log.info("%s is contains") continue log.info("Start load subs from comments and posts of %s" % user_name) user = reddit.get_redditor(user_name) us = RelationalElements() c_subs = set(map(lambda x: x.subreddit.display_name, user.get_comments())) p_subs = set(map(lambda x: x.subreddit.display_name, user.get_submitted())) u_subs = c_subs.union(p_subs) us.add_groups(u_subs, user_name) sub_queue.put(dict(us)) log.info("\tloaded %s subs of %s" % (len(u_subs), user_name)) sc_store.u_add(user_name) event.clear() def generate_subs(users): q_in, q_out = mQ(len(users)), mQ(len(users)) for u in users: q_in.put(u) te = [] for _ in range(8): e = Event() e.set() t = Process(target=get_subs_from_users, args=(q_in, q_out, e)) t.daemon = True t.start() te.append(e) while 1: result = qget(q_out) if not result: for e in te: if e.is_set(): continue break result = RelationalElements.create(result) yield result def qget(q): max_tryings = 5 while 1: try: return q.get() except Empty: if max_tryings > 0: max_tryings -= 1 sleep(1) continue except Exception as e: log.exception(e) return None def load_sub_users_and_reposts_connections(sub): users_queue = Q(500) p = Thread(target=get_sub_users, args=(sub, users_queue)) p.daemon = True p.start() all_users = set() while 1: su = qget(users_queue) if not su: break su.compile_subs(generate_subs) for r_sub, users in su.subs.iteritems(): if r_sub != all_elements: sc_store.add_connection(r_sub, sub, ons=users, ct="users") all_users.union(su.all) sc_store.set_sub_info(sub, {"unique_users_count": len(all_users)}) if __name__ == '__main__': # load_recommended("cringe") load_sub_users_and_reposts_connections("cringe") pass

28.448485

112

0.626545

673

4,694

4.123328

0.213967

0.025225

0.02018

0.014054

0.206126

0.110991

0.055496

0.032432

0

0.005557

0.271623

4,694

164

113

28.621951

0.806084

0.005539

0

0.094488

0

0.069653

0

1

0.047244

false

0.007874

0.102362

0

0.165354

0

null

0

null

0

1

0

21b594ae9bbaf94ccb0e9c28f5fa4f7f82326ce0

2,955

py

Python

Wrapping/Generators/Python/itk_generate_pyi.py

rflanz/ITK

7863da6db1fd541487c9e543308dcfcea26d3261

[ "Apache-2.0" ]

1

2021-03-17T14:09:24.000Z

Wrapping/Generators/Python/itk_generate_pyi.py

rflanz/ITK

7863da6db1fd541487c9e543308dcfcea26d3261

[ "Apache-2.0" ]

null

Wrapping/Generators/Python/itk_generate_pyi.py

rflanz/ITK

7863da6db1fd541487c9e543308dcfcea26d3261

[ "Apache-2.0" ]

null

from typing import List, Any import inspect import importlib import sys try: # First attempt using convention of build directory from pathlib import Path wrap_itk_pth: Path = Path(__file__).parent / "WrapITK.pth" if not wrap_itk_pth.is_file(): print( "ERROR: itk_generate_pyi.py must be run in the same directory as the WrapITK.pth file" ) with open(wrap_itk_pth, "r") as fid: itk_module_paths = [ itk_module_path.strip() for itk_module_path in fid.readlines() ] for pp in itk_module_paths: if not pp.startswith("#"): sys.path.append(pp) import itkConfig except: # Second attempt on the standard path import itkConfig itkConfig.LazyLoading = False itkConfig.DumpInterfaces = True requested_module_name = "itk" requested_module = importlib.import_module(requested_module_name) # Can not dump complete .pyi interface file if LazyLoading is ued class ITKSignaturesList: """ A pure static class to manage dumping a .pyi file for the itk_module. """ _itk_namespace_list: List[str] = [] _broken_introspection_signatures: List[str] = ["echo", "image", "string", "str"] @staticmethod def parse_object(obj_name: str, obj: Any): # builtin classes do not have introspection signatures. if inspect.isbuiltin(obj): return elif obj_name in ITKSignaturesList._broken_introspection_signatures: return elif obj_name.startswith("_"): return elif inspect.isclass(obj): ITKSignaturesList._itk_namespace_list.append(f"class {obj_name}:") methods_exists: bool = False for elem_name, elem_obj in obj.__dict__.items(): if inspect.ismethod(elem_obj) or inspect.isfunction(elem_obj): methods_exists = True ITKSignaturesList._itk_namespace_list.append( f" def {elem_name}{inspect.signature(elem_obj)}: ..." ) if not methods_exists: ITKSignaturesList._itk_namespace_list[-1] += " ..." elif inspect.isfunction(obj): ITKSignaturesList._itk_namespace_list.append( f"def {obj_name}{inspect.signature(obj)}: ..." ) # else: # print(f"{obj_name}: {type(obj)}") @staticmethod def dumps(dump_file: str) -> None: with open(dump_file, "w") as fid: for ln in ITKSignaturesList._itk_namespace_list: fid.write(f"{ln}\n") # Now iterate through all module items and print of signatures # of the objects collected. The generation of a .pyi file # allows IDE's and other tools to do better introspection all_items = list(requested_module.__dict__.items()) for k, v in all_items: ITKSignaturesList.parse_object(k, v) ITKSignaturesList.dumps(requested_module.__file__ + "i")

34.360465

98

0.643655

362

2,955

5.01105

0.367403

0.039691

0.052922

0.090959

0.072767

0

0.000463

0.268359

2,955

85

99

34.764706

0.838575

0.166497

0

0.15

0

0.100205

0.031212

0

1

0.033333

false

0

0.133333

0

0.266667

0.016667

0

null

0

null

0

1

0

21b789e1f1b1d8c14c64211b16ff443f97bbdcbe

33,445

py

Python

challenge_submissions/submit_767200/supplements/bot_agent_1.py

opendilab/GoBigger-Challenge-2021

794897e94caf15b69635dcb36eb64e1341131db9

[ "Apache-2.0" ]

121

2021-11-01T01:24:31.000Z

2022-03-31T10:53:47.000Z

challenge_submissions/submit_767200/supplements/bot_agent_1.py

opendilab/GoBigger-Challenge-2021

794897e94caf15b69635dcb36eb64e1341131db9

[ "Apache-2.0" ]

2

2021-11-06T14:13:00.000Z

2022-01-25T09:19:41.000Z

challenge_submissions/submit_767200/supplements/bot_agent_1.py

opendilab/GoBigger-Challenge-2021

794897e94caf15b69635dcb36eb64e1341131db9

[ "Apache-2.0" ]

39

2021-11-01T12:48:19.000Z

2022-03-01T11:23:38.000Z

import math import os import random import logging import copy import queue from pygame.math import Vector2 from gobigger.agents.base_agent import BaseAgent # from .base_agent import BaseAgent tabu_size=1 position_size=11 class BotAgent(BaseAgent): ''' Overview: A simple script bot ''' def __init__(self, name=None, level=3): self.name = name self.actions_queue = queue.Queue() self.last_clone_num = 1 self.last_total_size = 0 self.level = level self.my_score=0 # self.team_score=0 self.position_times=[] def step(self, obs): global_obs,obs=obs # self.team_score=global_obs['leaderboard'][str(int(self.name)//3)] ally_info,others_clone_balls=self.process_ally_balls(obs) obs=obs[self.name] overlap = obs['overlap'] overlap = self.preprocess(overlap) food_balls = overlap['food'] thorns_balls = overlap['thorns'] spore_balls = overlap['spore'] clone_balls = overlap['clone'] food_balls.extend(spore_balls) my_clone_balls, ally_clone_balls, near_other_balls = self.process_clone_balls_1(clone_balls) my_total_score=sum(my_ball['radius']**2 for my_ball in my_clone_balls) for my_ball in my_clone_balls: for position, time in self.position_times: if (my_ball['position'] - position).length() < 6.5: my_ball['time'] = time break else: my_ball['time'] = 101 my_ball['time'] = max(0, my_ball['time'] - 1) self.position_times = [(my_ball['position'], my_ball['time']) for my_ball in my_clone_balls] if abs(self.last_total_size-my_clone_balls[0]['radius'])<0.01: self.stay_same_times+=1 else: self.stay_same_times=0 if self.stay_same_times>5 and self.last_total_size<10 and len(my_clone_balls)==1: self.stay_same_times = 0 # print(fr'={self.name}===================') self.actions_queue.put([None, None, 2]) self.actions_queue.put([None, None, 2]) self.actions_queue.put([None, None, 2]) self.actions_queue.put([None, None, 2]) self.actions_queue.put([None, None, 2]) action_ret = self.actions_queue.get() return action_ret self.last_total_size=my_clone_balls[0]['radius'] direction_attact=self.attact(my_clone_balls,others_clone_balls,thorns_balls) if direction_attact and len(my_clone_balls)<16: direction=direction_attact.normalize() action_type = 1 # print(f'{self.name}nnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnattack') self.actions_queue.queue.clear() self.actions_queue.put([direction.x, direction.y, action_type]) action_ret = self.actions_queue.get() return action_ret direction_attact=self.attact2(my_clone_balls,others_clone_balls,thorns_balls) if direction_attact and len(my_clone_balls)<16: direction=direction_attact.normalize() action_type = 1 # print(f'{self.name}nnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnattack') self.actions_queue.queue.clear() self.actions_queue.put([direction.x, direction.y, action_type]) action_ret = self.actions_queue.get() return action_ret # if self.actions_queue.qsize() > 0: # return self.actions_queue.get() direction,danger = self.APF(my_clone_balls, others_clone_balls) if direction.length()>=1: direction=direction.normalize() action_type = -1 self.actions_queue.queue.clear() self.actions_queue.put([direction.x, direction.y, action_type]) action_ret = self.actions_queue.get() return action_ret direction0=self.near_thorns_balls(thorns_balls, my_clone_balls, others_clone_balls) if direction0: action_type = -1 self.actions_queue.queue.clear() self.actions_queue.put([direction0.x, direction0.y, action_type]) action_ret = self.actions_queue.get() return action_ret elif self.actions_queue.qsize() > 0: return self.actions_queue.get() elif direction.length()<0.5 and danger and len(my_clone_balls)>3: self.actions_queue.queue.clear() self.actions_queue.put([None, None, 2]) self.actions_queue.put([None, None, -1]) self.actions_queue.put([None, None, -1]) self.actions_queue.put([None, None, -1]) self.actions_queue.put([None, None, -1]) self.actions_queue.put([None, None, -1]) self.actions_queue.put([None, None, -1]) self.actions_queue.put([None, None, -1]) self.actions_queue.put([None, None, -1]) self.actions_queue.put([None, None, -1]) action_ret = self.actions_queue.get() return action_ret for other_ball in others_clone_balls: if 800<my_total_score/6<other_ball['radius']**2<0.47*my_total_score and (other_ball['position']-my_clone_balls[0]['position']).length()<2*math.sqrt(0.9*my_total_score): self.actions_queue.put([None, None, 2]) self.actions_queue.put([None, None, -1]) self.actions_queue.put([None, None, -1]) self.actions_queue.put([None, None, -1]) self.actions_queue.put([None, None, -1]) self.actions_queue.put([None, None, -1]) self.actions_queue.put([None, None, -1]) self.actions_queue.put([None, None, 0]) self.actions_queue.put([None, None, 0]) self.actions_queue.put([None, None, 0]) self.actions_queue.put([None, None, 0]) self.actions_queue.put([None, None, 0]) self.actions_queue.put([None, None, 0]) self.actions_queue.put([None, None, 0]) self.actions_queue.put([None, None, 0]) action_ret = self.actions_queue.get() # print('/////////////////////////////') return action_ret if (len(my_clone_balls) >= 9 and my_clone_balls[4]['radius'] > 20) or (my_clone_balls[0]['radius']**2<my_total_score/4 and self.my_score>5000) or \ (len(my_clone_balls)>1 and (my_clone_balls[0]['position']-my_clone_balls[1]['position']).length()<1.2*my_clone_balls[0]['radius']): self.actions_queue.put([None, None, 2]) self.actions_queue.put([None, None, -1]) self.actions_queue.put([None, None, -1]) self.actions_queue.put([None, None, -1]) self.actions_queue.put([None, None, -1]) self.actions_queue.put([None, None, -1]) self.actions_queue.put([None, None, -1]) self.actions_queue.put([None, None, 0]) self.actions_queue.put([None, None, 0]) self.actions_queue.put([None, None, 0]) self.actions_queue.put([None, None, 0]) self.actions_queue.put([None, None, 0]) self.actions_queue.put([None, None, 0]) self.actions_queue.put([None, None, 0]) self.actions_queue.put([None, None, 0]) action_ret = self.actions_queue.get() return action_ret else: direction = self.APF2(direction,my_clone_balls, others_clone_balls, ally_info, food_balls, thorns_balls) action_type=-1 self.actions_queue.put([direction.x, direction.y, action_type]) action_ret = self.actions_queue.get() return action_ret def process_clone_balls_1(self, clone_balls): my_clone_balls = [] ally_clone_balls=[] others_clone_balls = [] for clone_ball in clone_balls: if clone_ball['player'] == self.name: my_clone_balls.append(copy.deepcopy(clone_ball)) elif clone_ball['team'] == str(int(self.name) // 3): ally_clone_balls.append(copy.deepcopy(clone_ball)) else: others_clone_balls.append(copy.deepcopy(clone_ball)) my_clone_balls.sort(key=lambda a: a['radius'], reverse=True) ally_clone_balls.sort(key=lambda a: a['radius'], reverse=True) others_clone_balls.sort(key=lambda a: a['radius'], reverse=True) # others_clone_balls.sort(key=lambda a: (my_clone_balls[0]['position'] - a['position']).length()-2*a['radius'], reverse=False) # threaten_balls=[] # for other_ball in others_clone_balls: # for my_ball in my_clone_balls[0:int(2*len(my_clone_balls)/3)]: # if my_ball['radius']<other_ball['radius']: return my_clone_balls,ally_clone_balls, others_clone_balls def preprocess(self, overlap): new_overlap = {} for k, v in overlap.items(): if k == 'clone': new_overlap[k] = [] for index, vv in enumerate(v): tmp = {} tmp['position'] = Vector2(vv[0], vv[1]) tmp['radius'] = vv[2] tmp['player'] = str(int(vv[-2])) tmp['team'] = str(int(vv[-1])) new_overlap[k].append(tmp) else: new_overlap[k] = [] for index, vv in enumerate(v): tmp = {} tmp['position'] = Vector2(vv[0], vv[1]) tmp['radius'] = vv[2] new_overlap[k].append(tmp) return new_overlap def preprocess_tuple2vector(self, overlap): new_overlap = {} for k, v in overlap.items(): new_overlap[k] = [] for index, vv in enumerate(v): new_overlap[k].append(vv) new_overlap[k][index]['position'] = Vector2(*vv['position']) return new_overlap def covered(self,my_ball,other_ball,my_clone_balls): neighbor_balls=[] other_ball_newradius=math.sqrt(my_ball['radius']**2+other_ball['radius']**2/2) for my_ball0 in my_clone_balls: if my_ball0['radius']<other_ball_newradius and (my_ball0['position']-my_ball['position']).length()<other_ball_newradius: neighbor_balls.append(my_ball0) tmp=other_ball['radius']**2/2 for neighbor_ball in neighbor_balls: tmp+=neighbor_ball['radius']**2 new_radius=math.sqrt(tmp) for my_big_ball in my_clone_balls: if 0.71*my_big_ball['radius']>new_radius: if my_big_ball['radius']*2.12>(my_big_ball['position']-my_ball['position']).length()+0.71*other_ball['radius']: # print('==========',self.name) return True else: return False return False def APF(self,my_clone_balls,other_clone_balls): danger=False rep = Vector2(0, 0) # 所有障碍物总斥力 for my_ball in my_clone_balls: for other_ball in other_clone_balls: t_vec = my_ball['position'] - other_ball['position'] if my_ball['radius']>other_ball['radius'] or t_vec.length()>20+2.5*other_ball['radius']: pass elif t_vec.length()>20+other_ball['radius'] and self.covered(my_ball,other_ball,my_clone_balls): pass else: direction=t_vec.normalize() rep_tmp=direction * 10000 * (2.5 / (t_vec.length()-20)- 1.0 / other_ball['radius']) / (t_vec.length())*(other_ball['radius']/my_clone_balls[0]['radius'])**2 rep_tmp=rep_tmp * my_ball['radius'] ** 2 / self.my_score * 10 if my_ball['radius']<0.71*other_ball['radius'] and \ (other_ball['position']-my_ball['position']).length()<10+2.12*other_ball['radius']: #3倍根号2 rep_tmp=rep_tmp*3 new_radius = math.sqrt(my_ball['radius'] ** 2 + 0.5 * other_ball['radius'] ** 2) for my_ball_1 in my_clone_balls: if my_ball_1 !=my_ball and 0.71*new_radius >my_ball_1['radius'] and (my_ball['position']-my_ball_1['position']).length()<10+2.12*new_radius: rep+=rep_tmp*(my_ball_1['radius']/my_ball['radius']) # print('xxxxxxxxxxxxasfasfasd',self.name) # avg_radius=math.sqrt(sum(tmp_ball['radius']**2 for tmp_ball in my_clone_balls)/len(my_clone_balls)) # if my_ball['radius']<0.71*other_ball['radius'] and my_ball['radius']>avg_radius*0.8 and \ # (other_ball['position']-my_ball['position']).length()<20+2.212*other_ball['radius']: #3倍根号2 # rep_tmp=rep_tmp*len(my_clone_balls) # print('xxxxxxxxxxxxasfasfasd') rep +=rep_tmp if rep_tmp.length()>=5: danger=True return rep,danger def APF2(self,rep,my_clone_balls,other_clone_balls,ally_info,food_balls,thorns_balls): food_balls.extend(thorns_balls) neighbor_food_balls=[] att=Vector2(0.01, 0.01) #食物球,队友引力 #队友合并 min_time=10 direct=None for my_ball in my_clone_balls: for ally_ball in ally_info: if ally_ball[2] !=self.name and len(my_clone_balls)>4 and ally_ball[3]>2000 and self.my_score>2000: dis=(ally_ball[1]-my_ball['position']).length() time=(dis-max(my_ball['radius'],ally_ball[0]))/((500/(10+my_ball['radius']))+(500/(10+ally_ball[0]))) if dis<1.5*(my_ball['radius']+ally_ball[0]) and time<min_time: min_time=time direct=(ally_ball[1]-my_ball['position']).normalize() if direct: return direct # if len(my_clone_balls)>=15 and my_clone_balls[-1]['radius']<ally_info[0][0]: # direct=ally_info[0][1]-my_clone_balls[-1]['position'] # att+=direct.normalize()*math.sqrt(direct.length()) # if (ally_info[0][1]-my_clone_balls[0]['position']).length()-my_clone_balls[0]['radius']>350 and 3000<self.my_score<6000: # direct=ally_info[0][1]-my_clone_balls[0]['position'] # att+=direct.normalize()*math.sqrt(direct.length()) # if len(my_clone_balls)>=15: # if self.name != ally_info[0][2] and my_clone_balls[-1]['radius']<ally_info[0][0]: # direct=ally_info[0][1]-my_clone_balls[-1]['position'] # att+=direct.normalize()*math.sqrt(direct.length()) # elif my_clone_balls[-1]['radius']<ally_info[1][0]: # direct=ally_info[1][1]-my_clone_balls[-1]['position'] # att+=direct.normalize()*math.sqrt(direct.length()) # elif (ally_info[0][1]-my_clone_balls[0]['position']).length()-my_clone_balls[0]['radius']>400 and 30<my_clone_balls[0]['radius']<80: # direct=ally_info[0][1]-my_clone_balls[0]['position'] # att+=direct.normalize()*math.sqrt(direct.length()) # elif self.name != ally_info[0][2] and ally_info[0][3] > 5000: # self_score=[info[3] for info in ally_info if info[2]==self.name][0] # if self_score>2000: # direct = ally_info[0][1] - my_clone_balls[0]['position'] # att += direct.normalize() * math.sqrt(direct.length()) for my_ball in my_clone_balls: for food_ball in food_balls: x,y=food_ball['position'][0],food_ball['position'][1] dis=min(x+y,1000-x+y,1000-y+x,2000-x-y) if my_ball['radius']>=food_ball['radius'] and dis>my_ball['radius']: t_vec = food_ball['position'] - my_ball['position'] if food_ball['radius']>5 and len(my_clone_balls)<16 and t_vec.length()<50+my_ball['radius']/10 and \ (not self.is_thorns_ball_safe(my_ball, food_ball, other_clone_balls,16-len(my_clone_balls),my_clone_balls)): #如果荆棘球不安全，会产生斥力? if t_vec.length()>10+my_ball['radius']: continue else: t_vec=-t_vec att += t_vec.normalize() * (food_ball['radius'] ** 2) / t_vec.length()*10 else: att+=t_vec.normalize()*(food_ball['radius']**2)/t_vec.length() if t_vec.length()<100+my_ball['radius']/10 and food_ball not in neighbor_food_balls: neighbor_food_balls.append(food_ball) # elif dis>my_ball['radius'] and 0.8*my_ball['radius']>food_ball['radius']: # t_vec = food_ball['position'] - my_ball['position'] # att += t_vec.normalize() * (food_ball['radius'] ** 2) *min(t_vec.length(),50)/ 5000*(my_ball['radius']/food_ball['radius'])**2 # # print('222222222222222222') # for other_bal l in other_clone_balls: # if my_ball['radius']>other_ball['radius']: # t_vec = other_ball['position'] - my_ball['position'] # dis_x=1000-other_ball['position'][0]-other_ball['radius'] if t_vec[0]>0 else other_ball['position'][0]-other_ball['radius'] # dis_y=1000-other_ball['position'][1]-other_ball['radius'] if t_vec[1]>0 else other_ball['position'][1]-other_ball['radius'] # # time=(dis_x+dis_y-my_ball['radius'])/(250/(10+my_ball['radius'])) # if t_vec.length()<100+my_ball['radius']/10 and time<50 and other_ball['radius']>50: # print('------------------------------------',time,self.name,other_ball['player']) # # att += t_vec.normalize() * (other_ball['radius'] ** 2 / 5) / time # neighbor_food_balls.append(other_ball) try: direction_goal=(rep+att).normalize() except: direction_goal =att.normalize() print(f'att:{att}') print(f'rep:{rep}') best_direction=copy.deepcopy(direction_goal) min_div=100000 for my_ball in my_clone_balls: for food_ball in neighbor_food_balls: if my_ball['radius']>food_ball['radius']: copy_balls=copy.deepcopy(my_clone_balls) for copy_ball in copy_balls: copy_ball['position']=copy_ball['position']+(food_ball['position']-my_ball['position'])*(10+my_ball['radius'])/(10+copy_ball['radius']) direction,danger=self.APF(copy_balls,other_clone_balls) if direction.length()>2 or danger: continue t_vec=food_ball['position']-my_ball['position'] diverse=((direction_goal-t_vec.normalize()).length()+0.5)*(t_vec.length()-my_ball['radius'])/(250/(10+my_ball['radius']))/food_ball['radius'] if diverse<min_div: min_div=diverse best_direction=t_vec.normalize() if best_direction==direction_goal: for my_ball in my_clone_balls: for food_ball in neighbor_food_balls: if my_ball['radius'] > food_ball['radius']: t_vec = food_ball['position'] - my_ball['position'] diverse = ((direction_goal - t_vec.normalize()).length() + 0.5) * ( t_vec.length() - my_ball['radius']) / (250 / (10 + my_ball['radius'])) / food_ball[ 'radius'] if diverse < min_div: min_div = diverse best_direction = t_vec.normalize() return best_direction def near_thorns_balls(self,thorns_balls,my_clone_balls,others_clone_balls): min_div=3 best_direction=None for my_ball in my_clone_balls: for thorns_ball in thorns_balls: if my_ball['radius']>thorns_ball['radius'] and self.is_thorns_ball_safe(my_ball,thorns_ball,others_clone_balls,16-len(my_clone_balls),my_clone_balls): t_vec=thorns_ball['position']-my_ball['position'] diverse=(t_vec.length()-my_ball['radius'])/(250/(10+my_ball['radius'])) if diverse<min_div: min_div=diverse best_direction=t_vec.normalize() return best_direction def is_thorns_ball_safe(self,my_ball,thorns_ball,other_balls,len,my_clone_balls): for other_ball in other_balls: my_ball_tmp=copy.deepcopy(my_ball) my_ball_tmp['radius']=math.sqrt(my_ball_tmp['radius']**2+thorns_ball['radius']**2) if self.covered(my_ball_tmp,other_ball,my_clone_balls): continue new_radius_sqr=(my_ball['radius']**2+thorns_ball['radius']**2)/min(10,(len+1)) new_radius1_sqr=my_ball['radius']**2+thorns_ball['radius']**2-400*min(9,len) new_radius=math.sqrt(max(new_radius1_sqr,new_radius_sqr)) if other_ball['radius']*0.71>new_radius: enemy_radius=max(math.sqrt(other_ball['radius']**2/2+min(400,new_radius_sqr)*min(9,len)/3),other_ball['radius']) else: enemy_radius=other_ball['radius'] if (thorns_ball['position'] - my_ball['position']).length()-my_ball['radius']>(thorns_ball['position'] - other_ball['position']).length()-10-2.12*other_ball['radius'] and \ new_radius<enemy_radius and other_ball['radius']>10: return False return True def process_ally_balls(self,obs): ally_info=[] others_clone_balls = [] other_ball_position = [] for name,obs_player in obs.items(): # if name != self.name: overlap = obs_player['overlap'] overlap = self.preprocess(overlap) ally_clone_balls_obs = overlap['clone'] ally_clone_balls=[] totol_score=0 for clone_ball in ally_clone_balls_obs: if clone_ball['player'] == name: ally_clone_balls.append(copy.deepcopy(clone_ball)) totol_score+=clone_ball['radius']**2 elif clone_ball['team'] != str(int(self.name) // 3) and clone_ball['position'] not in other_ball_position: others_clone_balls.append(copy.deepcopy(clone_ball)) other_ball_position.append(clone_ball['position']) ally_clone_balls.sort(key=lambda a: a['radius'], reverse=True) ally_info.append([ally_clone_balls[0]['radius'],ally_clone_balls[0]['position'],name,totol_score]) if name==self.name: self.my_score=totol_score ally_info.sort(key=lambda a:a[3],reverse=True) others_clone_balls.sort(key=lambda a: a['radius'], reverse=True) for i in range(len(others_clone_balls)-1): for j in range(i+1,len(others_clone_balls)): other_ball=others_clone_balls[i] other_ball1=others_clone_balls[j] if (other_ball['position']-other_ball1['position']).length()<1.1*other_ball['radius']: other_ball['radius']=math.sqrt(other_ball['radius']**2+other_ball1['radius']**2) return ally_info,others_clone_balls def is_safe(self,my_ball,other_balls,direction,culed_reward_pos): loss=0 reward=0 new_position = my_ball['position'] + direction * (10 + 1.41 * my_ball['radius']) for other_ball in other_balls: if my_ball['radius']<1.01*other_ball['radius'] and (other_ball['position']-my_ball['position']).length()<15+2.2*other_ball['radius']: loss += my_ball['radius'] ** 2 / 2 if 0.7*my_ball['radius']<other_ball['radius'] and \ (new_position-other_ball['position']).length()<other_ball['radius']: loss+=my_ball['radius']**2/2 elif my_ball['radius']<1.01*other_ball['radius'] and (other_ball['position']-new_position).length()<15+2.2*other_ball['radius']: loss += my_ball['radius'] ** 2 / 2 elif 0.7*my_ball['radius']>other_ball['radius'] and (new_position-other_ball['position']).length()<0.7*my_ball['radius'] and other_ball['position'] not in culed_reward_pos: reward+=other_ball['radius']**2 culed_reward_pos.append(other_ball['position']) loss=min(loss,my_ball['radius'] ** 2) # if 0.7*my_ball['radius']>other_ball['radius']: # return loss return loss,reward def attact(self,my_clone_balls,other_clone_balls,thorns_balls): for other_ball in other_clone_balls: for i in range(max(min(len(my_clone_balls),16-len(my_clone_balls)),0)): if 0.7*my_clone_balls[i]['radius']>other_ball['radius'] and 0.05*self.my_score<other_ball['radius']**2 and \ (my_clone_balls[i]['position'] - other_ball['position']).length() <= max(0,15-(500 / (10 + other_ball['radius'])))+ 2.12*my_clone_balls[i]['radius']: flag=False if len(my_clone_balls)<8: new_position = my_clone_balls[i]['position'] + (other_ball['position'] - my_clone_balls[i]['position']).normalize() * (1.41 * my_clone_balls[i]['radius'] + 5) for throns_ball in thorns_balls: if (throns_ball['position']-new_position).length()<0.71*my_clone_balls[i]['radius']+5: # print('[[[[[[[[[[[[[[[[[[[[[[[[[[[[[') flag=True break if flag: continue direction=(other_ball['position'] - my_clone_balls[i]['position']).normalize() reward=other_ball['radius']**2 loss=0 culed_reward_pos=[other_ball['position']] for j in range(min(len(my_clone_balls),16-len(my_clone_balls))): loss1,reward1=self.is_safe(my_clone_balls[j],other_clone_balls,direction,culed_reward_pos) loss+=loss1 reward+=reward1 if reward-loss<0.05*self.my_score: break else: return direction return False def attact1(self,my_clone_balls,other_clone_balls,thorns_balls): if len(my_clone_balls)<=15 and my_clone_balls[0]['radius']>50: for other_ball in other_clone_balls: if 0.49*my_clone_balls[0]['radius']>other_ball['radius'] and 0.3*my_clone_balls[0]['radius']<other_ball['radius'] and\ (my_clone_balls[0]['position'] - other_ball['position']).length() <= (1.41+1.5) *my_clone_balls[0]['radius']: direction=(other_ball['position'] - my_clone_balls[0]['position']).normalize() position1=my_clone_balls[0]['position']+direction*1.41 *my_clone_balls[0]['radius'] if len(my_clone_balls)>7: for j in range(1,len(my_clone_balls)): if (my_clone_balls[j]['position']-position1).length()<0.71*my_clone_balls[0]['radius'] and my_clone_balls[j]['time']==0: break else: continue #判断是否有荆棘球 if self.attact_thorns(position1, my_clone_balls[0]['radius'],len(my_clone_balls),thorns_balls): continue my_new_clone_balls=copy.deepcopy(my_clone_balls) for i in range(min(len(my_clone_balls), 16 - len(my_clone_balls))): my_new_clone_balls[i]['radius']=0.71*my_new_clone_balls[i]['radius'] new_clone_tmp=copy.deepcopy(my_new_clone_balls[i]) new_clone_tmp['position']=new_clone_tmp['position']+direction*1.41 *new_clone_tmp['radius'] new_clone_tmp['radius']=new_clone_tmp['radius']+0.1 #保证新分裂的排第一个 my_new_clone_balls.append(new_clone_tmp) my_new_clone_balls.sort(key=lambda a: a['radius'], reverse=True) reward=other_ball['radius']**2 loss=0 culed_reward_pos=[other_ball['position']] for j in range(min(len(my_clone_balls),16-len(my_clone_balls))): loss1,reward1=self.is_safe(my_clone_balls[j],other_clone_balls,direction,culed_reward_pos) loss+=loss1 reward+=reward1 for j in range(max(1,min(len(my_new_clone_balls), 16 - len(my_new_clone_balls)))): loss1,reward1=self.is_safe(my_new_clone_balls[j], other_clone_balls, direction,culed_reward_pos) loss += loss1 reward += reward1 if reward-loss<0.05*self.my_score: continue else: return direction return False def attact2(self,my_clone_balls,other_clone_balls,thorns_balls): if (len(my_clone_balls)<=7 or (7<len(my_clone_balls)<16 and my_clone_balls[15-len(my_clone_balls)]['radius']<10)) and my_clone_balls[0]['radius']>50: for other_ball in other_clone_balls: if 0.49*my_clone_balls[0]['radius']>other_ball['radius'] and 0.3*my_clone_balls[0]['radius']<other_ball['radius'] and\ (my_clone_balls[0]['position'] - other_ball['position']).length() <= (1.41+1.5) *my_clone_balls[0]['radius']: direction=(other_ball['position'] - my_clone_balls[0]['position']).normalize() position1=my_clone_balls[0]['position']+direction*1.41 *my_clone_balls[0]['radius'] # if len(my_clone_balls)>7: # for j in range(1,len(my_clone_balls)): # if (my_clone_balls[j]['position']-position1).length()<0.71*my_clone_balls[0]['radius'] and my_clone_balls[j]['time']==0: # break # else: # continue #判断是否有荆棘球 if self.attact_thorns(position1, my_clone_balls[0]['radius'],len(my_clone_balls),thorns_balls): continue my_new_clone_balls=copy.deepcopy(my_clone_balls) for i in range(min(len(my_clone_balls), 16 - len(my_clone_balls))): my_new_clone_balls[i]['radius']=0.71*my_new_clone_balls[i]['radius'] new_clone_tmp=copy.deepcopy(my_new_clone_balls[i]) new_clone_tmp['position']=new_clone_tmp['position']+direction*1.41 *new_clone_tmp['radius'] new_clone_tmp['radius']=new_clone_tmp['radius']+0.1 #保证新分裂的排第一个 my_new_clone_balls.append(new_clone_tmp) my_new_clone_balls.sort(key=lambda a: a['radius'], reverse=True) reward=other_ball['radius']**2 loss=0 culed_reward_pos=[other_ball['position']] for j in range(min(len(my_clone_balls),16-len(my_clone_balls))): loss1,reward1=self.is_safe(my_clone_balls[j],other_clone_balls,direction,culed_reward_pos) loss+=loss1 reward+=reward1 for j in range(max(1,min(len(my_new_clone_balls), 16 - len(my_new_clone_balls)))): loss1,reward1=self.is_safe(my_new_clone_balls[j], other_clone_balls, direction,culed_reward_pos) loss += loss1 reward += reward1 if reward-loss<0.05*self.my_score: continue else: return direction return False def attact_thorns(self,new_position,radius,len,thorns_balls): if len<8: for thorns_ball in thorns_balls: if (thorns_ball['position']-new_position).length()<radius: return True return False

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false

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0.004175

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null

0

null

0

1

0

21c1c1fd0d436d4aa0d902e64ebd89bf247f49da

948

py

Python

chapter1/problem_1_20.py

JBannwarth/OrbitalMechanics

fe3c36eba7cadf977804fb2ad866e0e28a96aab5

[ "MIT" ]

1

2020-08-29T13:34:48.000Z

chapter1/problem_1_20.py

JBannwarth/OrbitalMechanics

fe3c36eba7cadf977804fb2ad866e0e28a96aab5

[ "MIT" ]

1

2020-09-06T21:17:51.000Z

2020-09-07T00:52:39.000Z

chapter1/problem_1_20.py

JBannwarth/OrbitalMechanics

fe3c36eba7cadf977804fb2ad866e0e28a96aab5

[ "MIT" ]

null

""" Orbital Mechanics for Engineering Students Problem 1.20 Question: Numerically solve the second order-differential equation t*yDDot + t^2*yDot - 2*y = 0 for y at t = 4, if the initial conditions at t=1 are: - y = 0 - yDot = 1 Written by: J.X.J. Bannwarth """ import numpy as np import matplotlib.pyplot as plt from orbitutils.solvers import rkf45 # Differential equations def Rates(t, Y): F = np.zeros(Y.shape) F[0] = Y[1] F[1] = 2.*Y[0]/t - t*Y[1] return F # Title print("Orbital Mechanics for Engineering Students Problem 1.20") # Parameters tSpan = np.array([1., 4.]) Y0 = np.array([0., 1.]) # Solve numerically y, t = rkf45(Rates, Y0, tSpan) # Show answer print(f"y({t[-1]:.3f}) = {y[-1,0]:.3f}") # Plot answer plt.figure() plt.plot(t, y[:, 0], label="y") plt.plot(t, y[:, 1], label="yDot") plt.xlabel("Time (-)") plt.ylabel("Value (-)") plt.legend() plt.show()

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false

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0.1

0

null

0

null

0

1

0

21c44a1f70cf3b10e10c40da9f79c822ef4ae113

8,798

py

Python

boilerplate_app/tests.py

rishabhst/graph_test_task

5d342b077401903cf642bfdc8c49a1edb83ec9d7

[ "MIT" ]

null

boilerplate_app/tests.py

rishabhst/graph_test_task

5d342b077401903cf642bfdc8c49a1edb83ec9d7

[ "MIT" ]

2

2020-06-05T20:42:56.000Z

2021-06-10T21:25:31.000Z

boilerplate_app/tests.py

rishabhst/graph_test_task

5d342b077401903cf642bfdc8c49a1edb83ec9d7

[ "MIT" ]

null

# from model_mommy import mommy # from django.test import TestCase # from rest_framework_jwt.serializers import JSONWebTokenSerializer # from boilerplate_app.models import User # from boilerplate_app.serializers import UserListSerializer, UserCreateSerializer # class APITests(TestCase): # def test_list_user(self): # user = mommy.make(User) # self.assertTrue(isinstance(user, User)) # user_serializer = UserListSerializer(user) # assert user.id == user_serializer.data.get('id') # assert user.first_name == user_serializer.data.get('first_name') # assert user.last_name == user_serializer.data.get('last_name') # assert user.email == user_serializer.data.get('email') # assert user.role == user_serializer.data.get('role') # def test_register(self): # request_data = { # "username": "username", # "first_name" : "first_name", # "last_name" : "last_name", # "email" : "email@gmail.com", # "password" : "qwerty1234", # "role" : "role" # } # user_serializer = UserCreateSerializer(data=request_data) # if user_serializer.is_valid(): # pass # else: # message = '' # for error in user_serializer.errors.values(): # message += " " # message += error[0] # print(message) # user = User(username=request_data.get('username'), first_name=request_data.get('first_name'), last_name=request_data.get('last_name'), email=request_data.get('email'), password=request_data.get('password'), role=request_data.get('role')) # assert request_data.get('username') == user_serializer.data.get('username') # assert request_data.get('first_name') == user_serializer.data.get('first_name') # assert request_data.get('last_name') == user_serializer.data.get('last_name') # assert request_data.get('email') == user_serializer.data.get('email') # assert request_data.get('role') == user_serializer.data.get('role') # -*- coding: utf-8 -*- from __future__ import unicode_literals # Django imports from django.test import TestCase from django.urls import reverse # rest framework imports from rest_framework.test import APIClient from rest_framework import status # third party imports from django.test import mock from unittest.mock import patch # local imports from boilerplate_app.models import Graph, Node, Edge class GraphAPITestCases(TestCase): def setUp(self): self.title = "TestGraph" self.nod_id1, self.nod_id2 = "X1", "X2" self.nod_id3 = "X3" self.nod_id4 = "X4" self.nod_id5 = "X5" self.nod_id6 = "X6" self.nod_title = "XYZ1" self.position = {"top": 10, "left": 15, "bottom": 30, "right": 50} self.weight = 0.5 self.weight1 = 0.7 self.URLS = { 'graph-list-create-api': reverse('boilerplate_app-api:graph-list-create-api'), 'graph-detail-update-delete-api': reverse('boilerplate_app-api:graph-detail-update-delete-api', kwargs={'title': self.title}), 'graph-weekly-connected-node-api': reverse('boilerplate_app-api:graph-weekly-connected-node-api', kwargs={'title': self.title}), 'parse-node-csv-api': reverse('boilerplate_app-api:parse-node-csv-api', kwargs={'title': self.title}), 'graph-list-islands': reverse('boilerplate_app-api:graph-list-islands', kwargs={'title': self.title}), } def create_graph(self): graph = Graph.objects.create(title=self.title) Node.objects.bulk_create([ Node(nod_id=self.nod_id1, title=self.nod_title, position=self.position, graph=graph), Node(nod_id=self.nod_id2, title=self.nod_title, position=self.position, graph=graph), Node(nod_id=self.nod_id3, title=self.nod_title, position=self.position, graph=graph), Node(nod_id=self.nod_id4, title=self.nod_title, position=self.position, graph=graph), Node(nod_id=self.nod_id5, title=self.nod_title, position=self.position, graph=graph), Node(nod_id=self.nod_id6, title=self.nod_title, position=self.position, graph=graph) ]) resp = Edge.objects.bulk_create([ Edge(source=Node.objects.get(nod_id=self.nod_id1, graph=graph), target=Node.objects.get(nod_id=self.nod_id2, graph=graph), weight=self.weight, graph=graph), Edge(source=Node.objects.get(nod_id=self.nod_id1, graph=graph), target=Node.objects.get(nod_id=self.nod_id3, graph=graph), weight=self.weight1, graph=graph), Edge(source=Node.objects.get(nod_id=self.nod_id4, graph=graph), target=Node.objects.get(nod_id=self.nod_id5, graph=graph), weight=self.weight, graph=graph) ]) def test_create_graph(self): client = APIClient() resp = client.post(self.URLS['graph-list-create-api'], { "title": "TestGraph1", "nodes": [ { "id": "x1", "title": "ABC", "position": {"top": 10, "left": 15, "bottom": 30, "right": 50} }, { "id": "x2", "title": "DEF", "position": {"top": 10, "left": 60, "bottom": 30, "right": 95} }, { "id": "x3", "title": "GHI", "position": {"top": 10, "left": 100, "bottom": 30, "right": 125} } ], "edges": [ {"source": "x1", "target": "x2", "weight": 0.5}, {"source": "x1", "target": "x3", "weight": 0.8} ] }, format='json') assert resp.status_code == status.HTTP_201_CREATED assert resp.data['status'] == 'created' def test_list_graph(self): client = APIClient() self.create_graph() resp = client.get(self.URLS['graph-list-create-api']) assert resp.status_code == status.HTTP_200_OK assert resp.data['Response'][0]['title'] == 'TestGraph' assert len(resp.data['Response'][0]['nodes']) != 0 assert len(resp.data['Response'][0]['edges']) != 0 def test_detail_graph(self): client = APIClient() self.create_graph() resp = client.get(self.URLS['graph-detail-update-delete-api']) assert resp.status_code == status.HTTP_200_OK assert resp.data['Response']['title'] == 'TestGraph' assert len(resp.data['Response']['nodes']) != 0 assert len(resp.data['Response']['edges']) != 0 def test_update_graph(self): client = APIClient() self.create_graph() resp = client.put(self.URLS['graph-detail-update-delete-api'], { "title": "TestGraph", "nodes": [ { "id": "v1", "title": "ABC", "position": {"top": 10, "left": 15, "bottom": 30, "right": 50} }, { "id": "v2", "title": "DEF", "position": {"top": 10, "left": 60, "bottom": 30, "right": 95} }, { "id": "v3", "title": "GHI", "position": {"top": 10, "left": 100, "bottom": 30, "right": 125} } ], "edges": [ {"source": "v1", "target": "v2", "weight": 0.5}, {"source": "v1", "target": "v3", "weight": 0.8} ] }, format='json') assert resp.status_code == status.HTTP_200_OK assert resp.data['status'] == 'updated' def test_delete_graph(self): client = APIClient() self.create_graph() resp = client.delete(self.URLS['graph-detail-update-delete-api']) assert resp.status_code == status.HTTP_204_NO_CONTENT assert resp.data['status'] == 'deleted' def test_weekly_connected_node(self): client = APIClient() self.create_graph() resp = client.get(self.URLS['graph-weekly-connected-node-api']) assert resp.status_code == status.HTTP_200_OK assert resp.data['Response'] == ['X2', 'X5'] def tearDown(self): graph = Graph.objects.filter(title=self.title) Graph.objects.filter(title=self.title).delete() Node.objects.filter(graph=graph).delete() Edge.objects.filter(graph=graph).delete()

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null

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0

21c45eed900d2bb1c867cbce3f567b86b198b8b5

984

py

Python

utils.py

NeuralBending/stylegan2-ada-pytorch

c1b33b7e6d67a8de3d483d64f9ca1a45a7c6d217

[ "BSD-Source-Code" ]

null

utils.py

NeuralBending/stylegan2-ada-pytorch

c1b33b7e6d67a8de3d483d64f9ca1a45a7c6d217

[ "BSD-Source-Code" ]

null

utils.py

NeuralBending/stylegan2-ada-pytorch

c1b33b7e6d67a8de3d483d64f9ca1a45a7c6d217

[ "BSD-Source-Code" ]

null

import torch def postprocess(img_out): return (img_out.permute(0,2,3,1)* 127.5 + 128).clamp(0, 255).to(torch.uint8).cpu().numpy() def flicker(frames, f = 30): frames[::f]*=0 frames[2::f]*=0 frames[1::f]=255-frames[1::f] return frames def latent_walk(w,num=20, width=1): lin = torch.linspace(0,width,num)[:,None,None] # ws=0 tensor=[] for i in range(w.shape[0]-1): ws= w[i].repeat((num,1,1))*(width-lin) ws+= w[i+1].repeat((num,1,1))*lin tensor.append(ws) return torch.cat(tensor) def noiseIt(x,scale=1): noise = scale*torch.zeros_like(x).normal_() return x+noise def noiseItT(x,scale=1,n=2): app=[] random_noise = torch.zeros_like(x).normal_()[1:n] lin = torch.linspace(0,1,x.shape[0]//n)[:,None,None,None].cuda() for noise in random_noise: noise = noise.repeat((x.shape[0]//n,1,1,1)) app+= [noise*lin] app+= [noise*(1-lin)] noise = torch.cat(app) # print (noise.shape, x.shape) x+=scale*noise.cuda() return x

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21c9f21c6cd6f96ad31cddc81cac13f24697fdd7

16,697

py

Python

tool/merganser/data_retrieval2.py

ualberta-smr/conflict-prediction

6cd2c54a8af991256d0a2f85c3ab5d828ea2c3f8

[ "MIT" ]

4

2019-08-04T03:36:51.000Z

2020-09-17T18:13:45.000Z

tool/merganser/data_retrieval2.py

ualberta-smr/conflict-prediction

6cd2c54a8af991256d0a2f85c3ab5d828ea2c3f8

[ "MIT" ]

null

tool/merganser/data_retrieval2.py

ualberta-smr/conflict-prediction

6cd2c54a8af991256d0a2f85c3ab5d828ea2c3f8

[ "MIT" ]

1

2019-06-11T21:15:34.000Z

import os import pandas as pd import numpy as np from io import StringIO import config import logging import pandas as pd import pymysql from sqlalchemy import create_engine class Data_Retreival: def __init__(self): self.code_complexity_query = 'SELECT Merge_Scenario_merge_commit_hash, measure1_diff, measure2_diff, ' \ 'measure3_diff, measure4_diff, measure5_diff, measure6_diff, measure7_diff, ' \ 'measure8_diff ' \ 'FROM Merge_Data.Code_Complexity' self.code_violation_query = 'SELECT Code_Violation.Merge_Scenario_merge_commit_hash, ' \ 'Code_Violation.parent1_style_violation_num - ' \ ' Code_Violation.parent2_style_violation_num ' \ 'FROM Merge_Data.Code_Style_Violation Code_Violation ' self.parallel_changes_query = 'SELECT merge_commit_hash, parallel_changed_file_num ' \ 'FROM Merge_Data.Merge_Scenario' self.commit_num_query = 'SELECT Merge_Scenario.merge_commit_hash, count(Commits.commit_hash) ' \ 'FROM Merge_Data.Merge_Scenario Merge_Scenario LEFT JOIN ' \ 'Merge_Data.Merge_Related_Commit Commits ' \ 'on Merge_Scenario.merge_commit_hash = Commits.Merge_Scenario_merge_commit_hash' \ ' AND Commits.merge_commit_parent = {} ' \ 'GROUP BY Merge_Scenario.merge_commit_hash' self.commit_density_two_weeks = 'SELECT Merge_Scenario.merge_commit_hash, count(Commits.commit_hash) FROM ' \ 'Merge_Data.Merge_Scenario Merge_Scenario' \ ' LEFT JOIN Merge_Data.Merge_Related_Commit Commits ' \ 'on Merge_Scenario.merge_commit_hash = Commits.Merge_Scenario_merge_commit_hash ' \ 'AND Commits.merge_commit_parent = {} AND ' \ 'TIMESTAMPDIFF(WEEK, Merge_Scenario.merge_commit_date, Commits.date) < 3 ' \ 'GROUP BY Merge_Scenario.merge_commit_hash' self.file_change_query = 'SELECT Merge_Scenario.merge_commit_hash, COALESCE(SUM(file_added_num), 0), ' \ 'COALESCE(SUM(file_removed_num), 0), COALESCE(SUM(file_renamed_num), 0), ' \ 'COALESCE(SUM(file_copied_num), 0), COALESCE(SUM(file_modified_num), 0) ' \ ' FROM Merge_Data.Merge_Scenario Merge_Scenario ' \ 'LEFT JOIN Merge_Data.Merge_Related_Commit Commits ' \ 'on Merge_Scenario.merge_commit_hash = Commits.Merge_Scenario_merge_commit_hash' \ ' AND Commits.merge_commit_parent = {} ' \ 'GROUP BY Merge_Scenario.merge_commit_hash' self.line_change_query = 'SELECT Merge_Scenario.merge_commit_hash, COALESCE(SUM(line_added_num), 0), COALESCE(SUM(line_removed_num), 0) ' \ 'FROM Merge_Data.Merge_Scenario Merge_Scenario ' \ 'LEFT JOIN Merge_Data.Merge_Related_Commit Commits ' \ 'on Merge_Scenario.merge_commit_hash = Commits.Merge_Scenario_merge_commit_hash ' \ 'AND Commits.merge_commit_parent = {} ' \ 'GROUP BY Merge_Scenario.merge_commit_hash' self.developer_num_query = 'SELECT merge_commit_hash, parent{}_developer_num ' \ 'FROM Merge_Data.Merge_Scenario' self.commit_message_quey = 'SELECT GROUP_CONCAT(Commits.message SEPARATOR \' ||| \') ' \ 'FROM Merge_Data.Merge_Scenario Merge_Scenario ' \ 'LEFT JOIN Merge_Data.Merge_Related_Commit Commits ' \ 'on Merge_Scenario.merge_commit_hash = Commits.Merge_Scenario_merge_commit_hash ' \ 'AND Commits.merge_commit_parent = {} ' \ 'GROUP BY Merge_Scenario.merge_commit_hash' self.branch_duration = 'SELECT Merge_Scenario.merge_commit_hash, TIMESTAMPDIFF(HOUR, ' \ 'Merge_Scenario.ancestor_date, Merge_Scenario.parent{}_date) ' \ 'FROM Merge_Data.Merge_Scenario Merge_Scenario' self.is_conflict_query = 'SELECT Merge_Replay.Merge_Scenario_merge_commit_hash, Merge_Replay.is_conflict ' \ 'FROM Merge_Data.Merge_Replay Merge_Replay' self.conflict_rate_query = """SELECT scenarios.name AS 'Repository Name' , scenarios.scenarios AS '# Merge Scenarios', conflicts.conflicts AS '# Merge Scenarios with Conflicts', 100 * conflicts.conflicts/scenarios.scenarios 'Conflict Rate (%)' FROM (SELECT Repository.name, COUNT(Merge_Replay.Merge_Scenario_merge_commit_hash) AS 'scenarios' FROM Merge_Data.Repository JOIN Merge_Data.Merge_Replay ON id = Merge_Scenario_Repository_id GROUP BY name ORDER BY name) scenarios INNER JOIN (SELECT name, COUNT(Merge_Replay.Merge_Scenario_merge_commit_hash) AS 'conflicts' FROM Merge_Data.Repository JOIN Merge_Data.Merge_Replay ON id = Merge_Scenario_Repository_id WHERE is_conflict = 1 GROUP BY name ORDER BY name) conflicts ON scenarios. name = conflicts.name""" self.repository_stat = """(SELECT MIN(star_num) as Min, AVG(star_num) as AVG,MAX(star_num) as Max FROM Merge_Data.Repository) UNION ALL (SELECT MIN(watch_num), AVG(watch_num),MAX(watch_num) FROM Merge_Data.Repository) UNION ALL (SELECT MIN(fork_num), AVG(fork_num),MAX(fork_num) FROM Merge_Data.Repository) UNION ALL (SELECT MIN(issue_num), AVG(issue_num),MAX(issue_num) FROM Merge_Data.Repository) UNION ALL (SELECT MIN(size) / 1024, AVG(size) / 1024,MAX(size) / 1024 FROM Merge_Data.Repository)""" self.parallel_changed_commits_query = """select merge_commit_hash from Merge_Data.Merge_Scenario sc Where parallel_changed_file_num > 0""" self.merge_commits_langs_query = """SELECT merge_commit_hash, language FROM Merge_Data.Repository JOIN Merge_Data.Merge_Scenario ON id = Repository_id WHERE language IN ({})""" def get_parallel_changed_commits(self): logging.info('Extracting parallel changes...') return self.get_data_frame_of_query_result(self.get_query_result(self.parallel_changed_commits_query)) def get_query_result(self, query): engine = create_engine('mysql+pymysql://{}:{}@localhost/{}'.format(config.DB_USER_NAME, config.DB_PASSWORD, config.DB_NAME)) df = pd.read_sql_query(query, engine) return df # return os.popen('mysql -u {} -e "{}"'.format(config.DB_USER_NAME, query)).read() def get_data_frame_of_query_result(self, query_result): return query_result if len(query_result) == 0: print('Empty result!') return -1 return pd.read_csv(StringIO(query_result), delimiter='\t') def get_complexity(self): logging.info('Extracting code complexity...') return self.get_data_frame_of_query_result(self.get_query_result(self.code_complexity_query)) def get_code_violation(self): logging.info('Extracting code style violation...') return self.get_data_frame_of_query_result(self.get_query_result(self.code_violation_query)) def get_parallel_changes(self): logging.info('Extracting code parallel changes...') return self.get_data_frame_of_query_result(self.get_query_result(self.parallel_changes_query)) def get_commit_num(self, parent): # TODO: The number of data in two branches is not the same. logging.info('Extracting the number of commits...') return self.get_data_frame_of_query_result(self.get_query_result(self.commit_num_query.format(parent))) def get_commit_density(self, parent): logging.info('Extracting commit density...') return self.get_data_frame_of_query_result(self.get_query_result(self.commit_density_two_weeks.format(parent))) def get_file_changes(self, parent): logging.info('Extracting file changes...') return self.get_data_frame_of_query_result(self.get_query_result(self.file_change_query.format(parent))) def get_line_changes(self, parent): logging.info('Extracting line changes...') return self.get_data_frame_of_query_result(self.get_query_result(self.line_change_query.format(parent))) def get_developer_num(self, parent): logging.info('Extracting developer num...') res = self.get_data_frame_of_query_result(self.get_query_result(self.developer_num_query.format(parent))).drop('merge_commit_hash', axis=1).values return pd.DataFrame([item for sublist in res for item in sublist], columns=['# Developers']) def get_merge_scenarios_in_lang(self, langs): logging.info('Extracting merges by language...') langs = ','.join(['\'{}\''.format(lang) for lang in langs]) return self.get_data_frame_of_query_result(self.get_query_result(self.merge_commits_langs_query.format(langs))) def get_commit_messege_characteristics(self, parent): logging.info('Extracting message characteristics...') commit_messages = self.get_query_result(self.commit_message_quey.format(parent)) commit_messages_list = commit_messages.values[1:] commit_messages_list = [item for sublist in commit_messages_list for item in sublist if item is not None] keywords = sorted(['fix', 'bug', 'feature', 'improve', 'document', 'refactor', 'update', 'add', 'remove', 'use', 'delete', 'change']) keywords_frequency = [] commit_messege_length_stats = [] for merge_scenrio_commits in commit_messages_list: keywords_frequency.append([merge_scenrio_commits.lower().count(word) for word in keywords]) if merge_scenrio_commits != 'NULL': seperated_commit_message = merge_scenrio_commits.replace(' ||| ', '\n').split('\n') commit_messege_length = [len(msg.split()) for msg in seperated_commit_message] commit_messege_length_stats.append([np.min(commit_messege_length), np.mean(commit_messege_length), np.median(commit_messege_length), np.max(commit_messege_length)]) else: commit_messege_length_stats.append([0.0, 0.0, 0.0, 0.0]) column_names_frequency = ['# fix', '# bug', '# feature', '# improve', '# document', '# refactor', '# update', '# add', '# remove', '# use', '# delete', '# change'] column_names_stats = ['Min Msg Length', 'Mean Msg Length', 'Median Msg Length', 'Max Msg Length'] return pd.DataFrame(keywords_frequency, columns=column_names_frequency), \ pd.DataFrame(commit_messege_length_stats, columns=column_names_stats) def get_branch_duration(self, parent): logging.info('Extracting branch duration...') res = self.get_data_frame_of_query_result(self.get_query_result(self.branch_duration.format(parent))).drop( 'merge_commit_hash', axis=1).values return pd.DataFrame([item for sublist in res for item in sublist], columns=['Branch Duration']) def get_is_conflict(self): res = self.get_data_frame_of_query_result(self.get_query_result(self.is_conflict_query.format())).drop( 'Merge_Scenario_merge_commit_hash', axis=1).values return pd.DataFrame([item for sublist in res for item in sublist], columns=['Is Conflict']) def get_merge_scenario_prediction_data(self, langs): keywords_frequency1, commit_messege_length_stats1 = self.get_commit_messege_characteristics(1) keywords_frequency2, commit_messege_length_stats2 = self.get_commit_messege_characteristics(2) git_features_scenario = self.get_parallel_changes() features = [git_features_scenario, self.get_commit_num(1).drop('merge_commit_hash', axis=1) - self.get_commit_num(2).drop('merge_commit_hash', axis=1), self.get_commit_density(1).drop('merge_commit_hash', axis=1) - self.get_commit_density(2).drop( 'merge_commit_hash', axis=1), self.get_file_changes(1).drop('merge_commit_hash', axis=1) - self.get_file_changes(2).drop( 'merge_commit_hash', axis=1), self.get_line_changes(1).drop('merge_commit_hash', axis=1) - self.get_line_changes(2).drop( 'merge_commit_hash', axis=1), self.get_developer_num(1) - self.get_developer_num(2), keywords_frequency1 - keywords_frequency2, commit_messege_length_stats1 - commit_messege_length_stats2, self.get_branch_duration(1) - self.get_branch_duration(2)] res = pd.concat([pd.concat(features, axis=1).sort_values(by=['merge_commit_hash']), self.get_merge_scenarios_in_lang(langs).sort_values(by=['merge_commit_hash'])], axis=1) res = res[res['language'].isin(langs)].drop('merge_commit_hash', axis=1).drop('language', axis=1) return res def save_prediction_data_to_csv(self, langs, post_name): self.get_merge_scenario_prediction_data(langs).drop('Merge_Scenario_merge_commit_hash', axis=1)\ .to_csv(path_or_buf=config.PREDICTION_CSV_PATH + config.PREDICTION_CSV_DATA_NAME + post_name) self.get_is_conflict().to_csv(path_or_buf=config.PREDICTION_CSV_PATH + config.PREDICTION_CSV_LABEL_NAME + post_name) def get_conflict_ratio(self): return self.get_data_frame_of_query_result(self.get_query_result(self.conflict_rate_query)) def get_repository_stats(self): return self.get_data_frame_of_query_result(self.get_query_result(self.repository_stat)).rename(index={0:'star', 1:'watch', 2: 'fork', 3: 'issue', 4:'size'}) def print_df_stats(self, df): print('DataFrame Stats:') print(' - # Data Points: {}'.format(df.shape[0])) print(' - # Features: {}'.format(df.shape[1])) print(' - Index: {}'.format(df.index)) print(' - Columns: {}'.format(df.columns)) # Logging logging.basicConfig(level=logging.INFO, format='%(levelname)s in %(threadName)s - %(asctime)s by %(name)-12s : %(message)s', datefmt='%y-%m-%d %H:%M:%S') obj = Data_Retreival() print('Start data saving') print(' - Java') obj.save_prediction_data_to_csv(['Java'], '_java') print(' - Python') obj.save_prediction_data_to_csv(['Python'], '_Python') print(' - PHP') obj.save_prediction_data_to_csv(['PHP'], '_PHP') print(' - Ruby') obj.save_prediction_data_to_csv(['Ruby'], '_Ruby') print(' - C++') obj.save_prediction_data_to_csv(['C++'], '_CPP') print(' - Java') obj.save_prediction_data_to_csv(['Java', 'Python', 'Ruby', 'PHP', 'C++'], '_ALL') print('Finish data saving') #print(obj.get_repository_stats())

61.386029

164

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0.366105

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16,697

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false

0.004367

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0.065502

0

null

0

null

0

1

0

21cd10ec0c43a2a5245ac89b17664c6b6824fc20

12,948

py

Python

gmusicapi_wrapper/utils.py

dmoebius/gmusicapi-wrapper

8708683cd33955def1378fc28319ef37805b851d

[ "MIT" ]

20

2015-08-29T15:35:22.000Z

2018-09-29T06:16:15.000Z

gmusicapi_wrapper/utils.py

dmoebius/gmusicapi-wrapper

8708683cd33955def1378fc28319ef37805b851d

[ "MIT" ]

17

2015-02-28T17:19:30.000Z

2018-06-08T10:33:29.000Z

gmusicapi_wrapper/utils.py

dmoebius/gmusicapi-wrapper

8708683cd33955def1378fc28319ef37805b851d

[ "MIT" ]

11

2015-12-11T15:20:24.000Z

2019-02-13T18:54:11.000Z

# coding=utf-8 """Utility functions for gmusicapi_wrapper. >>> import gmusicapi_wrapper.utils as gmw_utils >>> from gmusicapi_wrapper.utils import ... """ import logging import os import re import subprocess import mutagen from .constants import CHARACTER_REPLACEMENTS, CYGPATH_RE, TEMPLATE_PATTERNS from .decorators import cast_to_list logger = logging.getLogger(__name__) def convert_cygwin_path(path): """Convert Unix path from Cygwin to Windows path.""" try: win_path = subprocess.check_output(["cygpath", "-aw", path], universal_newlines=True).strip() except (FileNotFoundError, subprocess.CalledProcessError): logger.exception("Call to cygpath failed.") raise return win_path def _get_mutagen_metadata(filepath): """Get mutagen metadata dict from a file.""" try: metadata = mutagen.File(filepath, easy=True) except mutagen.MutagenError: logger.warning("Can't load {} as music file.".format(filepath)) raise return metadata def _mutagen_fields_to_single_value(metadata): """Replace mutagen metadata field list values in mutagen tags with the first list value.""" return dict((k, v[0]) for k, v in metadata.items() if v) def _split_field_to_single_value(field): """Convert number field values split by a '/' to a single number value.""" split_field = re.match(r'(\d+)/\d+', field) return split_field.group(1) or field def _filter_comparison_fields(song): """Filter missing artist, album, title, or track fields to improve match accuracy.""" # Need both tracknumber (mutagen) and track_number (Google Music) here. return [field for field in ['artist', 'album', 'title', 'tracknumber', 'track_number'] if field in song and song[field]] def _normalize_metadata(metadata): """Normalize metadata to improve match accuracy.""" metadata = str(metadata) metadata = metadata.lower() metadata = re.sub(r'\/\s*\d+', '', metadata) # Remove "/<totaltracks>" from track number. metadata = re.sub(r'^0+([0-9]+)', r'\1', metadata) # Remove leading zero(s) from track number. metadata = re.sub(r'^\d+\.+', '', metadata) # Remove dots from track number. metadata = re.sub(r'[^\w\s]', '', metadata) # Remove any non-words. metadata = re.sub(r'\s+', ' ', metadata) # Reduce multiple spaces to a single space. metadata = re.sub(r'^\s+', '', metadata) # Remove leading space. metadata = re.sub(r'\s+$', '', metadata) # Remove trailing space. metadata = re.sub(r'^the\s+', '', metadata, re.I) # Remove leading "the". return metadata def _normalize_song(song): """Convert filepath to song dict while leaving song dicts untouched.""" return song if isinstance(song, dict) else _mutagen_fields_to_single_value(_get_mutagen_metadata(song)) def compare_song_collections(src_songs, dst_songs): """Compare two song collections to find missing songs. Parameters: src_songs (list): Google Music song dicts or filepaths of local songs. dest_songs (list): Google Music song dicts or filepaths of local songs. Returns: A list of Google Music song dicts or local song filepaths from source missing in destination. """ def gather_field_values(song): return tuple((_normalize_metadata(song[field]) for field in _filter_comparison_fields(song))) dst_songs_criteria = {gather_field_values(_normalize_song(dst_song)) for dst_song in dst_songs} return [src_song for src_song in src_songs if gather_field_values(_normalize_song(src_song)) not in dst_songs_criteria] @cast_to_list(0) def get_supported_filepaths(filepaths, supported_extensions, max_depth=float('inf')): """Get filepaths with supported extensions from given filepaths. Parameters: filepaths (list or str): Filepath(s) to check. supported_extensions (tuple or str): Supported file extensions or a single file extension. max_depth (int): The depth in the directory tree to walk. A depth of '0' limits the walk to the top directory. Default: No limit. Returns: A list of supported filepaths. """ supported_filepaths = [] for path in filepaths: if os.name == 'nt' and CYGPATH_RE.match(path): path = convert_cygwin_path(path) if os.path.isdir(path): for root, __, files in walk_depth(path, max_depth): for f in files: if f.lower().endswith(supported_extensions): supported_filepaths.append(os.path.join(root, f)) elif os.path.isfile(path) and path.lower().endswith(supported_extensions): supported_filepaths.append(path) return supported_filepaths @cast_to_list(0) def exclude_filepaths(filepaths, exclude_patterns=None): """Exclude file paths based on regex patterns. Parameters: filepaths (list or str): Filepath(s) to check. exclude_patterns (list): Python regex patterns to check filepaths against. Returns: A list of filepaths to include and a list of filepaths to exclude. """ if not exclude_patterns: return filepaths, [] exclude_re = re.compile("|".join(pattern for pattern in exclude_patterns)) included_songs = [] excluded_songs = [] for filepath in filepaths: if exclude_patterns and exclude_re.search(filepath): excluded_songs.append(filepath) else: included_songs.append(filepath) return included_songs, excluded_songs def _check_field_value(field_value, pattern): """Check a song metadata field value for a pattern.""" if isinstance(field_value, list): return any(re.search(pattern, str(value), re.I) for value in field_value) else: return re.search(pattern, str(field_value), re.I) def _check_filters(song, include_filters=None, exclude_filters=None, all_includes=False, all_excludes=False): """Check a song metadata dict against a set of metadata filters.""" include = True if include_filters: if all_includes: if not all(field in song and _check_field_value(song[field], pattern) for field, pattern in include_filters): include = False else: if not any(field in song and _check_field_value(song[field], pattern) for field, pattern in include_filters): include = False if exclude_filters: if all_excludes: if all(field in song and _check_field_value(song[field], pattern) for field, pattern in exclude_filters): include = False else: if any(field in song and _check_field_value(song[field], pattern) for field, pattern in exclude_filters): include = False return include def filter_google_songs(songs, include_filters=None, exclude_filters=None, all_includes=False, all_excludes=False): """Match a Google Music song dict against a set of metadata filters. Parameters: songs (list): Google Music song dicts to filter. include_filters (list): A list of ``(field, pattern)`` tuples. Fields are any valid Google Music metadata field available to the Musicmanager client. Patterns are Python regex patterns. Google Music songs are filtered out if the given metadata field values don't match any of the given patterns. exclude_filters (list): A list of ``(field, pattern)`` tuples. Fields are any valid Google Music metadata field available to the Musicmanager client. Patterns are Python regex patterns. Google Music songs are filtered out if the given metadata field values match any of the given patterns. all_includes (bool): If ``True``, all include_filters criteria must match to include a song. all_excludes (bool): If ``True``, all exclude_filters criteria must match to exclude a song. Returns: A list of Google Music song dicts matching criteria and a list of Google Music song dicts filtered out using filter criteria. :: (matched, filtered) """ matched_songs = [] filtered_songs = [] if include_filters or exclude_filters: for song in songs: if _check_filters( song, include_filters=include_filters, exclude_filters=exclude_filters, all_includes=all_includes, all_excludes=all_excludes): matched_songs.append(song) else: filtered_songs.append(song) else: matched_songs += songs return matched_songs, filtered_songs def filter_local_songs(filepaths, include_filters=None, exclude_filters=None, all_includes=False, all_excludes=False): """Match a local file against a set of metadata filters. Parameters: filepaths (list): Filepaths to filter. include_filters (list): A list of ``(field, pattern)`` tuples. Fields are any valid mutagen metadata fields. Patterns are Python regex patterns. Local songs are filtered out if the given metadata field values don't match any of the given patterns. exclude_filters (list): A list of ``(field, pattern)`` tuples. Fields are any valid mutagen metadata fields. Patterns are Python regex patterns. Local songs are filtered out if the given metadata field values match any of the given patterns. all_includes (bool): If ``True``, all include_filters criteria must match to include a song. all_excludes (bool): If ``True``, all exclude_filters criteria must match to exclude a song. Returns: A list of local song filepaths matching criteria and a list of local song filepaths filtered out using filter criteria. Invalid music files are also filtered out. :: (matched, filtered) """ matched_songs = [] filtered_songs = [] for filepath in filepaths: try: song = _get_mutagen_metadata(filepath) except mutagen.MutagenError: filtered_songs.append(filepath) else: if include_filters or exclude_filters: if _check_filters( song, include_filters=include_filters, exclude_filters=exclude_filters, all_includes=all_includes, all_excludes=all_excludes): matched_songs.append(filepath) else: filtered_songs.append(filepath) else: matched_songs.append(filepath) return matched_songs, filtered_songs def get_suggested_filename(metadata): """Generate a filename for a song based on metadata. Parameters: metadata (dict): A metadata dict. Returns: A filename. """ if metadata.get('title') and metadata.get('track_number'): suggested_filename = '{track_number:0>2} {title}'.format(**metadata) elif metadata.get('title') and metadata.get('trackNumber'): suggested_filename = '{trackNumber:0>2} {title}'.format(**metadata) elif metadata.get('title') and metadata.get('tracknumber'): suggested_filename = '{tracknumber:0>2} {title}'.format(**metadata) else: suggested_filename = '00 {}'.format(metadata.get('title', '')) return suggested_filename def _replace_template_patterns(template, metadata, template_patterns): drive, path = os.path.splitdrive(template) parts = [] while True: newpath, tail = os.path.split(path) if newpath == path: break parts.append(tail) path = newpath parts.reverse() for i, part in enumerate(parts): for key in template_patterns: if key in part and template_patterns[key] in metadata: # Force track number to be zero-padded to 2 digits. if any(template_patterns[key] == tracknumber_field for tracknumber_field in ['tracknumber', 'track_number']): track_number = _split_field_to_single_value(metadata[template_patterns[key]]) metadata[template_patterns[key]] = track_number.zfill(2) parts[i] = parts[i].replace(key, metadata[template_patterns[key]]) for char in CHARACTER_REPLACEMENTS: if char in parts[i]: parts[i] = parts[i].replace(char, CHARACTER_REPLACEMENTS[char]) if drive: filepath = os.path.join(drive, os.sep, *parts) else: if os.path.isabs(template): filepath = os.path.join(os.sep, *parts) else: filepath = os.path.join(*parts) return filepath def template_to_filepath(template, metadata, template_patterns=None): """Create directory structure and file name based on metadata template. Parameters: template (str): A filepath which can include template patterns as defined by :param template_patterns:. metadata (dict): A metadata dict. template_patterns (dict): A dict of ``pattern: field`` pairs used to replace patterns with metadata field values. Default: :const TEMPLATE_PATTERNS: Returns: A filepath. """ if template_patterns is None: template_patterns = TEMPLATE_PATTERNS metadata = metadata if isinstance(metadata, dict) else _mutagen_fields_to_single_value(metadata) assert isinstance(metadata, dict) suggested_filename = get_suggested_filename(metadata).replace('.mp3', '') if template == os.getcwd() or template == '%suggested%': filepath = suggested_filename else: t = template.replace('%suggested%', suggested_filename) filepath = _replace_template_patterns(t, metadata, template_patterns) return filepath def walk_depth(path, max_depth=float('inf')): """Walk a directory tree with configurable depth. Parameters: path (str): A directory path to walk. max_depth (int): The depth in the directory tree to walk. A depth of '0' limits the walk to the top directory. Default: No limit. """ start_level = os.path.abspath(path).count(os.path.sep) for dir_entry in os.walk(path): root, dirs, _ = dir_entry level = root.count(os.path.sep) - start_level yield dir_entry if level >= max_depth: dirs[:] = []

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21cf17efec6eb019292dc869b1f00c24149cffb4

4,402

py

Python

restructuredBootstrap/custom_pygments_style.py

lakhman/restructuredBootstrap

57002c6db2d3b5b97dc23820b91711db4f00c07e

[ "MIT" ]

1

2020-08-30T08:58:46.000Z

restructuredBootstrap/custom_pygments_style.py

lakhman/restructuredBootstrap

57002c6db2d3b5b97dc23820b91711db4f00c07e

[ "MIT" ]

null

restructuredBootstrap/custom_pygments_style.py

lakhman/restructuredBootstrap

57002c6db2d3b5b97dc23820b91711db4f00c07e

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- # ----------------------------------------------------------------------------- # Copyright (c) 2017 - Anil Lakhman - MIT License # ----------------------------------------------------------------------------- from pygments.style import Style from pygments.token import Keyword, Name, Comment, String, Error, \ Number, Operator, Generic, Whitespace, Punctuation, Other, Literal, Text # https://github.com/jhermann/pygments-markdown-lexer/blob/master/src/pygments_markdown_lexer/__init__.py class StackOverflowStyle(Style): # pragma: no cover #: highlight background color background_color = "#eff0f1" highlight_color = 'rgba(174, 247, 174, 0.5)' default_style = "" styles = { # No corresponding class for the following: #Text: "", # class: '' Whitespace: "underline #F8F8F8", # class: 'w' Error: "#A40000 border:#EF2929", # class: 'err' Other: "#303336", # class 'x' Comment: "#858C93", # class: 'c' Comment.Single: "#858C93", # class: 'c1' Comment.Multiline: "#858C93", # class: 'cm' Comment.Preproc: "italic #AAA", # class: 'cp' Keyword: "#101094", # class: 'k' Keyword.Constant: "#303336", # class: 'kc' Keyword.Declaration: "#101094", # class: 'kd' Keyword.Namespace: "#101094", # class: 'kn' Keyword.Pseudo: "#101094", # class: 'kp' Keyword.Reserved: "#101094", # class: 'kr' Keyword.Type: "#7D2727", # class: 'kt' Operator: "#303336", # class: 'o' Operator.Word: "#1010B7", # class: 'ow' - like keywords Punctuation: "#3c3d3e", # class: 'p' Punctuation.Indicator: "#000", # class: 'p-Indicator' # because special names such as Name.Class, Name.Function, etc. # are not recognized as such later in the parsing, we choose them # to look the same as ordinary variables. Name: "#303336", # class: 'n' Name.Attribute: "#e64320", # class: 'na' - to be revised Name.Builtin: "#303336", # class: 'nb' Name.Builtin.Pseudo: "#3465A4", # class: 'bp' Name.Class: "#1010B7", # class: 'nc' - to be revised Name.Constant: "#303336", # class: 'no' - to be revised Name.Decorator: "#888", # class: 'nd' - to be revised Name.Entity: "#ce5c00", # class: 'ni' Name.Exception: "#cc0000", # class: 'ne' Name.Function: "#008000", # class: 'nf' Name.Property: "#303336", # class: 'py' Name.Label: "#f57900", # class: 'nl' Name.Namespace: "#303336", # class: 'nn' - to be revised Name.Other: "#303336", # class: 'nx' Name.Tag: "#7d2727", # class: 'nt' - like a keyword Name.Variable: "#303336", # class: 'nv' - to be revised Name.Variable.Class: "#303336", # class: 'vc' - to be revised Name.Variable.Global: "#303336", # class: 'vg' - to be revised Name.Variable.Instance: "#303336", # class: 'vi' - to be revised Number: "#7D2727", # class: 'm' Literal: "#303336", # class: 'l' Literal.Date: "#303336", # class: 'ld' Literal.Scalar.Plain: '#090977', # class: 'l-Scalar-Plain' String: "#1010B7", # class: 's' String.Backtick: "#008000", # class: 'sb' String.Char: "#008000", # class: 'sc' String.Doc: "italic #B729D9", # class: 'sd' - like a comment String.Double: "#008000", # class: 's2' String.Escape: "#008000", # class: 'se' String.Heredoc: "#008000", # class: 'sh' String.Interpol: "#008000", # class: 'si' String.Other: "#008000", # class: 'sx' String.Regex: "#008000", # class: 'sr' String.Single: "#7D2727", # class: 's1' String.Symbol: "#008000", # class: 'ss' Generic: "#000", # class: 'g' Generic.Deleted: "#A40000", # class: 'gd' Generic.Emph: "italic #444", # class: 'ge' Generic.Error: "#EF2929", # class: 'gr' Generic.Heading: "#1010b7", # class: 'gh' Generic.Inserted: "#00A000", # class: 'gi' Generic.Output: "#888", # class: 'go' Generic.Prompt: "#745334", # class: 'gp' Generic.Strong: "bold #444", # class: 'gs' Generic.Subheading: "#800080", # class: 'gu' Generic.Traceback: "#A40000", # class: 'gt' }

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21d2b7b6aeb4285360c3d0b80011bc93dc5f393f

1,225

py

Python

pyleecan/Methods/Geometry/Segment/discretize.py

IrakozeFD/pyleecan

5a93bd98755d880176c1ce8ac90f36ca1b907055

[ "Apache-2.0" ]

95

2019-01-23T04:19:45.000Z

2022-03-17T18:22:10.000Z

pyleecan/Methods/Geometry/Segment/discretize.py

IrakozeFD/pyleecan

5a93bd98755d880176c1ce8ac90f36ca1b907055

[ "Apache-2.0" ]

366

2019-02-20T07:15:08.000Z

2022-03-31T13:37:23.000Z

pyleecan/Methods/Geometry/Segment/discretize.py

IrakozeFD/pyleecan

5a93bd98755d880176c1ce8ac90f36ca1b907055

[ "Apache-2.0" ]

74

2019-01-24T01:47:31.000Z

2022-02-25T05:44:42.000Z

# -*- coding: utf-8 -*- from numpy import linspace from ....Methods.Machine import LINE_NPOINT_D from ....Methods.Geometry.Segment import NbPointSegmentDError def discretize(self, nb_point=LINE_NPOINT_D): """Return the discretize version of the Segment. Begin and end are always returned Parameters ---------- self : Segment A Segment object nb_point : int Number of points to add to discretize the line (Default value = LINE_NPOINT_D) Returns ------- list_point: list List of complex coordinate of the points Raises ------ NbPointSegmentDError nb_point must be an integer >= """ self.check() if not isinstance(nb_point, int): raise NbPointSegmentDError("discretize : nb_point must be an integer") if nb_point < 0: raise NbPointSegmentDError("nb_point must be >=0") # t start by 0 (begin) and end by 1 (end) # len(t) = nb_point +2 : begin + end + nb_point between t = linspace(0, 1, nb_point + 2) # We use the complex representation of the point z1 = self.begin z2 = self.end # Generate the points with the parametric representation of the line return z1 - (z1 - z2) * t

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null

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1

0

21d6f524d8375ab9d0bc8eed28866f2443f50ef1

2,340

py

Python

Grid/grid_multi_yaxis.py

pyecharts/pyecharts_gallery

8430c37df923860b36c9d1d86f2adc9d94b9d72c

[ "MIT" ]

759

2019-04-28T22:42:10.000Z

2022-03-31T12:32:10.000Z

Grid/grid_multi_yaxis.py

pyecharts/pyecharts_gallery

8430c37df923860b36c9d1d86f2adc9d94b9d72c

[ "MIT" ]

65

2019-06-10T07:38:25.000Z

2022-03-24T10:10:03.000Z

Grid/grid_multi_yaxis.py

pyecharts/pyecharts_gallery

8430c37df923860b36c9d1d86f2adc9d94b9d72c

[ "MIT" ]

505

2019-04-28T08:45:33.000Z

2022-03-29T07:08:33.000Z

from pyecharts import options as opts from pyecharts.charts import Bar, Grid, Line x_data = ["{}月".format(i) for i in range(1, 13)] bar = ( Bar() .add_xaxis(x_data) .add_yaxis( "蒸发量", [2.0, 4.9, 7.0, 23.2, 25.6, 76.7, 135.6, 162.2, 32.6, 20.0, 6.4, 3.3], yaxis_index=0, color="#d14a61", ) .add_yaxis( "降水量", [2.6, 5.9, 9.0, 26.4, 28.7, 70.7, 175.6, 182.2, 48.7, 18.8, 6.0, 2.3], yaxis_index=1, color="#5793f3", ) .extend_axis( yaxis=opts.AxisOpts( name="蒸发量", type_="value", min_=0, max_=250, position="right", axisline_opts=opts.AxisLineOpts( linestyle_opts=opts.LineStyleOpts(color="#d14a61") ), axislabel_opts=opts.LabelOpts(formatter="{value} ml"), ) ) .extend_axis( yaxis=opts.AxisOpts( type_="value", name="温度", min_=0, max_=25, position="left", axisline_opts=opts.AxisLineOpts( linestyle_opts=opts.LineStyleOpts(color="#675bba") ), axislabel_opts=opts.LabelOpts(formatter="{value} °C"), splitline_opts=opts.SplitLineOpts( is_show=True, linestyle_opts=opts.LineStyleOpts(opacity=1) ), ) ) .set_global_opts( yaxis_opts=opts.AxisOpts( name="降水量", min_=0, max_=250, position="right", offset=80, axisline_opts=opts.AxisLineOpts( linestyle_opts=opts.LineStyleOpts(color="#5793f3") ), axislabel_opts=opts.LabelOpts(formatter="{value} ml"), ), title_opts=opts.TitleOpts(title="Grid-多 Y 轴示例"), tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="cross"), ) ) line = ( Line() .add_xaxis(x_data) .add_yaxis( "平均温度", [2.0, 2.2, 3.3, 4.5, 6.3, 10.2, 20.3, 23.4, 23.0, 16.5, 12.0, 6.2], yaxis_index=2, color="#675bba", label_opts=opts.LabelOpts(is_show=False), ) ) bar.overlap(line) grid = Grid() grid.add(bar, opts.GridOpts(pos_left="5%", pos_right="20%"), is_control_axis_index=True) grid.render("grid_multi_yaxis.html")

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null

0

null

0

1

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21d9cfb9234f93c241128ce45315616d74f27dd4

1,128

py

Python

ketchuporo/main.py

v0y/ketchuporo

1e4f7e487ad2cbc36acbee5cb3271b2c20411a98

[ "MIT" ]

null

ketchuporo/main.py

v0y/ketchuporo

1e4f7e487ad2cbc36acbee5cb3271b2c20411a98

[ "MIT" ]

null

ketchuporo/main.py

v0y/ketchuporo

1e4f7e487ad2cbc36acbee5cb3271b2c20411a98

[ "MIT" ]

null

# -*- coding: utf-8 -*- from kivy.app import App from kivy.core.text import LabelBase from kivy.lang import Builder from kivy.uix.screenmanager import ScreenManager from ketchuporo.const import Files from ketchuporo.controllers import ( BreakScreen, BreaksOverScreen, PomodorosOverScreen, SettingsScreen, WelcomeScreen, ) from ketchuporo.controllers import TimerScreen Builder.load_file(Files.KV) LabelBase.register(name='RobotoLight', fn_regular='lib/fonts/roboto_light.ttf') # Create the screen manager screen_manager = ScreenManager() screen_manager.add_widget(WelcomeScreen(name='welcome')) screen_manager.add_widget( TimerScreen(screen_manager=screen_manager, name='timer') ) screen_manager.add_widget(PomodorosOverScreen(name='pomodoros_over')) screen_manager.add_widget( BreakScreen(screen_manager=screen_manager, name='break') ) screen_manager.add_widget(BreaksOverScreen(name='breaks_over')) screen_manager.add_widget(SettingsScreen(name='settings')) class KetchuporoApp(App): def build(self): return screen_manager if __name__ == '__main__': KetchuporoApp().run()

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0.032258

false

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0.225806

0.032258

0.322581

0

null

0

null

0

1

0

21dc9a19d80f7a66fcdb3bf1134d323402812861

379

py

Python

reading_plan/reading_plan.py

hidenori-t/snippet

d850bd429931f9268162419d3fb8d7803e58cff3

[ "MIT" ]

1

2020-06-19T10:06:37.000Z

reading_plan/reading_plan.py

hidenori-t/snippet

d850bd429931f9268162419d3fb8d7803e58cff3

[ "MIT" ]

null

reading_plan/reading_plan.py

hidenori-t/snippet

d850bd429931f9268162419d3fb8d7803e58cff3

[ "MIT" ]

null

# 読書計画用スニペット from datetime import date def reading_plan(title, total_number_of_pages, period): current_page = int(input("Current page?: ")) deadline = (date(int(period[0]), int(period[1]), int(period[2])) - date.today()).days print(title, period, "まで残り", deadline, "days", (total_number_of_pages - current_page) // deadline, "p/day")

34.454545

70

0.638522

49

379

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0.571429

0.141631

0.111588

0.154506

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0.009967

0.205805

379

10

71

37.9

0.76412

0.026385

0

0.076294

0

1

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false

0

0.142857

0

0.285714

0.142857

0

null

0

null

0

1

0

21e208b57e398b0bb168739f3f960a4bcb46ffb1

603

py

Python

FRMinutesDiscoutRate-BS.py

henchc/web-scrapers

e9f852524fce8ba8c3948f6de242185220a436cb

[ "MIT" ]

15

2017-11-03T20:44:26.000Z

2022-01-02T22:07:15.000Z

FRMinutesDiscoutRate-BS.py

henchc/web-scrapers

e9f852524fce8ba8c3948f6de242185220a436cb

[ "MIT" ]

null

FRMinutesDiscoutRate-BS.py

henchc/web-scrapers

e9f852524fce8ba8c3948f6de242185220a436cb

[ "MIT" ]

1

2020-09-26T22:52:51.000Z

from urllib.request import Request, urlopen from bs4 import BeautifulSoup html = urlopen("http://www.federalreserve.gov/monetarypolicy/discountrate.htm") bsObj = BeautifulSoup(html.read(), "lxml") d1 = bsObj.findAll("option") urls = [] for item in d1: if "PDF" in str(item.get_text()): prefix = "http://www.federalreserve.gov" url = prefix + str(item['value']) urls.append((url, str(item.get_text()))) urls = urls[:3] print(len(urls)) for url in urls: res = urlopen(Request(url[0])) pdf = open((url[1] + ".pdf"), 'wb') pdf.write(res.read()) pdf.close()

24.12

79

0.640133

84

603

4.571429

0.52381

0.054688

0.109375

0.125

0

0.012146

0.180763

603

24

80

25.125

0.765182

0

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0

1

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false

0

0.111111

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0.111111

0.055556

0

null

0

null

0

1

0

21e2ac869244887a79d078c7ec21be9ea889f1fc

644

py

Python

py_analysis_quick_start/temp_line.py

jack-zheng/olm-analysis

16bdc06ece9c2b42983722b37d1c1688eaa90e0f

[ "MIT" ]

null

py_analysis_quick_start/temp_line.py

jack-zheng/olm-analysis

16bdc06ece9c2b42983722b37d1c1688eaa90e0f

[ "MIT" ]

null

py_analysis_quick_start/temp_line.py

jack-zheng/olm-analysis

16bdc06ece9c2b42983722b37d1c1688eaa90e0f

[ "MIT" ]

null

import random import matplotlib from matplotlib import pyplot as plt # 显示中文 # Windows/Linus font = {'family' : 'MicroSoft Yahei', 'weight': 'bold', 'size': '9'} matplotlib.rc("font", **font) # 设置图片大小 20x8 fig = plt.figure(figsize=(20, 8), dpi=80) y = [random.randint(20, 35) for i in range(120)] x = range(120) plt.plot(x, y) # 调整 x 轴刻度 _xticks_labels = ["10点{}分".format(i) for i in range(60)] _xticks_labels += ["11点{}分".format(i) for i in range(60)] # 将 x 和 labels 一一绑定； rotation 旋转 label plt.xticks(list(x)[::3], _xticks_labels[::3], rotation=45) # 添加描述信息 plt.xlabel("时间") plt.ylabel("温度 'C") plt.title("10点到12点温度变化") # 展示 plt.show()

21.466667

68

0.664596

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644

3.836364

0.609091

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0.042654

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0.099526

0

0.063521

0.14441

644

30

69

21.466667

0.702359

0.135093

0

0.127273

0

1

0

false

0

0.1875

0

0.1875

0

null

0

null

0

1

0

21e4e94462869b5e4859a8999659718ac1c5f326

935

py

Python

main.py

gaurav-karna/ReminderBot

e9c37f1c62a6b0f3e9f69ec5cc983ce2334ae6db

[ "MIT" ]

null

main.py

gaurav-karna/ReminderBot

e9c37f1c62a6b0f3e9f69ec5cc983ce2334ae6db

[ "MIT" ]

null

main.py

gaurav-karna/ReminderBot

e9c37f1c62a6b0f3e9f69ec5cc983ce2334ae6db

[ "MIT" ]

null

from sys import exit import argparse ALL_ARGS = None # found a free server to execute blindly, will update with CRON if need be def sanity(): if ALL_ARGS.hour > 23 or ALL_ARGS.hour < 0: print('Error in hour provided, must be >= 0 and <= 23') exit(0) if ALL_ARGS.min > 59 or ALL_ARGS.min < 0: print('Error in minute provided, must be >= 0 and <= 59') exit(0) def set_cron(): pass if __name__ == "__main__": parser = argparse.ArgumentParser(description='CRON setup to send reminder texts') parser.add_argument('--cron', action='store_true') parser.add_argument('--hour', help='hour in EST', type=int) parser.add_argument('--min', help='min of hour, default = 0', type=int, default=0) parser.add_argument('--msg', help='message to send', type=str, default='None provided') ALL_ARGS = parser.parse_args() print(ALL_ARGS.cron) # sanity() # set_cron()

28.333333

91

0.652406

142

935

4.133803

0.443662

0.083475

0.115843

0.044293

0.061329

0

0.021798

0.214973

935

32

92

29.21875

0.777929

0.098396

0

0.1

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0.274136

0

1

0.1

false

0.05

0.1

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0.2

0.15

0

null

0

null

0

1

0

21e56d9e00f8e568b7ebb5d7d2e6c10697c117cb

1,846

py

Python

tests/test_model.py

JanCVanB/netflix

e3ff4feab832846640dbb3daa5877ef84a00adaf

[ "MIT" ]

22

2016-12-14T03:58:28.000Z

2021-03-07T00:10:05.000Z

tests/test_model.py

JanCVanB/netflix

e3ff4feab832846640dbb3daa5877ef84a00adaf

[ "MIT" ]

1

2015-06-16T07:30:35.000Z

2015-06-16T07:39:18.000Z

tests/test_model.py

jvanbrug/netflix

e3ff4feab832846640dbb3daa5877ef84a00adaf

[ "MIT" ]

5

2016-12-05T08:55:15.000Z

2017-11-01T23:33:05.000Z

import numpy as np import os import pickle import random from algorithms import model as model_algorithm from utils import data_paths def test_model_can_create_instance_with_no_arguments(): model_algorithm.Model() def test_model_load_creates_the_expected_instance(): model = model_algorithm.Model() model.x = random.random() model.y = np.array([random.random()]) load_file_name = 'test.p' load_file_path = os.path.join(data_paths.MODELS_DIR_PATH, load_file_name) assert not os.path.isfile(load_file_path), ('{} is for test use only' .format(load_file_path)) try: with open(load_file_path, 'wb+') as load_file: pickle.dump(model, load_file) loaded_model = model_algorithm.Model.load(load_file_name) assert loaded_model.x == model.x np.testing.assert_array_equal(loaded_model.y, model.y) finally: try: os.remove(load_file_path) except FileNotFoundError: pass def test_model_save_writes_the_expected_file(): model = model_algorithm.Model() model.x = random.random() model.y = np.array([random.random()]) save_file_name = 'test.p' save_file_path = os.path.join(model_algorithm.MODELS_DIR_PATH, save_file_name) assert not os.path.isfile(save_file_path), ('{} is for test use only' .format(save_file_path)) try: model.save(save_file_name) with open(save_file_path, 'rb') as save_file: saved_model = pickle.load(save_file) assert saved_model.x == model.x np.testing.assert_array_equal(saved_model.y, model.y) finally: try: os.remove(save_file_path) except FileNotFoundError: pass

33.563636

77

0.641387

247

1,846

4.481781

0.242915

0.072267

0.054201

0.065041

0.444444

0.34869

0.296296

0.187895

0.121048

0

0.269231

1,846

54

78

34.185185

0.820608

0

0.340426

0

0.034128

0

0.12766

1

0.06383

false

0.042553

0.12766

0

0.191489

0

null

0

null

0

1

0

21e6f2feb888d1690e95e165f59b5155700dbb10

533

py

Python

serenote/utils/alert.py

LeptoFlare/todo-bot

5c619abe421e4731c0dbc73561fbbd4a20839422

[ "MIT" ]

3

2020-10-07T04:41:25.000Z

2021-04-23T19:52:53.000Z

serenote/utils/alert.py

LeptoFlare/serenote

5c619abe421e4731c0dbc73561fbbd4a20839422

[ "MIT" ]

8

2020-10-07T00:13:00.000Z

2020-11-23T09:01:08.000Z

serenote/utils/alert.py

LeptoFlare/serenote

5c619abe421e4731c0dbc73561fbbd4a20839422

[ "MIT" ]

null

import discord class Alert(discord.Embed): alert_types = { "error": "<:error:807799799721230347>", } def __init__(self, alert_type, title: str, description: str = discord.Embed.Empty): super().__init__( color=discord.Color.blurple(), title=self.process_title(alert_type, title), description=description ) @classmethod def process_title(cls, alert_type, title): return f"{cls.alert_types[alert_type]} {alert_type.capitalize()}: **{title}**"

26.65

87

0.628518

57

533

5.578947

0.438596

0.141509

0.132075

0

0.044335

0.238274

533

19

88

28.052632

0.738916

0

0.187617

0.153846

0

1

0.142857

false

0

0.071429

0.428571

0

null

0

null

0

1

0

21e79dcdcdee9f92045ac51d4e85b03f25e5e3c8

2,616

py

Python

fixture/project.py

AleksandrSmoliak/mantis

be0f48a6a63a2b955755031a721db7fb8761fe97

[ "Apache-2.0" ]

null

fixture/project.py

AleksandrSmoliak/mantis

be0f48a6a63a2b955755031a721db7fb8761fe97

[ "Apache-2.0" ]

null

fixture/project.py

AleksandrSmoliak/mantis

be0f48a6a63a2b955755031a721db7fb8761fe97

[ "Apache-2.0" ]

null

from model.project import Project class ProjectHelper: def __init__(self, app): self.app = app def open_project_page(self): wd = self.app.wd if not (wd.current_url.endswith("/manage_proj_create_page.php")): wd.find_element_by_xpath("//div[@id='main-container']/div[@id='sidebar']/ul[@class='nav nav-list']/li[7]/a").click() wd.find_element_by_xpath("//div[@class='row']/ul/li[3]/a").click() def create_project(self, project): wd = self.app.wd # Открываем страницу создания проекта self.open_project_page() # Переход на форму создания проекта wd.find_element_by_xpath("//button[@class='btn btn-primary btn-white btn-round']").click() # Заполнение полей формы self.fill_project_form(project) # Нажание на кнопку добавления проекта wd.find_element_by_xpath("//input[@class='btn btn-primary btn-white btn-round']").click() def fill_project_form(self, project): self.change_field_value("name", project.name) self.change_field_value("description", project.description) def change_field_value(self, field_name, text): wd = self.app.wd if text is not None: wd.find_element_by_name(field_name).click() wd.find_element_by_name(field_name).clear() wd.find_element_by_name(field_name).send_keys(text) def project_count(self): wd = self.app.wd return len(wd.find_elements_by_xpath("//div[@class='widget-box widget-color-blue2']/div[2]/div[1]/div[2]/table/tbody/tr")) def get_project_list(self): wd = self.app.wd self.open_project_page() self.project_list = [] for element in wd.find_elements_by_xpath("//div[@class='table-responsive']/table/tr"): cells = element.find_elements_by_tag_name("td") name = cells[1] description = cells[5] self.project_list.append(Project(name=name, description=description)) return list(self.project_list) def open_project_by_index(self, index): wd = self.app.wd self.open_project_page() row = wd.find_elements_by_xpath("//div[@class='table-responsive']/table/tbody/tr")[index] cell = row.find_elements_by_tag_name("td")[0] cell.find_element_by_tag_name("a").click() def del_project_by_index(self, index): wd = self.app.wd self.open_project_by_index(index) wd.find_element_by_xpath("//input[@value='Удалить проект']").click() wd.find_element_by_xpath("//input[@value='Удалить проект']").click()

42.193548

130

0.65367

368

2,616

4.388587

0.271739

0.044582

0.080495

0.083591

0.450155

0.416718

0.320124

0.250155

0.229102

0.180805

0

0.004348

0.208716

2,616

62

131

42.193548

0.775845

0.049312

0

0.25

0

0.041667

0.200564

0.134112

0

1

0.1875

false

0

0.020833

0

0.270833

0

null

0

null

0

1

0

21e821b8f988e5658b9c301ffb5c85c84dc141ff

773

py

Python

main.py

icarus747/DCS_MGRS_Converter

3ce687a697f1313658726a8881c134d321d67c22

[ "MIT" ]

null

main.py

icarus747/DCS_MGRS_Converter

3ce687a697f1313658726a8881c134d321d67c22

[ "MIT" ]

1

2020-10-12T20:11:27.000Z

2020-10-12T22:33:23.000Z

main.py

icarus747/DCS_MGRS_Converter

3ce687a697f1313658726a8881c134d321d67c22

[ "MIT" ]

1

2020-10-12T16:43:15.000Z

#!/usr/bin/env python3 # By Icarus747 # Created 10/11/2020 # Used for converting DCS MGRS grid coordinates to Lat/Long coordinates. import mgrs import re def main(): m = mgrs.MGRS() # dcs = '38TLN046623' dcs = input("Enter MGRS cord.\n\r") dcs = validate_mgrs(dcs) dd = m.toLatLon(dcs) lat = m.ddtodms(dd[0]) Lat = round(lat[1] + lat[2] / 60, 1) long = m.ddtodms(dd[1]) Long = round(long[1] + long[2] / 60, 1) print(f"N {int(lat[0])} {Lat}") print(f"E 0{int(long[0])} {Long}") def validate_mgrs(dcs): pattern = r'^\b\d{2}[a-z]{3}\d{6,10}\b' if re.match(pattern, dcs.lower()): return dcs else: print("Verify MGRS grid and try again.\n\r") main() if __name__ == '__main__': main()

20.342105

72

0.575679

127

773

3.425197

0.480315

0.048276

0.068966

0

0.068376

0.243208

773

37

73

20.891892

0.675214

0.186287

0

0.090909

0

0.214744

0.041667

0

1

0.090909

false

0

0.090909

0

0.227273

0.136364

0

null

0

null

0

1

0

21ea48feb3e6f28dc69bc7b3d03c6225f2e7de16

637

py

Python

backend/management/commands/setentryfolder.py

Software-Engineering-Bachelor-Project/mycroft

8ca3b6bfaa7b573f67def06c637f3c57838440a4

[ "MIT" ]

5

2020-03-01T11:17:09.000Z

2021-07-08T20:45:47.000Z

backend/management/commands/setentryfolder.py

Software-Engineering-Bachelor-Project/mycroft

8ca3b6bfaa7b573f67def06c637f3c57838440a4

[ "MIT" ]

245

2020-03-28T11:59:12.000Z

2020-05-26T10:05:22.000Z

backend/management/commands/setentryfolder.py

Software-Engineering-Bachelor-Project/mycroft

8ca3b6bfaa7b573f67def06c637f3c57838440a4

[ "MIT" ]

5

2020-02-03T08:15:13.000Z

2020-04-15T07:22:47.000Z

from django.core.management.base import BaseCommand from backend.file_manager import build_file_structure class Command(BaseCommand): help = 'Sets entry folder to given file path.' def add_arguments(self, parser): parser.add_argument('folder', type=str, help='Path to entry folder') def handle(self, *args, **kwargs): folder = kwargs['folder'] try: build_file_structure(file_path=folder) self.stdout.write("Successfully added entry folder.") except ValueError as e: self.stdout.write(str(e)) self.stderr.write("Failed to add entry folder.")

33.526316

76

0.66876

81

637

5.160494

0.530864

0.105263

0.086124

0

0.230769

637

18

77

35.388889

0.853061

0

0.200942

0

1

0.142857

false

0

0.142857

0

0.428571

0

null

0

null

0

1

0

21eb2ec30e5683c312af0b66057c5639110b2683

2,149

py

Python

work/adder.py

MorrisMA/py65

bda1553ff88fc577944bde3d7cb3e75a3b83ccfa

[ "BSD-3-Clause" ]

null

work/adder.py

MorrisMA/py65

bda1553ff88fc577944bde3d7cb3e75a3b83ccfa

[ "BSD-3-Clause" ]

null

work/adder.py

MorrisMA/py65

bda1553ff88fc577944bde3d7cb3e75a3b83ccfa

[ "BSD-3-Clause" ]

null

from math import * N = 8 V = 4 Z = 2 C = 1 wordSign = 1 << 11 wordMask = (1 << 12) - 1 byteSign = 1 << 7 byteMask = (1 << 8) - 1 def adder(op, a, b, cin, siz=False): if siz: sign = wordSign mask = wordMask else: sign = byteSign mask = byteMask auL = mask & a if op == 1: auR = mask & ~b else: auR = mask & b din = mask & b sum = auL + auR + cin nvzc = 0 if sign & sum: nvzc |= N if (~(auL ^ auR) & (auL ^ sum)) & sign: nvzc |= V if (mask & sum) == 0: nvzc |= Z if sum > mask: nvzc |= C sum &= mask return nvzc, sum, auL, auR print(N, V, Z, C, '%04X %04X %02X %02X' % (wordSign, wordMask, \ byteSign, byteMask)) stat ={0 :'----', 1 :'---C', 2 :'--Z-', 3 :'--ZC', 4 :'-V--', 5 :'-V-C', 6 :'-VZ-', 7 :'-VZC', 8 :'N---', 9 :'N--C', 10:'N-Z-', 11:'N-ZC', 12:'NV--', 13:'NV-C', 14:'NVZ-', 15:'NVZC' } with open("adder08b.txt", "wt") as fout: k = 0 for i in range(256): for j in range(256): nvzc, sum, auL, auR = adder(0, i, j, 0) print('%s, %1d, %02X, %02X, %02X, %02X, %02X, %1d' % \ (stat[nvzc], 0, sum, i, j, auL, auR, 0), file=fout) nvzc, sum, auL, auR = adder(0, i, j, 1) print('%s, %1d, %02X, %02X, %02X, %02X, %02X, %1d' % \ (stat[nvzc], 0, sum, i, j, auL, auR, 1), file=fout) nvzc, sum, auL, auR = adder(1, i, j, 1) print('%s, %1d, %02X, %02X, %02X, %02X, %02X, %1d' % \ (stat[nvzc], 1, sum, i, j, auL, auR, 1), file=fout) nvzc, sum, auL, auR = adder(1, i, j, 0) print('%s, %1d, %02X, %02X, %02X, %02X, %02X, %1d' % \ (stat[nvzc], 1, sum, i, j, auL, auR, 0), file=fout) if k == 0: print('%02X' % (i), end='') else: print(', %02X' % (i), end='') k += 1 if k == 16: print() k = 0

24.988372

69

0.378781

302

2,149

2.695364

0.241722

0.125307

0.132678

0.117936

0.373464

0.304668

0

0.105221

0.420661

2,149

85

70

25.282353

0.548594

0

0.118421

0

0.052632

0.127967

0

1

0.013158

false

0

0.013158

0

0.039474

0.105263

0

null

0

null

0

1

0

21ed527c3321a9993fe603a88785851b9688cff9

2,289

py

Python

optimism/contact/PenaltyContact.py

btalamini/optimism

023e1b2a0b137900a7517e4c7ac5056255cf7bbe

[ "MIT" ]

null

optimism/contact/PenaltyContact.py

btalamini/optimism

023e1b2a0b137900a7517e4c7ac5056255cf7bbe

[ "MIT" ]

1

2022-03-12T00:01:12.000Z

optimism/contact/PenaltyContact.py

btalamini/optimism

023e1b2a0b137900a7517e4c7ac5056255cf7bbe

[ "MIT" ]

3

2021-12-23T19:53:31.000Z

2022-03-27T23:12:03.000Z

from optimism.JaxConfig import * from optimism import Mesh from optimism import QuadratureRule from optimism import Surface import numpy as onp def get_current_coordinates_at_quadrature_points(mesh, dispField, quadRule, edge): fieldIndex = Surface.get_field_index(edge, mesh.conns) edgeCoords = Surface.eval_field(mesh.coords, fieldIndex) edgeDisps = Surface.eval_field(dispField, fieldIndex) return QuadratureRule.eval_at_iso_points(quadRule.xigauss, edgeCoords+edgeDisps) def evaluate_levelset_on_edge(levelset, mesh, dispField, quadRule, edge): fieldIndex = Surface.get_field_index(edge, mesh.conns) edgeCoords = Surface.eval_field(mesh.coords, fieldIndex) edgeDisps = Surface.eval_field(dispField, fieldIndex) quadratureCurCoords = QuadratureRule.eval_at_iso_points(quadRule.xigauss, edgeCoords+edgeDisps) return levelset(quadratureCurCoords) def compute_edge_penalty_contact_energy(levelset, mesh, dispField, quadRule, edge, stiffness): fieldIndex = Surface.get_field_index(edge, mesh.conns) edgeCoords = Surface.eval_field(mesh.coords, fieldIndex) edgeDisps = Surface.eval_field(dispField, fieldIndex) quadratureCurCoords = QuadratureRule.eval_at_iso_points(quadRule.xigauss, edgeCoords+edgeDisps) lsetField = levelset(quadratureCurCoords) negativeLsetField = np.minimum(0.0, lsetField) return stiffness*Surface.integrate_values(quadRule, edgeCoords, np.square(negativeLsetField)) def evaluate_contact_constraints(levelset, dispField, mesh, quadRule, edges): return vmap(evaluate_levelset_on_edge, (None,None,None,None,0))(levelset, mesh, dispField, quadRule, edges) def compute_total_penalty_contact_energy(levelset, dispField, mesh, quadRule, edges, stiffness): return np.sum(vmap(compute_edge_penalty_contact_energy, (None,None,None,None,0,None))(levelset, mesh, dispField, quadRule, edges, stiffness)) def compute_fisher_burmeister_linearization(levelset, disp, mesh, quadRule, edges, lmbda): for edge in edges: quadratureCurCoords = get_current_coordinates_at_quadrature_points(mesh, dispField, quadRule, edge) phi = levelset(quadratureCurCoords) t = np.sqrt(phi**2 + lmbda**2) dlambda = ( t**2 - t*(phi+lmbda) ) / (t-lmbda)

40.875

145

0.7737

270

2,289

6.355556

0.244444

0.045455

0.073427

0.058275

0.613054

0.453963

0.417249

0

0.003543

0.136741

2,289

55

146

41.618182

0.864879

0

0.323529

0

1

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0

0.147059

0.058824

0.470588

0

null

0

null

0

1

0

21ee589b914a86f78fa5eda580da880b00125bd4

1,970

py

Python

nlplingo/oregon/event_models/uoregon/layers/elmo.py

BBN-E/nlplingo

32ff17b1320937faa3d3ebe727032f4b3e7a353d

[ "Apache-2.0" ]

3

2020-10-22T13:28:00.000Z

2022-03-24T19:57:22.000Z

nlplingo/oregon/event_models/uoregon/layers/elmo.py

BBN-E/nlplingo

32ff17b1320937faa3d3ebe727032f4b3e7a353d

[ "Apache-2.0" ]

null

nlplingo/oregon/event_models/uoregon/layers/elmo.py

BBN-E/nlplingo

32ff17b1320937faa3d3ebe727032f4b3e7a353d

[ "Apache-2.0" ]

1

2020-10-22T13:29:51.000Z

import torch from torch.nn import ParameterList, Parameter class Elmo(torch.nn.Module): """ Computes a parameterised scalar mixture of N tensors, `mixture = gamma * sum(s_k * tensor_k)` where `s = softmax(w)`, with `w` and `gamma` scalar parameters. In addition, if `do_layer_norm=True` then apply layer normalization to each tensor before weighting. """ def __init__( self, mixture_size, do_layer_norm=False, trainable=True, ): super().__init__() self.mixture_size = mixture_size self.do_layer_norm = do_layer_norm initial_scalar_parameters = [0.0] * mixture_size self.scalar_parameters = ParameterList( [ Parameter( torch.FloatTensor([initial_scalar_parameters[i]]), requires_grad=trainable ) for i in range(mixture_size) ] ) self.gamma = Parameter(torch.FloatTensor([1.0]), requires_grad=trainable) def forward(self, tensors): """ tensors.shape = num feature layers * [batch size, num tokens, rep dim] """ def _do_layer_norm(tensor, broadcast_mask, num_elements_not_masked): tensor_masked = tensor * broadcast_mask mean = torch.sum(tensor_masked) / num_elements_not_masked variance = ( torch.sum(((tensor_masked - mean) * broadcast_mask) ** 2) / num_elements_not_masked ) return (tensor - mean) / torch.sqrt(variance + 1e-12) normed_weights = torch.nn.functional.softmax( torch.cat([parameter for parameter in self.scalar_parameters]), dim=0 ) normed_weights = torch.split(normed_weights, split_size_or_sections=1) pieces = [] for weight, tensor in zip(normed_weights, tensors): pieces.append(weight * tensor) return self.gamma * sum(pieces)

34.561404

103

0.607614

223

1,970

5.116592

0.403587

0.070114

0.048203

0.052585

0

0.007262

0.301015

1,970

56

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35.178571

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0

0.052632

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0.210526

0

null

0

null

0

1

0

21f19e371c588730cf753a1e2c9dc29cf4e55481

5,082

py

Python

walter/common/walterWidgets/walterBaseVariantsMenu.py

all-in-one-of/OpenWalter

c2034f7fac20b36ffe3e500c01d40b87e84e2b97

[ "libtiff" ]

187

2018-08-14T19:06:20.000Z

2022-03-04T06:03:25.000Z

walter/common/walterWidgets/walterBaseVariantsMenu.py

all-in-one-of/OpenWalter

c2034f7fac20b36ffe3e500c01d40b87e84e2b97

[ "libtiff" ]

9

2018-08-22T15:34:48.000Z

2019-11-27T13:45:21.000Z

walter/common/walterWidgets/walterBaseVariantsMenu.py

all-in-one-of/OpenWalter

c2034f7fac20b36ffe3e500c01d40b87e84e2b97

[ "libtiff" ]

41

2018-08-14T19:06:09.000Z

2021-09-04T20:01:10.000Z

""" Base classes for USD variants. It's written on Python to be able to use complex UI and PySide. """ # Copyright 2017 Rodeo FX. All rights reserved. import json from .Qt import QtWidgets from walterComplexMenu import ComplexMenu class VariantSet(): """Per variantset variants list.""" def __init__(self, name): self.name = name self.variants = [] self.selectedVariant = '' def setVariants(self, variants, selection): self.variants = variants self.selectedVariant = selection class BaseVariantAction(QtWidgets.QAction): def __init__(self, primPath, index, variantName, variantSetName, isSelected, menu): super(BaseVariantAction, self).__init__(variantName, menu) self.menu = menu self.index = index self.primPath = primPath self.variantName = variantName self.variantSetName = variantSetName self.setCheckable(isSelected) self.setChecked(isSelected) self.triggered.connect(self.__trigger) def _setVariantValue(self): pass def __trigger(self): self.menu.uncheckedActions() self._setVariantValue() self.setCheckable(True) self.setChecked(True) class BaseVariantSetMenu(ComplexMenu): def __init__(self, primPath, index, variantSet, parent): super(BaseVariantSetMenu, self).__init__(parent) self.parent = parent self.setTitle(variantSet.name) self.actionList = [] for variant in variantSet.variants: isSelected = variant == variantSet.selectedVariant action = self._constructVariantAction( primPath, index, variant, variantSet.name, isSelected) self.addAction(action) self.actionList.append(action) def _constructVariantAction(self, primPath, index, variant, variantSet, isSelected): pass def uncheckedActions(self): for action in self.actionList: action.setCheckable(False) action.setChecked(False) def mouseReleaseEvent(self, event): """Implementation of ComplexMenu.""" action = self.activeAction() ctrlKey = self.ctrlKeyEvent() if action and action.isEnabled() and ctrlKey: # QMenu will not disapear if the user clicked by disabled action. action.setEnabled(False) action.trigger() super(ComplexMenu, self).mouseReleaseEvent(event) action.setEnabled(True) else: super(ComplexMenu, self).mouseReleaseEvent(event) class BaseVariantsMenu(QtWidgets.QMenu): """Menu for editing walter variants.""" class ObjectVariantSet(): """Per object variantsets list.""" def __init__(self, name): self.name = name self.variantSets = [] def addVariantSet(self, variantSet): self.variantSets.append(variantSet) def __init__(self, parent=None): super(BaseVariantsMenu, self).__init__(parent) self.parent = parent self.nodePath = '' self.primPath = '' def reset(self, nodePath, primPath='', title=None, addSeparators=True, tearOff=True, recursively=True): self.clear() self.nodePath = nodePath self.primPath = primPath title_ = title if not title_: title_ = nodePath + '-' + primPath self.setTitle(title_) self.setWindowTitle(title_) self.setTearOffEnabled(tearOff) self.setStyleSheet("menu-scrollable: 1;") if addSeparators: self.setSeparatorsCollapsible(False) self.__constructMenu( self._getVariantList(recursively), addSeparators) return not self.isEmpty() def _getVariantList(self, recursively): pass def _createMenu(self, primPath, index, variantSet): pass def __constructMenu(self, variantSetsStr, addSeparators): objectVariantSets = [] for variantStr in variantSetsStr: js = json.loads(variantStr) objectVariantSet = BaseVariantsMenu.ObjectVariantSet( js['prim']) for jsVariant in js['variants']: variantSet = VariantSet(jsVariant['set']) variantSet.setVariants(jsVariant['names'], jsVariant['selection']) objectVariantSet.addVariantSet(variantSet) objectVariantSets.append(objectVariantSet) # Creates a variantset menu per object for idx, objectVariantSet in enumerate(objectVariantSets): if addSeparators: action = self.addAction(objectVariantSet.name) # Cause a diplay bug. The titles are cropped # action.setSeparator(True) action.setEnabled(False) for variantSet in objectVariantSet.variantSets: self.addMenu(self._createMenu( objectVariantSet.name, idx, variantSet))

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82

0.62436

454

5,082

6.872247

0.306167

0.026923

0.017628

0.009615

0.086538

0.044231

0.022436

0

0.001386

0.29024

5,082

163

83

31.177914

0.863599

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0

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false

0.035714

0.026786

0

0.223214

0

null

0

null

0

1

0

21f244a8537b9d091ee8fe82b19616dc52ef2d25

4,983

py

Python

src/speechless/readers/audio.py

Exepp/SpeechLess

6e7424e979f39132650db0d7426c1e9449dc43b8

[ "MIT" ]

1

2022-03-17T14:51:41.000Z

src/speechless/readers/audio.py

Exepp/SpeechLess

6e7424e979f39132650db0d7426c1e9449dc43b8

[ "MIT" ]

14

2021-06-23T02:27:22.000Z

2021-11-27T15:43:39.000Z

src/speechless/readers/audio.py

Exepp/SpeechLess

6e7424e979f39132650db0d7426c1e9449dc43b8

[ "MIT" ]

null

import subprocess import av import numpy as np from av.audio.frame import format_dtypes from typing import Dict, Generator, Tuple from logging import Logger from pathlib import Path from enum import Enum, auto from speechless.utils.logging import NULL_LOGGER class StreamInfo(Enum): SAMPLE_RATE = auto() FRAME_SIZE = auto() class AudioReader: def __init__(self, file_path: str, logger: Logger = NULL_LOGGER): """Audio frame reader Args: file_path (str): Path to the recording logger (Logger, optional): Logger for messages. Defaults to NULL_LOGGER """ self.file_path = str(Path(file_path).resolve()) self.logger = logger self._container = None def __del__(self): if self._container is not None: self._container.close() def read_stream(self, aud_stream_idx: int = None) \ -> Tuple[Generator[np.ndarray, None, None], Dict[StreamInfo, object]]: """Creates a generator of audio frames of a given audio stream in the recording Args: aud_stream_idx (int, optional): Index of the audio stream (0 -> first, 1 -> second). \ Defaults to None (the first audio stream) Returns: Tuple[Generator[np.ndarray, None, None], Dict[StreamInfo, object]]: A generator of the \ audio frames and a dictionary with info about the stream """ if self._container is not None: self._container.close() self._container = av.open(self.file_path) if aud_stream_idx is None: aud_stream_idx = 0 if len(self._container.streams.audio) > 1: self.logger.warning( 'Unspecified audio stream for a file with multiple audio streams. Reading ' f'the first one (stream #{self._container.streams.audio[self._stream_idx].index})') def audio_iter(): for frame in self._container.decode(audio=aud_stream_idx): yield frame.to_ndarray() return (audio_iter(), AudioReader.prepare_stream_info(self._container, aud_stream_idx)) @staticmethod def prepare_stream_info(container: av.container.InputContainer, aud_stream_idx: int) \ -> Dict[StreamInfo, object]: """Creates a dictionary with info about a specific audio stream in the container Args: container (av.container.InputContainer): A container with the audio stream aud_stream_idx (int): The index of the audio stream (0 -> first, 1 -> second) Returns: Dict[StreamInfo, object]: A dictionary with info about the stream """ astream = container.streams.audio[aud_stream_idx] return { StreamInfo.SAMPLE_RATE: astream.sample_rate, StreamInfo.FRAME_SIZE: astream.codec_context.frame_size } def read_entire_audio(file_path: str, aud_stream_idx: int = None, aud_format: str = 'f32le', sample_rate: int = None, logger: Logger = NULL_LOGGER) -> Tuple[np.ndarray, Dict[StreamInfo, object]]: """Reads an entire audio stream from a recording Args: file_path (str): Path to the recording aud_stream_idx (int, optional): Index of the audio stream (0 -> first, 1 -> second). \ Defaults to None (the first audio stream) aud_format (str, optional): A desired audio format. Defaults to 'f32le' sample_rate (int, optional): A desired sample rate. This will be the sample rate of the \ returned signal. logger (Logger, optional): Logger for messages. Defaults to NULL_LOGGER Returns: Tuple[np.ndarray, Dict[StreamInfo, object]]: The entire audio stream in the specified format \ and a dictionary with info about the (original) stream - sample rate information will be \ that of the original stream, not the one specified here """ file_path = str(Path(file_path).resolve()) with av.open(file_path) as container: if aud_stream_idx is None: aud_stream_idx = 0 if len(container.streams.audio) > 1: logger.warning('Unspecified audio stream for a file with multiple audio streams. Reading ' f'the first one (stream #{container.streams.audio[aud_stream_idx].index})') acodec = f'pcm_{aud_format}' command = [ 'ffmpeg', '-i', f'{file_path}', '-map', f'0:a:{aud_stream_idx}', '-f', f'{aud_format}', '-acodec', f'{acodec}' ] command += ['-ar', f'{sample_rate}'] if sample_rate is not None else [] command += ['pipe:1'] process = subprocess.Popen(stdout=subprocess.PIPE, args=command) buffer, _ = process.communicate() astream = container.streams.audio[aud_stream_idx] acodec = av.Codec(acodec, 'r') dtype = np.dtype(format_dtypes[acodec.audio_formats[0].name]) if acodec.audio_formats[0].is_planar: data = np.frombuffer(buffer, dtype).reshape((astream.channels, -1)) else: data = np.frombuffer(buffer, dtype).reshape((-1, astream.channels)).T return (data, AudioReader.prepare_stream_info(container, aud_stream_idx))

37.75

100

0.675095

673

4,983

4.843982

0.200594

0.049693

0.062577

0.027607

0.412577

0.382822

0.330982

0.257669

0.237423

0.169939

0

0.005167

0.223159

4,983

131

101

38.038168

0.836993

0.328116

0

0.138889

0

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0.032787

0

1

0.083333

false

0

0.125

0

0.305556

0

null

0

null

0

1

0

21f39b184cd5ca7c84021b18a3faeb579b594682

413

py

Python

scripts/multi_sound.py

deeredman1991/Simple-Mod-Loader

00fd342b43132d4ee1bd8ef9ac0910d02fece737

[ "MIT" ]

null

scripts/multi_sound.py

deeredman1991/Simple-Mod-Loader

00fd342b43132d4ee1bd8ef9ac0910d02fece737

[ "MIT" ]

null

scripts/multi_sound.py

deeredman1991/Simple-Mod-Loader

00fd342b43132d4ee1bd8ef9ac0910d02fece737

[ "MIT" ]

null

from kivy.core.audio import SoundLoader class MultiSound(object): # for playing the same sound multiple times. def __init__(self, file, num): self.num = num self.sounds = [SoundLoader.load(file) for n in range(num)] self.index = 0 def play(self): self.sounds[self.index].play() self.index += 1 if self.index == self.num: self.index = 0

31.769231

70

0.598063

56

413

4.339286

0.553571

0.185185

0.098765

0.106996

0

0.010274

0.292978

413

13

71

31.769231

0.821918

0.101695

0

0.181818

0

1

0.181818

false

0

0.090909

0

0.363636

0

null

0

null

0

1

0

21f3edcb7d50ea036f5a264fb5f9a7a4e6b5cf1a

810

py

Python

datasets/orange.py

tkemps/mklaren

d9e7890aaa26cb3877e1a82114ab1e52df595d96

[ "BSD-2-Clause" ]

3

2019-10-28T17:20:37.000Z

2020-08-20T22:59:18.000Z

datasets/orange.py

tkemps/mklaren

d9e7890aaa26cb3877e1a82114ab1e52df595d96

[ "BSD-2-Clause" ]

null

datasets/orange.py

tkemps/mklaren

d9e7890aaa26cb3877e1a82114ab1e52df595d96

[ "BSD-2-Clause" ]

1

2019-10-28T17:20:35.000Z

from numpy import genfromtxt from os.path import join, realpath, dirname ORANGE_PATH = join(dirname(realpath(__file__)), "orange") def load_ionosphere(n=None): """ Load the ionosphere dataset. :param n: Maximum number of examples. :return: Dataset in standard form. """ header = genfromtxt(join(ORANGE_PATH, "ionosphere.csv"), delimiter=",", skip_header=0, dtype=str, max_rows=1) data = genfromtxt(join(ORANGE_PATH, "ionosphere.csv"), delimiter=",", skip_header=1, dtype=float) X = data[:, :-1] y = data[:, -1].ravel() if n is not None and n < X.shape[0]: X = X[:n, :] y = y[:n] labels = header[:-1] assert len(labels) == X.shape[1] return { "data": X, "target": y, "labels": labels }

32.4

74

0.583951

106

810

4.358491

0.481132

0.064935

0.08658

0.103896

0.242424

0

0.0134

0.262963

810

25

75

32.4

0.760469

0.124691

0

0.075802

0

0.055556

1

0.055556

false

0

0.111111

0

0.222222

0

null

0

null

0

1

0

21f493f41dab43945de7c3a7977465eb24defd66

2,524

py

Python

makahiki/scripts/initialize_instance.py

justinslee/Wai-Not-Makahiki

4b7dd685012ec64758affe0ecee3103596d16aa7

[ "MIT" ]

1

2015-07-22T11:31:20.000Z

makahiki/scripts/initialize_instance.py

justinslee/Wai-Not-Makahiki

4b7dd685012ec64758affe0ecee3103596d16aa7

[ "MIT" ]

null

makahiki/scripts/initialize_instance.py

justinslee/Wai-Not-Makahiki

4b7dd685012ec64758affe0ecee3103596d16aa7

[ "MIT" ]

null

#!/usr/bin/python """ Invocation: scripts/initialize_instance .py -t|--type[=] default|demo|test -r|--heroku[=] <heroku_app> Use this script to create an instance with different types of configuration: [default]: includes the basic configuration. The admin needs to create the settings for rounds, resources, resource goals, teams and users, prizes, etc. Uses internal authentication. [test] : includes all of "default" configuration, with more test users and data. Uses CAS authentication. if -r or --heroku is specified, it will initialize the instance in the specified heroku app. Performs the following: * installation of any modules in requirements.txt * re-create the database and database user * Synchronize and migrates the database schemas. * Collects and copies the static and media files to the specific location. * Loads the default or test configuration of data. """ import getopt import sys import os sys.path.append(os.path.dirname(os.path.realpath(__file__)) + os.sep + os.pardir + os.sep) from apps.utils import script_utils def main(argv): """main function.""" instance_type = None heroku_app = None manage_py = script_utils.manage_py_command() manage_command = "python " + manage_py fixture_path = "fixtures" try: opts, args = getopt.getopt(argv, "t:r:h", ["type=", "heroku=", "help"]) except getopt.GetoptError: script_utils.exit_with_help(__doc__) if not opts: script_utils.exit_with_help(__doc__) for opt in opts: if opt[0] == "-h" or opt[0] == "--help": script_utils.exit_with_help(__doc__) if opt[0] == "-t" or opt[0] == "--type": instance_type = opt[1] if opt[0] == "-r" or opt[0] == "--heroku": heroku_app = opt[1] manage_command = "heroku run --app %s python makahiki/manage.py" % heroku_app if not instance_type in ("default", "demo", "test", "uh12", "water"): script_utils.exit_with_help(__doc__) _ = args if not heroku_app: script_utils.install_requirements() else: script_utils.create_heroku_app(heroku_app) script_utils.push_to_heroku(heroku_app) script_utils.reset_db(heroku_app) script_utils.syncdb(manage_command) script_utils.copy_static_media(heroku_app) script_utils.load_data(manage_command, instance_type, fixture_path) if __name__ == '__main__': main(sys.argv[1:])

30.409639

90

0.666799

341

2,524

4.692082

0.375367

0.089375

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0.0675

0.035

0

0.005641

0.227417

2,524

82

91

30.780488

0.814872

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0

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0

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0

1

0.025641

false

0

0.102564

0

0.128205

0

null

0

null

0

1

0

21f70e00fd9069d8e81d9648fe78c6c73dc2b773

2,608

py

Python

python/xml_parsers.py

warlicks/Real_Estate_Data

137dc53a24a878cb96ec5132be6e81196902aec1

[ "MIT" ]

1

2021-01-24T23:59:18.000Z

python/xml_parsers.py

warlicks/Real_Estate_Data

137dc53a24a878cb96ec5132be6e81196902aec1

[ "MIT" ]

3

2016-08-13T22:39:14.000Z

2016-08-21T01:00:51.000Z

python/xml_parsers.py

warlicks/Real_Estate_Data

137dc53a24a878cb96ec5132be6e81196902aec1

[ "MIT" ]

null

# Sean Warlick # Seattle Housing Project # XML Parsing Functions # Date: July 12, 2016 ############################################################################### # Import packages import xml.etree.ElementTree as et def location_parse(xml_string): # Convert the data returned from api to XML xml_element = et.fromstring(xml_string) # Create to Element from string xml_tree = et.ElementTree(xml_element) # Convert Element to Element Tree # To correctly parse xml we need to idenitify the api funciton used. parameters = xml_tree.findall(".//Parameter") # There are multiple Parameter nodes, we need to find the child name is "function" for p in parameters: if p.find("name").text == 'function': function = p.find('value').text else: next # To store the data parsed from xml we need to set up a variable. We will use a dictionary with lists. output = {"name":[], "code":[], "lon":[], "lat":[]} # Each API function has a slightly different schema, thus different logic to parse. Following if statements set up the logic if function == 'getStates': data_node = xml_tree.findall('.//state') # Iterate through all state nodes & extract data for states in data_node: name = states.find('name').text output["name"].append(name) code = states.find('stateCode').text output["code"].append(code) lon = states.find('longitude').text output["lon"].append(lon) lat = states.find('latitude').text output["lat"].append(lat) elif function == 'getCountiesInState': data_node = xml_tree.findall('.//county') for county in data_node: name = county.find('name').text output["name"].append(name) lon = county.find('longitude').text output["lon"].append(lon) lat = county.find('latitude').text output["lat"].append(lat) elif function == 'getCitiesInState': data_node = xml_tree.findall('.//city') for city in data_node: name = city.find('name').text output["name"].append(name) lon = city.find('longitude').text output["lon"].append(lon) lat = city.find("latitude").text output["lat"].append(lat) elif function == 'getZipCodesInState': data_node = xml_tree.findall('.//zipCode') for code in data_node: ZIP = code.find('name').text output["code"].append(ZIP) elif function == 'getNeighborhoodsInCity': data_node = xml_tree.findall('.//neighborhood') for neighborhood in data_node: name = neighborhood.find('name').text iD = neighborhood.find('id').text output["name"].append(name) output["code"].append(iD) return output # Return output for use outside the function.

28.347826

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0.669095

352

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4.894886

0.306818

0.069646

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0.043529

0.283227

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0.166028

2,608

92

127

28.347826

0.789425

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0.018868

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0.056604

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null

0

null

0

1

0

21f9033f249e427aede9e4e1c1b360170f737e4c

11,009

py

Python

pygame_gui/elements/ui_horizontal_slider.py

jtiai/pygame_gui

3da0e1f2c4c60a2780c798d5592f2603ba786b34

[ "MIT" ]

null

pygame_gui/elements/ui_horizontal_slider.py

jtiai/pygame_gui

3da0e1f2c4c60a2780c798d5592f2603ba786b34

[ "MIT" ]

null

pygame_gui/elements/ui_horizontal_slider.py

jtiai/pygame_gui

3da0e1f2c4c60a2780c798d5592f2603ba786b34

[ "MIT" ]

null

import pygame import warnings from typing import Union, List, Tuple from .. import ui_manager from ..core import ui_container from ..core.ui_element import UIElement from ..elements.ui_button import UIButton class UIHorizontalSlider(UIElement): """ A horizontal slider is intended to help users adjust values within a range, for example a volume control. :param relative_rect: A rectangle describing the position and dimensions of the element. :param start_value: The value to start the slider at. :param value_range: The full range of values. :param manager: The UIManager that manages this element. :param container: The container that this element is within. If set to None will be the root window's container. :param parent_element: The element this element 'belongs to' in the theming hierarchy. :param object_id: A custom defined ID for fine tuning of theming. """ def __init__(self, relative_rect: pygame.Rect, start_value: Union[float, int], value_range: Tuple[Union[float, int], Union[float, int]], manager: ui_manager.UIManager, container: ui_container.UIContainer = None, parent_element: UIElement = None, object_id: Union[str, None] = None): new_element_ids, new_object_ids = self.create_valid_ids(parent_element=parent_element, object_id=object_id, element_id='horizontal_slider') super().__init__(relative_rect, manager, container, object_ids=new_object_ids, element_ids=new_element_ids, starting_height=1, layer_thickness=1) self.button_width = 20 self.current_percentage = 0.5 self.value_range = value_range self.border_width = 1 border_width_string = self.ui_theme.get_misc_data(self.object_ids, self.element_ids, 'border_width') if border_width_string is not None: self.border_width = int(border_width_string) self.shadow_width = 1 shadow_width_string = self.ui_theme.get_misc_data(self.object_ids, self.element_ids, 'shadow_width') if shadow_width_string is not None: self.shadow_width = int(shadow_width_string) self.background_colour = self.ui_theme.get_colour(self.object_ids, self.element_ids, 'dark_bg') self.border_colour = self.ui_theme.get_colour(self.object_ids, self.element_ids, 'normal_border') if self.shadow_width > 0: self.image = self.ui_manager.get_shadow(self.rect.size) else: self.image = pygame.Surface(self.rect.size, flags=pygame.SRCALPHA) border_rect = pygame.Rect((self.shadow_width, self.shadow_width), (self.rect.width - (2 * self.shadow_width), self.rect.height - (2 * self.shadow_width))) if self.border_width > 0: self.image.fill(self.border_colour, border_rect) relative_background_rect = pygame.Rect((self.border_width + self.shadow_width, self.border_width + self.shadow_width), (border_rect.width - (2 * self.border_width), border_rect.height - (2 * self.border_width))) background_rect = pygame.Rect((relative_background_rect.x + relative_rect.x, relative_background_rect.y + relative_rect.y), relative_background_rect.size) self.image.fill(self.background_colour, relative_background_rect) value_range_length = self.value_range[1] - self.value_range[0] self.current_value = int(self.value_range[0] + (self.current_percentage * value_range_length)) self.scrollable_width = background_rect.width - (3 * self.button_width) self.left_limit_position = 0.0 self.right_limit_position = self.scrollable_width self.scroll_position = self.scrollable_width/2 self.left_button = UIButton(pygame.Rect(background_rect.topleft, (self.button_width, background_rect.height)), '◀', self.ui_manager, self.ui_container, starting_height=2, parent_element=self, object_id='#left_button') self.right_button = UIButton(pygame.Rect((background_rect.x + background_rect.width - self.button_width, background_rect.y), (self.button_width, background_rect.height)), '▶', self.ui_manager, self.ui_container, starting_height=2, parent_element=self, object_id='#right_button') sliding_x_pos = background_rect.x + background_rect.width/2 - self.button_width/2 self.sliding_button = UIButton(pygame.Rect((sliding_x_pos, background_rect.y), (self.button_width, background_rect.height)), '', self.ui_manager, self.ui_container, starting_height=2, parent_element=self, object_id='#sliding_button') self.sliding_button.set_hold_range((background_rect.width, 100)) self.grabbed_slider = False self.starting_grab_x_difference = 0 self.has_moved_recently = False self.set_current_value(start_value) def kill(self): """ Overrides the normal sprite kill() method to also kill the button elements that help make up the slider. """ self.left_button.kill() self.right_button.kill() self.sliding_button.kill() super().kill() def update(self, time_delta: float): """ Takes care of actually moving the slider based on interactions reported by the buttons or based on movement of the mouse if we are gripping the slider itself. :param time_delta: the time in seconds between calls to update. """ if self.alive(): moved_this_frame = False if self.left_button.held and self.scroll_position > self.left_limit_position: self.scroll_position -= (250.0 * time_delta) self.scroll_position = max(self.scroll_position, self.left_limit_position) x_pos = self.scroll_position + self.rect.x + self.shadow_width + self.border_width + self.button_width y_pos = self.rect.y + self.shadow_width + self.border_width self.sliding_button.set_position(pygame.Vector2(x_pos, y_pos)) moved_this_frame = True elif self.right_button.held and self.scroll_position < self.right_limit_position: self.scroll_position += (250.0 * time_delta) self.scroll_position = min(self.scroll_position, self.right_limit_position) x_pos = self.scroll_position + self.rect.x + self.shadow_width + self.border_width + self.button_width y_pos = self.rect.y + self.shadow_width + self.border_width self.sliding_button.set_position(pygame.Vector2(x_pos, y_pos)) moved_this_frame = True mouse_x, mouse_y = pygame.mouse.get_pos() if self.sliding_button.held and self.sliding_button.in_hold_range((mouse_x, mouse_y)): if not self.grabbed_slider: self.grabbed_slider = True real_scroll_pos = (self.scroll_position + self.rect.x + self.shadow_width + self.border_width + self.button_width) self.starting_grab_x_difference = mouse_x - real_scroll_pos real_scroll_pos = (self.scroll_position + self.rect.x + self.shadow_width + self.border_width + self.button_width) current_grab_difference = mouse_x - real_scroll_pos adjustment_required = current_grab_difference - self.starting_grab_x_difference self.scroll_position = self.scroll_position + adjustment_required self.scroll_position = min(max(self.scroll_position, self.left_limit_position), self.right_limit_position) x_pos = self.scroll_position + self.rect.x + self.shadow_width + self.border_width + self.button_width y_pos = self.rect.y + self.shadow_width + self.border_width self.sliding_button.set_position(pygame.Vector2(x_pos, y_pos)) moved_this_frame = True elif not self.sliding_button.held: self.grabbed_slider = False if moved_this_frame: self.current_value = self.value_range[0] + ( (self.scroll_position / self.scrollable_width) * (self.value_range[1] - self.value_range[0])) if not self.has_moved_recently: self.has_moved_recently = True def get_current_value(self) -> Union[float, int]: """ Gets the current value the slider is set to. :return: The current value recorded by the slider. """ self.has_moved_recently = False return self.current_value def set_current_value(self, value: Union[float, int]): """ Sets the value of the slider, which will move the position of the slider to match. Will issue a warning if the value set is not in the value range. :param value: The value to set. """ if min(self.value_range[0], self.value_range[1]) <= value <= max(self.value_range[0], self.value_range[1]): self.current_value = float(value) value_range_size = (self.value_range[1] - self.value_range[0]) if value_range_size != 0: percentage = (self.current_value - self.value_range[0])/value_range_size self.scroll_position = self.scrollable_width * percentage x_pos = self.scroll_position + self.rect.x + self.shadow_width + self.border_width + self.button_width y_pos = self.rect.y + self.shadow_width + self.border_width self.sliding_button.set_position(pygame.Vector2(x_pos, y_pos)) self.has_moved_recently = True else: warnings.warn('value not in range', UserWarning)

51.929245

118

0.597239

1,303

11,009

4.768227

0.147352

0.044906

0.06084

0.053114

0.447449

0.398841

0.327217

0.301626

0.272815

0.246419

0

0.007133

0.325098

11,009

211

119

52.175355

0.828802

0.111273

0

0.248322

0

0.012595

0

1

0.033557

false

0

0.04698

0

0.09396

0

null

0

null

0

1

0

21fb519669ab124f568a6a6b5a2310b85412e2fa

2,614

py

Python

src/django_datatables/mixins.py

ryanbowen/django-datatables

995b82ecf666d4fcae3322861312d927cba07c82

[ "Apache-2.0" ]

null

src/django_datatables/mixins.py

ryanbowen/django-datatables

995b82ecf666d4fcae3322861312d927cba07c82

[ "Apache-2.0" ]

null

src/django_datatables/mixins.py

ryanbowen/django-datatables

995b82ecf666d4fcae3322861312d927cba07c82

[ "Apache-2.0" ]

null

from datetime import datetime import logging from django.core.serializers.json import DjangoJSONEncoder from django.http import HttpResponse, JsonResponse from django.utils.encoding import force_text from django.utils.functional import Promise from django.utils.translation import ugettext as _ from django.utils.cache import add_never_cache_headers from django.utils.html import strip_tags try: from openpyxl import Workbook import openpyxl.writer.excel as ExcelWriter except ImportError: ExcelWriter = None LOG = logging.getLogger(__name__) class LazyEncoder(DjangoJSONEncoder): """Encodes django's lazy i18n strings """ def default(self, obj): if isinstance(obj, Promise): return force_text(obj) return super(LazyEncoder, self).default(obj) class DataResponse(object): def create_excel_response(self, request): """ Return an excel writer as a response. """ headers = self.get_column_titles() rows = self.get_data(request) title = getattr(self._meta, "title", "Sheet") wb = Workbook(write_only=True) ws = wb.create_sheet(title) ws.append(headers) for row in rows: ws.append([strip_tags(c) for c in row]) response = HttpResponse( ExcelWriter.save_virtual_workbook(wb), content_type='application/vnd.ms-excel' ) response['Content-Disposition'] = \ 'attachment; filename="{0}"'.format(f'{title}-{datetime.now().strftime("%Y-%m-%d %H%m")}.xlsx') return response def create_data_response(self, func_val, request): try: assert isinstance(func_val, dict) response = dict(func_val) if 'result' not in response: response['result'] = 'ok' except KeyboardInterrupt: # Allow keyboard interrupts through for debugging. raise except Exception as e: LOG.exception('JSON view error: %s', request.path) msg = getattr(e, 'message', _('Internal error') + ': ') + str(e) response = {'result': 'error', 'sError': msg, 'text': msg} return JsonResponse(response) def dispatch(self, request, *args, **kwargs): self.request = request response = None if request.GET.get("export") == "excel": return self.create_excel_response(request) func_val = self.get_context_data(request) response = self.create_data_response(func_val, request) add_never_cache_headers(response) return response

30.752941

107

0.643458

302

2,614

5.437086

0.430464

0.042631

0.045676

0.024361

0

0.001542

0.25593

2,614

84

108

31.119048

0.842674

0.048585

0

0.067797

0

0.090317

0.026851

0

0.016949

1

0.067797

false

0

0.20339

0

0.40678

0

null

0

null

0

1

0

21fbcb929911a995b3f6095877a796a92d8d46dc

2,193

py

Python

src/dsa/misc/utils/class_generator.py

tvatter/dsa

e5ae217e38441d90914a55103e23d86f5821dc2f

[ "MIT" ]

null

src/dsa/misc/utils/class_generator.py

tvatter/dsa

e5ae217e38441d90914a55103e23d86f5821dc2f

[ "MIT" ]

null

src/dsa/misc/utils/class_generator.py

tvatter/dsa

e5ae217e38441d90914a55103e23d86f5821dc2f

[ "MIT" ]

null

import re from os import path def preprocess_file(class_name='MyClass', file_name=None, property_name=None): if file_name is None: file_name = class_name.lower() + '.py' found = [False] * 3 class_info = { 'name': class_name, 'definition': 'class {}:'.format(class_name) } if path.exists(file_name): found[0] = True file = open(file_name, 'r') file_str = file.read() file.close() if found[0] and class_info['definition'] in file_str: found[1] = True if found[0] and not found[1] and file_str != '': class_info['definition'] = '\n\n' + class_info['definition'] if found[0] and found[1] and property_name is not None: class_info = extend_class_info(class_info, file_str) if 'self._{} ='.format(property_name) in class_info['class_str']: found[2] = True return file_name, found, class_info def get_property_defaults(property_name='my_property'): self_property = 'self._{}'.format(property_name) init = """ self._{0} = {0}\n""".format(property_name) getter = """ @property def {}(self): return {}\n""".format(property_name, self_property) setter = """ @{0}.setter def {0}(self, new_{0}): {1} = new_{0}\n""".format(property_name, self_property) deleter = """ @{0}.deleter def {0}(self): del {1}\n""".format(property_name, self_property) return init, getter, setter, deleter def extend_class_info(class_info, file_str): # Start and end locations (could be improved) class_info['start'] = file_str.find(class_info['definition']) class_start_locations = [ x.start() for x in re.compile(r'class .*:').finditer(file_str) ] class_start_index = class_start_locations.index(class_info['start']) if class_start_index + 1 == len(class_start_locations): class_end_location = len(file_str) else: class_end_index = class_start_index + 1 class_end_location = class_start_locations[class_end_index] - 2 class_info['end'] = class_end_location # To facilitation the addition of the property class_info['file_str'] = file_str class_str = file_str[class_info['start']:class_info['end']] class_info['class_str'] = class_str return class_info

27.074074

78

0.679891

321

2,193

4.35514

0.214953

0.128755

0.077253

0.054363

0.164521

0.110873

0.044349

0

0.012168

0.175559

2,193

80

79

27.4125

0.761062

0.040128

0

0.159372

0

1

0.052632

false

0

0.035088

0

0.157895

0

null

0

null

0

1

0

21ff78f085a5d50f157b5c45478181711a1254eb

2,086

py

Python

kilojoule/templates/humidair_USCS_F.py

johnfmaddox/kilojoule

b4c146ded82e3ef51a0252ff48b1066a076e9aeb

[ "MIT" ]

null

kilojoule/templates/humidair_USCS_F.py

johnfmaddox/kilojoule

b4c146ded82e3ef51a0252ff48b1066a076e9aeb

[ "MIT" ]

null

kilojoule/templates/humidair_USCS_F.py

johnfmaddox/kilojoule

b4c146ded82e3ef51a0252ff48b1066a076e9aeb

[ "MIT" ]

null

import kilojoule.humidair import kilojoule.realfluid import kilojoule.idealgas as idealgas from kilojoule.organization import QuantityTable from kilojoule.display import Calculations, Summary from kilojoule.units import units, Quantity import kilojoule.magics humidair = kilojoule.humidair.Properties(unit_system='English_F') water = kilojoule.realfluid.Properties('Water',unit_system='English_F') properties_dict = { 'T':'degF', # Temperature 'p':'psi', # pressure 'v':'ft^3/lb_dry_air', # specific volume 'h':'Btu/lb_dry_air', # specific enthalpy 'h_w':'Btu/lb_water', # specific enthalpy 's':'Btu/lb_dry_air/degR', # specific entropy 's_w':'But/lb_water', # entropy of water 'x':'', # vapor quality 'm_a':'lb_dry_air', # mass 'm_w':'lb_water', # mass 'mdot_a':'lb_dry_air/s', # mass flow rate 'mdot_w':'lb_water/s', # mass flow rate of water 'Vol':'ft^3', # volume 'Vdot':'ft^3/s', # volumetric flow rate 'Vel':'ft/s', # velocity 'X':'Btu', # exergy 'Xdot':'hp', # exergy flow rate # 'phi':'Btu/lb_dry_air', # specific exergy 'psi':'Btu/lb_dry_air', # specific flow exergy 'y':'', # water mole fraction 'c_v':'Btu/lb_dry_air/degR', # constant volume specific heat 'c_p':'Btu/lb_dry_air/degR', # constant pressure specific heat 'k':'Btu/ft/degR', # conductivity 'T_wb':'degF', # Wet-bulb Temperature 'T_dp':'degF', # Dew-point Temperature 'p_w':'psi', # partial pressure of water vapor 'rel_hum':'', # relative humidity 'phi':'', # relative humidity 'omega':'lb_water/lb_dry_air'# humidity ratio } states = QuantityTable(properties_dict, unit_system='USCS_F', add_to_namespace=True)

47.409091

85

0.553691

243

2,086

4.559671

0.37037

0.045126

0.072202

0.059567

0.106498

0.041516

0

0.00211

0.318313

2,086

43

86

48.511628

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0

0.228916

0

1

0

false

0

0.175

0

0.175

0

null

0

null

0

1

0

1d0018b86cdd965ac8f6b7d70eb9745a2cec6bbc

7,640

py

Python

CRF/scan_text_rev.py

henchc/MHG-Scansion-BLSTM

551abd3da9a2f989fb770ee97dbd73445edd0d3c

[ "MIT" ]

4

2018-04-21T08:52:25.000Z

2020-01-08T13:57:05.000Z

CRF/scan_text_rev.py

henchc/MHG-Scansion-BLSTM

551abd3da9a2f989fb770ee97dbd73445edd0d3c

[ "MIT" ]

null

CRF/scan_text_rev.py

henchc/MHG-Scansion-BLSTM

551abd3da9a2f989fb770ee97dbd73445edd0d3c

[ "MIT" ]

null

# -*- coding: utf-8 -*- from __future__ import unicode_literals # for python2 compatibility from __future__ import division from __future__ import absolute_import # created at UC Berkeley 2015 # Authors: Christopher Hench # This program scans MHG epic poetry, returning data to analyze statistically import codecs import pycrfsuite import numpy as np import itertools import codecs import pycrfsuite import numpy as np from get_features import get_features import itertools from get_features import syllableweight def only_four_stresses(lines_w_features, tagger, sylls): labs = ["MORA_HAUPT", "MORA", "DOPPEL", "HALB_HAUPT", "HALB", "EL"] stressed = ["MORA_HAUPT", "DOPPEL", "HALB_HAUPT"] four_stress = [] for i, line in enumerate(lines_w_features): t_line = tagger.tag(line) line_sylls = sylls[i] stress = 0 for i2, l in enumerate(t_line): # increment stress if t_line[i2] in stressed: stress += 1 # no doppel can be light if l == "DOPPEL" and syllableweight(line_sylls[i2]) == "L": stress += 5 # auto sends to reweight probs # no halb-haupt can be heavy if l == "HALB_HAUPT" and syllableweight(line_sylls[i2]) == "H": stress += 5 # auto sends to reweight probs # no EL can be heavy if l == "EL" and syllableweight(line_sylls[i2]) == "H": stress += 5 # auto sends to reweight probs # error alternation, recount accs = ["MORA_HAUPT", "HALB_HAUPT"] if i2 > 0 and l in accs: # rule out if two if t_line[i2 - 1] in accs: stress += 5 # rule out if no stress following doppel if i2 < len(t_line) - 1 and l == "DOPPEL": if t_line[i2 + 1] not in stressed: stress += 5 if i2 > 0 and l == "DOPPEL": # rule out stress before double if t_line[i2 - 1] in accs: stress += 5 if 0 < i2 < len(t_line) - 1 and l == "EL": if t_line[i2 - 1] in accs and t_line[i2 + 1] in accs: stress += 5 if "DOPPELDOPPEL" in ''.join(t_line): stress += 5 # if > 4 stresses, look at probs if stress != 4: line_probs = [] for i3, l in enumerate(t_line): # marginal probablities probs = [(lb, tagger.marginal(lb, i3)) for lb in labs] # no doppel can be light if syllableweight(line_sylls[i3]) == "L": probs = [x for x in probs if x[0] != "DOPPEL"] # no halbhaupt or EL can be heavy if syllableweight(line_sylls[i3]) == "H": probs = [x for x in probs if x[0] != "HALB_HAUPT"] probs = [x for x in probs if x[0] != "EL"] probs = sorted(probs, key=lambda tup: tup[1], reverse=True) # if very certain, only take top 2, otherwise top 4 if probs[0][1] > .9: line_probs.append(probs[:2]) else: line_probs.append(probs[:4]) # get combinations of syll values with probs combos = itertools.product(*line_probs) # verify each combo and rank final_line = (0, 0) for c in combos: stress = 0 tot_prob = 0 for i4, l in enumerate(c): tot_prob += l[1] if l[0] in stressed: stress += 1 if i4 < ( len(c) - 1) and l[0] == "DOPPEL": # rule out if no stress after double if c[i4 + 1][0] not in stressed: stress += 5 # error alternation recount accs = ["MORA_HAUPT", "HALB_HAUPT"] if i4 > 0 and l[0] in accs: # rule out if two if c[i4 - 1][0] in accs: stress += 5 if i4 > 0 and l[0] == "DOPPEL": # no stress b4 double if c[i4 - 1][0] in accs: stress += 5 if 0 < i4 < len(c) - 1 and l[0] == "EL": if c[i4 - 1][0] in accs and c[i4 + 1][0] in accs: stress += 5 if "DOPPELDOPPEL" in ''.join([l[0] for l in c]): stress += 5 if stress == 4 and tot_prob > final_line[1]: final_line = (c, tot_prob) try: t_line = [x[0] for x in final_line[0]] except TypeError: pass # not continue, pass will do nothing four_stress.append(t_line) # will take orig if no errors # additional fixes final_labels = [] for line in four_stress: count = 0 new_line = [] for i, l in enumerate(line): # fix / X' ◡ / if (0 < i < (len(line) - 1) and line[i - 1] == "MORA_HAUPT" and l == "HALB" and line[i + 1] in stressed): new_line.append("MORA") else: new_line.append(l) final_labels.append(new_line) final_labels2 = [] for line in final_labels: new_line = [] for i, l in enumerate(line): # fix / X' ◡ X / X' if (1 < i < (len(line) - 1) and line[i - 1] == "HALB" and line[i - 2] == "MORA_HAUPT" and l == "MORA" and line[i + 1] in stressed): new_line.append("HALB") else: new_line.append(l) final_labels2.append(new_line) final_labels = final_labels2 final_labels2 = [] for line in final_labels: new_line = [] for i, l in enumerate(line): # fix / X' X ◡ / X' if (0 < i < (len(line) - 2) and line[i - 1] == "MORA_HAUPT" and l == "MORA" and line[i + 1] == "HALB" and line[i + 2] in stressed): new_line.append("HALB") else: new_line.append(l) final_labels2.append(new_line) final_labels = final_labels2 final_labels2 = [] for line in final_labels: new_line = line for i, l in enumerate(line): # fix / X' X X / X' if (0 < i < (len(line) - 2) and line[i - 1] == "MORA_HAUPT" and l == "MORA" and line[i + 1] == "MORA" and line[i + 2] in stressed): new_line[i] = "HALB" new_line[i + 1] = "HALB" final_labels2.append(new_line) final_labels = final_labels2 # # zweisilbig maennlich kadenz # final_labels2 = [] # prefixes = ["ge", "be", "en"] # for i, line in enumerate(final_labels): # new_line = line # if line[-4:] == ["MORA_HAUPT", "HALB", "HALB", "MORA_HAUPT"]: # if len(sylls[i][-1]) > 1 and sylls[ # i][-1][-2] not in prefixes and syllableweight(sylls[i][-1][-2]) == "L": # new_line[-4:] = ["MORA_HAUPT", "MORA", "HALB_HAUPT", "HALB"] # final_labels2.append(new_line) # final_labels = final_labels2 return(final_labels)

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0.170347

0.038218

0.025287

0.02069

0.540805

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7,640

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32.931034

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0.09589

0

null

0

null

0

1

0

1d01a7ba705971e5adcf5ebca0f865bd17b58593

12,084

py

Python

aps_32id/run/energy_scan.py

tomography/scanscripts

f7486fe1285da4684f3709661f112ccc15c2e4b8

[ "BSD-3-Clause" ]

null

aps_32id/run/energy_scan.py

tomography/scanscripts

f7486fe1285da4684f3709661f112ccc15c2e4b8

[ "BSD-3-Clause" ]

18

2017-03-27T01:35:35.000Z

2018-04-03T20:30:49.000Z

aps_32id/run/energy_scan.py

tomography/scanscripts

f7486fe1285da4684f3709661f112ccc15c2e4b8

[ "BSD-3-Clause" ]

8

2017-02-22T16:31:43.000Z

2017-10-15T18:22:30.000Z

# -*- coding: utf-8 -*- ####################### '''For each energy step, a projection and then a flat field are acquired. The script calls the move_energy method from the TXM class. ''' import time import os import logging import warnings import numpy as np import h5py import tqdm from scanlib.scan_variables import update_variable_dict, parse_list_variable from scanlib.tools import energy_range_from_points, loggingConfig from aps_32id.txm import new_txm __author__ = 'Mark Wolfman' __copyright__ = 'Copyright (c) 2017, UChicago Argonne, LLC.' __docformat__ = 'restructuredtext en' __platform__ = 'Unix' __all__ = ['run_energy_scan', 'getVariableDict'] variableDict = { 'PreDarkImages': 0, 'SampleXOut': 0.2, 'SampleYOut': 0.0, 'SampleZOut': 0.0, 'SampleRotOut': 90.0, # In degrees 'SampleXIn': 0.0, 'SampleYIn': 0.0, 'SampleZIn': 0.0, 'SampleRotIn': 0.0, # In degrees 'StartSleep_min': 0.0, 'StabilizeSleep_ms': 3000, 'ExposureTime': 1, 'Energy_limits': '8.33, 8.34, 8.37, 8.43', 'Energy_Step': '0.002, 0.001, 0.002', 'ZP_X_drift': 0., 'constant_mag': True, # will CCD move to maintain constant magnification? # 'BSC_diameter': 1320, # 'BSC_drn': 60 'Repetitions': 1, 'Pause': 0, # in minutes 'Use_Fast_Shutter': 1, # Logging: 0=UNSET, 10=DEBUG, 20=INFO, 30=WARNING, 40=ERROR, 50=CRITICAL 'Log_Level': logging.INFO, } SHUTTER_PERMIT = False log = logging.getLogger(__name__) def getVariableDict(): return variableDict def _capture_energy_frames(txm, energies, constant_mag, stabilize_sleep_ms, sample_pos, out_pos, ZP_X_drift_array): """A helper method for collected a set of energy frames. The TXM should already be set up before calling this function. Parameters ---------- txm : A NanoTXM or MicroCT object that this script will control. energies : np.ndarray An array with the energies (in keV) for capturing frames. constant_mag : bool Whether to move the detector at each energy to keep the magnification constant. stabilize_sleep_ms : int How long to wait after moving energy to allow the beamline (monochromator, etc.) to stabilize. sample_pos : 4-tuple (x, y, z, θ°) with the position for the sample. out_pos : 4-tuple (x, y, z, θ°) with the position for the flat field. ZP_X_drift_array : np.ndarray Each entry is the change in x position of the zoneplate needed to keep the sample centered at that energy. """ correct_backlash = True # First energy only for idx, energy in enumerate(tqdm.tqdm(energies, "Energy scan")): log.debug('Preparing to capture energy: %f keV', energy) # Check whether we should collect the sample or white field first sample_first = not bool(idx % 2) log.info("Collecting %s first.", "sample" if sample_first else "white-field") # Move sample, zone plate and energy txm.zone_plate_x = ZP_X_drift_array[idx] if sample_first: txm.move_sample(*sample_pos) else: txm.move_sample(*out_pos) txm.move_energy(energy, constant_mag=constant_mag, correct_backlash=correct_backlash) correct_backlash = False # Needed on first energy only # Pause for a moment to allow the beam to stabilize log.debug('Stabilize Sleep %f ms', stabilize_sleep_ms) time.sleep(stabilize_sleep_ms / 1000.0) # Sample projection acquisition (or white-field on odd passes) if sample_first: log.info("Acquiring sample position %s at %.4f eV", sample_pos, energy) txm.capture_projections() else: log.info("Acquiring white-field position %s at %.4f eV", out_pos, energy) txm.capture_white_field() # Flat-field projection acquisition (or sample on odd passes) if sample_first: txm.move_sample(*out_pos) log.info("Acquiring white-field position %s at %.4f eV", out_pos, energy) # time.sleep(3) txm.capture_white_field() else: txm.move_sample(*sample_pos) log.info("Acquiring sample position %s at %.4f eV", sample_pos, energy) txm.capture_projections() def run_energy_scan(energies, exposure=0.5, n_pre_dark=5, has_permit=True, sample_pos=(0.,), out_pos=(0.2,), ZP_X_drift_array=None, constant_mag=True, stabilize_sleep_ms=1000, repetitions=1, pause=0, use_fast_shutter=True, log_level=logging.INFO, txm=None): """Collect a series of 2-dimensional projections across a range of energies. At each position, a sample projection and white-field projection will be collected by moving the sample along the X direction. Parameters ---------- energies : np.ndarray An array with the list of energies to scan, in keV. exposure : float, optional How long to collect each frame for, in seconds. n_pre_dark : int, optional How many dark-field projections to collect before starting the energy scan. is_attached : bool, optional Determines whether the instrument is available. has_permit : bool, optional Does the user have permission to open the shutters and change source energy. sample_pos : 4-tuple, optional (x, y, z, θ) tuple for positioning the sample in the beam. out_pos : 4-tuple, optional (x, y, z, θ) tuple for removing the sample from the beam. ZP_X_drift_array : np.ndarray, optional Each entry is the change in x position of the zoneplate needed to keep the sample centered at that energy. constant_mag : bool, optional Whether to adjust the camera position to maintain a constant focus. stabilize_sleep_ms : int, optional How long, in milliseconds, to wait for the beam to stabilize before collecting projections. repetitions : int, optional How many times to run this energy scan, including the first one. pause : int, optional How long, in minute, the scan pause in between each energy scan repetition use_fast_shutter : bool, optional Whether to open and shut the fast shutter before triggering projections. log_level : int, optional Temporary log level to use. ``None`` does not change the logging. txm : optional An instance of the NanoTXM class. If not given, a new one will be created. Mostly used for testing. """ log.debug("Starting run_energy_scan()") start_time = time.time() total_projections = n_pre_dark + 2 * len(energies) # Fix up default parameters if ZP_X_drift_array is None: ZP_X_drift_array = np.zeros_like(energies) elif ZP_X_drift_array.shape != energies.shape: raise ValueError("ZP_X_drift_array shape does not match energies: " "{} vs {}".format(ZP_X_drift_array.shape, energies.shape)) log.debug('ZP x-drift corrections: {}'.format(ZP_X_drift_array)) # Create the TXM object for this scan if txm is None: txm = new_txm() # Execute the actual scan script with txm.run_scan(): if use_fast_shutter: txm.enable_fast_shutter() # Collect repetitions of the energy scan for rep in range(repetitions): # Prepare TXM for capturing data txm.setup_detector(exposure=exposure, num_projections=total_projections) if use_fast_shutter: txm.enable_fast_shutter() txm.setup_hdf_writer(num_projections=total_projections) time.sleep(5) txm.start_logging(log_level) # Capture pre dark field images if n_pre_dark > 0: txm.close_shutters() log.info('Capturing %d Pre Dark Field images', n_pre_dark) txm.capture_dark_field(num_projections=n_pre_dark) # Calculate the array of energies that will be scanned log.info('Capturing %d energies', len(energies)) # Collect frames at each energy txm.open_shutters() _capture_energy_frames(txm=txm, energies=energies, constant_mag=constant_mag, stabilize_sleep_ms=stabilize_sleep_ms, sample_pos=sample_pos, out_pos=out_pos, ZP_X_drift_array=ZP_X_drift_array) txm.close_shutters() # Add the energy array to the active HDF file hdf_filename = txm.hdf_filename print ('1', hdf_filename) if pause: log.info("Pausing between scans for %f min", pause) time.sleep(pause * 60.0) # convert min to sec try: print ('2', hdf_filename) with txm.hdf_file(hdf_filename, mode="r+") as hdf_f: log.debug('Saving energies to file: %s', hdf_filename) hdf_f.create_dataset('/exchange/energy', data=energies) except (OSError, IOError): # Could not load HDF file, so raise a warning msg = "Could not save energies to file %s" % hdf_filename warnings.warn(msg, RuntimeWarning) log.warning(msg) # Log the duration and output file duration = time.time() - start_time log.info('Energy scan took %d sec and saved in file %s', duration, hdf_filename) return txm def main(): # Enter the main script function update_variable_dict(variableDict) # Set up default logging # Choices are DEBUG, INFO, WARNING, ERROR, CRITICAL # logging.basicConfig(filename='energy_scan_debug.log', level=logging.DEBUG) log_level = variableDict['Log_Level'] loggingConfig(level=log_level) # Get the requested sample positions sample_pos = (variableDict.get('SampleXIn', None), variableDict.get('SampleYIn', None), variableDict.get('SampleZIn', None), variableDict.get('SampleRotIn', None)) out_pos = (variableDict.get('SampleXOut', None), variableDict.get('SampleYOut', None), variableDict.get('SampleZOut', None), variableDict.get('SampleRotOut', None)) # Prepare the list of energies requested energy_limits = parse_list_variable(variableDict['Energy_limits'], dtype=float) energy_steps = parse_list_variable(variableDict['Energy_Step'], dtype=float) energies = energy_range_from_points(energy_points=energy_limits, energy_steps=energy_steps) ZP_X_drift = float(variableDict['ZP_X_drift']) ZP_X_drift_array = (energies-energies[0]) * ZP_X_drift / (energies[-1]-energies[0]) # Start scan sleep in min so min * 60 = sec sleep_min = float(variableDict.get('StartSleep_min', 0)) stabilize_sleep_ms = float(variableDict.get("StabilizeSleep_ms")) repetitions = int(variableDict['Repetitions']) pause = float(variableDict['Pause']) constant_mag = bool(variableDict['constant_mag']) use_fast_shutter = bool(int(variableDict['Use_Fast_Shutter'])) if sleep_min > 0: log.debug("Sleeping for %f min", sleep_min) time.sleep(sleep_min * 60.0) # Start the energy scan run_energy_scan( energies=energies, has_permit=SHUTTER_PERMIT, exposure=float(variableDict['ExposureTime']), n_pre_dark=int(variableDict['PreDarkImages']), sample_pos=sample_pos, out_pos=out_pos, stabilize_sleep_ms=stabilize_sleep_ms, ZP_X_drift_array=ZP_X_drift_array, constant_mag=constant_mag, repetitions=repetitions, pause=pause, log_level=log_level, use_fast_shutter=use_fast_shutter, ) if __name__ == '__main__': main()

40.28

87

0.642337

1,578

12,084

4.723701

0.220532

0.008452

0.022538

0.027904

0.187416

0.151462

0.118326

0.098739

0.074054

0

0.014075

0.270937

12,084

299

88

40.414716

0.831782

0.328037

0

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0

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0

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false

0

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0.00565

0.090395

0.011299

0

null

0

null

0

1

0

1d02c486ba87d6a7adf60c5da4f6c32ac5c73634

308

py

Python

Section 1: Linux, Virtual Machines, Docker/home_exercise_4_-_flask/IMDB/data_test.py

MarkVoitov/DS_ABB

130ba32fe7187d41fee5651800b965211d1ccaa9

[ "MIT" ]

null

Section 1: Linux, Virtual Machines, Docker/home_exercise_4_-_flask/IMDB/data_test.py

MarkVoitov/DS_ABB

130ba32fe7187d41fee5651800b965211d1ccaa9

[ "MIT" ]

null

Section 1: Linux, Virtual Machines, Docker/home_exercise_4_-_flask/IMDB/data_test.py

MarkVoitov/DS_ABB

130ba32fe7187d41fee5651800b965211d1ccaa9

[ "MIT" ]

null

from contextlib import closing import sqlite3 def query(db_name, sql): with closing(sqlite3.connect(db_name)) as con, con, \ closing(con.cursor()) as cur: cur.execute(sql) print(cur.fetchall()) if __name__ == '__main__': query("IMDB.db","""SELECT * FROM title_basics LIMIT 200""")

23.692308

61

0.672078

43

308

4.55814

0.627907

0.061224

0

0.01992

0.185065

308

12

62

25.666667

0.760956

0

0.165584

0

1

0.111111

false

0

0.222222

0

0.333333

0.111111

0

null

0

null

0

1

0

1d0439f0057cae94a572c82de4fa0e43b8aad68d

4,551

py

Python

scripts/frontend/page_components/InformationBlock.py

MichaelLapshin/Virtual-Hand-Application

7c27317feae3d54fd10e616858ab0ab79bda6338

[ "MIT" ]

1

2021-08-31T05:22:04.000Z

scripts/frontend/page_components/InformationBlock.py

MichaelLapshin/Virtual-Hand-Application

7c27317feae3d54fd10e616858ab0ab79bda6338

[ "MIT" ]

null

scripts/frontend/page_components/InformationBlock.py

MichaelLapshin/Virtual-Hand-Application

7c27317feae3d54fd10e616858ab0ab79bda6338

[ "MIT" ]

null

import tkinter import tkinter.font from scripts import General, Parameters, Constants from scripts.frontend.custom_widgets.CustomLabels import InformationLabel from scripts.frontend.custom_widgets.WidgetInterface import WidgetInterface TITLE_FONT_SIZE = 8 class Frame(tkinter.Frame, WidgetInterface): def __init__(self, root, num_columns, num_rows, title=None, column=0, row=0, columnspan=1, rowspan=1): # Asserts dimensions assert num_columns > 0 and num_rows > 0 self.num_columns = num_columns self.num_rows = num_rows # Saves the colour self.frame_colour = None self.label_colour = None # Creates self frame tkinter.Frame.__init__(self, root, relief=tkinter.RIDGE, bd=1) self.grid(column=column, row=row, columnspan=columnspan, rowspan=rowspan, padx=Constants.STANDARD_SPACING, pady=Constants.STANDARD_SPACING, sticky=tkinter.NSEW) # Configure weights self.columnconfigure(0, weight=1) self.rowconfigure(1, weight=1) # Creates title bar self.titlebar = None if title is not None: self.titlebar = InformationLabel(self, text=title, column=0, row=0) self.titlebar.config(padx=Constants.STANDARD_SPACING, pady=Constants.STANDARD_SPACING) self.titlebar.config(font=TITLE_FONT_SIZE) self.titlebar.grid(padx=Constants.STANDARD_SPACING, pady=Constants.STANDARD_SPACING) # Dynamic number of columns self.info_frame = tkinter.Frame(self, relief=tkinter.RIDGE) self.info_frame.grid(column=0, row=1) self.info_frame.grid(padx=Constants.STANDARD_SPACING, pady=Constants.STANDARD_SPACING) self.info_frame.grid(sticky=tkinter.NSEW) # Configure info frame weights for x in range(0, num_columns): self.info_frame.columnconfigure(x, weight=1) # Creates the info spaces self.info_spaces = [] # spaces[x, y] = position for y in range(0, num_rows): self.info_spaces.append([]) for x in range(0, num_columns): widget = InformationLabel(self.info_frame, column=x, row=y) # widget.config(bg=General.washed_colour_hex(label_colour, Parameters.ColourGrad_C)) self.info_spaces[y].append(widget) def update_colour(self): super().update_colour() if self.titlebar is not None: self.titlebar.update_colour() for y in range(0, len(self.info_spaces)): for x in range(0, len(self.info_spaces[y])): self.info_spaces[y][x].update_colour() self.config(bg=General.washed_colour_hex(self.frame_colour, Parameters.ColourGrad_B)) self.info_frame.config(bg=General.washed_colour_hex(self.frame_colour, Parameters.ColourGrad_B)) if self.titlebar is not None: self.titlebar.config(bg=General.washed_colour_hex(self.label_colour, Parameters.ColourGrad_D)) def set_frame_colour(self, colour): self.frame_colour = colour def set_label_colour(self, colour): self.label_colour = colour # Functionality Methods def assert_within_grid(self, column, row): assert column >= 0 and row >= 0 assert column < self.num_columns and row < self.num_rows def set_info(self, column, row, text): self.assert_within_grid(column, row) self.info_spaces[row][column].config(text=text) def set_font(self, column, row, font_size): self.assert_within_grid(column, row) font = tkinter.font.Font(size=font_size) self.info_spaces[row][column].config(font=font) def set_justify(self, column, row, justify): self.assert_within_grid(column, row) self.info_spaces[row][column].config(justify=justify) def set_column_weight(self, column, weight): self.info_frame.columnconfigure(column, weight=weight) def set_row_weight(self, row, weight): self.info_frame.rowconfigure(row, weight=weight) def set_anchor(self, column, row, anchor): self.info_spaces[row][column].config(anchor=anchor) def add_info(self, column, row, text): self.assert_within_grid(column, row) if self.info_spaces[row][column]["text"] is not None: self.info_spaces[row][column].config(text=str(self.info_spaces[row][column]["text"]) + str(text)) else: self.info_spaces[row][column].config(text=str(text))

37.925

109

0.667985

594

4,551

4.93771

0.161616

0.062734

0.066826

0.046369

0.378111

0.342312

0.28401

0.239004

0.171497

0.108762

0

0.006849

0.230059

4,551

119

110

38.243697

0.830194

0.06548

0

0.101266

0

0.001886

0

0.101266

1

0.151899

false

0

0.063291

0

0.227848

0

null

0

null

0

1

0

1d04634957ca46a9855555b61ec6eb3e8a8ca0fd

2,320

py

Python

tests/st/ops/gpu/test_softmax_cross_entropy_with_logits_op.py

unseenme/mindspore

4ba052f0cd9146ac0ccc4880a778706f1b2d0af8

[ "Apache-2.0" ]

2

2020-04-28T03:49:10.000Z

2020-04-28T03:49:13.000Z

tests/st/ops/gpu/test_softmax_cross_entropy_with_logits_op.py

liyong126/mindspore

930a1fb0a8fa9432025442c4f4732058bb7af592

[ "Apache-2.0" ]

7

2020-03-30T08:31:56.000Z

2020-04-01T09:54:39.000Z

tests/st/ops/gpu/test_softmax_cross_entropy_with_logits_op.py

liyong126/mindspore

930a1fb0a8fa9432025442c4f4732058bb7af592

[ "Apache-2.0" ]

1

2020-03-30T17:07:43.000Z

# Copyright 2019 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 from mindspore import Tensor import mindspore.nn as nn from mindspore.common.api import ms_function import numpy as np import mindspore.context as context from mindspore.common.initializer import initializer from mindspore.common.parameter import Parameter class NetSoftmaxCrossEntropyWithLogits(nn.Cell): def __init__( self): super(NetSoftmaxCrossEntropyWithLogits, self).__init__() self.loss = nn.SoftmaxCrossEntropyWithLogits(sparse=False) def construct(self, logits, labels): return self.loss(logits, labels) @pytest.mark.level0 @pytest.mark.platform_x86_gpu_training @pytest.mark.env_onecard def test_softmax_cross_entropy_with_logits(): logits = Tensor(np.array([[1,1,10], [1,10,1], [10,1,1]]).astype(np.float32)) labels = Tensor(np.array([[0,0,1], [0,1,0], [1,0,0]]).astype(np.float32)) expect_loss = [0.00024673, 0.00024673, 0.00024673] context.set_context(mode=context.GRAPH_MODE, device_target='GPU') softmax_cross_entropy_with_logits = NetSoftmaxCrossEntropyWithLogits() output = softmax_cross_entropy_with_logits(logits, labels) error0 = 1.0e-6 diff0 = output.asnumpy() - expect_loss assert np.all(abs(diff0) < error0) context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU') softmax_cross_entropy_with_logits = NetSoftmaxCrossEntropyWithLogits() output = softmax_cross_entropy_with_logits(logits, labels) error0 = 1.0e-6 diff0 = output.asnumpy() - expect_loss assert np.all(abs(diff0) < error0)

39.322034

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0.693534

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5.290541

0.435811

0.038314

0.060664

0.073436

0.311622

0.265006

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0

null

0

null

0

1

0

1d058146fd52903447fe36684e99aa705f6fd6f5

2,747

py

Python

charts.py

glichfalls/covid-19-charts

52e9b78632388d3459bdc04f91513c00eaee000c

[ "MIT" ]

null

charts.py

glichfalls/covid-19-charts

52e9b78632388d3459bdc04f91513c00eaee000c

[ "MIT" ]

null

charts.py

glichfalls/covid-19-charts

52e9b78632388d3459bdc04f91513c00eaee000c

[ "MIT" ]

null

import matplotlib.pyplot as plt import matplotlib.ticker as ticker import matplotlib.dates as mdates from db import DatabaseConnection from datetime import datetime, timedelta class Charts: def __init__(self, db: DatabaseConnection): self._db = db # get pie chart of the total cases by continent and of the given date def show_pie_of_total_cases_by_continent(self, date: str = None): if date is None: date = datetime.strftime(datetime.now() - timedelta(1), '%Y-%m-%d') # load continent labels labels = [country[1] for country in self._db.get_continents()] # load cases by continent sizes = [case[1] for case in self._db.get_continent_cases(date)] fig1, ax1 = plt.subplots() ax1.pie(sizes, labels=labels, autopct='%1.1f%%') ax1.axis('equal') plt.show() def show_line_total_cases(self): cases = self._db.get_total_cases() vaccines = self._db.get_total_vaccinations() labels = [datetime.strptime(x[0], '%Y-%m-%d') for x in cases] plt.plot(labels, [x[1] for x in cases]) plt.plot(labels, [x[2] for x in cases]) plt.plot(labels, [x[1] for x in vaccines]) plt.title('total worldwide cases') plt.gcf().autofmt_xdate() plt.gca().yaxis.set_major_formatter(ticker.EngFormatter()) plt.gca().xaxis.set_major_locator(mdates.MonthLocator(interval=2)) plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m')) plt.grid(True) plt.show() def show_case_death_chart(self): data = self._db.get_total_cases() date = [datetime.strptime(x[0], '%Y-%m-%d') for x in data] cases = [x[1] for x in data] deaths = [x[2] for x in data] fig, ax1 = plt.subplots() color = 'tab:red' ax1.set_xlabel('Datum') ax1.set_ylabel('Fälle', color=color) ax1.plot(date, cases, color=color) ax1.tick_params(axis='y', labelcolor=color) plt.gca().yaxis.set_major_formatter(ticker.EngFormatter()) ax2 = ax1.twinx() # instantiate a second axes that shares the same x-axis color = 'tab:blue' ax2.set_ylabel('Gestorbene', color=color) # we already handled the x-label with ax1 ax2.plot(date, deaths, color=color) ax2.tick_params(axis='y', labelcolor=color) plt.gcf().autofmt_xdate() plt.gca().yaxis.set_major_formatter(ticker.EngFormatter()) plt.gca().xaxis.set_major_locator(mdates.MonthLocator(interval=2)) plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m')) plt.grid(True) fig.tight_layout() # otherwise the right y-label is slightly clipped plt.show()

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0.380896

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0.274764

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2,747

73

93

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null

0

null

0

1

0

1d06db36083ac9ceb764551230b0138b99515c1f

4,045

py

Python

soccer3d/soccerdepth/models/encoder_decoder.py

ngerstle/soccerontable

25426ff0f8fe0ce008b99c5c0fdbb35091d8d92c

[ "BSD-2-Clause" ]

465

2018-05-18T04:43:40.000Z

2022-02-27T03:09:25.000Z

soccer3d/soccerdepth/models/encoder_decoder.py

ngerstle/soccerontable

25426ff0f8fe0ce008b99c5c0fdbb35091d8d92c

[ "BSD-2-Clause" ]

18

2018-06-20T15:03:10.000Z

2021-05-05T04:33:38.000Z

soccer3d/soccerdepth/models/encoder_decoder.py

ngerstle/soccerontable

25426ff0f8fe0ce008b99c5c0fdbb35091d8d92c

[ "BSD-2-Clause" ]

97

2018-05-03T09:12:30.000Z

2022-01-25T12:49:33.000Z

import torch import torch.nn as nn class G(nn.Module): def __init__(self, input_nc, output_nc, ngf=64): super(G, self).__init__() # 128 x 128 self.conv1 = nn.Conv2d(input_nc, ngf, 4, 2, 1) # 64 x 64 x1 self.conv2 = nn.Conv2d(ngf, ngf*2, 4, 2, 1) # 32 x 32 x 2 self.conv3 = nn.Conv2d(ngf*2, ngf*4, 4, 2, 1) # 16 x 16 x 4 self.conv4 = nn.Conv2d(ngf*4, ngf*8, 4, 2, 1) # 8 x 8 x 8 self.conv5 = nn.Conv2d(ngf*8, ngf*8, 4, 2, 1) # 4 x 4 x 8 -------------------------------------------------------- self.conv6 = nn.Conv2d(ngf*8, ngf*8, 4, 2, 1) # 2 x 2 x 8--------------------------------------------------- | self.conv7 = nn.Conv2d(ngf*8, ngf*8, 4, 2, 1) # | | # 1 x 1 | | self.dconv1 = nn.ConvTranspose2d(ngf*8, ngf*8, 4, 2, 1) # | | # 2 x 2 x 8--------------------------------------------------- | self.dconv2 = nn.ConvTranspose2d(ngf*8 * 2, ngf*8, 4, 2, 1) # 4 x 4 x 8 -------------------------------------------------------- self.dconv3 = nn.ConvTranspose2d(ngf*8 * 2, ngf*8, 4, 2, 1) # 8 x 8 x 8 self.dconv4 = nn.ConvTranspose2d(ngf*8 * 2, ngf*4, 4, 2, 1) # 16 x 16 self.dconv5 = nn.ConvTranspose2d(ngf*4 * 2, ngf*2, 4, 2, 1) # 32 x 32 self.dconv6 = nn.ConvTranspose2d(ngf*2 * 2, ngf, 4, 2, 1) # 64 x 64 self.dconv7 = nn.ConvTranspose2d(ngf, output_nc, 4, 2, 1) # 128 x 128 self.batch_norm = nn.BatchNorm2d(ngf) self.batch_norm2 = nn.BatchNorm2d(ngf*2) self.batch_norm4 = nn.BatchNorm2d(ngf*4) self.batch_norm8 = nn.BatchNorm2d(ngf*8) self.leaky_relu = nn.LeakyReLU(0.2, True) self.relu = nn.ReLU(True) self.dropout = nn.Dropout(0.5) self.tanh = nn.Tanh() self.log_softmax = nn.LogSoftmax() self.sigmoid = nn.Sigmoid() def forward(self, input): # Encoder # Convolution layers: # input is (nc) x 128 x 128 e1 = self.conv1(input) # state size is (ngf) x 64 x 64 e2 = self.batch_norm2(self.conv2(self.leaky_relu(e1))) # state size is (ngf x 2) x 32 x 32 e3 = self.batch_norm4(self.conv3(self.leaky_relu(e2))) # state size is (ngf x 4) x 16 x 16 e4 = self.batch_norm8(self.conv4(self.leaky_relu(e3))) # state size is (ngf x 8) x 8 x 8 e5 = self.batch_norm8(self.conv5(self.leaky_relu(e4))) # state size is (ngf x 8) x 4 x 4 e6 = self.batch_norm8(self.conv6(self.leaky_relu(e5))) # state size is (ngf x 8) x 2 x 2 # No batch norm on output of Encoder e7 = self.conv7(self.leaky_relu(e6)) # Decoder # Deconvolution layers: # state size is (ngf x 8) x 1 x 1 d1_ = self.dropout(self.batch_norm8(self.dconv1(self.relu(e7)))) # state size is (ngf x 8) x 2 x 2 d1 = torch.cat((d1_, e6), 1) d2_ = self.dropout(self.batch_norm8(self.dconv2(self.relu(d1)))) # state size is (ngf x 8) x 4 x 4 d2 = torch.cat((d2_, e5), 1) d3_ = self.dropout(self.batch_norm8(self.dconv3(self.relu(d2)))) # state size is (ngf x 8) x 8 x 8 d3 = torch.cat((d3_, e4), 1) d4_ = self.batch_norm4(self.dconv4(self.relu(d3))) # state size is (ngf x 8) x 16 x 16 d4 = torch.cat((d4_, e3), 1) d5_ = self.batch_norm2(self.dconv5(self.relu(d4))) # state size is (ngf x 4) x 32 x 32 d5 = torch.cat((d5_, e2), 1) d6 = self.batch_norm(self.dconv6(self.relu(d5))) # state size is (ngf x 2) x 64 x 64 # d6 = torch.cat((d6_, e1), 1) d7 = self.dconv7(self.relu(d6)) # state size is (ngf) x 128 x 128 # output = self.tanh(d7) output = self.log_softmax(d7) # output = self.sigmoid(d7) # output = d7 return output

40.858586

76

0.500371

640

4,045

3.089063

0.135938

0.020233

0.021244

0.09914

0.313101

0.297926

0.243298

0.180577

0.168437

0.135559

0

0.113901

0.320643

4,045

99

77

40.858586

0.605531

0.274907

0

1

0.039216

false

0

0.039216

0

0.117647

0

null

0

null

0

1

0

1d07976c7f74517929e92feef1305db889f52a18

264

py

Python

slotter/__init__.py

saurabh-hirani/slotter

a6d9dcacb61b5e1111e383855d181ef782ea494e

[ "MIT" ]

5

2016-10-21T06:58:30.000Z

2016-11-22T16:12:07.000Z

slotter/__init__.py

saurabh-hirani/slotter

a6d9dcacb61b5e1111e383855d181ef782ea494e

[ "MIT" ]

null

slotter/__init__.py

saurabh-hirani/slotter

a6d9dcacb61b5e1111e383855d181ef782ea494e

[ "MIT" ]

1

2020-02-27T03:36:01.000Z

""" Slotter Slotter is used to slot elements in buckets """ from .version import __version__ __title__ = 'slotter' __author__ = 'Saurabh Hirani' __license__ = 'MIT' __copyright__ = 'Copyright 2016 Saurabh Hirani' from .slotter import Slotter create = Slotter

15.529412

47

0.757576

31

264

5.806452

0.645161

0.144444

0

0.0181

0.162879

264

16

48

16.5

0.79638

0.19697

0

0.259804

0

1

0

false

0

0.285714

0

0.285714

0

null

0

null

0

1

0

1d0814786340bf7cae3bfaf13859f75091f59cb9

941

py

Python

pytglib/api/types/chat_type_supergroup.py

iTeam-co/pytglib

e5e75e0a85f89b77762209b32a61b0a883c0ae61

[ "MIT" ]

6

2019-10-30T08:57:27.000Z

2021-02-08T14:17:43.000Z

pytglib/api/types/chat_type_supergroup.py

iTeam-co/python-telegram

e5e75e0a85f89b77762209b32a61b0a883c0ae61

[ "MIT" ]

1

2021-08-19T05:44:10.000Z

2021-08-19T07:14:56.000Z

pytglib/api/types/chat_type_supergroup.py

iTeam-co/python-telegram

e5e75e0a85f89b77762209b32a61b0a883c0ae61

[ "MIT" ]

5

2019-12-04T05:30:39.000Z

2021-05-21T18:23:32.000Z

from ..utils import Object class ChatTypeSupergroup(Object): """ A supergroup (i.e. a chat with up to GetOption("supergroup_max_size") other users), or channel (with unlimited members) Attributes: ID (:obj:`str`): ``ChatTypeSupergroup`` Args: supergroup_id (:obj:`int`): Supergroup or channel identifier is_channel (:obj:`bool`): True, if the supergroup is a channel Returns: ChatType Raises: :class:`telegram.Error` """ ID = "chatTypeSupergroup" def __init__(self, supergroup_id, is_channel, **kwargs): self.supergroup_id = supergroup_id # int self.is_channel = is_channel # bool @staticmethod def read(q: dict, *args) -> "ChatTypeSupergroup": supergroup_id = q.get('supergroup_id') is_channel = q.get('is_channel') return ChatTypeSupergroup(supergroup_id, is_channel)

25.432432

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0.147887

0.073944

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0

0.268863

941

36

125

26.138889

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false

0

0.090909

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0.545455

0

null

0

null

0

1

0

1d09e8d254b0e97154810e8229ddfdd861fe0345

12,370

py

Python

utils/logger_utils.py

sunhailin-Leo/TeamLeoX_BlogsCrawler

389ff31e02bdff415c8bc470a3a48da1acb14c4c

[ "MIT" ]

null

utils/logger_utils.py

sunhailin-Leo/TeamLeoX_BlogsCrawler

389ff31e02bdff415c8bc470a3a48da1acb14c4c

[ "MIT" ]

null

utils/logger_utils.py

sunhailin-Leo/TeamLeoX_BlogsCrawler

389ff31e02bdff415c8bc470a3a48da1acb14c4c

[ "MIT" ]

null

import os import sys import logging from typing import Dict from concurrent_log_handler import ConcurrentRotatingFileHandler LOG_NAME: str = "service.log" LOG_LEVEL_INT: int = 20 formatter_dict = { 1: logging.Formatter( "日志时间【%(asctime)s】 - 日志名称【%(name)s】 - 文件【%(filename)s】 - " "第【%(lineno)d】行 - 日志等级【%(levelname)s】 - 日志信息【%(message)s】", "%Y-%m-%d %H:%M:%S", ), 2: logging.Formatter( "%(asctime)s - %(name)s - %(filename)s - %(funcName)s - " "%(lineno)d - %(levelname)s - %(message)s", "%Y-%m-%d %H:%M:%S", ), 3: logging.Formatter( '%(asctime)s - %(name)s - 【File "%(pathname)s", ' "line %(lineno)d, in %(funcName)s】 - %(levelname)s - %(message)s", "%Y-%m-%d %H:%M:%S", ), # 一个模仿 traceback 异常的可跳转到打印日志地方的模板 4: logging.Formatter( '%(asctime)s - %(name)s - "%(filename)s" - %(funcName)s - %(lineno)d - ' '%(levelname)s - %(message)s - File "%(pathname)s", line %(lineno)d ', "%Y-%m-%d %H:%M:%S", ), # 支持日志跳转 5: logging.Formatter( '%(asctime)s - %(name)s - "%(pathname)s:%(lineno)d" - ' "%(funcName)s - %(levelname)s - %(message)s", "%Y-%m-%d %H:%M:%S", ), # 推荐模板 6: logging.Formatter( "%(name)s - %(asctime)-15s - %(filename)s - %(lineno)d - " "%(levelname)s: %(message)s", "%Y-%m-%d %H:%M:%S", ), # 一个只显示简短文件名和所处行数的日志模板 7: logging.Formatter("%(levelname)s - %(filename)s - %(lineno)d - %(message)s"), # uvicorn default 的 formatters -- without logger.Formatter 8: '%(asctime)s - %(name)s - "%(pathname)s:%(lineno)d" - %(funcName)s - ' "%(levelname)s - %(message)s", # uvicorn access 的 foramtters -- without logger.Formatter 9: '%(asctime)s - %(name)s - "%(pathname)s:%(lineno)d" - %(funcName)s - ' '%(levelname)s - %(client_addr)s - "%(request_line)s" - %(status_code)s', } class LogLevelException(Exception): def __init__(self, log_level): err = "设置的日志级别是 {0}，设置错误，请设置为 1 2 3 4 5 范围的数字".format(log_level) Exception.__init__(self, err) class ColorHandler(logging.Handler): blue = 96 if os.name == "nt" else 36 yellow = 93 if os.name == "nt" else 33 def __init__(self, stream=None): """Initialize the handler. If stream is not specified, sys.stderr is used.""" logging.Handler.__init__(self) if stream is None: stream = sys.stdout # stderr无彩。 self.stream = stream def flush(self): self.acquire() try: if self.stream and hasattr(self.stream, "flush"): self.stream.flush() finally: self.release() def emit(self, record: logging.LogRecord): """ 30 40 黑色 31 41 红色 32 42 绿色 33 43 黃色 34 44 蓝色 35 45 紫红色 36 46 青蓝色 37 47 白色 """ try: msg = self.format(record) stream = self.stream msg_color_dict = { 10: "\033[0;32m%s\033[0m" % msg, 20: "\033[0;%sm%s\033[0m" % (self.blue, msg), 30: "\033[0;%sm%s\033[0m" % (self.yellow, msg), 40: "\033[0;31m%s\033[0m" % msg, 50: "\033[0;34m%s\033[0m" % msg, } try: msg_color = msg_color_dict[record.levelno] except KeyError: msg_color = msg stream.write(msg_color) stream.write("\n") self.flush() except Exception as err: print(err) self.handleError(record) def __repr__(self): level = logging.getLevelName(self.level) name = getattr(self.stream, "name", "") if name: name += " " return "<%s %s(%s)>" % (self.__class__.__name__, name, level) class LogManager(object): """一个日志管理类，用于创建 logger 和添加 handler，支持将日志打印到控制台打印和写入日志文件和邮件。""" logger_name_list: list = [] logger_list: list = [] def __init__(self, logger_name=None): """ :param logger_name: 日志名称，当为 None 时候创建 root 命名空间的日志一般不要传 None，除非你确定需要这么做 """ self._logger_name = logger_name self.logger = logging.getLogger(logger_name) self._logger_level = None self._is_add_stream_handler = None self._do_not_use_color_handler = None self._log_path = None self._log_filename = None self._log_file_size = None self._formatter = None # 加 * 是为了强制在调用此方法时候使用关键字传参，如果以位置传参强制报错， # 因为此方法后面的参数中间可能以后随时会增加更多参数，造成之前的使用位置传参的代码参数意义不匹配。 def get_logger_and_add_handlers( self, log_level_int: int = LOG_LEVEL_INT, *, is_add_stream_handler=True, do_not_use_color_handler=False, log_path=os.getcwd() + "/log", log_filename=LOG_NAME, log_file_size=100, formatter_template=5, ): """ :param log_level_int: 日志输出级别，设置为 1 2 3 4 5，分别对应原生 logging.DEBUG(10)，logging.INFO(20)， logging.WARNING(30)，logging.ERROR(40)，logging.CRITICAL(50)级别现在可以直接用 10 20 30 40 50。 :param is_add_stream_handler: 是否打印日志到控制台, True / False :param do_not_use_color_handler: 是否禁止使用 color 彩色日志, True / False :param log_path: 设置存放日志的文件夹路径 :param log_filename: 日志的名字，仅当 log_path 和 log_filename 都不为 None 时候才写入到日志文件。 :param log_file_size: 日志大小，单位 M，默认 10M, 默认值 int :param formatter_template: 日志模板，1 为 formatter_dict 的详细模板，2 为简要模板, 5 为最好模板 """ self._logger_level = log_level_int * 10 if log_level_int < 10 else log_level_int self._is_add_stream_handler = is_add_stream_handler self._do_not_use_color_handler = do_not_use_color_handler self._log_path = log_path self._log_filename = log_filename self._log_file_size = log_file_size self._formatter = formatter_dict[formatter_template] self.__set_logger_level() self.__add_handlers() self.logger_name_list.append(self._logger_name) self.logger_list.append(self.logger) return self.logger def get_logger_without_handlers(self): """返回一个不带 handlers 的 logger, 就是一个带红色字体的 print 输出""" return self.logger def look_over_all_handlers(self): print(f"{self._logger_name}名字的日志的所有 handlers 是--> {self.logger.handlers}") def remove_all_handlers(self): for hd in self.logger.handlers: self.logger.removeHandler(hd) def remove_handler_by_handler_class(self, handler_class: type): """ 去掉指定类型的 handler :param handler_class: logging.StreamHandler,ColorHandler, ConcurrentRotatingFileHandler,CompatibleSMTPSSLHandler的一种 """ if handler_class not in ( ColorHandler, logging.StreamHandler, ConcurrentRotatingFileHandler, ): raise TypeError("设置的 handler 类型不正确") for handler in self.logger.handlers: if isinstance(handler, handler_class): self.logger.removeHandler(handler) def __set_logger_level(self): self.logger.setLevel(self._logger_level) def __remove_handlers_from_other_logger_when_logger_name_is_none( self, handler_class ): """ 当 logger name 为 None 时候需要移出其他 logger 的 handler，否则重复记录日志 :param handler_class: handler 类型 """ if self._logger_name is None: for logger in self.logger_list: for handler in logger.handlers: if isinstance(handler, handler_class): logger.removeHandler(handler) @staticmethod def __judge_logger_contain_handler_class(logger: logging.Logger, handler_class): for h in logger.handlers + logging.getLogger().handlers: if isinstance(h, (handler_class,)): return True def __add_handlers(self): if self._is_add_stream_handler: if not self.__judge_logger_contain_handler_class(self.logger, ColorHandler): # 主要是阻止给 logger 反复添加同种类型的 handler 造成重复记录 self.__remove_handlers_from_other_logger_when_logger_name_is_none( ColorHandler ) self.__add_stream_handler() if all([self._log_path, self._log_filename]): if not self.__judge_logger_contain_handler_class( self.logger, ConcurrentRotatingFileHandler ): self.__remove_handlers_from_other_logger_when_logger_name_is_none( ConcurrentRotatingFileHandler ) self.__add_file_handler() def __add_stream_handler(self): """ 日志显示到控制台 """ # stream_handler = logging.StreamHandler() # 不使用 streamhandler，使用自定义的彩色日志 stream_handler = ( ColorHandler() if not self._do_not_use_color_handler else logging.StreamHandler() ) stream_handler.setLevel(self._logger_level) stream_handler.setFormatter(self._formatter) self.logger.addHandler(stream_handler) def __add_file_handler(self): """日志写入日志文件""" if not os.path.exists(self._log_path): os.mkdir(self._log_path) log_file = os.path.join(self._log_path, self._log_filename) rotate_file_handler = None if os.name == "nt": # windows 下用这个，非进程安全 rotate_file_handler = ConcurrentRotatingFileHandler( log_file, maxBytes=self._log_file_size * 1024 * 1024, backupCount=3, encoding="utf-8", ) if os.name == "posix": # linux 下可以使用 ConcurrentRotatingFileHandler，进程安全的日志方式 rotate_file_handler = ConcurrentRotatingFileHandler( log_file, maxBytes=self._log_file_size * 1024 * 1024, backupCount=3, encoding="utf-8", ) rotate_file_handler.setLevel(self._logger_level) rotate_file_handler.setFormatter(self._formatter) self.logger.addHandler(rotate_file_handler) class LoggerMixin(object): subclass_logger_dict: dict = {} @property def logger(self): if self.__class__.__name__ + "1" not in self.subclass_logger_dict: logger_var = LogManager( self.__class__.__name__ ).get_logger_and_add_handlers() self.subclass_logger_dict[self.__class__.__name__ + "1"] = logger_var return logger_var else: return self.subclass_logger_dict[self.__class__.__name__ + "1"] @property def logger_with_file(self): if self.__class__.__name__ + "2" not in self.subclass_logger_dict: logger_var = LogManager(type(self).__name__).get_logger_and_add_handlers( log_filename=type(self).__name__ + ".log", log_file_size=50 ) self.subclass_logger_dict[self.__class__.__name__ + "2"] = logger_var return logger_var else: return self.subclass_logger_dict[self.__class__.__name__ + "2"] UVICORN_LOGGING_CONFIG: Dict = { "version": 1, "disable_existing_loggers": False, "formatters": { "default": { "()": "uvicorn.logging.DefaultFormatter", "fmt": formatter_dict[8], "use_colors": None, "datefmt": "%Y-%m-%d %H:%M:%S", }, "access": { "()": "uvicorn.logging.AccessFormatter", "fmt": formatter_dict[9], "datefmt": "%Y-%m-%d %H:%M:%S", }, }, "handlers": { "default": { "formatter": "default", "class": "logging.StreamHandler", "stream": "ext://sys.stderr", }, "access": { "formatter": "access", "class": "logging.StreamHandler", "stream": "ext://sys.stdout", }, }, "loggers": { "": {"handlers": ["default"], "level": "INFO"}, "uvicorn.error": {"level": "INFO"}, "uvicorn.access": {"handlers": ["access"], "level": "INFO", "propagate": False}, }, }

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0.219058

0.174172

0.16105

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0

0.021383

0.304365

12,370

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0.167584

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false

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0.007692

0

null

0

null

0

1

0

1d0a33a6f3a05c3c57bcd109bd1331137ac931d0

1,945

py

Python

Blogs/Automate table mappings JSON creation/Create_JSON.py

subepie/DMS_tool

de4d3b3295366bb2118b31eb5ec268a278d4a557

[ "Apache-2.0" ]

43

2017-11-09T23:01:35.000Z

2020-09-08T14:51:41.000Z

Blogs/Automate table mappings JSON creation/Create_JSON.py

subepie/DMS_tool

de4d3b3295366bb2118b31eb5ec268a278d4a557

[ "Apache-2.0" ]

1

2019-09-11T09:54:55.000Z

2020-05-12T11:37:44.000Z

Blogs/Automate table mappings JSON creation/Create_JSON.py

subepie/DMS_tool

de4d3b3295366bb2118b31eb5ec268a278d4a557

[ "Apache-2.0" ]

34

2017-07-19T14:37:31.000Z

2020-08-30T00:54:01.000Z

import json import csv import os data = {} line_count = 0 data['rules'] = [] def writeJSON(): with open('automated_json.json', 'w') as outfile: json.dump(data, outfile) def createJSON(csvfile,action): global line_count with open(csvfile) as file: csv_reader = csv.reader(file, delimiter=',') for row in csv_reader: counter = str(line_count + 1) data['rules'].append({ "rule-type": "selection", "rule-id": counter, "rule-name": counter, "object-locator": { "schema-name": row[0], "table-name": row[1] }, "rule-action": action }) line_count += 1 if __name__ == "__main__": print("This program expects a folder location from the user. ") print("The folder can have 2 different types of files in csv format.") print("The file types are include table list and exclude table list.") print(" ") print("The file name should start with include or exclude to indicate " "whether the content of a particular file has to included or excluded.") print(" ") print("Both include and exclude files should contain schema name and the table name " "to be included or excluded separated by comma.") print("It is not necessary to have both include and exclude files.") print(" ") File_Location = raw_input("Enter the Folder location: ") if("/" in File_Location): separater = "/" else: separater = "\\" listOfFiles = os.listdir(File_Location) for entry in listOfFiles: if (entry.startswith("include")): createJSON(File_Location+separater+entry,"include") elif (entry.startswith("exclude")): createJSON(File_Location+separater+entry, "exclude") writeJSON()

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0.1

0.18

0

null

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null

0

1

0

1d0ece57935690eb57b92110e051ae26ccac522f

502

py

Python

website/events/helpers/views.py

eamanu/asoc_members

bf2e99e9c63c60a59bdfd10ca1812d78851cbde6

[ "MIT" ]

9

2019-03-24T21:56:52.000Z

2022-03-14T04:21:48.000Z

website/events/helpers/views.py

eamanu/asoc_members

bf2e99e9c63c60a59bdfd10ca1812d78851cbde6

[ "MIT" ]

111

2018-04-30T03:26:58.000Z

2021-12-01T13:06:24.000Z

website/events/helpers/views.py

eamanu/asoc_members

bf2e99e9c63c60a59bdfd10ca1812d78851cbde6

[ "MIT" ]

23

2018-09-06T21:39:56.000Z

2022-03-25T15:08:52.000Z

from django.db.models import Q import functools import operator def search_filtered_queryset(base_queryset, search_fields, search_value): filters = [] for key, value in search_fields.items(): field_filter = key if value != 'equal': field_filter = field_filter + '__' + value filter_dict = {field_filter: search_value} filters.append(Q(**filter_dict)) queryset = base_queryset.filter(functools.reduce(operator.or_, filters)) return queryset

31.375

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0.47541

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0.213147

502

15

77

33.466667

0.840506

0

0.013944

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false

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0

0.384615

0

null

0

null

0

1

0

0dfb51cb719fc893ece63b1717e0432e40ccdc7b

1,103

py

Python

machine_vision/yolov2_20class.py

huningxin/MaixPy_scripts

a54015f97989c46cc6b3d58f985156e619666a51

[ "MIT" ]

1

2020-05-04T15:22:53.000Z

machine_vision/yolov2_20class.py

huningxin/MaixPy_scripts

a54015f97989c46cc6b3d58f985156e619666a51

[ "MIT" ]

null

machine_vision/yolov2_20class.py

huningxin/MaixPy_scripts

a54015f97989c46cc6b3d58f985156e619666a51

[ "MIT" ]

null

#refer to http://blog.sipeed.com/p/677.html import sensor,image,lcd,time import KPU as kpu lcd.init(freq=15000000) sensor.reset() sensor.set_pixformat(sensor.RGB565) sensor.set_framesize(sensor.QVGA) sensor.set_vflip(1) sensor.run(1) clock = time.clock() classes = ['aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor'] task = kpu.load(0x500000) anchor = (1.08, 1.19, 3.42, 4.41, 6.63, 11.38, 9.42, 5.11, 16.62, 10.52) a = kpu.init_yolo2(task, 0.5, 0.3, 5, anchor) while(True): clock.tick() img = sensor.snapshot() code = kpu.run_yolo2(task, img) print(clock.fps()) if code: for i in code: a=img.draw_rectangle(i.rect()) a = lcd.display(img) for i in code: lcd.draw_string(i.x(), i.y(), classes[i.classid()], lcd.RED, lcd.WHITE) lcd.draw_string(i.x(), i.y()+12, '%f1.3'%i.value(), lcd.RED, lcd.WHITE) else: a = lcd.display(img) a = kpu.deinit(task)

35.580645

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0.602901

176

1,103

3.732955

0.568182

0.041096

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0.071429

0.035714

0

null

0

null

0

1

0

0dfcdef8833ed5833ba57eeec1abdbc79c20e892

14,393

py

Python

DupLess.py

FadyMohareb/dupless

60fef3e36dab619bc3c543d05dcb6d2fa359b62f

[ "MIT" ]

1

2020-02-03T14:35:11.000Z

DupLess.py

FadyMohareb/dupless

60fef3e36dab619bc3c543d05dcb6d2fa359b62f

[ "MIT" ]

null

DupLess.py

FadyMohareb/dupless

60fef3e36dab619bc3c543d05dcb6d2fa359b62f

[ "MIT" ]

null

#!/usr/bin/python # DupLess is a python script to detect and remove artifact duplications in assemblies. # Assemblies from heterozygous genomes tend to create two contigs instead of # one in areas of high heterozygosity. DupLess detects these regions based on # read coverage and sequence similarity. # Dependencies: # python v2.7 or higher # samtools v1.9 or higher (important for the "-o" parameter) # bedtools v2.27 (lower version should now work) # blastn v2.6.0+ # pandas, numpy, matplotlib, multiprocessing, getopt, biopython, sys, os, subprocess # sed and awk import getopt import subprocess import sys import os import detect_het_regions_from_coverage as dh import detect_duplicates_from_het_regions as dd import processing as proc import utils_dupless as ud global VERSION VERSION = "0.1.0" # Default values for the input window_size = 1000 # The coverage of each window will be based on the median of all the coverages inside the window. coverage_bed = None # Bed file with the coverage value for each position. Can be produced with "bedtools coverage". assembly_name = None # Assembly in fasta format, used to extract the het regions and also check the scaffold lengths. expected_coverage = None # Any window with 0 < coverage < expected_cov/1.5 will be considered as heterozygous. gaps_bed = None # Optional. Used to draw gaps as grey bars on the coverage graphs. output_folder = "./DupLess_out/" nbThreads = 10 blast_identity_threshold = 90 # Two regions will be considered duplicated if... blast_length_threshold = 300 # ...these two blast thresholds are met (min identity and min length). blast_overlap_threshold = 90.0 # % of duplication to cover contig (default 90%). skip_het_dect = False # Possibility to skip the first step (het detection) if bed of heterozygous regions is provided. het_bed = None # Bed defining the heterozygous region (Created by DupLess, or given by the user if skip_het_dect = T). skip_blast = False # Possibility to skip the "het detection" and "pairwise blasting" and just filter the blast results. blast_output = None # Default output file for blast results (given by the user if skip_blast = T) skip_plot = False # Skip the generation of the coverage plots def print_version(): """Print the version. """ global VERSION print("DupLess v" + VERSION) def usage(): """Print the usage. """ print("\npython DupLess.py -t [nb_threads] -b [coverage.bed] -a [assembly.fasta] -w [window_size] -c [expected_coverage] -i [min_blast_identity] -l [min_blast_length] -o [output_folder]") print("\nRequired:") print(" -a/--assembly The assembly corresponding to the bed coverage in fasta format.") print("") print(" -b/--bed_cov The bed file containing the coverage at each base (can be generated with 'bedtools genomecov').") print(" /!\ If using paired end reads: make sure that you set the -w or -l option higher than the insert size,") print(" to avoid false positives due to coverage drop at the ends of contigs (because of unaligned mates).") print("\nOptional:") print(" -t/--nThreads The number of threads (default ",nbThreads,")") print(" -o/--out_folder The output folder (default '",output_folder,"')") print("") print(" -c/--expected_cov The expected read coverage for the homozygous regions. The homozygosity / heterozygosity will be determined based on this value.") print(" You can determine the value by plotting the coverage distribution. It should correspond to the homozygous peak") print(" If no value is given, it will be based on the mode of the coverage distribution (not reliable if high heterozygosity).") print("") print(" -w/--window_size The size of the windows in basepairs (default: ",window_size,")") print(" The value of the coverage for each window will be the median of the coverage at each base.") print(" All the windows classified as 'heterozygous' will be considered for the detection of duplication.") print("") print(" -g/--bed_gaps A bed file containing the gaps along the genome. If given, the graphs will contain a grey background where the gaps are.") print("") print(" -i/--blast_identity The minimum percentage of identity between the het regions to consider them duplicates (default: ",blast_identity_threshold,", range 0 to 100).") print(" -l/--blast_length The blast alignments with a length lower than this threshold will be filtered (default=",blast_length_threshold,").") print(" -p/--blast_overlap The contigs overlapped at least by this threshold are written to a filter list (default=",blast_overlap_threshold,").") print("") print(" -n/--no_plot Skip the creation of all the plots") print("\nSkipping part of pipeline:") print(" -s/--skip_het_detection Skip the detection of the heterozygous regions. If so, you must provide a bed identifying the heterozygous regions:") print(" python DupLess.py -s [het_regions_bed] -t [nb_threads] -a [assembly.fasta] -i [min_blast_identity] -l [min_blast_length] -o [new_output_folder]") print("") print(" -f/--filter_blast_only Skip the detection of the heterozygous regions AND the pairwise alignments. If so, you must provide a blast ouput with -oufmt 6:") print(" python DupLess.py -f [blast_output] -t [nb_threads] -a [assembly.fasta] -i [min_blast_identity] -l [min_blast_length] -o [new_output_folder]") print("\nOther:") print(" -h/--help Print the usage and help and exit.") print(" -v/--version Print the version and exit.") #================================================================= # GetOpt = #================================================================= try: opts, args = getopt.getopt(sys.argv[1:], "t:w:b:a:c:g:o:s:f:i:l:p:nhv", ["nThreads=", "window_size=", "bed_cov=", "assembly=", "expected_cov=", "bed_gaps=", "out_folder=", "skip_het_detection=", "filter_blast_only=", "blast_identity=", "blast_length=", "blast_overlap=", "no_plot", "help", "version"]) except getopt.GetoptError as err: print(str(err)) usage() sys.exit(2) for o,a in opts: if o in ("-t", "--nThreads"): nbThreads = int(a) elif o in ("-w", "--window_size"): window_size = int(a) elif o in ("-b", "--bed_cov"): coverage_bed = str(a) elif o in ("-a", "--assembly"): assembly_name = str(a) elif o in ("-c", "--expected_cov"): expected_coverage = int(a) elif o in ("-g", "--bed_gaps"): gaps_bed = str(a) elif o in ("-o", "--out_folder"): output_folder = str(a) elif o in ("-s", "--skip_het_dect"): het_bed = str(a) skip_het_dect = True elif o in ("-f", "--filter_blast_only"): blast_output = str(a) skip_het_dect = True skip_blast = True elif o in ("-i", "--blast_identity"): blast_identity_threshold = int(a) elif o in ("-l", "--blast_length"): blast_length_threshold = float(a) elif o in ("-p", "--blast_overlap"): blast_overlap_threshold = float(a) elif o in ("-n", "--no_plot"): skip_plot = True elif o in ("-h", "--help"): usage() sys.exit(1) elif o in ("-v", "--version"): print_version() sys.exit(1) else: assert False, "Unhandled option !" # If we do not skip the het detection step: if not skip_het_dect: # Then we need the coverage bed file_ok, error_mssg = ud.check_file(coverage_bed) if not file_ok: print("Error with option -b/--bed_cov: "+error_mssg) usage() sys.exit(2) # Exit if output folder already exists (to avoid overwriting an already existing project) if(os.path.isdir(output_folder)): print("\nFolder '"+output_folder+"' already exists, stopping now...\n") sys.exit(2) file_ok, error_mssg = ud.check_file(assembly_name) if not file_ok: print("Error with option -a/--assembly: "+error_mssg) usage() sys.exit(2) if(window_size <= 0): print("The window size can not be lower than 0 (-w/--window_size option).\n") usage() sys.exit(2) if(nbThreads <= 0): print("The number of threads can not be lower than 0 (-t/--nThreads option).\n") usage() sys.exit(2) if((blast_identity_threshold < 0) or (blast_identity_threshold > 100)): print("The blast identity treshold (-i/--blast_identity) must be between 0 and 100. Current value: "+str(blast_identity_threshold)+"\n") usage() sys.exit(2) if((blast_length_threshold < 0)): print("The blast coverage treshold (-l/--blast_length) can not be lower than 0. Current value: "+str(blast_length_threshold)+"\n") usage() sys.exit(2) #================================================================= # Main = #================================================================= # Creating the output folder architecture # individuals_beds/ contains the bed files describing the het. regions for each sequence. # invidual_blasts/ contains the blast results for each het. region. # graphs/ contains the coverage graphs for each sequence. # temp/ contains temp file for blast. # deduplicated/ contains the results of DupLess: deduplicated.fasta and discarded.fasta for folder in [output_folder, output_folder+"/individual_beds", output_folder+"/graphs", output_folder+"/individual_blasts", output_folder+"/temp", output_folder+"/deduplicated"]: try: pr = subprocess.Popen(["mkdir", folder], shell=False, stdout=subprocess.PIPE) pr.communicate() ud.check_return_code(pr.returncode, "mkdir "+folder) except Exception as e: print("Error during mkdir "+folder) print("Exception:"+str(e)) sys.exit() # Indexing the assembly, needed later on for extraction of het regions # Also a good way to check if samtools exists at the start of the script ud.index_fasta_file(assembly_name) #================================================== # Detection of the heterozygous regions #================================================== if not skip_het_dect: # If the user does not skip the het dect then we need the coverage bed file # We check if it exists file_ok, error_mssg = ud.check_file(coverage_bed) if file_ok: # Launch the bed and graph creation for heterozygous regions, detection based on coverage values. het_bed = dh.detect_het_regions(coverage_bed, gaps_bed, expected_coverage, window_size, output_folder, nbThreads, skip_plot) else: print("Error with the coverage bed file: "+error_mssg) usage() sys.exit(2) #================================================== # Pairwise alignment of heterozygous regions #================================================== if not skip_blast: # Check if the het_bed file exists (wether it has been created by the step before or just given by the user) file_ok, error_mssg = ud.check_file(het_bed) if file_ok: # Launch pairwise blast comparisons between the detected heterozygous regions to detect duplication blast_output = dd.detect_dupl_regions(assembly_name, het_bed, output_folder, nbThreads) else: print("Error with the heterozygous bed file: "+error_mssg) usage() sys.exit(2) #================================================== # Filtering the blast results #================================================== # Check if the blast output file exists (wether it has been created by the step before or just given by the user) file_ok, error_mssg = ud.check_file(blast_output) if file_ok: # Filter the blasts by identity and length toRemoveBed, discardedBed, toFilterList = dd.filter_blast_results(blast_output, blast_identity_threshold, blast_length_threshold, blast_overlap_threshold, assembly_name, output_folder) else: print("Error with the blast output file: "+error_mssg) usage() sys.exit(2) #================================================== # Generating the output files #================================================== deduplicated_assembly = output_folder+"/deduplicated/deduplicated_assembly.fasta" discarded_assembly = output_folder+"/deduplicated/discarded.fasta" print("Generating the deduplicated fasta files from the blast results...") proc.remove_duplications_assembly(deduplicated_assembly, assembly_name, toRemoveBed, output_folder) proc.generate_discarded_fasta(assembly_name, discardedBed, discarded_assembly) # If we skip blast, no intermediate files created, no need to clean if not skip_blast: # Cleaning the intermediate files: ud.remove_file(output_folder+"/All_Blasts_region_coord.tab") ud.remove_file(output_folder+"/assembly_HET_ONLY.fa") ud.remove_file(output_folder+"/assembly_HET_ONLY.fa.fai") print("Done !\n") print("Deduplicated assembly generated in: " + deduplicated_assembly) print("Discarded sequences in: " + discarded_assembly) print("Contigs with a blast hit covering more than ", blast_overlap_threshold, "% of their length in: toFilter.list")

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0

null

0

null

0

1

0

0dfe6c5821cbd16700bb97be17dc7d969044d263

6,889

py

Python

arduino_utils.py

diegocepedaw/lasergo

584a1b7f2c2bd96f5b8f1d4ede09f5992c774b1e

[ "MIT" ]

12

2020-12-30T23:50:56.000Z

2022-02-14T03:27:02.000Z

arduino_utils.py

diegocepedaw/lasergo

584a1b7f2c2bd96f5b8f1d4ede09f5992c774b1e

[ "MIT" ]

null

arduino_utils.py

diegocepedaw/lasergo

584a1b7f2c2bd96f5b8f1d4ede09f5992c774b1e

[ "MIT" ]

1

2022-01-26T02:00:40.000Z

import cv2 import numpy as np import time import math import serial import pickle LASER_START = (100,100) arduino = serial.Serial('COM6', 9600, timeout=5) def clear_leds(): # turn off all leds data = bytes("C0,0\r\n", "utf8") arduino.write(data) # write increment to serial port print("wrote: " + str(data)) reachedPos = str(arduino.readline()) # read serial port for arduino echo print("read: " + str(reachedPos)) def set_led_coordinates(x_coord,y_coord): y_coord += 19 # light up the leds on the board to indicate a coordinate data = bytes("O" + str(x_coord) + "," + str(y_coord)+'\r\n', 'utf8') arduino.write(data) # write position to serial port print("wrote: " + str(data)) reachedPos = str(arduino.readline()) # read serial port for arduino echo print("read: " + str(reachedPos)) def control_laser(x_travel,y_travel): data = bytes("I" + str(x_travel) + "," + str(y_travel)+'\r\n', 'utf8') arduino.write(data) # write increment to serial port reachedPos = str(arduino.readline()) # read serial port for arduino echo time.sleep(0.5) print(reachedPos) def set_laser_pos(x_travel,y_travel): data = bytes("S" + str(x_travel) + "," + str(y_travel)+'\r\n', 'utf8') arduino.write(data) # write position to serial port reachedPos = str(arduino.readline()) # read serial port for arduino echo time.sleep(1) print(reachedPos) def calibrate_laser(cap): ''' determine how many pixel sevo angle travels''' set_laser_pos(LASER_START[0],LASER_START[1]) ret, frame = cap.read() frame = image_resize(frame, maxLength = 720, inter = cv2.INTER_AREA) laser_coords = get_laser_coords(frame) print(laser_coords) # move laser by 1 pos and measure change in pixel x,y control_laser(3,3) ret, frame = cap.read() new_laser_coords = get_laser_coords(frame) x_ratio = (new_laser_coords[0] - laser_coords[0]) y_ratio = (new_laser_coords[1] - laser_coords[1]) print(new_laser_coords) print("ratio:") print(x_ratio,y_ratio) return(x_ratio,y_ratio) def image_resize(image, maxLength = 720, inter = cv2.INTER_AREA): # initialize the dimensions of the image to be resized and # grab the image size dim = None (h, w) = image.shape[:2] # check to see if height is larger than width if max(h, w) == h: # calculate the ratio of the height and construct the # dimensions r = maxLength / float(h) dim = (int(w * r), maxLength) # otherwise, the height is None else: # calculate the ratio of the width and construct the # dimensions r = maxLength / float(w) dim = (maxLength, int(h * r)) # resize the image resized = cv2.resize(image, dim, interpolation = inter) # return the resized image return resized def get_laser_coords(frame): hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) # in the HSV range red is split up in two parts so these masks capture different red values which work under different conditions # currently I am just manually setting one but in the future this should be done in a better way lower_red = np.array([0, 70, 50]) upper_red = np.array([10, 255, 255]) mask1 = cv2.inRange(hsv, lower_red, upper_red) lower_red = np.array([170, 70, 50]) upper_red = np.array([180, 255, 255]) mask2 = cv2.inRange(hsv, lower_red, upper_red) mask = mask2 thresh = cv2.threshold(mask, 25, 255, cv2.THRESH_BINARY)[1] cv2.imshow("mask", thresh) lel = cv2.findNonZero(thresh) if lel is not None: if len(lel) > 500: lel = lel[-450:] x = 0 y = 0 if lel is not None: for element in lel: x += element[0][0] y += element[0][1] x = x / len(lel) y = y / len(lel) laser_coords = (int(x),int(y)) #cv2.circle(frame, laser_coords, 10, (0, 0, 0), 2) return laser_coords def target_laser(target, cap, mask = True): x_ratio, y_ratio = calibrate_laser(cap) x_ratio, y_ratio = -35, -14 ret, frame = cap.read() frame = image_resize(frame, maxLength = 720, inter = cv2.INTER_AREA) with open('corner_original_coords.data', 'rb') as filehandle: # read the data as binary data stream pts1 = pickle.load(filehandle) #arrange points to be drawn as polygon poly = [pts1[0], pts1[2], pts1[3], pts1[1]] # MASK NON BOARD AREA fill_color = [0, 0, 0] # any BGR color value to fill with mask_value = 255 # 1 channel white (can be any non-zero uint8 value) # our stencil - some `mask_value` contours on black (zeros) background, # the image has same height and width as `img`, but only 1 color channel stencil = np.zeros(frame.shape[:-1]) cv2.fillPoly(stencil, np.array([poly], dtype=np.int32), mask_value) sel = stencil != mask_value # select everything that is not mask_value arrived = False while (1): ret, frame = cap.read() frame = image_resize(frame, maxLength = 720, inter = cv2.INTER_AREA) frame[sel] = fill_color # fill masked area with fill_color laser_coords = get_laser_coords(frame) print(laser_coords) cv2.circle(frame, target, 10, (0, 0, 0), 2) x_dist = abs(target[0] - laser_coords[0]) y_dist = abs(target[1] - laser_coords[1]) if x_dist < 8 and y_dist < 8 or arrived: arrived = True cv2.circle(frame, target, 17, (0, 255, 0), 2) else: x_travel = ((target[0] - laser_coords[0] ) / x_ratio) y_travel = ((target[1] - laser_coords[1] ) / y_ratio) if x_dist < 5: x_travel = 0 elif x_travel < 0: x_travel = math.floor(x_travel) else: x_travel = math.ceil(x_travel) if y_dist < 5: y_travel = 0 elif y_travel < 0: y_travel = math.floor(y_travel) else: y_travel = math.floor(y_travel) print(laser_coords, target) print(x_travel,y_travel) time.sleep(3) control_laser(x_travel,y_travel) #cv2.imshow('mask', mask) cv2.imshow('Track Laser', frame) waitkey = cv2.waitKey(1) if waitkey & 0xFF == ord('q') or waitkey & 0xFF == ord('q') or waitkey == 9: break set_laser_pos(0,0) cv2.destroyAllWindows() if __name__ == "__main__": cap = cv2.VideoCapture(0) #calibrate_laser(cap) target_laser((340, 200), cap) cap.release() if arduino.isOpen() == True: arduino.close()

35.510309

133

0.59791

974

6,889

4.090349

0.262834

0.060743

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0.013052

0.357932

0.314006

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0.212098

0.189006

0

0.039837

0.289447

6,889

194

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35.510309

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0

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0

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0

0.113475

0.092199

0

null

0

null

0

1

0

0dfea64ad5882b1a0e160dc452c8c73b059a7bdc

22,984

py

Python

sktime_dl/deeplearning/mcnn/_classifier.py

talhaanwarch/sktime-dl

da4db17b78b645667b9be18e80283d0af4768c40

[ "BSD-3-Clause" ]

1

2021-06-13T05:29:16.000Z

sktime_dl/deeplearning/mcnn/_classifier.py

oustella/sktime-dl

f454893012567519f12d04050991cbec53ab1ef0

[ "BSD-3-Clause" ]

null

sktime_dl/deeplearning/mcnn/_classifier.py

oustella/sktime-dl

f454893012567519f12d04050991cbec53ab1ef0

[ "BSD-3-Clause" ]

null

# todo keras/tesnorflow memory problem when search over network parameters # currently just deleting EVERY model and retraining the best parameters # at the end, see **1 __author__ = "Aaron Bostrom, James Large" import gc import numpy as np from sklearn.model_selection import train_test_split from tensorflow import keras from sktime_dl.deeplearning.base.estimators import BaseDeepClassifier from sktime_dl.utils import check_and_clean_data from sktime_dl.utils import check_is_fitted from sklearn.utils import check_random_state class MCNNClassifier(BaseDeepClassifier): """Multi-scale Convolutional Neural Network (MCNN). Adapted from the implementation from Fawaz et. al https://github.com/hfawaz/dl-4-tsc/blob/master/classifiers/mcnn.py Network originally defined in: @article{cui2016multi, title={Multi-scale convolutional neural networks for time series classification}, author={Cui, Zhicheng and Chen, Wenlin and Chen, Yixin}, journal={arXiv preprint arXiv:1603.06995}, year={2016} } """ def __init__( self, pool_factors=[2, 3, 5], filter_sizes=[0.05, 0.1, 0.2], window_size=0.2, nb_train_batch=10, nb_epochs=200, max_train_batch_size=256, slice_ratio=0.9, random_state=0, verbose=False, model_name="mcnn", model_save_directory=None, ): """ :param pool_factors: array of shape :param filter_sizes: array of shape :param window_size: int, :param nb_train_batch: int, :param nb_epochs: int, the number of epochs to train the model :param max_train_batch_size: int, :param slice_ratio: int, :param random_state: int, seed to any needed random actions :param verbose: boolean, whether to output extra information :param model_name: string, the name of this model for printing and file writing purposes :param model_save_directory: string, if not None; location to save the trained keras model in hdf5 format """ super(MCNNClassifier, self).__init__( model_save_directory=model_save_directory, model_name=model_name ) self.random_state = random_state self.verbose = verbose self.pool_factors = ( pool_factors # used for hyperparameters grid search ) self.filter_sizes = ( filter_sizes # used for hyperparameters grid search ) self.window_size = window_size self.nb_train_batch = nb_train_batch self.nb_epochs = nb_epochs self.max_train_batch_size = max_train_batch_size self.slice_ratio = slice_ratio self._is_fitted = False def set_hyperparameters(self): # *******set up the ma and ds********# self.ma_base = 5 self.ma_step = 6 self.ma_num = 1 self.ds_base = 2 self.ds_step = 1 self.ds_num = 4 def slice_data(self, data_x, data_y, slice_ratio): n = data_x.shape[0] length = data_x.shape[1] n_dim = data_x.shape[2] # for MTS length_sliced = int(length * slice_ratio) increase_num = ( length - length_sliced + 1 ) # if increase_num =5, it means one ori becomes 5 new instances. n_sliced = n * increase_num new_x = np.zeros((n_sliced, length_sliced, n_dim)) new_y = None if data_y is not None: nb_classes = data_y.shape[1] new_y = np.zeros((n_sliced, nb_classes)) for i in range(n): for j in range(increase_num): new_x[i * increase_num + j, :, :] = data_x[ i, j: j + length_sliced, : ] if data_y is not None: new_y[i * increase_num + j] = np.int_( data_y[i].astype(np.float32) ) return new_x, new_y def _downsample(self, data_x, sample_rate, offset=0): num = data_x.shape[0] length_x = data_x.shape[1] num_dim = data_x.shape[2] # for MTS last_one = 0 if length_x % sample_rate > offset: last_one = 1 new_length = int(np.floor(length_x / sample_rate)) + last_one output = np.zeros((num, new_length, num_dim)) for i in range(new_length): output[:, i] = np.array(data_x[:, offset + sample_rate * i]) return output def _movingavrg(self, data_x, window_size): num = data_x.shape[0] length_x = data_x.shape[1] num_dim = data_x.shape[2] # for MTS output_len = length_x - window_size + 1 output = np.zeros((num, output_len, num_dim)) for i in range(output_len): output[:, i] = np.mean(data_x[:, i: i + window_size], axis=1) return output def movingavrg(self, data_x, window_base, step_size, num): if num == 0: return (None, []) out = self._movingavrg(data_x, window_base) data_lengths = [out.shape[1]] for i in range(1, num): window_size = window_base + step_size * i if window_size > data_x.shape[1]: continue new_series = self._movingavrg(data_x, window_size) data_lengths.append(new_series.shape[1]) out = np.concatenate([out, new_series], axis=1) return (out, data_lengths) def downsample(self, data_x, base, step_size, num): # the case for dataset JapaneseVowels MTS if data_x.shape[1] == 26: return (None, []) # too short to apply downsampling if num == 0: return (None, []) out = self._downsample(data_x, base, 0) data_lengths = [out.shape[1]] # for offset in range(1,base): #for the base case # new_series = _downsample(data_x, base, offset) # data_lengths.append( new_series.shape[1] ) # out = np.concatenate( [out, new_series], axis = 1) for i in range(1, num): sample_rate = base + step_size * i if sample_rate > data_x.shape[1]: continue for offset in range(0, 1): # sample_rate): new_series = self._downsample(data_x, sample_rate, offset) data_lengths.append(new_series.shape[1]) out = np.concatenate([out, new_series], axis=1) return (out, data_lengths) def train(self, x_train, y_train, pool_factor, filter_size): # split train into validation set with validation_size = 0.2 train_size x_train, x_val, y_train, y_val = train_test_split( x_train, y_train, test_size=0.2 ) ori_len = x_train.shape[1] # original_length of time series kernel_size = int(ori_len * filter_size) # restrict slice ratio when data lenght is too large current_slice_ratio = self.slice_ratio if ori_len > 500: current_slice_ratio = ( self.slice_ratio if self.slice_ratio > 0.98 else 0.98 ) increase_num = ( ori_len - int(ori_len * current_slice_ratio) + 1 ) # this can be used as the bath size # print(increase_num) train_batch_size = int( x_train.shape[0] * increase_num / self.nb_train_batch ) current_n_train_batch = self.nb_train_batch if train_batch_size > self.max_train_batch_size: # limit the train_batch_size current_n_train_batch = int(x_train.shape[0] * increase_num / self.max_train_batch_size) # data augmentation by slicing the length of the series x_train, y_train = self.slice_data( x_train, y_train, current_slice_ratio ) x_val, y_val = self.slice_data(x_val, y_val, current_slice_ratio) train_set_x, train_set_y = x_train, y_train valid_set_x, valid_set_y = x_val, y_val valid_num = valid_set_x.shape[0] # print("increase factor is ", increase_num, ', ori len', ori_len) valid_num_batch = int(valid_num / increase_num) length_train = train_set_x.shape[1] # length after slicing. current_window_size = ( int(length_train * self.window_size) if self.window_size < 1 else int(self.window_size) ) ds_num_max = length_train / (pool_factor * current_window_size) current_ds_num = int(min(self.ds_num, ds_num_max)) ma_train, ma_lengths = self.movingavrg( train_set_x, self.ma_base, self.ma_step, self.ma_num ) ma_valid, ma_lengths = self.movingavrg( valid_set_x, self.ma_base, self.ma_step, self.ma_num ) ds_train, ds_lengths = self.downsample( train_set_x, self.ds_base, self.ds_step, current_ds_num ) ds_valid, ds_lengths = self.downsample( valid_set_x, self.ds_base, self.ds_step, current_ds_num ) # concatenate directly data_lengths = [length_train] # downsample part: if ds_lengths != []: data_lengths += ds_lengths train_set_x = np.concatenate([train_set_x, ds_train], axis=1) valid_set_x = np.concatenate([valid_set_x, ds_valid], axis=1) # moving average part if ma_lengths != []: data_lengths += ma_lengths train_set_x = np.concatenate([train_set_x, ma_train], axis=1) valid_set_x = np.concatenate([valid_set_x, ma_valid], axis=1) # print("Data length:", data_lengths) n_train_size = train_set_x.shape[0] # n_valid_size = valid_set_x.shape[0] batch_size = int(n_train_size / current_n_train_batch) n_train_batches = int(n_train_size / batch_size) # data_dim = train_set_x.shape[1] num_dim = train_set_x.shape[2] # For MTS nb_classes = train_set_y.shape[1] self.input_shapes, max_length = self.get_list_of_input_shapes( data_lengths, num_dim ) model = self.build_sub_model( self.input_shapes, nb_classes, pool_factor, kernel_size ) # print('submodel built', model) if self.verbose: model.summary() # early-stopping parameters patience = 10000 # look as this many examples regardless patience_increase = 2 # wait this much longer when a new best is # found improvement_threshold = 0.995 # a relative improvement of this much is # considered significant validation_frequency = min(n_train_batches, patience / 2) max_before_stopping = 500 best_validation_loss = np.inf # best_iter = 0 valid_loss = 0.0 epoch = 0 done_looping = False num_no_update_epoch = 0 epoch_avg_cost = float("inf") # epoch_avg_err = float("inf") while (epoch < self.nb_epochs) and (not done_looping): epoch = epoch + 1 epoch_train_err = 0.0 epoch_cost = 0.0 num_no_update_epoch += 1 if num_no_update_epoch == max_before_stopping: break for minibatch_index in range(n_train_batches): iteration = (epoch - 1) * n_train_batches + minibatch_index x = train_set_x[ minibatch_index * batch_size: (minibatch_index + 1) * batch_size] y = train_set_y[ minibatch_index * batch_size: (minibatch_index + 1) * batch_size] x = self.split_input_for_model(x, self.input_shapes) # print('\t pre train batch') cost_ij, accuracy = model.train_on_batch(x, y) # print('\t post train batch') train_err = 1 - accuracy epoch_train_err = epoch_train_err + train_err epoch_cost = epoch_cost + cost_ij if (iteration + 1) % validation_frequency == 0: valid_losses = [] for i in range(valid_num_batch): x = valid_set_x[ i * (increase_num): (i + 1) * (increase_num) ] y_pred = model.predict_on_batch( self.split_input_for_model(x, self.input_shapes) ) # convert the predicted from binary to integer y_pred = np.argmax(y_pred, axis=1) label = np.argmax(valid_set_y[i * increase_num]) ( unique_value, sub_ind, correspond_ind, count, ) = np.unique(y_pred, True, True, True) unique_value = unique_value.tolist() curr_err = 1.0 if label in unique_value: target_ind = unique_value.index(label) count = count.tolist() sorted_count = sorted(count) if count[target_ind] == sorted_count[-1]: if (len(sorted_count) > 1 and sorted_count[-1] == sorted_count[-2]): curr_err = 0.5 # tie else: curr_err = 0 valid_losses.append(curr_err) valid_loss = sum(valid_losses) / float(len(valid_losses)) # print('...epoch%i,valid err: %.5f |' % # (epoch, valid_loss)) # if we got the best validation score until now if valid_loss <= best_validation_loss: num_no_update_epoch = 0 # improve patience if loss improvement is good enough if ( valid_loss < best_validation_loss * improvement_threshold ): patience = max( patience, iteration * patience_increase ) # save best validation score and iteration number best_validation_loss = valid_loss # best_iter = iteration # save model in h5 format # self.model.save(self.output_directory+'best_model.hdf5') if patience <= iteration: done_looping = True break epoch_avg_cost = epoch_cost / n_train_batches # epoch_avg_err = epoch_train_err / n_train_batches # print('train err %.5f, cost %.4f' % (epoch_avg_err, # epoch_avg_cost)) if epoch_avg_cost == 0: break return best_validation_loss, model def split_input_for_model(self, x, input_shapes): res = [] indx = 0 for input_shape in input_shapes: res.append(x[:, indx: indx + input_shape[0], :]) indx = indx + input_shape[0] return res def get_list_of_input_shapes(self, data_lengths, num_dim): input_shapes = [] max_length = 0 for i in data_lengths: input_shapes.append((i, num_dim)) max_length = max(max_length, i) return input_shapes, max_length def build_sub_model( self, input_shapes, nb_classes, pool_factor, kernel_size ): input_layers = [] stage_1_layers = [] for input_shape in input_shapes: input_layer = keras.layers.Input(input_shape) input_layers.append(input_layer) conv_layer = keras.layers.Conv1D( filters=256, kernel_size=kernel_size, padding="same", activation="sigmoid", kernel_initializer="glorot_uniform", )(input_layer) # should all concatenated have the same length pool_size = int(int(conv_layer.shape[1]) / pool_factor) max_layer = keras.layers.MaxPooling1D(pool_size=pool_size)( conv_layer ) # max_layer = keras.layers.GlobalMaxPooling1D()(conv_layer) stage_1_layers.append(max_layer) concat_layer = keras.layers.Concatenate(axis=-1)(stage_1_layers) kernel_size = int( min(kernel_size, int(concat_layer.shape[1])) ) # kernel shouldn't exceed the length full_conv = keras.layers.Conv1D( filters=256, kernel_size=kernel_size, padding="same", activation="sigmoid", kernel_initializer="glorot_uniform", )(concat_layer) pool_size = int(int(full_conv.shape[1]) / pool_factor) full_max = keras.layers.MaxPooling1D(pool_size=pool_size)(full_conv) full_max = keras.layers.Flatten()(full_max) fully_connected = keras.layers.Dense( units=256, activation="sigmoid", kernel_initializer="glorot_uniform", )(full_max) output_layer = keras.layers.Dense( units=nb_classes, activation="softmax", kernel_initializer="glorot_uniform", )(fully_connected) model = keras.models.Model(inputs=input_layers, outputs=output_layer) model.compile( loss="categorical_crossentropy", optimizer=keras.optimizers.Adam(lr=0.1), metrics=["accuracy"], ) return model def fit(self, X, y, input_checks=True, **kwargs): """ Fit the classifier on the training set (X, y) ---------- X : a nested pd.Dataframe, or (if input_checks=False) array-like of shape = (n_instances, series_length, n_dimensions) The training input samples. If a 2D array-like is passed, n_dimensions is assumed to be 1. y : array-like, shape = [n_instances] The class labels. input_checks: boolean whether to check the X and y parameters Returns ------- self : object """ self.random_state = check_random_state(self.random_state) self.set_hyperparameters() X = check_and_clean_data(X, y, input_checks=input_checks) y_onehot = self.convert_y(y) # best_df_metrics = None best_valid_loss = np.inf # grid search for pool_factor in self.pool_factors: for filter_size in self.filter_sizes: # print('pretrain') valid_loss, model = self.train( X, y_onehot, pool_factor, filter_size ) # print('posttrain') if valid_loss < best_valid_loss: best_valid_loss = valid_loss self.best_pool_factor = pool_factor self.best_filter_size = filter_size # self.model = model # see **1 below # print('postbest', self.model) # clear memory in all the ways... **1 del model gc.collect() keras.backend.clear_session() # print('postclear',self.model) _, self.model = self.train(X, y_onehot, pool_factor, filter_size) self.save_trained_model() self._is_fitted = True return self def predict_proba(self, X, input_checks=True, **kwargs): """ Find probability estimates for each class for all cases in X. Parameters ---------- X : a nested pd.Dataframe, or (if input_checks=False) array-like of shape = (n_instances, series_length, n_dimensions) The training input samples. If a 2D array-like is passed, n_dimensions is assumed to be 1. input_checks: boolean whether to check the X parameter Returns ------- output : array of shape = [n_instances, n_classes] of probabilities """ check_is_fitted(self) X = check_and_clean_data(X, input_checks=input_checks) ori_len = X.shape[1] # original_length of time series # restrict slice ratio when data lenght is too large current_slice_ratio = self.slice_ratio if ori_len > 500: current_slice_ratio = ( self.slice_ratio if self.slice_ratio > 0.98 else 0.98 ) increase_num = ( ori_len - int(ori_len * current_slice_ratio) + 1 ) # this can be used as the bath size # will need to slice at some poin x_test, _ = self.slice_data(X, None, current_slice_ratio) length_train = x_test.shape[1] # length after slicing. current_window_size = ( int(length_train * self.window_size) if self.window_size < 1 else int(self.window_size) ) ds_num_max = length_train / ( self.best_pool_factor * current_window_size ) current_ds_num = int(min(self.ds_num, ds_num_max)) # need to batch and downsample the test data. ma_test, ma_lengths = self.movingavrg( x_test, self.ma_base, self.ma_step, self.ma_num ) ds_test, ds_lengths = self.downsample( x_test, self.ds_base, self.ds_step, current_ds_num ) test_set_x = x_test # concatenate directly data_lengths = [length_train] # downsample part: if ds_lengths != []: data_lengths += ds_lengths test_set_x = np.concatenate([test_set_x, ds_test], axis=1) # moving average part if ma_lengths != []: data_lengths += ma_lengths test_set_x = np.concatenate([test_set_x, ma_test], axis=1) test_num = x_test.shape[0] test_num_batch = int(test_num / increase_num) # get the true predictions of the test set y_predicted = [] for i in range(test_num_batch): x = test_set_x[i * (increase_num): (i + 1) * (increase_num)] preds = self.model.predict_on_batch( self.split_input_for_model(x, self.input_shapes) ) y_predicted.append(np.average(preds, axis=0)) y_pred = np.array(y_predicted) return y_pred

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22,984

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0.146955

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null

0

null

0

1

0

df011455a0e6333338175cf0393900bcda2fcb02

3,280

py

Python

bgm_block/models.py

MCG-NJU/BCN

c637dcdc436717104d4c14a6fbb21e2d2299b087

[ "MIT" ]

69

2020-08-21T01:19:24.000Z

2022-03-28T02:39:44.000Z

bgm_block/models.py

redwang/BCN

e5c494d8ca396d5a535309575a7a652db54f14b7

[ "MIT" ]

7

2020-11-12T08:21:08.000Z

2021-12-29T04:50:11.000Z

bgm_block/models.py

redwang/BCN

e5c494d8ca396d5a535309575a7a652db54f14b7

[ "MIT" ]

9

2020-08-28T06:46:17.000Z

2022-01-29T11:27:01.000Z

# -*- coding: utf-8 -*- import torch import torch.nn.functional as F import torch.nn as nn from torch.nn import init import copy class fullBGM(torch.nn.Module): def __init__(self): super(fullBGM, self).__init__() self.feat_dim = 2048 self.batch_size = 1 self.c_hidden = 256 self.bgm_best_loss = 10000000 self.bgm_best_f1 = -10000000 self.bgm_best_precision = -10000000 self.output_dim = 1 self.num_layers=3 self.conv_in = nn.Conv1d(self.feat_dim, self.c_hidden, 1) self.layers = nn.ModuleList([copy.deepcopy(DilatedResidualLayer(2 ** (i+2), self.c_hidden, self.c_hidden)) for i in range(self.num_layers)]) self.conv_out = nn.Conv1d(self.c_hidden, self.output_dim, 1) self.reset_params() @staticmethod def weight_init(m): if isinstance(m, nn.Conv2d): init.xavier_normal(m.weight) init.constant(m.bias, 0) def reset_params(self): for i, m in enumerate(self.modules()): self.weight_init(m) def forward(self, x): out = self.conv_in(x) for layer in self.layers: out = layer(out) out = self.conv_out(out) out = torch.sigmoid(0.01*out) return out class DilatedResidualLayer(nn.Module): def __init__(self, dilation, in_channels, out_channels): super(DilatedResidualLayer, self).__init__() self.conv_dilated = nn.Conv1d(in_channels, out_channels, 3, padding=dilation, dilation=dilation) self.conv_1x1 = nn.Conv1d(out_channels, out_channels, 1) self.dropout = nn.Dropout() # default value is 0.5 def forward(self, x): out = F.relu(self.conv_dilated(x)) out = self.conv_1x1(out) out = self.dropout(out) return (x + out) class resizedBGM(torch.nn.Module): def __init__(self, dataset): super(resizedBGM, self).__init__() self.feat_dim = 2048 if dataset == 'breakfast' or dataset == 'gtea': self.temporal_dim = 300 elif dataset == '50salads': self.temporal_dim = 400 self.batch_size = 40 self.batch_size_test = 10 self.c_hidden = 512 self.bgm_best_loss = 10000000 self.bgm_best_f1= -10000000 self.output_dim = 1 self.conv1 = torch.nn.Conv1d(in_channels=self.feat_dim, out_channels=self.c_hidden, kernel_size=3, stride=1, padding=1, groups=1) self.conv2 = torch.nn.Conv1d(in_channels=self.c_hidden, out_channels=self.c_hidden, kernel_size=3, stride=1, padding=1, groups=1) self.conv3 = torch.nn.Conv1d(in_channels=self.c_hidden, out_channels=self.output_dim, kernel_size=1, stride=1, padding=0) self.reset_params() @staticmethod def weight_init(m): if isinstance(m, nn.Conv2d): init.xavier_normal(m.weight) init.constant(m.bias, 0) def reset_params(self): for i, m in enumerate(self.modules()): self.weight_init(m) def forward(self, x): x = F.relu(self.conv1(x)) x = F.relu(self.conv2(x)) x = torch.sigmoid(self.conv3(x)) return x

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148

0.607622

450

3,280

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0.222222

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0.057895

0.037895

0.467895

0.428421

0.343684

0

0.049111

0.279878

3,280

93

149

35.268817

0.755292

0.012805

0

0.3875

0

0.006496

0

1

0.125

false

0

0.0625

0

0.2625

0

null

0

null

0

1

0

df012a7576e9516f4a23ace4d324b5a6e610a767

508

py

Python

Random Problems/Merge the Tools/solutions.py

matheuscordeiro/HackerRank

003ab87fdfa9e7c0535972abd06caebb1165423f

[ "MIT" ]

null

Random Problems/Merge the Tools/solutions.py

matheuscordeiro/HackerRank

003ab87fdfa9e7c0535972abd06caebb1165423f

[ "MIT" ]

null

Random Problems/Merge the Tools/solutions.py

matheuscordeiro/HackerRank

003ab87fdfa9e7c0535972abd06caebb1165423f

[ "MIT" ]

null

def merge_the_tools(string, k): substrings = int(len(string)/k) size_substring = int(len(string)/substrings) count = 0 frequency = {} word = "" for value in string: count += 1 if not value in frequency: word += value frequency[value] = True if count == size_substring: print(word) word = "" count = 0 frequency = {} if __name__ == "__main__": merge_the_tools("AABCAAADA", 3)

25.4

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0.521654

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508

4.563636

0.490909

0.063745

0.103586

0

0.012539

0.372047

508

20

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0

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false

0

0.055556

0

null

0

null

0

1

0

df019cd58e39253b69a98f540baa40894015701a

1,915

py

Python

utils/cmd_parser.py

SecureThemAll/CquenceR

b2d4d578a8a055df8370798769cdc6e6e1039721

[ "MIT" ]

null

utils/cmd_parser.py

SecureThemAll/CquenceR

b2d4d578a8a055df8370798769cdc6e6e1039721

[ "MIT" ]

null

utils/cmd_parser.py

SecureThemAll/CquenceR

b2d4d578a8a055df8370798769cdc6e6e1039721

[ "MIT" ]

null

#!/usr/bin/env python3 import argparse from collections import Callable from utils.command import Command from utils.commands.preprocess import Preprocess from utils.commands.test import Test from utils.commands.train import Train from utils.commands.repair import Repair from utils.commands.stats import Stats from utils.commands.clean import Clean COMMANDS = {} parser = argparse.ArgumentParser(prog="CquenceR", description='Program Repair Tool based on Sequence-to-Sequence Learning.') main_parser = argparse.ArgumentParser(add_help=False) main_parser.add_argument('-v', '--verbose', help='Verbose output.', action='store_true') main_parser.add_argument('-seed', default=0, type=int, help='Seed used for better reproducibility between experiments') main_parser.add_argument('-l', '--log_file', type=str, default=None, help='Log file to write the results to.') subparsers = parser.add_subparsers() def add_command(name: str, command: Command, description: str): cmd_parser = subparsers.add_parser(name=name, help=description, parents=[main_parser]) cmd_parser.set_defaults(command=command) cmd_parser.set_defaults(name=name) return cmd_parser def register(definition: Callable, arguments: Callable): """Register a command as a positional argument""" cmd_parser = add_command(**definition()) arguments(cmd_parser) register(definition=Preprocess.definition, arguments=Preprocess.add_arguments) register(definition=Train.definition, arguments=Train.add_arguments) register(definition=Test.definition, arguments=Test.add_arguments) register(definition=Repair.definition, arguments=Repair.add_arguments) register(definition=Stats.definition, arguments=Stats.add_arguments) register(definition=Clean.definition, arguments=Clean.add_arguments) def run(command: Command, **kwargs): cmd = command(**kwargs) cmd()

36.132075

110

0.768146

240

1,915

6.008333

0.316667

0.043689

0.070735

0.104022

0

0.001195

0.126371

1,915

52

111

36.826923

0.860729

0.033943

0

0.113341

0

1

0.085714

false

0

0.257143

0

0.371429

0

null

0

null

0

1

0

df035418a124dfc6d3a28d6cc0fd7c45993e05d4

3,884

py

Python

lanefinder/CamModel.py

sheayun-kmu/CarND-Advanced-Lane-Lines

f8f38767acb9a65bc942f53706c6d223dfb666a2

[ "MIT" ]

null

lanefinder/CamModel.py

sheayun-kmu/CarND-Advanced-Lane-Lines

f8f38767acb9a65bc942f53706c6d223dfb666a2

[ "MIT" ]

null

lanefinder/CamModel.py

sheayun-kmu/CarND-Advanced-Lane-Lines

f8f38767acb9a65bc942f53706c6d223dfb666a2

[ "MIT" ]

1

2020-09-23T12:43:22.000Z

import logging import numpy as np import cv2 from lanefinder.params import camera_params from lanefinder.params import perspective_params # Capture camera model # 1. calibrate using a set of images of chessboards # 2. undistort image based on calibration # 3. warp image to get a bird's eye view of it class CamModel: # Configure logger, initialize distortion parameters # and perspective transform parameters. def __init__(self): self.log = logging.getLogger(__name__) self.log.setLevel(logging.WARN) self.mtx = None self.dist = None self.M = None self.Minv = None # Given a set of chessboard images (and # of corners), # calibrate the camera and derive the conversion matrix. def calibrate(self, image_files, nx=0, ny=0): # Read from configuration if nx & ny are not specified. if nx == 0: nx = camera_params['nx'] if ny == 0: ny = camera_params['ny'] # Initialize empty imgpoints and objpoints imgpoints = [] objpoints = [] # Prepare uniform object points using simple arithmetic objp = np.zeros((ny * nx, 3), np.float32) objp[:, :2] = np.mgrid[0:nx, 0:ny].T.reshape(-1, 2) self.log.info("Beginning camera calibration with" " %d images" % len(image_files)) for fname in image_files: img = cv2.imread(fname) # Convert to grayscale gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # Find chessboard corners ret, corners = cv2.findChessboardCorners(gray, (nx, ny), None) # If found, add object points and image points if ret == True: imgpoints.append(corners) objpoints.append(objp) else: self.log.warn("Failed to find %d * %d corners from" " image %s" % (nx, ny, fname)) # Calibrate self.log.info("Gathered %d sets of corners" % len(imgpoints)) ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera( objpoints, imgpoints, img.shape[1::-1], None, None ) self.log.info("Finished calibrating camera") self.mtx = mtx self.dist = dist # Given an image, return its undistorted version, based on # calibration parameters determined in calibrate() def undistort(self, img): undist = cv2.undistort(img, self.mtx, self.dist, None, self.mtx) return undist # Given two sets of four points (corners) - src & dst - # compute perspective transformation (matrix M) and its inverse. def init_perspective(self, src=None, dst=None): if not src: src = np.float32([ perspective_params['src']['ul'], perspective_params['src']['ur'], perspective_params['src']['ll'], perspective_params['src']['lr'], ]) if not dst: dst = np.float32([ perspective_params['dst']['ul'], perspective_params['dst']['ur'], perspective_params['dst']['ll'], perspective_params['dst']['lr'], ]) self.M = cv2.getPerspectiveTransform(src, dst) self.Minv = cv2.getPerspectiveTransform(dst, src) # Warp image using previously initialized transform. def warp(self, img): r, c = img.shape[:2] warped = cv2.warpPerspective( img, self.M, (c, r), flags=perspective_params['flags'] ) return warped # Inverse-warp image using previously initialized transform. def inverse_warp(self, img): r, c = img.shape[:2] inverse = cv2.warpPerspective( img, self.Minv, (c, r), flags=perspective_params['flags'] ) return inverse

36.299065

74

0.58033

454

3,884

4.903084

0.328194

0.084007

0.035939

0.02336

0.093441

0.019766

0

0.013178

0.316169

3,884

106

75

36.641509

0.824925

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0.2

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null

0

null

0

1

0

df04f18f1abbee7ba636186d9cf666a08ecb1a2d

4,446

py

Python

poc/hex_grid/hex.py

CoryOwens/hex

83253b52f9ee86d13402fc7475a0cdd772921728

[ "MIT" ]

null

poc/hex_grid/hex.py

CoryOwens/hex

83253b52f9ee86d13402fc7475a0cdd772921728

[ "MIT" ]

null

poc/hex_grid/hex.py

CoryOwens/hex

83253b52f9ee86d13402fc7475a0cdd772921728

[ "MIT" ]

null

import math import kivy from kivy.graphics import Line from kivy.graphics import Color from kivy.properties import ListProperty, NumericProperty from kivy.uix.widget import Widget from kivy.logger import Logger kivy.require('1.10.0') class HexTile(Widget): corners = ListProperty() sides = ListProperty() wedge_size = NumericProperty() rotation_offset = NumericProperty() def __init__(self, **kwargs): super(HexTile, self).__init__() self.debug = kwargs.get('debug', False) size = kwargs.get('size', None) if size: self.size = size else: self.wedge_size = kwargs.get('wedge_size', 10) rotation_offset = kwargs.get('rotation_offset', 0) self.rotation_offset = rotation_offset center = kwargs.get('center', None) if not center: center = (self.center_x, self.center_y) self.center = center @staticmethod def calc_tile_width(size, deg): deg = deg % 60 h_rad = math.radians(deg) off = math.radians(60) max_width = min(size) angles = [ math.fabs(math.cos(h_rad)), math.fabs(math.cos(off - h_rad)) ] return max_width * max(angles) @staticmethod def calc_tile_height(size, deg): deg = deg % 60 v_rad = math.radians(deg + 60) off = math.radians(60) max_height = min(size) angles = [ math.fabs(math.sin(v_rad)), math.fabs(math.sin(off - v_rad)) ] return max_height * max(angles) @classmethod def calc_wedge_size(cls, size, deg): return max(cls.calc_tile_width(size, deg), cls.calc_tile_height(size, deg)) / 2 def update_wedge_size(self): self.wedge_size = self.calc_wedge_size(self.size, self.rotation_offset) def on_rotation_offset(self, instance, value): self.update_wedge_size() if not self.center: Logger.info('HexTile: on_rotation_offset -- center not set') return self.populate_corners() def on_size(self, instance, value): self.update_wedge_size() if not self.center: Logger.info('HexTile: on_size -- center not set') return self.populate_corners() def on_center(self, instance, value): if not self.wedge_size: Logger.info('HexTile: on_center -- wedge_size not set') return self.populate_corners() def populate_corners(self): corners = [] for i in range(6): angle_deg = 60 * i - self.rotation_offset angle_rad = math.pi / 180 * angle_deg corner = (self.center_x + self.wedge_size * math.cos(angle_rad), self.center_y + self.wedge_size * math.sin(angle_rad)) corners.append(corner) Logger.info('HexTile: populate_corners -- corners: {}'.format(corners)) self.corners = corners def on_corners(self, instance, value): self.populate_sides() def populate_sides(self): sides = [] for i in range(6): corner_a = self.corners[i] corner_b = self.corners[(i + 1) % 6] side = (corner_a, corner_b) sides.append(side) Logger.info('HexTile: populate_sides -- sides: {}'.format(sides)) self.sides = sides def on_sides(self, instance, value): self.draw() def draw(self): with self.canvas: self.canvas.clear() Color(1, 1, 1) Line(points=self.corners+[self.corners[0]], width=1) if self.debug: # Draw center line Line(points=[self.center, self.corners[0]], width=1) # Draw bounding box box_size = min(self.size) / 2 # Dist from center to orth edge box_corners = [ (self.center_x - box_size, self.center_y - box_size), (self.center_x + box_size, self.center_y - box_size), (self.center_x + box_size, self.center_y + box_size), (self.center_x - box_size, self.center_y + box_size), ] Line(points=box_corners + [box_corners[0]], width=1) if __name__ == '__main__': from kivy.app import App class HexApp(App): def build(self): return HexTile(debug=True) HexApp().run()

32.217391

79

0.580072

555

4,446

4.448649

0.181982

0.064804

0.031187

0.048198

0.269745

0.18307

0.162819

0.149048

0.115026

0

0.012076

0.310841

4,446

137

80

32.452555

0.793734

0.014395

0

0.175439

0

0.056875

0

1

0.122807

false

0

0.070175

0.017544

0.307018

0

null

0

null

0

1

0

df07eeff5a871ae595d36ebd4e1dcb711889d0e5

816

py

Python

run/gold-key-splitter.py

osmanbaskaya/mapping-impact

8024dd3b916ac2dfc336221dd32faba4c0a98442

[ "MIT" ]

1

2016-03-14T15:28:22.000Z

run/gold-key-splitter.py

osmanbaskaya/mapping-impact

8024dd3b916ac2dfc336221dd32faba4c0a98442

[ "MIT" ]

null

run/gold-key-splitter.py

osmanbaskaya/mapping-impact

8024dd3b916ac2dfc336221dd32faba4c0a98442

[ "MIT" ]

null

#! /usr/bin/python # -*- coding: utf-8 -*- __author__ = "Osman Baskaya" """ Split the keys according to target words. Our gold file is merged. All keys are in the same file. In order to create a development set in run/gold/twitter|gigaword, we need to split keys in dev set into separate files. input_file should be in the Semeval 2013 format. Please check the files in keys/gold Not: Gigaword icin calismiyor, formatta sorun var. """ import sys import os from collections import defaultdict as dd input_file = open(sys.argv[1]) output_dir = sys.argv[2] devset = set(sys.argv[3:]) d = dd(list) for line in input_file: tw = line.split()[0] if tw in devset: d[tw].append(line) for tw in devset: with open(os.path.join(output_dir, tw + ".key"), 'w') as f: f.write("".join(d[tw]))

24

69

0.688725

141

816

3.921986

0.595745

0.048825

0.036166

0

0.013699

0.194853

816

33

70

24.727273

0.828006

0.047794

0

0.044226

0

1

0

false

0

0.2

0

0.2

0

null

0

null

0

1

0

df08f0d2fd7a1589d78358cb6d71d6ae0bb1ece0

857

py

Python

resource/engine/add_worker.py

CybersecurityLuxembourg/cyberlux-cron

dc1a8e4fad067b6d00c73a434d2a278576bbafab

[ "BSD-2-Clause" ]

null

resource/engine/add_worker.py

CybersecurityLuxembourg/cyberlux-cron

dc1a8e4fad067b6d00c73a434d2a278576bbafab

[ "BSD-2-Clause" ]

null

resource/engine/add_worker.py

CybersecurityLuxembourg/cyberlux-cron

dc1a8e4fad067b6d00c73a434d2a278576bbafab

[ "BSD-2-Clause" ]

null

from flask_apispec import MethodResource from flask_apispec import doc from flask_restful import Resource from queue import Queue from flask_jwt_extended import jwt_required from decorator.catch_exception import catch_exception from decorator.verify_admin_access import verify_admin_access class AddWorker(MethodResource, Resource): def __init__(self, db, engine): self.db = db self.engine = engine @doc(tags=['engine']) @jwt_required @verify_admin_access @catch_exception def post(self): try: message_queue = Queue(1) self.engine.queue.put((message_queue, "ADD_WORKER")) res = message_queue.get(timeout=1) del message_queue except Exception as e: del message_queue # noqa: F821 return str(e), 200 return res, 200

26.78125

64

0.683781

108

857

5.185185

0.416667

0.107143

0.091071

0.078571

0

0.017161

0.252042

857

31

65

27.645161

0.856474

0.011669

0

0.08

0

0.018935

0

1

0.08

false

0

0.28

0

0.48

0

null

0

null

0

1

0

df09ac413fdfd3e722c4aa22f5197075d17783b1

1,463

py

Python

react-flask-app/api/TripDB.py

GabCas28/Smart-Mazda-On-Board

2c3aa53c34bd90868a55985a01dcb5a5954d6a8b

[ "MIT" ]

null

react-flask-app/api/TripDB.py

GabCas28/Smart-Mazda-On-Board

2c3aa53c34bd90868a55985a01dcb5a5954d6a8b

[ "MIT" ]

1

2022-02-13T13:14:46.000Z

react-flask-app/api/TripDB.py

GabCas28/Smart-Mazda-On-Board

2c3aa53c34bd90868a55985a01dcb5a5954d6a8b

[ "MIT" ]

null

from tinydb import TinyDB, Query, where from tinydb.operations import delete, add, set # import json from pymongo import MongoClient, ReplaceOne from bson import json_util, objectid from pymongo.errors import BulkWriteError import time import pprint class TripDB: database = None def __init__(self): self.database = self.getDataBase() def getDataBase(self): """ Retrieves the content of the database, it creates a new one if it doesn't exist """ db = TinyDB("Trips.json") return db def updateTrip(self, DBentry): """ Insert or Update new entry into the table """ self.database.upsert(DBentry, Query().startTime==DBentry['startTime']) def upload(self): """ Upload the content of the database to the remote server """ client = MongoClient("mongodb+srv://mazda:V2KMvmtixGkOxq2h@cluster0-lpt2w.gcp.mongodb.net/test?retryWrites=true&w=majority") remoteDB = client.SmartMazda.trips operations = [ReplaceOne( filter={"startTime": doc["startTime"]}, replacement=doc, upsert=True ) for doc in self.database.all()] try: result = remoteDB.bulk_write(operations) except BulkWriteError as bwe: print(bwe.details) return (json_util.dumps(result.bulk_api_result)) def clear(self): """ Delete the content of the local database """ self.database.truncate()

35.682927

132

0.656186

174

1,463

5.465517

0.54023

0.050473

0.037855

0.047319

0.04837

0

0.003626

0.24607

1,463

41

133

35.682927

0.858568

0.160629

0

0.032258

0.113787

0.083056

0

1

0.16129

false

0

0.225806

0

0.516129

0.064516

0

null

0

null

0

1

0

df0ae9e699494b11d2f763f0b863647a022b4718

940

py

Python

shebang/account/tasks.py

KNU-shebang/Shebang

117d6d55344442d76def5c7682ab826ea2521d15

[ "MIT" ]

2

2017-03-18T07:43:58.000Z

2017-05-02T16:21:42.000Z

shebang/account/tasks.py

KNU-shebang/Shebang

117d6d55344442d76def5c7682ab826ea2521d15

[ "MIT" ]

2

2017-03-24T09:24:35.000Z

2017-05-02T14:48:21.000Z

shebang/account/tasks.py

KNU-shebang/Shebang

117d6d55344442d76def5c7682ab826ea2521d15

[ "MIT" ]

1

2020-10-18T18:38:14.000Z

from django.core.mail import EmailMessage from celery.decorators import task from django.conf import settings import base64 @task(name='send_email_task') def send_email_task(email, name): email_value = email.split('@')[0].encode('utf-8') # 사용자 인증 url로 이메일의 @ 앞부분을 base64 기반으로 인코딩 encoded_email = base64.b64encode(email_value) # 인코딩 from_email = settings.EMAIL_HOST_USER # 발신 메일 주소 - settings 파일에 지정(현재 임의) subject = '{} 님 회원가입 알림'.format(name) # 메일 제목 refined_email = str(encoded_email)[1:].strip("'") # 이메일 인코딩 값 b'c2F6MDU0OQ==' -> c2F6MDU0OQ==로 변경. html_content = """<h1>{0}님 가입을 환영합니다.</h1> <p>가입 인증을 위해서 아래 링크를 클릭해주세요</p> <a href='http://127.0.0.1:8000/account/{1}/'>http://127.0.0.1:8000/account/{2}/</a> """.format(name, refined_email, refined_email) msg = EmailMessage( subject, html_content, from_email, [email]) msg.content_subtype = "html" msg.send()

33.571429

102

0.660638

144

940

4.1875

0.534722

0.059701

0.043118

0.029851

0.069652

0

0.05483

0.185106

940

27

103

34.814815

0.732376

0.138298

0

0.055556

0.245025

0

1

0.055556

false

0

0.222222

0

0.277778

0

null

0

null

0

1

0

df0cb59e9d0a265d0b3224ad16dd4f909e4faa37

5,703

py

Python

murasame/maker.py

amaotone/caruta-contest-manager

33bbbc8a8ff2903a2763a1270715f224c329e7a2

[ "MIT" ]

null

murasame/maker.py

amaotone/caruta-contest-manager

33bbbc8a8ff2903a2763a1270715f224c329e7a2

[ "MIT" ]

20

2016-07-21T16:01:36.000Z

2016-10-20T07:42:57.000Z

murasame/maker.py

amaotone/murasame

33bbbc8a8ff2903a2763a1270715f224c329e7a2

[ "MIT" ]

null

import os import warnings import numpy as np import pandas as pd from .utils import classname_sorted, match_count class Board(object): def __init__(self, match_count, keys=None): """Initialize match display board. Args: match_count (int): Number of matches. keys (list[str]): Keys for deciding whether the match is valid. Default value is `["club"]`. """ self._upper = list() self._lower = list() self.match_count = match_count self.keys = keys if keys else ["club"] def append(self, player): """Append player to board. This match-making algorithm acts upon a Guidelines for Caruta Competition proposed by All Japan Caruta Association. Args: player (pd.Series): A player to be added. See Also: http://www.karuta.or.jp/kitei/kyougikai.pdf """ assert len(self._upper) >= len(self._lower) if self._on_upper(): self._upper.append(player) return if self._is_valid(self._single_player, player): self._lower.append(player) return if not self._is_last(self._upper): self._upper.append(player) return if not self._is_last(self._lower): self._change_players(player) return warnings.warn("Match-making is already completed.") def validate(self): """Check all organized matches are valid.""" for a, b in zip(self._upper, self._lower): if not self._is_valid(a, b): return False return True def as_dataframe(self): return pd.DataFrame(self.all) @property def all(self): return list(sum(zip(self._upper, self._lower), ())) @property def completed(self): if not self.validate(): return False return len(self._upper) == len(self._lower) == self.match_count @property def _single_player(self): assert len(self._upper) > len(self._lower) return self._upper[len(self._lower)] def _change_players(self, player): opponent = self._single_player for i, (a, b) in enumerate(zip(self._upper, self._lower)): if self._is_valid(player, b) and self._is_valid(opponent, a): self._upper[i] = player self._lower.append(a) return if self._is_valid(a, player) and self._is_valid(opponent, b): self._lower[i] = player self._lower.append(b) return warnings.warn("No player is changeable.") self._lower.append(player) def _is_valid(self, a, b): for key in self.keys: if a.ix[key] == b.ix[key]: return False return True def _is_last(self, lst): return len(lst) >= self.match_count def _on_upper(self): return len(self._upper) == len(self._lower) def index(self, player): return self.all.index(player) def __contains__(self, item): return item in self.all def __getitem__(self, i): return self.all[i] def __len__(self): return len(self.all) class Maker(object): def __init__(self, file): self.dfs = pd.read_excel(file, sheetname=None) self.results = dict() def make_board(self, keys): def make(df, keys): player_count = df.shape[0] board = Board(match_count(player_count), keys=keys) shuffled = df.reindex(np.random.permutation(df.index)) for i, row in shuffled.iterrows(): board.append(row) if board.completed: break return board.as_dataframe() def trim(df, start_index=1): df.reset_index(drop=True, inplace=True) df.index += start_index return df start = 1 for classname, df in classname_sorted(self.dfs.items()): assert start % 2 == 1 board = make(df, keys=keys) board = trim(board, start_index=start) self.results[classname] = dict() self.results[classname]['board'] = board start += board.shape[0] def make_sheet(self, id_label, seat_label, fill): def make(df, board, id_label, seat_label, fill): ref = pd.DataFrame({id_label: board[id_label], seat_label: board.index}) sheet = df.merge(right=ref, on=id_label, how='left') sheet[seat_label] = sheet[seat_label].fillna(fill) return sheet assert len(self.results) != 0 for classname, df in classname_sorted(self.dfs.items()): board = self.results[classname]['board'] sheet = make(df, board, id_label, seat_label, fill) self.results[classname]['sheet'] = sheet def save_board(self, path): w = self.writer(path) for classname, res in classname_sorted(self.results.items()): res['board'].to_excel(w, classname, index=False) w.save() def save_sheet(self, path, sort_by=None): w = self.writer(path) for classname, res in classname_sorted(self.results.items()): sheet = res['sheet'].copy() if sort_by is not None: sheet.sort_values(by=sort_by, inplace=True) sheet.to_excel(w, classname, index=False) w.save() @staticmethod def writer(path): root, _ = os.path.split(path) os.makedirs(root, exist_ok=True) return pd.ExcelWriter(path, engine='xlsxwriter')

29.396907

75

0.575662

717

5,703

4.400279

0.23152

0.042789

0.020919

0.025357

0.279239

0.206973

0.183835

0.130586

0.09065

0.039937

0

0.001792

0.315097

5,703

193

76

29.549223

0.80594

0.08364

0

0.198473

0

0.019765

0

0.030534

1

0.183206

false

0

0.038168

0.061069

0.427481

0

null

0

null

0

1

0

df155ccc7955d0006b4f5d0a5362f1ace1aaba5e

1,367

py

Python

sweep.py

binshengliu/bm25f

6d6d8578494a2ee091639dc9fa2f40fbb9254c39

[ "MIT" ]

6

2020-12-11T03:54:10.000Z

2022-03-22T04:04:13.000Z

sweep.py

binshengliu/bm25f

6d6d8578494a2ee091639dc9fa2f40fbb9254c39

[ "MIT" ]

null

sweep.py

binshengliu/bm25f

6d6d8578494a2ee091639dc9fa2f40fbb9254c39

[ "MIT" ]

null

import logging import subprocess from pathlib import Path import hydra # type: ignore from omegaconf import DictConfig logger = logging.getLogger(__name__) initialized = False @hydra.main(config_path="conf/", config_name="ax") # type: ignore def main(cfg: DictConfig) -> float: field_wt_list = [f"{k[:-3]}:{v}" for k, v in cfg.items() if k.endswith("_wt")] field_wt = ",".join(field_wt_list) field_b_list = [f"{k[:-2]}:{v}" for k, v in cfg.items() if k.endswith("_b")] field_b = ",".join(field_b_list) output_path = f"k1={cfg.k1}-fieldWt={field_wt}-fieldB={field_b}.run" cmd = ( f"{cfg.script_path} -index={cfg.index_path} -k1={cfg.k1} " f"-fieldWt={field_wt} -fieldB={field_b} " f"-threads={cfg.threads} {cfg.query_path}" ) out = subprocess.run(cmd, shell=True, stdout=subprocess.PIPE, encoding="utf-8") with open(output_path, "w") as f: f.write(out.stdout) trec_cmd = f"trec_eval -m {cfg.metric} {cfg.qrels_path} {output_path}" proc = subprocess.run(trec_cmd, shell=True, stdout=subprocess.PIPE, text=True) value = proc.stdout.split()[2] new_path = ( f"k1={cfg.k1}-fieldWt={field_wt}-fieldB={field_b}-{cfg.metric}={value}.run" ) Path(output_path).rename(new_path) logger.info(new_path) return float(value) if __name__ == "__main__": main()

30.377778

83

0.649598

208

1,367

4.048077

0.355769

0.049881

0.024941

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null

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null

0

1

0

df17a532cbd4448e549e2c95877c8b22f5178d2e

2,127

py

Python

utils/compute_contrast.py

Mhaiyang/iccv

04a8ee52c2323d7ff5cdf03c0be1466e8180d2eb

[ "MIT" ]

2

2019-01-10T03:44:03.000Z

2019-05-24T08:50:14.000Z

utils/compute_contrast.py

Mhaiyang/iccv

04a8ee52c2323d7ff5cdf03c0be1466e8180d2eb

[ "MIT" ]

null

utils/compute_contrast.py

Mhaiyang/iccv

04a8ee52c2323d7ff5cdf03c0be1466e8180d2eb

[ "MIT" ]

null

""" @Time : 202/20/19 09:41 @Author : TaylorMei @Email : mhy845879017@gmail.com @Project : iccv @File : compute_contrast.py @Function: """ import os import numpy as np import cv2 import skimage.io from misc import data_write # image_path = '/home/iccd/data/2019/msd9_all/all_images/' # mask_path = '/home/iccd/data/2019/msd9_all/all_masks/' # image_path = '/home/iccd/data/MSRA10K/DUT-OMRON/image/' # mask_path = '/home/iccd/data/MSRA10K/DUT-OMRON/mask/' image_path = '/home/iccd/data/SBU-all/image/' mask_path = '/home/iccd/data/SBU-all/mask/' imglist = os.listdir(image_path) chi_sq_color = [] def chi2(arr1, arr2): return np.sum((arr1 - arr2)**2 / (arr1 + arr2 + np.finfo(np.float).eps)) for i, imgname in enumerate(imglist): print(i, imgname) image = skimage.io.imread(image_path + imgname) # name = imgname.split('.')[0] name = imgname[:-4] mask = skimage.io.imread(mask_path + name + '.png') mask_f = np.where(mask != 0, 1, 0).astype(np.uint8) mask_b = np.where(mask == 0, 1, 0).astype(np.uint8) if np.sum(mask_f) == 0: print('llllllllllllllllllllllllllllllllllllllllllllllll') continue hist_f_r = cv2.calcHist([image], [0], mask_f, [256], [0,256]) hist_f_g = cv2.calcHist([image], [1], mask_f, [256], [0,256]) hist_f_b = cv2.calcHist([image], [2], mask_f, [256], [0,256]) hist_b_r = cv2.calcHist([image], [0], mask_b, [256], [0,256]) hist_b_g = cv2.calcHist([image], [1], mask_b, [256], [0,256]) hist_b_b = cv2.calcHist([image], [2], mask_b, [256], [0,256]) chi_sq_r = chi2(hist_f_r.flatten()/np.sum(mask_f), hist_b_r.flatten()/np.sum(mask_b)) chi_sq_g = chi2(hist_f_g.flatten()/np.sum(mask_f), hist_b_g.flatten()/np.sum(mask_b)) chi_sq_b = chi2(hist_f_b.flatten()/np.sum(mask_f), hist_b_b.flatten()/np.sum(mask_b)) chi_sq_color.append(((chi_sq_r + chi_sq_g + chi_sq_b) / 3).item()) chi_sq_color = np.array(chi_sq_color) chi_sq_color = (chi_sq_color - np.min(chi_sq_color)) / (np.max(chi_sq_color - np.min(chi_sq_color))) print(chi_sq_color) data_write('./shadow_chi_sq.xlsx', [chi_sq_color])

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null

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null

0

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0

df199fd0ad58d48f73efca3982f9ee10e7470114

3,108

py

Python

sherry/inherit/bar.py

py-mu/sherry

af1e95f1eada663ba3b3fb607ca88f099f894f36

[ "MIT" ]

1

2021-06-27T05:54:23.000Z

sherry/inherit/bar.py

py-mu/sherry

af1e95f1eada663ba3b3fb607ca88f099f894f36

[ "MIT" ]

null

sherry/inherit/bar.py

py-mu/sherry

af1e95f1eada663ba3b3fb607ca88f099f894f36

[ "MIT" ]

1

2021-07-28T09:00:43.000Z

# encoding=utf-8 """ create by pymu on 2021/6/6 at 2:09 """ from sherry.inherit.component import Component class BaseBar(Component): bar_normal = None # 自定义标题栏的最大化最小化及关闭按钮 bar_close = None bar_mini = None def __init__(self, master, *args, **kwargs): super().__init__(master, *args, **kwargs) if not master: raise ValueError(self.resource.translate('Bar', '父类窗体不能为空')) # noinspection PyUnresolvedReferences def set_signal(self): """设置标题栏信号""" super(BaseBar, self).set_signal() if not self.master: return if self.bar_normal: self.bar_normal.clicked.connect(self.change_normal) if self.bar_close: self.bar_close.clicked.connect(self.master.accept) if self.bar_mini: self.bar_mini.clicked.connect(self.master.showMinimized) def configure(self): super(BaseBar, self).configure() self.set_default_btn_icon() translate = self.resource.translate self.bar_normal.setToolTip(translate('Bar', "最大化")) self.bar_mini.setToolTip(translate('Bar', "最小化")) self.bar_close.setToolTip(translate('Bar', "关闭")) def set_default_btn_icon(self): """设置默认按钮图标""" if self.bar_normal: self.bar_normal.setIcon(self.resource.font_icon("fa.window-maximize", color="black")) if self.bar_mini: self.bar_mini.setIcon(self.resource.font_icon("fa.window-minimize", color="black")) if self.bar_close: self.bar_close.setIcon(self.resource.font_icon("fa.close", color="black")) # noinspection PyUnresolvedReferences def change_normal(self): """ 切换到恢复窗口大小按钮, """ if not self.bar_normal: return self.master.layout().setContentsMargins(*[0] * 4) self.master.showMaximized() # 先实现窗口最大化 self.bar_normal.setIcon(self.resource.font_icon("fa.window-restore", color="black")) self.bar_normal.setToolTip(self.resource.translate('Bar', "恢复")) # 更改按钮提示 self.bar_normal.disconnect() # 断开原本的信号槽连接 self.bar_normal.clicked.connect(self.change_max) # 重新连接信号和槽 # noinspection PyUnresolvedReferences def change_max(self): """ 切换到最大化按钮 """ if not hasattr(self, "bar_normal"): return if not hasattr(self.master, "border_width"): self.master.border_width = 0 self.master.layout().setContentsMargins(*[self.master.border_width] * 4) self.master.showNormal() self.bar_normal.setIcon(self.resource.font_icon("fa.window-maximize", color="black")) self.bar_normal.setToolTip(self.resource.translate('Bar', "最大化")) self.bar_normal.disconnect() # 关闭信号与原始槽连接 self.bar_normal.clicked.connect(self.change_normal) def mouseDoubleClickEvent(self, event): """鼠标双击(在y轴上小于标题栏高度的双击均被认为是双击头部，随后进行窗体的最大化跟恢复效果)""" if not self.bar_normal: return if event.pos().y() < self.y() + self.height(): self.bar_normal.click()

35.724138

97

0.634813

358

3,108

5.346369

0.268156

0.098746

0.115465

0.060084

0.373041

0.33908

0.298851

0.193312

0.14838

0

0.005915

0.238417

3,108

86

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36.139535

0.802704

0.099421

0

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0

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0.12069

false

0

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null

0

null

0

1

0

df1ab6b4b0ece312421f86b8f892a47e6416b0f6

3,970

py

Python

build/env/lib/python2.7/site-packages/windmill-1.6-py2.7.egg/windmill/dep/_functest/collector.py

bopopescu/myhue

5f566970a5a1fa5af9f01832c9e9808c47634bc7

[ "Apache-2.0" ]

61

2015-03-16T18:36:06.000Z

2021-12-02T10:08:17.000Z

windmill/dep/_functest/collector.py

admc/windmill

4304ee7258eb0c2814f215d8ce90abf02b1f737f

[ "Apache-2.0" ]

8

2015-03-10T10:01:26.000Z

2020-05-18T10:51:24.000Z

windmill/dep/_functest/collector.py

admc/windmill

4304ee7258eb0c2814f215d8ce90abf02b1f737f

[ "Apache-2.0" ]

14

2015-01-29T16:28:33.000Z

2021-09-04T11:19:48.000Z

# Copyright (c) 2007 Mikeal Rogers # # 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 os import sys import inspect import new import imp import copy from time import sleep class Collector(object): post_collection_functions = [] def import_module(self, path): if os.path.isfile(path): sys.path.insert(0, os.path.dirname(path)) name = os.path.split(path)[-1].split('.')[0] filename, pathname, description = imp.find_module(name, [os.path.dirname(path)]) module = imp.load_module(name, filename, pathname, description) module.functest_module_path = path module.__file__ = os.path.abspath(path) sys.path.pop(0) elif os.path.isdir(path): if os.path.isfile(os.path.join(path, '__init__.py')): sys.path.insert(0, os.path.abspath(os.path.join(path, os.path.pardir))) name = os.path.split(path)[-1] filename, pathname, description = imp.find_module( name, [os.path.abspath(os.path.join(path, os.path.pardir))]) module = imp.load_module(name, filename, pathname, description) module.functest_module_path = path module.__file__ = os.path.abspath(os.path.join(path, '__init__.py')) sys.path.pop(0) else: module = new.module(os.path.split(path)[-1]) module.functest_module_path = path else: raise ImportError('path is not file or directory') return module def create_module_chain(self, path): path = os.path.abspath(path) module_chain = [] if not os.path.isdir(path): path = os.path.dirname(path) # For every valid python module the test is in we need to import it incase it contains setup/teardown while os.path.isfile(os.path.join(path, '__init__.py')): module_chain.append(self.import_module(path)) path = os.path.join(*os.path.split(path)[:-1]) module_chain.reverse() return module_chain def create_test_module(self, path): path = os.path.abspath(path) if os.path.isfile(path): test_module = self.import_module(path) for func in self.post_collection_functions: func(test_module) elif os.path.isdir(path): test_module = self.import_module(path) for func in self.post_collection_functions: func(test_module) for filename in [ f for f in os.listdir(path) if ( not f.startswith('.') ) and ( f.startswith('test') ) and ( ( f.endswith('.py') ) or ( os.path.isdir(os.path.join(path, f)) and os.path.isfile(os.path.join(path, f, '__init__.py')) ) ) ]: setattr(test_module, filename.split('.')[0], self.create_test_module(os.path.join(path, filename))) else: sys.__stdout__.write(path+' is not a valid python module path or filename\n') sys.__stdout__.flush() sleep(.5) sys.exit() return test_module def register_post_collection(func): test_collector.post_collection_functions.append(func)

42.234043

115

0.587406

500

3,970

4.518

0.276

0.087649

0.039841

0.049579

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0.385569

0.320496

0.283311

0.26826

0.198318

0

0.006917

0.30806

3,970

93

116

42.688172

0.815435

0.16927

0

0.309859

0

0.039939

0

1

0.056338

false

0

0.169014

0

0.295775

0

null

0

null

0

1

0

df1b51fe6d72128fafb48b80c98baaeec85bd5b2

2,643

py

Python

Tools/BlenderPlugins/scripts/addons/zmey_properties/ui.py

nikoladimitroff/Zmey

4bcea8d66cd3452c532fa68286aa03ad8528a3b4

[ "MIT" ]

22

2017-05-06T18:08:48.000Z

2022-01-12T02:10:22.000Z

Tools/BlenderPlugins/scripts/addons/zmey_properties/ui.py

nikoladimitroff/Zmey

4bcea8d66cd3452c532fa68286aa03ad8528a3b4

[ "MIT" ]

21

2017-12-11T18:42:49.000Z

2018-08-27T23:13:47.000Z

Tools/BlenderPlugins/scripts/addons/zmey_properties/ui.py

nikoladimitroff/Zmey

4bcea8d66cd3452c532fa68286aa03ad8528a3b4

[ "MIT" ]

null

import bpy # Lists class ZmeySceneTypeList(bpy.types.UIList): def draw_item(self, context, layout, data, item, icon, active_data, active_propname): if item: layout.prop(item, "name", text="", emboss=False) # Panels class ZmeyWorldPropertyPanel(bpy.types.Panel): """Zmey Scene Type Properties""" bl_label = "Zmey World Types" bl_idname = "ZMEY_SCENE_TYPES" bl_space_type = "PROPERTIES" bl_region_type = "WINDOW" bl_context = "world" @classmethod def poll(cls, context): return context.scene def draw(self, context): layout = self.layout world = context.world zmey = world.zmey_scene_types layout.row().prop(zmey, "name") layout.row().prop(zmey, "types") row = layout.row() row.template_list("ZmeySceneTypeList", "", zmey, "types", zmey, "active_type") column = row.column() column.operator("zmey.add_scene_type", icon="ZOOMIN", text="") column.operator("zmey.remove_scene_type", icon="ZOOMOUT", text="") if len(zmey.types): self.draw_type(zmey.types[zmey.active_type], layout.box()) def draw_type(self, item, layout): layout.row().prop(item, "name") mesh_box = layout.box() mesh_box.row().prop(item, "mesh_reference") item.components.draw_type(layout) class ZmeyObjectPropertiesPanel(bpy.types.Panel): """Zmey Object Properties""" bl_label = "Zmey Object" bl_idname = "ZMEY_OBJECT" bl_space_type = "PROPERTIES" bl_region_type = "WINDOW" bl_context = "object" def draw(self, context): layout = self.layout obj = context.object layout.row().prop(obj.zmey_props, "enabled", toggle=True) if obj.zmey_props.enabled: layout.row().prop(obj.zmey_props, "type") box = layout.box() zmey_type = bpy.context.scene.world.zmey_scene_types.types[int(obj.zmey_props.type)] box.box().prop( obj.zmey_props, "mesh_export", text="Export Mesh" if zmey_type.mesh_reference == None else "Override Type Mesh") obj.zmey_props.components.draw_type(box) def register(): bpy.utils.register_class(ZmeySceneTypeList) bpy.utils.register_class(ZmeyWorldPropertyPanel) bpy.utils.register_class(ZmeyObjectPropertiesPanel) def unregister(): bpy.utils.unregister_class(ZmeySceneTypeList) bpy.utils.unregister_class(ZmeyWorldPropertyPanel) bpy.utils.unregister_class(ZmeyObjectPropertiesPanel)

33.884615

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